title: Optimal waveform design for cognitive radar

abstract: A key component of a cognitive radar system is the method by which the transmitted waveform is adapted in response to information regarding the radar environment. The goal of such adaptation methods is to provide a flexible framework that can synthesize waveforms that provide different tradeoffs between a variety of performance objectives, and can do so efficiently. In this paper, we propose a waveform design method that efficiently synthesizes waveforms that provide a trade-off between estimation performance for a Gaussian ensemble of targets and detection performance for a specific target. In particular, the method synthesizes (finite length) waveforms that achieve an inherent trade-off between the (Gaussian) mutual information and the signal-to-noise ratio (SNR) for a particular target. In addition, the method can accommodate a variety of constraints on the transmitted spectrum. We show that the waveform design problem can be formulated as a convex optimization problem in the autocorrelation of the waveform, and we develop a customized interior point method for efficiently obtaining a globally optimal waveform.


title: I am Robot: (Deep) Learning to Break Semantic Image CAPTCHAs

abstract: Since their inception, captchas have been widely used for preventing fraudsters from performing illicit actions. Nevertheless, economic incentives have resulted in an arms race, where fraudsters develop automated solvers and, in turn, captcha services tweak their design to break the solvers. Recent work, however, presented a generic attack that can be applied to any text-based captcha scheme. Fittingly, Google recently unveiled the latest version of reCaptcha. The goal of their new system is twofold, to minimize the effort for legitimate users, while requiring tasks that are more challenging to computers than text recognition. ReCaptcha is driven by an "advanced risk analysis system" that evaluates requests and selects the difficulty of the captcha that will be returned. Users may be required to click in a checkbox, or solve a challenge by identifying images with similar content. In this paper, we conduct a comprehensive study of reCaptcha, and explore how the risk analysis process is influenced by each aspect of the request. Through extensive experimentation, we identify flaws that allow adversaries to effortlessly influence the risk analysis, bypass restrictions, and deploy large-scale attacks. Subsequently, we design a novel low-cost attack that leverages deep learning technologies for the semantic annotation of images. Our system is extremely effective, automatically solving 70.78% of the image reCaptcha challenges, while requiring only 19 seconds per challenge. We also apply our attack to the Facebook image captcha and achieve an accuracy of 83.5%. Based on our experimental findings, we propose a series of safeguards and modifications for impacting the scalability and accuracy of our attacks. Overall, while our study focuses on reCaptcha, our findings have wide implications, as the semantic information conveyed via images is increasingly within the realm of automated reasoning, the future of captchas relies on the exploration of novel directions.


A review of medical marijuana for the treatment of posttraumatic stress disorder: Real symptom re-leaf or just high hopes?


title: Digital data transmission via Visible Light Communication (VLC): Application to vehicle to vehicle communication

abstract: In this article, we present an implementation of the new digital communication, technology that uses visible light, known as LIFI (Light Fidelity) or VLC (Visual Light Communication), and apply it for inter-vehicle communication. This communication may improve driver's safety by allowing the vehicles to communicate easily with each other (V2V communication). The first prototype of an unidirectional VLC communication was developed at the laboratory of signals and images (LSI) of USTO-MB. The experimental results are more than satisfactory.


title: On full text download and citation distributions in scientific-scholarly journals

abstract: A statistical analysis of full text downloads of articles in Elsevier's ScienceDirect covering all disciplines reveals large differences in download frequencies, their skewness, and their correlation with Scopus-based citation counts, between disciplines, journals, and document types. Download counts tend to be 2 orders of magnitude higher and less skewedly distributed than citations. A mathematical model based on the sum of two exponentials does not adequately capture monthly download counts. The degree of correlation at the article level within a journal is similar to that at the journal level in the discipline covered by that journal, suggesting that the differences between journals are, to a large extent, discipline specific. Despite the fact that in all studied journals download and citation counts per article positively correlate, little overlap may exist between the set of articles appearing in the top of the citation distribution and that with the most frequently downloaded ones. Usage and citation leaks, bulk downloading, differences between reader and author populations in a subject field, the type of document or its content, differences in obsolescence patterns between downloads and citations, and different functions of reading and citing in the research process all provide possible explanations of differences between download and citation distributions.


title: Achieving effective cloud search services: multi-keyword ranked search over encrypted cloud data supporting synonym query

abstract: In recent years, consumer-centric cloud computing paradigm has emerged as the development of smart electronic devices combined with the emerging cloud computing technologies. A variety of cloud services are delivered to the consumers with the premise that an effective and efficient cloud search service is achieved. For consumers, they want to find the most relevant products or data, which is highly desirable in the "pay-as-you use" cloud computing paradigm. As sensitive data (such as photo albums, emails, personal health records, financial records, etc.) are encrypted before outsourcing to cloud, traditional keyword search techniques are useless. Meanwhile, existing search approaches over encrypted cloud data support only exact or fuzzy keyword search, but not semantics-based multi-keyword ranked search. Therefore, how to enable an effective searchable system with support of ranked search remains a very challenging problem. This paper proposes an effective approach to solve the problem of multi-keyword ranked search over encrypted cloud data supporting synonym queries. The main contribution of this paper is summarized in two aspects: multi-keyword ranked search to achieve more accurate search results and synonym-based search to support synonym queries. Extensive experiments on real-world dataset were performed to validate the approach, showing that the proposed solution is very effective and efficient for multikeyword ranked searching in a cloud environment.


title: Mapping to Declarative Knowledge for Word Problem Solving

abstract: Math word problems form a natural abstraction to a range of quantitative reasoning problems, such as understanding financial news, sports results, and casualties of war. Solving such problems requires the understanding of several mathematical concepts such as dimensional analysis, subset relationships, etc. In this paper, we develop declarative rules which govern the translation of natural language description of these concepts to math expressions. We then present a framework for incorporating such declarative knowledge into word problem solving. Our method learns to map arithmetic word problem text to math expressions, by learning to select the relevant declarative knowledge for each operation of the solution expression. This provides a way to handle multiple concepts in the same problem while, at the same time, support interpretability of the answer expression. Our method models the mapping to declarative knowledge as a latent variable, thus removing the need for expensive annotations. Experimental evaluation suggests that our domain knowledge based solver outperforms all other systems, and that it generalizes better in the realistic case where the training data it is exposed to is biased in a different way than the test data.


title: The evolutionary impact of invasive species

abstract: Since the Age of Exploration began, there has been a drastic breaching of biogeographic barriers that previously had isolated the continental biotas for millions of years. We explore the nature of these recent biotic exchanges and their consequences on evolutionary processes. The direct evidence of evolutionary consequences of the biotic rearrangements is of variable quality, but the results of trajectories are becoming clear as the number of studies increases. There are examples of invasive species altering the evolutionary pathway of native species by competitive exclusion, niche displacement, hybridization, introgression, predation, and ultimately extinction. Invaders themselves evolve in response to their interactions with natives, as well as in response to the new abiotic environment. Flexibility in behavior, and mutualistic interactions, can aid in the success of invaders in their new environment.


title: The Evaluation Process of a Computer Security Incident Ontology

abstract: Ontologies have been developed and used by several researchers in different knowledge domains aiming to ease the structuring and management of knowledge, and to create a unique standard to represent concepts of such a knowledge domain. Considering the computer security domain, several tools can be used to manage and store security information. These tools generate a great amount of security alerts, which are stored in different formats. This lack of standard and the amount of data make the tasks of the security administrators even harder, because they have to understand, using their tacit knowledge, different security alerts to make correlation and solve security problems. Aiming to assist the administrators in executing these tasks efficiently, this paper presents the main features of the computer security incident ontology developed to model, using a unique standard, the concepts of the security incident domain, and how the ontology has been evaluated.


title: Starting with Lives Supporting Older People's Strategies and Ways of Coping

abstract: • Summary: A recent summary of research findings concluded that social policy and practice should use older people's lives as the starting point to plan and evaluate services (Joseph Rowntree Foundation, 2004). Based on the findings of a small-scale qualitative study and related research, this article explores the strategies and ways of coping used by older people to manage changes and difficulties that accompany ageing.• Findings: It is suggested that older people's strategies and ways of coping can be understood as efforts to sustain a sense of self in later life and a model of some key processes involved is presented. The empirical findings of the study are related to theoretical models of ageing and identity management. Attention is directed to the interaction between structural resources and constraints, dominant cultural norms and values, and older people's individual capacities for reflexivity and interpretation.• Applications : It is argued that this understanding has significant implications for ...


title: Efficient multiple-click models in web search

abstract: Many tasks that leverage web search users' implicit feedback rely on a proper and unbiased interpretation of user clicks. Previous eye-tracking experiments and studies on explaining position-bias of user clicks provide a spectrum of hypotheses and models on how an average user examines and possibly clicks web documents returned by a search engine with respect to the submitted query. In this paper, we attempt to close the gap between previous work, which studied how to model a single click, and the reality that multiple clicks on web documents in a single result page are not uncommon. Specifically, we present two multiple-click models: the independent click model (ICM) which is reformulated from previous work, and the dependent click model (DCM) which takes into consideration dependencies between multiple clicks. Both models can be efficiently learned with linear time and space complexities. More importantly, they can be incrementally updated as new click logs flow in. These are well-demanded properties in reality. We systematically evaluate the two models on click logs obtained in July 2008 from a major commercial search engine. The data set, after preprocessing, contains over 110 thousand distinct queries and 8.8 million query sessions. Extensive experimental studies demonstrate the gain of modeling multiple clicks and their dependencies. Finally, we note that since our experimental setup does not rely on tweaking search result rankings, it can be easily adopted by future studies.


title: Self-regulation and selective exposure: the impact of depleted self-regulation resources on confirmatory information processing.

abstract: In the present research, the authors investigated the impact of self-regulation resources on confirmatory information processing, that is, the tendency of individuals to systematically prefer standpoint-consistent information to standpoint-inconsistent information in information evaluation and search. In 4 studies with political and economic decision-making scenarios, it was consistently found that individuals with depleted self-regulation resources exhibited a stronger tendency for confirmatory information processing than did individuals with nondepleted self-regulation resources. Alternative explanations based on processes of ego threat, cognitive load, and mood were ruled out. Mediational analyses suggested that individuals with depleted self-regulation resources experienced increased levels of commitment to their own standpoint, which resulted in increased confirmatory information processing. In sum, the impact of ego depletion on confirmatory information search seems to be more motivational than cognitive in nature.


title: High Accuracy Head Pose Tracking Survey

abstract: Head pose estimation is recently a more and more popular area of research. For the last three decades new approaches have constantly been developed, and steadily better accuracy was achieved. Unsurprisingly, a very broad range of methods was explored - statistical, geometrical and tracking-based to name a few. This paper presents a brief summary of the evolution of head pose estimation and a glimpse at the current state-of-the-art in this field.


title: Spatial databases: Accomplishments and researchneeds

abstract: Spatial databases, addressing the growing data management and analysis needs of spatial applications such as geographic information systems, have been an active area of research for more than two decades. This research has produced a taxonomy of models for space, spatial data types and operators, spatial query languages and processing strategies, as well as spatial indexes and clustering techniques. However, more research is needed to improve support for network and field data, as well as query processing (e.g., cost models, bulk load). Another important need is to apply spatial data management accomplishments to newer applications, such as data warehouses and multimedia information systems. The objective of this paper is to identify recent accomplishments and associated research needs of the near term.


title: Conducting user experiments in recommender systems

abstract: There is an increasing consensus in the field of recommender systems that we should move beyond the offline evaluation of algorithms towards a more user-centric approach. This tutorial teaches the essential skills involved in conducting user experiments, the scientific approach to user-centric evaluation. Such experiments are essential in uncovering how and why the user experience of recommender systems comes about.


title: PEST-a tool for implementing pseudo-exhaustive self test

abstract: PEST is a CAD tool for implementing pseudo-exhaustive self test in integrated circuits. PEST's unique features are: 1. Parallel testing of all cones, 2. Global test point selection, and 3. A new cost effective scheme for test vector generation. AT&T's Network Interface Controller chip was designed using PEST. Effective 100% fault coverage without fault simulation or test generation was obtained with less than 24% transistor overhead. This paper begins with an overview of PEST's approach. The test generation algorithm is then described. Finally, the implementation of PEST in the Network Interface Controller is presented.


title: From Entity Relationship to XML Schema: a graph-theoretic approach

abstract: We propose a mapping from the Enhanced Entity Relationship conceptual model to the W3C XML Schema Language with the following properties: information and integrity constraints are preserved, no redundance is introduced, different hierarchical views of the conceptual information are available, the resulting XML structure is highly connected, and the design is reversible. We investigate two different ways to nest the XML structure: a maximum connectivity nesting, that minimizes the number of schema constraints used in the mapping of the conceptual schema reducing the validation overhead, and a maximum depth nesting, that keeps low the number of (expensive) join operations that are necessary to reconstruct the information at query time using the mapped schema. We propose a graph-theoretic linear-time algorithm to find a maximum connectivity nesting and show that finding a maximum depth nesting is NP-complete. We complement our investigation with an implementation of the devised translation and we embed the implemented module in a software framework for the conceptual and logical design of spatio-temporal databases.


title: Wearable Obstacle Avoidance Electronic Travel Aids for Blind: A Survey

abstract: The last decades a variety of portable or wearable navigation systems have been developed to assist visually impaired people during navigation in known or unknown, indoor or outdoor environments. There are three main categories of these systems: electronic travel aids (ETAs), electronic orientation aids (EOAs), and position locator devices (PLDs). This paper presents a comparative survey among portable/wearable obstacle detection/avoidance systems (a subcategory of ETAs) in an effort to inform the research community and users about the capabilities of these systems and about the progress in assistive technology for visually impaired people. The survey is based on various features and performance parameters of the systems that classify them in categories, giving qualitative-quantitative measures. Finally, it offers a ranking, which will serve only as a reference point and not as a critique on these systems.


title: Activity Discovery and Activity Recognition: A New Partnership

abstract: Activity recognition has received increasing attention from the machine learning community. Of particular interest is the ability to recognize activities in real time from streaming data, but this presents a number of challenges not faced by traditional offline approaches. Among these challenges is handling the large amount of data that does not belong to a predefined class. In this paper, we describe a method by which activity discovery can be used to identify behavioral patterns in observational data. Discovering patterns in the data that does not belong to a predefined class aids in understanding this data and segmenting it into learnable classes. We demonstrate that activity discovery not only sheds light on behavioral patterns, but it can also boost the performance of recognition algorithms. We introduce this partnership between activity discovery and online activity recognition in the context of the CASAS smart home project and validate our approach using CASAS data sets.


title: Right parietal cortex plays a critical role in change blindness

abstract: There is increasing evidence from functional magnetic resonance imaging (fMRI) that visual awareness is not only associated with activity in ventral visual cortex but also with activity in the parietal cortex. However, due to the correlational nature of neuroimaging, it remains unclear whether this parietal activity plays a causal role in awareness. In the experiment presented here we disrupted activity in right or left parietal cortex by applying repetitive transcranial magnetic stimulation (rTMS) over these areas while subjects attempted to detect changes between two images separated by a brief interval (i.e. 1-shot change detection task). We found that rTMS applied over right parietal cortex but not left parietal cortex resulted in longer latencies to detect changes and a greater rate of change blindness compared with no TMS. These results suggest that the right parietal cortex plays a critical role in conscious change detection.


title: Cambricon-X: An accelerator for sparse neural networks

abstract: Neural networks (NNs) have been demonstrated to be useful in a broad range of applications such as image recognition, automatic translation and advertisement recommendation. State-of-the-art NNs are known to be both computationally and memory intensive, due to the ever-increasing deep structure, i.e., multiple layers with massive neurons and connections (i.e., synapses). Sparse neural networks have emerged as an effective solution to reduce the amount of computation and memory required. Though existing NN accelerators are able to efficiently process dense and regular networks, they cannot benefit from the reduction of synaptic weights. In this paper, we propose a novel accelerator, Cambricon-X, to exploit the sparsity and irregularity of NN models for increased efficiency. The proposed accelerator features a PE-based architecture consisting of multiple Processing Elements (PE). An Indexing Module (IM) efficiently selects and transfers needed neurons to connected PEs with reduced bandwidth requirement, while each PE stores irregular and compressed synapses for local computation in an asynchronous fashion. With 16 PEs, our accelerator is able to achieve at most 544 GOP/s in a small form factor (6.38 mm2 and 954 mW at 65 nm). Experimental results over a number of representative sparse networks show that our accelerator achieves, on average, 7.23x speedup and 6.43x energy saving against the state-of-the-art NN accelerator.


Pixel-wise classification of weeds and crops in images by using a Fully Convolutional neural network


title: The Treatment of Internet Gaming Disorder: a Brief Overview of the PIPATIC Program

abstract: Over the last decade, there has been an increase in children and adolescents accessing psychology services regarding problematic use of online videogames. Consequently, providing effective treatment is essential. The present paper describes the design process of a manualized PIPATIC (Programa Individualizado Psicoterapeutico para la Adiccion a las Tecnologias de la informacion y la comunicacion) intervention program for 12- to 18-year-old adolescents with Internet Gaming Disorder. The design and application of the PIPATIC program integrates several areas of intervention structured into six modules: psychoeducational, treatment as usual, intrapersonal, interpersonal, family intervention, and development of a new lifestyle. The program’s goals are to reduce the addiction symptoms related to online videogames and to improve the well-being of adolescents. Preliminary findings suggest positive and encouraging effects.


title: Performance analysis of Linux-based EtherCAT DC synchronization

abstract: We analyze the performance of Linux-based EtherCAT synchronization technique, namely the distributed clock (DC). The IgH EtherCAT master module is an open source program for the desktop Linux PC. In order to use the EtherCAT master in Linux system environment, we change and compile a Real-time patched kernel. As a result, we become able to use Linux-based EtherCAT master with the EtherCAT slave hardwares in the real experiments. Experiment results show that the system time differences between the reference clock and the slave clock are precisely measured in the real experiments; the DC mechanism appropriately compensates the time offsets; and the stability is achieved in the Linux-based real EtherCAT network.


Saudi License Plate Recognition


title: Simultaneous localization and mapping with detection and tracking of moving objects

abstract: Both simultaneous localization and mapping (SLAM) and detection and tracking of moving objects (DTMO) play key roles in robotics and automation. For certain constrained environments, SLAM and DTMO are becoming solved problems. But for robots working outdoors, and at high speeds, SLAM and DTMO are still incomplete. This paper presents a method to integrate SLAM and DTMO to solve both problems simultaneously for both indoor and outdoor applications. The results of experiments carried out with vehicles with the maximum speed of 45 mph in crowded urban and suburban areas verify the described work.


title: You Tweet What You Eat: Studying Food Consumption Through Twitter

abstract: Food is an integral part of our lives, cultures, and well-being, and is of major interest to public health. The collection of daily nutritional data involves keeping detailed diaries or periodic surveys and is limited in scope and reach. Alternatively, social media is infamous for allowing its users to update the world on the minutiae of their daily lives, including their eating habits. In this work we examine the potential of Twitter to provide insight into US-wide dietary choices by linking the tweeted dining experiences of 210K users to their interests, demographics, and social networks. We validate our approach by relating the caloric values of the foods mentioned in the tweets to the state-wide obesity rates, achieving a Pearson correlation of 0.77 across the 50 US states and the District of Columbia. We then build a model to predict county-wide obesity and diabetes statistics based on a combination of demographic variables and food names mentioned on Twitter. Our results show significant improvement over previous CHI research (Culotta 2014). We further link this data to societal and economic factors, such as education and income, illustrating that areas with higher education levels tweet about food that is significantly less caloric. Finally, we address the somewhat controversial issue of the social nature of obesity (Christakis & Fowler 2007) by inducing two social networks using mentions and reciprocal following relationships.


title: Planar Meander Monopole Antenna With Parasitic Strips and Sleeve Feed for DVB-H/LTE/GSM850/900 Operation in the Mobile Phone

abstract: The design and wideband performance of a planar meander monopole antenna in the mobile phone is presented. By using parasitic strips and a sleeve feed, wideband impedance characteristics are achieved. A measured impedance bandwidth for VSWR $\,\leq$ 3 of about 101.7% ranging from 440 to 1350 MHz is obtained. It is very suitable for DVB-H, LTE 13, GSM850, and GSM900 applications in the mobile phone.


title: Investigation of 1.2 kV SiC MOSFET for high frequency high power applications

abstract: SiC is among the most promising materials for next generation power electronic devices due to its superior physical properties to Si and relative mature technology. SiC MOSFET is expected to offer performance improvement over Si counterpart. This paper presents the characterization of 1.2 kV SiC MOSFET, including its static and dynamic characteristics, and its high-frequency (1 MHz), high-power (1.2 kW) zero-voltage switching (ZVS) operation in a half-bridge parallel resonant converter. In comparison with SiC JFET and Si CoolMOS, the advantages and disadvantages of the SiC MOSFET are summarized.


title: Stochastic Variance-reduced Gradient Descent for Low-rank Matrix Recovery from Linear Measurements

abstract: We study the problem of estimating low-rank matrices from linear measurements (a.k.a., matrix sensing) through nonconvex optimization. We propose an efficient stochastic variance reduced gradient descent algorithm to solve a nonconvex optimization problem of matrix sensing. Our algorithm is applicable to both noisy and noiseless settings. In the case with noisy observations, we prove that our algorithm converges to the unknown low-rank matrix at a linear rate up to the minimax optimal statistical error. And in the noiseless setting, our algorithm is guaranteed to linearly converge to the unknown low-rank matrix and achieves exact recovery with optimal sample complexity. Most notably, the overall computational complexity of our proposed algorithm, which is defined as the iteration complexity times per iteration time complexity, is lower than the state-of-the-art algorithms based on gradient descent. Experiments on synthetic data corroborate the superiority of the proposed algorithm over the state-of-the-art algorithms.


title: Location diversity in anonymity networks

abstract: Anonymity networks have long relied on diversity of node location for protection against attacks---typically an adversary who can observe a larger fraction of the network can launch a more effective attack. We investigate the diversity of two deployed anonymity networks, Mixmaster and Tor, with respect to an adversary who controls a single Internet administrative domain. Specifically, we implement a variant of a recently proposed technique that passively estimates the set of administrative domains (also known as autonomous systems, or ASes) between two arbitrary end-hosts without having access to either end of the path. Using this technique, we analyze the AS-level paths that are likely to be used in these anonymity networks. We find several cases in each network where multiple nodes are in the same administrative domain. Further, many paths between nodes, and between nodes and popular endpoints, traverse the same domain.


title: Cocoa flavanol consumption improves cognitive function, blood pressure control, and metabolic profile in elderly subjects: the Cocoa, Cognition, and Aging (CoCoA) Study—a randomized controlled trial1234

abstract: Background: Recent evidence has indicated that flavanol consumption may have many health benefits in humans, including improved cognitive activities. ::: ::: Objective: The aim was to evaluate the effect of flavanol consumption on cognitive performance in cognitively intact elderly subjects. ::: ::: Design: This was a double-blind, controlled, parallel-arm study conducted in 90 elderly individuals without clinical evidence of cognitive dysfunction who were randomly assigned to consume daily for 8 wk a drink containing 993 mg [high flavanol (HF)], 520 mg [intermediate flavanol (IF)], or 48 mg [low flavanol (LF)] cocoa flavanols (CFs). Cognitive function was assessed at baseline and after 8 wk by using the Mini-Mental State Examination (MMSE), the Trail Making Test (TMT) A and B, and the Verbal Fluency Test (VFT). ::: ::: Results: The changes in MMSE score in response to the 3 different treatments were not different. In contrast, there was a positive impact of the intervention on specific aspects of cognitive function. Mean changes (±SEs) in the time required to complete the TMT A and B after consumption of the HF (−8.6 ± 0.4 and −16.5 ± 0.8 s, respectively) and IF (−6.7 ± 0.5 and −14.2 ± 0.5 s, respectively) drinks significantly (P < 0.0001) differed from that after consumption of the LF drinks (−0.8 ± 1.6 and −1.1 ± 0.7 s, respectively). Similarly, VFT scores significantly improved among all treatment groups, but the magnitude of improvement in the VFT score was significantly (P < 0.0001) greater in the HF group (7.7 ± 1.1 words/60 s) than in the IF (3.6 ± 1.2 words/60 s) and LF (1.3 ± 0.5 words/60 s) groups. Significantly different improvements in insulin resistance (P < 0.0001), blood pressure (P < 0.0001), and lipid peroxidation (P = 0.001) were also observed for the HF and IF groups in comparison with the LF group. Changes in insulin resistance explained ∼17% of changes in composite z score (partial r2 = 0.1703, P < 0.0001). ::: ::: Conclusions: This dietary intervention study provides evidence that regular CF consumption can reduce some measures of age-related cognitive dysfunction, possibly through an improvement in insulin sensitivity. These data suggest that the habitual intake of flavanols can support healthy cognitive function with age. This trial was registered at www.controlled-trials.com as ISRCTN68970511.


title: Software Feature Request Detection in Issue Tracking Systems

abstract: Communication about requirements is often handled in issue tracking systems, especially in a distributed setting. As issue tracking systems also contain bug reports or programming tasks, the software feature requests of the users are often difficult to identify. This paper investigates natural language processing and machine learning features to detect software feature requests in natural language data of issue tracking systems. It compares traditional linguistic machine learning features, such as "bag of words", with more advanced features, such as subject-action-object, and evaluates combinations of machine learning features derived from the natural language and features taken from the issue tracking system meta-data. Our investigation shows that some combinations of machine learning features derived from natural language and the issue tracking system meta-data outperform traditional approaches. We show that issues or data fields (e.g. descriptions or comments), which contain software feature requests, can be identified reasonably well, but hardly the exact sentence. Finally, we show that the choice of machine learning algorithms should depend on the goal, e.g. maximization of the detection rate or balance between detection rate and precision. In addition, the paper contributes a double coded gold standard and an open-source implementation to further pursue this topic.


title: STRUCTURAL VERSUS REDUCED FORM MODELS: A NEW INFORMATION BASED PERSPECTIVE

abstract: This paper compares structural versus reduced form credit risk models from an information based perspective. We show that the difference between these two model types can be characterized in terms of the information assumed known by the modeler. Structural models assume that the modeler has the same information set as the firm's manager - complete knowledge of all the firm's assets and liabilities. In most situations, this knowledge leads to predictable default time. In contracts, reduced form models assume that the modeler has the same information set as the market - incomplete knowledge of the firm's condition. In most cases, the imperfect knowledge leads to an inaccessible default time. As such, we argue that the key distinction between structural and reduced form models is not whether the default is predictable or inaccessible, but whether the information set is observed by the market or not. Consequently, for pricing and hedging, reduced form models are the preferred methodology.


title: Boosting with Abstention

abstract: We present a new boosting algorithm for the key scenario of binary classification with abstention where the algorithm can abstain from predicting the label of a point, at the price of a fixed cost. At each round, our algorithm selects a pair of functions, a base predictor and a base abstention function. We define convex upper bounds for the natural loss function associated to this problem, which we prove to be calibrated with respect to the Bayes solution. Our algorithm benefits from general margin-based learning guarantees which we derive for ensembles of pairs of base predictor and abstention functions, in terms of the Rademacher complexities of the corresponding function classes. We give convergence guarantees for our algorithm along with a linear-time weak-learning algorithm for abstention stumps. We also report the results of several experiments suggesting that our algorithm provides a significant improvement in practice over two confidence-based algorithms.


title: Learning from User Interactions in Personal Search via Attribute Parameterization

abstract: User interaction data (e.g., click data) has proven to be a powerful signal for learning-to-rank models in web search. However, such models require observing multiple interactions across many users for the same query-document pair to achieve statistically meaningful gains. Therefore, utilizing user interaction data for improving search over personal, rather than public, content is a challenging problem. First, the documents (e.g., emails or private files) are not shared across users. Second, user search queries are of personal nature (e.g., "alice's address") and may not generalize well across users. In this paper, we propose a solution to these challenges, by projecting user queries and documents into a multi-dimensional space of fine-grained and semantically coherent attributes. We then introduce a novel parameterization technique to overcome sparsity in the multi-dimensional attribute space. Attribute parameterization enables effective usage of cross-user interactions for improving personal search quality -- which is a first such published result, to the best of our knowledge. Experiments with a dataset derived from interactions of users of one of the world's largest personal search engines demonstrate the effectiveness of the proposed attribute parameterization technique.


title: Qualities of Symbolic Play Among Children with Autism: A Social-Developmental Perspective

abstract: We hypothesized that the qualities of play shown by children with autism reflect their impoverished experience of identifying with other people’s attitudes and moving among person-anchored perspectives. On this basis, we predicted their play should manifest a relative lack of the social-developmental hallmarks that typify creative symbolic functioning. We videotaped the spontaneous and modelled symbolic play of matched groups of children with and without autism. The two groups were similar in the mechanics of play, for example in making one thing stand for another and using materials flexibly. By contrast, and as predicted, children with autism were rated as showing less playful pretend involving self-conscious awareness of pretending, investment in the symbolic meanings given to play materials, creativity, and fun.


title: $W$ -Band Waveguide Filters Fabricated by Laser Micromachining and 3-D Printing

abstract: This paper presents two W-band waveguide bandpass filters, one fabricated using laser micromachining and the other 3-D printing. Both filters are based on coupled resonators and are designed to have a Chebyshev response. The first filter is for laser micromachining and it is designed to have a compact structure allowing the whole filter to be made from a single metal workpiece. This eliminates the need to split the filter into several layers and therefore yields an enhanced performance in terms of low insertion loss and good durability. The second filter is produced from polymer resin using a stereolithography 3-D printing technique and the whole filter is plated with copper. To facilitate the plating process, the waveguide filter consists of slots on both the broadside and narrow side walls. Such slots also reduce the weight of the filter while still retaining the filter’s performance in terms of insertion loss. Both filters are fabricated and tested and have good agreement between measurements and simulations.


title: Fit between Agile practices and organizational cultures

abstract: Software engineering methodology such as Agile, establishes the governance of software development. Over the years, a number of Agile practices have been invented. These practices are built on the same Agile principles but emphasise on different rationales and values, and thus associated with various approaches, activities and artefacts. Given this variety, and necessity for organizations to incorporate Agile, many studies have attempted to examine the relationship between organizational attributes, particularly organizational culture, and Agile practices, which in configurations result in effective outcomes of software development. In this paper, we propose a theoretical framework that deals with an alternative way in which organizations identify their cultural types, and understand underlying principles of each Agile practice in order to form the fit between organizational culture and Agile. In other words, organizations do not necessarily change their current culture in order to use Agile effectively, as existing studies suggested. Rather, they select the Agile practice that fits with their cultural type. This framework is based on interpretation of existing literature and systematic qualitative approaches.


title: Characterizing microblogs with topic models

abstract: As microblogging grows in popularity, services like Twitter are coming to support information gathering needs above and beyond their traditional roles as social networks. But most users’ interaction with Twitter is still primarily focused on their social graphs, forcing the often inappropriate conflation of “people I follow” with “stuff I want to read.” We characterize some information needs that the current Twitter interface fails to support, and argue for better representations of content for solving these challenges. We present a scalable implementation of a partially supervised learning model (Labeled LDA) that maps the content of the Twitter feed into dimensions. These dimensions correspond roughly to substance, style, status, and social characteristics of posts. We characterize users and tweets using this model, and present results on two information consumption oriented tasks.


Two Knowledge-based Methods for High-Performance Sense Distribution Learning


title: Design of Secure User Authenticated Key Management Protocol for Generic IoT Networks

abstract: In recent years, the research in generic Internet of Things (IoT) attracts a lot of practical applications including smart home, smart city, smart grid, industrial Internet, connected healthcare, smart retail, smart supply chain and smart farming. The hierarchical IoT network (HIoTN) is a special kind of the generic IoT network, which is composed of the different nodes, such as the gateway node, cluster head nodes, and sensing nodes organized in a hierarchy. In HIoTN, there is a need, where a user can directly access the real-time data from the sensing nodes for a particular application in generic IoT networking environment. This paper emphasizes on the design of a new secure lightweight three-factor remote user authentication scheme for HIoTNs, called the user authenticated key management protocol (UAKMP). The three factors used in UAKMP are the user smart card, password, and personal biometrics. The security of the scheme is thoroughly analyzed under the formal security in the widely accepted real-or-random model, the informal security as well as the formal security verification using the widely accepted automated validation of Internet security protocols and applications tool. UAKMP offers several functionality features including offline sensing node registration, freely password and biometric update facility, user anonymity, and sensing node anonymity compared to other related existing schemes. In addition, UAKMP is also comparable in computation and communication costs as compared to other existing schemes.


title: Enhancing Image Quality via Style Transfer for Single Image Super-Resolution

abstract: Recently, by feat of the Generative Adversarial Network (GAN), single image super-resolution (SISR) has achieved great breakthroughs in enhancing the perceptual image quality. However, since the network is trained by minimizing the perceptual loss, the GAN based SISR method (SRGAN) [1] results in images with very low objective quality, i.e., peak signal-to-noise ratio (PSNR). In this letter, we aim to solve this problem in an image style transfer way, to generate an image with similar perceptual quality as SRGAN, but with much higher objective quality. Moreover, we propose a threshold-based method to automatically alter the objective and perceptual quality of the reconstructed image through adjusting only one parameter. Experimental results show that our method can achieve more than 1.6 dB PSNR improvement over SRGAN with similar Mean Opinion Score value. Also, with the same objective quality, our method can provide significantly better perceptual results than other state-of-the-art SISR methods.


title: The impact of exposure to domestic violence on children and young people: A review of the literature

abstract: Abstract Objective This article reviews the literature concerning the impact of exposure to domestic violence on the health and developmental well-being of children and young people. Impact is explored across four separate yet inter-related domains (domestic violence exposure and child abuse; impact on parental capacity; impact on child and adolescent development; and exposure to additional adversities), with potential outcomes and key messages concerning best practice responses to children's needs highlighted. Method A comprehensive search of identified databases was conducted within an 11-year framework (1995–2006). This yielded a vast literature which was selectively organized and analyzed according to the four domains identified above. Results This review finds that children and adolescents living with domestic violence are at increased risk of experiencing emotional, physical and sexual abuse, of developing emotional and behavioral problems and of increased exposure to the presence of other adversities in their lives. It also highlights a range of protective factors that can mitigate against this impact, in particular a strong relationship with and attachment to a caring adult, usually the mother. Conclusion Children and young people may be significantly affected by living with domestic violence, and impact can endure even after measures have been taken to secure their safety. It also concludes that there is rarely a direct causal pathway leading to a particular outcome and that children are active in constructing their own social world. Implications for interventions suggest that timely, appropriate and individually tailored responses need to build on the resilient blocks in the child's life. Practice implications This study illustrate the links between exposure to domestic violence, various forms of child abuse and other related adversities, concluding that such exposure may have a differential yet potentially deleterious impact for children and young people. From a resilient perspective this review also highlights range of protective factors that influence the extent of the impact of exposure and the subsequent outcomes for the child. This review advocates for a holistic and child-centered approach to service delivery, derived from an informed assessment, designed to capture a picture of the individual child's experience, and responsive to their individual needs.


title: Benchmarking a Multimodal and Multiview and Interactive Dataset for Human Action Recognition

abstract: Human action recognition is an active research area in both computer vision and machine learning communities. In the past decades, the machine learning problem has evolved from conventional single-view learning problem, to cross-view learning, cross-domain learning and multitask learning, where a large number of algorithms have been proposed in the literature. Despite having large number of action recognition datasets, most of them are designed for a subset of the four learning problems, where the comparisons between algorithms can further limited by variances within datasets, experimental configurations, and other factors. To the best of our knowledge, there exists no dataset that allows concurrent analysis on the four learning problems. In this paper, we introduce a novel multimodal and multiview and interactive ( $\text{M}^{{2}}\text{I}$ ) dataset, which is designed for the evaluation of human action recognition methods under all four scenarios. This dataset consists of 1760 action samples from 22 action categories, including nine person-person interactive actions and 13 person-object interactive actions. We systematically benchmark state-of-the-art approaches on $\text{M}^{{2}}\text{I}$ dataset on all four learning problems. Overall, we evaluated 13 approaches with nine popular feature and descriptor combinations. Our comprehensive analysis demonstrates that $\text{M}^{{2}}\text{I}$ dataset is challenging due to significant intraclass and view variations, and multiple similar action categories, as well as provides solid foundation for the evaluation of existing state-of-the-art algorithms.


title: Approximate Boolean Operations on Large Polyhedral Solids with Partial Mesh Reconstruction

abstract: We present a new approach to compute the approximate Boolean operations of two freeform polygonal mesh solids efficiently with the help of Layered Depth Images (LDIs). After applying the LDI sampling-based membership classification, the most challenging part, a trimmed adaptive contouring algorithm, is developed to reconstruct the mesh surface from the LDI samples near the intersected regions and stitch it to the boundary of the retained surfaces. Our method of approximate Boolean operations holds the advantage of numerical robustness as the approach uses volumetric representation. However, unlike other methods based on volumetric representation, we do not damage the facets in nonintersected regions, thus preserving geometric details much better and speeding up the computation as well. We show that the proposed method can successfully compute the Boolean operations of free-form solids with a massive number of polygons in a few seconds.


title: Data lineage model for Taverna workflows with lightweight annotation requirements

abstract: The provenance, or lineage , of a workflow data product can be reconstructed by keeping a complete trace of workflow execution. This lineage information, however, is likely to be both imprecise, because of the black-box nature of the services that compose the workflow, and noisy, because of the many trivial data transformations that obscure the intended purpose of the workflow. In this paper we argue that these shortcomings can be alleviated by introducing a small set of optional lightweight annotations to the workflow, in a principled way. We begin by presenting a baseline, annotation-free lineage model for the Taverna workflow system, and then show how the proposed annotations improve the results of fundamental lineage queries.


title: Design of Hough transform hardware accelerator for lane detection

abstract: The Hough transform is a well-known straight line detection algorithm and it has been widely used for many lane detection algorithms. However, its real-time operation is not guaranteed due to its high computational complexity. In this paper, we designed a Hough transform hardware accelerator on FPGA to process it in real time. Its FPGA logic area usage was reduced by limiting the angles of the lines to (-20, 20) degrees which are enough for lane detection applications, and its arithmetic computations were performed in parallel to speed up the processing time. As a result of FPGA synthesis using Xilinx Vertex-5 XC5VLX330 device, it occupies 4,521 slices and 25.6Kbyte block memory giving performance of 10,000fps in VGA images(5000 edge points). The proposed hardware on FPGA (0.1ms) is 450 times faster than the software implementation on ARM Cortex-A9 1.4GHz (45ms). Our Hough transform hardware was verified by applying it to the newly developed LDWS (lane departure warning system).


title: Automatic Analysis of Rhythmic Poetry with Applications to Generation and Translation

abstract: We employ statistical methods to analyze, generate, and translate rhythmic poetry. We first apply unsupervised learning to reveal word-stress patterns in a corpus of raw poetry. We then use these word-stress patterns, in addition to rhyme and discourse models, to generate English love poetry. Finally, we translate Italian poetry into English, choosing target realizations that conform to desired rhythmic patterns.


title: Small slot antenna for metal mountable UHF RFID tags

abstract: Small slot antenna on a high-permittivity ceramic Barium Titanate (BaTiO3) substrate for metal mountable passive UHF RFID tags is presented. The tag has extremely small size of 0.09λ×0.09λ, simple structure based on only a single conductor layer, and it achieves competitive performance against state of the art.


title: Top 10 plant pathogenic bacteria in molecular plant pathology

abstract: Many plant bacteriologists, if not all, feel that their particular microbe should appear in any list of the most important bacterial plant pathogens. However, to our knowledge, no such list exists. The aim of this review was to survey all bacterial pathologists with an association with the journal Molecular Plant Pathology and ask them to nominate the bacterial pathogens they would place in a 'Top 10' based on scientific/economic importance. The survey generated 458 votes from the international community, and allowed the construction of a Top 10 bacterial plant pathogen list. The list includes, in rank order: (1) Pseudomonas syringae pathovars; (2) Ralstonia solanacearum; (3) Agrobacterium tumefaciens; (4) Xanthomonas oryzae pv. oryzae; (5) Xanthomonas campestris pathovars; (6) Xanthomonas axonopodis pathovars; (7) Erwinia amylovora; (8) Xylella fastidiosa; (9) Dickeya (dadantii and solani); (10) Pectobacterium carotovorum (and Pectobacterium atrosepticum). Bacteria garnering honourable mentions for just missing out on the Top 10 include Clavibacter michiganensis (michiganensis and sepedonicus), Pseudomonas savastanoi and Candidatus Liberibacter asiaticus. This review article presents a short section on each bacterium in the Top 10 list and its importance, with the intention of initiating discussion and debate amongst the plant bacteriology community, as well as laying down a benchmark. It will be interesting to see, in future years, how perceptions change and which bacterial pathogens enter and leave the Top 10.


title: Prediction of Arctic Sea Ice Concentration Using a Fully Data Driven Deep Neural Network

abstract: The Arctic sea ice is an important indicator of the progress of global warming and climate change. Prediction of Arctic sea ice concentration has been investigated by many disciplines and predictions have been made using a variety of methods. Deep learning (DL) using large training datasets, also known as deep neural network, is a fast-growing area in machine learning that promises improved results when compared to traditional neural network methods. Arctic sea ice data, gathered since 1978 by passive microwave sensors, may be an appropriate input for training DL models. In this study, a large Arctic sea ice dataset was employed to train a deep neural network and this was then used to predict Arctic sea ice concentration, without incorporating any physical data. We compared the results of our methods quantitatively and qualitatively to results obtained using a traditional autoregressive (AR) model, and to a compilation of results from the Sea Ice Prediction Network, collected using a diverse set of approaches. Our DL-based prediction methods outperformed the AR model and yielded results comparable to those obtained with other models.


title: Data Analytics in Free-Floating Carsharing: Evidence from the City of Berlin

abstract: Carsharing has emerged as an alternative to vehicle ownership and is a rapidly expanding global market. Particularly through the flexibility of free-floating models, car sharing complements public transport since customers do not need to return cars to specific stations. We present a novel data analytics approach that provides decision support to car sharing operators -- from local start-ups to global players -- in maneuvering this constantly growing and changing market environment. Using a large set of rental data, as well as zero-inflated and geographically weighted regression models, we derive indicators for the attractiveness of certain areas based on points of interest in their vicinity. These indicators are valuable for a variety of operational and strategic decisions. As a demonstration project, we present a case study of Berlin, where the indicators are used to identify promising regions for business area expansion.


title: A Novel Frequency Domain Method for Estimating Blood Pressure from Photoplethysmogram

abstract: A novel method of estimating blood pressure (BP) from Photoplethysmogram (PPG) is provided. The first 15 points of the discrete cosine transform (DCT) sequence of the PPG signal are trained as inputs of the Backpropagation neural network (BPNN), the systolic blood pressure (SBP) and diastolic blood pressure (DBP) extracted from the Arterial blood pressure (ABP) signal which is corresponding to the PPG signal are used as outputs of the BPNN. Combining the idea of AdaBoost algorithm, 10 BPNN with different initial values are chosen as "weak predictors" to form a "strong predictor" for predicting blood pressure. The PhysioNet/CinC Challenge 2010 data set was used in this work for training and testing and more than 10,000 separate PPG heartbeats with corresponding ABP signals were extracted from this data set. The experimental results show that this method provided in this article can predict blood pressure effectively.


title: A Survey on Position-Based Routing Algorithms in Wireless Sensor Networks

abstract: Wireless sensor networks (WSN) have attracted much attention in recent years for its unique characteristics and wide use in many different applications. Routing protocol is one of key technologies in WSN. In this paper, the position-based routing protocols are surveyed and classified into four categories: flooding-based, curve-based, grid-based and ant algorithm-based intelligent. To each category, the main contribution of related routing protocols is shown including the relationship among the routing protocols. The different routing algorithms in the same category and the different categories are compared based on popular metrics. Moreover, some open research directions in WSN are also discussed.


title: A real-time phenotyping framework using machine learning for plant stress severity rating in soybean

abstract: BACKGROUND ::: Phenotyping is a critical component of plant research. Accurate and precise trait collection, when integrated with genetic tools, can greatly accelerate the rate of genetic gain in crop improvement. However, efficient and automatic phenotyping of traits across large populations is a challenge; which is further exacerbated by the necessity of sampling multiple environments and growing replicated trials. A promising approach is to leverage current advances in imaging technology, data analytics and machine learning to enable automated and fast phenotyping and subsequent decision support. In this context, the workflow for phenotyping (image capture → data storage and curation → trait extraction → machine learning/classification → models/apps for decision support) has to be carefully designed and efficiently executed to minimize resource usage and maximize utility. We illustrate such an end-to-end phenotyping workflow for the case of plant stress severity phenotyping in soybean, with a specific focus on the rapid and automatic assessment of iron deficiency chlorosis (IDC) severity on thousands of field plots. We showcase this analytics framework by extracting IDC features from a set of ~4500 unique canopies representing a diverse germplasm base that have different levels of IDC, and subsequently training a variety of classification models to predict plant stress severity. The best classifier is then deployed as a smartphone app for rapid and real time severity rating in the field. ::: ::: ::: RESULTS ::: We investigated 10 different classification approaches, with the best classifier being a hierarchical classifier with a mean per-class accuracy of ~96%. We construct a phenotypically meaningful 'population canopy graph', connecting the automatically extracted canopy trait features with plant stress severity rating. We incorporated this image capture → image processing → classification workflow into a smartphone app that enables automated real-time evaluation of IDC scores using digital images of the canopy. ::: ::: ::: CONCLUSION ::: We expect this high-throughput framework to help increase the rate of genetic gain by providing a robust extendable framework for other abiotic and biotic stresses. We further envision this workflow embedded onto a high throughput phenotyping ground vehicle and unmanned aerial system that will allow real-time, automated stress trait detection and quantification for plant research, breeding and stress scouting applications.


title: Sequential Operations in Digital Picture Processing

abstract: An extensible jib assembly for a crane boom of the extensible and retractable type which has a main boom member and at least one outer boom member telescoped within the main boom member. The jib assembly consists of a plurality of rectangular jib sections with a base section telescopingly receiving the remainder of the sections. First cooperating means between the base section and the main boom member normally maintain the jib assembly in a stored position on the lower surface of the main boom member. The jib assembly is located in a manner that releasable coupling means carried by one end of the base section may be connected to the outer end of the outer boom section when the boom is fully retracted so as to pivotally connect the jib assembly to the boom while the jib assembly is in the stored position.


title: Biologically Plausible Multi-Dimensional Reinforcement Learning in Neural Networks

abstract: How does the brain learn to map multi-dimensional sensory inputs to multi-dimensional motor outputs when it can only observe single rewards for the coordinated outputs of the whole network of neurons that make up the brain? We introduce Multi-AGREL, a novel, biologically plausible multi-layer neural network model for multi-dimensional reinforcement learning. We demonstrate that Multi-AGREL can learn non-linear mappings from inputs to multi-dimensional outputs by using only scalar reward feedback. We further show that in Multi-AGREL, the changes in the connection weights follow the gradient that minimizes global prediction error, and that all information required for synaptic plasticity is locally present.


title: Approximation algorithms for some routing problems

abstract: Several polynomial time approximation algorithms for some NP-complete routing problems are presented, and the worst-case ratios of the cost of the obtained route to that of an optimal are determined. A mixed-strategy heuristic with a bound of 9/5 is presented for the Stacker-Crane problem (a modified Traveling Salesman problem). A tour-splitting heuristic is given for k-person variants of the Traveling Salesman problem, the Chinese Postman problem, and the Stacker-Crane problem, for which a minimax solution is sought. This heuristic has a bound of e + 1 - 1/k, where e is the bound for the corresponding 1-person algorithm.


title: Options for Effective Treatment of Visceral Leishmaniasis

abstract: Visceral Leishmaniasis (VL), also known as kala-azar, is caused by several species of Leishmania, a protozoan parasite (Leishmania donovani) transmitted to humans by the bite of infected phelobotomine argentipes sandflies. VL is a disease of poverty, affecting the poorest of the poor. It is a major cause of morbidity and mortality in some areas (localized). If the infection is left untreated, the patient dies in about 2 years. Several drugs are now available for the treatment of VL. However, some of them are very costly (miltefosine, lipid amphotericin B). Sodium stibogluconate is an effective drug and the backbone of VL treatment for about six decades. Unfortunately, parasites developed resistance against the drug. In some areas in India, for example in North Bihar, approximately 60% of isolates are resistant to this treatment. In addition, the compound exhibits high cardio-toxity, which is an important limiting factor for its use. Based on the new data, which became available, the WHO/SEARO Regional Technical Advisory Group (RTAG) especially constituted for the kala-azar elimination program undertaken by India, Nepal, and Bangladesh in 2005, recommended that miltefosine should be used as the first line drug. However, the RTAG at its meeting in Dhaka (Bangladesh) in 2009 modified the above recommendation and advised that miltefosine should be phased out and replaced by lipid amphotericin B. The decision to switch over to lipid amphotericin B could have been delayed, because the program had already made substantial progress using miltefosine. In view of drug resistance, low compliance, availability and cost, it is imperative that serious efforts should be made to develop new drugs, preferably oral, for the treatment of VL and PKDL.


title: Effect of disorder on the Kondo behavior of thin Cu(Mn) films

abstract: We have studied the influence of disorder on the Kondo effect in thin films of Cu(Mn), i.e., Cu doped with a small amount of Mn. We find that the Kondo contribution to the resistivity is suppressed when the elastic mean-free-path, $\lambda$, is reduced. While this is qualitatively similar to results found previously by our group in a different material, our new experiments reveal in detail how this suppression depends on both film thickness and $\lambda$. These results are compared with the theory of Martin, Wan, and Phillips. While there is general qualitative agreement with this theory, there appear to be some quantitative discrepancies.


title: Measuring scientific impact beyond academia: An assessment of existing impact metrics and proposed improvements

abstract: How does scientific research affect the world around us? Being able to answer this question is of great importance in order to appropriately channel efforts and resources in science. The impact by scientists in academia is currently measured by citation based metrics such as h-index, i-index and citation counts. These academic metrics aim to represent the dissemination of knowledge among scientists rather than the impact of the research on the wider world. In this work we are interested in measuring scientific impact beyond academia, on the economy, society, health and legislation (comprehensive impact). Indeed scientists are asked to demonstrate evidence of such comprehensive impact by authoring case studies in the context of the Research Excellence Framework (REF). We first investigate the extent to which existing citation based metrics can be indicative of comprehensive impact. We have collected all recent REF impact case studies from 2014 and we have linked these to papers in citation networks that we constructed and derived from CiteSeerX, arXiv and PubMed Central using a number of text processing and information retrieval techniques. We have demonstrated that existing citation-based metrics for impact measurement do not correlate well with REF impact results. We also consider metrics of online attention surrounding scientific works, such as those provided by the Altmetric API. We argue that in order to be able to evaluate wider non-academic impact we need to mine information from a much wider set of resources, including social media posts, press releases, news articles and political debates stemming from academic work. We also provide our data as a free and reusable collection for further analysis, including the PubMed citation network and the correspondence between REF case studies, grant applications and the academic literature.


title: Weakly-supervised discovery of named entities using web search queries

abstract: A seed-based framework for textual information extraction allows for weakly supervised extraction of named entities from anonymized Web search queries. The extraction is guided by a small set of seed named entities, without any need for handcrafted extraction patterns or domain-specific knowledge, allowing for the acquisition of named entities pertaining to various classes of interest to Web search users. Inherently noisy search queries are shown to be a highly valuable, albeit little explored, resource for Web-based named entity discovery.


title: TAXONOMY AWARE CATALOG INTEGRATION WITH HIDDEN MARKOV MODEL (HMM) ABSTRACT

abstract: Integration of data is the major important task for online ecommerce based web portals and commerce search engine based application. The integration of task faced by online commercial portals and e-commerce search engines are the integration of products coming from multiple providers to their creation of product catalogs. Cataloging of products from the data provider into the master taxonomy and while formulate use of the information provider taxonomy data become major problem. Conquer this difficulty classify the products based on textual based classifier and taxonomy-aware step with the purpose of adjust the outcome of a textual based classifier to make sure that products that are close as one in the provider taxonomy. In taxonomy aware calibration process base classifier that is text base classifier update the results based on parameters values specified at calibration step, but these values are manually given by user, at a halt it becomes major issues to identify candidate products for category the products, to conquer these problem Proposed a HMM based machine learning method to derive the parameter values automatically and continuously retrain the base classifier with fundamentals chosen during the taxonomy-aware calibration step. HMMs allow you to estimate probabilities of unobserved products in product categorization. In our approach estimate the probability between the categorized products and unobserved products .Unobserved categorization products are derived from the Transitional probability, observation probability products are used to find the observed or categorized products in the system .In this machine learning algorithm involves a large quantity of unlabeled product data with only a small number of labeled product data. It finds the each candidate parameter ߠ ௜ and after that get the optimal parameter ߛ from that the validation set is accuracy is maximized. An experimental result shows that the HMM based machine learning systems are efficient and thus appropriate to the huge data sets to be representative on the web.


title: Traffic Management for Emergency Vehicle Priority Based on Visual Sensing

abstract: Vehicular traffic is endlessly increasing everywhere in the world and can cause terrible traffic congestion at intersections. Most of the traffic lights today feature a fixed green light sequence, therefore the green light sequence is determined without taking the presence of the emergency vehicles into account. Therefore, emergency vehicles such as ambulances, police cars, fire engines, etc. stuck in a traffic jam and delayed in reaching their destination can lead to loss of property and valuable lives. This paper presents an approach to schedule emergency vehicles in traffic. The approach combines the measurement of the distance between the emergency vehicle and an intersection using visual sensing methods, vehicle counting and time sensitive alert transmission within the sensor network. The distance between the emergency vehicle and the intersection is calculated for comparison using Euclidean distance, Manhattan distance and Canberra distance techniques. The experimental results have shown that the Euclidean distance outperforms other distance measurement techniques. Along with visual sensing techniques to collect emergency vehicle information, it is very important to have a Medium Access Control (MAC) protocol to deliver the emergency vehicle information to the Traffic Management Center (TMC) with less delay. Then only the emergency vehicle is quickly served and can reach the destination in time. In this paper, we have also investigated the MAC layer in WSNs to prioritize the emergency vehicle data and to reduce the transmission delay for emergency messages. We have modified the medium access procedure used in standard IEEE 802.11p with PE-MAC protocol, which is a new back off selection and contention window adjustment scheme to achieve low broadcast delay for emergency messages. A VANET model for the UTMS is developed and simulated in NS-2. The performance of the standard IEEE 802.11p and the proposed PE-MAC is analysed in detail. The NS-2 simulation results have shown that the PE-MAC outperforms the IEEE 802.11p in terms of average end-to-end delay, throughput and energy consumption. The performance evaluation results have proven that the proposed PE-MAC prioritizes the emergency vehicle data and delivers the emergency messages to the TMC with less delay compared to the IEEE 802.11p. The transmission delay of the proposed PE-MAC is also compared with the standard IEEE 802.15.4, and Enhanced Back-off Selection scheme for IEEE 802.15.4 protocol [EBSS, an existing protocol to ensure fast transmission of the detected events on the road towards the TMC] and the comparative results have proven the effectiveness of the PE-MAC over them. Furthermore, this research work will provide an insight into the design of an intelligent urban traffic management system for the effective management of emergency vehicles and will help to save lives and property.


title: Testing database transaction concurrency

abstract: Database application programs are often designed to be executed concurrently by many users. By grouping related database queries into transactions, DBMS (database management system) can guarantee that each transaction satisfies the well-known ACID properties: atomicity, consistency, isolation, and durability. However, if a database application is decomposed into transactions in an incorrect manner, the application may fail when executed concurrently due to potential offline concurrency problems. This paper presents a dataflow analysis technique for identifying schedules of transaction execution aimed at revealing concurrency faults of this nature, along with techniques for controlling the DBMS or the application so that execution of transaction sequences follows generated schedules. The techniques have been integrated into AGENDA, a tool set for testing relational database application programs. Preliminary empirical evaluation is presented.


title: State-aware Network Access Management for Software-Defined Networks

abstract: OpenFlow, as the prevailing technique for Software-Defined Networks (SDNs), introduces significant programmability, granularity, and flexibility for many network applications to effectively manage and process network flows. However, because OpenFlow attempts to keep the SDN data plane simple and efficient, it focuses solely on L2/L3 network transport and consequently lacks the fundamental ability of stateful forwarding for the data plane. Also, OpenFlow provides a very limited access to connection-level information in the SDN controller. In particular, for any network access management applications on SDNs that require comprehensive network state information, these inherent limitations of OpenFlow pose significant challenges in supporting network services. To address these challenges, we propose an innovative connection tracking framework called STATEMON that introduces a global state-awareness to provide better access control in SDNs. STATEMON is based on a lightweight extension of OpenFlow for programming the stateful SDN data plane, while keeping the underlying network devices as simple as possible. To demonstrate the practicality and feasibility of STATEMON, we implement and evaluate a stateful network firewall and port knocking applications for SDNs, using the APIs provided by STATEMON. Our evaluations show that STATEMON introduces minimal message exchanges for monitoring active connections in SDNs with manageable overhead (3.27% throughput degradation).


title: Characteristics of chronic ischemic pain in patients with peripheral arterial disease

abstract: Chronic ischemic pain is a leading cause of pain in the lower extremities. A neuropathic component in ischemic pain has been shown. Neuropathic pain questionnaires are established as a common tool in pain research. The aim of this study was to analyze the clinical nature and the character of chronic ischemic pain in peripheral arterial disease (PAD). One hundred and two patients suffering from symptomatic PAD (Fontaine stages II–IV) were surveyed using validated pain questionnaires (VAS, NPSI, S-LANSS, PDI, SF-MPQ). Pain related disability was 22.7 ± 1.7 (mean ± SEM) in patients with intermittent claudication (CI) and 34.0 ± 2.3 in patients with critical limb ischemia (CLI). Neuropathic pain questionnaires revealed distinctly higher scores for CLI than for CI: The S-LANSS indicated pain of predominantly neuropathic origin in patients with CLI (17.2 ± 0.8) compared to CI (6.7 ± 0.8; p < 0.001). Global NPSI scores were 34.1 ± 3.1 for CLI and 6.6 ± 1.1 for CI (p < 0.001). S-LANSS and NPSI correlated well (Spearman’s ρ = 0.779; p < 0.001). The SF-MPQ revealed that patients with CLI scored significantly higher for pain descriptors stabbing, hot-burning, tender and cruel-punishing compared to those with CI. The results suggest that the character of ischemic pain changes from nociceptive pain in patients with CI to predominantly neuropathic pain in patients with CLI. A neuropathic pain component seems to be a serious aspect in CLI, while it is not in CI. Questionnaires might be a helpful tool to investigate and diagnose ischemic pain.


title: SpiderMAV: Perching and stabilizing micro aerial vehicles with bio-inspired tensile anchoring systems

abstract: Whilst Micro Aerial Vehicles (MAVs) possess a variety of promising capabilities, their high energy consumption severely limits applications where flight endurance is of high importance. Reducing energy usage is one of the main challenges in advancing aerial robot utility. To address this bottleneck in the development of unmanned aerial vehicle applications, this work proposes an bioinspired mechanical approach and develops an aerial robotic system for greater endurance enabled by low power station-keeping. The aerial robotic system consists of an multirotor MAV and anchoring modules capable of launching multiple tensile anchors to fixed structures in its operating envelope. The resulting tensile perch is capable of providing a mechanically stabilized mode for high accuracy operation in 3D workspace. We explore generalised geometric and static modelling of the stabilisation concept using screw theory. Following the analytical modelling of the integrated robotic system, the tensile anchoring modules employing high pressure gas actuation are designed, prototyped and then integrated to a quadrotor platform. The presented design is validated with experimental tests, demonstrating the stabilization capability even in a windy environment.


title: An Approach to Vehicle Recognition Using Supervised Learning

abstract: SUMMARY To enhance safety and traffic efficiency, a driver assistance system and an autonomous vehicle system are being developed. A preceding vehicle recognition method is important to develop such systems. In this paper, a vision-based preceding vehicle recognition method, based on supervised learning from sample images is proposed. The improvement for Modified Quadratic Discriminant Function (MQDF) classifier that is used in the proposed method is also shown. And in the case of road environment recognition including the preceding vehicle recognition, many researches have been reported. However in those researches, a quantitative evaluation with large number of images has rarely been done. Whereas, in this paper, over 1,000 sample images for passenger vehicles, which are recorded on a highway during daytime, are used for an evaluation. The evaluation result shows that the performance in a low order case is improved from the ordinary MQDF. Accordingly, the calculation time is reduced more than 20% by using the proposed method. And the feasibility of the proposed method is also proved, due to the result that the proposed method indicates over 98% as classification rate.


title: A spatiotemporal compression based approach for efficient big data processing on Cloud

abstract: It is well known that processing big graph data can be costly on Cloud. Processing big graph data introduces complex and multiple iterations that raise challenges such as parallel memory bottlenecks, deadlocks, and inefficiency. To tackle the challenges, we propose a novel technique for effectively processing big graph data on Cloud. Specifically, the big data will be compressed with its spatiotemporal features on Cloud. By exploring spatial data correlation, we partition a graph data set into clusters. In a cluster, the workload can be shared by the inference based on time series similarity. By exploiting temporal correlation, in each time series or a single graph edge, temporal data compression is conducted. A novel data driven scheduling is also developed for data processing optimisation. The experiment results demonstrate that the spatiotemporal compression and scheduling achieve significant performance gains in terms of data size and data fidelity loss.


title: Duadic codes and difference sets

abstract: This note illustrates the strong relationship between duadic codes and difference sets. Sufficient conditions are given for the code of a difference set to be embedded into a duadic code. This result generalizes some wellknown results on projective planes and is related to Wilbrink's Theorem.


title: Endogenous peroxidase activity in human cutaneous and adenoidal mast cells.

abstract: We have studied peroxidase activity in human cutaneous and adenoidal mast cells using different methods, in order to determine the optimal technical conditions for its demonstration. In 1.25% glutaraldehyde-fixed cells, no peroxidase activity was seen. On the contrary, in tannic acid-aldehyde-fixed cells or in unfixed cells peroxidase activity was revealed independently of the DAB concentration or the incubation time in DAB medium. The reaction product was localized in perinuclear cisternae and endoplasmic reticulum. Granules were always unreactive with all techniques employed. Golgi apparatus was generally negative and only occasional cells exhibited one or two positive peripheral cisternae. This activity appears sensitive to fixation by glutaraldehyde and is inhibited by 3-amino-1,2,4-triazole (AMT) and by lack of H2O2 or DAB in the incubation medium, but not by potassium cyanide, sodium azide, or sodium pyruvate, at the concentrations used. The peroxidase activity described in this report is an endogenous peroxidase and is not related to uptake of exogenous peroxidase by mast cells. It can therefore be considered as an ultracytochemical marker of human mast cells.


title: Dual-Layer Ridged Waveguide Slot Array Fed by a Butler Matrix With Sidelobe Control in the 60-GHz Band

abstract: A novel multibeam antenna based on a $\mathbf{4} \times \mathbf{4}$ Butler matrix with sidelobe level (SLL) control is proposed in the 60-GHz band. The SLL is controlled using a dedicated circuit composed by four 90-degree couplers with different coupling factors and several cross couplers. This dual-layer antenna is made by hollow waveguides and is assembled using diffusion bonding technique. The radiating part is a compact array of ridged waveguides designed to avoid appearance of grating lobes in the visible range for large scanning angles. The antenna field of view is $\pm \mathbf{43}^\circ $ with sidelobes lower than $- \mathbf{17.5}\;\mathbf{dB}$ over the entire scanning range. The isolation between the input ports of the Butler matrix is better than 25 dB at the design frequency of 61.5 GHz. The reflection bandwidth for VSWR $ is larger than 6.5%. At the design frequency, the measured conductor losses of the antenna are estimated lower than 0.64 dB for all ports.


Assessment in an Online Mathematics Course


title: On the importance of temporal dynamics in modeling urban activity

abstract: The vast amount of available spatio-temporal data of human activities and mobility has given raise to the rapidly emerging field of urban computing/informatics. Central to the latter is understanding the dynamics of the activities that take place in an urban area (e.g., a city). This can significantly enhance functionalities such as resource and service allocation within a city. Existing literature has paid a lot of attention on spatial dynamics, with the temporal ones often being neglected and left out. However, this can lead to non-negligible implications. For instance, while two areas can appear to exhibit similar activity when the latter is aggregated in time, they can be significantly different when introducing the temporal dimension. Furthermore, even when considering a specific area X alone, the transitions of the activity that takes place within X are important themselves. Using data from the most prevalent location-based social network (LBSN for short), Foursquare, we analyze the temporal dynamics of activities in New York City and San Francisco. Our results clearly show that considering the temporal dimension provides us with a different and more detailed description of urban dynamics. We envision this study to lead to more careful and detailed consideration of the temporal dynamics when analyzing urban activities.


title: Deep patch-wise colorization model for grayscale images

abstract: To handle the colorization problem, we propose a deep patch-wise colorization model for grayscale images. Distinguished with some constructive color mapping models with complicated mathematical priors, we alternately apply two loss metric functions in the deep model to suppress the training errors under the convolutional neural network. To address the potential boundary artifacts, a refinement scheme is presented inspired by guided filtering. In the experiment section, we summarize our network parameters setting in practice, including the patch size, amount of layers and the convolution kernels. Our experiments demonstrate this model can output more satisfactory visual colorizations compared with the state-of-the-art methods. Moreover, we prove our method has extensive application domains and can be applied to stylistic colorization.


title: Argentine tango dance compared to mindfulness meditation and a waiting-list control: A randomised trial for treating depression

abstract: Summary Objectives To determine whether tango dancing is as effective as mindfulness meditation in reducing symptoms of psychological stress, anxiety and depression, and in promoting well-being. Design This study employed analysis of covariance (ANCOVA) and multiple regression analysis. Participants Ninety-seven people with self-declared depression were randomised into tango dance or mindfulness meditation classes, or to control/waiting-list. Setting classes were conducted in a venue suitable for both activities in the metropolitan area of Sydney, Australia. Interventions Participants completed six-week programmes (1½h/week of tango or meditation). The outcome measures were assessed at pre-test and post-test. Main outcome measures Depression, Anxiety and Stress Scale; The Self Esteem Scale; Satisfaction with Life Scale, and Mindful Attention Awareness Scale. Results Sixty-six participants completed the program and were included in the statistical analysis. Depression levels were significantly reduced in the tango (effect size d =0.50, p =.010), and meditation groups (effect size d =0.54, p =.025), relative to waiting-list controls. Stress levels were significantly reduced only in the tango group (effect size d =0.45, p =.022). Attending tango classes was a significant predictor for the increased levels of mindfulness R 2 =.10, adjusted R 2 =.07, F (2,59)=3.42, p =.039. Conclusion Mindfulness-meditation and tango dance could be effective complementary adjuncts for the treatment of depression and/or inclusion in stress management programmes. Subsequent trials are called to explore the therapeutic mechanisms involved.


title: Body Mass Index Estimation by Using an Adaptive Neuro Fuzzy Inference System

abstract: Abstract In the last few decades, the early diagnosis and prediction of disease, particularly the diseases related to Body Mass Index (BMI), have increased dramatically. In this regard, the soft computing techniques, such as neural networks, machine learning, Fuzzy Inference Systems (FIS) etc. are the principle elements in these studies. The Artificial Neural Network particular form with the hybrid intelligent system called Adaptive Neuro-Fuzzy Inference System (ANFIS) was chosen and applied in this study. ANFIS benefits from the ANN’s superior learning algorithms and FIS’ excellent estimation functions. The main aim of the current study is to examine the performance of ANFIS in order to estimate the BMI by using correlated explanatory variables with the response variable BMI. Two ANFIS models with the same attributes and structures were developed separately for both gender data sets. The low RMSE results of both models (female RMSE=1.914 and male RMSE=1.817) revealed that these models are biologically acceptable and provide methods for predicting the BMI based on five factors, which have acceptably strong correlations with BMI values. Consequently, it is suggested that obesity could be prevented if the changeable risk factors with various lifestyle modifications could be controlled.


title: Ensuring quality of highest-risk population care management in a teleworking environment.

abstract: Abstract Disease management (DM) has been shown to improve the care process and health outcomes for individuals with particu-lar chronic conditions, but what about people who struggle with several chronic conditions and are at greatest risk for imminent hospitalization? Even the most effective DM initiatives do not always reach health plan members with maximum risks and impending costs. Single-condition DM programs may not always fully address the multiple medical and psychosocial issues prevalent among highest-risk members.


title: Learning User Preferences By Adaptive Pairwise Comparison

abstract: Users make choices among multi-attribute objects in a data set in a variety of domains including used car purchase, job search and hotel room booking. Individual users sometimes have strong preferences between objects, but these preferences may not be universally shared by all users. If we can cast these preferences as derived from a quantitative user-specific preference function, then we can predict user preferences by learning their preference function, even though the preference function itself is not directly observable, and may be hard to express. ::: ::: In this paper we study the problem of preference learning with pairwise comparisons on a set of entities with multiple attributes. We formalize the problem into two subproblems, namely preference estimation and comparison selection. We propose an innovative approach to estimate the preference, and introduce a binary search strategy to adaptively select the comparisons. We introduce the concept of an orthogonal query to support this adaptive selection, as well as a novel S-tree index to enable efficient evaluation of orthogonal queries. ::: ::: We integrate these components into a system for inferring user preference with adaptive pairwise comparisons. Our experiments and user study demonstrate that our adaptive system significantly outperforms the naive random selection system on both real data and synthetic data, with either simulated or real user feedback. We also show our preference learning approach is much more effective than existing approaches, and our S-tree can be constructed efficiently and perform orthogonal query at interactive speeds.


title: Psychosocial Factors Affecting Smartphone Addiction in University Students

abstract: Smartphone addiction is a recent concern that has resulted from the dramatic increase in worldwide smartphone use. The purpose of this cross-sectional study was to evaluate psychosocial factors affecting smartphone addiction in university students. The study was performed among students at the Ondokuz Mayis University Samsun School of Health (Samsun, Turkey) on October-December 2015. Four hundred ninety-four students possessing smartphones and agreeing to participate were included. A sociodemographic data form produced by the authors and consisting of 10 questions was administered together with a questionnaire involving the Smartphone Addiction Scale-Short Version (SAS-SV), the Flourishing Scale, the General Health Questionnaire, and the Multidimensional Scale of Perceived Social Support. The questionnaires were applied in a class environment at face-to-face interviews. SAS-SV scores of 6.47% of students were "significantly higher" than the participating group mean SAS-SV score. Multiple regression analysis revealed that depression, anxiety and insomnia, and familial social support statistically, significantly predicted smartphone addiction. Further studies of smartphone addiction in different age groups and with different educational levels are now needed.


title: Who will Lead and Who will Follow? a Social Process of Leadership Identity Construction in Organizations

abstract: We propose that a leadership identity is coconstructed in organizations when individuals claim and grant leader and follower identities in their social interactions. Through this claiming-granting process, individuals internalize an identity as leader or follower, and those identities become relationally recognized through reciprocal role adoption and collectively endorsed within the organizational context. We specify the dynamic nature of this process, antecedents to claiming and granting, and an agenda for research on leadership identity and development.


title: Configurable Architecture for Double/Two-Parallel Single Precision Floating Point Division

abstract: This paper presents a dynamically configurable and area-efficient multi-precision architecture for Floating Point (FP) division. FP division is a core arithmetic in scientific and engineering domain. We propose an architecture for double precision (DP) division which is also capable of processing dual (two-parallel) single precision (SP) computation, named as DPdSP FP divider. The architecture is based on series expansion methodology ofcomputing division. Key components involved in the floatingpoint division architecture are re-designed in order to efficiently enable the resource sharing and tune the data-path for processing both precision operands with minimum hardware overhead. We have targeted the proposed architecture using "OSUcells Cell Library" 0.18μm technology ASIC implementation. Compared to a standalone double precision divider, the proposed dual modeunified architecture needs ≈ 7% extra hardware, with ≈ 5% delay overhead. When compared to the previous work in literature, the proposed dual mode architecture out-perform them in terms of required area, throughput, and area × delay, has smaller area & delay overhead over only DP divider, and has more computational support.


title: General Video Game Playing

abstract: One of the grand challenges of AI is to create general intelligence: an agent that can excel at many tasks, not just one. In the area of games, this has given rise to the challenge of General Game Playing (GGP). In GGP, the game (typically a turn-taking board game) is defined declaratively in terms of the logic of the game (what happens when a move is made, how the scoring system works, how the winner is declared, and so on). The AI player then has to work out how to play the game and how to win. In this work, we seek to extend the idea of General Game Playing into the realm of video games, thus forming the area of General Video Game Playing (GVGP). In GVGP, computational agents will be asked to play video games that they have not seen before. At the minimum, the agent will be given the current state of the world and told what actions are applicable. Every game tick the agent will have to decide on its action, and the state will be updated, taking into account the actions of the other agents in the game and the game physics. We envisage running a competition based on GVGP playing, using arcadestyle (e.g. similar to Atari 2600) games as our starting point. These games are rich enough to be a formidable challenge to a GVGP agent, without introducing unnecessary complexity. The competition that we envisage could have a number of tracks, based on the form of the state (frame buffer or object model) and whether or not a forward model of action execution is available. We propose that the existing Physical Travelling Salesman (PTSP) software could be extended for our purposes and that a variety of GVGP games could be created in this framework by AI and Games students and other developers. Beyond this, we envisage the development of a Video Game Description Language (VGDL) as a way of concisely specifying video games. For the competition, we see this as being an interesting challenge in terms of deliberative search, machine learning and transfer of existing knowledge into new domains.


title: ULTRASONOGRAPHIC FEATURES OF GRASS AWNS IN THE URINARY BLADDER

abstract: Grass awns are a common cause of foreign body disease in animals, but little is known about their presence in the lower urinary tract. The ultrasonographic features of grass awns in vivo and in vitro have been described in detail. The purpose of this report is to describe the clinical and sonographic features of grass awns in the urinary bladder of dogs and cats. Three male Yorkshire terriers (one of which was examined twice) and one female domestic short-haired cat were evaluated for signs of lower urinary tract disease, and an intravesicular grass awn was suspected based on ultrasound examination. The grass awn appeared ultrasonographically as a bladder stone (n ¼ 1) or a linear hyperechoic structure (n ¼ 4) with or without acoustic shadowing that was easy to identify due to contrast with surrounding urine. The presence of a grass awn within the urinary bladder was confirmed during exploratory surgery. In all patients, the route of entry of the grass awn was thought to have been retrograde migration from the urethral opening. The ultrasonographic appearance of grass awns in the bladder is consistent with that in other tissues. r 2010 Veterinary Radiology & Ultrasound, Vol. 51, No. 4, 2010, pp 462–465.