title: Maternal and infant outcomes following third trimester exposure to marijuana in opioid dependent pregnant women maintained on buprenorphine

abstract: Abstract Background To determine whether maternal and infant outcomes are associated with exposure to marijuana during the third trimester in a population of opioid dependent pregnant women maintained on buprenorphine. Methods This retrospective cohort study of 191 maternal-infant dyads exposed to buprenorphine during pregnancy examines a variety of variables including gestational age, birthweight, method of delivery, Apgar scores at one and five minutes, duration of infant hospital stay, peak neonatal abstinence syndrome (NAS) score, duration of NAS and incidence of pharmacologic treatment of NAS in infants exposed to marijuana during the third trimester as compared to infants not exposed to marijuana during the third trimester. Results Analyses failed to support any significant relationship between marijuana use in the third trimester and a variety of maternal and infant outcomes. Two important variables – the likelihood of requiring pharmacologic treatment for NAS (27.6% in marijuana exposed infants vs. 15.7% in non-marijuana exposed infants, p = 0.066) and the duration of infant hospital stay (7.7 days in marijuana exposed infants vs. 6.6 days in non-exposed infants, p = 0.053) trended toward significance. Conclusions Preliminary results indicate that marijuana exposure in the third trimester does not complicate the pregnancy or the delivery process. However, the severity of the infant withdrawal syndrome in the immediate postnatal period may be impacted by marijuana exposure. Because previous study of prenatal marijuana exposure has yielded mixed results, further analysis is needed to determine whether these findings are indeed significant.


title: Fine-Grained Recognition via Attribute-Guided Attentive Feature Aggregation

abstract: Fine-grained object recognition is challenging due to large intra-class variation and inter-class ambiguity. A good algorithm should be able to: 1) discover discriminative local details and 2) align and aggregate these local discriminative patch-level features in an effective way to facilitate object level classification. Towards this end, we propose a novel local feature discovery, discriminative alignment and aggregation framework, inspired by the recent success of deep recurrent attention model. First, we develop a novel attribute-guided attentive network to sequentially discover informative parts/regions, by seeking a good registration between attentive regions and predefined object attributes. This could be considered as a semantic guided salient region discovery and alignment network, which might be more robust than conventional attention model. Second, these discovered regions are actively and progressively fed into a recurrent neural network, to yield the object-level representation. This could be considered as a discriminant aggregation network and informative patch-level features are propagated and accumulated to the deeper nodes of the recurrent network for final classification. We extensively test our framework on two fine-grained image benchmarks and the results demonstrate the effectiveness of the proposed framework.


title: Conceptual framework for tasks in information studies

abstract: Today information-intensive work tasks in professional settings involve highly dynamic information utilization in which information seeking and searching tasks are taking a more central role. This article considers the concept of task in the context of information studies in order to provide a definitional clarity for task-based information seeking and retrieval studies. We identify (1) the central task levels as well as (2) the kinds of dimensions connected to the levels from the perspective of information studies. The analysis is aimed to serve as a conceptual starting point for empirical studies in the research area. The focus is on some central aspects of tasks that are recognized within information studies as well as related research areas (e.g., organizational studies). We define two levels of information-related subtasks: information seeking tasks and information search tasks. Information retrieval tasks are explicitly considered as a specific type of information search task. We describe differences and connections between these task levels. Finally, the implications of the proposed conceptual framework for information studies are discussed.


title: Experimental Design and Data Analysis for Biologists

abstract: 1. Introduction 2. Estimation 3. Hypothesis testing 4. Graphical exploration of data 5. Correlation and regression 6. Multiple regression and correlation 7. Design and power analysis 8. Comparing groups or treatments - analysis of variance 9. Multifactor analysis of variance 10. Randomized blocks and simple repeated measures: unreplicated two-factor designs 11. Split plot and repeated measures designs: partly nested anovas 12. Analysis of covariance 13. Generalized linear models and logistic regression 14. Analyzing frequencies 15. Introduction to multivariate analyses 16. Multivariate analysis of variance and discriminant analysis 17. Principal components and correspondence analysis 18. Multidimensional scaling and cluster analysis 19. Presentation of results.


title: A survey, classification and comparative analysis of medium access control protocols for ad hoc networks

abstract: Recent technological advances in wireless communications offer new opportunities and challenges for wireless ad hoc networking. In the absence of the fixed infrastructure that characterizes traditional wireless networks, control and management of wireless ad hoc networks must be distributed across the nodes, thus requiring carefully designed medium access control (MAC) layer protocols. In this article we survey, classify, and analyze 34 MAC layer protocols for wireless ad hoc networks, ranging from industry standards to research proposals. Through this analysis, six key features emerge: (1) channel separation and access; (2) topology; (3) power; (4) transmission initiation; (5) traffic load and scalability; and (6) range. These features allow us to characterize and classify the protocols, to analyze the tradeoffs produced by different design decisions, and to assess the suitability of various design combinations for ad hoc network applications. The classification and the tradeoff analysis yield design guidelines for future wireless ad hoc network MAC layer protocols.


title: Simulation to scaled city: zero-shot policy transfer for traffic control via autonomous vehicles

abstract: Using deep reinforcement learning, we successfully train a set of two autonomous vehicles to lead a fleet of vehicles onto a round-about and then transfer this policy from simulation to a scaled city without fine-tuning. We use Flow, a library for deep reinforcement learning in microsimulators, to train two policies, (1) a policy with noise injected into the state and action space and (2) a policy without any injected noise. In simulation, the autonomous vehicles learn an emergent metering behavior for both policies which allows smooth merging. We then directly transfer this policy without any tuning to the University of Delaware's Scaled Smart City (UDSSC), a 1:25 scale testbed for connected and automated vehicles. We characterize the performance of the transferred policy based on how thoroughly the ramp metering behavior is captured in UDSSC. We show that the noise-free policy results in severe slowdowns and only, occasionally, it exhibits acceptable metering behavior. On the other hand, the noise-injected policy consistently performs an acceptable metering behavior, implying that the noise eventually aids with the zero-shot policy transfer. Finally, the transferred, noise-injected policy leads to a 5% reduction of average travel time and a reduction of 22% in maximum travel time in the UDSSC. Videos of the proposed self-learning controllers can be found at https://sites.google.com/view/iccps-policy-transfer.


title: Facets: Fast Comprehensive Mining of Coevolving High-order Time Series

abstract: Mining time series data has been a very active research area in the past decade, exactly because of its prevalence in many high-impact applications, ranging from environmental monitoring, intelligent transportation systems, computer network forensics, to smart buildings and many more. It has posed many fascinating research questions. Among others, three prominent challenges shared by a variety of real applications are (a) high-order; (b) contextual constraints and (c) temporal smoothness. The state-of-the-art mining algorithms are rich in addressing each of these challenges, but relatively short of comprehensiveness in attacking the coexistence of multiple or even all of these three challenges. In this paper, we propose a comprehensive method, FACETS, to simultaneously model all these three challenges. We formulate it as an optimization problem from a dynamic graphical model perspective. The key idea is to use tensor factorization to address multi-aspect challenges, and perform careful regularizations to attack both contextual and temporal challenges. Based on that, we propose an effective and scalable algorithm to solve the problem. Our experimental evaluations on three real datasets demonstrate that our method (1) outperforms its competitors in two common data mining tasks (imputation and prediction); and (2) enjoys a linear scalability w.r.t. the length of time series.


title: HIP Security Architecture for the IP-Based Internet of Things

abstract: The IP-based Internet of Things refers to the pervasive interactions of smart objects and people enabling new applications by means of IP protocols. An application scenario is a Smart City in which the city infrastructure, cars, and people exchange information to enable new services. IP protocols, such as IPv6, TCP and HTTP will be further complemented by IPv6 over Low power Wireless Personal Area Networks and Constrained Application Protocol currently in development in IETF. Security and privacy are a must for the IP-based IoTs in order to ensure its acceptance. However, mobility, limited bandwidth, and resource-constrained devices pose new challenges and require for a sound and efficient security architecture. In particular, dynamic association of mobile smart objects and the management of keys in large-scale networks remain an open challenge. In this context, we propose a flexible security architecture based on the Host Identity Protocol and Multimedia Internet KEYing protocols allowing for secure network association and key management. HIP - based on asymmetric-key cryptography - ensures unambiguous thing identification, mobility support, as well as a lightweight and secure method for network association. In our solution, HIP is extended with MIKEY capabilities to provide enhanced key management using polynomials, which allow to generate pair wise keys with any node based on its identity. This combination of protocols and crypto-algorithms ensures both strong security and very good performance as shown by our implementation and presents clear advantages compared with other alternatives.


title: Jumping mini-robot with bio-inspired legs

abstract: This paper presents a bio-inspired concept for an autonomous centimeter-scale robot for locomotion on un-even terrains with bionic legs. Our previous work, the jumping robot “GRILLO I” (Fig. 1A), is firstly introduced, as well as its limitations. For improving the performance of this prototype, we made biological observations on jumping insects by means of a high speed camera. It was found that the legs configuration let map muscle-like force into a constant force at feet-ground interface, which represents an optimum design for jumping. That gives us the bionic inspiration to redesign the legs that can reproduce the dynamic characteristics of insect jumping. Based on this idea, a new jumping robot prototype, GRILLO II (Fig. 1B), is then presented, as well as jumping details measured by the high speed camera and accelerometer.


title: Excited-state acid-base kinetics and equilibria in norharman

abstract: Absorption and CW and time-resolved fluorescence measurements were performed to investigate the excited-state acid-base kinetics and equilibria of norharman in aqueous solutions of different pH. Four different excited-state species, namely cation, neutral molecule, anion, and zwitterion, can be distinguished. Rate equations were applied to explain the observed decay curves and to calculate the pH-dependent kinetic parameters for the transitions between the species


title: A low variation GHz ring oscillator with addition-based current source

abstract: A 3-stage current-starved ring oscillator with 65.1% reduction in process variation in a 90nm CMOS process is presented. The low variation is achieved without degrading the mean operating frequency through the implementation of an addition-based current source to replace a single transistor current source in each inverter stage. No post-fabrication trimming or calibration is required. Circuit simulations indicate that the proposed circuitry is well suited for scaling beyond 90nm. Measurements that are taken from 2 separate wafers and 167 test chips show 65.1% less process variation in output frequency, compared to a conventional current-starved ring oscillator. The power overhead for the additional circuitry is 33µW.


title: A 94 GHz, 1.4 dB Insertion Loss Single-Pole Double-Throw Switch Using Reverse-Saturated SiGe HBTs

abstract: This work demonstrates two 94 GHz SPDT quarter-wave shunt switches using saturated SiGe HBTs. A new mode of operation, called reverse saturation, using the emitter at the RF output node of the switch, is utilized to take advantage of the higher emitter doping and improved isolation from the substrate. The switches were designed in a 180 nm SiGe BiCMOS technology featuring 90 nm SiGe HBTs (selective emitter shrink) with fT/fmax of 250/300+ GHz. The forward-saturated switch achieves an insertion loss and isolation at 94 GHz of 1.8 dB and 19.3 dB, respectively. The reverse-saturated switch achieves a similar isolation, but reduces the insertion loss to 1.4 dB. This result represents a 30% improvement in insertion loss in comparison to the best CMOS SPDT at 94 GHz.


title: Objects Talk - Object Detection and Pattern Tracking Using TensorFlow

abstract: Objects in household that are frequently in use often follow certain patterns with respect to time and geographical movement. Analysing these patterns can help us keep better track of our objects and maximise efficiency by minimizing time wasted in forgetting or searching for them. In our project, we used TensorFlow, a relatively new library from Google, to model our neural network. The TensorFlow Object Detection API is used to detect multiple objects in real-time video streams. We then introduce an algorithm to detect patterns and alert the user if an anomaly is found. We consider the research presented by Laube et al., Finding REMO-detecting relative motion patterns in geospatial lifelines, 201–214, (2004)[1].


title: Privado: Practical and Secure DNN Inference

abstract: Recently, cloud providers have extended support for trusted hardware primitives such as Intel SGX. Simultaneously, the field of deep learning is seeing enormous innovation and increase in adoption. In this paper, we therefore ask the question:"Can third-party cloud services use SGX to provide practical, yet secure DNN Inference-as-a-service?"Our work addresses the three main challenges that SGX-based DNN inferencing faces, namely, security, ease-of-use, and performance. We first demonstrate that side-channel based attacks on DNN models are indeed possible. We show that, by observing access patterns, we can recover inputs to the DNN model. This motivates the need for Privado, a system we have designed for secure inference-as-a-service. Privado is input-oblivious: it transforms any deep learning framework written in C/C++ to be free of input-dependent access patterns. Privado is fully-automated and has a low TCB: with zero developer effort, given an ONNX description, it generates compact C code for the model which can run within SGX-enclaves. Privado has low performance overhead: we have used Privado with Torch, and have shown its overhead to be 20.77\% on average on 10 contemporary networks.


title: Impact of Social Network Structure on Content Propagation: A Study using YouTube Data

abstract: We study how the size and structure of the local network around a node affects the aggregate diffusion of products seeded by it. We examine this in the context of YouTube, the popular video-sharing site. We address the endogeneity problems common to this setting by using a rich dataset and a careful estimation methodology. We empirically demonstrate that the size and structure of an author’s local network is a significant driver of the popularity of videos seeded by her, even after controlling for observed and unobserved video characteristics, unobserved author characteristics, and endogenous network formation. Our findings are distinct from those in the peer effects literature, which examines neighborhood effects on individual behavior, since we document the causal relationship between a node’s local network position and the global diffusion of products seeded by it. Our results provide guidelines for identifying seeds that provide the best return on investment, thereby aiding managers conducting buzz marketing campaigns on social media forums. Further, our study sheds light on the other substantive factors that affect video consumption on YouTube.


title: Movie summarization based on audiovisual saliency detection

abstract: Based on perceptual and computational attention modeling studies, we formulate measures of saliency for an audiovisual stream. Audio saliency is captured by signal modulations and related multi-frequency band features, extracted through nonlinear operators and energy tracking. Visual saliency is measured by means of a spatiotemporal attention model driven by various feature cues (intensity, color, motion). Audio and video curves are integrated in a single attention curve, where events may be enhanced, suppressed or vanished. The presence of salient events is signified on this audiovisual curve by geometrical features such as local extrema, sharp transition points and level sets. An audiovisual saliency-based movie summarization algorithm is proposed and evaluated. The algorithm is shown to perform very well in terms of summary informativeness and enjoyability for movie clips of various genres.


title: User validation in ontology alignment

abstract: User validation is one of the challenges facing the ontology alignment community, as there are limits to the quality of automated alignment algorithms. In this paper we present a broad study on user validation of ontology alignments that encompasses three distinct but interrelated aspects: the profile of the user, the services of the alignment system, and its user interface. We discuss key issues pertaining to the alignment validation process under each of these aspects, and provide an overview of how current systems address them. Finally, we use experiments from the Interactive Matching track of the Ontology Alignment Evaluation Initiative (OAEI) 2015 to assess the impact of errors in alignment validation, and how systems cope with them as function of their services.


title: A Theoretical Framework for Organizing the Effect of the Internet on Cognitive Development

abstract: The number of children and adolescents accessing the Internet as well as the amount of time online are steadily increasing. The most common online activities include playing video games, navigating web sites, and communicating via chat rooms, email, and instant messaging. A theoretical framework for understanding the effects of Internet use on cognitive development is presented. The proposed framework, based on the cognitive information processing model, the sociocultural perspective, and the PASS cognitive processing model, organizes previous research in terms of the cognitive consequences of common online activities. From a cognitive-developmental perspective, the Internet is a cultural tool that influences cognitive processes and an environmental stimulus that contributes to the formation of specific cognitive architecture.


title: Using virtual reality to assess user experience.

abstract: OBJECTIVE ::: The aim of this article is to discuss how user experience (UX) evaluation can benefit from the use of virtual reality (VR). ::: ::: ::: BACKGROUND ::: UX is usually evaluated in laboratory settings. However, considering that UX occurs as a consequence of the interaction between the product, the user, and the context of use, the assessment of UX can benefit from a more ecological test setting. VR provides the means to develop realistic-looking virtual environments with the advantage of allowing greater control of the experimental conditions while granting good ecological validity. ::: ::: ::: METHOD ::: The methods used to evaluate UX, as well as their main limitations, are identified.The currentVR equipment and its potential applications (as well as its limitations and drawbacks) to overcome some of the limitations in the assessment of UX are highlighted. ::: ::: ::: RESULTS ::: The relevance of VR for UX studies is discussed, and a VR-based framework for evaluating UX is presented. ::: ::: ::: CONCLUSION ::: UX research may benefit from a VR-based methodology in the scopes of user research (e.g., assessment of users' expectations derived from their lifestyles) and human-product interaction (e.g., assessment of users' emotions since the first moment of contact with the product and then during the interaction). ::: ::: ::: APPLICATION ::: This article provides knowledge to researchers and professionals engaged in the design of technological interfaces about the usefulness of VR in the evaluation of UX.


title: Mathematically gifted adolescents use more extensive and more bilateral areas of the fronto-parietal network than controls during executive functioning and fluid reasoning tasks

abstract: article i nfo The main goal of this study was to investigate the neural substrates of fluid reasoning and visuospatial working memory in adolescents with precocious mathematical ability. The study population comprised two groups of adolescents: 13 math-gifted adolescents and 14 controls with average mathematical skills. Patterns of activation specific to reasoning tasks in math-gifted subjects were examined using functional magnetic resonance images acquired while the subjects were performing Raven's Advanced Progressive Matrices (RAPM) and the Tower of London (TOL) tasks. During the tasks, both groups showed significant activations in the frontoparietal network. In the math-gifted group, clusters of activation were always bilateral and more regions were recruited, especially in the right hemisphere. In the TOL task, math-gifted adolescents showed significant hyper-activations relative to controls in the precuneus, superior occipital lobe (BA 19), and medial temporal lobe (BA 39). The maximum differences between the groups were detected during RAPM tasks at the highest level of difficulty, where math-gifted subjects showed significant activations relative to controls in the right inferior parietal lobule (BA 40), anterior cingulated gyrus (BA 32), and frontal (BA 9, and BA 6) areas. Our results support the hypothesis that greater ability for complex mathematical reasoning may be related to more bilateral patterns of activation and that increased activation in the parietal and frontal regions of math-gifted adolescents is associated with enhanced skills in visuospatial processing and logical reasoning.


title: Relationship-based approach to leadership: Development of leader-member exchange (LMX) theory of leadership over 25 years: Applying a multi-level multi-domain perspective

abstract: Abstract Research into Leader-Member Exchange (LMX) theory has been gaining momentum in recent years, with a multitude of studies investigating many aspects of LMX in organizations. Theoretical development in this area also has undergone many refinements, and the current theory is far different from the early Vertical Dyad Linkage (VDL) work. This article uses a levels perspective to trace the development of LMX through four evolutionary stages of theorizing and investigation up to the present. The article also uses a domains perspective to develop a new taxonomy of approaches to leadership, and LMX is discussed within this taxonomy as a relationship-based approach to leadership. Common questions and issues concerning LMX are addressed, and directions for future research are provided.


title: Learning to rank relevant files for bug reports using domain knowledge

abstract: When a new bug report is received, developers usually need to reproduce the bug and perform code reviews to find the cause, a process that can be tedious and time consuming. A tool for ranking all the source files of a project with respect to how likely they are to contain the cause of the bug would enable developers to narrow down their search and potentially could lead to a substantial increase in productivity. This paper introduces an adaptive ranking approach that leverages domain knowledge through functional decompositions of source code files into methods, API descriptions of library components used in the code, the bug-fixing history, and the code change history. Given a bug report, the ranking score of each source file is computed as a weighted combination of an array of features encoding domain knowledge, where the weights are trained automatically on previously solved bug reports using a learning-to-rank technique. We evaluated our system on six large scale open source Java projects, using the before-fix version of the project for every bug report. The experimental results show that the newly introduced learning-to-rank approach significantly outperforms two recent state-of-the-art methods in recommending relevant files for bug reports. In particular, our method makes correct recommendations within the top 10 ranked source files for over 70% of the bug reports in the Eclipse Platform and Tomcat projects.


title: Bounding pipeline and instruction cache performance

abstract: Predicting the execution time of code segments in real-time systems is challenging. Most recently designed machines contain pipelines and caches. Pipeline hazards may result in multicycle delays. Instruction or data memory references may not be found in cache and these misses typically require several cycles to resolve. Whether an instruction will stall due to a pipeline hazard or a cache miss depends on the dynamic sequence of previous instructions executed and memory references performed. Furthermore, these penalties are not independent since delays due to pipeline stalls and cache miss penalties may overlap. This paper describes an approach for bounding the worst and best case performance of large code segments on machines that exploit both pipelining and instruction caching. First, a method is used to analyze a program's control flow to statically categorize the caching behavior of each instruction. Next, these categorizations are used in the pipeline analysis of sequences of instructions representing paths within the program. A timing analyzer uses the pipeline path analysis to estimate the worst and best-case execution performance of each loop and function in the program. Finally, a graphical user interface is invoked that allows a user to request timing predictions on portions of the program. The results indicate that the timing analyzer efficiently produces tight predictions of worst and best-case performance for pipelining and instruction caching.


title: BRAD 1.0: Book reviews in Arabic dataset

abstract: The availability of rich datasets is a pre-requisite for proposing robust sentiment analysis systems. A variety of such datasets exists in English language. However, it is rare or nonexistent for the Arabic language except for a recent LABR dataset, which consists of a little bit over 63,000 book reviews extracted from. Goodreads. com. We introduce BRAD 1.0, the largest Book Reviews in Arabic Dataset for sentiment analysis and machine language applications. BRAD comprises of almost 510,600 book records. Each record corresponds for a single review and has the review in Arabic language and the reviewer's rating on a scale of 1 to 5 stars. In this paper, we present and describe the properties of BRAD. Further, we provide two versions of BRAD: the complete unbalanced dataset and the balanced version of BRAD. Finally, we implement four sentiment analysis classifiers based on this dataset and report our findings. When training and testing the classifiers on BRAD as opposed to LABR, an improvement rate growth of 46% is reported. The highest accuracy attained is 91%. Our core contribution is to make this benchmark-dataset available and accessible to the research community on Arabic language.


title: Webshell detection techniques in web applications

abstract: With widely adoption of online services, malicious web sites have become a malignant tumor of the Internet. Through system vulnerabilities, attackers can upload malicious files (which are also called webshells) to web server to create a backdoor for hackers' further attacks. Therefore, finding and detecting webshell inside web application source code are crucial to secure websites. In this paper, we propose a novel method based on the optimal threshold values to identify files that contain malicious codes from web applications. Our detection system will scan and look for malicious codes inside each file of the web application, and automatically give a list of suspicious files and a detail log analysis table of each suspicious file for administrators to check further. The Experimental results show that our approach is applicable to identify webshell more efficient than some other approaches.


title: SDMEC: Software Defined System for Mobile Edge Computing

abstract: Mobile Edge Computing (MEC) promises a paradigm shift in enabling efficient Mobile Cloud Computing (MCC) services by providing storage and processing capacity within the access range of the mobile devices. In MEC, Mobile Edge (ME) servers are placed at the edge of the mobile networks eliminating the need to offload compute-/storage-intensive tasks of the mobile devices to the core of the network (the centralized cloud data center). This reduces the network latency and enhances the quality of service provided for the mobile end users. Different applications can benefit from the large scale deployments of ME servers such as smart grid applications, content delivery networks, content sharing, traffic management, and E-health applications. This promising paradigm comes with its own downside related to the management complexity of large scale deployments that offers hundreds of applications to millions of users. In this paper, we introduce a software defined based framework to enable efficient MCC services through the integration of different software defined system components with the MEC system.


title: Automatic red blood cell counting using hough transform

abstract: The major issue in clinical laboratory is to produce a precise result for every test especially in the area of Red Blood Cell (RBC) count. The number of red blood cell is very important to detect as well as to follow the treatment of many diseases like anaemia, leukaemia etc. Red blood cell count gives the vital information that help diagnosis many of the patient's sickness. The old conventional method of RBC counting under microscope gives an unreliable and inaccurate result depends on clinical laboratory technician skill. This method puts a lot of strain on the technician. Another method for RBC counting uses the automatic hematology analyzer, this machine is very costlier. So it is not possible all the hospital's clinical laboratory implement such an expensive machine to count the blood cell in their laboratory. This paper introduces an efficient and cost effective computer vision system for automatic red blood cell counting using image based analysis.


title: Challenges and Solutions in Big Data Management -- An Overview

abstract: Currently, a huge explosion of data is observed in many organizations in the world. Industry analysts and businesses are looking towards big data as the next big thing to provide opportunities, insights, solutions and a new way to increase profits in business. From social networking sites to records in a hospital, big data has played an important role to improve businesses and innovation. Businesses strive to get quality information and retrieve them for data analysis and business purposes. Though big data is obtained from numerous resources, there are many issues and challenges that companies face while storing and handling big data. Proper data management practices, techniques, technology and infrastructure can help overcome these challenges, problems and issues. This paper gives an overview about the issues and challenges of big data management, also their solutions and practices being used to handle them.


title: Palm lines extraction using PCNN and image data field

abstract: In this paper, an approach of using image data field and pulse-coupled neuron network (PCNN) to fast extract palm lines is proposed for online palmprint images. Each pixel in an enhanced palmprint image is seen as a particle with the mass, which produces a data field. The data field is introduced to map the enhanced palmprint image from grayscale space to the corresponding potential space. By selecting the relative mass, a relative image data field is obtained. Then the enhanced palmprint image and its relative data field are input into two PCNNs with different parameters, separately. Their outputs are two binary images, and each has complementary advantages and disadvantages. In order to extract palm lines, a non-connectedness value (NCV) is defined and used to fusion the two binary images. At last, the morphological operators are used to remove noise and isolated points. The experimental results indicate that the speed of palm-line extraction promotes greatly and it can satisfy the practical requirements.


title: A new Big Five: fundamental principles for an integrative science of personality.

abstract: Despite impressive advances in recent years with respect to theory and research, personality psychology has yet to articulate clearly a comprehensive framework for understanding the whole person. In an effort to achieve that aim, the current article draws on the most promising empirical and theoretical trends in personality psychology today to articulate 5 big principles for an integrative science of the whole person. Personality is conceived as (a) an individual's unique variation on the general evolutionary design for human nature, expressed as a developing pattern of (b) dispositional traits, (c) characteristic adaptations, and (d) self-defining life narratives, complexly and differentially situated (e) in culture and social context. The 5 principles suggest a framework for integrating the Big Five model of personality traits with those self-defining features of psychological individuality constructed in response to situated social tasks and the human need to make meaning in culture.


Advances in Data Mining. Applications and Theoretical Aspects


title: Greedy forwarding in scale-free networks embedded in hyperbolic metric spaces

abstract: We show that complex (scale-free) network topologies naturally emerge from hyperbolic metric spaces. Hyperbolic geometry facilitates maximally efficient greedy forwarding in these networks. Greedy forwarding is topology-oblivious. Nevertheless, greedy packets find their destinations with 100% probability following almost optimal shortest paths. This remarkable efficiency sustains even in highly dynamic networks. Our findings suggest that forwarding information through complex networks, such as the Internet, is possible without the overhead of existing routing protocols, and may also find practical applications in overlay networks for tasks such as application-level routing, information sharing, and data distribution.


title: Sex differences in personality traits and gender-related occupational preferences across 53 nations: testing evolutionary and social-environmental theories.

abstract: Using data from over 200,000 participants from 53 nations, I examined the cross-cultural consistency of sex differences for four traits: extraversion, agreeableness, neuroticism, and male-versus-female-typical occupational preferences. Across nations, men and women differed significantly on all four traits (mean ds = −.15, −.56, −.41, and 1.40, respectively, with negative values indicating women scoring higher). The strongest evidence for sex differences in SDs was for extraversion (women more variable) and for agreeableness (men more variable). United Nations indices of gender equality and economic development were associated with larger sex differences in agreeableness, but not with sex differences in other traits. Gender equality and economic development were negatively associated with mean national levels of neuroticism, suggesting that economic stress was associated with higher neuroticism. Regression analyses explored the power of sex, gender equality, and their interaction to predict men’s and women’s 106 national trait means for each of the four traits. Only sex predicted means for all four traits, and sex predicted trait means much more strongly than did gender equality or the interaction between sex and gender equality. These results suggest that biological factors may contribute to sex differences in personality and that culture plays a negligible to small role in moderating sex differences in personality.


title: FPGA based design of low power reconfigurable router for Network on Chip (NoC)

abstract: FPGA based design of reconfigurable router for NoC applications is proposed in the present work. Design entry of the proposed router is done using Verilog Hardware Description Language (Verilog HDL). The router designed in the present work has four channels (namely, east, west, north and south) and a crossbar switch. Each channel consists of First in First out (FIFO) buffers and multiplexers. FIFO buffers are used to store the data and the input and output of the data are controlled using multiplexers. Firstly, south channel is designed which includes the design of FIFO and multiplexers. After that, the crossbar switch and other three channels are designed. All these designed channels, FIFO buffers, multiplexers and crossbar switches are integrated to form the complete router architecture. The proposed design is simulated using Modelsim and the RTL view is obtained using Xilinx ISE 13.4. Xilinx SPARTAN-6 FPGAs are used for synthesis of proposed design. Power dissipation of the proposed reconfigurable router is reduced using Power gating technique. Total power is calculated by the use of XPower Analyzer tool. Obtained results show that the proposed design consumes less power compared to the previously designed reconfigurable routers.


title: MILD: Multi-index hashing for appearance based loop closure detection

abstract: Loop Closure Detection (LCD) has been proved to be extremely useful in global consistent visual Simultaneously Localization and Mapping (SLAM) and appearance-based robot relocalization. Methods exploiting binary features in bag of words representation have recently gained a lot of popularity for their efficiency, but suffer from low recall due to the inherent drawback that high dimensional binary feature descriptors lack well-defined centroids. In this paper, we propose a realtime LCD approach called MILD (Multi-Index Hashing for Loop closure Detection), in which image similarity is measured by feature matching directly to achieve high recall without introducing extra computational complexity with the aid of Multi-Index Hashing (MIH). A theoretical analysis of the approximate image similarity measurement using MIH is presented, which reveals the trade-off between efficiency and accuracy from a probabilistic perspective. Extensive comparisons with state-of-the-art LCD methods demonstrate the superiority of MILD in both efficiency and accuracy.


title: Skill learning and task outcome prediction for manipulation

abstract: Learning complex motor skills for real world tasks is a hard problem in robotic manipulation that often requires painstaking manual tuning and design by a human expert. In this work, we present a Reinforcement Learning based approach to acquiring new motor skills from demonstration. Our approach allows the robot to learn fine manipulation skills and significantly improve its success rate and skill level starting from a possibly coarse demonstration. Our approach aims to incorporate task domain knowledge, where appropriate, by working in a space consistent with the constraints of a specific task. In addition, we also present an approach to using sensor feedback to learn a predictive model of the task outcome. This allows our system to learn the proprioceptive sensor feedback needed to monitor subsequent executions of the task online and abort execution in the event of predicted failure. We illustrate our approach using two example tasks executed with the PR2 dual-arm robot: a straight and accurate pool stroke and a box flipping task using two chopsticks as tools.


title: Diversity and spatial distribution of epiphytic flora associated with four tree species of partially disturbed ecosystem in tropical rainforest zone.

abstract: As components of forest communities, epiphytes influence litter and nutrient cycling while providing shelter, nesting materials and food for arboreal animals, thereby promoting diversity. Their preference for certain tree species (phorophytes) influences biodiversity and distribution, but this association is poorly understood in the tropics. We assessed the diversity and spatial distribution of epiphytes associated with four phorophytes ( Alstonia booneii , Peltophorum pterocarpum , Mangifera indica and Terminalia catappa ) in southern Nigeria, with number of trees sampled as 4, 29, 32 and 44, respectively. The epiphytes were delineated, sampled and identified. Soil samples were collected from tree pockets for textural and pH analyses. On the four phorophytes, 265 epiphytes under seven species ( Platycerium elephantotis , Microgamma owariensis , Nephrolepis biserrata , Funaria hygrometrica , Axonopus compressus , Commelina benghalensis and Ficus spp.) and five families ( Polypoidaceae , Funariaceae , Poaceae , Commelinaceae and Moraceae ) were recorded. The three most abundant were Funaria hygrometrica (109), Platycerium elephantotis (102) and Microgamma owariensis (44), being present on all four phorophytes. Funaria hygrometrica and Platycerium elephantotis were the most abundant on M. indica (32 each) and T. catappa (44 and 39, respectively). Nephrolepis biserrata appeared only once on T. catappa ; so too did Axonopus compressus and Commelina benghalensis on P. pterocarpum . Altogether, A. booneii , P. pterocarpum , M. indica and T. catappa had 11, 62, 89 and 103 epiphytes, respectively. Funaria hygrometrica traversed three tree strata, while others were restricted to two or even one stratum. Canopy layer, middle stratum and lower portion hosted four, five and three epiphytic species, respectively out of the seven recorded, pointing to the relative importance of light, spaciousness and moisture, respectively in epiphyte abundance on trees. Epiphytes distribution on phorophytes was not influenced by texture of the arboreal soils, but was inversely related to their pH. This study has provided useful information on epiphyte-phorophyte association in tropical environments and deserves repetition with more tree species in more natural forests. Keywords: forest tree species, tree spatial strata, epiphyte distribution, species abundance, Nigeria


title: A framework for preventing money laundering in banks

abstract: Purpose – The purpose of this paper is to form a new framework for preventing money laundering by mapping COBIT (Control for Information and Related Technology) processes to COSO (Committee of Sponsoring Organisation) components.Design/methodology/approach – First, a new framework for preventing money laundering in banks is formed by mapping COBIT to COSO. Further, the potential of the mapped framework to comply with the Bank Secrecy Act requirements is analysed.Findings – The mapped framework effectively supports all the activities of financial sectors through defining efficient information technology‐based processes and control methods. Information systems play a key role for financial sectors in producing financial statements, managing customer databases, detecting frauds, etc.Research limitations/implications – Case studies of banks of different sizes, and in different countries are needed. It is necessary to improve the mapped framework by considering Basel III regulations.Practical implications – CO...


title: Improving zero-shot learning by mitigating the hubness problem

abstract: The zero-shot paradigm exploits vector-based word representations extracted from text corpora with unsupervised methods to learn general mapping functions from other feature spaces onto word space, where the words associated to the nearest neighbours of the mapped vectors are used as their linguistic labels. We show that the neighbourhoods of the mapped elements are strongly polluted by hubs, vectors that tend to be near a high proportion of items, pushing their correct labels down the neighbour list. After illustrating the problem empirically, we propose a simple method to correct it by taking the proximity distribution of potential neighbours across many mapped vectors into account. We show that this correction leads to consistent improvements in realistic zero-shot experiments in the cross-lingual, image labeling and image retrieval domains.


title: Comparison of Image Steganography Techniques

abstract: Steganography is an important area of research in recent years involving a number of applications. It is the science of embedding information into the cover image viz., text, video,and image (payload) without causing statistically significant modification to the cover image. The modern secure image steganography presents a challenging task of transferring the embedded information to the destination without being detected.This paper deals with hiding text in an image file using Least Significant Bit (LSB) based Steganography, Discrete Cosine Transform (DCT) based Steganography and Discrete Wavelet Transform (DWT) based steganography.The LSB algorithm is implemented in spatial domain in which the payload bits are embedded into the least significant bits of cover image to derive the stego-image whereas DCT & DWT algorithm are implemented in frequency domain in which the stego-image is transformed from spatial domain to the frequency domain and the payload bits are embedded into the frequency components of the cover image.The performance and comparison of these three techniques is evaluated on the basis of the parameters MSE, PSNR, NC, processing time, Capacity& Robustness.


title: Comparison of inverse kinematics solutions using neural network for 6R robot manipulator with offset

abstract: An artificial neural network (ANN) using backpropagation algorithm is applied to solve inverse kinematics problems of industrial robot manipulator. 6R robot manipulator with offset wrist was chosen as industrial robot manipulator because geometric feature of this robot does not allow solving inverse kinematics problems analytically. In other words, there is no closed form solution for this problem. In order to define orientation of robot end-effector, three different representations are used here: homogeneous transformation matrix, Euler angles and equivalent angle axis. These representations were compared to obtain inverse kinematics solutions for 6R robot manipulator with offset wrist. Simulation results show that prediction performance from the approximation accuracy point of view is satisfactory with low effective errors based on 10 degrees data resolution


title: Reliability of Tanner stage assessments in a multi-center study.

abstract: Tanner stage indices have been frequently used in research studies to characterize the level of sexual maturation among adolescents. The Cooperative Study of Sickle Cell Disease (CSSCD) is a large multi-center study in which the assessment of Tanner stage indices has been performed to describe delays in sexual maturation and correlates of delays. Rigorous training, however, in rating Tanner stages was not provided for the clinicians in the study. Data from the CSSCD were analyzed to characterize the reliability of the assessment of sexual maturation in this study, and demonstrate the potential for increasing the reliability by more careful training. In lieu of a gold standard, a maximum likelihood algorithm was employed to examine the longitudinal consistency of Tanner stage assessments. Based on the assumption that the underlying process of sexual maturation is progressive, models were developed that allowed one to estimate jointly the incidence distribution for the attainment of Tanner stages and the error rates of assessment. Estimated false-positive rates for assessing the attainment of Tanner stages from the CSSCD ranged from 2% to 19%. Estimated false-negative rates ranged from 1% to 18%. The occurrence of these errors was found not to vary markedly with the subject's age. Error rates of this magnitude can introduce considerable bias to the estimation of the distribution of Tanner stages and result in serious losses in relative efficiency. While the validity of Tanner stage indices can be demonstrated from study data, the loss in statistical efficiency and power associated with the study error rates was severe. This underscores the utility of careful training in pubertal rating for research studies.


title: A novel microfluidic chip and antibody-aptamer based multianalysis method for simultaneous determination of several tumor markers with polymerization nicking reactions for homogenous signal amplification

abstract: Abstract To detect trace level of tumor markers (TMs) plays a crucial role in the early diagnosis of cancer. However, the fabrication of assays to simultaneously detect trace levels of TMs was still challengeable. The study was aimed to develop a novel microchip electrophoresis (MC) and antibody-aptamer based hybrid detection strategy for simultaneous determination of prostate specific antigen (PSA), carcino-embryonic antigen (CEA) and carbohydrate antigen 125 (CA125) in human serum. The assay comprises the generation of magnetic aptamer capture probes and anti-TMs labeled encoded signal tags, then employment of nicking enzyme for signal amplification. Firstly, the aptamers of TMs were co-immobilized on surface of Fe3O4@AuNPs (AuMPs) as capture probes. Simultaneously, the antibodies of TMs labeled with different double stranded DNA (dsDNA) as nicked fragment-induced strand was prepared as encoded signal tags. Secondly, the TMs, capture probes and encoded signal tags were simultaneously incubated to form sandwich complex. After magnetic separation, the complex was collected by adding the nicking enzyme. As a result, the dsDNA on the complex can initiate nicking enzyme cleavage reaction to produce many single stranded DNA (ssDNA) products with different length corresponding to different targets. Finally, the ssDNA products were injected into MC for separation and determination of TMs, respectively. Under optimized conditions, the assay can simultaneously detect three TMs with detection limits of 0.1, 0.15, 0.12 pg mL−1 for PSA, CEA and CA125, respectively (S/N = 3). Moreover, the magnetic aptamer probes exhibited good stability and can be reused for 20 times with the recovery above 80% after heat treatments. The multianalysis method exhibits distinct advantages such as high-throughput, -sensitivity, easy-to-operate and offers a powerful tool for early diagnosis of cancers.


title: Spatiotemporal Saliency Estimation by Spectral Foreground Detection

abstract: We present a novel approach for spatiotemporal saliency detection by optimizing a unified criterion of color contrast, motion contrast, appearance, and background cues. To this end, we first abstract the video by temporal superpixels. Second, we propose a novel graph structure exploiting the saliency cues to assign the edge weights. The salient segments are then extracted by applying a spectral foreground detection method, quantum cuts, on this graph. We evaluate our approach on several public datasets for video saliency and activity localization to demonstrate the favorable performance of the proposed video quantum cuts compared to the state of the art.


title: Exceptional energy and new insight with a sodium–selenium battery based on a carbon nanosheet cathode and a pseudographite anode

abstract: We created a unique sodium ion battery (NIB, SIB) cathode based on selenium in cellulose-derived carbon nanosheets (CCNs), termed Se-CCN. The elastically compliant two-dimensional CCN host incorporates a high mass loading of amorphous Se (53 wt%), which is primarily impregnated into 1 cm3 g−1 nanopores. The results in facile sodiation kinetics due to short solid-state diffusion distances and a large charge transfer area of the nanosheets were established. The architecture also leads to an intrinsic resistance to polyselenide shuttle and to disintegration/coarsening. As a Na half-cell, the Se-CCN cathode delivers a reversible capacity of 613 mA h g−1 with 88% retention over 500 cycles. The exceptional stability is achieved by using a standard electrolyte (1 M NaClO4 EC-DMC) without secondary additives or high salt concentrations. The rate capability is also superb, achieving 300 mA h g−1 at 10C. Compared to recent state-of-the-art literature, the Se-CCN is the most cyclically stable and offers the highest rate performance. As a Se–Na battery, the system achieves 992 W h kg−1 at 68 W kg−1 and 384 W h kg−1 at 10144 W kg−1 (by active mass in a cathode). We are the first to fabricate and test a Se-based full NIB, which is based on Se-CCN coupled to a Na intercalating pseudographitic carbon (PGC) anode. It is demonstrated that the PGC anode increases its structural order in addition to dilating as a result of Na intercalation at voltages below 0.2 V vs. Na/Na+. The {110} Na reflections are distinctly absent from the XRD patterns of PGC sodiated down to 0.001 V, indicating that the Na metal pore filling is not significant for pseudographitic carbons. The battery delivers highly promising Ragone chart characteristics, for example yielding 203 and 50 W h kg−1 at 70 and 14000 W kg−1 (via total material mass in the anode and cathode).


title: Automatic Container Code Recognition via Spatial Transformer Networks and Connected Component Region Proposals

abstract: Container identification and recognition is still performed manually or in a semi-automatic fashion in multiple ports globally. This results in errors and inefficiencies in port operations. The problem of automatic container identification and recognition is challenging as the ISO standard only prescribes the pattern of the code and does not specify other parameters such as the foreground and background colors, font type and size, orientation of characters (horizontal or vertical) so on. Additionally, the corrugated surface of container body makes the two dimensional projection of the text on three dimensional containers slanted and jagged. We propose a solution in the form of an end-to-end pipeline that uses Region Proposals generated based on Connected Components for text detection in conjunction with Spatial Transformer Networks for text recognition. We demonstrate via our experimental results that the pipeline is reliable and robust even in situations when the code characters are highly distorted and outperforms the state-of-the-art results for text detection and recognition over the containers. We achieve text coverage rate of 100% and text recognition rate of 99.64%.


title: Design Data Management in Model-Based Design

abstract: One of the keys to successfully harnessing the benefits of graphical modeling in ModelBased Design is the effective management of associated design data. Design data in the form of model parameters and simulation configuration settings are as important to the simulation as the algorithm itself. Together they determine the form and range of the simulation outputs and internal states of the model. However, control engineers have been inclined towards graphical editing and layout than design data management leading to several key design issues especially for large scale systems. These are separation, logical partitioning, change detection, dependency analysis and traceability. In this paper, we present an approach to address these issues using a demonstration within Simulink, an exemplary Model-Based Design environment. A tradeoff decision on embedding of design parameters in the graphical model as opposed to keeping them separate can determine the ease of carrying out a parameter sweep. Another advantage is in the case of modular design platforms where the control variables required to switch variant configurations must be separated out from the design data. Moreover, the use of multiple configuration settings can be used to optimize the performance of the simulation depending on the inherent dynamics present in the model. Lack of logical partitioning of the data can present understandability and data corruption issues within a collaborative environment. Parallel development of graphical components without clear data separation increases the risk of design error. Furthermore, innovative partitioning schemes require a careful understanding of sharing and logical relationships among the data which can be independent of the graphical model componentization. Another decision point in logical partitioning is regarding the storage of design data in a centralized or a decentralized repository system with each offering unique advantages. For example, a decentralized system consisting of multiple files can be put under source control and differenced to earlier versions to detect changes. A change detection workflow that integrates changes in the graphical design with those in the data is key to getting a complete understanding of the changes in the system. As the design begins to scale, it may become necessary to store the design data in a decentralized repository system. However, this does present additional challenges. For example, the tracking of the interdependencies between the graphical model components and the design data partitions becomes critical. An analysis of such dependencies must be carried out to identify any missing components from the project. At a lower level, the traceability of the individual design data to the components is critical to establish the necessary and sufficient conditions for executing a component. Such analysis can also help with the logical partitioning schemes and removing redundant data. One of the big challenges of utilizing the recommendations presented above is in migrating a legacy system to use them. Such migration presents constraints on how much porting can be done in practice. In this paper, we outline several recommendations for addressing these key questions. In conclusion, we offer a set of best practices that engineers can use to manage their data effectively within a team-based environment.


title: To Click or Not To Click: Automatic Selection of Beautiful Thumbnails from Videos

abstract: Thumbnails play such an important role in online videos. As the most representative snapshot, they capture the essence of a video and provide the first impression to the viewers; ultimately, a great thumbnail makes a video more attractive to click and watch. We present an automatic thumbnail selection system that exploits two important characteristics commonly associated with meaningful and attractive thumbnails: high relevance to video content and superior visual aesthetic quality. Our system selects attractive thumbnails by analyzing various visual quality and aesthetic metrics of video frames, and performs a clustering analysis to determine the relevance to video content, thus making the resulting thumbnails more representative of the video. On the task of predicting thumbnails chosen by professional video editors, we demonstrate the effectiveness of our system against six baseline methods, using a real-world dataset of 1,118 videos collected from Yahoo Screen. In addition, we study what makes a frame a good thumbnail by analyzing the statistical relationship between thumbnail frames and non-thumbnail frames in terms of various image quality features. Our study suggests that the selection of a good thumbnail is highly correlated with objective visual quality metrics, such as the frame texture and sharpness, implying the possibility of building an automatic thumbnail selection system based on visual aesthetics.


title: Privacy preserving record linkage approaches

abstract: Privacy-preserving record linkage is a very important task, mostly because of the very sensitive nature of the personal data. The main focus in this task is to find a way to match records from among different organisation data sets or databases without revealing competitive or personal information to non-owners. Towards accomplishing this task, several methods and protocols have been proposed. In this work, we propose a certain methodology for preserving the privacy of various record linkage approaches and we implement, examine and compare four pairs of privacy preserving record linkage methods and protocols. Two of these protocols use n-gram based similarity comparison techniques, the third protocol uses the well known edit distance and the fourth one implements the Jaro-Winkler distance metric. All of the protocols used are enhanced by private key cryptography and hash encoding. This paper presents also a blocking scheme as an extension to the privacy preserving record linkage methodology. Our comparison is backed up by extended experimental evaluation that demonstrates the performance achieved by each of the proposed protocols.


Bilingual Lexicon Induction by Learning to Combine Word-Level and Character-Level Representations


title: The need for a socio-cultural perspective in the implementation of virtual environments

abstract: For many researchers, virtual reality (VR) is first of all a technology. This vision is also well reflected in the growing research work concerned with virtual environments: most of it has been addressed primarily the development of new rendering technologies rather than the highly interactive and dynamic nature of user-system interaction that VR supports. However, this focus on technology is disappointing for developers and researchers. To overcome this limitation, this paper describes VR as an advanced communication tool: a communication interface in single-user VR, and a communication medium in the case of multi-user VR. This leads us to propose acultural concept of presence as a social construction. Lying at the base of this view are two elements that guarantee an elevated sense of presence: acultural framework and the possibility ofnegotiation, both of actions and of their meaning. Within this view, experiencing presence and telepresence does not depend so much on the faithfulness of the reproduction of ‘physical’ aspects of ‘external reality’ as on the capacity of simulation to produce a context in which social actors may communicate and cooperate. The consequences of this approach for the design and the development of VR systems are presented.


title: Lightly Supervised Learning of Procedural Dialog Systems

abstract: Procedural dialog systems can help users achieve a wide range of goals. However, such systems are challenging to build, currently requiring manual engineering of substantial domain-specific task knowledge and dialog management strategies. In this paper, we demonstrate that it is possible to learn procedural dialog systems given only light supervision, of the type that can be provided by non-experts. We consider domains where the required task knowledge exists in textual form (e.g., instructional web pages) and where system builders have access to statements of user intent (e.g., search query logs or dialog interactions). To learn from such textual resources, we describe a novel approach that first automatically extracts task knowledge from instructions, then learns a dialog manager over this task knowledge to provide assistance. Evaluation in a Microsoft Office domain shows that the individual components are highly accurate and can be integrated into a dialog system that provides effective help to users.


title: Data-driven persona development

abstract: Much has been written on creating personas --- both what they are good for, and how to create them. A common problem with personas is that they are not based on real customer data, and if they are, the data set is not of a sample size that can be considered statistically significant. In this paper, we describe a new method for creating and validating personas, based on the statistical analysis of data, which is fast and cost effective.


TasselNet: counting maize tassels in the wild via local counts regression network


title: Fire detection for early fire alarm based on optical flow video processing

abstract: We propose a method for early fire detection based on the Lucas-Kanade optical flow algorithm that is able to detect fire in real time in a video stream from a monocular camera. The method works both indoors and in open areas. It detects fire at the beginning of the burning process, enabling an earlier response than would be possible with a conventional fire detector. The method performs background subtraction to identify moving pixels in the scene then filters for colors consistent with fire. Growth rate analysis is applied to the contours identified in the previous step. We further propose the use of a Lucas-Kanade optical flow pyramid in the regions identified in the growth analysis step and analysis of the variance of the optical flow feature points' motion. In an experiment comparing the growth rate method alone with the combination of growth rate and optical flow analysis, we find that the algorithm improves the accuracy of fire detection for early fire alarming and reduce false alarm by solving the fire-like color.


title: Elastic stream processing in the Cloud

abstract: Stream processing is a computing paradigm that has emerged from the necessity of handling high volumes of data in real time. In contrast to traditional databases, stream-processing systems perform continuous queries and handle data on-the-fly. Today, a wide range of application areas relies on efficient pattern detection and queries over streams. The advent of Cloud computing fosters the development of elastic stream-processing platforms, which are able to dynamically adapt based on different cost–benefit trade-offs. This article provides an overview of the historical evolution and the key concepts of stream processing, with special focus on adaptivity and Cloud-based elasticity.


Using analogy to learn about phenomena at scales outside human perception


3D Printed Models of Cleft Palate Pathology for Surgical Education


title: Power-Handling Capabilities of Circular Dielectric Waveguide at Millimeter Wavelengths

abstract: The power-handling capabilities of a circular dielectric waveguide propagating the single HE/sub 11/ mode are discussed. Simple calculations illustrate the limits imposed by dielectric heating and dielectric breakdown. Examples are presented for polystyrene and polytetrafluoroethylene (PTFE) guides at 70 GHz. The results suggest that maximum power levels for circular dielectric waveguide in the millimeter-wave specturm will be of the orders of 10 to 100 W and that dielectric heating is the limiting phenomenon.


"True Lies": Children's abuse history and power attributions as influences on deception detection.


title: Generative Adversarial Networks: An Overview

abstract: Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this by deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image synthesis, semantic image editing, style transfer, image superresolution, and classification. The aim of this review article is to provide an overview of GANs for the signal processing community, drawing on familiar analogies and concepts where possible. In addition to identifying different methods for training and constructing GANs, we also point to remaining challenges in their theory and application.


title: Circle Packing for Origami Design Is Hard

abstract: We show that deciding whether a given set of circles can be packed into a rectangle, an equilateral triangle, or a unit square are NP-hard problems, settling the complexity of these natural packing problems. On the positive side, we show that any set of circles of total area 1 can be packed into a square of size 4/\sqrt{pi}=2.2567... These results are motivated by problems arising in the context of origami design.


title: Optimizing human-interpretable dialog management policy using Genetic Algorithm

abstract: Automatic optimization of spoken dialog management policies that are robust to environmental noise has long been the goal for both academia and industry. Approaches based on reinforcement learning have been proved to be effective. However, the numerical representation of dialog policy is human-incomprehensible and difficult for dialog system designers to verify or modify, which limits its practical application. In this paper we propose a novel framework for optimizing dialog policies specified in domain language using genetic algorithm. The human-interpretable representation of policy makes the method suitable for practical employment. We present learning algorithms using user simulation and real human-machine dialogs respectively. Empirical experimental results are given to show the effectiveness of the proposed approach.


title: Automated Variational Inference in Probabilistic Programming

abstract: We present a new algorithm for approximate inference in probabilistic programs, based on a stochastic gradient for variational programs. This method is efficient without restrictions on the probabilistic program; it is particularly practical for distributions which are not analytically tractable, including highly structured distributions that arise in probabilistic programs. We show how to automatically derive mean-field probabilistic programs and optimize them, and demonstrate that our perspective improves inference efficiency over other algorithms.


title: Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

abstract: One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intra-subject differences, as well as to inherent noise associated with such data. Herein, we propose a novel approach for learning such representations from multi-channel EEG time-series, and demonstrate its advantages in the context of mental load classification task. First, we transform EEG activities into a sequence of topology-preserving multi-spectral images, as opposed to standard EEG analysis techniques that ignore such spatial information. Next, we train a deep recurrent-convolutional network inspired by state-of-the-art video classification to learn robust representations from the sequence of images. The proposed approach is designed to preserve the spatial, spectral, and temporal structure of EEG which leads to finding features that are less sensitive to variations and distortions within each dimension. Empirical evaluation on the cognitive load classification task demonstrated significant improvements in classification accuracy over current state-of-the-art approaches in this field.


title: Improved cyberbullying detection using gender information

abstract: As a result of the invention of social networks, friendships, relationships and social communication are all undergoing changes and new definitions seem to be applicable. One may have hundreds of ‘friends’ without even seeing their faces. Meanwhile, alongside this transition there is increasing evidence that online social applications are used by children and adolescents for bullying. State-of-the-art studies in cyberbullying detection have mainly focused on the content of the conversations while largely ignoring the characteristics of the actors involved in cyberbullying. Social studies on cyberbullying reveal that the written language used by a harasser varies with the author’s features including gender. In this study we used a support vector machine model to train a gender-specific text classifier. We demonstrated that taking gender-specific language features into account improves the discrimination capacity of a classifier to detect cyberbullying.


title: SemFuzz: Semantics-based Automatic Generation of Proof-of-Concept Exploits

abstract: Patches and related information about software vulnerabilities are often made available to the public, aiming to facilitate timely fixes. Unfortunately, the slow paces of system updates (30 days on average) often present to the attackers enough time to recover hidden bugs for attacking the unpatched systems. Making things worse is the potential to automatically generate exploits on input-validation flaws through reverse-engineering patches, even though such vulnerabilities are relatively rare (e.g., 5% among all Linux kernel vulnerabilities in last few years). Less understood, however, are the implications of other bug-related information (e.g., bug descriptions in CVE), particularly whether utilization of such information can facilitate exploit generation, even on other vulnerability types that have never been automatically attacked. In this paper, we seek to use such information to generate proof-of-concept (PoC) exploits for the vulnerability types never automatically attacked. Unlike an input validation flaw that is often patched by adding missing sanitization checks, fixing other vulnerability types is more complicated, usually involving replacement of the whole chunk of code. Without understanding of the code changed, automatic exploit becomes less likely. To address this challenge, we present SemFuzz, a novel technique leveraging vulnerability-related text (e.g., CVE reports and Linux git logs) to guide automatic generation of PoC exploits. Such an end-to-end approach is made possible by natural-language processing (NLP) based information extraction and a semantics-based fuzzing process guided by such information. Running over 112 Linux kernel flaws reported in the past five years, SemFuzz successfully triggered 18 of them, and further discovered one zero-day and one undisclosed vulnerabilities. These flaws include use-after-free, memory corruption, information leak, etc., indicating that more complicated flaws can also be automatically attacked. This finding calls into question the way vulnerability-related information is shared today.


title: Toward a standard benchmark for computer security research: the worldwide intelligence network environment (WINE)

abstract: Unlike benchmarks that focus on performance or reliability evaluations, a benchmark for computer security must necessarily include sensitive code and data. Because these artifacts could damage systems or reveal personally identifiable information about the users affected by cyber attacks, publicly disseminating such a benchmark raises several scientific, ethical and legal challenges. We propose the Worldwide Intelligence Network Environment (WINE), a security-benchmarking approach based on rigorous experimental methods. WINE includes representative field data, collected worldwide from 240,000 sensors, for new empirical studies, and it will enable the validation of research on all the phases in the lifecycle of security threats. We tackle the key challenges for security benchmarking by designing a platform for repeatable experimentation on the WINE data sets and by collecting the metadata required for understanding the results. In this paper, we review the unique characteristics of the WINE data, we discuss why rigorous benchmarking will provide fresh insights on the security arms race and we propose a research agenda for this area.


title: An Adaptive Lossless Data Compression Scheme for Wireless Sensor Networks

abstract: Energy is an important consideration in the design and deployment of wireless sensor networks (WSNs) since sensor nodes are typically powered by batteries with limited capacity. Since the communication unit on a wireless sensor node is the major power consumer, data compression is one of possible techniques that can help reduce the amount of data exchanged between wireless sensor nodes resulting in power saving. However, wireless sensor networks possess significant limitations in communication, processing, storage, bandwidth, and power. Thus, any data compression scheme proposed for WSNs must be lightweight. In this paper, we present an adaptive lossless data compression (ALDC) algorithm for wireless sensor networks. Our proposed ALDC scheme performs compression losslessly using multiple code options. Adaptive compression schemes allow compression to dynamically adjust to a changing source. The data sequence to be compressed is partitioned into blocks, and the optimal compression scheme is applied for each block. Using various real-world sensor datasets we demonstrate the merits of our proposed compression algorithm in comparison with other recently proposed lossless compression algorithms for WSNs.


title: DeltaPhish: Detecting Phishing Webpages in Compromised Websites

abstract: The large-scale deployment of modern phishing attacks relies on the automatic exploitation of vulnerable websites in the wild, to maximize profit while hindering attack traceability, detection and blacklisting. To the best of our knowledge, this is the first work that specifically leverages this adversarial behavior for detection purposes. We show that phishing webpages can be accurately detected by highlighting HTML code and visual differences with respect to other (legitimate) pages hosted within a compromised website. Our system, named DeltaPhish, can be installed as part of a web application firewall, to detect the presence of anomalous content on a website after compromise, and eventually prevent access to it. DeltaPhish is also robust against adversarial attempts in which the HTML code of the phishing page is carefully manipulated to evade detection. We empirically evaluate it on more than 5,500 webpages collected in the wild from compromised websites, showing that it is capable of detecting more than 99% of phishing webpages, while only misclassifying less than 1% of legitimate pages. We further show that the detection rate remains higher than 70% even under very sophisticated attacks carefully designed to evade our system.


title: A capacitive intrabody communication channel from 100 kHz to 100 MHz

abstract: Intrabody communication (IBC) uses the human body as a signal transmission medium. In the capacitive coupling IBC approach the signal is transmitted through the body and the signal return path is closed through the environment. The received signal level is affected by the orientation of the transmitter with respect to the receiver, the number of ground electrodes connected to the body, the size of the receiver ground plane, and the surrounding environment. In this paper we present a characterization of the capacitive IBC channel in the frequency range from 100 kHz to 100 MHz, obtained using a network analyzer and a pair of baluns. In order to better understand the transmission path in the frequency range of interest, we analyze the intrabody channel transmission characteristics using different electrode arrangements. The transmission gain increases with frequency for 20 dB/decade up to 1 MHz, and for the higher frequencies the increase depends on the transmitter to the receiver distance and the electrode arrangements.


On creativity, economics, and baseball


title: On the inverse kinematics of an a priori unknown general 6R-Robot

abstract: This paper looks at the inverse kinematics problem of an a priori unknown 6R-Robot from the representation point of view. It describes a new representation of the Euclidean motion group. With this representation, the inverse kinematics problem can be treated entirely numerical. No symbolical methods are required.


title: Security and Privacy in the Internet of Medical Things: Taxonomy and Risk Assessment

abstract: As the number of devices (things) connected to the Internet (Internet of things: IoT) is growing, achieving robust security and privacy (SaP) is becoming increasingly challenging. With the heavy use of medical things (MT), the SaP in the medical domain poses a serious issue that continues to grow. Due to the criticality and sensitivity of the data in the healthcare domain, ensuring the SaP of the Internet of medical things (IoMT) makes matters even more problematic. Lack of proper SaP in IoMT will not only leave patients' privacy at risk but may also put patients' lives at risk. In this paper, we provide a taxonomy of the SaP issues of IoMT. We also provide an approach to quantify IoMT risks and demonstrate how to assess risks in two IoMT devices. This work aims to increase the SaP awareness among IoMT stakeholders by enabling them to identify and quantify potential IoMT SaP risks.


title: Securing wireless communications of connected vehicles with artificial intelligence

abstract: This work applies artificial intelligence (AI) to secure wireless communications of Connected Vehicles. Vehicular Ad-hoc Network (VANET) facilitates exchange of safety messages for collision avoidance, leading to self-driving cars. An AI system continuously learns to augment its ability in discerning and recognizing its surroundings. Such ability plays a vital role in evaluating the authenticity and integrity of safety messages for cars driven by computers. Falsification of meter readings, disablement of brake function, and other unauthorized controls by spoofed messages injected into VANET emerge as security threats. Countermeasures must be considered at design stage, as opposed to afterthought patches, effectively against cyber-attacks. However, current standards oversubscribe security measures by validating every message circulating among Connected Vehicles, making VANET subject to denial-of-service (DoS) Attacks. This interdisciplinary research shows promising results by searching the pivot point to balance between message authentication and DoS prevention, making security measures practical for the real-world deployment of Connected Vehicles. Message authentication adopts Context-Adaptive Signature Verification strategy, applying AI filters to reduce both communication and computation overhead. Combining OMNET++, a data network simulator, and SUMO, a road traffic simulator, with Veins, an open source framework for VANET simulation, the study evaluates AI filters comparatively under various attacking scenarios. The results lead to an effective design choice of securing wireless communications for Connected Vehicles.


title: Occlusion Boundary Detection via Deep Exploration of Context

abstract: Occlusion boundaries contain rich perceptual information about the underlying scene structure. They also provide important cues in many visual perception tasks such as scene understanding, object recognition, and segmentation. In this paper, we improve occlusion boundary detection via enhanced exploration of contextual information (e.g., local structural boundary patterns, observations from surrounding regions, and temporal context), and in doing so develop a novel approach based on convolutional neural networks (CNNs) and conditional random fields (CRFs). Experimental results demonstrate that our detector significantly outperforms the state-of-the-art (e.g., improving the F-measure from 0.62 to 0.71 on the commonly used CMU benchmark). Last but not least, we empirically assess the roles of several important components of the proposed detector, so as to validate the rationale behind this approach.


title: Semisupervised Feature Selection Based on Relevance and Redundancy Criteria

abstract: Feature selection aims to gain relevant features for improved classification performance and remove redundant features for reduced computational cost. How to balance these two factors is a problem especially when the categorical labels are costly to obtain. In this paper, we address this problem using semisupervised learning method and propose a max-relevance and min-redundancy criterion based on Pearson’s correlation (RRPC) coefficient. This new method uses the incremental search technique to select optimal feature subsets. The new selected features have strong relevance to the labels in supervised manner, and avoid redundancy to the selected feature subsets under unsupervised constraints. Comparative studies are performed on binary data and multicategory data from benchmark data sets. The results show that the RRPC can achieve a good balance between relevance and redundancy in semisupervised feature selection. We also compare the RRPC with classic supervised feature selection criteria (such as mRMR and Fisher score), unsupervised feature selection criteria (such as Laplacian score), and semisupervised feature selection criteria (such as sSelect and locality sensitive). Experimental results demonstrate the effectiveness of our method.


title: Analytic Methods for Questions Pertaining to a Randomized Pretest , Posttest , Follow-Up Design

abstract: Delineates 5 questions regarding group differences that are likely to be of interest to researchers within the framework of a randomized pretest, posttest, follow-up (PPF) design. These 5 questions are examined from a methodological perspective by comparing and discussing analysis of variance (ANOVA) and analysis of covariance (ANCOVA) methods and briefly discussing hierarchical linear modeling (HLM) for these questions. This article demonstrates that the pretest should be utilized as a covariate in the model rather than as a level of the time factor or as part of the dependent variable within the analysis of group differences. It is also demonstrated that how the posttest and the follow-up are utilized in the analysis of group differences is determined by the specific question asked by the researcher.


title: Virtual Laboratory for On-board ETCS Equipment

abstract: Available ETCS equipment suffered from a long process before being put into service, due to inconsistencies in the specifications and the inability of the laboratory certification procedures to completely address all the needs of the system testing. To overcome those problems, this paper presents a novel ETCS Testing Laboratory, which will help in the reduction of on-site testing amount, and a Golden Reference Model of the ERTMS on-board equipment, that will help in the interpretation of the current normative. The design, implementation, operation and validation of these developments will be presented in this research work.


title: Use of Bivalirudin in Pediatric Patients on Ventricular Assist Device Support

abstract: Purpose Little reported data exist regarding bivalirudin anticoagulation in pediatric patients supported with ventricular assist devices (VAD). Methods Single center retrospective cohort study of all patients supported with VAD who received bivalirudin for anticoagulation from 10/2016 to 10/2018. Demographic variables and clinical features, including indication for bivalirudin, details of therapy, and complications were analyzed. Results Bivalirudin was utilized in 19 HeartWare and 4 Berlin EXCOR patients (23 runs in 19 unique patients) - 39% (9/23) male, median age 13.6yr (IQR 6.3 - 17.2yr). Indications included: initial anticoagulation after VAD in 34% (4 HeartWare, 4 Berlin EXCOR), rescue anticoagulation for sub-therapeutic heparin in 26% (6 HeartWare), wash-out for pump thrombosis based on log file analysis and laboratory findings of hemolysis in 26% (6 HeartWare), and anticoagulation after a new ischemic stroke (once neurologically stable) in 14% (3 HeartWare). The overall median duration of bivalirudin was 23 days (IQR 12 - 35 days). The median starting dose was 0.15 mg/kg/hr (IQR 0.1 - 0.15 mg/kg/hr), and the median target activated partial thromboplastin time (PTT) Hepzyme was 60 - 70 sec. The median initial lactate dehydrogenase level was 1213 IU/mL (IQR 599 - 2072 IU/mL), and the median initial plasma hemoglobin level was 40 mg/dL (IQR 30 - 110 mg/dL). The median time to reach goal anticoagulation parameters was 1 day (IQR 1-2 days), and the median therapeutic dose was 0.2 mg/kg/hr (IQR 0.15 - 0.20 mg/kg/hr). Two of the 6 patients who received bivalirudin infusion for washout of pump thrombosis did not achieve therapeutic PTT hepzyme levels, and underwent pump exchange. The remaining 4 patients had normalization of flow patterns on their log file downloads after bivalirudin. While on bivalirudin, 1 patient had a bleeding complication (mediastinal hematoma in HeartWare), while 3 patients had thrombotic complications (all on bivalirudin for primary anticoagulation after VAD implantation - 1 embolic stroke in Berlin EXCOR, 1 embolic stroke in HeartWare, 1 VAD thrombus in HeartWare). Conclusion In this small pediatric series, bivalirudin use for post-operative anticoagulation as well as pump thrombosis achieved therapeutic success in 91% (21/23) cases, with complications in 4 cases. Increasing use of bivalirudin will help elucidate its role in pediatric VAD management.


title: MEDICAL DEONTOLOGY AND REGULATIONS OF THE FIRST COLLEGE PHYSICIANS OF IN ROMANIA

abstract: Medical deontology in Romania started to develop at the beginning of the XX th century, founded mainly by the works of professors Mina Minovici in Bucharest and George Bogdan in Jassy. These norms were however difficult to implement and be controlled in clinical practice without a professional association able to take the necessary measures to do so. The purpose of this article is to summarize what was considered as a disciplinary and/or as a deontological breach in the Regulations regarding the Physicians College in the Interwar period in Romania.


title: A case study of LEGO Mindstorms'#8482; suitability for artificial intelligence and robotics courses at the college level

abstract: This paper examines LEGO Mindstorms'™ suitability as a hardware platform for integrating robotics into an Artificial Intelligence course organized around the agent paradigm popularized by Russell and Norvig. This evaluation discusses how kits and projects based on Mindstorms supported students' exploration of the issues behind the design of agents from three classes in Russell and Norvig's intelligent agent taxonomy. The paper's investigation also examines several popularly-perceived limitations of the Mindstorms package for college-level robotics projects and shows that most of these "limitations" are not serious impediments to Mindstorms' use, while certain other of these "limitations" do indeed present challenges to the platform's use.


title: Honing Theory: A Complex Systems Framework for Creativity

abstract: This paper proposes a theory of creativity, referred to as honing theory, which posits that creativity fuels the process by which culture evolves through communal exchange amongst minds that are self-organizing, self-maintaining, and self-reproducing. According to honing theory, minds, like other selforganizing systems, modify their contents and adapt to their environments to minimize entropy. Creativity begins with detection of high psychological entropy material, which provokes uncertainty and is arousalinducing. The creative process involves recursively considering this material from new contexts until it is sufficiently restructured that arousal dissipates. Restructuring involves neural synchrony and dynamic binding, and may be facilitated by temporarily shifting to a more associative mode of thought. A creative work may similarly induce restructuring in others, and thereby contribute to the cultural evolution of more nuanced worldviews. Since lines of cultural descent connecting creative outputs may exhibit little continuity, it is proposed that cultural evolution occurs at the level of self-organizing minds, outputs reflect their evolutionary state. Honing theory addresses challenges not addressed by other theories of creativity, such as the factors that guide restructuring, and in what sense creative works evolve. Evidence comes from empirical studies, an agent-based computational model of cultural evolution, and a model of concept combination.


title: Fusion of 3D-LIDAR and camera data for scene parsing

abstract: One geometry segmentation algorithm is proposed to parse scanner pointclouds.One efficient multilayer perception classifier is trained to parse camera images.We propose one fuzzy logic based fusion method to integrate results of two sensors.We propose one Markov random field based temporal fusion method.The fused results are more reliable than those of individual sensors. Fusion of information gathered from multiple sources is essential to build a comprehensive situation picture for autonomous ground vehicles. In this paper, an approach which performs scene parsing and data fusion for a 3D-LIDAR scanner (Velodyne HDL-64E) and a video camera is described. First of all, a geometry segmentation algorithm is proposed for detection of obstacles and ground areas from data collected by the Velodyne scanner. Then, corresponding image collected by the video camera is classified patch by patch into more detailed categories. After that, parsing result of each frame is obtained by fusing result of Velodyne data and that of image using the fuzzy logic inference framework. Finally, parsing results of consecutive frames are smoothed by the Markov random field based temporal fusion method. The proposed approach has been evaluated with datasets collected by our autonomous ground vehicle testbed in both rural and urban areas. The fused results are more reliable than that acquired via analysis of only images or Velodyne data.


Geometry and Uncertainty in Deep Learning for Computer Vision


title: Million-scale near-duplicate video retrieval system

abstract: In this paper, we present a novel near-duplicate video retrieval system serving one million web videos. To achieve both the effectiveness and efficiency, a visual word based approach is proposed, which quantizes each video frame into a word and represents the whole video as a bag of words. The system can respond to a query in 41ms with 78.4% MAP on average.


title: Directivity Enhancement to Vivaldi Antennas Using Compactly Anisotropic Zero-Index Metamaterials

abstract: The traditional Vivaldi antenna has an ultrawide bandwidth, but low directivity. To enhance the directivity, we propose a high-gain Vivaldi antenna based on compactly anisotropic zero-index metamaterials (ZIM). Such anisotropic ZIM are designed and fabricated using resonant meander-line structures, which are integrated with the Vivaldi antenna smoothly and hence have compact size. Measurement results show that the directivity and gain of the Vivaldi antenna have been enhanced significantly in the designed bandwidth of anisotropic ZIM (9.5-10.5 GHz), but not affected in other frequency bands (2.5-9.5 GHz and 10.5-13.5 GHz).


title: The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks

abstract: Abstract Bayesian belief networks provide a natural, efficient method for representing probabilistic dependencies among a set of variables. For these reasons, numerous researchers are exploring the use of belief networks as a knowledge representation in artificial intelligence. Algorithms have been developed previously for efficient probabilistic inference using special classes of belief networks. More general classes of belief networks, however, have eluded efforts to develop efficient inference algorithms. We show that probabilistic inference using belief networks is NP-hard. Therefore, it seems unlikely that an exact algorithm can be developed to perform probabilistic inference efficiently over all classes of belief networks. This result suggests that research should be directed away from the search for a general, efficient probabilistic inference algorithm, and toward the design of efficient special-case, average-case, and approximation algorithms.


title: Effect of abdominal and pelvic floor tasks on muscle activity, abdominal pressure and bladder neck

abstract: INTRODUCTION AND HYPOTHESIS ::: Although the bladder neck is elevated during a pelvic floor muscle (PFM) contraction, it descends during straining. This study aimed to investigate the relationship between bladder neck displacement, electromyography (EMG) activity of the pelvic floor and abdominal muscles and intra-abdominal pressure (IAP) during different pelvic floor and abdominal contractions. ::: ::: ::: METHODS ::: Nine women without PFM dysfunction performed maximal, gentle and moderate PFM contractions, maximal and gentle transversus abdominis (TrA) contractions, bracing, Valsalva and head lift. Bladder neck position was assessed with perineal ultrasound. PFM and abdominal muscle activities were recorded with a vaginal probe and fine-wire electrodes, respectively. IAP was recorded with a rectal balloon. ::: ::: ::: RESULTS ::: Bladder neck elevation only occurred during PFM and TrA contractions. PFM EMG and IAP increased during all tasks from 0.5 (gentle TrA) to 45.7 cmH2O (maximal Valsalva). ::: ::: ::: CONCLUSION ::: Bladder neck elevation was only observed when the activity of PFM EMG was high relative to the IAP increase.