title: Three-dimensional numerical simulation of a vertical axis tidal turbine using the two-way fluid structure interaction approach *

abstract: The objective of this study was to develop, as well as validate the strongly coupled method (two-way fluid structural interaction (FSI)) used to simulate the transient FSI response of the vertical axis tidal turbine (VATT) rotor, subjected to spatially varying inflow. Moreover, this study examined strategies on improving techniques used for mesh deformation that account for large displacement or deformation calculations. The blade’s deformation for each new time step is considered in transient two-way FSI analysis, to make the design more reliable. Usually this is not considered in routine one-way FSI simulations. A rotor with four blades and 4-m diameter was modeled and numerically analyzed. We observed that two-way FSI, utilizing the strongly coupled method, was impossible for a complex model; and thereby using ANSYS-CFX and ANSYS-MECHANICAL in work bench, as given in ANSYS-WORKBENCH, helped case examples 22 and 23, by giving an error when the solution was run. To make the method possible and reduce the computational power, a novel technique was used to transfer the file in ANSYS-APDL to obtain the solution and results. Consequently, the results indicating a two-way transient FSI analysis is a time- and resource-consuming job, but with our proposed technique we can reduce the computational time. The ANSYS STRUCTURAL results also uncover that stresses and deformations have higher values for two-way FSI as compared to one-way FSI. Similarly, fluid flow CFX results for two-way FSI are closer to experimental results as compared to one-way simulation results. Additionally, this study shows that, using the proposed method we can perform coupled simulation with simple multi-node PCs (core i5).


title: Neonatal Marfan Syndrome: Report of Two Cases

abstract: Background:Neonatal Marfan syndrome is a rare and severe phenotype of ::: this disease. A poor prognosis is anticipated due to the high ::: probability of congestive heart failure, and mitral and tricuspid ::: regurgitations with suboptimal response to medical therapy and ::: difficulties in surgical management at an early age. Case Presentation: ::: We present two consecutive patients with this disease who are the first ::: reported cases from Iran to the best of our knowledge. Unfortunately ::: both of them died shortly after diagnosis. Conclusion: Neonatal Marfan ::: syndrome is reported from Iran and has a poor prognosis like the ::: patients reported from elsewhere.


Ontology based Semantic Search Engine 1 N.Vanjulavalli,


title: Microstrip Branch-Line Couplers for Crossover Application

abstract: The branch-line coupler may be redesigned for crossover application. The bandwidth of such a coupler can be extended by suitably incorporating additional sections into the composite design. Laboratory tests on microstrip prototypes have shown the return loss and isolation of the three- and four-section couplers to be better than 20 dB over bandwidths of 22% and 33%, respectively. The insertion losses and group delays vary by less than ±0.05 dB and ±1 ns, respectively, for both prototypes.


title: Link Cost and Reliability of Frame Preamble MAC Protocols

abstract: Previous studies have shown that preamble MAC protocols have higher energy efficiency compared to traditional low power MAC protocols based on common sleep/wakeup schedules. One efficient implementation of preamble MAC protocols is frame preamble MAC in which the MAC preamble is replaced by a series of frames. In this paper, we consider four frame preamble MAC protocols, namely: persistent MFP (micro frame preamble), non persistent MFP, persistent DFP (data frame preamble), and non persistent DFP. We study their energy cost and communication reliability by assuming a simple binary symmetric channel error model


title: Simulation of the Flow Control of Radial Piston Pump Based on Simulink

abstract: The flow control of radial piston is widely used in speed,position servo system. In this paper the mathematic model of the test radial piston pump is established and the simulation diagram is established in the simulink of MATLAB.Then the response curves of the system are achieved.The performance of the system is improved after adding the fuzzy-PID controller.


title: Access Control Framework for Social Network System using Ontology

abstract: In recent years, we have seen unprecedented growth in the area of Online Social Networking (OSN) that is still keeping on day by day. Social networking websites such as Facebook, Google+, and Twitter are using widely by people to share personal and public information with friends, coworkers, colleagues, family and even with strangers .Facebook, one of the most popular social network sites, has million of active users and billions of pieces of content or data that use daily like web links, news stories, blog posts, notes, photo albums, etc. shared each month. To protect such kind for huge or big data or information need more secured and flexible access control model. There are so many access control policies are available for controlling online social network, but all social networking sites like Facebook or Twitter has their own access control mechanism that is not standard and still not more secured or flexible. To protect such kind of publically oriented user data need more dynamic access control model. In order to protect OSN, in this paper an innovative or dynamic access control framework for social networking systems using semantic web ontology has been proposed which addresses the protection of semantic-rich information in a knowledge base ontology


title: Automatic question answering using the web: Beyond the Factoid

abstract: In this paper we describe and evaluate a Question Answering (QA) system that goes beyond answering factoid questions. Our approach to QA assumes no restrictions on the type of questions that are handled, and no assumption that the answers to be provided are factoids. We present an unsupervised approach for collecting question and answer pairs from FAQ pages, which we use to collect a corpus of 1 million question/answer pairs from FAQ pages available on the Web. This corpus is used to train various statistical models employed by our QA system: a statistical chunker used to transform a natural language-posed question into a phrase-based query to be submitted for exact match to an off-the-shelf search engine; an answer/question translation model, used to assess the likelihood that a proposed answer is indeed an answer to the posed question; and an answer language model, used to assess the likelihood that a proposed answer is a well-formed answer. We evaluate our QA system in a modular fashion, by comparing the performance of baseline algorithms against our proposed algorithms for various modules in our QA system. The evaluation shows that our system achieves reasonable performance in terms of answer accuracy for a large variety of complex, non-factoid questions.


title: The active site residue Valine 867 in human telomerase reverse transcriptase influences nucleotide incorporation and fidelity

abstract: Human telomerase reverse transcriptase (hTERT), the catalytic subunit of human telomerase, contains conserved motifs common to retroviral reverse transcriptases and telomerases. Within the C motif of hTERT is the Leu866-Val867-Asp868-Asp869 tetrapeptide that includes a catalytically essential aspartate dyad. Site-directed mutagenesis of Tyr183 and Met184 residues in HIV-1 RT, residues analogous to Leu866 and Val867, revealed that they are key determinants of nucleotide binding, processivity and fidelity. In this study, we show that substitutions at Val867 lead to significant changes in overall enzyme activity and telomere repeat extension rate, but have little effect on polymerase processivity. All Val867 substitutions examined (Ala, Met, Thr) led to reduced repeat extension rates, ranging from ~20 to 50% of the wild-type rate. Reconstitution of V867M hTERT and telomerase RNAs (TRs) with mutated template sequences revealed the effect on extension rate was associated with a template copying defect specific to template A residues. Furthermore, the Val867 hTERT mutants also displayed increased nucleotide incorporation fidelity, implicating Val867 as a determinant of telomerase fidelity. These findings suggest that by evolving to have a valine at position 867, the wild-type hTERT protein may have partially compromised polymerase fidelity for optimal and rapid repeat synthesis.


title: A Dolev-Yao Model for Zero Knowledge

abstract: We propose an extension of the standard Dolev-Yao model of cryptographic protocols to facilitate symbolic reasoning about zero-knowledge proofs. This is accomplished by communicating typed terms, and providing a proof amounts to certifying that a term is of a particular type. We present a proof system for term derivability, which is employed to yield a decision procedure for checking whether a given protocol meets its zero knowledge specification.


Thanks ! Title ΦCrAss 001 represents the most abundant bacteriophage family in the human gut and infects Bacteroides intestinalis


title: Design of a parabolic patch antenna in band L, with double layer and air substrate, for weather satellite reception

abstract: This paper presents the design of a parabolic patch antenna. The reflector of primary focus has 120cm of diameter, the proposed feeder has double layer and air substrate between its layers. The upper and lower square layers are supported by non-conductive material. The experimental axial ratio obtained is 0.8dB at 1.7 GHz, phi=0deg. The gain obtained is 23.4dB, the reflection coefficient obtained in 1.7 GHz is -14dB. The antenna can be used to receive information from polar orbit weather satellites in L band, at 1.7 GHz. The antenna must be used with a rotor with synchronized movement in azimuth and elevation for the satellite tracking.


title: A unified framework for generalized Linear Discriminant Analysis

abstract: Linear discriminant analysis (LDA) is one of the well- known methods for supervised dimensionality reduction. Over the years, many LDA-based algorithms have been developed to cope with the curse of dimensionality. In essence, most of these algorithms employ various techniques to deal with the singularity problem, which occurs when the data dimensionality is larger than the sample size. They have been applied successfully in various applications. However, there is a lack of a systematic study of the commonalities and differences of these algorithms, as well as their intrinsic relationships. In this paper, a unified framework for generalized LDA is proposed via a transfer function. The proposed framework elucidates the properties of various algorithms and their relationships. Based on the presented analysis, we propose an efficient model selection algorithm for LDA. We conduct extensive experiments using a collection of high-dimensional data, including text documents, face images, gene expression data, and gene expression pattern images, to evaluate the proposed theories and algorithms.


Abstract ODAC: Hierarchical Clustering of Time Series Data Streams ∗


title: DeepPatent: patent classification with convolutional neural networks and word embedding

abstract: Patent classification is an essential task in patent information management and patent knowledge mining. However, this task is still largely done manually due to the unsatisfactory performance of current algorithms. Recently, deep learning methods such as convolutional neural networks (CNN) have led to great progress in image processing, voice recognition, and speech recognition, which has yet to be applied to patent classification. We proposed DeepPatent, a deep learning algorithm for patent classification based on CNN and word vector embedding. We evaluated the algorithm on the standard patent classification benchmark dataset CLEF-IP and compared it with other algorithms in the CLEF-IP competition. Experiments showed that DeepPatent with automatic feature extraction achieved a classification precision of 83.98%, which outperformed all the existing algorithms that used the same information for training. Its performance is better than the state-of-art patent classifier with a precision of 83.50%, whose performance is, however, based on 4000 characters from the description section and a lot of feature engineering while DeepPatent only used the title and abstract information. DeepPatent is further tested on USPTO-2M, a patent classification benchmark data set that we contributed with 2,000,147 records after data cleaning of 2,679,443 USA raw utility patent documents in 637 categories at the subclass level. Our algorithms achieved a precision of 73.88%.


title: Higher-order Mode Rectangular Dielectric Resonator Antenna for 5G Applications

abstract: The excitation of the higher-order mode, in rectangular dielectric resonator designed was explored to enhance the antenna gain and detailed elaboration is presented in this paper. The antenna was fed by a 50Ω microstrip line through an aperture cut in the ground plane. Besides avoiding spurious radiation, this feeding technique gives flexibility in controlling the amount of coupling in order to reduce the Q-factor in the higher-order mode RDRA. A design was developed and subsequently simulated using Ansoft HFSS ver 16.0 by utilizing Duroid 5880 dielectric substrate with a thickness (ts) of 0.254 mm, a permittivity (es) of 2.2 and a loss tangent (δ) of 0.001 at 15 GHz. The higher-order mode, RDRA achieved the measured gain at 9.76 dBi and the measured impedance bandwidth as much 2.5 GHz which is 4.7% more compared to the fundamental mode. The result should be considered suitable for 5G applications.


title: Performance Evaluation and Optimization of Communication Infrastructure for the Next Generation Air Transportation System

abstract: Automatic dependent surveillance-broadcast (ADS-B) is one of the fundamental surveillance technologies to improve the safety, capacity, and efficiency of the national airspace system. ADS-B shares its frequency band with current radar systems that use the same 1,090 MHz band. The coexistence of radar systems and ADS-B systems is a key issue to detect and resolve conflicts in the next generation air transportation system (NextGen). This paper focuses on the performance evaluation of ADS-B with existing radar systems and performance optimization of ADS-B systems to improve the safety and efficiency of conflict detection and resolution in NextGen. We have developed a simulation environment which models the complex interplay among the air traffic load, the radar systems, the ADS-B systems, and the wireless channel. A simple model is used to derive an analytical expression for a performance metric of ADS-B. This model is then used to design an adaptive ADS-B protocol for maximizing the information coverage while guaranteeing reliable and timely communication in air traffic surveillance networks. Simulation results show that the effect of ADS-B interference on the current radar system is negligible. The operational ability of ADS-B meets the performance requirements of conflict detection and resolution in air traffic control. However, upgrades are required in the current radar system for operation within an ADS-B environment since the current radars can significantly degrade the ADS-B performance. Numerical results indicate that the proposed adaptive protocol has the potential to improve the performance of conflict detection and resolution in air traffic control.


title: Dolphin-like propulsive mechanism based on an adjustable Scotch yoke

abstract: This paper addresses the design, construction, and motion control of an adjustable Scotch yoke mechanism generating desired kinematics for dolphin-like robots. Since dolphins propel themselves by dorsoventral oscillations following a sinusoidal path with alterable amplitudes, a two-motor-driven Scotch yoke mechanism is adopted as the primary propulsor to produce sinusoidal oscillations, where a certain combination of a leading screw mechanism and a rack and pinion mechanism driven by the slave motor are incorporated to independently change the length of the crank actuated by the master motor. Meanwhile, the output of the Scotch yoke, i.e., reciprocating motion, is converted into the up-and-down oscillations via a rack and gear transmission. A DSP-based built-in motion control scheme is then brought forth and applied to achieve dolphin-like propulsion. Preliminary tests, in a robotics context, confirm the feasibility of the devised mechanism severing as a dedicated propulsor for bio-inspired movements.


title: Data Mining and Machine Learning---Towards Reducing False Positives

abstract: Intrusion Detection Systems (IDSs) are used to monitor computer systems for signs of security violations. Having detected such signs, IDSs trigger alerts to report them. These alerts are presented to a human analyst, who evaluates them and initiates an adequate response. In practice, IDSs have been observed to trigger thousands of alerts per day, most of which are mistakenly triggered by benign events (i.e., false positives). This makes it extremely difficult for the analyst to correctly identify alerts related to attacks (i.e., true positives). In this paper, we present two orthogonal and complementary approaches to reduce the number of false positives in intrusion detection using alert postprocessing by data mining and machine learning. Moreover, these two techniques, because of their complementary nature, can be used together in an alert-management system. These concepts have been verified on a variety of data sets, and achieved a significant reduction in the number of false positives in both simulated and real environments.


title: Environmental influences on photosynthesis within the crown of a white oak

abstract: Photosynthesis, leaf conductance, xylem pressure potential as well as six environmental factors were measured within the crown of a dominant, 18.9 m, white oak tree (Quercus alba L.) during the summers of 1974 and 1975. Photosynthesis was measured with a 14CO2 apparatus which enabled a description of the within crown and with leaf variations to be made. Photosynthesis averaged 7.0, 5.6 and 4.6 mgCO2 dm-2 h-1 for top, middle and bottom crown positions. High leaf temperatures and mid-day stomatal closure appeared to limit photosynthesis in the upper crown while light appeared limiting in the lower. Under conditions of a moderate drought, the lower crown's photosynthethic activity approached that of the upper crown. Seasonal shifts in photosynthesis were noted and these were related to shifts in leaf conductance associated with temperature or drought or both pre-conditioning. For example, the xylem pressure potential threshold for stomatal closure varied from-18.9 to-26.1 bars depending upon drought history. With the 14CO2 technique, photosynthetic activity was statistically uniform over the entire leaf surface; thus sampling location within a white oak leaf was not a matter of concern. Similarly, no statistically significant within-leaf variation was found with respect to leaf conductance.


title: Light interaction with human skin: from believable images to predictable models

abstract: Recent research efforts in image synthesis have been directed toward the rendering of believable and predictable images of biological materials. This course addresses an important topic in this area, namely the predictive simulation of skin's appearance. The modeling approaches, algorithms and data examined during this course can be also applied to the rendering of other organic materials such as hair and ocular tissues.


Automatic Differentiation and Backpropagation CS 701


title: Cyber-physical attacks in power networks: Models, fundamental limitations and monitor design

abstract: Future power networks will be characterized by safe and reliable functionality against physical malfunctions and cyber attacks. This paper proposes a unified framework and advanced monitoring procedures to detect and identify network components malfunction or measurements corruption caused by an omniscient adversary. We model a power system under cyber-physical attack as a linear time-invariant descriptor system with unknown inputs. Our attack model generalizes the prototypical stealth, (dynamic) false-data injection and replay attacks. We characterize the fundamental limitations of both static and dynamic procedures for attack detection and identification. Additionally, we design provably-correct (dynamic) detection and identification procedures based on tools from geometric control theory. Finally, we illustrate the effectiveness of our method through a comparison with existing (static) detection algorithms, and through a numerical study.


title: Visual Object-Action Recognition : Inferring Object Affordances from Human Demonstration I

abstract: This paper investigates object categorization according to function, i.e., learning the affordances of objects from human demonstration. Object affordances (functionality) are inferred from observations of humans using the objects in different types of actions. The intended application is learning from demonstration, in which a robot learns to employ objects in household tasks, from observing a human performing the same tasks with the objects. We present a method for categorizing manipulated objects and human manipulation actions in context of each other. The method is able to simultaneously segment and classify human hand actions, and detect and classify the objects involved in the action. This can serve as an initial step in a learning from demonstration method. Experiments show that the contextual information improves the classification of both objects and actions.


title: Automatic Determination of Shoulder Joint Limits Using Quaternion Field Boundaries

abstract: To provide efficient tools for the capture and modeling of acceptable virtual human poses, we propose a method for constraining the underlying joint structures based on real life data. Current tools for delimiting valid postures often employ techniques that do not represent joint limits in an intuitively satisfying manner, and furthermore are seldom directly derived from experimental data. Here, we propose a semi-automatic scheme for determining ball-and-socket joint limits by actual measurement and we apply it to modeling the shoulder complex, which—along with the hip complex—can be approximated by a three-degree-of-freedom ball-and-socket joint. Our first step is to measure the joint motion range using optical motion capture. We next convert the recorded values to joint poses encoded using a coherent quaternion field representation of the joint orientation space. Finally, we obtain a closed, continuous implicit surface approximation for the quaternion orientation-space boundary whose interior represents...


title: Context-aware query suggestion by mining click-through and session data

abstract: Query suggestion plays an important role in improving the usability of search engines. Although some recently proposed methods can make meaningful query suggestions by mining query patterns from search logs, none of them are context-aware - they do not take into account the immediately preceding queries as context in query suggestion. In this paper, we propose a novel context-aware query suggestion approach which is in two steps. In the offine model-learning step, to address data sparseness, queries are summarized into concepts by clustering a click-through bipartite. Then, from session data a concept sequence suffix tree is constructed as the query suggestion model. In the online query suggestion step, a user's search context is captured by mapping the query sequence submitted by the user to a sequence of concepts. By looking up the context in the concept sequence sufix tree, our approach suggests queries to the user in a context-aware manner. We test our approach on a large-scale search log of a commercial search engine containing 1:8 billion search queries, 2:6 billion clicks, and 840 million query sessions. The experimental results clearly show that our approach outperforms two baseline methods in both coverage and quality of suggestions.


title: DeftNN: addressing bottlenecks for DNN execution on GPUs via synapse vector elimination and near-compute data fission

abstract: Deep neural networks (DNNs) are key computational building blocks for emerging classes of web services that interact in real time with users via voice, images and video inputs. Although GPUs have gained popularity as a key accelerator platform for deep learning workloads, the increasing demand for DNN computation leaves a significant gap between the compute capabilities of GPU-enabled datacenters and the compute needed to service demand. The state-of-the-art techniques to improve DNN performance have significant limitations in bridging the gap on real systems. Current network pruning techniques remove computation, but the resulting networks map poorly to GPU architectures, yielding no performance benefit or even slowdowns. Meanwhile, current bandwidth optimization techniques focus on reducing off-chip band-width while overlooking on-chip bandwidth, a key DNN bottleneck. To address these limitations, this work introduces DeftNN, a GPU DNN execution framework that targets the key architectural bottlenecks of DNNs on GPUs to automatically and transparently improve execution performance. DeftNN is composed of two novel optimization techniques–(l) synapse vector elimination, a technique that identifies non-contributing synapses in the DNN and carefully transforms data and removes the computation and data movement of these synapses while fully utilizing the GPU to improve performance, and (2) near-compute data fission, a mechanism for scaling down the on-chip data movement requirements within DNN computations. Our evaluation of DeftNN spans 6 state-of-the-art DNNs. By applying both optimizations in concert, DeftNN is able to achieve an average speedup of $2.1\times$ on real GPU hardware. We also introduce a small additional hardware unit per GPU core to facilitate efficient data fission operations, increasing the speedup achieved by DeftNN to $2.6\times$CCS CONCEPTS• Computing methodologies $\rightarrow $Neural networks;• Computer systems organization $\rightarrow $Single instruction, multiple data; • General and reference $\rightarrow $Performance;


title: Personality and Demographic Characteristics of Dropouts and Completers in a Nonhospital Residential Alcohol Treatment Program

abstract: This study investigated the relationship of scores on the Psychopathic Deviate, Denial, Dependency, and MacAndrew Scales of the MMPI; intelligence; age; income; and education to continuance in a residential alcohol treatment program. Dropouts scored higher on the Psychopathic Deviate Scale than completers did. For completers, Psychopathic Deviate scores were correlated with age and Denial scores were


title: Effects of welding speed on absorption rate in partial and full penetration welding of stainless steel with high brightness and high power laser

abstract: Abstract The objectives of this paper are to measure the absorption rate based on water calorimetric method for both full and partial penetration welding and to evaluate the energy loss by spatter and metallic plume in 6 kW laser welding of 304 austenitic stainless steel according to the X-ray observed keyhole characteristic and loss of specimen weight. In partial penetration welds of 9 mm thickness, the absorption rate decreased from 73% to 62% with the welding speed from 50 to 150 mm/s due to the decrease in multiple reflections of laser beam inside the keyhole. In full penetration welds of 3 mm thickness, the absorption rate increased from 42% to 61% with the welding speed owing to the decrease in the amount of beam escaped from the keyhole inlet on the bottom surface of the sheet. At 50 mm/s in full penetration welds, fluctuation in keyhole diameter appeared, and the minimum absorption rate might come near 0 when the keyhole diameter was 3 times of the normal value.


Design and Control of a Four-Wheeled Omnidirectional Mobile Robot with Steerable Omnidirectional Wheels


title: How to Train Your DRAGAN

abstract: Generative Adversarial Networks have emerged as an effective technique for estimating data distributions. The basic setup consists of two deep networks playing against each other in a zero-sum game setting. However, it is not understood if the networks reach an equilibrium eventually and what dynamics makes this possible. The current GAN training procedure, which involves simultaneous gradient descent, lacks a clear game-theoretic justification in the literature. In this paper, we introduce regret minimization as a technique to reach equilibrium in games and use this to justify the success of simultaneous GD in GANs. In addition, we present a hypothesis that mode collapse, which is a common occurrence in GAN training, happens due to the existence of spurious local equilibria in non-convex games. Motivated by these insights, we develop an algorithm called DRAGAN that is fast, simple to implement and achieves competitive performance in a stable fashion across different architectures (150 random setups), datasets (MNIST, CIFAR-10, and CelebA), and divergence measures with almost no hyperparameter tuning. We show significant improvements over the recently proposed Wasserstein GAN variants.


title: Adversarial Generation of Training Examples for Vehicle License Plate Recognition

abstract: Generative Adversarial Networks (GAN) have attracted much research attention recently, leading to impressive results for natural image generation. However, to date little success was observed in using GAN generated images for improving classification tasks. Here we attempt to explore, in the context of car license plate recognition, whether it is possible to generate synthetic training data using GAN to improve recognition accuracy. With a carefully-designed pipeline, we show that the answer is affirmative. First, a large-scale image set is generated using the generator of GAN, without manual annotation. Then, these images are fed to a deep convolutional neural network (DCNN) followed by a bidirectional recurrent neural network (BRNN) with long short-term memory (LSTM), which performs the feature learning and sequence labelling. Finally, the pre-trained model is fine-tuned on real images. Our experimental results on a few data sets demonstrate the effectiveness of using GAN images: an improvement of 7.5% over a strong baseline with moderate-sized real data being available. We show that the proposed framework achieves competitive recognition accuracy on challenging test datasets. We also leverage the depthwise separate convolution to construct a lightweight convolutional RNN, which is about half size and 2x faster on CPU. Combining this framework and the proposed pipeline, we make progress in performing accurate recognition on mobile and embedded devices.


title: SCAN: Learning Abstract Hierarchical Compositional Visual Concepts

abstract: The natural world is infinitely diverse, yet this diversity arises from a relatively small set of coherent properties and rules, such as the laws of physics or chemistry. We conjecture that biological intelligent systems are able to survive within their diverse environments by discovering the regularities that arise from these rules primarily through unsupervised experiences, and representing this knowledge as abstract concepts. Such representations possess useful properties of compositionality and hierarchical organisation, which allow intelligent agents to recombine a finite set of conceptual building blocks into an exponentially large set of useful new concepts. This paper describes SCAN (Symbol-Concept Association Network), a new framework for learning such concepts in the visual domain. We first use the previously published beta-VAE (Higgins et al., 2017a) architecture to learn a disentangled representation of the latent structure of the visual world, before training SCAN to extract abstract concepts grounded in such disentangled visual primitives through fast symbol association. Our approach requires very few pairings between symbols and images and makes no assumptions about the choice of symbol representations. Once trained, SCAN is capable of multimodal bi-directional inference, generating a diverse set of image samples from symbolic descriptions and vice versa. It also allows for traversal and manipulation of the implicit hierarchy of compositional visual concepts through symbolic instructions and learnt logical recombination operations. Such manipulations enable SCAN to invent and learn novel visual concepts through recombination of the few learnt concepts.


Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning.


title: Adversarial Examples that Fool both Human and Computer Vision

abstract: Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich. However, it is still an open question whether humans are prone to similar mistakes. Here, we create the first adversarial examples designed to fool humans, by leveraging recent techniques that transfer adversarial examples from computer vision models with known parameters and architecture to other models with unknown parameters and architecture, and by modifying models to more closely match the initial processing of the human visual system. We find that adversarial examples that strongly transfer across computer vision models influence the classifications made by time-limited human observers.


title: Effective Dimensionality Reduction for Word Embeddings

abstract: Word embeddings have become the basic building blocks for several natural language processing and information retrieval tasks. Recently, there has been an emphasis on further improving the pre-trained word vectors through post-processing algorithms. One such area of improvement is the dimensionality reduction of word embeddings. Reducing the size of word embeddings through dimensionality reduction can improve their utility in memory constrained devices. In this work, we devise an algorithm that effectively combines PCA based dimensionality reduction with a post-processing algorithm, to construct word embeddings of lower dimensions. Empirical evalutions on 12 standard word similarity benchmarks show that our algorithm reduces the embedding dimensionality by 50\%, while achieving similar or (more often) better performance.


title: Unsupervised Real-to-Virtual Domain Unification for End-to-End Highway Driving

abstract: In the spectrum of vision-based autonomous driving, vanilla end-to-end models are not interpretable and suboptimal in performance, while mediated perception models require additional intermediate representations such as segmentation masks or detection bounding boxes, whose annotation can be prohibitively expensive as we move to a larger scale. Raw images and existing intermediate representations are also loaded with nuisance details that are irrelevant to the prediction of vehicle commands, e.g. the style of the car in front or the view beyond the road boundaries. More critically, all prior works fail to deal with the notorious domain shift if we were to merge data collected from different sources, which greatly hinders the model generalization ability. In this work, we address the above limitations by taking advantage of virtual data collected from driving simulators, and present DU-drive, an unsupervised real to virtual domain unification framework for end-to-end driving. It transforms real driving data to its canonical representation in the virtual domain, from which vehicle control commands are predicted. Our framework has several advantages: 1) it maps driving data collected from different source distributions into a unified domain, 2) it takes advantage of annotated virtual data which is free to obtain, 3) it learns an interpretable, canonical representation of driving image that is specialized for vehicle command prediction. Extensive experiments on two public highway driving datasets clearly demonstrate the performance superiority and interpretive capability of DU-drive.


FutureGAN: Anticipating the Future Frames of Video Sequences using Spatio-Temporal 3d Convolutions in Progressively Growing Autoencoder GANs


The 128 Point New Mixed Radix-2 and Radix-4 Pipeline Combined SDF-MDC FFT / IFFT Processor For MIMO-OFDM Transceiver


title: Histogram refinement for content-based image retrieval

abstract: Color histograms are widely used for content-based image retrieval. Their advantages are efficiency, and insensitivity to small changes in camera viewpoint. However, a histogram is a coarse characterization of an image, and so images with very different appearances can have similar histograms. We describe a technique for comparing images called histogram refinement, which imposes additional constraints on histogram based matching. Histogram refinement splits the pixels in a given bucket into several classes, based upon some local property. Within a given bucket, only pixels in the same class are compared. We describe a split histogram called a color coherence vector (CCV), which partitions each histogram bucket based on spatial coherence. CCVs can be computed at over 5 images per second on a standard workstation. A database with 15,000 images can be queried using CCVs in under 2 seconds. We demonstrate that histogram refinement can be used to distinguish images whose color histograms are indistinguishable.


title: Apache Flink: Stream Analytics at Scale

abstract: Apache Flink is an open source system for expressive, declarative, fast, and efficient data analysis on both historical (batch) and real-time (streaming) data. Flink combines the scalability and programming flexibility of distributed MapReduce-like platforms with the efficiency, out-of-core execution, and query optimization capabilities found in parallel databases. At its core, Flink builds on a distributed dataflow runtime that unifies batch and incremental computations over a true-streaming pipelined execution. Its programming model allows for stateful, fault tolerant computations, flexible user-defined windowing semantics for streaming and unique support for iterations. Flink is converging into a use-case complete system for parallel data processing with a wide range of top level libraries ranging from machine learning through to graph processing. Apache Flink originates from the Stratosphere project led by TU Berlin and has led to various scientific papers (e.g., in VLDBJ, SIGMOD, (P)VLDB, ICDE, and HPDC). In this half-day tutorial we will introduce Apache Flink, and give a tutorial on its streaming capabilities using concrete examples of application scenarios, focusing on concepts such as stream windowing, and stateful operators.


title: The associations of mobile touch screen device use with musculoskeletal symptoms and exposures: A systematic review

abstract: Background ::: The use of mobile touch screen devices (MTSDs) has increased rapidly over the last decade, and there are concerns that their use may have negative musculoskeletal consequences; yet evidence on the association of MTSD use with musculoskeletal symptoms and exposures is currently dispersed. The aim of this study was to systematically review available literature on musculoskeletal symptoms and exposures associated with MTSD use. The synthesised information may facilitate wise use of MTSDs and may identify areas in need of further research. ::: ::: ::: Methods ::: EMBASE, Medline, Scopus, PsycINFO and Proquest electronic databases were searched for articles published up to June 2016, using keywords describing MTSD, musculoskeletal symptoms (e.g. pain, discomfort) and musculoskeletal exposures (e.g. posture, muscle activity). Two reviewers independently screened the articles, extracted relevant data and assessed methodological quality of included studies. Due to heterogeneity in the studies, a meta-analysis was not possible and a structured narrative synthesis of the findings was undertaken. ::: ::: ::: Results ::: A total of 9,908 articles were screened for eligibility with 45 articles finally included for review. Included articles were of cross-sectional, case-control or experimental laboratory study designs. No longitudinal studies were identified. Findings were presented and discussed in terms of the amount, features, tasks and positions of MTSD use and its association with musculoskeletal symptoms and musculoskeletal exposures. ::: ::: ::: Conclusions ::: There is limited evidence that MTSD use, and various aspects of its use (i.e. amount of usage, features, tasks and positions) are associated with musculoskeletal symptoms and exposures. This is due to mainly low quality experimental and case-control laboratory studies, with few cross-sectional and no longitudinal studies. Further research is warranted in order to develop guidelines for wise use of MTSDs.


title: On developing system dynamics model for business process simulation

abstract: Business operations can be formally described in business process models that capture activities, information, and flow embedded in business operation. System dynamics modeling is a set of conceptual tools that enable business process designers to build computer simulations of complex business process behaviors. System dynamics models provide accurate description of system behavior along the time dimension. It gives a convenient tool to conduct what if analysis though dynamics points of view. However, to develop system dynamics models requires keen understanding of the "physics" of the target business operations, business organizations, and financial structures and so on. This paper is aimed to provide heuristics and guidelines of developing system dynamics models based on given business process models along with associated reference contexts. An example, from supply chain management domain, of using business process models to derive system dynamics models will be given in the paper.


title: Acceleration of Gradient-based Path Integral Method for Efficient Optimal and Inverse Optimal Control

abstract: This paper deals with a new accelerated path integral method, which iteratively searches optimal controls with a small number of iterations. This study is based on the recent observations that a path integral method for reinforcement learning can be interpreted as gradient descent. This observation also applies to an iterative path integral method for optimal control, which sets a convincing argument for utilizing various optimization methods for gradient descent, such as momentum-based acceleration, step-size adaptation and their combination. We introduce these types of methods to the path integral and demonstrate that momentum-based methods, like Nesterov Accelerated Gradient and Adam, can significantly improve the convergence rate to search for optimal controls in simulated control systems. We also demonstrate that the accelerated path integral could improve the performance on model predictive control for various vehicle navigation tasks. Finally, we represent this accelerated path integral method as a recurrent network, which is the accelerated version of the previously proposed path integral networks (PI-Net). We can train the accelerated PI-Net more efficiently for inverse optimal control with less RAM than the original PI-Net.


title: Clustering and Embedding Using Commute Times

abstract: This paper exploits the properties of the commute time between nodes of a graph for the purposes of clustering and embedding and explores its applications to image segmentation and multibody motion tracking. Our starting point is the lazy random walk on the graph, which is determined by the heat kernel of the graph and can be computed from the spectrum of the graph Laplacian. We characterize the random walk using the commute time (that is, the expected time taken for a random walk to travel between two nodes and return) and show how this quantity may be computed from the Laplacian spectrum using the discrete Green's function. Our motivation is that the commute time can be anticipated to be a more robust measure of the proximity of data than the raw proximity matrix. In this paper, we explore two applications of the commute time. The first is to develop a method for image segmentation using the eigenvector corresponding to the smallest eigenvalue of the commute time matrix. We show that our commute time segmentation method has the property of enhancing the intragroup coherence while weakening intergroup coherence and is superior to the normalized cut. The second application is to develop a robust multibody motion tracking method using an embedding based on the commute time. Our embedding procedure preserves commute time and is closely akin to kernel PCA, the Laplacian eigenmap, and the diffusion map. We illustrate the results on both synthetic image sequences and real-world video sequences and compare our results with several alternative methods.


title: Low-voltage MOS linear transconductor/squarer and four-quadrant multiplier for analog VLSI

abstract: Analog computations such as four-quadrant multiplication, linear voltage-to-current conversion and sum-square or difference-square are fundamental for many analog signal processing systems. All these functions can be realized based on the principle of the linearized differential pair using floating-voltage sources. This paper describes an improved practical realization of this principle, which is particularly suited to analog VLSI computational systems. The proposed class-AB analog cells are very compact, exhibit low total harmonic distortion and low nonlinearity, have a wide bandwidth, and are compatible with low-power and low-voltage operation. A mathematical discussion on stability and harmonic distortion of the proposed realization is presented. Both simulated results and measurements from fabricated cell samples in a 0.8-/spl mu/m CMOS process are given. The described circuits operate from a single 2-V power supply.


title: Further studies on the avoidance behaviour of Periplaneta Americana to electric shock

abstract: Abstract Several experiments involving learning of a leg position by the cockroach were carried out. The main results were as follows: 1. 1. Animals retained the learned habit during extinction but animals trained at a higher intensity of shock carried over their previous poorer performance in acquisition into the period without shock. 2. 2. Extinction tests made after only 6 min of training showed no sign of retention, but there was retention after 20 min of training. 3. 3. An attempt to establish light as a secondary reinforcer to enhance resistance to extinction was a failure. The additional stimulation simply brought about a deterioration of performance. 4. 4. An examination of reversal learning showed no successive improvement over trials, though most animals did maintain a fairly steady performance and changed their responses consistently with the change of experimental conditions.


title: Comparing effect sizes in follow-up studies: ROC Area, Cohen's d, and r.

abstract: In order to facilitate comparisons across follow-up studies that have used different measures of effect size, we provide a table of effect size equivalencies for the three most common measures: ROC area (AUC), Cohen's d, and r. We outline why AUC is the preferred measure of predictive or diagnostic accuracy in forensic psychology or psychiatry, and we urge researchers and practitioners to use numbers rather than verbal labels to characterize effect sizes.


title: Computer Analysis of Jazz Chord Sequences : Is Solar a Blues ?

abstract: This chapter investigates the issue of the role of the computer in musical analysis. Starting with a survey of the main approaches in computer analysis, we focus on the particular problem of Jazz chord sequences harmonic analysis. We propose a theory of chord sequence analysis, based on an explicit conceptual hierarchy of analysis objects. We discuss the implementation of the theory and its results on a typical example (Blues for Alice, by Charlie Parker), for which the system produces an analysis which conforms exactly to human interpretation. We also exhibit a chord sequence, Solar (by Miles Davis), for which the results of the system do not conform to human perception, i.e. it does not find it is a Blues. We conclude on the issue of the role for the computer in musical analysis.


title: Benchmarking Deep Sequential Models on Volatility Predictions for Financial Time Series

abstract: Volatility is a quantity of measurement for the price movements of stocks or options which indicates the uncertainty within financial markets. As an indicator of the level of risk or the degree of variation, volatility is important to analyse the financial market, and it is taken into consideration in various decision-making processes in financial activities. On the other hand, recent advancement in deep learning techniques has shown strong capabilities in modelling sequential data, such as speech and natural language. In this paper, we empirically study the applicability of the latest deep structures with respect to the volatility modelling problem, through which we aim to provide an empirical guidance for the theoretical analysis of the marriage between deep learning techniques and financial applications in the future. We examine both the traditional approaches and the deep sequential models on the task of volatility prediction, including the most recent variants of convolutional and recurrent networks, such as the dilated architecture. Accordingly, experiments with real-world stock price datasets are performed on a set of 1314 daily stock series for 2018 days of transaction. The evaluation and comparison are based on the negative log likelihood (NLL) of real-world stock price time series. The result shows that the dilated neural models, including dilated CNN and Dilated RNN, produce most accurate estimation and prediction, outperforming various widely-used deterministic models in the GARCH family and several recently proposed stochastic models. In addition, the high flexibility and rich expressive power are validated in this study.


title: Securing Mobile Healthcare Data: A Smart Card Based Cancelable Finger-Vein Bio-Cryptosystem

abstract: Most existing biometric systems designed for healthcare applications only use biometrics for authentication/access control without considering its other function—data encryption. In this paper, we propose a cancelable finger-vein-based bio-cryptosystem, which not only can provide authentication but also can encrypt sensitive healthcare data through a biometric cryptographic technique, fuzzy commitment scheme (FCS). The proposed bio-cryptosystem stores both the encrypted version of healthcare data and the biometric template on a smart card for the reason that it is safer if the biometric data never leaves the card. The employment of the cancelable biometrics further enhances the system security. The experimental results and security analysis show the validity of the proposed scheme.


title: Towards Realistic Energy Profiling of Blockchains for Securing Internet of Things

abstract: Internet of Things (IoTs) offers a plethora of opportunities for remote monitoring and communication of everyday objects known as things with applications in numerous domains. The advent of blockchains can be a significant enabler for IoTs towards conducting and verifying transactions in a secure manner. However, applying blockchains to IoTs is challenging due to the resource constrained nature of the embedded devices coupled with significant delay incurred in processing and verifying transactions in the blockchain. Thus there exists a need for profiling the energy consumption of blockchains for securing IoTs and analyzing energy-performance trade-offs. Towards this goal, we profile the impact of workloads based on Smart Contracts and further quantify the power consumed by different operations performed by the devices on the Ethereum platform. In contrast to existing approaches that are focused on performance, we characterize performance and energy consumption for real workloads and analyse energy-performance trade-offs. Our proposed methodology is generic in that it can be applied to other platforms. The insights obtained from the study can be used to develop secure protocols for IoTs using blockchains.


title: Guided Locality Preserving Feature Matching for Remote Sensing Image Registration

abstract: Feature matching, which refers to establishing reliable correspondences between two sets of feature points, is a critical prerequisite in feature-based image registration. This paper proposes a simple yet surprisingly effective approach, termed as guided locality preserving matching, for robust feature matching of remote sensing images. The key idea of our approach is merely to preserve the neighborhood structures of potential true matches between two images. We formulate it into a mathematical model, and derive a simple closed-form solution with linearithmic time and linear space complexities. This enables our method to accomplish the mismatch removal from thousands of putative correspondences in only a few milliseconds. To handle extremely large proportions of outliers, we further design a guided matching strategy based on the proposed method, using the matching result on a small putative set with a high inlier ratio to guide the matching on a large putative set. This strategy can also significantly boost the true matches without sacrifice in accuracy. Experiments on various real remote sensing image pairs demonstrate the generality of our method for handling both rigid and nonrigid image deformations, and it is more than two orders of magnitude faster than the state-of-the-art methods with better accuracy, making it practical for real-time applications.


title: Language input and child syntax q

abstract: Abstract Existing work on the acquisition of syntax has been concerned mainly with the early stages of syntactic development. In the present study we examine later syntactic development in children. Also, existing work has focused on commonalities in the emergence of syntax. Here we explore individual differences among children and their relation to variations in language input. In Study 1 we find substantial individual differences in children’s mastery of multiclause sentences and a significant relation between those differences and the proportion of multiclause sentences in parent speech. We also find individual differences in the number of noun phrases in children’s utterances and a significant relation between those differences and the number of noun phrases in parent speech. In Study 2 we find greater syntactic growth over a year of preschool in classes where teachers’ speech is more syntactically complex. The implications of our findings for the understanding of the sources of syntactic development are discussed.


Mn-Catalyzed Oxidation of Naphthalenediol GENE WHELAN


title: Barriers to adopting ICT and e-commerce with SMEs in developing countries : an exploratory study in Sri Lanka

abstract: Research indicates that e-commerce offers a promising way for organizations to meet challenges of an ever-changing environment. However, the few available studies related to SMEs in developing countries reveal that they lag behind and are skeptical about adopting the technologies irrespective of their effectiveness. Literature reveals many significant reasons contributing towards this reluctance. The various factors identified as causes for the reticence can be broadly classified as Internal Barriers and External Barriers. Internal Barriers can be resolved within the organization by the organization itself, while External Barriers need to be addressed either by government intervention or by collaboration of SMEs. This paper presents a model for barriers to adoption of ICT and e-commerce, collected from the available literature and the results of an exploratory pilot studies and subsequent survey. In addition to the barriers facing SMEs, this research identifies the relevant support required by SMEs in a developing country, Sri Lanka. The methodology and initial issues found in the study are also discussed. Keyword: E-commerce, SMEs, adoption, developing countries, factors, barriers, model


title: Using the Business Process Model and Notation for Modeling Enterprise Integration Patterns

abstract: Enterprise Integration Patterns (EIP) are a collection of widely used stencils for integrating enterprise applications and business processes. These patterns represent a "de-facto" standard reference for design decisions when integrating enterprise applications. ::: For each of these patterns we present the integration semantics (model) and the conceptual translation (syntax) to the Business Process Model and Notation (BPMN), which is a "de-facto" standard for modelling business process semantics and their runtime behavior.


title: How do students perceive the educational value of peer assessment in relation to its social nature? A survey study in Flanders

abstract: This study explores the relationship between students’ perceptions of peer assessment (PA) and its social nature. A quantitative survey study (N = 3680) was conducted in secondary education in Flanders, examining the students’ perceptions of PA interpersonal variables and their beliefs on the educational value of PA. The structural equation modeling (SEM) results show that the educational value students attribute to PA was positively predicted through trust in their own and their peers’ evaluative capabilities, awareness of negative interpersonal processes (e.g. fear of disapproval and friendship marking), and beliefs about PA accuracy. The importance attributed to anonymity appeared to be a negative predictor of PA conceptions. Tests of mean latent differences were performed to explore the differences between educational levels, PA experience and gender.


title: Prevalence of Overweight and Obesity Among US Children , Adolescents , and Adults , 1999-2002

abstract: ContextThe prevalence of overweight and obesity has increased markedly in the ::: last 2 decades in the United States.ObjectiveTo update the US prevalence estimates of overweight in children and ::: obesity in adults, using the most recent national data of height and weight ::: measurements.Design, Setting, and ParticipantsAs part of the National Health and Nutrition Examination Survey (NHANES), ::: a complex multistage probability sample of the US noninstitutionalized civilian ::: population, both height and weight measurements were obtained from 4115 adults ::: and 4018 children in 1999-2000 and from 4390 adults and 4258 children in 2001-2002.Main Outcome MeasurePrevalence of overweight (body mass index [BMI] ≥95th percentile ::: of the sex-specific BMI-for-age growth chart) among children and prevalence ::: of overweight (BMI, 25.0-29.9), obesity (BMI ≥30.0), and extreme obesity ::: (BMI ≥40.0) among adults by sex, age, and racial/ethnic group.ResultsBetween 1999-2000 and 2001-2002, there were no significant changes among ::: adults in the prevalence of overweight or obesity (64.5% vs 65.7%), obesity ::: (30.5% vs 30.6%), or extreme obesity (4.7% vs 5.1%), or among children aged ::: 6 through 19 years in the prevalence of at risk for overweight or overweight ::: (29.9% vs 31.5%) or overweight (15.0% vs 16.5%). Overall, among adults aged ::: at least 20 years in 1999-2002, 65.1% were overweight or obese, 30.4% were ::: obese, and 4.9% were extremely obese. Among children aged 6 through 19 years ::: in 1999-2002, 31.0% were at risk for overweight or overweight and 16.0% were ::: overweight. The NHANES results indicate continuing disparities by sex and ::: between racial/ethnic groups in the prevalence of overweight and obesity.ConclusionsThere is no indication that the prevalence of obesity among adults and ::: overweight among children is decreasing. The high levels of overweight among ::: children and obesity among adults remain a major public health concern.


title: Programming with Arrows

abstract: Consider this simple Haskell definition, of a function which counts the number of occurrences of a given word w in a string: ::: ::: count w = length . filter (==w) . words ::: ::: This is an example of “point-free” programming style, where we build a function by composing others, and make heavy use of higher-order functions such as filter. Point-free programming is rightly popular: used appropriately, it makes for concise and readable definitions, which are well suited to equational reasoning in the style of Bird and Meertens [2]. It’s also a natural way to assemble programs from components, and closely related to connecting programs via pipes in the UNIX shell.


title: Perceptions of safety at work: a framework for linking safety climate to safety performance, knowledge, and motivation

abstract: Research in the areas of organizational climate and work performance was used to develop a framework for measuring perceptions of safety at work. The framework distinguished perceptions of the work environment from perceptions of performance related to safety. Two studies supported application of the framework to employee perceptions of safety in the workplace. Safety compliance and safety participation were distinguished as separate components of safety-related performance. Perceptions of knowledge about safety and motivation to perform safely influenced individual reports of safety performance and also mediated the link between safety climate and safety performance. Specific dimensions of safety climate were identified and constituted a higher order safety climate factor. The results support conceptualizing safety climate as an antecedent to safety performance in organizations.


title: Parents’ roles in shaping early adolescents’ occupational aspirations

abstract: Relations among dimensions of parenting and adolescents' occupational aspirations were examined in two specific domains: academics and sports. The sample consisted of 444 seventh graders, with approximately equal numbers of African American and European American males and females, from two-parent nondivorced families. Multiple measures were used as indicators of parents' values and behaviors, youths' values and beliefs, positive identification with parents, and adolescents' occupational aspirations. In the academic domain, parents' values predicted youths' values directly rather than indirectly through their behaviors. In contrast, fathers' behaviors mediated the relation between parents' and youths' values in the sports domain. Positive identification was directly related to adolescents' values (especially about academics); however, positive identification did not moderate the transmission of values from parent to child in either domain. Parents' values predicted adolescents' occupational aspirations via both direct and indirect pathways. Similar results were obtained for African American and European American males and females. These findings highlight the potential role of parents as socializers of achievement-related values, and, ultimately, adolescents' occupational visions of themselves in the future.


title: SCface – surveillance cameras face database

abstract: In this paper we describe a database of static images of human faces. Images were taken in uncontrolled indoor environment using five video surveillance cameras of various qualities. Database contains 4,160 static images (in visible and infrared spectrum) of 130 subjects. Images from different quality cameras should mimic real-world conditions and enable robust face recognition algorithms testing, emphasizing different law enforcement and surveillance use case scenarios. In addition to database description, this paper also elaborates on possible uses of the database and proposes a testing protocol. A baseline Principal Component Analysis (PCA) face recognition algorithm was tested following the proposed protocol. Other researchers can use these test results as a control algorithm performance score when testing their own algorithms on this dataset. Database is available to research community through the procedure described at www.scface.org .


title: Automatic Image Annotation using Deep Learning Representations

abstract: We propose simple and effective models for the image annotation that make use of Convolutional Neural Network (CNN) features extracted from an image and word embedding vectors to represent their associated tags. Our first set of models is based on the Canonical Correlation Analysis (CCA) framework that helps in modeling both views - visual features (CNN feature) and textual features (word embedding vectors) of the data. Results on all three variants of the CCA models, namely linear CCA, kernel CCA and CCA with k-nearest neighbor (CCA-KNN) clustering, are reported. The best results are obtained using CCA-KNN which outperforms previous results on the Corel-5k and the ESP-Game datasets and achieves comparable results on the IAPRTC-12 dataset. In our experiments we evaluate CNN features in the existing models which bring out the advantages of it over dozens of handcrafted features. We also demonstrate that word embedding vectors perform better than binary vectors as a representation of the tags associated with an image. In addition we compare the CCA model to a simple CNN based linear regression model, which allows the CNN layers to be trained using back-propagation.


title: Humanoid Robot LOLA — Research Platform for High-SpeedWalking

abstract: This paper describes the design concept of the performance enhanced humanoid robot LOLA. Our goal is to realize a fast, human-like walking motion. The robot has 22 degrees of freedom, including 7-DoF legs with actively driven toe joints. It is characterized by its lightweight construction, a modular, multi-sensory joint design with brushless motors and an electronics architecture using decentralized joint controllers. Special emphasis was paid on an improved mass distribution of the leg apparatus to achieve good dynamic performance. The sensor system comprises absolute angular sensors in all links, two custom-made force/torque sensors in the feet and a high-precision inertial sensor on the upper body. The trajectory generation and control system currently being developed aim at faster, more flexible, and more robust walking patterns.


title: Performance bias: Why judgments of learning are not affected by learning

abstract: Past research has shown a performance bias: People expect their future performance level on a task to match their current performance level, even when there are good reasons to expect future performance to differ from current performance. One explanation of this bias is that judgments are controlled by what learners can observe, and while current performance is usually observable, changes in performance (i.e., learning or forgetting) are not. This explanation makes a prediction that we tested here: If learning becomes observable, it should begin to affect judgments. In three experiments, after practicing a skill, participants estimated how they performed in the past and how they expected to perform in the future. In Experiments 1 and 2, participants knew they had been improving, as shown by their responses, yet they did not predict that they would improve in the future. This finding was particularly striking because (a) they did improve in the future and (b) as Experiment 3 showed, they did hold the conscious belief that past improvement predicted future improvement. In short, when learning and performance are both observable, judgments of learning seem to be guided by performance and not learning.


title: IoT based ransomware growth rate evaluation and detection using command and control blacklisting

abstract: Internet of things (IoT) is internetworking of various physical devices to provide a range of services and applications. IoT is a rapidly growing field, on an account of this; the security measurements for IoT should be at first concern. In the modern day world, the most emerging cyber-attack threat for IoT is ransomware attack. Ransomware is a kind of malware with the aim of rendering a victim's computer unusable or inaccessible, and then asking the user to pay a ransom to revert the destruction. In this paper we are evaluating ransomware attacks statistics for the past 2 years and the present year to estimate growth rate of the most emerging ransomware families from the last 3 years to evaluate most threatening ransomware attacks for IoT. Growth rate results shows that the number of attacks for Cryptowall and locky ransomware are notably increasing therefore, these ransomware families are potential threat to IoT. Moreover, we present a Cryptowall ransomware attack detection model based on the communication and behavioral study of Cryptowall for IoT environment. The proposed model observes incoming TCP/IP traffic through web proxy server then extracts TCP/IP header and uses command and control (C&C) server black listing to detect ransomware attacks.


title: Introduction to Reliable and Secure Distributed Programming

abstract: In modern computing a program is usually distributed among several processes. The fundamental challenge when developing reliable and secure distributed programs is to support the cooperation of processes required to execute a common task, even when some of these processes fail. Failures may range from crashes to adversarial attacks by malicious processes.Cachin, Guerraoui, and Rodrigues present an introductory description of fundamental distributed programming abstractions together with algorithms to implement them in distributed systems, where processes are subject to crashes and malicious attacks. The authors follow an incremental approach by first introducing basic abstractions in simple distributed environments, before moving to more sophisticated abstractions and more challenging environments. Each core chapter is devoted to one topic, covering reliable broadcast, shared memory, consensus, and extensions of consensus. For every topic, many exercises and their solutions enhance the understanding This book represents the second edition of "Introduction to Reliable Distributed Programming". Its scope has been extended to include security against malicious actions by non-cooperating processes. This important domain has become widely known under the name "Byzantine fault-tolerance".


title: Flexible artificial muscle by bundle of McKibben fiber actuators

abstract: McKibben actuator is well known as an artificial muscle because it realizes contraction force and displacement with high energy density. Therefore mechanical engineers have been applied the actuators to biomimetic robots, power assisting wear mechanisms and so on. Generally, “one” actuator has been used as “one” artificial muscle. Namely, if one artificial muscle of 10mm in diameter is necessary, one actuator of 10mm in diameter has been utilized.


title: The Design and Implementation of an ANN-based Non-linearity Compensator of LVDT Sensor

abstract: Linear variable differential transformer (LVDT) sensors are used in engineering applications due to their fine-grained measurements. However, these sensors exhibit non-linear input-output characteristics, which decrease the reliability of the sensing system. The contribution of this article is three-fold. First, it provides an experimental study of the non-linearity problem of the LVDT. Second, it proposes the design of a functional link artificial neural network (FLANN) based non-linearity compensator model for overcoming it. Finally, it validates the feasibility of the solution in simulation, and presents a proof-of-concept hardware implementation on a SPARTAN-II (PQ208)FPGA using VHDL in Xilinx. The model has been mathematically derived, and its simulation study has been presented that achieves nearly 100% linearity range. The result obtained from the FPGA implementation is in good agreement with the simulation result, which establishes its actualization as part of a general manufacturing process for linearity compensated LVDT sensors.


title: New defected ground structures (DGSs) to reduce cross-polarized radiation of circular microstrip antennas

abstract: In this paper, suppression of cross-polarized (XP) radiation of a circular microstrip patch antenna (CMPA) employing two new geometries of defected ground structures (DGSs), is experimentally investigated. One of the antennas employs a circular ring shaped defect in the ground plane, located bit away from the edge of the patch. This structure provides an improvement of XP level by 5 to 7 dB compared to an identical patch with normal ground plane. The second structure incorporates two arc-shaped DGSs in the H-plane of the patch. This configuration improves the XP radiation by about 7 to 12 dB over and above a normal CMPA. For demonstration of the concept, a set of prototypes have been examined at C-band. The experimental results have been presented.


title: Argus: the digital doorman

abstract: When you've visited someone in a large apartment or office complex, chances are that a security guard in the lobby granted you access. Perhaps over time, the guard has learned to associate you with the person you plan to visit and immediately notifies that person over the building intercom when you arrive. Argus, named after the vigilant watchman from Greek mythology, is an automated version of such a security guard: a system for automatic visitor identification. We successfully implemented and tested Argus at Just Research. To detect visitors, Argus's digital camera photographs the building entrance at regular intervals, and a motion detection algorithm identifies potential scenes containing visitors. Using a neural-network-based face detector, Argus extracts faces from these images. A memory-based face recognition system examines these faces and attempts to find visually similar matches in its stored database of visitors. An interface agent notifies system users whenever visitors arrive. Users can also provide feedback to Argus in the event of misclassified visitors. Because the face recognizer can learn online, Argus immediately incorporates these corrections into its face recognition data set.


title: BOINC: a system for public-resource computing and storage

abstract: BOINC (Berkeley Open Infrastructure for Network Computing) is a software system that makes it easy for scientists to create and operate public-resource computing projects. It supports diverse applications, including those with large storage or communication requirements. PC owners can participate in multiple BOINC projects, and can specify how their resources are allocated among these projects. We describe the goals of BOINC, the design issues that we confronted, and our solutions to these problems.


title: A Short Text Similarity Algorithm for Finding Similar Police 110 Incidents

abstract: Finding similar police 110 incidents from the incident dataset plays an important role in recognising related cases from which the investigators could find more clues and make a better decision on police deployment. We aim at finding 110 incidents with similar case features and semantic compared against a given incident. A short text similarity algorithm is presented. Our algorithm is developed from a novel semantic similarity algorithm Word Mover'd Distance(WMD). In order to emphasize the significance of case features in incident text, the method introduces the traditional term frequency-inverted document frequency(TF-IDF) as term weights to the WMD. Then the algorithm is verified on the practical dataset of public security department to find similar incidents, and experiments show that the algorithm is effective and can improve the accuracy in finding similar police incidents.


title: A novel resolver-to-360/spl deg/ linearized converter

abstract: A novel and simple resolver-to-dc converter is presented. It is shown that by appropriate processing of the sine and cosine resolver signals, the proposed converter may produce an output voltage proportional to the shaft angle. A dedicated compensation method is applied to produce an almost perfectly linear output. This enables determination of the angle with reasonable accuracy without a processor and/or a look-up table. The tests carried out under various operating conditions are satisfactory and in good agreement with theory. This paper gives the theoretical analysis, the computer simulation, the full circuit details, and experimental results of the proposed scheme.


State of the Art in Real-time Registration of RGB-D Images


title: Real-time 3D eyelids tracking from semantic edges

abstract: State-of-the-art real-time face tracking systems still lack the ability to realistically portray subtle details of various aspects of the face, particularly the region surrounding the eyes. To improve this situation, we propose a technique to reconstruct the 3D shape and motion of eyelids in real time. By combining these results with the full facial expression and gaze direction, our system generates complete face tracking sequences with more detailed eye regions than existing solutions in real-time. To achieve this goal, we propose a generative eyelid model which decomposes eyelid variation into two low-dimensional linear spaces which efficiently represent the shape and motion of eyelids. Then, we modify a holistically-nested DNN model to jointly perform semantic eyelid edge detection and identification on images. Next, we correspond vertices of the eyelid model to 2D image edges, and employ polynomial curve fitting and a search scheme to handle incorrect and partial edge detections. Finally, we use the correspondences in a 3D-to-2D edge fitting scheme to reconstruct eyelid shape and pose. By integrating our fast fitting method into a face tracking system, the estimated eyelid results are seamlessly fused with the face and eyeball results in real time. Experiments show that our technique applies to different human races, eyelid shapes, and eyelid motions, and is robust to changes in head pose, expression and gaze direction.


title: Active Contour External Force Using Vector Field Convolution for Image Segmentation

abstract: Snakes, or active contours, have been widely used in image processing applications. Typical roadblocks to consistent performance include limited capture range, noise sensitivity, and poor convergence to concavities. This paper proposes a new external force for active contours, called vector field convolution (VFC), to address these problems. VFC is calculated by convolving the edge map generated from the image with the user-defined vector field kernel. We propose two structures for the magnitude function of the vector field kernel, and we provide an analytical method to estimate the parameter of the magnitude function. Mixed VFC is introduced to alleviate the possible leakage problem caused by choosing inappropriate parameters. We also demonstrate that the standard external force and the gradient vector flow (GVF) external force are special cases of VFC in certain scenarios. Examples and comparisons with GVF are presented in this paper to show the advantages of this innovation, including superior noise robustness, reduced computational cost, and the flexibility of tailoring the force field.


title: An Intrinsic Graphical Signature Based on Alert Correlation Analysis for Intrusion Detection

abstract: We propose a graphical signature for intrusion detection given alert sequences. By correlating alerts with their temporal proximity, we build a probabilistic graph-based model to describe a group of alerts that form an attack or normal behavior. Using the models, we design a pairwise measure based on manifold learning to measure the dissimilarities between different groups of alerts. A large dissimilarity implies different behaviors between the two groups of alerts. Such measure can therefore be combined with regular classification methods for intrusion detection. We evaluate our framework mainly on Acer 2007, a private dataset gathered from a well-known Security Operation Center in Taiwan. The performance on the real data suggests that the proposed method can achieve high detection accuracy. Moreover, the graphical structures and the representation from manifold learning naturally provide the visualized result suitable for further analysis from domain experts.


title: High performance work systems: the gap between policy and practice in health care reform

abstract: PURPOSE ::: Studies of high-performing organisations have consistently reported a positive relationship between high performance work systems (HPWS) and performance outcomes. Although many of these studies have been conducted in manufacturing, similar findings of a positive correlation between aspects of HPWS and improved care delivery and patient outcomes have been reported in international health care studies. The purpose of this paper is to bring together the results from a series of studies conducted within Australian health care organisations. First, the authors seek to demonstrate the link found between high performance work systems and organisational performance, including the perceived quality of patient care. Second, the paper aims to show that the hospitals studied do not have the necessary aspects of HPWS in place and that there has been little consideration of HPWS in health system reform. ::: ::: ::: DESIGN/METHODOLOGY/APPROACH ::: The paper draws on a series of correlation studies using survey data from hospitals in Australia, supplemented by qualitative data collection and analysis. To demonstrate the link between HPWS and perceived quality of care delivery the authors conducted regression analysis with tests of mediation and moderation to analyse survey responses of 201 nurses in a large regional Australian health service and explored HRM and HPWS in detail in three casestudy organisations. To achieve the second aim, the authors surveyed human resource and other senior managers in all Victorian health sector organisations and reviewed policy documents related to health system reform planned for Australia. ::: ::: ::: FINDINGS ::: The findings suggest that there is a relationship between HPWS and the perceived quality of care that is mediated by human resource management (HRM) outcomes, such as psychological empowerment. It is also found that health care organisations in Australia generally do not have the necessary aspects of HPWS in place, creating a policy and practice gap. Although the chief executive officers of health service organisations reported high levels of strategic HRM, the human resource and other managers reported a distinct lack of HPWS from their perspectives. The authors discuss why health care organisations may have difficulty in achieving HPWS. ::: ::: ::: ORIGINALITY/VALUE ::: Leaders in health care organisations should focus on ensuring human resource management systems, structures and processes that support HPWS. Policy makers need to consider HPWS as a necessary component of health system reform. There is a strong need to reorient organisational human resource management policies and procedures in public health care organisations towards high performing work systems.


title: Parametric Study of Losses in Cross-Bonded Cables: Conductors Transposed Versus Conductors Nontransposed

abstract: Conductor transposition, for cross-bonded cables, is recommended in the ANSI/IEEE Standard 575-1988 as a means to reduce interference with communication systems. In this paper, it is shown that reduced interference comes at the price of increased cable losses, which is an issue not addressed in the 1998 edition of the IEEE Standard 575. Analytical formulas are obtained for the calculation of the positive-sequence resistance for transposed and not transposed conductors to shed light on the reasons why the losses increase when the conductors are transposed. It has been found that for cross-bonded cables installed in flat formations, the positive-sequence resistance of transposed conductors is always larger than that of nontransposed conductors. Parametric studies are performed by varying all of the construction and installation parameters that affect the value of the positive-sequence resistance. In particular, we have changed the separation distance between cables, the insulation thickness, the number and resistance of the concentric wires, and the resistivity of the soil among other parameters. Examples on transmission and distribution cables are discussed.


title: Supporting domain experts to construct conceptual ontologies: A holistic approach

abstract: Abstract A recent trend in ontology engineering research aims at encouraging the active participation of domain experts in the ontology creation process. Ontology construction methodologies together with appropriate tools and technologies, such as controlled natural languages, semantic wikis, intelligent user interfaces and social computing, are being proposed to enable the direct input from domain experts and to minimize the dependency on knowledge engineers at every step of ontology development. The time is ripe for consolidating methodological and technological advancements to create intuitive ontology engineering tools which can make Semantic Web technologies usable by a wide variety of people without formal knowledge engineering skills. A novel, holistic approach to facilitate the involvements of domain experts in the ontology authoring process is presented here. It integrates (i) an ontology construction methodology, (ii) the use of a controlled natural language, and (iii) appropriate tool support. The integrated approach is illustrated with the design, implementation and evaluation of ROO – a unique ontology authoring tool which combines intelligent techniques to assist domain experts in constructing ontologies. The benefits and limitations of the proposed approach are analyzed based on user studies with ROO. A broader discussion is provided pointing at issues to be taken into account when assisting the involvement of domain experts in ontology construction.


title: High-performance speed measurement by suppression of systematic resolver and encoder errors

abstract: The subject of this paper is a method which suppresses systematic errors of resolvers and optical encoders with sinusoidal line signals. The proposed method does not require any additional hardware and the computational efforts are minimal. Since this method does not cause any time delay, the dynamic of the speed control is not affected. By means of this new scheme, dynamic and smooth running characteristics of drive systems are improved considerably.


title: FPGA-based convolutional neural network accelerator design using high level synthesize

abstract: Convolutional Neural Networks are well known for their outstanding results in recent years in computer vision applications. Two hardware architectures for implementing these networks are proposed. The first one is more application specific which is suitable for smaller convolutional networks and the second one is more extendable and can be easily used for larger networks. A new method for computing hyperbolic tangent activation function is proposed to reduce computation time. LeNet convolutional network architecture is implemented to recognize handwritten digits using MNIST dataset as a sample. With the proposed architectures, it is indicated that latency is reduced while maintaining accuracy. Experiments are done on Xilinx Zynq FPGA using High Level Synthesis.


title: Robot Positioning Using Camera-Space Manipulation With a Linear Camera Model

abstract: This paper presents a new version of the camera-space-manipulation method (CSM). The set of nonlinear view parameters of the classic CSM is replaced with a linear model. Simulations and experiments show a similar precision error for the two methods. However, the new approach is simpler to implement and is faster.


title: Error-floors of LDPC codes

abstract: We introduce a computational technique that accurately predicts performance for a given LDPC code in the error floor region. We present some results obtained by applying the technique and describe certain aspects of it.


title: An Occupancy Grid Map Merging Algorithm Invariant to Scale, Rotation and Translation

abstract: In this paper, we consider the problem of merging occupancy grid maps obtained by different autonomous robotic agents exploring the same environment. These robots may have different sensors, processing power, memory capacities, and mapping features. Maps produced can present variations on scale, accuracy or orientation. Nowadays, merging such maps is a challenge which led us to propose an alternative heuristic approach which considers these variations. The algorithm implemented uses Scale Invariant Feature Transform (SIFT) to detect key-points while calculating transformations (rotation, translation, and scale) to merge the maps. Merging is accomplished without a priori information about a robot’s initial position and orientation. Public available data sets were used to test the algorithm, and it produced reliable combined maps. Finally, an analysis based on different tests is presented and discussed.


title: Rats’ orientation at the start point is important for spatial learning in a water T-maze ☆

abstract: Abstract When trained to locate a hidden platform in a T-maze moved between two positions, rats appear to adopt a conditional strategy based on start point discrimination. To determine if location cues or orientation cues at the start point underlie this discrimination, separate groups of rats were trained on two place problems, each with unique start locations at the two maze positions (Experiment 1). The group with the start arms oriented in different directions was more successful than the group with start arms oriented in the same direction. In Experiment 2, rats were trained from two maze positions to swim in a consistent direction from the choice point to the platform. When the maze was later moved to two new positions, rats trained to make the same response based on start point orientation showed no disruption in performance while rats trained to make the same response based on start point location did show an initial disruption in performance. When the start point location cues were masked, by making the room dark prior to placing the rats in the maze, performance did not deteriorate if rats were required to respond based on orientation of the start point but was disrupted if they were required to respond based on start point location cues (Experiment 3). This sense of direction requires exposure to the room cues to get oriented, as rats brought into an already darkened room (Experiment 4) were disrupted regardless of whether responses were tied to orientation cues or location cues. These findings are consistent with views of spatial learning that attribute a strong role to a rats’ sense of direction.


title: A glass-box interactive machine learning approach for solving NP-hard problems with the human-in-the-loop

abstract: The goal of Machine Learning to automatically learn from data, extract knowledge and to make decisions without any human intervention. Such automatic (aML) approaches show impressive success. Recent results even demonstrate intriguingly that deep learning applied for automatic classification of skin lesions is on par with the performance of dermatologists, yet outperforms the average. As human perception is inherently limited, such approaches can discover patterns, e.g. that two objects are similar, in arbitrarily high-dimensional spaces what no human is able to do. Humans can deal only with limited amounts of data, whilst big data is beneficial for aML; however, in health informatics, we are often confronted with a small number of data sets, where aML suffer of insufficient training samples and many problems are computationally hard. Here, interactive machine learning (iML) may be of help, where a human-in-the-loop contributes to reduce the complexity of NP-hard problems. A further motivation for iML is that standard black-box approaches lack transparency, hence do not foster trust and acceptance of ML among end-users. Rising legal and privacy aspects, e.g. with the new European General Data Protection Regulations, make black-box approaches difficult to use, because they often are not able to explain why a decision has been made. In this paper, we present some experiments to demonstrate the effectiveness of the human-in-the-loop approach, particularly in opening the black-box to a glass-box and thus enabling a human directly to interact with an learning algorithm. We selected the Ant Colony Optimization framework, and applied it on the Traveling Salesman Problem, which is a good example, due to its relevance for health informatics, e.g. for the study of protein folding. From studies of how humans extract so much from so little data, fundamental ML-research also may benefit.


title: Dehumanizing the Lowest of the Low Neuroimaging Responses to Extreme Out-Groups

abstract: Traditionally, prejudice has been conceptualized as simple animosity. The stereotype content model (SCM) shows that some prejudice is worse. The SCM previously demonstrated separate stereotype dimensions of warmth (low-high) and competence (low-high), identifying four distinct out-group clusters. The SCM predicts that only extreme out-groups, groups that are both stereotypically hostile and stereotypically incompetent (low warmth, low competence), such as addicts and the homeless, will be dehumanized. Prior studies show that the medial prefrontal cortex (mPFC) is necessary for social cognition. Functional magnetic resonance imaging provided data for examining brain activations in 10 participants viewing 48 photographs of social groups and 12 participants viewing objects; each picture dependably represented one SCM quadrant. Analyses revealed mPFC activation to all social groups except extreme (low-low) out-groups, who especially activated insula and amygdala, a pattern consistent with disgust, the emotion...


"Self-concept clarity: Measurement, personality correlates, and cultural boundaries": Correction.


title: Interactive metagenomic visualization in a Web browser

abstract: BackgroundA critical output of metagenomic studies is the estimation of abundances of taxonomical or functional groups. The inherent uncertainty in assignments to these groups makes it important to consider both their hierarchical contexts and their prediction confidence. The current tools for visualizing metagenomic data, however, omit or distort quantitative hierarchical relationships and lack the facility for displaying secondary variables.ResultsHere we present Krona, a new visualization tool that allows intuitive exploration of relative abundances and confidences within the complex hierarchies of metagenomic classifications. Krona combines a variant of radial, space-filling displays with parametric coloring and interactive polar-coordinate zooming. The HTML5 and JavaScript implementation enables fully interactive charts that can be explored with any modern Web browser, without the need for installed software or plug-ins. This Web-based architecture also allows each chart to be an independent document, making them easy to share via e-mail or post to a standard Web server. To illustrate Krona's utility, we describe its application to various metagenomic data sets and its compatibility with popular metagenomic analysis tools.ConclusionsKrona is both a powerful metagenomic visualization tool and a demonstration of the potential of HTML5 for highly accessible bioinformatic visualizations. Its rich and interactive displays facilitate more informed interpretations of metagenomic analyses, while its implementation as a browser-based application makes it extremely portable and easily adopted into existing analysis packages. Both the Krona rendering code and conversion tools are freely available under a BSD open-source license, and available from: http://krona.sourceforge.net.


title: Dust and Reflection Removal from Videos Captured in Moving Car

abstract: The quality of videos captured in moving cars suffer from dust on the wind screen glass. Dust contaminates the captured videos and makes the videos blurred. Removing dust and restoring high quality dust-free and reflection-free video is a challenging task in the field of video stream processing. In this work, we present a pipeline of dust and reflection removal from the corrupted video streams. To the best of our knowledge, this paper is the first study how to effectively remove dust from video streams captured in a moving car. The pipeline is comprises of two steps. In the first step, it uses the state of the art image matting to model and resolve the captured frames as the merging result of a dust layer, a background layer and a matte layer. To refine the result, the second step employs variances of pixel streams to constrain the matte layer and optimizes the extracted dust layer, background layer and matte layer to make them spatially and temporally consistent in the streams. The test results demonstrate that our proposed method can effectively remove the dust and reflections in the video streams captured in moving cars.


title: A scalable parallel framework for analyzing terascale molecular dynamics simulation trajectories

abstract: As parallel algorithms and architectures drive the longest molecular dynamics (MD) simulations towards the millisecond scale, traditional sequential post-simulation data analysis methods are becoming increasingly untenable. Inspired by the programming interface of Google's MapReduce, we have built a new parallel analysis framework called HiMach, which allows users to write trajectory analysis programs sequentially, and carries out the parallel execution of the programs automatically. We introduce (1) a new MD trajectory data analysis model that is amenable to parallel processing, (2) a new interface for defining trajectories to be analyzed, (3) a novel method to make use of an existing sequential analysis tool called VMD, and (4) an extension to the original MapReduce model to support multiple rounds of analysis. Performance evaluations on up to 512 cores demonstrate the efficiency and scalability of the HiMach framework on a Linux cluster.