title: Job satisfaction theories: Traceability to employee performance in organizations

abstract: Job satisfaction and employee performance has been a topic of research for decades. Whether job satisfaction influences employee satisfaction in organizations remains a crucial issue to managers and psychologists. That is where the problem lies. Therefore, the objective of this paper is to trace the relationship between job satisfaction and employee performance in organizations with particular reference to Nigeria. Related literature on the some theories of job satisfaction such as affective events, two-factor, equity and job characteristics was reviewed and findings from these theories indicate that a number of factors like achievement, recognition, responsibility, pay, work conditions and so on, have positive influence on employee performance in organizations. The paper adds to the theoretical debate on whether job satisfaction impacts positively on employee performance. It concludes that though the concept of job satisfaction is complex, using appropriate variables and mechanisms can go a long way in enhancing employee performance. It recommends that managers should use those factors that impact employee performance to make them happy, better their well being and the environment. It further specifies appropriate mechanisms using a theoretical approach to support empirical approaches which often lack clarity as to why the variables are related.


title: Bridging the Gap Between Value and Policy Based Reinforcement Learning

abstract: We establish a new connection between value and policy based reinforcement learning (RL) based on a relationship between softmax temporal value consistency and policy optimality under entropy regularization. Specifically, we show that softmax consistent action values correspond to optimal entropy regularized policy probabilities along any action sequence, regardless of provenance. From this observation, we develop a new RL algorithm, Path Consistency Learning (PCL), that minimizes a notion of soft consistency error along multi-step action sequences extracted from both on- and off-policy traces. We examine the behavior of PCL in different scenarios and show that PCL can be interpreted as generalizing both actor-critic and Q-learning algorithms. We subsequently deepen the relationship by showing how a single model can be used to represent both a policy and the corresponding softmax state values, eliminating the need for a separate critic. The experimental evaluation demonstrates that PCL significantly outperforms strong actor-critic and Q-learning baselines across several benchmarks.


title: Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding.

abstract: One of the key problems in spoken language understanding (SLU) is the task of slot filling. In light of the recent success of applying deep neural network technologies in domain detection and intent identification, we carried out an in-depth investigation on the use of recurrent neural networks for the more difficult task of slot filling involving sequence discrimination. In this work, we implemented and compared several important recurrent-neural-network architectures, including the Elman-type and Jordan-type recurrent networks and their variants. To make the results easy to reproduce and compare, we implemented these networks on the common Theano neural network toolkit, and evaluated them on the ATIS benchmark. We also compared our results to a conditional random fields (CRF) baseline. Our results show that on this task, both types of recurrent networks outperform the CRF baseline substantially, and a bi-directional Jordantype network that takes into account both past and future dependencies among slots works best, outperforming a CRFbased baseline by 14% in relative error reduction.


title: SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks

abstract: We propose a fast approximation method of a softmax function with a very large vocabulary using singular value decomposition (SVD). SVD-softmax targets fast and accurate probability estimation of the topmost probable words during inference of neural network language models. The proposed method transforms the weight matrix used in the calculation of the output vector by using SVD. The approximate probability of each word can be estimated with only a small part of the weight matrix by using a few large singular values and the corresponding elements for most of the words. We applied the technique to language modeling and neural machine translation and present a guideline for good approximation. The algorithm requires only approximately 20\% of arithmetic operations for an 800K vocabulary case and shows more than a three-fold speedup on a GPU.


title: Two Decades of Recommender Systems at Amazon.com

abstract: Amazon is well-known for personalization and recommendations, which help customers discover items they might otherwise not have found. In this update to their original paper, the authors discuss some of the changes as Amazon has grown.


title: Evaluation Metrics For Generation

abstract: Certain generation applications may profit from the use of stochastic methods. In developing stochastic methods, it is crucial to be able to quickly assess the relative merits of different approaches or models. In this paper, we present several types of intrinsic (system internal) metrics which we have used for baseline quantitative assessment. This quantitative assessment should then be augmented to a fuller evaluation that examines qualitative aspects. To this end, we describe an experiment that tests correlation between the quantitative metrics and human qualitative judgment. The experiment confirms that intrinsic metrics cannot replace human evaluation, but some correlate significantly with human judgments of quality and understandability and can be used for evaluation during development.


title: Potsdam Commentary Corpus 2.0: Annotation for Discourse Research

abstract: We present a revised and extended version of the Potsdam Commentary Corpus, a collection of 175 German newspaper commentaries (op-ed pieces) that has been annotated with syntax trees and three layers of discourse-level information: nominal coreference, connectives and their arguments (similar to the PDTB, (Prasad et al., 2008)), and trees reflecting discourse structure according to Rhetorical Structure Theory (Mann and Thompson, 1988). Connectives have been annotated with the help of a semi-automatic tool (Conano, (Stede and Heintze, 2004)) that identifies most connectives and suggests arguments based on their syntactic category. The other layers have been created manually with dedicated annotation tools. The corpus is made available on the one hand as a set of original XML files produced with the annotation tools, based on identical tokenization. On the other hand, it will be distributed together with the open-source linguistic database ANNIS3 (Chiarcos et al., 2008; Zeldes et al., 2009), which provides multi-layer search functionality and layer-specific visualization modules. This allows for comfortable qualitative evaluation of the correlations between annotation layers.


title: Guided Music Synthesis with Variable Markov Oracle.

abstract: In this work the problem of guided improvisation is approached and elaborated; then a new method, Variable Markov Oracle, for guided music synthesis is proposed as the first step to tackle the guided improvisation problem. Variable Markov Oracle is based on previous results from Audio Oracle, which is a fast indexing and recombination method of repeating sub-clips in an audio signal. The newly proposed Variable Markov Oracle is capable of identifying inherent datapoint clusters in an audio signal while tracking the sequential relations among clusters at the same time. With a target audio signal indexed by Variable Markov Oracle, a query-matching algorithm is devised to synthesize new music materials by recombination of the target audio matched to a query audio. This approach makes the query-matching algorithm a solution to the guided music synthesis problem. The query-matching algorithm is efficient and intelligent since it follows the inherent clusters discovered by Variable Markov Oracle, creating a query-by-content result which allows numerous applications in concatenative synthesis, machine improvisation and interactive music system. Examples of using Variable Markov Oracle to synthesize new musical materials based on given music signals in the style of Jazz are shown.


title: Artificial Intelligence in the NASA Volcano Sensorweb: Over a Decade in Operations

abstract: Volcanic activity can occur with little or no warning. Increasing numbers of space borne assets can enable coordinated measurements of volcanic events to enhance both scientific study and hazard response. We describe the use of space and ground measurements to target further measurements as part of a worldwide volcano monitoring system. We utilize a number of alert systems including the MODVOLC, GOESVOLC, US Air Force Weather Advisory, and Volcanic Ash Advisory Center (VAAC) alert systems. Additionally we use in-situ data from ground instrumentation at a number of volcanic sites, including Iceland. Artificial Intelligence Software plays a key role in the Volcano Sensorweb. First, several in-situ volcano monitoring networks use “intelligent” data interpretation software to trigger alerts that can then be used to allocate network resources, notify human agents, and even task space observations. Second, the Earth Observing One (EO-1) spacecraft uses Artificial Intelligence Software to automatically task the spacecraft to execute observations. Third, EO-1 also interprets thermal data onboard to allow for faster notifications of volcanic activity. Finally some data interpretation steps use intelligent software such as Random Decision Forest Methods used to automatically estimate volcanic plume heights in Worldview-2 Imagery.


title: Loss minimization-based charging strategy for lithium-ion battery

abstract: In this paper, an optimal charging strategy for lithium-ion batteries is proposed to minimize charging loss. To reach this target, a one-RC electric model is employed to model the loss for the battery, and an efficiency map is measured for the charger, considering different charging currents and voltages. A dynamic programming algorithm is applied to determine the optimal charging current profiles for minimizing the losses of the battery and the charger separately and collectively. Experiment results prove that the proposed method is more efficient compared with a constant-current charging method without influencing the charging time.


title: Using GUI ripping for automated testing of Android applications

abstract: We present AndroidRipper, an automated technique that tests Android apps via their Graphical User Interface (GUI). AndroidRipper is based on a user-interface driven ripper that automatically explores the app’s GUI with the aim of exercising the application in a structured manner. We evaluate AndroidRipper on an open-source Android app. Our results show that our GUI-based test cases are able to detect severe, previously unknown, faults in the underlying code, and the structured exploration outperforms a random approach.


title: Noncontact Measurement of Complex Permittivity and Thickness by Using Planar Resonators

abstract: This paper presents a novel noncontact measurement technique that entails using a single-compound triple complementary split-ring resonator (SC-TCSRR) to determine the complex permittivity and thickness of a material under test (MUT). The proposed technique overcomes the problem engendered by the existence of air gaps between the sensor ground plane and the MUT. In the proposed approach, a derived governing equation of the resonance frequencies is used to estimate the thickness and complex permittivity of the MUT by calculating the resonant frequency $(f_{{ r}})$ and magnitude response in a single-step noncontact measurement process. This study theoretically analyzed and experimentally verified a simple and low-cost SC-TCSRR measurement method for assessing materials in a noncontact method. For a 0.2-mm air gap, the experiments yielded average measurement errors of 4.32% and 5.05% for the thickness and permittivity, respectively. The proposed SC-TCSRR technique provides excellent solutions for reducing the effect of air-gap conditions on permittivity, thickness, and loss tangent in noncontact measurements.


title: Stochastic Gradient Push for Distributed Deep Learning

abstract: Distributed data-parallel algorithms aim to accelerate the training of deep neural networks by parallelizing the computation of large mini-batch gradient updates across multiple nodes. Approaches that synchronize nodes using exact distributed averaging (e.g., via AllReduce) are sensitive to stragglers and communication delays. The PushSum gossip algorithm is robust to these issues, but only performs approximate distributed averaging. This paper studies Stochastic Gradient Push (SGP), which combines PushSum with stochastic gradient updates. We prove that SGP converges to a stationary point of smooth, non-convex objectives at the same sub-linear rate as SGD, and that all nodes achieve consensus. We empirically validate the performance of SGP on image classification (ResNet-50, ImageNet) and machine translation (Transformer, WMT'16 En-De) workloads. Our code will be made publicly available.


title: A Novel Image Steganography Method via Deep Convolutional Generative Adversarial Networks

abstract: The security of image steganography is an important basis for evaluating steganography algorithms. Steganography has recently made great progress in the long-term confrontation with steganalysis. To improve the security of image steganography, steganography must have the ability to resist detection by steganalysis algorithms. Traditional embedding-based steganography embeds the secret information into the content of an image, which unavoidably leaves a trace of the modification that can be detected by increasingly advanced machine-learning-based steganalysis algorithms. The concept of steganography without embedding (SWE), which does not need to modify the data of the carrier image, appeared to overcome the detection of machine-learning-based steganalysis algorithms. In this paper, we propose a novel image SWE method based on deep convolutional generative adversarial networks. We map the secret information into a noise vector and use the trained generator neural network model to generate the carrier image based on the noise vector. No modification or embedding operations are required during the process of image generation, and the information contained in the image can be extracted successfully by another neural network, called the extractor, after training. The experimental results show that this method has the advantages of highly accurate information extraction and a strong ability to resist detection by state-of-the-art image steganalysis algorithms.


Cavs: An Efficient Runtime System for Dynamic Neural Networks


title: Smart parking service based on Wireless Sensor Networks

abstract: In this paper, we present the design and implementation of a prototype system of Smart Parking Services based on Wireless Sensor Networks (WSNs) that allows vehicle drivers to effectively find the free parking places. The proposed scheme consists of wireless sensor networks, embedded web-server, central web-server and mobile phone application. In the system, low-cost wireless sensors networks modules are deployed into each parking slot equipped with one sensor node. The state of the parking slot is detected by sensor node and is reported periodically to embedded web-server via the deployed wireless sensor networks. This information is sent to central web-server using Wi-Fi networks in real-time, and also the vehicle driver can find vacant parking lots using standard mobile devices.


Visual Query Systems: A Taxonomy


title: Interrogative suggestibility, compliance and false confessions among prisoners and their relationship with attention deficit hyperactivity disorder (ADHD) symptoms

abstract: Background Interrogative suggestibility and compliance are important psychological vulnerabilities during interrogation. The aim of the study was to investigate the relationship of suggestibility and compliance with childhood and current symptoms of attention deficit hyperactivity disorder (ADHD). Compliance has not been studied previously in relation to ADHD. A further aim was to investigate the relationship between ADHD and the reporting of having made a false confession to the police. Method The participants were 90 male prisoners, all of whom had completed the Gudjonsson Suggestibility and Compliance Scales (GSS and GCS) within 10 days of admission to the prison. Childhood ADHD symptoms were screened by the Wender Utah Rating Scale (WURS) and current adult symptoms by the DSM-IV Checklist criteria for ADHD. Results Half of the prisoners (50%) were found on screening to meet criteria for ADHD in childhood and, of those, over half (60%) were either fully symptomatic or in partial remission of their symptoms. ADHD symptoms were found to be significantly associated with compliance, but not with suggestibility. The relationship with compliance was stronger (effect size) in relation to current than childhood symptoms. The ADHD symptomatic groups were significantly more likely to claim that they had made a false confession to the police in the past. Conclusions The findings raise important questions about the potential vulnerability of adults with ADHD symptoms in terms of their ability to cope with interrogation.


title: Randomized controlled trial of abductor muscle damage in relation to the surgical approach for primary total hip replacement: minimally invasive anterolateral versus modified direct lateral approach

abstract: Introduction ::: Minimally invasive total hip arthroplasty (THA) is claimed to be superior to the standard technique, due to the potential reduction of soft tissue damage via a smaller and tissue-sparing approach. As a result of the lack of objective evidence of fewer muscle and tendon defects, controversy still remains as to whether minimally invasive total hip arthroplasty truly minimizes muscle and tendon damage. Therefore, the objective was to compare the influence of the surgical approach on abductor muscle trauma and to analyze the relevance to postoperative pain and functional recovery.


title: Network Topology Inference from Spectral Templates

abstract: We address the problem of identifying a graph structure from the observation of signals defined on its nodes. Fundamentally, the unknown graph encodes direct relationships between signal elements, which we aim to recover from observable indirect relationships generated by a diffusion process on the graph. The fresh look advocated here permeates benefits from convex optimization and stationarity of graph signals, in order to identify the graph shift operator (a matrix representation of the graph) given only its eigenvectors. These spectral templates can be obtained, e.g., from the sample covariance of independent graph signals diffused on the sought network. The novel idea is to find a graph shift that, while being consistent with the provided spectral information, endows the network with certain desired properties such as sparsity. To that end we develop efficient inference algorithms stemming from provably-tight convex relaxations of natural nonconvex criteria, particularizing the results for two shifts: the adjacency matrix and the normalized Laplacian. Algorithms and theoretical recovery conditions are developed not only when the templates are perfectly known, but also when the eigenvectors are noisy or when only a subset of them are given. Numerical tests showcase the effectiveness of the proposed algorithms in recovering social, brain, and amino-acid networks.


title: Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations

abstract: We put forth a deep learning approach for discovering nonlinear partial differential equations from scattered and potentially noisy observations in space and time. Specifically, we approximate the unknown solution as well as the nonlinear dynamics by two deep neural networks. The first network acts as a prior on the unknown solution and essentially enables us to avoid numerical differentiations which are inherently ill-conditioned and unstable. The second network represents the nonlinear dynamics and helps us distill the mechanisms that govern the evolution of a given spatiotemporal data-set. We test the effectiveness of our approach for several benchmark problems spanning a number of scientific domains and demonstrate how the proposed framework can help us accurately learn the underlying dynamics and forecast future states of the system. In particular, we study the Burgers', Korteweg-de Vries (KdV), Kuramoto-Sivashinsky, nonlinear Schrodinger, and Navier-Stokes equations.


title: PersaLog: Personalization of News Article Content

abstract: Content personalization automatically modifying text and multimedia features within articles based on the reader's individual features'is evolving as a new form of journalism. Informed by constraints articulated through a survey of journalists, we have implemented PersaLog, a novel system for creating personalized content (e.g., text and interactive visualizations). Because crafting, and validating, personalized content can be challenging to scale across articles (unlike feed personalization), we offer a simple Domain Specific Language (DSL), and editing environment, to support this task. PersaLog is particularly designed to support the personalization of existing text and visualizations. Our work provides guidelines for personalization as well as a system that allows for both subtle and dramatic personalization-driven content changes. We validate PersaLog using case and lab studies.


title: Improvement of Text Feature Selection Method Based on TFIDF

abstract: TFIDF is a kind of common methods used to select the text feature, but it has many disadvantages. First, the method undervalues that this term can represent the characteristic of the documents of this class if it only frequently appears in the documents belongs to the same class while infrequently in the documents of the other class. Second TFIDF neglects the relations between the feature and the class. The paper proposed the improved TFIDF strategy, and combined with the text classification method of simple distance vector to compare to traditional TFIDF, and obtained the very good classified effect, the experiment proved its feasibility.


title: Data for Development: the D4D Challenge on Mobile Phone Data

abstract: The Orange \Data for Development" (D4D) challenge is an open data challenge on anonymous call patterns of Orange's mobile phone users in Ivory Coast. The goal of the challenge is to help address society development questions in novel ways by contributing to the socio-economic development and well-being of the Ivory Coast population. Participants to the challenge are given access to four mobile phone datasets and the purpose of this paper is to describe the four datasets. The website http://www.d4d.orange.com contains more information about the participation rules. The datasets are based on anonymized Call Detail Records (CDR) of phone calls and SMS exchanges between


title: A Low Power and Low Complexity Automatic White Balance Algorithm for AMOLED Driving Using Histogram Matching

abstract: This paper presents a low power, low complexity and high hardware efficiency automatic white balance algorithm based on histogram matching (AWB-HM) for active-matrix organic light-emitting diode (AMOLED) driving. The main idea of AWB-HM algorithm is to reduce the display power by power constraint, and then increase the overlap area (OA) of three color histograms by histogram matching. The computation complexity in the proposed algorithm is reduced without estimating the gains of red, green, and blue channels in the conventional automatic white balance (AWB) algorithms. Moreover, adders rather than multipliers are mainly used in the algorithm, which is easier for the hardware realization. The simulation results show that the value of OA for the images processed by the proposed algorithm is 26.4% and 58.7% larger than that of gray-world (GW) and white-patch (WP) algorithm, respectively. Furthermore, the power dissipation and the time complexity is reduced compared with other algorithms. The proposed algorithm has been applied to AMOLED and the color cast of image is improved significantly.


title: Two-stage Neural Network for Volume Segmentation

abstract: Abstract A new system to segment and label CT/MRI brain slices using feature extraction and unsupervised clustering is presented. Each volume element (voxel) is assigned a feature pattern consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is then assigned to a specific region using a two-stage neural network system. The first stage is a self-organizing principal components analysis (SOPCA) network that is used to project the feature vector onto its leading principal axes found by using principal components analysis. This step provides an effective basis for feature extraction. The second stage consists of a self-organizing feature map (SOFM) which automatically clusters the input vector into different regions. A 3D connected component labeling algorithm is then applied to ensure region connectivity. We demonstrate the power of this approach to volume segmentation of medical images.


title: On the Practicality of Cryptographically Enforcing Dynamic Access Control Policies in the Cloud

abstract: The ability to enforce robust and dynamic access controls on cloud-hosted data while simultaneously ensuring confidentiality with respect to the cloud itself is a clear goal for many users and organizations. To this end, there has been much cryptographic research proposing the use of (hierarchical) identity-based encryption, attribute-based encryption, predicate encryption, functional encryption, and related technologies to perform robust and private access control on untrusted cloud providers. However, the vast majority of this work studies static models in which the access control policies being enforced do not change over time. This is contrary to the needs of most practical applications, which leverage dynamic data and/or policies. In this paper, we show that the cryptographic enforcement of dynamic access controls on untrusted platforms incurs computational costs that are likely prohibitive in practice. Specifically, we develop lightweight constructions for enforcing role-based access controls (i.e., RBAC0) over cloud-hosted files using identity-based and traditional public-key cryptography. This is done under a threat model as close as possible to the one assumed in the cryptographic literature. We prove the correctness of these constructions, and leverage real-world RBAC datasets and recent techniques developed by the access control community to experimentally analyze, via simulation, their associated computational costs. This analysis shows that supporting revocation, file updates, and other state change functionality is likely to incur prohibitive overheads in even minimally-dynamic, realistic scenarios. We identify a number of bottlenecks in such systems, and fruitful areas for future work that will lead to more natural and efficient constructions for the cryptographic enforcement of dynamic access controls. Our findings naturally extend to the use of more expressive cryptographic primitives (e.g., HIBE or ABE) and richer access control models (e.g., RBAC1 or ABAC).


title: Neighbor-Aware Search for Approximate Labeled Graph Matching using the Chi-Square Statistics

abstract: Labeled graphs provide a natural way of representing entities, relationships and structures within real datasets such as knowledge graphs and protein interactions. Applications such as question answering, semantic search, and motif discovery entail efficient approaches for subgraph matching involving both label and structural similarities. Given the NP-completeness of subgraph isomorphism and the presence of noise, approximate graph matching techniques are required to handle queries in a robust and real-time manner. This paper presents a novel technique to characterize the subgraph similarity based on statistical significance captured by chi-square statistic. The statistical significance model takes into account the background structure and label distribution in the neighborhood of vertices to obtain the best matching subgraph and, therefore, robustly handles partial label and structural mismatches. Based on the model, we propose two algorithms, VELSET and NAGA, that, given a query graph, return the top-k most similar subgraphs from a (large) database graph. While VELSET is more accurate and robust to noise, NAGA is faster and more applicable for scenarios with low label noise. Experiments on large real-life graph datasets depict significant improvements in terms of accuracy and running time in comparison to the state-of-the-art methods.


title: Knowledge and common knowledge in a distributed environment

abstract: Reasoning about knowledge seems to play a fundamental role in distributed systems. Indeed, such reasoning is a central part of the informal intuitive arguments used in the design of distributed protocols. Communication in a distributed system can be viewed as the act of transforming the system's state of knowledge. This paper presents a general framework for formalizing and reasoning about knowledge in distributed systems. It is shown that states of knowledge of groups of processors are useful concepts for the design and analysis of distributed protocols. In particular, distributed knowledge corresponds to knowledge that is “distributed” among the members of the group, while common knowledge corresponds to a fact being “publicly known.” The relationship between common knowledge and a variety of desirable actions in a distributed system is illustrated. Furthermore, it is shown that, formally speaking, in practical systems common knowledge cannot be attained. A number of weaker variants of common knowledge that are attainable in many cases of interest are introduced and investigated.


title: VoxSegNet: Volumetric CNNs for Semantic Part Segmentation of 3D Shapes

abstract: Voxel is an important format to represent geometric data, which has been widely used for 3D deep learning in shape analysis due to its generalization ability and regular data format. However, fine-grained tasks like part segmentation require detailed structural information, which increases voxel resolution and thus causes other issues such as the exhaustion of computational resources. In this paper, we propose a novel volumetric convolutional neural network, which could extract discriminative features encoding detailed information from voxelized 3D data under a limited resolution. To this purpose, a spatial dense extraction (SDE) module is designed to preserve the spatial resolution during the feature extraction procedure, alleviating the loss of detail caused by sub-sampling operations such as max-pooling. An attention feature aggregation (AFA) module is also introduced to adaptively select informative features from different abstraction scales, leading to segmentation with both semantic consistency and high accuracy of details. Experiment results on the large-scale dataset demonstrate the effectiveness of our method in 3D shape part segmentation.


title: Group Sparsity and Geometry Constrained Dictionary Learning for Action Recognition from Depth Maps

abstract: Human action recognition based on the depth information provided by commodity depth sensors is an important yet challenging task. The noisy depth maps, different lengths of action sequences, and free styles in performing actions, may cause large intra-class variations. In this paper, a new framework based on sparse coding and temporal pyramid matching (TPM) is proposed for depth-based human action recognition. Especially, a discriminative class-specific dictionary learning algorithm is proposed for sparse coding. By adding the group sparsity and geometry constraints, features can be well reconstructed by the sub-dictionary belonging to the same class, and the geometry relationships among features are also kept in the calculated coefficients. The proposed approach is evaluated on two benchmark datasets captured by depth cameras. Experimental results show that the proposed algorithm repeatedly achieves superior performance to the state of the art algorithms. Moreover, the proposed dictionary learning method also outperforms classic dictionary learning approaches.


title: Current surgical management of penile cancer.

abstract: Surgical resection of the primary penile tumor should result in complete removal of the cancerous lesion with as much organ preservation as possible to preserve functional and cosmetic outcomes and avoid psychological distress. This is especially important in patients who develop penile cancer at a younger age (approximately 20% o40 years of age at presentation). It is essential, however, to remove all malignant tissue with negative surgical margins in order to optimize oncological outcomes and minimize risk of recurrence. Selection of the most appropriate technique is dependent on the stage and location of the primary tumor, the effect of surgery on penile length, and the patient's age and comorbid conditions (Table).


title: Factorised spatial representation learning: application in semi-supervised myocardial segmentation

abstract: The success and generalisation of deep learning algorithms heavily depend on learning good feature representations. In medical imaging this entails representing anatomical information, as well as properties related to the specific imaging setting. Anatomical information is required to perform further analysis, whereas imaging information is key to disentangle scanner variability and potential artefacts. The ability to factorise these would allow for training algorithms only on the relevant information according to the task. To date, such factorisation has not been attempted. In this paper, we propose a methodology of latent space factorisation relying on the cycle-consistency principle. As an example application, we consider cardiac MR segmentation, where we separate information related to the myocardium from other features related to imaging and surrounding substructures. We demonstrate the proposed method’s utility in a semi-supervised setting: we use very few labelled images together with many unlabelled images to train a myocardium segmentation neural network. Specifically, we achieve comparable performance to fully supervised networks using a fraction of labelled images in experiments on ACDC and a dataset from Edinburgh Imaging Facility QMRI. Code will be made available at https://github.com/agis85/spatial_factorisation.


title: ECG monitoring using Android mobile phone and Bluetooth

abstract: This paperwork describes the development and test of circuitry and software to enable the use of Android mobile phones equipped with Bluetooth to receive the incoming electrocardiogram (ECG) signal from a user and show it in real-time on the cell phone screen. The system comprises three distinct subsystems. The first one is dedicated to condition the analog ECG signal, preparing it for conversion to the digital world. The second one consists of a microcontroller and a Bluetooth module. This unit samples the ECG, serializes the samples and transmits them via the Bluetooth module to the Android cell phone. The third subsystem is the cell phone itself. An application program written to the cell phone receives the ECG samples and suitably charts the ECG signal on the screen for analysis. The good quality of the ECG signal allows for identification of arrhythmias.


title: Enriching object detection with 2D-3D registration and continuous viewpoint estimation

abstract: A large body of recent work on object detection has focused on exploiting 3D CAD model databases to improve detection performance. Many of these approaches work by aligning exact 3D models to images using templates generated from renderings of the 3D models at a set of discrete viewpoints. However, the training procedures for these approaches are computationally expensive and require gigabytes of memory and storage, while the viewpoint discretization hampers pose estimation performance. We propose an efficient method for synthesizing templates from 3D models that runs on the fly - that is, it quickly produces detectors for an arbitrary viewpoint of a 3D model without expensive dataset-dependent training or template storage. Given a 3D model and an arbitrary continuous detection viewpoint, our method synthesizes a discriminative template by extracting features from a rendered view of the object and decorrelating spatial dependences among the features. Our decorrelation procedure relies on a gradient-based algorithm that is more numerically stable than standard decomposition-based procedures, and we efficiently search for candidate detections by computing FFT-based template convolutions. Due to the speed of our template synthesis procedure, we are able to perform joint optimization of scale, translation, continuous rotation, and focal length using Metropolis-Hastings algorithm. We provide an efficient GPU implementation of our algorithm, and we validate its performance on 3D Object Classes and PASCAL3D+ datasets.


title: Projection-Based Augmented Reality in Disney Theme Parks

abstract: Walt Disney Imagineering and Disney Research Zurich are building a projector- camera toolbox to help create spatially augmented 3D objects and dynamic, interactive spaces that enhance the theme park experience by immersing guests in magical worlds. A related video can be seen here: http://youtu.be/wjrylXl0tTk. It shows examples of projection-based augmented reality techniques being employed in Disney theme parks.


title: A case for end system multicast (keynote address)

abstract: The conventional wisdom has been that IP is the natural protocol layer for implementing multicast related functionality. However, ten years after its initial proposal, IP Multicast is still plagued with concerns pertaining to scalability, network management, deployment and support for higher layer functionality such as error, flow and congestion control. In this paper, we explore an alternative architecture for small and sparse groups, where end systems implement all multicast related functionality including membership management and packet replication. We call such a scheme End System Multicast. This shifting of multicast support from routers to end systems has the potential to address most problems associated with IP Multicast. However, the key concern is the performance penalty associated with such a model. In particular, End System Multicast introduces duplicate packets on physical links and incurs larger end-to-end delay than IP Multicast. In this paper, we study this question in the context of the Narada protocol. In Narada, end systems self-organize into an overlay structure using a fully distributed protocol. In addition, Narada attempts to optimize the efficiency of the overlay based on end-to-end measurements. We present details of Narada and evaluate it using both simulation and Internet experiments. Preliminary results are encouraging. In most simulations and Internet experiments, the delay and bandwidth penalty are low. We believe the potential benefits of repartitioning multicast functionality between end systems and routers significantly outweigh the performance penalty incurred.


title: Detecting Trojan horses based on system behavior using machine learning method

abstract: The Research of detection malware using machine learning method attracts much attention recent years. However, most of research focused on code analysis which is signature-based or analysis of system call sequence in Linux environment. Obviously, all methods have their strengths and weaknesses. In this paper, we concentrate on detection Trojan horse by operation system information in Windows environment using data mining technology. Our main content and contribution contains as follows: First, we collect Trojan horse samples in true network environment and classify them by scanner. Secondly, we collect operation system behavior features under infected and clean circumstances separately by WMI manager tools. And then, several classic classification algorithms are applied and a performance comparison is given. Feature selection methods are applied to those features and we get a feature order list which reflects the relevance order of Trojan horse activities and the system feature. We believe the instructive meaning of the list is significant. Finally, a feature combination method is applied and features belongs different groups are combined according their characteristic for high classification performance. Results of experiments demonstrate the feasibility of our assumption that detecting Trojan horses by system behavior information is feasible and affective.


title: A 5Gb/s equalizer for USB 3.0 receiver in 65 nm CMOS technology

abstract: In this paper a 5Gb/s equalizer has been presented. It is designed to operate within USB 3.0 transceiver and compensates for frequency dependent losses introduced by transmission channel. For the reference signal, the clock and data recovery circuit has been used. This approach allowed to minimize the equalizer components. Critical equalizer building blocks have been implemented in GLOBALFOUNDRIES 65 nm Low Power CMOS technology. Other blocks are modeled in hardware description language. Mixed-signal system simulation results show full functionality of the proposed solution.


title: BioWatch — A wrist watch based signal acquisition system for physiological signals including blood pressure

abstract: A wrist watch based system, which can measure electrocardiogram (ECG) and photoplethysmogram (PPG), is presented in this work. By using both ECG and PPG we also measure pulse transit time (PTT), which studies show to correlate well with blood pressure (BP). The system is also capable of monitoring heart rate using either ECG or PPG and can monitor blood oxygenation by easily replacing the PPG sensors with a different set. In this work, we investigate methods to train a fitting function to convert a PTT measurement to its corresponding systolic BP. We also validate measurements on different postures and show the value of calibrating the device for each posture. This system, called BioWatch, can potentially facilitate continuous and ubiquitous monitoring of ECG, PPG, heart rate, blood oxygenation and BP.


title: The meaning of compassion fatigue to student nurses: an interpretive phenomenological study

abstract: Background ::: Compassion fatigue is a form of occupational stress which occurs when individuals are exposed to suffering and trauma on an ongoing basis. The purpose of this study was to explore the experiences of compassion fatigue among student nurses following their first clinical placement in a UK health care setting during 2015.


title: Degradation mechanisms of silicone rubber (SiR) by accelerated ageing for cables of nuclear power plant

abstract: The degradation behavior of SiR for the cable insulation by accelerated thermal and radiation ageing was studied and the degradation mechanism was proposed. The degradation was observed by the change of tensile properties, the distribution of crosslinking, and the change of weight. The chemical reaction under the both ageing in oxidation conditions was crosslinking and the oxidation mechanism was found to be the same between thermal and radiation ageing. The yield of crosslinking was proportional to the ageing time and also the dose. The effect of irradiation temperature on oxidation was accelerated with an increase of temperature above around 120°C, which might be due to the specific radiation chemical reactions. Therefore, the degradation by simultaneous ageing at higher temperatures above 155°C was much higher than that for sequential ageing, such as irradiation followed by thermal ageing or thermal ageing followed irradiation. At a high temperature, the degradation by thermal ageing under vacuum (without oxidation) was more progressed than that for the ageing in air (with oxidation). The reason was assumed to be the thermal decomposition of crosslinks between SiR molecules formed by the chemical crosslinking agent. The hardness (Shore hardness) reflected well the degradation of the SiR material for any ageing conditions.


title: Identifying and retrieving distress images from road pavement surveys

abstract: This paper proposes a system capable of identifying and retrieving distress images from a road pavement survey image database. A database of images acquired during road pavement surface surveys along Portuguese roads is considered. Regions corresponding to cracks are detected over the acquired images, based on a subdivision of the images into a set of non-overlapping windows, which may be classified as containing cracks, or not. Crack detection results, represented by binary images where windows containing crack pixels are set to one, undergo a second classification stage to distinguish several crack types. This classification follows the structure proposed by the Portuguese Distresses Catalog, produced by the national entity in charge of road maintenance. Three crack types are identified at this stage: longitudinal cracks, transversal cracks and miscellaneous cracks. The experimental results, obtained by processing real survey imagery over Portuguese roads, present encouraging results for automating the process of identifying road distresses from images.


title: Zero-Shot Learning with Semantic Output Codes

abstract: We consider the problem of zero-shot learning, where the goal is to learn a classifier f : X → Y that must predict novel values of Y that were omitted from the training set. To achieve this, we define the notion of a semantic output code classifier (SOC) which utilizes a knowledge base of semantic properties of Y to extrapolate to novel classes. We provide a formalism for this type of classifier and study its theoretical properties in a PAC framework, showing conditions under which the classifier can accurately predict novel classes. As a case study, we build a SOC classifier for a neural decoding task and show that it can often predict words that people are thinking about from functional magnetic resonance images (fMRI) of their neural activity, even without training examples for those words.


title: Netprobe: a fast and scalable system for fraud detection in online auction networks

abstract: Given a large online network of online auction users and their histories of transactions, how can we spot anomalies and auction fraud? This paper describes the design and implementation of NetProbe, a system that we propose for solving this problem. NetProbe models auction users and transactions as a Markov Random Field tuned to detect the suspicious patterns that fraudsters create, and employs a Belief Propagation mechanism to detect likely fraudsters. Our experiments show that NetProbe is both efficient and effective for fraud detection. We report experiments on synthetic graphs with as many as 7,000 nodes and 30,000 edges, where NetProbe was able to spot fraudulent nodes with over 90% precision and recall, within a matter of seconds. We also report experiments on a real dataset crawled from eBay, with nearly 700,000 transactions between more than 66,000users, where NetProbe was highly effective at unearthing hidden networks of fraudsters, within a realistic response time of about 6 minutes. For scenarios where the underlying data is dynamic in nature, we propose IncrementalNetProbe, which is an approximate, but fast, variant of NetProbe. Our experiments prove that Incremental NetProbe executes nearly doubly fast as compared to NetProbe, while retaining over 99% of its accuracy.


title: Back pain in the elderly: a review.

abstract: Low back pain is a common symptom in the older person. Whilst the majority of cases are thought to be mechanical or idiopathic and benign in nature, its multiple potential causes and concerns regarding missed diagnosis of less common but more serious underlying pathological diagnoses mean many physicians find the assessment, investigation and treatment of chronic low back pain in older adults challenging. This narrative review describes the classification of low back pain in older adults, discusses both mechanical and sinister causes of pain, highlights the appropriate use of medical imaging and provides an overview of surgical and non-surgical management of these patients.


title: A potential maze solving algorithm for a micromouse robot

abstract: Discretely assigned potential levels can be effectively used in making autonomous route decisions for a mobile robot to reach a goal. This paper demonstrates methods of assigning and manipulating these artificial potentials to provide locally optimized path choices while maintaining the integrity of the potentials. The basic algorithm is improved by retaining information of the number of decisions that have been made. Results from implementation and simulation of the algorithm for a micromouse maze-solving robot are presented. Consideration is given to implementation using a limited power microprocessor.


title: Rapidandbrief communication Evolutionary extreme learning machine

abstract: Extreme learning machine (ELM) [G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, Extreme learning machine: a new learning scheme of feedforward neural networks, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN2004), Budapest, Hungary, 25-29 July 2004], a novel learning algorithm much faster than the traditional gradient-based learning algorithms, was proposed recently for single-hidden-layer feedforward neural networks (SLFNs). However, ELM may need higher number of hidden neurons due to the random determination of the input weights and hidden biases. In this paper, a hybrid learning algorithm is proposed which uses the differential evolutionary algorithm to select the input weights and Moore-Penrose (MP) generalized inverse to analytically determine the output weights. Experimental results show that this approach is able to achieve good generalization performance with much more compact networks.


title: A Low-Cost Dual-Band CPW-Fed Printed LPDA for Wireless Communications

abstract: A dual-band printed log-periodic dipole array (LPDA) antenna for wireless communications, designed on a low-cost PET substrate and implemented by inkjet-printing conductive ink, is presented. The proposed antenna can be used for wireless communications both within the UHF (2.4–2.484 GHz) and SHF (5.2-5.8 GHz) wireless frequency bands, and presents a good out-of-band rejection, without the need of stopband filters. The antenna has been designed using a general-purpose 3-D computer-aided design software (CAD), CST Microwave Studio, and then realized. Measured results are in very good agreement with simulations.


title: Skin Lesion Classification from Dermoscopic Images Using Deep Learning Techniques

abstract: The recent emergence of deep learning methods for medical image analysis has enabled the development of intelligent medical imaging-based diagnosis systems that can assist the human expert in making better decisions about a patients health. In this paper we focus on the problem of skin lesion classification, particularly early melanoma detection, and present a deep-learning based approach to solve the problem of classifying a dermoscopic image containing a skin lesion as malignant or benign. The proposed solution is built around the VGGNet convolutional neural network architecture and uses the transfer learning paradigm. Experimental results are encouraging: on the ISIC Archive dataset, the proposed method achieves a sensitivity value of 78.66%, which is significantly higher than the current state of the art on that dataset.


title: Intensity of challenge to the balance system is not reported in the prescription of balance exercises in randomised trials: a systematic review

abstract: Question How has balance challenge intensity been reported in trials of balance exercise interventions? Are there any instruments designed to measure the intensity of balance challenge in balance training exercises? Design Systematic review of randomised trials of balance training exercises. Participants Older adults, ie, the majority of subjects were aged over 55 years. Intervention Balance exercise intervention, or multi-dimensional intervention that included a balance exercise intervention. Outcome measures The included trials were examined for descriptions and instruments used to report the intensity of the challenge to the patient's balance system provided by the balance exercise prescribed. The other included studies were examined for instruments that measure balance challenge intensity. Results In most of the 148 randomised trials identified, measures of reported balance challenge ‘intensity' were actually measures of some other aspect of the exercise, eg, aerobic intensity or a hierarchy of task difficulty without reference to the patient's ability. Three potential systems of measuring the balance challenge intensity were identified. Two were not described in any detail. One was defined in terms of the limits of the patient's postural stability, but this system appears not to have been validated. No adequate measures of balance challenge intensity were found among the other types of studies identified. Conclusion The review highlights a serious gap in the methods used to prescribe, implement, and evaluate the effect of balance exercise programs. Comprehensive work in this area is required to develop a psychometrically sound measure of balance exercise intensity.


title: Compact megavolt pulse transformer with inner magnetic core and conical secondary windings

abstract: Pulse power systems have been stimulated intense interest towards high power, compact structure and high reliability. In this paper, a compact megavolt pulse autotransformer with inner cylindrical magnetic core and conical secondary windings is presented. First, the operation principal and the geometrical structure are described. At the same time, the electric c ircuit is analyzed and the parametric relation between the auto-transformer and the traditional transformer is derived. And the auto-transformer can raise voltage ratio with 5%. Second, the compact pulse transformer is experimentally investigated from low voltage to high voltage. And then the lifetime of the pulse transformer is experimentally studied and statistically estimated. Moreover, the reliability is statistically analyzed. Furthermore, the reliability growth experiment is carried out through improving inner magnetic core and conical secondary windings' insulting strength. Nowadays, the insulating failure of the pulse transformer focus on the high voltage output terminal. Finally, the compact megavolt pulse transformer is development. It can operate at 1 MV secondary voltage with 6 μs secondary charging time. And the reliability is above 0.9 and the lifetime is 10 pulses. These efforts set a good foundation of a compact megavolt pulse transformer and show a promising application for the further.


Application of Katharine Kolcaba Comfort Theory in Post Operative Child: Delivering Integrative Comfort Care Intervention by using Theory of Comfort


title: Dynamic Security Risk Management Using Bayesian Attack Graphs

abstract: Security risk assessment and mitigation are two vital processes that need to be executed to maintain a productive IT infrastructure. On one hand, models such as attack graphs and attack trees have been proposed to assess the cause-consequence relationships between various network states, while on the other hand, different decision problems have been explored to identify the minimum-cost hardening measures. However, these risk models do not help reason about the causal dependencies between network states. Further, the optimization formulations ignore the issue of resource availability while analyzing a risk model. In this paper, we propose a risk management framework using Bayesian networks that enable a system administrator to quantify the chances of network compromise at various levels. We show how to use this information to develop a security mitigation and management plan. In contrast to other similar models, this risk model lends itself to dynamic analysis during the deployed phase of the network. A multiobjective optimization platform provides the administrator with all trade-off information required to make decisions in a resource constrained environment.


title: Willingness to Pay for E-Mobility Services : A Case Study from Germany

abstract: We analyse the relevance of and willingness to pay for services connected to operation and charging of electric vehicles. Our survey data comprises fleet manager answers from 109 German organizations, mainly small and medium-sized enterprises. The results indicate that semi-public charging infrastructure at the organizations connected to services enabling the operation of an interorganizational charging network are relevant to most of the fleet managers. In most of the organizations willingness to pay for connected charging services is available, but only sporadic usage of inter-organizational charging activities could be observed.


title: Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network

abstract: Magnetic resonance imaging (MRI) is a non-invasive diagnostic tool very frequently used for brain imaging. The classification of MRI images of normal and pathological brain conditions pose a challenge from technological and clinical point of view, since MR imaging focuses on soft tissue anatomy and generates a large information set and these can act as a mirror reflecting the conditions of the brain. A new approach by integrating wavelet entropy based spider web plots and probabilistic neural network is proposed for the classification of MRI brain images. The two step method for classification uses (1) wavelet entropy based spider web plots for the feature extraction and (2) probabilistic neural network for the classification. The spider web plot is a geometric construction drawn using the entropy of the wavelet approximation components and the areas calculated are used as feature set for classification. Probabilistic neural network provides a general solution to the pattern classification problems and the classification accuracy is found to be 100%.


title: Interactive indirect illumination using voxel-based cone tracing: an insight

abstract: Indirect illumination is an important element for realistic image synthesis, but its computation is expensive and highly dependent on the complexity of the scene and of the BRDF of the surfaces involved. While off-line computation and pre-baking can be acceptable for some cases, many applications (games, simulators, etc.) require real-time or interactive approaches to evaluate indirect illumination. We present a novel algorithm to compute indirect lighting in real-time that avoids costly precomputation steps and is not restricted to low frequency illumination. It is based on a hierarchical voxel octree representation generated and updated on-the-fly from a regular scene mesh coupled with an approximate voxel cone tracing that allows a fast estimation of the visibility and incoming energy. Our approach can manage two light bounces for both Lambertian and Glossy materials at interactive framerates (25-70FPS). It exhibits an almost scene-independent performance and allows for fully dynamic content thanks to an interactive octree voxelization scheme. In addition, we demonstrate that our voxel cone tracing can be used to efficiently estimate Ambient Occlusion.


title: Design of Air Pollution Monitoring System Using ZigBee Networks for Ubiquitous-City

abstract: The innovative Ubiquitous-City was born through convergence of construction, home applications and culture, with development of up to date information technologies and ubiquitous IT. The U-City can be applied to a variety of special situations for managing functions of each city efficiently. This paper focuses on implementation of air pollution monitoring system. First, each sensor was tested after survey about market trends of a variety of sensors for detecting air pollution. Second, wireless communication modules for monitoring system were developed using wireless sensor networks technologies based on ZigBee. And then a performance of modules was estimated in the real-fields. Through software programs written in nesC for efficient routing in wireless networks were simulated using TOSSIM simulator. Finally, integrated wireless sensor board which employs dust, CO2, temperature/humidity sensor and a ZigBee module was developed. The board is embedded device based on AT91SAM7S64 porting uCOS. This paper accelerates the digital convergence age through continual research and development of technologies related the U-City.


Neural Networks: A Systematic Introduction


Using Scenarios in Architecture Evaluations


title: Stock market forecast using sentiment analysis

abstract: Public opinion and stock market sentiment analysis have been used in this paper to find a relation between public moods and the stock market. We have used twitter data for predicting public emotion and past stock values to predict stock market movements. Thus by doing so we have been successful at obtaining high accuracy and capturing large scale trends.


title: User Conditional Hashtag Prediction for Images

abstract: Understanding the content of user's image posts is a particularly interesting problem in social networks and web settings. Current machine learning techniques focus mostly on curated training sets of image-label pairs, and perform image classification given the pixels within the image. In this work we instead leverage the wealth of information available from users: firstly, we employ user hashtags to capture the description of image content; and secondly, we make use of valuable contextual information about the user. We show how user metadata (age, gender, etc.) combined with image features derived from a convolutional neural network can be used to perform hashtag prediction. We explore two ways of combining these heterogeneous features into a learning framework: (i) simple concatenation; and (ii) a 3-way multiplicative gating, where the image model is conditioned on the user metadata. We apply these models to a large dataset of de-identified Facebook posts and demonstrate that modeling the user can significantly improve the tag prediction quality over current state-of-the-art methods.


title: Microstrip fed star super wideband fractal antenna

abstract: A microstrip line fed star fractal antenna for super wideband applications is presented. Second iterative star shaped fractal geometry of the radiator, modified feedline and notch loaded semi-elliptical ground plane are used to achieve an impedance bandwidth of 4.6–52 GHz. A good agreement is observed between the simulated and experimental results. It has an overall volume of 19.7×19×1.6 mm3. It has advantages of miniaturized dimensions and wider bandwidth over previously reported structures.


title: Appendices MISQ Archivist for Business Process Change: A Study of Methodologies, Techniques and Tools By

abstract: Growth in Business Process Reengineering (BPR) consulting services has led to a proliferation of methods for conducting BPR. Sifting through vendor promotional hype and identifying a set of techniques and tools that best meets a particular project's needs can be a daunting task. This article investigates BPR Methods, Techniques, and Tools (MTTs) and places them within an empirically derived reference framework. A comprehensive picture of BPR emerges that includes MTTs that help in reengineering strategy, people, management, structure, and technology dimensions of business processes. A BPR planning approach for customizing this framework based on unique project characteristics is then offered to assist in selecting those BPR project activities and techniques to be emphasized. This flexible framework and comprehensive survey of commonly used BPR techniques and tools forms a knowledge base to improve business process change practice and provides a basis for future BPR research.


title: Nighttime Vehicle Detection for Driver Assistance and Autonomous Vehicles

abstract: This study presents an effective method for detecting vehicles in front of the camera-assisted car during nighttime driving. The proposed method detects vehicles based on detecting and locating vehicle headlights and taillights using techniques of image segmentation and pattern analysis. First, to effectively extract bright objects of interest, a segmentation process based on automatic multilevel thresholding is applied on the grabbed road-scene images. Then the extracted bright objects are processed by a rule-based procedure, to identify the vehicles by locating and analyzing their vehicle light patterns, and estimate their distances to the camera-assisted car. Experimental results demonstrate the effectiveness of the proposed method on detecting vehicles at night.


title: Feature extraction for identification of sugarcane rust disease

abstract: This research propose an image pattern classification to identify rust disease in sugarcane leaf with a combination of texture and color feature extraction. The purpose of this research is to find appropriate features that can identify sugarcane rust disease. Firstly, normal and diseased images are collected and pre-processed. Then, features of shape, color and texture are extracted from these images. After that, these images are classified by support vector machine classifier. A combination of several features are used to evaluate the appropriate features to find distinctive features for identification of rust disease. When a single feature is used, shape feature has the lowest accuracy of 51% and texture feature has the highest accuracy of 96.5%. A combination of texture and color feature extraction results a highest classification accuracy of 97.5%. A combination of texture and color feature extraction with polynomial kernel results in 98.5 % classification accuracy.


title: Movement Error Rate for Evaluation of Machine Learning Methods for sEMG-Based Hand Movement Classification

abstract: There has been increasing interest in applying learning algorithms to improve the dexterity of myoelectric prostheses. In this work, we present a large-scale benchmark evaluation on the second iteration of the publicly released NinaPro database, which contains surface electromyography data for 6 DOF force activations as well as for 40 discrete hand movements. The evaluation involves a modern kernel method and compares performance of three feature representations and three kernel functions. Both the force regression and movement classification problems can be learned successfully when using a nonlinear kernel function, while the exp- χ 2 kernel outperforms the more popular radial basis function kernel in all cases. Furthermore, combining surface electromyography and accelerometry in a multimodal classifier results in significant increases in accuracy as compared to when either modality is used individually. Since window-based classification accuracy should not be considered in isolation to estimate prosthetic controllability, we also provide results in terms of classification mistakes and prediction delay. To this extent, we propose the movement error rate as an alternative to the standard window-based accuracy. This error rate is insensitive to prediction delays and it allows us therefore to quantify mistakes and delays as independent performance characteristics. This type of analysis confirms that the inclusion of accelerometry is superior, as it results in fewer mistakes while at the same time reducing prediction delay.


title: Automated extractive single-document summarization: beating the baselines with a new approach

abstract: Single document summarization, which is as important as multiple document summarization for a variety of reasons, has been attracting declining interest recently. The goal of this study is to introduce a new approach to single document summarization and its implementation, SynSem. Our approach fuses syntactic, semantic, and statistical methodologies and reflects the importance of text headings in articles along with the presence of thematic keywords in sentences. Successful summary evaluation results are demonstrated when SynSem is tested on the Document Understanding Conference (DUC) 2002 data set using ROUGE, which compares single document summaries to baselines.


title: Wavelet-based multi-resolution GARCH model for financial spillover effects

abstract: Abstract: This study proposes a wavelet-based multi-resolution BEKK-GARCH model to investigate spillover effects across financial markets. Compared with traditional multivariate GARCH analysis, the proposed model can identify or decompose cross-market spillovers on multiple resolutions. Taking two highly correlated indices, the NASDAQ (U.S.) and TWSI (Taiwan composite stock index) for analysis, the empirical results show that the NASDAQ returns strongly predict the movements of TWSI on the raw data level, but via wavelet-based multi-resolution analysis we find that the prediction power unevenly spreads over each time scale, and the spillover patterns are totally different as that revealed on the raw data level. The direction and magnitude of return and volatility spillovers significantly vary with their time scales. Considering the fact that heterogeneous groups of investors trade on different time horizons, the results of this study help investors to uncover the complex pattern of return and volatility spillovers on their own horizon, and make a good hedge on their risk.


title: Spatio-temporal interest points for video analysis

abstract: In this paper, we discuss the need for an effective representation of video data to aid analysis of large datasets of video clips and describe a prototype developed to explore the use of spatio-temporal interest points for action recognition. Our focus is on ways that computation can assist analysis.


Hybrid Systems: Computation and Control


A Visually Interpretable Iris Recognition System with Crypt Features


The Ethos Framework: Generating Contextually Linked Dynamic Quests


title: Thinking Smart Cities as Systems-of-Systems: A Perspective Study

abstract: Smart cities typically encompass several of distributed systems engaged in complex relationships including the integration and interaction with other systems towards providing new functionalities. These relationships lead to the so-called systems-of-systems (SoS), a widespread set of independent, heterogeneous complex systems that have their own purpose and collaborate with each other to fulfill common global missions. The SoS concept has indeed emerged as an active domain of research in recent years and it is of crucial importance for the societal challenges facing the world. In this perspective, we herein present how a smart city itself can be holistically viewed as a complex SoS and shed light on some challenges related to the development of this type of systems in the context of smart cities.


title: EVALUATION OF GIS AND BIM ROLES FOR THE INFORMATION MANAGEMENT OF HISTORICAL BUILDINGS

abstract: Abstract. An architectural heritage object carries heterogeneous and multi-layered information beyond physical characteristics. It requires an integrated representation of various types of information for understanding and management prior to the decision-making process of conservation. This requirement is a twofold manner consisting of representation and management processes. There exists a variety of approaches for representation of heritage objects in digital three-dimensional (3D) environment, but the selection of the appropriate one according to the needs is crucial. On one hand, there have been recently great attempts to adopt Building Information Modeling (BIM) for historical buildings. Nevertheless, the related works in the topic focus mainly on pre-processing of data, such as the integration of born-digital material into a BIM environment and the creation of parametric objects according to historical building characteristics. As the information management of a historical building requires enhanced attribute management and integration of different datasets, further investigation on the BIM capabilities in management terms is crucial. On the other hand, Geographical Information Systems (GIS) have great potentials in exploring spatial relationships, but their potential in 3D representation is still somehow limited. The paper reviews and evaluates the roles of BIM and GIS, highlighting their advantages and disadvantages for integration, retrieval and management of heterogeneous data in the context of historical buildings.


title: Markov Chain Hebbian Learning Algorithm With Ternary Synaptic Units

abstract: In spite of remarkable progress in machine learning techniques, the state-of-the-art machine learning algorithms often keep machines from real-time learning (online learning) due in part to computational complexity in parameter optimization. As an alternative, a learning algorithm to train a memory in real time is proposed, which is named as the Markov chain Hebbian learning algorithm. The algorithm pursues efficient memory use during training in that (i) the weight matrix has ternary elements (-1, 0, 1) and (ii) each update follows a Markov chain--the upcoming update does not need past weight memory. The algorithm was verified by two proof-of-concept tasks (handwritten digit recognition and multiplication table memorization) in which numbers were taken as symbols. Particularly, the latter bases multiplication arithmetic on memory, which may be analogous to humans' mental arithmetic. The memory-based multiplication arithmetic feasibly offers the basis of factorization, supporting novel insight into the arithmetic.


title: Evaluating the SDN control traffic in large ISP networks

abstract: Scalability of Software Defined Networking (SDN) approach is one of the key issues that many network operators are willing to address and understand. Indeed, the promised programmability and flexibility of a SDN network is paid with a non-negligible control traffic exchanged between the network nodes and the SDN controllers. We consider a network controlled by a real OpenFlow-enabled controller, i.e. OpenDaylight. We evaluate analytically the number of OpenFlow messages for installing a new traffic flow, assuming the default reactive forwarding application available in OpenDaylight. We apply these results to the specific case of a large ISP network, comprising a backbone interconnecting many POPs. By evaluating exactly the amount of generated control traffic, we are able to assess the scalability of the reactive forwarding application in a practical relevant scenario for ISPs.


title: Low-Cost Compact ECG With Graphic LCD and Phonocardiogram System Design

abstract: Till today, many different ECG devices are made in developing countries. In this study, low cost, small size, portable LCD screen ECG device, and phonocardiograph were designed. With designed system, heart sounds that take synchronously with ECG signal are heard as sensitive. Improved system consist three units; Unit 1, ECG circuit, filter and amplifier structure. Unit 2, heart sound acquisition circuit. Unit 3, microcontroller, graphic LCD and ECG signal sending unit to computer. Our system can be used easily in different departments of the hospital, health institution and clinics, village clinic and also in houses because of its small size structure and other benefits. In this way, it is possible that to see ECG signal and hear heart sounds as synchronously and sensitively. In conclusion, heart sounds are heard on the part of both doctor and patient because sounds are given to environment with a tiny speaker. Thus, the patient knows and hears heart sounds him/herself and is acquainted by doctor about healthy condition.


title: Stemming The Qur'an

abstract: In natural language, a stem is the morphological base of a word to which affixes can be attached to form derivatives. Stemming is a process of assigning morphological variants of words to equivalence classes such that each class corresponds to a single stem. Different stemmers have been developed for a wide range of languages and for a variety of purposes. Arabic, a highly inflected language with complex orthography, requires good stemming for effective text analysis. Preliminary investigation indicates that existing approaches to Arabic stemming fail to provide effective and accurate equivalence classes when applied to a text like the Qur'an written in Classical Arabic. Therefore, I propose a new stemming approach based on a light stemming technique that uses a transliterated version of the Qur'an in western script.


title: Residual-Based External Torque Estimation in Series Elastic Actuators Over a Wide Stiffness Range: Frequency Domain Approach

abstract: This letter presents an enhanced external torque estimation algorithm for series elastic actuators (SEAs) expanding the usability of the residual-based technique. Although the residual method demonstrates online torque estimation capability in diverse applications and thus becomes popular, it is practically challenging to achieve accurate estimation performance in a wide stiffness range of SEAs. The performance degradation is mainly induced by inaccurate transmission torque information, which is stemmed from unexpected errors in the spring deflection based torque sensing. To overcome the issue, this letter analyses the limitation of the conventional approach in frequency domain and proposes the enhanced residual to be used over a wide stiffness range of SEAs. The performance of proposed method is comparatively verified with conventional method in the both of simulations and experiments.


title: Whoo.ly: facilitating information seeking for hyperlocal communities using social media

abstract: Social media systems promise powerful opportunities for people to connect to timely, relevant information at the hyper local level. Yet, finding the meaningful signal in noisy social media streams can be quite daunting to users. In this paper, we present and evaluate Whoo.ly, a web service that provides neighborhood-specific information based on Twitter posts that were automatically inferred to be hyperlocal. Whoo.ly automatically extracts and summarizes hyperlocal information about events, topics, people, and places from these Twitter posts. We provide an overview of our design goals with Whoo.ly and describe the system including the user interface and our unique event detection and summarization algorithms. We tested the usefulness of the system as a tool for finding neighborhood information through a comprehensive user study. The outcome demonstrated that most participants found Whoo.ly easier to use than Twitter and they would prefer it as a tool for exploring their neighborhoods.


title: Living with joint hypermobility syndrome: patient experiences of diagnosis, referral and self-care

abstract: BACKGROUND: Musculoskeletal problems are common reasons for seeking primary health care. It has been suggested that many people with ‘everyday’ non-inflammatory musculoskeletal problems may have undiagnosed joint hypermobility syndrome (JHS), a complex multi-systemic condition. JHS is characterised by joint laxity, pain, fatigue and a wide range of other symptoms. Physiotherapy is usually the preferred treatment option for JHS, although diagnosis can be difficult. The lived experience of those with JHS requires investigation. ::: ::: OBJECTIVE: To examine patients’ lived experience of JHS, their views and experiences of JHS diagnosis and management. ::: ::: METHODS: Focus groups in four locations in the United Kingdom were convened, involving 25 participants with a prior diagnosis of JHS. The focus groups were audio recorded, fully transcribed and analysed using the constant comparative method to inductively derive a thematic account of the data. ::: ::: RESULTS: Pain, fatigue, proprioception difficulties and repeated cycles of injury were among the most challenging features of living with JHS. Participants perceived a lack of awareness of JHS from health professionals and more widely in society and described how diagnosis and access to appropriate health care services was often slow and convoluted. Education for patients and health professionals was considered to be essential. ::: ::: CONCLUSIONS: Timely diagnosis, raising awareness and access to health professionals who understand JHS may be particularly instrumental in helping to ameliorate symptoms and help patients to self-manage their condition. Physiotherapists and other health professionals should receive training to provide biopsychosocial support for people with this condition.


title: Blur-Aware Disparity Estimation from Defocus Stereo Images

abstract: Defocus blur usually causes performance degradation in establishing the visual correspondence between stereo images. We propose a blur-aware disparity estimation method that is robust to the mismatch of focus in stereo images. The relative blur resulting from the mismatch of focus between stereo images is approximated as the difference of the square diameters of the blur kernels. Based on the defocus and stereo model, we propose the relative blur versus disparity (RBD) model that characterizes the relative blur as a second-order polynomial function of disparity. Our method alternates between RBD model update and disparity update in each iteration. The RBD model in return refines the disparity estimation by updating the matching cost and aggregation weight to compensate the mismatch of focus. Experiments using both synthesized and real datasets demonstrate the effectiveness of our proposed algorithm.


Persian Plagiarism Detection Using Sentence Correlations


title: ECOMIPS: an economic MIPS CPU design on FPGA

abstract: In recently years, many traditional ASIC applications are moving towards a more flexible FPGA based design. To establish a complete application system, it often needs a separate processor to achieve some interactive system functionalities such as I/O operations and control-sensitive tasks. Modern chip producers (Xilinx, Altera, etc) are promoting economic, yet powerful FPGA chips that have the capacity of migrating a general DSP or microprocessor into one FPGA chip. The trend is that both a general CPU module and the application specific circuit are to coexist on a single chip. This article introduces a MIPs CPU architecture or ECOMIPS to be implemented for that purpose. It focuses on economic resource utilization on modern chips (Xilinx Spartan 3 families). Traditional MIPs architecture was modified to avoid resource conflicts with the ASIC part. The key principles of designing such MIPs HDL IP cores are covered and analyzed with implementation results. As a second objective, ECOMIPS also tries to make itself a customizable and reusable architecture for bridging the gap between microprocessor and ASIC.


title: Large Margin Local Metric Learning

abstract: Linear metric learning is a widely used methodology to learn a dissimilarity function from a set of similar/dissimilar example pairs. Using a single metric may be a too restrictive assumption when handling heterogeneous datasets. Recently, local metric learning methods have been introduced to overcome this limitation. However, they are subjects to constraints preventing their usage in many applications. For example, they require knowledge of the class label of the training points. In this paper, we present a novel local metric learning method, which overcomes some limitations of previous approaches. The method first computes a Gaussian Mixture Model from a low dimensional embedding of training data. Then it estimates a set of local metrics by solving a convex optimization problem; finally, a dissimilarity function is obtained by aggregating the local metrics. Our experiments show that the proposed method achieves state-of-the-art results on four datasets.


title: Comparative Research of Portfolio Model Using Fuzzy Theory

abstract: By discussing both linear and nonlinear fuzzy situations, we give the relevant model and the solution method. First of all, regarding the target level of investment return and investment risk as the half trapezoidal fuzzy number, we give the fuzzy portfolio selection model of linear satisfaction degree. Then, to describe the investor satisfaction degree of investment return and investment risk better, a logarithmic membership function has been imported. Following, we give the fuzzy portfolio selection model of nonlinear satisfaction degree. Nonlinear optimize model can be transferred to linear programming model, so that model calculation has been simplified significantly. Finally, according to the actual data of Chinese securities market, both of models have been compared by using genetic algorithm. Result indicates that portfolio selection model based on linear and nonlinear membership function includes not only historical data, but also investors' expectation. That accords with human psychology and fact. However, by adopting the nonlinear membership function and adjusting its parameter, it can furthest get portfolio that accords with investor's expectation. Therefore, nonlinear membership function is better than linear membership function.


title: The critical period hypothesis revisited

abstract: Nowadays there seems to be a wide acceptance that there is a CP for first language (FL) acquisition, with compelling evidence that, unless they are exposed to language in the early years of life, humans lose the ability to learn a language, especially its grammatical system. The situation with (adult) SL acquisition however appears to be far less clear. While it is true that very few adult SL learners achieve native competence in the SL, some competence is nevertheless acquired, which seems to go against the notion of a biological constraint on language learning. It is true that prepubescent learners as a rule achieve higher levels of proficiency in the SL than adult learners and that generally only very young starters can hope to achieve native competence in the SL. Critics of the CP hypothesis however point out that the attested age-related decline in adult SL learning ability is too gradual to be seen as the result of a critical period. They also point to the nature of errors which adult SL learners have been found to make: more often than not these errors affect SL structures which are different from the learners' FL. If a biological constraint was at play‐critics have argued‐then SL learners should make errors across the board, not only where the learners' FL and SL differ. The issue of CP is closely related to the issue of access to Universal Grammar (UG) in SL learning: a CP hypothesis would entail that after a certain age (e.g. early teens) UG is no longer available. Following the Fundamental Difference (FD) hypothesis proposed in Bley-Vroman (1989), the paper assumes that, in contrast to first language acquisition, SL learning uses the FL (rather than UG) as a knowledge base and involves general (rather than domain-specific) cognitive procedures. The paper argues that such an approach provides a neat account for some of the issues claimed to be problematic for the CP hypothesis.


title: An examination of the determinants of customer loyalty in mobile commerce contexts

abstract: While the importance of customer loyalty has been recognized in marketing literature for at least three decades, the development and empirical validation of a customer loyalty model in a mobile commerce (m-commerce) context had not been addressed. The purpose of our study was to develop and validate such a customer loyalty model. Based on IS and marketing literature, a comprehensive set of constructs and hypotheses were compiled with a methodology for testing them. A questionnaire was constructed and data were collected from 255 users of m-commerce systems in Taiwan. Structural modeling techniques were then applied to analyze the data. The results indicated that customer loyalty was affected by perceived value, trust, habit, and customer satisfaction, with customer satisfaction playing a crucial intervening role in the relationship of perceived value and trust to loyalty. Based on the findings, its implications and limitations are discussed.


title: Filtering near-duplicate documents

abstract: The mathematical concept of document resemblance captures well the informal notion of syntactic similarity. The resemblance can be estimated using a fixed size "sketch" for each document. For a large collection of documents (say hundreds of millions) the size of this sketch is of the order of a few hundred bytes per document. ::: ::: However, for effcient large scale web indexing it is not necessary to determine the actual resemblance value: it suffces to determine whether newly encountered documents are duplicates or near-duplicates of documents already indexed. In other words, it suffces to determine whether the resemblance is above a certain threshold. In this talk we show how this determination can be made using a "sample" of less than 50 bytes per document. ::: ::: The basic approach for computing resemblance has two aspects: first, resemblance is expressed as a set (of strings) intersection problem, and second, the relative size of intersections is evaluated by a process of random sampling that can be done independently for each document. The process of estimating the relative size of intersection of sets and the threshold test discussed above can be applied to arbitrary sets, and thus might be of independent interest. ::: ::: The algorithm for filtering near-duplicate documents discussed here has been successfully implemented and has been used for the last three years in the context of the AltaVista search engine.


1 Text Mining Systems for Predicting Market Response to News : A Survey


title: Managing Traumatic Brain Injury: Appropriate Assessment and a Rationale for Using Neurofeedback and Biofeedback to Enhance Recovery in Postconcussion Syndrome

abstract: Impairments that may result from a mild traumatic brain injury (TBI) or concussion can be both severe and long-lasting. This article will list some of the common persisting symptoms that may occur and give a brief description of the neuropathological processes that can be triggered by TBI, including diffuse axonal injury and its effects on the mitochondrial Kreb's cycle and the production of adenosine triphosphate, the brain's source of energy. This is followed by a summary of a comprehensive assessment process that includes quantitative electroencephalography, evoked potentials, heart rate variability (HRV) measures, neuropsychological testing, and blood and urine analysis. Details concerning a neurophysiological approach to effective treatment are given. These include conventional single-channel neurofeedback (NFB), also called brain-computer interface training, low-resolution electromagnetic tomography z-score neurofeedback, HRV training, and counseling on diet, sleep, and exercise. The authors expand ...


title: IOTSim: a Cloud based Simulator for Analysing IoT Applications

abstract: A disruptive technology that is influencing not only computing paradigm but every other business is the rise of big data. Internet of Things (IoT) applications are considered to be a major source of big data. Such IoT applications are in general supported through clouds where data is stored and processed by big data processing systems. In order to improve the efficiency of cloud infrastructure so that they can efficiently support IoT big data applications, it is important to understand how these applications and the corresponding big data processing systems will perform in cloud computing environments. However, given the scalability and complex requirements of big data processing systems, an empirical evaluation on actual cloud infrastructure can hinder the development of timely and cost effective IoT solutions. Therefore, a simulator supporting IoT applications in cloud environment is highly demanded, but such work is still in its infancy. To fill this gap, we have designed and implemented IOTSim which supports and enables simulation of IoT big data processing using MapReduce model in cloud computing environment. A real case study validates the efficacy of the simulator.


title: Fraud Detection And Management In Mobile Telecommunications Networks

abstract: The paper discusses the status of research on detection of fraud undertaken as part of the European Commission-funded ACTS ASPeCT (Advanced security for personal communications technologies) project. A first task has been the identification of possible fraud scenarios and of typical fraud indicators which can be mapped to data in toll tickets. Currently, the project is exploring the detection of fraudulent behaviour based on a combination of absolute and differential usage. Three approaches are being investigated: a rule based approach and two approaches based on neural networks, where both supervised and unsupervised learning are considered. Special attention is being paid to the feasibility of the implementations.


title: Most apparent distortion: full-reference image quality assessment and the role of strategy

abstract: The mainstream approach to image quality assessment has centered around accurately modeling the single most relevant strategy employed by the human visual system (HVS) when judging image quality (e.g., detecting visible differences, and extracting image structure/information). In this work, we suggest that a single strategy may not be sufficient; rather, we advocate that the HVS uses multiple strategies to determine image quality. For images containing near-threshold distortions, the image is most apparent, and thus the HVS attempts to look past the image and look for the distortions (a detection-based strategy). For images containing clearly visible distortions, the distortions are most apparent, and thus the HVS attempts to look past the distortion and look for the image's subject matter (an appearance-based strategy). Here, we present a quality assessment method [most apparent distortion (MAD)], which attempts to explicitly model these two separate strategies. Local luminance and contrast masking are used to estimate detection-based perceived distortion in high-quality images, whereas changes in the local statistics of spatial-frequency components are used to estimate appearance-based perceived distortion in low-quality images. We show that a combination of these two measures can perform well in predicting subjective ratings of image quality.


title: Understanding probabilistic classifiers

abstract: Probabilistic classifiers are developed by assuming generative models which are product distributions over the original attribute space (as in naive Bayes) or more involved spaces (as in general Bayesian networks). While this paradigm has been shown experimentally successful on real world applications, despite vastly simplified probabilistic assumptions, the question of why these approaches work is still open. ::: ::: This paper resolves this question.We show that almost all joint distributions with a given set of marginals (i.e., all distributions that could have given rise to the classifier learned) or, equivalently, almost all data sets that yield this set of marginals, are very close (in terms of distributional distance) to the product distribution on the marginals; the number of these distributions goes down exponentially with their distance from the product distribution. Consequently, as we show, for almost all joint distributions with this set of marginals, the penalty incurred in using the marginal distribution rather than the true one is small. In addition to resolving the puzzle surrounding the success of probabilistic classifiers our results contribute to understanding the tradeoffs in developing probabilistic classifiers and will help in developing better classifiers.


title: Accelerated Gossip via Stochastic Heavy Ball Method

abstract: In this paper we show how the stochastic heavy ball method (SHB) -- a popular method for solving stochastic convex and non-convex optimization problems --operates as a randomized gossip algorithm. In particular, we focus on two special cases of SHB: the Randomized Kaczmarz method with momentum and its block variant. Building upon a recent framework for the design and analysis of randomized gossip algorithms, [Loizou Richtarik, 2016] we interpret the distributed nature of the proposed methods. We present novel protocols for solving the average consensus problem where in each step all nodes of the network update their values but only a subset of them exchange their private values. Numerical experiments on popular wireless sensor networks showing the benefits of our protocols are also presented.


Learning from Notes: Organizational Issues in Groupware Implementation