title: A survey of domain adaptation for statistical machine translation

abstract: Differences in domains of language use between training data and test data have often been reported to result in performance degradation for phrase-based machine translation models. Throughout the past decade or so, a large body of work aimed at exploring domain-adaptation methods to improve system performance in the face of such domain differences. This paper provides a systematic survey of domain-adaptation methods for phrase-based machine-translation systems. The survey starts out with outlining the sources of errors in various components of phrase-based models due to domain change, including lexical selection, reordering and optimization. Subsequently, it outlines the different research lines to domain adaptation in the literature, and surveys the existing work within these research lines, discussing how these approaches differ and how they relate to each other.


title: Group Atmosphere, Shared Understanding, and Perceived Conflict in Virtual Teams: Findings from an Experiment

abstract: In this paper, we present a theoretical model on the relationships among group atmosphere, shared understanding, and perceived task conflict in virtual teams. We validate the theoretical model by analyzing data that was collected in a laboratory experiment on virtual teams. We find that cultural diversity of virtual team adversely affects group atmosphere and group atmosphere has a positive influence on the development of shared understanding in these teams. We also find that the development of shared understanding weakens perceived task conflict in virtual teams. However, we do not find a strong support for the moderating effect of avoidance conflict management style on the relationship between shared understanding and perceived task conflict.


title: HeatPipe: High Throughput, Low Latency Big Data Heatmap with Spark Streaming

abstract: Heatmap visualization is a well-known type of visualization to alleviate the overplot problem of point visualization. As such, it is well suited to visualize Big Data. In order to tackle the velocity problem of Big Data, one has to leverage streaming computations. Recently, canopy clustering was shown to be well suited for Big Data heatmap visualization. In this article, we present how to design a streaming algorithm to compute canopy clustering using Apache Spark. This result is directly applicable to be included into a lambda architecture.


title: Cadaver validation of skeletal muscle measurement by magnetic resonance imaging and computerized tomography

abstract: Magnetic resonance imaging (MRI) and computerized tomography (CT) are promising reference methods for quantifying whole body and regional skeletal muscle mass. Earlier MRI and CT validation studies used data-acquisition techniques and data-analysis procedures now outdated, evaluated anatomic rather than adipose tissue-free skeletal muscle (ATFSM), studied only the relatively large thigh, or found unduly large estimation errors. The aim of the present study was to compare arm and leg ATFSM cross-sectional area estimates (cm2) by using standard MRI and CT acquisition and image-analysis methods with corresponding cadaver estimates. A second objective was to validate MRI and CT measurements of adipose tissue embedded within muscle (interstitial adipose tissue) and surrounding muscle (subcutaneous adipose tissue). ATFSM area ( n = 119) by MRI [38.9 ± 22.3 (SD) cm2], CT (39.7 ± 22.8 cm2), and cadaver (39.5 ± 23.0 cm2) were not different ( P > 0.001), and both MRI and CT estimates of ATFSM were highly correlated with corresponding cadaver values [MRI: r = 0.99, SE of estimate (SEE) 3.9 cm2, P < 0.001; and CT: r = 0.99, SEE = 3.8 cm2, P < 0.001]. Similarly good results were observed between MRI- and CT-measured vs. cadaver-measured interstitial and subcutaneous adipose tissue. For MRI-ATFSM the intraobserver correlation for duplicate measurements in vivo was 0.99 [SEE = 8.7 cm2(2.9%), P < 0.001]. These findings strongly support the use of MRI and CT as reference methods for appendicular skeletal muscle, interstitial and subcutaneous adipose tissue measurement in vivo.


title: A DVB-S2 compliant LDPC decoder integrating the Horizontal Shuffle Scheduling

abstract: Low-density parity check codes (LDPC) are a class of channel decoding codes used in digital communications. Very high error correcting performances can be reached with such codes but they require both a great computing effort and randomly constructed decoding matrices. LDPC codes are used to perform the channel coding of the satellite television broadcast standard DVB-S2. This paper proposes a way to design massively parallel hardware architecture of DVB-S2 compliant LDPC decoders. It is based on a particular way to schedule all the algorithm's calculations. The proposed architecture speeds-up the decoding process, allowing the algorithm to converge faster with no significant performance loss. Moreover, a particular data update mechanism has been developed in order to avoid all data conflicts inherent to DVB-S2 matrices even for highly parallel implementations. This paper describes our hardware LDPC decoder architecture and its processing elements. Estimated silicon area of this decoder is 11 mm in ST 90 nm technology and the decoding throughput reaches 591 M bps at 300 MHz for rate 1/2 and code size of 64800


title: Constrained Sampling and Counting: Universal Hashing Meets SAT Solving

abstract: Constrained sampling and counting are two fundamental problems in artificial intelligence with a diverse range of applications, spanning probabilistic reasoning and planning to constrained-random verification. While the theory of these problems was thoroughly investigated in the 1980s, prior work either did not scale to industrial size instances or gave up correctness guarantees to achieve scalability. Recently, we proposed a novel approach that combines universal hashing and SAT solving and scales to formulas with hundreds of thousands of variables without giving up correctness guarantees. This paper provides an overview of the key ingredients of the approach and discusses challenges that need to be overcome to handle larger real-world instances.


title: Design and Simulation of Multi Channel UART for Serial Communication N.B.S.Naveen

abstract: Universal Asynchronous Receiver Transmitter) is used for serial communication. It is used for long distance and low cost process for transfer of data between pc and its devices. In general a UART operated with specific baud rate. To meet the complex communication demands it is not sufficient. To overcome this difficulty a multi channel UART is proposed in this paper. And the whole design is simulated with modelsim and synthesized with Xilinx software KeywordsBaud rate generator, First in First Out, Simulation.


title: Gaussian Processes to Speed up Hybrid Monte Carlo for Expensive Bayesian Integrals

abstract: Hybrid Monte Carlo (HMC) is often the method of choice for computing Bayesian integrals that are not analytically tractable. However the success of this method may require a very large number of evaluations of the (un-normalized) posterior and its partial derivatives. In situations where the posterior is computationally costly to evaluate, this may lead to an unacceptable computational load for HMC. I propose to use a Gaussian Process model of the (log of the) posterior for most of the computations required by HMC. Within this scheme only occasional evaluation of the actual posterior is required to guarantee that the samples generated have exactly the desired distribution, even if the GP model is somewhat inaccurate. The method is demonstrated on a 10 dimensional problem, where 200 evaluations suffice for the generation of 100 roughly independent points from the posterior. Thus, the proposed scheme allows Bayesian treatment of models with posteriors that are computationally demanding, such as models involving computer simulation.


title: Multilinear Dynamical Systems for Tensor Time Series

abstract: Data in the sciences frequently occur as sequences of multidimensional arrays called tensors. How can hidden, evolving trends in such data be extracted while preserving the tensor structure? The model that is traditionally used is the linear dynamical system (LDS) with Gaussian noise, which treats the latent state and observation at each time slice as a vector. We present the multilinear dynamical system (MLDS) for modeling tensor time series and an expectation-maximization (EM) algorithm to estimate the parameters. The MLDS models each tensor observation in the time series as the multilinear projection of the corresponding member of a sequence of latent tensors. The latent tensors are again evolving with respect to a multilinear projection. Compared to the LDS with an equal number of parameters, the MLDS achieves higher prediction accuracy and marginal likelihood for both artificial and real datasets.


title: A trust-semantic fusion-based recommendation approach for e-business applications

abstract: Collaborative Filtering (CF) is the most popular recommendation technique but still suffers from data sparsity, user and item cold-start problems, resulting in poor recommendation accuracy and reduced coverage. This study incorporates additional information from the users' social trust network and the items' semantic domain knowledge to alleviate these problems. It proposes an innovative Trust-Semantic Fusion (TSF)-based recommendation approach within the CF framework. Experiments demonstrate that the TSF approach significantly outperforms existing recommendation algorithms in terms of recommendation accuracy and coverage when dealing with the above problems. A business-to-business recommender system case study validates the applicability of the TSF approach.


title: Preserving the privacy of sensitive relationships in graph data

abstract: In this paper, we focus on the problem of preserving the privacy of sensitive relationships in graph data. We refer to the problem of inferring sensitive relationships from anonymized graph data as link reidentification. We propose five different privacy preservation strategies, which vary in terms of the amount of data removed (and hence their utility) and the amount of privacy preserved. We assume the adversary has an accurate predictive model for links, and we show experimentally the success of different link re-identification strategies under varying structural characteristics of the data.


title: AI BASED, AUTOMATIC PRODUCTION SYSTEM DECOMPOSITION

abstract: This paper introduces a new approach for automatic plant decomposition based on artificial intelligence (AI) techniques. A novel automatic solution based on a generalized feature selection technique and on artificial neural network (ANN) training was developed by the author. The main goal is to explore connections among parameters of a given database, and based on the modelled dependency sets validated groups of parameters can form individual parts of the analysed system. Applying this technique to a production database containing data typically inherited from the shop-floor level through process monitoring systems (or based on virtual simulation models) results groups of connected production parameters. Consequently it allows decomposing a running or simulated manufacturing system into smaller, individual and autonomous components, so, the approach can provide the basis for production system decomposition and reconfiguration, too.


title: Metal artifact reduction in CT images by sinogram TV inpainting

abstract: In this paper, a total variation (TV) inpainting method is proposed for metal artifact reduction in medical computed tomography (CT). Digital inpainting is an image processing method to fill in the lost image information in a consistent way. In our work, projection data with metal projection region (MPR) are treated as a damaged image, and TV inpainting is applied to “inpaint” the information missing region. Compared to conventional interpolation methods, the advantage of our algorithm lies in dealing with complicate cases such as an image with multiple metal objects. In numerical experiments, both TV inpainting and linear interpolation method are performed on noise-free and additive noisy projection of a modified Shepp-Logan phantom. Results show that the algorithm proposed fills metal projection gaps more smoothly and accurately than linear interpolation, hence produces images of superior quality after reconstruction. Relevant practical issues including the limitation of the algorithm and possible improvements for future work are discussed.


title: Parameter-free Sentence Embedding via Orthogonal Basis

abstract: We propose a simple and robust training-free approach for building sentence representations. Inspired by the Gram-Schmidt Process in geometric theory, we build an orthogonal basis of the subspace spanned by a word and its surrounding context in a sentence. We model the semantic meaning of a word in a sentence based on two aspects. One is its relatedness to the word vector subspace already spanned by its contextual words. The other is the word's novel semantic meaning which shall be introduced as a new basis vector perpendicular to this existing subspace. Following this motivation, we develop an innovative method based on orthogonal basis to combine pre-trained word embeddings into sentence representations. This approach requires zero training and zero parameters, along with efficient inference performance. We evaluate our approach on 11 downstream NLP tasks. Experimental results show that our model outperforms all existing zero-training alternatives in all the tasks and it is competitive to other approaches relying on either large amounts of labelled data or prolonged training time.


title: Wide and Deep Model of Multi-Source Information-Aware Recommender System

abstract: Collaborative filtering recommendation suffers from the problems of high data sparsity, poor expansibility, cold start, and the difficulty of modeling user preferences, among which data sparsity is the greatest issue. Although our previous work on matrix completion model, named low rank non-negative matrix factorization and completion algorithm (LR-NMFC) and stochastic sub-gradient based low rank matrix completion algorithm, could effectively alleviate the sparsity problem, they customarily model the linear feature interactions instead of the complex nonlinear structures between users and items when making recommendations. To better depict user preferences and item features, we deepen the linear model LR-NMFC to establish a wide and deep model, which we named Wide and Deep model of Multi-source information-Aware recommender system (WDMMA), based on multi-source information composed of user-item interaction matrix, attributes, and context. The wide part mainly handles the linear interactions between users and items, while the deep part portrays the high-order nonlinear interactions. We pre-train both the wide and the deep part using LR-NMFC in the embedding layer. In the pooling layer, we define a pooling operation, AC-pooling, which is used to model the various interactions among users, items, attributes, and context information. Upon the pooling layer, we stack some hidden layers to capture the high-order nonlinear feature interactions. Experiments on two public datasets show that WDMMA can learn complex nonlinear feature patterns successfully and effectively and is beneficial to improve the recommendation performance. Therefore, it is an effective way to consider both linear user-item interactions and multi-source information-aware nonlinear interactions in a deep learning framework when making recommendations.


title: Learning Robust, Transferable Sentence Representations for Text Classification

abstract: Despite deep recurrent neural networks (RNNs) demonstrate strong performance in text classification, training RNN models are often expensive and requires an extensive collection of annotated data which may not be available. To overcome the data limitation issue, existing approaches leverage either pre-trained word embedding or sentence representation to lift the burden of training RNNs from scratch. In this paper, we show that jointly learning sentence representations from multiple text classification tasks and combining them with pre-trained word-level and sentence level encoders result in robust sentence representations that are useful for transfer learning. Extensive experiments and analyses using a wide range of transfer and linguistic tasks endorse the effectiveness of our approach.


A Preliminary Literature Review of Digital Transformation Case Studies


title: How schema and novelty augment memory formation

abstract: Information that is congruent with existing knowledge (a schema) is usually better remembered than less congruent information. Only recently, however, has the role of schemas in memory been studied from a systems neuroscience perspective. Moreover, incongruent (novel) information is also sometimes better remembered. Here, we review lesion and neuroimaging findings in animals and humans that relate to this apparent paradoxical relationship between schema and novelty. In addition, we sketch a framework relating key brain regions in medial temporal lobe (MTL) and medial prefrontal cortex (mPFC) during encoding, consolidation and retrieval of information as a function of its congruency with existing information represented in neocortex. An important aspect of this framework is the efficiency of learning enabled by congruency-dependent MTL–mPFC interactions.


title: "Eigenlips" for robust speech recognition

abstract: We improve the performance of a hybrid connectionist speech recognition system by incorporating visual information about the corresponding lip movements. Specifically, we investigate the benefits of adding visual features in the presence of additive noise and crosstalk (cocktail party effect). Our study extends our previous experiments by using a new visual front end, and an alternative architecture for combining the visual and acoustic information. Furthermore, we have extended our recognizer to a multi-speaker, connected letters recognizer. Our results show a significant improvement for the combined architecture (acoustic and visual information) over just the acoustic system in the presence of additive noise and crosstalk. >


title: Graph-Based Clustering for Computational Linguistics: A Survey

abstract: In this survey we overview graph-based clustering and its applications in computational linguistics. We summarize graph-based clustering as a five-part story: hypothesis, modeling, measure, algorithm and evaluation. We then survey three typical NLP problems in which graph-based clustering approaches have been successfully applied. Finally, we comment on the strengths and weaknesses of graph-based clustering and envision that graph-based clustering is a promising solution for some emerging NLP problems.


title: Gait Simulation via a 6-DOF Parallel Robot With Iterative Learning Control

abstract: We have developed a robotic gait simulator (RGS) by leveraging a 6-degree of freedom parallel robot, with the goal of overcoming three significant challenges of gait simulation, including: 1) operating at near physiologically correct velocities; 2) inputting full scale ground reaction forces; and 3) simulating motion in all three planes (sagittal, coronal and transverse). The robot will eventually be employed with cadaveric specimens, but as a means of exploring the capability of the system, we have first used it with a prosthetic foot. Gait data were recorded from one transtibial amputee using a motion analysis system and force plate. Using the same prosthetic foot as the subject, the RGS accurately reproduced the recorded kinematics and kinetics and the appropriate vertical ground reaction force was realized with a proportional iterative learning controller. After six gait iterations the controller reduced the root mean square (RMS) error between the simulated and in situ vertical ground reaction force to 35 N during a 1.5 s simulation of the stance phase of gait with a prosthetic foot. This paper addresses the design, methodology and validation of the novel RGS.


title: A Taxonomy for Neural Memory Networks

abstract: An increasing number of neural memory networks have been developed, leading to the need for a systematic approach to analyze and compare their underlying memory structures. Thus, in this paper, we first create a framework for memory organization and then compare four popular dynamic models: vanilla recurrent neural network, long short-term memory, neural stack, and neural RAM. This analysis helps to open the dynamic neural networks' black box from the memory usage prospective. Accordingly, a taxonomy for these networks and their variants is proposed and proved using a unifying architecture. With the taxonomy, both network architectures and learning tasks are classified into four classes, and a one-to-one mapping is built between them to help practitioners select the appropriate architecture. To exemplify each task type, four synthetic tasks with different memory requirements are selected. Moreover, we use some signal processing applications and two natural language processing applications to evaluate the methodology in a realistic setting.


title: AdaBoost.RT: a boosting algorithm for regression problems

abstract: A boosting algorithm, AdaBoost.RT, is proposed for regression problems. The idea is to filter out examples with a relative estimation error that is higher than the pre-set threshold value, and then follow the AdaBoost procedure. Thus it requires to select the sub-optimal value of relative error threshold to demarcate predictions from the predictor as correct or incorrect. Some experimental results using the M5 model tree as a weak learning machine for benchmark data sets and for hydrological modeling are reported, and compared to other boosting methods, bagging and artificial neural networks, and to a single M5 model tree. AdaBoost.Rt is proved to perform better on most of the considered data sets.


Intelligent Computing Theory


title: Epoxy putty external skeletal fixation for fractures of the four main metacarpal /metatarsal bones in cats and dogs

abstract: The objective of this retrospective study was to report the clinical use, advantages, disadvantages, complications and outcome of an epoxy putty external skeletal fixator (EPESF) used for the repair of fractures of all four main metatarsal or metacarpal bones in a single limb in 11 dogs and 11 cats. The aim of this technique was to achieve satisfactory alignment of the proximal and distal joints and the digits, rather than anatomical reconstruction of the metacarpal/metatarsal bones themselves. All of the cases achieved fracture union and they all recovered fully without any residual lameness.


Demonstrative Movement in Spanish : a comparative approach


title: Improved Neural Relation Detection for Knowledge Base Question Answering

abstract: Relation detection is a core component for many NLP applications including Knowledge Base Question Answering (KBQA). In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning that detects KB relations given an input question. Our method uses deep residual bidirectional LSTMs to compare questions and relation names via different hierarchies of abstraction. Additionally, we propose a simple KBQA system that integrates entity linking and our proposed relation detector to enable one enhance another. Experimental results evidence that our approach achieves not only outstanding relation detection performance, but more importantly, it helps our KBQA system to achieve state-of-the-art accuracy for both single-relation (SimpleQuestions) and multi-relation (WebQSP) QA benchmarks.


A Flexible Architecture for Ray Tracing Terrain Heightfields


title: Efficient Batched Synchronization in Dropbox-like Cloud Storage Services

abstract: As tools for personal storage, file synchronization and data sharing, cloud storage services such as Dropbox have quickly gained popularity. These services provide users with ubiquitous, reliable data storage that can be automatically synced across multiple devices, and also shared among a group of users. To minimize the network overhead, cloud storage services employ binary diff, data compression, and other mechanisms when transferring updates among users. However, despite these optimizations, we observe that in the presence of frequent, short updates to user data, the network traffic generated by cloud storage services often exhibits pathological inefficiencies. Through comprehensive measurements and detailed analysis, we demonstrate that many cloud storage applications generate session maintenance traffic that far exceeds the useful update traffic. We refer to this behavior as the traffic overuse problem. To address this problem, we propose the update-batched delayed synchronization (UDS) mechanism. Acting as a middleware between the user’s file storage system and a cloud storage application, UDS batches updates from clients to significantly reduce the overhead caused by session maintenance traffic, while preserving the rapid file synchronization that users expect from cloud storage services. Furthermore, we extend UDS with a backwards compatible Linux kernel modification that further improves the performance of cloud storage applications by reducing the CPU usage.


title: Fully Printed Chipless RFID Tags towards Item-Level Tracking Applications

abstract: An ID generating circuit is unquestionably the core of a chipless RFID tag. For convenience of printing process and cost consideration, the circuit should be kept as simple as possible. Based on th ...


title: m-SNE: Multiview Stochastic Neighbor Embedding

abstract: In many real world applications, different features (or multiview data) can be obtained and how to duly utilize them in dimension reduction is a challenge. Simply concatenating them into a long vector is not appropriate because each view has its specific statistical property and physical interpretation. In this paper, we propose a multiview stochastic neighbor embedding (m-SNE) that systematically integrates heterogeneous features into a unified representation for subsequent processing based on a probabilistic framework. Compared with conventional strategies, our approach can automatically learn a combination coefficient for each view adapted to its contribution to the data embedding. Also, our algorithm for learning the combination coefficient converges at a rate of O(1/k2), which is the optimal rate for smooth problems. Experiments on synthetic and real datasets suggest the effectiveness and robustness of m-SNE for data visualization and image retrieval.


title: A cloud-based TaaS infrastructure with tools for SaaS validation, performance and scalability evaluation

abstract: With the fast advancements in cloud computing and software- as-a-service (SaaS), testing and evaluation of cloud-based software and SaaS applications became an important task for engineers. Since most existing tools are not developed to support cloud-based software testing and SaaS evaluation, there is a strong demand for a new cloud-based testing infrastructure and evaluation environment for SaaS applications. This paper proposes a testing-as-service (TaaS) infrastructure and reports a cloud-based TaaS environment with tools (known as CTaaS) developed to meet the needs in SaaS testing, performance and scalability evaluation. The paper presents TaaS concepts and CTaaS, including their infrastructure, design and implementation. In addition, the paper demonstrates the application results of our previously proposed graphic models and metrics for SaaS performance and scalability evaluation. Moreover, the paper reports one case study for a selected SaaS (OrangeHRM) using the developed TaaS environment.


title: Abu-MaTran at WMT 2016 Translation Task: Deep Learning, Morphological Segmentation and Tuning on Character Sequences

abstract: This paper presents the systems submitted by the Abu-MaTran project to the Englishto-Finnish language pair at the WMT 2016 news translation task. We applied morphological segmentation and deep learning in order to address (i) the data scarcity problem caused by the lack of in-domain parallel data in the constrained task and (ii) the complex morphology of Finnish. We submitted a neural machine translation system, a statistical machine translation system reranked with a neural language model and the combination of their outputs tuned on character sequences. The combination and the neural system were ranked first and second respectively according to automatic evaluation metrics and tied for the first place in the human evaluation.


title: A deep learning approach for VM workload prediction in the cloud

abstract: In order to manage the resources in cloud efficiently, ensure the performance of cloud services and reduce the power consumption, it is critical to predict the workload of virtual machines (VM) accurately. In this paper, a new approach for VM workload prediction based on deep learning was proposed. A deep learning prediction model was designed with a deep belief network (DBN) composed of multiple-layered restricted Boltzmann machines (RBMs) and a regression layer. The DBN is used to extract the high level features from all VMs workload data and the regression layer is used to predict the workload of the VMs in the future. With little prior knowledge, DBN could learn the features efficiently for the VM workload prediction in an unsupervised fashion. Experimental results show that the proposed approach improves the workload prediction performance compared with other widely used workload prediction approaches.


title: A Survey of Intelligent Language Tutoring Systems

abstract: Abstract — Intelligent Languages Tutoring Systems (ILTSs) plays a significant role in evaluating students’ answers through interaction with them. ILTSs implements Natural Language processing (NLP) techniques in order to allow free input of words and sentences. ILTSs have the capability of identifying the input errors and provide an immediate feedback along with the errors source. It has been observed that ILTSs were not surveyed intensively; the reason that motivates us to conduct this research. Some NLP recent trends such as Latent Sematic Analysis and entailment were demonstrated. Different ILTSs have been discussed with a dedicated section about the development of Arabic ILTSs. Arabic share many of its characteristics with Semitic and morphologically rich languages. In our presentation we point out new trends that have been emerged while conducting survey. Keywords—Intelligent Language Tutoring systems; ILTS; ITS; Natural Language Processing, Arabic Langauge. I. I NTRODUCTION Nowadays, computers are essential part in our lives. Computer applications have become widely used in different domains to provide useful facilities for their users. Examples of educational applications which find the functionalities of computers fit into their purposes are lesson tutoring, language testing, errors diagnosing and handling, and documents archival. These are good reasons that enable researchers and developers to think about designing an Intelligent Tutoring Systems (ITSs). These systems attempt to mimic the one-to-one human tutor in order to deliver learning material in the cyber space rather than using books and traditional learning environment. Natural Language Processing (NLP), as one of the Artificial Intelligence fields, plays a significant role in how computers could interpret and process human natural languages (text or speech) in order to develop useful learning applications. In this review, we will shed the light on the relevant areas of Intelligent Language Tutoring Systems (ILTSs). ILTSs attempt to develop NLP techniques that focus on processing and assessing the learner’s free text input. The use of ILTSs in education makes it easy to diagnose the leaner’s error and provide convenient feedback along with the source of error (which is main goal of ILTS). We describe different ILTSs in this survey, including auto-tutor ([21], [22], [35]), web-based German Tutor [23], Beetle II [28], and CIRCSIM-Tutor ([29], [30]). It should be noted that Authoring Systems are different from ILITS as they just enable the author to represent his/her knowledge in natural language without resorting to any intelligent programming. As learning Arabic language is a challenge task, we discuss some characteristics and issues related to Arabic NLP which have led to the progress in Arabic ILTSs research. To the best of our knowledge, ILTSs have not yet been surveyed extensively, which has motivated us to conduct this survey. The rest of this paper is structured as follows: Section II provides background about: Intelligent Tutoring Systems, Natural Language Processing with focus on two main issues (


title: Topical analysis of interactions between news and social media

abstract: The analysis of interactions between social media and traditional news streams is becoming increasingly relevant for a variety of applications, including: understanding the underlying factors that drive the evolution of data sources, tracking the triggers behind events, and discovering emerging trends. Researchers have explored such interactions by examining volume changes or information diffusions, however, most of them ignore the semantical and topical relationships between news and social media data. Our work is the first attempt to study how news influences social media, or inversely, based on topical knowledge. We propose a hierarchical Bayesian model that jointly models the news and social media topics and their interactions. We show that our proposed model can capture distinct topics for individual datasets as well as discover the topic influences among multiple datasets. By applying our model to large sets of news and tweets, we demonstrate its significant improvement over baseline methods and explore its power in the discovery of interesting patterns for real world cases.


title: A family of test adequacy criteria for database-driven applications

abstract: Although a software application always executes within a particular environment, current testing methods have largely ignored these environmental factors. Many applications execute in an environment that contains a database. In this paper, we propose a family of test adequacy criteria that can be used to assess the quality of test suites for database-driven applications. Our test adequacy criteria use dataflow information that is associated with the entities in a relational database. Furthermore, we develop a unique representation of a database-driven application that facilitates the enumeration of database interaction associations. These associations can reflect an application's definition and use of database entities at multiple levels of granularity. The usage of a tool to calculate intraprocedural database interaction associations for two case study applications indicates that our adequacy criteria can be computed with an acceptable time and space overhead.


title: Ultra-broadband V-band fractal T-square antenna

abstract: A corner CPW-fed T-square fractal antenna is proposed. This fractal layout provides a wider bandwidth compared to typical square patches. The proposed antenna is designed to operate within the V-band millimeter-wave frequency range and found to cover a 10 dB return loss bandwidth of 19.2 GHz which presents 54.9% of the entire V-band. The ground plane of the antenna is limited to the feeding line, and a monopole-like radiation pattern is achieved. The gain values gradually increase throughout the ultra-broadband operational bandwidth and reaches a maximum value of 5.7 dBi. The efficiency of the antenna is considered high with a maximum value of 87.6%. The layout of the antenna can be scaled in order to fit other frequency bands; such as the ultra-wideband frequency band.


title: Multiple classifier application to credit risk assessment

abstract: Credit risk prediction models seek to predict quality factors such as whether an individual will default (bad applicant) on a loan or not (good applicant). This can be treated as a kind of machine learning (ML) problem. Recently, the use of ML algorithms has proven to be of great practical value in solving a variety of risk problems including credit risk prediction. One of the most active areas of recent research in ML has been the use of ensemble (combining) classifiers. Research indicates that ensemble individual classifiers lead to a significant improvement in classification performance by having them vote for the most popular class. This paper explores the predicted behaviour of five classifiers for different types of noise in terms of credit risk prediction accuracy, and how such accuracy could be improved by using classifier ensembles. Benchmarking results on four credit datasets and comparison with the performance of each individual classifier on predictive accuracy at various attribute noise levels are presented. The experimental evaluation shows that the ensemble of classifiers technique has the potential to improve prediction accuracy.


title: Unfolded recurrent neural networks for speech recognition.

abstract: We introduce recurrent neural networks (RNNs) for acoustic modeling which are unfolded in time for a fixed number of time steps. The proposed models are feedforward networks with the property that the unfolded layers which correspond to the recurrent layer have time-shifted inputs and tied weight matrices. Besides the temporal depth due to unfolding, hierarchical processing depth is added by means of several non-recurrent hidden layers inserted between the unfolded layers and the output layer. The training of these models: (a) has a complexity that is comparable to deep neural networks (DNNs) with the same number of layers; (b) can be done on frame-randomized minibatches; (c) can be implemented efficiently through matrix-matrix operations on GPU architectures which makes it scalable for large tasks. Experimental results on the Switchboard 300 hours English conversational telephony task show a 5% relative improvement in word error rate over state-of-the-art DNNs trained on FMLLR features with i-vector speaker adaptation and hessianfree sequence discriminative training. Index Terms: recurrent neural networks, speech recognition


title: Analysis and Design of a Transformer-Feedback-Based Wideband Receiver

abstract: This paper proposes a multistage transformer-feedback-based design approach for a high fractional-bandwidth (fBW) IF stage in a 60-GHz heterodyne receiver. An in-depth analysis of source-to-gate transformer-feedback amplifiers, including the design of the matching network, is presented. Analytic expressions for the input resistance, quality factor, and noise figure (NF) as a function of the transformer turns-ratio (n ) and magnetic coupling factor (k) are derived. To validate the proposed analysis, a wideband IF amplifier and mixer were designed in a 40-nm CMOS process. From measured results, this device achieves a 16% -3-dB fBW, a peak power gain of 27.6 dB, an NF of 5.3 dB while consuming 28.8 mW from a 0.9-V supply.


title: Challenge: Processing web texts for classifying job offers

abstract: Today the Web represents a rich source of labour market data for both public and private operators, as a growing number of job offers are advertised through Web portals and services. In this paper we apply and compare several techniques, namely explicit-rules, machine learning, and LDA-based algorithms to classify a real dataset of Web job offers collected from 12 heterogeneous sources against a standard classification system of occupations.


title: DeepCham: Collaborative Edge-Mediated Adaptive Deep Learning for Mobile Object Recognition

abstract: Deep learning techniques achieve state-of-the-art performance on many computer vision related tasks, e.g. large-scale object recognition. In this paper we show that recognition accuracy degrades when used in daily mobile scenarios due to context variations caused by different locations, time of a day, etc. To solve this problem, we present DeepCham - the first adaptive mobile object recognition framework that allows deep learning techniques to be used successfully in mobile environments. Specifically, DeepCham is mediated by an edge master server which coordinates with participating mobile users to collaboratively train a domain-aware adaptation model which can yield much better object recognition accuracy when used together with a domain-constrained deep model. DeepCham generates high-quality domain-aware training instances for adaptation from in-situ mobile photos using two major steps: (i) a distributed algorithm which identifies qualifying images stored in each mobile device for training, (ii) a user labeling process for recognizable objects identified from qualifying images using suggestions automatically generated by a generic deep model. Using a newly collected dataset with smartphone images collected from different locations, time of a day, and device types, we show that DeepCham improves the object recognition accuracy by 150% when compared to that achieved merely using a generic deep model. In addition, we investigated how major design factors affect the performance of DeepCham. Finally, we demonstrate the feasibility of DeepCham using an implemented prototype.


title: A Direct Least-Squares (DLS) method for PnP

abstract: In this work, we present a Direct Least-Squares (DLS) method for computing all solutions of the perspective-n-point camera pose determination (PnP) problem in the general case (n ≥ 3). Specifically, based on the camera measurement equations, we formulate a nonlinear least-squares cost function whose optimality conditions constitute a system of three third-order polynomials. Subsequently, we employ the multiplication matrix to determine all the roots of the system analytically, and hence all minima of the LS, without requiring iterations or an initial guess of the parameters. A key advantage of our method is scalability, since the order of the polynomial system that we solve is independent of the number of points. We compare the performance of our algorithm with the leading PnP approaches, both in simulation and experimentally, and demonstrate that DLS consistently achieves accuracy close to the Maximum-Likelihood Estimator (MLE).


title: High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks

abstract: Advances in deep learning for natural images have prompted a surge of interest in applying similar techniques to medical images. The majority of the initial attempts focused on replacing the input of a deep convolutional neural network with a medical image, which does not take into consideration the fundamental differences between these two types of images. Specifically, fine details are necessary for detection in medical images, unlike in natural images where coarse structures matter most. This difference makes it inadequate to use the existing network architectures developed for natural images, because they work on heavily downscaled images to reduce the memory requirements. This hides details necessary to make accurate predictions. Additionally, a single exam in medical imaging often comes with a set of views which must be fused in order to reach a correct conclusion. In our work, we propose to use a multi-view deep convolutional neural network that handles a set of high-resolution medical images. We evaluate it on large-scale mammography-based breast cancer screening (BI-RADS prediction) using 886,000 images. We focus on investigating the impact of the training set size and image size on the prediction accuracy. Our results highlight that performance increases with the size of training set, and that the best performance can only be achieved using the original resolution. In the reader study, performed on a random subset of the test set, we confirmed the efficacy of our model, which achieved performance comparable to a committee of radiologists when presented with the same data.


Resistance to change and the adoption of digital libraries: An integrative model


title: Standardized Method of Measuring Acanthamoeba Antibodies in Sera from Healthy Human Subjects

abstract: Acanthamoeba species can cause serious, debilitating, and sometimes life-threatening infections. Three groups have been identified using morphological and immunological comparisons. Previous serological studies have utilized a variety of antigen preparations and assay methods and reported disparate (3 to 100%) results. This study was designed to (i) optimize an enzyme-linked immunosorbent assay for detecting serum antibodies to each of the Acanthamoeba serogroups and (ii) test 55 healthy individuals for specific immunoglobulin G reactivity. The highest signal-to-background ratio was found when 3,000 fixed, intact trophozoites per well were used with a 1:10 serum dilution. Sera yielding optical densities of <0.25 against all three Acanthamoeba serogroups were used to define the cutoff for positive results. The highest background reactivity with these sera was seen with Acanthamoeba polyphaga (serogroup 2), followed by Acanthamoeba culbertsoni (serogroup 3) and Acanthamoeba astronyxis (serogroup 1). Of 55 subjects tested, the highest number of positive results was seen with A. polyphaga (81.8%), followed by A. astronyxis (52.8%) and A. culbertsoni (40%). Seven serum samples (12.7%) were negative for all three Acanthamoeba serogroups, 16 (29.1%) were positive for one serogroup only, 16 were positive for two serogroups, and 16 reacted to all three serogroups. Further analysis showed no significant associations between serogroup reactivity and age or gender. However, some ethnic differences were noted, especially with A. polyphaga antigens. In that case, serum samples from Hispanic subjects were 14.5 times less likely to be positive (P = 0.0025) and had lower mean absorbance values (P = 0.047) than those from Caucasian subjects. Overall, these data suggest that Acanthamoeba colonization or infection is more common than previously thought. Mild or asymptomatic infections may contribute to the observed serum reactivities.


title: An average model of solid state transformer for dynamic system simulation

abstract: The Solid State Transformer (SST) is one of the key elements proposed in the National Science Foundation (NSF) Generation-III Engineering Research Center (ERC) “Future Renewable Electric Energy Delivery and Management” (FREEDM) Systems Center established in 2008. The SST is used to enable active management of distributed renewable energy resources, energy storage devices and loads. In this paper, a 20kVA solid state transformer based on 6.5kV IGBT is proposed for interface with 12kV distribution system voltage. The average model and control scheme of SST including AC/DC rectifier, Dual Active Bridge (DAB) converter and DC/AC inverter are developed to enable dynamic system level simulation. The developed average model is verified by comparing with the detailed switching model simulation. The dynamic system level SST simulation verifies the proposed controller and the corresponding average model illustrates the functionalities and advantages of the SST in FREEDM system.


title: Deep Neural Networks

abstract: In this chapter, we introduce deep neural networks (DNNs)—multilayer perceptrons with many hidden layers. DNNs play an important role in the modern speech recognition systems, and are the focus of the rest of the book. We depict the architecture of DNNs, describe the popular activation functions and training criteria, illustrate the famous backpropagation algorithm for learning DNN model parameters, and introduce practical tricks that make the training process robust.


title: An augmented reality C-arm for intraoperative assessment of the mechanical axis: a preclinical study

abstract: Background ::: Determination of lower limb alignment is a prerequisite for successful orthopedic surgical treatment. Traditional methods include the electrocautery cord, alignment rod, or axis board which rely solely on C-arm fluoroscopy navigation and are radiation intensive.


title: Tangible user interfaces in order to improve Collaborative Interactions and Decision Making

abstract: The paper introduces a research based on an innovative multi-user tangible interface system that aims at introducing an instrument to improve the response phase of the decision-making process. The purpose is to set up and develop a decision support system to overcome some of the problems that are still causing inefficiency in the decisionmaking process and analyze the reactions of users upon the new introduced technology. The paper consists of two parts The first part of the describes theories upon which the research project is based, explaining why and how a multi-user tangible interface can help collaborative communication during crisis response, the challenge, the chosen solution and the architecture. The second part describes how the presented solution will be tested to prove if the new system will be useful and accepted by users.


title: Improving the Performance of K-Means for Color Quantization

abstract: Color quantization is an important operation with many applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. However, despite its popularity as a general purpose clustering algorithm, k-means has not received much respect in the color quantization literature because of its high computational requirements and sensitivity to initialization. In this paper, we investigate the performance of k-means as a color quantizer. We implement fast and exact variants of k-means with several initialization schemes and then compare the resulting quantizers to some of the most popular quantizers in the literature. Experiments on a diverse set of images demonstrate that an efficient implementation of k-means with an appropriate initialization strategy can in fact serve as a very effective color quantizer.


title: Infant Perception of Surface Texture and Relative Height as Distance Information: A Preferential-Reaching Study.

abstract: Two preferential-reaching experiments explored 5- and 7-month-olds’ sensitivity to pictorial depth cues. In the first experiment, infants viewed a display in which texture gradients, linear perspective of the surface contours, and relative height in the visual field provided information that two objects were at different distances. Five- and 7-month-old infants reached preferentially for the apparently nearer object under monocular but not binocular viewing conditions, indicating that infants in both age groups respond to pictorial depth cues. In the second experiment, texture gradients and linear perspective of the surface contours were eliminated from the experimental display, making relative height the sole pictorial depth cue. Seven-month-olds again reached more often for the apparently nearer object under monocular, but not binocular viewing conditions. By contrast, the 5-month-olds’ reaching behavior did not differ between viewing conditions. These results indicate that 7-month-olds respond to the depth cue of relative height but provide no evidence of responsiveness to relative height in 5-month-olds. Both age groups responded more consistently to pictorial depth in Experiment 1 than in Experiment 2.


title: Sensitivity analysis of a wireless power transfer (WPT) system for electric vehicle application

abstract: This paper presents a detailed parametric sensitivity analysis for a wireless power transfer (WPT) system in an electric vehicle application. Specifically, several key parameters for sensitivity analysis of a series-parallel (SP) WPT system are derived first based on analytical modeling approach, which includes the equivalent input impedance, active / reactive power, and DC voltage gain. Based on the derivation, the impact of primary side compensation capacitance, coupling coefficient, transformer leakage inductance, and different load conditions on the DC voltage gain curve and power curve are studied and analyzed. It is shown that the desired power can be achieved by just changing frequency or voltage depending on the design value of coupling coefficient. However, in some cases both have to be modified in order to achieve the required power transfer at high efficiencies.


title: Chill-Out: Relaxation Training through Respiratory Biofeedback in a Mobile Casual Game

abstract: We present Chill-Out, an adaptive biofeedback game that teaches relaxation skills by monitoring the breathing rate of the player. The game uses a positive feedback loop that penalizes fast breathing by means of a proportional-derivative control law: rapid (and/or increasing) breathing rates increase game difficulty and reduce the final score of the game. We evaluated Chill-Out against a conventional non-biofeedback game and traditional relaxation based on deep breathing. Measurements of breathing rate, electrodermal activity, and heart rate variability show that playing Chill-Out leads to lower arousal during a subsequent task designed to induce stress.


title: SuperQ: Computing Supernetworks from Quartets

abstract: Supertrees are a commonly used tool in phylogenetics to summarize collections of partial phylogenetic trees. As a generalization of supertrees, phylogenetic supernetworks allow, in addition, the visual representation of conflict between the trees that is not possible to observe with a single tree. Here, we introduce SuperQ, a new method for constructing such supernetworks (SuperQ is freely available at >www.uea.ac.uk/computing/superq.). It works by first breaking the input trees into quartet trees, and then stitching these together to form a special kind of phylogenetic network, called a split network. This stitching process is performed using an adaptation of the QNet method for split network reconstruction employing a novel approach to use the branch lengths from the input trees to estimate the branch lengths in the resulting network. Compared with previous supernetwork methods, SuperQ has the advantage of producing a planar network. We compare the performance of SuperQ to the Z-closure and Q-imputation supernetwork methods, and also present an analysis of some published data sets as an illustration of its applicability.


title: A New Bumpless Rotor-Flux Position Estimation Scheme for Vector-Controlled Washing Machine

abstract: For a washing machine, permanent-magnet (PM) motors are commonly used due to their superior efficiency, high torque-to-volume ratio, and so on. PM motor driving system with vector control scheme requires rotor-flux position information. Hall-effect position sensors are often used for that purpose because their volume is small and is cost effective compared with other types of position sensor such as an encoder, even though they have low resolution of position information. Generally, two low-resolution hall-effect sensors are employed to provide rotor flux position for cost reduction purpose and it means four resolutions per one electrical 360° rotation. The detected rotor-flux position by hall sensor signals is manipulated to change linearly by interpolation method. However, because of electrical and mechanical nonuniformity associated with hall sensors, the calculated or estimated position is not linearly changed. In other words, there exist bumps at the point where hall signal occurs even at the constant speed operation and it degrades control performance. To solve this problem, this paper proposes a novel scheme for bumpless position estimation. In addition, the proposed reduced-order observer does not use mechanical parameters such as inertia. The validity and effectiveness of the proposed algorithm are verified by experiments on a top-load washing machine vector-controlled with two Hall-effect sensors.


New Perspectives on the Correlation of Scholastic Assessment Test Scores , High School Grades , and Socioeconomic Factors


SPACE SYNTAX IN ARCHITECTURAL DESIGN 056


title: A sketch-based interface for detail-preserving mesh editing

abstract: In this paper we present a method for the intuitive editing of surface meshes by means of view-dependent sketching. In most existing shape deformation work, editing is carried out by selecting and moving a handle, usually a set of vertices. Our system lets the user easily determine the handle, either by silhouette selection and cropping, or by sketching directly onto the surface. Subsequently, an edit is carried out by sketching a new, view-dependent handle position or by indirectly influencing differential properties along the sketch. Combined, these editing and handle metaphors greatly simplify otherwise complex shape modeling tasks.


title: DeepThin: A Self-Compressing Library for Deep Neural Networks

abstract: As the industry deploys increasingly large and complex neural networks to mobile devices, more pressure is put on the memory and compute resources of those devices. Deep compression, or compression of deep neural network weight matrices, is a technique to stretch resources for such scenarios. Existing compression methods cannot effectively compress models smaller than 1-2% of their original size. We develop a new compression technique, DeepThin, building on existing research in the area of low rank factorization. We identify and break artificial constraints imposed by low rank approximations by combining rank factorization with a reshaping process that adds nonlinearity to the approximation function. We deploy DeepThin as a plug-gable library integrated with TensorFlow that enables users to seamlessly compress models at different granularities. We evaluate DeepThin on two state-of-the-art acoustic models, TFKaldi and DeepSpeech, comparing it to previous compression work (Pruning, HashNet, and Rank Factorization), empirical limit study approaches, and hand-tuned models. For TFKaldi, our DeepThin networks show better word error rates (WER) than competing methods at practically all tested compression rates, achieving an average of 60% relative improvement over rank factorization, 57% over pruning, 23% over hand-tuned same-size networks, and 6% over the computationally expensive HashedNets. For DeepSpeech, DeepThin-compressed networks achieve better test loss than all other compression methods, reaching a 28% better result than rank factorization, 27% better than pruning, 20% better than hand-tuned same-size networks, and 12% better than HashedNets. DeepThin also provide inference performance benefits ranging from 2X to 14X speedups, depending on the compression ratio and platform cache sizes.


title: Automatic Generation of Mobile Malwares Using Genetic Programming

abstract: The number of mobile devices has increased dramatically in the past few years. These smart devices provide many useful functionalities accessible from anywhere at anytime, such as reading and writing e-mails, surfing on the Internet, showing facilities nearby, and the like. Hence, they become an inevitable part of our daily lives. However the popularity and adoption of mobile devices also attract virus writers in order to harm our devices. So, many security companies have already proposed new solutions in order to protect our mobile devices from such malicious attempts. However developing methodologies that detect unknown malwares is a research challenge, especially on devices with limited resources. This study presents a method that evolves automatically variants of malwares from the ones in the wild by using genetic programming (GP). We aim to evaluate the efficacy of current anti-virus products, using static analysis techniques, in the market. The experimental results show the weaknesses of the static analysis tools available in the market, and the need of new detection techniques suitable for mobile devices.


title: "I care about him as a pal": conceptions of robotic pets in online AIBO discussion forums

abstract: In this study, we analyzed people's conceptions of AIBO, a robotic pet, through thier spontaneous postings in online AIBO discussion forums. Results showed that AIBO psychologically engaged this group of participants, particularly by drawing forth conceptions of essences (79%), agency (60%), and social standing (59%). However, participants seldom attributed moral standing to AIBO (e.g., that AIBO deserves respect, has rights, or can be held morally accountable for action). Our discussion focuses on the societal implications of these results.


title: A comparative study of hash based approximate nearest neighbor learning and its application in image retrieval

abstract: Plenty of data are available due to the growth of digital technology that creates a high expectation in retrieving the relevant images, accurately and efficiently for a given query image. For searching the relevant images efficiently for the Large Scale dataset, the searching algorithm should have fast access capability. The existing Exact Nearest Neighbor search performs in linear time and so it takes more time as both the dataset and data dimension increases. As a remedy to provide sub-linear/logarithmic time complexity, usage of Approximate Nearest Neighbor (ANN) algorithms is emerging at a rapid rate. This paper discusses about the importance of ANN and their general classification; the different categories involved in Learning to Hash has been analyzed with their pros and cons; different bit assignment types and methods to minimize the Quantization Errors have been reviewed along with its merits and demerits. Therefore, it serves to increase the efficiency of the Image Retrieval process in Large Scale.


title: A nested-graph model for the representation and manipulation of complex objects

abstract: Three recent trends in database research are object-oriented and deductive databases and graph-based user interfaces. We draw these trends together in a data model we call the Hypernode Model. The single data structure of this model is the hypernode , a graph whose nodes can themselves be graphs. Hypernodes are typed, and types, too, are nested graphs. We give the theoretical foundations of hypernodes and types, and we show that type checking is tractable. We show also how conventional type-forming operators can be simulated by our graph types, including cyclic types. The Hypernode Model comes equipped with a rule-based query language called Hyperlog, which is complete with respect to computation and update. We define the operational semantics of Hyperlog and show that the evaluation can be performed efficiently. We discuss also the use of Hyperlog for supporting database browsing, an essential feature of Hypertext databases. We compare our work with other graph-based data models—unlike previous graph-based models, the Hypernode Model provides inherent support for data abstraction via its nesting of graphs. Finally, we briefly discuss the implementation of a DBMS based on the Hypernode Model.


title: Automated valet parking as part of an integrated travel assistance

abstract: The integration of automated valet parking (automated search of a parking space and execution of the parking maneuver) in a comprehensive travel assistance approach promises great benefits for a traveler. In particular, it aims at increasing comfort, optimizing travel time and improving energy efficiency for changing means of transport (where one of them is a car) within a whole multimodal travel chain. In this paper an automated valet parking system as part of a travel assistant is presented. Besides giving an overview of the overall system, the main components (namely the environment perception and automation modules of the fully automated vehicle, a mobile phone application as human machine interface and a parking space occupancy detection camera as part of the parking area infrastructure) are described. The system was successfully tested on three different parking areas, where one of these areas was located at Braunschweig main station.


title: Physiciansʼ Empathy and Clinical Outcomes for Diabetic Patients

abstract: Purpose To test the hypothesis that physicians’ empathy is associated with positive clinical outcomes for diabetic patients. Method A correlational study design was used in a university-affiliated outpatient setting. Participants were 891 diabetic patients, treated between July 2006 and June 2009, by 29 family physicians. Results of the most recent hemoglobin A1c and LDL-C tests were extracted from the patients’ electronic records. The results of hemoglobin A1c tests were categorized into good control (7.0%) and poor control (9.0%). Similarly, the results of the LDL-C tests were grouped into good control (100) and poor control (130). The physicians, who completed the Jefferson Scale of Empathy in 2009, were grouped into high, moderate, and low empathy scorers. Associations between physicians’ level of empathy scores and patient outcomes were examined. Results Patients of physicians with high empathy scores were significantly more likely to have good control of hemoglobin A1c (56%) than were patients of physicians with low empathy scores (40%, P .001). Similarly, the proportion of patients with good LDL-C control was significantly higher for physicians with high empathy scores (59%) than physicians with low scores (44%, P .001). Logistic regression analyses indicated that physicians’ empathy had a unique contribution to the prediction of optimal clinical outcomes after controlling for physicians’ and patients’ gender and age, and patients’ health insurance. Conclusions The hypothesis of a positive relationship between physicians’ empathy and patients’ clinical outcomes was confirmed, suggesting that physicians’ empathy is an important factor associated with clinical competence and patient outcomes. Empathy, an essential component of the physician–patient relationship, may be linked to positive patient outcomes. Although this notion is consistent with the conceptual view of physician–patient relationships, 1–3 empirical data supporting the association between physicians’ empathy and tangible clinical outcomes are difficult to find. Several studies generally support the notion that the quality of the physician–patient relationship (as a proxy for empathic engagement in patient care) has a positive influence on patient


title: DroNet: Learning to Fly by Driving

abstract: Civilian drones are soon expected to be used in a wide variety of tasks, such as aerial surveillance, delivery, or monitoring of existing architectures. Nevertheless, their deployment in urban environments has so far been limited. Indeed, in unstructured and highly dynamic scenarios, drones face numerous challenges to navigate autonomously in a feasible and safe way. In contrast to traditional “map-localize-plan” methods, this letter explores a data-driven approach to cope with the above challenges. To accomplish this, we propose DroNet: a convolutional neural network that can safely drive a drone through the streets of a city. Designed as a fast eight-layers residual network, DroNet produces two outputs for each single input image: A steering angle to keep the drone navigating while avoiding obstacles, and a collision probability to let the UAV recognize dangerous situations and promptly react to them. The challenge is however to collect enough data in an unstructured outdoor environment such as a city. Clearly, having an expert pilot providing training trajectories is not an option given the large amount of data required and, above all, the risk that it involves for other vehicles or pedestrians moving in the streets. Therefore, we propose to train a UAV from data collected by cars and bicycles, which, already integrated into the urban environment, would not endanger other vehicles and pedestrians. Although trained on city streets from the viewpoint of urban vehicles, the navigation policy learned by DroNet is highly generalizable. Indeed, it allows a UAV to successfully fly at relative high altitudes and even in indoor environments, such as parking lots and corridors. To share our findings with the robotics community, we publicly release all our datasets, code, and trained networks.


title: LL(*): the foundation of the ANTLR parser generator

abstract: Despite the power of Parser Expression Grammars (PEGs) and GLR, parsing is not a solved problem. Adding nondeterminism (parser speculation) to traditional LL and LR parsers can lead to unexpected parse-time behavior and introduces practical issues with error handling, single-step debugging, and side-effecting embedded grammar actions. This paper introduces the LL(*) parsing strategy and an associated grammar analysis algorithm that constructs LL(*) parsing decisions from ANTLR grammars. At parse-time, decisions gracefully throttle up from conventional fixed k>=1 lookahead to arbitrary lookahead and, finally, fail over to backtracking depending on the complexity of the parsing decision and the input symbols. LL(*) parsing strength reaches into the context-sensitive languages, in some cases beyond what GLR and PEGs can express. By statically removing as much speculation as possible, LL(*) provides the expressivity of PEGs while retaining LL's good error handling and unrestricted grammar actions. Widespread use of ANTLR (over 70,000 downloads/year) shows that it is effective for a wide variety of applications.


title: Meniscus pathology, osteoarthritis and the treatment controversy

abstract: The menisci are internal structures that are of central importance for a healthy knee joint; they have a key role in the structural progression of knee osteoarthritis (OA), and the risk of the disease dramatically increases if they are damaged by injury or degenerative processes. Meniscus damage might be considered a signifying feature of incipient OA in middle-aged and elderly people. As approximately every third knee of people in these groups has a damaged meniscus, tears are common incidental findings of knee MRI. However, as most tears do not cause symptoms, careful clinical evaluation is required to determine if a damaged meniscus is likely to directly impact a patient's symptoms. Conservative management of patients with knee pain and a degenerative meniscal tear should be considered as a first-line therapy before surgical treatment is contemplated. Patients with mechanical interference of joint movements, such as painful catching or locking, might need surgical treatment with meniscal repair if possible. In a subset of patients, meniscal resection might relieve pain and other symptoms that potentially originate directly from the torn meniscus. However, the possibility of an increased risk of OA if functional meniscal tissue is removed cannot be overlooked.


title: A Comprehensive Survey on Domain Adaptation for Visual Applications

abstract: The aim of this chapter is to give an overview of domain adaptation and transfer learning with a specific view to visual applications. After a general motivation, we first position domain adaptation in the more general transfer learning problem. Second, we try to address and analyze briefly the state-of-the-art methods for different types of scenarios, first describing the historical shallow methods, addressing both the homogeneous and heterogeneous domain adaptation methods. Third, we discuss the effect of the success of deep convolutional architectures which led to the new type of domain adaptation methods that integrate the adaptation within the deep architecture. Fourth, we review DA methods that go beyond image categorization, such as object detection, image segmentation, video analyses or learning visual attributes. We conclude the chapter with a section where we relate domain adaptation to other machine learning solutions.


title: Perceived Maternal Parenting Self-Efficacy (PMP S-E) tool: development and validation with mothers of hospitalized preterm neonates.

abstract: AIM ::: This paper is a report of a study to develop and test the psychometric properties of the Perceived Maternal Parenting Self-Efficacy tool. ::: ::: ::: BACKGROUND ::: Mothers' perceptions of their ability to parent (maternal parenting self-efficacy) is a critical mechanism guiding their interactions with their preterm newborns. A robust measure is needed which can measure mothers' perceptions of their ability to understand and care for their hospitalized preterm neonates as well as being sensitive to the various levels and tasks in parenting. ::: ::: ::: METHODS ::: Using a mixed sampling methodology (convenience or randomized cluster control trial) 165 relatively healthy and hospitalized mother-preterm infant dyads were recruited in 2003-2005 from two intensive care neonatal units in the United Kingdom (UK). Mothers were recruited within the first 28 days after giving birth to a preterm baby. The Perceived Maternal Parenting Self-Efficacy tool, which is made up of 20 items representing four theorized subscales, was tested for reliability and validity. ::: ::: ::: RESULTS ::: Internal consistency reliability of the Perceived Maternal Parenting Self-Efficacy tool was 0.91, external/test-retest reliability was 0.96, P<0.01. Divergent validity using the Maternal Self-Report Inventory was r(s)=0.4, P<0.05 and using the Maternal Postnatal Attachment Scale was r(s)=0.31, P<0.01. ::: ::: ::: CONCLUSION ::: The Perceived Maternal Parenting Self-Efficacy tool is a psychometrically robust, reliable and valid measure of parenting self-efficacy in mothers of relatively healthy hospitalized preterm neonates. Although application outside the UK will require further cross-cultural validation, the tool has the potential to provide healthcare professionals with a reliable method of identifying mothers of preterm hospitalized babies who are in need of further support.


title: Potentials of Gamification in Learning Management Systems: A Qualitative Evaluation

abstract: Besides game-based learning, gamification is an upcoming trend in education, studied in various empirical studies and found in many major learning management systems. Employing a newly developed qualitative instrument for assessing gamification in a system, we studied five popular LMS for their specific implementations. The instrument enabled experts to extract affordances for gamification in the five categories experiential, mechanics, rewards, goals, and social. Results show large similarities in all of the systems studied and few varieties in approaches to gamification.


title: Phishing Detection using Content Based Associative Classification Data Mining

abstract: Phishing is a technique imitating an official websites of any company such as banks, institutes, etc. The purpose of phishing is that to theft a private and sensitive credentials of users such as password, username, PIN etc. Phishing detection is a technique to deal with this kind of malware activity. It is intended to prevent a phishing using data mining technique. MCAC Algorithm is gives higher efficiency towards to detect phishing activity. In MCAC algorithm does not consider a content based features of websites. It is intended to add content and page style features in that algorithm and change the system for better performance. This paper shows proposed method and flow chart. This paper also shows all the features of website which are considered during experimental analysis.


title: Book chapter in Data Warehousing and Data Mining Techniques for Computer Security, Springer, 2006 Data Mining for Cyber Security

abstract: Summary. This chapter provides an overview of the Minnesota Intrusion Detection System (MINDS), which uses a suite of data mining based algorithms to address difierent aspects of cyber security. The various components of MINDS such as the scan detector, anomaly detector and the proflling module detect difierent types of attacks and intrusions on a computer network. The scan detector aims at detecting scans which are the percusors to any network attack. The anomaly detection algorithm is very efiective in detecting behavioral anomalies in the network tra‐c which typically translate to malicious activities such as denial-of-service (DoS) tra‐c, worms, policy violations and inside abuse. The proflling module helps a network analyst to understand the characteristics of the network tra‐c and detect any deviations from the normal proflle. Our analysis shows that the intrusions detected by MINDS are complementary to those of traditional signature based systems, such as SNORT, which implies that they both can be combined to increase overall attack coverage. MINDS has shown great operational success in detecting network intrusions in two live deployments at the University of Minnesota and as a part of the Interrogator architecture at the US Army Research Labs Center for Intrusion Monitoring and Protection (ARL-CIMP). The conventional approach to securing computer systems against cyber threats is to design mechanisms such as flrewalls, authentication tools, and virtual private networks that create a protective shield. However, these mechanisms almost always have vulnerabilities. They cannot ward ofi attacks that are continually being adapted to exploit system weaknesses, which are often caused by careless design and implementation ∞aws. This has created the need for intrusion detection [6], security technology that complements conventional security approaches by monitoring systems and identifying computer attacks. Traditional intrusion detection methods are based on human experts’ extensive knowledge of attack signatures which are character strings in a messages payload


title: Statistical Relational Artificial Intelligence: Logic, Probability, and Computation

abstract: An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.


title: Fingerprint Liveness Detection using Binarized Statistical Image Features

abstract: Recent experiments, reported in the third edition of Fingerprint Liveness Detection competition (LivDet 2013), have clearly shown that fingerprint liveness detection is a very difficult and challenging task. Although the number of approaches is large, none of them can be claimed as able to detect liveness of fingerprint traits with an acceptable error rate. In our opinion, in order to investigate at which extent this error can be reduced, novel feature sets must be proposed, and, eventually, integrated with existing ones. In this paper, a novel fingerprint liveness descriptor named “BSIF” is described, which, similarly to Local Binary Pattern and Local Phase Quantization-based representations, encodes the local fingerprint texture on a feature vector. Experimental results on LivDet 2011 data sets appear to be encouraging and make this descriptor worth of further investigations.


title: Dynamical pose filtering for mixtures of gaussian processes

abstract: In this paper we propose a novel method for discriminative monocular human pose tracking using a mixture of Gaussian processes and a dynamic programming algorithm for selecting the optimal expert at each frame. The proposed tracking mechanism incorporates a dynamical model into the predictive distribution which is combined with the appearance model in a principled manner. This model is able to give a smoother predicted pose and resolves ambiguities in the image to pose mapping. We introduce a mixture of Gaussian processes model which optimises the size and location of each expert ensuring that each expert models a coherent region of the dataset resulting in an accurate predictive density. We compare our method to other state of the art methods on 2D and 3D monocular pose estimation on ballet and sign language data sets.


title: Teamwork and moral hazard: : Evidence from the emergency department

abstract: I investigate how teamwork may reduce moral hazard by joint monitoring and management. I study two organizational systems differing in the extent to which physicians may mutually manage work: Physicians are assigned patients in a “nurse-managed” system but divide patients between themselves in a “self-managed” system. The self-managed system increases throughput productivity by reducing a “foot-dragging” moral hazard, in which physicians prolong patient stays as expected future work increases. I find evidence that physicians in the same location have better information about each other and that, in the self-managed system, they use this information to assign patients.


Dual Properties for Vibration Analysis Via Screw Theory


title: Relevant and Redundant Feature Analysis with Ensemble Classification

abstract: Feature selection and ensemble classification increase system efficiency and accuracy in machine learning, data mining and biomedical informatics. This research presents an analysis of the effect of removing irrelevant and redundant features with ensemble classifiers using two datasets from UCI machine learning repository. Accuracy and computational time were evaluated by four base classifiers; NaiveBayes, Multilayer Perceptron, Support Vector Machines and Decision Tree. Eliminating irrelevant features improves accuracy and reduces computational time while removing redundant features reduces computational time and reduces accuracy of the ensemble.


title: Partition testing does not inspire confidence (program testing)

abstract: Theoretical models are used to study partition testing in the abstract and to describe the circumstances under which it should perform well at failure detection. Partition testing is shown to be more valuable when the partitions are narrowly based on expected failures and there is a good chance that failures occur. It is concluded that for gaining confidence from successful tests, partition testing as usually practiced has little value. >


title: Bias in analytic research.

abstract: Publisher Summary ::: This chapter focuses on the bias in analytic research. Case-control studies are attractive. They can be executed quickly and at low cost, even when the disorders of interest are rare. The execution of pilot case-control studies is becoming automated; strategies have been devised for the “computer scanning” of large files of hospital admission diagnoses and prior drug exposures, with detailed analyses carried out in the same data set on an ad hoc basis. As evidence of their growing popularity, when one original article was randomly selected from each issue of The New England Journal of Medicine, The Lancet, and the Journal of the American Medical Association for the years 1956, 1966, and 1976, the proportion that reported case-control analytic studies increased fourfold over these two decades; however, the proportion reporting cohort analytic studies fell by half; a general trend toward fewer study subjects but more study authors was also noted.


title: Modality of input and vocabulary acquisition

abstract: The results indicate that groups with captions (VAC and VC) scored higher on written than on aural recognition of word forms, while the reverse applied to the VA group. The VAC group learned more word meanings than the VA group. Results from the questionnaire suggest that learners paid most attention to captions, followed by video and audio, and acquired most words by associating them with visual images. Pedagogical implications of this study are that captioned video tends to aid recognition of written word forms and the learning of word meaning, while non-captioned video tends to improve listening comprehension as it facilitates recognition of aural word forms.


title: Privacy-preserving Collaborative Filtering based on Randomized Perturbation Techniques and Secure Multiparty Computation

abstract: With the evolution of the Internet, collaborative filtering techniques are becoming increasingly popular in E-commerce recommender systems. Such techniques recommend items to users employing similar users' preference data. People use recommender systems to cope with information overload. Although collaborative filtering systems are widely used by E-commerce sites, they fail to protect users' privacy. Since many users might decide to give false information because of privacy concerns, collecting high quality data from users is not an easy task. Collaborative filtering systems using these data might produce inaccurate recommendations. To reserve privacy in collaborative filtering recommender systems, this paper presented a collaborative filtering algorithm based on randomized perturbation techniques and secure multiparty computation. The randomized perturbation techniques are used in the course of user data collection and can generate recommendations with decent accuracy. Employing secure multiparty computation to protect the privacy of collaborative filtering by distributing the user profiles between multiple repositories and exchange only a subset of the profile data, which is useful for the recommendation. Theoretical analysis shows that the algorithm based on randomized perturbation techniques and secure multiparty computation not only protect the users’ privacy, but also can keep the accuracy.


title: Identification Topic Area : Caching infrastructure / meshes Title : Adaptive Web Caching : Towards a New Caching Architecture

abstract: Abstract An adaptive, highly scalable, and robust web caching system is needed to effectively handle the exponential growth and extreme dynamic environment of the World Wide Web. Our work presented last year sketched out the basic design of such a system. This sequel paper reports our progress over the past year. To assist caches making web query forwarding decisions, we sketch out the basic design of a URL routing framework. To assist fast searching within each cache group, we let neighbor caches share content information. Equipped with the URL routing table and neighbor cache contents, a cache in the revised design can now search the local group, and forward all missing queries quickly and efficiently, thus eliminating both the waiting delay and the overhead associated with multicast queries. The paper also presents a proposal for incremental deployment that provides a smooth transition from the currently deployed cache infrastructure to the new design.


Sandbox: An Encapsulated Exploratory Environment


title: RIPless compressed sensing from anisotropic measurements

abstract: Abstract Compressed sensing is the art of reconstructing a sparse vector from its inner products with respect to a small set of randomly chosen measurement vectors. It is usually assumed that the ensemble of measurement vectors is in isotropic position in the sense that the associated covariance matrix is proportional to the identity matrix. In this paper, we establish bounds on the number of required measurements in the anisotropic case, where the ensemble of measurement vectors possesses a non-trivial covariance matrix. Essentially, we find that the required sampling rate grows proportionally to the condition number of the covariance matrix. In contrast to other recent contributions to this problem, our arguments do not rely on any restricted isometry properties (RIP’s), but rather on ideas from convex geometry which have been systematically studied in the theory of low-rank matrix recovery. This allows for a simple argument and slightly improved bounds, but may lead to a worse dependency on noise (which we do not consider in the present paper).


title: Dynamic background subtraction using Local Binary Pattern and Histogram of oriented Gradients

abstract: Moving object detection in the presence of complex dynamic backgrounds such as swaying of trees, spouting of water from fountain, ripples in water, flag fluttering in the wind, camera jitters, noise, etc., is known to be very difficult and challenging task. In addition to this, illumination variation, camouflage and real-time constraint aggravate the problem further. Background subtraction (BS) is a widely used algorithm for moving object detection in the presence of static cameras. Its performance purely depends on the choice of features used for background modeling. In this paper, we have proposed a novel multi-feature and multi-modal based background subtraction using Local Binary Pattern (LBP) and Histogram of oriented Gradients (HOG) for complex dynamic scene. Each pixel is modeled as a set of multi-feature calculated from its neighborhood and multi-modal BS is performed using Gaussian mixture model (GMM). To show its efficacy, the proposed algorithm is compared with some of the state-of-the-art BS techniques. In order to evaluate the algorithm in uncontrolled environments, a collection of publicly available database has been used. Quantitative and qualitative results justify our algorithm for efficient moving object detection in the presence of swaying of trees, camouflage and ripples in the water surface.


title: The challenge of simultaneous object detection and pose estimation: a comparative study

abstract: Detecting objects and estimating their pose remains as one of the major challenges of the computer vision research community. There exists a compromise between localizing the objects and estimating their viewpoints. The detector ideally needs to be view-invariant, while the pose estimation process should be able to generalize towards the category-level. This work is an exploration of using deep learning models for solving both problems simultaneously. For doing so, we propose three novel deep learning architectures, which are able to perform a joint detection and pose estimation, where we gradually decouple the two tasks. We also investigate whether the pose estimation problem should be solved as a classification or regression problem, being this still an open question in the computer vision community. We detail a comparative analysis of all our solutions and the methods that currently define the state of the art for this problem. We use PASCAL3D+ and ObjectNet3D datasets to present the thorough experimental evaluation and main results. With the proposed models we achieve the state-of-the-art performance in both datasets.


Cyber Security and the Internet of Things: Vulnerabilities, Threats, Intruders and Attacks


title: Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset

abstract: Ultrasound imaging has been widely used for tumor detection and diagnosis. In ultrasound based computer-aided diagnosis, feature representation is a crucial step. In recent years, deep learning (DL) has achieved great success in feature representation learning. However, it generally suffers from the small sample size problem. Since the medical datasets usually have small training samples, texture features are still very commonly used for small ultrasound image datasets. Compared with the commonly used DL algorithms, the newly proposed deep polynomial network (DPN) algorithm not only shows superior performance on large scale data, but also has the potential to learn effective feature representation from a relatively small dataset. In this work, a stacked DPN (S-DPN) algorithm is proposed to further improve the representation performance of the original DPN, and S-DPN is then applied to the task of texture feature learning for ultrasound based tumor classification with small dataset. The task tumor classification is performed on two image dataset, namely the breast B-mode ultrasound dataset and prostate ultrasound elastography dataset. In both cases, experimental results show that S-DPN achieves the best performance with classification accuracies of 92.40?1.1% and 90.28?2.78% on breast and prostate ultrasound datasets, respectively. This level of accuracy is significantly superior to all other compared algorithms in this work, including stacked auto-encoder and deep belief network. It suggests that S-DPN can be a strong candidate for the texture feature representation learning on small ultrasound datasets. We employ DPN to learn texture feature representation for small ultrasound dataset.We propose the stacked DPN (S-DPN) algorithm for representation learning.We apply S-DPN to the ultrasound-based tumor classification task.S-DPN can significantly improve representation performance for small ultrasound dataset.


title: Mobile App Usability Index (MAUI) for improving mobile banking adoption

abstract: India has 790+ million active mobile connections and 80.57 million smartphone users. However, as per Reserve Bank of India, the number of transactions performed using smartphone based mobile banking applicationsis less than 12% of the overall banking transactions. One of the major reasons for such low numbers is the usability of the mobile banking app. In this paper, we focus on usability issues related tomobile banking apps and propose a Mobile App Usability Index (MAUI) for enhancing the usability of a mobile banking app. The proposed Index has been validatedwith mobile banking channel managers, chief information security officers, etc.


title: Torque ripple improvement for synchronous reluctance motor using an asymmetric flux barrier arrangement

abstract: An interior permanent-magnet synchronous motor (IPMSM) is a highly efficient motor and operates in a wide speed range; therefore, it is used in many industrial and home appliance applications. However, the torque ripple of synchronous motors such as the IPMSM and synchronous reluctance motor is very large. The variation of magnetic resistance between the flux barriers and teeth causes the torque ripple. In this paper, flux barriers are asymmetrically designed so that the relative positions between the outer edges of the flux barriers and the teeth do not correspond. As a result, torque ripple can be reduced dramatically.


title: Online Learning and Sequential Anomaly Detection in Trajectories.

abstract: Detection of anomalous trajectories is an important problem in the surveillance domain. Various algorithms based on learning of normal trajectory patterns have been proposed for this problem. Yet, these algorithms typically suffer from one or more limitations: They are not designed for sequential analysis of incomplete trajectories or online learning based on an incrementally updated training set. Moreover, they typically involve tuning of many parameters, including ad-hoc anomaly thresholds, and may therefore suffer from overfitting and poorly-calibrated alarm rates. In this article, we propose and investigate the Sequential Hausdorff Nearest-Neighbour Conformal Anomaly Detector (SHNN-CAD) for online learning and sequential anomaly detection in trajectories. This is a parameter-light algorithm that offers a well-founded approach to the calibration of the anomaly threshold. The discords algorithm, originally proposed by Keogh et al, is another parameter-light anomaly detection algorithm that has previously been shown to have good classification performance on a wide range of time-series datasets, including trajectory data. We implement and investigate the performance of SHNN-CAD and the discords algorithm on four different labelled trajectory datasets. The results show that SHNN-CAD achieves competitive classification performance with minimum parameter tuning during unsupervised online learning and sequential anomaly detection in trajectories.


title: Anonymity in Social Media: Effects of Content Controversiality and Social Endorsement on Sharing Behavior

abstract: The amount of information shared via social media is rapidly increasing amid growing concerns over online privacy. This study investigates the effect of controversiality and social endorsement of media content on sharing behavior when choosing between sharing publicly or anonymously. Anonymous sharing is found to be a popular choice (59% of shares), especially for controversial content which is 3.2x more likely to be shard anonymously. Social endorsement was not found to affect sharing behavior, except for sports-related content. Implications for social media interface design are discussed.


title: A Study of Cross-domain Generative Models applied to Cartoon Series

abstract: We investigate Generative Adversarial Networks (GANs) to model one particular kind of image: frames from TV cartoons. Cartoons are particularly interesting because their visual appearance emphasizes the important semantic information about a scene while abstracting out the less important details, but each cartoon series has a distinctive artistic style that performs this abstraction in different ways. We consider a dataset consisting of images from two popular television cartoon series, Family Guy and The Simpsons. We examine the ability of GANs to generate images from each of these two domains, when trained independently as well as on both domains jointly. We find that generative models may be capable of finding semantic-level correspondences between these two image domains despite the unsupervised setting, even when the training data does not give labeled alignments between them.


title: Acoustic echo cancellation and doubletalk detection using estimated loudspeaker impulse responses

abstract: In this paper, we present a new approach to acoustic echo cancellation and doubletalk detection for a teleconferencing system including a loudspeaker for which an estimate of the loudspeaker impulse response is available. The approach is general in the sense that it may be applied to most existing acoustic echo cancellation and doubletalk detection algorithms. We show that the new approach reduces the computational complexity for both the echo cancellation and the doubletalk detection algorithms. Furthermore, the numerical examples show that the new approach also may increase the echo cancellation and doubletalk detection performances.