title: Using Text Comprehension Model for Learning Concepts, Context, and Topic of Web Content

abstract: Concepts in web ontologies help machines to understand data through the meanings they hold. Furthermore, learning contexts and topics of web documents also have helped in better semantic-oriented structuring and retrieval of data on the web. In this short paper we present a novel approach for domain-independent open learning of domain concepts, context and topic of any given web document. Our approach is based on a computational version of the Construction-Integration (CI) model of text comprehension. Our proposed system mimics the way humans learn the meanings of textual units and identify domain concepts, contexts and topics in the form of semantic networks. We apply our system on a number of web documents with a range of topics and domains. The resulting semantic networks provide a quantitative and qualitative insights into the nature of the given web documents.


title: Tolerance design of a 2-DOF overconstrained translational parallel robot

abstract: This paper deals with the tolerance design of a two-degree-of-freedom translational parallel robot module for high-speed pick-and-place operations. The module is an overconstrained parallel mechanism using two sets of parallelograms in each limb. A probabilistic model of the uncompensatable pose error is formulated, together with a compatibility condition to ensure the mobility of the robot. Based upon this model, optimization of the tolerances of the geometric source errors and joint clearances is conducted, subject to a set of appropriate constraints. Simulation and experimental results are given to demonstrate the effectiveness of this approach.


title: Muscle-Based Control for Character Animation

abstract: Muscle-based control is transforming the field of physics-based character animation through the integration of knowledge from neuroscience, biomechanics and robotics, which enhance motion realism. Since any physics-based animation system can be extended to a muscle-actuated system, the possibilities of growth are tremendous. However, modelling muscles and their control remains a difficult challenge. We present an organized review of over a decade of research in muscle-based control for character animation, its fundamental concepts and future directions for development. The core of this review contains a classification of control methods, tables summarizing their key aspects and popular neuromuscular functions used within these controllers, all with the purpose of providing the reader with an overview of the field.


title: A social media platform in higher education

abstract: This paper reports on the successful use of Graasp, a social media platform, by university students for their collaborative work. Graasp features a number of innovations, such as administrator-free creation of collaborative spaces, a context-aware recommendation and privacy management. In the context of a EU-funded project involving large test beds, we have been able to extend this platform with lightweight tools (widgets) aimed for learning and competence development and to validate its usefulness in a collaborative learning context.


Autonomous navigation of planetary rovers: A fuzzy logic approach”, Submitted to Intern


Post-hoc and multiple comparison test – An overview with SAS and R Statistical Package


title: Kqueue: A generic and scalable event notification facility

abstract: Applications running on a UNIX platform need to be notified when some activity occurs on a socket or other descriptor, and this is traditionally done with the select() or poll() system calls. However, it has been shown that the performance of these calls does not scale well with an increasing number of descriptors. These interfaces are also limited in the respect that they are unable to handle other potentially interesting activities that an application might be interested in, these might include signals, file system changes, and AIO completions. This paper presents a generic event delivery mechanism, which allows an application to select from a wide range of event sources, and be notified of activity on these sources in a scalable and efficient manner. The mechanism may be extended to cover future event sources without changing the application interface.


title: Video and accelerometer-based motion analysis for automated surgical skills assessment

abstract: Basic surgical skills of suturing and knot tying are an essential part of medical training. Having an automated system for surgical skills assessment could help save experts time and improve training efficiency. There have been some recent attempts at automated surgical skills assessment using either video analysis or acceleration data. In this paper, we present a novel approach for automated assessment of OSATS-like surgical skills and provide an analysis of different features on multi-modal data (video and accelerometer data). We conduct a large study for basic surgical skill assessment on a dataset that contained video and accelerometer data for suturing and knot-tying tasks. We introduce “entropy-based” features—approximate entropy and cross-approximate entropy, which quantify the amount of predictability and regularity of fluctuations in time series data. The proposed features are compared to existing methods of Sequential Motion Texture, Discrete Cosine Transform and Discrete Fourier Transform, for surgical skills assessment. We report average performance of different features across all applicable OSATS-like criteria for suturing and knot-tying tasks. Our analysis shows that the proposed entropy-based features outperform previous state-of-the-art methods using video data, achieving average classification accuracies of 95.1 and 92.2% for suturing and knot tying, respectively. For accelerometer data, our method performs better for suturing achieving 86.8% average accuracy. We also show that fusion of video and acceleration features can improve overall performance for skill assessment. Automated surgical skills assessment can be achieved with high accuracy using the proposed entropy features. Such a system can significantly improve the efficiency of surgical training in medical schools and teaching hospitals.


title: On the anonymity of two-factor authentication schemes for wireless sensor networks

abstract: Display Omitted We demonstrate privacy breaches into two password authentication schemes for WSNs.Public-key techniques are indispensible to achieve user untraceability.Our principle is applicable to two-factor authentication for universal environments.We discuss the viable solutions to practical realization of user anonymity.Experimental timings of related public-key operations on small devices are reported. Anonymity is among the important properties of two-factor authentication schemes for wireless sensor networks (WSNs) to preserve user privacy. Though impressive efforts have been devoted to designing schemes with user anonymity by only using lightweight symmetric-key primitives such as hash functions and block ciphers, to the best of our knowledge none has succeeded so far. In this work, we take an initial step to shed light on the rationale underlying this prominent issue. Firstly, we scrutinize two previously-thought sound schemes, namely Fan et al.'s scheme and Xue et al.'s scheme, and demonstrate the major challenges in designing a scheme with user anonymity.Secondly, using these two foremost schemes as case studies and on the basis of the work of Halevi-Krawczyk (1999) 44 and Impagliazzo-Rudich (1989) 43, we put forward a general principle: Public-key techniques are intrinsically indispensable to construct a two-factor authentication scheme that can support user anonymity. Furthermore, we discuss the practical solutions to realize user anonymity. Remarkably, our principle can be applied to two-factor schemes for universal environments besides WSNs, such as the Internet, global mobility networks and mobile clouds. We believe that our work contributes to a better understanding of the inherent complexity in achieving user privacy, and will establish a groundwork for developing more secure and efficient privacy-preserving two-factor authentication schemes.


title: Function and regulation in MAPK signaling pathways: Lessons learned from the yeast Saccharomyces cerevisiae

abstract: Signaling pathways that activate different mitogen-activated protein kinases (MAPKs) elicit many of the responses that are evoked in cells by changes in certain environmental conditions and upon exposure to a variety of hormonal and other stimuli. These pathways were first elucidated in the unicellular eukaryote Saccharomyces cerevisiae (budding yeast). Studies of MAPK pathways in this organism continue to be especially informative in revealing the molecular mechanisms by which MAPK cascades operate, propagate signals, modulate cellular processes, and are controlled by regulatory factors both internal to and external to the pathways. Here we highlight recent advances and new insights about MAPK-based signaling that have been made through studies in yeast, which provide lessons directly applicable to, and that enhance our understanding of, MAPK-mediated signaling in mammalian cells.


title: Filter design for 5G BF-OFDM waveform

abstract: A flexible and efficient use of the frequency resource is a key challenge for future wireless technologies. The new requirements are not satisfied by the CP-OFDM which has motivated the research on alternative waveforms. Recently, Block-Filtered OFDM modulation scheme has been proposed. This waveform addresses most of the CP-OFDM's drawbacks at the price of a slight complexity increase of the transmitter. The scheme is based on a filter-bank, a precoding stage that ensures the Near Perfect Reconstruction property and a pre-compensation stage which avoids filtering at the receiver. In this paper, the filter specifications for BF-OFDM are derived and an optimization method based on self-interference rejection is proposed. Optimized filter shape can provide up to 70 dB of Signal-to-Interference Ratio which justifies the Near Perfect Reconstruction property with an enhanced spectral confinement and no channel performance degradation.


title: Survey on Neural Networks Used for Medical Image Processing.

abstract: This paper aims to present a review of neural networks used in medical image processing. We classify neural networks by its processing goals and the nature of medical images. Main contributions, advantages, and drawbacks of the methods are mentioned in the paper. Problematic issues of neural network application for medical image processing and an outlook for the future research are also discussed. By this survey, we try to answer the following two important questions: (1) What are the major applications of neural networks in medical image processing now and in the nearby future? (2) What are the major strengths and weakness of applying neural networks for solving medical image processing tasks? We believe that this would be very helpful researchers who are involved in medical image processing with neural network techniques.


title: De Novo 3q22.3q24 Microdeletion in a Patient With Blepharophimosis–Ptosis–Epicanthus Inversus Syndrome, Dandy-Walker Malformation, and Wisconsin Syndrome

abstract: Interstitial deletions affecting the long arm of chromosome 3 have been associated with a broad phenotype. This has included the features of blepharophimosis-ptosis-epicanthus inversus syndrome, Dandy-Walker malformation, and the rare Wisconsin syndrome. The authors report a young female patient presenting with features consistent with all 3 of these syndromes. This has occurred in the context of a de novo 3q22.3q24 microdeletion including FOXL2, ZIC1, and ZIC4. This patient provides further evidence for the role of ZIC1 and ZIC4 in Dandy-Walker malformation and is the third reported case of Dandy-Walker malformation to have associated corpus callosum thinning. This patient is also only the seventh to be reported with the rare Wisconsin syndrome phenotype.


title: Design of a Traffic Junction Controller Using Classifier Systems and Fuzzy Logic

abstract: Traffic control in large cities is a difficult and non-trivial optimization problem. Most of the automated urban traffic control systems are based on deterministic algorithms and have a multi-level architecture; to achieve global optimality, hierarchical control algorithms are generally employed. However, these algorithms are often slow to react to varying conditions, and it has been recognized that incorporating computational intelligence into the lower levels can remove some burdens of algorithm calculation and decision making from higher levels. An alternative approach is to use a fully distributed architecture in which there is effectively only one (low) level of control. Such systems are aimed at increasing the response time of the controller and, again, these often incorporate computational intelligence techniques. This paper presents preliminary work into designing an intelligent local controller primarily for distributed traffic control systems. The idea is to use a classifier system with a fuzzy rule representation to determine useful junction control rules within the dynamic environment.


title: miRCancer: a microRNA–cancer association database constructed by text mining on literature

abstract: Motivation: Research interests in microRNAs have increased rapidly in the past decade. Many studies have showed that microRNAs have close relationships with various human cancers, and they potentially could be used as cancer indicators in diagnosis or as a suppressor for treatment purposes. There are several databases that contain microRNA–cancer associations predicted by computational methods but few from empirical results. Despite the fact that abundant experiments investigating microRNA expressions in cancer cells have been carried out, the results have remain scattered in the literature. We propose to extract microRNA–cancer associations by text mining and store them in a database called miRCancer. Results: The text mining is based on 75 rules we have constructed, which represent the common sentence structures typically used to state microRNA expressions in cancers. The microRNA–cancer association database, miRCancer, is updated regularly by running the text mining algorithm against PubMed. All miRNA–cancer associations are confirmed manually after automatic extraction. miRCancer currently documents 878 relationships between 236 microRNAs and 79 human cancers through the processing of426 000 published articles. Availability: miRCancer is freely available on the web at http://mircan


title: Thompson Sampling for Contextual Bandits with Linear Payoffs

abstract: Thompson Sampling is one of the oldest heuristics for multi-armed bandit problems. It is a randomized algorithm based on Bayesian ideas, and has recently generated significant interest after several studies demonstrated it to have better empirical performance compared to the state-of-the-art methods. However, many questions regarding its theoretical performance remained open. In this paper, we design and analyze a generalization of Thompson Sampling algorithm for the stochastic contextual multi-armed bandit problem with linear payoff functions, when the contexts are provided by an adaptive adversary. This is among the most important and widely studied version of the contextual bandits problem. We prove a high probability regret bound of O(d2/e√T1+e) in time T for any 0 < e < 1, where d is the dimension of each context vector and e is a parameter used by the algorithm. Our results provide the first theoretical guarantees for the contextual version of Thompson Sampling, and are close to the lower bound of Ω(d√T) for this problem. This essentially solves a COLT open problem of Chapelle and Li [COLT 2012].


title: Building Continuous Arterial Blood Pressure Prediction Models Using Recurrent Networks

abstract: This paper presents a methodology for developing highly-accurate, continuous Arterial Blood Pressure (ABP) models using only Photoplethysmography (PPG). In contrast to prior approaches, we develop a system that exhibits dynamic temporal behavior which leads to increased accuracy in modeling ABP. We validate our approach using data from patients in the intensive care unit (ICU). We show that it is possible to build highly accurate, continuous blood pressure models using only finger pulse oximeters. Our methodology achieves accurate systolic blood pressure estimation with a root mean square error 2.58 ± 1.23 across the patient sample used. Furthermore, the continuous ABP signal is estimated with a root mean square error of 6.042 ± 3.26 and correlation coefficient of 0.95 ± 0.045. Our method enables designing robust Remote Health Monitoring Systems (RMS) for Heart Failure patients without requiring traditional blood pressure monitors.


title: Adapting learned robotics behaviours through policy adjustment

abstract: We present an approach to learning control policies for physical robots that achieves high efficiency by adjusting existing policies that have been learned on similar source systems, such as a similar robot with different physical parameters, or an approximate dynamics model simulator. This can be viewed as calibrating a policy learned on a source system, to match a desired behaviour in similar target systems. Our approach assumes that the trajectories described by the source robot are feasible on the target robot. By making this assumption, we only need to learn a mapping from the source robot state and action spaces to the target robot action space, which we call a policy adjustment model. We demonstrate our approach in simulation in the cart-pole balancing task and a two link double pendulum. We also validate our approach with a physical cart-pole system, where we adjust a learned policy under changes to the weight of the pole.


title: Lifetime Prevalence of Mental Disorders in U.S. Adolescents: Results from the National Comorbidity Survey Replication–Adolescent Supplement (NCS-A)

abstract: Objective To present estimates of the lifetime prevalence of DSM-IV mental disorders with and without severe impairment, their comorbidity across broad classes of disorder, and their sociodemographic correlates. Method The National Comorbidity Survey–Adolescent Supplement NCS-A is a nationally representative face-to-face survey of 10,123 adolescents aged 13 to 18 years in the continental United States. DSM-IV mental disorders were assessed using a modified version of the fully structured World Health Organization Composite International Diagnostic Interview. Results Anxiety disorders were the most common condition (31.9%), followed by behavior disorders (19.1%), mood disorders (14.3%), and substance use disorders (11.4%), with approximately 40% of participants with one class of disorder also meeting criteria for another class of lifetime disorder. The overall prevalence of disorders with severe impairment and/or distress was 22.2% (11.2% with mood disorders, 8.3% with anxiety disorders, and 9.6% behavior disorders). The median age of onset for disorder classes was earliest for anxiety (6 years), followed by 11 years for behavior, 13 years for mood, and 15 years for substance use disorders. Conclusions These findings provide the first prevalence data on a broad range of mental disorders in a nationally representative sample of U.S. adolescents. Approximately one in every four to five youth in the U.S. meets criteria for a mental disorder with severe impairment across their lifetime. The likelihood that common mental disorders in adults first emerge in childhood and adolescence highlights the need for a transition from the common focus on treatment of U.S. youth to that of prevention and early intervention.


title: WIDEBAND DUAL-POLARIZED CROSSED-DIPOLE ANTENNA WITH PARASITICAL CROSSED-STRIP FOR BASE STATION APPLICATIONS

abstract: A wideband dual-polarized crossed-dipole antenna with parasitical crossed-strip for base- station applications is presented. By using a pair of orthogonal crossed-dipoles, two linear polarizations (§45 - ) are obtained. A parasitical crossed-strip is introduced to improve the impendence bandwidth and enhance the isolation (S12) between the two orthogonal polarizations of the upper band. The antenna shows a wideband impedance characteristic about 34.9% for S11 • i10dB (+45 - polarization) and S22 • i10dB (i45 - polarization). High isolation (S12 • i32dB) between the two polarizations in the required band are obtained. The stable peak gain, unidirectional radiation patterns and low cross- polarization over the whole operating band are also achieved. Due to its good performance, simple fabrication technique and low cost, the antenna is very suitable for potential base station applications in mobile communication such as DCS, PCS and UMTS.


title: Selective Term Proximity Scoring Via BP-ANN

abstract: When two terms occur together in a document, the probability of a close relationship between them and the document itself is greater if they are in nearby positions. However, ranking functions including term proximity (TP) require larger indexes than traditional document-level indexing, which slows down query processing. Previous studies also show that this technique is not effective for all types of queries. Here we propose a document ranking model which decides for which queries it would be beneficial to use a proximity-based ranking, based on a collection of features of the query. We use a machine learning approach in determining whether utilizing TP will be beneficial. Experiments show that the proposed model returns improved rankings while also reducing the overhead incurred as a result of using TP statistics.


title: Vision-based bicycle detection and tracking using a deformable part model and an EKF algorithm

abstract: Bicycles that share the road with intelligent vehicles present particular challenges for automated perception systems. Bicycle detection is important because bicycles share the road with vehicles and can move at comparable speeds in urban environments. From a computer vision standpoint, bicycle detection is challenging as bicycle's appearance can change dramatically between viewpoints and a person riding on the bicycle is a non-rigid object. In this paper, we present a vision-based framework to detect and track bicycles that takes into account these issues. A mixture model of multiple viewpoints is defined and trained via a Support Vector Machine (SVM) to detect bicycles under a variety of circumstances. Each component of the model uses a part-based representation and known geometric context is used to improve overall detection efficiency. An extended Kalman filter (EKF) is used to estimate the position and velocity of the bicycle in vehicle coordinates. We demonstrate the effectiveness of this approach through a series of experiments run on video data of moving bicycles captured from a vehicle-mounted camera


title: A Low Cost Wireless Data Acquisition System for a Remote Photovoltaic (PV) Water Pumping System

abstract: This paper presents the design and development of a 16F877 microcontroller-based wireless data acquisition system and a study of the feasibility of different existing methodologies linked to field data acquisition from remote photovoltaic (PV) water pumping systems. Various existing data transmission techniques were studied, especially satellite, radio, Global System for Mobile Communication (GSM) and General Packet Radio Service (GPRS). The system’s hardware and software and an application to test its performance are described. The system will be used for reading, storing and analyzing information from several PV water pumping stations situated in remote areas in the arid region of the south of Tunisia. The remote communications are based on the GSM network and, in particular, on the Short text Message Service (SMS). With this integrated system, we can compile a complete database of the different parameters related to the PV water pumping systems of Tunisia. This data could be made available to interested parties over the Internet.


title: A Proposition on Memes and Meta-Memes in Computing for Higher-Order Learning

abstract: In computational intelligence, the term ‘memetic algorithm’ has come to be associated with the algorithmic pairing of a global search method with a local search method. In a sociological context, a ‘meme’ has been loosely defined as a unit of cultural information, the social analog of genes for individuals. Both of these definitions are inadequate, as ‘memetic algorithm’ is too specific, and ultimately a misnomer, as much as a ‘meme’ is defined too generally to be of scientific use. In this paper, we extend the notion of memes from a computational viewpoint and explore the purpose, definitions, design guidelines and architecture for effective memetic computing. Utilizing two conceptual case studies, we illustrate the power of high-order meme-based learning. With applications ranging from cognitive science to machine learning, memetic computing has the potential to provide much-needed stimulation to the field of computational intelligence by providing a framework for higher order learning.


title: Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization

abstract: Asynchronous parallel implementations of stochastic gradient (SG) have been broadly used in solving deep neural network and received many successes in practice recently. However, existing theories cannot explain their convergence and speedup properties, mainly due to the nonconvexity of most deep learning formulations and the asynchronous parallel mechanism. To fill the gaps in theory and provide theoretical supports, this paper studies two asynchronous parallel implementations of SG: one is on the computer network and the other is on the shared memory system. We establish an ergodic convergence rate $O(1/\sqrt{K})$ for both algorithms and prove that the linear speedup is achievable if the number of workers is bounded by $\sqrt{K}$ ($K$ is the total number of iterations). Our results generalize and improve existing analysis for convex minimization.


title: Empirical Findings in Agile Methods

abstract: In recent years, the use of, interest in, and controversy about Agile methodologies have realized dramatic growth. Anecdotal evidence is rising regarding the effectiveness of agile methodologies in certain environments and for specified projects. However, collection and analysis of empirical evidence of this effectiveness and classification of appropriate environments for Agile projects has not been conducted. Researchers from four institutions organized an eWorkshop to synchronously and virtually discuss and gather experiences and knowledge from eighteen Agile experts spread across the globe. These experts characterized Agile Methods and communicated experiences using these methods on small to very large teams. They discussed the importance of staffing Agile teams with highly skilled developers. They shared common success factors and identified warning signs of problems in Agile projects. These and other findings and heuristics gathered through this valuable exchange can be useful to researchers and to practitioners as they establish an experience base for better decision making.


title: TIMELY: RTT-based Congestion Control for the Datacenter

abstract: Datacenter transports aim to deliver low latency messaging together with high throughput. We show that simple packet delay, measured as round-trip times at hosts, is an effective congestion signal without the need for switch feedback. First, we show that advances in NIC hardware have made RTT measurement possible with microsecond accuracy, and that these RTTs are sufficient to estimate switch queueing. Then we describe how TIMELY can adjust transmission rates using RTT gradients to keep packet latency low while delivering high bandwidth. We implement our design in host software running over NICs with OS-bypass capabilities. We show using experiments with up to hundreds of machines on a Clos network topology that it provides excellent performance: turning on TIMELY for OS-bypass messaging over a fabric with PFC lowers 99 percentile tail latency by 9X while maintaining near line-rate throughput. Our system also outperforms DCTCP running in an optimized kernel, reducing tail latency by $13$X. To the best of our knowledge, TIMELY is the first delay-based congestion control protocol for use in the datacenter, and it achieves its results despite having an order of magnitude fewer RTT signals (due to NIC offload) than earlier delay-based schemes such as Vegas.


title: Molecular signatures database (MSigDB) 3.0

abstract: Motivation: Well-annotated gene sets representing the universe of the biological processes are critical for meaningful and insightful interpretation of large-scale genomic data. The Molecular Signatures Database (MSigDB) is one of the most widely used repositories of such sets. ::: ::: Results: We report the availability of a new version of the database, MSigDB 3.0, with over 6700 gene sets, a complete revision of the collection of canonical pathways and experimental signatures from publications, enhanced annotations and upgrades to the web site. ::: ::: Availability and Implementation: MSigDB is freely available for non-commercial use at http://www.broadinstitute.org/msigdb. ::: ::: Contact: gsea@broadinstitute.org


title: Context aware mobile application architecture (CAMAA) for health care systems

abstract: Context awareness was introduced recently in several fields in quotidian human activities. Among context aware applications, health care systems are the most important ones. Such applications, in order to perceive the context, rely on sensors which may be physical or virtual. However, these applications lack of standardization in handling the context and the perceived sensors data. In this work, we propose a formal context aware application architecture model to deal with the context taking into account the scalability and interoperability as key features towards an abstraction of the context relatively to end user applications. As a proof of concept, we present also a case study and simulation explaining the operational aspect of this architecture in health care systems.


title: Evolutionary Training of Sparse Artificial Neural Networks: A Network Science Perspective

abstract: Through the success of deep learning, Artificial Neural Networks (ANNs) are among the most used artificial intelligence methods nowadays. ANNs have led to major breakthroughs in various domains, such as particle physics, reinforcement learning, speech recognition, computer vision, and so on. Taking inspiration from the network properties of biological neural networks (e.g. sparsity, scale-freeness), we argue that (contrary to general practice) Artificial Neural Networks (ANN), too, should not have fully-connected layers. We show how ANNs perform perfectly well with sparsely-connected layers. Following a Darwinian evolutionary approach, we propose a novel algorithm which evolves an initial random sparse topology (i.e. an Erd\H{o}s-R\'enyi random graph) of two consecutive layers of neurons into a scale-free topology, during the ANN training process. The resulting sparse layers can safely replace the corresponding fully-connected layers. Our method allows to quadratically reduce the number of parameters in the fully conencted layers of ANNs, yielding quadratically faster computational times in both phases (i.e. training and inference), at no decrease in accuracy. We demonstrate our claims on two popular ANN types (restricted Boltzmann machine and multi-layer perceptron), on two types of tasks (supervised and unsupervised learning), and on 14 benchmark datasets. We anticipate that our approach will enable ANNs having billions of neurons and evolved topologies to be capable of handling complex real-world tasks that are intractable using state-of-the-art methods.


title: Collaborative data mining for clinical trial analytics

abstract: This paper proposes a collaborative data mining technique to provide multi-level analysis from clinical trials data. Clinical trials for clinical research and drug development generate large amount of data. Due to dispersed nature of clinical trial data, it remains a challenge to harness this data for analytics. In this paper, we propose a novel method using master data management (MDM) for analyzing clinical trial data, scattered across multiple databases, through collaborative data mining. Our aim is to validate findings by collaboratively utilizing multiple data mining techniques such as classification, clustering, and association rule mining. We complement our results with the help of interactive visualizations. The paper also demonstrates use of data stratification for identifying disparities between various subgroups of clinical trial participants. Overall, our approach aims at extracting useful knowledge from clinical trial data in order to improve design of clinical trials by gaining confidence in the outcomes using multi-level analysis. We provide experimental results in drug abuse clinical trial data.


title: Integrated Wideband Self-Interference Cancellation in the RF Domain for FDD and Full-Duplex Wireless

abstract: A fully integrated technique for wideband cancellation of transmitter (TX) self-interference (SI) in the RF domain is proposed for multiband frequency-division duplexing (FDD) and full-duplex (FD) wireless applications. Integrated wideband SI cancellation (SIC) in the RF domain is accomplished through: 1) a bank of tunable, reconfigurable second-order high-Q RF bandpass filters in the canceller that emulate the antenna interface’s isolation (essentially frequency-domain equalization in the RF domain) and 2) a linear $N$ -path $G_m$ - $C$ filter implementation with embedded variable attenuation and phase shifting. A 0.8–1.4 GHz receiver (RX) with the proposed wideband SIC circuits is implemented in a 65 nm CMOS process. In measurement, $>20\;\text{MHz}\;20\;\text{dB}$ cancellation bandwidth (BW) is achieved across frequency-selective antenna interfaces: 1) a custom-designed LTE-like 0.780/0.895 GHz duplexer with TX/RX isolation peak magnitude of 30 dB, peak group delay of 11 ns, and 7 dB magnitude variation across the TX band for FDD and 2) a 1.4 GHz antenna pair for FD wireless with TX/RX isolation peak magnitude of 32 dB, peak group delay of 9 ns, and 3 dB magnitude variation over 1.36–1.38 GHz. For FDD, SIC enhances the effective out-of-band (OOB) IIP3 and IIP2 to $+\text{25}\text{-}27\;\text{dBm}$ and $+90\;\text{dBm}$ , respectively (enhancements of 8–10 and 29 dB, respectively). For FD, SIC eliminates RX gain compression for as high as $-8\;\text{dBm}$ of peak in-band (IB) SI, and enhances effective IB IIP3 and IIP2 by 22 and 58 dB.


title: Validation of a Psycho-Sensory hAllucinations Scale (PSAS) in schizophrenia and Parkinson's disease

abstract: Abstract Objective If hallucinations are the most common of schizophrenic symptoms, they have been described in other pathologies such as Parkinson's disease (PD) but may differ considerably in their phenomenology. However, no multi-modal clinical scale with a transnosographic approach has been developed today. The purpose of this study was to create and validate a new tool for the hetero-assessment of all sensory modalities of hallucinations schizophrenia (SCZ) and in PD. Method Scale items were generated by literature review and validated by medical board. A study was then made to evaluate psychometric properties of the Psycho-Sensory hAllucinations Scale (PSAS) that include four domains (auditory, visual, olfactory and gustatory, cenesthetic modalities) and one specific item ‘guardian angel’. Results It was then validated in 137 patients: 86 PD (53.5% male; mean age = 53.3 years) and 51 SCZ (64.7% male; mean age = 38.5 years). Factorial analysis of the PSAS found four factors. The PSAS showed good internal consistency [Kuder–Richardson alpha coefficient 0.49 to 0.77] and good test–retest reliability [Agreement % = 0.75 to 0.97] and inter-rater reliability [Agreement % = 0.78 to 1.0]. The convergent validity illustrates the concomitant evaluation of the concept between PSAS and PANSS P3 and UPDRS1 I2. Conclusion The PSAS can be useful to describe the whole hallucination and its evolution during the course of the disease and treatment in schizophrenia and PD. Moreover, it can allow us to undertake a clinic-pathological comparison of hallucination modalities between these two diseases, to enhance our understanding of their precise neurological mechanisms.


Intelligent Mobile Agents for Information Retrieval and Knowledge Discovery from Distributed Data and Knowledge Sources


title: Sequence Prediction with Neural Segmental Models

abstract: Segments that span contiguous parts of inputs, such as phonemes in speech, named entities in sentences, actions in videos, occur frequently in sequence prediction problems. Segmental models, a class of models that explicitly hypothesizes segments, have allowed the exploration of rich segment features for sequence prediction. However, segmental models suffer from slow decoding, hampering the use of computationally expensive features. ::: In this thesis, we introduce discriminative segmental cascades, a multi-pass inference framework that allows us to improve accuracy by adding higher-order features and neural segmental features while maintaining efficiency. We also show that instead of including more features to obtain better accuracy, segmental cascades can be used to speed up training and decoding. ::: Segmental models, similarly to conventional speech recognizers, are typically trained in multiple stages. In the first stage, a frame classifier is trained with manual alignments, and then in the second stage, segmental models are trained with manual alignments and the out- puts of the frame classifier. However, obtaining manual alignments are time-consuming and expensive. We explore end-to-end training for segmental models with various loss functions, and show how end-to-end training with marginal log loss can eliminate the need for detailed manual alignments. ::: We draw the connections between the marginal log loss and a popular end-to-end training approach called connectionist temporal classification. We present a unifying framework for various end-to-end graph search-based models, such as hidden Markov models, connectionist temporal classification, and segmental models. Finally, we discuss possible extensions of segmental models to large-vocabulary sequence prediction tasks.


title: Feature selection for clustering - a filter solution

abstract: Processing applications with a large number of dimensions has been a challenge for the KDD community. Feature selection, an effective dimensionality reduction technique, is an essential pre-processing method to remove noisy features. In the literature only a few methods have been proposed for feature selection for clustering, and almost all these methods are 'wrapper' techniques that require a clustering algorithm to evaluate candidate feature subsets. The wrapper approach is largely unsuitable in real-world applications due to its heavy reliance on clustering algorithms that require parameters such as the number of clusters, and the lack of suitable clustering criteria to evaluate clustering in different subspaces. In this paper we propose a 'filter' method that is independent of any clustering algorithm. The proposed method is based on the observation that data with clusters has a very different point-to-point distance histogram to that of data without clusters. By exploiting this we propose an entropy measure that is low if data has distinct clusters and high if it does not. The entropy measure is suitable for selecting the most important subset of features because it is invariant with the number of dimensions, and is affected only by the quality of clustering. Extensive performance evaluation over synthetic, benchmark, and real datasets shows its effectiveness.


title: Probabilistic diffusion tractography with multiple fibre orientations : What can we gain ?

abstract: We present a direct extension of probabilistic diffusion tractography to the case of multiple fibre orientations. Using automatic relevance determination, we are able to perform online selection of the number of fibre orientations supported by the data at each voxel, simplifying the problem of tracking in a multi-orientation field. We then apply the identical probabilistic algorithm to tractography in the multi- and single-fibre cases in a number of example systems which have previously been tracked successfully or unsuccessfully with single-fibre tractography. We show that multi-fibre tractography offers significant advantages in sensitivity when tracking non-dominant fibre populations, but does not dramatically change tractography results for the dominant pathways.


title: Nonnegative Matrix Factorization for the Identification of EMG Finger Movements: Evaluation Using Matrix Analysis

abstract: Surface electromyography (sEMG) is widely used in evaluating the functional status of the hand to assist in hand gesture recognition, prosthetics and rehabilitation applications. The sEMG is a noninvasive, easy to record signal of superficial muscles from the skin surface. Considering the nonstationary characteristics of sEMG, recent feature selection of hand gesture recognition using sEMG signals necessitate designers to use nonnegative matrix factorization (NMF)-based methods. This method exploits both the additive and sparse nature of signals by extracting accurate and reliable measurements of sEMG features using a minimum number of sensors. The testing has been conducted for simple and complex finger flexions using several experiments with artificial neural network classification scheme. It is shown, both by simulation and experimental studies, that the proposed algorithm is able to classify ten finger flexions (five simple and five complex finger flexions) recorded from two sEMG sensors up to 92% (95% for simple and 87% for complex flexions) accuracy. The recognition performances of simple and complex finger flexions are also validated with NMF permutation matrix analysis.


title: XOR-based frame loss recovery scheme for video streaming

abstract: High coding dependencies among video frames suffer from vulnerability to packet loss, which impacts the playback quality of video streaming. In this paper, according to the characteristics of MPEG4/H.264 encoding methods, we propose a simple and low-complexity XOR-based FEC frame loss recovery scheme. Within an entire Group of Pictures (GOP), the proposed scheme shows the ability to recover simultaneously I-frame loss and one P-frame loss. The high frame loss resilience improves the playback QoS of compressed video streaming. The mathematical analysis reveals that the proposed scheme has 72.7% performance improvement than no frame loss protection in term of full GOP frames successful decoding rate.


title: External Knowledge and Query Strategies in Active Learning: a Study in Clinical Information Extraction

abstract: This paper presents a new active learning query strategy for information extraction, called Domain Knowledge Informativeness (DKI). Active learning is often used to reduce the amount of annotation effort required to obtain training data for machine learning algorithms. A key component of an active learning approach is the query strategy, which is used to iteratively select samples for annotation. Knowledge resources have been used in information extraction as a means to derive additional features for sample representation. DKI is, however, the first query strategy that exploits such resources to inform sample selection. To evaluate the merits of DKI, in particular with respect to the reduction in annotation effort that the new query strategy allows to achieve, we conduct a comprehensive empirical comparison of active learning query strategies for information extraction within the clinical domain. The clinical domain was chosen for this work because of the availability of extensive structured knowledge resources which have often been exploited for feature generation. In addition, the clinical domain offers a compelling use case for active learning because of the necessary high costs and hurdles associated with obtaining annotations in this domain. Our experimental findings demonstrated that (1) amongst existing query strategies, the ones based on the classification model's confidence are a better choice for clinical data as they perform equally well with a much lighter computational load, and (2) significant reductions in annotation effort are achievable by exploiting knowledge resources within active learning query strategies, with up to 14% less tokens and concepts to manually annotate than with state-of-the-art query strategies.


title: The Role of Requirements Engineering Practices in Agile Development: An Empirical Study

abstract: Requirements Engineering (RE) plays a fundamental role in all sorts of software development processes. Recently, agile software development has been growing in popularity. However, in contrast to the extensive research of RE in traditional software development, the role of RE in agile development has not yet been studied in depth. In this paper, we present a survey with three research questions to explore the treatment of RE in the practical agile development by investigating eight agile groups from four software development organizations. To answer the three research questions, we targeted at 108 participants with rich agile experiences and designed a questionnaire to collect their answers. Our survey shows that agile RE practices play a crucial role in agile development and they are an important prerequisite for projects’ success though many agile methods advocate coding without waiting for formal requirements and design specifications.


title: "Help Me Pull That Cursor" A Collaborative Interactive Floor Enhancing Community Interaction

abstract: In this paper we describe the development, experiments and evaluation of the iFloor, an interactive floor prototype installed at the local central municipality library. The primary purpose of the iFloor prototype is to support and stimulate community interaction between collocated people. The context of the library demands that any user can walk up and use the prototype without any devices or prior introduction. To achieve this, the iFloor proposes innovative interaction (modes/paradigms/patterns) for floor surfaces through the means of video tracking. Browsing and selecting content is done in a collaborative process and mobile phones are used for posting messages onto the floor. The iFloor highlights topics on social issues of ubiquitous computing environments in public spaces, and provides an example of how to exploit human spatial movements, positions and arrangements in interaction with computers.


title: Rotor loss calculation and thermal analysis of a dual-stator axial-flux permanent magnet machine with combined rectangle-shaped magnets

abstract: This paper presents thermal analysis of a water-cooled dual-stator axial-flux permanent magnet (AFPM) machine with focus on the sandwiched rotor. The rotor of the machine is featured by combined rectangle-shaped magnets. Firstly, the eddy current losses in the PM are calculated and analyzed. Secondly, lumped-parameter and FE thermal models are established to calculate the temperature distribution of the machine. The results of the two methods show good agreement. Additionally, a detailed thermal analysis of the rotor is carried out. It shows that the segmented PMs can be efficient to reduce the eddy current loss and the non-uniform eddy current loss density of PM has a significant influence on the location of the rotor hot spot.


title: Line segment based map building and localization using 2D laser rangefinder

abstract: We study a new scheme for map building and describe localization techniques for a mobile robot equipped with a 2D laser rangefinder. We propose to use line segments as the basic element for the purpose of localization and to build the map. Line segments do provide considerable geometric information about the scene that can also be used for accurate and fast localization. We introduce a new closed line segment (CLS) map which consists of only line segments and defines a closed and connected region. Virtual line segments are drawn, for the spots that do not adequately describe a line segment on the range data. These are further explored via navigation and we argue that the CLS map provides an efficient mobile robot exploration scheme. All these techniques have been implemented on our Nomad XR4000 mobile robot and results are described in this paper.


title: The hitchhiking effect on the site frequency spectrum of DNA polymorphisms.

abstract: The level of DNA sequence variation is reduced in regions of the Drosophila melanogaster genome where the rate of crossing over per physical distance is also reduced. This observation has been interpreted as support for the simple model of genetic hitchhiking, in which directional selection on rare variants, e.g., newly arising advantageous mutants, sweeps linked neutral alleles to fixation, thus eliminating polymorphisms near the selected site. However, the frequency spectra of segregating sites of several loci from some populations exhibiting reduced levels of nucleotide diversity and reduced numbers of segregating sites did not appear different from what would be expected under a neutral equilibrium model. Specifically, a skew toward an excess of rare sites was not observed in these samples, as measured by Tajima's D. Because this skew was predicted by a simple hitchhiking model, yet it had never been expressed quantitatively and compared directly to DNA polymorphism data, this paper investigates the hitchhiking effect on the site frequency spectrum, as measured by Tajima's D and several other statistics, using a computer simulation model based on the coalescent process and recurrent hitchhiking events. The results presented here demonstrate that under the simple hitchhiking model (1) the expected value of Tajima's D is large and negative (indicating a skew toward rare variants), (2) that Tajima's test has reasonable power to detect a skew in the frequency spectrum for parameters comparable to those from actual data sets, and (3) that the Tajima's Ds observed in several data sets are very unlikely to have been the result of simple hitchhiking. Consequently, the simple hitchhiking model is not a sufficient explanation for the DNA polymorphism at those loci exhibiting a decreased number of segregating sites yet not exhibiting a skew in the frequency spectrum.


title: Demystifying theory and its use in improvement

abstract: The role and value of theory in improvement work in healthcare has been seriously underrecognised. We join others in proposing that more informed use of theory can strengthen improvement programmes and facilitate the evaluation of their effectiveness. Many professionals, including improvement practitioners, are unfortunately mystified-and alienated-by theory, which discourages them from using it in their work. In an effort to demystify theory we make the point in this paper that, far from being discretionary or superfluous, theory ('reason-giving'), both informal and formal, is intimately woven into virtually all human endeavour. We explore the special characteristics of grand, mid-range and programme theory; consider the consequences of misusing theory or failing to use it; review the process of developing and applying programme theory; examine some emerging criteria of 'good' theory; and emphasise the value, as well as the challenge, of combining informal experience-based theory with formal, publicly developed theory. We conclude that although informal theory is always at work in improvement, practitioners are often not aware of it or do not make it explicit. The germane issue for improvement practitioners, therefore, is not whether they use theory but whether they make explicit the particular theory or theories, informal and formal, they actually use.


title: The Pemberton Happiness Index: Validation of the Universal Portuguese version in a large Brazilian sample

abstract: The Pemberton Happiness Index (PHI) is a recently developed integrative measure of well-being that includes components of hedonic, eudaimonic, social, and experienced well-being. The PHI has been validated in several languages, but not in Portuguese. Our aim was to cross-culturally adapt the Universal Portuguese version of the PHI and to assess its psychometric properties in a sample of the Brazilian population using online surveys.An expert committee evaluated 2 versions of the PHI previously translated into Portuguese by the original authors using a standardized form for assessment of semantic/idiomatic, cultural, and conceptual equivalence. A pretesting was conducted employing cognitive debriefing methods. In sequence, the expert committee evaluated all the documents and reached a final Universal Portuguese PHI version. For the evaluation of the psychometric properties, the data were collected using online surveys in a cross-sectional study. The study population included healthcare professionals and users of the social network site Facebook from several Brazilian geographic areas. In addition to the PHI, participants completed the Satisfaction with Life Scale (SWLS), Diener and Emmons' Positive and Negative Experience Scale (PNES), Psychological Well-being Scale (PWS), and the Subjective Happiness Scale (SHS). Internal consistency, convergent validity, known-group validity, and test-retest reliability were evaluated. Satisfaction with the previous day was correlated with the 10 items assessing experienced well-being using the Cramer V test. Additionally, a cut-off value of PHI to identify a "happy individual" was defined using receiver-operating characteristic (ROC) curve methodology.Data from 1035 Brazilian participants were analyzed (health professionals = 180; Facebook users = 855). Regarding reliability results, the internal consistency (Cronbach alpha = 0.890 and 0.914) and test-retest (intraclass correlation coefficient = 0.814) were both considered adequate. Most of the validity hypotheses formulated a priori (convergent and know-group) was further confirmed. The cut-off value of higher than 7 in remembered PHI was identified (AUC = 0.780, sensitivity = 69.2%, specificity = 78.2%) as the best one to identify a happy individual.We concluded that the Universal Portuguese version of the PHI is valid and reliable for use in the Brazilian population using online surveys.


title: The NumPy array: a structure for efficient numerical computation

abstract: In the Python world, NumPy arrays are the standard representation for numerical data and enable efficient implementation of numerical computations in a high-level language. As this effort shows, NumPy performance can be improved through three techniques: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts.


title: SCADA security: a review and enhancement for DNP3 based systems

abstract: Supervisory control and data acquisition (SCADA) systems are large-scale industrial control sys- tems often spread across geographically dispersed locations that let human operators control entire physical systems, from a single control room. Early multi-site SCADA sys- tems used closed networks and propriety industrial com- munication protocols like Modbus, DNP3 etc to reach remote sites. But with time it has become more convenient and more cost-effective to connect them to the Internet. However, internet connections to SCADA systems build in new vulnerabilities, as SCADA systems were not designed with internet security in mind. This can become matter of national security if these systems are power plants, water treatment facilities, or other pieces of critical infrastructure. Compared to IT systems, SCADA systems have a higher requirement concerning reliability, latency and uptime, so it is not always feasible to apply IT security measures deployed in IT systems. This paper provides an overview of security issues and threats in SCADA networks. Next, attention is focused on security assessment of the SCADA. This is followed by an overview of relevant SCADA security solutions. Finally we propose our security solution approach which is embedded in bump-in-the-wire is discussed.


title: Smart cars on smart roads: Problems of control

abstract: Key features of one automated intelligent vehicle/highway system (IVHS) are outlined, it is shown how core driver decisions are improved, a basic IVHS control system architecture is proposed, and a design of some control subsystems is offered. Some experimental work is summarized. A system that promises a threefold increase in capacity is outlined, and a four-layer hierarchical control architecture that decomposes this problem into more manageable units is proposed. >


title: Organisational Security Culture and Information Security Compliance for E-Government Development: The Moderating Effect of Social Pressure.

abstract: Rapid development of e-government has exposed critical public information to the possibility of cybercrime. Information security has become a critical issue that needs to be adequately addressed in e-government development. This paper develops an information security compliance model by drawing insights from organizational and institutional theory literature to examine how organizational security culture influences information security compliance in public organizations for e-government development. It also investigates the role of social pressure in moderating the relationship between information security culture and information security compliance. The study explores three specific dimensions of information security culture: management commitments, accountability and information security awareness. The result of a hierarchical regression analysis indicates that management commitments, accountability, information security awareness, and social pressure have a significant positive impact on information security compliance in public organizations. The moderating role of social pressure, however, is only significant in augmenting the relationship between accountability and information security compliance. This study contributes to the information security compliance research by highlighting the criticality of establishing an information security culture within public organisations to promote information security compliance.


title: Magnesite Enrichment with Pseudomonas oryzihabitans Isolated from Magnesite Ore

abstract: Magnesite is a primary source of magnesium and its compounds. The major problem in its practical use are the impurities such as silicon, iron and calcium carbonate. Some magnesite ores in Turkey cannot be used due to a high amount of CaCO3 (≥3%). In this study, bacterial isolates from magnesite quarries in Mersin were tested by plate assay for their ability to decalcify magnesite. A bacterial strain producing the largest clear zones in the plate assay was identified as Pseudomonas oryzihabitans by 16S rDNA-PCR and applied to magnesite ore. It was found to be effective in decalcifying magnesite ore without significant concurrent dissolution of the magnesium carbonate.


title: Fast beam propagation method for the analysis of second-order nonlinear phenomena

abstract: We adopt an implicit scheme to solve both the dispersive/diffractive and the nonlinear step in the beam propagation method (BPM) code. Unlike the Runge-Kutta implementation used in the nonlinear part of the propagation, the totally implicit scheme is considerably fast. Transparent boundary conditions can be also applied to effectively treat radiation problems. To validate the method, numerical examples are given and results compared with analytical solutions.


title: Exploring Small Extracellular Vesicles for Precision Medicine in Prostate Cancer

abstract: Tumor microenvironment constitutes a complex network in which tumor cells communicate among them and with stromal and immune cells. It has been shown that cancer cells are able to exchange genetic materials through small extracellular vesicles (EVs), a heterogeneous group of vesicles with different size and shape, cargo content, and function. The importance to investigate populations of circulating EVs would be of great importance as prostate cancer (PCa) biomarkers. In several neoplasms as well as in PCa, nanometer-sized EVs of endosomal origin are implicated in supporting tumor growth and metastatic spread by both altering local stroma cells and creating a protumor environment that favors the formation of pre-metastatic niches. Several techniques are applicable for the isolation and analysis of PCa-derived small EVs and are illustrated in this article. Due to the high sensitivity and specificity of these techniques, small EVs have become ideal candidates for early diagnosis. Moreover, we discuss the role of small EVs during PCa carcinogenesis, as well as in modulating the development of drug resistance to hormonal therapy and chemotherapy, thus underlining the potential of EV-tailored strategies in PCa patients.


title: A navigation model for exploring scientific workflow provenance graphs

abstract: Many scientific workflow systems record provenance information in the form of data and process dependencies as part of workflow execution. Users often wish to explore these dependencies to reproduce, validate, and explain workflow results, e.g., by examining the data and processes that were used to produce particular workflow outputs. A natural interface for determining relevant provenance information, which is adopted by many systems, is to display the complete provenance dependency graph. However, for many workflows, provenance graphs can be large, with thousands or more nodes and edges. Displaying an entire provenance graph for such workflows can result in "provenance overload," where the large amount of provenance information available makes it difficult for users to find relevant information and explore data and process dependencies. In this paper, we address the challenges of "provenance overload" through a novel navigation model that provides operations for creating different views of provenance graphs along with approaches for easily navigating between different views. Further, our proposed navigation model provides an integrated approach for exploring, summarizing, and querying portions of provenance graphs. We also discuss different architectures for efficiently navigating large provenance graphs against an underlying provenance database.


title: A fingerprint pattern classification approach based on the coordinate geometry of singularities

abstract: The problem of Automatic Fingerprint Pattern Classification (AFPC) has been studied by many fingerprint biometric practitioners. It is an important concept because, in instances where a relatively large database is being queried for the purposes of fingerprint matching, it serves to reduce the duration of the query. The fingerprint classes discussed in this document are the Central Twins (CT), Tented Arch (TA), Left Loop (LL), Right Loop (RL) and the Plain Arch (PA). The classification rules employed in this problem involve the use of the coordinate geometry of the detected singular points. Using a confusion matrix to evaluate the performance of the fingerprint classifier, a classification accuracy of 83.5% is obtained on the five-class problem. This performance evaluation is done by making use of fingerprint images from one of the databases of the year 2002 version of the Fingerprint Verification Competition (FVC2002).


title: Skyline Community Search in Multi-valued Networks

abstract: Given a scientific collaboration network, how can we find a group of collaborators with high research indicator (e.g., h-index) and diverse research interests? Given a social network, how can we identify the communities that have high influence (e.g., PageRank) and also have similar interests to a specified user? In such settings, the network can be modeled as a multi-valued network where each node has d ($d \ge 1$) numerical attributes (i.e., h-index, diversity, PageRank, similarity score, etc.). In the multi-valued network, we want to find communities that are not dominated by the other communities in terms of d numerical attributes. Most existing community search algorithms either completely ignore the numerical attributes or only consider one numerical attribute of the nodes. To capture d numerical attributes, we propose a novel community model, called skyline community, based on the concepts of k-core and skyline. A skyline community is a maximal connected k-core that cannot be dominated by the other connected k-cores in the d-dimensional attribute space. We develop an elegant space-partition algorithm to efficiently compute the skyline communities. Two striking advantages of our algorithm are that (1) its time complexity relies mainly on the size of the answer s (i.e., the number of skyline communities), thus it is very efficient if s is small; and (2) it can progressively output the skyline communities, which is very useful for applications that only require part of the skyline communities. Extensive experiments on both synthetic and real-world networks demonstrate the efficiency, scalability, and effectiveness of the proposed algorithm.


title: Beacon placement for range-based indoor localization

abstract: In this paper, we address the problem of range-based beacon placement given a floor plan to support indoor localization systems. Existing approaches for trilateration require three or more beacons to determine a unique position solution. We show that with prior knowledge of the map and a model of beacon coverage, it is possible to uniquely localize with only two beacons. This not only reduces installation cost by requiring fewer nodes, but can also improve robustness. One of the main challenges with respect to beacon placement algorithms is defining a metric for estimating performance. We propose augmenting the commonly used Geometric Dilution of Precision (GDOP) metric to account for indoor spaces. We then use this enhanced GDOP metric as part of a toolchain to compare various beacon placement algorithms in terms of coverage and expected accuracy. When applied to a set of real floor plans, our approach is able to reduce the number of beacons between 22% and 60% (33% on an average) as compared to standard trilateration.


title: mmm: an R package for analyzing multivariate longitudinal data with multivariate marginal models.

abstract: Modeling multivariate longitudinal data has many challenges in terms of both statistical and computational aspects. Statistical challenges occur due to complex dependence structures. Computational challenges are due to the complex algorithms, the use of numerical methods, and potential convergence problems. Therefore, there is a lack of software for such data. This paper introduces an R package mmm prepared for marginal modeling of multivariate longitudinal data. Parameter estimations are achieved by generalized estimating equations approach. A real life data set is applied to illustrate the core features of the package, and sample R code snippets are provided. It is shown that the multivariate marginal models considered in this paper and mmm are valid for binary, continuous and count multivariate longitudinal responses.


A Glimpse Far into the Future: Understanding Long-term Crowd Worker Accuracy


title: Deep learning approach for Network Intrusion Detection in Software Defined Networking

abstract: Software Defined Networking (SDN) has recently emerged to become one of the promising solutions for the future Internet. With the logical centralization of controllers and a global network overview, SDN brings us a chance to strengthen our network security. However, SDN also brings us a dangerous increase in potential threats. In this paper, we apply a deep learning approach for flow-based anomaly detection in an SDN environment. We build a Deep Neural Network (DNN) model for an intrusion detection system and train the model with the NSL-KDD Dataset. In this work, we just use six basic features (that can be easily obtained in an SDN environment) taken from the forty-one features of NSL-KDD Dataset. Through experiments, we confirm that the deep learning approach shows strong potential to be used for flow-based anomaly detection in SDN environments.


title: English Teachers ‘On-line’: Developing a New Community of Discourse

abstract: This article presents the case study of an on-line conference for students of English studying on an initial teacher education course. It is argued that electronic conferencing reveals the inadequacy of the concept of the ‘reflective practitioner’ both to describe and analyse the complex process of teacher development. Whilst the primary focus of reflective practice is the individual, this on-line community demonstrates the essentially social character of learning, highlighting the place of joint practice in the process of becoming a teacher.


title: Viscoelastic Properties of an Epoxy Resin during Cure

abstract: The cure dependent relaxation modulus of an epoxy resin was investigated over the entire range of cure extent. Parallel plate rheometry was used to measure the material behavior below the gel point of the epoxy network. Creep testing in three-point bend was used for specimens cured past gelation. All data were converted to the stress relaxation modulus for comparison of the material behavior among the various cure states and between the two experimental techniques. The data were used to develop a practical model for predicting the cure dependence of the relaxation modulus throughout cure under varying processing conditions.


title: Management of diabetic foot by natural honey.

abstract: BACKGROUND ::: Many studies have demonstrated that honey has antibacterial activity in vitro, and a small number of clinical case studies have shown that application of honey to severely infected cutaneous wounds is capable of clearing infection from the wound and improving tissue healing. Research has also indicated that honey may possess anti-inflammatory activity and stimulate immune responses within a wound. The overall effect is to reduce infection and to enhance wound healing in burns, ulcers, and other cutaneous wounds. The objective of the study was to find out the results of topical wound dressings in diabetic wounds with natural honey. ::: ::: ::: METHODS ::: The study was conducted at department of Orthopaedics, Unit-1, Liaquat University of Medical and Health Sciences, Jamshoro from July 2006 to June 2007. Study design was experimental. The inclusion criteria were patients of either gender with any age group having diabetic foot Wagner type I, II, III and II. The exclusion criteria were patients not willing for studies and who needed urgent amputation due to deteriorating illness. Initially all wounds were washed thoroughly and necrotic tissues removed and dressings with honey were applied and continued up to healing of wounds. ::: ::: ::: RESULTS ::: Total number of patients was 12 (14 feet). There were 8 males (66.67%) and 4 females (33.33%), 2 cases (16.67%) were presented with bilateral diabetic feet. The age range was 35 to 65 years (46 +/- 9.07 years). Amputations of big toe in 3 patients (25%), second and third toe ray in 2 patients (16.67%) and of fourth and fifth toes at the level of metatarsophalengeal joints were done in 3 patients (25%). One patient (8.33%) had below knee amputation. ::: ::: ::: CONCLUSION ::: In our study we observed excellent results in treating diabetic wounds with dressings soaked with natural honey. The disability of diabetic foot patients was minimized by decreasing the rate of leg or foot amputations and thus enhancing the quality and productivity of individual life.


title: Measuring computer performance : A practitioner's guide

abstract: 1. Introduction 2. Metrics of performance 3. Average performance and variability 4. Errors in experimental measurements 5. Comparing alternatives 6. Measurement tools and techniques 7. Benchmark programs 8. Linear regression models 9. Design of experiments 10. Simulation and random number generation 11. Queueing analysis Glossary Appendices.


title: A tutorial on human activity recognition using body-worn inertial sensors

abstract: The last 20 years have seen ever-increasing research activity in the field of human activity recognition. With activity recognition having considerably matured, so has the number of challenges in designing, implementing, and evaluating activity recognition systems. This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition. It specifically focuses on activity recognition using on-body inertial sensors. We first discuss the key research challenges that human activity recognition shares with general pattern recognition and identify those challenges that are specific to human activity recognition. We then describe the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems. We detail each component of the framework, provide references to related research, and introduce the best practice methods developed by the activity recognition research community. We conclude with the educational example problem of recognizing different hand gestures from inertial sensors attached to the upper and lower arm. We illustrate how each component of this framework can be implemented for this specific activity recognition problem and demonstrate how different implementations compare and how they impact overall recognition performance.


title: Intelligent phishing url detection using association rule mining

abstract: Phishing is an online criminal act that occurs when a malicious webpage impersonates as legitimate webpage so as to acquire sensitive information from the user. Phishing attack continues to pose a serious risk for web users and annoying threat within the field of electronic commerce. This paper focuses on discerning the significant features that discriminate between legitimate and phishing URLs. These features are then subjected to associative rule mining--apriori and predictive apriori. The rules obtained are interpreted to emphasize the features that are more prevalent in phishing URLs. Analyzing the knowledge accessible on phishing URL and considering confidence as an indicator, the features like transport layer security, unavailability of the top level domain in the URL and keyword within the path portion of the URL were found to be sensible indicators for phishing URL. In addition to this number of slashes in the URL, dot in the host portion of the URL and length of the URL are also the key factors for phishing URL.


title: StackGuard: Automatic Adaptive Detection and Prevention of Buffer-Overflow Attacks

abstract: This paper presents a systematic solution to the persistent problem of buffer overflow attacks. Buffer overflow attacks gained notoriety in 1988 as part of the Morris Worm incident on the Internet. While it is fairly simple to fix individual buffer overflow vulnerabilities, buffer overflow attacks continue to this day. Hundreds of attacks have been discovered, and while most of the obvious vulnerabilities have now been patched, more sophisticated buffer overflow attacks continue to emerge. ::: ::: We describe StackGuard: a simple compiler technique that virtually eliminates buffer overflow vulnerabilities with only modest performance penalties. Privileged programs that are recompiled with the StackGuard compiler extension no longer yield control to the attacker, but rather enter a fail-safe state. These programs require no source code changes at all, and are binary-compatible with existing operating systems and libraries. We describe the compiler technique (a simple patch to gcc), as well as a set of variations on the technique that trade-off between penetration resistance and performance. We present experimental results of both the penetration resistance and the performance impact of this technique.


title: Novel Ultra-Wideband (UWB) Multilayer Slotline Power Divider With Bandpass Response

abstract: A novel ultra-wideband (UWB) multilayer slotline power divider with bandpass filtering response is presented. In the proposed structure, a single isolation resistor is properly placed between the two output ports. Based on the transmission-line equivalent-circuit method, the design equations of the proposed UWB power divider has been derived with the even and odd mode analyses. Experimental results show that excellent impedance matching at all three ports, amplitude and phase balance at the two output ports, isolation between the two output ports, and out-of-band rejection are observed at the UWB band.


title: Energy-efficient and high-throughput FPGA-based accelerator for Convolutional Neural Networks

abstract: Convolutional Neural Networks (CNN) is widely applied in modern machine learning and pattern recognition area. Not only performance, more and more attention is paid on energy efficient and scalable devices like FPGA as a better solution than CPU and GPU. In this paper, we propose methods to optimize CNN by fixed-point quantization, activation function approximation, loops and tasks pipelining and parallelization, memory reorganization, and implement an energy-efficient and high-throughput FPGA-based CNN accelerator for LeNet-5 based on Zynq-7000 platform. The accelerator can run at 166MHz and achieve a low error rate of 0.99%, the same as software implementations, and has 37% higher throughput and 93.7% less energy dissipation than a GPU implementation.


title: Spontaneous speech and opinion detection: mining call-centre transcripts

abstract: Opinion mining on conversational telephone speech tackles two challenges: the robustness of speech transcriptions and the relevance of opinion models. The two challenges are critical in an industrial context such as marketing. The paper addresses jointly these two issues by analyzing the influence of speech transcription errors on the detection of opinions and business concepts. We present both modules: the speech transcription system, which consists in a successful adaptation of a conversational speech transcription system to call-centre data and the information extraction module, which is based on a semantic modeling of business concepts, opinions and sentiments with complex linguistic rules. Three models of opinions are implemented based on the discourse theory, the appraisal theory and the marketers' expertise, respectively. The influence of speech recognition errors on the information extraction module is evaluated by comparing its outputs on manual versus automatic transcripts. The F-scores obtained are 0.79 for business concepts detection, 0.74 for opinion detection and 0.67 for the extraction of relations between opinions and their target. This result and the in-depth analysis of the errors show the feasibility of opinion detection based on complex rules on call-centre transcripts.


title: Stray gassing of transformer insulating oils: impact of materials, oxygen content, additives, incubation time and temperature, and its relationship to oxidation stability

abstract: For many years, dissolved gas analysis (DGA) has been considered worldwide as the most important technique for evaluating the state of health of oil-filled power transformers. Based on regular analysis and trending of the dissolved gases extracted from insulating oil samples, an assessment can be made whether internal transformer abnormalities are present and how critical these abnormalities are [1], [2].


title: Developing Leadership Character in Business Programs

abstract: Our objective is to encourage and enable leadership character development in business education. Building on a model of character strengths and their link to virtues, values, and ethical decision making, we describe an approach to develop leadership character at the individual, group, and organizational levels. We contrast this approach to existing practices that have focused on teaching functional content over character and address how business educators can enable leadership character development through their own behaviors, relationships, and structures. Most important, we provide concrete suggestions on how to integrate a focus on character development into existing business programs, both in terms of individual courses as well as the overall curriculum. We highlight that the development of leadership character must extend beyond student engagement in a course since “it takes a village” to develop character.


title: A Regression-Based Approach for Scaling-Up Personalized Recommender Systems in E-Commerce

abstract: Automated collaborative filtering is one of the key techniques for providing a customization for Ecommerce sites. Various neighbor-based recommendation methods are popular choices for collaborative filtering. However, their latency can be a serious drawback for scaling up to a large number of requests that should be processed in real-time. In this paper we propose an alternative regression-based approach that searches for relationships among items instead of looking for similarities among users. Experiments on a movie database provide evidence that the proposed regression-based approach provides significantly better accuracy and is two orders of magnitude faster than the neighbor-based alternatives. Even faster time response with accuracy similar to neighbor-based recommendations was obtained by adjusting the generic recommendations with only the average preference of an active user.


title: Computation and Memory Efficient Image Segmentation

abstract: In this paper, we address the segmentation problem under limited computation and memory resources. Given a segmentation algorithm, we propose a framework that can reduce its computation time and memory requirement simultaneously , while preserving its accuracy. The proposed framework uses standard pixel-domain downsampling and includes two main steps. Coarse segmentation is first performed on the downsampled image. Refinement is then applied to the coarse segmentation results. We make two novel contributions to enable competitive accuracy using this simple framework. First, we rigorously examine the effect of downsampling on segmentation using a signal processing analysis. The analysis helps to determine the uncertain regions , which are small image regions where pixel labels are uncertain after the coarse segmentation. Second, we propose an efficient minimum spanning tree-based algorithm to propagate the labels into the uncertain regions. We perform extensive experiments using several standard data sets. The experimental results show that our segmentation accuracy is comparable to state-of-the-art methods, while requiring much less computation time and memory than those methods.


title: Lingual nerve paresthesia following third molar surgery: A retrospective clinical study

abstract: Lingual nerve anesthesia, paresthesia, and dysesthesia are possible side effects of third molar extraction. These unwanted complications are frequently disturbing to both the patient and practitioner. The incidence of lingual nerve damage following third molar surgery is more frequent than once thought. Six hundred questionnaires were sent to randomly selected Fellows of the American Association of Oral and Maxillofacial Surgeons in 50 states to determine the parameters surrounding this phenomenon. Of the 452 respondents, 76.05% reported having had patients with lingual anesthesia, dysesthesia, or paresthesia. Of all the reported cases, 18.64% of the cases failed to resolve. Of the reported cases, only three underwent surgical intervention. Because many cases of lingual nerve dysfunction do not resolve, it is important to inform patients that microsurgical nerve repair techniques are available as a modality of treatment following diagnosis. It has also been recommended that if the paresthesia does not resolve within 10 to 12 weeks, then management options including microsurgical nerve reconstruction within a short period of time should be discussed as a plan with the patient.


title: In-Vehicle Software Defined Networking: An Enabler for Data Interoperability

abstract: As future transportation systems evolve, new in-vehicle network designs are required to handle the heterogeneous data generated by different Electronic Control Modules (ECUs). Enabling interaction between these data sources can trigger innovation and the emergence of new smart features significantly impacting upon security and riders experience. The interoperability between the ECUs is of high value in the context of autonomous transportation systems. Indeed, it enables different technologies to collaborate for achieving complex tasks. Without this interoperability, features like radar system connected to the Media Oriented Systems Transport bus (MOST) cannot trigger the electronic stability control connected to the Controller Area Network (CAN). These features allow the car to mitigate a high-risk situation using existing modules. In this work, we propose a Software Defined Network (SDN) approach that enables in-vehicle data sources interoperability that allows ECUs to share a medium. The benefits of the proposed approach are backed by the implementation of a relevant use case and the generation of simulation results.


title: Two simple sufficient conditions for FDR control

abstract: We show that the control of the false discovery rate (FDR) for a multiple testing procedure is implied by two coupled simple sufficient conditions. The first one, which we call ``self-consistency condition'', concerns the algorithm itself, and the second, called ``dependency control condition'' is related to the dependency assumptions on the $p$-value family. Many standard multiple testing procedures are self-consistent (e.g. step-up, step-down or step-up-down procedures), and we prove that the dependency control condition can be fulfilled when choosing correspondingly appropriate rejection functions, in three classical types of dependency: independence, positive dependency (PRDS) and unspecified dependency. As a consequence, we recover earlier results through simple and unifying proofs while extending their scope to several regards: weighted FDR, $p$-value reweighting, new family of step-up procedures under unspecified $p$-value dependency and adaptive step-up procedures. We give additional examples of other possible applications. This framework also allows for defining and studying FDR control for multiple testing procedures over a continuous, uncountable space of hypotheses.


title: Recent advances in image processing techniques for automated harvesting purposes: A review

abstract: Image processing has been proved to be an effective tool for analysis in various human activity areas, namely, agricultural applications. Interpreting a digital color image of fruit orchard captured in field environment is extremely challenging due to adverse weather conditions, luminance variability and the presence of dust, insects and other unavoidable image noises. The purpose of this survey is to categorize and briefly review the literature on computer analysis of fruit images in agricultural operations, which comprises more than 60 papers published in the last 10 years. With the aim to perform applied research in agricultural imaging, this paper intends to focus on advanced image processing and analysis techniques used in applications for detection and classifications of fruits, developed in the last decade. For the reviewed techniques, some performance evaluation metrics achieved in various experiments are emphasized to help the researchers when making choices and develop new computer vision applications in fruit images.


title: Acquisition-probability model of noncooperative maneuvering target detection in space

abstract: In order to deal with the acquisition-probability of pointing,acquisition,tracking(PAT) system on satellite-based photoelectric detection platform,a theoretical model of the acquisition was founded,the acquisition-probabilities of photoelectric detection system on satellite-based platform in uncertain region and difficult maneuvering conditions were analyzed.At first,the time-related movement model of space maneuvering target was proposed.Then,the spiral scan method was used to establish the model of acquisition-probability of target in space.At last,the Monte-Carlo simulation method was used to analyze the acquiring procedure,with resolution angle range from 0.6 mrad to 0.8 mrad,the scan angle range from 0.42 mrad to 0.702 mrad.The result shows that through 5 000 times,the theoretical model proposed in this paper is coincide with target acquisition of the actual situation in space.It provides theorical foundation for the design of photoelectric detection system on satellite-based platform.


title: Design and Implementation of PUF-Based "Unclonable" RFID ICs for Anti-Counterfeiting and Security Applications

abstract: Physical unclonable functions (PUFs) exploit the physical characteristics of the silicon and the IC manufacturing process variations to uniquely characterize each and every silicon chip. Since it is practically impossible to model, copy, or control the IC manufacturing process variations, PUFs not only make these chips unique, but also effectively unclonable. Exploiting the inherent variations in the IC manufacturing process, PUFs provide a secure, robust, low cost mechanism to authenticate silicon chips. This makes PUFs attractive for RFID ICs where cost and security are the key requirements. In this paper we present the design and implementation of PUF enabled "unclonable" RFIDs. The PUF-enabled RFID has been fabricated in 0.18 mu technology, and extensive testing results demonstrate that PUFs can securely authenticate an RFID with minimal overheads. We also highlight the advantages of PUF based RFIDs in anti-counterfeiting and security applications.


title: Interactive exploration over RDF data using formal concept analysis

abstract: With an increased interest in machine processable data, many datasets are now published in RDF (Resource Description Framework) format in Linked Data Cloud. These data are distributed over independent resources which need to be centralized and explored for domain specific applications. This paper proposes a new approach based on interactive data exploration paradigm using Pattern Structures, an extension of Formal Concept Analysis, to provide exploration and navigation over Linked Data through concept lattices. It takes RDF triples and RDF Schema based on user requirements and provides one navigation space resulting from several RDF resources. This navigation space allows user to navigate and search only the part of data that is interesting for her.


title: Redesigning the jMetal Multi-Objective Optimization Framework

abstract: jMetal, an open source, Java-based framework for multi-objective optimization with metaheuristics, has become a valuable tool for many researches in the area as well as for some industrial partners in the last ten years. Our experience using and maintaining it during that time, as well as the received comments and suggestions, have helped us improve the jMetal design and identify significant features to incorporate. This paper revisits the jMetal architecture, describing its refined new design, which relies on design patterns, principles from object-oriented design, and a better use of the Java language features to improve the quality of the code, without disregarding jMetal ever goals of simplicity, facility of use, flexibility, extensibility and portability. Among the newly incorporated features, jMetal supports live interaction with running algorithms and parallel execution of algorithms.


title: Smurf-based Distributed Denial of Service (DDoS) Attack Amplification in Internet

abstract: The Smurf-based distributed denial of service (DDoS) attack is an amplification attack where the attacker uses unprotected intermediate networks to amplify the attack traffic load and direct it to the victim computer. In this paper, we investigate the factors that contribute to the amplification of the smurf attack traffic and understand the relation among the original attack traffic, intermediate unprotected network and the final amplified attack traffic. We also define a new term called attack amplification factor which represents the degree of amplification that original attack traffic undergoes during its transmission towards the victim computer. It is also shown in this paper that utilizing this attack, it is possible for an attacker to just use a dialup modem and an unprotected intermediary network to exhaust even an ultra high speed optical line such as OC-192 of the victim network.


title: Building automation interoperability - A review

abstract: The development of building automation systems (BAS) is strategic to answer comfort, security and energy saving requirements. In large buildings energy can represent a significant percentage of the energy year's bill. An integrated BAS is expected to contribute for a significant decreased of costs, development, assembling and management cost, and it might contribute to a reduction of CO2. The goal is to develop an integration strategy aiming to establish an integrated control of the building sub-systems (e.g. heating, ventilation and air-conditioning (HVAC), lightning, security, etc.). To accomplish this integrated control there is a need to establish a communication strategy among the involved sub-systems. One of the challenges to develop such a BAS is on how to establish interoperability between sub-systems since they are from multi-manufactures and developed on heterogeneous technologies. A solution is to build a system that implements one of the emergent open protocols such as BACnet, LonWorks or KNX. They are open, interoperable, multi-vendor and provide an end-to-end solution. The paper presents a comparison between the characteristics of the mentioned protocols and the best one to implement an open building automation system is chosen.


title: A 2.2-MHz Active-Clamp Buck Converter for Automotive Applications

abstract: Automotive point-of-load dc–dc converters have a wide input voltage range from 4.5 to 42 V and, in order to avoid the AM band interference and comply with the strict CISPR-25 standard for electromagnetic interference (EMI) performance, the switching frequency is desired to be higher than 2 MHz. Buck converter is currently employed for such applications owing to its simplicity and low cost, but it has low efficiency at high switching frequencies and high EMI noise emission due to hard switching. A new active-clamp buck converter is proposed in this paper, which features zero-voltage-switching for main and auxiliary switches, and soft turnoff for synchronous rectifier over a wide input voltage range. The detailed operating principle analysis and several important design considerations are presented. Extensive design equations that can be adapted for other specifications and applications are also derived. A 2.2 MHz, 5V/5A prototype converter with only Silicon mosfets has been designed, built, and tested. The experimental results verify the converter operation and demonstrate the superior performance of the proposed active-clamp buck converter.


title: Fueling Performance: Ketones Enter the Mix

abstract: Ketone body metabolites serve as alternative energy substrates during prolonged fasting, calorie restriction, or reduced carbohydrate (CHO) availability. Using a ketone ester supplement, Cox et al. (2016) demonstrate that acute nutritional ketosis alters substrate utilization patterns during exercise, reduces lactate production, and improves time-trial performance in elite cyclists.


title: Essential oils: their antibacterial properties and potential applications in foods—a review

abstract: Abstract In vitro studies have demonstrated antibacterial activity of essential oils (EOs) against Listeria monocytogenes, Salmonella typhimurium, Escherichia coli O157:H7, Shigella dysenteria, Bacillus cereus and Staphylococcus aureus at levels between 0.2 and 10 μl ml−1. Gram-negative organisms are slightly less susceptible than gram-positive bacteria. A number of EO components has been identified as effective antibacterials, e.g. carvacrol, thymol, eugenol, perillaldehyde, cinnamaldehyde and cinnamic acid, having minimum inhibitory concentrations (MICs) of 0.05–5 μl ml−1 in vitro. A higher concentration is needed to achieve the same effect in foods. Studies with fresh meat, meat products, fish, milk, dairy products, vegetables, fruit and cooked rice have shown that the concentration needed to achieve a significant antibacterial effect is around 0.5–20 μl g−1 in foods and about 0.1–10 μl ml−1 in solutions for washing fruit and vegetables. EOs comprise a large number of components and it is likely that their mode of action involves several targets in the bacterial cell. The hydrophobicity of EOs enables them to partition in the lipids of the cell membrane and mitochondria, rendering them permeable and leading to leakage of cell contents. Physical conditions that improve the action of EOs are low pH, low temperature and low oxygen levels. Synergism has been observed between carvacrol and its precursor p-cymene and between cinnamaldehyde and eugenol. Synergy between EO components and mild preservation methods has also been observed. Some EO components are legally registered flavourings in the EU and the USA. Undesirable organoleptic effects can be limited by careful selection of EOs according to the type of food.


title: A Deep Learning Architecture for Limited-Angle Computed Tomography Reconstruction

abstract: Limited-angle computed tomography suffers from missing data in the projection domain, which results in intensity inhomogeneities and streaking artifacts in the image domain. We address both challenges by a two-step deep learning architecture: First, we learn compensation weights that account for the missing data in the projection domain and correct for intensity changes. Second, we formulate an image restoration problem as a variational network to eliminate coherent streaking artifacts. We perform our experiments on realistic data and we achieve superior results for destreaking compared to state-of-the-art non-linear filtering methods in literature. We show that our approach eliminates the need for manual tuning and enables joint optimization of both correction schemes.