title: Learn to Detect Objects Incrementally

abstract: Intelligent vehicles need to detect new classes of traffic objects while keeping the performance of old ones. Deep convolution neural network (DCNN) based detector has shown superior performance, however, DCNN is ill-equipped for incremental learning, i.e., a DCNN based vehicle detector trained on traffic sign dataset will catastrophic forget how to detect vehicles. In this paper, we propose a novel method to alleviate this problem, our key insight is that the original class of objects also appears in new task data, by utilizing these objects, our method effectively keeps the detection accuracy of original models while incremental learning to detect new classes of objects. Detailed experiments on PASCAL VOC dataset and TSD-max database verified the effectiveness of our method.


title: Multi- and Cross-Modal Semantics Beyond Vision: Grounding in Auditory Perception

abstract: Multi-modal semantics has relied on feature norms or raw image data for perceptual input. In this paper we examine grounding semantic representations in raw auditory data, using standard evaluations for multi-modal semantics, including measuring conceptual similarity and relatedness. We also evaluate cross-modal mappings, through a zero-shot learning task mapping between linguistic and auditory modalities. In addition, we evaluate multimodal representations on an unsupervised musical instrument clustering task. To our knowledge, this is the first work to combine linguistic and auditory information into multi-modal representations.


title: Provably Secure Active IC Metering Techniques for Piracy Avoidance and Digital Rights Management

abstract: In the horizontal semiconductor business model where the designer's intellectual property (IP) is transparent to foundry and to other entities on the production chain, integrated circuits (ICs) overbuilding and IP piracy are prevalent problems. Active metering is a suite of methods enabling the designers to control their chips postfabrication. We provide a comprehensive description of the first known active hardware metering method and introduce new formal security proofs. The active metering method uniquely and automatically locks each IC upon manufacturing, such that the IP rights owner is the only entity that can provide the specific key to unlock or otherwise control each chip. The IC control mechanism exploits: 1) the functional description of the design, and 2) unique and unclonable IC identifiers. The locks are embedded by modifying the structure of the hardware computation model, in the form of a finite state machine (FSM). We show that for each IC hiding the locking states within the modified FSM structure can be constructed as an instance of a general output multipoint function that can be provably efficiently obfuscated. The hidden locks within the FSM may also be used for remote enabling and disabling of chips by the IP rights owner during the IC's normal operation. An automatic synthesis method for low overhead hardware implementation is devised. Attacks and countermeasures are addressed. Experimental evaluations demonstrate the low overhead of the method. Proof-of-concept implementation on the H.264 MPEG decoder automatically synthesized on a Xilinix Virtex-5 field-programmable gate array (FPGA) further shows the practicality, security, and the low overhead of the new method.


title: A New Design of Dual Band Fractal Antenna for LEO Applications

abstract: In this paper, a new design of dual band printed antenna based on Minkowski fractal geometry has been presented. The antenna offers a very light weight, low profile, and very low coast, which satisfy the requirement o f Low Earth Orbit (LEO) applications. Some techniques have been used to qualify the printed antenna for space appli cations. Simulation results show the advantage of Minkowski fractal geometry in terms of multiband and bandwidth enhancement. The proposed antenna operates in S and L, Ultra High Frequency (UHF) band efficiently, and has a small size, so it is useful for small satellite communication applicatio ns.


title: Enhanced quantum efficiency in polymer/layered silicate nanocomposite light-emitting devices

abstract: Light-emitting devices based on environmentally stable polymer/layered silicate nanocomposite were fabricated by blending poly[2-methoxy-5-(2'-ethyl-hexyloxy)-1,4-phenylene vinylene] (MEH-PPV) with organo-clay. The ratio of MEH-PPV/organo-clay was regulated from 5/1 (w/w) to 1/1 (w/w). The light-emitting device of 1/1 (w/w) blend gives us the highest quantum efficiency, 0.38 % ph/el, which is enhanced 100 times compared with the pure MEH-PPV layer device. This 2-dimensional lamellar-type nanocomposite device forms a similar electronic structure to that of commonly known quantum well devices. Thus, this method can promote to confine both the electrons and holes to enhance the recombination rate.


title: Extending the definition of modularity to directed graphs with overlapping communities

abstract: Complex network topologies present interesting and surprising properties, such as community structures, which can be exploited to optimize communication, to find new efficient and context-aware routing algorithms or simply to understand the dynamics and meaning of relationships among nodes. Complex networks are gaining more and more importance as a reference model and are a powerful interpretation tool for many different kinds of natural, biological and social networks, where directed relationships and contextual belonging of nodes to many different communities is a matter of fact. This paper starts from the definition of a modularity function, given by Newman to evaluate the goodness of network community decompositions, and extends it to the more general case of directed graphs with overlapping community structures. Interesting properties of the proposed extension are discussed, a method for finding overlapping communities is proposed and results of its application to benchmark case-studies are reported. We also propose a new data set which could be used as a reference benchmark for overlapping community structures identification.


title: Detection of Computer-Generated Papers in Scientific Literature

abstract: Meaningless computer generated scientific texts can be used in several ways. For example, they have allowed Ike Antkare to become one of the most highly cited scientists of the modern world. Such fake publications are also appearing in real scientific conferences and, as a result, in the bibliographic services (Scopus, ISI-Web of Knowledge, Google Scholar,...). Recently, more than 120 papers have been withdrawn from subscription databases of two high-profile publishers, IEEE and Springer, because they were computer generated thanks to the SCIgen software. This software, based on a Probabilistic Context Free Grammar (PCFG), was designed to randomly generate computer science research papers. Together with PCFG, Markov Chains (MC) are the mains ways to generated Meaningless texts. This paper presents the mains characteristic of texts generated by PCFG and MC. For the time being, PCFG generators are quite easy to spot by an automatic way, using intertextual distance combined with automatic clustering, because these generators are behaving like authors with specifics features such as a very low vocabulary richness and unusual sentence structures. This shows that quantitative tools are effective to characterize originality (or banality) of authors' language.


title: Does cost-sensitive learning beat sampling for classifying rare classes?

abstract: A highly-skewed class distribution usually causes the learned classifier to predict the majority class much more often than the minority class. This is a consequence of the fact that most classifiers are designed to maximize accuracy. In many instances, such as for medical diagnosis, the minority class is the class of primary interest and hence this classification behavior is unacceptable. In this paper, we compare two basic strategies for dealing with data that has a skewed class distribution and non-uniform misclassification costs. One strategy is based on cost-sensitive learning while the other strategy employs sampling to create a more balanced class distribution in the training set. We compare two sampling techniques, up-sampling and down-sampling, to the cost-sensitive learning approach. The purpose of this paper is to determine which technique produces the best overall classifier---and under what circumstances.


title: Design and analysis of a highly integrated 9-phase drivetrain for EV applications

abstract: Cost and compactness are major success factors for electrical drivetrains for (hybrid) electric vehicles (EV). To increase the system power density, the focus is set on system integration, meaning to integrate e-machine, inverter, control, and gearing into one common housing. The inverter integration is facilitated by multiphase motors leading to lower phase powers and thus smaller inverter modules. To overcome price uncertainties of rare earth magnets, improved hard ferrite magnets are chosen as an alternative. In this paper, a new highly integrated drivetrain with a ferrite based 9-phase synchronous motor concept is presented with special focus on the main choices leading to the particular electromagnetic and mechanical design. The advantages of an integrated triplex inverter power supply and the new mechanical arrangement of a spoke-type rotor design will be presented, without focusing on the detailed electromagnetic behavior of the machine.


Modified and Combined Lateral and Posterolateral Approach an Alternative Treatment to Tibial Plateau Fractures


title: Secure matrix generation for Compressive Sensing embedded cryptography

abstract: Confidentiality, entity authentication and nonrepudiation are security mechanisms, used to protect data against unauthorized access or manipulation. Hereby, the security and the reliability directly depend on the key. To secure a communication using Compressive Sensing (CS), the measurement matrix can be used as a key if it fulfill some requirements. However, the negotiation and the exchange of this matrix as required in standard cryptography, will leads to a massive overhead. Instead, both sides of the communication should share a secret key and additional parameters, which are used to generate the secret measurement matrix. Prior work on the measurement matrix generation mainly focus on sensing requirements and provides neither the requirements for the secret key nor the requirements for the key generation or derivation mechanisms. In this paper we first define the secret matrix requirements as well as the requirements for secret matrix generation or derivation mechanisms, in accordance with the NIST SP 800-90A standard. Then we propose a solution for the generation of secret matrices for CS embedded cryptography. The designed Secure CS Matrix Generator (SCS-MG) uses existing deterministic matrix constructions, which are appropriate for particular applications, as input. The generated secret matrices fulfill the requirements of the corresponding CS based applications and the requirements of a cryptographic key. The SCS-MG, considers the invariance of the Spark, the NSP order and the RIP order of CS measurement matrices, under elementary column and row transformation of this matrices.


title: Improving the co-word analysis method based on semantic distance

abstract: We propose an improvement over the co-word analysis method based on semantic distance. This combines semantic distance measurements with concept matrices generated from ontologically based concept mapping. Our study suggests that the co-word analysis method based on semantic distance produces a preferable research situation in terms of matrix dimensions and clustering results. Despite this method's displayed advantages, it has two limitations: first, it is highly dependent on domain ontology; second, its efficiency and accuracy during the concept mapping progress merit further study. Our method optimizes co-word matrix conditions in two aspects. First, by applying concept mapping within the labels of the co-word matrix, it combines words at the concept level to reduce matrix dimensions and create a concept matrix that contains more content. Second, it integrates the logical relationships and concept connotations among studied concepts into a co-word matrix and calculates the semantic distance between concepts based on domain ontology to create the semantic matrix.


title: Visual Neural Development

abstract: INTRODUCTION 478 Species 479 METHODOLOGY IN DEVELOPMENTAL STUDIES 479 Rearing 479 Electrophysiology 480 Physiology •....... 480 Recording ....... ...... 480 Visual stimulation ...... ... 480 Experimental design ...... 481 NORMAL VISUAL DEVELOPMENT AND EFFECTS OF TOTAL DEPRIVATION 482 Visual Optics 482 Normal development . 482 Effects of deprivation .... ....... .... ......... ..... 483 Retina 483 Normal development 483 Effects of deprivation 484 Lateral Geniculate Nucleus (LGN) 484 Normal development 484 Effects of deprivation .. ... ... 485 Visual Cortex 486 Normal development ... .... 486 Effects of deprivation. 488


title: Compliant leg behaviour explains basic dynamics of walking and running

abstract: The basic mechanics of human locomotion are associated with vaulting over stiff legs in walking and rebounding on compliant legs in running. However, while rebounding legs well explain the stance dynamics of running, stiff legs cannot reproduce that of walking. With a simple bipedal spring-mass model, we show that not stiff but compliant legs are essential to obtain the basic walking mechanics; incorporating the double support as an essential part of the walking motion, the model reproduces the characteristic stance dynamics that result in the observed small vertical oscillation of the body and the observed out-of-phase changes in forward kinetic and gravitational potential energies. Exploring the parameter space of this model, we further show that it not only combines the basic dynamics of walking and running in one mechanical system, but also reveals these gaits to be just two out of the many solutions to legged locomotion offered by compliant leg behaviour and accessed by energy or speed.


title: Linearizing Oscillometric Blood-Pressure Measurements: (Non)Sense?

abstract: This paper proposes a simplified method to compute the systolic and diastolic blood pressures from measured oscillometric blood-pressure waveforms. Therefore, the oscillometric waveform is analyzed in the frequency domain, which reveals that the measured blood-pressure signals are heavily disturbed by nonlinear contributions. The proposed approach will linearize the measured oscillometric waveform in order to obtain a more accurate and transparent estimation of the systolic and diastolic pressure based on a robust preprocessing technique. This new approach will be compared with the Korotkoff method and a commercially available noninvasive blood-pressure meter. This allows verification if the linearized approach contains as much information as the Korotkoff method in order to calculate a correct systolic and diastolic blood pressure.


title: Visualizing and understanding players' behavior in video games: discovering patterns and supporting aggregation and comparison

abstract: As video games become more popular, there is an urge for procedures that can support the analysis and understanding of players' behaviors within game environments. Such data would inform game and level designers of game design issues that should be fixed or improved upon. By logging user-initiated events in video games, analysts have exhaustive information regarding players' actions within games. However, visualizing such data is a challenging task due to the amount of data one has to deal with; the necessity of a deep understanding of the game and players' possible actions within the game plus a deep understanding of questions one wants to answer; the computation that has to be done on the data; and the limitations and/or complexities of current analysis tools. In this paper, we present a new visualization system that allows analysts to build visualization and interact with telemetry data, to identify patterns and identify game design issues efficiently. Besides the system itself, we propose a new approach to visualize players' behavior that has not been explored so far. For example, instead of using heat maps to visualize a single metric (e.g. deaths), our system allows analysts to superimpose and visualize a series of actions players take in the game. This is especially important when one should understand cause and effect within the game. We present examples of the visualizations using an RPG game, Dragon Age Origins (BioWare/EA, 2009). It should be noted that the system is currently under development and testing with analysts working at BioWare.


title: Algorithms For Automatic And Robust Registration Of 3D Head Scans

abstract: Two methods for registering laser-scans of human heads and transforming them to a new semantically consistent topology defined by a user-provided template mesh are described. Both algorithms are stated within the Iterative Closest Point framework. The first method is based on finding landmark correspondences by iteratively registering the vicinity of a landmark with a re-weighted error function. Thin-plate spline interpolation is then used to deform the template mesh and finally the scan is resampled in the topology of the deformed template. The second algorithm employs a morphable shape model, which can be computed from a database of laser-scans using the first algorithm. It directly optimizes pose and shape of the morphable model. The use of the algorithm with PCA mixture models, where the shape is split up into regions each described by an individual subspace, is addressed. Mixture models require either blending or regularization strategies, both of which are described in detail. For both algorithms, strategies for filling in missing geometry for incomplete laser-scans are described. While an interpolation-based approach can Digital Peer Publishing Licence Any party may pass on this Work by electronic means and make it available for download under the terms and conditions of the current version of the Digital Peer Publishing Licence (DPPL). The text of the licence may be accessed and retrieved via Internet at http://www.dipp.nrw.de/. First presented at the Conference for Visual Media Prodction 2008, extended and revised for JVRB be used to fill in small or smooth regions, the modeldriven algorithm is capable of fitting a plausible complete head mesh to arbitrarily small geometry, which is known as “shape completion”. The importance of regularization in the case of extreme shape completion is shown.


title: An FPGA Based All-Digital Transmitter with Radio Frequency Output for Software Defined Radio

abstract: This paper presents the architecture and implementation of an all-digital transmitter with radio frequency output targeting an FPGA device. FPGA devices have been widely adopted in the applications of digital signal processing (DSP) and digital communication. They are typically well suited for the evolving technology of software defined radios (SDR) due to their reconfigurability and programmability. However, FPGA devices are mostly used to implement digital baseband and intermediate frequency (IF) functionalities. Therefore, significant analog and RF components are still needed to fulfill the radio communication requirements. The all-digital transmitter presented in this paper directly synthesizes RF signal in the digital domain, therefore eliminates the need for most of the analog and RF components. The all-digital transmitter consists of one QAM modulator and one RF pulse width modulator (RFPWM). The binary output waveform from RFPWM is centered at 800MHz with 64QAM signaling format. The entire transmitter is implemented using Xilinx Virtex2pro device with on chip multi-gigabit transceiver (MGT). The adjacent channel leakage ratio (ACLR) measured in the 20 MHz passband is 45dB, and the measured error vector magnitude (EVM) is less than 1%. Our work extends the digital implementation of communication applications on an FPGA platform to radio frequency, therefore making a significant evolution towards an ideal SDR


title: Online Anomaly Detection for Sensor Systems: a Simple and Efficient Approach

abstract: Wireless sensor systems aid scientific studies by instrumenting the real world and collecting measurements. Given the large volume of measurements collected by sensor systems, one problem arises-an automated approach to identifying the ''interesting'' parts of these datasets, or anomaly detection. A good anomaly detection methodology should be able to accurately identify many types of anomaly, be robust, require relatively few resources, and perform detection in (near) real time. Thus, in this paper, we focus on an approach to online anomaly detection in measurements collected by sensor systems, where our evaluation, using real-world datasets, shows that our approach is accurate (it detects over 90% of the anomalies with few false positives), works well over a range of parameter choices, and has a small (CPU, memory) footprint.


A novel 60 GHz wideband coupled half-mode / quarter-mode substrate integrated waveguide antenna


title: Efficient Real-Time Auditing and Proof of Violation for Cloud Storage Systems

abstract: In this paper we study how to develop an efficient real-time auditing and proof of violation (POV) scheme for cloud storage systems. A POV scheme enables a user or a service provider to produce cryptographic proofs which can be used to prove either the occurrence of the violation of properties or the innocence of the service provider. POV schemes are solutions for obtaining mutual nonrepudiation between users and the service provider in the cloud. After each file operation, a real-time auditing should be performed so that the violation of the service provider can be found instantly. Existing solutions need to cache the hash values of files in client devices and thus the overhead for storing and synchronizing hash values in client devices which share files is huge. We propose a novel scheme in which client devices do not need to cache any hash values of files. A small portion called slice of a binary hash tree is transferred to the client device for real-time auditing and it can support POV whenever the audit does not pass. Experimental results are presented that demonstrate the feasibility of the proposed scheme and show that our scheme outperforms previous work by one to two order of magnitude. Service providers of cloud storage can use the proposed scheme to provide a mutual nonrepudiation guarantee in their service-level agreements.


title: Generating abstract paintings in Kandinsky style

abstract: This paper presents a recent project on automatic generation of Kandinsky style of abstract paintings using the programming language Processing. It first offers an analysis of Kandinsky's paintings based on his art theories and the author's own understanding and observation. The generation process is described in details and sample generated images styled on four of Kandinsky's paintings are also demonstrated and discussed. Our approach is highly scalable, limited only by the memory space set in Processing. Using random generation, every styled image generated can be unique. A selection of the images generated in the required resolution is also submitted and 70 images are made into a video companion.


title: A 1 GS/s 6 Bit 6.7 mW Successive Approximation ADC Using Asynchronous Processing

abstract: An asynchronous 6 bit 1 GS/s ADC is achieved by time interleaving two ADCs based on the binary successive approximation (SA) algorithm using a series capacitive ladder. The semi-closed loop asynchronous technique eliminates the high internal clocks and significantly speeds up the SA algorithm. A key feature to reduce the power in this design involves relaxing the comparator requirements using an error correction technique, which can be viewed as an extension of the SA algorithm to remove degradation due to metastability. Fabricated in 65 nm CMOS with an active area of 0.11 mm 2 , it achieves a peak SNDR of 31.5 dB at 1 GS/s sampling rate and has a total power consumption of 6.7 mW.


title: Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change

abstract: Words shift in meaning for many reasons, including cultural factors like new technologies and regular linguistic processes like subjectification. Understanding the evolution of language and culture requires disentangling these underlying causes. Here we show how two different distributional measures can be used to detect two different types of semantic change. The first measure, which has been used in many previous works, analyzes global shifts in a word's distributional semantics; it is sensitive to changes due to regular processes of linguistic drift, such as the semantic generalization of promise ("I promise." "It promised to be exciting."). The second measure, which we develop here, focuses on local changes to a word's nearest semantic neighbors; it is more sensitive to cultural shifts, such as the change in the meaning of cell ("prison cell" "cell phone"). Comparing measurements made by these two methods allows researchers to determine whether changes are more cultural or linguistic in nature, a distinction that is essential for work in the digital humanities and historical linguistics.


Market Orientation: The Construct, Research Propositions, and Managerial Implications


title: Mirror neurons: action observation treatment as a tool in stroke rehabilitation.

abstract: Abstract The observation of actions performed by others activate in an observer the same neural structures (including mirror neurons) as when he/she actually performs the same actions. The aim of the present study was to assess whether action observation treatment may improve upper limb motor impairment in chronic stroke patients. This was an observational study. Patients were recruited by three Italian Centres for Neurorehabilitation between 2006 and 2008. Twenty-eight chronic stroke patients with upper limb impairment have undergone for four weeks, five days a week, a rehabilitation treatment based on observation of video-clips presenting hand daily actions, followed by the imitation of those same actions with the affected limb. Functional evaluation by means of Modified Barthel Index (MBI), Frenchay Arm Test (FAT) and Fugl Meyer (FM) was carried out twice before treatment (BT1 and BT2), at an interval of 15 days, then after treatment (AT1) and finally at a two-month follow-up (AT2). Wilcoxon Signed Rank test was applied to test differences between scores obtained from functional scales before and after treatment (BT1 vs. BT2; BT2 vs. AT1; AT1 vs. AT2). In all scales, scores did not differ when comparing BT1 with BT2. Scores improved significantly in all scales at AT1 as compared to BT2 (MBI, P=0.026; FAT, P=0.005; FM, P=0.001). This improvement was still present at the two-month follow-up as testified by no score difference between AT1 and AT2. Action Observation Treatment may become a useful strategy in the rehabilitation of stroke patients. The present preliminary study suggests that stimulation of neural structures (including mirror neurons), activated when the patients actually perform the same actions as those observed could constitute a good alternative rehabilitative approach in chronic stroke patients.


title: Detecting text in natural scenes with stroke width transform

abstract: We present a novel image operator that seeks to find the value of stroke width for each image pixel, and demonstrate its use on the task of text detection in natural images. The suggested operator is local and data dependent, which makes it fast and robust enough to eliminate the need for multi-scale computation or scanning windows. Extensive testing shows that the suggested scheme outperforms the latest published algorithms. Its simplicity allows the algorithm to detect texts in many fonts and languages.


title: Anchoring: accessibility as a cause of judgmental assimilation

abstract: Anchoring denotes assimilation of judgment toward a previously considered value — an anchor. The selective accessibility model argues that anchoring is a result of selective accessibility of information compatible with an anchor. The present review shows the similarities between anchoring and knowledge accessibility effects. Both effects depend on the applicability of the accessible information, which is also used similarly. Furthermore, both knowledge accessibility and anchoring influence the time needed for the judgment and both display temporal robustness. Finally, we offer recent evidence for the selective accessibility model and demonstrate how the model can be applied to reducing the anchoring effect.


title: CSIFT based locality-constrained linear coding for image classification

abstract: In the past decade, SIFT descriptor has been witnessed as one of the most robust local invariant feature descriptors and widely used in various vision tasks. Most traditional image classification systems depend on the luminance-based SIFT descriptors, which only analyze the gray level variations of the images. Misclassification may happen since their color contents are ignored. In this article, we concentrate on improving the performance of existing image classification algorithms by adding color information. To achieve this purpose, different kinds of colored SIFT descriptors are introduced and implemented. Locality-constrained Linear Coding (LLC), a state-of-the-art sparse coding technology, is employed to construct the image classification system for the evaluation. The real experiments are carried out on several benchmarks. With the enhancements of color SIFT, the proposed image classification system obtains approximate 3% improvement of classification accuracy on the Caltech-101 dataset and approximate 4% improvement of classification accuracy on the Caltech-256 dataset.


title: Effect of a Quality Improvement Project to Use Exclusive Mother's Own Milk on Rate of Necrotizing Enterocolitis in Preterm Infants.

abstract: OBJECTIVE ::: Mother's own milk (MOM) remains the key strategy to prevent necrotizing enterocolitis (NEC) in preterm infants. We developed and implemented quality improvement (QI) initiatives to improve rate of using MOM in preterm infants of <32 weeks of gestation. The objective was to evaluate the impact of this QI project on NEC incidence in this population. ::: ::: ::: STUDY DESIGN ::: In November 2009, we established a QI multidisciplinary team that reviewed literature and practices of other institutions to promote exclusive use of MOM in preterm infants of <32 weeks of gestation. The team developed practice changes that were implemented in February 2010. We tracked use of MOM and occurrence of NEC stage ≥ 2 from January 2009 until March 2012 when donor human milk was introduced. The subjects were divided into three groups: baseline (year 2009; n = 189), intervention phase (year 2010; n = 202), and sustain phase (January 2011-March 2012; n = 254). Clinical outcomes of the baseline and sustain phases were compared. ::: ::: ::: RESULTS ::: Exclusive use of MOM improved significantly from 60.8% to 73.6% at time of first feed (p = 0.004) without any delay in feed introduction (median [interquartile range], 40 [26, 75.5] hours versus 43.5 [24, 69] hours; p = 0.96). There was a nonsignificant increase in exclusive use of MOM at time of full feed (from 79.5% to 85.2%; p = 0.11); however; use of any MOM increased significantly from 80.4% to 91.3% (p < 0.001) at discharge. Time to reach full feed and length of stay remained similar between the baseline and sustain periods. On regression analysis, risk of NEC decreased significantly in the sustain period (odds ratio = 0.32; 95% confidence interval, 0.11-0.93). ::: ::: ::: CONCLUSIONS ::: QI initiatives were effective in increasing use of exclusive MOM at time of first feed and of any MOM at time of discharge and in reducing NEC incidence in our neonatal intensive care unit.


title: Training to enhance the physiological determinants of long-distance running performance: can valid recommendations be given to runners and coaches based on current scientific knowledge?

abstract: This article investigates whether there is currently sufficient scientific knowledge for scientists to be able to give valid training recommendations to long-distance runners and their coaches on how to most effectively enhance the maximal oxygen uptake, lactate threshold and running economy. Relatively few training studies involving trained distance runners have been conducted, and these studies have often included methodological factors that make interpretation of the findings difficult. For example, the basis of most of the studies was to include one or more specific bouts of training in addition to the runners' 'normal training', which was typically not described or only briefly described. The training status of the runners (e.g. off-season) during the study period was also typically not described. This inability to compare the runners' training before and during the training intervention period is probably the main factor that hinders the interpretation of previous training studies. Arguably, the second greatest limitation is that only a few of the studies included more than one experimental group. Consequently, there is no comparison to allow the evaluation of the relative efficacy of the particular training intervention. Other factors include not controlling the runners' training load during the study period, and employing small sample sizes that result in low statistical power. Much of the current knowledge relating to chronic adaptive responses to physical training has come from studies using sedentary individuals; however, directly applying this knowledge to formulate training recommendations for runners is unlikely to be valid. Therefore, it would be difficult to argue against the view that there is insufficient direct scientific evidence to formulate training recommendations based on the limited research. Although direct scientific evidence is limited, we believe that scientists can still formulate worthwhile training recommendations by integrating the information derived from training studies with other scientific knowledge. This knowledge includes the acute physiological responses in the various exercise domains, the structures and processes that limit the physiological determinants of long-distance running performance, and the adaptations associated with their enhancement. In the future, molecular biology may make an increasing contribution in identifying effective training methods, by identifying the genes that contribute to the variation in maximal oxygen uptake, the lactate threshold and running economy, as well as the biochemical and mechanical signals that induce these genes. Scientists should be cautious when giving training recommendations to runners and coaches based on the limited available scientific knowledge. This limited knowledge highlights that characterising the most effective training methods for long-distance runners is still a fruitful area for future research.


title: A 9.1–10.7 GHz 10-W, 40-dB Gain Four-Stage PHEMT MMIC Power Amplifier

abstract: This letter presents a compact X-band high gain and high power four-stage AlGaAs/InGaAs/GaAs pseudomorphic high electron mobility transistor (PHEMT) monolithic microwave integrated circuit (MMIC) high power amplifier (PA). This amplifier is designed to fully match a 50-Omega input and output impedance. Based on 0.35-mum gate-length power PHEMT technology, this PA MMIC is fabricated on a 3-mil thick wafer. While operating under 8 V and 2700-mA dc bias condition, the characteristics of 40-dB small-signal gain, a 10-W continuous-wave saturation output power, and 33% power added efficiency at 9.7GHz can be achieved


MetaDisk A Blockchain-Based Decentralized File Storage Application


title: Facebook Use of Sensitive Data for Advertising in Europe

abstract: The upcoming European General Data Protection Regulation (GDPR) prohibits the processing and exploitation of some categories of personal data (health, political orientation, sexual preferences, religious beliefs, ethnic origin, etc.) due to the obvious privacy risks that may be derived from a malicious use of such type of information. These categories are referred to as sensitive personal data. Facebook has been recently fined EUR 1.2M in Spain for collecting, storing and processing sensitive personal data for advertising purposes. This paper quantifies the portion of Facebook users in the European Union (EU) who are labeled with interests linked to sensitive personal data. The results of our study reveal that Facebook labels 73% EU users with sensitive interests. This corresponds to 40% of the overall EU population. We also estimate that a malicious third-party could unveil the identity of Facebook users that have been assigned a sensitive interest at a cost as low as EUR 0.015 per user. Finally, we propose and implement a web browser extension to inform Facebook users of the sensitive interests Facebook has assigned them.


title: Stability analysis of swarms

abstract: In this note, we specify an "individual-based" continuous-time model for swarm aggregation in n-dimensional space and study its stability properties. We show that the individuals (autonomous agents or biological creatures) will form a cohesive swarm in a finite time. Moreover, we obtain an explicit bound on the swarm size, which depends only on the parameters of the swarm model.


title: Graph Clustering Based on Structural/Attribute Similarities

abstract: The goal of graph clustering is to partition vertices in a large graph into different clusters based on various criteria such as vertex connectivity or neighborhood similarity. Graph clustering techniques are very useful for detecting densely connected groups in a large graph. Many existing graph clustering methods mainly focus on the topological structure for clustering, but largely ignore the vertex properties which are often heterogenous. In this paper, we propose a novel graph clustering algorithm, SA-Cluster, based on both structural and attribute similarities through a unified distance measure. Our method partitions a large graph associated with attributes into k clusters so that each cluster contains a densely connected subgraph with homogeneous attribute values. An effective method is proposed to automatically learn the degree of contributions of structural similarity and attribute similarity. Theoretical analysis is provided to show that SA-Cluster is converging. Extensive experimental results demonstrate the effectiveness of SA-Cluster through comparison with the state-of-the-art graph clustering and summarization methods.


title: Automated topic naming to support cross-project analysis of software maintenance activities

abstract: Researchers have employed a variety of techniques to extract underlying topics that relate to software development artifacts. Typically, these techniques use semi-unsupervised machine-learning algorithms to suggest candidate word-lists. However, word-lists are difficult to interpret in the absence of meaningful summary labels. Current topic modeling techniques assume manual labelling and do not use domainspecific knowledge to improve, contextualize, or describe results for the developers. We propose a solution: automated labelled topic extraction. Topics are extracted using Latent Dirichlet Allocation (LDA) from commit-log comments recovered from source control systems such as CVS and Bit-Keeper. These topics are given labels from a generalizable cross-project taxonomy, consisting of non-functional requirements. Our approach was evaluated with experiments and case studies on two large-scale RDBMS projects: MySQL and MaxDB. The case studies show that labelled topic extraction can produce appropriate, context-sensitive labels relevant to these projects, which provides fresh insight into their evolving software development activities.


Battery storage for supplementing renewable energy systems


title: Exploring LOD through metadata extraction and data-driven visualizations

abstract: Purpose – The purpose of this paper is to present a new approach toward automatically visualizing Linked Open Data (LOD) through metadata analysis. Design/methodology/approach – By focussing on the data within a LOD dataset, the authors can infer its structure in a much better way than current approaches, generating more intuitive models to progress toward visual representations. Findings – With no technical knowledge required, focussing on metadata properties from a semantically annotated dataset could lead to automatically generated charts that allow to understand the dataset in an exploratory manner. Through interactive visualizations, users can navigate LOD sources using a natural approach, in order to save time and resources when dealing with an unknown resource for the first time. Research limitations/implications – This approach is suitable for available SPARQL endpoints and could be extended for resource description framework dumps loaded locally. Originality/value – Most works dealing with LOD vi...


title: Empirical Evidences in Citation-Based Search Engines: Is Microsoft Academic Search dead?

abstract: The goal of this working paper is to summarize the main empirical evidences provided by the scientific community as regards the comparison between the two main citation based academic search engines: Google Scholar and Microsoft Academic Search, paying special attention to the following issues: coverage, correlations between journal rankings, and usage of these academic search engines. Additionally, selfelaborated data is offered, which are intended to provide current evidence about the popularity of these tools on the Web, by measuring the number of rich files PDF, PPT and DOC in which these tools are mentioned, the amount of external links that both products receive, and the search queries frequency from Google Trends. The poor results obtained by MAS led us to an unexpected and unnoticed discovery: Microsoft Academic Search is outdated since 2013. Therefore, the second part of the working paper aims at advancing some data demonstrating this lack of update. For this purpose we gathered the number of total records indexed by Microsoft Academic Search since 2000. The data shows an abrupt drop in the number of documents indexed from 2,346,228 in 2010 to 8,147 in 2013 and 802 in 2014. This decrease is offered according to 15 thematic areas as well. In view of these problems it seems logical not only that Microsoft Academic Searchwas poorly used to search for articles by academics and students, who mostly use Google or Google Scholar, but virtually ignored by bibliometricians


title: A COMPACT QUAD BAND-NOTCHED UWB MONOPOLE ANTENNA LOADED ONE LATERAL L-SHAPED SLOT

abstract: A novel compact microstrip-fed planar monopole antenna with quad-notched bands is presented. The proposed antenna is based on one rectangular-stepped-patch. To achieve the higher resonance over the 12GHz, one lateral L-shaped structure is embedded in the ground. By inserting four U-shaped slots in the radiation patch, quad band-notched properties in the WiMAX (3.3{3.6GHz), INSAT (4.5{4.8GHz), lower WLAN (5.15{5.35GHz) and higher WLAN (5.725{5.825GHz) are obtained. Experimental results indicate that the designed UWB antenna can obtain broadband matched impedance values, good frequency selectivity over the notched bands, relatively ∞at group delay and nearly omni-directional transmission characteristics across the UWB frequencies. More importantly, the quad-notched bands can be reconflgurable by shorting the corresponding U-shaped slots.


title: Purification and characterization of polyamine-stimulated protein kinase (casein kinase II) from bovine spermatozoa.

abstract: Casein kinase II from bovine epididymal spermatozoa was purified to apparent homogeneity by repeated chromatography with phosphocellulose and gel filtration with sephacryl S-200. The purified enzyme exhibited a molecular mass of 130 kDa by gel filtration and displayed three polypeptide bands with molecular masses of 26, 33, and 36 kDa by SDS-polyacrylamide gel electrophoresis. Antibodies raised against calf thymus casein kinase II cross reacted with the three sperm polypeptides. Incubation of the holoenzyme with either [gamma-32P]ATP or [gamma-32P]GTP resulted in the phosphorylation of the 26-kDa subunit. The enzymatic activity with casein as substrate was strongly inhibited by nanomolar heparin and greatly stimulated by micromolar spermine. With casein as substrate, the specific activity of the pure enzyme (0.5 mumol/min/mg protein) was comparable to that of casein kinase II from other sources. Endogenous substrates of the kinase were demonstrated by incubating sperm cytosolic extracts with [gamma-32P]GTP, under conditions that limit the expression of other protein kinases, and analyzing the products by SDS-PAGE and autoradiography. Similar results were obtained when sperm extracts, suitably diluted to minimize endogenous casein kinase II, were incubated with [gamma-32P]GTP and aliquots of pure sperm casein kinase II. Low concentrations (50 microM) spermine strongly enhanced the phosphorylation of 92- and 106-kDa cytosolic proteins. Our results clearly show that casein kinase II is present in spermatozoa and that it shares many of the properties of the enzyme from other sources. Further, they indicate that the enzyme plays a role in mediating the phosphorylation state of sperm proteins.


title: System performance of cooperative massive MIMO downlink 5G cellular systems

abstract: Massive MIMO (multiple-input multiple-output) antenna technology can provide significant performance improvement for cellular systems in terms of both throughput and energy efficiency. It is widely recognized that inter-user interference can be eliminated with a large number of antennas because of the asymptotical orthogonality among users when linear MF (Matched Filter) downlink precoding is used in the eNodeB. Due to the complexity and deployment consideration in practical scenarios at individual eNodeBs, cooperative massive MIMO [CM-MIMO] where multiple base stations cooperate together and form a distributed antenna array to serve multiple users simultaneously is an attractive alternative. Furthermore, cooperative massive MIMO can also help increase the system performance especially for cell edge users because of the cooperative transmission among neighboring cells. In this paper, system level simulation performance for the downlink, based upon current LTE systems, provides an indication of the achievable potential system performance improvement by employing CM-MIMO in future (5G) cellular networks. It is demonstrated that CM-MIMO can improve the system performance of cell edge users significantly even if the cell average performance is very slightly degraded or maintained caused by the power imbalance of received signal from different cooperative neighboring cells.


title: Digital image splicing detection based on Markov features in block DWT domain

abstract: Image splicing is very common and fundamental in image tampering. Many splicing detection schemes based on Markov features in transform domain have been proposed. Based on previous studies, the traditional DWT based schemes perform not better than the DCT based schemes. In this paper, a block DWT based scheme is proposed to improve the detection performance of the DWT based scheme. Firstly, the block DWT is applied on the source image. Then, the Markov features are constructed in block DWT domain to characterize the dependency among wavelet coefficients across positions. After that, feature selection method SVM-RFE is used to reduce the dimensionality of features. Finally, Support Vector Machine is exploited to classify the authentic and spliced images. Experiment results show that the detection performance of the features extracted in DWT domain can be improved with block DWT based scheme. And then, in order to further clarify the phenomenon about the traditional DWT based schemes perform not better than the DCT based schemes, a detail comparison between the two kinds of schemes is proposed based on a set of experiments. The results show that the DWT based scheme is more applicable and powerful than the DCT based scheme, and the DCT based scheme is more suitable for handling these datasets which generated with the process of JPEG compression.


title: Wideband slotted planar inverted-F antenna for millimeter-wave 5G mobile devices

abstract: A compact slotted planar inverted-F antenna (PIFA) with a superstrate is presented in this paper. The proposed antenna covers 28 GHz millimeter wave frequency bands and can be useful for future 5G hand held devices. The antenna has been designed to be integrated to a mobile phone substrate of dimensions 110 mm × 60 mm, while reserving 70 mm × 35 mm area for a battery, and occupies a small volume of 4 mm × 4 mm × 1.34 mm. It has a wide operating bandwidth (|S11| < − 10dB) of 3.18 GHz (27.57–30.75 GHz) and a peak gain of 8.8 dBi with a gain variation of less than 0.83 dB within the operating bandwidth. The proposed antenna has stable broadside radiation in yz and xz planes with low side lobe levels. It features wide half power beamwidths of 65.90 in the xz plane and 61.10 in the yz plane at 28 GHz. Radiation efficiency and total efficiency of the antenna vary between 97.9%–98.8% and 69.3%–97.3%, respectively.


title: Effects of gait speed on the body’s center of mass motion relative to the center of pressure during over-ground walking

abstract: Abstract Preferred walking speed (PWS) reflects the integrated performance of the relevant physiological sub-systems, including energy expenditure. It remains unclear whether the PWS during over-ground walking is chosen to optimize one’s balance control because studies on the effects of speed on the body’s balance control have been limited. The current study aimed to bridge the gap by quantifying the effects of the walking speed on the body’s center of mass (COM) motion relative to the center of pressure (COP) in terms of the changes and directness of the COM-COP inclination angle (IA) and its rate of change (RCIA). Data of the COM and COP were measured from fifteen young healthy males at three walking speeds including PWS using a motion capture system. The values of IAs and RCIAs at key gait events and their average values over gait phases were compared between speeds using one-way repeated measures ANOVA. With increasing walking speed, most of the IA and RCIA related variables were significantly increased (p


title: The effectiveness of community interventions targeting HIV and AIDS prevention at young people in developing countries.

abstract: OBJECTIVE ::: To identify successful HIV/AIDS prevention interventions targeting youths and delivered in geographically bounded communities (for example, rural villages, urban settlements or neighbourhoods) in developing countries. ::: ::: ::: METHODS ::: A systematic review and synthesis of studies evaluating interventions that were published between January 1990 and December 2004 was conducted. Using predetermined criteria, all interventions were summarized into multiple tables to facilitate comparison. Results of the evaluations of each of four types of intervention were reviewed using predetermined thresholds of evidence. The four types of interventions were classified as follows. Type 1 interventions were those targeting youths and delivered through existing organizations or centres that served youths. Type 2 were those targeting youths but not affiliated with existing organizations or centres. Type 3 were those targeting all community members and delivered through traditional kinship networks. Type 4 were those targeting communities as a whole and delivered through community-wide events. ::: ::: ::: FINDINGS ::: Evaluations of 22 interventions were reviewed. Type 1 interventions produced primarily positive results at the required threshold of evidence. They are recommended for use in scaling-up projects but should be subject to continued rigorous evaluations. Studies of all other intervention types produced primarily positive results, but the evaluations were less rigorous so clear conclusions could not be drawn about their effectiveness. It is recommended that these interventions be continued and that priority should be given to implementing rigorous evaluations of these interventions. ::: ::: ::: CONCLUSIONS ::: Considerable creativity, ingenuity and commitment is demonstrated in designing and delivering HIV interventions but there is a paucity of adequate evidence of their effectiveness. This precludes identification of the types of interventions that actually produce the targeted changes. It is essential that governments and donor agencies invest in high quality process and outcome evaluations and cost-benefit analyses so that effective interventions can be identified and promoted.


title: GATED FAST WEIGHTS FOR ASSOCIATIVE RETRIEVAL

abstract: We improve previous end-to-end differentiable neural networks (NNs) with fast weight memories. A gate mechanism updates fast weights at every time step of a sequence through two separate outer-product-based matrices generated by slow parts of the net. The system is trained on a complex sequence to sequence variation of the Associative Retrieval Problem with roughly 70 times more temporal memory (i.e. time-varying variables) than similar-sized standard recurrent NNs (RNNs). In terms of accuracy and number of parameters, our architecture outperforms a variety of RNNs, including Long Short-Term Memory, Hypernetworks, and related fast weight architectures.


title: Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring

abstract: Droughtisoneof themajornaturalhazards thatbringabout billionsof dollarsinlosstothefarmingcommunityaroundtheworld each year. Drought is most often caused by a departure of precipitation from the normal amount, and agriculture is often the first sector to be affected by the onset of drought due to its dependence on water resources and soil moisture reserves during various stages of crop growth. Currently used drought indices like the Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI) have coarse spatial (7000‐100,000 km 2 ) and temporal resolution (monthly). Hence, the distributed hydrologic model SWAT was used to simulate soil moisture and evapotranspiration from daily weather data at a high spatial resolution (16 km 2 ) using GIS. Using this simulated data the drought indices Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) were developed based on weekly soil moisture deficit and evapotranspiration deficit, respectively. SMDI was computed at four different levels, using soil water available in the entire soil profile, then soil water available at the top 2 ft. (SMDI-2), 4 ft. (SMDI-4), and 6 ft. (SMDI-6). This was done because the potential of the crop to extract water from depths varies during different stages of the crop growth and also by crop type. ETDI and SMDI-2 had less autocorrelation lag, indicating that they could be used as good indicators of short-term drought. The developed drought indices showed high spatial variability (spatial standard deviation � 1.00) in the study watersheds, primarily due to high spatial variability of precipitation. The wheat and sorghum crop yields were highly correlated (r > 0.75) with the ETDI and SMDI’s during theweeks of critical crop growth stages, indicating that the developed drought indices can be used for monitoring agricultural drought. # 2005 Elsevier B.V. All rights reserved.


title: Stability analysis of method of fundamental solutions for mixed boundary value problems of Laplace’s equation

abstract: Since the stability of the method of fundamental solutions (MFS) is a severe issue, the estimation on the bounds of condition number Cond is important to real application. In this paper, we propose the new approaches for deriving the asymptotes of Cond, and apply them for the Dirichlet problem of Laplace’s equation, to provide the sharp bound of Cond for disk domains. Then the new bound of Cond is derived for bounded simply connected domains with mixed types of boundary conditions. Numerical results are reported for Motz’s problem by adding singular functions. The values of Cond grow exponentially with respect to the number of fundamental solutions used. Note that there seems to exist no stability analysis for the MFS on non-disk (or non-elliptic) domains. Moreover, the expansion coefficients obtained by the MFS are oscillatingly large, to cause the other kind of instability: subtraction cancelation errors in the final harmonic solutions.


title: Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights

abstract: This paper presents incremental network quantization (INQ), a novel method, targeting to efficiently convert any pre-trained full-precision convolutional neural network (CNN) model into a low-precision version whose weights are constrained to be either powers of two or zero. Unlike existing methods which are struggled in noticeable accuracy loss, our INQ has the potential to resolve this issue, as benefiting from two innovations. On one hand, we introduce three interdependent operations, namely weight partition, group-wise quantization and re-training. A well-proven measure is employed to divide the weights in each layer of a pre-trained CNN model into two disjoint groups. The weights in the first group are responsible to form a low-precision base, thus they are quantized by a variable-length encoding method. The weights in the other group are responsible to compensate for the accuracy loss from the quantization, thus they are the ones to be re-trained. On the other hand, these three operations are repeated on the latest re-trained group in an iterative manner until all the weights are converted into low-precision ones, acting as an incremental network quantization and accuracy enhancement procedure. Extensive experiments on the ImageNet classification task using almost all known deep CNN architectures including AlexNet, VGG-16, GoogleNet and ResNets well testify the efficacy of the proposed method. Specifically, at 5-bit quantization, our models have improved accuracy than the 32-bit floating-point references. Taking ResNet-18 as an example, we further show that our quantized models with 4-bit, 3-bit and 2-bit ternary weights have improved or very similar accuracy against its 32-bit floating-point baseline. Besides, impressive results with the combination of network pruning and INQ are also reported. The code is available at this https URL.


title: BACE-1 Inhibitors: From Recent Single-Target Molecules to Multitarget Compounds for Alzheimer’s Disease

abstract: The amyloid hypothesis has long been the central dogma in drug discovery for Alzheimer’s disease (AD), leading to many small-molecule and biological drug candidates. One major target has been the β-site amyloid-precursor-protein-cleaving enzyme 1 (BACE-1), with many big pharma companies expending great resources in the search for BACE-1 inhibitors. The lack of efficacy of verubecestat in mild-to-moderate AD raises important questions about the timing of intervention with BACE-1 inhibitors, and anti-amyloid therapies in general, in AD treatment. It also suggests new possibilities for discovering BACE-1-targeted compounds with more complex mechanisms of actions and improved efficacy. Herein, we review the major advances in BACE-1 drug discovery, from single-target small molecule inhibitors to multitarget compounds. We discuss these compounds as innovative tools for better understanding the complexity of AD and for identifying efficacious drug candidates to treat this devastating disease.


title: Inducing Frame Semantic Verb Classes From WordNet And LDOCE

abstract: This paper presents SemFrame, a system that induces frame semantic verb classes from WordNet and LDOCE. Semantic frames are thought to have significant potential in resolving the paraphrase problem challenging many language-based applications.When compared to the handcrafted FrameNet, SemFrame achieves its best recall-precision balance with 83.2% recall (based on SemFrame's coverage of FrameNet frames) and 73.8% precision (based on SemFrame verbs' semantic relatedness to frame-evoking verbs). The next best performing semantic verb classes achieve 56.9% recall and 55.0% precision.


title: Discrete Optimization Algorithms for the variable sized bin packing problem

abstract: In this paper, we consider the variable sized bin packing problem where the objective is to minimize the total cost of used bins when the cost of unit size of each bin does not increase as the bin size increases. Two greedy algorithms are described, and analyzed in three special cases: (a) the sizes of items and bins are divisible, respectively, (b) only the sizes of bins are divisible, and (c) the sizes of bins are not divisible. Here, we say that a list of numbers a1,a2,…,am are divisible when aj exactly divides aj−1, for each 1<j⩽m. In the case of (a), the algorithms give optimal solutions, and in the case of (b), each algorithm gives a solution whose value is less than 119C(B*)+4119, where C(B*) is the optimal value. In the case of (c), each algorithm gives a solution whose value is less than 32C(B*)+1.


title: From structure-from-motion point clouds to fast location recognition

abstract: Efficient view registration with respect to a given 3D reconstruction has many applications like inside-out tracking in indoor and outdoor environments, and geo-locating images from large photo collections. We present a fast location recognition technique based on structure from motion point clouds. Vocabulary tree-based indexing of features directly returns relevant fragments of 3D models instead of documents from the images database. Additionally, we propose a compressed 3D scene representation which improves recognition rates while simultaneously reducing the computation time and the memory consumption. The design of our method is based on algorithms that efficiently utilize modern graphics processing units to deliver real-time performance for view registration. We demonstrate the approach by matching hand-held outdoor videos to known 3D urban models, and by registering images from online photo collections to the corresponding landmarks.


title: Multiple service load-balancing with OpenFlow

abstract: Load balancers have a decisive role in every enterprise network as they serve often as an entry point and have major impact on the performance and the availability of the network. While current load balancers are mostly implemented as specific hardware components, we developed a load balancer service based on the OpenFlow controllers to handle the load of multiple services without the necessity for a specific piece of hardware. This approach integrates the network and the load balancing functionality and reduces the maintenance effort. We increase the efficiency by providing dedicated and thus specifically adapted load balancing algorithms for the involved services. For example one controller handles the standard network traffic between the server nodes and other network components, while another controller handles the load-balancing of the web-servers and another one the load-balancing of e-mail servers. Furthermore experimental measurements in the local OpenFlow island using the FlowVisor and NOX controllers prove the performance capabilities of the developed prototype.


title: A survey on hardware design issues in RF energy harvesting for wireless sensor networks (WSN)

abstract: Today, the lifetime improvement in the wireless sensor networks is one of the most critical issue for researchers. The efficient Radio Frequency (RF) Energy harvesting technique provides themicro-power needed for sensor node and flexibility of charging the battery. Many researchers are working on the hardware issues like new emerging technologies to optimize the efficiency of the circuit. Some are working on commercially available devices (ICs, Schottky diodes, Antennas etc.), component level (using VLSI design) and mathematical modeling to generate efficient models for RF energy Harvesting. Powercast companies provide circuits which input the RF signal and output it in the form of charging voltage and current. In this paper, a detailed theoretical study and literature has been surveyed to find current and new era of research in this domain. This paper may help the researchers to find new points for the research in the domain of Radio Frequency Energy Harvesting


title: A Wearable 8-Channel Active-Electrode EEG/ETI Acquisition System for Body Area Networks

abstract: This paper describes an 8-channel gel-free EEG/electrode-tissue impedance (ETI) acquisition system, consisting of nine active electrodes (AEs) and one back-end (BE) analog signal processor. The AEs amplify the weak EEG signals, while their low output impedance suppresses cable-motion artifacts and 50/60 Hz mains interference. A common-mode feed-forward (CMFF) scheme boosts the CMRR of the AE pairs by 25 dB. The BE post-processes and digitizes the analog outputs of the AEs, it also can configure them via a single-wire pulse width modulation (PWM) protocol. Together, the AEs and BE are capable of recording 8-channel EEG and ETI signals. With EEG recording enabled, ETIs of up to 60 kΩ can be measured, which increases to 550 kΩ when EEG recording is disabled. Each EEG channel has a 1.2 GΩ input impedance (at 20 Hz), 1.75 μVrms (0.5-100 Hz) input-referred noise, 84 dB CMRR and ±250 mV electrode offset rejection capability. The EEG acquisition system was implemented in a standard 0.18 μm CMOS process, and dissipates less than 700 μW from a 1.8 V supply.


title: Effects of Vibrio anguillarum on immune activities of Chlamys farreri

abstract: The content of intracellular ROS and the activities of CAT, ACP and ALP of Chlamys farreri were studied at 5, 24, 48 and 72 h after challenged with Vibrio anguillarum injection. The results showed that the intracellular ROS content of experimental groups were much higher than that of control groups at 24, 48, 72 h. The CAT activities of experimental groups in haemocytes were higher than that of control group but not significantly. The ACP and ALP activities of experimental groups were much higher than that of control group at 5, 24, 48, 72 h. In conclusion, V.anguillarum could influence the immune activities of C.farreri.


title: Optimizing routine collection efficiency in IoT based garbage collection monitoring systems

abstract: Ubiquitous objects are getting “smarter” and more “connected”, every day. With this ever-growing Internet of Things, every object can now be uniquely identified and made to communicate with each other. This approach has been applied to dustbins too, to monitor garbage collection, throwing light on numerous valuable insights. Our project too employs a similar approach, to not only monitor garbage collection but also optimize it, using machine learning. The method of unsupervised learning we utilize is K Means Clustering, widely used in data mining and analytics. Our physical device uses an ultrasonic sensor to be aware of a dustbin's current content level. If the level reaches or exceeds a threshold percentage of the total capacity of the dustbin, it informs our servers, via an online application programming interface (API) developed for this purpose. The API also stores related data – fill time, cleanup time, and location, to name a few. This dynamic dataset generated is analyzed by our algorithm, to determine the times of the day, when a regular cleanup should be performed, such that the dustbins are clean, for the maximum possible portion of the day. The algorithm also shows the locations, where another dustbin should be installed, for further optimization. This is found out by inspecting each cluster individually and scanning out – items which are the furthest away from its closest centroid; and multiple items related to the same dustbin. In either case, a new dustbin installation is advised at such locations. Data henceforth generated revealed that the installation has had a positive effect on the optimization.


title: Student Privacy and Educational Data Mining: Perspectives from Industry

abstract: While the field of educational data mining (EDM) has generated many innovations for improving educational software and student learning, the mining of student data has recently come under a great deal of scrutiny. Many stakeholder groups, including public officials, media outlets, and parents, have voiced concern over the privacy of student data and their efforts have garnered national attention. The momentum behind and scrutiny of student privacy has made it increasingly difficult for EDM applications to transition from academia to industry. Based on experience as academic researchers transitioning into industry, we present three primary areas of concern related to student privacy in practice: policy, corporate social responsibility, and public opinion. Our discussion will describe the key challenges faced within these categories, strategies for overcoming them, and ways in which the academic EDM community can support the adoption of innovative technologies in large-scale production.


title: A Language Support for Cloud Elasticity Management

abstract: Elasticity is the intrinsic element that differentiates Cloud computing from traditional computing paradigm, since it allows service providers to rapidly adjust their needs for resources to absorb the demand and hence guarantee a minimum level of Quality of Service (QoS) that respects the Service Level Agreements (SLAs) previously defined with their clients. However, due to non-negligible resource initiation time, network fluctuations or unpredictable workload, it becomes hard to guarantee QoS levels and SLA violations may occur. This paper proposes a language support for Cloud elasticity management that relies on CSLA (Cloud Service Level Agreement). CSLA offers new features such as QoS/functionality degradation and an advanced penalty model that allow providers to finely express contracts so that services self-adaptation capabilities are improved and SLA violations minimized. The approach was evaluated with a real infrastructure and application test bed. Experimental results show that the use of CSLA makes Cloud services capable of absorbing more peaks and oscillations by trading-off the QoS levels and costs due to penalties.


title: Semantic Similarity of Arabic Sentences with Word Embeddings

abstract: Semantic textual similarity is the basis of countless applications and plays an important role in diverse areas, such as information retrieval, plagiarism detection, information extraction and machine translation. This article proposes an innovative word embedding-based system devoted to calculate the semantic similarity in Arabic sentences. The main idea is to exploit vectors as word representations in a multidi-mensional space in order to capture the semantic and syntactic properties of words. IDF weighting and Part-of-Speech tagging are applied on the examined sentences to support the identification of words that are highly descriptive in each sentence. The performance of our proposed system is confirmed through the Pearson correlation between our assigned semantic similarity scores and human judgments.


title: Comparison among three analytical methods for knowledge communities group-decision analysis

abstract: Knowledge management can greatly facilitate an organization's learning via strategic insight. Assessing the achievements of knowledge communities (KC) includes both a theoretical basis and practical aspect; however, a cautionary word is in order, because using improper measurements will increase complexity and reduce applicability. Group decision-making, the essence of knowledge communities, lets one considers multi-dimensional problems for the decision-maker, sets priorities for each decision factor, and assesses rankings for all alternatives. The purpose of this study is to establish the objective and measurable patterns to obtain anticipated achievements of KC through conducting a group-decision comparison. The three multiple-criteria decision-making methods we used, simple average weight (SAW), ''Technique for Order Preference by Similarity to an Ideal Solution'' (TOPSIS) and ''VlseKriterijumska Optimizacija I Kompromisno Resenje'' (VIKOR), are based on an aggregating function representing ''closeness to the ideal point''. The TOPSIS and VIKOR methods were used to highlight our innovative idea, academic analysis, and practical appliance value. Simple average weight (SAW) is known to be a common method to get the preliminary outcome. Our study provides a comparison analysis of the above-three methods. An empirical case is illustrated to demonstrate the overall KC achievements, showing their similarities and differences to achieve group decisions. Our results showed that TOPSIS and simple average weight (SAW) had identical rankings overall, but TOPSIS had better distinguishing capability. TOPSIS and VIKOR had almost the same success setting priorities by weight. However, VIKOR produced different rankings than those from TOPSIS and SAW, and VIKOR also made it easy to choose appropriate strategies. Both the TOPSIS and VIKOR methods are suitable for assessing similar problems, provide excellent results close to reality, and grant superior analysis.


title: V-REP: A versatile and scalable robot simulation framework

abstract: From exploring planets to cleaning homes, the reach and versatility of robotics is vast. The integration of actuation, sensing and control makes robotics systems powerful, but complicates their simulation. This paper introduces a versatile, scalable, yet powerful general-purpose robot simulation framework called V-REP. The paper discusses the utility of a portable and flexible simulation framework that allows for direct incorporation of various control techniques. This renders simulations and simulation models more accessible to a general-public, by reducing the simulation model deployment complexity. It also increases productivity by offering built-in and ready-to-use functionalities, as well as a multitude of programming approaches. This allows for a multitude of applications including rapid algorithm development, system verification, rapid prototyping, and deployment for cases such as safety/remote monitoring, training and education, hardware control, and factory automation simulation.


title: 3-D Path Planning and Target Trajectory Prediction for the Oxford Aerial Tracking System

abstract: For the Oxford Aerial Tracking System (OATS) we are developing a robot helicopter that can track moving ground objects. Here we describe algorithms for the device to perform path planning and trajectory prediction. The path planner uses superquadratic potential fields and incorporates a height change mechanism that is triggered where necessary and in order to avoid local minima traps. The goal of the trajectory prediction system is to reliably predict the target trajectory during occlusion caused by ground obstacles. For this we use two artificial neural networks in parallel whose retraining is automatically triggered if major changes in the target behaviour pattern are detected. Simulation results for both systems are presented.


title: Gender differences in the perception and acceptance of online games

abstract: With the proliferation of online games, understanding users' intention to play online games has become a new issue for academics and practitioners. Prior studies have investigated the factors affecting behavioural intention to play online games. However, little research has been conducted to investigate the gender differences in the acceptance of online games. Thus, this study is to investigate the effects of perceived playfulness and its potential antecedents (ie, computer self-efficacy, computer anxiety, challenge, speed and feedback) on the behavioural intention to play online games, and to examine the gender differences in the perception and acceptance of online games. Data collected from 281 respondents in Taiwan were tested against the research model using analysis of variance and structural equation modelling approaches. The theoretical and practical implications of the results were discussed. [ABSTRACT FROM AUTHOR]


title: An approach to the automatic design of multiple classifier systems

abstract: Abstract Multiple classifier systems (MCSs) based on the combination of outputs of a set of different classifiers have been proposed in the field of pattern recognition as a method for the development of high performance classification systems. Previous work clearly showed that multiple classifier systems are effective only if the classifiers forming them are accurate and make different errors. Therefore, the fundamental need for methods aimed to design “accurate and diverse” classifiers is currently acknowledged. In this paper, an approach to the automatic design of multiple classifier systems is proposed. Given an initial large set of classifiers, our approach is aimed at selecting the subset made up of the most accurate and diverse classifiers. A proof of the optimality of the proposed design approach is given. Reported results on the classification of multisensor remote sensing images show that this approach allows the design of effective multiple classifier systems.


title: Motivational climate, resilience, and burnout in youth sport

abstract: Purpose ::: Grounded in the theoretical framework of achievement goal theory, the purpose of this study was to examine the role of some personal factors (perceived competence and resilience) and situational variables (motivational climate) on burnout in young athletes practicing team sports.


title: Overview of Multi-MW Wind Turbines and Wind Parks

abstract: Multimegawatt wind-turbine systems, often organized in a wind park, are the backbone of the power generation based on renewable-energy systems. This paper reviews the most-adopted wind-turbine systems, the adopted generators, the topologies of the converters, the generator control and grid connection issues, as well as their arrangement in wind parks.


title: Planar inverted-f antenna (PIFA) design for millimeter wave application

abstract: This paper presented the analysis and characterization of a single planar inverted-F antenna (PIFA) operating with 28 GHz frequency. The PIFA design is extended to dual element Multiple Input Multiple Output (MIMO) antenna. PIFA design is used as it is one of the most promising antenna types because it is small and has a low profile, making it suitable for mounting in mobile devices. Computer Simulation Technology (CST) software is used to design the proposed antenna.


title: BinSign: Fingerprinting Binary Functions to Support Automated Analysis of Code Executables

abstract: Binary code fingerprinting is a challenging problem that requires an in-depth analysis of binary components for deriving identifiable signatures. Fingerprints are useful in automating reverse engineering tasks including clone detection, library identification, authorship attribution, cyber forensics, patch analysis, malware clustering, binary auditing, etc. In this paper, we present BinSign, a binary function fingerprinting framework. The main objective of BinSign is providing an accurate and scalable solution to binary code fingerprinting by computing and matching structural and syntactic code profiles for disassemblies. We describe our methodology and evaluate its performance in several use cases, including function reuse, malware analysis, and indexing scalability. Additionally, we emphasize the scalability aspect of BinSign. We perform experiments on a database of 6 million functions. The indexing process requires an average time of 0.0072 s per function. We find that BinSign achieves higher accuracy compared to existing tools.


title: Measuring self-regulated learning in a novel e-learning platform: eLDa

abstract: Massive Open Online Courses (MOOCs) have proliferated internationally. Despite the high profile of MOOCs, many people who enrol do not complete their course, leading to a widely publicized high dropout rate. This research introduces a novel MOOC learning platform known as 'eLDa', which implements a new approach to MOOC structure and incorporates several theory-based features specifically aimed at addressing problems associated with high attrition. In particular, the framework supports users in establishing their own learning objectives and establishing individual learning paths. We seek to investigate the relationships between learner choice, learner engagement, and development of capacity for self-regulated learning. Our approach allows "success" to be defined not in terms of full completion of a course, but whether learners achieve their objectives. The eLDa platform is being trialled with an adapted version of a course previously run in traditional MOOC style. Data is being gathered from learners in each of two modes: (1) learners set their own goals and study in a self-regulated manner and at their own pace; (2) learners follow a system-led, structured path of study.


title: Efficient Correlated Topic Modeling with Topic Embedding

abstract: Correlated topic modeling has been limited to small model and problem sizes due to their high computational cost and poor scaling. In this paper, we propose a new model which learns compact topic embeddings and captures topic correlations through the closeness between the topic vectors. Our method enables efficient inference in the low-dimensional embedding space, reducing previous cubic or quadratic time complexity to linear w.r.t the topic size. We further speedup variational inference with a fast sampler to exploit sparsity of topic occurrence. Extensive experiments show that our approach is capable of handling model and data scales which are several orders of magnitude larger than existing correlation results, without sacrificing modeling quality by providing competitive or superior performance in document classification and retrieval.


title: Does contact work in protracted asymmetrical conflict? Appraising 20 years of reconciliation-aimed encounters between Israeli Jews and Palestinians

abstract: In the past few decades, planned contact interventions between groups in conflict have played an important role in attempts at improving intergroup relations and achieving peace and reconciliation. This article focuses on such reconciliation-aimed intergroup encounters between Israeli Jews and Palestinians that seek to reduce hostility and increase understanding and cooperation between the two nationalities. Like other contact interventions conducted in settings of intergroup conflict, encounters between Israeli Jews and Palestinians represent a paradoxical project: this is a project that aspires to generate equality and cooperation between groups that are embedded in a protracted asymmetrical conflict. Though existing research teaches us valuable lessons on the effectiveness of contact conducted under optimal conditions, little is said about contact between groups involved in asymmetrical protracted dispute. The goal of this analysis is to examine the evolution of reconciliation-aimed contact interventions between Israeli Jews and Palestinians in the past 20 years. The research method is qualitative, relying on ethnographic data assembled during the relevant period of time. The findings identify and trace the evolution of four major models of Jewish-Palestinian planned encounters: the Coexistence Model, the Joint Projects Model, the Confrontational Model, and the Narrative-Story-Telling Model. The strengths and limitations of each model in transforming intergroup attitudes in asymmetric conflict are discussed.


title: Combining Temporal Features by Local Binary Pattern for Acoustic Scene Classification

abstract: The popular frequency-domain features Mel-frequency cepstral coefficients MFCCs have been widely used for the task of acoustic scene classification ASC. The MFCC feature vector describes only the power spectral envelope of a single frame, but it seems like environmental audio signal would benefit from information in the temporal dynamics. However, the classic approach of integrating them would lose this important information. Here, we adopt local binary pattern LBP as a tool to characterize the latent information on the temporal dynamics. The frame-level MFCC features are viewed as a 2-D image, where we use LBP to encode the evolution process. Besides, some complementary spectral features such as spectral centroid SC, spectral bandwidth SBW is utilized to further improve the ASC performance. The proposed features are then fed into an ensemble classifier called D3C for recognizing environmental sounds. The results show that the proposed method was able to achieve a classification improvement of 8% compared to the baseline system. Our work presented a new method for combing the temporal features, demonstrating the significance of the temporal evolution features for characterizing the environmental sound.


title: “What Does My Classifier Learn?” A Visual Approach to Understanding Natural Language Text Classifiers

abstract: Neural Networks have been utilized to solve various tasks such as image recognition, text classification, and machine translation and have achieved exceptional results in many of these tasks. However, understanding the inner workings of neural networks and explaining why a certain output is produced are no trivial tasks. Especially when dealing with text classification problems, an approach to explain network decisions may greatly increase the acceptance of neural network supported tools. In this paper, we present an approach to visualize reasons why a classification outcome is produced by convolutional neural networks by tracing back decisions made by the network. The approach is applied to various text classification problems, including our own requirements engineering related classification problem. We argue that by providing these explanations in neural network supported tools, users will use such tools with more confidence and also may allow the tool to do certain tasks automatically.


title: AMOLED Pixel Circuit With Electronic Compensation of Luminance Degradation

abstract: A new voltage-programmed pixel circuit using hydrogenated amorphous silicon (a-Si:H) thin-film transistors (TFTs) for active-matrix organic light-emitting diodes (AMOLEDs) is presented. In addition to compensating for the shift in threshold voltage of TFTs, the circuit is capable of compensating for OLED luminance degradation by employing the shift in OLED voltage as a feedback of OLED degradation


title: Effects of oleothermal treatment and polydimethylsiloxane (PDMS) coating on natural weathering of beech and fir woods

abstract: Effects of natural weathering on oleothermally treated wood and coated with an organic silicone compound were studied. Slats of beech and fir woods were cut in sizes of 20×10×1 cm. The slats were treated by soybean oil at 230˚C for 1 h. Then, the half of the untreated and oleothermally treated slats were coated with polydimethylsiloxane (PDMS) and the rest of them were considered as control. All slats were hanged in 45˚ angle at the southwest (SW) direction for 90 days. They were analyzed for color changes, roughness, and contact angle properties at 30-day intervals. Results showed that the oleothermal treatment of wood reduced discoloration of fir samples against natural weathering; however, the reducing effect was lower in the Beech samples. This treatment also reduced the surface roughness of the fir samples during weathering while it increased the surface roughness of beech. Wettability of the samples in both species, before and after weathering, was reduced by the oleothermal treatment. Results also revealed that coating of the wood surface with PDMS does not prevent the discoloration and surface roughness against weathering. Nevertheless, the coating could considerably decrease the hydrophilicity of the woods even after weathering.


Review and Performance Analysis of Shortest Path Problem Solving Algorithms


Lean Software Development in Action


title: Understanding the difficulty of training deep feedforward neural networks

abstract: Whereas before 2006 it appears that deep multilayer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with experimental results showing the superiority of deeper vs less deep architectures. All these experimental results were obtained with new initialization or training mechanisms. Our objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future. We first observe the influence of the non-linear activations functions. We find that the logistic sigmoid activation is unsuited for deep networks with random initialization because of its mean value, which can drive especially the top hidden layer into saturation. Surprisingly, we find that saturated units can move out of saturation by themselves, albeit slowly, and explaining the plateaus sometimes seen when training neural networks. We find that a new non-linearity that saturates less can often be beneficial. Finally, we study how activations and gradients vary across layers and during training, with the idea that training may be more difficult when the singular values of the Jacobian associated with each layer are far from 1. Based on these considerations, we propose a new initialization scheme that brings substantially faster convergence. 1 Deep Neural Networks Deep learning methods aim at learning feature hierarchies with features from higher levels of the hierarchy formed by the composition of lower level features. They include Appearing in Proceedings of the 13 International Conference on Artificial Intelligence and Statistics (AISTATS) 2010, Chia Laguna Resort, Sardinia, Italy. Volume 9 of JMLR: WC Weston et al., 2008). Much attention has recently been devoted to them (see (Bengio, 2009) for a review), because of their theoretical appeal, inspiration from biology and human cognition, and because of empirical success in vision (Ranzato et al., 2007; Larochelle et al., 2007; Vincent et al., 2008) and natural language processing (NLP) (Collobert & Weston, 2008; Mnih & Hinton, 2009). Theoretical results reviewed and discussed by Bengio (2009), suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Most of the recent experimental results with deep architecture are obtained with models that can be turned into deep supervised neural networks, but with initialization or training schemes different from the classical feedforward neural networks (Rumelhart et al., 1986). Why are these new algorithms working so much better than the standard random initialization and gradient-based optimization of a supervised training criterion? Part of the answer may be found in recent analyses of the effect of unsupervised pretraining (Erhan et al., 2009), showing that it acts as a regularizer that initializes the parameters in a “better” basin of attraction of the optimization procedure, corresponding to an apparent local minimum associated with better generalization. But earlier work (Bengio et al., 2007) had shown that even a purely supervised but greedy layer-wise procedure would give better results. So here instead of focusing on what unsupervised pre-training or semi-supervised criteria bring to deep architectures, we focus on analyzing what may be going wrong with good old (but deep) multilayer neural networks. Our analysis is driven by investigative experiments to monitor activations (watching for saturation of hidden units) and gradients, across layers and across training iterations. We also evaluate the effects on these of choices of activation function (with the idea that it might affect saturation) and initialization procedure (since unsupervised pretraining is a particular form of initialization and it has a drastic impact).


title: Refined Rademacher Chaos Complexity Bounds with Applications to the Multikernel Learning Problem

abstract: Estimating the Rademacher chaos complexity of order two is important for understanding the performance of multikernel learning (MKL) machines. In this letter, we develop a novel entropy integral for Rademacher chaos complexities. As compared to the previous bounds, our result is much improved in that it introduces an adjustable parameter ε to prohibit the divergence of the involved integral. With the use of the iteration technique in Steinwart and Scovel (2007), we also apply our Rademacher chaos complexity bound to the MKL problems and improve existing learning rates.


title: Supervised Adaptive Incremental Clustering for data stream of chunks

abstract: Many supervised clustering algorithms have been developed to find the optimal clusters for static datasets by presetting some parameters, but they are seldom suitable for dynamic datasets, such as the data stream of chunks. To find the optimal clusters of the data stream of chunks, a novel Supervised Adaptive Incremental Clustering (SAIC) algorithm is proposed. SAIC can cluster dynamic datasets of arbitrary shapes and sizes automatically. It includes learning and post-processing phases. In the learning phase, each cluster updates adaptively according to its learning rate that is calculated from its counter value. All data points are shuffled at each iteration in order to make SAIC insensitive to the input order of data points. In the post-processing phase, the outliers or boundary points are eliminated according to the counter value of each cluster and the number of iterations. Four synthetic datasets and fourteen UCI datasets are used to evaluate the performance of SAIC, respectively. The experiments on UCI datasets show that SAIC reaches to or outperforms some other supervised clustering algorithms and several unsupervised incremental clustering algorithms. In addition, three data stream of chunks are used to evaluate SAIC from different aspects, which shows SAIC has the scalability and incremental learning ability for the clustering of data streams of chunks.


title: Editing Syntax Trees on the Surface

abstract: We describe a system for interactive modification of syntax trees by intuitive editing operations on the surface string. The system has a graphical interface, where the user can move, replace, add, and in other ways modify, words or phrases. During editing, the sentence is kept grammatical, by automatically rearranging words and changing inflection, if necessary. This is accomplished by combining constraints on syntax trees with a distance measure between trees.


title: Distance-based Camera Network Topology Inference for Person Re-identification

abstract: In this paper, we propose a novel distance-based camera network topology inference method for efficient person re-identification. To this end, we first calibrate each camera and estimate relative scales between cameras. Using the calibration results of multiple cameras, we calculate the speed of each person and infer the distance between cameras to generate distance-based camera network topology. The proposed distance-based topology can be applied adaptively to each person according to its speed and handle diverse transition time of people between non-overlapping cameras. To validate the proposed method, we tested the proposed method using an open person re-identification dataset and compared to state-of-the-art methods. The experimental results show that the proposed method is effective for person re-identification in the large-scale camera network with various people transition time.


title: Evaluation of a Multichannel Non-Contact ECG System and Signal Quality Algorithms for Sleep Apnea Detection and Monitoring

abstract: Sleep-related conditions require high-cost and low-comfort diagnosis at the hospital during one night or longer. To overcome this situation, this work aims to evaluate an unobtrusive monitoring technique for sleep apnea. This paper presents, for the first time, the evaluation of contactless capacitively-coupled electrocardiography (ccECG) signals for the extraction of sleep apnea features, together with a comparison of different signal quality indicators. A multichannel ccECG system is used to collect signals from 15 subjects in a sleep environment from different positions. Reference quality labels were assigned for every 30-s segment. Quality indicators were calculated, and their signal classification performance was evaluated. Features for the detection of sleep apnea were extracted from capacitive and reference signals. Sleep apnea features related to heart rate and heart rate variability achieved high similarity to the reference values, with p-values of 0.94 and 0.98, which is in line with the more than 95% beat-matching obtained. Features related to signal morphology presented lower similarity with the reference, although signal similarity metrics of correlation and coherence were relatively high. Quality-based automatic classification of the signals had a maximum accuracy of 91%. Best-performing quality indicators were based on template correlation and beat-detection. Results suggest that using unobtrusive cardiac signals for the automatic detection of sleep apnea can achieve similar performance as contact signals, and indicates clinical value of ccECG. Moreover, signal segments can automatically be classified by the proposed quality metrics as a pre-processing step. Including contactless respiration signals is likely to improve the performance and provide a complete unobtrusive cardiorespiratory monitoring solution; this is a promising alternative that will allow the screening of more patients with higher comfort, for a longer time, and at a reduced cost.