title: Diagnostic Testing and Decision-Making: Beauty Is Not Just in the Eye of the Beholder

abstract: To use a diagnostic test effectively and consistently in their practice, clinicians need to know how well the test distinguishes between those patients who have the suspected acute or chronic disease and those patients who do not. Clinicians are equally interested and usually more concerned whether, based on the results of a screening test, a given patient actually: (1) does or does not have the suspected disease; or (2) will or will not subsequently experience the adverse event or outcome. Medical tests that are performed to screen for a risk factor, diagnose a disease, or to estimate a patient's prognosis are frequently a key component of a clinical research study. Like therapeutic interventions, medical tests require proper analysis and demonstrated efficacy before being incorporated into routine clinical practice. This basic statistical tutorial, thus, discusses the fundamental concepts and techniques related to diagnostic testing and medical decision-making, including sensitivity and specificity, positive predictive value and negative predictive value, positive and negative likelihood ratio, receiver operating characteristic curve, diagnostic accuracy, choosing a best cut-point for a continuous variable biomarker, comparing methods on diagnostic accuracy, and design of a diagnostic accuracy study.


title: A Practical Guide to CNNs and Fisher Vectors for Image Instance Retrieval

abstract: With deep learning becoming the dominant approach in computer vision, the use of representations extracted from Convolutional Neural Nets (CNNs) is quickly gaining ground on Fisher Vectors (FVs) as favoured state-of-the-art global image descriptors for image instance retrieval. While the good performance of CNNs for image classification are unambiguously recognised, which of the two has the upper hand in the image retrieval context is not entirely clear yet.We propose a comprehensive study that systematically evaluates FVs and CNNs for image instance retrieval. The first part compares the performances of FVs and CNNs on multiple publicly available data sets and for multiple criteria. We show that no descriptor is systematically better than the other and that performance gains can usually be obtained by using both types together. The second part of the study focuses on the impact of geometrical transformations. We show that performance of CNNs can quickly degrade in the presence of certain transformations and propose a number of ways to incorporate the required invariances in the CNN pipeline.Our findings are organised as a reference guide offering practically useful and simply implementable guidelines to anyone looking for state-of-the-art global descriptors best suited to their specific image instance retrieval problem. HighlightsCNNs exhibit very limited invariance to rotation changes compared to FVDoG.CNNs are more robust to scale changes than any variants of FV.Max-pooling across rotated/scaled database images gains rotation/scale invariance.Combining FV with CNN can improve retrieval accuracy by a significant margin.


title: Kalman Filtering with State Constraints : A Survey of Linear and Nonlinear Algorithms

abstract: The Kalman filter is the minimum-variance state estimator for linear dynamic systems with Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is the best linear estimator. For nonlinear systems it is not possible, in general, to derive the optimal state estimator in closed form, but various modifications of the Kalman filter can be used to estimate the state. These modifications include the extended Kalman filter, the unscented Kalman filter, and the particle filter. Although the Kalman filter and its modifications are powerful tools for state estimation, we might have information about a system that the Kalman filter does not incorporate. For example, we may know that the states satisfy equality or inequality constraints. In this case we can modify the Kalman filter to exploit this additional information and get better filtering performance than the Kalman filter provides. This paper provides an overview of various ways to incorporate state constraints in the Kalman filter and its nonlinear modifications. If both the system and state constraints are linear, then all of these different approaches result in the same state estimate, which is the optimal constrained linear state estimate. If either the system or constraints are nonlinear, then constrained filtering is, in general, not optimal, and different approaches give different results.


title: Low profile ultra-wideband antennas for software defined radio

abstract: There is great interest in developing low profile ultra-wideband (UWB) antennas to support software-defined radio (SDR). Typical bandwidth requirements for SDR are 2 MHz to 2000 MHz, and achieving such a bandwidth using a single small antenna is rather challenging. This large bandwidth requirement stems from high data rate transmission and reception requirements (>5 MB/s). In designing such antennas for airframe or ground vehicles, aperture size and profile should be reduced in size by a factor of 10 or more from currently available wavelength size antennas (e.g. 5 m or ~15 ft at 30 MHz for a typical λ/2 dipole). Unfortunately, bandwidths of electrically-small antennas decrease inversely with the third power of their electrical length ka [1]. Thus, there is a concurrent need for high bandwidth and small size antennas, implying significant challenges in RF aperture design.


title: Beyond Age Comparisons: A Plea for the Use of a Modified Brunswikian Approach to Experimental Designs in the Study of Adult Development and Aging

abstract: This article makes a plea for experimental designs in the field of adult development and aging using an approach to research inspired by the work of Egon Brunswik. Our recommendations are intended to


title: Pressure-Based Clutch Control for Automotive Transmissions Using a Sliding-Mode Controller

abstract: Clutch shift control is critical for efficient and high-performance transmission designs, including automatic, dual clutch, and hybrid transmissions. To ensure a smooth clutch to clutch shift, appropriate controls for two consecutive processes are critical. One is the precise coordination between the on-coming and off-going clutches, which requires the on-coming clutch to be filled and ready for engagement at the predetermined time (clutch fill). The other is the proper torque control during clutch engagement. In this paper, we will investigate the closed-loop “wet” clutch control enabled by a pressure sensor in the clutch chamber. The main challenges of the pressure-based “wet” clutch control lie in the complex nonlinear dynamics due to the interactions between the fluid and the mechanical systems, the ON/OFF behavior of the clutch assembly, the time-varying clutch loading condition, the required short time duration for a precise and robust clutch shift, and the lack of the displacement information. To enable precise and robust pressure-based control, this paper focuses on the following three aspects. First, a clutch dynamic model is constructed and validated, which precisely captures the system dynamics in a wide pressure range. Second, a sliding-mode controller is designed to achieve robust pressure control while avoiding the chattering effect. Finally, an observer is constructed to estimate the clutch piston motion, which is not only a necessary term in the nonlinear controller design but also a diagnosis tool for the clutch fill process. To validate the proposed methods, a transmission clutch fixture has been designed and built in the laboratory. The experimental results demonstrate the effectiveness and robustness of the proposed controller and observer.


title: An Iterative BP-CNN Architecture for Channel Decoding

abstract: Inspired by the recent advances in deep learning, we propose a novel iterative belief propagation – convolutional neural network (BP-CNN) architecture for channel decoding under correlated noise. This architecture concatenates a trained CNN with a standard BP decoder. The standard BP decoder is used to estimate the coded bits, followed by a CNN to remove the estimation errors of the BP decoder and obtain a more accurate estimation of the channel noise. Iterating between BP and CNN will gradually improve the decoding SNR and, hence, result in better decoding performance. To train a well-behaved CNN model, we define a new loss function that involves not only the accuracy of the noise estimation but also the normality test for the estimation errors, i.e., to measure how likely the estimation errors follow a Gaussian distribution. The introduction of the normality test to the CNN training shapes the residual noise distribution and further reduces the bit error rate of the iterative decoding, compared to using the standard quadratic loss function. We carry out extensive experiments to analyze and verify the proposed framework. 1 1 Code is available at https://github.com/liangfei-info/Iterative-BP-CNN .


title: Fashion coordinates recommendation based on user behavior and visual clothing style

abstract: Fashion coordinates recommendation focuses on coordinating the pattern and colors of fashion items. For example, which shirt out of the candidates matches the pants best? The traditional recommender system based on Latent Factor Model (LFM) uses user behavior features to deal with the recommendation of two matching item, but it is weak in recommending more than two items because the problem of cold start becomes significant. Furthermore, fashion coordinates are correlated with visual features of items directly, which is another factor that can be imported into the recommendation calculation. In this paper, we propose a fashion coordinates system which considers both user behaviors and visual fashion styles. We extend LFM to dealing with user behavior features, and a deep learning model called Denoising Autoencoder is used to process visual features. Combining those two methods, our system can recommend multi-items more accurate than traditional methods and support cold start. Initial experiments on coordinating three kinds of items (tops, trousers and shoes) are demonstrated in detail.


title: Detecting Volcano Deformation in InSAR using Deep learning

abstract: Globally 800 million people live within 100 km of a volcano and currently 1500 volcanoes are considered active, but half of these have no ground-based monitoring. Alternatively, satellite radar (InSAR) can be employed to observe volcanic ground deformation, which has shown a significant statistical link to eruptions. Modern satellites provide large coverage with high resolution signals, leading to huge amounts of data. The explosion in data has brought major challenges associated with timely dissemination of information and distinguishing volcano deformation patterns from noise, which currently relies on manual inspection. Moreover, volcano observatories still lack expertise to exploit satellite datasets, particularly in developing countries. This paper presents a novel approach to detect volcanic ground deformation automatically from wrapped-phase InSAR images. Convolutional neural networks (CNN) are employed to detect unusual patterns within the radar data.


title: Bricolage: example-based retargeting for web design

abstract: The Web provides a corpus of design examples unparalleled in human history. However, leveraging existing designs to produce new pages is often difficult. This paper introduces the Bricolage algorithm for transferring design and content between Web pages. Bricolage employs a novel, structured-prediction technique that learns to create coherent mappings between pages by training on human-generated exemplars. The produced mappings are then used to automatically transfer the content from one page into the style and layout of another. We show that Bricolage can learn to accurately reproduce human page mappings, and that it provides a general, efficient, and automatic technique for retargeting content between a variety of real Web pages.


title: Unifying Message Passing Algorithms Under the Framework of Constrained Bethe Free Energy Minimization

abstract: Variational message passing (VMP), belief propagation (BP) and expectation propagation (EP) have found their wide applications in complex statistical signal processing problems. In addition to viewing them as a class of algorithms operating on graphical models, this paper unifies them under an optimization framework, namely, Bethe free energy minimization with differently and appropriately imposed constraints. This new perspective in terms of constraint manipulation can offer additional insights on the connection between different message passing algorithms and is valid for a generic statistical model. It also founds a theoretical framework to systematically derive message passing variants. Taking the sparse signal recovery (SSR) problem as an example, a low-complexity EP variant can be obtained by simple constraint reformulation, delivering better estimation performance with lower complexity than the standard EP algorithm. Furthermore, we can resort to the framework for the systematic derivation of hybrid message passing for complex inference tasks. Notably, a hybrid message passing algorithm is exemplarily derived for joint SSR and statistical model learning with near-optimal inference performance and scalable complexity.


title: Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark

abstract: This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convolutional neural networks (CNNs) as a work horse, which already show remarkable performance improvements over state-of-the-art. CNNs have become the de-facto standard for many tasks in computer vision and machine learning like semantic segmentation or object detection in images, but have no yet led to a true breakthrough for 3D point cloud labelling tasks due to lack of training data. With the massive data set presented in this paper, we aim at closing this data gap to help unleash the full potential of deep learning methods for 3D labelling tasks. Our semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains 8 semantic classes and covers a wide range of urban outdoor scenes: churches, streets, railroad tracks, squares, villages, soccer fields and castles. We describe our labelling interface and show that our data set provides more dense and complete point clouds with much higher overall number of labelled points compared to those already available to the research community. We further provide baseline method descriptions and comparison between methods submitted to our online system. We hope semantic3D.net will pave the way for deep learning methods in 3D point cloud labelling to learn richer, more general 3D representations, and first submissions after only a few months indicate that this might indeed be the case.


title: Current BCI technologies in brain engineering

abstract: Various neuroimaging modalities have appeared to acquire brain signals for developing a brain-computer interface (BCI). In this article, we review studies on different modalities including both invasive and non-invasive techniques for the implementation of BCIs, for brain signals detection, decoding, feature extraction, and classification. We discuss their advantages, disadvantages, and implementation issues to design of BCIs. Finally, we focus on fundamental principles, recent developments, and applications by using optical imaging as a promising modality for BCI applications.


title: Automatic Classification of Musical Instrument Sounds

abstract: We present an exhaustive review of research on automatic classification of sounds from musical instruments. Two different but complementary approaches are examined, the perceptual approach and the taxonomic approach. The former is targeted to derive perceptual similarity functions in order to use them for timbre clustering and for searching and retrieving sounds by timbral similarity. The latter is targeted to derive indexes for labeling sounds after culture- or user-biased taxonomies. We review the relevant features that have been used in the two areas and then we present and discuss different techniques for similarity-based clustering of sounds and for classification into pre-defined instrumental categories.


title: Enhancing the Malloc System with Pollution Awareness for Better Cache Performance

abstract: Cache pollution, by which weak-locality data unduly replaces strong-locality data, may notably degrade application performance in a shared-cache multicore machine. This paper presents NightWatch, a cache management subsystem that provides general, transparent and low-overhead pollution control to applications. NightWatch is based on the observation that data within the same memory chunk or chunks within the same allocation context often share similar locality property. NightWatch embodies this observation by online monitoring current cache locality to predict future behavior and restricting potential cache polluters proactively. We have integrated NightWatch into two popular allocators, tcmalloc and ptmalloc2 . Experiments with SPEC CPU2006 show that NightWatch improves application performance by up to 45 percent (18 percent on average), with an average monitoring overhead of 0.57 percent (up to 3.02 percent).


title: New normal limits for the paediatric electrocardiogram.

abstract: AIMS ::: Previous studies that determined the normal limits for the paediatric ECG had their imperfections: ECGs were recorded at a relatively low sampling rate, ECG measurements were conducted manually, or normal limits were presented for only a limited set of parameters. The aim of this study was to establish an up-to-date and complete set of clinically relevant normal limits for the paediatric ECG. ::: ::: ::: METHODS AND RESULTS ::: ECGs from 1912 healthy Dutch children (age 11 days to 16 years) were recorded at a sampling rate of 1200 Hz. The digitally stored ECGs were analysed using a well-validated ECG computer program. The normal limits of all clinically relevant ECG measurements were determined for nine age groups. Clinically significant differences were shown to exist, compared with previously established normal limits. Sex differences could be demonstrated for QRS duration and several amplitude measurements. ::: ::: ::: CONCLUSIONS ::: These new normal limits differ substantially from those commonly used and suggest that diagnostic criteria for the paediatric ECG should be adjusted.


title: Information security governance in Saudi organizations: an empirical study

abstract: Purpose – This paper seeks to empirically examine the existence and implementation of information security governance (ISG) in Saudi organizations.Design/methodology/approach – An empirical survey, using a self‐administered questionnaire, is conducted to explore and evaluate the current status and the main features of ISG in the Saudi environment. The questionnaire is developed based on ISG guidelines for boards of directors and executive management issued by the Information Technology (IT) Governance Institute and other related materials available in the literature. A total of 167 valid questionnaires are collected and processed using the Statistical Package for Social Sciences, version 16.Findings – The results of the study reveal that although the majority of Saudi organizations recognize the importance of ISG as an integrant factor for the success of IT and corporate governance, most of them have no clear information security strategies or written information security policy statements. The majority o...


title: Visualized decision making: development and applications of information visualization techniques to improve decision quality of nursing home choice

abstract: An individual’s decision to place a close family member in a nursing home is both difficult and crucial. To assist consumers with such a decision, several initiatives have led to the creation of public websites designed to communicate quality indicators for nursing homes. However, a majority of consumers fail to fully utilize this information for various reasons, such as the multidimensionality, complexity, and uncertainty of the information. Some of the difficulties may be alleviated by information visualization (InfoVis) techniques. However, several unsuccessful attempts in applying InfoVis to decision making suggest that a thorough understanding of the user’s perspective is necessary. ::: Accordingly, the author has developed an InfoVis tool for the decision domain of choice of a nursing home. First, a framework of overarching InfoVis and decision theories, called the “visualized decision making (VDM)” framework, has been developed and contextualized within the selection of a nursing home. Second, a decision-support tool using several InfoVis techniques such as the weighting slider bar and the distribution view have been designed for application within the framework, and the designed tool, called “VDM,” was implemented. Third, VDM was empirically tested through a webbased experiment and follow-up interviews. ::: The results of this study showed that individuals faced with the decision of selecting a nursing home could make fairly high quality decisions when they used VDM. Though the effects of proposed InfoVis techniques were not evident, this study provided the theoretical framework and empirical results which may help other designers of InfoVis techniques because this work addresses several issues consumers face when choosing a nursing home that can be generalized to other decision making contexts.


title: The state of the art in underwater acoustic telemetry

abstract: Progress in underwater acoustic telemetry since 1982 is reviewed within a framework of six current research areas: (1) underwater channel physics, channel simulations, and measurements; (2) receiver structures; (3) diversity exploitation; (4) error control coding; (5) networked systems; and (6) alternative modulation strategies. Advances in each of these areas as well as perspectives on the future challenges facing them are presented. A primary thesis of this paper is that increased integration of high-fidelity channel models into ongoing underwater telemetry research is needed if the performance envelope (defined in terms of range, rate, and channel complexity) of underwater modems is to expand.


AUTHOR IDENTIFICATION BASED ON A HYBRID FEATURE SET USING MACHINE LEARNING AND CLUSTERING TECHNIQUES


title: Performance of a Deep-Learning Neural Network Model in Assessing Skeletal Maturity on Pediatric Hand Radiographs

abstract: A deep-learning convolutional neural network model can estimate skeletal maturity with accuracy similar to that of an expert radiologist and to that of current state-of-the-art feature extraction–based automated models for assessment of bone age.


From Vision to NLP : A Merge


title: LLLR Parsing: a Combination of LL and LR Parsing

abstract: A new parsing method called LLLR parsing is defined and a method for producing LLLR parsers is described. An LLLR parser uses an LL parser as its backbone and parses as much of its input string using LL parsing as possible. To resolve LL conflicts it triggers small embedded LR parsers. An embedded LR parser starts parsing the remaining input and once the LL conflict is resolved, the LR parser produces the left parse of the substring it has just parsed and passes the control back to the backbone LL parser. The LLLR(k) parser can be constructed for any LR(k) grammar. It produces the left parse of the input string without any backtracking and, if used for a syntax-directed translation, it evaluates semantic actions using the top-down strategy just like the canonical LL(k) parser. An LLLR(k) parser is appropriate for grammars where the LL(k) conflicting nonterminals either appear relatively close to the bottom of the derivation trees or produce short substrings. In such cases an LLLR parser can perform a significantly better error recovery than an LR parser since the most part of the input string is parsed with the backbone LL parser. LLLR parsing is similar to LL(^*) parsing except that it (a) uses LR(k) parsers instead of finite automata to resolve the LL(k) conflicts and (b) does not perform any backtracking.


title: Why are women with cervical cancer not being diagnosed in preinvasive phase? An analysis of risk factors using a hierarchical model.

abstract: OBJECTIVE ::: To assess the risk factors for delayed diagnosis of uterine cervical lesions. ::: ::: ::: MATERIALS AND METHODS ::: This is a case-control study that recruited 178 women at 2 Brazilian hospitals. The cases (n = 74) were composed of women with a late diagnosis of a lesion in the uterine cervix (invasive carcinoma in any stage). The controls (n = 104) were composed of women with cervical lesions diagnosed early on (low- or high-grade intraepithelial lesions). The analysis was performed by means of logistic regression model using a hierarchical model. The socioeconomic and demographic variables were included at level I (distal). Level II (intermediate) included the personal and family antecedents and knowledge about the Papanicolaou test and human papillomavirus. Level III (proximal) encompassed the variables relating to individuals' care for their own health, gynecologic symptoms, and variables relating to access to the health care system. ::: ::: ::: RESULTS ::: The risk factors for late diagnosis of uterine cervical lesions were age older than 40 years (odds ratio [OR] = 10.4; 95% confidence interval [CI], 2.3-48.4), not knowing the difference between the Papanicolaou test and gynecological pelvic examinations (OR, = 2.5; 95% CI, 1.3-4.9), not thinking that the Papanicolaou test was important (odds ratio [OR], 4.2; 95% CI, 1.3-13.4), and abnormal vaginal bleeding (OR, 15.0; 95% CI, 6.5-35.0). Previous treatment for sexually transmissible disease was a protective factor (OR, 0.3; 95% CI, 0.1-0.8) for delayed diagnosis. ::: ::: ::: CONCLUSIONS ::: Deficiencies in cervical cancer prevention programs in developing countries are not simply a matter of better provision and coverage of Papanicolaou tests. The misconception about the Papanicolaou test is a serious educational problem, as demonstrated by the present study.


title: SGX-Bomb: Locking Down the Processor via Rowhammer Attack

abstract: Intel Software Guard Extensions (SGX) provides a strongly isolated memory space, known as an enclave, for a user process, ensuring confidentiality and integrity against software and hardware attacks. Even the operating system and hypervisor cannot access the enclave because of the hardware-level isolation. Further, hardware attacks are neither able to disclose plaintext data from the enclave because its memory is always encrypted nor modify it because its integrity is always verified using an integrity tree. When the processor detects any integrity violation, it locks itself to prevent further damages; that is, a system reboot is necessary. The processor lock seems a reasonable solution against such a powerful hardware attacker; however, if a software attacker has a way to trigger integrity violation, the lock could result in a severe denial-of-service (DoS) attack. In this paper, we introduce the SGX-Bomb attack that launches the Rowhammer attack against enclave memory to trigger the processor lockdown. The SGX-Bomb attack is simple yet alarming. Inside an enclave, this attack first finds conflicting row addresses at the same DRAM bank, and then repeatedly accesses them while bypassing the cache. If arbitrary bit flips have occurred inside the enclave because of the Rowhammer attack, any read attempts to the enclave memory results in a failure of integrity check so that the processor will be locked, and the system should be rebooted. The SGX-Bomb attack is a serious threat especially to the public cloud providers who are supposed to run unknown enclave programs received from their clients, which might shut down their servers shared with other clients. We evaluate the effectiveness of the SGX-Bomb attack in a real environment with DDR4 DRAM; it takes 283 s to hang the entire system with the default DRAM refresh rate, 64 ms.


title: Gait feature extraction in Parkinson's disease using low-cost accelerometers

abstract: The clinical hallmarks of Parkinson's disease (PD) are movement poverty and slowness (i.e. bradykinesia), muscle rigidity, limb tremor or gait disturbances. Parkinson's gait includes slowness, shuffling, short steps, freezing of gait (FoG) and/or asymmetries in gait. There are currently no validated clinical instruments or device that allow a full characterization of gait disturbances in PD. As a step towards this goal, a four accelerometer-based system is proposed to increase the number of parameters that can be extracted to characterize parkinsonian gait disturbances such as FoG or gait asymmetries. After developing the hardware, an algorithm has been developed, that automatically epoched the signals on a stride-by-stride basis and quantified, among others, the gait velocity, the stride time, the stance and swing phases, the single and double support phases or the maximum acceleration at toe-off, as validated by visual inspection of video recordings during the task. The results obtained in a PD patient and a healthy volunteer are presented. The FoG detection will be improved using time-frequency analysis and the system is about to be validated with a state-of-the-art 3D movement analysis system.


title: High-Q MEMS Resonators for Laser Beam Scanning Displays

abstract: This paper reports on design, fabrication and characterization of high-Q MEMS resonators to be used in optical applications like laser displays and LIDAR range sensors. Stacked vertical comb drives for electrostatic actuation of single-axis scanners and biaxial MEMS mirrors were realized in a dual layer polysilicon SOI process. High Q-factors up to 145,000 have been achieved applying wafer level vacuum packaging technology including deposition of titanium thin film getters. The effective reduction of gas damping allows the MEMS actuator to achieve large amplitudes at high oscillation frequencies while driving voltage and power consumption can be minimized. Exemplarily shown is a micro scanner that achieves a total optical scan angle of 86 degrees at a resonant frequency of 30.8 kHz, which fulfills the requirements for HD720 resolution. Furthermore, results of a new wafer based glass-forming technology for fabrication of three dimensionally shaped glass lids with tilted optical windows are presented.


title: Torus data center network with smart flow control enabled by hybrid optoelectronic routers [Invited]

abstract: A torus-topology photonic data center network with smart flow control is presented, where optical packet switching (OPS), optical circuit switching (OCS), and virtual OCS (VOCS) schemes are all enabled on a unified hardware platform via the combination of hybrid optoelectronic routers (HOPR) and a centralized network controller. An upgraded HOPR is being developed with the goal of handling 100 Gbps optical packets with a high energy efficiency of 90 mW/Gbps and low latency of less than 100 ns. The architecture of HOPR and its enabling technologies are reviewed, including a label processor, optical switch, and optoelectronic shared buffer. We also explain the concept and operation mechanisms of both the data and control planes in the hybrid OPS/OCS/VOCS torus network. The performance of these three transmission modes is evaluated by numerical simulations for a network composed of 4096 HOPRs.


title: The BeiHang Keystroke Dynamics Authentication System

abstract: Keystroke Dynamics is an important biometric solution for person authentication. Based upon keystroke dynamics, this paper designs an embedded password protection device, develops an online system, collects two public databases for promoting the research on keystroke authentication, exploits the Gabor filter bank to characterize keystroke dynamics, and provides benchmark results of three popular classification algorithms, one-class support vector machine, Gaussian classifier, and nearest neighbour classifier.


title: Discriminating Instance Generation for Automated Constraint Model Selection

abstract: One approach to automated constraint modelling is to generate, and then select from, a set of candidate models. This method is used by the automated modelling system Conjure. To select a preferred model or set of models for a problem class from the candidates Conjure produces, we use a set of training instances drawn from the target class. It is important that the training instances are discriminating. If all models solve a given instance in a trivial amount of time, or if no models solve it in the time available, then the instance is not useful for model selection. This paper addresses the task of generating small sets of discriminating training instances automatically. The instance space is determined by the parameters of the associated problem class. We develop a number of methods of finding parameter configurations that give discriminating training instances, some of them leveraging existing parameter-tuning techniques. Our experimental results confirm the success of our approach in reducing a large set of input models to a small set that we can expect to perform well for the given problem class.


title: CrimeNet explorer: a framework for criminal network knowledge discovery

abstract: Knowledge about the structure and organization of criminal networks is important for both crime investigation and the development of effective strategies to prevent crimes. However, except for network visualization, criminal network analysis remains primarily a manual process. Existing tools do not provide advanced structural analysis techniques that allow extraction of network knowledge from large volumes of criminal-justice data. To help law enforcement and intelligence agencies discover criminal network knowledge efficiently and effectively, in this research we proposed a framework for automated network analysis and visualization. The framework included four stages: network creation, network partition, structural analysis, and network visualization. Based upon it, we have developed a system called CrimeNet Explorer that incorporates several advanced techniques: a concept space approach, hierarchical clustering, social network analysis methods, and multidimensional scaling. Results from controlled experiments involving student subjects demonstrated that our system could achieve higher clustering recall and precision than did untrained subjects when detecting subgroups from criminal networks. Moreover, subjects identified central members and interaction patterns between groups significantly faster with the help of structural analysis functionality than with only visualization functionality. No significant gain in effectiveness was present, however. Our domain experts also reported that they believed CrimeNet Explorer could be very useful in crime investigation.


title: A novel particle swarm optimization algorithm with Levy flight

abstract: Particle swarm optimization (PSO) is one of the well-known population-based techniques used in global optimization and many engineering problems. Despite its simplicity and efficiency, the PSO has problems as being trapped in local minima due to premature convergence and weakness of global search capability. To overcome these disadvantages, the PSO is combined with Levy flight in this study. Levy flight is a random walk determining stepsize using Levy distribution. Being used Levy flight, a more efficient search takes place in the search space thanks to the long jumps to be made by the particles. In the proposed method, a limit value is defined for each particle, and if the particles could not improve self-solutions at the end of current iteration, this limit is increased. If the limit value determined is exceeded by a particle, the particle is redistributed in the search space with Levy flight method. To get rid of local minima and improve global search capability are ensured via this distribution in the basic PSO. The performance and accuracy of the proposed method called as Levy flight particle swarm optimization (LFPSO) are examined on well-known unimodal and multimodal benchmark functions. Experimental results show that the LFPSO is clearly seen to be more successful than one of the state-of-the-art PSO (SPSO) and the other PSO variants in terms of solution quality and robustness. The results are also statistically compared, and a significant difference is observed between the SPSO and the LFPSO methods. Furthermore, the results of proposed method are also compared with the results of well-known and recent population-based optimization methods.


Fisher Score Dimensionality Reduction for Svm Classification


title: Software Defined Integrated Satellite-Terrestrial Network: A Survey

abstract: SDN paradigm has successfully manage to pave the way toward next-generation networking, but the research on SDN-based integrated satellite and terrestrial network has just started. SDN-enabled management and deployment architecture of integrated satellite-terrestrial network eases the complexity of management of infrastructures and networks, improves the maintaining and deployment costs, achieves efficient resource allocation and improves network performance of overall system. In this paper, we started introducing the SDN-based integrated satellite-terrestrial network architecture and discuss the unified and simple system functional architecture. Then we illustrate the two fundamental aspects of integrated network application functions. Following the demonstration of recent research works, we identify three challenges and discuss the emerging topics requiring further research.


title: Harnessing Sequence Labeling for Sarcasm Detection in Dialogue from TV Series 'Friends'

abstract: This paper is a novel study that views sarcasm detection in dialogue as a sequence labeling task, where a dialogue is made up of a sequence of utterances. We create a manuallylabeled dataset of dialogue from TV series ‘Friends’ annotated with sarcasm. Our goal is to predict sarcasm in each utterance, using sequential nature of a scene. We show performance gain using sequence labeling as compared to classification-based approaches. Our experiments are based on three sets of features, one is derived from information in our dataset, the other two are from past works. Two sequence labeling algorithms (SVM-HMM and SEARN) outperform three classification algorithms (SVM, Naive Bayes) for all these feature sets, with an increase in F-score of around 4%. Our observations highlight the viability of sequence labeling techniques for sarcasm detection of dialogue.


title: Dietary methionine intake and neural tube defects in Mexican-American women.

abstract: BACKGROUND ::: Nutrients other than maternal folic acid are also thought to play a role in preventing neural tube defects (NTDs). Evidence suggests that methionine interacts with folic acid and vitamin B(12) in the methylation of contractile proteins involved in closing the neural folds. The role of dietary intake of methionine in NTD risk has not been specifically studied among Mexican Americans, a population with one of the highest prevalences of NTDs in the United States. ::: ::: ::: METHODS ::: We conducted a case-control study of 184 Mexican American women with NTD-affected pregnancies (case women) and 225 women with normal offspring (control women) who resided along the Texas-Mexico border. The average daily intakes of methionine were calculated from periconceptional food frequency questionnaire data. Women were categorized according to quartiles of daily methionine intake, based on the control mothers' distribution, and the risk for an NTD-affected pregnancy was calculated using the lowest quartile of intake as the referent. ::: ::: ::: RESULTS ::: With adjustment for income, body mass index, hyperinsulinemia, and diarrhea, the odds ratios for increasing quartile of methionine intake were: 0.95 (95% confidence interval [CI], 0.48,1.90), 0.92 (95% CI, 0.46,1.84), and 0.66 (95% CI, 0.30,1.45). Some evidence of interaction between dietary methionine and serum vitamin B(12) was noted particularly at higher levels of both components. ::: ::: ::: CONCLUSIONS ::: This study was limited by a small sample size but examined this association in an exclusively Hispanic population. Results were suggestive of a potential protective effect for NTDs with increasing maternal dietary methionine intake.


title: Service innovation capability: Proposing a new framework

abstract: Service organizations face the challenge of offering their customers continuously improved or completely new services and, hence, require service innovations to sustain themselves in the market. We interpret the design and implementation of new or enhanced service offerings as a dynamic capability because the service organization is required to sense impulses for innovation, seize meaningful ways for change, and to finally transform its operational capabilities to the desired state. Accordingly, we propose a new framework which structures service innovation capability into the areas of sensing, seizing, and transformation. We further identify and describe the key activities in all of these three areas based on an analysis of existing literature. With this conceptual paper, we contribute to a better understanding of service innovation capability by proposing a novel framework which is grounded in dynamic capability theory. This framework is beneficial to both practice and academia. It offers an overview of service innovation capability areas and activities against which service organizations can critically reflect their service innovation initiatives. As for academia, it stipulates promising directions for future research.


title: Identification of Malicious Web Pages with Static Heuristics

abstract: Malicious web pages that launch client-side attacks on web browsers have become an increasing problem in recent years. High-interaction client honeypots are security devices that can detect these malicious web pages on a network. However, high-interaction client honeypots are both resource-intensive and known to miss attacks. This paper presents a novel classification method for detecting malicious web pages that involves inspecting the underlying static attributes of the initial HTTP response and HTML code. Because malicious web pages import exploits from remote resources and hide exploit code, static attributes characterizing these actions can be used to identify a majority of malicious web pages. Combining high-interaction client honeypots and this new classification method into a hybrid system leads to significant performance improvements.


title: Foundations for Model-Based Engineering of Systems of Systems

abstract: The engineering of Systems of Systems presents daunting challenges. In this paper it is argued that rigorous semantic foundations for model-based techniques are potentially beneficial in addressing these. Three priorities for current research are identified: contractual interface definition, the verification of emergent behaviour, and the need to deal with semantic heterogeneity of constituent systems and their models. We describe the foundations of an approach in which architectural models in SysML may be analysed in a formal modelling language that has an extensible semantics given using the Unifying Theories of Programming (UTP).


title: FP-BNN : Binarized neural network on FPGA

abstract: Deep neural networks (DNNs) have attracted significant attention for their excellent accuracy especially in areas such as computer vision and artificial intelligence. To enhance their performance, technologies for their hardware acceleration are being studied. FPGA technology is a promising choice for hardware acceleration, given its low power consumption and high flexibility which makes it suitable particularly for embedded systems. However, complex DNN models may need more computing and memory resources than those available in many current FPGAs. This paper presents FP-BNN, a binarized neural network (BNN) for FPGAs, which drastically cuts down the hardware consumption while maintaining acceptable accuracy. We introduce a Resource-Aware Model Analysis (RAMA) method, and remove the bottleneck involving multipliers by bit-level XNOR and shifting operations, and the bottleneck of parameter access by data quantization and optimized on-chip storage. We evaluate the FP-BNN accelerator designs for MNIST multi-layer perceptrons (MLP), Cifar-10 ConvNet, and AlexNet on a Stratix-V FPGA system. An inference performance of Tera opartions per second with acceptable accuracy loss is obtained, which shows improvement in speed and energy efficiency over other computing platforms.


Congenital Chloride Diarrhea: Is it a Common Disorder in Saudi Arabia?


title: A Dual-Buck–Boost AC/DC Converter for DC Nanogrid With Three Terminal Outputs

abstract: Due to the widely used dc characterized loads and more distributed power generation sources, the dc nanogrid becomes more and more popular, and it is seen as an alternative to the ac grid. For safety considerations, the dc nanogrid should provide reliable grounding for the residential loads such as the low-voltage ac power system. There are three typical grounding configurations for a dc nanogrid: the united grounding, the unidirectional grounding, and the virtual isolated grounding. Each grounding configuration has its own specifications to ac/dc converters. In this paper, a dual-buck–boost ac/dc converter for use in the united-grounding-configuration-based dc nanogrid with three terminal outputs is proposed. The working principle of this converter is presented in detail through analyzing the equivalent circuits. Experiments are carried out to verify the theoretical analysis.


title: Exploring Big Data Opportunities for Online Customer Segmentation

abstract: In today's competitive business environment, more and more organizations move or extent their business online. Thus, there is an increasing need for organizations to build concrete online marketing strategies in order to engage with their customers. One basic step towards achieving the objectives related to online marketing is the segmentation of online customers, based on the customer data gathered online. Since there is an onslaught of customer information collected from online sources, new techniques are required for managing and analyzing the huge amount of data, and this is where the concept of Big Data can play an essential role. This research sheds light on three fields: Online Marketing, Customer Segmentation, and Big Data Analytics. The three domains are integrated into the Online Customer Segmentation (OCS) framework, which attempts to show how online marketing objectives can be supported by techniques and tools applicable to extremely large datasets. For the creation of the OCS framework a set of main online marketing objectives is defined. Moreover, the differences among customer attributes gathered from offline and online channels are discussed and OCS categories are identified. Finally, the concept of Big Data is introduced and relevant techniques and tools suitable for analyzing customer segmentation categories and segmenting customers effectively are described. This work demonstrates the OCS framework by applying it on a hypothetical business scenario using an online customer data set.


title: Actual Emissions from Urban Buses Powered with Diesel and Gas Engines

abstract: Abstract Due to the growing demand for traditionally sourced fossil fuels such as oil or natural gas, it became necessary to search for new unconventional sources of these raw materials. The latest results of research work targeted the exploration and exploitation of resources of yet another type of fuel – shale gas. According to estimates, Poland alone may have reserves of 5.3 billion cubic meters of natural gas, accumulated in non-conventional sources. This potential can be used in many sectors of the economy, including in transport. Thus, it became reasonable to consider the use of natural gas as an alternative power source for vehicles. The article presents the results of emissions from public buses powered by diesel fuel and compressed natural gas. The study was conducted in real traffic conditions on a regular bus line in Poznan. The measurement of toxic exhaust elements was conducted with the use of a mobile measuring system of exhaust gas analyzers PEMS (Portable Emissions Measurement System). The data obtained were used to determine the average emissions of individual components. Based on the obtained values it can be concluded that the use of different types of fuels to drive public transport vehicles leads to a reduction of air pollutants and thus to improvement in the quality of life in the city.


title: Mobile Robot for Object Detection Using Image Processing

abstract: This paper describes a robotic application that tracks a moving object by utilizing a mobile robot with sensors and image processing. The robotic platform uses a visual camera to sense the movement of the desired object and a range sensor to help the robot detect and then avoid obstacles in real time while continuing to track and follow the desired object. In terms of real-time obstacle avoidance capacity, this paper also presents a algorithm for this robotic application specifically. Experimental results show that the robotic and intelligent system can fulfill the requirements of tracking an object and avoiding obstacles simultaneously.


title: 3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks

abstract: We propose a method for reconstructing 3D shapes from 2D sketches in the form of line drawings. Our method takes as input a single sketch, or multiple sketches, and outputs a dense point cloud representing a 3D reconstruction of the input sketch(es). The point cloud is then converted into a polygon mesh. At the heart of our method lies a deep, encoder-decoder network. The encoder converts the sketch into a compact representation encoding shape information. The decoder converts this representation into depth and normal maps capturing the underlying surface from several output viewpoints. The multi-view maps are then consolidated into a 3D point cloud by solving an optimization problem that fuses depth and normals across all viewpoints. Based on our experiments, compared to other methods, such as volumetric networks, our architecture offers several advantages, including more faithful reconstruction, higher output surface resolution, better preservation of topology and shape structure.


i FEM : AN INNOVATIVE FINITE ELEMENT METHOD PACKAGE IN MATLAB


A comparison of Named-Entity Disambiguation and Word Sense Disambiguation.


title: The Trojan Player Typology: A cross-genre, cross-cultural, behaviorally validated scale of video game play motivations

abstract: New scale developed for motivations to play video games.Validated using a North American MOBA and a Chinese MMO.Validated using both survey and server-side behavioral measures. While many video game researchers have built scales to tackle the motivations that people have for playing video games, these scales are often limited by their focus on specific game genres or player cultures as well as their lack of behavioral validation. The present research offers a new scale for player motivations and then examines its validity across two distinct gaming genres and cultures, drawing from server-side data combined with survey data of 18,627 players of the Multiplayer Online Battle Arena League of Legends and 18,819 players of the Chinese Massively Multiplayer Online Game Chevalier's Romance Online 3. Six types of player motivations were found: socializer, completionist, competitor, escapist, story-driven, and smarty-pants. Consistent with previous research on player motivations, this typology offers new insights into why people play video games and how player motivations can be used to infer players' in-game behaviors.


title: Parallel Recursive Deep Model for Sentiment Analysis

abstract: Sentiment analysis has now become a popular research problem to tackle in Artificial Intelligence (AI) and Natural Language Processing (NLP) field. We introduce a novel Parallel Recursive Deep Model (PRDM) for predicting sentiment label distributions. The main trait of our model is to not only use the composition units, i.e., the vector of word, phrase and sentiment label with them, but also exploit the information encoded among the structure of sentiment label, by introducing a sentiment Recursive Neural Network (sentiment-RNN) together with RNTN. The two parallel neural networks together compose of our novel deep model structure, in which Sentiment-RNN and RNTN cooperate with each other. On predicting sentiment label distributions task, our model outperforms previous state of the art approaches on both full sentences level and phrases level by a large margin.


title: On the selection of ground-motion prediction equations for seismic hazard analysis

abstract: A key element in any seismic hazard analysis is the selection of appropriate ground-motion prediction equations (GMPEs). In an earlier paper, focused on the selection and adjustment of ground-motion models for probabilistic seismic hazard analysis (PSHA) in moderately active regions--with limited data and few, if any, indigenous models--Cotton et al. (2006) proposed seven criteria as the basis for selecting GMPEs. Recent experience in applying these criteria, faced with several new GMPEs developed since the Cotton et al. (2006) paper was published and a significantly larger strong-motion database, has led to consideration of how the criteria could be refined and of other conditions that could be included to meet the original objectives of Cotton et al. (2006). In fact, about a dozen new GMPEs are published each year, and this number appears to be increasing. Additionally, Cotton et al. (2006) concluded that the criteria should not be excessively specific, tied to the state-of-the-art in ground-motion modeling at the time of writing and thus remaining static, but rather should be sufficiently flexible to be adaptable to the continuing growth of the global strong-motion database and the continued evolution of GMPEs


title: Smart Substation: State of the Art and Future Development

abstract: The smart substation, revolutionarily changing every aspect of the modern substation, is developing fast in the world and being massively deployed in China quickly. A smart substation is typically implemented with a sophisticated combination of smart primary high-voltage equipment and hierarchically networked secondary devices. Based on the IEC61850 communication protocol, the functionalities, such as the information sharing and interoperability among smart electric equipment, are realized in smart substations. It is regarded as the basis for the development of smart grid and represents the future development trend of substation technologies. This paper reviews the fundamental vision of the smart substation. The state of the art and the challenges encountered in the practice of engineering implementation are presented. Future developments to solve the present challenges and promote the development of smart substations are also described.


title: Mapping science through bibliometric triangulation: An experimental approach applied to water research

abstract: The idea of constructing science maps based on bibliographic data has intrigued researchers for decades, and various techniques have been developed to map the structure of research disciplines. Most science mapping studies use a single method. However, as research fields have various properties, a valid map of a field should actually be composed of a set of maps derived from a series of investigations using different methods. That leads to the question of what can be learned from a combination-triangulation-of these different science maps. In this paper we propose a method for triangulation, using the example of water science. We combine three different mapping approaches: journal-journal citation relations JJCR, shared author keywords SAK, and title word-cited reference co-occurrence TWRC. Our results demonstrate that triangulation of JJCR, SAK, and TWRC produces a more comprehensive picture than each method applied individually. The outcomes from the three different approaches can be associated with each other and systematically interpreted to provide insights into the complex multidisciplinary structure of the field of water research.


title: SVO: Semidirect Visual Odometry for Monocular and Multicamera Systems

abstract: Direct methods for visual odometry (VO) have gained popularity for their capability to exploit information from all intensity gradients in the image. However, low computational speed as well as missing guarantees for optimality and consistency are limiting factors of direct methods, in which established feature-based methods succeed instead. Based on these considerations, we propose a semidirect VO (SVO) that uses direct methods to track and triangulate pixels that are characterized by high image gradients, but relies on proven feature-based methods for joint optimization of structure and motion. Together with a robust probabilistic depth estimation algorithm, this enables us to efficiently track pixels lying on weak corners and edges in environments with little or high-frequency texture. We further demonstrate that the algorithm can easily be extended to multiple cameras, to track edges, to include motion priors, and to enable the use of very large field of view cameras, such as fisheye and catadioptric ones. Experimental evaluation on benchmark datasets shows that the algorithm is significantly faster than the state of the art while achieving highly competitive accuracy.


title: ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras

abstract: We present ORB-SLAM2 a complete SLAM system for monocular, stereo and RGB-D cameras, including map reuse, loop closing and relocalization capabilities. The system works in real-time on standard CPUs in a wide variety of environments from small hand-held indoors sequences, to drones flying in industrial environments and cars driving around a city. Our back-end based on bundle adjustment with monocular and stereo observations allows for accurate trajectory estimation with metric scale. Our system includes a lightweight localization mode that leverages visual odometry tracks for unmapped regions and matches to map points that allow for zero-drift localization. The evaluation on 29 popular public sequences shows that our method achieves state-of-the-art accuracy, being in most cases the most accurate SLAM solution. We publish the source code, not only for the benefit of the SLAM community, but with the aim of being an out-of-the-box SLAM solution for researchers in other fields.


title: Adaptive Threshold Scheme for Touchscreen Gesture Continuous Authentication Using Sensor Trust

abstract: In this study we produce a continuous authentication scheme for mobile devices that adjusts an adaptive threshold for touchscreen interactions based on trust in passively collected sensor data. Our framework unobtrusively compares real-time sensor data of a user to historic data and adjusts a trust parameter based on the similarity. We show that the trust parameter can be used to adjust an adaptive threshold in continuous authentication schemes. The framework passively models temporal, spatial and activity scenarios using sensor data such as location, surrounding devices, wi-fi networks, ambient noise, movements, user activity, ambient light, proximity to objects and atmospheric pressure from study participants. Deviations from the models increases the level of threat the device perceives from the scenario. We also model the user touchscreen interactions. The touchscreen interactions are authenticated against a threshold that is continually adjusted based on the perceived trust. This scheme provides greater nuance between security and usability, enabling more refined decisions. We present our novel framework and threshold adjustment criteria and validate our framework on two state-of-the-art sensor datasets. Our framework more than halves the false acceptance and false rejection rates of a static threshold system.


title: © 2002 Springer-Verlag New York Inc. Approximations for a Bottleneck Steiner Tree Problem

abstract: In the design of wireless communication networks, due to a budget limit, suppose we could put totally n+k stations in the plane. However, n of them must be located at given points. Of course, one would like to have the distance between stations as small as possible. The problem is how to choose locations for other k stations to minimize the longest distance between stations. This problem is NP-hard. We show that if NP \neq P , no polynomial-time approximation for the problem in the rectilinear plane has a performance ratio less than 2 and no polynomial-time approximation for the problem in the Euclidean plane has a performance ratio less than \sqrt 2 and that there exists a polynomial-time approximation with performance ratio 2 for the problem in both the rectilinear plane and the Euclidean plane.


title: A Feature Learning Based Approach for Automated Fruit Yield Estimation

abstract: This paper demonstrates a generalised multi-scale feature learning approach to multi-class segmentation, applied to the estimation of fruit yield on treecrops. The learning approach makes the algorithmflexible and adaptable to different classification problems, and hence applicable to a wide variety of tree-crop applications. Extensive experiments were performed on a dataset consisting of 8000 colour images collected in an apple orchard. This paper shows that the algorithm was able to segment apples with different sizes and colours in an outdoor environment with natural lighting conditions, with a single model obtained from images captured using a monocular colour camera. The segmentation results are applied to the problem of fruit counting and the results are compared against manual counting. The results show a squared correlation coefficient of R 2 = 0.81.


SOIL WATER CHARACTERISTIC 1 RETENTION OF WATER IN SOIL AND THE SOIL WATER CHARACTERISTIC CURVE ( Revised 09 / 13 / 2003 )


Opportunistic Maintenance in Aircraft using Relevant Condition Parameter based Approach


title: Forensic Data Recovery from Flash Memory

abstract: Current forensic tools for examination of embedded systems like mobile phones and PDA’s mostly perform data extraction on a logical level and do not consider the type of storage media during data analysis. This paper suggests a low level approach for the forensic examination of flash memories and describes three low-level data acquisition methods for making full memory copies of flash memory devices. Results are presented of a file system study in which USB memory sticks from 45 different make and models were used. For different mobile phones is shown how full memory copies of their flash memories can be made and which steps are needed to translate the extracted data into a format that can be understood by common forensic media analysis tools. Artifacts, caused by flash specific operations like block erasing and wear leveling, are discussed and directions are given for enhanced data recovery and analysis on data originating from flash memory.


title: The Fuzzy Logic Control of a Multilevel Converter in a Variable Speed Wind Turbine

abstract: In this paper, a fuzzy logic controller has been developed to obtain a stable current output adjusting the modulation index of a multilevel converter in a variable speed wind turbine system. The variable speed wind turbine system has been modeled containing a squirrel-cage induction generator (SCIG), and SPWM controlled AC-DC-AC converter which is constituted of a 6-pulse rectifier and 3-level multilevel inverter. The simulation of the power conversion system has been evaluated using Matlab/Simulink and Fuzzy Toolbox. The input parameters have been modeled with a variable voltage-variable frequency (VVVF) generating system according to various wind speeds. The input values of converter have been limited between 350-700V and 40-60Hz. The fuzzy logic controller tracks the input values and controls the converter in order to supply desired current output. The control process has been developed selecting the most accurate modulation index for multilevel inverter which directly affects the output power.


title: Hierarchical models of object recognition in cortex

abstract: Visual processing in cortex is classically modeled as a hierarchy of increasingly sophisticated representations, naturally extending the model of simple to complex cells of Hubel and Wiesel. Surprisingly, little quantitative modeling has been done to explore the biological feasibility of this class of models to explain aspects of higher-level visual processing such as object recognition. We describe a new hierarchical model consistent with physiological data from inferotemporal cortex that accounts for this complex visual task and makes testable predictions. The model is based on a MAX-like operation applied to inputs to certain cortical neurons that may have a general role in cortical function.


title: EEG-based biometric identification with deep learning

abstract: Despite the recent increasing interest in biometric identification using electroencephalogram (EEG) signals, the state of the art still lacks a simple and robust model that is useful in real applications. This work proposes a new approach based on convolutional neural network CNN. The proposed CNN works directly on raw EEG data, thus alleviating the need for engineering features. We investigate the performance of the CNN on datasets of 100 subjects collected from one driving fatigue experiment. Our results show that the CNN model is fast highly efficient in training ( 100K training epochs) and highly robust, achieving 97% accuracy in identifying ∼14K testing epochs from 100 subjects with non-time-locked natural driving fatigue data and much higher than from randomly sampled epochs (90%). Overall, this work demonstrates the potential of deep learning solutions for real-life EEG-based biometric identification.


title: Rescuing Memristor-based Neuromorphic Design with High Defects

abstract: Memristor-based synaptic network has been widely investigated and applied to neuromorphic computing systems for the fast computation and low design cost. As memristors continue to mature and achieve higher density, bit failures within crossbar arrays can become a critical issue. These can degrade the computation accuracy significantly. In this work, we propose a defect rescuing design to restore the computation accuracy. In our proposed design, significant weights in a specified network are first identified and retraining and remapping algorithms are described. For a two layer neural network with 92.64% classification accuracy on MNIST digit recognition, our evaluation based on real device testing shows that our design can recover almost its full performance when 20% random defects are present.


title: A multi-layered annotated corpus of scientific papers

abstract: Comunicacio presentada a la Tenth International Conference on Language Resources and Evaluation (LREC 2016), celebrada els dies 23 a 28 de maig de 2016 a Portorož, Eslovenia.


title: Linking land use, erosion and sediment yields in river basins

abstract: Results obtained from erosion plots and catchment experiments provide clear evidence of the sensitivity of erosion rates to land use change and related human activity. Evidence for the impact of land use on the sediment yields of world rivers is less clear, although examples of rivers where sediment yields have both increased and decreased in recent decades can be identified. The apparent lack of sensitivity of river sediment loads to land use change reflects, at least in part, the buffering capacity associated with many river basins. This buffering capacity is closely related to the sediment delivery ratio of a river basin, in that basins with high sediment delivery ratios are likely to exhibit a reduced buffering capacity. Investigations of the impact of land use and related human activity on sediment yields should consider the overall sediment budget of a catchment rather than simply the sediment output. Information on the sediment budget of a drainage basin is difficult to assemble using traditional techniques, but recent developments in the application of fingerprinting techniques to establish sediment sources and in the use of environmental radionuclides, such as caesium-137 and lead-210, to document sediment storage offer considerable potential for providing such information. Sediment storage within a river basin can give rise to environmental problems where sediment-associated pollutants accumulate in sediment sinks. The accumulation of phosphorus on river floodplains as a result of overbank sedimentation can, for example, represent an important phosphorus sink.


title: Web technologies for environmental Big Data

abstract: Recent evolutions in computing science and web technology provide the environmental community with continuously expanding resources for data collection and analysis that pose unprecedented challenges to the design of analysis methods, workflows, and interaction with data sets. In the light of the recent UK Research Council funded Environmental Virtual Observatory pilot project, this paper gives an overview of currently available implementations related to web-based technologies for processing large and heterogeneous datasets and discuss their relevance within the context of environmental data processing, simulation and prediction. We found that, the processing of the simple datasets used in the pilot proved to be relatively straightforward using a combination of R, RPy2, PyWPS and PostgreSQL. However, the use of NoSQL databases and more versatile frameworks such as OGC standard based implementations may provide a wider and more flexible set of features that particularly facilitate working with larger volumes and more heterogeneous data sources. We review web service related technologies to manage, transfer and process Big Data.We examine international standards and related implementations.Many existing algorithms can be easily exposed as services and cloud-enabled.The adoption of standards facilitate the implementation of workflows.Use of web technologies to tackle environmental issues is acknowledged worldwide.


title: European option pricing with transaction costs and trading restrictions

abstract: In this paper, using the utility based option pricing approach, pioneered by Hodges and Neuberger, we propose a new model in a continuous-time market with proportional transaction costs and several trading restrictions. According to this model, we study the problem of option pricing and prove that the value function of our model is a unique constrained viscosity solution of a HJB equation.


title: Sphere tracing: a geometric method for the antialiased ray tracing of implicit surfaces

abstract: Sphere tracing is a new technique for rendering implicit surfaces that uses geometric distance. Sphere tracing marches along the ray toward its first intersection in steps guaranteed not to penetrate the implicit surface. It is particularly adept at rendering pathological surfaces. Creased and rough implicit surfaces are defined by functions with discontinuous or undefined derivatives. Sphere tracing requires only a bound on the magnitude of the derivative, robustly avoiding problems where the derivative jumps or vanishes. It is an efficient direct visualization system for the design and investigation of new implicit models. Sphere tracing efficiently approximates cone tracing, supporting symbolic-prefiltered antialiasing. Signed distance functions for a variety of primitives and operations are derived.


title: EDITORIAL Combining Cloud and sensors in a smart city

abstract: In the current worldwide ICT scenario, a constantly growing number of ever more powerful devices (smartphones, sensors, household appliances, RFID devices, etc.) join the Internet, significantly impacting the global traffic volume (data sharing, voice, multimedia, etc.) and foreshadowing a world of (more or less) smart devices, or “things” in the Internet of Things (IoT) perspective. Heterogeneous resources can be aggregated and abstracted according to tailored thing-like semantics, thus enabling Things as a Service paradigm, or better a “Cloud of Things”. In the Future Internet initiatives, sensor networks will assume even more of a crucial role, especially for making smarter cities. Smarter sensors will be the peripheral elements of a complex future ICT world. However, due to differences in the “appliances” being sensed, smart sensors are very heterogeneous in terms of communication technologies, sensing features and elaboration capabilities. This article intends to contribute to the design of a pervasive infrastructure where new generation services interact with the surrounding environment, thus creating new opportunities for contextualization and geo-awareness. The architecture proposal is based on Sensor Web Enablement standard specifications and makes use of the Contiki Operating System for accomplishing the IoT. Smart cities are assumed as the reference scenario.


title: A Review of Subsequence Time Series Clustering

abstract: Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.


title: Development of 1-axis torque sensor with different shape of support and measurement spoke

abstract: This paper describes the design of a new 1-axis torque sensor that consists of two different type spokes. The sensor has three trapezoidal shape spokes and three support spokes. It serves a double purpose that the sensitivity and stiffness for driving torque. We suggest trapezoid-typed spokes rather than conventional rectangular-typed spoke for sensitivity, and support spokes are used to tolerate to decrease the deformation. The sensor is verified by finite element model analysis and experiments. The result showed the linear relationship between the applied weight and the measured value.


title: Space-domain non-iterative approach for SPECT/CT systems considering attenuation and space-variant detector response

abstract: A quantitative analysis of emission planar image reconstruction from projections by an object dependent, exact, direct approach in the space-domain considering both object attenuation and space-variant impulse response of SPECT/CT systems is proposed. That approach is compared with iterative methods and non-object-dependent exact methods in both the space domain and the frequency one. Since the mean-projection precorrection method is the concern of some actual 3D methods of compensation for distance-dependent spatial resolution and is thought right for competing with different methods able to quantify the tracer density in the object of interest, it is also examined in the course of the analysis. The direct approach may also augment the simulation power of the Matlab Image Processing Toolbox concerning the direct and inverse Radon transform from parallel projection data, the Toolbox being actually restricted to the ideal transform in the frequency domain.


Affectively Aligned Assistive Technology for Persons with Dementia 1


title: How to initialize the CNN for small datasets: Extracting discriminative filters from pre-trained model

abstract: In this paper, we study how to initialize the convolutional neural network (CNN) model for training on a small dataset. Specially, we try to extract discriminative filters from the pre-trained model for a target task. On the basis of relative entropy and linear reconstruction, two methods, Minimum Entropy Loss (MEL) and Minimum Reconstruction Error (MRE), are proposed. The CNN models initialized by the proposed MEL and MRE methods are able to converge fast and achieve better accuracy. We evaluate MEL and MRE on the CIFAR10, CIFAR100, SVHN, and STL-10 public datasets. The consistent performances demonstrate the advantages of the proposed methods.


title: Effect of pectin and wheat fibers on quality attributes of extruded cornstarch

abstract: The effect of pectin alone or in combination with wheat fiber on the physical and structural properties of extruded cornstarch, under specific moisture content, barrel temperature and screw speed conditions were studied using a Laboratory single screw extruder. The expansion ratio, the moisture content and the porosity decreased as the concentration of the fibers increased, while hardness increased. Fibers also reduced the size of the cells and increased their number. Pectin increased porosity and reduced expansion ratio and hardness but there was not any apparent effect of pectin on the size and the number of the cells. Interaction between fibers and pectin does not give a statistically significant effect on any of the attributes studied.


title: Nonsubsampled shearlet based CT and MR medical image fusion using biologically inspired spiking neural network

abstract: Abstract This paper presents a new fusion scheme for the CT and MR medical images that utilizes both the features of the nonsubsampled shearlet transform (NSST) and spiking neural network. As a new image representation with the different features, the NSST is utilized to provide an effective representation of the image coefficients. Firstly, the source CT and MR images are decomposed by the NSST into several subimages. The regional energy is used to fuse the low frequency coefficients. High frequency coefficients are also fused using a pulse coupled neural network model that is used as a biologically inspired type neural network. It also retains the edges and detail information from the source images. Finally, the inverse NSST is used to produce the fused image. The performance of the proposed fusion method is evaluated by conducting several experiments on the different CT and MR medical image datasets. Experimental results demonstrate that the proposed method does not only produce better results by successfully fusing the different CT and MR images, but also ensures an improvement in the various quantitative parameters as compared to other existing methods.


title: Skull stripping of MRI brain images using mathematical morphology

abstract: Skull stripping is a major phase in MRI brain imaging applications and it refers to the removal of its non-cerebral tissues. The main problem in skull-stripping is the segmentation of the non-cerebral and the intracranial tissues due to their homogeneity intensities. As morphology requires prior binarization of the image, this paper proposed mathematical morphology segmentation using double and Otsu's thresholding. The purpose is to identify robust threshold values to remove the non-cerebral tissue from MRI brain images. Ninety collected samples of T1-weighted, T2-weighted and FLAIR MRI brain images are used in the experiments. The results showed promising use of double threholding as a robust threshold value in handling intensity inhomogeneities compared to Otsu's thresholding.


title: Comparison of Different Classification Techniques Using WEKA for Breast Cancer

abstract: The development of data-mining applications such as classification and clustering has shown the need for machine learning algorithms to be applied to large scale data. In this paper we present the comparison of different classification techniques using Waikato Environment for Knowledge Analysis or in short, WEKA. WEKA is an open source software which consists of a collection of machine learning algorithms for data mining tasks. The aim of this paper is to investigate the performance of different classification or clustering methods for a set of large data. The algorithm or methods tested are Bayes Network, Radial Basis Function, Pruned Tree, Single Conjunctive Rule Learner and Nearest Neighbors Algorithm. A fundamental review on the selected technique is presented for introduction purposes. The data breast cancer data with a total data of 6291 and a dimension of 699 rows and 9 columns will be used to test and justify the differences between the classification methods or algorithms. Subsequently, the classification technique that has the potential to significantly improve the common or conventional methods will be suggested for use in large scale data, bioinformatics or other general applications.


title: Spinal Inhibitory Interneuron Diversity Delineates Variant Motor Microcircuits

abstract: Animals generate movement by engaging spinal circuits that direct precise sequences of muscle contraction, but the identity and organizational logic of local interneurons that lie at the core of these circuits remain unresolved. Here, we show that V1 interneurons, a major inhibitory population that controls motor output, fractionate into highly diverse subsets on the basis of the expression of 19 transcription factors. Transcriptionally defined V1 subsets exhibit distinct physiological signatures and highly structured spatial distributions with mediolateral and dorsoventral positional biases. These positional distinctions constrain patterns of input from sensory and motor neurons and, as such, suggest that interneuron position is a determinant of microcircuit organization. Moreover, V1 diversity indicates that different inhibitory microcircuits exist for motor pools controlling hip, ankle, and foot muscles, revealing a variable circuit architecture for interneurons that control limb movement.


title: Characterizing Lifting and Lowering Activities with Insole FSR sensors in Industrial Exoskeletons

abstract: This paper presents an insole FSR (Force Sensing Resistor) to dynamically detect weight variations in an exoskeleton system. The proposed methodology is intended for tasks of lifting and lowering heavy weights with an industrial exoskeleton to substantially reduce spinal loads during these manual handling activities. Instead of extensively placing high-dense force sensors by covering the whole plantar area, as most of commercial applications do, we integrate only a few force sensors in specific plantar area, so that the sensory system is not restricted to an individual foot size and shape, and on the other hand has relatively lower material cost. Industrial exoskeletons are intended to assist workers when handling heavy goods. With this in mind, wearers are not able to use their hands to control the exoskeleton since they use them to handle the goods. Therefore, the exoskeleton controller is required to indirectly infer how much and when the wearer requires assistance for lifting or lowering a heavy weight. Our approach of dynamically detect and characterize the increment/decrement of weight, as well as the rising/falling edge, enables the exoskeleton's controller to trigger the request of assistive force to the actuators.


title: Local and Global Algorithms for Disambiguation to Wikipedia

abstract: Disambiguating concepts and entities in a context sensitive way is a fundamental problem in natural language processing. The comprehensiveness of Wikipedia has made the online encyclopedia an increasingly popular target for disambiguation. Disambiguation to Wikipedia is similar to a traditional Word Sense Disambiguation task, but distinct in that the Wikipedia link structure provides additional information about which disambiguations are compatible. In this work we analyze approaches that utilize this information to arrive at coherent sets of disambiguations for a given document (which we call "global" approaches), and compare them to more traditional (local) approaches. We show that previous approaches for global disambiguation can be improved, but even then the local disambiguation provides a baseline which is very hard to beat.


title: Guided Search 2.0 A revised model of visual search

abstract: An important component of routine visual behavior is the ability to find one item in a visual world filled with other, distracting items. This ability to performvisual search has been the subject of a large body of research in the past 15 years. This paper reviews the visual search literature and presents a model of human search behavior. Built upon the work of Neisser, Treisman, Julesz, and others, the model distinguishes between a preattentive, massively parallel stage that processes information about basic visual features (color, motion, various depth cues, etc.) across large portions of the visual field and a subsequent limited-capacity stage that performs other, more complex operations (e.g., face recognition, reading, object identification) over a limited portion of the visual field. The spatial deployment of the limited-capacity process is under attentional control. The heart of the guided search model is the idea that attentional deployment of limited resources isguided by the output of the earlier parallel processes. Guided Search 2.0 (GS2) is a revision of the model in which virtually all aspects of the model have been made more explicit and/or revised in light of new data. The paper is organized into four parts: Part 1 presents the model and the details of its computer simulation. Part 2 reviews the visual search literature on preattentive processing of basic features and shows how the GS2 simulation reproduces those results. Part 3 reviews the literature on the attentional deployment of limited-capacity processes in conjunction and serial searches and shows how the simulation handles those conditions. Finally, Part 4 deals with shortcomings of the model and unresolved issues.


title: Perceptual Models of Human-Robot Proxemics

abstract: To enable socially situated human-robot interaction, a robot must both understand and control proxemics—the social use of space—to employ communication mechanisms analogous to those used by humans. In this work, we considered how proxemic behavior is influenced by human speech and gesture production, and how this impacts robot speech and gesture recognition in face-to-face social interactions. We conducted a data collection to model these factors conditioned on distance. This resulting models of pose, speech, and gesture were consistent with related work in human-human interactions, but were inconsistent with related work in human-human interactions—participants in our data collection pos itioned themselves much farther away than has been observed in related work. These models have been integrated into a situated autonomous proxemic robot controller, in which the robot selects interagent pose parameters to maximize its expectation to recognize natural human speech and body gestures during an interaction. This work contributes to the understanding of the underlying per-cultural processes that govern human proxemic behavior, and has implications for the development of robust proxemic controllers for sociable and socially assistive robots situated in complex interactions (e.g., with multiple people or individuals with hearing/visual impairments) and environments (e.g., in which there is loud noise, reverberation, low lighting, or visual occlusion).


title: An Evolutionary Approach to the Full Optimization of the Traveling Thief Problem

abstract: Real-World problems usually consist of several different small sub-problems interacting with each other. These interactions promote a relation of interdependence, where the quality of a solution to one sub-problem influences the quality of another partial solution. The Traveling Thief Problem (TTP) is a recent benchmark that results from the combination of the Traveling Salesman Problem (TSP) and the Knapsack Problem (KP). Thus far, existing approaches solve the TTP by fixing one of the components, usually the TSP, and then tackling the KP. We follow in a different direction and propose an Evolutionary Algorithm that addresses both sub-problems at the same time. Experimental results show that solving the TTP as whole creates conditions for discovering solutions with enhanced quality, and that fixing one of the components might compromise the overall results.


title: 2010), “Electoral Manipulation via Voter-Friendly Spending: Theory and

abstract: We present a model of the political budget cycle in which incumbents try to influence voters by changing the composition of government spending, rather than overall spending or revenues. Rational voters may support an incumbent who targets them with spending before the election even though such spending may be due to opportunistic manipulation, because it may also reflect sincere preference of the incumbent for types of spending voters favor. Classifying expenditures into those which are likely targeted to voters and those that are not, we provide evidence supporting our model in data on local public finances for all Colombian municipalities. Our findings indicate both a pre-electoral increase in targeted expenditures, combined with a contraction of other types of expenditure, and a voter response to targeting.


title: Improving Security for SCADA Control Systems

abstract: The continuous growth of cyber security threats and attacks including the increasing sophistica-tion of malware is impacting the security of critical infrastructure, industrial control systems, and Supervisory Control and Data Acquisition (SCADA) control systems. The reliable operation of modern infrastructures depends on computerized systems and SCADA systems. Since the emer-gence of Internet and World Wide Web technologies, these systems were integrated with business systems and became more exposed to cyber threats. There is a growing concern about the security and safety of the SCADA control systems. The Presidential Decision Directive 63 document es-tablished the framework to protect the critical infrastructure and the Presidential document of 2003, the National Strategy to Secure Cyberspace stated that securing SCADA systems is a na-tional priority. The critical infrastructure includes telecommunication, transportation, energy, banking, finance, water supply, emergency services, government services, agriculture, and other fundamental systems and services that are critical to the security, economic prosperity, and social well-being of the public. The critical infrastructure is characterized by interdependencies (physi-cal, cyber, geographic, and logical) and complexity (collections of interacting components). Therefore, information security management principles and processes need to be applied to SCADA systems without exception. Critical infrastructure disruptions can directly and indirectly affect other infrastructures, impact large geographic regions, and send ripples throughout the na-tional and global economy. For example, under normal operating conditions, the electric power infrastructure requires fuels (natural gas and petroleum), transportation, water, banking and fi-nance, telecommunication, and SCADA systems for monitoring and control. In this paper, we provide an analysis of key developments, architecture, potential vulnerabilities, and security concerns including recommendations toward improving security for SCADA control systems. We discuss the most important issues concerning the security of SCADA systems in-cluding a perspective on enhancing security of these systems. We briefly describe the SCADA architecture, and identify the attributes that increase the complexity of these systems including the key developments that mark the evolution of the SCADA control systems along with the growth of potential vulnerabilities and security concerns. Then, we provide recommendations toward an enhanced security for SCADA control systems. More efforts should be planned on reducing the vulnerabilities and improving the security operations of these systems. It is necessary to address not only the individual vulnerabiltiies, but the breadth of risks that can interfere with critical operations. We describe key requirements and fea-tures needed to improve the security of the current SCADA control systems. For example, in assessing the risk for SCADA systems, use of general meth-ods for risk analysis including specific conditions and characteristics of a con-


title: Fluid Vector Flow and Applications in Brain Tumor Segmentation

abstract: In this paper, we propose a new approach that we call the ldquofluid vector flowrdquo (FVF) active contour model to address problems of insufficient capture range and poor convergence for concavities. With the ability to capture a large range and extract concave shapes, FVF demonstrates improvements over techniques like gradient vector flow, boundary vector flow, and magnetostatic active contour on three sets of experiments: synthetic images, pediatric head MRI images, and brain tumor MRI images from the Internet brain segmentation repository.


title: Intelligent Tutoring Systems using Reinforcement Learning to teach Autistic Students

abstract: Many Intelligent Tutoring Systems have been developed using different Artificial Intelligence techniques. In this paper we propose to use Reinforcement Learning for building an intelligent tutoring system to teach autistic students, who can’t communicate well with others. In reinforcement learning, a policy is updated for taking appropriate action to teach the student. The main advantage of using reinforcement learning is that, it eliminates the need for encoding pedagogical rules. Various issues in using reinforcement learning for intelligent tutoring systems are discussed in this paper.


title: HAHE: Hierarchical Attentive Heterogeneous Information Network Embedding

abstract: Heterogeneous information network (HIN) embedding has recently attracted much attention due to its effectiveness in dealing with the complex heterogeneous data. Meta path, which connects different object types with various semantic meanings, is widely used by existing HIN embedding works. However, several challenges have not been addressed so far. First, different meta paths convey different semantic meanings, while existing works assume that all nodes share same weights for meta paths and ignore the personalized preferences of different nodes on different meta paths. Second, given a meta path, nodes in HIN are connected by path instances while existing works fail to fully explore the differences between path instances that reflect nodes' preferences in the semantic space. rTo tackle the above challenges, we propose aHierarchical Attentive Heterogeneous information network Embedding (HAHE) model to capture the personalized preferences on meta paths and path instances in each semantic space. As path instances are based on a particular meta path, a hierarchical attention mechanism is naturally utilized to model the personalized preference on meta paths and path instances. Extensive experiments on several real-world datasets show that our proposed \model model significantly outperforms the state-of-the-art methods in terms of various data mining tasks.


title: Adversarial Domain Adaptation for Stance Detection

abstract: This paper studies the problem of stance detection which aims to predict the perspective (or stance) of a given document with respect to a given claim. Stance detection is a major component of automated fact checking. As annotating stances in different domains is a tedious and costly task, automatic methods based on machine learning are viable alternatives. In this paper, we focus on adversarial domain adaptation for stance detection where we assume there exists sufficient labeled data in the source domain and limited labeled data in the target domain. Extensive experiments on publicly available datasets show the effectiveness of our domain adaption model in transferring knowledge for accurate stance detection across domains.


title: Device-to-device communications: The physical layer security advantage

abstract: In systems that allow device-to-device (D2D) communications, user pairs in close proximity communicate directly without using an access point (AP) as an intermediary. D2D communications leads to improved throughput, reduced power consumption and interference, and more flexible resource allocation. We show that the D2D paradigm also provides significantly improved security at the physical layer, by reducing exposure of the information to eavesdroppers from two relatively high-power transmissions to a single low-power hop. We derive the secrecy outage probability (SOP) for the D2D and cellular systems, and compare performance for D2D scenarios in the presence of a multi-antenna eavesdropper. The cellular approach is only seen to have an advantage in certain cases when the AP has a large number of antennas and perfect channel state information.