title: Survey of Nonlinear Attitude Estimation Methods

abstract: This paper provides a survey of modern nonlinear filtering methods for attitude estimation. Early applications relied mostly on the extended Kalman filter for attitude estimation. Since these applications, several new approaches have been developed that have proven to be superior to the extended Kalman filter. Several of these approaches maintain the basic structure of the extended Kalman filter, but employ various modifications in order to provide better convergence or improve other performance characteristics. Examples of such approaches include: filter QUEST, extended QUEST and the backwards-smoothing extended Kalman filter. Filters that propagate and update a discrete set of sigma points rather than using linearized equations for the mean and covariance are also reviewed. A twostep approach is discussed with a first-step state that linearizes the measurement model and an iterative second step to recover the desired attitude states. These approaches are all based on the Gaussian assumption that the probability density function is adequately specified by its mean and covariance. Other approaches that do not require this assumption are reviewed, Associate Professor, Department of Mechanical & Aerospace Engineering. Email: johnc@eng.buffalo.edu. Associate Fellow AIAA. Aerospace Engineer, Guidance, Navigation and Control Systems Engineering Branch. Email: Landis.Markley@nasa.gov. Fellow AIAA. Postdoctoral Research Fellow, Department of Mechanical & Aerospace Engineering. Email: cheng3@eng.buffalo.edu. Member AIAA.


The Blurring Line Between Electronic and Physical Channels: Reconceptualising Multichannel Commerce


title: A Multi-Sensor Fusion MAV State Estimation from Long-Range Stereo, IMU, GPS and Barometric Sensors

abstract: State estimation is the most critical capability for MAV (Micro-Aerial Vehicle) localization, autonomous obstacle avoidance, robust flight control and 3D environmental mapping. There are three main challenges for MAV state estimation: (1) it can deal with aggressive 6 DOF (Degree Of Freedom) motion; (2) it should be robust to intermittent GPS (Global Positioning System) (even GPS-denied) situations; (3) it should work well both for low- and high-altitude flight. In this paper, we present a state estimation technique by fusing long-range stereo visual odometry, GPS, barometric and IMU (Inertial Measurement Unit) measurements. The new estimation system has two main parts, a stochastic cloning EKF (Extended Kalman Filter) estimator that loosely fuses both absolute state measurements (GPS, barometer) and the relative state measurements (IMU, visual odometry), and is derived and discussed in detail. A long-range stereo visual odometry is proposed for high-altitude MAV odometry calculation by using both multi-view stereo triangulation and a multi-view stereo inverse depth filter. The odometry takes the EKF information (IMU integral) for robust camera pose tracking and image feature matching, and the stereo odometry output serves as the relative measurements for the update of the state estimation. Experimental results on a benchmark dataset and our real flight dataset show the effectiveness of the proposed state estimation system, especially for the aggressive, intermittent GPS and high-altitude MAV flight.


Artificial Neural Networks' Application in Weather Forecasting - Using RapidMiner


title: Real-time ball tracking in a semi-automated foosball table

abstract: In this article a method is proposed for ball tracking using 100 Hz computer vision in a semi-automated foosball table. In this application the behavior of the ball is highly dynamic with speeds up to 10 m/s and frequent bounces occur against the sides of the table and the puppets. Moreover, in the overhead camera view of the field the ball is often fully or partially occluded and there are other objects present that resemble the ball. The table is semi-automated to enable single user game play. This article shows that it is possible to perform fast and robust ball tracking by combining efficient image processing algorithms with a priori knowledge of the stationary environment and position information of the automated rods.


title: Forensic carving of network packets and associated data structures

abstract: Using validated carving techniques, we show that popular operating systems (e.g. Windows, Linux, and OSX) frequently have residual IP packets, Ethernet frames, and associated data structures present in system memory from long-terminated network traffic. Such information is useful for many forensic purposes including establishment of prior connection activity and services used; identification of other systems present on the system's LAN or WLAN; geolocation of the host computer system; and cross-drive analysis. We show that network structures can also be recovered from memory that is persisted onto a mass storage medium during the course of system swapping or hibernation. We present our network carving techniques, algorithms and tools, and validate these against both purpose-built memory images and a readily available forensic corpora. These techniques are valuable to both forensics tasks, particularly in analyzing mobile devices, and to cyber-security objectives such as malware analysis.


title: Combining click-stream data with NLP tools to better understand MOOC completion

abstract: Completion rates for massive open online classes (MOOCs) are notoriously low. Identifying student patterns related to course completion may help to develop interventions that can improve retention and learning outcomes in MOOCs. Previous research predicting MOOC completion has focused on click-stream data, student demographics, and natural language processing (NLP) analyses. However, most of these analyses have not taken full advantage of the multiple types of data available. This study combines click-stream data and NLP approaches to examine if students' on-line activity and the language they produce in the online discussion forum is predictive of successful class completion. We study this analysis in the context of a subsample of 320 students who completed at least one graded assignment and produced at least 50 words in discussion forums, in a MOOC on educational data mining. The findings indicate that a mix of click-stream data and NLP indices can predict with substantial accuracy (78%) whether students complete the MOOC. This predictive power suggests that student interaction data and language data within a MOOC can help us both to understand student retention in MOOCs and to develop automated signals of student success.


title: A Circularly Polarized Broadside Radiating “Herringbone” Array Design With the Leaky-Wave Approach

abstract: A series-fed “herringbone” microstrip traveling wave circularly polarized antenna array is designed to achieve broadside radiation. It is shown that such an antenna can be modeled as a periodic leaky-wave (LW) structure, providing insight into radiation characteristics of interest, such as the directivity and beam angle of the structure, particularly at broadside. Full-wave simulations as well as a prototype of the design verified the approach. The extracted leaky-wave number properly predicts the behavior seen in radiation measurements, and no degradation in antenna gain is seen at or near broadside.


title: Size and Weight Reduction of an In-Wheel Axial-Gap Motor Using Ferrite Permanent Magnets for Electric Commuter Cars

abstract: In-wheel type permanent-magnet synchronous motors for electric commuter cars are required to be compact and light to use limited wheel space effectively and reduce unspringing weight. Therefore, our previous papers presented a low-cost in-wheel axial-gap motor that uses ferrite permanent magnets. This motor adopted an open slot structure to reduce production costs. However, to achieve further size and weight reduction, we examine here in detail the adoption of a semiclosed slot structure instead of an open slot structure. Experimental results show that the semiclosed slot structure is effective in realizing size and weight reduction.


title: A glimpse on big data analytics in the framework of marketing strategies

abstract: Mining and analyzing the valuable knowledge hidden behind the amount of data available in social media is becoming a fundamental prerequisite for any effective and successful strategic marketing campaign. Anyway, to the best of our knowledge, a systematic analysis and review of the very recent literature according to a marketing framework is still missing. In this work, we intend to provide, first and foremost, a clear understanding of the main concepts and issues regarding social big data, as well as their features and technologies. Secondly, we focus on marketing, describing an operative methodology to get useful insights from social big data. Then, we carry out a brief but accurate classification of recent use cases from the literature, according to the decision support and the competitive advantages obtained by enterprises whenever they exploit the analytics available from social big data sources. Finally, we outline some open issues and suggestions in order to encourage further research in the field.


title: Simplified Technique for Creating a Youthful Umbilicus in Abdominoplasty

abstract: Reimplantation of the umbilicus remains a critical aesthetic component in abdominoplasty and transverse rectus abdominis musculocutaneous breast reconstruction. Although the ideal shape of the umbilicus has been debated, recent studies have shown the young, thin female with an attractive abdomen tends to have a small, vertically oriented umbilicus. The aesthetic considerations for reimplantation include position, depth, shape, and location of scar. The authors present a technique that is expedient and reliable and that addresses each of these variables. The umbilicus is sutured to the rectus fascia and reimplanted through a vertical incision in the abdominal flap. Subdermal sutures are placed from the umbilicus to the linea alba superiorly and inferiorly. These sutures create a vertically oriented shape and place the umbilicus in the midline. Shortening the umbilical stalk establishes depth and hides the closure of the umbilicus and abdominal flap within the stalk. The stalk length is easily varied, depending on the thickness of the panniculus. Defatting is performed through the vertical incision to allow easy visualization of the umbilicus. This technique creates depth, ensures optimal position, pulls the scar deep in the umbilicus, and produces a vertically oriented, youthful umbilicus. More importantly, a questionnaire given to patients who have undergone abdominoplasty with this procedure (n = 21) confirms that patients have a high level of satisfaction with the resulting shape, position, and overall appearance.


title: Reclassification of Bacillus beijingensis Qiu et al. 2009 and Bacillus ginsengi Qiu et al. 2009 as Bhargavaea beijingensis comb. nov. and Bhargavaea ginsengi comb. nov. and emended description of the genus Bhargavaea

abstract: We have carried out a polyphasic taxonomic characterization of ::: Bacillus beijingensis ::: DSM 19037T and ::: Bacillus ginsengi ::: DSM 19038T, which are closely related phylogenetically to ::: Bhargavaea cecembensis ::: LMG 24411T. All three strains are Gram-stain-positive, non-motile, moderately halotolerant and non-spore-forming. 16S rRNA gene sequence analyses showed that the strains constituted a coherent cluster, with sequence similarities between 99.7 and 98.7 %. The percentage similarity on the basis of amino acid sequences deduced from partial gyrB gene nucleotide sequences of these three type strains was 96.1–92.7 %. Phylogenetic trees based on the 16S rRNA gene and GyrB amino acid sequences, obtained by using three different algorithms, were consistent and showed that these three species constituted a deeply rooted cluster separated from the clades represented by the genera ::: Bacillus ::: , ::: Planococcus ::: , ::: Planomicrobium ::: , ::: Sporosarcina ::: , ::: Lysinibacillus ::: , ::: Viridibacillus ::: , ::: Kurthia ::: and ::: Geobacillus ::: , supporting their placement in the genus ::: Bhargavaea ::: . All three type strains have menaquinone MK-8 as the major respiratory quinone and showed similar fatty acid profiles. The main polar lipids present in the three type strains were diphosphatidylglycerol and phosphatidylglycerol, and the three strains showed peptidoglycan type A4α with l-lysine as the diagnostic diamino acid. The DNA G+C contents of ::: Bacillus beijingensis ::: DSM 19037T, ::: Bacillus ginsengi ::: DSM 19038T and ::: Bhargavaea cecembensis ::: LMG 24411T were 53.1, 50.2 and 53.7 mol%, respectively. The level of DNA–DNA hybridization among the three strains was 57–39 %, indicating that they are members of different species of the genus ::: Bhargavaea ::: . The phenotypic data are consistent with the placement of these three species in a single genus and support their differentiation at the species level. On the basis of these data, we have emended the description of the genus ::: Bhargavaea ::: and propose the reclassification of ::: Bacillus beijingensis ::: and ::: Bacillus ginsengi ::: to the genus ::: Bhargavaea ::: , as Bhargavaea beijingensis comb. nov. (type strain ge10T = DSM 19037T = CGMCC 1.6762T) and Bhargavaea ginsengi comb. nov. (type strain ge14T = DSM 19038T = CGMCC 1.6763T).


title: Comparing image classification methods: K-nearest-neighbor and support-vector-machines

abstract: In order for a robot or a computer to perform tasks, it must recognize what it is looking at. Given an image a computer must be able to classify what the image represents. While this is a fairly simple task for humans, it is not an easy task for computers. Computers must go through a series of steps in order to classify a single image. In this paper, we used a general Bag of Words model in order to compare two different classification methods. Both K-Nearest-Neighbor (KNN) and Support-Vector-Machine (SVM) classification are well known and widely used. We were able to observe that the SVM classifier outperformed the KNN classifier. For future work, we hope to use more categories for the objects and to use more sophisticated classifiers.


title: SVM-based Brain-Machine Interface for controlling a robot arm through four mental tasks

abstract: Abstract Human–Machine Interfaces can be very useful to improve the quality of life of physically impaired users. In this work, a non-invasive spontaneous Brain–Machine Interface (BMI) has been designed to control a robot arm through the mental activity of the users. This BMI uses the classification of four mental tasks in order to manage the movements of the robot. The high accuracy in the classification of these tasks (around 70%) allows a quick accomplishment of the experiment designed, even with the low signal-to-noise ratio of this kind of signals. The experiment consists of reaching four points in the workspace of an industrial robot in the established order. After a brief training, the volunteers are able to control the robot in a real time activity. The real time test shows that the system can be applied to do more complex activity such as pick and place tasks if a supplementary system is added. These interfaces are very adequate in the control of rehabilitation or assistance systems for people suffering from motor impairment.


Team cornell’s skynet: Robust perception and planning in an urban environment


title: COMPRESSION BASED FACE RECOGNITION USING DWT AND SVM

abstract: The biometric is used to identify a person effectively and employ in almost all applications of day to day activities. In this paper, we propose compression based face recognition using Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM). The novel concept of converting many images of single person into one image using averaging technique is introduced to reduce execution time and memory. The DWT is applied on averaged face image to obtain approximation (LL) and detailed bands. The LL band coefficients are given as input to SVM to obtain Support vectors (SV’s). The LL coefficients of DWT and SV’s are fused based on arithmetic addition to extract final features. The Euclidean Distance (ED) is used to compare test image features with database image features to compute performance parameters. It is observed that, the proposed algorithm is better in terms of performance compared to existing algorithms .


title: A Multichannel airborne radar dataset

abstract: The U.S. Naval Research Laboratory (NRL) recently developed an airborne Multichannel Synthetic Aperture Radar (MSAR) test bed system consisting of 32 along-track phase centers using two transmit horns and 16 receive antennas. NRL has deployed this system to perform extensive and systematic data collections on a variety of small maritime craft under different collection approaches, subject motions, and environmental conditions. These data collections are detailed in this paper.


title: How primary school learners conceptualize the environment and environmental education

abstract: Several researchers have noted that the knowledge and awareness of children with regard to environmental concepts and issues are at a low level. The major aim of this study was to investigate the knowledge and understanding of primary school children about selected environmental concepts and issues such as conservation, recycling, pollution and environmental protection. This study also aimed at understanding how the children’s conceptions are developed and how the knowledge of these critical concepts shape their environmental awareness and practices outside the classroom. This information will assist those who have responsibilities for Environmental Education within schools and for those who develop support programmes for the protection, restoration and enhancement of the quality of the environment. The study was conducted at three primary schools and four children were selected from each school. This study involved the use of semistructured interviews to ascertain details of children’s knowledge and conceptions about environmental issues. The study has established that the majority of children that took part in this study understand the environment only from a physical and biophysical perspective. This is characterised by a limited conception of the environment where the environment is thought to be a place, a place with living things and a place with living things and people. The research findings in the study also indicated that the majority of children that took part in investigation utilized by this study recognised basic facts concerning environmental problems; however most of them could not apply their knowledge to comprehend the consequences or suggest potential solutions to the environmental problems. This study will help towards the establishment of a sound basis for Environmental education in schools.


Phraseology and linguistic theory -- a brief survey


3D reconstruction is not just a low-level task: retrospect and survey


title: Maximum Power Extraction from Wind Energy System by Using Perturbation and Observation Method

abstract: This paper analyses and simulates the Maximum Power Point Tracking (MPPT) method for a wind energy system. Permanent Magnet Synchronous Generator (PMSG) is used to generate power from wind energy. The MPPT method used in this work is Perturbation & Observation method which is also called as Hill Climb Searching method. Meaning of perturbation is disturbance. Disturbance parameter in wind energy system is wind speed (v). Due to this variable wind speed output power is also variable throughout the day. By using this perturbed power input to the P & O algorithm it generates the PWM signal to vary the duty cycle of buck converter according to requirement of maximum power. A prototype for the complete system is developed in MATLAB Simulink tool and power output with and without MPPT results are observed.


Numerical methods in a differential geometry approach to problems in nonlinear control theory


title: Confocal Microwave Imaging for Breast Cancer Detection: Delay-Multiply-and-Sum Image Reconstruction Algorithm

abstract: A new image reconstruction algorithm, termed as delay-multiply-and-sum (DMAS), for breast cancer detection using an ultra-wideband confocal microwave imaging technique is proposed. In DMAS algorithm, the backscattered signals received from numerical breast phantoms simulated using the finite-difference time-domain method are time shifted, multiplied in pair, and the products are summed to form a synthetic focal point. The effectiveness of the DMAS algorithm is shown by applying it to backscattered signals received from a variety of numerical breast phantoms. The reconstructed images illustrate improvement in identification of embedded malignant tumors over the delay-and-sum algorithm. Successful detection and localization of tumors as small as 2 mm in diameter are also demonstrated.


Enhanced Features Ranking and Selection using Recursive Feature Elimination ( RFE ) and k-Nearest Neighbor Algorithms in Support Vector Machine for Intrusion Detection System


title: Genetic process mining: an experimental evaluation

abstract: One of the aims of process mining is to retrieve a process model from an event log. The discovered models can be used as objective starting points during the deployment of process-aware information systems (Dumas et al., eds., Process-Aware Information Systems: Bridging People and Software Through Process Technology. Wiley, New York, 2005) and/or as a feedback mechanism to check prescribed models against enacted ones. However, current techniques have problems when mining processes that contain non-trivial constructs and/or when dealing with the presence of noise in the logs. Most of the problems happen because many current techniques are based on local information in the event log. To overcome these problems, we try to use genetic algorithms to mine process models. The main motivation is to benefit from the global search performed by this kind of algorithms. The non-trivial constructs are tackled by choosing an internal representation that supports them. The problem of noise is naturally tackled by the genetic algorithm because, per definition, these algorithms are robust to noise. The main challenge in a genetic approach is the definition of a good fitness measure because it guides the global search performed by the genetic algorithm. This paper explains how the genetic algorithm works. Experiments with synthetic and real-life logs show that the fitness measure indeed leads to the mining of process models that are complete (can reproduce all the behavior in the log) and precise (do not allow for extra behavior that cannot be derived from the event log). The genetic algorithm is implemented as a plug-in in the ProM framework.


title: Skill Acquisition: An Event Approach with Special Reference to Searching for the Optimum of a Function of Several Variables

abstract: Publisher Summary This chapter provides an overview on skill acquisition. In reference to the nature of the actor, the relationships among muscles are sufficiently plastic so that within limits actors are able to constrain or organize their musculature into different systems. On this perspective, learning a skill involves discovering an optimal self-organization. In reference to the nature of skills, skills have structure and discovering an optimal self-organization is in reference to those variables of stimulation corresponding to environmental and bio kinematic relations that specify the essential features of the skill the actor is to perform. The useful skill-related information must be discovered, the actor must engage certain search methods that reveal the useful information to him. These search methods must be compatible with the actor; that is, they must be compatible with, for example, real-world mechanical and temporal constraints that natural actors must obey. In seeking an explanation of anything, it is important that the forms of theoretical and investigatory attention be a domain of entities and functions that is optimal to the particular problem under investigation.


title: Flower image segmentation based on color analysis and a supervised evaluation

abstract: We propose a flower segmentation schema which overcomes some limits of previous works. Indeed, it does not involve any interaction with the user, or make assumptions based on the domain knowledge. To achieve segmentation, we used OTSU thresholding on Lab color space. The thresholding was performed, separately, on the three component L, a and b, and the best result is selected relatively to the ground truth. The experimentation of the proposed method, performed using the dataset from the Oxford flower collection, make better the results, while consuming less CPU time, than the method proposed by Nilsback and Zisserman[5].


title: A scriptable rapid application deployment framework for sensor networks

abstract: Wireless sensor networks are finding application in heterogeneous scenarios with varied deployment constraints and consumer requirements. Effective instrumentation, visualization, archival, analysis, and third-party notification are the cornerstones of any sensor network deployment. Unfortunately, the manifestation of these generalized needs can differ widely between deployments leading to creation of multiple specialized applications. We present RAPIDSNAP-a flexible, generic and scriptable rapid sensor network application deployment framework that can be quickly customized to realize end-user centric sensor network deployments. Algorithms for routing, clustering and localization have been implemented on sensor nodes and integrated with RAPIDSNAP. Along with standard components for control and visualization, RAPIDSNAP allows each deployment specific extension to be captured in a JavaScript based script called a Rapidapp. RAPIDSNAP acts as an execution platform for Rapidapps and an abstraction layer for them to transparently communicate with nodes in the network.


3 D model classification using convolutional neural network


title: Software defined apertures for 5G wireless network communications

abstract: Commercialized, low C-SWAP (Cost, Size, Weight and Power), software defined communications apertures will substantially increase network capacity in future 5G wireless networks. We advocate using high gain, electronically steerable antennas for small cell radio access networks, backhaul and millimeter wave applications. The steerable beam functionality can provide elevated data rates, reduced power consumption, allow spectrum reuse and enable dynamic network reconfiguration. Additionally, network operating costs would be reduced and deployment schedules would become more flexible. In this paper we elucidate the advantages of using such an antenna in 5G network topologies. We also discuss some of the effects of high-gain, electronically steerable antennas on standards development.


title: Gemini: A Natural Language System For Spoken-Language Understanding

abstract: Gemini is a natural language understanding system developed for spoken language applications. This paper describes the details of the system, and includes relevant measurements of size, efficiency, and performance of each of its sub-components in detail.


title: High-Quality Real-Time Video Inpaintingwith PixMix

abstract: While image inpainting has recently become widely available in image manipulation tools, existing approaches to video inpainting typically do not even achieve interactive frame rates yet as they are highly computationally expensive. Further, they either apply severe restrictions on the movement of the camera or do not provide a high-quality coherent video stream. In this paper we will present our approach to high-quality real-time capable image and video inpainting. Our PixMix approach even allows for the manipulation of live video streams, providing the basis for real Diminished Reality (DR) applications. We will show how our approach generates coherent video streams dealing with quite heterogeneous background environments and non-trivial camera movements, even applying constraints in real-time.


title: Varieties of Transgender/Transsexual Lives and Their Relationship with Transphobia

abstract: The conceptualization of gender variant populations within studies have consisted of imposed labels and a diversity of individual identities that preclude any attempt at examining the variations found among gender variant populations, while at the same time creating artificial distinctions between groups that may not actually exist. Data were collected from 90 transgender/transsexual people using confidential, self-administered questionnaires. Factors like age of transition, being out to others, and participant's race and class were associated with experiences of transphobic life events. Discrimination can have profound impact on transgender/transsexual people's lives, but different factors can influence one's experience of transphobia. Further studies are needed to examine how transphobia manifests, and how gender characteristics impact people's lives.


title: Learning to summarize web image and text mutually

abstract: We consider the problem of learning to summarize images by text and visualize text utilizing images, which we call Mutual-Summarization. We divide the web image-text data space into three subspaces, namely pure image space (PIS), pure text space (PTS) and image-text joint space (ITJS). Naturally, we treat the ITJS as a knowledge base. For summarizing images by sentence issue, we map images from PIS to ITJS via image classification models and use text summarization on the corresponding texts in ITJS to summarize images. For text visualization problem, we map texts from PTS to ITJS via text categorization models and generate the visualization by choosing the semantic related images from ITJS, where the selected images are ranked by their confidence. In above approaches images are represented by color histograms, dense visual words and feature descriptors at different levels of spatial pyramid; and the texts are generated according to the Latent Dirichlet Allocation (LDA) topic model. Multiple Kernel (MK) methodologies are used to learn classifiers for image and text respectively. We show the Mutual-Summarization results on our newly collected dataset of six big events ("Gulf Oil Spill", "Haiti Earthquake", etc.) as well as demonstrate improved cross-media retrieval performance over existing methods in terms of MAP, Precision and Recall.


title: An Autonomous Underwater Vehicle Data-Driven Control Strategy for Target Tracking

abstract: This paper presents a data-driven approach to control the movement of autonomous underwater vehicles (AUVs) operating as receivers of a multistatic sonar surveillance network. The algorithm adopts a nonmyopic receding horizon policy to control the AUV heading, with the objective of minimizing the expected estimate error in the target location produced by the onboard tracker. Minimizing this error is of the utmost interest in target state estimation since it is one way of maintaining tracks. The proposed policy builds on an automated perception module which produces a target track and on an acoustic model to estimate the target measurement signal-to-noise ratio. Driven by the mission objectives, it explores the best decisions for the AUV by evaluating the evolution of the tactical situation over a future time window. Nonmyopic approaches can offer increased performance if compared to myopic ones. The downside is an increase of the computational burden. Methods are described to simplify and solve effectively the resulting decision tree to allow the execution of these kinds of computational intensive algorithms onboard autonomous vehicles. At the same time, they aim at producing AUV paths robust to possible misleading target measurements, which might cause meandering trajectories. These are crucial factors to use data-driven strategies for AUVs which must work in real applications. Results from the COLLAB13 sea trial are reported and discussed. They show both the feasibility of executing the algorithm in realtime on an onboard computer and the benefits of using the proposed approach over conventional predefined paths. These results are, to the best of our knowledge, the first successful demonstration at sea of a complex nonmyopic algorithm running in realtime onboard AUVs in a realistic multistatic littoral surveillance scenario.


title: Performance and microbial analysis of anaerobic digesters treating food waste and dairy manure.

abstract: The stability and microbial population of completely mixed anaerobic digesters were evaluated during the treatment of manure, food waste, and their mixtures at a temperature of 35°C ± 2°C and a hydraulic retention time of 20 days. Two mixtures were tested; the first mixture composed of 32% and 68% and the second of 48% and 52% food waste and dairy manure, respectively, based on volatile solids (VS). The digesters treating manure and the two mixtures showed stable performance at an organic loading rate (OLR) of 4 g VS L-1 day-1. However, the digester treating food waste was not stable at this OLR nor at the reduced OLR of 2 g VS L-1 day-1, as indicated by high volatile fatty acid concentrations and low pH in the digester and low biogas producion rate. The results of this study show that the co-digestion of food waste and dairy manure significantly increases the biogas production rate as compared to digestion of dairy manure alone. The measured biogas production rates were 1.26, 1.91, and 2.02 L L-1 day-1 for manure, first mixture, and second mixture, respectively, at OLR of 4 g VS L-1 day-1. Thus, the addition of food waste into dairy digesters is a feasible method for improving the economics of dairy farm digesters. Microbial analysis of the digester contents revealed that the food waste digester contained statistically greater numbers of clones related to the phyla Thermotogae and Actinobacteria, and the manure digester contained greater amounts of the phyla Firmicutes, Bacteriodetes, and Spirochetes. The archaeal population structure had little difference in the digesters fed with different substrates and was composed of hydrogenotrophic, acetotrophyic, and methylotrophic methanogens.


title: Arabic Online Handwriting Recognition (AOHR): A Survey

abstract: This article comprehensively surveys Arabic Online Handwriting Recognition (AOHR). We address the challenges posed by online handwriting recognition, including ligatures, dots and diacritic problems, online/offline touching of text, and geometric variations. Then we present a general model of an AOHR system that incorporates the different phases of an AOHR system. We summarize the main AOHR databases and identify their uses and limitations. Preprocessing techniques that are used in AOHR, viz. normalization, smoothing, de-hooking, baseline identification, and delayed stroke processing, are presented with illustrative examples. We discuss different techniques for Arabic online handwriting segmentation at the character and morpheme levels and identify their limitations. Feature extraction techniques that are used in AOHR are discussed and their challenges identified. We address the classification techniques of non-cursive (characters and digits) and cursive Arabic online handwriting and analyze their applications. We discuss different classification techniques, viz. structural approaches, Support Vector Machine (SVM), Fuzzy SVM, Neural Networks, Hidden Markov Model, Genetic algorithms, decision trees, and rule-based systems, and analyze their performance. Post-processing techniques are also discussed. Several tables that summarize the surveyed publications are provided for ease of reference and comparison. We summarize the current limitations and difficulties of AOHR and future directions of research.


title: Managed care, time pressure, and physician job satisfaction: Results from the physician worklife study

abstract: OBJECTIVE ::: To assess the association between HMO practice, time pressure, and physician job satisfaction. ::: ::: ::: DESIGN ::: National random stratified sample of 5,704 primary care and specialty physicians in the United States. Surveys contained 150 items reflecting 10 facets (components) of satisfaction in addition to global satisfaction with current job, one's career and one's specialty. Linear regression-modeled satisfaction (on 1-5 scale) as a function of specialty, practice setting (solo, small group, large group, academic, or HMO), gender, ethnicity, full-time versus part-time status, and time pressure during office visits. "HMO physicians" (9% of total) were those in group or staff model HMOs with > 50% of patients capitated or in managed care. ::: ::: ::: RESULTS ::: Of the 2,326 respondents, 735 (32%) were female, 607 (26%) were minority (adjusted response rate 52%). HMO physicians reported significantly higher satisfaction with autonomy and administrative issues when compared with other practice types (moderate to large effect sizes). However, physicians in many other practice settings averaged higher satisfaction than HMO physicians with resources and relationships with staff and community (small to moderate effect sizes). Small and large group practice and academic physicians had higher global job satisfaction scores than HMO physicians (P <.05), and private practice physicians had quarter to half the odds of HMO physicians of intending to leave their current practice within 2 years (P <.05). Time pressure detracted from satisfaction in 7 of 10 satisfaction facets (P <.05) and from job, career, and specialty satisfaction (P <.01). Time allotted for new patients in HMOs (31 min) was less than that allotted in solo (39 min) and academic practices (44 min), while 83% of family physicians in HMOs felt they needed more time than allotted for new patients versus 54% of family physicians in small group practices (P <.05 after Bonferroni's correction). ::: ::: ::: CONCLUSIONS ::: HMO physicians are generally less satisfied with their jobs and more likely to intend to leave their practices than physicians in many other practice settings. Our data suggest that HMO physicians' satisfaction with staff, community, resources, and the duration of new patient visits should be assessed and optimized. Whether providing more time for patient encounters would improve job satisfaction in HMOs or other practice settings remains to be determined.


title: Dealing with Uncertainty: A Survey of Theories and Practices

abstract: Uncertainty accompanies our life processes and covers almost all fields of scientific studies. Two general categories of uncertainty, namely, aleatory uncertainty and epistemic uncertainty, exist in the world. While aleatory uncertainty refers to the inherent randomness in nature, derived from natural variability of the physical world (e.g., random show of a flipped coin), epistemic uncertainty origins from human's lack of knowledge of the physical world, as well as ability of measuring and modeling the physical world (e.g., computation of the distance between two cities). Different kinds of uncertainty call for different handling methods. Aggarwal, Yu, Sarma, and Zhang et al. have made good surveys on uncertain database management based on the probability theory. This paper reviews multidisciplinary uncertainty processing activities in diverse fields. Beyond the dominant probability theory and fuzzy theory, we also review information-gap theory and recently derived uncertainty theory. Practices of these uncertainty handling theories in the domains of economics, engineering, ecology, and information sciences are also described. It is our hope that this study could provide insights to the database community on how uncertainty is managed in other disciplines, and further challenge and inspire database researchers to develop more advanced data management techniques and tools to cope with a variety of uncertainty issues in the real world.


title: Anthropometric measurement and analysis of the lower face in Turkish rhinoplasty patients

abstract: BackgroundLower face dimensions have a great impact on the perception of nasal beauty. For this reason, evaluation of the lower face is important in patients applied for rhinoplasty. In this study, we aimed to describe the values of lower face anthropometric measurements in Turkish patients who applied for rhinoplasty and to compare these values with measurements of individuals who are pleased with their nasal appearance.MethodsA Turkish population of 252 rhinoplasty-negative individuals and 171 rhinoplasty patients were included in this study as the control and rhinoplasty groups. Using the preoperative photographs of the facial profile, seven vertical measurements were taken and seven indices were used to determine the relationships between measurements of the lower face.ResultsIn the rhinoplasty group, most vertical profile measurements were greater in males. The only variables with no gender differences were lower and upper vermilion heights. Only variables with a significant difference between two groups were upper lip vermilion height in females and upper lip height in males.ConclusionsThis study provides objective reference material for the evaluation of the lower face when planning for rhinoplasty. Besides, the differences found between two groups emphasize the importance of the nasolabial region when planning for rhinoplasty.Level of Evidence: Level II, diagnostic study.


title: Literature Review on Electrospinning Process (A Fascinating Fiber Fabrication Technique)

abstract: A literature review that discussed the early attempts at electrospinning, the process of fiber formation, were discussed in some detail. Here electrospinning process considerations were also discussed .operating parameters and material properties which affect the electrospinning process and the resulting fibre morphology has also been discussed. Early Attempts on Electrospinning are mentioned here.


Borderline personality disorder: patterns of self-harm, reported childhood trauma and clinical outcome


title: A low-profile film antenna design for in-car DAB VHF band applications

abstract: A novel low-profile inverted-F film antenna design for in-car DAB Band III (170-240 MHz) applications is presented. This antenna is attached on the front windshield of the car along the edge of the A-pillar, which occupies an area of only 238(L)×27(W)mm2 to reduce sight blockages. Good antenna performance has been obtained.


title: Experimental evaluation of oxygen free radical scavengers in the prevention of reperfusion injury to skeletal muscle.

abstract: A prolonged preoperative ischemic interval decreases the chances for successful replantation of an amputated limb. Skeletal muscle is especially sensitive to periods of prolonged ischemia. It is now hypothesized that significant tissue injury occurs during reperfusion, when oxygen-rich blood contacts anaerobic metabolites forming toxic oxygen free radicals. A replantation model, using the rabbit hind limb tibialis anterior muscle, was developed to assess muscle function and histological appearance following ischemic intervals of five and eight hours. Muscle strength five weeks after injury was used as a functional measurement of tissue damage. The effects of the superoxide free radical scavenger superoxide dismutase (SOD) and the hydroxyl radical scavenger dimethylsulfoxide (DMSO), administered systemically just before reperfusion, were studied. Muscle treated with SOD following five hours of ischemia had essentially normal strength and histological appearance; however, there was no protective effect after eight hours. DMSO treatment had no beneficial effects after five hours of ischemia, but after eight hours DMSO-treated muscle had significantly better function than untreated muscle. Histological examination confirmed the functional results. Clinical treatment of ischemic limbs with free radical scavengers before revascularization may aid in avoiding reperfusion injury and may improve survival and later muscle function.


Open-Domain Event Detection using Distant Supervision


title: Mitigating Sybils in Federated Learning Poisoning

abstract: Machine learning (ML) over distributed data is relevant to a variety of domains. Existing approaches, such as federated learning, compose the outputs computed by a group of devices at a central aggregator and run multi-round algorithms to generate a globally shared model. Unfortunately, such approaches are susceptible to a variety of attacks, including model poisoning, which is made substantially worse in the presence of sybils. In this paper we first evaluate the vulnerability of federated learning to sybil-based poisoning attacks. We then describe FoolsGold, a novel defense to this problem that identifies poisoning sybils based on the diversity of client contributions in the distributed learning process. Unlike prior work, our system does not assume that the attackers are in the minority, requires no auxiliary information outside of the learning process, and makes fewer assumptions about clients and their data. In our evaluation we show that FoolsGold exceeds the capabilities of existing state of the art approaches to countering ML poisoning attacks. Our results hold for a variety of conditions, including different distributions of data, varying poisoning targets, and various attack strategies.


title: The effects of divided attention on encoding and retrieval processes in human memory.

abstract: The authors examined the effects of divided attention (DA) at encoding and retrieval in free recall, cued recall, and recognition memory in 4 experiments. Lists of words or word pairs were presented auditorily and recalled orally; the secondary task was a visual continuous reaction-time (RT) task with manual responses. At encoding, DA was associated with large reductions in memory performance, but small increases in RT; trade-offs between memory and RT were under conscious control. In contrast, DA at retrieval resulted in small or no reductions in memory, but in comparatively larger increases in RT, especially in free recall. Memory performance was sensitive to changes in task emphasis at encoding but not at retrieval. The results are discussed in terms of controlled and automatic processes and speculatively linked to underlying neuropsychological mechanisms. The main purpose of this article is to examine the similarities and differences between encoding and retrieval processes in human memory. At first it seems that the two sets of processes are very similar. This position follows from Tulving's encoding specificity principle, Kolers's views on repetition of operations, and the concept of transfer-appropriate processing. In the same vein, Craik (1983) has suggested that encoding processes are essentially those involved in the perception and comprehension of events, and that retrieval processes represent an attempt to recapitulate these initial processes. In addition, evidence from neuropsychology and neuroscience suggests that the pathways involved in retrieval overlap substantially with those involved in perception and storage of the same type of information. If encoding and retrieval processes are indeed similar, experimental manipulations that affect one set of processes


title: The care and feeding of wild-caught mutants

abstract: Mutation testing of a test suite and a program provides a way to measure the quality of the test suite. In essence, mutation testing is a form of sensitivity testing: by running mutated versions of the program against the test suite, mutation testing measures the suite's sensitivity for detecting bugs that a programmer might introduce into the program. This paper introduces a technique to improve mutation testing that we call wild-caught mutants; it provides a method for creating potential faults that are more closely coupled with changes made by actual programmers. This technique allows the mutation tester to have more certainty that the test suite is sensitive to the kind of changes that have been observed to have been made by programmers in real-world cases.


title: Rare fetal monster in Holstein crossbred cow

abstract: This report describes a rare case of conjoined female twin monster (Monocephalus Thoracopagus Tetrabrachius Tetrapus Dicaudatus) in a Holstein Friesian pluriparous crossbred cow.


title: Anhedonia in schizophrenia and major depression: state or trait?

abstract: BackgroundIn schizophrenia and major depressive disorder, anhedonia (a loss of capacity to feel pleasure) had differently been considered as a premorbid personological trait or as a main symptom of their clinical picture. The aims of this study were to examine the pathological features of anhedonia in schizophrenic and depressed patients, and to investigate its clinical relations with general psychopathology (negative, positive, and depressive dimensions).MethodsA total of 145 patients (80 schizophrenics and 65 depressed subjects) were assessed using the Physical Anhedonia Scale and the Social Anhedonia Scale (PAS and SAS, respectively), the Scales for the Assessment of Positive and Negative Symptoms (SAPS and SANS, respectively), the Calgary Depression Scale for Schizophrenics (CDSS), and the Hamilton Depression Rating Scale (HDRS). The statistical analysis was performed in two steps. First, the schizophrenic and depressed samples were dichotomised into 'anhedonic' and 'normal hedonic' subgroups (according to the 'double (PAS/SAS) cut-off') and were compared on the general psychopathology scores using the Mann-Whitney Z test. Subsequently, for the total schizophrenic and depressed samples, Spearman correlations were calculated to examine the relation between anhedonia ratings and the other psychopathological parameters.ResultsIn the schizophrenic sample, anhedonia reached high significant levels only in 45% of patients (n = 36). This 'anhedonic' subgroup was distinguished by high scores in the disorganisation and negative dimensions. Positive correlations of anhedonia with disorganised and negative symptoms were also been detected. In the depressed sample, anhedonia reached high significant levels in only 36.9% of subjects (n = 24). This 'anhedonic' subgroup as distinguished by high scores in the depression severity and negative dimensions. Positive correlations of anhedonia with depressive and negative symptoms were also been detected.ConclusionIn the schizophrenic sample, anhedonia seems to be a specific subjective psychopathological experience of the negative and disorganised forms of schizophrenia. In the depressed sample, anhedonia seems to be a specific subjective psychopathological experience of those major depressive disorder forms with a marked clinical depression severity.


title: Revisiting Classifier Two-Sample Tests

abstract: The goal of two-sample tests is to assess whether two samples, $S_P \sim P^n$ and $S_Q \sim Q^m$, are drawn from the same distribution. Perhaps intriguingly, one relatively unexplored method to build two-sample tests is the use of binary classifiers. In particular, construct a dataset by pairing the $n$ examples in $S_P$ with a positive label, and by pairing the $m$ examples in $S_Q$ with a negative label. If the null hypothesis"$P = Q$"is true, then the classification accuracy of a binary classifier on a held-out subset of this dataset should remain near chance-level. As we will show, such Classifier Two-Sample Tests (C2ST) learn a suitable representation of the data on the fly, return test statistics in interpretable units, have a simple null distribution, and their predictive uncertainty allow to interpret where $P$ and $Q$ differ. The goal of this paper is to establish the properties, performance, and uses of C2ST. First, we analyze their main theoretical properties. Second, we compare their performance against a variety of state-of-the-art alternatives. Third, we propose their use to evaluate the sample quality of generative models with intractable likelihoods, such as Generative Adversarial Networks (GANs). Fourth, we showcase the novel application of GANs together with C2ST for causal discovery.


title: Self-Supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation

abstract: Enabling robots to autonomously navigate complex environments is essential for real-world deployment. Prior methods approach this problem by having the robot maintain an internal map of the world, and then use a localization and planning method to navigate through the internal map. However, these approaches often include a variety of assumptions, are computationally intensive, and do not learn from failures. In contrast, learning-based methods improve as the robot acts in the environment, but are difficult to deploy in the real-world due to their high sample complexity. To address the need to learn complex policies with few samples, we propose a generalized computation graph that subsumes value-based model-free methods and model-based methods, with specific instantiations interpolating between model-free and model-based. We then instantiate this graph to form a navigation model that learns from raw images and is sample efficient. Our simulated car experiments explore the design decisions of our navigation model, and show our approach outperforms single-step and $N$-step double Q-learning. We also evaluate our approach on a real-world RC car and show it can learn to navigate through a complex indoor environment with a few hours of fully autonomous, self-supervised training. Videos of the experiments and code can be found at github.com/gkahn13/gcg


title: An Interactive RFID-based Bracelet for Airport Luggage Tracking System

abstract: Radio Frequency Identification (RFID) is a promising technology that has been implemented lately in airports. RFID tags are used to identify and track the location of passengers' luggage. This paper investigates the use of an interactive bracelet that communicates with the RFID system by mean of a database application. The database system interacts with the bracelet using messages that inform the passenger about his luggage status. The proposed database design and implementation are also discussed to describe the different functionalities of the application.


title: The perception of affective touch in anorexia nervosa

abstract: Anorexia nervosa (AN) is a disorder characterized by restricted eating, fears of gaining weight, and body image distortions. The etiology remains unknown; however impairments in social cognition and reward circuits contribute to the onset and maintenance of the disorder. One possibility is that AN is associated with reduced perceived pleasantness during social interactions. We therefore examined the perception of interpersonal, ‘affective touch’ and its social modulation in AN. We measured the perceived pleasantness of light, dynamic stroking touches applied to the forearm of 25 AN patients and 30 healthy controls using C Tactile (CT) afferents-optimal (3 cm/s) and non-optimal (18 cm/s) velocities, while simultaneously displaying images of faces showing rejecting, neutral and accepting expressions. CT-optimal touch, but not CT non-optimal touch, elicited significantly lower pleasantness ratings in AN patients compared with healthy controls. Pleasantness ratings were modulated by facial expressions in both groups in a similar fashion; namely, presenting socially accepting faces increased the perception of touch pleasantness more than neutral and rejecting faces. Our findings suggest that individuals with AN have a disordered, CT-based affective touch system. This impairment may be linked to their weakened interoceptive perception and distorted body representation.


Computer-mediated input and the acquisition of L2 vowels.


Verbs, Nouns, and Postpositives in Attic Prose


title: Templated synthesis of mesoporous aluminas by graft copolymer and their CO2 adsorption capacities

abstract: Mesoporous aluminas were synthesized via a sol–gel process by templating an amphiphilic graft copolymer, PVC–g–POEM, consisting of a poly(vinyl chloride) (PVC) backbone and poly(oxyethylene methacrylate) (POEM) side chains. The mesoporous structures of aluminas with large surface areas were confirmed by X-ray diffraction, transmission electron microscopy, and nitrogen adsorption/desorption analysis. Aluminas synthesized with PVC–g–POEM graft copolymer exhibited higher CO2 adsorption capacities (0.7 mol CO2/kg sorbent) than aluminas synthesized without graft copolymer (0.6 mol CO2/kg sorbent). The adsorption capacity of alumina strongly depends on its structure and calcination temperature; amorphous (400 °C) > γ phase (800 °C) > α phase (1000 °C).


title: A Routing Algorithm based on Semi-supervised Learning for Cognitive Radio Sensor Networks

abstract: In Cognitive Radio Sensor Networks (CRSNs), the cognitive radio technology enables sensor nodes to occupy licensed bands in a opportunistic manner and provides advantages in terms of spectrum utilization and system throughput. This paper proposes a routing scheme based on semi-supervised learning, which jointly considers energy efficiency, context-awareness, and optimal path configuration to enhance communication efficiency. A context-aware module is developed to collect and learn context information in an energy-efficient way and a new semi-supervised learning algorithm is proposed to estimate dynamic changes in network environment. A novel routing metric is used to select the most reliable and stable path. Our simulation study shows that the proposed routing algorithm enhances the reliability and stability for CRSNs, and at the same time, significantly improves the packet delivery ratio.


Parametric Excitation of a Pendulum With Bilinear Hysteresis


title: Taxonomic study of Ascocotyle (Phagicola) longa Ransom, 1920 (Digenea: Heterophyidae) and related taxa

abstract: Species of Ascocotyle Looss, 1899 (subgenus Phagicola Faust, 1920) (Digenea: Heterophyidae) related to A. (P.) longa Ransom, 1920, i.e. those with a bipartite gonotyl and a single crown of 16 circumoral spines, were revised on the basis of examination of type and voucher specimens. The species A. (P.) arnaldoi Travassos, 1929, A. (P.) byrdi (Robinson, 1956) and A. (P.) longicollis (Kuntz & Chandler, 1956), all possessing 16 circumoral spines, are synonymised with A. (P.) longa. A. (P.) ascolonga Witenberg, 1929, the types of which were not available, is provisionally retained as a valid species despite its close similarity to A. (P.) longa (identical number of circumoral spines and morphology of the ventrogenital sac). It differs from A. (P.) longa in the presence of uterine loops posterior to the testes and intestinal caeca ending anterior to the ovary (versus the pretesticular uterus and intestinal caeca reaching to the posterior margin of the ovary in A. (P.) longa). A. (P.) inglei (Hutton & Sogandares-Bernal, 1959), found only once in a single specimen, is also tentatively considered a valid species because it differs in number of circumoral spines (19), slightly larger eggs (23–26×13–16 μm) and the distribution of refractile bodies of the gonotyl arranged in four separate groups from A. (P.) longa and A. (P.) ascolonga (16 circumoral spines; eggs 15–20×–12 μm; refractile bodies in two groups).


title: Mass spectrometry–based proteomics turns quantitative

abstract: The field of proteomics is built on technologies to analyze large numbers of proteins--ideally the entire proteome--in the same experiment. Mass spectrometry (MS) has been successfully used to characterize proteins in complex mixtures, but results so far have largely been qualitative. Two recently developed methodologies offer the opportunity to obtain quantitative proteomic information. Comparing the signals from the same peptide under different conditions yields a rough estimate of relative protein abundance between two proteomes. Alternatively, and more accurately, peptides are labeled with stable isotopes, introducing a predictable mass difference between peptides from two experimental conditions. Stable isotope labels can be incorporated 'post-harvest', by chemical approaches or in live cells through metabolic incorporation. This isotopic handle facilitates direct quantification from the mass spectra. Using these quantitative approaches, precise functional information as well as temporal changes in the proteome can be captured by MS.


Molar distalization with repelling magnets


title: Vital Sign Monitoring Through the Back Using an UWB Impulse Radar With Body Coupled Antennas

abstract: Radar devices can be used in nonintrusive situations to monitor vital sign, through clothes or behind walls. By detecting and extracting body motion linked to physiological activity, accurate simultaneous estimations of both heart rate (HR) and respiration rate (RR) is possible. However, most research to date has focused on front monitoring of superficial motion of the chest. In this paper, body penetration of electromagnetic (EM) wave is investigated to perform back monitoring of human subjects. Using body-coupled antennas and an ultra-wideband (UWB) pulsed radar, in-body monitoring of lungs and heart motion was achieved. An optimised location of measurement in the back of a subject is presented, to enhance signal-to-noise ratio and limit attenuation of reflected radar signals. Phase-based detection techniques are then investigated for back measurements of vital sign, in conjunction with frequency estimation methods that reduce the impact of parasite signals. Finally, an algorithm combining these techniques is presented to allow robust and real-time estimation of both HR and RR. Static and dynamic tests were conducted, and demonstrated the possibility of using this sensor in future health monitoring systems, especially in the form of a smart car seat for driver monitoring.


title: Multi-View Stereo Revisited

abstract: We present an extremely simple yet robust multi-view stereo algorithm and analyze its properties. The algorithm first computes individual depth maps using a window-based voting approach that returns only good matches. The depth maps are then merged into a single mesh using a straightforward volumetric approach. We show results for several datasets, showing accuracy comparable to the best of the current state of the art techniques and rivaling more complex algorithms.


title: Automatic translation UML activity diagrams to Petri net

abstract: Provides a method for automatic translation UML diagrams to Petri nets, which is to convert formats like structure .xmi and .cpn. We consider the transformation of the most frequently used items on activity diagram - state action, condition, fork and join based on rules transformation. These elements are shown in activity diagram and its corresponding Petri net. It is noted that in active diagram presence four types elements - state action, a pseudostate, final state and transition, in Petri nets involved three types elements - place, transition and arc. Discussed in detail the comparison of initial state, state action and final state of activity diagram and places Petri nets - element name and its properties.


title: OAHO: an Effective Algorithm for Multi-Class Learning from Imbalanced Data

abstract: This paper presents our research in multi-class pattern learning from imbalanced data. In many real world applications, the data among different pattern classes are imbalanced; some classes may have far more training data than the others. Typically a neural network classifier has troubles to learn from the imbalanced data distribution among different pattern classes. In this paper we propose a new pattern classification algorithm, One-Against-Higher-Order (OAHO), that effectively learn multi-class patterns from the imbalanced data, and a theoretical analysis of data imbalance problem related to other popular multi-class pattern classification approaches. We have conducted experiments on the two highly imbalanced data sets posted at the UCI site, and the results show that the neural network system trained with the proposed OAHO algorithm gives better performances on minority pattern classes over the neural network systems trained with the two other popular multi-class classification methods: OAO and OAA.


title: A meta-model for software development resource expenditures

abstract: One of the basic goals of software engineering is the establishment of useful models and equations to predict the cost of any given programming project. Many models have been proposed over the last several years, but, because of differences in the data collected, types of projects and environmental factors among software development sites, these models are not transportable and are only valid within the organization where they were developed. This result seems reasonable when one considers that a model developed at a certain environment will only be able to capture the impact of the factors which have a variable effect within that environment. Those factors which are constant at that environment, and therefore do not cause variations in the productivity among projects produced there, may have different or variable effects at another environment. This paper presents a model-generation process which permits the development of a resource estimation model for any particular organization. The model is based on data collected by that organization which captures its particular environmental factors and the differences among its particular projects. The process provides the capability of producing a model tailored to the organization which can be expected to be more effective than any model originally developed for another environment. It is demonstrated here using data collected from the Software Engineering Laboratory at the NASA/Goddard Space Flight Center.


title: Evoked EMG and muscle fatigue during isokinetic FES-cycling in individuals with SCI.

abstract: PURPOSE ::: This study investigated whether muscle fatigue during functional electrical stimulation (FES)-induced cycling was associated with changes occurring in evoked electromyographic signals (eEMG, M-waves) in individuals with spinal cord injury. We also explored the effects of recovery intervals between exercise sessions on the relationship between eEMG and muscle torque. ::: ::: ::: METHODS ::: Eight individuals with spinal cord injury performed three FES-cycling sessions of 15-min duration, with 5 min of recovery between them. The quadriceps muscles were electrically stimulated as the prime agonist to produce cycling. Pedal torques and surface eEMG signals were synchronously processed and recorded for offline analysis. ::: ::: ::: RESULTS ::: Large Torque decreases (20-44%) were observed in the first 5 min of cycling during the three exercise bouts, while changes of similar magnitude did not occur on any of the M-wave time-series (less than 19%). Between 5 and 15 min of cycling, muscle fatigue lowered the plateau baselines of Torque (ranging from 41% to 62%), M-wave peak-to-peak amplitude (PtpA) and Area (ranging from 60% to 98%) time-series, yet the magnitudes of these reductions were not consistent between them. ::: ::: ::: CONCLUSION ::: We concluded that muscle fatigue during FES-cycling was not associated with, nor could be predicted by, eEMG signals. Nonetheless, the consistency between M-waves and Torque time-curves in their direction of change clearly warrants further investigation.


title: Internet Banking and Customers’ Acceptance in Jordan: The Unified Model’s Perspective

abstract: Internet banking in Jordan has developed rapidly since the year 2001 as most of the Jordanian banks have adopted some form of Internet usage and launched websites to serve their customers. Such efforts will not be successful without customers accepting Internet banking. The study added to the body of knowledge in the technology acceptance domain by testing and validating the Unified Theory of Acceptance and Use of Technology. This study extends the model by adding personality dimensions and validates two relationships that were suggested in previous literature (the influence of performance expectancy in mediating the relationship between effort expectancy and social influence and behavioral intention). This study explores the individuals' Internet banking acceptance using multiple regression. The population of the study was "counter bank customers" sampled in three Jordanian banks and in three major cities in the country. The model proposed is a comprehensive model with ten independent variables and one dependent variable. The study involved translating the instrument to Arabic and using it to explore the hypothesized relationships. ::: The results of the study indicated a support of the UTAUT with respect to the predictors' effect on behavioral intentions, and a partial support in the moderation effect. Also, the hypothesized mediation effect was supported for both relationships (effort expectancy and social influence and their relationship to behavioral intention). The proposed model of this study supported the influence of performance expectancy, social influence, self-efficacy, perceived trust and locus of control on Jordanian's intentions to use Internet banking. Part of these relationships was moderated by gender, age, education and experience. Effort expectancy (part of the UTAUT), perceived facilitating conditions, anxiety, personal innovativeness and perceived risk were not significant in predicting behavioral intention. Finally, this study developed an Arabic instrument that can be used in future research in the area of technology acceptance and with acceptable level of reliability. Implications for research and practice, limitations, future research, and conclusions are discussed at the end of the study.


title: Saliency and Gist Features for Target Detection in Satellite Images

abstract: Reliably detecting objects in broad-area overhead or satellite images has become an increasingly pressing need, as the capabilities for image acquisition are growing rapidly. The problem is particularly difficult in the presence of large intraclass variability, e.g., finding “boats” or “buildings,” where model-based approaches tend to fail because no good model or template can be defined for the highly variable targets. This paper explores an automatic approach to detect and classify targets in high-resolution broad-area satellite images, which relies on detecting statistical signatures of targets, in terms of a set of biologically-inspired low-level visual features. Broad-area images are cut into small image chips, analyzed in two complementary ways: “attention/saliency” analysis exploits local features and their interactions across space, while “gist” analysis focuses on global nonspatial features and their statistics. Both feature sets are used to classify each chip as containing target(s) or not, using a support vector machine. Four experiments were performed to find “boats” (Experiments 1 and 2), “buildings” (Experiment 3) and “airplanes” (Experiment 4). In experiment 1, 14 416 image chips were randomly divided into training (300 boat, 300 nonboat) and test sets (13 816), and classification was performed on the test set (ROC area: 0.977 ±0.003). In experiment 2, classification was performed on another test set of 11 385 chips from another broad-area image, keeping the same training set as in experiment 1 (ROC area: 0.952 ±0.006). In experiment 3, 600 training chips (300 for each type) were randomly selected from 108 885 chips, and classification was conducted (ROC area: 0.922 ±0.005). In experiment 4, 20 training chips (10 for each type) were randomly selected to classify the remaining 2581 chips (ROC area: 0.976 ±0.003). The proposed algorithm outperformed the state-of-the-art SIFT, HMAX, and hidden-scale salient structure methods, and previous gist-only features in all four experiments. This study shows that the proposed target search method can reliably and effectively detect highly variable target objects in large image datasets.


Artificial arm checkout procedures.


title: Factors influencing the adoption of Internet banking

abstract: A research framework based on the theory of planned behavior (Ajzen 1985) and the diffusion of innovations theory (Rogers 1983) was used to identify the attitudinal, social and perceived behavioral control factors that would influence the adoption of Internet banking. An online questionnaire was designed on the World Wide Web (WWW). Respondents participated through extensive personalized email invitations as well as postings to newsgroups and hyperlinks from selected Web sites.


title: PEG-based micelles as carriers of contrast agents for different imaging modalities.

abstract: The review deals with the fast growing field of diagnostic micelles. The need and requirements for microparticulate contrast agents are discussed. Brief analysis of the micellization process and micelle properties shows that micelles made of amphiphilic co-polymers seem to be the most attractive for practical application. These micelles can be prepared from the variety of co-polymers including hydrophilic polymers grafted on one terminus with lipid residues. Polymeric micelles are considered loaded or modified with various contrast reporter moieties for gamma-scintigraphy, magnetic resonance imaging (MRI), and computed tomography (CT). Their in vitro and in vivo properties are discussed and the results of the initial animal experiments are presented. Mixed micelles were prepared from diacyllipid-polyethylene glycol (PEG) conjugates and polymeric amphiphilic chelates, containing entrapped metals, such as 111-In or Gd, and used for the experimental gamma- and MR imaging of various components of lymphatic system in rabbits. The method is also described to prepare polymeric iodine-containing PEG-based micelles which may act as a long-circulating blood pool imaging agent for CT. Experimental CT-imaging performed in mice and rabbits demonstrated high potential of a micellar contrast agent.


title: A Broadband Center-Fed Circular Patch-Ring Antenna With a Monopole Like Radiation Pattern

abstract: A center-fed circular microstrip patch antenna with a coupled annular ring is presented. This antenna has a low profile configuration with a monopole like radiation pattern. Compared to the center-fed circular patch antenna (CPA), the proposed antenna has a large bandwidth and similar radiation pattern. The proposed antenna is fabricated and tested. It resonates at 5.8 GHz, the corresponding impedance bandwidth and gain are 12.8% and 5.7 dBi, respectively. Very good agreement between the measurement and simulation for the return loss and radiation patterns is achieved.


Face Recognition using Principal Component Analysis with DCT


title: Teaching students global software engineering skills using distributed Scrum

abstract: In this paper we describe distributed Scrum augmented with best practices in global software engineering (GSE) as an important paradigm for teaching critical competencies in GSE. We report on a globally distributed project course between the University of Victoria, Canada and Aalto University, Finland. The project-driven course involved 16 students in Canada and 9 students in Finland, divided into three cross-site Scrum teams working on a single large project. To assess learning of GSE competencies we employed a mixed-method approach including 13 post-course interviews, pre-, post-course and iteration questionnaires, observations, recordings of Daily Scrums as well as collection of project asynchronous communication data. Our analysis indicates that the Scrum method, along with supporting collaboration practices and tools, supports the learning of important GSE competencies, such as distributed communication and teamwork, building and maintaining trust, using appropriate collaboration tools, and inter-cultural collaboration.


title: Interacting Multiple Model Filter-Based Sensor Fusion of GPS With In-Vehicle Sensors for Real-Time Vehicle Positioning

abstract: Vehicle position estimation for intelligent vehicles requires not only highly accurate position information but reliable and continuous information provision as well. A low-cost Global Positioning System (GPS) receiver has widely been used for conventional automotive applications, but it does not guarantee accuracy, reliability, or continuity of position data when GPS errors occur. To mitigate GPS errors, numerous Bayesian filters based on sensor fusion algorithms have been studied. The estimation performance of Bayesian filters primarily relies on the choice of process model. For this reason, the change in vehicle dynamics with driving conditions should be addressed in the process model of the Bayesian filters. This paper presents a positioning algorithm based on an interacting multiple model (IMM) filter that integrates low-cost GPS and in-vehicle sensors to adapt the vehicle model to various driving conditions. The model set of the IMM filter is composed of a kinematic vehicle model and a dynamic vehicle model. The algorithm developed in this paper is verified via intensive simulation and evaluated through experimentation with a real-time embedded system. Experimental results show that the performance of the positioning system is accurate and reliable under a wide range of driving conditions.


title: A robust lane detection method for autonomous car-like robot

abstract: Due to illumination variation, view changes, and complex road conditions, automatic lane detection is crucial for path finding and planning for autonomous car-like robots. In this paper, a robust lane detection method is proposed. Firstly, in order to extract the edges of lanes in traffic scenarios, we adopt an adaptive thresholding strategy to binarize a gradient image and trace edges by using their local gradient information. Moreover, by integrating gradient constraints and introducing a reverse voting strategy to the standard Hough transform, we greatly improved speed and stability of line extraction. Further, through inverse perspective mapping the endpoints of extracted lines to the world coordination, we can combine the extracted lines from different cameras. Finally, the lane could be detected by matching two points instead of two parallel lines in parameter space. Extensive experiments and comparisons show the efficiency of the proposed method.


title: A time window neural network based framework for Remaining Useful Life estimation

abstract: This paper develops a framework for determining the Remaining Useful Life (RUL) of aero-engines. The framework includes the following modular components: creating a moving time window, a suitable feature extraction method and a multi-layer neural network as the main machine learning algorithm. The proposed framework is evaluated on the publicly available C-MAPSS dataset. The prognostic accuracy of the proposed algorithm is also compared against other state-of-the-art methods available in the literature and it has been shown that the proposed framework has the best overall performance.


title: Action classification on product manifolds

abstract: Videos can be naturally represented as multidimensional arrays known as tensors. However, the geometry of the tensor space is often ignored. In this paper, we argue that the underlying geometry of the tensor space is an important property for action classification. We characterize a tensor as a point on a product manifold and perform classification on this space. First, we factorize a tensor relating to each order using a modified High Order Singular Value Decomposition (HOSVD). We recognize each factorized space as a Grassmann manifold. Consequently, a tensor is mapped to a point on a product manifold and the geodesic distance on a product manifold is computed for tensor classification. We assess the proposed method using two public video databases, namely Cambridge-Gesture gesture and KTH human action data sets. Experimental results reveal that the proposed method performs very well on these data sets. In addition, our method is generic in the sense that no prior training is needed.


title: Design of Reconfigurable CNC System Based on FPGA

abstract: This paper brings forward a hardware design method of reconfigurable CNC system which makes use of the abundant logic resources of FPGA and programmable characteristics. It introduces the scheme of a CNC system which has regarded FPGA as the control centre. It mainly represents the thought of reconfigurable design, design of programmable pulse signal generator, the speed control of stepping motor, and the communication of FPGA and PC. As a result, this system has fast response and strong reconfiguration ability. It can reduce the number of peripheral interface devices and result in cost-reduction.


Test Case Prioritization Using Fault Severity


Face Verification using Correlation Filters


Semi-Supervised Deep Hashing with a Bipartite Graph


title: Equivalent negative stiffness mechanism using three bundled needles inspired by mosquito for achieving easy insertion

abstract: The mosquito proboscis is composed of seven micro parts. Among them, labrum and maxillae play important roles in insertion. The labrum is a central needle which sucks blood; two maxillae are left/right needles aside the labrum, respectively. They cooperatively move, i.e., one maxilla goes forward, labrum goes forward, another maxilla goes forward, successively. We noticed that the maxilla has a jagged harpoon-like shape on its tip area, which has an anchoring effect. We proposed the easy insertion mechanism as follows: the equivalent stiffness, which is the ratio of the resistance force relative to the moving distance, is considered. The total stiffness in pushing out the central needle can be made smaller than zero due to the anchoring effect of the side needles. We named it as “negative stiffness mechanism.” Equivalent stiffness during pulling/pushing a fabricated needle was experimentally estimated, which indicated the validity of proposed mechanism.


title: Automatically Constructing a Corpus of Sentential Paraphrases

abstract: An obstacle to research in automatic paraphrase identification and generation is the lack of large-scale, publiclyavailable labeled corpora of sentential paraphrases. This paper describes the creation of the recently-released Microsoft Research Paraphrase Corpus, which contains 5801 sentence pairs, each hand-labeled with a binary judgment as to whether the pair constitutes a paraphrase. The corpus was created using heuristic extraction techniques in conjunction with an SVM-based classifier to select likely sentence-level paraphrases from a large corpus of topicclustered news data. These pairs were then submitted to human judges, who confirmed that 67% were in fact semantically equivalent. In addition to describing the corpus itself, we explore a number of issues that arose in defining guidelines for the human raters.


title: Fiat-Shamir with aborts: Applications to lattice and factoring-based signatures

abstract: We demonstrate how the framework that is used for creating efficient number-theoretic ID and signature schemes can be transferred into the setting of lattices. This results in constructions of the most efficient to-date identification and signature schemes with security based on the worst-case hardness of problems in ideal lattices. In particular, our ID scheme has communication complexity of around 65,000 bits and the length of the signatures produced by our signature scheme is about 50,000 bits. All prior lattice-based identification schemes required on the order of millions of bits to be transferred, while all previous lattice-based signature schemes were either stateful, too inefficient, or produced signatures whose lengths were also on the order of millions of bits. The security of our identification scheme is based on the hardness of finding the approximate shortest vector to within a factor of $\tilde{O}(n^2)$ in the standard model, while the security of the signature scheme is based on the same assumption in the random oracle model. Our protocols are very efficient, with all operations requiring $\tilde{O}(n)$ time. ::: ::: We also show that the technique for constructing our lattice-based schemes can be used to improve certain number-theoretic schemes. In particular, we are able to shorten the length of the signatures that are produced by Girault's factoring-based digital signature scheme ([10][11][31]).


title: Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data

abstract: Early detection of lung nodules is currently the one of the most effective ways to predict and treat lung cancer. As a result, the past decade has seen a lot of focus on computer aided diagnosis (CAD) of lung nodules, whose goal is to efficiently detect, segment lung nodules and classify them as being benign or malignant. Effective detection of such nodules remains a challenge due to their arbitrariness in shape, size and texture. In this paper, we propose to employ 3D convolutional neural networks (CNN) to learn highly discriminative features for nodule detection in lieu of hand-engineered ones such as geometric shape or texture. While 3D CNNs are promising tools to model the spatio-temporal statistics of data, they are limited by their need for detailed 3D labels, which can be prohibitively expensive when compared obtaining 2D labels. Existing CAD methods rely on obtaining detailed labels for lung nodules, to train models, which is also unrealistic and time consuming. To alleviate this challenge, we propose a solution wherein the expert needs to provide only a point label, i.e., the central pixel of of the nodule, and its largest expected size. We use unsupervised segmentation to grow out a 3D region, which is used to train the CNN. Using experiments on the SPIE-LUNGx dataset, we show that the network trained using these weak labels can produce reasonably low false positive rates with a high sensitivity, even in the absence of accurate 3D labels.


title: Representation Learning for Clustering: A Statistical Framework

abstract: We address the problem of communicating domain knowledge from a user to the designer of a clustering algorithm. We propose a protocol in which the user provides a clustering of a relatively small random sample of a data set. The algorithm designer then uses that sample to come up with a data representation under which $k$-means clustering results in a clustering (of the full data set) that is aligned with the user's clustering. We provide a formal statistical model for analyzing the sample complexity of learning a clustering representation with this paradigm. We then introduce a notion of capacity of a class of possible representations, in the spirit of the VC-dimension, showing that classes of representations that have finite such dimension can be successfully learned with sample size error bounds, and end our discussion with an analysis of that dimension for classes of representations induced by linear embeddings.


title: Anthropological contributions to the study of climate: past, present, future

abstract: In the world of climate science there is an increasing demand for contributions from the social sciences, given that the current processes of climate change deeply affect societies. This article is a response to this call, with specific focus on past and potential contributions from anthropology, as we have known it from the 19th century onwards. It is shown how through the ages, different anthropological interests have shaped distinct perspectives on the entanglement of society and nature. It is argued that the present global concerns about climate change necessitate a refashioning of anthropology, and make it expedient to pay attention to the emergent global imaginaries. WIREs Clim Change 2013, 4:269–281. doi: 10.1002/wcc.219 ::: ::: Conflict of interest: The author has declared no conflicts of interest for this article. ::: ::: For further resources related to this article, please visit the WIREs website.


title: Transition flight control of the quad-tilting rotor convertible MAV

abstract: This paper presents a model of a particular class of a convertible MAV with fixed wings. This vehicle can operate as a helicopter as well as a conventional airplane, i.e. the aircraft is able to switch their flight configuration from hover to level flight and vice versa by means of a transition maneuver. The paper focuses on finding a controller capable of performing such transition via the tilting of their four rotors. The altitude should remain on a predefined value throughout the transition stage. For this purpose a nonlinear control strategy based on saturations and Lyapunov design is given. The use of this control law enables to make the transition maneuver while maintaining the aircraft in flight. Numerical results are presented, showing the effectiveness of the proposed methodology to deal with the transition stage.


title: Privacy-Aware Framework for Matching Online Social Identities in Multiple Social Networking Services

abstract: With various emerging Social Networking Services (SNS), it is possible for users to join multiple SNS for social relationships with other users and to collect a large amount of information (e.g., statuses on Facebook and tweets on Twitter). However, these users have been facing difficulties in managing all the data collected from the multiple SNS. It is important to match social identities from the multiple SNS. In this study, we propose a privacy-aware framework for a social identity matching (SIM) method across these multiple SNS. It means that the proposed approach can protect user privacy, because only the public information (e.g., username and the social relationships of the users) is employed to find the best matches between social identities. As a result, we have shown by evaluation that the F-measure of the proposed SIM method is about 60%.


title: Indonesian's presidential social media campaigns

abstract: Social media has been used for political campaigns of presidential candidates in a number of democratic countries, including Indonesia.The candidates for president and vice president have been following the latest trends in the virtual world by using social media such as: 1) Facebook, and 2) Twitter.The author's continuing research with a focus on the popularity of candidates for president and vice president of the Indonesian presidential election in July 9, 2014.The authors found that Facebook remains a social media most widely used in Indonesia for presidential campaigns. Prabowo secured the most number of fans on Facebook. While Joko Widodo secured the most number of followers on Twitter.Facebook users is dominated by the user in the age range 18-24 years, and most cities are using Facebook is Jakarta.


Innovation and Entrepreneurship in Biotechnology, An International Perspective


A Model of Reference-Dependent Preferences


title: How far did we get in face spoofing detection?

abstract: The growing use of control access systems based on face recognition shed light over the need for even more accurate systems to detect face spoofing attacks. In this paper, an extensive analysis on face spoofing detection works published in the last decade is presented. The analyzed works are categorized by their fundamental parts, i.e., descriptors and classifiers. This structured survey also brings the temporal evolution of the face spoofing detection field, as well as a comparative analysis of the works considering the most important public data sets in the field. The methodology followed in this work is particularly relevant to observe trends in the existing approaches, to discuss still opened issues, and to propose new perspectives for the future of face spoofing detection.


title: A linguistically motivated taxonomy for Machine Translation error analysis

abstract: A detailed error analysis is a fundamental step in every natural language processing task, as to be able to diagnose what went wrong will provide cues to decide which research directions are to be followed. In this paper we focus on error analysis in Machine Translation (MT). We significantly extend previous error taxonomies so that translation errors associated with Romance language specificities can be accommodated. Furthermore, based on the proposed taxonomy, we carry out an extensive analysis of the errors generated by four different systems: two mainstream online translation systems Google Translate (Statistical) and Systran (Hybrid Machine Translation), and two in-house MT systems, in three scenarios representing different challenges in the translation from English to European Portuguese. Additionally, we comment on how distinct error types differently impact translation quality.


title: Facebook Use Predicts Declines in Subjective Well-Being in Young Adults

abstract: Over 500 million people interact daily with Facebook. Yet, whether Facebook use influences subjective well-being over time is unknown. We addressed this issue using experience-sampling, the most reliable method for measuring in-vivo behavior and psychological experience. We text-messaged people five times per day for two-weeks to examine how Facebook use influences the two components of subjective well-being: how people feel moment-to-moment and how satisfied they are with their lives. Our results indicate that Facebook use predicts negative shifts on both of these variables over time. The more people used Facebook at one time point, the worse they felt the next time we text-messaged them; the more they used Facebook over two-weeks, the more their life satisfaction levels declined over time. Interacting with other people “directly” did not predict these negative outcomes. They were also not moderated by the size of people's Facebook networks, their perceived supportiveness, motivation for using Facebook, gender, loneliness, self-esteem, or depression. On the surface, Facebook provides an invaluable resource for fulfilling the basic human need for social connection. Rather than enhancing well-being, however, these findings suggest that Facebook may undermine it.


title: Quantum cognition goes beyond-quantum: modeling the collective participant in psychological measurements

abstract: In psychological measurements, two levels should be distinguished: the 'individual level', relative to the different participants in a given cognitive situation, and the 'collective level', relative to the overall statistics of their outcomes, which we propose to associate with a notion of 'collective participant'. When the distinction between these two levels is properly formalized, it reveals why the modeling of the collective participant generally requires beyond-quantum - non-Bornian - probabilistic models, when sequential measurements at the individual level are considered, and this though a pure quantum description remains valid for single measurement situations.


title: 5G mm-Wave front-end-module design with advanced SOI process

abstract: In this paper, we first introduce the RF performance of Globalfoundries 45RFSOI process. NFET F t > 290GHz and F max >380GHz. Then we present several mm-Wave circuit block designs, i.e., Switch, Power Amplifier, and LNA, based on 45RFSOI process for 5G Front End Module (FEM) applications. For the SPDT switch, insertion loss (IL) 25dBm P max . For the PA, with a 2.9V power supply, the PA achieves 13.1dB power gain and a saturated output power (P sat ) of 16.2dBm with maximum power-added efficiency (PAE) of 41.5% at 24Ghz continuous-wave (CW). With 960Mb/s 64QAM signal, 22.5% average PAE, −29.6dB EVM, and −30.5dBc ACLR are achieved with 9.5dBm average output power.


title: Enhancing Security Event Management Systems with Unsupervised Anomaly Detection

abstract: Security Information and Event Management (SIEM) systems are today a key component of complex enterprise networks. They usually aggregate and correlate events from different machines and perform a rule-based analysis to detect threats. In this paper we present an enhancement of such systems which makes use of unsupervised anomaly detection algorithms without the need for any prior training of the system. For data acquisition, events are exported from an existing SIEM appliance, parsed, unified and preprocessed to fit the requirements of unsupervised anomaly detection algorithms. Six different algorithms are evaluated qualitatively and finally a global k-NN approach was selected for a practical deployment. The new system was able to detect misconfigurations and gave the security operation center team more insight about processes in the


title: Machine Learning for Vehicular Networks

abstract: The emerging vehicular networks are expected to make everyday vehicular operation safer, greener, and more efficient, and pave the path to autonomous driving in the advent of the fifth generation (5G) cellular system. Machine learning, as a major branch of artificial intelligence, has been recently applied to wireless networks to provide a data-driven approach to solve traditionally challenging problems. In this article, we review recent advances in applying machine learning in vehicular networks and attempt to bring more attention to this emerging area. After a brief overview of the major concept of machine learning, we present some application examples of machine learning in solving problems arising in vehicular networks. We finally discuss and highlight several open issues that warrant further research.


title: A Compliant Parallel XY Micromotion Stage With Complete Kinematic Decoupling

abstract: This paper presents a novel compliant parallel XY micromotion stage driven by piezoelectric actuators (PZT). With the purpose to obtain complete kinematic decoupling and good stiffness performance, the stage is designed using a symmetric 4-PP structure in which double four-bar flexure is chosen as the prismatic joint. Matrix method is employed to establish the compliance model of the mechanism. Based on the model, dynamic analysis is investigated after static analysis is carried out. The dimensions of the mechanism are optimized using the particle swarm optimization (PSO) algorithm in order to maximize the natural frequencies. Finite-element analysis (FEA) result indicates that the mechanism has an almost linear force-deflection relationship, high first natural frequency (720.52 Hz), and ideal decoupling property. To cope with the nonlinearities such as hysteresis that exists in the PZT, the control system is constructed by a proportional-integral-derivative (PID) feedback controller with a feedforward compensator based on Preisach model. The fabricated prototype has a 19.2 μm × 8.8 μm rectangular workspace with coupling less than 5%. The result of the closed-loop test shows that the XY stage can achieve positioning, tracking and contouring tasks with small errors.