title: Measurements of interference between microwave and personal communications systems for spectrum sharing

abstract: Future Personal Communications Services (PCS) may be able to share frequency spectrum in bands currently occupied by microwave point-to-point communications systems without causing harmful interference. Telesis Technologies Laboratory (TTL) has undertaken experimental spectrum sharing feasibility measurements in the San Francisco Bay area to quantify the extent to which microwave link spectrum is available for use on a shared basis, and to identify constraints that need to be placed on PCS systems in order to share with fixed link microwave users. The measurements indicate that exclusion zones for potential PCS transmission exist along the main beam of the fixed microwave link and mainly near the microwave terminals. PCS users located in high-rise buildings and hilltops produce significantly higher interference to the fixed microwave link. The results of measurements made in PacTel building show, for worst case, a 45 dB increase in interference level from the ground floor to the roof. In dense urban areas, higher interference also results from specular reflections off building. In downtown San Francisco, a 40 to 50dB higher interference level was observed due to building reflections in a wide area outside the microwave main beam. Because the microwave links are fixed and of known location with directional energy, the exclusion areas are usually local. It is suggested that a system with a channel bandwidth less than 5 MHz is more feasible for spectrum sharing through deliberate frequency planning.


Towards Internet of Things: Survey and Future Vision


title: W-Band Characterizations of Printed Circuit Board Based on Substrate Integrated Waveguide Multi-Resonator Method

abstract: This paper provides a broadband measurement method at W-band for printed circuit board (PCB) properties using multiple substrate integrated waveguide (SIW) resonators, i.e., SIW multi-resonator method. Eight SIW resonators are designed in series fed by a rectangular waveguide through coupling slots. All resonators operate at TE101 mode with different resonant frequencies. Multi-resonator Foster form is employed to obtain unloaded resonant frequency of each SIW cavity. After introducing the testing procedure, a number of samples based on different PCBs are fabricated and accurately measured to verify the feasibility of this method.


title: Measuring Volunteerability and the Capacity to Volunteer among Non-volunteers: Implications for Social Policy

abstract: As volunteering and its benefits gain global recognition, social policymakers can sustain and increase volunteering through social policy, legislation and other types of involvement. A key performance practice is to measure the rate of volunteering based on the percentage of the population that volunteer or the number of hours donated. The focus of this article, however, is on the capacity to volunteer by non-volunteers as well as by volunteers. The concept and theory of volunteerability (an individual's ability to overcome related obstacles and volunteer, based on his or her willingness, capability and availability) offers a richer understanding of how people can be assisted to overcome barriers to maximize their volunteer potential and thus increase volunteering. The article details the definitions and benefits of volunteering and covers examples of related social policy, as well as explaining the concept of volunteerability and how it can be measured using existing and new scales. Based on a mixed methods study in Australia, the article offers specific measures to examine the concept of volunteerability and reveals important differences between volunteers and non-volunteers. The article also details major barriers to volunteering and how social policies can be developed to overcome them.


title: Visual Causal Feature Learning

abstract: We react to what we see, but what exactly is it that we react to? What are the visual causes of be- havior? Can we identify such causes from raw image data? If the visual features are causes, how can we manipulate them? Here we provide a rigorous definition of the visual cause of a behavior that is broadly applicable to the visually driven behavior in humans, animals, neurons, robots and other per- ceiving systems. Our framework generalizes standard accounts of causal learning to settings in which the causal variables need to be constructed from microvariables (raw image pixels in this case). We prove the Causal Coarsening Theorem, which allows us to gain causal knowledge from observational data with minimal experimental effort. The theorem provides a connection to standard inference techniques in machine learning that identify features of an image that correlate with, but may not cause, the target behavior. Finally, we propose an active learning scheme to learn a manipulator function that performs optimal manipulations on the image to automatically identify the visual cause of a target behavior. We illustrate our inference and learning algorithms in experiments based on both synthetic and real data. To our knowledge, our account is the first demonstration of true causal feature learning in the literature.


title: Biosocial Studies of Antisocial and Violent Behavior in Children and Adults: A Review

abstract: Despite increasing knowledge of social and biological risk factors for antisocial and violent behavior, we know surprisingly little about how these two sets of risk factors interact. This paper documents 39 empirical examples of biosocial interaction effects for antisocial behavior from the areas of genetics, psychophysiology, obstetrics, brain imaging, neuropsychology, neurology, hormones, neurotransmitters, and environmental toxins. Two main themes emerge. First, when biological and social factors are grouping variables and when antisocial behavior is the outcome, then the presence of both risk factors exponentially increases the rates of antisocial and violent behavior. Second, when social and antisocial variables are grouping variables and biological functioning is the outcome, then the social variable invariably moderates the antisocial‐biology relationship such that these relationships are strongest in those from benign home backgrounds. It is argued that further biosocial research is critical for establishing a new generation of more successful intervention and prevention research.


abstract: In China, the methods of time-control, optical-control and time-optical-control are in common used to control street lamp, particularly in small and medium-sized cities. But due to the backward lighting control and administrative method, the precision is bad, and the result of work is also poor. Through many kinds of sensor combination sense environment's change, the multi-sensor exhibition can combinatory logically control the new intelligent street light controller system. And based on the degree of illumination control fixed time, in the automatic foundation fixed time, according to the multi-sensing exhibition survey data's special combination change, to control the street light nimbly; the system can also realize the automatic timing control, by pre-installing time to control street light switch and ultimately to control the street light time. Simultaneously the system can also realize the automatic sunshine control, which may act according to the actual determination of the sunlight degree of illumination and the degree of illumination control criterion and automatically control street light. On the basis of the merits of the regular control and the optical control, a new street smart controller is designed, with dual functions including timing control and automatic photoelectric control. It allows street lamps automatically lit in the evening, lighting the road for a few hours (adjustable time). After 0:00, when few vehicles or pedestrians go past, it turn off automatically. And terminal controller has wake-up function. After the street light turn out automatically, when the vehicles or pedestrians are going through, street light will be waken up by terminal controller. When the vehicles or pedestrians go past, the lights are off automatically. Design of new intelligent street light control system does not only achieve energy-saving power but also extend the service life of lighting equipment. Moreover, it is controllable, ease of maintenance. At the same time, it is helpful to highlight the festive and other characteristics, and ultimately make street light network, intelligence, humanity and art. An optimal configuration is reported. Finally, the results of experiment are obtained that: when a motor is closed to 15 meters, LED lights are on automatically. While people passed, the distance is proximity 10 meters.


title: Diversity of references as an indicator of the interdisciplinarity of journals: Taking similarity between subject fields into account

abstract: The objective of this article is to further the study of journal interdisciplinarity, or, more generally, knowledge integration at the level of individual articles. Interdisciplinarity is operationalized by the diversity of subject fields assigned to cited items in the article's reference list. Subject fields and subfields were obtained from the Leuven-Budapest ECOOM subject-classification scheme, while disciplinary diversity was measured taking variety, balance, and disparity into account. As diversity measure we use a Hill-type true diversity in the sense of Jost and Leinster-Cobbold. The analysis is conducted in 3 steps. In the first part, the properties of this measure are discussed, and, on the basis of these properties it is shown that the measure has the potential to serve as an indicator of interdisciplinarity. In the second part the applicability of this indicator is shown using selected journals from several research fields ranging from mathematics to social sciences. Finally, the often-heard argument, namely, that interdisciplinary research exhibits larger visibility and impact, is studied on the basis of these selected journals. Yet, as only 7 journals, representing a total of 15,757 articles, are studied, albeit chosen to cover a large range of interdisciplinarity, further research is still needed.


title: Modeling Genre with the Music Genome Project: Comparing Human-Labeled Attributes and Audio Features

abstract: Genre provides one of the most convenient categorizations of music, but it is often regarded as a poorly defined or largely subjective musical construct. In this work, we provide evidence that musical genres can to a large extent be objectively modeled via a combination of musical attributes. We employ a data-driven approach utilizing a subset of 48 hand-labeled musical attributes comprising instrumentation, timbre, and rhythm across more than one million examples from Pandorar Internet Radio’s Music Genome Projectr. A set of audio features motivated by timbre and rhythm are then implemented to model genre both directly and through audio-driven models derived from the hand-labeled musical attributes. In most cases, machine learning models built directly from hand-labeled attributes outperform models based on audio features. Among the audio-based models, those that combine audio features and learned musical attributes perform better than those derived from audio features alone.


title: User Behavior Modeling for Estimating Residential Energy Consumption

abstract: Residential energy constitutes a significant portion of the total US energy consumption. Several researchers proposed energy-aware solutions for houses, promising significant energy and cost savings. However, it is important to evaluate the outcomes of these methods on larger scale, with hundreds of houses. This paper presents a human-activity based residential energy modeling framework, that can create power demand profiles considering the characteristics of household members. It constructs a mathematical model to show the detailed relationships between human activities and house power consumption. It can be used to create various house profiles with different energy demand characteristics in a reproducible manner. Comparison with real data shows that our model captures the power demand differences between different family types and accurately follows the trends seen in real data. We also show a case study that evaluates voltage deviation in a neighborhood, which requires accurate estimation of the trends in power consumption.


title: To what extent does the anxiety scale of the Four-Dimensional Symptom Questionnaire (4DSQ) detect specific types of anxiety disorder in primary care? A psychometric study

abstract: Anxiety scales may help primary care physicians to detect specific anxiety disorders among the many emotionally distressed patients presenting in primary care. The anxiety scale of the Four-Dimensional Symptom Questionnaire (4DSQ) consists of an admixture of symptoms of specific anxiety disorders. The research questions were: (1) Is the anxiety scale unidimensional or multidimensional? (2) To what extent does the anxiety scale detect specific DSM-IV anxiety disorders? (3) Which cut-off points are suitable to rule out or to rule in (which) anxiety disorders? We analyzed 5 primary care datasets with standardized psychiatric diagnoses and 4DSQ scores. Unidimensionality was assessed through confirmatory factor analysis (CFA). We examined mean scores and anxiety score distributions per disorder. Receiver operating characteristic (ROC) analysis was used to determine optimal cut-off points. Total n was 969. CFA supported unidimensionality. The anxiety scale performed slightly better in detecting patients with panic disorder, agoraphobia, social phobia, obsessive compulsive disorder (OCD) and post traumatic stress disorder (PTSD) than patients with generalized anxiety disorder (GAD) and specific phobia. ROC-analysis suggested that ≥4 was the optimal cut-off point to rule out and ≥10 the cut-off point to rule in anxiety disorders. The 4DSQ anxiety scale measures a common trait of pathological anxiety that is characteristic of anxiety disorders, in particular panic disorder, agoraphobia, social phobia, OCD and PTSD. The anxiety score detects the latter anxiety disorders to a slightly greater extent than GAD and specific phobia, without being able to distinguish between the different anxiety disorder types. The cut-off points ≥4 and ≥10 can be used to separate distressed patients in three groups with a relatively low, moderate and high probability of having one or more anxiety disorders.


title: Do You Want Your Autonomous Car To Drive Like You?

abstract: With progress in enabling autonomous cars to drive safely on the road, it is time to start asking how they should be driving. A common answer is that they should be adopting their users' driving style. This makes the assumption that users want their autonomous cars to drive like they drive - aggressive drivers want aggressive cars, defensive drivers want defensive cars. In this paper, we put that assumption to the test. We find that users tend to prefer a significantly more defensive driving style than their own. Interestingly, they prefer the style they think is their own, even though their actual driving style tends to be more aggressive. We also find that preferences do depend on the specific driving scenario, opening the door for new ways of learning driving style preference.


title: Enhancing First Story Detection using Word Embeddings

abstract: In this paper we show how word embeddings can be used to increase the effectiveness of a state-of-the art Locality Sensitive Hashing (LSH) based first story detection (FSD) system over a standard tweet corpus. Vocabulary mismatch, in which related tweets use different words, is a serious hindrance to the effectiveness of a modern FSD system. In this case, a tweet could be flagged as a first story even if a related tweet, which uses different but synonymous words, was already returned as a first story. In this work, we propose a novel approach to mitigate this problem of lexical variation, based on tweet expansion. In particular, we propose to expand tweets with semantically related paraphrases identified via automatically mined word embeddings over a background tweet corpus. Through experimentation on a large data stream comprised of 50 million tweets, we show that FSD effectiveness can be improved by 9.5% over a state-of-the-art FSD system.


Clinicians approaches to management of background treatment in patients with SLE in clinical remission: results of an international observational survey


An active learning-enabled annotation system for clinical named entity recognition


title: Functional safety methodologies for automotive applications

abstract: Safety-critical automotive applications have stringent demands for functional safety and reliability. Traditionally, functional safety requirements have been managed by car manufacturers and system providers. However, with the increasing complexity of electronics involved, the responsibility of addressing functional safety is now propagating through the supply chain to semiconductor companies and design tool providers. This paper introduces some basic concepts of functional safety analysis and optimization and shows the bridge with the tradition design flow. Considerations are presented on how design methodologies are capturing and addressing the new safety metrics.


The Average Packet Latency and Fault Tolerant Routing Algorithm for Generalized-Star Crossed Cube


CLASSIFICATION OF TWEETS VIA CLUSTERING OF HASHTAGS EECS 545 FINAL PROJECT, FALL, 2011


User Classification in Twitter Based on Sentiment Analysis on user Tweets


title: Trans-dimensional Markov chain Monte Carlo

abstract: Summary In the context of sample-based computation of Bayesian posterior distributions in complex stochastic systems, this chapter discusses some of the uses for a Markov chain with a prescribed invariant distribution whose support is a union of euclidean spaces of differing dimensions. This leads into a re-formulation of the reversible jump MCMC framework for constructing such ‘trans-dimensional’ Markov chains. This framework is compared to alternative approaches for the same task, including methods that involve separate sampling within different fixed-dimension models. We consider some of the difficulties researchers have encountered with obtaining adequate performance with some of these methods, attributing some of these to misunderstandings, and offer tentative recommendations about algorithm choice for various classes of problem. The chapter concludes with a look towards desirable future developments.


title: A Non-Parametric Factor Microfacet Model for Isotropic BRDFs

abstract: We investigate the expressiveness of the microfacet model for isotropic bidirectional reflectance distribution functions (BRDFs) measured from real materials by introducing a non-parametric factor model that represents the model’s functional structure but abandons restricted parametric formulations of its factors. We propose a new objective based on compressive weighting that controls rendering error in high-dynamic-range BRDF fits better than previous factorization approaches. We develop a simple numerical procedure to minimize this objective and handle dependencies that arise between microfacet factors. Our method faithfully captures a more comprehensive set of materials than previous state-of-the-art parametric approaches yet remains compact (3.2KB per BRDF). We experimentally validate the benefit of the microfacet model over a naive orthogonal factorization and show that fidelity for diffuse materials is modestly improved by fitting an unrestricted shadowing/masking factor. We also compare against a recent data-driven factorization approach [Bilgili et al. 2011] and show that our microfacet-based representation improves rendering accuracy for most materials while reducing storage by more than 10 ×.


title: Efficient 3-D placement of an aerial base station in next generation cellular networks

abstract: Agility and resilience requirements of future cellular networks may not be fully satisfied by terrestrial base stations in cases of unexpected or temporary events. A promising solution is assisting the cellular network via low-altitude unmanned aerial vehicles equipped with base stations, i.e., drone-cells. Although drone-cells provide a quick deployment opportunity as aerial base stations, efficient placement becomes one of the key issues. In addition to mobility of the drone-cells in the vertical dimension as well as the horizontal dimension, the differences between the air-to-ground and terrestrial channels cause the placement of the drone-cells to diverge from placement of terrestrial base stations. In this paper, we first highlight the properties of the drone-cell placement problem, and formulate it as a 3-D placement problem with the objective of maximizing the revenue of the network. After some mathematical manipulations, we formulate an equivalent quadratically-constrained mixed integer non-linear optimization problem and propose a computationally efficient numerical solution for this problem. We verify our analytical derivations with numerical simulations and enrich them with discussions which could serve as guidelines for researchers, mobile network operators, and policy makers.


title: SMART in Mathematics? Exploring the effects of in‐class‐level differentiation using SMARTboard on math proficiency

abstract: This paper explored the effects of in-class-level differentiation by making innovative use of an interactive whiteboard (SMARTboard) on math proficiency. Therefore, this paper evaluates the use of SMARTboard in class, in combination with teacher training, using a randomized field experiment among 199 pre-vocational students in seventh grade in the Netherlands. During 6 weeks, students in the intervention group participated in math classes in which the SMARTboard was used to apply level differentiation. The teachers of these classes received a specific training (Technological Pedagogical and Content Knowledge [TPACK]) in using the SMARTboard in class. Control classes were taught by teachers without the training, who did not use the SMARTboard in class. The results showed that level differentiation in class, which was possible because of the efficient use of the SMARTboard, significantly increased math proficiency with 0.25 points.


title: Robust mixture models for anomaly detection

abstract: We propose robust density estimation in a low dimensional space for anomaly detection. The outline of the method is as follows: first a low dimensional representation of the original data is learnt. Then, a robust density mixture model is estimated in the learnt space. Finally, the likelihood of a data point given the model parameters is used to apply anomaly detection. An efficient way for adapting the model parameters when the data distribution is changing with time is proposed. We further show how to identify the actual parameters in the original feature space that accounts for the occurrence of the anomaly. We present experimental results that demonstrate the effectiveness of the proposed methods.


title: Motion control of a wheeled mobile robot using model predictive control: a survey

abstract: Model predictive control (MPC) has been one of the most promising control strategies in industrial processes for decades. Due to its remarkable advantages, it has been extended to many areas of robotic research, especially motion control. Therefore, the goal of this paper is to review motion control of wheeled mobile robots (WMRs) using MPC. Principles as well as key issues in real-time implementations are first addressed. We then update the current literature of MPC for motion control. We also classify publications by using three criteria, i.e., MPC models, robot kinematic models, and basic motion tasks. MPC models categorized here include nonlinear MPC, linear MPC, neural network MPC, and generalized predictive control (GPC), while robot kinematic models we focus on consist of unicycle-type vehicles, car-like vehicles, and omnidirectional vehicles. Basic motion tasks, in general, are classified into three groups, i.e., trajectory tracking, path following, and point stabilization. To show that MPC strategies are capable of real-time implementations, some experimental scenarios from our previous work are given. We also conclude by identifying some future research directions.


Abstract Cartoon Style Rendering


title: Tracking Control of Shape-Memory-Alloy Actuators Based on Self-Sensing Feedback and Inverse Hysteresis Compensation

abstract: Shape memory alloys (SMAs) offer a high power-to-weight ratio, large recovery strain, and low driving voltages, and have thus attracted considerable research attention. The difficulty of controlling SMA actuators arises from their highly nonlinear hysteresis and temperature dependence. This paper describes a combination of self-sensing and model-based control, where the model includes both the major and minor hysteresis loops as well as the thermodynamics effects. The self-sensing algorithm uses only the power width modulation (PWM) signal and requires no heavy equipment. The method can achieve high-accuracy servo control and is especially suitable for miniaturized applications.


title: Analysis of series-fed microstrip array antennas

abstract: Series-fed array of square patches designed at 10 GHz, with three different configurations has been analyzed for sidelobe level and cross-polarization characteristics. A series-fed 8×2 planar array is analyzed to provide a sidelobe level of − 24.2dB and cross-polarization level of −21.8dB. The second configuration; a 2-line series array of 8 elements oriented back to back and fed with 180° out of phase exhibits a sidelobe level of − 25dB and low cross-polarization better than −51.6dB over all the angles. In the third configuration, a single line series array of 8 elements displaced on both the sides of the feed line is analyzed. The sidelobe level has been increased to −13.3dB with maximum cross-polarization level −27.7dB. The impedance bandwidth of 220MHz, 270MHz and 350MHz for a VSWR of 2, is observed for the three antenna types respectively.


FPGA-oriented Security


title: Zero-Resource Translation with Multi-Lingual Neural Machine Translation

abstract: In this paper, we propose a novel finetuning algorithm for the recently introduced multi-way, mulitlingual neural machine translate that enables zero-resource machine translation. When used together with novel many-to-one translation strategies, we empirically show that this finetuning algorithm allows the multi-way, multilingual model to translate a zero-resource language pair (1) as well as a single-pair neural translation model trained with up to 1M direct parallel sentences of the same language pair and (2) better than pivot-based translation strategy, while keeping only one additional copy of attention-related parameters.


title: DISCOVER: Keyword Search in Relational Databases

abstract: DISCOVER operates on relational databases and facilitates information discovery on them by allowing its user to issue keyword queries without any knowledge of the database schema or of SQL. DISCOVER returns qualified joining networks of tuples, that is, sets of tuples that are associated because they join on their primary and foreign keys and collectively contain all the keywords of the query. DISCOVER proceeds in two steps. First the Candidate Network Generator generates all candidate networks of relations, that is, join expressions that generate the joining networks of tuples. Then the Plan Generator builds plans for the efficient evaluation of the set of candidate networks, exploiting the opportunities to reuse common subexpressions of the candidate networks. ::: ::: We prove that DISCOVER finds without redundancy all relevant candidate networks, whose size can be data bound, by exploiting the structure of the schema. We prove that the selection of the optimal execution plan (way to reuse common subexpressions) is NP-complete. We provide a greedy algorithm and we show that it provides near-optimal plan execution time cost. Our experimentation also provides hints on tuning the greedy algorithm.


title: LLVM: a compilation framework for lifelong program analysis & transformation

abstract: We describe LLVM (low level virtual machine), a compiler framework designed to support transparent, lifelong program analysis and transformation for arbitrary programs, by providing high-level information to compiler transformations at compile-time, link-time, run-time, and in idle time between runs. LLVM defines a common, low-level code representation in static single assignment (SSA) form, with several novel features: a simple, language-independent type-system that exposes the primitives commonly used to implement high-level language features; an instruction for typed address arithmetic; and a simple mechanism that can be used to implement the exception handling features of high-level languages (and setjmp/longjmp in C) uniformly and efficiently. The LLVM compiler framework and code representation together provide a combination of key capabilities that are important for practical, lifelong analysis and transformation of programs. To our knowledge, no existing compilation approach provides all these capabilities. We describe the design of the LLVM representation and compiler framework, and evaluate the design in three ways: (a) the size and effectiveness of the representation, including the type information it provides; (b) compiler performance for several interprocedural problems; and (c) illustrative examples of the benefits LLVM provides for several challenging compiler problems.


title: Optimized cabin power supply with a +/− 270 V DC grid on a modern aircraft

abstract: This paper deals with an onboard power supply of +/− 270 V DC (HVDC, high voltage DC). Conventional aircraft grids have a main voltage level of 115 V AC with a variable frequency of 360…800 Hz. In the future, fuel cells might replace the conventional auxiliary power unit (APU) to optimize the eco efficiency. With that technology HVDC-grids can be integrated in an aircraft. Changing to a HVDC-grid has a lot of advantages. Higher voltage means lower cable weight. Furthermore, the electrical converter architecture can be optimized. Especially the converters inside the loads can be build much lighter when using a +/− 270 V DC supply. This paper gives an overview of the electrical architecture on a modern short- and midrange aircraft and the effect of a +/− 270 V DC power grid thus focusing on the converter architecture in a modern aircraft. Concluding all benefits are summarized.


title: Neuroscience: Big brain, big data

abstract: Neuroscientists are starting to share and integrate data — but shifting to a team approach isn't easy.


title: A comparison of static, adaptive, and adaptable menus

abstract: Software applications continue to grow in terms of the number of features they offer, making personalization increasingly important. Research has shown that most users prefer the control afforded by an adaptable approach to personalization rather than a system-controlled adaptive approach. No study, however, has compared the efficiency of the two approaches. In a controlled lab study with 27 subjects we compared the measured and perceived efficiency of three menu conditions: static, adaptable and adaptive. Each was implemented as a split menu, in which the top four items remained static, were adaptable by the subject, or adapted according to the subject's frequently and recently used items. The static menu was found to be significantly faster than the adaptive menu, and the adaptable menu was found to be significantly faster than the adaptive menu under certain conditions. The majority of users preferred the adaptable menu overall. Implications for interface design are discussed.


title: Automatic Extraction of Social Networks from Literary Text: A Case Study on Alice in Wonderland

abstract: In this paper we present results for two tasks: social event detection and social network extraction from a literary text, Alice in Wonderland. For the first task, our system trained on a news corpus using tree kernels and support vector machines beats the baseline systems by a statistically significant margin. Using this system we extract a social network from Alice in Wonderland. We show that while we achieve an F-measure of about 61% on social event detection, our extracted unweighted network is not statistically distinguishable from the un-weighted gold network according to popularly used network measures.


IEEE 802.11ax: Performance of OFDM Uplink Multi-User MIMO over Non-Ideal Scenarios


title: Outlier Detection with Kernel Density Functions

abstract: Outlier detection has recently become an important problem in many industrial and financial applications. In this paper, a novel unsupervised algorithm for outlier detection with a solid statistical foundation is proposed. First we modify a nonparametric density estimate with a variable kernel to yield a robust local density estimation. Outliers are then detected by comparing the local density of each point to the local density of its neighbors. Our experiments performed on several simulated data sets have demonstrated that the proposed approach can outperform two widely used outlier detection algorithms (LOF and LOCI).


title: Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning

abstract: With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) for delivering adapted learning content to mobile learners. The ANFIS model was designed using trial and error based on various experiments. This study was conducted to illustrate that ANFIS is effective with hybrid learning, for the adaptation of learning content according to learners' needs. Study results show that ANFIS has been successfully implemented for learning content adaptation within different learning context scenarios. The performance of the ANFIS model was evaluated using standard error measurements which revealed the optimal setting necessary for better predictability. The MATLAB simulation results indicate that the performance of the ANFIS approach is valuable and easy to implement. The study results are based on analysis of different model settings; they confirm that the m-learning application is functional. However, it should be noted that an increase in the number of inputs being considered by the model will increase the system response time, and hence the delay for the mobile learner.


title: Analysis and design of HBT Cherry-Hooper amplifiers with emitter-follower feedback for optical communications

abstract: In this article, the large-signal, small-signal, and noise performance of the Cherry-Hooper amplifier with emitter-follower feedback are analyzed from a design perspective. A method for choosing the component values to obtain a low group delay distortion or Bessel transfer function is given. The design theory is illustrated with an implementation of the circuit in a 47-GHz SiGe process. The amplifier has 19.7-dB gain, 13.7-GHz bandwidth, and /spl plusmn/10-ps group delay distortion. The amplifier core consumes 34 mW from a -3.3-V supply.


title: Ankle Anatomy for the Arthroscopist. Part I: The Portals

abstract: Over the last decade, arthroscopy of the ankle has become an important tool for the diagnosis and treatment of numerous pathologies. The considerable increase in the use of endoscopic techniques has led to significant changes in the field of orthopedic surgery and a new concept of human anatomy. The typical visualization in three dimensions on the part of surgeons and anatomists has become a two-dimensional, magnified view with the capability to observe anatomic structures that cannot be accessed by open surgery or dissection without altering their location or morphology. Because endoscopic procedures are performed using small incisions through which instruments are passed from the surface to the depths of the area under treatment, an anatomic focus is required that is based on the relationships between the structures susceptible to lesion and the arthroscopic access routes or portals. Hence, adequate knowledge of the anatomy of the joint to be treated should cover not only the most common anatomic configurations (extra-articular and intra-articular) in statistical terms but also the possible anatomic variations to avoid confusion and serious technical errors. This anatomic knowledge is particularly important in arthroscopy of the ankle because of the significant risk of associated complications, which can be prevented or decreased only by profound familiarity with the anatomy of the region and the use of a ‘‘protocolled,’’ reproducible technique [1].


The Strength of Weak Ties Author ( s )


Generalized support vector machines


title: Adaptive Regularization With the Structure Tensor

abstract: Natural images exhibit geometric structures that are informative of the properties of the underlying scene. Modern image processing algorithms respect such characteristics by employing regularizers that capture the statistics of natural images. For instance, total variation (TV) respects the highly kurtotic distribution of the pointwise gradient by allowing for large magnitude outlayers. However, the gradient magnitude alone does not capture the directionality and scale of local structures in natural images. The structure tensor provides a more meaningful description of gradient information as it describes both the size and orientation of the image gradients in a neighborhood of each point. Based on this observation, we propose a variational model for image reconstruction that employs a regularization functional adapted to the local geometry of image by means of its structure tensor. Our method alternates two minimization steps: 1) robust estimation of the structure tensor as a semidefinite program and 2) reconstruction of the image with an adaptive regularizer defined from this tensor. This two-step procedure allows us to extend anisotropic diffusion into the convex setting and develop robust, efficient, and easy-to-code algorithms for image denoising, deblurring, and compressed sensing. Our method extends naturally to nonlocal regularization, where it exploits the local self-similarity of natural images to improve nonlocal TV and diffusion operators. Our experiments show a consistent accuracy improvement over classic regularization.


title: Image Reconstruction using Matched Wavelet Estimated from Data Sensed Compressively using Partial Canonical Identity Matrix

abstract: This paper proposes a joint framework wherein lifting-based, separable, image-matched wavelets are estimated from compressively sensed (CS) images and used for the reconstruction of the same. Matched wavelet can be easily designed if full image is available. Also matched wavelet may provide better reconstruction results in CS application compared to standard wavelet sparsifying basis. Since in CS application, we have compressively sensed image instead of full image, existing methods of designing matched wavelet cannot be used. Thus, we propose a joint framework that estimates matched wavelet from the compressively sensed images and also reconstructs full images. This paper has three significant contributions. First, lifting-based, image-matched separable wavelet is designed from compressively sensed images and is also used to reconstruct the same. Second, a simple sensing matrix is employed to sample data at sub-Nyquist rate such that sensing and reconstruction time is reduced considerably without any noticeable degradation in the reconstruction performance. Third, a new multi-level L-Pyramid wavelet decomposition strategy is provided for separable wavelet implementation on images that leads to improved reconstruction performance. Compared to CS-based reconstruction using standard wavelets with Gaussian sensing matrix and with existing wavelet decomposition strategy, the proposed methodology provides faster and better image reconstruction in compressive sensing application.


title: Flexible Similarity Search of Semantic Vectors Using Fulltext Search Engines

abstract: Vector representations and vector space modeling (VSM) play a central role in modern machine learning. In our recent research we proposed a novel approach to ‘vector similarity searching’ over dense semantic vector representations. This approach can be deployed on top of traditional inverted-index-based fulltext engines, taking advantage of their robustness, stability, scalability and ubiquity. In this paper we validate our method using varied datasets ranging from text representations and embeddings (LSA, doc2vec, GloVe) to SIFT descriptors of image data. We show how our approach handles the indexing and querying in these domains, building a fast and scalable vector database with a tunable trade-off between vector search performance and quality, backed by a standard fulltext engine such as Elasticsearch.


title: Multichannel airborne ultrasonic ranging system based on the Piccolo C2000 MCU

abstract: The combination of different characteristics of airborne ultrasonic transducers for ranging applications opens up new applications for autonomous navigation systems in robotics. The features of a Piccolo C2000 MCU are suitable for the design of a ranging system with four channels. Each channel has different operating range and accuracy depending on the transducer characteristics and the implemented ranging technique. This paper presents the system architecture and the experimental results obtained with transducers operating at 40kHz, 100kHz and 300kHz.


title: Effects of hormones on skin wrinkles and rigidity vary by race/ethnicity: four-year follow-up from the ancillary skin study of the Kronos Early Estrogen Prevention Study.

abstract: OBJECTIVE ::: To measure skin wrinkles and rigidity in menopausal women of varying race/ethnicity with or without hormone therapy (HT) for up to four years. ::: ::: ::: DESIGN ::: Randomized, double-blind, placebo-controlled trial. ::: ::: ::: SETTING ::: Academic medical centers. ::: ::: ::: PATIENT(S) ::: Women (42-58 years of age) within 36 months of last menstrual period and enrolled in the Kronos Early Estrogen Prevention Study (KEEPS). ::: ::: ::: INTERVENTION(S) ::: Treatment with 0.45 mg oral conjugated equine estrogens (CEE), transdermal E2 (50 μg/d) with micronized P (200 mg daily), or placebo for 4 years. ::: ::: ::: MAIN OUTCOME MEASURE(S) ::: Skin wrinkles were assessed at 11 locations on the face and neck, and skin rigidity was assessed at the forehead and cheek at baseline and yearly for 4 years. ::: ::: ::: RESULT(S) ::: Neither total wrinkle score nor total rigidity score was significantly different at baseline or over the 4-year follow-up among patients randomized to CEE, E2, or placebo. Skin wrinkle and rigidity scores were primarily affected by race/ethnicity, with scores being significantly different between races for almost all of the wrinkle parameters and for all of the rigidity measures. There was no association between race and response to HT for total wrinkle or rigidity scores. Black women had the lowest wrinkle scores compared with white women across all 4 years. In general, skin rigidity decreased in all groups over time, but black women had significantly reduced total facial rigidity compared with white women after 4 years. ::: ::: ::: CONCLUSION(S) ::: Race is the strongest predictor of the advancement of skin aging in the 4 years following menopause. HT does not appear to affect skin wrinkles or rigidity at most facial locations. ::: ::: ::: CLINICAL TRIAL REGISTRATION NUMBER ::: NCT00154180.


Sequence to Sequence Modeling for User Simulation in Dialog Systems


title: Using Nonfinancial Measures to Assess Fraud Risk

abstract: ABSTRACT This study examines whether auditors can effectively use nonfinancial measures (NFMs) to assess the reasonableness of financial performance and, thereby, help detect financial statement fraud (hereafter, fraud). If auditors or other interested parties (e.g., directors, lenders, investors, or regulators) can identify NFMs (e.g., facilities growth) that are correlated with financial measures (e.g., revenue growth), inconsistent patterns between the NFMs and financial measures can be used to detect firms with high fraud risk. We find that the "difference" between financial and nonfinancial performance is significantly greater for firms that committed fraud than for their nonfraud competitors. We also find that this difference is a significant fraud indicator when included in a model containing variables that have previously been linked to the likelihood of fraud. Overall, our results provide empirical evidence suggesting that NFMs can be effectively used to assess fraud risk. Copyright (c), University of Chicago on behalf of the Accounting Research Center, 2009.


title: Panel Size and Overbooking Decisions for Appointment‐Based Services under Patient No‐Shows

abstract: Many service systems that work with appointments, particularly those in healthcare, suffer from high no-show rates. While there are many reasons why patients become no-shows, empirical studies found that the probability of a patient being a no-show typically increases with the patient's appointment delay, i.e., the time between the call for the appointment and the appointment date. This paper investigates how demand and capacity control decisions should be made while taking this relationship into account. We use stylized single server queueing models to model the appointments scheduled for a provider, and consider two different problems. In the first problem, the service capacity is fixed and the decision variable is the panel size; in the second problem, both the panel size and the service capacity (i.e., overbooking level) are decision variables. The objective in both cases is to maximize some net reward function, which reduces to system throughput for the first problem. We give partial or complete characterizations for the optimal decisions, and use these characterizations to provide insights into how optimal decisions depend on patient's no-show behavior in regards to their appointment delay. These insights especially provide guidance to service providers who are already engaged in or considering interventions such as sending reminders in order to decrease no-show probabilities. We find that in addition to the magnitudes of patient show-up probabilities, patients' sensitivity to incremental delays is an important determinant of how demand and capacity decisions should be adjusted in response to anticipated changes in patients' no-show behavior.


title: CactusNets: Layer Applicability as a Metric for Transfer Learning

abstract: Deep neural networks trained over large datasets learn features that are both generic to the whole dataset, and specific to individual classes in the dataset. Learned features tend towards generic in the lower layers and specific in the higher layers of a network. Methods like fine-tuning are made possible because of the ability for one filter to apply to multiple target classes. Much like the human brain this behavior, can also be used to cluster and separate classes. However, to the best of our knowledge there is no metric for how applicable learned features are to specific classes. In this paper we propose a definition and metric for measuring the applicability of learned features to individual classes, and use this applicability metric to estimate input applicability and produce a new method of unsupervised learning we call the CactusNet.


title: Esthetic outcome evaluation of maxillary anterior single-tooth bone-level implants with metal or ceramic abutments and ceramic crowns.

abstract: PURPOSE ::: The aim of this study was to evaluate the treatment outcome of implants placed in the maxillary anterior segment. ::: ::: ::: MATERIALS AND METHODS ::: Patients were treated with bone-level implants (Straumann Bone Level Implant, Institut Straumann) that supported all-ceramic single crowns and were followed for 2 to 4 years. Titanium or ceramic abutments were used according to the quality of the soft tissue at each site. Esthetic parameters were recorded to assess treatment outcomes. Pink esthetic scores (PES) and white esthetic scores (WES) were used to evaluate the esthetic outcome of anterior single-tooth implant-supported crowns. Patient satisfaction was also evaluated by means of a questionnaire. ::: ::: ::: RESULTS ::: Fifty-five implants were placed in 47 patients. At the recall examinations, all implants were confirmed to have successfully integrated and demonstrated healthy peri-implant soft tissues, as documented by generally accepted clinical parameters. Overall, the esthetic results were considered favorable, and there were no significant differences between restorations with ceramic or titanium abutments. WES values were slightly superior to PES values. None of the implants had mucosal recession. ::: ::: ::: CONCLUSION ::: Objective and subjective evaluation of maxillary bone-level implants in the esthetic zone yielded satisfactory results. Pleasing esthetic outcomes and stable facial soft tissues were achieved.


title: Vibration mode investigation of a resonant silicon tube structure for use as a fluid density sensor

abstract: We present a new sensor for on-line measurements of fluid density. The sensor consists of a tube system in single crystalline silicon which oscillates in mechanical resonance. The fluid is conveyed into and through two identical, serially and planarly arranged tube loops. A change in the density of the fluid in the tube changes the resonance frequency due to the change in the mass of the vibrating tube system. The sensor is fabricated using double-sided silicon KOH-etching and silicon fusion bonding. The silicon tube system (without the frame) is 8.6 by 17.7 mm with a thickness of 1 mm. The tubes have an inner diameter of approximately 0.8 mm and an outer diameter of approximately 1 mm. The micromachining process results in a total tube length of 61 mm and a sample volume of 0.035 ml. Fluid density measurements are presented and the vibration modes investigated.


title: Health Care Information Systems: Architectural Models and Governance

abstract: The adoption of ICT within health care has been characterized by a series of phases evolving since the 1960s (Khoumbati et al., 2009). Health informatics adoption started mainly from financial systems, providing support to the organization’s billing, payroll, accounting and reporting systems. Clinical departments launched a major initiative during the 1970s that supported such internal activities as radiology, laboratory and pharmacy (Wickramasinghe & Geisler, 2008), where machinery could support high-volume operations with the implementation of standardized procedures. Financial systems once again became prominent in the 1980s, with major investments in cost accounting and materials management systems (Grimson, 2001). During the 1990s, attention turned towards enterprise-wide clinical systems, including clinical data repositories and visions of a fully computerized Electronic Medical Record (EMR) (Bates, 2005).


title: Acoustic Echo and Noise Control Frequency-Domain Integration of Acoustic Echo Cancellation and a Generalized Sidelobe Canceller with Improved Robustness

abstract: For hands-free acoustic human/machine interfaces, as required, e.g., for automatic speech recognition, teleconferencing, and other multimedia services, microphone arrays using generalized sidelobe cancellers (GSCs) in conjunction with acoustic echo cancellation (AEC) can be efficiently applied for optimum communication. This contribution first devises a hew structure for combining AEC and GSC in the frequency domain. We show that computational complexity is reduced by more than a factor of ten compared to a time-domain arrangement. Second, robustness issues of the GSC adaptation mechanism are addressed. We illustrate how robust adaptation is assured in the new structure. Third, we propose alternatives for the fixed beamformer part of the GSC, which allow to specify (a) the width of the target tracking region and (b) improve the suppression of low frequencies. GSC robustness constraints are included using linear optimization with configurable penalty functions.


title: Optimizing for Efficiency or Battery Life in a Battery/Supercapacitor Electric Vehicle

abstract: A novel energy control strategy for a battery/supercapacitor vehicle, which is designed to be tunable to achieve different goals, is described. Two possible goals for adding a pack of supercapacitors are examined for a test vehicle using lead-acid batteries: 1) improving the vehicle's efficiency and range and 2) reducing the peak currents in the battery pack to increase battery life. The benefits of hybridization are compared with those achievable by increasing the size of the battery pack by a comparable mass to the supercapacitors. The availability of energy from regenerative braking and the characteristics of the supercapacitors are considered as impact factors. Supercapacitors were found to be effective at reducing peak battery currents; however, the benefits to range extension were found to be limited. A battery life extension of at least 50% is necessary to make supercapacitors cost effective for the test vehicle at current prices.


title: Multi-Stage Holomorphic Embedding Method for Calculating the Power-Voltage Curve

abstract: The recently proposed non-iterative load flow method, called the holomorphic embedding method, may encounter the precision issue, i.e. nontrivial round-off errors caused by the limit of digits used in computation when calculating the power-voltage (P-V) curve for a heavily loaded power system. This letter proposes a multi-stage scheme to solve such a precision issue and calculate an accurate P-V curve. The scheme is verified on the New Eng-land 39-bus power system and benchmarked with the result from the traditional continuation power flow method.


title: Smoothed Sarsa: Reinforcement learning for robot delivery tasks

abstract: Our goal in this work is to make high level decisions for mobile robots. In particular, given a queue of prioritized object delivery tasks, we wish to find a sequence of actions in real time to accomplish these tasks efficiently. We introduce a novel reinforcement learning algorithm called Smoothed Sarsa that learns a good policy for these delivery tasks by delaying the backup reinforcement step until the uncertainty in the state estimate improves. The state space is modeled by a Dynamic Bayesian Network and updated using a Region-based Particle Filter. We take advantage of the fact that only discrete (topological) representations of entity locations are needed for decision-making, to make the tracking and decision making more efficient. Our experiments show that policy search leads to faster task completion times as well as higher total reward compared to a manually crafted policy. Smoothed Sarsa learns a policy orders of magnitude faster than previous policy search algorithms. We demonstrate our results on the Player/Stage simulator and on the Pioneer robot.


title: HDR Deghosting: How to Deal with Saturation?

abstract: We present a novel method for aligning images in an HDR (high-dynamic-range) image stack to produce a new exposure stack where all the images are aligned and appear as if they were taken simultaneously, even in the case of highly dynamic scenes. Our method produces plausible results even where the image used as a reference is either too dark or bright to allow for an accurate registration.


title: Image Encryption Algorithm Based on Logistic Map and Pixel Mapping Table

abstract: In this paper, we propose a new image encryption algorithm based on logistic map chaotic function. Our algorithm consists of two replacement approaches; to change the value of the pixel without shuffling the image itself. To do that, we suggest using a Pixel Mapping Table (PMT) with the random shifting value to increase the uncertainty of the image. After that, we modified the pixels value by using the rows and columns replacement approach. However, by analyzing our algorithm, we show that it’s strong against different types of attacks and it’s sensitive to the initial conditions.


title: Applying convolutional neural networks for pre-detection of alzheimer's disease from structural MRI data

abstract: During the recent years, there have been many studies implemented on the automatic diagnosis of Alzheimer's Disease (AD) using different methods. The focus of most of these studies has relied upon the detection of AD from neu-roimaging data. However, recognizing symptoms early as much as possible(Pre-detection) is crucial as disease modifying drugs will be most effective if administered early in the course of disease, before the occurrence of irreversible brain damages. Therefore, there is a high importance of utilizing automated techniques for the purpose of pre-detection of AD symptoms from such data. We report an experimental approach to evaluate the best pre-detection method of AD. Our study consists of two main experiments. Those two experiments were implemented using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Prior to our first experiment we have stated an assumption which is, if there is a successful AD detection method that will be successful in AD pre-detection also. Different studies have used different data sets and different diagnosing methods. Therefore we have verified the existing and the most successful detection method which is Support Vector Machine (SVMs) as the first experiment. According to the results obtained from the initial experiment (detection study) the sensitivity is 95.3%, the specificity is 71.4% and the accuracy is 84.4% with use of a SVM. Since those results were not successful, a deep learning based technique (Convolutional Neural Network) was proposed as the second experiment. The proposed Convolutional Neural Network (CNN) model was being tested using different image segmentation methods and different datasets. Finally the best image segmentation method obtained a high accuracy around 96% (sensitivity — 96%, specificity — 98%). And the CNN model remains unbiased to the dataset. Results of those experiments suggest an important role for early diagnosis of Alzheimer's disease using image processing and deep learning techniques.


title: Executive Personality, Capability Cues, and Risk Taking: How Narcissistic CEOs React to Their Successes and Stumbles

abstract: We adopt an interactionist logic to study the determinants of risk taking by chief executive officers (CEOs). We introduce the concept of “capability cues”—contextual signals that decision makers m...


title: Improving automated source code summarization via an eye-tracking study of programmers

abstract: Source Code Summarization is an emerging technology for automatically generating brief descriptions of code. Current summarization techniques work by selecting a subset of the statements and keywords from the code, and then including information from those statements and keywords in the summary. The quality of the summary depends heavily on the process of selecting the subset: a high-quality selection would contain the same statements and keywords that a programmer would choose. Unfortunately, little evidence exists about the statements and keywords that programmers view as important when they summarize source code. In this paper, we present an eye-tracking study of 10 professional Java programmers in which the programmers read Java methods and wrote English summaries of those methods. We apply the findings to build a novel summarization tool. Then, we evaluate this tool and provide evidence to support the development of source code summarization systems.


title: ASTD: Arabic Sentiment Tweets Dataset

abstract: This paper introduces ASTD, an Arabic social sentiment analysis dataset gathered from Twitter. It consists of about 10,000 tweets which are classified as objective, subjective positive, subjective negative, and subjective mixed. We present the properties and the statistics of the dataset, and run experiments using standard partitioning of the dataset. Our experiments provide benchmark results for 4 way sentiment classification on the dataset.


title: Layout Dependent Effects mitigation in current mirrors

abstract: This paper presents a study on how the different physical realization of the transistors constituting a circuit, keeping the same (W/L) ratio, can dramatically alter the circuit specifications, and even functionality. This goes back to the Layout Dependent Effects (LDE), and its effect is increasingly important as the technology scales down into deep sub-micron processes. In this study, different layouts for each building block of an analog circuit, are formulated composing different aspect ratios, and every time the layouts are simulated to see the effect of the extracted views on the schematic results. Different current mirror configurations using a 65nm process are used to show the Shallow Trench Isolation (STI) and Well Proximity Effects (WPE).


title: BT-Nets: Simplifying Deep Neural Networks via Block Term Decomposition

abstract: Recently, deep neural networks (DNNs) have been regarded as the state-of-the-art classification methods in a wide range of applications, especially in image classification. Despite the success, the huge number of parameters blocks its deployment to situations with light computing resources. Researchers resort to the redundancy in the weights of DNNs and attempt to find how fewer parameters can be chosen while preserving the accuracy at the same time. Although several promising results have been shown along this research line, most existing methods either fail to significantly compress a well-trained deep network or require a heavy fine-tuning process for the compressed network to regain the original performance. In this paper, we propose the \textit{Block Term} networks (BT-nets) in which the commonly used fully-connected layers (FC-layers) are replaced with block term layers (BT-layers). In BT-layers, the inputs and the outputs are reshaped into two low-dimensional high-order tensors, then block-term decomposition is applied as tensor operators to connect them. We conduct extensive experiments on benchmark datasets to demonstrate that BT-layers can achieve a very large compression ratio on the number of parameters while preserving the representation power of the original FC-layers as much as possible. Specifically, we can get a higher performance while requiring fewer parameters compared with the tensor train method.


title: The benefits of logistics clustering

abstract: Purpose – The purpose of this paper is to analyze the benefits of logistics clustering, with the intent to explain their growth and popularity among private agents and policy makers during the last decade. Design/methodology/approach – Because of limited knowledge about the benefits of logistics clustering a main objective of this paper is theory building. The authors employed a grounded theory approach and conducted 135 open and semi structured interviews with logistics executives, government officials, academics, and chambers of commerce representatives. Findings – Study results revealed that, the agglomeration of logistics firms provides several key benefits to companies. Specifically, it was found that logistics clustering facilitates collaboration-related benefits, offering of value added services, career mobility for the logistics workforce within the cluster, and promotes job growth at multiple levels within the cluster. The authors offer a rich description of these benefits and the mechanisms that...


title: Training on multiple sub-flows to optimise the use of Machine Learning classifiers in real-world IP networks

abstract: Literature on the use of machine learning (ML) algorithms for classifying IP traffic has relied on full-flows or the first few packets of flows. In contrast, many real-world scenarios require a classification decision well before a flow has finished even if the flow's beginning is lost. This implies classification must be achieved using statistics derived from the most recent N packets taken at any arbitrary point in a flow's lifetime. We propose training the classifier on a combination of short sub-flows (extracted from full-flow examples of the target application's traffic). We demonstrate this optimisation using the Naive Bayes ML algorithm, and show that our approach results in excellent performance even when classification is initiated mid-way through a flow with windows as small as 25 packets long. We suggest future use of unsupervised ML algorithms to identify optimal sub-flows for training.


A Reinforcement Learning Approach for Adaptive Single- and Multi-Document Summarization


title: Measuring text similarity with dynamic time warping

abstract: In this work, we describe an approach which aims to make typed texts comparable with temporal data mining methods. This proposal was made in earlier work [11], but to our knowledge no significant research on this subject has been done yet. The basic idea is to derive artificial time series from texts by counting the occurrences of relevant keywords in a sliding window applied to them, and these time series can be compared with techniques of time series analysis. In this particular case the Dynamic Time Warping distance [3] was used. By extensive testing adequate parameters for time series calculation were derived, and we show that this approach might aid in the recognition of similar texts since the observed distances between similar documents are significantly lower than those between unrelated texts. Our idea might also be especially suitable for comparison in different languages since only the keyword translations must be known.


title: A Survey of Approaches and Issues in Machine-Aided Translation Systems

abstract: The much-proclaimed "information explosion" of recent years has produced an ever-increasing flow of scientific, technical, and scholarly literature, much of which is relevant to an audience wide enough to span several languages. This has, in turn, created a growing demand for more, faster, and better translation of printed material. The growth of international cooperative ventures, such as the Commission of European Communities and of multinational corporations, has provided further demand for quick and accurate translation capabilities. Translators and translation facilities, struggling to keep up with their burgeoning load, have turned more and more to the view that their problems can be solved only with help from computers. The earliest approach, first investigated three decades ago, was to get computers to do translation entirely by themselves. But as time progressed, this goal began to look unobtainable (at least in the immediate future), and interest shifted toward using computers in supporting, rather than starring roles, as aids to human translators. Progress on this front has been substantial and rapid, until today there are over twenty "computer-aided," or "machine-aided" translation systems in operation in the United States, Canada, and Western Europe.


title: Convolutional Video Steganography with Temporal Residual Modeling

abstract: Steganography represents the art of unobtrusively concealing a secrete message within some cover data. The key scope of this work is about visual steganography techniques that hide a full-sized color image / video within another. A majority of existing works are devoted to the image case, where both secret and cover data are images. We empirically validate that image steganography model does not naturally extend to the video case (i.e., hiding a video into another video), mainly because it completely ignores the temporal redundancy within consecutive video frames. Our work proposes a novel solution to the problem of video steganography. The technical contributions are two-fold: first, the residual between two consecutive frames tends to zero at most pixels. Hiding such highly-sparse data is significantly easier than hiding the original frames. Motivated by this fact, we propose to explicitly consider inter-frame residuals rather than blindly applying image steganography model on every video frame. Specifically, our model contains two branches, one of which is specially designed for hiding inter-frame difference into a cover video frame and the other instead hides the original secret frame. A simple thresholding method determines which branch a secret video frame shall choose. When revealing the concealed secret video, two decoders are devised, revealing difference or frame respectively. Second, we develop the model based on deep convolutional neural networks, which is the first of its kind in the literature of video steganography. In experiments, comprehensive evaluations are conducted to compare our model with both classic least significant bit (LSB) method and pure image steganography models. All results strongly suggest that the proposed model enjoys advantages over previous methods. We also carefully investigate key factors in the success of our deep video steganography model.


title: MSR-CNN: Applying motion salient region based descriptors for action recognition

abstract: In recent years the most popular video-based human action recognition methods rely on extracting feature representations using Convolutional Neural Networks (CNN) and then using these representations to classify actions. In this work, we propose a fast and accurate video representation that is derived from the motion-salient region (MSR), which represents features most useful for action labeling. By improving a well-performed foreground detection technique, the region of interest (ROI) corresponding to actors in the foreground in both the appearance and the motion field can be detected under various realistic challenges. Furthermore, we propose a complementary motion salient measure to select a secondary ROI - the major moving part of the human. Accordingly, a MSR-based CNN descriptor (MSR-CNN) is formulated to recognize human action, where the descriptor incorporates appearance and motion features along with tracks of MSR. The computation can be efficiently implemented due to two characteristics: 1) only part of the RGB image and the motion field need to be processed; 2) less data is used as input for the CNN feature extraction. Comparative evaluation on JHMDB and UCF Sports datasets shows that our method outperforms the state-of-the-art in both efficiency and accuracy.


title: Emotional intelligence: New ability or eclectic traits?

abstract: Some individuals have a greater capacity than others to carry out sophisticated information processing about emotions and emotion-relevant stimuli and to use this information as a guide to thinking and behavior. The authors have termed this set of abilities emotional intelligence (El). Since the introduction of the concept, however, a schism has developed in which some researchers focus on El as a distinct group of mental abilities, and other researchers instead study an eclectic mix of positive traits such as happiness, self-esteem, and optimism. Clarifying what El is and is not can help the field by better distinguishing research that is truly pertinent to El from research that is not. El-conceptualized as an ability-is an important variable both conceptually and empirically, and it shows incremental validity for predicting socially relevant outcomes.


title: Acupuncture for the treatment of tinnitus: a systematic review of randomized clinical trials

abstract: Complementary and alternative medicine (CAM) has frequently been used to treat tinnitus, and acupuncture is a particularly popular option. The objective of this review was to assess the evidence concerning the effectiveness of acupuncture as a treatment for tinnitus. Fourteen databases were searched from the dates of their creation to July 4th, 2012. Randomized clinical trials (RCTs) were included if acupuncture was used as the sole treatment. The Cochrane risk of bias tool was used to assess the risk of bias. A total of 9 RCTs met all the inclusion criteria. Their methodological quality was mostly poor. Five RCTs compared the effectiveness of acupuncture or electroacupuncture with sham acupuncture for treating tinnitus. The results failed to show statistically significant improvements. Two RCTs compared a short one-time scalp acupuncture treatment with the use of penetrating sham acupuncture at non-acupoints in achieving subjective symptom relief on a visual analog scale; these RCTs demonstrated significant positive effects with scalp acupuncture. Two RCTs compared acupuncture with conventional drug treatments. One of these RCTs demonstrated that acupuncture had statistically significant effects on the response rate in patients with nervous tinnitus, but the other RCT did not demonstrate significant effects in patients with senile tinnitus. The number, size and quality of the RCTs on the effectiveness of acupuncture for the treatment of tinnitus are not sufficient for drawing definitive conclusions. Further rigorous RCTs that overcome the many limitations of the current evidence are warranted.


title: Wide-baseline image matching using Line Signatures

abstract: We present a wide-baseline image matching approach based on line segments. Line segments are clustered into local groups according to spatial proximity. Each group is treated as a feature called a Line Signature. Similar to local features, line signatures are robust to occlusion, image clutter, and viewpoint changes. The descriptor and similarity measure of line signatures are presented. Under our framework, the feature matching is not only robust against affine distortion but also a considerable range of 3D viewpoint changes for non-planar surfaces. When compared to matching approaches based on existing local features, our method shows improved results with low-texture scenes. Moreover, extensive experiments validate that our method has advantages in matching structured non-planar scenes under large viewpoint changes and illumination variations.


title: Three-dimensional pose determination for a humanoid robot using binocular head system

abstract: There has been much interests on three-dimensional pose estimation of an object since there are a lot of robotic applications such as intelligent robotic assembly and/or vision-guided control of a humanoid or human-friendly robot. In this paper, there are proposed a simple and fast stereo matching algorithm for real-time robotic applications and an improved scheme for feature matching using three-dimensional information by adopting backprojection as a feedback element measuring the degree of feature matching. The degree of feature matching is determined by checking existence ratio of each feature point in real images after back-projecting an object model into image plane. The proposed pose determination algorithm is applied for a humanoid robot in KIST, whose name is CENTAUR, successfully and determine the pose of several polyhedral objects using 3D information of vertexes on the outline of an object in image plane.


title: Particle swarm optimization in electromagnetics

abstract: The particle swarm optimization (PSO), new to the electromagnetics community, is a robust stochastic evolutionary computation technique based on the movement and intelligence of swarms. This paper introduces a conceptual overview and detailed explanation of the PSO algorithm, as well as how it can be used for electromagnetic optimizations. This paper also presents several results illustrating the swarm behavior in a PSO algorithm developed by the authors at UCLA specifically for engineering optimizations (UCLA-PSO). Also discussed is recent progress in the development of the PSO and the special considerations needed for engineering implementation including suggestions for the selection of parameter values. Additionally, a study of boundary conditions is presented indicating the invisible wall technique outperforms absorbing and reflecting wall techniques. These concepts are then integrated into a representative example of optimization of a profiled corrugated horn antenna.


title: RAAC: Robust and Auditable Access Control With Multiple Attribute Authorities for Public Cloud Storage

abstract: Data access control is a challenging issue in public cloud storage systems. Ciphertext-policy attribute-based encryption (CP-ABE) has been adopted as a promising technique to provide flexible, fine-grained, and secure data access control for cloud storage with honest-but-curious cloud servers. However, in the existing CP-ABE schemes, the single attribute authority must execute the time-consuming user legitimacy verification and secret key distribution, and hence, it results in a single-point performance bottleneck when a CP-ABE scheme is adopted in a large-scale cloud storage system. Users may be stuck in the waiting queue for a long period to obtain their secret keys, thereby resulting in low efficiency of the system. Although multi-authority access control schemes have been proposed, these schemes still cannot overcome the drawbacks of single-point bottleneck and low efficiency, due to the fact that each of the authorities still independently manages a disjoint attribute set. In this paper, we propose a novel heterogeneous framework to remove the problem of single-point performance bottleneck and provide a more efficient access control scheme with an auditing mechanism. Our framework employs multiple attribute authorities to share the load of user legitimacy verification. Meanwhile, in our scheme, a central authority is introduced to generate secret keys for legitimacy verified users. Unlike other multi-authority access control schemes, each of the authorities in our scheme manages the whole attribute set individually. To enhance security, we also propose an auditing mechanism to detect which attribute authority has incorrectly or maliciously performed the legitimacy verification procedure. Analysis shows that our system not only guarantees the security requirements but also makes great performance improvement on key generation.


title: Feature-based ROI generation for stereo-based pedestrian detection

abstract: Region of interest (ROI) generation is an important step in stereo-based pedestrian detection systems. In this paper, we propose an ROI generation method by fusing the color and depth information obtained from a stereo camera mounted on a vehicle. In our proposed method, a feature-based method which uses contour properties of the image is used to find the ROIs. In our feature-based ROI extraction method, we extract four features which are contour density, maximum area, maximum perimeter and matching score. Then we create a feature vector from these features and classify them using SVM. ROIs are then classified into the pedestrian and non-pedestrian classes using Histogram of Oriented Gradients (HOG)/Linear SVM. We have tested our proposed method on the Daimler dataset and experimental results show that our proposed method has a 96.5% accuracy for 1 false positive per frame and outperforms existing monocular and stereo-based methods.


title: Impact of privacy concern in social networking web sites

abstract: Purpose – This study aims to understand the impact of users' privacy concerns on their acceptance of social networking web sites (SNWs).Design/methodology/approach – This paper develops two research models, with privacy concern conceptualized either as an antecedent of acceptance intention, or as a moderator of the relationships in the technology acceptance model (TAM). Using a survey questionnaire, empirical data were collected from 428 undergraduate college students. Structured equation modeling was used to test the validity of the proposed research models.Findings – The privacy concerns of the research respondents were found to be statistically significant. However, they did not directly affect users' acceptance of social networking web sites. Instead, privacy concerns did moderate the effects of perceived usefulness, and perceived ease of use, on users' intention to continue to use SNWs.Research limitations/implications – The study identifies the theoretical foundations of privacy and privacy concerns...


title: Human Action Recognition Using Multi-Velocity STIPs and Motion Energy Orientation Histogram

abstract: Local image features in space-time or spatio-temporal interest points provide compact and abstract representations of patterns in a video sequence. In this paper, we present a novel human action recognition method based on multi-velocity spatio-temporal interest points (MVSTIPs) and a novel local descriptor called motion energy (ME) orientation histogram (MEOH). The MVSTIP detection includes three steps: first, filtering video frames with multi-direction ME filters at different speeds to detect significant changes at the pixel level; thereafter, a surround suppression model is employed to rectify the ME deviation caused by the camera motion and complicated backgrounds (e.g., dynamic texture); finally, MVSTIPs are obtained with local maximum filters at multi-speeds. After detection, we develop MEOH descriptor to capture the motion features in local regions around interest points. The performance of the proposed method is evaluated on KTH, Weizmann, and UCF sports human action datasets. Results show that our method is robust to both simple and complex backgrounds and the method is superior to other methods that are based on local features.


title: Challenges, Designs, and Performances of Large-Scale Open-P2SP Content Distribution

abstract: Content distribution on today's Internet operates primarily in two modes: server-based and peer-to-peer (P2P). To leverage the advantages of both modes while circumventing their key limitations, a third mode: peer-to-server/peer (P2SP) has emerged in recent years. Although P2SP can provide efficient hybrid server-P2P content distribution, P2SP generally works in a closed manner by only utilizing its private owned servers to accelerate its private organized peer swarms. Consequently, P2SP still has its limitations in both content abundance and server bandwidth. To this end, the fourth mode (or says a generalized mode of P2SP) has appeared as "open-P2SP" that integrates various third-party servers, contents, and data transfer protocols all over the Internet into a large, open, and federated P2SP platform. In this paper, based on a large-scale commercial open-P2SP system named "QQXuanfeng" , we investigate the key challenging problems, practical designs and real-world performances of open-P2SP. Such "white-box" study of open-P2SP provides solid experiences and helpful heuristics to the designers of similar systems.


title: Nephrology Training Programs and Applicants: A Very Good Match

abstract: A critical mission of academic nephrology programs is training the next generation of nephrologists. Attracting high-quality candidates into nephrology training programs is the first step in this process. The training program directors (TPDs) in nephrology launched an initiative several years ago to standardize the application and recruitment process for applicants to their programs. This has recently culminated in the completion of a formal matching process using the Medical Specialties Matching Program (MSMP). This review will outline the history of this process and present the results of the recent matching process in nephrology for applicants who applied to training programs with a start date of July 1, 2009. The authors report that 89% of participating programs and 95% of applicant positions were filled. On the basis of a survey of nephrology TPDs, the match process was successful, with almost all respondents planning on participating in the match with a start date of July 1, 2010. Certain issues remain, including determining the correct number of interviews, how many applicants to rank, and whether four tracks are necessary. It is concluded that the nephrology match is of benefit to both training programs and applicants.


title: Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches.

abstract: Radiomics describes a broad set of computational methods that extract quantitative features from radiographic images. The resulting features can be used to inform imaging diagnosis, prognosis, and therapy response in oncology. However, major challenges remain for methodologic developments to optimize feature extraction and provide rapid information flow in clinical settings. Equally important, to be clinically useful, predictive radiomic properties must be clearly linked to meaningful biologic characteristics and qualitative imaging properties familiar to radiologists. Here we use a cross-disciplinary approach to highlight studies in radiomics. We review brain tumor radiologic studies (eg, imaging interpretation) through computational models (eg, computer vision and machine learning) that provide novel clinical insights. We outline current quantitative image feature extraction and prediction strategies with different levels of available clinical classes for supporting clinical decision-making. We further discuss machine-learning challenges and data opportunities to advance radiomic studies.


title: Quantification of bleomycin-induced murine lung damage in vivo with micro-computed tomography.

abstract: RATIONALE AND OBJECTIVES ::: We explored noninvasive, in vivo cone-beam microcomputed tomography (micro-CT) to visualize and quantify fibrotic and inflammatory damage over the entire lung volume of mice. ::: ::: ::: MATERIALS AND METHODS ::: We used bleomycin to induce pulmonary damage in vivo and compared the results from micro-CT with histologic measurements. Ten C57BL/6 mice were given 5 U/kg bleomycin intratracheally. Seven surviving mice were scanned with micro-CT before administration of bleomycin, and again before sacrifice. The resulting images were analyzed for lung volume measurements. After the final scan, all lungs were examined histologically and pulmonary damage was quantified. Damaged lung tissue regions were matched between micro-CT images and histologic sections for each mouse. ::: ::: ::: RESULTS ::: The percent lung damage calculated from micro-CT and histology were correlated (r(2) = 0.49, r = 0.64 with P = 0.12), and the means of their respective distributions were not different (P > 0.05). ::: ::: ::: CONCLUSION ::: This study shows that micro-CT is a promising alternative to predicting lung damage caused by bleomycin. CT image volumes of the thorax allow for global tissue sampling, which may be useful when following nonuniform lung damage that can occur from intratracheal administration of bleomycin.


title: A general methodology for machine tool accuracy enhancement by error compensation

abstract: Abstract A general methodology for increasing the accuracy of machine tools by compensating for the inherent systematic errors is described. This methodology in


title: Anthropometric and fitness characteristics of international, professional and amateur male graduate soccer players from an elite youth academy.

abstract: We compared anthropometric and fitness performance data from graduate male youth players from an elite soccer academy who on leaving the institution were either successful or not in progressing to higher standards of play. Altogether, 161 players were grouped according to whether they achieved international or professional status or remained amateur. Measures were taken across three age categories (under 14, 15 and 16 years of age). Players were assessed using standard measures of anthropometric and fitness characteristics. The skeletal age of players was also measured to determine maturity status. Multivariate analysis (MANCOVA) identified a significant (p<0.001) effect for playing status. Univariate analysis revealed a significant difference in maturity status in amateurs and professionals versus internationals (p<0.05), in body mass in professionals versus amateurs (d=0.56, p<0.05), in height (d=0.85, p<0.01) and maximal anaerobic power (d=0.79, p<0.01) in both professionals and internationals versus amateurs. There was also a significant difference in counter-movement jump (d=0.53, p<0.05) and 40-m sprint time (d=0.50, p<0.05) in internationals versus amateurs, as well as a significant main effect for age and playing position (p<0.001). Significant differences were reported for maturity status, body mass, height, peak concentric torque, maximal anaerobic power, and sprint and jump performance with results dependant on age category and playing position. These results suggest that anthropometric and fitness assessments of elite youth soccer players can play a part in determining their chances of proceeding to higher achievement levels.


Integration , Motivation , Strengths and Optimism : Retention Theories Past , Present and Future


title: Sliding Line Point Regression for Shape Robust Scene Text Detection

abstract: Traditional text detection methods mostly focus on quadrangle text. In this study we propose a novel method named sliding line point regression (SLPR) in order to detect arbitrary-shape text in natural scene. SLPR regresses multiple points on the edge of text line and then utilizes these points to sketch the outlines of the text. The proposed SLPR can be adapted to many object detection architectures such as Faster R-CNN and R-FCN. Specifically, we first generate the smallest rectangular box including the text with region proposal network (RPN), then isometrically regress the points on the edge of text by using the vertically and horizontally sliding lines. To make full use of information and reduce redundancy, we calculate x-coordinate or y-coordinate of target point by the rectangular box position, and just regress the remaining y-coordinate or x-coordinate. Accordingly we can not only reduce the parameters of system, but also restrain the points which will generate more regular polygon. Our approach achieved competitive results on traditional ICDAR2015 Incidental Scene Text benchmark and curve text detection dataset CTW1500.


title: Dual-band feed systems for SATCOM antenna applications

abstract: Dual and multi-band feed systems that enable reflector antennas to operate over multiple frequency bands have been used for some time. With newer generations of satellites, there is an increased demand for these types of feeds in satellite communications. At the same time, the requirements for performance such as bandwidth and band separation are becoming increasingly challenging, leading to the development of new techniques to achieve multi-band operation. This paper gives an overview of dual and multi-band feed development at BAE Systems Australia, and shows examples of some of the different techniques used to achieve the required performance.


title: Denoising of Hyperspectral Image Using Low-Rank Matrix Factorization

abstract: Restoration of hyperspectral images (HSIs) is a challenging task, owing to the reason that images are inevitably contaminated by a mixture of noise, including Gaussian noise, impulse noise, dead lines, and stripes, during their acquisition process. Recently, HSI denoising approaches based on low-rank matrix approximation have become an active research field in remote sensing and have achieved state-of-the-art performance. These approaches, however, unavoidably require to calculate full or partial singular value decomposition of large matrices, leading to the relatively high computational cost and limiting their flexibility. To address this issue, this letter proposes a method exploiting a low-rank matrix factorization scheme, in which the associated robust principal component analysis is solved by the matrix factorization of the low-rank component. Our method needs only an upper bound of the rank of the underlying low-rank matrix rather than the precise value. The experimental results on the simulated and real data sets demonstrate the performance of our method by removing the mixed noise and recovering the severely contaminated images.


title: A PPG sensor for continuous cuffless blood pressure monitoring with self-adaptive signal processing

abstract: A new portable PPG-BP device designed for continuously measuring blood pressure (BP) without a cuff is proposed in this study. This continuous and long-time BP monitoring enabled herein by the proposed portable cuffless BP sensor. Towards the aforementioned goal, the sensor is designed capable of detecting in real time, non-invasively and continuously the temporal intravascular blood volume change based on the principle of photoplethysmograph (PPG) for estimating BP. The hardware of the sensor consists mainly of light emitting diodes (LEDs) in wavelengths of 660 nm, a photo detectors (PD), and also signal processing chips to read output signals of the PD. The PD readout circuit includes a PD pre-amplifier, a band-pass filter, a programmable gain amplifier (PGA), a microcontroller unit for calculation and a wireless module for communication. A laptop is also used to display continuous BPs and conducts statistical analysis and displaying results. 27 subjects participated in the experimental validation, in which the obtained BPs are calibrated by and then compared with the results from a commercial blood pressure monitor by OMRON. The resultant signal-to-noise ratio (SNR) is capable of rising more than 15%, correlation coefficient, R2, for systolic blood pressure (SBP) and diastolic blood pressure (DBP) are 0.89 and 0.98, respectively.


title: The different stages of PRPD pattern for positive point to plane corona driven by a DC voltage containing ripple

abstract: The different phase resolved partial discharge patterns, which can be recognized when a positive dc voltage containing a ripple due to a half-wave rectification is used are presented in this paper. Results are given from corona onset to the appearance of breakdown streamers. The outcomes are five different patterns, which represent the four stages of positive corona and their transition zones. Three of them are clearly identifiable with respect to patterns from other discharge sources that have already been reported for dc voltages containing a ripple. For an assessment of pattern formation discharge pulses as recorded at the test object are presented. Finally, the appearance of patterns is explained with support of the measured discharge pulses and the underlying physical processes.


title: Detection of Behavior Change in People with Depression

abstract: Major Depressive Disorder (MDD) is the most common mental health disorder and remains a leading cause of disability and lost productivity with huge costs for society. MDD has high rates of relapse and recurrence, and it has strong correlations with feelings of low social support and disrupted sleep. However, MDD is also commonly misdiagnosed by primary care providers, which leads to delayed treatment and unnecessary suffering. Changes in technology now make it possible to cheaply and effectively monitor social and sleep behaviors, offering the potential of early detection of the onset of MDD. We report on the design of Big Black Dog, a smartphone-based system for gathering data about social and sleep behaviors. We also report on the results of a pilot evaluation to understand the feasibility of gathering and using data from smartphones for inferring the onset of depression.


title: Highly-cited papers in Library and Information Science (LIS): Authors, institutions, and network structures

abstract: As a follow-up to the highly cited authors list published by Thomson Reuters in June 2014, we analyzed the top 1% most frequently cited papers published between 2002 and 2012 included in the Web of Science WoS subject category "Information Science & Library Science." In all, 798 authors contributed to 305 top 1% publications; these authors were employed at 275 institutions. The authors at Harvard University contributed the largest number of papers, when the addresses are whole-number counted. However, Leiden University leads the ranking if fractional counting is used. Twenty-three of the 798 authors were also listed as most highly cited authors by Thomson Reuters in June 2014 http://highlycited.com/. Twelve of these 23 authors were involved in publishing 4 or more of the 305 papers under study. Analysis of coauthorship relations among the 798 highly cited scientists shows that coauthorships are based on common interests in a specific topic. Three topics were important between 2002 and 2012: a collection and exploitation of information in clinical practices; b use of the Internet in public communication and commerce; and c scientometrics.


MDA: A formal approach to game design and game research


title: Interleaver Design for Turbo Codes

abstract: The performance of a turbo code with short block length depends critically on the interleaver design. There are two major criteria in the design of an interleaver: the distance spectrum of the code and the correlation between the information input data and the soft output of each decoder corresponding to its parity bits. This paper describes a new interleaver design for turbo codes with short block length based on these two criteria. A deterministic interleaver suitable for turbo codes is also described. Simulation results compare the new interleaver design to different existing interleavers.


title: Vision-based road-following using a small autonomous aircraft

abstract: This paper describes the vision-based control of a small autonomous aircraft following a road. The computer vision system detects natural features of the scene and tracks the roadway in order to determine relative yaw and lateral displacement between the aircraft and the road. Using only the vision measurements and onboard inertial sensors, a control strategy stabilizes the aircraft and follows the road. The road detection and aircraft control strategies have been verified by hardware in the loop (HIL) simulations over long stretches (several kilometers) of straight roads and in conditions of up to 5 m/s of prevailing wind. Hardware experiments have also been conducted using a modified radio-controlled aircraft. Successful road following was demonstrated over an airfield runway under variable lighting and wind conditions. The development of vision-based control strategies for unmanned aerial vehicles (UAVs), such as the ones presented here, enables complex autonomous missions in environments where typical navigation sensor like GPS are unavailable.