title: Antialiasing Filter Design for Subpixel Downsampling via Frequency-Domain Analysis

abstract: In this paper, we are concerned with image downsampling using subpixel techniques to achieve superior sharpness for small liquid crystal displays (LCDs). Such a problem exists when a high-resolution image or video is to be displayed on low-resolution display terminals. Limited by the low-resolution display, we have to shrink the image. Signal-processing theory tells us that optimal decimation requires low-pass filtering with a suitable cutoff frequency, followed by downsampling. In doing so, we need to remove many useful image details causing blurring. Subpixel-based downsampling, taking advantage of the fact that each pixel on a color LCD is actually composed of individual red, green, and blue subpixel stripes, can provide apparent higher resolution. In this paper, we use frequency-domain analysis to explain what happens in subpixel-based downsampling and why it is possible to achieve a higher apparent resolution. According to our frequency-domain analysis and observation, the cutoff frequency of the low-pass filter for subpixel-based decimation can be effectively extended beyond the Nyquist frequency using a novel antialiasing filter. Applying the proposed filters to two existing subpixel downsampling schemes called direct subpixel-based downsampling (DSD) and diagonal DSD (DDSD), we obtain two improved schemes, i.e., DSD based on frequency-domain analysis (DSD-FA) and DDSD based on frequency-domain analysis (DDSD-FA). Experimental results verify that the proposed DSD-FA and DDSD-FA can provide superior results, compared with existing subpixel or pixel-based downsampling methods.


title: Low-swing interconnect interface circuits

abstract: This paper reviews a number of low-swing on-chip interconnect schemes, and presents a thorough analysis of their effectiveness and limitations. In addition, several new interface circuits, presenting even more energy savings, are proposed. Some of these circuits not only reduce the interconnect swing, but also use very-low supply voltages, so as to obtain quadratic energy savings. The performance of each of the presented circuits is thoroughly examined using simulation on a benchmark interconnect circuit. Energy savings with a factor of seven have been observed for some of the schemes.


title: High Speed Pattern Matching for Network IDS/IPS

abstract: The phenomenal growth of the Internet in the last decade and society?s increasing dependence on it has brought along, a flood of security attacks on the networking and computing infrastructure. Intrusion detection/prevention systems provide defenses against these attacks by monitoring headers and payload of packets flowing through the network. Multiple string matching that can compare hundreds of string patterns simultaneously is a critical component of these systems, and is a well-studied problem. Most of the string matching solutions today are based on the classic Aho-Corasick algorithm, which has an inherent limitation; they can process only one input character in one cycle. As memory speed is not growing at the same pace as network speed, this limitation has become a bottleneck in the current network, having speeds of tens of gigabits per second. In this paper, we propose a novel multiple string matching algorithm that can process multiple characters at a time thus achieving multi-gigabit rate search speeds. We also propose an architecture for an efficient implementation on TCAM-based hardware. We additionally propose novel optimizations by making use of the properties of TCAMs to significantly reduce the memory requirements of the proposed algorithm. We finally present extensive simulation results of network-based virus/worm detection using real signature databases to illustrate the effectiveness of the proposed scheme.


African Labour Productivity Reconsidered


title: RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitation

abstract: Imitation Learning has empowered recent advances in learning robotic manipulation tasks by addressing shortcomings of Reinforcement Learning such as exploration and reward specification. However, research in this area has been limited to modest-sized datasets due to the difficulty of collecting large quantities of task demonstrations through existing mechanisms. This work introduces RoboTurk to address this challenge. RoboTurk is a crowdsourcing platform for high quality 6-DoF trajectory based teleoperation through the use of widely available mobile devices (e.g. iPhone). We evaluate RoboTurk on three manipulation tasks of varying timescales (15-120s) and observe that our user interface is statistically similar to special purpose hardware such as virtual reality controllers in terms of task completion times. Furthermore, we observe that poor network conditions, such as low bandwidth and high delay links, do not substantially affect the remote users' ability to perform task demonstrations successfully on RoboTurk. Lastly, we demonstrate the efficacy of RoboTurk through the collection of a pilot dataset; using RoboTurk, we collected 137.5 hours of manipulation data from remote workers, amounting to over 2200 successful task demonstrations in 22 hours of total system usage. We show that the data obtained through RoboTurk enables policy learning on multi-step manipulation tasks with sparse rewards and that using larger quantities of demonstrations during policy learning provides benefits in terms of both learning consistency and final performance. For additional results, videos, and to download our pilot dataset, visit $\href{this http URL}{\texttt{roboturk.stanford.edu}}$


title: Convolutional neural networks and multimodal fusion for text aided image classification

abstract: With the exponential growth of web meta-data, exploiting multimodal online sources via standard search engine has become a trend in visual recognition as it effectively alleviates the shortage of training data. However, the web meta-data such as text data is usually not as cooperative as expected due to its unstructured nature. To address this problem, this paper investigates the numerical representation of web text data. We firstly adopt convolutional neural network (CNN) for web text modeling on top of word vectors. Combined with CNN for image, we present a multimodal fusion to maximize the discriminative power of visual and textual modality data for decision level and feature level simultaneously. Experimental results show that the proposed framework achieves significant improvement in large-scale image classification on Pascal VOC-2007 and VOC-2012 datasets.


title: On structuring probabilistic dependences in stochastic language modelling

abstract: Abstract In this paper, we study the problem of stochastic language modelling from the viewpoint of introducing suitable structures into the conditional probability distributions. The task of these distributions is to predict the probability of a new word by looking at M or even all predecessor words. The conventional approach is to limit M to 1 or 2 and to interpolate the resulting bigram and trigram models with a unigram model in a linear fashion. However, there are many other structures that can be used to model the probabilistic dependences between the predecessor word and the word to be predicted. The structures considered in this paper are: nonlinear interpolation as an alternative to linear interpolation; equivalence classes for word histories and single words; cache memory and word associations. For the optimal estimation of nonlinear and linear interpolation parameters, the leaving-one-out method is systematically used. For the determination of word equivalence classes in a bigram model, an automatic clustering procedure has been adapted. To capture long-distance dependences, we consider various models for word-by-word dependences; the cache model may be viewed as a special type of self-association. Experimental results are presented for two text databases, a Germany database and an English database.


title: A frontier-based approach for autonomous exploration

abstract: We introduce a new approach for exploration based on the concept of frontiers, regions on the boundary between open space and unexplored space. By moving to new frontiers, a mobile robot can extend its map into new territory until the entire environment has been explored. We describe a method for detecting frontiers in evidence grids and navigating to these frontiers. We also introduce a technique for minimizing specular reflections in evidence grids using laser-limited sonar. We have tested this approach with a real mobile robot, exploring real-world office environments cluttered with a variety of obstacles. An advantage of our approach is its ability to explore both large open spaces and narrow cluttered spaces, with walls and obstacles in arbitrary orientation.


title: 1-Bit compressive sensing

abstract: Compressive sensing is a new signal acquisition technology with the potential to reduce the number of measurements required to acquire signals that are sparse or compressible in some basis. Rather than uniformly sampling the signal, compressive sensing computes inner products with a randomized dictionary of test functions. The signal is then recovered by a convex optimization that ensures the recovered signal is both consistent with the measurements and sparse. Compressive sensing reconstruction has been shown to be robust to multi-level quantization of the measurements, in which the reconstruction algorithm is modified to recover a sparse signal consistent to the quantization measurements. In this paper we consider the limiting case of 1-bit measurements, which preserve only the sign information of the random measurements. Although it is possible to reconstruct using the classical compressive sensing approach by treating the 1-bit measurements as plusmn 1 measurement values, in this paper we reformulate the problem by treating the 1-bit measurements as sign constraints and further constraining the optimization to recover a signal on the unit sphere. Thus the sparse signal is recovered within a scaling factor. We demonstrate that this approach performs significantly better compared to the classical compressive sensing reconstruction methods, even as the signal becomes less sparse and as the number of measurements increases.


title: Weapon design patterns in shooter games

abstract: With the increasing complexity of interactive games, the need for a common vocabulary to describe patterns grows. Action-oriented games focus heavily on players using weapons, but categorizations used for weapons are borrowed from real-world patterns, classifying them according to the physical inner-workings of the weapons rather than their gameplay effects. This terminology draws lines between similar weapons that elicit similar gameplay and creates unnecessary distinctions between fictional and non-fictional weapons. We aim to classify weapons in a way that defines weapons by the gameplay behaviors they elicit using a language of common weapon design patterns. This paper expands the taxonomy of game design patterns to better describe how weapons can be used to influence gameplay. These patterns act as a language for communicating game design concepts.


STUDY ON THE CELLULASE ENZYME PRODUCING ACTIVITY OF BACTERIA ISOLATED FROM MANURE WASTE AND DEGRADING SOIL


title: Video2Shop: Exact Matching Clothes in Videos to Online Shopping Images

abstract: In recent years, both online retail and video hosting service are exponentially growing. In this paper, we explore a new cross-domain task, Video2Shop, targeting for matching clothes appeared in videos to the exact same items in online shops. A novel deep neural network, called AsymNet, is proposed to explore this problem. For the image side, well-established methods are used to detect and extract features for clothing patches with arbitrary sizes. For the video side, deep visual features are extracted from detected object regions in each frame, and further fed into a Long Short-Term Memory (LSTM) framework for sequence modeling, which captures the temporal dynamics in videos. To conduct exact matching between videos and online shopping images, LSTM hidden states, representing the video, and image features, which represent static object images, are jointly modeled under the similarity network with reconfigurable deep tree structure. Moreover, an approximate training method is proposed to achieve the efficiency when training. Extensive experiments conducted on a large cross-domain dataset have demonstrated the effectiveness and efficiency of the proposed AsymNet, which outperforms the state-of-the-art methods.


title: Bone tissue regeneration: the role of scaffold geometry

abstract: The geometry of porous scaffolds that are used for bone tissue engineering and/or bone substitution has recently been shown to significantly influence the cellular response and the rate of bone tissue regeneration. Most importantly, it has been shown that the rate of tissue generation increases with curvature and is much larger on concave surfaces as compared to convex and planar surfaces. In this work, recent discoveries concerning the effects of geometrical features of porous scaffolds such as surface curvature, pore shape, and pore size on the cellular response and bone tissue regeneration process are reviewed. In addition to reviewing the recent experimental observations, we discuss the mechanisms through which geometry affects the bone tissue regeneration process. Of particular interest are the theoretical models that have been developed to explain the role of geometry in the bone tissue regeneration process. We then follow with a section on the implications of the observed phenomena for geometrical design of porous scaffolds including the application of predictive computational models in geometrical design of porous scaffolds. Moreover, some geometrical concepts in the design of porous scaffolds such as minimal surfaces and porous structures with geometrical gradients that have not been explored before are suggested for future studies. We especially focus on the porous scaffolds manufactured using additive manufacturing techniques where the geometry of the porous scaffolds could be precisely controlled. The paper concludes with a general discussion of the current state-of-the-art and recommendations for future research.


title: Mining the Relationship between Emoji Usage Patterns and Personality

abstract: Emojis have been widely used in textual communications as a new way to convey nonverbal cues. An interesting observation is the various emoji usage patterns among different users. In this paper, we investigate the correlation between user personality traits and their emoji usage patterns, particularly on overall amounts and specific preferences. To achieve this goal, we build a large Twitter dataset which includes 352,245 users and over 1.13 billion tweets associated with calculated personality traits and emoji usage patterns. Our correlation and emoji prediction results provide insights into the power of diverse personalities that lead to varies emoji usage patterns as well as its potential in emoji recommendation tasks.


title: Cultural Differences in Spontaneous Trait and Situation Inferences

abstract: Previous findings indicated that when people observe someone’s behavior, they spontaneously infer the traits and situations that cause the target person’s behavior. These inference processes are ca...


title: Highly Efficient Doherty Amplifier Based on Class-E Topology for WCDMA Applications

abstract: This letter reports a high-efficiency gallium nitride (GaN) high-electron mobility transistor (HEMT) Doherty power amplifier (DPA) based on the class-E topology for wideband code-division multiple-access (WCDMA) applications. The class-E topology is employed as the carrier and peaking cells of the Doherty configuration. For validations, the proposed DPA is designed and implemented with 25-W GaN HEMTs at 2.14 GHz. For the proposed DPA, the power-added efficiency (PAE) and drain efficiency of 56.1% and 61.2% are achieved at 40 dBm (6-dB backoff power from Psat) for a continuous wave. For a 1-carrier WCDMA signal, the PAE of 44.8% is obtained with an adjacent channel leakage ratio (ACLR) of -31 dBc at 37 dBm, which is an 8.9% improvement over the conventional DPA with an ACLR of -36.4 dBc.


title: Evaluation of machine learning classifiers for mobile malware detection

abstract: Mobile devices have become a significant part of people's lives, leading to an increasing number of users involved with such technology. The rising number of users invites hackers to generate malicious applications. Besides, the security of sensitive data available on mobile devices is taken lightly. Relying on currently developed approaches is not sufficient, given that intelligent malware keeps modifying rapidly and as a result becomes more difficult to detect. In this paper, we propose an alternative solution to evaluating malware detection using the anomaly-based approach with machine learning classifiers. Among the various network traffic features, the four categories selected are basic information, content based, time based and connection based. The evaluation utilizes two datasets: public (i.e. MalGenome) and private (i.e. self-collected). Based on the evaluation results, both the Bayes network and random forest classifiers produced more accurate readings, with a 99.97 % true-positive rate (TPR) as opposed to the multi-layer perceptron with only 93.03 % on the MalGenome dataset. However, this experiment revealed that the k-nearest neighbor classifier efficiently detected the latest Android malware with an 84.57 % true-positive rate higher than other classifiers.


title: ORIGINAL ARTICLE Extreme learning machines: a survey

abstract: Computational intelligence techniques have been used in wide applications. Out of numerous computational intelligence techniques, neural networks and support vector machines (SVMs) have been playing the dominant roles. However, it is known that both neural networks and SVMs face some challenging issues such as: (1) slow learning speed, (2) trivial human intervene, and/or (3) poor computational scalability. Extreme learning machine (ELM) as emergent technology which overcomes some challenges faced by other techniques has recently attracted the attention from more and more researchers. ELM works for generalized single-hidden layer feedforward networks (SLFNs). The essence of ELM is that the hidden layer of SLFNs need not be tuned. Compared with those traditional computational intelligence techniques, ELM provides better generalization performance at a much faster learning speed and with least human intervene. This paper gives a survey on ELM and its variants, especially on (1) batch learning mode of ELM, (2) fully complex ELM, (3) online sequential ELM, (4) incremental ELM, and (5) ensemble of ELM.


title: The St. Thomas common sense symposium: designing architectures for human-level intelligence

abstract: To build a machine that has "common sense" was once a principal goal in the field of artificial intelligence. But most researchers in recent years have retreated from that ambitious aim. Instead, each developed some special technique that could deal with some class of problem well, but does poorly at almost everything else. We are convinced, however, that no one such method will ever turn out to be "best," and that instead, the powerful AI systems of the future will use a diverse array of resources that, together, will deal with a great range of problems. To build a machine that's resourceful enough to have humanlike common sense, we must develop ways to combine the advantages of multiple methods to represent knowledge, multiple ways to make inferences, and multiple ways to learn. We held a two-day symposium in St. Thomas, U.S. Virgin Islands, to discuss such a project--to develop new architectural schemes that can bridge between different strategies and representations. This article reports on the events and ideas developed at this meeting and subsequent thoughts by the authors on how to make progress.


title: A Novel Antenna-in-Package With LTCC Technology for W-Band Application

abstract: In this letter, a novel antenna-in-package (AiP) technology at W-band has been proposed. This technology is presented for solving the special case that the metallic package should be used to accommodate high mechanical strength. By taking advantages of the multilayer low temperature co-fired ceramic (LTCC) technology, the radiation efficiency of the antenna can be maintained. Meanwhile, high mechanical strength and shielding performance are achieved. A prototype of AiP has been designed. The prototype constitutes integrated LTCC antenna, low-loss feeder, and metallic package with a tapered horn aperture. This LTCC feeder is realized by laminated waveguide (LWG). An LWG cavity that is buried in LTCC is employed to broaden the antenna impedance bandwidth. Electromagnetic (EM) simulations and measurements of antenna performances agree well over the whole frequency range of interest. The proposed prototype achieves a -10-dB impedance bandwidth of 10 GHz from 88 to 98 GHz and a peak gain of 12.3 dBi at 89 GHz.


title: Differential information use for near and distant decisions

abstract: Whether choosing a cell phone, a senator, or a kitchen appliance, consumers today quickly find themselves awash in information from commercials, magazines, and websites. Whereas some of this information is broad, decontextualized, and abstracted across multiple individuals and instances, other information is more closely tied to a single experience within one specific context. The present research asks: under what circumstances do people rely on abstracted averages, and when are they swayed by another individual’s particular experience? Across three studies, we show that temporal distance increases the relative weight placed on aggregate vs. individualized information when participants are asked to choose between two sleeping pills, migraine medications, or kitchen appliances, and that this process impacts not only evaluation but also willingness to pay and choice. Potential implications for evaluation, decision-making, and base-rate utilization are discussed.


title: Fast simple linear iterative clustering for superpixel segmentation

abstract: In this paper, a fast implementation of simple linear iterative clustering is presented. By exploiting spatial redundancy within natural images, we successfully reduce the number of distance calculation. It is confirmed that the proposed method runs about two times faster than the conventional method with almost same segmentation results.


title: Fundamental Limits of Caching

abstract: Caching is a technique to reduce peak traffic rates by prefetching popular content in memories at the end users. This paper proposes a novel caching approach that can achieve a significantly larger reduction in peak rate compared to previously known caching schemes. In particular, the improvement can be on the order of the number of end users in the network. Conventionally, cache memories are exploited by delivering requested contents in part locally rather than through the network. The gain offered by this approach, which we term local caching gain, depends on the local cache size (i.e., the cache available at each individual user). In this paper, we introduce and exploit a second, global, caching gain, which is not utilized by conventional caching schemes. This gain depends on the aggregate global cache size (i.e., the cumulative cache available at all users), even though there is no cooperation among the caches. To evaluate and isolate these two gains, we introduce a new, information-theoretic formulation of the caching problem focusing on its basic structure. For this setting, the proposed scheme exploits both local and global caching gains, leading to a multiplicative improvement in the peak rate compared to previously known schemes. Moreover, we argue that the performance of the proposed scheme is within a constant factor from the information-theoretic optimum for all values of the problem parameters.


Optimized Agar Extraction from Gracilaria eucheumoides Harvey


title: Deep Structured Energy-Based Image Inpainting

abstract: In this paper, we propose a structured image inpainting method employing an energy based model. In order to learn structural relationship between patterns observed in images and missing regions of the images, we employ an energy-based structured prediction method. The structural relationship is learned by minimizing an energy function which is defined by a simple convolutional neural network. The experimental results on various benchmark datasets show that our proposed method significantly outperforms the state-of-the-art methods which use Generative Adversarial Networks (GANs). We obtained 497.35 mean squared error (MSE) on the Olivetti face dataset compared to 833.0 MSE provided by the state-of-the-art method. Moreover, we obtained 28.4 dB peak signal to noise ratio (PSNR) on the SVHN dataset and 23.53 dB on the CelebA dataset, compared to 22.3 dB and 21.3 dB, provided by the state-of-the-art methods, respectively. The code is publicly available.11https:llgithub.com/cvlab-tohoku/DSEBImageInpainting.


title: Automated Social Skills Trainer

abstract: Social skills training is a well-established method to decrease human anxiety and discomfort in social interaction, and acquire social skills. In this paper, we attempt to automate the process of social skills training by developing a dialogue system named "automated social skills trainer," which provides social skills training through human-computer interaction. The system includes a virtual avatar that recognizes user speech and language information and gives feedback to users to improve their social skills. Its design is based on conventional social skills training performed by human participants, including defining target skills, modeling, role-play, feedback, reinforcement, and homework. An experimental evaluation measuring the relationship between social skill and speech and language features shows that these features have a relationship with autistic traits. Additional experiments measuring the effect of performing social skills training with the proposed application show that most participants improve their skill by using the system for 50 minutes.


title: POIsam: a System for Efficient Selection of Large-scale Geospatial Data on Maps

abstract: In this demonstration we present POIsam, a visualization system supporting the following desirable features: representativeness, visibility constraint, zooming consistency, and panning consistency. The first two constraints aim to efficiently select a small set of representative objects from the current region of user's interest, and any two selected objects should not be too close to each other for users to distinguish in the limited space of a screen. One unique feature of POISam is that any similarity metrics can be plugged into POISam to meet the user's specific needs in different scenarios. The latter two consistencies are fundamental challenges to efficiently update the selection result w.r.t. user's zoom in, zoom out and panning operations when they interact with the map. POISam drops a common assumption from all previous work, i.e. the zoom levels and region cells are pre-defined and indexed, and objects are selected from such region cells at a particular zoom level rather than from user's current region of interest (which in most cases do not correspond to the pre-defined cells). It results in extra challenge as we need to do object selection via online computation. To our best knowledge, this is the first system that is able to meet all the four features to achieve an interactive visualization map exploration system.


title: A Fully Integrated Auto-Calibrated Super-Regenerative Receiver in 0.13-$\mu{\hbox {m}}$ CMOS

abstract: Super-regeneration is re-examined for its simplicity and power efficiency for low-power, short-range communication. A fully integrated super-regenerative receiver in 0.13-mum CMOS is designed to operate in the 2.4 GHz ISM band. A frequency synthesizer scheme tunes the passband. Successive approximation register (SAR) logic driving a current digital-to-analog converter (DAC) calibrates the quench signal to enhance the selectivity of a Q-enhanced filter and the sensitivity of super-regeneration. A single-chip prototype receiver occupies less than 1 mm2, has a turn-on time of 83.6 mus, a channel spacing of 10 MHz, and a sensitivity of -90 dBm. A data rate of 500 kb/s is achieved with a power consumption of 2.8 mW, corresponding to energy consumption of 5.6 nJ per received bit.


title: Traveling Slow Oscillations During Sleep: A Marker of Brain Connectivity in Childhood

abstract: Slow oscillations, a defining characteristic of the nonrapid eye movement sleep electroencephalogram (EEG), proliferate across the scalp in highly reproducible patterns. In adults, the propagation of slow oscillations is a recognized fingerprint of brain connectivity and excitability. In this study, we (1) describe for the first time maturational features of sleep slow oscillation propagation in children (n = 23; 2-13 years) using high-density (hd) EEG and (2) examine associations between sleep slow oscillatory propagation characteristics (ie, distance, traveling speed, cortical involvement) and white matter myelin microstructure as measured with multicomponent Driven Equilibrium Single Pulse Observation of T1 and T2-magnetic resonance imaging (mcDESPOT-MRI). Results showed that with increasing age, slow oscillations propagated across longer distances (average growth of 0.2 cm per year; R(21) = 0.50, p < .05), while traveling speed and cortical involvement (ie, slow oscillation expanse) remained unchanged across childhood. Cortical involvement (R(20) = 0.44) and slow oscillation speed (R(20) = -0.47; both p < .05, corrected for age) were associated with myelin content in the superior longitudinal fascicle, the largest anterior-posterior, intrahemispheric white matter connectivity tract. Furthermore, slow oscillation distance was moderately associated with whole-brain (R(21) = 0.46, p < .05) and interhemispheric myelin content, the latter represented by callosal myelin water fraction (R(21) = 0.54, p < .01, uncorrected). Thus, we demonstrate age-related changes in slow oscillation propagation distance, as well as regional associations between brain activity during sleep and the anatomical connectivity of white matter microstructure. Our findings make an important contribution to knowledge of the brain connectome using a noninvasive and novel analytic approach. These data also have implications for understanding the emergence of neurodevelopmental disorders and the role of sleep in brain maturation trajectories.


title: New dissimilarity measures for image phylogeny reconstruction

abstract: Image phylogeny is the problem of reconstructing the structure that represents the history of generation of semantically similar images (e.g., near-duplicate images). Typical image phylogeny approaches break the problem into two steps: (1) estimating the dissimilarity between each pair of images and (2) reconstructing the phylogeny structure. Given that the dissimilarity calculation directly impacts the phylogeny reconstruction, in this paper, we propose new approaches to the standard formulation of the dissimilarity measure employed in image phylogeny, aiming at improving the reconstruction of the tree structure that represents the generational relationships between semantically similar images. These new formulations exploit a different method of color adjustment, local gradients to estimate pixel differences and mutual information as a similarity measure. The results obtained with the proposed formulation remarkably outperform the existing counterparts in the literature, allowing a much better analysis of the kinship relationships in a set of images, allowing for more accurate deployment of phylogeny solutions to tackle traitor tracing, copyright enforcement and digital forensics problems.


title: Bundled Payment vs. Fee-for-Service: Impact of Payment Scheme on Performance

abstract: Healthcare reimbursements in the United States have been traditionally based on a fee-for-service FFS scheme, providing incentives for high volume of care, rather than efficient care. The new healthcare legislation tests new payment models that remove such incentives, such as the bundled payment BP system. We consider a population of patients beneficiaries. The provider may reject patients based on the patient's cost profile and selects the treatment intensity based on a risk-averse utility function. Treatment may result in success or failure, where failure means that unforeseen complications require further care. Our interest is in analyzing the effect of different payment schemes on outcomes such as the presence and extent of patient selection, the treatment intensity, the provider's utility and financial risk, and the total system payoff. Our results confirm that FFS provides incentives for excessive treatment intensity and results in suboptimal system payoff. We show that BP could lead to suboptimal patient selection and treatment levels that may be lower or higher than desirable for the system, with a high level of financial risk for the provider. We also find that the performance of BP is extremely sensitive to the bundled payment value and to the provider's risk aversion. The performance of both BP and FFS degrades when the provider becomes more risk averse. We design two payment systems, hybrid payment and stop-loss mechanisms, that alleviate the shortcomings of FFS and BP and may induce system optimum decisions in a complementary manner. ::: ::: This paper was accepted by Serguei Netessine, operations management.


title: Design of ocean intelligent sensor based on STM32

abstract: In order to realize a long time unattended data acquisition in a complex and harsh environment in the ocean, a low-power intelligent sensor based on STM32 chip is designed. The sensor system is mainly composed of the central control unit based on the ARM chip STM32F103R6, sensor probes, storage and power control unit. Through the detailed design of some key issues such as high-precision sampling, low-power processing, communication, an intelligent ocean sensor has been developed, which has many functions including self-test, self-calibration, self-storage and communication. The test shows that the intelligent sensor work with high stability, and can provide detailed data on deep-sea environment for research.


title: An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach

abstract: Abstract Epilepsy is a life-threatening and challenging neurological disorder, which is affecting a large number of people all over the world. For its detection, encephalography (EEG) is a commonly used clinical approach, but manual inspection of EEG brain signals is a time-consuming and laborious process, which puts a heavy burden on neurologists and affects their performance. Several automatic systems have been proposed using traditional approaches to assist neurologists, which perform well in detecting binary epilepsy scenarios e.g. normal vs. ictal, but their performance degrades in classifying ternary case e.g. ictal vs. normal vs. inter-ictal. To overcome this problem, we propose a system that is an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models. Though a CNN model learns the internal structure of data and outperforms hand-engineered techniques, the main issue is the large number of learnable parameters, whose learning requires a huge volume of data. To overcome this issue, P-1D-CNN works on the concept of refinement approach and it involves 61% fewer parameters compared to standard CNN models and as such it has better generalization. Further to overcome the limitations of the small amount of data, we propose two augmentation schemes. We tested the system on the University of Bonn dataset, a benchmark dataset; in almost all the cases concerning epilepsy detection, it gives an accuracy of 99.1 ± 0.9% and outperforms the state-of-the-art systems. In addition, while enjoying the strength of a CNN model, P-1D-CNN model requires 61% less memory space and its detection time is very short (


Unresolved Issues in Longitudinal Telomere Length Research: Response to Susser et al.


title: Deterministic Replay: A Survey

abstract: Deterministic replay is a type of emerging technique dedicated to providing deterministic executions of computer programs in the presence of nondeterministic factors. The application scopes of deterministic replay are very broad, making it an important research topic in domains such as computer architecture, operating systems, parallel computing, distributed computing, programming languages, verification, and hardware testing. In this survey, we comprehensively review existing studies on deterministic replay by introducing a taxonomy. Basically, existing deterministic replay schemes can be classified into two categories, single-processor (SP) schemes and multiprocessor (MP) schemes. By reviewing the details of these two categories of schemes respectively, we summarize and compare how existing schemes address technical issues such as log size, record slowdown, replay slowdown, implementation cost, and probe effect, which may shed some light on future studies on deterministic replay.


title: Self-adjusting binary search trees

abstract: The splay tree, a self-adjusting form of binary search tree, is developed and analyzed. The binary search tree is a data structure for representing tables and lists so that accessing, inserting, and deleting items is easy. On an n -node splay tree, all the standard search tree operations have an amortized time bound of O (log n ) per operation, where by “amortized time” is meant the time per operation averaged over a worst-case sequence of operations. Thus splay trees are as efficient as balanced trees when total running time is the measure of interest. In addition, for sufficiently long access sequences, splay trees are as efficient, to within a constant factor, as static optimum search trees. The efficiency of splay trees comes not from an explicit structural constraint, as with balanced trees, but from applying a simple restructuring heuristic, called splaying , whenever the tree is accessed. Extensions of splaying give simplified forms of two other data structures: lexicographic or multidimensional search trees and link/cut trees.


title: App traffic mutation: Toward defending against mobile statistical traffic analysis

abstract: Fingerprinting of mobile device apps is currently an attractive and affordable data gathering technique. Even in the presence of encryption, it is possible to fingerprint a user's app by means of packet-level traffic analysis in which side-channel information is used to determine specific patterns in packets. Knowing the specific apps utilized by smartphone users is a serious privacy concern. In this study, we address the issue of defending against statistical traffic analysis of Android apps. First, we present a methodology for the identification of mobile apps using traffic analysis. Further, we propose confusion models in which we obfuscate packet lengths information leaked by mobile traffic, and we shape one class of app traffic to obscure its class features with minimum overhead. We assess the efficiency of our model using different apps and against a recently published approach for mobile apps classification. We focus on making it hard for intruders to differentiate between the altered app traffic and the actual one using statistical analysis. Additionally, we study the tradeoff between shaping cost and traffic privacy protection, specifically the needed overhead and realization feasibility. We were able to attain 91.1% classification accuracy. Using our obfuscation technique, we were able to reduce this accuracy to 15.78%.


title: End-to-End Race Driving with Deep Reinforcement Learning

abstract: We present research using the latest reinforcement learning algorithm for end-to-end driving without any mediated perception (object recognition, scene understanding). The newly proposed reward and learning strategies lead together to faster convergence and more robust driving using only RGB image from a forward facing camera. An Asynchronous Actor Critic (A3C) framework is used to learn the car control in a physically and graphically realistic rally game, with the agents evolving simultaneously on tracks with a variety of road structures (turns, hills), graphics (seasons, location) and physics (road adherence). A thorough evaluation is conducted and generalization is proven on unseen tracks and using legal speed limits. Open loop tests on real sequences of images show some domain adaption capability of our method.


title: Automatic Video Object Segmentation using Graph Cut

abstract: This paper presents an algorithm for automatic video object planes extraction from coarse to fine. For the case of single moving object in a scene, block-based segmentation defines regions of foreground, background and boundary blocks. Then, the segmentation problem is formulated as an energy minimization problem which is settled by using graph cut algorithm. Automatic segmentation can be realized by obtaining prior knowledge from foreground and background blocks and computation complex is reduced by restricting the refined segmentation region to boundary blocks. Experimental results show the effectiveness of proposed algorithm. It is can be implemented in the head-and-shoulder video sequence segmentation applications.


title: Tapping the educational potential of Facebook: Guidelines for use in higher education

abstract: Facebook is a frequently used Computer Mediated Environment (CME) for students and others to build social connections, with identities and deposited self-expression. Its widespread use makes it appropriate for consideration as an educational tool; though one that does not yet have clear guidelines for use. Whether a social networking site can be used for educational objectives remains largely unexplored as a research question. This paper discusses a study conducted at the University of Auckland and at Manchester Metropolitan University on how their students use Facebook, and its impact on their social and academic lives. Using theories of social capital and knowledge management, we explore some potential educational uses of Facebook. Guidelines are included for the educational use of Facebook by tutors in a university environment. These include both positive recommendations and activities and approaches to avoid; and include educational, administrative and legal issues.


title: Toward metadata removal to preserve privacy of social media users

abstract: Social media site users are constantly uploading images onto social media platforms to document their lives for others. These users may be unaware, though, that the pictures they upload have metadata embedded within them. The metadata includes a wide variety of different types of information about the pictures and the camera used to take them, including Global Positioning System location data. After the picture is uploaded, data is collected by data mining companies that are allowed access to the data by social media sites. What these companies do with this data is uncertain and raises privacy concerns for users. This research provides insight into how much information is stored within pictures and how this may affect users’ privacy. We have also supplied a programming example in the Python programming language that performs the removal of all the metadata, or just the Global Positioning System location data, from images before they are uploaded. Instead of completely stripping away the metadata, our solution allows the users to create identical copies of their images that are free of metadata. These images can now be uploaded free of information the user may not be comfortable sharing with data mining companies or other social media users.


title: Querying massive graph data: A compress and search approach

abstract: Querying graph data is a fundamental problem that witnesses an increasing interest especially for massive graph databases which come as a promising alternative to relational databases for big data modeling. In this paper, we study the problem of subgraph isomorphism search which consists to enumerate the embedding of a query graph in a data graph. The most known solutions of this NP-complete problem are backtracking-based and result in a high computational cost when we deal with massive graph databases. We address this problem and its challenges via graph compression with modular decomposition. In our approach, subgraph isomorphism search is performed on compressed graphs without decompressing them yielding substantial reduction of the search space and consequently a significant saving in processing time as well as in storage space for the graphs. We evaluated our algorithms on nine real-word datasets. The experimental results show that our approach is efficient and scalable.


title: A world full of surprises: bayesian theory of surprise to quantify degrees of uncertainty

abstract: In the specific area of Software Engineering (SE) for self-adaptive systems (SASs) there is a growing research awareness about the synergy between SE and Artificial Intelligence (AI). However, just few significant results have been published so far. In this paper, we propose a novel and formal Bayesian definition of surprise as the basis for quantitative analysis to measure degrees of uncertainty and deviations of self-adaptive systems from normal behavior. A surprise measures how observed data affects the models or assumptions of the world during runtime. The key idea is that a "surprising'' event can be defined as one that causes a large divergence between the belief distributions prior to and posterior to the event occurring. In such a case the system may decide either to adapt accordingly or to flag that an abnormal situation is happening. In this paper, we discuss possible applications of Bayesian theory of surprise for the case of self-adaptive systems using Bayesian dynamic decision networks.


title: Random Forest Clustering and Application to Video Segmentation

abstract: This paper considers the problem of clustering large data sets in a high-dimensional space. Using a random forest, we first generate multiple partitions of the same input space, one per tree. The partitions from all trees are merged by intersecting them, resulting in a partition of higher resolution. A graph is then constructed by assigning a node to each region and linking adjacent nodes. This Graph of Superimposed Partitions (GSP) represents a remapped space of the input data where regions of high density are mapped to a larger number of nodes. Generating such a graph turns the clustering problem in the feature space into a graph clustering task which we solve with the Markov cluster algorithm (MCL). The proposed algorithm is able to capture non-convex structure while being computationally efficient, capable of dealing with large data sets. We show the clustering performance on synthetic data and apply the method to the task of video segmentation.


title: Equilibrium points in n-person games

abstract: One may define a concept of an n -person game in which each player has a finite set of pure strategies and in which a definite set of payments to the n players corresponds to each n -tuple of pure strategies, one strategy being taken for each player. For mixed strategies, which are probability distributions over the pure strategies, the pay-off functions are the expectations of the players, thus becoming polylinear forms …


Bioremediation of Contaminated Soils : A Comparison of In Situ and Ex Situ Techniques


title: Dynamic High-Resolution Sonography Compared to Magnetic Resonance Imaging for Diagnosis of Temporomandibular Joint Disk Displacement

abstract: OBJECTIVES ::: The purpose of this study was to determine the value of dynamic high-resolution sonography for evaluation of temporomandibular joint (TMJ) disk displacement compared to magnetic resonance imaging (MRI) with the mouth closed and during the maximal mandibular range of motion. ::: ::: ::: METHODS ::: Dynamic high-resolution sonography with the mouth closed and during the maximal mandibular range of motion was performed on 39 consecutive patients (78 joints; 13 male and 26 female; age range, 18-77 years; mean age ± SD, 37.23 ± 16.26 years) with TMJ disorders. A TMJ MRI study was performed 1 to 7 days after sonography. We searched for signs of disk displacement and findings compatible with degenerative joint disease. Both studies were performed and interpreted independently by blinded operators. ::: ::: ::: RESULTS ::: Magnetic resonance imaging depicted 22 normal joints (28.2%), 21 (26.9%) with anterior disk displacement with reduction, 15 (19.2%) with anterior disk displacement without reduction, and 20 (25.6%) with degenerative disease. Sonography depicted 30 normal joints (38.5%), 22 (28.2%) with anterior disk displacement with reduction, 12 (15.4%) with anterior disk displacement without reduction, and 14 (17.9%) with degenerative disease. The overall sensitivity, specificity, and accuracy of sonography for diagnosis of disk displacement were 74.3%, 84.2%, and 77.7%, respectively. The sensitivity, specificity, and accuracy for diagnosis of disk displacement with reduction were 78.6%, 66.7%, and 73.0%, and the values for diagnosis of disk displacement without reduction were 66.7%, 78.6%, and 73.0%. ::: ::: ::: CONCLUSIONS ::: Dynamic high-resolution sonography is a potential imaging method for diagnosis of TMJ disk displacement and degenerative diseases. Further studies are needed to make dynamic high-resolution sonography the first-line test for diagnosis of TMJ disk displacement.


title: Keyword clustering for user interest profiling refinement within paper recommender systems

abstract: To refine user interest profiling, this paper focuses on extending scientific subject ontology via keyword clustering and on improving the accuracy and effectiveness of recommendation of the electronic academic publications in online services. A clustering approach is proposed for domain keywords for the purpose of the subject ontology extension. Based on the keyword clusters, the construction of user interest profiles is presented on a rather fine granularity level. In the construction of user interest profiles, we apply two types of interest profiles: explicit profiles and implicit profiles. The explicit profiles are obtained by relating users' interest-topic relevance factors to users' interest measurements of these topics computed by a conventional ontology-based method, and the implicit profiles are acquired on the basis of the correlative relationships among the topic nodes in topic network graphs. Three experiments are conducted which reveal that the uses of the subject ontology extension approach as well as the two types of interest profiles satisfyingly contribute to an improvement in the accuracy of recommendation.


title: Pore‐scale modeling and upscaling of nonaqueous phase liquid mass transfer

abstract: A pore-scale model is developed to simulate mass transfer between two fluid phases in a porous medium. The approach uses a network description of the pore space and builds on explicit tracking of the fluid-fluid interfaces in the pore network. Mass transfer is computed as local mass fluxes across each interface, and transport equations are solved in the pore network by a characteristic method. The concept of stagnant-layer diffusion is used to describe the interface mass transfer, where calculated local concentrations control the rates of mass transfer. The model results predict dissolution fronts developed in column experiments of porous media initially at residual nonaqueous phase saturation. The definition of macroscopic mass transfer coefficients is investigated, and comparisons are made for rigorously upscaled quantities.


title: OOV Sensitive Named-Entity Recognition in Speech

abstract: Named Entity Recognition (NER), an information extraction task, is typically applied to spoken documents by cascading a large vocabulary continuous speech recognizer (LVCSR) and a named entity tagger. Recognizing named entities in automatically decoded speech is difficult since LVCSR errors can confuse the tagger. This is especially true of out-of-vocabulary (OOV) words, which are often named entities and always produce transcription errors. In this work, we improve speech NER by including features indicative of OOVs based on a OOV detector, allowing for the identification of regions of speech containing named entities, even if they are incorrectly transcribed. We construct a new speech NER data set and demonstrate significant improvements for this task.


title: Secure Logging as a Service—Delegating Log Management to the Cloud

abstract: Securely maintaining log records over extended periods of time is very important to the proper functioning of any organization. Integrity of the log files and that of the logging process need to be ensured at all times. In addition, as log files often contain sensitive information, confidentiality and privacy of log records are equally important. However, deploying a secure logging infrastructure involves substantial capital expenses that many organizations may find overwhelming. Delegating log management to the cloud appears to be a viable cost saving measure. In this paper, we identify the challenges for a secure cloud-based log management service and propose a framework for doing the same.


title: The relations of obesity indicators and early metabolic disturbance with upper extremity pain.

abstract: OBJECTIVES ::: To determine whether signs of metabolic disturbance and especially visceral obesity are associated with upper extremity pain. ::: ::: ::: DESIGN ::: Cohort study. ::: ::: ::: SUBJECTS ::: One hundred and seventy-seven workers (154 women, 23 men; age 20-64 years, mean 45) seeking medical advice in the occupational health service for incipient upper extremity disorders were included. ::: ::: ::: MEASURES ::: Weight, height, waist circumference, and hip circumference were measured. Visceral and liver fat content and carotid artery intima-media thickness were estimated with ultrasound. Pain intensity and pain interference with sleep were assessed with visual analog scales at baseline and after 2, 8, 12, 52, and 104 weeks follow-up. Generalized estimating equation approach was used to analyze the repeated measures data. ::: ::: ::: RESULTS ::: All obesity indicators were associated with both pain intensity and pain interference with sleep. Visceral fat thickness was the strongest predictor of pain intensity and pain interference with sleep. Carotid intima-media thickness was neither associated with pain intensity nor with pain interference with sleep. ::: ::: ::: CONCLUSIONS ::: Visceral obesity seems to be a risk factor for upper extremity pain. Further studies are needed to elucidate the underlying mechanisms and to clarify whether weight loss can be helpful in pain management.


title: Deep Generative Adversarial Networks for Compressed Sensing Automates MRI

abstract: Magnetic resonance image (MRI) reconstruction is a severely ill-posed linear inverse task demanding time and resource intensive computations that can substantially trade off {\it accuracy} for {\it speed} in real-time imaging. In addition, state-of-the-art compressed sensing (CS) analytics are not cognizant of the image {\it diagnostic quality}. To cope with these challenges we put forth a novel CS framework that permeates benefits from generative adversarial networks (GAN) to train a (low-dimensional) manifold of diagnostic-quality MR images from historical patients. Leveraging a mixture of least-squares (LS) GANs and pixel-wise $\ell_1$ cost, a deep residual network with skip connections is trained as the generator that learns to remove the {\it aliasing} artifacts by projecting onto the manifold. LSGAN learns the texture details, while $\ell_1$ controls the high-frequency noise. A multilayer convolutional neural network is then jointly trained based on diagnostic quality images to discriminate the projection quality. The test phase performs feed-forward propagation over the generator network that demands a very low computational overhead. Extensive evaluations are performed on a large contrast-enhanced MR dataset of pediatric patients. In particular, images rated based on expert radiologists corroborate that GANCS retrieves high contrast images with detailed texture relative to conventional CS, and pixel-wise schemes. In addition, it offers reconstruction under a few milliseconds, two orders of magnitude faster than state-of-the-art CS-MRI schemes.


title: Spatiotemporal inferences for use in building detection using series of very-high-resolution space-borne stereo images

abstract: Automatic building detection from very-high-resolution VHR satellite images is a difficult task. The detection accuracy is usually limited by spectral ambiguities and the uncertainties of the available height information. Feature extraction and training sampling collection for supervised methods are other sources of uncertainty. Most widely used VHR sensors have shorter revisit cycles IKONOS/GeoEye-1/2, 3 days; WorldView 1/2, 1.1 days due to large off-nadir viewing angles and hence are able to perform consistent acquisition of mono or stereo images. In this article, we investigate the possibility of using high-temporal stereo VHR images to enhance remote-sensing image interpretation under the context of building detection. Digital surface models, which contain the height information, are generated for each date using semi-global matching. Pre-classification is performed combining the height and spectral information to obtain an initial building probability map. With a reference land cover map available for one date, the training samples of the other dates are automatically derived using a rule-based validating procedure. A spatiotemporal inference filter is developed considering the spectral, spatial, and temporal aspects to enhance the building probability maps. This aims at homogenizing the building probability values of spectrally similar pixels in the spatial domain and geometrically similar pixels in the temporal domain, while being robust to the silhouette of the images and geometric discrepancies of the multitemporal data. The effectiveness and robustness of the proposed method are evaluated by performing three experiments on six stereo pairs of the same region over a time period of five years 2006–2011. The area under curve AUC of the receiver operating characteristic and kappa statistic κ are employed to assess the results. These experiments show that spatiotemporal inference filtering largely improves the accuracy of the building probability map average AUC = 0.95 while facilitating building extraction in snow-covered images. The resulting building probability maps can be further used for other applications e.g. building footprint updating.


title: Recommender systems in e-commerce

abstract: Internet is speeding up and modifying the manner in which daily tasks such as online shopping, paying utility bills, watching new movies, communicating, etc., are accomplished. As an example, in older shopping methods, products were mass produced for a single market and audience but that approach is no longer viable. Markets based on long product and development cycles can no longer survive. To stay competitive, markets need to provide different products and services to different customers with different needs. The shift to online shopping has made it incumbent on producers and retailers to customize for customers' needs while providing more options than were possible before. This, however, poses a problem for customers who must now analyze every offering in order to determine what they actually need and will benefit from. To aid customers in this scenario, we discuss about common recommender systems techniques that have been employed and their associated trade-offs.


title: Applications of Data Mining Techniques in Telecom Churn Prediction

abstract: In this competitive world, business becomes highly saturated. Especially, the field of telecommunication faces complex challenges due to a number of vibrant competitive service providers. Therefore it has become very difficult for them to retain existing customers. Since the cost of acquiring new customers is much higher than the cost of retaining the existing customers, it is the time for the telecom industries to take necessary steps to retain the customers to stabilize their market value. This paper explores the application of data mining techniques in predicting likely churners and the impact of attribute selection on identifying the churn. It also compares the efficiency of Decision tree and Neural Network classifiers and lists their performances.


title: Malware Virtualization-Resistant Behavior Detection

abstract: Many researchers monitor malicious software (malware) behavior using Virtual Machines (VM) to protect the underlying operating system. For virtual machines, the malware monitor process exists at the same layer as the real system so the monitor can get detailed behavior information without being discovered. There are some Anti-VM techniques employed by malware authors to ward off collection, analysis and reverse engineering of their malicious programs. Therefore, malware researchers may obtain inaccurate analysis from VM aware programs. This paper presents a solution to detect Anti-VM techniques. We collect behavioral information from malware and use an enhanced behavior distance algorithm to calculate the difference between real and virtual environments to distinguish if the malware has Anti-VM capability. Our experiments show this algorithm works well. This idea can improve malware analysis results and reduce malware misdetection.


title: A sensor fusion methodology for obstacle avoidance robot

abstract: Obstacle detection and navigation of dynamic environments is a challenge in mobile robotics. To address this challenge, this paper presents an efficient sensor fusion methodology to detect the size and location of obstacles and navigate the mobile robot with high accuracy. This is done by leveraging upon the unique advantages of accuracy in both ultrasonic sensor and a Kinect sensor for near-field and far-fields respectively. Further, an efficient Kalman filter is implemented to reduce the systematic errors in encoder data to track robot pose of the robot in real-time and reach the destination with high accuracy. Implemented on differential drive-based mobile robot, the proposed system has been validated with a high efficiency of detecting obstacles and reaching the destination with an accuracy of 5cm.


title: FPGA Design and Implementation of a Real-Time Stereo Vision System

abstract: Stereo vision is a well-known ranging method because it resembles the basic mechanism of the human eye. However, the computational complexity and large amount of data access make real-time processing of stereo vision challenging because of the inherent instruction cycle delay within conventional computers. In order to solve this problem, the past 20 years of research have focused on the use of dedicated hardware architecture for stereo vision. This paper proposes a fully pipelined stereo vision system providing a dense disparity image with additional sub-pixel accuracy in real-time. The entire stereo vision process, such as rectification, stereo matching, and post-processing, is realized using a single field programmable gate array (FPGA) without the necessity of any external devices. The hardware implementation is more than 230 times faster when compared to a software program operating on a conventional computer, and shows stronger performance over previous hardware-related studies.


title: Experimental Study of Unsupervised Feature Learning for HEp-2 Cell Images Clustering

abstract: Automatic identification of HEp-2 cell images has received an increasing research attention. Feature representations play a critical role in achieving good identification performance. Much recent work has focused on supervised feature learning. Typical methods consist of BoW model (based on hand-crafted features) and deep learning model (learning hierarchical features). However, these labels used in supervised feature learning are very labour-intensive and time-consuming. They are commonly manually annotated by specialists and very expensive to obtain. In this paper, we follow this fact and focus on unsupervised feature learning. We have verified and compared the features of these two typical models by clustering. Experimental results show the BoW model generally perform better than deep learning models. Also, we illustrate BoW model and deep learning models have complementarity properties.


title: Perceptrons: An Introduction to Computational Geometry

abstract: Cambridge, Mass.: MIT Press, 1972. 2nd. ed. The book's aim is to seek general results from the close study of abstract version of devices known as perceptrons


title: Causal Inference on Multidimensional Data Using Free Probability Theory

abstract: In this paper, we deal with the problem of inferring causal relations for multidimensional data. Based on the postulate that the distribution of the cause and the conditional distribution of the effect given cause are generated independently, we show that the covariance matrix of the mean embedding of the cause in reproducing kernel Hilbert space (RKHS) is free independent with the covariance matrix of the conditional embedding of the effect given cause. This, called freeness condition, induces a cause–effect asymmetry that a designed measurement is 0 in the causal direction but smaller than 0 in the anticausal direction, and it uncovers the causal direction. One important novel aspect of this paper is that we interpret the independence as a freeness condition between covariance matrices of RKHS distribution embeddings, and it has a wide applicability. We show that our freeness condition-based inference method succeeds in scenarios like additive noise cases, where other methods fail, by theoretical analysis and experimental results.


Forest Fires Prevention in Portugal- Using GIS to Help Improving Early Fire Detection Effectiveness


Low-resource OCR error detection and correction in French Clinical Texts


Character-focused narrative planning


title: Causal Perception in Virtual Reality and its Implications for Presence Factors

abstract: Causality is an important aspect of how we construct reality. Yet, while many psychological phenomena have been studied in their relation to virtual reality (VR), very little work has been dedicated specifically to causal perception, despite its potential relevance for user interaction and presence. In this paper, we describe the development of a virtual environment supporting experiments with causal perception. The system, inspired from psychological data, operates by intercepting events in the virtual world, so as to create artificial co-occurrences between events and their subsequent effects. After recognizing high-level events and formalizing them with a symbolic representation inspired from robotics planning, it modifies the events' effects using knowledge-based operators. The re-activation of the modified events creates co-occurrences inducing causal impressions in the user. We conducted experiments with fifty-three subjects who had to interact with virtual world objects and were presented with alternative consequences for their actions, generated by the system using various levels of plausibility. At the same time, these subjects had to answer ten items from the Presence Questionnaire corresponding mainly to control and realism factors: causal perception appears to have a positive impact on these items. The implications of this work are twofold: first, causal perception can provide an interesting experimental setting for some presence determinants, and second, the elicitation of causal impressions can become part of VR technologies to provide new forms of VR experiences.


title: Qualitative Case Study Guidelines

abstract: Although widely used, the qualitative case study method is not well understood. Due to conflicting epistemological presuppositions and the complexity inherent in qualitative case-based studies, scientific rigor can be difficult to demonstrate, and any resulting findings can be difficult to justify. For that reason, this paper discusses methodological problems associated with qualitative case-based research and offers guidelines for overcoming them. Due to its nearly universal acceptance, Yin’s six-stage case study process is adopted and elaborated on. Moreover, additional principles from the wider methodological literature are integrated and explained. Finally, some modifications to the dependencies between the six case study stages are suggested. It is expected that following the guidelines presented in this paper may facilitate the collection of the most relevant data in the most efficient and effective manner, simplify the subsequent analysis, as well as enhance the validity of the resulting findings. The paper should be of interest to students (honour, masters, doctoral), academics, and practitioners involved with conducting and reviewing qualitative case-based studies.


title: Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection

abstract: The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not. It has many security applications, including plagiarism detection, malware detection, vulnerability search, etc. Existing approaches rely on approximate graph-matching algorithms, which are inevitably slow and sometimes inaccurate, and hard to adapt to a new task. To address these issues, in this work, we propose a novel neural network-based approach to compute the embedding, i.e., a numeric vector, based on the control flow graph of each binary function, then the similarity detection can be done efficiently by measuring the distance between the embeddings for two functions. We implement a prototype called Gemini. Our extensive evaluation shows that Gemini outperforms the state-of-the-art approaches by large margins with respect to similarity detection accuracy. Further, Gemini can speed up prior art's embedding generation time by 3 to 4 orders of magnitude and reduce the required training time from more than 1 week down to 30 minutes to 10 hours. Our real world case studies demonstrate that Gemini can identify significantly more vulnerable firmware images than the state-of-the-art, i.e., Genius. Our research showcases a successful application of deep learning on computer security problems.


title: System on Chip Signal Conditioner for LVDT Sensors

abstract: Linear variable differential transformer (LVDT) sensors are widely used in hydraulic and pneumatic mechatronic systems for measuring physical quantities like displacement, force or pressure. The LVDT sensor consists of two magnetic coupled coils with a common core and this sensor converts the displacement of core into reluctance variation of magnetic circuit. LVDT sensors combines good accuracy (0.1 % error) with low cost, but they require relative complex electronics. Standard electronics for LVDT sensor conditioning is analog $the coupled coils constitute an inductive half-bridge supplied with 5 kHz sinus excitation from a quadrate oscillator. The output phase span is amplified and synchronous demodulated. This analog technology works well but has its drawbacks - hard to adjust, many components and packages, no connection to computer systems. To eliminate all these disadvantages, our team from "Politehnica" University of Bucharest has developed a LVDT signal conditioner using system on chip microcontroller MSP430F149 from Texas Instruments. This device integrates all peripherals required for LVDT signal conditioning (pulse width modulation modules, analog to digital converter, timers, enough memory resources and processing power) and offers also excellent low-power options. Resulting electronic module is a one-chip solution made entirely in SMD technology and its small dimensions allow its integration into sensor's body. Present paper focuses on specific issues of this digital solution for LVDT conditioning and compares it with classic analog solution from different points of view: error curve, power consumption, communication options, dimensions and production cost. Microcontroller software (firmware) and digital signal conditioning techniques for LVDT are also analyzed. Use of system on chip devices for signal conditioning allows realization of low cost compact transducers with same or better performances than their analog counterparts, but with extra options like serial communication channels, self-calibration, local storage of measured values and fault detection


title: The Search-Transfer Problem: The Role of Weak Ties in Sharing Knowledge across Organization Subunits

abstract: This paper combines the concept of weak ties from social network research and the notion of complex knowledge to explain the role of weak ties in sharing knowledge across organization subunits in a multiunit organization. I use a network study of 120 new-product development projects undertaken by 41 divisions in a large electronics company to examine the task of developing new products in the least amount of time. Findings show that weak interunit ties help a project team search for useful knowledge in other subunits but impede the transfer of complex knowledge, which tends to require a strong tie between the two parties to a transfer. Having weak interunit ties speeds up projects when knowledge is not complex but slows them down when the knowledge to be transferred is highly complex. I discuss the implications of these findings for research on social networks and product innovation.


title: Sympathy, empathy, and compassion: A grounded theory study of palliative care patients’ understandings, experiences, and preferences

abstract: Background:Compassion is considered an essential element in quality patient care. One of the conceptual challenges in healthcare literature is that compassion is often confused with sympathy and em...


title: How Latent is Latent Semantic Analysis

abstract: Latent semantic analysis (LSA) is a technique for comparing texts using a vector-based representation that is learned from a corpus. This article begins with a description of the history of LSA and its basic functionality. LSA enjoys both theoretical support and empirical results that show how it matches human behavior. A number of the experiments that compare LSA with humans are described here. The article also describes a few of the many successful applications of LSA to text-processing problems and finishes by presenting a number of current research directions.


title: Chronic Kidney Disease Japan Cohort (CKD-JAC) Study: Design and Methods

abstract: The prevalence and incidence of end-stage renal disease (ESRD) in Japan are the highest and the third highest, respectively, in the world, while the incidence of cardiac death in Japan is the lowest among developed countries. A recent study showed that the prevalence of chronic kidney disease (CKD), defined as an estimated glomerular filtration rate (GFR) of less than 60 mL/min/1.73 m2, is extremely high in Japan, about 20% of the adult population. However, the risk of ESRD and cardiovascular disease (CVD) in the CKD population has not been determined nationwide. For this observational study, we will establish a Chronic Kidney Disease Japan Cohort (CKD-JAC) by enrolling 3,000 patients with CKD in 17 clinical centers around Japan, which will be used to determine the incidence of ESRD and CVD in Japanese CKD patients. Risk factors associated with the development of CVD will also be examined. Comorbidity of diabetes in CKD patients will be analyzed to determine whether it is a risk for rapid progression of CKD and high incidence of CVD. In addition, we will study whether the burden of CKD decreases the QOL of patients, and increases hospitalization or health resource utilization. Insights from the CKD-JAC study will provide a basis for future interventional trials focused on reducing the burden of ESRD and CVD in patients with CKD in Japan.


title: Code Churn: A Neglected Metric in Effort-Aware Just-in-Time Defect Prediction

abstract: Background: An increasing research effort has devoted to just-in-time (JIT) defect prediction. A recent study by Yang et al. at FSE'16 leveraged individual change metrics to build unsupervised JIT defect prediction model. They found that many unsupervised models performed similarly to or better than the state-of-the-art supervised models in effort-aware JIT defect prediction. Goal: In Yang et al.'s study, code churn (i.e. the change size of a code change) was neglected when building unsupervised defect prediction models. In this study, we aim to investigate the effectiveness of code churn based unsupervised defect prediction model in effort-aware JIT defect prediction. Methods: Consistent with Yang et al.'s work, we first use code churn to build a code churn based unsupervised model (CCUM). Then, we evaluate the prediction performance of CCUM against the state-of-the-art supervised and unsupervised models under the following three prediction settings: cross-validation, time-wise cross-validation, and cross-project prediction. Results: In our experiment, we compare CCUM against the state-of-the-art supervised and unsupervised JIT defect prediction models. Based on six open-source projects, our experimental results show that CCUM performs better than all the prior supervised and unsupervised models. Conclusions: The result suggests that future JIT defect prediction studies should use CCUM as a baseline model for comparison when a novel model is proposed.


title: Oxaloacetate: a novel neuroprotective for acute ischemic stroke.

abstract: It is well established that glutamate acts as an important mediator of neuronal degeneration during cerebral ischemia. Different kind of glutamate antagonists have been used to reduce the deleterious effects of glutamate. However, their preclinical success failed to translate into practical treatments. Far from the classical use of glutamate antagonists employed so far, the systemic administration of oxaloacetate represents a novel neuroprotective strategy to minimize the deleterious effect of glutamate in the brain tissue after ischemic stroke. The neuroprotective effect of oxaloacetate is based on the capacity of this molecule to reduce the brain and blood glutamate levels as a result of the activation of the blood-resident enzyme glutamate-oxaloacetate transaminase. Here we review the recent experimental and clinical results where it is demonstrated the potential applicability of oxaloacetate as a novel and powerful neuroprotective treatment against ischemic stroke.


Software-as-a-Service: The iPlant Foundation API


title: Measure of Landmark Semantic Salience through Geosocial Data Streams

abstract: Research in the area of spatial cognition demonstrated that references to landmarks are essential in the communication and the interpretation of wayfinding instructions for human being. In order to detect landmarks, a model for the assessment of their salience has been previously developed by Raubal and Winter. According to their model, landmark salience is divided into three categories: visual, structural, and semantic. Several solutions have been proposed to automatically detect landmarks on the basis of these categories. Due to a lack of relevant data, semantic salience has been frequently reduced to objects' historical and cultural significance. Social dimension (i.e., the way an object is practiced and recognized by a person or a group of people) is systematically excluded from the measure of landmark semantic salience even though it represents an important component. Since the advent of mobile Internet and smartphones, the production of geolocated content from social web platforms—also described as geosocial data—became commonplace. Actually, these data allow us to have a better understanding of the local geographic knowledge. Therefore, we argue that geosocial data, especially Social Location Sharing datasets, represent a reliable source of information to precisely measure landmark semantic salience in urban area.


title: A 2.4-GHz 22-Mbps CMOS OOK transmitter for wireless body area network

abstract: A 2.4-GHz CMOS on-off keying (OOK) transmitter is developed for applications that require high data rate over 10Mbps in the area of wireless body area network (WBAN) and medical implant communication service (MICS). A novel analog pulse-shaping circuit for reducing power consumption and circuit complexity is employed for the OOK transmitter. Fabricated in 0.13µm CMOS, the transmitter core excluding the 50 ohm driving buffer dissipates 0.95mW from 1.2-V supply. Measurement shows that it can support the data rate up to 22Mbps. With the pulse-shaping capability enabled, the transmitter output spectrum shows successful suppression of the sidelobe power below -42dBc at 100MHz offset from the center frequency.


title: Fast (Spherical) Light Field Rendering with Per-Pixel Depth

abstract: Image-based rendering techniques are a powerful alternative to traditional polygon-based computer graphics. This paper presents a novel light field rendering technique which performs per-pixel depth correction of rays for high-quality reconstruction. Our technique stores combined RGB and depth values in a parabolic 2D texture for every light field sample acquired at discrete positions on a uniformly sampled sphere. Image synthesis is implemented on the GPU as a fragment program which extracts the correct image information from adjacent cameras for each fragment by applying a per-pixel depth correction of rays. We show that the presented image-based rendering technique provides a significant improvement compared to previous approaches. We explain two different rendering implementations which make use of a uniformly sampled parameterisation to minimize disparity problems and ensure full six degrees of freedom for virtual view synthesis. While one rendering algorithm implements a run-time efficient iterative refinement approach for rendering light fields with per pixel depth correction the other approach employs a raycaster for per pixel depth correction and provides superior rendering quality at moderate frame rates. GPU based per-fragment depth correction of rays, used in both implementations, reduces ghosting artefacts to a non noticeable amount and provides a rendering technique that performs without exhaustive pre-processing for 3D object reconstruction or real-time ray-object intersection calculations at rendering time. Index Terms—Image-Based Rendering, Light Field.


title: A Comparative Study of Relational and Non- Relational Database Models in a Web- Based Application

abstract: The purpose of this paper is to present a comparative study between relational and non-relational database models in a web-based application, by executing various operations on both relational and on non-relational databases thus highlighting the results obtained during performance comparison tests. The study was based on the implementation of a web-based application for population records. For the non-relational database, we used MongoDB and for the relational database, we used MSSQL 2014. We will also present the advantages of using a non-relational database compared to a relational database integrated in a web-based application, which needs to manipulate a big amount of data.


title: Broadband and planar microstrip-to-waveguide transitions in millimeter-wave band

abstract: Broadband and planar microstrip-to-waveguide transitions are developed in the millimeter-wave band. Novel printed pattern is applied to the microstrip substrate in the ordinary back-short-type transition to operate over extremely broad frequency bandwidth. Furthermore, in order to realize flat and planar transition which does not need back-short waveguide, the transition is designed in multi-layer substrate. Both transitions are fabricated and their performances are measured and simulated in the millimeter-wave band.


title: Decoding the visual and subjective contents of the human brain

abstract: The potential for human neuroimaging to read out the detailed contents of a person's mental state has yet to be fully explored. We investigated whether the perception of edge orientation, a fundamental visual feature, can be decoded from human brain activity measured with functional magnetic resonance imaging (fMRI). Using statistical algorithms to classify brain states, we found that ensemble fMRI signals in early visual areas could reliably predict on individual trials which of eight stimulus orientations the subject was seeing. Moreover, when subjects had to attend to one of two overlapping orthogonal gratings, feature-based attention strongly biased ensemble activity toward the attended orientation. These results demonstrate that fMRI activity patterns in early visual areas, including primary visual cortex (V1), contain detailed orientation information that can reliably predict subjective perception. Our approach provides a framework for the readout of fine-tuned representations in the human brain and their subjective contents.


title: Sparse Passive-Aggressive Learning for Bounded Online Kernel Methods

abstract: One critical deficiency of traditional online kernel learning methods is their unbounded and growing number of support vectors in the online learning process, making them inefficient and non-scalable for large-scale applications. Recent studies on scalable online kernel learning have attempted to overcome this shortcoming, e.g., by imposing a constant budget on the number of support vectors. Although they attempt to bound the number of support vectors at each online learning iteration, most of them fail to bound the number of support vectors for the final output hypothesis, which is often obtained by averaging the series of hypotheses over all the iterations. In this article, we propose a novel framework for bounded online kernel methods, named “Sparse Passive-Aggressive (SPA)” learning, which is able to yield a final output kernel-based hypothesis with a bounded number of support vectors. Unlike the common budget maintenance strategy used by many existing budget online kernel learning approaches, the idea of our approach is to attain the bounded number of support vectors using an efficient stochastic sampling strategy that samples an incoming training example as a new support vector with a probability proportional to its loss suffered. We theoretically prove that SPA achieves an optimal mistake bound in expectation, and we empirically show that it outperforms various budget online kernel learning algorithms. Finally, in addition to general online kernel learning tasks, we also apply SPA to derive bounded online multiple-kernel learning algorithms, which can significantly improve the scalability of traditional Online Multiple-Kernel Classification (OMKC) algorithms while achieving satisfactory learning accuracy as compared with the existing unbounded OMKC algorithms.


title: FPGA-Optimised Uniform Random Number Generators Using LUTs and Shift Registers

abstract: FPGA-optimised Random Number Generators (RNGs) are more resource efficient than software-optimised RNGs, as they can take advantage of bit-wise operations and FPGA-specific features. However, it is difficult to concisely describe FPGA-optimised RNGs, so they are not commonly used in real-world designs. This paper describes a new type of FPGA RNG called a LUT-SR RNG, which takes advantage of bit-wise XOR operations and the ability to turn LUTs into shift-registers of varying lengths. This provides a good resource-quality balance compared to previous FPGA-optimised generators, between the previous high-resource high-quality LUT-FIFO RNGs and low-resource low-quality LUT-OPT RNGs. The LUT-SR generators can also be expressed using a simple C++ algorithm contained within the paper, allowing 60 fully-specified LUT-SR RNGs with different characteristics to be embedded in the paper, backed up by an online set of VHDL generators and test-benches.


title: Cloudbus Toolkit for Market-Oriented Cloud Computing

abstract: This keynote paper: (1) presents the 21st century vision of computing and identifies various IT paradigms promising to deliver computing as a utility; (2) defines the architecture for creating market-oriented Clouds and computing atmosphere by leveraging technologies such as virtual machines; (3) provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; (4) presents the work carried out as part of our new Cloud Computing initiative, called Cloudbus: (i) Aneka, a Platform as a Service software system containing SDK (Software Development Kit) for construction of Cloud applications and deployment on private or public Clouds, in addition to supporting market-oriented resource management; (ii) internetworking of Clouds for dynamic creation of federated computing environments for scaling of elastic applications; (iii) creation of 3rd party Cloud brokering services for building content delivery networks and e-Science applications and their deployment on capabilities of IaaS providers such as Amazon along with Grid mashups; (iv) CloudSim supporting modelling and simulation of Clouds for performance studies; (v) Energy Efficient Resource Allocation Mechanisms and Techniques for creation and management of Green Clouds; and (vi) pathways for future research.


title: Multiple kernel learning algorithms

abstract: In recent years, several methods have been proposed to combine multiple kernels instead of using a single one. These different kernels may correspond to using different notions of similarity or may be using information coming from multiple sources (different representations or different feature subsets). In trying to organize and highlight the similarities and differences between them, we give a taxonomy of and review several multiple kernel learning algorithms. We perform experiments on real data sets for better illustration and comparison of existing algorithms. We see that though there may not be large differences in terms of accuracy, there is difference between them in complexity as given by the number of stored support vectors, the sparsity of the solution as given by the number of used kernels, and training time complexity. We see that overall, using multiple kernels instead of a single one is useful and believe that combining kernels in a nonlinear or data-dependent way seems more promising than linear combination in fusing information provided by simple linear kernels, whereas linear methods are more reasonable when combining complex Gaussian kernels.


title: On Periodic Properties of Interpolation and Their Application To Image Authentication

abstract: In our society digital images are a powerful and widely used communication medium. They have an important impact on our life. In recent years, due to the advent of high-performance consumer hardware and improved human computer interfaces, it has become relatively easy to create fake images. Typically, to create high quality and consistent forgeries, several types of tampering techniques are employed simultaneously. One of the most often used techniques is resampling (especially when two or more images are spliced together). Generally, resampling is based on an interpolation procedure. Typically, this procedure brings into the signal specific statistical changes. In this work we analyze and analytically describe specific periodic properties presence in interpolated signals and their derivatives. Furthermore, we propose an efficient and blind method to automatically detect the traces of geometric transformations in images and their portions. The results of the proposed method are demonstrated on several examples.


title: Unsupervised Discovery of Discourse Relations for Eliminating Intra-sentence Polarity Ambiguities

abstract: Polarity classification of opinionated sentences with both positive and negative sentiments is a key challenge in sentiment analysis. This paper presents a novel unsupervised method for discovering intra-sentence level discourse relations for eliminating polarity ambiguities. Firstly, a discourse scheme with discourse constraints on polarity was defined empirically based on Rhetorical Structure Theory (RST). Then, a small set of cuephrase-based patterns were utilized to collect a large number of discourse instances which were later converted to semantic sequential representations (SSRs). Finally, an unsupervised method was adopted to generate, weigh and filter new SSRs without cue phrases for recognizing discourse relations. Experimental results showed that the proposed methods not only effectively recognized the defined discourse relations but also achieved significant improvement by integrating discourse information in sentence-level polarity classification.


title: Tracking control for multi-agent consensus with an active leader and variable topology

abstract: In this paper, we consider a multi-agent consensus problem with an active leader and variable interconnection topology. The state of the considered leader not only keeps changing but also may not be measured. To track such a leader, a neighbor-based local controller together with a neighbor-based state-estimation rule is given for each autonomous agent. Then we prove that, with the proposed control scheme, each agent can follow the leader if the (acceleration) input of the active leader is known, and the tracking error is estimated if the input of the leader is unknown.


title: Broadband Dual-Polarized Base Station Antenna for Fifth-Generation (5G) Applications

abstract: A broadband dual-polarized base station antenna with special designed feeding structures is investigated in this paper. The proposed antenna contains two pairs of crossed dipoles, two specially designed feeding connectors, two pieces of dielectric pads, a supporter (also a balun), and a reflector. To verify the designed antenna, a prototype is fabricated and measured. The antenna attains a bandwidth of around 46.5% operating over 3.14⁻5.04 GHz under reflection coefficient lower than -15 dB, and the port-to-port isolation is higher than 32.5 dB. It also achieves very stable radiation patterns with half power beam widths of 71.8° ± 2.5° in both the horizontal and vertical planes and gains of around 8 dBi over its operating band. Besides, the mechanism of the obtained good performances is clearly explained from the angle of current. All of the features ensure that the proposed antenna is suitable for the fifth-generation (5G) mobile communications.


title: Author-conference topic-connection model for academic network search

abstract: This paper proposes a novel topic model, Author-Conference Topic-Connection (ACTC) Model for academic network search. The ACTC Model extends the author-conference-topic (ACT) model by adding subject of the conference and the latent mapping information between subjects and topics. It simultaneously models topical aspects of papers, authors and conferences with two latent topic layers: a subject layer corresponding to conference topic, and a topic layer corresponding to the word topic. Each author would be associated with a multinomial distribution over subjects of conference (eg., KM, DB, IR for CIKM 2012), the conference(CIKM 2012), and the topics are respectively generated from a sampled subject. Then the words are generated from the sampled topics. We conduct experiments on a data set with 8,523 authors, 22,487 papers and 1,243 conferences from the well-known Arnetminer website, and train the model with different number of subjects and topics. For a qualitative evaluation, we compare ACTC with three others models LDA, Author-Topic (AT) and ACT in academic search services. Experiments show that ACTC can effectively capture the semantic connection between different types of information in academic network and perform well in expert searching and conference searching.


title: Integrating SNOMED CT into the UMLS: An Exploration of Different Views of Synonymy and Quality of Editing

abstract: Abstract Objective The integration of SNOMED CT into the Unified Medical Language System (UMLS) involved the alignment of two views of synonymy that were different because the two vocabulary systems have different intended purposes and editing principles. The UMLS is organized according to one view of synonymy, but its structure also represents all the individual views of synonymy present in its source vocabularies. Despite progress in knowledge-based automation of development and maintenance of vocabularies, manual curation is still the main method of determining synonymy. The aim of this study was to investigate the quality of human judgment of synonymy. Design Sixty pairs of potentially controversial SNOMED CT synonyms were reviewed by 11 domain vocabulary experts (six UMLS editors and five noneditors), and scores were assigned according to the degree of synonymy. Measurements The synonymy scores of each subject were compared to the gold standard (the overall mean synonymy score of all subjects) to assess accuracy. Agreement between UMLS editors and noneditors was measured by comparing the mean synonymy scores of editors to noneditors. Results Average accuracy was 71% for UMLS editors and 75% for noneditors (difference not statistically significant). Mean scores of editors and noneditors showed significant positive correlation (Spearman's rank correlation coefficient 0.654, two-tailed p Conclusion The accuracy in the judgment of synonymy was comparable for UMLS editors and nonediting domain experts. There was reasonable agreement between the two groups.