title: The "Which Blair Project": a quick visual method for evaluating perceptual color maps

abstract: We have developed a fast, perceptual method for selecting color scales for data visualization that takes advantage of our sensitivity to luminance variations in human faces. To do so, we conducted experiments in which we mapped various color scales onto the intensitiy values of a digitized photograph of a face and asked observers to rate each image. We found a very strong correlation between the perceived naturalness of the images and the degree to which the underlying color scales increased monotonically in luminance. Color scales that did not include a monotonically-increasing luminance component produced no positive rating scores. Since color scales with monotonic luminance profiles are widely recommended for visualizing continuous scalar data, a purely visual technique for identifying such color scales could be very useful, especially in situations where color calibration is not integrated into the visualization environment, such as over the Internet.


title: An ontology for clinical questions about the contents of patient notes

abstract: Objective: Many studies have been completed on question classification in the open domain, however only limited work focuses on the medical domain. As well, to the best of our knowledge, most of these medical question classifications were designed for literature based question and answering systems. This paper focuses on a new direction, which is to design a novel question processing and classification model for answering clinical questions applied to electronic patient notes. Methods: There are four main steps in the work. Firstly, a relatively large set of clinical questions was collected from staff in an Intensive Care Unit. Then, a clinical question taxonomy was designed for question and answering purposes. Subsequently an annotation guideline was created and used to annotate the question set. Finally, a multilayer classification model was built to classify the clinical questions. Results: Through the initial classification experiments, we realized that the general features cannot contribute to high performance of a minimum classifier (a small data set with multiple classes). Thus, an automatic knowledge discovery and knowledge reuse process was designed to boost the performance by extracting and expanding the specific features of the questions. In the evaluation, the results show around 90% accuracy can be achieved in the answerable subclass classification and generic question templates classification. On the other hand, the machine learning method does not perform well at identifying the category of unanswerable questions, due to the asymmetric distribution. Conclusions: In this paper, a comprehensive study on clinical questions has been completed. A major outcome of this work is the multilayer classification model. It serves as a major component of a patient records based clinical question and answering system as our studies continue. As well, the question collections can be reused by the research community to improve the efficiency of their own question and answering systems.


title: Derivative Dynamic Time Warping

abstract: Time series are a ubiquitous form of data occurring in virtually every scientific discipline. A common task with time series data is comparing one sequence with another. In some domains a very simple distance measure, such as Euclidean distance will suffice. However, it is often the case that two sequences have the approximately the same overall component shapes, but these shapes do not line up in X-axis. Figure 1 shows this with a simple example. In order to find the similarity between such sequences, or as a preprocessing step before averaging them, we must "warp" the time axis of one (or both) sequences to achieve a better alignment. Dynamic time warping (DTW), is a technique for efficiently achieving this warping. In addition to data mining (Keogh & Pazzani 2000, Yi et. al. 1998, Berndt & Clifford 1994), DTW has been used in gesture recognition (Gavrila & Davis 1995), robotics (Schmill et. al 1999), speech processing (Rabiner & Juang 1993), manufacturing (Gollmer & Posten 1995) and medicine (Caiani et. al 1998).


title: Experimental Analysis of BRDF Models

abstract: The Bidirectional Reflectance Distribution Function (BRDF) describes the appearance of a material by its interaction with light at a surface point. A variety of analytical models have been proposed to represent BRDFs. However, analysis of these models has been scarce due to the lack of high-resolution measured data. In this work we evaluate several well-known analytical models in terms of their ability to fit measured BRDFs. We use an existing high-resolution data set of a hundred isotropic materials and compute the best approximation for each analytical model. Furthermore, we have built a new setup for efficient acquisition of anisotropic BRDFs, which allows us to acquire anisotropic materials at high resolution. We have measured four samples of anisotropic materials (brushed aluminum, velvet, and two satins). Based on the numerical errors, function plots, and rendered images we provide insights into the performance of the various models. We conclude that for most isotropic materials physically-based analytic reflectance models can represent their appearance quite well. We illustrate the important difference between the two common ways of defining the specular lobe: around the mirror direction and with respect to the half-vector. Our evaluation shows that the latter gives a more accurate shape for the reflection lobe. Our analysis of anisotropic materials indicates current parametric reflectance models cannot represent their appearances faithfully in many cases. We show that using a sampled microfacet distribution computed from measurements improves the fit and qualitatively reproduces the measurements.


title: Neural Color Transfer between Images

abstract: We propose a new algorithm for color transfer between images that have perceptually similar semantic structures. We aim to achieve a more accurate color transfer that leverages semantically-meaningful dense correspondence between images. To accomplish this, our algorithm uses neural representations for matching. Additionally, the color transfer should be spatially-variant and globally coherent. Therefore, our algorithm optimizes a local linear model for color transfer satisfying both local and global constraints. Our proposed approach jointly optimize matching and color transfer, adopting a coarse-to-fine strategy. The proposed method can be successfully extended from "one-to-one" to "one-to-many" color transfers. The latter further addresses the problem of mismatching elements of the input image. We validate our proposed method by testing it on a large variety of image content.


title: FSCNMF: Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks

abstract: Network embedding has been a central topic of information network mining in the last couple of years. Network embedding learns a compact lower dimensional vector representation for each node of the network, and uses this lower dimensional representation for different machine learning and mining applications. The existing methods for network embedding consider mainly the structure of the network. But many real world networks also contain rich textual or other type of content associated with each node, which can help to understand the underlying semantics of the network. It is not straightforward to integrate the content of each node in the state-of-the-art network embedding methods. ::: In this work, we propose a nonnegative matrix factorization based optimization framework, namely FSCNMF which considers both the network structure and the content of the nodes while learning a lower dimensional vector representation of each node in the network. Our approach systematically exploits the consistency of the network structure and the content of the nodes. We further extend the basic FSCNMF to an advanced method, namely FSCNMF++ to capture the higher order proximities in the network. We conduct experiments on real world information networks for different types of machine learning applications such as node clustering, visualization, and multi-class classification. The results show that our method can represent the network significantly better than the state-of-the-art algorithms and improve the performance across all the applications that we consider.


title: Multibeam Pillbox Antenna With Low Sidelobe Level and High-Beam Crossover in SIW Technology Using the Split Aperture Decoupling Method

abstract: A novel dual-layer multibeam pillbox antenna with a slotted waveguide radiating part in substrate-integrated waveguide (SIW) technology is proposed. In contrast to previous works, the design goal is to have a multibeam antenna with arbitrary low sidelobes and at the same time a high crossing level between adjacent radiated beams. These two constraints cannot be satisfied simultaneously for any passive and lossless multibeam antenna systems with a single radiating aperture due to beam orthogonality. Here, this limitation is overcome using the “split aperture decoupling” method which consists in using two radiating apertures. Each aperture is associated with a pillbox quasi-optical system with several integrated feed horns in its focal plane so as to steer the main beam in the azimuthal plane. The antenna operates at 24.15 GHz and presents very good scanning performance over an angular sector of $\pm 40^\circ $ , with a good agreement between full-wave simulations and measurements. The crossover level between adjacent beams is about $- 3\;{\text {dB}}$ with a sidelobe level lower than $- 24\;{\text{dB}}$ for the central beam and better than $- 11\;{\text{dB}}$ for the extreme beam positions. The isolation between feed horns in the same pillbox system is better than 20 dB.


title: Moving Obstacle Detection using Monocular Vision

abstract: This paper proposes a method for detecting moving obstacles on roads, by using a vehicle mounted monocular camera. To detect various moving obstacles, such as vehicles and pedestrians, the ego-motion of the vehicle is initially estimated from images captured by the camera. There are two problems in ego-motion estimation. Firstly, a typical road scene contains moving obstacles. This causes false estimation of the ego-motion. Secondly, roads possess fewer features, when compared to the number associated with background structures. This reduces the accuracy of ego-motion estimation. In our approach, the ego-motion is estimated from the correspondences of dispersed feature points extracted from various regions other than those that contain moving obstacles. After estimating the ego-motion, any moving obstacles are detected by tracking the feature points over consecutive frames. In our experiments, it has been shown that the proposed method is able to detect moving obstacles.


title: Like bees around the hive: a comparative study of a mobile augmented reality map

abstract: We present findings from field trials of MapLens, a mobile augmented reality (AR) map using a magic lens over a paper map. Twenty-six participants used MapLens to play a location-based game in a city centre. Comparisons to a group of 11 users with a standard 2D mobile map uncover phenomena that arise uniquely when interacting with AR features in the wild. The main finding is that AR features facilitate place-making by creating a constant need for referencing to the physical, and in that it allows for ease of bodily configurations for the group, encourages establishment of common ground, and thereby invites discussion, negotiation and public problem-solving. The main potential of AR maps lies in their use as a collaborative tool.


title: The Xeon® Processor E5-2600 v3: a 22 nm 18-Core Product Family

abstract: The next generation enterprise Xeon server processor maximum configuration supports 18 dual-threaded 64 bit Haswell cores, 45 MB L3 cache, 4 DDR4–2133 MHz memory channels, 40 8 GT/s PCIe lanes, and 40 9.6 GT/s QPI lanes. The processor has 5.56 B transistors on a 31.9 mm $\,\times\,$ 20.8 mm die in Intel's Hi-K metal-gate tri-gate 22 nm CMOS technology with 11 metal layers and achieves up to 33% performance boost. The design supports a wide range of configurations including thermal design power ranging from 55 to 160 W and frequencies ranging from 1.6 to 3.7 GHz. Key architectural innovations include the addition of AVX2 technology, DDR4, and fully integrated voltage regulators (FIVR) that enable per core p-states and uncore frequency scaling.


title: Autonomic Intelligent Cyber-Sensor to Support Industrial Control Network Awareness

abstract: The proliferation of digital devices in a networked industrial ecosystem, along with an exponential growth in complexity and scope, has resulted in elevated security concerns and management complexity issues. This paper describes a novel architecture utilizing concepts of autonomic computing and a simple object access protocol (SOAP)-based interface to metadata access points (IF-MAP) external communication layer to create a network security sensor. This approach simplifies integration of legacy software and supports a secure, scalable, and self-managed framework. The contribution of this paper is twofold: 1) A flexible two-level communication layer based on autonomic computing and service oriented architecture is detailed and 2) three complementary modules that dynamically reconfigure in response to a changing environment are presented. One module utilizes clustering and fuzzy logic to monitor traffic for abnormal behavior. Another module passively monitors network traffic and deploys deceptive virtual network hosts. These components of the sensor system were implemented in C++ and PERL and utilize a common internal D-Bus communication mechanism. A proof of concept prototype was deployed on a mixed-use test network showing the possible real-world applicability. In testing, 45 of the 46 network attached devices were recognized and 10 of the 12 emulated devices were created with specific operating system and port configurations. In addition, the anomaly detection algorithm achieved a 99.9% recognition rate. All output from the modules were correctly distributed using the common communication structure.


An Extension of the Technology Acceptance Model tailored to Wearable Device Technology


title: Price manipulation in the Bitcoin ecosystem

abstract: We identify and analyze the impact of suspicious trading activity (STA) on the Mt. Gox Bitcoin currency exchange between February and November 2013. We discuss two distinct STA periods in which approximately 600,000 bitcoin (BTC) valued at $188 million were acquired by agents who did not pay for the bitcoins. During the second period, the USD-BTC exchange rate rose by an average of $20 at Mt. Gox on days when suspicious trades took place, compared to a slight decline on days without suspicious activity. Based on rigorous analysis with extensive robustness checks, we conclude that the suspicious trading activity caused the unprecedented spike in the USD-BTC exchange rate in late 2013, when the rate jumped from around $150 to more than $1,000 in two months.


title: Handling Homographs in Neural Machine Translation

abstract: Homographs, words with different meanings but the same surface form, have long caused difficulty for machine translation systems, as it is difficult to select the correct translation based on the context. However, with the advent of neural machine translation (NMT) systems, which can theoretically take into account global sentential context, one may hypothesize that this problem has been alleviated. In this paper, we first provide empirical evidence that existing NMT systems in fact still have significant problems in properly translating ambiguous words. We then proceed to describe methods, inspired by the word sense disambiguation literature, that model the context of the input word with context-aware word embeddings that help to differentiate the word sense be- fore feeding it into the encoder. Experiments on three language pairs demonstrate that such models improve the performance of NMT systems both in terms of BLEU score and in the accuracy of translating homographs.


title: Phenotypic plasticity and the epigenetics of human disease

abstract: It is becoming clear that epigenetic changes are involved in human disease as well as during normal development. A unifying theme of disease epigenetics is defects in phenotypic plasticity--cells' ability to change their behaviour in response to internal or external environmental cues. This model proposes that hereditary disorders of the epigenetic apparatus lead to developmental defects, that cancer epigenetics involves disruption of the stem-cell programme, and that common diseases with late-onset phenotypes involve interactions between the epigenome, the genome and the environment. Increased understanding of epigenetic-disease mechanisms could lead to disease-risk stratification for targeted intervention and to targeted therapies.


title: An online probabilistic road intersection detector

abstract: In this paper we propose a probabilistic approach for detecting and classifying urban road intersections from a moving vehicle. The approach is based on images from an onboard stereo rig; it relies on the detection of the road ground plane on one side, and on a pixel-level classification of the road on the other. The two processing pipelines are then integrated and the parameters of the road components, i.e., the intersection geometry, are inferred. As opposed to other state-of-the-art offline methods, which require processing of the whole video sequence, our approach integrates the image data by means of an online procedure. The experiments have been performed on well-known KITTI datasets, allowing for future comparisons.


title: Quantitative disease resistance to the bacterial pathogen X anthomonas campestris involves an Arabidopsis immune receptor pair and a gene of unknown function

abstract: Although quantitative disease resistance (QDR) is a durable and broad-spectrum form of resistance in plants, the identification of the genes underlying QDR is still in its infancy. RKS1 (Resistance related KinaSe1) has been reported recently to confer QDR in Arabidopsis thaliana to most but not all races of the bacterial pathogen Xanthomonas campestris pv. campestris (Xcc). We therefore explored the genetic bases of QDR in A. thaliana to diverse races of X. campestris (Xc). A nested genome-wide association mapping approach was used to finely map the genomic regions associated with QDR to Xcc12824 (race 2) and XccCFBP6943 (race 6). To identify the gene(s) implicated in QDR, insertional mutants (T-DNA) were selected for the candidate genes and phenotyped in response to Xc. We identified two major QTLs that confer resistance specifically to Xcc12824 and XccCFBP6943. Although QDR to Xcc12824 is conferred by At5g22540 encoding for a protein of unknown function, QDR to XccCFBP6943 involves the well-known immune receptor pair RRS1/RPS4. In addition to RKS1, this study reveals that three genes are involved in resistance to Xc with strikingly different ranges of specificity, suggesting that QDR to Xc involves a complex network integrating multiple response pathways triggered by distinct pathogen molecular determinants.


title: Convergence Analysis of Batch Normalization for Deep Neural Nets

abstract: Batch normalization (BN) is very effective in accelerating the convergence of a neural network training phase that it has become a common practice. We propose a generalization of BN, the diminishing batch normalization (DBN) algorithm. We provide an analysis of the convergence of the DBN algorithm that converges to a stationary point with respect to trainable parameters. We analyze a two layer model with linear activation. The main challenge of the analysis is the fact that some parameters are updated by gradient while others are not. In the numerical experiments, we use models with more layers and ReLU activation. We observe that DBN outperforms the original BN algorithm on MNIST, NI and CIFAR-10 datasets with reasonable complex FNN and CNN models.


title: Untapped Potential of Wind Power

abstract: Technology costs have come down hugely, and wind is now knocking at the door of coal, nuclear, and even gas as the least-cost energy source in developed countries. The U.K. has experienced wind farms that have put in bids under the Non-Fossil Fuel Obligation (NFFO) at about US$0.03/kWh, and this is probably a benchmark for many projects in the United States. Yet in many of the World Bank’s client countries, which have good and in some cases outstanding wind regimes, costs are much higher, installed capacity is low, and the prospects are gloomy. Sector Structure Electricity is a sector that is still emerging from dominance with most of its clients in state-owned monopoly utilities, where generation, transmission, and distribution are bundled together. Many electricity sectors are undergoing reform and restructuring, but progress is uneven. Some countries are further down the path of reform than others. There are few, if any, truly open electricity markets where a generator can set up shop to sell electricity. Often utilities resist moves to include renewables in their generation mix: immaturity of the technology, small size of the projects, and fears for system stability are the most frequently cited reasons. At best, generators may have the option to respond to set-piece requests for proposals to supply electricity either under engineer, procure, and construct (EPC) or build, own, and operate (BOO); build, own, operate, and transfer (BOOT); or build, operate, and transfer (BOT) arrangements that are published by electricity utility or government authorities. Quite often it is difficult to separate one from the other, given closeness of the policy and the regulatory and operational framework in many countries. Some of the bids have resulted in strong competition and quite low prices, but they: Do not represent a proper market since they are called at a time, frequency, site, and size of the utility’s choosing Are not sustainable over the long term because BOO/BOOT/BOT arrangements are not compatible with the development of wholesale electricity markets.


title: Part-Pair Representation for Part Localization

abstract: In this paper, we propose a novel part-pair representation for part localization. In this representation, an object is treated as a collection of part pairs to model its shape and appearance. By changing the set of pairs to be used, we are able to impose either stronger or weaker geometric constraints on the part configuration. As for the appearance, we build pair detectors for each part pair, which model the appearance of an object at different levels of granularities. Our method of part localization exploits the part-pair representation, featuring the combination of non-parametric exemplars and parametric regression models. Non-parametric exemplars help generate reliable part hypotheses from very noisy pair detections. Then, the regression models are used to group the part hypotheses in a flexible way to predict the part locations. We evaluate our method extensively on the dataset CUB-200-2011 [32], where we achieve significant improvement over the state-of-the-art method on bird part localization. We also experiment with human pose estimation, where our method produces comparable results to existing works.


title: Adult neurogenesis and its promise as a hope for brain repair

abstract: After the pioneer report by Joseph Altman of adult neurogenesis (AN) in mammals in 1962, the phenomenon of AN was “rediscovered” some 20 years later, first in songbirds and then in mammals. Since the 1990s, interest in AN was fueled by the hope that it could lead to the treatment of neurological deficits by grafting these neurons or their progenitors into brain areas affected by disease or injury. Unfortunately, after 20 years of intense research efforts there is no clear indication that AN can be harnessed for the repair of brain circuits. We argue that the exuberant optimism regarding the potential application of AN for brain repair was misguided by the belief that neurons and their precursors had extensive developmental plasticity. Many of the experiments investigating the potential of AN for brain repair were inspired by the idea that neuronal precursors would be able to adapt, and easily change their developmental fate to replace the lost neurons. However, research during the last 20 years has shown that, in most cases, the fate of neurons is strongly determined and that it rarely changes. Understanding the mechanisms that control neural cell fate may allow for the engineering of adult stem cells so that they can give rise to neurons with properties appropriate for the host circuit to be repaired. The lack of phenotypic flexibility of neuronal progenitors may eventually prove to be advantageous, as this may provide a high degree of predictability (and safety) in the properties of reprogrammed cells. We suggest that AN is still a useful model to understand how neurons integrate into adult brain circuits, and that brain repair will require a thorough understanding of the genetic programs that control neuronal fate and neuronal migration.


Measuring student engagement in upper elementary through high school : a description of 21 instruments January 2011


title: A Fully Convolutional Two-Stream Fusion Network for Interactive Image Segmentation

abstract: Abstract In this paper, we propose a novel fully convolutional two-stream fusion network (FCTSFN) for interactiveimage segmentation. The proposed network includes two sub-networks: a two-stream late fusion network (TSLFN) that predicts the foreground at a reduced resolution, and a multi-scale refining network (MSRN) that refines the foreground at full resolution. The TSLFN includes two distinct deep streams followed by a fusion network. The intuition is that, since user interactions are more direct information on foreground/background than the image itself, the two-stream structure of the TSLFN reduces the number of layers between the pure user interaction features and the network output, allowing the user interactions to have a more direct impact on the segmentation result. The MSRN fuses the features from different layers of TSLFN with different scales, in order to seek the local to global information on the foreground to refine the segmentation result at full resolution. We conduct comprehensive experiments on four benchmark datasets. The results show that the proposed network achieves competitive performance compared to current state-of-the-art interactive image segmentation methods. 1


title: An Analysis of Cyber Security Attack Taxonomies

abstract: Taxonomies have been developed as a mechanism for cyber attack categorisation. However, when one considers the recent and rapid evolution of attacker techniques and targets, the applicability and effectiveness of these taxonomies should be questioned. This paper applies two approaches to the evaluation of seven taxonomies. The first employs a criteria set, derived through analysis of existing works in which critical components to the creation of taxonomies are defined. The second applies historical attack data to each taxonomy under review, more specifically, attacks in which industrial control systems have been targeted. This combined approach allows for a more in-depth understanding of existing taxonomies to be developed, from both a theoretical and practical perspective.


title: Zero-shot Domain Adaptation without Domain Semantic Descriptors

abstract: We propose a method to infer domain-specific models such as classifiers for unseen domains, from which no data are given in the training phase, without domain semantic descriptors. When training and test distributions are different, standard supervised learning methods perform poorly. Zero-shot domain adaptation attempts to alleviate this problem by inferring models that generalize well to unseen domains by using training data in multiple source domains. Existing methods use observed semantic descriptors characterizing domains such as time information to infer the domain-specific models for the unseen domains. However, it cannot always be assumed that such metadata can be used in real-world applications. The proposed method can infer appropriate domain-specific models without any semantic descriptors by introducing the concept of latent domain vectors, which are latent representations for the domains and are used for inferring the models. The latent domain vector for the unseen domain is inferred from the set of the feature vectors in the corresponding domain, which is given in the testing phase. The domain-specific models consist of two components: the first is for extracting a representation of a feature vector to be predicted, and the second is for inferring model parameters given the latent domain vector. The posterior distributions of the latent domain vectors and the domain-specific models are parametrized by neural networks, and are optimized by maximizing the variational lower bound using stochastic gradient descent. The effectiveness of the proposed method was demonstrated through experiments using one regression and two classification tasks.


title: A novel method for fast arbitrary-oriented scene text detection

abstract: In this paper, we propose a novel method for fast arbitrary-oriented text detection in scene images. Our proposed method is simple and effective which can predict word-level bounding boxes via a single fully convolutional network. Our method extracts features from the input images by residual network and apply multi-level fusion over the extracted features. It has two outputs, pixel-wise classification between text and non-text and word-level bounding boxes. Our method achieves an F-measure of 83.46% and 56.39% on ICDAR2015 Incidental Scene Text benchmark and COCO-Text dataset respectively, outperforming the previous methods by a large margin. Also, it can run at over 11 FPS on 704 × 1280 images, which is much faster than the previous works.


title: Driving behavior classification based on sensor data fusion using LSTM recurrent neural networks

abstract: We are presenting a novel approach for the task of driving behavior classification based on stacked LSTM Recurrent Neural Networks. Given a nine different sensor data captured using a smart phone internal sensors during a naturalistic driving sessions, we formulated the driving behavior classification problem as time-series classification task. Whereas, given a window sequence of fused feature vectors of sensor data at any time step of a driving trip, we can accurately classify the driving behavior during that window sequence from a three distinctive driving behavior classes, namely normal, aggressive or drowsy driving. We evaluated our proposed Stacked-LSTM model on one of the recent naturalistic driving behavior analysis and classification dataset, UAH-DriveSet. Our proposed Stacked-LSTM model has achieved state-of-the-art results on the UAH-DriveSet with much higher true positive rate as well as lower false positive rate in comparison to the baseline approach. We have also compared the performance of our proposed Stacked-LSTM model against a number of the common classification algorithms used in the driving behavior classification and analysis studies and we achieved F1-measure score of 91% with an improvement of more than 10% over the closest compared approaches.


title: Cardiac arrhythmia detection from ECG combining convolutional and long short-term memory networks

abstract: Objectives: Atrial fibrillation (AF) is a common heart rhythm disorder associated with deadly and debilitating consequences including heart failure, stroke, poor mental health, reduced quality of life and death. Having an automatic system that diagnoses various types of cardiac arrhythmias would assist cardiologists to initiate appropriate preventive measures and to improve the analysis of cardiac disease. To this end, this paper introduces a new approach to detect and classify automatically cardiac arrhythmias in electrocardiograms (ECG) recordings. Methods: The proposed approach used a combination of Convolution Neural Networks (CNNs) and a sequence of Long Short-Term Memory (LSTM) units, with pooling, dropout and normalization techniques to improve their accuracy. The network predicted a classification at every 18th input sample and we selected the final prediction for classification. Results were cross-validated on the Physionet Challenge 2017 training dataset, which contains 8,528 single lead ECG recordings lasting from 9s to just over 60s. Results: Using the proposed structure and no explicit feature selection, 10-fold stratified cross-validation gave an overall F-measure of 0.83.10±0.015 on the held-out test data (mean ± standard deviation over all folds) and 0.80 on the hidden dataset of the Challenge entry server.


title: Statistical Modeling of RNA-Seq Data

abstract: Recently, ultra high-throughput sequencing of RNA (RNA-Seq) has been developed as an approach for analysis of gene expression. By obtaining tens or even hundreds of millions of reads of transcribed sequences, an RNA-Seq experiment can offer a comprehensive survey of the population of genes (transcripts) in any sample of interest. This paper introduces a statistical model for estimating isoform abundance from RNA-Seq data and is flexible enough to accommodate both single end and paired end RNA-Seq data and sampling bias along the length of the transcript. Based on the derivation of minimal sufficient statistics for the model, a computationally feasible implementation of the maximum likelihood estimator of the model is provided. Further, it is shown that using paired end RNA-Seq provides more accurate isoform abundance estimates than single end sequencing at fixed sequencing depth. Simulation studies are also given.


title: Predicting Future Lane Changes of Other Highway Vehicles using RNN-based Deep Models

abstract: In the event of sensor failure, it is necessary for autonomous vehicles to safely execute emergency maneuvers while avoiding other vehicles on the road. In order to accomplish this, the sensor-failed vehicle must predict the future semantic behaviors of other drivers, such as lane changes, as well as their future trajectories given a small window of past sensor observations. We address the first issue of semantic behavior prediction in this paper, by introducing a prediction framework that leverages the power of recurrent neural networks (RNNs) and graphical models. Our prediction goal is to predict the future categorical driving intent, for lane changes, of neighboring vehicles up to three seconds into the future given as little as a one-second window of past LIDAR, GPS, inertial, and map data. ::: We collect real-world data containing over 500,000 samples of highway driving using an autonomous Toyota vehicle. We propose a pair of models that leverage RNNs: first, a monolithic RNN model that tries to directly map inputs to future behavior through a long-short-term-memory network. Second, we propose a composite RNN model by adopting the methodology of Structural Recurrent Neural Networks (RNNs) to learn factor functions and take advantage of both the high-level structure of graphical models and the sequence modeling power of RNNs, which we expect to afford more transparent modeling and activity than the monolithic RNN. To demonstrate our approach, we validate our models using authentic interstate highway driving to predict the future lane change maneuvers of other vehicles neighboring our autonomous vehicle. We find that both RNN models outperform baselines, and they outperform each other in certain conditions.


title: Knowledge Sharing in Agile Software Teams

abstract: Traditionally, software development teams follow Tayloristic approaches favoring division of labor and, hence, the use of role-based teams. Role-based teams require the transfer of knowledge from one stage of the development process to the next. As multiple stages are involved, the problem of miscommunication due to indirect and long communication path is amplified. Agile development teams address this problem by using cross-functional teams that encourages direct communication and reduces the likelihood of miscommunication. Agile approaches usually require team members to be co-located and only facilitate intra-team learning. To overcome the restriction in co-location and support organizational inter-team learning while supporting the social context critical to the sharing of tacit knowledge is the focus of this paper. We also highlight that humans are good at making sense of incomplete and approximative information.


title: Game Theory Based Network Security

abstract: The interactions between attackers and network administrator are modeled as a non-cooperative non-zero-sum dynamic game with incomplete information, which considers the uncertainty and the special properties of multi-stage attacks. The model is a Fictitious Play approach along a special game tree when the attacker is the leader and the administrator is the follower. Multi-objective optimization methodology is used to predict the attacker’s best actions at each decision node. The administrator also keeps tracking the attacker’s actions and updates his knowledge on the attacker’s behavior and objectives after each detected attack, and uses it to update the prediction of the attacker’s future actions. Instead of searching the entire game tree, appropriate time horizons are dynamically determined to reduce the size of the game tree, leading to a new, fast, adaptive learning algorithm. Numerical experiments show that our algorithm has a significant reduction in the damage of the network and it is also more efficient than other existing algorithms.


title: Automatic quality evaluation and (semi-) automatic improvement of OCR models for historical printings

abstract: Good OCR results for historical printings rely on the availability of recognition models trained on diplomatic transcriptions as ground truth, which is both a scarce resource and time-consuming to generate. Instead of having to train a separate model for each historical typeface, we propose a strategy to start from models trained on a combined set of available transcriptions in a variety of fonts. These \emph{mixed models} result in character accuracy rates over 90\% on a test set of printings from the same period of time, but without any representation in the training data, demonstrating the possibility to overcome the typography barrier by generalizing from a few typefaces to a larger set of (similar) fonts in use over a period of time. The output of these mixed models is then used as a baseline to be further improved by both fully automatic methods and semi-automatic methods involving a minimal amount of manual transcriptions. In order to evaluate the recognition quality of each model in a series of models generated during the training process in the absence of any ground truth, we introduce two readily observable quantities that correlate well with true accuracy. These quantities are \emph{mean character confidence C} (as given by the OCR engine OCRopus) and \emph{mean token lexicality L} (a distance measure of OCR tokens from modern wordforms taking historical spelling patterns into account, which can be calculated for any OCR engine). Whereas the fully automatic method is able to improve upon the result of a mixed model by only 1-2 percentage points, already 100-200 hand-corrected lines lead to much better OCR results with character error rates of only a few percent. This procedure minimizes the amount of ground truth production and does not depend on the previous construction of a specific typographic model.


title: Simulation and analysis of DQ frame and P+Resonant controls for voltage source inverter to distributed generation

abstract: An inverter control design in DQ reference frame takes 3-phase sinusoidal signals and converts them to the DQ-frame to make a 2-phase based DC variable control. The error is calculated by subtracting the feedback from reference and then feedding it to PI controllers. The outputs of PI controllers are of the voltage form and transferred back to the ABC frame and fed to SPWM modules. P+Resonant controllers suggest establishing the control strategy in stationary (alpha-beta) frame. In this frame the axis are fix and three phase variables are mapped in a rotating vector with the same angular speed of the original signals. The P+Resonant controller has zero steady state error at the resonant frequency. This paper explains how to apply DQ-frame and P+Resonant for inverter controller design for grid connected single-phase Voltage Source Inverters (VSI). To evaluate the effectiveness of both control techniques, it is also presented simulated results with application of these controllers.


title: Detecting Semantically Equivalent Questions in Online User Forums

abstract: Two questions asking the same thing could be too different in terms of vocabulary and syntactic structure, which makes identifying their semantic equivalence challenging. This study aims to detect semantically equivalent questions in online user forums. We perform an extensive number of experiments using data from two different Stack Exchange forums. We compare standard machine learning methods such as Support Vector Machines (SVM) with a convolutional neural network (CNN). The proposed CNN generates distributed vector representations for pairs of questions and scores them using a similarity metric. We evaluate in-domain word embeddings versus the ones trained with Wikipedia, estimate the impact of the training set size, and evaluate some aspects of domain adaptation. Our experimental results show that the convolutional neural network with in-domain word embeddings achieves high performance even with limited training data.


title: Research Challenges for Data Mining in Science and Engineering ∗

abstract: With the rapid development of computer and information technology in the last several decades, an enormous amount of data in science and engineering has been and will continuously be generated in massive scale, either being stored in gigantic storage devices or flowing into and out of the system in the form of data streams. Moreover, such data has been made widely available, e.g., via the Internet. Such tremendous amount of data, in the order of terato petabytes, has fundamentally changed science and engineering, transforming many disciplines from data-poor to increasingly data-rich, and calling for new, data-intensive methods to conduct research in science and engineering. In this paper, we discuss the research challenges in science and engineering, from the data mining perspective, with a focus on the following issues: (1) information network analysis, (2) discovery, usage, and understanding of patterns and knowledge, (3) stream data mining, (4) mining moving object data, RFID data, and data from sensor networks, (5) spatiotemporal and multimedia data mining, (6) mining text, Web, and other unstructured data, (7) data cube-oriented multidimensional online analytical mining, (8) visual data mining, and (9) data mining by integration of sophisticated scientific and engineering domain knowledge.


title: Accurate Measurement of the Air-Core Inductance of Iron-Core Transformers With a Non-Ideal Low-Power Rectifier

abstract: The air-core inductance of power transformers is measured using a nonideal low-power rectifier. Its dc output serves to drive the transformer into deep saturation, and its ripple provides low-amplitude variable excitation. The principal advantage of the proposed method is its simplicity. For validation, the experimental results are compared with 3-D finite-element simulations.


title: Auricular reconstruction: indications for autogenous and prosthetic techniques.

abstract: The indications for autogenous auricular reconstruction versus prosthetic reconstruction with osseointegrated implant-retained prostheses were outlined in. Plastic and Reconstructive Surgery in 1994 by Wilkes et al. of Canada, but because of the relatively recent Food and Drug Administration approval (1995) of extraoral osseointegrated implants, these indications had not been examined by a surgical unit in the United States. The purpose of this article is to present an evolving algorithm based on an experience with 98 patients who underwent auricular reconstruction over a 10-year period. From this experience, the authors conclude that autogenous reconstruction is the procedure of choice in the majority of pediatric patients with microtia. Prosthetic reconstruction of the auricle is considered in such pediatric patients with congenital deformities for the following three relative indications: (1) failed autogenous reconstruction, (2) severe soft-tissue/skeletal hypoplasia, and/or (3) a low or unfavorable hairline. A fourth, and in our opinion the ideal, indication for prosthetic ear reconstruction is the acquired total or subtotal auricular defect, most often traumatic or ablative in origin, which is usually encountered in adults. Although prosthetic reconstruction requires surgical techniques that are less demanding than autogenous reconstruction, construction of the prosthesis is a time-consuming task requiring experience and expertise. Although autogenous reconstruction presents a technical challenge to the surgeon, it is the prosthetic reconstruction that requires lifelong attention and may be associated with late complications. This article reports the first American series of auricular reconstruction containing both autogenous and prosthetic methods by a single surgical team.


title: LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks

abstract: Abstract In this work, a deep learning approach has been developed to carry out road detection by fusing LIDAR point clouds and camera images. An unstructured and sparse point cloud is first projected onto the camera image plane and then upsampled to obtain a set of dense 2D images encoding spatial information. Several fully convolutional neural networks (FCNs) are then trained to carry out road detection, either by using data from a single sensor, or by using three fusion strategies: early, late, and the newly proposed cross fusion. Whereas in the former two fusion approaches, the integration of multimodal information is carried out at a predefined depth level, the cross fusion FCN is designed to directly learn from data where to integrate information; this is accomplished by using trainable cross connections between the LIDAR and the camera processing branches. To further highlight the benefits of using a multimodal system for road detection, a data set consisting of visually challenging scenes was extracted from driving sequences of the KITTI raw data set. It was then demonstrated that, as expected, a purely camera-based FCN severely underperforms on this data set. A multimodal system, on the other hand, is still able to provide high accuracy. Finally, the proposed cross fusion FCN was evaluated on the KITTI road benchmark where it achieved excellent performance, with a MaxF score of 96.03%, ranking it among the top-performing approaches.


title: Access to treatment for alcohol use disorders following Oregon's health care reforms and Medicaid expansion

abstract: Abstract The study examines impacts of delivery system reforms and Medicaid expansion on treatment for alcohol use disorders within the Oregon Health Plan (Medicaid). Diagnoses, services and pharmacy claims related to alcohol use disorders were extracted from Medicaid encounter data. Logistic regression and interrupted time series analyses assessed the percent with alcohol use disorder entering care and the percent receiving pharmacotherapy before (January 2010–June 2012) and after (January 2013–June 2015) the initiation of Oregon's Coordinated Care Organization (CCO) model (July 2012–December 2012). Analyses also examined changes in access following Medicaid expansion (January 2014). Treatment entry rates increased from 35% in 2010 to 41% in 2015 following the introduction of CCOs and Medicaid expansion. The number of Medicaid enrollees with a diagnosed alcohol use disorder increased about 150% from 10,360 (2013) to 25,454 (2014) following Medicaid expansion. Individuals with an alcohol use disorder who were prescribed a medication to support recovery increased from 2.3% (2010) to 3.8% (2015). In Oregon, Medicaid expansion and health care reforms enhanced access and improved treatment initiation for alcohol use disorders.


title: A voice-controlled personal assistant robot

abstract: Personal robotic assistants help reducing the manual efforts being put by humans in their day-to-day tasks. In this paper, we develop a voice-controlled personal assistant robot. The human voice commands are given to the robotic assistant remotely, by using a smart mobile phone. The robot can perform different movements, turns, start/stop operations and relocate an object from one place to another. The voice commands are processed in real-time, using an online cloud server. The speech signal commands converted to text form are communicated to the robot over a Bluetooth network. The personal assistant robot is developed on a micro-controller based platform and can be aware of its current location. The effectiveness of the voice control communicated over a distance is measured through several experiments. Performance evaluation is carried out with encouraging results of the initial experiments. Possible improvements are also discussed towards potential applications in home, hospitals and industries.


title: Psyllium as therapeutic and drug delivery agent.

abstract: There is no doubt that fibers, in particular viscous dietary fibers, have positive effects on human health, both in the prevention and in treatment of chronic diseases. Dietary fibers from psyllium have been used extensively both as pharmacological supplements, food ingredients, in processed food to aid weight control, to regulation of glucose control for diabetic patients and reducing serum lipid levels in hyperlipidemics. Keeping in view, the pharmacological importance of psyllium polysaccharide and its gel-forming nature, this article discusses the therapeutic value of psyllium for the treatment of constipation, diarrhea, irritable bowel syndrome, inflammatory bowel disease-ulcerative colitis, colon cancer, diabetes and hypercholesterolemia and exploitation of psyllium for developing drug delivery systems.


Guidelines for usability testing with children


title: SHORT-TERM TRAFFIC FLOW PREDICTION: NEURAL NETWORK APPROACH

abstract: Much of the current activity in the area of intelligent vehicle-highway systems (IVHSs) focuses on one simple objective: to collect more data. Clearly, improvements in sensor technology and communication systems will allow transportation agencies to more closely monitor the condition of the surface transportation system. However, monitoring alone cannot improve the safety or efficiency of the system. It is imperative that surveillance data be used to manage the system in a proactive rather than a reactive manner. Proactive traffic management will require the ability to predict traffic conditions. Previous predictive modeling approaches can be grouped into three categories: (a) historical, data-based algorithms; (b) time-series models; and (c) simulations. A relatively new mathematical model, the neural network, offers an attractive alternative because neural networks can model undefined, complex nonlinear surfaces. In a comparison of a backpropagation neural network model with the more traditional approaches of an historical, data-based algorithm and a time-series model, the backpropagation model was clearly superior, although all three models did an adequate job of predicting future traffic volumes. The backpropagation model was more responsive to dynamic conditions than the historical, data-based algorithm, and it did not experience the lag and overprediction characteristics of the time-series model. Given these advantages and the backpropagation model's ability to run in a parallel computing environment, it appears that such neural network prediction models hold considerable potential for use in real-time IVHS applications.


title: Scheduled Control of a Small Unmanned Underwater Vehicle Using Zero Optimization

abstract: In this paper a zero optimizing methodology is used to tune a scheduled PID controller for Gavia, a small autonomous underwater vehicle (AUV). Applying this methodology to a linear parametrically varying (LPV) model results in a scheduled controller effectively linearizing the system. Using the zero optimizing methodology is very simple and only requires a predefined reference system with the desired transient behavior.


Measuring the effectiveness of privacy tools for limiting behavioral advertising


title: Maximum energy harvesting from medium voltage electric-field energy using power line insulators

abstract: One essential requirement for a modern Smart Grid is continuous monitoring of the grid variables. This task is efficiently achieved using self-powered electronic devices deployed throughout the grid network. Within the vicinity of the grid power lines, the high voltages of these lines can provide a source of energy for such loads using capacitive coupling. This is known as Electric-Field Energy Harvesting (EFEH). However, the reported strategies that use this principle have not explored how to maximise their power transfer, which is essential for practical systems since the available energy from EFEH is invariably quite small. In this paper, a novel approach for optimum energy harvesting is proposed using the parasitic capacitance of medium-voltage power line insulators. A followon power electronic conversion system is then used to maintain the load conditions that maximise the extracted energy. The system performance has been analysed using theoretical studies and computer simulations to identify the operating conditions that maximise the extracted energy. Results obtained so far suggest that up to 100 mW can be harvested from 22 kV grid feeders, using only the capacitive coupling that exists between the line insulator harvester and the feeder conductor.


Feature The Singing Neanderthals : the Origins of Music , Language , Mind and Body by


title: Routing or Computing? The Paradigm Shift Towards Intelligent Computer Network Packet Transmission Based on Deep Learning

abstract: Recent years, Software Defined Routers (SDRs) (programmable routers) have emerged as a viable solution to provide a cost-effective packet processing platform with easy extensibility and programmability. Multi-core platforms significantly promote SDRs’ parallel computing capacities, enabling them to adopt artificial intelligent techniques, i.e., deep learning, to manage routing paths. In this paper, we explore new opportunities in packet processing with deep learning to inexpensively shift the computing needs from rule-based route computation to deep learning based route estimation for high-throughput packet processing. Even though deep learning techniques have been extensively exploited in various computing areas, researchers have, to date, not been able to effectively utilize deep learning based route computation for high-speed core networks. We envision a supervised deep learning system to construct the routing tables and show how the proposed method can be integrated with programmable routers using both Central Processing Units (CPUs) and Graphics Processing Units (GPUs). We demonstrate how our uniquely characterized input and output traffic patterns can enhance the route computation of the deep learning based SDRs through both analysis and extensive computer simulations. In particular, the simulation results demonstrate that our proposal outperforms the benchmark method in terms of delay, throughput, and signaling overhead.


title: Moderating role of trait aggressiveness in the effects of violent media on aggression.

abstract: Three studies were conducted to test the hypothesis that high trait aggressive individuals are more affected by violent media than are low trait aggressive individuals. In Study 1, participants read film descriptions and then chose a film to watch. High trait aggressive individuals were more likely to choose a violent film to watch than were low trait aggressive individuals. In Study 2, participants reported their mood before and after the showing of a violet or nonviolent videotape. High trait aggressive individuals felt more angry after viewing the violent videotape than did low trait aggressive individuals. In Study 3, participants first viewed either a violent or a nonviolent videotape and then competed with an "opponent" on a reaction time task in which the loser received a blast of unpleasant noise. Videotape violence was more likely to increase aggression in high trait aggressive individuals than in low trait aggressive individuals. Language: en


title: A reconfigurable microstrip cross parasitic patch antenna with two-dimensional beam scanning capability

abstract: A reconfigurable planar cross parasitic patch antenna is designed and simulated to achieve continuous two-dimensional beam scanning at 2.45 GHz. The antenna realizes complete azimuthal beam scanning with a maximum elevation angle of 34°. This antenna consists of a central driven element and four hexagonal slotted tunable parasitic patches placed on each side of the driven element. The effective electrical size of the parasitic element is changed with respect to the driven element, by varying the capacitance of varactor diodes loaded in the hexagonal slot. The reflector and director properties of the tunable parasitic elements are used to tilt the main beam of antenna away from the broadside direction. The antenna achieves continuous beam scanning from θ = 0° to 34° for φ = 45°, 90°, 135°, 225°, 270° and 315° planes. The advantage of the antenna is that the design is simple and it maintains a common impedance bandwidth from 2.42–2.47 GHz in all configurations. The two-dimensional beam scanning capability of the proposed antenna can be effectively used in the next generation wireless communication networks.


title: Evaluation of LoRa LPWAN technology for remote health and wellbeing monitoring

abstract: Low power consumption, low transceiver chip cost and large coverage area are the main characteristics of the low power wide area networks (LPWAN) technologies. We expect that LPWAN can be part of enabling new human-centric health and wellness monitoring applications. Therefore in this work we study the indoor performance of one LPWAN technology, namely LoRa, by the means of real-life measurements. The measurements were conducted using the commercially available equipment in the main campus of the University of Oulu, Finland, which has an indoor area spanning for over 570 meters North to South and over 320 meters East to West. The measurements were executed for a sensor node operating close to human body that was periodically reporting the sensed data to a base station. The obtained results show that when using 14 dBm transmit power and the largest spreading factor of 12 for the 868 MHz ISM band, the whole campus area can be covered. Measured packet success delivery ratio was 96.7 % without acknowledgements and retransmissions.


title: Half-Bridge Integrated ZVS Full-Bridge Converter With Reduced Conduction Loss for Electric Vehicle Battery Chargers

abstract: A half-bridge integrated zero-voltage-switching (ZVS) full-bridge converter with reduced conduction loss for battery on-board chargers in electric vehicles (EVs) or plug-in hybrid electric vehicles (PHEVs) is proposed in this paper. The proposed converter features a reduction in primary-conduction loss and a lower secondary-voltage stress. In addition, the proposed converter has the most favorable characteristics as battery chargers as follows: a full ZVS capability and a significantly reduced output filter size due to the improved output waveform. In this paper, the circuit configuration, operation principle, and relevant analysis results of the proposed converter are described, followed by the experimental results on a prototype converter realized with a scale-downed 2-kW battery charger for EVs or PHEVs. The experimental results validate the theoretical analysis and show the effectiveness of the proposed converter as battery on-board chargers for EVs or PHEVs.


title: Operator Splitting Methods in Compressive Sensing and Sparse Approximation

abstract: Compressive sensing and sparse approximation have many emerging applications, and are a relatively new driving force for the development of splitting methods in optimization. Many sparse coding problems are well described by variational models with l1-norm penalties and constraints that are designed to promote sparsity. Successful algorithms need to take advantage of the separable structure of potentially complicated objectives by “splitting” them into simpler pieces and iteratively solving a sequence of simpler convex minimization problems. In particular, isolating l1 terms from the rest of the objective leads to simple soft thresholding updates or l1 ball projections. A few basic splitting techniques can be used to design a huge variety of relevant algorithms. This chapter will focus on operator splitting strategies that are based on proximal operators, duality, and alternating direction methods. These will be explained in the context of basis pursuit variants and through compressive sensing applications.


title: Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data

abstract: Google uses continuous streams of data from industry partners in order to deliver accurate results to users. Unexpected drops in traffic can be an indication of an underlying issue and may be an early warning that remedial action may be necessary. Detecting such drops is non-trivial because streams are variable and noisy, with roughly regular spikes (in many different shapes) in traffic data. We investigated the question of whether or not we can predict anomalies in these data streams. Our goal is to utilize Machine Learning and statistical approaches to classify anomalous drops in periodic, but noisy, traffic patterns. Since we do not have a large body of labeled examples to directly apply supervised learning for anomaly classification, we approached the problem in two parts. First we used TensorFlow to train our various models including DNNs, RNNs, and LSTMs to perform regression and predict the expected value in the time series. Secondly we created anomaly detection rules that compared the actual values to predicted values. Since the problem requires finding sustained anomalies, rather than just short delays or momentary inactivity in the data, our two detection methods focused on continuous sections of activity rather than just single points. We tried multiple combinations of our models and rules and found that using the intersection of our two anomaly detection methods proved to be an effective method of detecting anomalies on almost all of our models. In the process we also found that not all data fell within our experimental assumptions, as one data stream had no periodicity, and therefore no time based model could predict it.


title: Uptraining for Accurate Deterministic Question Parsing

abstract: It is well known that parsing accuracies drop significantly on out-of-domain data. What is less known is that some parsers suffer more from domain shifts than others. We show that dependency parsers have more difficulty parsing questions than constituency parsers. In particular, deterministic shift-reduce dependency parsers, which are of highest interest for practical applications because of their linear running time, drop to 60% labeled accuracy on a question test set. We propose an uptraining procedure in which a deterministic parser is trained on the output of a more accurate, but slower, latent variable constituency parser (converted to dependencies). Uptraining with 100K unlabeled questions achieves results comparable to having 2K labeled questions for training. With 100K unlabeled and 2K labeled questions, uptraining is able to improve parsing accuracy to 84%, closing the gap between in-domain and out-of-domain performance.


ABSTRACT OSINT ANALYSIS USING ADAPTIVE RESONANCE THEORY FOR COUNTERTERRORISM WARNINGS


title: Mobile ad hoc cloud: A survey

abstract: The unabated flurry of research activities to augment various mobile devices in terms of compute-intensive task execution by leveraging heterogeneous resources of available devices in the local vicinity has created a new research domain called mobile ad hoc cloud MAC or mobile cloud. It is a new type of mobile cloud computing MCC. MAC is deemed to be a candidate blueprint for future compute-intensive applications with the aim of delivering high functionalities and rich impressive experience to mobile users. However, MAC is yet in its infancy, and a comprehensive survey of the domain is still lacking. In this paper, we survey the state-of-the-art research efforts carried out in the MAC domain. We analyze several problems inhibiting the adoption of MAC and review corresponding solutions by devising a taxonomy. Moreover, MAC roots are analyzed and taxonomized as architectural components, applications, objectives, characteristics, execution model, scheduling type, formation technologies, and node types. The similarities and differences among existing proposed solutions by highlighting the advantages and disadvantages are also investigated. We also compare the literature based on objectives. Furthermore, our study advocates that the problems stem from the intrinsic characteristics of MAC by identifying several new principles. Lastly, several open research challenges such as incentives, heterogeneity-ware task allocation, mobility, minimal data exchange, and security and privacy are presented as future research directions. Copyright © 2016 John Wiley & Sons, Ltd.


title: Dynamic Pricing in the Presence of Strategic Consumers and Oligopolistic Competition

abstract: We present a dynamic pricing model for oligopolistic firms selling differentiated perishable goods to multiple finite segments of strategic consumers who are aware that pricing is dynamic and may time their purchases accordingly. This model encompasses strategic behavior by both firms and consumers in a unified stochastic dynamic game in which each firm's objective is to maximize its total expected revenues, and each consumer responds according to a shopping-intensity-allocation consumer choice model. We prove the existence of a unique subgame-perfect equilibrium, provide equilibrium optimality conditions, and prove monotonicity results for special cases. The model provides insights about equilibrium price dynamics under different levels of competition, asymmetry between firms, and multiple market segments with varying properties. We demonstrate that strategic behavior by consumers can have serious impacts on revenues if firms ignore that behavior in their dynamic pricing policies. Moreover, ideal equilibrium responses to consumer strategic behavior can recover only a portion of the lost revenues. A key conclusion is that firms may benefit more from limiting the information available to consumers than from allowing full information and responding to the resulting strategic behavior in an optimal fashion.


title: Bimanual task division preferences for volume selection

abstract: Using both hands for 3D interaction allows users to transfer ingrained interaction skills, significantly increase performance on certain tasks, and reduce training [Bowman et al. 2005]. Guiard's framework of Bimanual manipulation states that different classes of bimanual actions exist [1997]. The Bimanual Asymmetric classification consists of both hands, performing different actions, coordinated to accomplish the same task. The Bimanual Symmetric classification involves each hand performing identical actions, either synchronously or asynchronously. Latulipe et al. compared a symmetric, dual-mouse technique for manipulation of spline curves, to two asymmetric dual-mouse techniques and a standard single-mouse technique. The symmetric technique performed best and was most preferred by participants [2006].


Artificial Neural Networks Architecture For Intrusion Detection Systems and Classification of Attacks


title: VHT: Vertical hoeffding tree

abstract: IoT Big Data requires new machine learning methods able to scale to large size of data arriving at high speed. Decision trees are popular machine learning models since they are very effective, yet easy to interpret and visualize. In the literature, we can find distributed algorithms for learning decision trees, and also streaming algorithms, but not algorithms that combine both features. In this paper we present the Vertical Hoeffding Tree (VHT), the first distributed streaming algorithm for learning decision trees. It features a novel way of distributing decision trees via vertical parallelism. The algorithm is implemented on top of Apache SAMOA, a platform for mining distributed data streams, and thus able to run on real-world clusters. We run several experiments to study the accuracy and throughput performance of our new VHT algorithm, as well as its ability to scale while keeping its superior performance with respect to non-distributed decision trees.


Fashion, Feminism, and the Pleasures and Perils of Consumer Fantasy


title: Generalized Mongue-Elkan Method for Approximate Text String Comparison

abstract: The Mongue-Elkan method is a general text string comparison method based on an internal character-based similarity measure (e.g. edit distance) combined with a token level (i.e. word level) similarity measure. We propose a generalization of this method based on the notion of the generalized arithmetic mean instead of the simple average used in the expression to calculate the Monge-Elkan method. The experiments carried out with 12 well-known name-matching data sets show that the proposed approach outperforms the original Monge-Elkan method when character-based measures are used to compare tokens.


Monitoring Cyber-Physical Systems


title: Realistic Biomechanical Simulation and Control of Human Swimming

abstract: We address the challenging problem of controlling a complex biomechanical model of the human body to synthesize realistic swimming animation. Our human model includes all of the relevant articular bones and muscles, including 103 bones (163 articular degrees of freedom) plus a total of 823 muscle actuators embedded in a finite element model of the musculotendinous soft tissues of the body that produces realistic deformations. To coordinate the numerous muscle actuators in order to produce natural swimming movements, we develop a biomimetically motivated motor control system based on Central Pattern Generators (CPGs), which learns to produce activation signals that drive the numerous muscle actuators.


title: Motor control and learning in altered dynamic environments

abstract: Dynamic perturbations of reaching movements are an important technique for studying motor learning and adaptation. Adaptation to non-contacting, velocity-dependent inertial Coriolis forces generated by arm movements during passive body rotation is very rapid, and when complete the Coriolis forces are no longer sensed. Adaptation to velocity-dependent forces delivered by a robotic manipulandum takes longer and the perturbations continue to be perceived even when adaptation is complete. These differences reflect adaptive self-calibration of motor control versus learning the behavior of an external object or ‘tool’. Velocity-dependent inertial Coriolis forces also arise in everyday behavior during voluntary turn and reach movements but because of anticipatory feedforward motor compensations do not affect movement accuracy despite being larger than the velocity-dependent forces typically used in experimental studies. Progress has been made in understanding: the common features that determine adaptive responses to velocity-dependent perturbations of jaw and limb movements; the transfer of adaptation to mechanical perturbations across different contact sites on a limb; and the parcellation and separate representation of the static and dynamic components of multiforce perturbations.


title: High-Efficiency ZVS AC-DC LED Driver Using a Self-Driven Synchronous Rectifier

abstract: This paper proposes a high-efficiency zero-voltage-switching (ZVS) AC-DC light-emitting-diode (LED) driver. The structure of the proposed converter is based on a buck-boost power-factor-correction (PFC) converter. Through replacement of an output diode with a self-driven synchronous rectifier (SR), the conduction loss decreases significantly, and there is no switching loss because of the ZVS operation of the switching devices. In addition, no additional control circuit is required and cost reduction is achieved because the SR is self-driven. The efficiency of the proposed converter is higher than that of the conventional critical-conduction-mode (CRM) buck-boost PFC converter, owing to the reduced conduction loss of the high-side output rectifier and no switching loss of either of the switches. For verifying the ZVS operation and efficiency improvement of the proposed AC-DC LED driver, theoretical analysis and experimental results of a 48-[V] and 1.4-[A] prototype for LED driver are discussed.


title: Nuclear neighborhoods and gene expression.

abstract: The eukaryotic nucleus is a highly compartmentalized and dynamic environment. Chromosome territories are arranged nonrandomly within the nucleus and numerous studies have indicated that a gene's position in the nucleus can impact its transcriptional activity. Here, we focus on recent advances in our understanding of the influence of specific nuclear neighborhoods on gene expression or repression. Nuclear neighborhoods associated with transcriptional repression include the inner nuclear membrane/nuclear lamina and perinucleolar chromatin, whereas neighborhoods surrounding the nuclear pore complex, PML nuclear bodies, and nuclear speckles seem to be transcriptionally permissive. While nuclear position appears to play an important role in gene expression, it is likely to be only one piece of a flexible puzzle that incorporates numerous parameters. We are still at a very early, yet exciting stage in our journey toward deciphering the mechanism(s) that govern(s) the permissiveness of gene expression/repression within different nuclear neighborhoods.


title: A Fundamental Storage-Communication Tradeoff in Distributed Computing with Straggling Nodes

abstract: The optimal storage-computation tradeoff is characterized for a MapReduce-like distributed computing system with straggling nodes, where only a part of the nodes can be utilized to compute the desired output functions. The result holds for arbitrary output functions and thus generalizes previous results that restricted to linear functions. Specifically, in this work, we propose a new information-theoretical converse and a new matching coded computing scheme, that we call coded computing for straggling systems (CCS).


title: SirenAttack: Generating Adversarial Audio for End-to-End Acoustic Systems

abstract: Despite their immense popularity, deep learning-based acoustic systems are inherently vulnerable to adversarial attacks, wherein maliciously crafted audios trigger target systems to misbehave. In this paper, we present SirenAttack, a new class of attacks to generate adversarial audios. Compared with existing attacks, SirenAttack highlights with a set of significant features: (i) versatile -- it is able to deceive a range of end-to-end acoustic systems under both white-box and black-box settings; (ii) effective -- it is able to generate adversarial audios that can be recognized as specific phrases by target acoustic systems; and (iii) stealthy -- it is able to generate adversarial audios indistinguishable from their benign counterparts to human perception. We empirically evaluate SirenAttack on a set of state-of-the-art deep learning-based acoustic systems (including speech command recognition, speaker recognition and sound event classification), with results showing the versatility, effectiveness, and stealthiness of SirenAttack. For instance, it achieves 99.45% attack success rate on the IEMOCAP dataset against the ResNet18 model, while the generated adversarial audios are also misinterpreted by multiple popular ASR platforms, including Google Cloud Speech, Microsoft Bing Voice, and IBM Speech-to-Text. We further evaluate three potential defense methods to mitigate such attacks, including adversarial training, audio downsampling, and moving average filtering, which leads to promising directions for further research.


title: Closed type families with overlapping equations

abstract: Open, type-level functions are a recent innovation in Haskell that move Haskell towards the expressiveness of dependent types, while retaining the look and feel of a practical programming language. This paper shows how to increase expressiveness still further, by adding closed type functions whose equations may overlap, and may have non-linear patterns over an open type universe. Although practically useful and simple to implement, these features go beyond conventional dependent type theory in some respects, and have a subtle metatheory.


title: Personality and Character Selection in World of Warcraft

abstract: The present study examined the relationship between players’ personality characteristics and their online behaviors, including character faction, class selection, and game play in the massively multiplayer online role playing game the World of Warcraft (WoW). Data were collected from 205 WoW players who participated in an online survey that included the Big 5 Personality Inventory (Extroversion, Agreeableness, Conscientiousness, Emotional Stability and Openness to Experience) and portions of the California Personality Index, as well as original questions pertaining to WoW (Goldberg, et al. 2006). Statistical analysis showed that although there was not a significant relationship between player personality traits and their class or faction selection, there were significant relationships between personality traits and engagement in player versus player game play.


title: Integrative Therapies in Anxiety Treatment with Special Emphasis on the Gut Microbiome

abstract: Over the past decade, research has shown that diet and gut health affects symptoms expressed in stress related disorders, depression, and anxiety through changes in the gut microbiota. Psycho-behavioral function and somatic health interaction have often been ignored in health care with resulting deficits in treatment quality and outcomes. While mental health care requires the professional training in counseling, psychotherapy and psychiatry, complimentary therapeutic strategies, such as attention to a nutritional and diverse diet and supplementation of probiotic foods, may be integrated alongside psychotherapy treatment models. Development of these alternative strategies is predicated on experimental evidence and diligent research on the biology of stress, fear, anxiety-related behaviors, and the gut-brain connection. This article provides a brief overview on biological markers of anxiety and the expanding nutritional literature relating to brain health and mental disorders. A case study demonstrates an example of a biopsychosocial approach integrating cognitive psychotherapy, dietary changes, and mindfulness activities, in treating symptoms of anxiety. This case study shows a possible treatment protocol to explore the efficacy of targeting the gut-brain-axis that may be used as an impetus for future controlled studies.


title: Retinal vessel extraction using first-order derivative of Gaussian and morphological processing

abstract: The change in morphology, diameter, branching pattern and/or tortuosity of retinal blood vessels is an important indicator of various clinical disorders of the eye and the body. This paper reports an automated method for segmentation of blood vessels in retinal images by means of a unique combination of differential filtering and morphological processing. The centerlines are extracted by the application of first order derivative of Gaussian in four orientations and then the evaluation of derivative signs and average derivative values is made. The shape and orientation map of the blood vessel is obtained by applying a multidirectional morphological top-hat operator followed by bit plane slicing of a vessel enhanced grayscale image. The centerlines are combined with these maps to obtain the segmented vessel tree. The approach is tested on two publicly available databases and results show that the proposed algorithm can obtain robust and accurate vessel tracings with a performance comparable to other leading systems.


title: Increasing the opportunities for aging in place

abstract: A growing social problem in the U.S. and elsewhere is supporting older adults who want to continue living independently as opposed to moving to an institutional care setting. The “Aging in Place” project strives to delay taking that first step away from the family home. Through the careful placement of technological support we believe older adults can continue living in their own homes longer. The goal of our research is to take a three-pronged approach to understanding the potential of such environmental supports. The research team combines expertise in human-computer-interaction, computational perception, and cognitive aging. Together the team is assessing the feasibility of designing environments that aid older individuals in maintaining their independence. Based on our initial research, we are dividing this work into three parts: recognizing and adverting crisis, assisting daily routines, and supporting peace of mind for adult children.


title: Identifying emotional states using keystroke dynamics

abstract: The ability to recognize emotions is an important part of building intelligent computers. Emotionally-aware systems would have a rich context from which to make appropriate decisions about how to interact with the user or adapt their system response. There are two main problems with current system approaches for identifying emotions that limit their applicability: they can be invasive and can require costly equipment. Our solution is to determine user emotion by analyzing the rhythm of their typing patterns on a standard keyboard. We conducted a field study where we collected participants' keystrokes and their emotional states via self-reports. From this data, we extracted keystroke features, and created classifiers for 15 emotional states. Our top results include 2-level classifiers for confidence, hesitance, nervousness, relaxation, sadness, and tiredness with accuracies ranging from 77 to 88%. In addition, we show promise for anger and excitement, with accuracies of 84%.


Ray Marching Distance Fields in Real-time on WebGL


title: Compact Bilinear Pooling

abstract: Bilinear models has been shown to achieve impressive performance on a wide range of visual tasks, such as semantic segmentation, fine grained recognition and face recognition. However, bilinear features are high dimensional, typically on the order of hundreds of thousands to a few million, which makes them impractical for subsequent analysis. We propose two compact bilinear representations with the same discriminative power as the full bilinear representation but with only a few thousand dimensions. Our compact representations allow back-propagation of classification errors enabling an end-to-end optimization of the visual recognition system. The compact bilinear representations are derived through a novel kernelized analysis of bilinear pooling which provide insights into the discriminative power of bilinear pooling, and a platform for further research in compact pooling methods. Experimentation illustrate the utility of the proposed representations for image classification and few-shot learning across several datasets.


title: Spatio-temporal dynamics of online memes: a study of geo-tagged tweets

abstract: We conduct a study of the spatio-temporal dynamics of Twitter hashtags through a sample of 2 billion geo-tagged tweets. In our analysis, we (i) examine the impact of location, time, and distance on the adoption of hashtags, which is important for understanding meme diffusion and information propagation; (ii) examine the spatial propagation of hashtags through their focus, entropy, and spread; and (iii) present two methods that leverage the spatio-temporal propagation of hashtags to characterize locations. Based on this study, we find that although hashtags are a global phenomenon, the physical distance between locations is a strong constraint on the adoption of hashtags, both in terms of the hashtags shared between locations and in the timing of when these hashtags are adopted. We find both spatial and temporal locality as most hashtags spread over small geographical areas but at high speeds. We also find that hashtags are mostly a local phenomenon with long-tailed life spans. These (and other) findings have important implications for a variety of systems and applications, including targeted advertising, location-based services, social media search, and content delivery networks.


title: Optimal design of antenna arrays

abstract: A universal method for the optimal design of antenna arrays is introduced in this paper. The method is based on the optimization of the power transmission efficiency between the antenna array to be designed and a test antenna array whose elements are placed in the desired directions in which radiations need to be maximized. The optimized distribution of excitations for the antenna array is determined by an eigenvalue equation derived from the Rayleigh quotient for the power transmission efficiency. The method is applicable to any type of antenna array. To demonstrate its validity, different antenna arrays for various applications have been designed using the proposed method.


title: Pressure Ulcer Prevention in the Hospital Setting Using Silicone Foam Dressings

abstract: Patient care is of the utmost importance in the hospital setting. Bedrest and immobility during hospitalization, especially in the surgical and intensive care setting, place the patient at high risk for pressure ulcers. It is very important to prevent or notice a pressure ulcer forming due to the significant health care costs involved and patient health associated with them. Various measures are in place to prevent patients from getting pressure ulcers, but a newer material, silicone foam dressings, has been introduced as an alternative solution for the prevention of these ulcers. We review the current literature to examine whether the standard protocol or silicone material is superior to the prevention of pressure ulcer formation. We conclude that silicone foam dressings, when used as prophylactic treatment, seems very promising and may even be superior to the standard care of prevention. However, there were limitations to some studies and further research is needed to confirm the role of silicone foam dressings.


title: A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition

abstract: Decomposition is a well-known strategy in traditional multiobjective optimization. However, the decomposition strategy was not widely employed in evolutionary multiobjective optimization until Zhang and Li proposed multiobjective evolutionary algorithm based on decomposition (MOEA/D) in 2007. MOEA/D proposed by Zhang and Li decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them in a collaborative manner using an evolutionary algorithm (EA). Each subproblem is optimized by utilizing the information mainly from its several neighboring subproblems. Since the proposition of MOEA/D in 2007, decomposition-based MOEAs have attracted significant attention from the researchers. Investigations have been undertaken in several directions, including development of novel weight vector generation methods, use of new decomposition approaches, efficient allocation of computational resources, modifications in the reproduction operation, mating selection and replacement mechanism, hybridizing decomposition- and dominance-based approaches, etc. Furthermore, several attempts have been made at extending the decomposition-based framework to constrained multiobjective optimization, many-objective optimization, and incorporate the preference of decision makers. Additionally, there have been many attempts at application of decomposition-based MOEAs to solve complex real-world optimization problems. This paper presents a comprehensive survey of the decomposition-based MOEAs proposed in the last decade.


title: Efficiency Degradation in Wideband Power Amplifiers

abstract: This paper addresses the efficiency degradation observed in wideband power amplifiers. It starts by presenting a detailed explanation that relates this observed performance degradation with the terminations at baseband. Then, a comparison between two implemented power amplifiers with the same fundamental and harmonic terminations, but with different baseband networks is presented, showing that an optimized bias network design can improve the observed efficiency degradation.


title: Low-Power High-Throughput BCH Error Correction VLSI Design for Multi-Level Cell NAND Flash Memories

abstract: As the reliability is a critical issue for new generation multi-level cell (MLC) flash memories, there is growing call for fast and compact error correction code (ECC) circuit with minimum impact on memory access time and chip area. This paper presents a high-throughput and low-power ECC scheme for MLC NAND flash memories. The BCH encoder and decoder architecture features byte-wise processing and a low complexity key equation solver using a simplified Berlekamp-Massey algorithm. Resource sharing and power reduction techniques are also applied. Synthesized using 0.25-mum CMOS technology in a supply voltage of 2.5 V, the proposed BCH (4148,4096) encoder/decoder achieves byte-wise processing, and it needs an estimated cell area of 0.2 mm2, and an average power of 3.18 mW with 50 MB/s throughput


title: Provenance semirings

abstract: We show that relational algebra calculations for incomplete databases, probabilistic databases, bag semantics and why-provenance are particular cases of the same general algorithms involving semirings. This further suggests a comprehensive provenance representation that uses semirings of polynomials. We extend these considerations to datalog and semirings of formal power series. We give algorithms for datalog provenance calculation as well as datalog evaluation for incomplete and probabilistic databases. Finally, we show that for some semirings containment of conjunctive queries is the same as for standard set semantics.


Vulnerability Assessment and Penetration Testing


title: Combined Depth and Outlier Estimation in Multi-View Stereo

abstract: In this paper, we present a generative model based approach to solve the multi-view stereo problem. The input images are considered to be generated by either one of two processes: (i) an inlier process, which generates the pixels which are visible from the reference camera and which obey the constant brightness assumption, and (ii) an outlier process which generates all other pixels. Depth and visibility are jointly modelled as a hiddenMarkov Random Field, and the spatial correlations of both are explicitly accounted for. Inference is made tractable by an EM-algorithm, which alternates between estimation of visibility and depth, and optimisation of model parameters. We describe and compare two implementations of the E-step of the algorithm, which correspond to the Mean Field and Bethe approximations of the free energy. The approach is validated by experiments on challenging real-world scenes, of which two are contaminated by independently moving objects.


title: Analyzing extended property graphs with Apache Flink

abstract: Graphs are an intuitive way to model complex relationships between real-world data objects. Thus, graph analytics plays an important role in research and industry. As graphs often reflect heterogeneous domain data, their representation requires an expressive data model including the abstraction of graph collections, for example, to analyze communities inside a social network. Further on, answering complex analytical questions about such graphs entails combining multiple analytical operations. To satisfy these requirements, we propose the Extended Property Graph Model, which is semantically rich, schema-free and supports multiple distinct graphs. Based on this representation, it provides declarative and combinable operators to analyze both single graphs and graph collections. Our current implementation is based on the distributed dataflow framework Apache Flink. We present the results of a first experimental study showing the scalability of our implementation on social network data with up to 11 billion edges.


title: Faces and text attract gaze independent of the task: Experimental data and computer model

abstract: Previous studies of eye gaze have shown that when looking at images containing human faces, observers tend to rapidly ::: focus on the facial regions. But is this true of other high-level image features as well? We here investigate the extent to ::: which natural scenes containing faces, text elements, and cell phones - as a suitable control - attract attention by tracking ::: the eye movements of subjects in two types of tasks - free viewing and search. We observed that subjects in free-viewing ::: conditions look at faces and text 16.6 and 11.1 times more than similar regions normalized for size and position of the face ::: and text. In terms of attracting gaze, text is almost as effective as faces. Furthermore, it is difﬁcult to avoid looking at faces ::: and text even when doing so imposes a cost. We also found that subjects took longer in making their initial saccade when ::: they were told to avoid faces/text and their saccades landed on a non-face/non-text object. We reﬁne a well-known bottom–up ::: computer model of saliency-driven attention that includes conspicuity maps for color, orientation, and intensity by adding ::: high-level semantic information (i.e., the location of faces or text) and demonstrate that this signiﬁcantly improves the ::: ability to predict eye ﬁxations in natural images. Our enhanced model’s predictions yield an area under the ROC curve ::: over 84% for images that contain faces or text when compared against the actual ﬁxation pattern of subjects. This ::: suggests that the primate visual system allocates attention using such an enhanced saliency map.


title: Ontology-Based Text Classification into Dynamically Defined Topics

abstract: We present a method for the automatic classification of text documents into a dynamically defined set of topics of interest. The proposed approach requires only a domain ontology and a set of user-defined classification topics, specified as contexts in the ontology. Our method is based on measuring the semantic similarity of the thematic graph created from a text document and the ontology sub-graphs resulting from the projection of the defined contexts. The domain ontology effectively becomes the classifier, where classification topics are expressed using the defined ontological contexts. In contrast to the traditional supervised categorization methods, the proposed method does not require a training set of documents. More importantly, our approach allows dynamically changing the classification topics without retraining of the classifier. In our experiments, we used the English language Wikipedia converted to an RDF ontology to categorize a corpus of current Web news documents into selection of topics of interest. The high accuracy achieved in our tests demonstrates the effectiveness of the proposed method, as well as the applicability of Wikipedia for semantic text categorization purposes.


title: Knowledge Elicitation Methods for Affect Modelling in Education

abstract: Research on the relationship between affect and cognition in Artificial Intelligence in Education AIEd brings an important dimension to our understanding of how learning occurs and how it can be facilitated. Emotions are crucial to learning, but their nature, the conditions under which they occur, and their exact impact on learning for different learners in diverse contexts still needs to be mapped out. The study of affect during learning can be challenging, because emotions are subjective, fleeting phenomena that are often difficult for learners to report accurately and for observers to perceive reliably. Context forms an integral part of learners' affect and the study thereof. This review provides a synthesis of the current knowledge elicitation methods that are used to aid the study of learners' affect and to inform the design of intelligent technologies for learning. Advantages and disadvantages of the specific methods are discussed along with their respective potential for enhancing research in this area, and issues related to the interpretation of data that emerges as the result of their use. References to related research are also provided together with illustrative examples of where the individual methods have been used in the past. Therefore, this review is intended as a resource for methodological decision making for those who want to study emotions and their antecedents in AIEd contexts, i.e. where the aim is to inform the design and implementation of an intelligent learning environment or to evaluate its use and educational efficacy.