title: X-pire 2.0: a user-controlled expiration date and copy protection mechanism

abstract: During the last decade, social networks' free dissemination of personal information has reached a wide acceptance. Most of today's Internet users, especially teenagers, publish various kinds of sensitive information, without realizing that this information is preserved and might be detrimental to their future life and career. We present X-pire 2.0, which overcomes this unsatisfactory situation and puts users back in control of their published data. X-pire 2.0 is the first system that provides a digital expiration date and integrates robust protection against attackers creating digital copies of content before the expiration date has been reached. Our system leverages state-of-the-art trusted computing technologies and seamlessly integrates into the existing infrastructure on the Internet. X-pire 2.0 allows to add and enforce a digital expiration date on images that can be uploaded to existing social networks, since our enforcement is even robust against image recompression during upload. We implemented X-pire 2.0 for Android and show its practicality by publishing protected images in Google+ and Flickr.


title: A Grounded Theory Analysis of Introductory Computer Science Pedagogy

abstract: Planning is a critical, early step on the path to successful program writing and a skill that is often lacking in novice programmers. As practitioners we are continually search- ing for or creating interventions to help our students, particularly those who struggle in the early stages of their computer science education. In this paper we report on our ongoing research of novice programming skills that utilizes the qualitative research method of grounded theory to develop theories and inform the construction of these interventions. We describe how grounded theory, a popular research method in the social sciences since the 1960's, can lend formality and structure to the common practice of simply asking students what they did and why they did it. Further, we aim to inform the reader not only about our emerging theories on interventions for planning but also how they might collect and analyze their own data in this and other areas that trouble novice programmers. In this way those who lecture and design CS1 interventions can do so from a more informed perspective.


A free boundary value problem of nematic liquid crystals with variable degree of orientation


title: A Comparison of Weak Supervision methods for Knowledge Base Construction

abstract: We present a comparison of weak and distant supervision methods for producing proxy examples for supervised relation extraction. We find that knowledge-based weak supervision tends to outperform popular distance supervision techniques, providing a higher yield of positive examples and more accurate models.


title: Synthetic and Bio-Artificial Tactile Sensing: A Review

abstract: This paper reviews the state of the art of artificial tactile sensing, with a particular focus on bio-hybrid and fully-biological approaches. To this aim, the study of physiology of the human sense of touch and of the coding mechanisms of tactile information is a significant starting point, which is briefly explored in this review. Then, the progress towards the development of an artificial sense of touch are investigated. Artificial tactile sensing is analysed with respect to the possible approaches to fabricate the outer interface layer: synthetic skin versus bio-artificial skin. With particular respect to the synthetic skin approach, a brief overview is provided on various technologies and transduction principles that can be integrated beneath the skin layer. Then, the main focus moves to approaches characterized by the use of bio-artificial skin as an outer layer of the artificial sensory system. Within this design solution for the skin, bio-hybrid and fully-biological tactile sensing systems are thoroughly presented: while significant results have been reported for the development of tissue engineered skins, the development of mechanotransduction units and their integration is a recent trend that is still lagging behind, therefore requiring research efforts and investments. In the last part of the paper, application domains and perspectives of the reviewed tactile sensing technologies are discussed.


title: Emotion Classification of Duterte Administration Tweets Using Hybrid Approach

abstract: Nowadays, people engage in social networking sites to gain information, share thoughts and ideas, and give reaction to certain topics. The works of Duterte Administration has been a hot topic in the country since the day it started its term. This research has gathered tweets and determined the emotions based on the Ekman's six basic emotion classification namely happiness, sadness, anger, fear, disgust, and surprise. A hybrid approach was used to determine the classification using lexicon-based and machine learning. Support Vector Machine (SVM) and Naive Bayes Classifier (NBC) were used to train the classifier. Result shows that SVM gains a higher accuracy of 80.48% over NBC. Most tweets result to anger and a few express surprises as classification of emotions.


title: ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software

abstract: Gephi is a network visualization software used in various disciplines (social network analysis, biology, genomics…). One of its key features is the ability to display the spatialization process, aiming at transforming the network into a map, and ForceAtlas2 is its default layout algorithm. The latter is developed by the Gephi team as an all-around solution to Gephi users’ typical networks (scale-free, 10 to 10,000 nodes). We present here for the first time its functioning and settings. ForceAtlas2 is a force-directed layout close to other algorithms used for network spatialization. We do not claim a theoretical advance but an attempt to integrate different techniques such as the Barnes Hut simulation, degree-dependent repulsive force, and local and global adaptive temperatures. It is designed for the Gephi user experience (it is a continuous algorithm), and we explain which constraints it implies. The algorithm benefits from much feedback and is developed in order to provide many possibilities through its settings. We lay out its complete functioning for the users who need a precise understanding of its behaviour, from the formulas to graphic illustration of the result. We propose a benchmark for our compromise between performance and quality. We also explain why we integrated its various features and discuss our design choices.


title: Acute Appendicitis, Somatosensory Disturbances (“Head Zones”), and the Differential Diagnosis of Anterior Cutaneous Nerve Entrapment Syndrome (ACNES)

abstract: BACKGROUND ::: Anterior cutaneous nerve entrapment syndrome (ACNES) is a neuropathic abdominal wall pain syndrome typically characterized by locally altered skin sensations. On the other hand, visceral disease may also be associated with similar painful and altered skin sensations ("Head zones"). Aim of the study was to determine if patients with acute appendicitis demonstrated somatosensory disturbances in the corresponding right lower quadrant Head zone. ::: ::: ::: METHODS ::: The presence of somatosensory disturbances such as hyperalgesia, hypoesthesia, altered cool perception, or positive pinch test was determined in 100 patients before and after an appendectomy. Potential associations between altered skin sensations and various items including age, sex, history, body temperature, C-reactive protein (CRP), leukocyte count, and type of appendicopathy (normal, inflamed, necrotic, or perforated) were assessed. ::: ::: ::: RESULTS ::: A total of 39 patients demonstrated at least one right lower abdominal quadrant skin somatosensory disturbance before the laparoscopic appendectomy. However, locoregional skin sensation normalized in all but 2 patients 2 weeks postoperatively. No differences were found concerning patient characteristics or type of appendicopathy between populations with or without altered lower abdominal skin sensations. ::: ::: ::: CONCLUSION ::: A substantial portion of patients with acute appendicitis demonstrate right lower abdominal somatosensory disturbances that are similar as observed in acute ACNES. Both may be different sides of the same coin and are possibly expressions of segmental phenomena as described by Head. McBurney's point, a landmark area of maximum pain in acute appendicitis, is possibly a trigger point within a Head zone. Differentiating acute appendicitis from acute ACNES is extremely difficult, but imaging and observation may aid in the diagnostic process.


title: Reactive Vega: A Streaming Dataflow Architecture for Declarative Interactive Visualization

abstract: We present Reactive Vega, a system architecture that provides the first robust and comprehensive treatment of declarative visual and interaction design for data visualization. Starting from a single declarative specification, Reactive Vega constructs a dataflow graph in which input data, scene graph elements, and interaction events are all treated as first-class streaming data sources. To support expressive interactive visualizations that may involve time-varying scalar, relational, or hierarchical data, Reactive Vega's dataflow graph can dynamically re-write itself at runtime by extending or pruning branches in a data-driven fashion. We discuss both compile- and run-time optimizations applied within Reactive Vega, and share the results of benchmark studies that indicate superior interactive performance to both D3 and the original, non-reactive Vega system.


Towards Multi-Stakeholder Utility Evaluation of Recommender Systems


title: Automatic online news topic ranking using media focus and user attention based on aging theory

abstract: News topics, which are constructed from news stories using the techniques of Topic Detection and Tracking (TDT), bring convenience to users who intend to see what is going on through the Internet. However, it is almost impossible to view all the generated topics, because of the large amount. So it will be helpful if all topics are ranked and the top ones, which are both timely and important, can be viewed with high priority. Generally, topic ranking is determined by two primary factors. One is how frequently and recently a topic is reported by the media; the other is how much attention users pay to it. Both media focus and user attention varies as time goes on, so the effect of time on topic ranking has already been included. However, inconsistency exists between both factors. In this paper, an automatic online news topic ranking algorithm is proposed based on inconsistency analysis between media focus and user attention. News stories are organized into topics, which are ranked in terms of both media focus and user attention. Experiments performed on practical Web datasets show that the topic ranking result reflects the influence of time, the media and users. The main contributions of this paper are as follows. First, we present the quantitative measure of the inconsistency between media focus and user attention, which provides a basis for topic ranking and an experimental evidence to show that there is a gap between what the media provide and what users view. Second, to the best of our knowledge, it is the first attempt to synthesize the two factors into one algorithm for automatic online topic ranking.


title: A Hybrid Discriminative/Generative Approach for Modeling Human Activities

abstract: Accurate recognition and tracking of human activities is an important goal of ubiquitous computing. Recent advances in the development of multi-modal wearable sensors enable us to gather rich datasets of human activities. However, the problem of automatically identifying the most useful features for modeling such activities remains largely unsolved. In this paper we present a hybrid approach to recognizing activities, which combines boosting to discriminatively select useful features and learn an ensemble of static classifiers to recognize different activities, with hidden Markov models (HMMs) to capture the temporal regularities and smoothness of activities. We tested the activity recognition system using over 12 hours of wearable-sensor data collected by volunteers in natural unconstrained environments. The models succeeded in identifying a small set of maximally informative features, and were able identify ten different human activities with an accuracy of 95%.


Data-Driven Design : Using Web Analytics to Validate Heuristics


title: A Novel Fully Electronic Active Real-Time Imager Based on a Planar Multistatic Sparse Array

abstract: The interest in active millimeter-wave and microwave imaging systems is increasing due to their utilization in security, medical, and industrial applications. Imaging systems using monostatic arrays suffer from the high number of antenna positions, making a fully electronic solution very costly and impractical. Multistatic arrays, however, offer the chance for high reduction factors in the number of antennas, and then allow for fully electronic solutions. Here, a new array architecture is presented, which is suitable for real-time operation and is capable of delivering high image quality in means of resolution and dynamic range. Various technological solutions were developed for the imaging purpose, which are introduced in detail. An example array for operation from 72 to 80 GHz is demonstrated. The imaging system hardware is introduced, along with several imaging results of humans with concealed objects.


title: Why do linear SVMs trained on HOG features perform so well?

abstract: Linear Support Vector Machines trained on HOG features are now a de facto standard across many visual perception tasks. Their popularisation can largely be attributed to the step-change in performance they brought to pedestrian detection, and their subsequent successes in deformable parts models. This paper explores the interactions that make the HOG-SVM symbiosis perform so well. By connecting the feature extraction and learning processes rather than treating them as disparate plugins, we show that HOG features can be viewed as doing two things: (i) inducing capacity in, and (ii) adding prior to a linear SVM trained on pixels. From this perspective, preserving second-order statistics and locality of interactions are key to good performance. We demonstrate surprising accuracy on expression recognition and pedestrian detection tasks, by assuming only the importance of preserving such local second-order interactions.


title: Fetal middle cerebral artery Doppler indices and clinical application at Korle Bu Teaching Hospital, Accra, Ghana

abstract: Abstract Objective To determine normal ranges for various Doppler flow velocity indices of the fetal middle cerebral artery (MCA) and their trends in normal pregnancies at Korle Bu Teaching Hospital, Accra, Ghana. Methods A prospective cross-sectional study was conducted at Korle Bu Teaching Hospital in 2015. Included women had a singleton pregnancy of 20–40 weeks' duration, dated using an early ultrasonography scan, and normal fetal growth. Interviews were conducted to collect data on sociodemographic characteristics, followed by Doppler ultrasonography of the MCA. The resistive index, pulsatility index, systolic-to-diastolic ratio, and peak systolic velocity of the MCA were determined for all participants. Results Overall, 458 pregnant women were recruited. The peak systolic velocity increased with advancing gestational age and a positive correlation of r = 0.725 (P Conclusion The reference curve for the peak systolic velocity increases with gestational age, whereas the other indices decrease in a parabolic pattern.


title: Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding

abstract: This paper introduces Explicit Semantic Ranking (ESR), a new ranking technique that leverages knowledge graph embedding. Analysis of the query log from our academic search engine, SemanticScholar.org, reveals that a major error source is its inability to understand the meaning of research concepts in queries. To addresses this challenge, ESR represents queries and documents in the entity space and ranks them based on their semantic connections from their knowledge graph embedding. Experiments demonstrate ESR's ability in improving Semantic Scholar's online production system, especially on hard queries where word-based ranking fails.


title: Multiple Scan Data Association by Convex Variational Inference

abstract: Data association, the reasoning over correspondence between targets and measurements, is a problem of fundamental importance in target tracking. Recently, belief propagation (BP) has emerged as a promising method for estimating the marginal probabilities of measurement to target association, providing fast, accurate estimates. The excellent performance of BP in the particular formulation used may be attributed to the convexity of the underlying free energy which it implicitly optimises. This paper studies multiple scan data association problems, i.e., problems that reason over correspondence between targets and several sets of measurements, which may correspond to different sensors or different time steps. We find that the multiple scan extension of the single scan BP formulation is non-convex and demonstrate the undesirable behaviour that can result. A convex free energy is constructed using the recently proposed fractional free energy (FFE). A convergent, BP-like algorithm is provided for the single scan FFE, and employed in optimising the multiple scan free energy using primal-dual coordinate ascent. Finally, based on a variational interpretation of joint probabilistic data association (JPDA), we develop a sequential variant of the algorithm that is similar to JPDA, but retains consistency constraints from prior scans. The performance of the proposed methods is demonstrated on a bearings only target localisation problem.


title: LabelRankT: Incremental Community Detection in Dynamic Networks via Label Propagation

abstract: An increasingly important challenge in network analysis is efficient detection and tracking of communities in dynamic networks for which changes arrive as a stream. There is a need for algorithms that can incrementally update and monitor communities whose evolution generates huge real-time data streams, such as the Internet or on-line social networks. In this paper, we propose LabelRankT, an on-line distributed algorithm for detection of communities in large-scale dynamic networks through stabilized label propagation. Results of tests on real-world networks demonstrate that LabelRankT has much lower computational costs than other algorithms. It also improves the quality of the detected communities compared to dynamic detection methods and matches the quality achieved by static detection approaches. Unlike most of other algorithms which apply only to binary networks, LabelRankT works on weighted and directed networks, which provides a flexible and promising solution for real-world applications.


title: Design of a D-PHY chip for mobile display interface supporting MIPI standard

abstract: This paper presents a MIPI (Mobile Industry Processor Interface) D-PHY (physical layer) analog part that meets the MIPI Alliance standard that supports high-speed (HS) transmitter (HS-TX) and receiver (HS-RX) modes as well as low-power (LP)-TX, LP-RX, and LP-contention detection (CD) modes. MIPI is a flexible, source synchronous serial interface standard connecting a host processor to display and camera modules on mobile devices. The standard supports signal levels of 1.2Vpp at 10Mbps in LP mode and 0.2Vpp at 80-1000Mbps in HS mode. In the design, we propose the use of special circuits: LP-TX controls the slew-rate and limits current with a push-pull driver that reduces electromagnetic interference, LP-RX maintains good noise immunity using a hysteresis comparator and a set/reset (SR) latch, HS-TX supports synchronous differential high-speed data transmission based on Scalable Low Voltage Signaling (SLVS), and HS-RX stably receives transferred data with DC variations and AC noise using a very-wide-common-mode range differential amplifier (VCDA). We implemented the MIPI D-PHY analog chip using [email protected] CMOS process under a 1.2V supply. We found that the HS-RX block shows a jitter of less than 5% at 1Gbps and a power consumption of 0.74mW that is suitable for the standard.


title: Student use of Facebook for informal learning and peer support

abstract: Purpose – As the use of social networking and social media technologies (SNT) has become pandemic amongst young people (Tess, 2013; Falahah and Rosmala, 2012) there has been an increasing drive amongst educators and researchers to explore the ways in which SNT may be utilised within the classroom (Junco, 2012). Whilst there is therefore an increasing amount of literature available in relation to the use of SNT within the classroom it does not appear that there has been sufficient research considering the manner in which SNT results in the development of a complex, invisible and organic social network amongst students. When these networks exist outside of the classroom they may allow informal learning and peer support to occur. The purpose of this paper is to investigate these issues. Additionally this paper seeks to determine if the use of Facebook by students may provide an indication of the likelihood of student success on their course. Design/methodology/approach – This paper utilises an empirical appr...


title: A natural language approach to automated cryptanalysis of two-time pads

abstract: While keystream reuse in stream ciphers and one-time pads has been a well known problem for several decades, the risk to real systems has been underappreciated. Previous techniques have relied on being able to accurately guess words and phrases that appear in one of the plaintext messages, making it far easier to claim that "an attacker would never be able to do that." In this paper, we show how an adversary can automatically recover messages encrypted under the same keystream if only the type of each message is known (e.g. an HTML page in English). Our method, which is related to HMMs, recovers the most probable plaintext of this type by using a statistical language model and a dynamic programming algorithm. It produces up to 99% accuracy on realistic data and can process ciphertexts at 200ms per byte on a $2,000 PC. To further demonstrate the practical effectiveness of the method, we show that our tool can recover documents encrypted by Microsoft Word 2002 [22].


title: Development and control of a three DOF spherical induction motor

abstract: This paper reports, to our knowledge, the first spherical induction motor (SIM) operating with closed loop control. The motor can produce up to 4 Nm of torque along arbitrary axes with continuous speeds up to 300 rpm. The motor's rotor is a two-layer copper-over-iron spherical shell. The stator has four independent inductors that generate thrust forces on the rotor surface. The motor is also equipped with four optical mouse sensors that measure surface velocity to estimate the rotor's angular velocity, which is used for vector control of the inductors and control of angular velocity and orientation. Design considerations including torque distribution for the inductors, angular velocity sensing, angular velocity control, and orientation control are presented. Experimental results show accurate tracking of velocity and orientation commands.


title: A Hassle-Free Unsupervised Domain Adaptation Method Using Instance Similarity Features

abstract: We present a simple yet effective unsupervised domain adaptation method that can be generally applied for different NLP tasks. Our method uses unlabeled target domain instances to induce a set of instance similarity features. These features are then combined with the original features to represent labeled source domain instances. Using three NLP tasks, we show that our method consistently outperforms a few baselines, including SCL, an existing general unsupervised domain adaptation method widely used in NLP. More importantly, our method is very easy to implement and incurs much less computational cost than SCL.


title: Moral Disengagement in the Perpetration of Inhumanities

abstract: Moral agency is manifested in both the power to refrain from behaving inhumanely and the proactive power to behave humanely. Moral agency is embedded in a broader sociocognitive self theory encompassing self-organizing, proactive, self-reflective, and self-regulatory mechanisms rooted in personal standards linked to self-sanctions. The self-regulatory mechanisms governing moral conduct do not come into play unless they are activated, and there are many psychosocial maneuvers by which moral self-sanctions are selectively disengaged from inhumane conduct. The moral disengagement may center on the cognitive restructuring of inhumane conduct into a benign or worthy one by moral justification, sanitizing language, and advantageous comparison; disavowal of a sense of personal agency by diffusion or displacement of responsibility; disregarding or minimizing the injurious effects of one 's actions; and attribution of blame to, and dehumanization of those who are victimized. Many inhumanities operate through a sup...


title: Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks

abstract: Importance Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide. The decision to treat is primarily based on the presence of plus disease, defined as dilation and tortuosity of retinal vessels. However, clinical diagnosis of plus disease is highly subjective and variable. Objective To implement and validate an algorithm based on deep learning to automatically diagnose plus disease from retinal photographs. Design, Setting, and Participants A deep convolutional neural network was trained using a data set of 5511 retinal photographs. Each image was previously assigned a reference standard diagnosis (RSD) based on consensus of image grading by 3 experts and clinical diagnosis by 1 expert (ie, normal, pre–plus disease, or plus disease). The algorithm was evaluated by 5-fold cross-validation and tested on an independent set of 100 images. Images were collected from 8 academic institutions participating in the Imaging and Informatics in ROP (i-ROP) cohort study. The deep learning algorithm was tested against 8 ROP experts, each of whom had more than 10 years of clinical experience and more than 5 peer-reviewed publications about ROP. Data were collected from July 2011 to December 2016. Data were analyzed from December 2016 to September 2017. Exposures A deep learning algorithm trained on retinal photographs. Main Outcomes and Measures Receiver operating characteristic analysis was performed to evaluate performance of the algorithm against the RSD. Quadratic-weighted κ coefficients were calculated for ternary classification (ie, normal, pre–plus disease, and plus disease) to measure agreement with the RSD and 8 independent experts. Results Of the 5511 included retinal photographs, 4535 (82.3%) were graded as normal, 805 (14.6%) as pre–plus disease, and 172 (3.1%) as plus disease, based on the RSD. Mean (SD) area under the receiver operating characteristic curve statistics were 0.94 (0.01) for the diagnosis of normal (vs pre–plus disease or plus disease) and 0.98 (0.01) for the diagnosis of plus disease (vs normal or pre–plus disease). For diagnosis of plus disease in an independent test set of 100 retinal images, the algorithm achieved a sensitivity of 93% with 94% specificity. For detection of pre–plus disease or worse, the sensitivity and specificity were 100% and 94%, respectively. On the same test set, the algorithm achieved a quadratic-weighted κ coefficient of 0.92 compared with the RSD, outperforming 6 of 8 ROP experts. Conclusions and Relevance This fully automated algorithm diagnosed plus disease in ROP with comparable or better accuracy than human experts. This has potential applications in disease detection, monitoring, and prognosis in infants at risk of ROP.


title: Energy Propagation in Deep Convolutional Neural Networks

abstract: Many practical machine learning tasks employ very deep convolutional neural networks. Such large depths pose formidable computational challenges in training and operating the network. It is therefore important to understand how fast the energy contained in the propagated signals (a.k.a. feature maps) decays across layers. In addition, it is desirable that the feature extractor generated by the network be informative in the sense of the only signal mapping to the all-zeros feature vector being the zero input signal. This “trivial null-set” property can be accomplished by asking for “energy conservation” in the sense of the energy in the feature vector being proportional to that of the corresponding input signal. This paper establishes conditions for energy conservation (and thus for a trivial null-set) for a wide class of deep convolutional neural network-based feature extractors and characterizes corresponding feature map energy decay rates. Specifically, we consider general scattering networks employing the modulus non-linearity and we find that under mild analyticity and high-pass conditions on the filters (which encompass, inter alia, various constructions of Weyl-Heisenberg filters, wavelets, ridgelets, ( $\alpha $ )-curvelets, and shearlets) the feature map energy decays at least polynomially fast. For broad families of wavelets and Weyl-Heisenberg filters, the guaranteed decay rate is shown to be exponential. Moreover, we provide handy estimates of the number of layers needed to have at least $((1-\varepsilon )\cdot 100)\%$ of the input signal energy be contained in the feature vector.


title: Visualizing multiple evolution metrics

abstract: Observing the evolution of very large software systems needs the analysis of large complex data models and visualization of condensed views on the system. For visualization software metrics have been used to compute such condensed views. However, current techniques concentrate on visualizing data of one particular release providing only insufficient support for visualizing data of several releases.In this paper we present the RelVis visualization approach that concentrates on providing integrated condensed graphical views on source code and release history data of up to n releases. Measures of metrics of source code entities and relationships are composed in Kiviat diagrams as annual rings. Diagrams highlight the good and bad times of an entity and facilitate the identification of entities and relationships with critical trends. They represent potential refactoring candidates that should be addressed first before further evolving the system. The paper provides needed background information and evaluation of the approach with a large open source software project.


title: FACE spoofing detection using LDP-TOP

abstract: In this paper, we propose a novel approach for face spoofing detection using the high-order Local Derivative Pattern from Three Orthogonal Planes (LDP-TOP). The proposed method is not only simple to derive and implement, but also highly efficient, since it takes into account both spatial and temporal information in different directions of subtle face movements. According to experimental results, the proposed approach outperforms state-of-the-art methods on three reference datasets, namely Idiap REPLAY-ATTACK, CASIA-FASD, and MSU MFSD. Moreover, it requires only 25 video frames from each video, i.e., only one second, and thus potentially can be performed in real time even on low-cost devices.


title: Local Geometry of One-Hidden-Layer Neural Networks for Logistic Regression

abstract: We study the local geometry of a one-hidden-layer fully-connected neural network where the training samples are generated from a multi-neuron logistic regression model. We prove that under Gaussian input, the empirical risk function employing quadratic loss exhibits strong convexity and smoothness uniformly in a local neighborhood of the ground truth, for a class of smooth activation functions satisfying certain properties, including sigmoid and tanh, as soon as the sample complexity is sufficiently large. This implies that if initialized in this neighborhood, gradient descent converges linearly to a critical point that is provably close to the ground truth without requiring a fresh set of samples at each iteration. This significantly improves upon prior results on learning shallow neural networks with multiple neurons. To the best of our knowledge, this is the first global convergence guarantee for one-hidden-layer neural networks using gradient descent over the empirical risk function without resampling at the near-optimal sampling and computational complexity.


Artificial Intelligence and Games


title: Feasibility of moisture and humidity determination using optical fibre sensing in polymer matrix composites.

abstract: Knowing the distribution and level of moisture uptake in polymer composites is essential for determining the long-term property retention in aggressive environments. The deleterious effects of moisture on the interface, matrix and occasionally on the reinforcing fibres themselves results in changes to the mechanical behaviour and life expectancy of the material. Ultimately, the uncertainty in moisture content and distribution contributes to conservative design practices to compensate for the worst case scenario property reductions. With fuel/energy savings driving the demand for lightweight structures, more economical designs which still ensure satisfactory lifetime and performance are important. The high strength-to-weight ratio of polymer composites make them ideal candidates for lightweight construction. ::: ::: Bulk material data on moisture absorption can be applied to component design, but doesn't take into consideration local variations in the moisture content from region to region in the structure or from surface to core. In order to minimise design requirements, a system for measuring moisture locally within a composite structure, both in-situ and real-time, would be advantageous. ::: ::: This Measurement Note provides an evaluation of the most viable optical fibre techniques which could potentially be robust enough to be embedded within a composite. All aspects of experimentation are covered, from specimen preparation through testing procedures and results to data analysis. The methods were assessed both in humid air environments at different temperatures and subsequently within a thermosetting epoxy resin to determine any additional problems caused by the embedding process.


title: Mind how you answer me!: transparently authenticating the user of a smartphone when answering or placing a call

abstract: In this paper we propose a new biometric measure to authenticate the user of a smartphone: the movement the user performs when answering (or placing) a phone call. The biometric measure leverages features that are becoming commodities in new smartphones, i.e. accelerometer and orientation sensors. We argue that this new biometric measure has a unique feature. That is, it allows a transparent authentication (not requiring an additional specific interaction for this) to check that the user that is answering (or placing) a phone call is the one authorized to do that. At the same time, this biometric measure can also be used as a non transparent authentication method, e.g. the user may need to move the phone as if answering a call, in order to unlock the phone to get access to SMSs or emails. As a consequence of being a biometric measure, an adversary that spies on the movement (e.g. captures it with a camera) and tries to replicate it, will not be granted access to the phone. We prototyped our solution and conducted several experiments to assess its feasibility. Results show that the method is effective, and the performance is comparable to that of other transparent authentication methods, like face or voice recognition.


title: Lip reading using optical flow and support vector machines

abstract: This paper presents a lip reading technique to classify the discrete utterances without evaluating the acoustic signals. The reported technique analysis the video data of lip motions by computing the optical flow (OF). The statistical properties of the vertical OF component were used to form the feature vectors for training the support vector machines (SVM) classifier. The impact of the variation in speed/velocity of speaking on the performance of the system was minimized by removing the zero energy frames and normalizing the number of frames by interpolation. The resulting system is an efficient visual viseme classifier with high accuracy (95.9%), specificity (98.1%) and sensitivity (66.4%). The results of the experiments demonstrate the developed technique is insensitive to inter speaker variations.


title: Modeling attention to salient proto-objects

abstract: Selective visual attention is believed to be responsible for serializing visual information for recognizing one object at a time in a complex scene. But how can we attend to objects before they are recognized? In coherence theory of visual cognition, so-called proto-objects form volatile units of visual information that can be accessed by selective attention and subsequently validated as actual objects. We propose a biologically plausible model of forming and attending to proto-objects in natural scenes. We demonstrate that the suggested model can enable a model of object recognition in cortex to expand from recognizing individual objects in isolation to sequentially recognizing all objects in a more complex scene.


title: A New Design Method for Single-Feed Circular Polarization Microstrip Antenna With an Arbitrary Impedance Matching Condition

abstract: This paper introduces a new analytical method suitable for the design of the single-feed circular polarization (CP) microstrip patch antenna. Specifically, the proposed method imposes simultaneously the circular polarization condition as well as an arbitrary input impedance matching condition. The two conditions are enforced by an analytical method derived from an equivalent circuit model of a quasi-symmetrical patch antenna, and manipulated to control the modal detuning. This method can be used as an aid to speed up the design procedure for CP antennas even working with a numeric CAD tool. The validation of this approach is proven designing and fabricating a prototype implementing an original design, which consists of a circular disc slotted by a concentric elliptical cut with coaxial feed and operating at the center frequency of 2.45 GHz. The fabricated prototype exhibited a return loss of about 19 dB, within an impedance bandwidth of 130 MHz, a measured gain of 3.85 dB and a 0 dB axial ratio at 2.45 GHz with a corresponding modal phase difference of 87 degrees.


title: A Stylometric Inquiry into Hyperpartisan and Fake News

abstract: This paper reports on a writing style analysis of hyperpartisan (i.e., extremely one-sided) news in connection to fake news. It presents a large corpus of 1,627 articles that were manually fact-checked by professional journalists from BuzzFeed. The articles originated from 9 well-known political publishers, 3 each from the mainstream, the hyperpartisan left-wing, and the hyperpartisan right-wing. In sum, the corpus contains 299 fake news, 97% of which originated from hyperpartisan publishers. ::: We propose and demonstrate a new way of assessing style similarity between text categories via Unmasking---a meta-learning approach originally devised for authorship verification---, revealing that the style of left-wing and right-wing news have a lot more in common than any of the two have with the mainstream. Furthermore, we show that hyperpartisan news can be discriminated well by its style from the mainstream (F1=0.78), as can be satire from both (F1=0.81). Unsurprisingly, style-based fake news detection does not live up to scratch (F1=0.46). Nevertheless, the former results are important to implement pre-screening for fake news detectors.


title: TextRank: Bringing Order Into Texts

abstract: In this paper, the authors introduce TextRank, a graph-based ranking model for text processing, and show how this model can be successfully used in natural language applications.


The Character of a Coach: Success Depends on Trustworthiness.


title: Multi-label Based Learning for Better Multi-criteria Ranking of Ontology Reasoners

abstract: A growing number of highly optimized reasoning algorithms have been developed to allow inference tasks on expressive ontology languages such as OWL(DL). Nevertheless, there is broad agreement that a reasoner could be optimized for some, but not all the ontologies. This particular fact makes it hard to select the best performing reasoner to handle a given ontology, especially for novice users. In this paper, we present a novel method to support the selection ontology reasoners. Our method generates a recommendation in the form of reasoner ranking. The efficiency as well as the correctness are our main ranking criteria. Our solution combines and adjusts multi-label classification and multi-target regression techniques. A large collection of ontologies and 10 well-known reasoners are studied. The experimental results show that the proposed method performs significantly better than several state-of-the-art ranking solutions. Furthermore, it proves that our introduced ranking method could effectively be evolved to a competitive meta-reasoner.


title: Hashtag-based topic evolution in social media

abstract: The rise of online social media has led to an explosion of metadata-containing user generated content. The tracking of metadata distribution is essential to understand social media. This paper presents two statistical models that detect interpretable topics over time along with their hashtags distribution. A topic is represented by a cluster of words that frequently occur together, and a context is represented by a cluster of hashtags, i.e., the hashtag distribution. The models combine a context with a related topic by jointly modeling words with hashtags and time. Experiments with real-world datasets demonstrate that the proposed models discover topics over time with related contexts effectively.


title: Keyframe Reduction Techniques for Motion Capture Data

abstract: Two methods for keyframe reduction of motion capture data are presented. Keyframe reduction of motion capture data enables animators to easily edit motion data with smaller number of keyframes. One of the approaches achieves keyframe reduction and noise removal simultaneously by fitting a curve to the motion information using dynamic programming. The other approach uses curve simplification algorithms on the motion capture data until a predefined threshold of number of keyframes is reached. Although the error rate varies with different motions, the results show that curve fitting with dynamic programming performs as good as curve simplification methods.


title: Multi-Level Structured Models for Document-Level Sentiment Classification

abstract: In this paper, we investigate structured models for document-level sentiment classification. When predicting the sentiment of a subjective document (e.g., as positive or negative), it is well known that not all sentences are equally discriminative or informative. But identifying the useful sentences automatically is itself a difficult learning problem. This paper proposes a joint two-level approach for document-level sentiment classification that simultaneously extracts useful (i.e., subjective) sentences and predicts document-level sentiment based on the extracted sentences. Unlike previous joint learning methods for the task, our approach (1) does not rely on gold standard sentence-level subjectivity annotations (which may be expensive to obtain), and (2) optimizes directly for document-level performance. Empirical evaluations on movie reviews and U.S. Congressional floor debates show improved performance over previous approaches.


title: The Differentiation of Self Inventory: Development and initial validation.

abstract: Despite the importance of Bowen theory (M.Bowen, 1976,1978; M. E. KerrB Nichols & Schwartz, 1998). Indeed, Bowen theory provides a foundation for the field of family therapy that renders i t distinct from the multitude of theoretica l approaches to individual psychotherapy. At present, many of Bowen's (1976, 1978) concepts (e.g., differentiation of self, interlocking triangles , o r reactiv e emotiona l distancing ) pervade th e famil y system s literature . Despit e th e vas t attention Bowen theory has received from clinicians and theorists alike, there have been, to date, few programmatic attempts t o tes t it s validit y wit h respec t t o personalit y functioning or quality of interpersonal relations or to changes as a result of psychotherapy. To begin filling this notable gap, we developed the Differentiation o f Self Inventory, a self-report instrument for adults (ages 25+). In this article, we present psychometric support for the measure, validation studies to date, and implications for theory, research, and practice.


title: Effect of the carbon source on biosurfactant production byPseudomonas aeruginosa 44T1

abstract: Pseudomonas aeruginosa 44T1 produces rhamnolipids when grown on C12 n-alkane but not with other hydrocarbons tested. Best results were obtained with olive oil as carbon source; a final production of 7.65 g rhamnolipid/l with a production yield of 38.2% was detected.


Overview and validation work of the North American Land Data Assimilation System (NLDAS)


title: WIKI: A TECHNOLOGY FOR CONVERSATIONAL KNOWLEDGE MANAGEMENT AND GROUP COLLABORATION

abstract: Wikis (from wikiwiki, meaning “fast” in Hawaiian) are a promising new technology that supports “conversational” knowledge creation and sharing. A Wiki is a collaboratively created and iteratively improved set of web pages, together with the software that manages the web pages. Because of their unique way of creating and managing knowledge, Wikis combine the best elements of earlier conversational knowledge management technologies, while avoiding many of their disadvantages. This article introduces Wiki technology, the behavioral and organizational implications of Wiki use, and Wiki applicability as groupware and help system software. The article concludes that organizations willing to embrace the “Wiki way” with collaborative, conversational knowledge management systems, may enjoy better than linear knowledge growth while being able to satisfy ad-hoc, distributed knowledge needs.


title: Burnout and psychiatric morbidity among physicians engaged in end-of-life care for cancer patients: a cross-sectional nationwide survey in Japan

abstract: PURPOSE ::: To determine the prevalence of burnout and psychiatric morbidity among physicians engaged in end-of-life care for cancer patients in Japan and to explore associated factors related to end-of-life care. ::: ::: ::: METHODS ::: Questionnaires were mailed to 1436 Japanese clinical oncologists and palliative care physicians with a request to complete the Maslach Burnout Inventory (MBI), the General Health Questionnaire (GHQ-12), and to report on individual factors, including confidence in patient care. High levels of burnout and psychiatric morbidity were identified using cut-off scores of the MBI and GHQ-12. ::: ::: ::: RESULTS ::: A total of 697 physicians returned the questionnaires (response rate, 49.6%). Twenty-two percent of the respondents had a high level of emotional exhaustion, 11% had a high level of depersonalization, 62% had a low level of personal accomplishment, and 20% had psychiatric morbidity. Clinical oncologists showed a significantly higher psychiatric morbidity than palliative care physicians. Confidence in having sufficient time to communicate with patients was significantly associated with all the burnout subscales. ::: ::: ::: CONCLUSIONS ::: A low level of personal accomplishment was relatively high among Japanese physicians compared with previous studies. Insufficient confidence in the psychological care of patients was associated with physician burnout rather than involvement in end-of-life care.


title: Smart self-regulatory health system

abstract: Healthcare industry has proven its technically abundant applications at par other industries with the help of many concepts like IoT and cloud computing. It has transformed its techniques by using intelligent gadgets that can be acquainted with patients in different configurations. The data obtained from devices like pulse rate monitors, weighing scales, blood glucose measuring devices, etc. is to be stocked and tracked as awareness of health statistics is vital for all. A hefty portion of these measures requires a follow-off with a specialist. This commences the preference of using IoT based smart devices to deliver more valuable data to the patient and drop off the necessity for direct patient-doctor interaction. Improving the standard of service, it brings particularly high value for the patients requiring constant supervision and quick data access and analysis. This paper will demonstrate automatic communication between the smart devices and android mobile application via Bluetooth and data storage on Cloud. Also, it shows automatic control over oxygen cylinder using mobile app for admitted patients who require supplementary oxygen. Thus, our system serves the purpose of minimizing doctors' efforts and providing seamless experience to the patient with IoT, Cloud and Wireless Technology like Bluetooth helping in saving millions of lives.


title: Image-sensor-based visible light communication for automotive applications

abstract: The present article introduces VLC for automotive applications using an image sensor. In particular, V2I-VLC and V2V-VLC are presented. While previous studies have documented the effectiveness of V2I and V2V communication using radio technology in terms of improving automotive safety, in the present article, we identify characteristics unique to image-sensor-based VLC as compared to radio wave technology. The two primary advantages of a VLC system are its line-of-sight feature and an image sensor that not only provides VLC functions, but also the potential vehicle safety applications made possible by image and video processing. Herein, we present two ongoing image-sensor-based V2I-VLC and V2VVLC projects. In the first, a transmitter using an LED array (which is assumed to be an LED traffic light) and a receiver using a high-framerate CMOS image sensor camera is introduced as a potential V2I-VLC system. For this system, real-time transmission of the audio signal has been confirmed through a field trial. In the second project, we introduce a newly developed CMOS image sensor capable of receiving highspeed optical signals and demonstrate its effectiveness through a V2V communication field trial. In experiments, due to the high-speed signal reception capability of the camera receiver using the developed image sensor, a data transmission rate of 10 Mb/s has been achieved, and image (320 × 240, color) reception has been confirmed together with simultaneous reception of various internal vehicle data, such as vehicle ID and speed.


title: Clarifying the Origin of Biological Abnormalities in PTSD Through the Study of Identical Twins Discordant for Combat Exposure

abstract: A biological abnormality found to be associated with post-traumatic stress disorder (PTSD) may be, among other things, a pre-trauma vulnerability factor, that is, it may have been present prior to the event’s occurrence and increased the individual’s likelihood of developing PTSD upon traumatic exposure. Alternately, it may be an acquired PTSD sign, that is, it may have developed after the traumatic exposure, along with the PTSD. We have studied pairs of Vietnam combat veterans and their noncombat-exposed, identical twins in an effort to resolve these competing origins. Combat veterans were diagnosed as current PTSD or non-PTSD (i.e., never had). Average heart rate responses (HRRs) to a series of sudden, loud-tone presentations were larger in Vietnam combat veteran twins with PTSD, but these larger responses were not shared by their noncombat-exposed cotwins, whose responses were similar to those of the non-PTSD combat veterans and their noncombat-exposed cotwins. These results suggest that larger HRRs to sudden, loud tones represent an acquired sign of PTSD. In contrast, increased neurological soft signs (NSSs), diminished hippocampal volume, and presence of abnormal cavum septum pellucidum (CSP) were found in Vietnam combat veteran twins with PTSD and their “high-risk,” unexposed cotwins compared to Vietnam combat veteran twins without PTSD and their “low-risk,” unexposed cotwins. These results support the conclusion that the latter abnormalities represent antecedent, familial vulnerability factors for developing chronic PTSD upon exposure to a traumatic event.


title: The Aponeurotic Tension Model of Craniofacial Growth in Man

abstract: Craniofacial growth is a scientific crossroad for the fundamental mechanisms of musculoskeletal physiology. Better understanding of growth and development will provide new insights into repair, regeneration and adaptation to ap- plied loads. Traditional craniofacial growth concepts are insufficient to explain the dynamics of airway/vocal tract devel- opment, cranial rotation, basicranial flexion and the role of the cranial base in expression of facial proportions. A testable hypothesis is needed to explore the physiological pressure propelling midface growth and the role of neural factors in ex- pression of musculoskeletal adaptation after the cessation of anterior cranial base growth. A novel model for craniofacial growth is proposed for: 1. brain growth and craniofacial adaptation up to the age of 20; 2. explaining growth force vectors; 3. defining the role of muscle plasticity as a conduit for craniofacial growth forces; and 4. describing the effect of cranial rotation in the expression of facial form. Growth of the viscerocranium is believed to be influenced by the superficial musculoaponeurotic systems (SMAS) of the head through residual tension in the occipitofrontalis muscle as a result of cephalad brain growth and cranial rotation. The coordinated effects of the regional SMAS develop a craniofacial musculoaponeurotic system (CFMAS), which is believed to affect maxillary and mandibular development.


title: The Unique Brain Anatomy of Meditation Practitioners: Alterations in Cortical Gyrification

abstract: Several cortical regions are reported to vary in meditation practitioners. However, prior analyses have focused primarily on examining gray matter or cortical thickness. Thus, additional effects with respect to other cortical features might have remained undetected. Gyrification (the pattern and degree of cortical folding) is an important cerebral characteristic related to the geometry of the brain’s surface. Thus, exploring cortical gyrification in long-term meditators may provide additional clues with respect to the underlying anatomical correlates of meditation. This study examined cortical gyrification in a large sample (n = 100) of meditators and controls, carefully matched for sex and age. Cortical gyrification was established by calculating mean curvature across thousands of vertices on individual cortical surface models. Pronounced group differences indicating larger gyrification in meditators were evident within the left precentral gyrus, right fusiform gyrus, right cuneus, as well as left and right anterior dorsal insula (the latter representing the global significance maximum). Positive correlations between gyrification and the number of meditation years were similarly pronounced in the right anterior dorsal insula. Although the exact functional implications of larger cortical gyrification remain to be established, these findings suggest the insula to be a key structure involved in aspects of meditation. For example, variations in insular complexity could affect the regulation of well-known distractions in the process of meditation, such as daydreaming, mind-wandering, and projections into past or future. Moreover, given that meditators are masters in introspection, awareness, and emotional control, increased insular gyrification may reflect an integration of autonomic, affective, and cognitive processes. Due to the cross-sectional nature of this study, further research is necessary to determine the relative contribution of nature and nurture to links between cortical gyrification and meditation.


Miniaturized Wilkinson Power Divider with nth Harmonic Suppression using Front Coupled Tapered CMRC


title: Highlight lines for conveying shape

abstract: Recent work has shown that sparse lines defined on 3D shapes, including occluding contours and suggestive contours, are effective at conveying shape. We introduce two new families of lines called suggestive highlights and principal highlights, based on definitions related to suggestive contours and geometric creases. We show that when these are drawn in white on a gray background they are naturally interpreted as highlight lines, and complement contours and suggestive contours. We provide object-space definitions and algorithms for extracting these lines, explore several stylization possibilities, and compare the lines to ridges and valleys of intensity in diffuse-shaded images.


title: Rules based event extraction from natural language text

abstract: The notion of events plays a crucial role in narrative texts. Events are the situations that happen or occur at a particular place and time. Extraction and representation of event has a significant role in many of the natural language text and applications like text summarization, question answering systems etc. Several methods were developed so far but they addressed the problem in domain aspect point of view. In this paper we demonstrate a rule based system for event extraction which is generic in nature. A generic rules based event extraction algorithm was developed that maps the framed rules to derive the events from natural language text. Experiments were conducted on the MUC data set and the results are found to be encouraging.


title: Low voltage CMOS Schmitt Trigger in 0.18μm technology

abstract: This paper presents the effect of source voltage on performance of proposed Schmitt Trigger circuit. The proposed circuit was designed based on Conventional Schmitt Trigger by manipulating the arrangement of transistors and the width-length ratio. The simulation results have been carried out based on Mentor Graphics software in term of propagation delay. The circuit layout has been designed and checked by using design rule check (DRC) and layout versus schematic (LVS) method. From these results, the proposed full swing CMOS Schmitt Trigger was able to operate at low voltage (0.8V-1.5V)


title: Visualizing association rules for text mining

abstract: An association rule in data mining is an implication of the form X/spl rarr/Y where X is a set of antecedent items and Y is the consequent item. For years researchers have developed many tools to visualize association rules. However, few of these tools can handle more than dozens of rules, and none of them can effectively manage rules with multiple antecedents. Thus, it is extremely difficult to visualize and understand the association information of a large data set even when all the rules are available. This paper presents a novel visualization technique to tackle many of these problems. We apply the technology to a text mining study on large corpora. The results indicate that our design can easily handle hundreds of multiple antecedent association rules in a three-dimensional display with minimum human interaction, low occlusion percentage, and no screen swapping.


title: Investigating Teaching Leadership in the Capstone Marketing Course.

abstract: Marketing graduates are ultimately expected to perform in managerial roles, yet limited course work is devoted to leadership training for marketing management. In the capstone marketing course, group projects with partner organizations can serve as an opportunity for student leadership development. Marketing students working in groups on experiential-learning projects rated others in their project group on the extent to which selected leadership behaviors were evidenced. Three key leadership constructs surfaced in the peer evaluations: vision, rational communication, and heart. Students exposed to leadership concepts in the capstone marketing course rated their peers higher on leadership skills and performed better on the group project.


title: Stability of Helicopters in Compliant Contact Under PD-PID Control

abstract: Aerial vehicles are difficult to stabilize, especially when acted upon by external forces. A hovering vehicle interacting with objects and surfaces must be robust to contact forces and torques transmitted to the airframe. These produce coupled dynamics that are distinctly different from those of free flight. While external contact is generally avoided, extending aerial robot functionality to include contact with the environment during flight opens up new and useful areas such as perching, object grasping, andmanipulation. Thesemechanicsmay be modeled as elastic couplings between the aircraft and the ground, represented by springs in ℝ xSO(3). We show that proportional derivative and proportional integral derivative (PID) attitude and position controllers that stabilize a rotorcraft in free flight will also stabilize the aircraft during contact for a range of contact displacements and stiffnesses. Simulation of the coupled aircraft dynamics demonstrates stable and unstablemodes of the system.We find analytical measures that predict the stability of these systems and consider, in particular, the planar system in which the contact point is directly beneath the rotor. We show through explicit solution of the linearized system that the planar dynamics of the object-helicopter system in vertical, horizontal, and pitch motion around equilibrium remain stable, within a range of contact stiffnesses, under unmodified PID attitude control. Flight experiments with a small-scale PID-stabilized helicopter fitted with a compliant gripper for capturing objects affirm our model's stability predictions.


title: Galaxies and chaos

abstract: Order and Chaos.- Orbit Theory.- Observations.- Formation and Evolution of Galaxies.- Solar System Dynamics.


title: Intelligent Device-to-Device Communication in the Internet of Things

abstract: Analogous to the way humans use the Internet, devices will be the main users in the Internet of Things (IoT) ecosystem. Therefore, device-to-device (D2D) communication is expected to be an intrinsic part of the IoT. Devices will communicate with each other autonomously without any centralized control and collaborate to gather, share, and forward information in a multihop manner. The ability to gather relevant information in real time is key to leveraging the value of the IoT as such information will be transformed into intelligence, which will facilitate the creation of an intelligent environment. Ultimately, the quality of the information gathered depends on how smart the devices are. In addition, these communicating devices will operate with different networking standards, may experience intermittent connectivity with each other, and many of them will be resource constrained. These characteristics open up several networking challenges that traditional routing protocols cannot solve. Consequently, devices will require intelligent routing protocols in order to achieve intelligent D2D communication. We present an overview of how intelligent D2D communication can be achieved in the IoT ecosystem. In particular, we focus on how state-of-the-art routing algorithms can achieve intelligent D2D communication in the IoT.


title: Perspectives of granular computing

abstract: Granular computing emerges as a new multi-disciplinary study and has received much attention in recent years. A conceptual framework is presented by extracting shared commonalities from many fields. The framework stresses multiple views and multiple levels of understanding in each view. It is argued that granular computing is more about a philosophical way of thinking and a practical methodology of problem solving. By effectively using levels of granularity, granular computing provides a systematic, natural way to analyze, understand, represent, and solve real world problems. With granular computing, one aims at structured thinking at the philosophical level, and structured problem solving at the practical level.


Microstrip Coupler with High Isolation


title: Cache contention aware Virtual Machine placement and migration in cloud datacenters

abstract: In cloud datacenters, multiple Virtual Machines (VMs) are co-located in a Physical Machine (PM) to serve different applications. Prior VM consolidation methods for cloud datacenters schedule VMs mainly based on resource (CPU and memory) constraints in PMs but neglect serious shared Last Level cache contention between VMs. This may cause severe VM performance degradation due to cache thrashing and starvation for VMs. Current cache contention aware VM consolidation strategies either estimate cache contention by coarse VM classification for each individual VM without considering co-location and (or) require the aid of hardware to monitor the online miss rate of each VM. Therefore, these strategies are insufficiently accurate and (or) difficult to adopt for VM consolidation in clouds. In this paper, we formalize the problem of cache contention aware VM placement and migration in cloud datacenters using integer linear programming. We then propose a cache contention aware VM placement and migration algorithm (CacheVM). It estimates the total cache contention degree of co-locating a given VM with a group of VMs in a PM based on the cache stack distance profiles of the VMs. Then, it places the VM to the PM with the minimum cache contention degree and chooses the VM from a PM that generates the maximum cache contention degree to migrate out. We implemented CacheVM and its comparison methods on a supercomputing cluster. Trace-driven simulation and real-testbed experiments show that CacheVM outperforms other methods in terms of the number of cache misses, execution time and throughput.


title: Encrypted accelerated least squares regression

abstract: Information that is stored in an encrypted format is, by definition, usually not amenable to statistical analysis or machine learning methods. In this paper we present detailed analysis of coordinate and accelerated gradient descent algorithms which are capable of fitting least squares and penalised ridge regression models, using data encrypted under a fully homomorphic encryption scheme. Gradient descent is shown to dominate in terms of encrypted computational speed, and theoretical results are proven to give parameter bounds which ensure correctness of decryption. The characteristics of encrypted computation are empirically shown to favour a non-standard acceleration technique. This demonstrates the possibility of approximating conventional statistical regression methods using encrypted data without compromising privacy.


title: Ultra-Wideband Single and Dual Baluns Based on Substrate Integrated Coaxial Line Technology

abstract: In this paper, ultra-wideband (UWB) Marchand baluns using substrate integrated coaxial line (SICL) technology are first proposed. SICL is a shielded planar coaxial line, which is very suitable for wideband and high-speed applications due to its broad unimodal operation band, low insertion loss, very small dispersion, and easy integration with other planar circuits. In order to achieve wider bandwidth and better VSWR performance of the SICL balun, a pair of quarter-wave impedance transformers are inserted into the balanced line. The impedance characteristics of the proposed SICL baluns are analyzed, and the parameters that determine the balun bandwidth are discussed. Then, curves of the balun resistance, reactance, and the SICL characteristic impedance are presented. According to these curves, the design parameters of the proposed SICL baluns can easily be determined. To verify these ideas, an UWB SICL single balun and an UWB SICL dual balun are designed, fabricated, and measured. Both full-wave simulated and measured results are presented, and good agreement between them is observed. The proposed SICL baluns have a simple structure and good performance, which are suitable for applications in UWB communication systems.


title: Organizational Mindfulness and Mindful Organizing: A Reconciliation and Path Forward

abstract: Ray, Baker, and Plowman's (2011) study of organizational mindfulness highlights latent tensions in the mindfulness literature and promising avenues for future research. Their study provides a springboard for reconciling the literature by differentiating organizational mindfulness from mindful organizing, establishing where organizational mindfulness and mindful organizing are most important, and clarifying how and when each construct can be most fruitfully deployed in research and practice. Clearer theorizing leads to a set of research questions that seek to integrate multiple conceptions of individual and organizational mindfulness, establish their individual and organizational antecedents, explore the consequences for individuals and organizations, and in so doing, further increase the relevance of organizational mindfulness for business schools.


title: SynPAnal: Software for Rapid Quantification of the Density and Intensity of Protein Puncta from Fluorescence Microscopy Images of Neurons

abstract: Continuous modification of the protein composition at synapses is a driving force for the plastic changes of synaptic strength, and provides the fundamental molecular mechanism of synaptic plasticity and information storage in the brain. Studying synaptic protein turnover is not only important for understanding learning and memory, but also has direct implication for understanding pathological conditions like aging, neurodegenerative diseases, and psychiatric disorders. Proteins involved in synaptic transmission and synaptic plasticity are typically concentrated at synapses of neurons and thus appear as puncta (clusters) in immunofluorescence microscopy images. Quantitative measurement of the changes in puncta density, intensity, and sizes of specific proteins provide valuable information on their function in synaptic transmission, circuit development, synaptic plasticity, and synaptopathy. Unfortunately, puncta quantification is very labor intensive and time consuming. In this article, we describe a software tool designed for the rapid semi-automatic detection and quantification of synaptic protein puncta from 2D immunofluorescence images generated by confocal laser scanning microscopy. The software, dubbed as SynPAnal (for Synaptic Puncta Analysis), streamlines data quantification for puncta density and average intensity, thereby increases data analysis throughput compared to a manual method. SynPAnal is stand-alone software written using the JAVA programming language, and thus is portable and platform-free.


title: A Comparitive Survey of ANN and Hybrid HMM/ANN Architectures for Robust Speech Recognition

abstract: Advanced Technologies for Medical and Signals (ATMS) Research Unit, National School of Engineering of Sfax, B.P.W, Sfax ,Tunisia Abstract This paper proposes two hybrid connectionist structural acoustical models for robust context independent phone like and word like units for speaker-independent recognition system. Such structure combines strength of Hidden Markov Models (HMM) in modeling stochastic sequences and the non-linear classification capability of Artificial Neural Networks (ANN). Two kinds of Neural Networks (NN) are investigated: Multilayer Perceptron (MLP) and Elman Recur- rent Neural Networks (RNN). The hybrid connectionist-HMM systems use discriminatively trained NN to estimate the a posteriori probability distribution among subword units given the acoustic observations. We efficiently tested the perform- ance of the conceived systems using the TIMIT database in clean and noisy environments with two perceptually motivated features: MFCC and PLP. Finally, the robustness of the systems is evaluated by using a new preprocessing stage for de- noising based on wavelet transform. A significant improvement in performance is obtained with the proposed method.


title: Intelligent scissors for image composition

abstract: We present a new, interactive tool called Intelligent Scissors which we use for image segmentation and composition. Fully automated segmentation is an unsolved problem, while manual tracing is inaccurate and laboriously unacceptable. However, Intelligent Scissors allow objects within digital images to be extracted quickly and accurately using simple gesture motions with a mouse. When the gestured mouse position comes in proximity to an object edge, a live-wire boundary “snaps” to, and wraps around the object of interest. Live-wire boundary detection formulates discrete dynamic programming (DP) as a two-dimensional graph searching problem. DP provides mathematically optimal boundaries while greatly reducing sensitivity to local noise or other intervening structures. Robustness is further enhanced with on-the-fly training which causes the boundary to adhere to the specific type of edge currently being followed, rather than simply the strongest edge in the neighborhood. Boundary cooling automatically freezes unchanging segments and automates input of additional seed points. Cooling also allows the user to be much more free with the gesture path, thereby increasing the efficiency and finesse with which boundaries can be extracted. Extracted objects can be scaled, rotated, and composited using live-wire masks and spatial frequency equivalencing. Frequency equivalencing is performed by applying a Butterworth filter which matches the lowest frequency spectra to all other image components. Intelligent Scissors allow creation of convincing compositions from existing images while dramatically increasing the speed and precision with which objects can be extracted.


title: Neural Motifs: Scene Graph Parsing with Global Context

abstract: We investigate the problem of producing structured graph representations of visual scenes. Our work analyzes the role of motifs: regularly appearing substructures in scene graphs. We present new quantitative insights on such repeated structures in the Visual Genome dataset. Our analysis shows that object labels are highly predictive of relation labels but not vice-versa. We also find there are recurring patterns even in larger subgraphs: more than 50% of graphs contain motifs involving at least two relations. This analysis leads to a new baseline that is simple, yet strikingly powerful. While hardly considering the overall visual context of an image, it outperforms previous approaches. We then introduce Stacked Motif Networks, a new architecture for encoding global context that is crucial for capturing higher order motifs in scene graphs. Our best model for scene graph detection achieves a 7.3% absolute improvement in recall@50 (41% relative gain) over prior state-of-the-art.


title: Text Localization in Natural Images Using Stroke Feature Transform and Text Covariance Descriptors

abstract: In this paper, we present a new approach for text localization in natural images, by discriminating text and non-text regions at three levels: pixel, component and text line levels. Firstly, a powerful low-level filter called the Stroke Feature Transform (SFT) is proposed, which extends the widely-used Stroke Width Transform (SWT) by incorporating color cues of text pixels, leading to significantly enhanced performance on inter-component separation and intra-component connection. Secondly, based on the output of SFT, we apply two classifiers, a text component classifier and a text-line classifier, sequentially to extract text regions, eliminating the heuristic procedures that are commonly used in previous approaches. The two classifiers are built upon two novel Text Covariance Descriptors (TCDs) that encode both the heuristic properties and the statistical characteristics of text stokes. Finally, text regions are located by simply thresholding the text-line confident map. Our method was evaluated on two benchmark datasets: ICDAR 2005 and ICDAR 2011, and the corresponding F-measure values are 0.72 and 0.73, respectively, surpassing previous methods in accuracy by a large margin.


title: Scalable and incremental software bug detection

abstract: An important, but often neglected, goal of static analysis for detecting bugs is the ability to show defects to the programmer quickly. Unfortunately, existing static analysis tools scale very poorly, or are shallow and cannot find complex interprocedural defects. Previous attempts at reducing the analysis time by adding more resources (CPU, memory) or by splitting the analysis into multiple sub-analyses based on defect detection capabilities resulted in limited/negligible improvements. We present a technique for parallel and incremental static analysis using top-down, bottom-up and global specification inference based around the concept of a work unit, a self-contained atom of analysis input, that deterministically maps to its output. A work unit contains both abstract and concrete syntax to analyze, a supporting fragment of the class hierarchy, summarized interprocedural behavior, and defect reporting information, factored to ensure a high level of reuse when analyzing successive versions incrementally. Work units are created and consumed by an analysis master process that coordinates the multiple analysis passes, the flow of information among them, and incrementalizes the computation. Meanwhile, multiple analysis worker processes use abstract interpretation to compute work unit results. Process management and interprocess communication is done by a general-purpose computation distributor layer. We have implemented our approach and our experimental results show that using eight processor cores, we can perform complete analysis of code bases with millions of lines of code in a few hours, and even faster after incremental changes to that code. The analysis is thorough and accurate: it usually reports about one crash-causing defect per thousand lines of code, with a false positive rate of 10--20%


title: Vehicular fog computing: Vision and challenges

abstract: The emerging vehicular applications demand for a lot more computing and communication capacity to excel in their compute-intensive and latency-sensitive tasks. Fog computing, which focuses on moving computing resources to the edge of networks, complements cloud computing by solving the latency constraints and reducing ingress traffic to the cloud. This paper presents a visionary concept on vehicular fog computing that turns connected vehicles into mobile fog nodes and utilises mobility of vehicles for providing cost-effective and on-demand fog computing for vehicular applications. Besides system design, this paper also discusses the remained technical challenges.


title: Orko: Facilitating Multimodal Interaction for Visual Exploration and Analysis of Networks

abstract: Data visualization systems have predominantly been developed for WIMP-based direct manipulation interfaces. Only recently have other forms of interaction begun to appear, such as natural language or touch-based interaction, though usually operating only independently. Prior evaluations of natural language interfaces for visualization have indicated potential value in combining direct manipulation and natural language as complementary interaction techniques. We hypothesize that truly multimodal interfaces for visualization, those providing users with freedom of expression via both natural language and touch-based direct manipulation input, may provide an effective and engaging user experience. Unfortunately, however, little work has been done in exploring such multimodal visualization interfaces. To address this gap, we have created an architecture and a prototype visualization system called Orko that facilitates both natural language and direct manipulation input. Specifically, Orko focuses on the domain of network visualization, one that has largely relied on WIMP-based interfaces and direct manipulation interaction, and has little or no prior research exploring natural language interaction. We report results from an initial evaluation study of Orko, and use our observations to discuss opportunities and challenges for future work in multimodal network visualization interfaces.


title: An RF-input outphasing power amplifier with RF signal decomposition network

abstract: This work presents an outphasing power amplifier that directly amplifies a modulated RF input. The approach eliminates the need for multiple costly IQ modulators and baseband signal component separation found in conventional outphasing power amplifier systems, which have previously required both an RF carrier input and a separate baseband input to synthesize a modulated RF output. A novel RF signal decomposition network enables direct RF-input / RF-output outphasing by directly synthesizing the phase- and amplitude-modulated RF signals that drive the branch PAs from the modulated RF input waveform. The technique is demonstrated at 2.14 GHz in a four-way lossless outphasing amplifier system with transmission-line-based power combiner. The resulting proof-of-concept outphasing power amplifier has a peak CW output power of 95 W, and peak drain efficiency of 72%.


title: Capturing the Shifting Shapes: Enabling Efficient Screen-Camera Communication with a Pattern-based Dynamic Barcode

abstract: With the increasing availability of LCD displays and phone cameras in today's environment, screen-camera communication using dynamic barcode has emerged as a convenient infrastructure-free form to establish impromptu communication channel among mobile devices. Due to the short wavelengths and narrow beams of visible light, screen-camera communication is highly directional, low-interference and secure, which envisions a wide range of application scenarios. Conventional screen-camera communication systems encode data bits with color in dynamic barcodes, which suffers from the frame mixture problem caused by the rolling shutter effect of CMOS camera in high capturing rate. In this paper, we propose a novel design of dynamic barcode called ShiftCode that encodes data bits with shifting shape patterns, which provide a new way to expand the barcode capacity for screen-camera communications. ShiftCode adopts a pattern-based layout design to embed multiple data bits in a symbol representation. With such layout, it exploits a decoding mechanism to solve the frame mixture problem and achieves high frame capturing rate. It further intruduces a two-level reliability technique for intra-frame error correction and inter-frame redundancy, which reduces the overhead and delay of retransmission. The proposed ShiftCode is implemented on the Android platform, and extensive experiments show that it achieves at least two-fold improvement on goodput compared with the conventional screen-camera communication systems.


title: Genetic Aspects of Gender Identity Development and Gender Dysphoria

abstract: The development of gender identity, its variance, and gender dysphoria is thought to be a complex process involving biological and psychosocial factors. Heritability studies have demonstrated a genetic factor for the development of gender dysphoria. The brain is regarded as the anatomical substrate of gender identity, and sex differences of the brain are studied to elucidate the process of gender identity development. Many sex differences have been attributed to hormonal action, and the first genetic studies in transsexuals were focused on sex-steroid-related genes. To this day, a convincing candidate gene has not been identified, and it is now known that sex chromosomes have a direct effect on sex differentiation and that they may play a role in gender identity development. For future studies of the genetic base of gender dysphoria, new techniques, such as genome-wide studies, have become available. In addition, epigenetic studies may provide for a different association perspective of the genetics of gender dysphoria.


title: Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding

abstract: Object categories inherently form a hierarchy with different levels of concept abstraction, especially for fine-grained categories. For example, birds (Aves) can be categorized according to a four-level hierarchy of order, family, genus, and species. This hierarchy encodes rich correlations among various categories across different levels, which can effectively regularize the semantic space and thus make prediction less ambiguous. However, previous studies of fine-grained image recognition primarily focus on categories of one certain level and usually overlook this correlation information. In this work, we investigate simultaneously predicting categories of different levels in the hierarchy and integrating this structured correlation information into the deep neural network by developing a novel Hierarchical Semantic Embedding (HSE) framework. Specifically, the HSE framework sequentially predicts the category score vector of each level in the hierarchy, from highest to lowest. At each level, it incorporates the predicted score vector of the higher level as prior knowledge to learn finer-grained feature representation. During training, the predicted score vector of the higher level is also employed to regularize label prediction by using it as soft targets of corresponding sub-categories. To evaluate the proposed framework, we organize the 200 bird species of the Caltech-UCSD birds dataset with the four-level category hierarchy and construct a large-scale butterfly dataset that also covers four level categories. Extensive experiments on these two and the newly-released VegFru datasets demonstrate the superiority of our HSE framework over the baseline methods and existing competitors.


title: Exploiting Massive Parallelism for IndexingMulti-Dimensional Datasets on the GPU

abstract: Inherently multi-dimensional n-ary indexing structures such as R-trees are not well suited for the GPU because of their irregular memory access patterns and recursive back-tracking function calls. It has been known that traversing hierarchical tree structures in an irregular manner makes it difficult to exploit parallelism and to maximize the utilization of GPU processing units. Moreover, the recursive tree search algorithms often fail with large indexes because of the GPU’s tiny runtime stack size. In this paper, we propose a novel parallel tree traversal algorithm— massively parallel restart scanning (MPRS) for multi-dimensional range queries that avoids recursion and irregular memory access. The proposed MPRS algorithm traverses hierarchical tree structures with mostly contiguous memory access patterns without recursion, which offers more chances to optimize the parallel SIMD algorithm. We implemented the proposed MPRS range query processing algorithm on n-ary bounding volume hierarchies including R-trees and evaluated its performance using real scientific datasets on an NVIDIA Tesla M2090 GPU. Our experiments show braided parallel SIMD friendly MPRS range query algorithm achieves at least 80 percent warp execution efficiency while task parallel tree traversal algorithm shows only 9-15 percent efficiency. Moreover, braided parallel MPRS algorithm accesses 7-20 times less amount of global memory than task parallel parent link algorithm by virtue of minimal warp divergence.


title: Application of robust and adaptive control techniques to cooperative manipulation

abstract: This paper presents a study on application of adaptive and robust control methods to a cooperative manipulation system, which is developed for handling an object by two-link planar robot manipulators. Adaptive control algorithm ensures a parameter adaptation law satisfying the stability condition of uncertain systems. In designing robust control structure, contact and friction constraints for grasp and bearing conditions, structural flexibility or such similar factors as various unmodeled dynamics are considered as the uncertainties that determine the available values of control parameters. The novelty of results in the present paper is to define new control inputs using the parametric uncertainties and the Lyapunov-based theory of guaranteed stability of uncertain systems.


title: Rapid race perception despite individuation and accuracy goals

abstract: ABSTRACTPerceivers rapidly process social category information and form stereotypic impressions of unfamiliar others. However, a goal to individuate a target or to accurately predict their behavior can result in individuated impressions. It is unknown how the combination of both accuracy and individuation goals affects perceptual category processing. To explore this, participants were given both the goal to individuate targets and accurately predict behavior. We then recorded event-related brain potentials while participants viewed photos of black and white males along with four pieces of individuating information in the form of descriptions of past behavior. Even with explicit individuation and accuracy task goals, participants rapidly differentiated targets by race within 200 ms. Importantly, this rapid categorical processing did not influence behavioral outcomes as participants made individuated predictions. These findings indicate that individuals engage in category processing even when provided with ...


title: A high-speed end-to-end approach for retinal arteriovenous segmentation

abstract: Retinal artery/vein segmentation plays a significant role in retinal relevant diseases diagnosis. This paper presents a novel, end-to-end and multi-task convolutional neural network structure for retinal arteriovenous segmentation task. The proposed method can simultaneously output the same size as the original image of the artery map and the vein map, respectively. Then we evaluate the neural network on the AVDRIVE dataset. This method achieves sensitivity values of 0.86, specificity values of 0.82, precision values of 0.87, F1score values of 0.86 and accuracy values of 0.94. In this case the time it cost on segment one image only needed 0.13 seconds, which are 60 times faster than other studies we cite.


title: Personalized Photograph Ranking and Selection System Considering Positive and Negative User Feedback

abstract: In this article, we propose a novel personalized ranking system for amateur photographs. The proposed framework treats the photograph assessment as a ranking problem and we introduce the idea of personalized ranking, which ranks photographs considering both their aesthetic qualities and personal preferences. Photographs are described using three types of features: photo composition, color and intensity distribution, and personalized features. An aesthetic prediction model is learned from labeled photographs by using the proposed image features and RBF-ListNet learning algorithm. The experimental results show that the proposed framework outperforms in the ranking performance: a Kendall's tau value of 0.432 is significantly higher than those obtained by the features proposed in one of the state-of-the-art approaches (0.365) and by learning based on support vector regression (0.384). To realize personalization in ranking, three approaches are proposed: the feature-based approach allows users to select photographs with specific rules, the example-based approach takes the positive feedback from users to rerank the photograph, and the list-based approach takes both positive and negative feedback from users into consideration. User studies indicate that all three approaches are effective in both aesthetic and personalized ranking.


title: Gold-standard for Topic-specific Sentiment Analysis of Economic Texts

abstract: Public opinion, as measured by media sentiment, can be an important indicator in the financial and economic context. These are domains where traditional sentiment estimation techniques often struggle, and existing annotated sentiment text collections are of less use. Though considerable progress has been made in analyzing sentiments at sentence-level, performing topic-dependent sentiment analysis is still a relatively uncharted territory. The computation of topic-specific sentiments has commonly relied on naive aggregation methods without much consideration to the relevance of the sentences to the given topic. Clearly, the use of such methods leads to a substantial increase in noise-to-signal ratio. To foster development of methods for measuring topic-specific sentiments in documents, we have collected and annotated a corpus of financial news that have been sampled from Thomson Reuters newswire. In this paper, we describe the annotation process and evaluate the quality of the dataset using a number of inter-annotator agreement metrics. The annotations of 297 documents and over 9000 sentences can be used for research purposes when developing methods for detecting topic-wise sentiment in financial text.


title: Workflow Management Systems and ERP Systems: Differences, Commonalities, and Applications

abstract: Two important classes of information systems, Workflow Management Systems (WfMSs) and Enterprise Resource Planning (ERP) systems, have been used to support e-business process redesign, integration, and management. While both technologies can help with business process automation, data transfer, and information sharing, the technological approach and features of solutions provided by WfMS and ERP are different. Currently, there is a lack of understanding of these two classes of information systems in the industry and academia, thus hindering their effective applications. In this paper, we present a comprehensive comparison between these two classes of systems. We discuss how the two types of systems can be used independently or together to develop intra- and inter-organizational application solutions. In particular, we also explore the roles of WfMS and ERP in the next generation of IT architecture based on Web Services. Our findings should help businesses make better decisions in the adoption of both WfMS and ERP in their e-business strategies.


title: Sentiue: Target and Aspect based Sentiment Analysis in SemEval-2015 Task 12

abstract: This paper describes our participation in SemEval-2015 Task 12, and the opinion mining system sentiue. The general idea is that systems must determine the polarity of the sentiment expressed about a certain aspect of a target entity. For slot 1, entity and attribute category detection, our system applies a supervised machine learning classifier, for each label, followed by a selection based on the probability of the entity/attribute pair, on that domain. The target expression detection, for slot 2, is achieved by using a catalog of known targets for each entity type, complemented with named entity recognition. In the opinion sentiment slot, we used a 3 class polarity classifier, having BoW, lemmas, bigrams after verbs, presence of polarized terms, and punctuation based features. Working in unconstrained mode, our results for slot 1 were assessed with precision between 57% and 63%, and recall varying between 42% and 47%. In sentiment polarity, sentiue’s result accuracy was approximately 79%, reaching the best score in 2 of the 3 domains.


title: FAIR: A Hadoop-based Hybrid Model for Faculty Information Retrieval System

abstract: In era of ever-expanding data and knowledge, we lack a centralized system that maps all the faculties to their research works. This problem has not been addressed in the past and it becomes challenging for students to connect with the right faculty of their domain. Since we have so many colleges and faculties this lies in the category of big data problem. In this paper, we present a model which works on the distributed computing environment to tackle big data. The proposed model uses apache spark as an execution engine and hive as database. The results are visualized with the help of Tableau that is connected to Apache Hive to achieve distributed computing.


title: Vehicle Applications of Controller Area Network

abstract: The Controller Area Network (CAN) is a serial bus communications protocol developed by Bosch in the early 1980s. It defines a standard for efficient and reliable communication between sensor, actuator, controller, and other nodes in real-time applications. CAN is the de facto standard in a large variety of networked embedded control systems. The early CAN development was mainly supported by the vehicle industry: CAN is found in a variety of passenger cars, trucks, boats, spacecraft, and other types of vehicles. The protocol is also widely used today in industrial automation and other areas of networked embedded control, with applications in diverse products such as production machinery, medical equipment, building automation, weaving machines, and wheelchairs.The purpose of this chapter is to give an introduction to CAN and some of its vehicle applications. The outline is as follows. Section 2 describes the CAN protocol, including its message formats and error handling. The section is concluded by a brief history of CAN. Examples of vehicle application architectures based on CAN are given in Section 3. A few specific control loops closed over CAN buses are discussed in Section 4. The paper is concluded with some perspectives in Section 5, where current research issues such as x-by-wire and standardized software architectures are considered. The examples are described in more detail in [14]. A detailed description of CAN is given in the textbook [6]. Another good resource for further information is the homepage of the organization CAN-in-Automation (CiA) [3]. The use of CAN as a basis for distributed control systems is discussed in [13].