title: Tackling Over-pruning in Variational Autoencoders

abstract: Variational autoencoders (VAE) are directed generative models that learn factorial latent variables. As noted by Burda et al. (2015), these models exhibit the problem of factor over-pruning where a significant number of stochastic factors fail to learn anything and become inactive. This can limit their modeling power and their ability to learn diverse and meaningful latent representations. In this paper, we evaluate several methods to address this problem and propose a more effective model-based approach called the epitomic variational autoencoder (eVAE). The so-called epitomes of this model are groups of mutually exclusive latent factors that compete to explain the data. This approach helps prevent inactive units since each group is pressured to explain the data. We compare the approaches with qualitative and quantitative results on MNIST and TFD datasets. Our results show that eVAE makes efficient use of model capacity and generalizes better than VAE.


title: Clinical comparison of unilateral biportal endoscopic technique versus open microdiscectomy for single-level lumbar discectomy: a multicenter, retrospective analysis

abstract: The unilateral biportal endoscopic (UBE) technique is a minimally invasive procedure for spinal surgery, while open microscopic discectomy is the most common surgical treatment for ruptured or herniated discs of the lumbar spine. A new endoscopic technique that uses a UBE approach has been applied to conventional arthroscopic systems for the treatment of spinal disease. In this study, we aimed to compare and evaluate the perioperative parameters and clinical outcomes, including recovery from surgery, pain and life quality modification, patient’s satisfaction, and complications, between UBE and open lumbar microdiscectomy (OLM) for single-level discectomy procedures. This study included 141 patients with degenerative disc disease requiring discectomy at a single level from L2–L3 to L5–S1. A total of 60 and 81 patients underwent UBE and OLM, respectively. Analysis was based on comparison of perioperative metrics, operation time (OT); estimated blood loss (EBL); length of hospital stay (HS); clinical outcomes, including assessment using the Visual Analogue Scale (VAS) and Oswestry Disability Index (ODI); patient satisfaction (the MacNab score); and the incidence of reoperation and complications. The study cohort was 56.7% women, and the mean patient age was 50.98 ± 18.23 years. The mean VAS (the back and leg), MacNab score, and ODI improved significantly from the preoperative period to the last follow-up (12.92 ± 3.92) in both groups (p < 0.001). One week after operation, the back VAS score in the UBE group showed significantly more improvement than that in the OLM group. However, the 1-week, 3-month, and 12-month VAS (the back and leg), ODI improvement, modified MacNab score, and OT were not significantly different between the two groups. In the UBE group, EBL (34.67 ± 16.92) was smaller and HS (2.77 ± 1.2) was shorter than that of the OLM group (140.05 ± 57.8, 6.37 ± 1.39). However, OT (70.15 ± 22.0) was longer in the UBE group than in the OLM group (60.38 ± 15.5), and the difference was statistically significant. Meanwhile, the differences in the rate of surgical conversion and complications between the two groups were not statistically significant. The UBE for single-level discectomy yielded similar clinical outcomes to OLM, including pain control, functional disability, and patient satisfaction, but incurred minimal EBL, HS, and postoperative back pain. Not applicable.


title: The DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure as a Screening Tool

abstract: The DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure was developed to aid clinicians with a dimensional assessment of psychopathology; however, this measure resembles a screening tool for several symptomatic domains. The objective of the current study was to examine the basic parameters of sensitivity, specificity, positive and negative predictive power of the measure as a screening tool. One hundred and fifty patients in a correctional community center filled out the measure prior to a psychiatric evaluation, including the Mini International Neuropsychiatric Interview screen. The above parameters were calculated for the domains of depression, mania, anxiety, and psychosis. The results showed that the sensitivity and positive predictive power of the studied domains was poor because of a high rate of false positive answers on the measure. However, when the lowest threshold on the Cross-Cutting Symptom Measure was used, the sensitivity of the anxiety and psychosis domains and the negative predictive values for mania, anxiety and psychosis were good. In conclusion, while it is foreseeable that some clinicians may use the DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure as a screening tool, it should not be relied on to identify positive findings. It functioned well in the negative prediction of mania, anxiety and psychosis symptoms.


title: Scalable Hypergrid k-NN-Based Online Anomaly Detection in Wireless Sensor Networks

abstract: Online anomaly detection (AD) is an important technique for monitoring wireless sensor networks (WSNs), which protects WSNs from cyberattacks and random faults. As a scalable and parameter-free unsupervised AD technique, k-nearest neighbor (kNN) algorithm has attracted a lot of attention for its applications in computer networks and WSNs. However, the nature of lazy-learning makes the kNN-based AD schemes difficult to be used in an online manner, especially when communication cost is constrained. In this paper, a new kNN-based AD scheme based on hypergrid intuition is proposed for WSN applications to overcome the lazy-learning problem. Through redefining anomaly from a hypersphere detection region (DR) to a hypercube DR, the computational complexity is reduced significantly. At the same time, an attached coefficient is used to convert a hypergrid structure into a positive coordinate space in order to retain the redundancy for online update and tailor for bit operation. In addition, distributed computing is taken into account, and position of the hypercube is encoded by a few bits only using the bit operation. As a result, the new scheme is able to work successfully in any environment without human interventions. Finally, the experiments with a real WSN data set demonstrate that the proposed scheme is effective and robust.


title: Probabilistic Forecast of PV Power Generation Based on Higher Order Markov Chain

abstract: This paper presents a method to forecast the probability distribution function (PDF) of the generated power of PV systems based on the higher order Markov chain (HMC). Since the output power of the PV system is highly influenced by ambient temperature and solar irradiance, they are used as important features to classify different operating conditions of the PV system. The classification procedure is carried out by applying the pattern discovery method on the historical data of the mentioned variables. An HMC is developed based on the categorized historical data of PV power in each operating point. The 15-min ahead PDF of the PV output power is forecasted through the Gaussian mixture method (GMM) by combining several distribution functions and by using the coefficients defined based on parameters of the HMC-based model. In order to verify the proposed method, the genetic algorithm is applied to minimize a well-defined objective function to achieve the optimal GMM coefficients. Numerical tests using real data demonstrate that the forecast results follow the real probability distribution of the PV power well under different weather conditions.


title: A Miniaturized Semantic Segmentation Method for Remote Sensing Image

abstract: In order to save the memory, we propose a miniaturization method for neural network to reduce the parameter quantity existed in remote sensing (RS) image semantic segmentation model. The compact convolution optimization method is first used for standard U-Net to reduce the weights quantity. With the purpose of decreasing model performance loss caused by miniaturization and based on the characteristics of remote sensing image, fewer down-samplings and improved cascade atrous convolution are then used to improve the performance of the miniaturized U-Net. Compared with U-Net, our proposed Micro-Net not only achieves 29.26 times model compression, but also basically maintains the performance unchanged on the public dataset. We provide a Keras and Tensorflow hybrid programming implementation for our model: https://github.com/Isnot2bad/Micro-Net


title: Information ethics: On the philosophical foundation of computer ethics

abstract: The essential difficulty about Computer Ethics‘ (CE) philosophical status is a methodological problem: standard ethical theories cannot easily be adapted to deal with CE-problems, which appear to strain their conceptual resources, and CE requires a conceptual foundation as an ethical theory. Information Ethics (IE), the philosophical foundational counterpart of CE, can be seen as a particular case of ’’environmental‘‘ ethics or ethics of the infosphere. What is good for an information entity and the infosphere in general? This is the ethical question asked by IE. The answer is provided by a minimalist theory of deseerts: IE argues that there is something more elementary and fundamental than life and pain, namely being, understood as information, and entropy, and that any information entity is to be recognised as the centre of a minimal moral claim, which deserves recognition and should help to regulate the implementation of any information process involving it. IE can provide a valuable perspective from which to approach, with insight and adequate discernment, not only moral problems in CE, but also the whole range of conceptual and moral phenomena that form the ethical discourse.


title: Validity , trustworthiness and rigour : quality and the idea of qualitative research

abstract: Aim. In this paper, I call into question the widely-held assumption of a single, more or less unified paradigm of ‘qualitative research’ whose methodologies share certain epistemological and ontological characteristics, and explore the implications of this position for judgements about the quality of research studies. ::: ::: Background. After a quarter of a century of debate in nursing about how best to judge the quality of qualitative research, we appear to be no closer to a consensus, or even to deciding whether it is appropriate to try to achieve a consensus. The literature on this issue can be broadly divided into three positions: those writers who wish qualitative research to be judged according to the same criteria as quantitative research; those who believe that a different set of criteria is required; and those who question the appropriateness of any predetermined criteria for judging qualitative research. Of the three positions, the second appears to have generated most debate, and a number of different frameworks and guidelines for judging the quality of qualitative research have been devised over recent years. ::: ::: Discussion. The second of the above positions is rejected in favour of the third. It argues that, if there is no unified qualitative research paradigm, then it makes little sense to attempt to establish a set of generic criteria for making quality judgements about qualitative research studies. We need either to acknowledge that the commonly perceived quantitative–qualitative dichotomy is in fact a continuum which requires a continuum of quality criteria, or to recognize that each study is individual and unique, and that the task of producing frameworks and predetermined criteria for assessing the quality of research studies is futile. ::: ::: Conclusion. Some of the implications of this latter position are explored, including the requirement that all published research reports should include a reflexive research diary.


title: Generalization Challenges for Neural Architectures in Audio Source Separation

abstract: Recent work has shown that recurrent neural networks can be trained to separate individual speakers in a sound mixture with high fidelity. Here we explore convolutional neural network models as an alternative and show that they achieve state-of-the-art results with an order of magnitude fewer parameters. We also characterize and compare the robustness and ability of these different approaches to generalize under three different test conditions: longer time sequences, the addition of intermittent noise, and different datasets not seen during training. For the last condition, we create a new dataset, RealTalkLibri, to test source separation in real-world environments. We show that the acoustics of the environment have significant impact on the structure of the waveform and the overall performance of neural network models, with the convolutional model showing superior ability to generalize to new environments. The code for our study is available at https://github.com/ShariqM/source_separation.


title: An Experimental Demonstration of GaN CMOS Technology

abstract: This letter reports the first demonstration of gallium nitride (GaN) complementary metal–oxide–semi-conductor (CMOS) field-effect-transistor technology. Selective area epitaxy was employed to have both GaN N-channel MOSFET (NMOS) and P-channel MOSFET (PMOS) structures on the same wafer. An AlN/SiN dielectric stack grown by metal–organic chemical vapor deposition served as the gate oxide for both NMOS and PMOS, yielding enhancement-mode N- and P-channel with the electron mobility of 300 cm2/V-s and hole mobility of 20 cm2/V-s, respectively. Using the GaN CMOS technology, a functional inverter integrated circuit was fabricated and characterized.


title: Data-Driven Content Analysis of Social Media

abstract: Researchers have long measured people’s thoughts, feelings, and personalities using carefully designed survey questions, which are often given to a relatively small number of volunteers. The proliferation of social media, such as Twitter and Facebook, offers alternative measurement approaches: automatic content coding at unprecedented scales and the statistical power to do open-vocabulary exploratory analysis. We describe a range of automatic and partially automatic content analysis techniques and illustrate how their use on social media generates insights into subjective well-being, health, gender differences, and personality.


title: Electrical filamentation in ggMOS protection structures

abstract: On the basis of the two-dimensional numerical simulation the isothermal current instability and filamentation are studied in the grounded gate MOS ESD protection (ggMOS) structures with lightly doped drain (LDD). It is demonstrated that avalanche injection breakdown in the LDD MOS structures results in two stage secondary breakdown and filamentation process. In the first stage a low amplitude filament limited by current saturation in LDD region is formed. In the second stage high amplitude filaments are formed between the source and drain contact n+-regions. The local burnout of the structure is clarified by the resulting high local overheating in the isothermal filament.


title: In silico characterization of thermostable lipases

abstract: Thermostable lipases are of high priority for industrial applications as they are endowed with the capability of carrying out diversified reactions at elevated temperatures. Extremophiles are their potential source. Sequence and structure annotation of thermostable lipases can elucidate evolution of lipases from their mesophilic counterparts with enhanced thermostability hence better industrial potential. Sequence analysis highlighted the conserved residues in bacterial and fungal thermostable lipases. Higher frequency of AXXXA motif and poly Ala residues in lid domain of thermostable Bacillus lipases were distinguishing characteristics. Comparison of amino acid composition among thermostable and mesostable lipases brought into light the role of neutral, charged and aromatic amino acid residues in enhancement of thermostability. Structural annotation of thermostable lipases with that of mesostable lipases revealed some striking features which are increment of gamma turns in thermostable lipases; being first time reported in our paper, longer beta strands, lesser beta-branched residues in helices, increase in charged-neutral hydrogen bonding pair, hydrophobic-hydrophobic contact and differences in the N-cap and C-cap residues of the α helices. Conclusively, it can be stated that subtle changes in the arrangement of amino acid residues in the tertiary structure of lipases contributes to enhanced thermostability.


title: Measuring usability: preference vs. performance

abstract: Simplistically stated, usability engineering aims at improving interactive systems and their user interfaces. Defined slightly more precisely [8], usability is a general concept that cannot be measured but is related to several usability parameters that can be measured. Measurable usability parameters fall into two broad categories: subjective user preference measures, assessing how much the users like the system, and objective performance measures, which measure how capable the users are at using the system


title: simTD: a car-to-X system architecture for field operational tests [Topics in Automotive Networking]

abstract: Car-to-car and car-to-infrastructure communications are considered as a key technology for safe and intelligent mobility in the future. In the German project simTD, car-to-x communication is shifting from a pure research topic toward a first deployment of such a system: the goal of simTD is to test car-to-x applications in a real metropolitan field trial. The simTD scenario includes vehicles, roadside units, and infrastructural facilities for traffic and test management. Besides these domains, several third parties are involved to provide access to additional services. The main communication partners are furthermore distributed over a wide area including highway, suburban, and urban scenarios. As a result, such a system requires a commonly accepted architecture of the individual components and a seamless communication network for reliable and efficient information interchange. In this article we propose the simTD system architecture and the components necessary for successful deployment in the large-scale field trial.


title: Trajectory segmentation for the Autonomous Control of Backward Motion for Truck and

abstract: For logistics centers the disposition and dispatch of goods has reached a high standard of automation. However, the trucks and trailers bringing and taking wares are driven manually. This offers a enormous potential for automation for the yards of logistics centers. The availability of this potential is correlated to the autonomous forward and backward motion control for standard transportation vehicles. By an experimental system it has been shown that truck and trailer can be maneuvered autonomously at a high degree of precision. For purposes of safety it is necessary to guarantee that at any instant of time the vehicle is within some predefined area. Therefore, this paper introduces the principle of safe cover segments which are never left by the vehicle in motion and defines rules for the connection of such segments forming a safe corridor.


title: TagScan: Simultaneous Target Imaging and Material Identification with Commodity RFID Devices

abstract: Target imaging and material identification play an important role in many real-life applications. This paper introduces TagScan, a system that can identify the material type and image the horizontal cut of a target simultaneously with cheap commercial off the-shelf (COTS) RFID devices. The key intuition is that different materials and target sizes cause different amounts of phase and RSS (Received Signal Strength) changes when radio frequency (RF) signal penetrates through the target. Multiple challenges need to be addressed before we can turn the idea into a functional system including (i) indoor environments exhibit rich multipath which breaks the linear relationship between the phase change and the propagation distance inside a target; (ii) without knowing either material type or target size, trying to obtain these two information simultaneously is challenging; and (iii) stitching pieces of the propagation distances inside a target for an image estimate is non-trivial. We propose solutions to all the challenges and evaluate the system's performance in three different environments. TagScan is able to achieve higher than 94% material identification accuracies for 10 liquids and differentiate even very similar objects such as Coke and Pepsi. TagScan can accurately estimate the horizontal cut images of more than one target behind a wall.


title: 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions

abstract: Matching local geometric features on real-world depth images is a challenging task due to the noisy, low-resolution, and incomplete nature of 3D scan data. These difficulties limit the performance of current state-of-art methods, which are typically based on histograms over geometric properties. In this paper, we present 3DMatch, a data-driven model that learns a local volumetric patch descriptor for establishing correspondences between partial 3D data. To amass training data for our model, we propose a self-supervised feature learning method that leverages the millions of correspondence labels found in existing RGB-D reconstructions. Experiments show that our descriptor is not only able to match local geometry in new scenes for reconstruction, but also generalize to different tasks and spatial scales (e.g. instance-level object model alignment for the Amazon Picking Challenge, and mesh surface correspondence). Results show that 3DMatch consistently outperforms other state-of-the-art approaches by a significant margin. Code, data, benchmarks, and pre-trained models are available online at http://3dmatch.cs.princeton.edu.


title: Fully Character-Level Neural Machine Translation without Explicit Segmentation

abstract: Most existing machine translation systems operate at the level of words, relying on explicit segmentation to extract tokens. We introduce a neural machine translation (NMT) model that maps a source character sequence to a target character sequence without any segmentation. We employ a character-level convolutional network with max-pooling at the encoder to reduce the length of source representation, allowing the model to be trained at a speed comparable to subword-level models while capturing local regularities. Our character-to-character model outperforms a recently proposed baseline with a subword-level encoder on WMT'15 DE-EN and CS-EN, and gives comparable performance on FI-EN and RU-EN. We then demonstrate that it is possible to share a single character-level encoder across multiple languages by training a model on a many-to-one translation task. In this multilingual setting, the character-level encoder significantly outperforms the subword-level encoder on all the language pairs. We observe that on CS-EN, FI-EN and RU-EN, the quality of the multilingual character-level translation even surpasses the models specifically trained on that language pair alone, both in terms of BLEU score and human judgment.


title: Image Stitching System Based on ORB Feature-Based Technique and Compensation Blending

abstract: The construction of a high-resolution panoramic image from a sequence of input overlapping images of the same scene is called image stitching/mosaicing. It is considered as an important, challenging topic in computer vision, multimedia, and computer graphics. The quality of the mosaic image and the time cost are the two primary parameters for measuring the stitching performance. Therefore, the main objective of this paper is to introduce a high-quality image stitching system with least computation time. First, we compare many different features detectors. We test Harris corner detector, SIFT, SURF, FAST, GoodFeaturesToTrack, MSER, and ORB techniques to measure the detection rate of the corrected keypoints and processing time. Second, we manipulate the implementation of different common categories of image blending methods to increase the quality of the stitching process. From experimental results, we conclude that ORB algorithm is the fastest, more accurate, and with higher performance. In addition, Exposure Compensation is the highest stitching quality blending method. Finally, we have generated an image stitching system based on ORB using Exposure Compensation blending method.


title: Perceptual Quality Assessment of Screen Content Images

abstract: Research on screen content images (SCIs) becomes important as they are increasingly used in multi-device communication applications. In this paper, we present a study on perceptual quality assessment of distorted SCIs subjectively and objectively. We construct a large-scale screen image quality assessment database (SIQAD) consisting of 20 source and 980 distorted SCIs. In order to get the subjective quality scores and investigate, which part (text or picture) contributes more to the overall visual quality, the single stimulus methodology with 11 point numerical scale is employed to obtain three kinds of subjective scores corresponding to the entire, textual, and pictorial regions, respectively. According to the analysis of subjective data, we propose a weighting strategy to account for the correlation among these three kinds of subjective scores. Furthermore, we design an objective metric to measure the visual quality of distorted SCIs by considering the visual difference of textual and pictorial regions. The experimental results demonstrate that the proposed SCI perceptual quality assessment scheme, consisting of the objective metric and the weighting strategy, can achieve better performance than 11 state-of-the-art IQA methods. To the best of our knowledge, the SIQAD is the first large-scale database published for quality evaluation of SCIs, and this research is the first attempt to explore the perceptual quality assessment of distorted SCIs.


title: Information Sharing for Supply Chain Management Based on Block Chain Technology

abstract: Supply Chain Management systems provide information sharing and analysis to companies and support their planning activities. They are not based on the real data because there is asymmetric information between companies, then leading to disturbance of the planning algorithms. On the other hand, sharing data between manufacturers, suppliers and customers becomes very important to ensure reactivity towards markets variability. Especially, double marginalization is a widespread and serious problem in supply chain management. Decentralized systems under wholesale price contracts are investigated, with double marginalization effects shown to lead to supply insufficiencies, in the cases of both deterministic and random demands. This paper proposes a blockchain based solution to address the problems of supply chain such as Double Marginalization and Information Asymmetry etc.


title: Statistical similarity of binaries

abstract: We address the problem of finding similar procedures in stripped binaries. We present a new statistical approach for measuring the similarity between two procedures. Our notion of similarity allows us to find similar code even when it has been compiled using different compilers, or has been modified. The main idea is to use similarity by composition: decompose the code into smaller comparable fragments, define semantic similarity between fragments, and use statistical reasoning to lift fragment similarity into similarity between procedures. We have implemented our approach in a tool called Esh, and applied it to find various prominent vulnerabilities across compilers and versions, including Heartbleed, Shellshock and Venom. We show that Esh produces high accuracy results, with few to no false positives -- a crucial factor in the scenario of vulnerability search in stripped binaries.


title: Lyapunov Approach for the stabilization of the Inverted Spherical Pendulum

abstract: A nonlinear controller is presented for the stabilization of the spherical inverted pendulum system. The control strategy is based on the Lyapunov approach in conjunction with LaSalle's invariance principle. The proposed controller is able to bring the pendulum to the unstable upright equilibrium point with the position of the movable base at the origin. The obtained closed-loop system has a very large domain of attraction, that can be as large as desired, for any initial position of the pendulum which lies above the horizontal plane.


title: Attention Inequality in Social Media

abstract: Social media can be viewed as a social system where the currency is attention. People post content and interact with others to attract attention and gain new followers. In this paper, we examine the distribution of attention across a large sample of users of a popular social media site Twitter. Through empirical analysis of these data we conclude that attention is very unequally distributed: the top 20\% of Twitter users own more than 96\% of all followers, 93\% of the retweets, and 93\% of the mentions. We investigate the mechanisms that lead to attention inequality and find that it results from the "rich-get-richer" and "poor-get-poorer" dynamics of attention diffusion. Namely, users who are "rich" in attention, because they are often mentioned and retweeted, are more likely to gain new followers, while those who are "poor" in attention are likely to lose followers. We develop a phenomenological model that quantifies attention diffusion and network dynamics, and solve it to study how attention inequality grows over time in a dynamic environment of social media.


title: Shunt active power filter connected to a photovoltaic array for compensating harmonics and reactive power simultaneously

abstract: In this paper, we present an analysis and simulation of a PV interactive shunt power active filter. This system is used to eliminate harmonics generated by a nonlinear load and reactive power compensation. In the day-time with intensive sunlight, the PV interactive Shunt Active Filter system brings all its functions into operation. At night and during no sunlight periods, the power required by the loads is received from the distribution system while the inverter system only provides reactive power compensation and filter harmonic currents. For the Shunt Active Filter reference current computation. We used the synchronous d-q-o reference frame algorithm (SRF) and for gating signal generation we apply the carrier-based PWM modulation. The Simulation results (using MATLAB/SIMULINK) are presented and discussed. They show the effectiveness of the photovoltaic compensation system, the sinusoidal form of the current and the effectiveness of the reactive power compensation. The proposed solution has achieved a low THD (Total Harmonic Distortion), demonstrating the effectiveness of the presented method.


title: 3-Coloring in Time O(1.3289^n)

abstract: We consider worst case time bounds for NP-complete problems including 3-SAT, 3-coloring, 3-edge-coloring, and 3-list-coloring. Our algorithms are based on a constraint satisfaction (CSP) formulation of these problems. 3-SAT is equivalent to (2,3)-CSP while the other problems above are special cases of (3,2)-CSP; there is also a natural duality transformation from (a,b)-CSP to (b,a)-CSP. We give a fast algorithm for (3,2)-CSP and use it to improve the time bounds for solving the other problems listed above. Our techniques involve a mixture of Davis-Putnam-style backtracking with more sophisticated matching and network flow based ideas.


title: SIFM: A network architecture for seamless flow mobility between LTE and WiFi networks - Analysis and Testbed Implementation

abstract: This paper deals with cellular (e.g. LTE) networks that selectively offload the mobile data traffic onto WiFi (IEEE 802.11) networks to improve network performance. We propose the Seamless Internetwork Flow Mobility (SIFM) architecture that provides seamless flow-mobility support using concepts of Software Defined Networking (SDN). The SDN paradigm decouples the control and data plane, leading to a centralized network intelligence and state. The SIFM architecture utilizes this aspect of SDN and moves the mobility decisions to a centralized Flow Controller (FC). This provides a global network view while making mobility decisions and also reduces the complexity at the PGW. We implement and evaluate both basic PMIPv6 and the SIFM architectures by incorporating salient LTE and WiFi network features in the ns-3 simulator. Performance experiments validate that seamless mobility is achieved. Also, the SIFM architecture shows an improved network performance when compared to the base PMIPv6 architecture. A proof-of-concept prototype of the SIFM architecture has been implemented on an experimental testbed. The LTE network is emulated by integrating USRP B210x with the OpenLTE eNodeB and OpenLTE EPC. The WiFi network is emulated using hostapd and dnsmasq daemons running on Ubuntu 12.04. An off-the-shelf LG G2 mobile phone running Android 4.2.2 is used as the user equipment. We demonstrate seamless mobility between the LTE network and the WiFi network with the help of ICMP ping and a TCP chat application.


title: Least-Squares Fitting of Two 3-D Point Sets

abstract: Two point sets {pi} and {p'i}; i = 1, 2,..., N are related by p'i = Rpi + T + Ni, where R is a rotation matrix, T a translation vector, and Ni a noise vector. Given {pi} and {p'i}, we present an algorithm for finding the least-squares solution of R and T, which is based on the singular value decomposition (SVD) of a 3 × 3 matrix. This new algorithm is compared to two earlier algorithms with respect to computer time requirements.


title: Classification of multi-class motor imagery with a novel hierarchical SVM algorithm for brain–computer interfaces

abstract: Pattern classification algorithm is the crucial step in developing brain–computer interface (BCI) applications. In this paper, a hierarchical support vector machine (HSVM) algorithm is proposed to address an EEG-based four-class motor imagery classification task. Wavelet packet transform is employed to decompose raw EEG signals. Thereafter, EEG signals with effective frequency sub-bands are grouped and reconstructed. EEG feature vectors are extracted from the reconstructed EEG signals with one versus the rest common spatial patterns (OVR-CSP) and one versus one common spatial patterns (OVO-CSP). Then, a two-layer HSVM algorithm is designed for the classification of these EEG feature vectors, where “OVO” classifiers are used in the first layer and “OVR” in the second layer. A public dataset (BCI Competition IV-II-a)is employed to validate the proposed method. Fivefold cross-validation results demonstrate that the average accuracy of classification in the first layer and the second layer is 67.5 ± 17.7% and 60.3 ± 14.7%, respectively. The average accuracy of the classification is 64.4 ± 16.7% overall. These results show that the proposed method is effective for four-class motor imagery classification.


title: Cardiorespiratory Fitness and Motor Skills in Relation to Cognition and Academic Performance in Children – A Review

abstract: Different elements of physical fitness in children have shown a declining trend during the past few decades. Cardiorespiratory fitness and motor skills have been associated with cognition, but the magnitude of this association remains unknown. The purpose of this review is to provide an overview of the relationship of cardiorespiratory fitness and motor skills with cognitive functions and academic performance in children up to 13 years of age. Cross-sectional studies suggest that children with higher cardiorespiratory fitness have more efficient cognitive processing at the neuroelectric level, as well as larger hippocampal and basal ganglia volumes, compared to children with lower cardiorespiratory fitness. Higher cardiorespiratory fitness has been associated with better inhibitory control in tasks requiring rigorous attention allocation. Better motor skills have been related to more efficient cognitive functions including inhibitory control and working memory. Higher cardiorespiratory fitness and better motor skills have also been associated with better academic performance. Furthermore, none of the studies on cardiorespiratory fitness have revealed independent associations with cognitive functions by controlling for motor skills. Studies concerning the relationship between motor skills and cognitive functions also did not consider cardiorespiratory fitness in the analyses. The results of this review suggest that high levels of cardiorespiratory fitness and motor skills may be beneficial for cognitive development and academic performance but the evidence relies mainly on cross-sectional studies.


title: Bird Classification using Ensemble Classifiers

abstract: This working note summarizes our submission to the LifeCLEF 2014 Bird Task which combines the outputs from a Python and Matlab classification system. The features used for both systems include Mel-Frequency Cepstral Coefficients (MFCC), time-averaged spectrograms and the provided meta-data. The Python subsystem combines a large ensemble of different classifiers with different subsets of the features while the Matlab subsystem is an ensemble of the Random Forest and Linear Discriminant Analysis (LDA) classifiers using local spectral and meta features. By combining this disparate set of features and classifiers, we managed to achieve a Mean Average Precision (MAP) score that is far superior to what is possible with any single classifier.


title: Combining wearable accelerometer and physiological data for activity and energy expenditure estimation

abstract: Physical Activity (PA) is one of the most important determinants of health. Wearable sensors have great potential for accurate assessment of PA (activity type and Energy Expenditure (EE)) in daily life. In this paper we investigate the benefit of multiple physiological signals (Heart Rate (HR), respiration rate, Galvanic Skin Response (GSR), skin humidity) as well as accelerometer (ACC) data from two locations (wrist - combining ACC, GSR and skin humidity - and chest - combining ACC and HR) on PA type and EE estimation. We implemented single regression, activity recognition and activity-specific EE models on data collected from 16 subjects, while performing a set of PAs, grouped into six clusters (lying, sedentary, dynamic, walking, biking and running). Our results show that combining ACC and physiological signals improves performance for activity recognition (by 2 and 8% for the chest and wrist) and EE (by 36 - chest - and 35% - wrist - for single regression models, and by 18 - chest - and 46% - wrist - for activity-specific models). Physiological signals other than HR showed a coarser relation with level of physical exertion, resulting in being better predictors of activity cluster type and separation between inactivity and activity than EE, due to the weak correlation to EE within an activity cluster.


title: Revisiting Cross Modal Retrieval

abstract: This paper proposes a cross-modal retrieval system that leverages on image and text encoding. Most multimodal architectures employ separate networks for each modality to capture the semantic relationship between them. However, in our work image-text encoding can achieve comparable results in terms of cross-modal retrieval without having to use a separate network for each modality. We show that text encodings can capture semantic relationships between multiple modalities. In our knowledge, this work is the first of its kind in terms of employing a single network and fused image-text embedding for cross-modal retrieval. We evaluate our approach on two famous multimodal datasets: MS-COCO and Flickr30K.


title: Photovoltaic Energy Harvesting Wireless Sensor Node for Telemetry Applications Optimized for Low Illumination Levels

abstract: This paper reports the design of a photovoltaic energy harvesting device used as a telemetry node in wireless sensor networks. The device draws power from the small solar cell, stores it into the primary energy buffer and backup supercapacitor, collects measured data from various sensors and transmits them over low power radio link at 868 MHz. Its design ensures reliable cold booting under very poor illumination conditions (down to 20 lx). The solar cell also enables indirect illumination level detection for the subcircuit that manages stored energy (day/night detector). The device is allowed to draw power from the backup supercapacitor only when it is not possible to gather enough energy from the solar cell during the sleep period. Short lasting and sudden drops of the illumination do not activate the backup power supply. A wireless sensor node design is adjusted to the proposed photovoltaic harvesting circuitry, so the overall power consumption in the sleep mode is less than 25 μW. Also, due to adaptive power consumption, proposed device topology ensures its autonomy time in the total darkness of 81 h. The device has been produced using commercially available components enabling versatile telemetric functionality by the implementation of different sensors.


title: A Taxonomy of Data Quality Challenges in Empirical Software Engineering

abstract: Reliable empirical models such as those used in software effort estimation or defect prediction are inherently dependent on the data from which they are built. As demands for process and product improvement continue to grow, the quality of the data used in measurement and prediction systems warrants increasingly close scrutiny. In this paper we propose a taxonomy of data quality challenges in empirical software engineering, based on an extensive review of prior research. We consider current assessment techniques for each quality issue and proposed mechanisms to address these issues, where available. Our taxonomy classifies data quality issues into three broad areas: first, characteristics of data that mean they are not fit for modeling, second, data set characteristics that lead to concerns about the suitability of applying a given model to another data set, and third, factors that prevent or limit data accessibility and trust. We identify this latter area as of particular need in terms of further research.


title: Limbic-cortical dysregulation: a proposed model of depression

abstract: A working model of depression implicating failure of the coordinated interactions of a distributed network of limbic-cortical pathways is proposed. Resting state patterns of regional glucose metabolism in idiopathic depressed patients, changes in metabolism with antidepressant treatment, and blood flow changes with induced sadness in healthy subjects were used to test and refine this hypothesis. Dorsal neocortical decreases and ventral paralimbic increases characterize both healthy sadness and depressive illness; concurrent inhibition of overactive paralimbic regions and normalization of hypofunctioning dorsal cortical sites characterize disease remission. Normal functioning of the rostral anterior cingulate, with its direct connections to these dorsal and ventral areas, is postulated to be additionally required for the observed reciprocal compensatory changes, since pretreatment metabolism in this region uniquely predicts antidepressant treatment response. This model is offered as an adaptable framework to facilitate continued integration of clinical imaging findings with complementary neuroanatomical, neurochemical, and electrophysiological studies in the investigation of the pathogenesis of affective disorders.


title: Integrated aviation security for defense-in-depth of next generation air transportation system

abstract: To meet the significant increase in civil air traffic demand in the National Airspace System (NAS), the Federal Aviation Administration (FAA) is cooperating with other departments to develop Next Generation Air Transportation System (NextGen). NextGen will greatly increase the NAS capacity, efficiency, safety, flexibility, and environmental protection. Aviation Security is one of the core components in NextGen. Without security protection, all NextGen functions will be vulnerable to various attacks and cannot work robustly. In this paper, we propose an Integrated Aviation Security (IAS) framework for defense-in-depth of various aspects of NextGen security such as cyber security and aircraft anti-hijacking. IAS uses a multi-layered, cross-component, and multi-planed (data plane and control plane) structure. Multiple components in NextGen such as communication, navigation, surveillance, air traffic management, aircraft anti-hijacking, access control, biometrics, risk analysis and management, etc, are processed integratedly through information fusion to enhance the security performance of each module. We will present a detailed implementation approach of IAS including the AlertEnterprise based Integrated Risk Management (IRM) Model, defense-in-depth of communication, navigation, surveillance, and aircraft anti-hijacking. Compared with disparate systems, the proposed IAS has much higher security performance to be effective against both outsider and insider threats.


title: Hybrid PWM-Resonant Converter for Electric Vehicle On-Board Battery Chargers

abstract: A novel hybrid pulse-width-modulation resonant converter is presented in this paper for electric vehicle (EV) 3.3-kW on-board battery chargers (OBCs). While the proposed converter has all the benefits of the earlier developed hybrid converters for OBCs, the proposed converter has fewer components and achieves much lower voltage stress in the rectifying diodes compared to the earlier hybrid converters. As a result, it is possible to employ superior diodes such as Schottky barrier diodes below 300 V featuring low forward-voltage drop and better reverse-recovery for EV 3.3-kW OBC applications. In addition, the proposed converter achieves much better transformer utilization compared to the earlier hybrid converters. Due to this, the proposed converter can achieve more optimal efficiency over the overall battery charging profile. The effectiveness of the proposed converter has been verified with the experimental results under an output voltage range of 250–420-V dc at 3.3 kW.


title: 3D printing: the new industrial revolution

abstract: This article examines the characteristics and applications of 3-D printing and compares it with mass customization and other manufacturing processes. 3-D printing enables small quantities of customized goods to be produced at relatively low costs. While currently used primarily to manufacture prototypes and mockups, a number of promising applications exist in the production of replacement parts, dental crowns, and artificial limbs, as well as in bridge manufacturing. 3-D printing has been compared to such disruptive technologies as digital books and music downloads that enable consumers to order their selections online, allow firms to profitably serve small market segments, and enable companies to operate with little or no unsold finished goods inventory. Some experts have also argued that 3-D printing will significantly reduce the advantages of producing small lot sizes in low-wage countries via reduced need for factory workers.


title: The Sorry State of TLS Security in Enterprise Interception Appliances

abstract: Network traffic inspection, including TLS traffic, in enterprise environments is widely practiced. Reasons for doing so are primarily related to improving enterprise security (e.g., malware detection) and meeting legal requirements. To analyze TLS-encrypted data, network appliances implement a Man-in-the-Middle TLS proxy, by acting as the intended web server to a requesting client (e.g., a browser), and acting as the client to the outside web server. As such, the TLS proxy must implement both a TLS client and a server, and handle a large amount of traffic, preferably, in real-time. However, as protocol and implementation layer vulnerabilities in TLS/HTTPS are quite frequent, these proxies must be, at least, as secure as a modern, up-to-date web browser, and a properly configured web server. As opposed to client-end TLS proxies (e.g., as in several anti-virus products), the proxies in network appliances may serve hundreds to thousands of clients, and any vulnerability in their TLS implementations can significantly downgrade enterprise security. ::: To analyze TLS security of network appliances, we develop a comprehensive framework, by combining and extending tests from existing work on client-end and network-based interception studies. We analyze thirteen representative network appliances over a period of more than a year (including versions before and after notifying affected vendors, a total of 17 versions), and uncover several security issues. For instance, we found that four appliances perform no certificate validation at all, three use pre-generated certificates, and eleven accept certificates signed using MD5, exposing their clients to MITM attacks. Our goal is to highlight the risks introduced by widely-used TLS proxies in enterprise and government environments, potentially affecting many systems hosting security, privacy, and financially sensitive data.


title: An evaluation study of BigData frameworks for graph processing

abstract: When Google first introduced the Map/Reduce paradigm in 2004, no comparable system had been available to the general public. The situation has changed since then. The Map/Reduce paradigm has become increasingly popular and there is no shortage of Map/Reduce implementations in today's computing world. The predominant solution is currently Apache Hadoop, started by Yahoo. Besides employing custom Map/Reduce installations, customers of cloud services can now exploit ready-made made installations (e.g. the Elastic Map/Reduce System). In the mean time, other, second generation frameworks have started to appear. They either fine tune the Map/Reduce model for specific scenarios, or change the paradigm altogether, such as Google's Pregel. In this paper, we present a comparison between these second generation frameworks and the current de-facto standard Hadoop, by focusing on a specific scenario: large-scale graph analysis. We analyze the different means of fine-tuning those systems by exploiting their unique features. We base our analysis on the k-core decomposition problem, whose goal is to compute the centrality of each node in a given graph; we tested our implementation in a cluster of Amazon EC2 nodes with realistic datasets made publicly available by the SNAP project.


title: SEGAN: Speech Enhancement Generative Adversarial Network

abstract: Current speech enhancement techniques operate on the spectral domain and/or exploit some higher-level feature. The majority of them tackle a limited number of noise conditions and rely on first-order statistics. To circumvent these issues, deep networks are being increasingly used, thanks to their ability to learn complex functions from large example sets. In this work, we propose the use of generative adversarial networks for speech enhancement. In contrast to current techniques, we operate at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them. We evaluate the proposed model using an independent, unseen test set with two speakers and 20 alternative noise conditions. The enhanced samples confirm the viability of the proposed model, and both objective and subjective evaluations confirm the effectiveness of it. With that, we open the exploration of generative architectures for speech enhancement, which may progressively incorporate further speech-centric design choices to improve their performance.


The Spam-Filtering Accuracy Plateau at 99 . 9 % Accuracy and How to Get Past It .


title: A Roadmap Towards Resilient Internet of Things for Cyber-Physical Systems

abstract: The Internet of Things (IoT) is a ubiquitous system connecting many different devices—the things —which can be accessed from the distance. The cyber-physical systems (CPSs) monitor and control the things from the distance. As a result, the concepts of dependability and security get deeply intertwined. The increasing level of dynamicity, heterogeneity, and complexity adds to the system’s vulnerability, and challenges its ability to react to faults. This paper summarizes the state of the art of existing work on anomaly detection, fault-tolerance, and self-healing, and adds a number of other methods applicable to achieve resilience in an IoT. We particularly focus on non-intrusive methods ensuring data integrity in the network. Furthermore, this paper presents the main challenges in building a resilient IoT for the CPS, which is crucial in the era of smart CPS with enhanced connectivity (an excellent example of such a system is connected autonomous vehicles). It further summarizes our solutions, work-in-progress and future work to this topic to enable “Trustworthy IoT for CPS”. Finally, this framework is illustrated on a selected use case: a smart sensor infrastructure in the transport domain.


title: Test-driven evaluation of linked data quality

abstract: Linked Open Data (LOD) comprises an unprecedented volume of structured data on the Web. However, these datasets are of varying quality ranging from extensively curated datasets to crowdsourced or extracted data of often relatively low quality. We present a methodology for test-driven quality assessment of Linked Data, which is inspired by test-driven software development. We argue that vocabularies, ontologies and knowledge bases should be accompanied by a number of test cases, which help to ensure a basic level of quality. We present a methodology for assessing the quality of linked data resources, based on a formalization of bad smells and data quality problems. Our formalization employs SPARQL query templates, which are instantiated into concrete quality test case queries. Based on an extensive survey, we compile a comprehensive library of data quality test case patterns. We perform automatic test case instantiation based on schema constraints or semi-automatically enriched schemata and allow the user to generate specific test case instantiations that are applicable to a schema or dataset. We provide an extensive evaluation of five LOD datasets, manual test case instantiation for five schemas and automatic test case instantiations for all available schemata registered with Linked Open Vocabularies (LOV). One of the main advantages of our approach is that domain specific semantics can be encoded in the data quality test cases, thus being able to discover data quality problems beyond conventional quality heuristics.


title: Higher-order Wavelet Statistics and their Application to Digital Forensics

abstract: We describe a statistical model for natural images that is built upon a multi-scale wavelet decomposition. The model consists of first- and higher-order statistics that capture certain statistical regularities of natural images. We show how this model can be useful in several digital forensic applications, specifically in detecting various types of digital tampering.


title: An Ultra-Low Power Fully Integrated Energy Harvester Based on Self-Oscillating Switched-Capacitor Voltage Doubler

abstract: This paper presents a fully integrated energy harvester that maintains >35% end-to-end efficiency when harvesting from a 0.84 mm 2 ::: solar cell in low light condition of 260 lux, converting 7 nW input power from 250 mV to 4 V. Newly proposed self-oscillating switched-capacitor (SC) DC-DC voltage doublers are cascaded to form a complete harvester, with configurable overall conversion ratio from 9× to 23×. In each voltage doubler, the oscillator is completely internalized within the SC network, eliminating clock generation and level shifting power overheads. A single doubler has >70% measured efficiency across 1 nA to 0.35 mA output current ( >10 5 ::: range) with low idle power consumption of 170 pW. In the harvester, each doubler has independent frequency modulation to maintain its optimum conversion efficiency, enabling optimization of harvester overall conversion efficiency. A leakage-based delay element provides energy-efficient frequency control over a wide range, enabling low idle power consumption and a wide load range with optimum conversion efficiency. The harvester delivers 5 nW-5 μW output power with >40% efficiency and has an idle power consumption 3 nW, in test chip fabricated in 0.18 μm CMOS technology.


title: An Intelligent Personalized Fashion Recommendation System

abstract: In this paper, we propose a novel system-Intelligent Personalized Fashion Recommendation System, which creates a new space in web multimedia mining and recommendation. The proposed system significantly helps customers find their most suitable fashion choices in mass fashion information in the virtual space based on multimedia mining. There are three stand-alone models developed in this paper to optimize the analysis of fashion features in mass fashion trend: (i). Interaction and recommender model, which associated clients' personalized demand with the current fashion trend, and helps clients find the most favorable fashion factors in trend. (ii). Evolutionary hierachical fashion multimedia mining model, which creates a hierachical structure to filer the key components of fashion multimedia information in the virtual space, and it proves to be more efficient for web mass multimedia mining in an evolutionary way. (iii). Color tone analysis model, a relevant and straightforward approach for analysis of main color tone as to the skin and clothing is used. In this model, a refined contour extraction of the fashion model method is also developed to solve the dilemma that the accuracy and efficiency of contour extraction in the dynamic and complex video scene. As evidenced by the experiment, the proposed system outperforms in effectiveness on mass fashion information in the virtual space compared with human, and thus developing a personalized and diversified way for fashion recommendation.


title: Lynn: A Multi-dimensional Dynamic Resource Management System for Distributed Applications in Clouds

abstract: We present Lynn, a multi-dimensional dynamic resource management system for distributed applications in clouds. Lynn is the first to uniformly manage full kinds of resources, including CPU, memory and particularly, the network I/O bandwidth resource as a whole to schedule tasks. This comprehensive policy allows for more fine-grained control over hardware resources, and thus more efficiency. To support today's sophisticated distributed applications in clouds and to ease the burden of software developers, Lynn defines the concept of virtual cluster in this case and provides a transparent and user-friendly interface of deploying the virtual cluster to run applications. Distributed Applications do not need to make any change to be adapted to Lynn for use. In addition, Lynn puts forward a mechanism that allows different kinds of virtualization techniques to coexist within one single machine, particularly the light-weighted visualization technology Linux container LXC and the famous full virtualization technology KVM. Lynn can handle the difference of the underlying virtualization technology and can manage their resources properly. Then, Lynn breaks the usual rule of static resource allocation of virtual machines. Creatively, Lynn takes careful account of the resources used by each virtual cluster and dynamically adapts their amounts of resources according to their respective workloads. Dynamic policy improves cluster resource utilizations and provides more room for new applications. Our results show that Lynn's multidimensional dynamic resource management policy is feasible and effective.


title: Call for AI Research in RTS Games

abstract: This position paper discusses AI challenges in the area of real‐time strategy games and presents a research agenda aimed at improving AI performance in these popular multi‐ player computer games.


A News Chain Evaluation Methodology along with a Lattice-based Approach for News Chain Construction


title: Real-Time Detection of False Data Injection Attacks in Smart Grid: A Deep Learning-Based Intelligent Mechanism

abstract: Application of computing and communications intelligence effectively improves the quality of monitoring and control of smart grids. However, the dependence on information technology also increases vulnerability to malicious attacks. False data injection (FDI), that attack on the integrity of data, is emerging as a severe threat to the supervisory control and data acquisition system. In this paper, we exploit deep learning techniques to recognize the behavior features of FDI attacks with the historical measurement data and employ the captured features to detect the FDI attacks in real-time. By doing so, our proposed detection mechanism effectively relaxes the assumptions on the potential attack scenarios and achieves high accuracy. Furthermore, we propose an optimization model to characterize the behavior of one type of FDI attack that compromises the limited number of state measurements of the power system for electricity theft. We illustrate the performance of the proposed strategy through the simulation by using IEEE 118-bus test system. We also evaluate the scalability of our proposed detection mechanism by using IEEE 300-bus test system.


title: Optimising thermal efficiency of direct contact membrane distillation by brine recycling for small-scale seawater desalination

abstract: Abstract A technique to optimise thermal efficiency using brine recycling during direct contact membrane distillation (DCMD) of seawater was investigated. By returning the hot brine to the feed tank, the system water recovery could be increased and the sensible heat of the hot brine was recovered to improve thermal efficiency. The results show that in the optimal water recovery range of 20 to 60% facilitated by brine recycling, the specific thermal energy consumption of the process could be reduced by more than half. It is also noteworthy that within this optimal water recovery range, the risk of membrane scaling is negligible — DCMD of seawater at a constant water recovery of 70% was achieved for over 24 h without any scale formation on the membrane surface. In contrast, severe membrane scaling was observed when water recovery reached 80%. In addition to water recovery, other operating conditions such as feed temperature and water circulation rates could influence the process thermal efficiency. Increasing the feed temperature and reducing the circulation flow rates increased thermal efficiency. Increasing the feed temperature could also mitigate the negative effect of elevated feed concentration on the distillate flux, particularly at a high water recovery.


Social network site changes over time: The case of MySpace


title: Induction and synchronous reluctance motors comparison

abstract: The aim of this paper is to investigate and compare the performances of two induction motors and two transverse laminated synchronous reluctance motors (induction motor nameplate data: 2.2 and 4 kW, 380 V, 50 Hz, 4 pole). Each induction motor is compared with a synchronous reluctance motor, that has the same stator lamination and winding, but, obviously, different rotor. The analysis has been based both on an analytical and experimental approach. The results have shown that the synchronous reluctance motor, compared to the induction motor, is capable of around a 10% to 15% larger rated torque for a given frame size. The direct comparison of the performances of the motor types has been made both for constant load and constant dissipated power conditions.


title: Concept Based Query Expansion Using WordNet

abstract: Query expansion is a widely studied technique for improving information retrieval effectiveness. In this paper we proposed a new query expansion technique using the comprehensive thesaurus WordNet and its semantic relatedness measure modules. Word sense disambiguation are performed on original query sentence, yielding the concept of each term in the query. Based on those recovered concepts, expanded query terms are generated from WordNet lexical database. The proposed method has been evaluated in document retrieval on the Web using query sentence. Our extensive experimental results demonstrate a 7% precision improvement over retrieval methods not employing query expansion techniques.


EmoWordNet: Automatic Expansion of Emotion Lexicon Using English WordNet


title: Bumps and Bruises: Mining Presidential Campaign Announcements on Twitter

abstract: Online social media plays an increasingly significant role in shaping the political discourse during elections worldwide. In the 2016 U.S. presidential election, political campaigns strategically designed candidacy announcements on Twitter to produce a significant increase in online social media attention. We use large-scale online social media communications to study the factors of party, personality, and policy in the Twitter discourse following six major presidential campaign announcements for the 2016 U.S. presidential election. We observe that all campaign announcements result in an instant bump in attention, with up to several orders of magnitude increase in tweets. However, we find that Twitter discourse as a result of this bump in attention has overwhelmingly negative sentiment. The bruising criticism, driven by crosstalk from Twitter users of opposite party affiliations, is organized by hashtags such as #NoMoreBushes and #WhyImNotVotingForHillary. We analyze how people take to Twitter to criticize specific personality traits and policy positions of presidential candidates.


title: Heterogeneous Information Network Embedding for Recommendation

abstract: Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in recommender systems, called HIN based recommendation. It is challenging to develop effective methods for HIN based recommendation in both extraction and exploitation of the information from HINs. Most of HIN based recommendation methods rely on path based similarity, which cannot fully mine latent structure features of users and items. In this paper, we propose a novel heterogeneous network embedding based approach for HIN based recommendation, called HERec. To embed HINs, we design a meta-path based random walk strategy to generate meaningful node sequences for network embedding. The learned node embeddings are first transformed by a set of fusion functions, and subsequently integrated into an extended matrix factorization (MF) model. The extended MF model together with fusion functions are jointly optimized for the rating prediction task. Extensive experiments on three real-world datasets demonstrate the effectiveness of the HERec model. Moreover, we show the capability of the HERec model for the cold-start problem, and reveal that the transformed embedding information from HINs can improve the recommendation performance.


title: Dialogue Intent Classification with Long Short-Term Memory Networks

abstract: Dialogue intent analysis plays an important role for dialogue systems. In this paper, we present a deep hierarchical LSTM model to classify the intent of a dialogue utterance. The model is able to recognize and classify user’s dialogue intent in an efficient way. Moreover, we introduce a memory module to the hierarchical LSTM model, so that our model can utilize more context information to perform classification. We evaluate the two proposed models on a real-world conversational dataset from a Chinese famous e-commerce service. The experimental results show that our proposed model outperforms the baselines.


title: Cuff-less PPG based continuous blood pressure monitoring — A smartphone based approach

abstract: Cuff-less estimation of systolic (SBP) and diastolic (DBP) blood pressure is an efficient approach for non-invasive and continuous monitoring of an individual's vitals. Although pulse transit time (PTT) based approaches have been successful in estimating the systolic and diastolic blood pressures to a reasonable degree of accuracy, there is still scope for improvement in terms of accuracies. Moreover, PTT approach requires data from sensors placed at two different locations along with individual calibration of physiological parameters for deriving correct estimation of systolic and diastolic blood pressure (BP) and hence is not suitable for smartphone deployment. Heart Rate Variability is one of the extensively used non-invasive parameters to assess cardiovascular autonomic nervous system and is known to be associated with SBP and DBP indirectly. In this work, we propose a novel method to extract a comprehensive set of features by combining PPG signal based and Heart Rate Variability (HRV) related features using a single PPG sensor. Further, these features are fed into a DBP feedback based combinatorial neural network model to arrive at a common weighted average output of DBP and subsequently SBP. Our results show that using this current approach, an accuracy of ±6.8 mmHg for SBP and ±4.7 mmHg for DBP is achievable on 1,750,000 pulses extracted from a public database (comprising 3000 people). Since most of the smartphones are now equipped with PPG sensor, a mobile based cuff-less BP estimation will enable the user to monitor their BP as a vital parameter on demand. This will open new avenues towards development of pervasive and continuous BP monitoring systems leading to an early detection and prevention of cardiovascular diseases.


title: Combined Task and Motion Planning as Classical AI Planning

abstract: Planning in robotics is often split into task and motion planning. The high-level, symbolic task planner decides what needs to be done, while the motion planner checks feasibility and fills up geometric detail. It is known however that such a decomposition is not effective in general as the symbolic and geometrical components are not independent. In this work, we show that it is possible to compile task and motion planning problems into classical AI planning problems; i.e., planning problems over finite and discrete state spaces with a known initial state, deterministic actions, and goal states to be reached. The compilation is sound, meaning that classical plans are valid robot plans, and probabilistically complete, meaning that valid robot plans are classical plans when a sufficient number of configurations is sampled. In this approach, motion planners and collision checkers are used for the compilation, but not at planning time. The key elements that make the approach effective are 1) expressive classical AI planning languages for representing the compiled problems in compact form, that unlike PDDL make use of functions and state constraints, and 2) general width-based search algorithms capable of finding plans over huge combinatorial spaces using weak heuristics only. Empirical results are presented for a PR2 robot manipulating tens of objects, for which long plans are required.


Fluency Boost Learning and Inference for Neural Grammatical Error Correction


title: High Fidelity Semantic Shape Completion for Point Clouds Using Latent Optimization

abstract: Semantic shape completion is a challenging problem in 3D computer vision where the task is to generate a complete 3D shape using a partial 3D shape as input. We propose a learning-based approach to complete incomplete 3D shapes through generative modeling and latent manifold optimization. Our algorithm works directly on point clouds. We use an autoencoder and a GAN to learn a distribution of embeddings for point clouds of object classes. An input point cloud with missing regions is first encoded to a feature vector. The representations learnt by the GAN are then used to find the best latent vector on the manifold using a combined optimization that finds a vector in the manifold of plausible vectors that is close to the original input (both in the feature space and the output space of the decoder). Experiments show that our algorithm is capable of successfully reconstructing point clouds with large missing regions with very high fidelity without having to rely on exemplar based database retrieval.


title: Deep recurrent neural network for mobile human activity recognition with high throughput

abstract: In this paper, we propose a method of human activity recognition with high throughput from raw accelerometer data applying a deep recurrent neural network (DRNN), and investigate various architectures and its combination to find the best parameter values. The “high throughput” refers to short time at a time of recognition. We investigated various parameters and architectures of the DRNN by using the training dataset of 432 trials with 6 activity classes from 7 people. The maximum recognition rate was 95.42% and 83.43% against the test data of 108 segmented trials each of which has single activity class and 18 multiple sequential trials, respectively. Here, the maximum recognition rates by traditional methods were 71.65% and 54.97% for each. In addition, the efficiency of the found parameters was evaluated using additional dataset. Further, as for throughput of the recognition per unit time, the constructed DRNN was requiring only 1.347 ms, while the best traditional method required 11.031 ms which includes 11.027 ms for feature calculation. These advantages are caused by the compact and small architecture of the constructed real time oriented DRNN.


title: Equivalent-circuit models for split-ring resonators and complementary split-ring resonators coupled to planar transmission lines

abstract: In this paper, a new approach for the development of planar metamaterial structures is developed. For this purpose, split-ring resonators (SRRs) and complementary split-ring resonators (CSRRs) coupled to planar transmission lines are investigated. The electromagnetic behavior of these elements, as well as their coupling to the host transmission line, are studied, and analytical equivalent-circuit models are proposed for the isolated and coupled SRRs/CSRRs. From these models, the stopband/passband characteristics of the analyzed SRR/CSRR loaded transmission lines are derived. It is shown that, in the long wavelength limit, these stopbands/passbands can be interpreted as due to the presence of negative/positive values for the effective /spl epsiv/ and /spl mu/ of the line. The proposed analysis is of interest in the design of compact microwave devices based on the metamaterial concept.


title: Caching Algorithms and Rational Models of Memory

abstract: People face a problem similar to that faced by algorithms that manage the memory of computers: trying to organize information to maximize the chance it will be available when needed in the future. In computer science, this problem is known as “caching”. Inspired by this analogy, we compared the properties of a model of human memory proposed by Anderson and Schooler (1991) and caching algorithms used in computer science. We tested each algorithm on a dataset relevant to human cognition: headlines from the New York Times. In addition to overall performance, we investigated whether the algorithms from computer science replicated the well-documented effects of recency, practice, and spacing on human memory. Anderson and Schooler’s model performed comparably to the worst caching algorithms, but was the only model that captured the spacing effects seen in human memory data. All models showed similar effects of recency and practice.


UNBIASING TRUNCATED BACKPROPAGATION THROUGH TIME


title: Examining the relationship between game type, player disposition and aggression

abstract: As technology advances the need for more comprehensive analyses of the relationship between human aggression and gaming increases. First Person Shooter gaming environments provide realistic opportunities for many interactions between players. Violent and aggressive video games have been blamed for many aggressive acts. Early meta-analyses of gaming and aggression concluded that gaming does increase aggression, although at levels lower than that of television and other media. More recent meta-analyses claim that there is no correlation between gaming and aggression. However, previous studies do not consider the possible effect of a live opponent on player aggression and therefore the current study includes this as a putative factor in gaming and aggression. Results indicate that neither the disposition of the opponent (confederate) nor the aggressive level of the game type affected participant aggression.


title: On the effectiveness of dynamic taint analysis for protecting against private information leaks on Android-based devices

abstract: We investigate the limitations of using dynamic taint analysis for tracking privacy-sensitive information on Android-based mobile devices. Taint tracking keeps track of data as it propagates through variables, interprocess messages and files, by tagging them with taint marks. A popular taint-tracking system, TaintDroid, uses this approach in Android mobile applications to mark private information, such as device identifiers or user's contacts details, and subsequently issue warnings when this information is misused (e.g., sent to an un-desired third party). We present a collection of attacks on Android-based taint tracking. Specifically, we apply generic classes of anti-taint methods in a mobile device environment to circumvent this security technique. We have implemented the presented techniques in an Android application, ScrubDroid. We successfully tested our app with the TaintDroid implementations for Android OS versions 2.3 to 4.1.1, both using the emulator and with real devices. Finally, we evaluate the success rate and time to complete of the presented attacks. We conclude that, although taint tracking may be a valuable tool for software developers, it will not effectively protect sensitive data from the black-box code of a motivated attacker applying any of the presented anti-taint tracking methods.


title: Efficient SPARQL-to-SQL with R2RML mappings

abstract: Abstract Existing SPARQL-to-SQL translation techniques have limitations that reduce their robustness, efficiency and dependability. These limitations include the generation of inefficient or even incorrect SQL queries, lack of formal background, and poor implementations. Moreover, some of these techniques cannot be used over arbitrary DB schemas due to the lack of support for RDB to RDF mapping languages, such as R2RML. In this paper we present a technique (implemented in the -ontop- system) that tackles all these issues. We propose a formal approach for SPARQL-to-SQL translation that (i) generates efficient SQL by combining optimization techniques from the logic programming and SQL optimization fields; (ii) provides a well-defined specification of the SPARQL semantics used in the translation; and (iii) supports R2RML mappings over general relational schemas. We provide extensive benchmarks using the -ontop- system for Ontology Based Data Access (OBDA) and show that by using these techniques -ontop- is able to outperform well known SPARQL-to-SQL systems, as well as commercial triple stores, by several orders of magnitude.


title: Current challenges in automatic software repair

abstract: The abundance of defects in existing software systems is unsustainable. Addressing them is a dominant cost of software maintenance, which in turn dominates the life cycle cost of a system. Recent research has made significant progress on the problem of automatic program repair, using techniques such as evolutionary computation, instrumentation and run-time monitoring, and sound synthesis with respect to a specification. This article serves three purposes. First, we review current work on evolutionary computation approaches, focusing on GenProg, which uses genetic programming to evolve a patch to a particular bug. We summarize algorithmic improvements and recent experimental results. Second, we review related work in the rapidly growing subfield of automatic program repair. Finally, we outline important open research challenges that we believe should guide future research in the area.


title: Mirror neurons responding to the observation of ingestive and communicative mouth actions in the monkey ventral premotor cortex

abstract: In theventral premotor cortex (area F5) of the monkeythere are neurons that discharge both when the monkeyperforms specific motor actions and when it observes another individual performing a similar action (mirror neurons). Previous studies on mirror neurons concerned hand actions. Here, we describe the mirror responses of F5 neurons that motorically code mouth actions. The results showed that about one-third of mouth motor neurons also discharge when the monkey observes another individual performing mouth actions. The majority of these ‘mouth mirror neurons’ become active during the execution and observation of mouth actions related to ingestive functions such as grasping, sucking or breaking food. Another population of mouth mirror neurons also discharges during the execution of ingestive actions, but the most effective visual stimuli in triggering them are communicative mouth gestures (e.g. lip smacking). Some also fire when the monkey makes communicative gestures. These findings extend the notion of mirror system from hand to mouth action and suggest that area F5, the area considered to be the homologue of human Broca’s area, is also involved in communicative functions.


title: Generative modeling of repositories of health records for predictive tasks

abstract: Repositories of health records are collections of events with varying number and sparsity of occurrences within and among patients. Although a large number of predictive models have been proposed in the last decade, they are not yet able to simultaneously capture cross-attribute and temporal dependencies associated with these repositories. Two major streams of predictive models can be found. On one hand, deterministic models rely on compact subsets of discriminative events to anticipate medical conditions. On the other hand, generative models offer a more complete and noise-tolerant view based on the likelihood of the testing arrangements of events to discriminate a particular outcome. However, despite the relevance of generative predictive models, they are not easily extensible to deal with complex grids of events. In this work, we rely on the Markov assumption to propose new predictive models able to deal with cross-attribute and temporal dependencies. Experimental results hold evidence for the utility and superior accuracy of generative models to anticipate health conditions, such as the need for surgeries. Additionally, we show that the proposed generative models are able to decode temporal patterns of interest (from the learned lattices) with acceptable completeness and precision levels, and with superior efficiency for voluminous repositories.


title: Automatic Extraction of Moving Objects from Image and LIDAR Sequences

abstract: Detecting and segmenting moving objects in an image sequence has always been a crucial task for many computer vision applications. This task becomes especially challenging for real-world image sequences of busy street scenes, where moving objects are ubiquitous. Although it remains technologically elusive to develop an effective and scalable image-based moving object detection, modern street side imagery are often augmented with sparse point clouds captured with depth sensors. This paper develops a simple but effective system for moving object detection that fully harnesses the complementary nature of 2D image and 3D LIDAR point clouds. We demonstrate how moving objects can be much more easily and reliably detected with sparse 3D measurements and how such information can significantly improve segmentation for moving objects in the image sequences. The results of our system are highly accurate "joint segmentation" of 2D images and 3D points for all moving objects in street scenes, which can serve many subsequent tasks such as object removal in images, 3D reconstruction and rendering.


title: State Space Model based Trust Evaluation over Wireless Sensor Networks: An Iterative Particle Filter Approach

abstract: In this paper we propose a state space modeling approach for trust evaluation in wireless sensor networks. In our state space trust model (SSTM), each sensor node is associated with a trust metric, which measures to what extent the data transmitted from this node would better be trusted by the server node. Given the SSTM, we translate the trust evaluation problem to be a nonlinear state filtering problem. To estimate the state based on the SSTM, a component-wise iterative state inference procedure is proposed to work in tandem with the particle filter, and thus the resulting algorithm is termed as iterative particle filter (IPF). The computational complexity of the IPF algorithm is theoretically linearly related with the dimension of the state. This property is desirable especially for high dimensional trust evaluation and state filtering problems. The performance of the proposed algorithm is evaluated by both simulations and real data analysis.


title: Targeting Ultimate Accuracy: Face Recognition via Deep Embedding

abstract: Face Recognition has been studied for many decades. As opposed to traditional hand-crafted features such as LBP and HOG, much more sophisticated features can be learned automatically by deep learning methods in a data-driven way. In this paper, we propose a two-stage approach that combines a multi-patch deep CNN and deep metric learning, which extracts low dimensional but very discriminative features for face verification and recognition. Experiments show that this method outperforms other state-of-the-art methods on LFW dataset, achieving 99.77% pair-wise verification accuracy and significantly better accuracy under other two more practical protocols. This paper also discusses the importance of data size and the number of patches, showing a clear path to practical high-performance face recognition systems in real world.


title: Image Fusion: An Overview

abstract: An extensive overview of the field of image fusion is presented in this paper. The study firstly delves into the problem of multiple modalities that form the motivation for fusion, and discusses the main advantages of image fusion. Further, it discusses in detail the history of fusion algorithms that comprise various transform-domain and data driven methods. A section on image fusion applications, ranging from geo-spatial, medical to security fields, is also presented. Overall the paper aims to bring to light the advances and state-of-the-art within the image fusion research area so as to benefit other fields.


title: Combining Business Intelligence with Semantic Web : Overview and Challenges

abstract: Under today’s highly complex and dynamic business environment, external data (most often issued from web) need to be included in traditional On-Line Analytical Processing (OLAP) analysis so that decision-makers would be well-informed before making effective decision. Including external web data requires knowing the exact semantic meaning in order to use the right information at the right time. Semantic Web (SW) technologies allow semantically annotating data so that we can exchange several descriptions over web data, do reasoning over these descriptions and ensure interoperability between humans and systems. A combination of BI technologies with SW will both enhance BI analysis with web data and allow analyzing SW data through BI tools. In this paper, we firstly introduce basic concepts of the BI and SW domains. Then, we present recent research results using SW to enhance OLAP analysis. At last, we identify challenges requiring future research efforts to achieve a complete incorporation of BI with SW.


title: Event templates in the lexical representations of verbs q

abstract: Four experiments support the hypothesis that syntactically relevant information about verbs is encoded in the lexicon in semantic event templates. A verb's event template represents the participants in an event described by the verb and the relations among the participants. The experiments show that lexical decision times are longer for verbs with more complex templates than verbs with less complex templates and that, for both transitive and intransitive sentences, sentences containing verbs with more complex templates take longer to process. In contrast, sentence processing times did not depend on the probabilities with which the verbs appear in transitive versus intransitive constructions in a large corpus of naturally produced sentences.


title: Real-valued Fast Fourier Transform Algorithms

abstract: This tutorial paper describes the methods for constructing fast algorithms for the computation of the discrete Fourier transform (DFT) of a real-valued series. The application of these ideas to all the major fast Fourier transform (FFT) algorithms is discussed, and the various algorithms are compared. We present a new implementation of the real-valued split-radix FFT, an algorithm that uses fewer operations than any other real-valued power-of-2-length FFT. We also compare the performance of inherently real-valued transform algorithms such as the fast Hartley transform (FHT) and the fast cosine transform (FCT) to real-valued FFT algorithms for the computation of power spectra and cyclic convolutions. Comparisons of these techniques reveal that the alternative techniques always require more additions than a method based on a real-valued FFT algorithm and result in computer code of equal or greater length and complexity.


Obtaining Low Rank Coal Biotransforming Bacteria from Microhabitats Enriched with Carbonaceos Residues


title: GRE: A Graph Runtime Engine for Large-Scale Distributed Graph-Parallel Applications

abstract: Large-scale distributed graph-parallel computing is challenging. On one hand, due to the irregular computation pattern and lack of locality, it is hard to express parallelism efficiently. On the other hand, due to the scale-free nature, real-world graphs are hard to partition in balance with low cut. To address these challenges, several graph-parallel frameworks including Pregel and GraphLab (PowerGraph) have been developed recently. In this paper, we present an alternative framework, Graph Runtime Engine (GRE). While retaining the vertex-centric programming model, GRE proposes two new abstractions: 1) a Scatter-Combine computation model based on active message to exploit massive fined-grained edge-level parallelism, and 2) a Agent-Graph data model based on vertex factorization to partition and represent directed graphs. GRE is implemented on commercial off-the-shelf multi-core cluster. We experimentally evaluate GRE with three benchmark programs (PageRank, Single Source Shortest Path and Connected Components) on real-world and synthetic graphs of millions billion of vertices. Compared to PowerGraph, GRE shows 2.5~17 times better performance on 8~16 machines (192 cores). Specifically, the PageRank in GRE is the fastest when comparing to counterparts of other frameworks (PowerGraph, Spark,Twister) reported in public literatures. Besides, GRE significantly optimizes memory usage so that it can process a large graph of 1 billion vertices and 17 billion edges on our cluster with totally 768GB memory, while PowerGraph can only process less than half of this graph scale.


title: An Overview of Tiles in HEVC

abstract: Tiles is a new feature in the High Efficiency Video Coding (HEVC) standard that divides a picture into independent, rectangular regions. This division provides a number of advantages. Specifically, it increases the “parallel friendliness” of the new standard by enabling improved coding efficiency for parallel architectures, as compared to previous sliced based methods. Additionally, tiles facilitate improved maximum transmission unit (MTU) size matching, reduced line buffer memory, and additional region-of-interest functionality. In this paper, we introduce the tiles feature and survey the performance of the tool. Coding efficiency is reported for different parallelization factors and MTU size requirements. Additionally, a tile-based region of interest coding method is developed.


title: User Interface Design and Evaluation

abstract: Whether you are a professional new to the user-centered design field, or an experienced designer who needs to learn the fundamentals of user interface design and evaluation, this book can lead the way. ::: What will you get from this book? Based on a course from the Open University, UK which has been taught to over a thousand professionals and students, this book presents an overview of the field. It illustrates the benefits of a user-centered approach to the design of software, computer systems, and web sites, and provides a clear and practical discussion of requirements gathering; developing interaction design from user requirements; and user interface evaluation. The book's coverage includes established HCI topics-for example, visibility, affordance, feedback, metaphors, mental models, and the like-combined with practical guidelines for contemporary designs and current trends, which makes for a winning combination. You get a clear presentation of ideas, illustrations of concepts, using real-world applications. ::: This book will help you develop all the skills necessary for iterative user-centered design, and provides a firm foundation for user interface design and evaluation on which to build. ::: The readership for this book will include seasoned professionals in user interface design and usability engineering (looking for new tools with which to expand their knowledge), new people who enter the HCI field with no prior educational experience, and software developers, web application developers, and information appliance designers, who need to know more about interaction design and evaluation.


title: Learning belief networks from data: an information theory based approach

abstract: This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional independence (CI) tests in typical cases. We provide precise conditions that specify when these algorithms are guaranteed to be correct as well as empirical evidence (from real world applications and simulation tests) that demonstrates that these systems work efficiently and reliably in practice.  2002 Elsevier Science B.V. All rights reserved.


title: Prediction of seasonal influenza epidemics in Tehran using artificial neural networks

abstract: Prediction of seasonal influenza epidemics is certainly a forming and effective step towards taking appropriate preventive actions. Improvement on public informing, decreasing the number of infected cases, undesirable effects and deaths due to influenza and also increasing vigilance of Iranian Influenza Surveillance System (IISS), have been practical goals of this research. A forecasting system has been designed and developed using Artificial Neural Networks (ANNs). It is a novel research as a novel dataset has been exploited. The data are categorized in two groups of climatic parameters (temperature, humidity, precipitation, wind speed & sea level pressure) and number of patients (number of total referrals and number of patients with Influenza-Like Illnesses (ILI)). In order to evaluate the model performance, different cost functions are defined and results indicate that the model provides the possibility of a satisfactory forecasting and is practically helpful to achieve the objectives already claimed.


title: Hiding the Trace of JPEG Compression History

abstract: Identifying and disguising the JPEG compression history has become a hot research point in recent days. The image forger could easily get the JPEG images as tampering materials, while the JPEG loss compression left a big influence on the image's DCT histogram. This JPEG compression trace has become an important standpoint of compression forensic. In order to fool the compression forensic, the anti-forensic dithers have been added into the JPEG compressed image and became an uncompressed image format. However, that kind of fake lossless image could be identified by measuring how 'noisy' the uncompressed image is after recompressing. This paper would help the forger hide JPEG compression history by recompressing and adding noise at different quality factor and restore the image quality by TV denoising. It could defend forensic attack aiming at anti-forensic dither by measuring the total variation (TV) of the image. The simulation results have proved that it can fool the TV detection forensic with a high image quality.


title: Dynamic Guarding of Marine Assets Through Cluster Control of Automated Surface Vessel Fleets

abstract: There is often a need to mark or patrol marine areas in order to prevent boat traffic from approaching critical regions, such as the location of a high-value vessel, a dive site, or a fragile marine ecosystem. In this paper, we describe the use of a fleet of robotic kayaks that provides such a function: the fleet circum- navigates the critical area until a threatening boat approaches, at which point the fleet establishes a barrier between the ship and the protected area. Coordinated formation control of the fleet is implemented through the use of the cluster-space control architecture, which is a full-order controller that treats the fleet as a virtual, articulating, kinematic mechanism. An application-specific layer interacts with the cluster-space controller in order for an operator to directly specify and monitor guarding-related parameters, such as the spacing between boats. This system has been experimentally verified in the field with a fleet of robotic kayaks. In this paper, we describe the control architecture used to establish the guarding behavior, review the design of the robotic kayaks, and present experimental data regarding the functionality and performance of the system.