title: Computational power of neural networks: a characterization in terms of kolmogorov complexity

abstract: The computational power of recurrent neural networks is shown to depend ultimately on the complexity of the real constants (weights) of the network. The complexity, or information contents, of the weights is measured by a variant of resource-bounded Kolmogorov (1965) complexity, taking into account the time required for constructing the numbers. In particular, we reveal a full and proper hierarchy of nonuniform complexity classes associated with networks having weights of increasing Kolmogorov complexity.


title: Robust place recognition for 3D range data based on point features

abstract: The problem of place recognition appears in different mobile robot navigation problems including localization, SLAM, or change detection in dynamic environments. Whereas this problem has been studied intensively in the context of robot vision, relatively few approaches are available for three-dimensional range data. In this paper, we present a novel and robust method for place recognition based on range images. Our algorithm matches a given 3D scan against a database using point features and scores potential transformations by comparing significant points in the scans. A further advantage of our approach is that the features allow for a computation of the relative transformations between scans which is relevant for registration processes. Our approach has been implemented and tested on different 3D data sets obtained outdoors. In several experiments we demonstrate the advantages of our approach also in comparison to existing techniques.


title: Improving rail network velocity: A machine learning approach to predictive maintenance

abstract: Abstract Rail network velocity is defined as system-wide average speed of line-haul movement between terminals. To accommodate increased service demand and load on rail networks, increase in network velocity, without compromising safety, is required. Among many determinants of overall network velocity, a key driver is service interruption, including lowered operating speed due to track/train condition and delays caused by derailments. Railroads have put significant infrastructure and inspection programs in place to avoid service interruptions. One of the key measures is an extensive network of wayside mechanical condition detectors (temperature, strain, vision, infrared, weight, impact, etc.) that monitor the rolling-stock as it passes by. The detectors are designed to alert for conditions that either violate regulations set by governmental rail safety agencies or deteriorating rolling-stock conditions as determined by the railroad. Using huge volumes of historical detector data, in combination with failure data, maintenance action data, inspection schedule data, train type data and weather data, we are exploring several analytical approaches including, correlation analysis, causal analysis, time series analysis and machine learning techniques to automatically learn rules and build failure prediction models. These models will be applied against both historical and real-time data to predict conditions leading to failure in the future, thus avoiding service interruptions and increasing network velocity. Additionally, the analytics and models can also be used for detecting root cause of several failure modes and wear rate of components, which, while do not directly address network velocity, can be proactively used by maintenance organizations to optimize trade-offs related to maintenance schedule, costs and shop capacity. As part of our effort, we explore several avenues to machine learning techniques including distributed learning and hierarchical analytical approaches.


title: Claims and supporting evidence for self-adaptive systems: A literature study

abstract: Despite the vast body of work on self-adaption, no systematic study has been performed on the claims associated with self-adaptation and the evidence that exists for these claims. As such an insight is crucial for researchers and engineers, we performed a literature study of the research results from SEAMS since 2006 and the associated Dagstuhl seminar in 2008. The study shows that the primary claims of self-adaptation are improved flexibility, reliability, and performance of the system. On the other hand, the tradeoffs implied by self-adaptation have not received much attention. Evidence is obtained from basic examples, or simply lacking. Few systematic empirical studies have been performed, and no industrial evidence is reported. From the study, we offer the following recommendations to move the field forward: to improve evaluation, researchers should make their assessment methods, tools and data publicly available; to deal with poor discussion of limitations, conferences/workshops should require an explicit section on limitations in engineering papers; to improve poor treatment of tradeoffs, this aspect should be an explicit subject of reviews; and finally, to enhance industrial validation, the best academy-industry efforts could be formally recognized by the community.


title: 2-d Robotic Arm Control using EMG Signal Aditya Veer Singh Rana

abstract: the presented work, a robotic arm is controlled using the EMG signal acquired from the electrodes attached to the human arm. The EMG signals are acquired from three different muscle groups of the upper forearm. The EMG signal is very noisy and of the order of micro volts. Faithful extraction of the EMG signal is the prime objective in the presented work. The acquired signal is then fed to signal conditioning unit consisting of signal acquisition, amplification, rectification and final filtration. The output of the signal conditioning unit is then converted to digital signal using a 16-bit serial ADC. The digitized signal is used as feedback and control signal for final control of the robotic arm. The robotic arm is attached to the shaft of the stepper motor for motion. Stepper motor is interfaced using microcontroller. There may be more than one stepper motor to give multi-dimensional motion to robotic arm. It can be assumed that with one single stepper motor, ideally we can have 360/θ no. of positions (θ being the step angle). Presently we have used only two stepper motors for up-down motion at two joints thereby enabling to give (360/θ)2 no. of positions.


title: Incremental Maintenance for Leapfrog Triejoin

abstract: We present an incremental maintenance algorithm for leapfrog triejoin. The algorithm maintains rules in time proportional (modulo log factors) to the edit distance between leapfrog triejoin traces.


Mixed-signal SOC verification using analog behavioral models


title: Testing network-based intrusion detection signatures using mutant exploits

abstract: Misuse-based intrusion detection systems rely on models of attacks to identify the manifestation of intrusive behavior. Therefore, the ability of these systems to reliably detect attacks is strongly affected by the quality of their models, which are often called "signatures." A perfect model would be able to detect all the instances of an attack without making mistakes, that is, it would produce a 100% detection rate with 0 false alarms. Unfortunately, writing good models (or good signatures) is hard. Attacks that exploit a specific vulnerability may do so in completely different ways, and writing models that take into account all possible variations is very difficult. For this reason, it would be beneficial to have testing tools that are able to evaluate the "goodness" of detection signatures. This work describes a technique to test and evaluate misuse detection models in the case of network-based intrusion detection systems. The testing technique is based on a mechanism that generates a large number of variations of an exploit by applying mutant operators to an exploit template. These mutant exploits are then run against a victim host protected by a network-based intrusion detection system. The results of the systems in detecting these variations provide a quantitative basis for the evaluation of the quality of the corresponding detection model.


title: Age of Menarche and Knowledge about Menstrual Hygiene Management among Adolescent School Girls in Amhara Province, Ethiopia: Implication to Health Care Workers & School Teachers

abstract: BACKGROUND ::: Effective menstrual hygiene has direct and indirect effect on achieving millennium development goals two (universal education), three (gender equality and women empowerment) and, five (improving maternal health). However, in Ethiopiait is an issue which is insufficiently acknowledged in the reproductive health sector. The objective of this study therefore, is to assess the age of menarche and knowledge of adolescents about menstrual hygiene management in Amhara province. ::: ::: ::: METHOD ::: School based cross sectional study was conducted from November 2012 to June 2013. Multistage stage sampling technique was used. The school was first clustered in to grades & sections and thenparticipants were selected by lottery method. A pretested &structured questionnaire was used. Data were entered, cleaned and analyzed using SPSS version 16.0. Finally, multivariate analysis was used to assess independent effect of predictors. ::: ::: ::: FINDINGS ::: In this study, 492 students were included, making a response rate of 100%. Mean age at menarche was 14.1±1.4 years. The main sources of information about menstrual hygiene management were teachers for 212 (43.1%). Four hundred forty six (90.7%) respondents had high level knowledge about menstrual hygiene management. Most of the respondents 457 (92.9%) and 475 (96.5%) had access for water and toilet facility respectively. Place of residence (AOR = 1.8, 95%CI: [1.42-1.52]) and educational status of their mothers' (AOR = 95%CI: [1.15-13.95]) were independent predictors of knowledge about menstrual hygiene management. ::: ::: ::: CONCLUSION ::: Knowledge of respondents about menstrual hygiene management was very high. School teachers were the primary source of information. Place of residence and their mother's educational status were independent predictors of menstrual hygiene management. Thus, the government of Ethiopia in collaboration with its stalk holders should develop and disseminatereproductive health programmes on menstrual hygiene management targeting both parents and their adolescents. Moreover, parents should be made aware about the need to support their children with appropriate sanitary materials.


title: Discriminative Representation Learning with Supervised Auto-encoder

abstract: Auto-encoders have been proved to be powerful unsupervised learning methods that able to extract useful features from input data or construct deep artificial neural networks by recent studies. In such settings, the extracted features or the initialized networks only learn the data structure while contain no class information which is a disadvantage in classification tasks. In this paper, we aim to leverage the class information of input to learn a reconstructive and discriminative auto-encoder. More specifically, we introduce a supervised auto-encoder that combines the reconstruction error and the classification error to form a unified objective function while taking the noisy concatenate data and label as input. The noisy concatenate input is constructed in such a method that one third has only original data and zero labels, one third has only label and zero data, the last one third has both original data and label. We show that the representations learned by the proposed supervised auto-encoder are more discriminative and more suitable for classification tasks. Experimental results demonstrate that our model outperforms many existing learning algorithms.


title: Clickers in the Classroom : An Active Learning Approach Further research will determine whether clickers complement or surpass other active learning approaches in improving learning outcomes

abstract: Current research describes the benefits of active learning approaches. Clickers, or student response systems, are a technology used to promote active learning. Most research on the benefits of using clickers in the classroom has shown that students become engaged and enjoy using them. However, research on learning outcomes has only compared the use of clickers to traditional lecture methods. Although learning outcomes are higher when using clickers, the question is whether the clickers or the active learning pedagogies are the cause. For this reason, I conducted a study that compared learning outcomes resulting from the use of clickers versus another active learning method—class discussion. Even though both techniques employ active learning, would using clickers increase learning outcomes more than another active learning approach? Two key features distinguish clicker use: ■ Clickers provide a mechanism for students to participate anonymously. ■ Clickers integrate a “game approach” that may engage students more than traditional class discussion. The study also investigated students’ perceptions of their learning using clickers versus classroom discussion.


title: Semi-supervised classification trees

abstract: In many real-life problems, obtaining labelled data can be a very expensive and laborious task, while unlabeled data can be abundant. The availability of labeled data can seriously limit the performance of supervised learning methods. Here, we propose a semi-supervised classification tree induction algorithm that can exploit both the labelled and unlabeled data, while preserving all of the appealing characteristics of standard supervised decision trees: being non-parametric, efficient, having good predictive performance and producing readily interpretable models. Moreover, we further improve their predictive performance by using them as base predictive models in random forests. We performed an extensive empirical evaluation on 12 binary and 12 multi-class classification datasets. The results showed that the proposed methods improve the predictive performance of their supervised counterparts. Moreover, we show that, in cases with limited availability of labeled data, the semi-supervised decision trees often yield models that are smaller and easier to interpret than supervised decision trees.


title: Texture Synthesis Using Convolutional Neural Networks

abstract: Here we introduce a new model of natural textures based on the feature spaces of convolutional neural networks optimised for object recognition. Samples from the model are of high perceptual quality demonstrating the generative power of neural networks trained in a purely discriminative fashion. Within the model, textures are represented by the correlations between feature maps in several layers of the network. We show that across layers the texture representations increasingly capture the statistical properties of natural images while making object information more and more explicit. The model provides a new tool to generate stimuli for neuroscience and might offer insights into the deep representations learned by convolutional neural networks.


title: Linearized Dual-Band Power Amplifiers With Integrated Baluns in 65 nm CMOS for a 2$\, \times \,$2 802.11n MIMO WLAN SoC

abstract: Fully integrated dual-band power amplifiers with on-chip baluns for 802.11n MIMO WLAN applications are presented. With a 3.3 V supply, the PAs produce a saturated output power of 28.3 dBm and 26.7 dBm with peak drain efficiency of 35.3% and 25.3% for the 2.4 GHz and 5 GHz bands, respectively. By utilizing multiple fully self-contained linearization algorithms, an EVM of -25 dB is achieved at 22.4 dBm for the 2.4 GHz band and 20.5 dBm for the 5 GHz band while transmitting 54 Mbs OFDM. The chip is fabricated in standard 65 nm CMOS and the PAs occupy 0.31 mm2 (2.4 GHz) and 0.27 mm2 (5 GHz) area. To examine the reliability of the PAs, accelerated aging tests are performed for several hundreds parts without a single failure.


title: Enhanced Network Embeddings via Exploiting Edge Labels

abstract: Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. While achieving competitive performance on a variety of network inference tasks such as node classification and link prediction, these methods treat the relations between nodes as a binary variable and ignore the rich semantics of edges. In this work, we attempt to learn network embeddings which simultaneously preserve network structure and relations between nodes. Experiments on several real-world networks illustrate that by considering different relations between different node pairs, our method is capable of producing node embeddings of higher quality than a number of state-of-the-art network embedding methods, as evaluated on a challenging multi-label node classification task.


title: Vector ℓ0 Sparse Conditional Independence Graphs

abstract: One of the main approaches to system identification of networks of time series or signals is conditional independence graphical (CIG) modeling. In the Gaussian case, the conditional dependence structure of the nodal time series is determined by the location of zeros in the precision matrix (inverse covariance matrix). And this determines the graph structure of the network. Despite the many applications of CIG models, the theory and algorithms have so far only dealt with networks of univariate or scalar signals. But in most applications the nodes carry multivariate or vector signals. Here we extend CIG modeling to handle such data by posing a group l 0 sparse penalised block precision matrix estimation problem. We develop a double cyclic descent algorithm to solve it. And we compare the method with a group l 1 penalised alternative in simulations.


title: Hierarchical Graphical Models for Context-Aware Hybrid Brain-Machine Interfaces

abstract: We present a novel hierarchical graphical model based context-aware hybrid brain-machine interface (hBMI) using probabilistic fusion of electroencephalographic (EEG) and electromyographic (EMG) activities. Based on experimental data collected during stationary executions and subsequent imageries of five different hand gestures with both limbs, we demonstrate feasibility of the proposed hBMI system through within session and online across sessions classification analyses. Furthermore, we investigate the context-aware extent of the model by a simulated probabilistic approach and highlight potential implications of our work in the field of neurophysiologically-driven robotic hand prosthetics.


title: MeshCNN: a network with an edge

abstract: Polygonal meshes provide an efficient representation for 3D shapes. They explicitly captureboth shape surface and topology, and leverage non-uniformity to represent large flat regions as well as sharp, intricate features. This non-uniformity and irregularity, however, inhibits mesh analysis efforts using neural networks that combine convolution and pooling operations. In this paper, we utilize the unique properties of the mesh for a direct analysis of 3D shapes using MeshCNN, a convolutional neural network designed specifically for triangular meshes. Analogous to classic CNNs, MeshCNN combines specialized convolution and pooling layers that operate on the mesh edges, by leveraging their intrinsic geodesic connections. Convolutions are applied on edges and the four edges of their incident triangles, and pooling is applied via an edge collapse operation that retains surface topology, thereby, generating new mesh connectivity for the subsequent convolutions. MeshCNN learns which edges to collapse, thus forming a task-driven process where the network exposes and expands the important features while discarding the redundant ones. We demonstrate the effectiveness of MeshCNN on various learning tasks applied to 3D meshes.


title: Query Adaptive Multiview Object Instance Search and Localization Using Sketches

abstract: Sketch-based object search is a challenging problem mainly due to three difficulties: 1) how to match the primary sketch query with the colorful image; 2) how to locate the small object in a big image that is similar to the sketch query; and 3) given the large image database, how to ensure an efficient search scheme that is reasonably scalable. To address the above challenges, we propose leveraging object proposals for object search and localization. However, instead of purely relying on sketch features, we propose fully utilizing the appearance features of object proposals to resolve the ambiguities between the matching sketch query and object proposals. Our proposed query adaptive search is formulated as a subgraph selection problem, which can be solved by the maximum flow algorithm. By performing query expansion, it can accurately locate the small target objects in a cluttered background or densely drawn deformation-intensive cartoon (Manga like) images. To improve the computing efficiency of matching proposal candidates, the proposed Multi View Spatially Constrained Proposal Selection encodes each identified object proposal in terms of a small local basis of anchor objects. The results on benchmark datasets validate the advantages of utilizing both the sketch and appearance features for sketch-based search, while ensuring sufficient scalability at the same time.


title: Design of Frictional 2D-Anisotropy Surface for Wriggle Locomotion of Printable Soft-Bodied Robots

abstract: Soft-bodied and continuum robots have shown great adaptability to the environment thanks to its flexibility of the body. They have great potential in environment exploring or rescuing mission. One of those robots is snake-like soft-bodied robots. A snake robot is often made by attaching passive wheels along a long body to achieve frictional anisotropy. This anisotropic structure helps to propel the body with serpentine locomotion and prevents it from sliding laterally. However, with a snake-like soft-bodied robot, attaching wheels is not only clumsy but also adding weight to the robot. In this paper, being inspired by the scales on the skin of a snake, we propose a designing scheme to achieve an all-printed wriggle soft-bodied robot by patterning high and low friction material to the ventral side of the robot. Compared to a totally flat ventral, we are able to speed-up the serpentine locomotion 2.8 times. Besides, by changing the configuration of high/low friction material, our wriggle soft-bodied robot can easily move forward or backward just by switching the controlling signal. The fabrication time is just less than 1 hour and the robot can achieve the speed of 26 mm/s.


Retrofitting Word Vectors of MeSH Terms to Improve Semantic Similarity Measures


title: Design and experimental study of a compliant feet quadruped robot with closed-chain mechanism legs

abstract: The locomotion performances of a quadruped robot with compliant feet based on closed-chain mechanism legs are presented. The legs of this quadruped robot were made up of six-bar linkage mechanism with one degree of freedom. And a special foot trajectory could be gained through kinematic analysis and optimum design of the six-bar linkage mechanism. In order to reduce the impact force of quadruped robot's walking on the ground, two semicircle feet with different thickness were designed as compliant feet. The experimental results of this quadruped robot with different stiffness feet showed that the semicircle feet could reduce the driving torque and current of motors. This primary investigation illustrated that the compliant feet could improve the locomotion performance of a quadruped robot based on closed-chain mechanism legs.


title: EEG Control of a Virtual Helicopter in 3-Dimensional Space Using Intelligent Control Strategies

abstract: Films like Firefox, Surrogates, and Avatar have explored the possibilities of using brain-computer interfaces (BCIs) to control machines and replacement bodies with only thought. Real world BCIs have made great progress toward that end. Invasive BCIs have enabled monkeys to fully explore 3-D space using neuroprosthetics. However, noninvasive BCIs have not been able to demonstrate such mastery of 3-D space. Here, we report our work, which demonstrates that human subjects can use a noninvasive BCI to fly a virtual helicopter to any point in a 3-D world. Through use of intelligent control strategies, we have facilitated the realization of controlled flight in 3-D space. We accomplished this through a reductionist approach that assigns subject-specific control signals to the crucial components of 3-D flight. Subject control of the helicopter was comparable when using either the BCI or a keyboard. By using intelligent control strategies, the strengths of both the user and the BCI system were leveraged and accentuated. Intelligent control strategies in BCI systems such as those presented here may prove to be the foundation for complex BCIs capable of doing more than we ever imagined.


title: Building a database on S3

abstract: There has been a great deal of hype about Amazon's simple storage service (S3). S3 provides infinite scalability and high availability at low cost. Currently, S3 is used mostly to store multi-media documents (videos, photos, audio) which are shared by a community of people and rarely updated. The purpose of this paper is to demonstrate the opportunities and limitations of using S3 as a storage system for general-purpose database applications which involve small objects and frequent updates. Read, write, and commit protocols are presented. Furthermore, the cost ($), performance, and consistency properties of such a storage system are studied.


title: WaveGlow: A Flow-based Generative Network for Speech Synthesis

abstract: In this paper we propose WaveGlow: a flow-based network capable of generating high quality speech from mel-spectrograms. WaveGlow combines insights from Glow [1] and WaveNet [2] in order to provide fast, efficient and high-quality audio synthesis, without the need for auto-regression. WaveGlow is implemented using only a single network, trained using only a single cost function: maximizing the likelihood of the training data, which makes the training procedure simple and stable. Our PyTorch implementation produces audio samples at a rate of more than 500 kHz on an NVIDIA V100 GPU. Mean Opinion Scores show that it delivers audio quality as good as the best publicly available WaveNet implementation. All code will be made publicly available online [3].


title: Using anomaly detection based techniques to detect HTTP-based botnet C&C traffic

abstract: HTTP is becoming the most preferred channel for command and control (C&C) communication of botnets. One of the main reasons is that it is very easy to hide the C&C traffic in the massive amount of browser generated Web traffic. However, detecting these HTTP-based C&C packets which constitute only a minuscule portion of the overall everyday HTTP traffic is a formidable task. In this paper, we present an anomaly detection based approach to detect HTTP-based C&C traffic using statistical features based on client generated HTTP request packets and DNS server generated response packets. We use three different unsupervised anomaly detection techniques to isolate suspicious communications that have a high probability of being part of a botnet's C&C communication. Results indicate that our method can achieve more than 90% detection rate while maintaining a reasonably low false positive rate.


title: Managing a Mobile Agricultural Robot Swarm for a seeding task

abstract: Mobile Agricultural Robot Swarms (MARS) is an approach for autonomous farming operations by a coordinated group of robots. One key aspect of the MARS concept is the low individual intelligence, meaning that each robot is equipped with only a minimum of sensor technology in order to achieve a low cost and energy efficient system that provides scalability and reliability for field tasks. The robot swarms are coordinated by a centralized entity (OptiVisor) which is responsible for path planning, optimization and supervision. It also serves as a mediator between the robots and different cloud services responsible for the documentation of the procedure. This paper focuses on the architecture and function of OptiVisor within the overall MARS system. An OptiVisor in combination with a simulation environment for a robot swarm is presented and shows the feasibility of the general concept and the current state of the algorithms. Furthermore, the paper shows results about the current progress of OptiVisor integration using real robots.


title: A First Step Towards Automated Detection of Buffer Overrun Vulnerabilities

abstract: We describe a new technique for finding potential buffer overrun vulnerabilities in security-critical C code. The key to success is to use static analysis: we formulate detection of buffer overruns as an integer range analysis problem. One major advantage of static analysis is that security bugs can be eliminated before code is deployed. We have implemented our design and used our prototype to find new remotely-exploitable vulnerabilities in a large, widely deployed software package. An earlier hand audit missed


title: Geodesic Object Proposals

abstract: We present an approach for identifying a set of candidate objects in a given image. This set of candidates can be used for object recognition, segmentation, and other object-based image parsing tasks. To generate the proposals, we identify critical level sets in geodesic distance transforms computed for seeds placed in the image. The seeds are placed by specially trained classifiers that are optimized to discover objects. Experiments demonstrate that the presented approach achieves significantly higher accuracy than alternative approaches, at a fraction of the computational cost.


title: Cluster size optimization in sensor networks with decentralized cluster-based protocols

abstract: Network lifetime and energy-efficiency are viewed as the dominating considerations in designing cluster-based communication protocols for wireless sensor networks. This paper analytically provides the optimal cluster size that minimizes the total energy expenditure in such networks, where all sensors communicate data through their elected cluster heads to the base station in a decentralized fashion. LEACH, LEACH-Coverage, and DBS comprise three cluster-based protocols investigated in this paper that do not require any centralized support from a certain node. The analytical outcomes are given in the form of closed-form expressions for various widely-used network configurations. Extensive simulations on different networks are used to confirm the expectations based on the analytical results. To obtain a thorough understanding of the results, cluster number variability problem is identified and inspected from the energy consumption point of view.


title: Gender identity development in adolescence

abstract: Abstract This article is part of a Special Issue "Puberty and Adolescence".This article aims to provide an outline of what is currently known on trajectories, and contributing factors to gender identity development in adolescence. We give a historical overview of the concept of gender identity, and describe general identity development in adolescence, gender identity development in the general population and in gender variant youth. Possible psychosocial (such as child and parental characteristics) and biological factors (such as the effects of prenatal exposure to gonadal hormones and the role of genetics) contributing to a gender variant identity are discussed. Studies focusing on a number of psychosocial and biological factors separately, indicate that each of these factors influence gender identity formation, but little is known about the complex interplay between the factors, nor about the way individuals themselves contribute to the process. Research into normative and gender variant identity development of adolescents is clearly lagging behind. However, studies on persons with gender dysphoria and disorders of sex development, show that the period of adolescence, with its changing social environment and the onset of physical puberty, seems to be crucial for the development of a non-normative gender identity.


title: Kinetostatic analysis of underactuated fingers

abstract: The aim of this paper is to establish a fundamental basis for the analysis of underactuated fingers with a general approach. A new method to obtain the force capabilities of any underactuated fingers will be presented. Force capability is defined as the ability to generate an external wrench onto a fixed object with a given set of phalanges. This method is based on the introduction of two new matrices which completely describe the relationship between the input torque of the finger actuator(s) and the contact forces on the phalanges. Using this tool, one can study the conditions under which certain phalanx forces vanish, and compare different underactuation mechanisms with a rigorous approach.


title: Neural mechanisms of top-down control during spatial and feature attention

abstract: Abstract Theories of visual selective attention posit that both spatial location and nonspatial stimulus features (e.g., color) are elementary dimensions on which top-down attentional control mechanisms can selectively influence visual processing. Neuropsychological and neuroimaging studies have demonstrated that regions of superior frontal and parietal cortex are critically involved in the control of visual–spatial attention. This frontoparietal control network has also been found to be activated when attention is oriented to nonspatial stimulus features (e.g., motion). To test the generality of the frontoparietal network in attentional control, we directly compared spatial and nonspatial attention in a cuing paradigm. Event-related fMRI methods permitted the isolation of attentional control activity during orienting to a location or to a nonspatial stimulus feature (color). Portions of the frontoparietal network were commonly activated to the spatial and nonspatial cues. However, direct statistical comparisons of cue-related activity revealed subregions of the frontoparietal network that were significantly more active during spatial than nonspatial orienting when all other stimulus, task, and attentional factors were equated. No regions of the frontal–parietal network were more active for nonspatial cues in comparison to spatial cues. These findings support models suggesting that subregions of the frontal–parietal network are highly specific for controlling spatial selective attention.


Parallelizing Boosting and Arcing


Semantic Web and Information Visualization


title: A Survey on Human-in-the-Loop Applications Towards an Internet of All

abstract: Our tools and appliances are becoming increasingly more intelligent and interconnected, giving birth to an “Internet of Things” that can be used to support new types of cyber-physical systems (CPSs). While many CPSs are human-centric applications where humans are an essential part of the system, unfortunately, most of these systems still consider the human as an external and unpredictable element to the control loop. In order for these systems to better serve human needs, future CPSs will need to bolster a closer tie with the human element, through Human-in-the-Loop controls that take into consideration human intents, psychological states, emotions and actions inferred through sensory data. This area is a natural confluence of multidisciplinary focus but currently lacks a general understanding of the underlying requirements, principles and theory. As far as we know, this survey is the first effort towards extending the field's knowledge through an in-depth research of the state-of-the-art and a critical overview of the current taxonomic efforts in the area of Human-in-the-Loop CPSs. On top of this research, a novel taxonomic exercise focused on the general roles of the human component together with a requirement analysis, are presented.


title: Achieving 3D-MIMO With Massive Antennas From Theory to Practice With Evaluation and Field Trial Results

abstract: Exploiting multiple-input–multiple-output (MIMO) techniques by using massive antennas can significantly improve the capacity and reliability of wireless systems and has been considered as one key technique in future fifth-generation mobile communications system. Considering the limitation of antenna panel size, placing the massive antennas in 2-D grid is an effective way for its commercialization, which is termed as “3D-MIMO.” In this paper, we study the performance of 3D-MIMO with massive antennas by system-level simulation using practical assumption and 3-D channel model and test the 3D-MIMO in field trial with commercial terminal and networks. In addition, we compare the system-level simulation results and the field trial test measurements. Our system-level simulation results show that the 3D-MIMO with 64 antennas can significantly improve the performance with 56% and 147% gain on cell-average and cell-edge throughputs compared with 2D-MIMO. We perform field trial to test the 3D-MIMO, and the results verify its performance gain obtained by simulation. Furthermore, the measurement in typical high-rise scenario shows that the 3D-MIMO can significantly enhance the data rate of users located at higher floors due to the capability of flexible elevation beamforming.


title: Structured Pruning of Deep Convolutional Neural Networks

abstract: Real-time application of deep learning algorithms is often hindered by high computational complexity and frequent memory accesses. Network pruning is a promising technique to solve this problem. However, pruning usually results in irregular network connections that not only demand extra representation efforts but also do not fit well on parallel computation. We introduce structured sparsity at various scales for convolutional neural networks: feature map-wise, kernel-wise, and intra-kernel strided sparsity. This structured sparsity is very advantageous for direct computational resource savings on embedded computers, in parallel computing environments, and in hardware-based systems. To decide the importance of network connections and paths, the proposed method uses a particle filtering approach. The importance weight of each particle is assigned by assessing the misclassification rate with a corresponding connectivity pattern. The pruned network is retrained to compensate for the losses due to pruning. While implementing convolutions as matrix products, we particularly show that intra-kernel strided sparsity with a simple constraint can significantly reduce the size of the kernel and feature map tensors. The proposed work shows that when pruning granularities are applied in combination, we can prune the CIFAR-10 network by more than 70% with less than a 1% loss in accuracy.


title: Three Senses of ‘Argument

abstract: In AI approaches to argumentation, different senses of argument are often conflated. We propose a three-level distinction between arguments, cases, and debates. This allows us to modularise issues into separate levels and identify systematic relations between levels. Arguments, comprised of rules, facts, and a claim, are the basic units; they instantiate argument schemes; they have no sub-arguments. Cases are sets of arguments supporting a claim. Debates are sets of arguments in an attack relation; they include cases for and against a particular claim. Critical questions, which are characteristic of the particular argument schemes, are used to determine the attack relation between arguments. In a debate, rankings on arguments or argument relations are given as components based on features of argument schemes. Our analysis clarifies the role and contribution of distinct approaches in the construction of rational debate. It identifies the source of properties used for evaluating the status of arguments in Argumentation Frameworks.


title: Belonging nowhere: Marginalization & radicalization risk among Muslim immigrants

abstract: In the last 15 years, the threat of Muslim violent extremists emerging within Western countries has grown. Terrorist organizations based in the Middle East are recruiting Muslims in the United States and Europe via social media. Yet we know little about the factors that would drive Muslim immigrants in a Western country to heed this call and become radicalized, even at the cost of their own lives. Research into the psychology of terrorism suggests that a person's cultural identity plays a key role in radicalization, so we surveyed 198 Muslims in the United States about their cultural identities and attitudes toward extremism. We found that immigrants who identify with neither their heritage culture nor the culture they are living in feel marginalized and insignificant. Experiences of discrimination make the situation worse and lead to greater support for radicalism, which promises a sense of meaning and life purpose. Such insights could be of use to policymakers engaged in efforts against violent extremism, including terrorism. Language: en


title: Bringing gesture recognition to all devices

abstract: Existing gesture-recognition systems consume significant power and computational resources that limit how they may be used in low-end devices. We introduce AllSee, the first gesture-recognition system that can operate on a range of computing devices including those with no batteries. AllSee consumes three to four orders of magnitude lower power than state-of-the-art systems and can enable always-on gesture recognition for smartphones and tablets. It extracts gesture information from existing wireless signals (e.g., TV transmissions), but does not incur the power and computational overheads of prior wireless approaches. We build AllSee prototypes that can recognize gestures on RFID tags and power-harvesting sensors. We also integrate our hardware with an off-the-shelf Nexus S phone and demonstrate gesture recognition in through-the-pocket scenarios. Our results show that AllSee achieves classification accuracies as high as 97% over a set of eight gestures.


title: ZooKeeper: Wait-free Coordination for Internet-scale Systems

abstract: In this paper, we describe ZooKeeper, a service for coordinating processes of distributed applications. Since ZooKeeper is part of critical infrastructure, ZooKeeper aims to provide a simple and high performance kernel for building more complex coordination primitives at the client. It incorporates elements from group messaging, shared registers, and distributed lock services in a replicated, centralized service. The interface exposed by Zoo-Keeper has the wait-free aspects of shared registers with an event-driven mechanism similar to cache invalidations of distributed file systems to provide a simple, yet powerful coordination service. ::: ::: The ZooKeeper interface enables a high-performance service implementation. In addition to the wait-free property, ZooKeeper provides a per client guarantee of FIFO execution of requests and linearizability for all requests that change the ZooKeeper state. These design decisions enable the implementation of a high performance processing pipeline with read requests being satisfied by local servers. We show for the target workloads, 2:1 to 100:1 read to write ratio, that ZooKeeper can handle tens to hundreds of thousands of transactions per second. This performance allows ZooKeeper to be used extensively by client applications.


title: Simulation of Rovers for Precision Agriculture

abstract: Precision agriculture (PA) is nowadays one of the main strategies to fulfill the increasing food demand in the world. PA was defined as the set of technologies and procedures employed to optimize the agriculture production system. This article evaluate three types of rovers to be applied in precision agriculture, they are type Shrimp, Crab and Rocker Bogie. These rovers are modeled and simulated using parametric CAD software (Solid Works®) in order to assess their performance in terms of torque and power consumption. These physical variables would help in developing compact and autonomous agricultural rovers


title: Structure and Hydration of Highly Branched, Monodisperse Phytoglycogen Nanoparticles

abstract: Phytoglycogen is a naturally occurring polysaccharide nanoparticle made up of extensively branched glucose monomers. It has a number of unusual and advantageous properties, such as high water retention, low viscosity, and high stability in water, which make this biomaterial a promising candidate for a wide variety of applications. In this study, we have characterized the structure and hydration of aqueous dispersions of phytoglycogen nanoparticles using neutron scattering. Small angle neutron scattering results suggest that the phytoglycogen nanoparticles behave similar to hard sphere colloids and are hydrated by a large number of water molecules (each nanoparticle contains between 250% and 285% of its mass in water). This suggests that phytoglycogen is an ideal sample in which to study the dynamics of hydration water. To this end, we used quasielastic neutron scattering (QENS) to provide an independent and consistent measure of the hydration number, and to estimate the retardation factor (or degree of water slow-down) for hydration water translational motions. These data demonstrate a length-scale dependence in the measured retardation factors that clarifies the origin of discrepancies between retardation factor values reported for hydration water using different experimental techniques. The present approach can be generalized to other systems containing nanoconfined water.


title: Logout in Single Sign-on Systems

abstract: Single sign-on (SSO) helps users to cope with many online services that require authentication. Systems such as OpenID and SAML-based Shibboleth offer federated identity management where an Identity Provider authenticates the user on behalf of the services. Much research concentrates on making authentication stronger, preventing phishing and making the systems more user friendly but less attention has been paid to the termination of the authentication sessions i.e. logout. It is, however, equally important that the sessions do not remain open when, for example, a student using shared computers in a university library leaves the workstation. In this article, we describe challenges related to logout in federated identity management on web based services and give guidelines for implementing reliable logout from services that use single sign-on.


title: Chapter 6 - Online Advertising

abstract: This chapter explores what makes online advertising different from traditional advertising channels. We argue that online advertising differs from traditional advertising channels in two important ways: measurability and targetability. Measurability is higher because the digital nature of online advertising means that responses to ads can be tracked relatively easily. Targetability is higher because data can be automatically tracked at an individual level, and it is relatively easy to show different people different ads. We discuss recent advances in search advertising, display advertising, and social media advertising and explore the key issues that arise for firms and consumers from measurability and targetability. We then explore possible public policy consequences, with an in depth discussion of the implications for consumer privacy.


title: Multi-level multi-phase VSI control using multi-dimensional feedback quantization

abstract: A switching strategy for multi-level multi-phase voltage source inverter (VSI) based on optimal feedback quantization is derived. The contributions include the improved harmonics distortion comparing with the conventional SVPWM and the realizable block diagram of quantization to produce optimal switching state. By finding the most significant switching state, the implementation of quantization block needs only adders and comparators. Further, the implementation complexity of quantization is independent of phase/level numbers. In the simulation, the comparison of the proposed method and SVPWM confirms the effectiveness of the proposed method.


title: The Advantages of IoT and Cloud Applied to Smart Cities

abstract: The European Commission and the Japanese National Institute of Information and Communication Technologies (NICT) have made a big effort to promote collaboration between the IoT and Cloud communities and define a common baseline for future research. ClouT (Cloud+IoT) project is co-funded as part of the first FP7 EU-Japan cooperation call and gives life to a fruitful collaboration between six European and seven Japanese organizations. The overarching objective is to provide enhanced solutions for smarter cities by using cloud computing to overcome some of the current challenges and limitations in the IoT domain. Through the combination of IoT and cloud computing, smart cities will be able to build new and enhanced services by using the large amounts of data stored in the cloud and by processing it in quasi-real time. This paper describes ClouT Reference Architecture which was outlined by leveraging on existing works performed by established IoT and cloud Research Communities both in Europe and Japan. ClouT Reference Architecture establishes a common ground of objects, definitions and rules mapping the IoT and cloud advantages into a unique context.


The Event-Camera Dataset: Event-based Data for Pose Estimation, Visual Odometry, and SLAM


title: Theoretical Insights Into the Optimization Landscape of Over-Parameterized Shallow Neural Networks

abstract: In this paper, we study the problem of learning a shallow artificial neural network that best fits a training data set. We study this problem in the over-parameterized regime where the numbers of observations are fewer than the number of parameters in the model. We show that with the quadratic activations, the optimization landscape of training, such shallow neural networks, has certain favorable characteristics that allow globally optimal models to be found efficiently using a variety of local search heuristics. This result holds for an arbitrary training data of input/output pairs. For differentiable activation functions, we also show that gradient descent, when suitably initialized, converges at a linear rate to a globally optimal model. This result focuses on a realizable model where the inputs are chosen i.i.d. from a Gaussian distribution and the labels are generated according to planted weight coefficients.


title: Esthetic outcomes following immediate and early implant placement in the anterior maxilla--a systematic review.

abstract: PURPOSE ::: The objectives of this systematic review are (1) to quantitatively estimate the esthetic outcomes of implants placed in postextraction sites, and (2) to evaluate the influence of simultaneous bone augmentation procedures on these outcomes. ::: ::: ::: MATERIALS AND METHODS ::: Electronic and manual searches of the dental literature were performed to collect information on esthetic outcomes based on objective criteria with implants placed after extraction of maxillary anterior and premolar teeth. All levels of evidence were accepted (case series studies required a minimum of 5 cases). ::: ::: ::: RESULTS ::: From 1,686 titles, 114 full-text articles were evaluated and 50 records included for data extraction. The included studies reported on single-tooth implants adjacent to natural teeth, with no studies on multiple missing teeth identified (6 randomized controlled trials, 6 cohort studies, 5 cross-sectional studies, and 33 case series studies). Considerable heterogeneity in study design was found. A meta-analysis of controlled studies was not possible. The available evidence suggests that esthetic outcomes, determined by esthetic indices (predominantly the pink esthetic score) and positional changes of the peri-implant mucosa, may be achieved for single-tooth implants placed after tooth extraction. Immediate (type 1) implant placement, however, is associated with a greater variability in outcomes and a higher frequency of recession of > 1 mm of the midfacial mucosa (eight studies; range 9% to 41% and median 26% of sites, 1 to 3 years after placement) compared to early (type 2 and type 3) implant placement (2 studies; no sites with recession > 1 mm). In two retrospective studies of immediate (type 1) implant placement with bone graft, the facial bone wall was not detectable on cone beam CT in 36% and 57% of sites. These sites had more recession of the midfacial mucosa compared to sites with detectable facial bone. Two studies of early implant placement (types 2 and 3) combined with simultaneous bone augmentation with GBR (contour augmentation) demonstrated a high frequency (above 90%) of facial bone wall visible on CBCT. Recent studies of immediate (type 1) placement imposed specific selection criteria, including thick tissue biotype and an intact facial socket wall, to reduce esthetic risk. There were no specific selection criteria for early (type 2 and type 3) implant placement. ::: ::: ::: CONCLUSIONS ::: Acceptable esthetic outcomes may be achieved with implants placed after extraction of teeth in the maxillary anterior and premolar areas of the dentition. Recession of the midfacial mucosa is a risk with immediate (type 1) placement. Further research is needed to investigate the most suitable biomaterials to reconstruct the facial bone and the relationship between long-term mucosal stability and presence/absence of the facial bone, the thickness of the facial bone, and the position of the facial bone crest.


title: An Image Multiresolution Representation for Lossless and Lossy Compression

abstract: We propose a new image multiresolution transform that is suited for both lossless (reversible) and lossy compression. The new transformation is similar to the subband decomposition, but can be computed with only integer addition and bit-shift operations. During its calculation, the number of bits required to represent the transformed image is kept small through careful scaling and truncations. Numerical results show that the entropy obtained with the new transform is smaller than that obtained with predictive coding of similar complexity. In addition, we propose entropy-coding methods that exploit the multiresolution structure, and can efficiently compress the transformed image for progressive transmission (up to exact recovery). The lossless compression ratios are among the best in the literature, and simultaneously the rate versus distortion performance is comparable to those of the most efficient lossy compression methods.


title: A Deep Learning Approach to Structured Signal Recovery

abstract: In this paper, we develop a new framework for sensing and recovering structured signals. In contrast to compressive sensing (CS) systems that employ linear measurements, sparse representations, and computationally complex convex/greedy algorithms, we introduce a deep learning framework that supports both linear and mildly nonlinear measurements, that learns a structured representation from training data, and that efficiently computes a signal estimate. In particular, we apply a stacked denoising autoencoder (SDA), as an unsupervised feature learner. SDA enables us to capture statistical dependencies between the different elements of certain signals and improve signal recovery performance as compared to the CS approach.


title: Compressive Spectrum Sensing for Cognitive Radio Networks

abstract: A cognitive radio system has the ability to observe and learn from the environment, adapt to the environmental conditions, and use the radio spectrum more efficiently. It allows secondary users (SUs) to use the primary users (PUs) channels when they are not being utilized. Cognitive radio involves three main processes: spectrum sensing, deciding, and acting. In the spectrum sensing process, the channel occupancy is measured with spectrum sensing techniques in order to detect unused channels. In the deciding process, sensing results are analyzed and decisions are made based on these results. In the acting process, actions are made by adjusting the transmission parameters to enhance the cognitive radio performance. One of the main challenges of cognitive radio is the wideband spectrum sensing. Existing spectrum sensing techniques are based on a set of observations sampled by an ADC at the Nyquist rate. However, those techniques can sense only one channel at a time because of the hardware limitations on the sampling rate. In addition, in order to sense a wideband spectrum, the wideband is divided into narrow bands or multiple frequency bands. SUs have to sense each band using multiple RF frontends simultaneously, which can result in a very high processing time, hardware cost, and computational complexity. In order to overcome this problem, the signal sampling should be as fast as possible even with high dimensional signals. Compressive sensing has been proposed as a low-cost solution to reduce the processing time and accelerate the scanning process. It allows reducing the number of samples required for high dimensional signal acquisition while keeping the essential information.


title: Joint Hybrid Backhaul and Access Links Design in Cloud-Radio Access Networks

abstract: The cloud-radio access network (CRAN) is expected to be the core network architecture for next generation mobile radio system. In this paper, we consider the downlink of a CRAN formed of one central processor (the cloud) and several base-station (BS), where each BS is connected to the cloud via either a wireless or capacity-limited wireline backhaul link. The paper addresses the joint design of the hybrid backhaul links (i.e., designing the wireline and wireless backhaul connections from cloud to BSs) and the access links (i.e., determining the sparse beamforming solution from the BSs to the users). The paper formulates the hybrid backhaul and access link design problem by minimizing the total network power consumption. The paper solves the problem using a two-stage heuristic algorithm. At one stage, the sparse beamforming solution is found using a weighted mixed $\ell_1/\ell_2$ norm minimization approach; the correlation matrix of the quantization noise of the wireline backhaul links is computed using the classical rate-distortion theory. At the second stage, the transmit powers of the wireless backhaul links are found by solving a power minimization problem subject to quality-of-service constraints, based on the principle of conservation of rate by utilizing the rates found in the first stage. Simulation results suggest that the performance of the proposed algorithm approaches the global optimum solution, especially at high SINR.


title: Joint Headlight Pairing and Vehicle Tracking by Weighted Set Packing in Nighttime Traffic Videos

abstract: We propose a set packing (SP) framework for joint headlight pairing and vehicle tracking. Given headlight detections, traditional nighttime vehicle tracking methods usually first pair headlights and then track these pairs. However, the poor photometric condition often introduces tremendous noises in headlight detection and pairing, which leads to unrecoverable errors for vehicle tracking. To overcome the challenge, we propose to jointly model these two tasks in a weighted SP framework. Specifically, a graph is built which takes candidate pair track hypotheses as nodes and encodes in edges both the disjoint constraints for tracking and the no-sharing-headlight constraints for pairing. Solving a weighted SP problem on such a graph produces vehicle trajectories, and facilitates pairing with temporal context and in turn produces high quality vehicle trajectories. The solution, however, raises the issue of unmanageable graph scale since the number of track hypotheses grows exponentially over time. To address this issue, pruning strategies are developed to solve the joint model efficiently. The proposed system is evaluated on two traffic data sets, including videos under various challenging conditions. Both quantitative and qualitative results show that our method outperforms other tested methods, both in nighttime vehicle tracking and in multi-target tracking, confirming the benefits of jointly modeling the two tasks.


title: A Class of Parallel Tiled Linear Algebra Algorithms for Multicore Architectures

abstract: As multicore systems continue to gain ground in the high performance computing world, linear algebra algorithms have to be reformulated or new algorithms have to be developed in order to take advantage of the architectural features on these new processors. Fine grain parallelism becomes a major requirement and introduces the necessity of loose synchronization in the parallel execution of an operation. This paper presents algorithms for the Cholesky, LU and QR factorization where the operations can be represented as a sequence of small tasks that operate on square blocks of data. These tasks can be dynamically scheduled for execution based on the dependencies among them and on the availability of computational resources. This may result in out of order execution of tasks which will completely hide the presence of intrinsically sequential tasks in the factorization. Performance comparisons are presented with LAPACK algorithms where parallelism can only be exploited at the level of the BLAS operations and vendor implementations.


Post-ICU Syndrome and the Post-ICU Clinic


title: Scale Invariant Feature Transform with Irregular Orientation Histogram Binning

abstract: The SIFT (Scale Invariant Feature Transform) descriptor is a widely used method for matching image features. However, perfect scale invariance can not be achieved in practice because of sampling artefacts, noise in the image data, and the fact that the computational effort limits the number of analyzed scale space images. In this paper we propose a modification of the descriptor's regular grid of orientation histogram bins to an irregular grid. The irregular grid approach reduces the negative effect of scale error and significantly increases the matching precision for image features. Results with a standard data set are presented that show that the irregular grid approach outperforms the original SIFT descriptor and other state-of-the-art extentions.


title: Leveraging the Wisdom of the Crowd for Fine-Grained Recognition

abstract: Fine-grained recognition concerns categorization at sub-ordinate levels, where the distinction between object classes is highly local. Compared to basic level recognition, fine-grained categorization can be more challenging as there are in general less data and fewer discriminative features. This necessitates the use of a stronger prior for feature selection. In this work, we include humans in the loop to help computers select discriminative features. We introduce a novel online game called “Bubbles” that reveals discriminative features humans use. The player's goal is to identify the category of a heavily blurred image. During the game, the player can choose to reveal full details of circular regions (“bubbles”), with a certain penalty. With proper setup the game generates discriminative bubbles with assured quality. We next propose the “BubbleBank” representation that uses the human selected bubbles to improve machine recognition performance. Finally, we demonstrate how to extend BubbleBank to a view-invariant 3D representation. Experiments demonstrate that our approach yields large improvements over the previous state of the art on challenging benchmarks.


title: MARS SMART LANDER SIMULATIONS FOR ENTRY , DESCENT , AND LANDING

abstract: Two primary simulations have been developed and are being updated for the Mars Smart Lander Entry, Descent, and Landing (EDL). The high fidelity engineering end-to-end EDL simulation that is based on NASA Langley’s Program to Optimize Simulated Trajectories (POST) and the end-to-end real-time, hardware-in-the-loop simulation test bed, which is based on NASA JPL’s Dynamics Simulator for Entry, Descent and Surface landing (DSENDS). This paper presents the status of these Mars Smart Lander EDL end-to-end simulations at this time. Various models, capabilities, as well as validation and verification for these simulations are discussed. NOMENCLATURE


title: A miniature conformal spiral antenna using inductive and dielectric loading

abstract: In this paper we introduce a new concept for a miniature conformal antenna combines inductive and dielectric material loading for size reduction.


title: Automated Accident Alert

abstract: The rapid growth of technology and infrastructure has made our lives easier. The advent of technology has also increased the traffic hazards and the road accidents take place frequently which causes huge loss of life and property because of the poor emergency facilities. Our paper will provide an optimum solution to this drawback. According to the paper when an accident occurs the sensor will detect the values and send to the controller. Once the threshold crosses it will send the alert message to control station or rescue team via ZigBee module. So the rescue team(ambulance vehicle) can immediately trace the location necessary action will be taken. If the person inside the vehicle is not injured or no threats to any one's life, there is provision to terminate the alert message by pressing a switch. So that the valuable time of the rescue team can be saved. This paper proposes a system that ensures immediate medical service to a person who meets with the accident without an human aid and within a minimum time.


title: A Path Loss Simulator for the 3GPP 5G Channel Models

abstract: This paper presents a web based simulator for large-scale path loss models, according to the 3GPP TR 38.901 report, for 5G network frequencies. In order to implement the simulator was necessary to analyze the model presented in the TR 38.901. The necessary tools were chosen to program this simulator. The parameters necessary for the calculation are specified in the TR 38.901 according to the selected scenario. The simulator allows choosing between the four scenarios contemplated in the 3GPP report, and at the same time, it requests the parameters corresponding to the scenario. As a result, it returns the path loss value, a tabulation and a graph that explain the behavior of the path loss for a range previously specified by the user. The simulator was tested and analyzed using Monte Carlo simulations. This simulator can be used for mobile service provider and for the training and teaching.


title: DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning

abstract: Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require time-consuming, error-prone calculations using static variable thresholds. These methods do not capitalize on the emerging availability of streaming electronic health record data or capture time-sensitive individual physiological patterns, a critical task in the intensive care unit. We propose a novel acuity score framework (DeepSOFA) that leverages temporal measurements and interpretable deep learning models to assess illness severity at any point during an ICU stay. We compare DeepSOFA with SOFA (Sequential Organ Failure Assessment) baseline models using the same model inputs and find that at any point during an ICU admission, DeepSOFA yields significantly more accurate predictions of in-hospital mortality. A DeepSOFA model developed in a public database and validated in a single institutional cohort had a mean AUC for the entire ICU stay of 0.90 (95% CI 0.90–0.91) compared with baseline SOFA models with mean AUC 0.79 (95% CI 0.79–0.80) and 0.85 (95% CI 0.85–0.86). Deep models are well-suited to identify ICU patients in need of life-saving interventions prior to the occurrence of an unexpected adverse event and inform shared decision-making processes among patients, providers, and families regarding goals of care and optimal resource utilization.


title: Significant Change Spotting for Periodic Human Motion Segmentation of Cleaning Tasks Using Wearable Sensors

abstract: The proportion of the aging population is rapidly increasing around the world, which will cause stress on society and healthcare systems. In recent years, advances in technology have created new opportunities for automatic activities of daily living (ADL) monitoring to improve the quality of life and provide adequate medical service for the elderly. Such automatic ADL monitoring requires reliable ADL information on a fine-grained level, especially for the status of interaction between body gestures and the environment in the real-world. In this work, we propose a significant change spotting mechanism for periodic human motion segmentation during cleaning task performance. A novel approach is proposed based on the search for a significant change of gestures, which can manage critical technical issues in activity recognition, such as continuous data segmentation, individual variance, and category ambiguity. Three typical machine learning classification algorithms are utilized for the identification of the significant change candidate, including a Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Naive Bayesian (NB) algorithm. Overall, the proposed approach achieves 96.41% in the F1-score by using the SVM classifier. The results show that the proposed approach can fulfill the requirement of fine-grained human motion segmentation for automatic ADL monitoring.


title: 3D Finger CAPE: Clicking Action and Position Estimation under Self-Occlusions in Egocentric Viewpoint

abstract: In this paper we present a novel framework for simultaneous detection of click action and estimation of occluded fingertip positions from egocentric viewed single-depth image sequences. For the detection and estimation, a novel probabilistic inference based on knowledge priors of clicking motion and clicked position is presented. Based on the detection and estimation results, we were able to achieve a fine resolution level of a bare hand-based interaction with virtual objects in egocentric viewpoint. Our contributions include: (i) a rotation and translation invariant finger clicking action and position estimation using the combination of 2D image-based fingertip detection with 3D hand posture estimation in egocentric viewpoint. (ii) a novel spatio-temporal random forest, which performs the detection and estimation efficiently in a single framework. We also present (iii) a selection process utilizing the proposed clicking action detection and position estimation in an arm reachable AR/VR space, which does not require any additional device. Experimental results show that the proposed method delivers promising performance under frequent self-occlusions in the process of selecting objects in AR/VR space whilst wearing an egocentric-depth camera-attached HMD.


title: Mechanisms for database intrusion detection and response

abstract: Data represent today a valuable asset for companies and organizations and must be protected. Most of an organization's sensitive and proprietary data resides in a Database Management System (DBMS). The focus of this thesis is to develop advanced security solutions for protecting the data residing in a DBMS. Our strategy is to develop an Intrusion Detection (ID) mechanism, implemented within the database server, that is capable of detecting anomalous user requests to a DBMS. The key idea is to learn profiles of users and applications interacting with a database. A database request that deviates from these profiles is then termed as anomalous. A major component of this work involves prototype implementation of this ID mechanism in the Post-greSQL database server. We also propose to augment the ID mechanism with an Intrusion Response engine that is capable of issuing an appropriate response to an anomalous database request.


title: Making sense of complex data using interactive data visualization

abstract: Abstract An important role for accountants today is to provide decision support to senior management by assisting them in the analysis of large, complex data sets. Interactive data visualization (IDV) facilitates this process by allowing users to navigate, select, and display data via an easy-to-use interface often used as a component of data analytics. Given the increasing popularity of IDV as a tool for making sense of complex data, it is important that accountants become familiar with and learn how to use this technology. This case provides a hands-on opportunity to organize complex accounting data to create IDVs for decision makers to use. Further, the case enables students to understand the potential impact of IDVs on preparers and users of accounting information. Students will assume the role of a division controller in a hypothetical company and create an IDV to assist the chief executive officer (CEO) in decision making.


title: A Practical Animal Detection and Collision Avoidance System Using Computer Vision Technique

abstract: One serious problem that all the developed nations are facing today is death and injuries due to road accidents. The collision of an animal with the vehicle on the highway is one such big issue, which leads to such road accidents. In this paper, a simple and a low-cost approach for automatic animal detection on highways for preventing animal-vehicle collision using computer vision techniques are proposed. A method for finding the distance of the animal in real-world units from the camera mounted vehicle is also proposed. The proposed system is trained on more than 2200 images consisting of positive and negatives images and tested on various video clips of animals on highways with varying vehicle speed. As per the two-second rule, our proposed method can alert the driver when the vehicle speed is up to 35 km/h. Beyond this speed, though the animal gets detected correctly, the driver does not get enough time to prevent a collision. An overall accuracy of almost 82.5% is achieved regarding detection using our proposed method.


title: Automatic bladder segmentation from CT images using deep CNN and 3D fully connected CRF-RNN

abstract: Automatic approach for bladder segmentation from computed tomography (CT) images is highly desirable in clinical practice. It is a challenging task since the bladder usually suffers large variations of appearance and low soft-tissue contrast in CT images. In this study, we present a deep learning-based approach which involves a convolutional neural network (CNN) and a 3D fully connected conditional random fields recurrent neural network (CRF-RNN) to perform accurate bladder segmentation. We also propose a novel preprocessing method, called dual-channel preprocessing, to further advance the segmentation performance of our approach. The presented approach works as following: first, we apply our proposed preprocessing method on the input CT image and obtain a dual-channel image which consists of the CT image and an enhanced bladder density map. Second, we exploit a CNN to predict a coarse voxel-wise bladder score map on this dual-channel image. Finally, a 3D fully connected CRF-RNN refines the coarse bladder score map and produce final fine-localized segmentation result. We compare our approach to the state-of-the-art V-net on a clinical dataset. Results show that our approach achieves superior segmentation accuracy, outperforming the V-net by a significant margin. The Dice Similarity Coefficient of our approach (92.24%) is 8.12% higher than that of the V-net. Moreover, the bladder probability maps performed by our approach present sharper boundaries and more accurate localizations compared with that of the V-net. Our approach achieves higher segmentation accuracy than the state-of-the-art method on clinical data. Both the dual-channel processing and the 3D fully connected CRF-RNN contribute to this improvement. The united deep network composed of the CNN and 3D CRF-RNN also outperforms a system where the CRF model acts as a post-processing method disconnected from the CNN.


title: Comparative analysis of single and three-phase dual active bridge bidirectional DC-DC converters

abstract: Many modern power conversion systems require a bi-directional energy transfer capability as a central part of their system operation. Preferably, such systems should use a single high efficiency power electronic conversion system to reduce size, weight and cost. For higher power bi-directional conversion, the common topology proposed is a dual active bridge structure, where two DC-AC converters are coupled back-to-back through an AC inductor/transformer. Either single-phase or three-phase converters have been proposed for such a system, but to date no clear-cut basis for selecting between these two alternatives has been established. This paper presents a comparative evaluation of single and three-phase back-to-back converters for use in a bi-directional power transfer application. The paper considers these two alternatives from the perspective of operating performance and losses, and provides a basis for selecting between the two topologies in any particular operating context.


title: Extending the roofline model: Bottleneck analysis with microarchitectural constraints

abstract: Software, even if carefully optimized, rarely reaches the peak performance of a processor. Understanding which hardware resource is the bottleneck is difficult but important as it can help with both further optimizing the code or deciding which hardware component to upgrade for higher performance. If the bottleneck is the memory bandwidth, the roofline model provides a simple but instructive analysis and visualization. In this paper, we take the roofline analysis further by including additional performance-relevant hardware features such as latency, throughput, capacity information for a multilevel cache hierarchy and out-of-order execution buffers. Two key ideas underlie our analysis. First, we estimate performance based on a scheduling of the computation DAG on a high-level model of a microarchitecture and extract data including utilization of resources and overlaps from a cycle-by-cycle analysis of the schedule. Second, we show how to use this data to create only one plot with multiple rooflines that visualize performance bottlenecks. We validate our model against performance data obtained from a real system, and then apply our bottleneck analysis to a number of floating-point kernels to identify and interpret bottlenecks.


title: Librarians’ Role in Development and Achievement of Central Library Users’ Information Literacy (a Case Study: Iran)

abstract: Introduction: Due to the development of technologies, communications, databases and information resource varieties in today’s information age, our various social, economic, cultural and political needs cannot be fulfilled by relying merely on past knowledge and skills as done previously. Information literacy (IL) as a set of necessary skills for all of us is an effective way of treating new technologies and their effective application in our lives. The study aimed to survey the library users’ views in the Central Library of Babol University of Medical Sciences (The Library), Iran, on the role and influences of librarians on their IL development and improvement. Methods: This analytical survey used a researcher-made questionnaire for data collection. Research population consisted of all users referring Information Unit of The Library during 22 September - 20 December 2010. Of them, 150 users participating in at least 5 workshops held by The Library were selected as the study sample. Results: Based on the findings, 52.7 percent of the subjects rated the influence of the librarians on their IL development much and very much. 44.7 percent claimed that they more acquired IL skills from librarians rather than others. 100 (63.3%) subjects preferred workshops held by the librarian to other workshops. Conclusion: The users of Information Unit of The Library perceived the training IL skills by librarians as a main influencing factor in their IL development and achievement. This emphasized the necessity of teaching IL to users and training the librarians in better teaching IL skills to library user


Private Collaborative Neural Network Learning


title: A case study of a five-year-old child with pervasive developmental disorder-not otherwise specified using sound-based interventions

abstract: The aim of this study was to determine the effi cacy of The Listening Program (TLP) in treating a child with pervasive developmental disorder-not other- wise specifi ed (PDD-NOS). Using a single-subject case study design, one child with PDD-NOS was administered a 20-week TLP intervention focused on improving sensory processing and language function. Data collection included pre- and post- evaluations using video footage, and Sensory Profi le and Listening Checklist ques- tionnaires. Results of the study indicated improved behaviour and sensory tolerance in the post-intervention video footage, including active participation in singing and movements to song. Sensory Profi le and Listening Checklist questionnaires indicated signifi cant improvements in sensory processing, receptive/expressive listening and language, motor skills, and behavioural/social adjustment at the post-intervention assessment. Although small in scope, this study highlights the need for continued research by occupational therapists into sound-based interventions. Particularly, occupational therapists need to perform larger-scale studies utilizing TLP to verify the effi cacy of this alternative treatment method. Copyright © 2009 John Wiley & Sons, Ltd.


Word Embeddings based on Fixed-Size Ordinally Forgetting Encoding


title: Unsupervised feature learning for 3D scene labeling

abstract: This paper presents an approach for labeling objects in 3D scenes. We introduce HMP3D, a hierarchical sparse coding technique for learning features from 3D point cloud data. HMP3D classifiers are trained using a synthetic dataset of virtual scenes generated using CAD models from an online database. Our scene labeling system combines features learned from raw RGB-D images and 3D point clouds directly, without any hand-designed features, to assign an object label to every 3D point in the scene. Experiments on the RGB-D Scenes Dataset v.2 demonstrate that the proposed approach can be used to label indoor scenes containing both small tabletop objects and large furniture pieces.


title: A Large-Scale Active Measurement Study on the Effectiveness of Piece-Attack on BitTorrent Networks

abstract: The peer to peer (P2P) file sharing applications have allocated a significant amount of today's Internet traffic. Among various P2P file sharing protocols, BitTorrent is the most common and popular one that attracts monthly a quarter of a billion users from all over the world. Similar to other P2P file sharing protocols, BitTorrent is mostly used for illegal sharing of copyright protected files such as movies, music and TV series. To impede this huge amount of illegal file distributions, anti-P2P companies have arisen to stand against these applications (specially the BitTorrent). To this end, they have begun to fire large-scale Internet attacks against BitTorrent networks. In this paper, we are going to actively measure the impact of the piece-attack against BitTorrent networks. Our measurement is divided into five scenarios in order to figure out the constraint factors that influence the success of the attack. To be able to evaluate the attack in different experiments, we defined attack effectiveness to quantitatively verify the success of the attack. Based on the measurement results, we discovered how it is possible to achieve significant outcome with modest amount of resources used by the attackers in hampering the illegal distribution of files in BitTorrent networks.


title: Panel and Speech Balloon Extraction from Comic Books

abstract: Comic books represent an important cultural heritage in many countries. However, few researches have been done in order to analyse the content of comics such as panels, speech balloons or characters. At first glance, the structure of a comic page may appear easy to determine. In practice, the configuration of the page, the size and the shape of the panels can be different from one page to the next. Moreover, authors often draw extended contents (speech balloon or comic art) that overlap two panels or more. In some situations, the panel extraction can become a real challenge. Speech balloons are other important elements of comics. Full text indexing is only possible if the text can be extracted. However the text is usually embedded among graphic elements. Moreover, unlike newspapers, the text layout in speech balloons can be irregular. Classic text extraction method can fail. We propose, in this paper, a method based on region growing and mathematical morphology to extract automatically the panels of a comic page and a method to detect speech balloons. Our approach is compared with other methods find in the literature. Results are presented and discussed.


title: Radar interferogram filtering for geophysical applications

abstract: The use of SAR interferometry is often impeded by decorrelation from thermal noise, temporal change, and baseline geometry. Power spectra of interferograms are typically the sum of a narrow-band component combined with broad-band noise. We describe a new adaptive filtering algorithm that dramatically lowers phase noise, improving both measurement accuracy and phase unwrapping, while demonstrating graceful degradation in regions of pure noise. The performance of the filter is demonstrated with SAR data from the ERS satellites over the Jakobshavns glacier of Greenland.


Pedestrian behavior analysis using 110-car naturalistic driving data in USA


title: An Assessment of Systems and Software Engineering Scholars and Institutions (2003-2007 and 2004-2008)

abstract: This paper presents the findings of a five-year study of the top scholars and institutions in the Systems and Software Engineering field, as measured by the quantity of papers published in the journals of the field in 2000-2004. The top scholar is Hai Zhuge of the Chinese Academy of Sciences, and the top institution is Korea Advanced Institute of Science and Technology. This paper is part of an ongoing study, conducted annually, that identifies the top 15 scholars and institutions in the most recent five-year period.


States in Armed Conflict 1998


Parallelizing Clique and Quasi-Clique Detection over Graph Data


title: The Unified Logging Infrastructure for Data Analytics at Twitter

abstract: In recent years, there has been a substantial amount of work on large-scale data analytics using Hadoop-based platforms running on large clusters of commodity machines. A less-explored topic is how those data, dominated by application logs, are collected and structured to begin with. In this paper, we present Twitter's production logging infrastructure and its evolution from application-specific logging to a unified "client events" log format, where messages are captured in common, well-formatted, flexible Thrift messages. Since most analytics tasks consider the user session as the basic unit of analysis, we pre-materialize "session sequences", which are compact summaries that can answer a large class of common queries quickly. The development of this infrastructure has streamlined log collection and data analysis, thereby improving our ability to rapidly experiment and iterate on various aspects of the service.


title: Tricorder: building a program analysis ecosystem

abstract: Static analysis tools help developers find bugs, improve code readability, and ensure consistent style across a project. However, these tools can be difficult to smoothly integrate with each other and into the developer workflow, particularly when scaling to large codebases. We present T ricorder , a program analysis platform aimed at building a data-driven ecosystem around program analysis. We present a set of guiding principles for our program analysis tools and a scalable architecture for an analysis platform implementing these principles. We include an empirical, in-situ evaluation of the tool as it is used by developers across Google that shows the usefulness and impact of the platform.


title: A Deep Learning Approach to IoT Authentication

abstract: At its peak, the Internet-of-Things will largely be composed of low-power devices with wireless radios attached. Yet, secure authentication of these devices amidst adversaries with much higher power and computational capability remains a challenge, even for advanced cryptographic and wireless security protocols. For instance, a high-power software radio could simply replay chunks of signals from a low-power device to emulate it. This paper presents a deep-learning classifier that learns hardware imperfections of low-power radios that are challenging to emulate, even for high- power adversaries. We build an LSTM framework, specifically sensitive to signal imperfections that persist over long durations. Experimental results from a testbed of 30 low-power nodes demonstrate high resilience to advanced software radio adversaries.


title: Design of advanced waveguide filters for passive intermodulation measurement setups

abstract: This paper addresses the design and development of advanced waveguide filters for passive intermodulation (PIM) measurement set-ups. After describing the severe specifications that such filters must met, some key guidelines to choose a particular filter configuration, response and layout are discussed. The design procedure is also briefly described. A design example, which is included in a manufactured waveguide multiplexer for measuring rejected PIM, is also shown.


Building an End-to-End Spatial-Temporal Convolutional Network for Video Super-Resolution.


title: ScriptGen: an automated script generation tool for Honeyd

abstract: Honeyd (N. Provos, 2004) is a popular tool developed by Niels Provos that offers a simple way to emulate services offered by several machines on a single PC. It is a so called low interaction honeypot. Responses to incoming requests are generated thanks to ad hoc scripts that need to be written by hand. As a result, few scripts exist, especially for services handling proprietary protocols. In this paper, we propose a method to alleviate these problems by automatically generating new scripts. We explain the method and describe its limitations. We analyze the quality of the generated scripts thanks to two different methods. On the one hand, we have launched known attacks against a machine running our scripts; on the other hand, we have deployed that machine on the Internet, next to a high interaction honeypot during two months. For those attackers that have targeted both machines, we can verify if our scripts have, or not, been able to fool them. We also discuss the various tuning parameters of the algorithm that can be set to either increase the quality of the script or, at the contrary, to reduce its complexity


title: G2o: A general framework for graph optimization

abstract: Many popular problems in robotics and computer vision including various types of simultaneous localization and mapping (SLAM) or bundle adjustment (BA) can be phrased as least squares optimization of an error function that can be represented by a graph. This paper describes the general structure of such problems and presents g2o, an open-source C++ framework for optimizing graph-based nonlinear error functions. Our system has been designed to be easily extensible to a wide range of problems and a new problem typically can be specified in a few lines of code. The current implementation provides solutions to several variants of SLAM and BA. We provide evaluations on a wide range of real-world and simulated datasets. The results demonstrate that while being general g2o offers a performance comparable to implementations of state-of-the-art approaches for the specific problems.


title: Visualization of vessel movements

abstract: We propose a geographical visualization to support operators of coastal surveillance systems and decision making analysts to get insights in vessel movements. For a possibly unknown area, they want to know where significant maritime areas, like highways and anchoring zones, are located. We show these features as an overlay on a map. As source data we use AIS data: Many vessels are currently equipped with advanced GPS devices that frequently sample the state of the vessels and broadcast them. Our visualization is based on density fields that are derived from convolution of the dynamic vessel positions with a kernel. The density fields are shown as illuminated height maps. Combination of two fields, with a large and small kernel provides overview and detail. A large kernel provides an overview of area usage revealing vessel highways. Details of speed variations of individual vessels are shown with a small kernel, highlighting anchoring zones where multiple vessels stop. Besides for maritime applications we expect that this approach is useful for the visualization of moving object data in general.


title: Data mining models for student careers

abstract: We presents a data mining methodology to analyze the careers of University graduated students.We present different approaches based on clustering and sequential patterns techniques.We introduce the concept of ideal career.We compare the career of a generic student with the ideal one.We apply the methodology to a real case study and interpret the results. This paper presents a data mining methodology to analyze the careers of University graduated students. We present different approaches based on clustering and sequential patterns techniques in order to identify strategies for improving the performance of students and the scheduling of exams. We introduce an ideal career as the career of an ideal student which has taken each examination just after the end of the corresponding course, without delays. We then compare the career of a generic student with the ideal one by using the different techniques just introduced. Finally, we apply the methodology to a real case study and interpret the results which underline that the more students follow the order given by the ideal career the more they get good performance in terms of graduation time and final grade.


title: Spatial clustering in the presence of obstacles

abstract: Clustering in spatial data mining is to group similar objects based on their distance, connectivity, or their relative density in space. In the real world there exist many physical obstacles such as rivers, lakes and highways, and their presence may affect the result of clustering substantially. We study the problem of clustering in the presence of obstacles and define it as a COD (Clustering with Obstructed Distance) problem. As a solution to this problem, we propose a scalable clustering algorithm, called COD-CLARANS. We discuss various forms of pre-processed information that could enhance the efficiency of COD-CLARANS. In the strictest sense, the COD problem can be treated as a change in distance function and thus could be handled by current clustering algorithms by changing the distance function. However, we show that by pushing the task of handling obstacles into COD-CLARANS instead of abstracting it at the distance function level, more optimization can be done in the form of a pruning function E'. We conduct various performance studies to show that COD-CLARANS is both efficient and effective.


1 Marine anthropogenic litter on British beaches : a 10-year nationwide assessment using citizen science


title: Credit decision tool using mobile application data for microfinance in agriculture

abstract: Smallholder agriculture is vital for food security and poverty reduction. Poor, unbanked smallholder farmers face challenges in obtaining loans for agriculture due to missing financial credit history. Although credit decisions tools with alternatives such as big data from mobile phones currently exist, these are mainly targeted towards consumer credit solutions. There is need for research focused on applying such data to credit decisions in agriculture, considering the unique challenges of the field. This research proposes a credit decision tool using alternative data from a mobile phone application used for reporting agricultural activities. 213 users submitted 41613 reports of farm activity by 11336 farmers over a 5-year period. Users were classified groups based on personal information, association with farmers, and reports. Three methods are applied for analysis: multiple logistic regression, a support vector machine with a linear kernel, and a support vector machine with a radial basis function kernel. Three sets of features, selected with different methods, are used. The results are evaluated by accuracy, and Area Under the Receiver operating curve (AUC). The support vector machine with a radial basis function kernel shows the best performance on the original data set with an Area Under the Receiver operating curve (AUC) value of 0.983. Feasibility of applying each method in agricultural microfinance is considered. Finally, further development of the models using Geographic Information System analysis of geospatial data which affects agricultural productivity, such as soil type and water resources, is suggested to incorporate the ability of the borrower to repay loans from farm activity.