title: Implicit authentication for smartphone security

abstract: Common authentication methods based on passwords, or fingerprints in smartphones, depend on user participation. They do not protect against the threat of an attacker getting hold of the phone after the user has been authenticated. Using a victim’s smartphone, the attacker can launch impersonation attacks, which threaten the data that can be accessed from the smartphone and also the security of other users in the network. In this paper, we propose an implicit authentication method using the sensors already built into smartphones. We utilize machine learning algorithms for smartphones to continuously and implicitly authenticate the current user. We compare two typical machine learning methods, SVM and KRR, for authenticating the user. We show that our method achieves high performance (more than 90 % authentication accuracy) and high efficiency. Our method needs less than 10 s to train the model and 20 s to detect an abnormal user. We also show that the combination of more sensors provides better accuracy. Furthermore, our method enables adjusting the security level by changing the sampling rate.


title: Word Embeddings vs Word Types for Sequence Labeling: the Curious Case of CV Parsing

abstract: We explore new methods of improving Curriculum Vitae (CV) parsing for German documents by applying recent research on the application of word embeddings in Natural Language Processing (NLP). Our approach integrates the word embeddings as input features for a probabilistic sequence labeling model that relies on the Conditional Random Field (CRF) framework. Best-performing word embeddings are generated from a large sample of German CVs. The best results on the extraction task are obtained by the model which integrates the word embeddings together with a number of hand-crafted features. The improvements are consistent throughout different sections of the target documents. The effect of the word embeddings is strongest on semi-structured, out-of-sample data.


title: Tappan Zee (north) bridge: mining memory accesses for introspection

abstract: The ability to introspect into the behavior of software at runtime is crucial for many security-related tasks, such as virtual machine-based intrusion detection and low-artifact malware analysis. Although some progress has been made in this task by automatically creating programs that can passively retrieve kernel-level information, two key challenges remain. First, it is currently difficult to extract useful information from user-level applications, such as web browsers. Second, discovering points within the OS and applications to hook for active monitoring is still an entirely manual process. In this paper we propose a set of techniques to mine the memory accesses made by an operating system and its applications to locate useful places to deploy active monitoring, which we call tap points. We demonstrate the efficacy of our techniques by finding tap points for useful introspection tasks such as finding SSL keys and monitoring web browser activity on five different operating systems (Windows 7, Linux, FreeBSD, Minix and Haiku) and two processor architectures (ARM and x86).


title: Mining the impact of object oriented metrics for change prediction using Machine Learning and Search-based techniques

abstract: Change in a software is crucial to incorporate defect correction and continuous evolution of requirements and technology. Thus, development of quality models to predict the change proneness attribute of a software is important to effectively utilize and plan the finite resources during maintenance and testing phase of a software. In the current scenario, a variety of techniques like the statistical techniques, the Machine Learning (ML) techniques and the Search-based techniques (SBT) are available to develop models to predict software quality attributes. In this work, we assess the performance of ten machine learning and search-based techniques using data collected from three open source software. We first develop a change prediction model using one data set and then we perform inter-project validation using two other data sets in order to obtain unbiased and generalized results. The results of the study indicate comparable performance of SBT with other employed statistical and ML techniques. This study also supports inter project validation as we successfully applied the model created using the training data of one project on other similar projects and yield good results.


title: AMC: an adaptive multi-level cache algorithm in hybrid storage systems

abstract: Summary ::: Hybrid storage systems that consist of flash-based solid state drives (SSDs) and traditional disks are now widely used. In hybrid storage systems, there exists a two-level cache hierarchy that regard dynamic random access memory (DRAM) as the first level cache and SSD as the second level cache for disk storage. However, this two-level cache hierarchy typically uses independent cache replacement policies for each level, which makes cache resource management inefficient and reduces system performance. In this paper, we propose a novel adaptive multi-level cache (AMC) replacement algorithm in hybrid storage systems. The AMC algorithm adaptively adjusts cache blocks between DRAM and SSD cache levels using an integrated solution. AMC uses combined selective promote and demote operations to dynamically determine the level in which the blocks are to be cached. In this manner, the AMC algorithm achieves multi-level cache exclusiveness and makes cache resource management more efficient. By using real-life storage traces, our evaluation shows the proposed algorithm improves hybrid multi-level cache performance and also increases the SSD lifetime compared with traditional multi-level cache replacement algorithms. Copyright © 2015 John Wiley & Sons, Ltd.


title: Residual Connections Encourage Iterative Inference

abstract: Residual networks (Resnets) have become a prominent architecture in deep learning. However, a comprehensive understanding of Resnets is still a topic of ongoing research. ::: A recent view argues that Resnets perform iterative refinement of features. We attempt to further expose properties of this aspect. To this end, we study Resnets both analytically and empirically. We formalize the notion of iterative refinement in Resnets by showing that residual connections naturally encourage features of residual blocks to move along the negative gradient of loss as we go from one block to the next. In addition, our empirical analysis suggests that Resnets are able to perform both representation learning and iterative refinement. In general, a Resnet block tends to concentrate representation learning behavior in the first few layers while higher layers perform iterative refinement of features. Finally we observe that sharing residual layers naively leads to representation explosion and counterintuitively, overfitting, and we show that simple existing strategies can help alleviating this problem.


title: Distributed Cooperative Control and Stability Analysis of Multiple DC Electric Springs in a DC Microgrid

abstract: Recently, dc electric springs (dc-ESs) have been proposed to realize voltage regulation and power quality improvement in dc microgrids. This paper establishes a distributed cooperative control framework for multiple dc-ESs in a dc microgrid and presents the small-signal stability analysis of the system. The primary level implements a droop control to coordinate the operations of multiple dc-ESs. The secondary control is based on a consensus algorithm to regulate the dc-bus voltage reference, incorporating the state-of-charge (SOC) balance among dc-ESs. With the design, the cooperative control can achieve average dc-bus voltage consensus and maintain SOC balance among different dc-ESs using only neighbor-to-neighbor information. Furthermore, a small-signal model of a four dc-ESs system with the primary and secondary controllers is developed. The eigenvalue analysis is presented to show the effect of the communication weight on system stability. Finally, the effectiveness of the proposed control scheme and the small-signal model is verified in an islanded dc microgrid under different scenarios through simulation and experimental studies.


title: Wearable technologies: The role of usefulness and visibility in smartwatch adoption

abstract: Although still in the early stages of diffusion, smartwatches represent the most popular type of wearable devices. Yet, little is known about why some people are more likely to adopt smartwatches than others. To deepen the understanding of underlying factors prompting adoption behavior, the authors develop a theoretical model grounded in technology acceptance and social psychology literatures. Empirical results reveal perceived usefulness and visibility as important factors that drive adoption intention, suggesting that smartwatches represent a type of ‘fashnology’ (i.e., fashion and technology). The magnitude of these antecedents is influenced by an individual's perception of viewing smartwatches as a technology and/or as a fashion accessory. Theoretical and managerial implications are discussed.


title: Development and verification of tendon-driven rotary actuator for haptics with flexible actuators and a PE line

abstract: In the future, robots will be important device widely in our daily lives to achieve complicated tasks. To achieve the tasks, there are some demands for the robots. In this paper, two strong demands of them are taken attention. First one is multiple-degrees of freedom (DOF), and the second one is miniaturization of the robots. Although rotary actuators is necessary to get multiple-DOF, miniaturization is difficult with rotary motors which are usually utilized for multiple-DOF robots. Here, tendon-driven rotary actuator is a candidate to solve the problems of the rotary actuators. The authors proposed a type of tendon-driven rotary actuator using thrust wires. However, big mechanical loss and frictional loss occurred because of the complicated structure of connection points. As the solution for the problems, this paper proposes a tendon-driven rotary actuator for haptics with thrust wires and polyethylene (PE) line. In the proposed rotary actuator, a PE line is used in order to connect the tip points of thrust wires and the end effector. The validity of the proposed rotary actuator is evaluated by experiments.


title: Autospecific gammadelta thymocytes that escape negative selection find sanctuary in the intestine.

abstract: alphabeta or gammadelta thymocytes whose T-cell receptors (TCRs) recognize endogenously expressed antigens (Ag) are autospecific and, thus, potentially self-reactive. In the thymus, such T cells are eliminated during T-cell development through a process known as negative selection. As a model of negative selection of gammadelta T cells, we have used G8 gammadelta-T cell transgenic mice, which express a gammadelta TCR that recognizes the nonpolymorphic MHC class I TL(b) molecule. Here, we demonstrate that negative selection of autospecific gammadelta T cells is almost complete in the adult thymus but is markedly attenuated in the neonatal thymus. A consequence of this attenuated negative selection is that potentially self-reactive gammadelta thymocytes are allowed to escape negative selection, undergo extrathymic differentiation, and find sanctuary in the intestinal epithelium. Interestingly, the ability of these potentially self-reactive gammadelta T cells to find sanctuary requires both the intestinal epithelial environment and the extrathymic presence of the self-Ag. The implications of these findings on the development and persistence of autoreactive T cells in autoimmune disease are discussed.


title: Respiratory Physiology in Pregnancy

abstract: This review discusses respiratory physiologic changes during normal pregnancy. Cardiovascular physiology is also reviewed, given the important interactions between the respiratory and cardiovascular systems in pregnancy. The combination of hormonal changes, mechanical effects of the enlarging uterus, and marked circulatory changes result in significant changes in pulmonary and cardiovascular physiology. These adaptations are necessary to meet the increased metabolic demands of the mother and fetus. It is important for the clinician to be familiar with the normal physiologic changes in pregnancy. Understanding these changes is critical in distinguishing the common dyspnea that occurs during normal pregnancy from pathophysiologic states associated with cardiopulmonary diseases seen in pregnancy, 1 and in anticipating disease worsening in pregnancy and the peripartum period in those women with cardiopulmonary disorders.


title: Semantic Role Labeling: An Introduction to the Special Issue

abstract: Semantic role labeling, the computational identification and labeling of arguments in text, has become a leading task in computational linguistics today. Although the issues for this task have been studied for decades, the availability of large resources and the development of statistical machine learning methods have heightened the amount of effort in this field. This special issue presents selected and representative work in the field. This overview describes linguistic background of the problem, the movement from linguistic theories to computational practice, the major resources that are being used, an overview of steps taken in computational systems, and a description of the key issues and results in semantic role labeling (as revealed in several international evaluations). We assess weaknesses in semantic role labeling and identify important challenges facing the field. Overall, the opportunities and the potential for useful further research in semantic role labeling are considerable.


title: MobiMine: monitoring the stock market from a PDA

abstract: This paper describes an experimental mobile data mining system that allows intelligent monitoring of time-critical financial data from a hand-held PDA. It presents the overall system architecture and the philosophy behind the design. It explores one particular aspect of the system---automated construction of personalized focus area that calls for user's attention. This module works using data mining techniques. The paper describes the data mining component of the system that employs a novel Fourier analysis-based approach to efficiently represent, visualize, and communicate decision trees over limited bandwidth wireless networks. The paper also discusses a quadratic programming-based personalization module that runs on the PDAs and the multi-media based user-interfaces. It reports experimental results using an ad hoc peer-to-peer IEEE 802.11 wireless network.


title: Harmonic Colormaps for Volume Visualization

abstract: Color design forms a crucial part in visual aesthetics, and it has been shown that a visually aesthetic visualization will be looked at more carefully. An important role plays here the choice of a colormap that is composed of harmonic colors. This paper presents an interface that allows users to choose harmonic colors in volume visualization applications. In addition, it describes mechanisms by which non-harmonic colormaps can be converted to harmonic ones, but keeping lightness constant to preserve the original contrast relationships. Finally, we also show how harmonic colors can be used for the highlighting of important volume features.


title: Local Intensity Order Pattern for feature description

abstract: This paper presents a novel method for feature description based on intensity order. Specifically, a Local Intensity Order Pattern(LIOP) is proposed to encode the local ordinal information of each pixel and the overall ordinal information is used to divide the local patch into subregions which are used for accumulating the LIOPs respectively. Therefore, both local and overall intensity ordinal information of the local patch are captured by the proposed LIOP descriptor so as to make it a highly discriminative descriptor. It is shown that the proposed descriptor is not only invariant to monotonic intensity changes and image rotation but also robust to many other geometric and photometric transformations such as viewpoint change, image blur and JEPG compression. The proposed descriptor has been evaluated on the standard Oxford dataset and four additional image pairs with complex illumination changes. The experimental results show that the proposed descriptor obtains a significant improvement over the existing state-of-the-art descriptors.


title: Design of a Ka-band Microstrip quasi-Yagi antenna array based on LTCC substrate

abstract: A Microstrip-fed quasi-Yagi phased array antenna at Ka-band is designed. This linear antenna array is constituted by 8 microstrip patch unit based on low-temperature co-fired-ceramic (LTCC) substrate(FerroA6M), it has several advantages, e.g., strong direction, broad bandwidth, easy-integration with LTCC circuit systems. 3D EM simulator was utilized to analyse the simulation of the antenna array. Simulation results indicate that the proposed antenna has 16dB gain at 35GHz and 2G bandwidth with less than −14 dB return loss, meanwhile, achieves the scan range of ± 25° without grating lobes. The bandwidth of the array antenna are 22.9% when VSWR<2, respectively. There is a very broad application prospect in LTCC-technology wireless communication system on the antenna.


title: A gradient descent rule for spiking neurons emitting multiple spikes

abstract: A supervised learning rule for Spiking Neural Networks (SNNs) is presented that can cope with neurons that spike multiple times. The rule is developed by extending the existing SpikeProp algorithm which could only be used for one spike per neuron. The problem caused by the discontinuity in the spike process is counteracted with a simple but effective rule, which makes the learning process more efficient. Our learning rule is successfully tested on a classification task of Poisson spike trains. We also applied the algorithm on a temporal version of the XOR problem and show that it is possible to learn this classical problem using only one spiking neuron making use of a hair-trigger situation.


title: SparseNet: A Sparse DenseNet for Image Classification

abstract: Deep neural networks have made remarkable progresses on various computer vision tasks. Recent works have shown that depth, width and shortcut connections of networks are all vital to their performances. In this paper, we introduce a method to sparsify DenseNet which can reduce connections of a L-layer DenseNet from O(L^2) to O(L), and thus we can simultaneously increase depth, width and connections of neural networks in a more parameter-efficient and computation-efficient way. Moreover, an attention module is introduced to further boost our network's performance. We denote our network as SparseNet. We evaluate SparseNet on datasets of CIFAR(including CIFAR10 and CIFAR100) and SVHN. Experiments show that SparseNet can obtain improvements over the state-of-the-art on CIFAR10 and SVHN. Furthermore, while achieving comparable performances as DenseNet on these datasets, SparseNet is x2.6 smaller and x3.7 faster than the original DenseNet.


title: Second-Order Cone Programming

abstract: Second-order cone programming (SOCP) problems are convex optimization problems in which a linear function is minimized over the intersection of an affine linear manifold with the Cartesian product of second-order (Lorentz) cones. Linear programs, convex quadratic programs and quadratically constrained convex quadratic programs can all be formulated as SOCP problems, as can many other problems that do not fall into these three categories. These latter problems model applications from a broad range of fields from engineering, control and finance to robust optimization and combinatorial optimization. On the other hand semidefinite programming (SDP)—that is the optimization problem over the intersection of an affine set and the cone of positive semidefinite matrices—includes SOCP as a special case. Therefore, SOCP falls between linear (LP) and quadratic (QP) programming and SDP. Like LP, QP and SDP problems, SOCP problems can be solved in polynomial time by interior point methods. The computational effort per iteration required by these methods to solve SOCP problems is greater than that required to solve LP and QP problems but less than that required to solve SDP’s of similar size and structure. Because the set of feasible solutions for an SOCP problem is not polyhedral as it is for LP and QP problems, it is not readily apparent how to develop a simplex or simplex-like method for SOCP. While SOCP problems can be solved as SDP problems, doing so is not advisable both on numerical grounds and computational complexity concerns. For instance, many of the problems presented in the survey paper of Vandenberghe and Boyd [VB96] as examples of SDPs can in fact be formulated as SOCPs and should be solved as such. In §2, 3 below we give SOCP formulations for four of these examples: the convex quadratically constrained quadratic programming (QCQP) problem, problems involving fractional quadratic functions ∗RUTCOR, Rutgers University, e-mail:alizadeh@rutcor.rutgers.edu. Research supported in part by the U.S. National Science Foundation grant CCR-9901991 †IEOR, Columbia University, e-mail: gold@ieor.columbia.edu. Research supported in part by the Department of Energy grant DE-FG02-92ER25126, National Science Foundation grants DMS-94-14438, CDA-97-26385 and DMS-01-04282.


title: FPGA Security: Motivations, Features, and Applications

abstract: Since their inception, field-programmable gate arrays (FPGAs) have grown in capacity and complexity so that now FPGAs include millions of gates of logic, megabytes of memory, high-speed transceivers, analog interfaces, and whole multicore processors. Applications running in the FPGA include communications infrastructure, digital cinema, sensitive database access, critical industrial control, and high-performance signal processing. As the value of the applications and the data they handle have grown, so has the need to protect those applications and data. Motivated by specific threats, this paper describes FPGA security primitives from multiple FPGA vendors and gives examples of those primitives in use in applications.


title: Visual landmarks sharpen grid cell metric and confer context specificity to neurons of the medial entorhinal cortex

abstract: Neurons of the medial entorhinal cortex (MEC) provide spatial representations critical for navigation. In this network, the periodic firing fields of grid cells act as a metric element for position. The location of the grid firing fields depends on interactions between self-motion information, geometrical properties of the environment and nonmetric contextual cues. Here, we test whether visual information, including nonmetric contextual cues, also regulates the firing rate of MEC neurons. Removal of visual landmarks caused a profound impairment in grid cell periodicity. Moreover, the speed code of MEC neurons changed in darkness and the activity of border cells became less confined to environmental boundaries. Half of the MEC neurons changed their firing rate in darkness. Manipulations of nonmetric visual cues that left the boundaries of a 1D environment in place caused rate changes in grid cells. These findings reveal context specificity in the rate code of MEC neurons.


An Introduction to Temporal Difference Learning


title: Maritime situation analysis framework: Vessel interaction classification and anomaly detection

abstract: Maritime domain awareness is critical for protecting sea lanes, ports, harbors, offshore structures like oil and gas rigs and other types of critical infrastructure against common threats and illegal activities. Typical examples range from smuggling of drugs and weapons, human trafficking and piracy all the way to terror attacks. Limited surveillance resources constrain maritime domain awareness and compromise full security coverage at all times. This situation calls for innovative intelligent systems for interactive situation analysis to assist marine authorities and security personal in their routine surveillance operations. In this article, we propose a novel situation analysis approach to analyze marine traffic data and differentiate various scenarios of vessel engagement for the purpose of detecting anomalies of interest for marine vessels that operate over some period of time in relative proximity to each other. We consider such scenarios as probabilistic processes and analyze complex vessel trajectories using machine learning to model common patterns. Specifically, we represent patterns as left-to-right Hidden Markov Models and classify them using Support Vector Machines. To differentiate suspicious activities from unobjectionable behavior, we explore fusion of data and information, including kinematic features, geospatial features, contextual information and maritime domain knowledge. Our experimental evaluation shows the effectiveness of the proposed approach using comprehensive real-world vessel tracking data from coastal waters of North America.


title: Direct solid sampling of fire assay beads by spark ablation inductively coupled plasma mass spectrometry

abstract: A spark-based, solid-sampling cell is described for inductively coupled plasma mass spectrometry (ICP-MS). The cell is devised for the direct sampling of gold and silver beads produced by the classical lead fire assay procedure. The sampler produces a solid aerosol composed of submicron-sized vapor condensates and small (< 2 μm) spherules. In contrast to solution nebulization, the mass spectrum for spark-ICP-MS is relatively free of interfering metal oxide, polyatomic, and multiply-charged ions. The measurement precision is 3% RSD for Pt, Pd, and Rh preconcentrated into fire assay beads, but is 6% RSD for Ir due to its heterogeneous distribution in a silver bead. Detection limits determined for Pt, Pd, Rh, and Ir in fire assay beads range from 0.6 μg/g (Pt) to 1.2 μg/g (Pd). Calibration curves for these elements are linear up to the highest concentration in the bead studied (2000 μg/g). The quantitative potential of the method is evaluated using the South African Reference Material (SARM-7) geologic standard.


title: Gene expression in Pseudomonas aeruginosa biofilms

abstract: Bacteria often adopt a sessile biofilm lifestyle that is resistant to antimicrobial treatment1,2,3,4,5. Opportunistic pathogenic bacteria like Pseudomonas aeruginosa can develop persistent infections1,2,3. To gain insights into the differences between free-living P. aeruginosa cells and those in biofilms, and into the mechanisms underlying the resistance of biofilms to antibiotics, we used DNA microarrays. Here we show that, despite the striking differences in lifestyles, only about 1% of genes showed differential expression in the two growth modes; about 0.5% of genes were activated and about 0.5% were repressed in biofilms. Some of the regulated genes are known to affect antibiotic sensitivity of free-living P. aeruginosa. Exposure of biofilms to high levels of the antibiotic tobramycin caused differential expression of 20 genes. We propose that this response is critical for the development of biofilm resistance to tobramycin. Our results show that gene expression in biofilm cells is similar to that in free-living cells but there are a small number of significant differences. Our identification of biofilm-regulated genes points to mechanisms of biofilm resistance to antibiotics.


title: Adhesive force of a single gecko foot-hair

abstract: Geckos are exceptional in their ability to climb rapidly up smooth vertical surfaces1,2,3. Microscopy has shown that a gecko's foot has nearly five hundred thousand keratinous hairs or setae. Each 30–130 µm long seta is only one-tenth the diameter of a human hair and contains hundreds of projections terminating in 0.2–0.5 µm spatula-shaped structures2,4. After nearly a century of anatomical description2,4,5,6, here we report the first direct measurements of single setal force by using a two-dimensional micro-electro-mechanical systems force sensor7 and a wire as a force gauge. Measurements revealed that a seta is ten times more effective at adhesion than predicted from maximal estimates on whole animals. Adhesive force values support the hypothesis that individual seta operate by van der Waals forces8,9. The gecko's peculiar behaviour of toe uncurling and peeling2 led us to discover two aspects of setal function which increase their effectiveness. A unique macroscopic orientation and preloading of the seta increased attachment force 600-fold above that of frictional measurements of the material. Suitably orientated setae reduced the forces necessary to peel the toe by simply detaching above a critical angle with the substratum.


title: Optical nano-imaging of gate-tunable graphene plasmons

abstract: Propagating optical plasmons — collective electron excitations coupled to photons — are launched in graphene and studied with near-field optical microscopy, revealing ultra-strong optical field confinement and gate-tunable control of optical fields at nanoscale dimensions.


title: Dead-time elimination method and current polarity detection circuit for three-phase PWM-controlled inverter

abstract: This paper will present a dead-time elimination scheme and the related current polarity detection circuit without separate power sources for three-phase inverters. The presented scheme includes the freewheeling current polarity detection circuit and the PWM control generator without dead time. It will be shown that the presented scheme eliminates the dead time of PWM control for inverter and therefore dramatically improving the output voltage loss and current distortion. Experimental results derived from an FPGA-based PWM-controlled inverter are shown for confirmation.


title: Response Generation by Context-aware Prototype Editing

abstract: Open domain response generation has achieved remarkable progress in recent years, but sometimes yields short and uninformative responses. We propose a new paradigm, prototypethen-edit for response generation, that first retrieves a prototype response from a pre-defined index and then edits the prototype response according to the differences between the prototype context and current context. Our motivation is that the retrieved prototype provides a good start-point for generation because it is grammatical and informative, and the post-editing process further improves the relevance and coherence of the prototype. In practice, we design a contextaware editing model that is built upon an encoder-decoder framework augmented with an editing vector. We first generate an edit vector by considering lexical differences between a prototype context and current context. After that, the edit vector and the prototype response representation are fed to a decoder to generate a new response. Experiment results on a large scale dataset demonstrate that our new paradigm significantly increases the relevance, diversity and originality of generation results, compared to traditional generative models. Furthermore, our model outperforms retrieval-based methods in terms of relevance and originality.


title: Improving Protein Gamma-Turn Prediction Using Inception Capsule Networks

abstract: Protein gamma-turn prediction is useful in protein function studies and experimental design. Several methods for gamma-turn prediction have been developed, but the results were unsatisfactory with Matthew correlation coefficients (MCC) around 0.2-0.4. One reason for the low prediction accuracy is the limited capacity of the methods; in particular, the traditional machine-learning methods like SVM may not extract high-level features well to distinguish between turn or non-turn. Hence, it is worthwhile exploring new machine-learning methods for the prediction. A cutting-edge deep neural network, named Capsule Network (CapsuleNet), provides a new opportunity for gamma-turn prediction. Even when the number of input samples is relatively small, the capsules from CapsuleNet are very effective to extract high-level features for classification tasks. Here, we propose a deep inception capsule network for gamma-turn prediction. Its performance on the gamma-turn benchmark GT320 achieved an MCC of 0.45, which significantly outperformed the previous best method with an MCC of 0.38. This is the first gamma-turn prediction method utilizing deep neural networks. Also, to our knowledge, it is the first published bioinformatics application utilizing capsule network, which will provides a useful example for the community.


title: Mapping the Americanization of English in space and time

abstract: As global political preeminence gradually shifted from the United Kingdom to the United States, so did the capacity to culturally influence the rest of the world. In this work, we analyze how the world-wide varieties of written English are evolving. We study both the spatial and temporal variations of vocabulary and spelling of English using a large corpus of geolocated tweets and the Google Books datasets corresponding to books published in the US and the UK. The advantage of our approach is that we can address both standard written language (Google Books) and the more colloquial forms of microblogging messages (Twitter). We find that American English is the dominant form of English outside the UK and that its influence is felt even within the UK borders. Finally, we analyze how this trend has evolved over time and the impact that some cultural events have had in shaping it.


title: Quantifying and Bursting the Online Filter Bubble

abstract: In this thesis, we develop methods to (i) detect and quantify the existence of filter bubbles in social media, (ii) monitor their evolution over time, and finally, (iii) devise methods to overcome the effects caused by filter bubbles. We are the first to propose an end-to-end system that solves the problem of filter bubbles completely algorithmically. We build on top of existing studies and ideas from social science with principles from graph theory to design algorithms which are language independent, domain agnostic and scalable to large number of users.


title: Data Augmentation for Recognition of Handwritten Words and Lines Using a CNN-LSTM Network

abstract: We introduce two data augmentation and normalization techniques, which, used with a CNN-LSTM, significantly reduce Word Error Rate (WER) and Character Error Rate (CER) beyond best-reported results on handwriting recognition tasks. (1) We apply a novel profile normalization technique to both word and line images. (2) We augment existing text images using random perturbations on a regular grid. We apply our normalization and augmentation to both training and test images. Our approach achieves low WER and CER over hundreds of authors, multiple languages and a variety of collections written centuries apart. Image augmentation in this manner achieves state-of-the-art recognition accuracy on several popular handwritten word benchmarks.


title: Capturing Investor Sentiment: Advancing Predictability in Finance with Computer Science Approaches

abstract: This discussion paper reviews both finance and computer science literature on sentiment. The toolset for addressing the sentiment analysis problem in computer science goes beyond methods commonly used in finance literature. The broader toolset of computer science has potential to improve the performance of sentiment measurement and predictability in financial markets.


title: KnowNER: Incremental Multilingual Knowledge in Named Entity Recognition

abstract: KnowNER is a multilingual Named Entity Recognition (NER) system that leverages different degrees of external knowledge. A novel modular framework divides the knowledge into four categories according to the depth of knowledge they convey. Each category consists of a set of features automatically generated from different information sources (such as a knowledge-base, a list of names or document-specific semantic annotations) and is used to train a conditional random field (CRF). Since those information sources are usually multilingual, KnowNER can be easily trained for a wide range of languages. In this paper, we show that the incorporation of deeper knowledge systematically boosts accuracy and compare KnowNER with state-of-the-art NER approaches across three languages (i.e., English, German and Spanish) performing amongst state-of-the art systems in all of them.


title: The changing psychological contract: challenges and implications for HRM, organisations and employees

abstract: Organisations and their employees face ongoing challenges in the form of new strategic initiatives designed to keep pace in an increasingly complex business environment. In order for these challenges to be successfully met, new behaviours are required on the part of employees (Sims 1994). Defining these new behaviours is initiated through the organisation's human resource (HR) practices (Rousseau & Wade-Benzoni 1994). However, actual change in individual employees' behaviour is determined by interpreting their employers' HR practices. Such interpretation affects employee behaviour by altering perceptions of the terms of the individually held psychological contract.


title: Correction of the Bony Rings During the Aesthetic Rhinoplasty: Apologia of the Transpalpebral Osteotomy

abstract: It is observed that in many cases of rhinoplasty, the final aesthetic result on the face of patients was not satisfactory. The reason is the persistence of "rings" making a shadow beside the root of the nose. These "rings" seem to be palpebral but are actually made by the frontal process of the maxilla. Osteotomy in rhinoplasties classically involve the nasal bones. However, the osteotomy in our cases is made on the frontal process of the maxilla. This surgical refinement is detailed with regard to a minute palpebral incision, precisely placing the lateral osteotomy on each frontal process of the maxilla, in-fracture and rasping equally. While the osteotomy procedure is well documented in rhinoplasties, the improvement of the patients' final appearance at the root of the nose after rhinoplasty is less clearly established. This report will detail an innovation based on the author's 15-year surgical experience with primary and secondary rhinoplasty. The improved result is remarkable on the final appearance of the nose. Close-up photography demonstrates the anatomy as well as the technique.


title: Comparative Study of CNN and LSTM based Attention Neural Networks for Aspect-Level Opinion Mining

abstract: Aspect-level opinion mining aims to find and aggregate opinions on opinion targets. Previous work has demonstrated that precise modeling of opinion targets within the surrounding context can improve performances. However, how to effectively and efficiently learn hidden word semantics and better represent targets and the context still needs to be further studied. In this paper, we propose and compare two interactive attention neural networks for aspect-level opinion mining, one employs two bi-directional Long-Short-Term-Memory (BLSTM) and the other employs two Convolutional Neural Networks (CNN). Both frameworks learn opinion targets and the context respectively, followed by an attention mechanism that integrates hidden states learned from both the targets and context. We compare our model with state-of-the-art baselines on two SemEval 2014 datasets1. Experiment results show that our models obtain competitive performances against the baselines on both datasets. Our work contributes to the improvement of state-of-the-art aspect-level opinion mining methods and offers a new approach to support human decision-making process based on opinion mining results. The quantitative and qualitative comparisons in our work aim to give basic guidance for neural network selection in similar tasks.


title: Nursing reflective practice: An empirical literature review

abstract: Reflective practice is a widespread concept in nursing; however, few empirical studies have demonstrated the possible effects of such a practice. The goal of this article is to present a review of the empirical literature on nursing reflective practice. Thirty-seven studies published between 1995 and 2012 were selected and analyzed to identify their common characteristics and structure. Most of them are qualitative in nature and were conducted in an academic context. Generally, few authors clearly define the reflective practice concept and the frames of reference on which their research is based. Furthermore, when reflective practice interventions are described, they have few points of comparison, which makes it hard to choose which intervention to prioritize to maximize the positive effects on nursing practice. Although a lot has been written on the advantages of reflective practice, nursing research on this concept remains at an early stage. The authors recommend various paths for future research.


title: Quantized Hydration Energies of Ions and Structure of Hydration Shell from the Experimental Gas-Phase Data

abstract: With previous data on alkali metal and halide ions included [Rais, J.; Okada, T. Anal. Sci. 2006, 22, 533], we analyzed rather broad data on ionic hydration from the point of view of gaseous cluster energetics. We have now added alkaline earth cations, Zn2+, H+, OH-, Cu+, Ag+, Bi+, Pb+, and alkylammonium cations. The present analysis revealed the octa-coordinated nature of alkaline earth cations, which is not fully pronounced for Be2+ and Zn2+, existence of Eigen protonium complex, which is trigonally hydrated, and particular property of the first OH-, H2O cluster. Whereas these findings are generally in accordance with theoretical model calculation studies, we have foreseen in addition tetrahedral hydration for halide anions and Rb+ and Cs+, as well as for alkylammonium ions. The obtained picture of the quantized solvation of ions is mirrored in the ionization potentials of outer electrons of pertinent atoms. This is a second independent phenomenon, and together, they invoked a common pattern formation (...


title: A multi-agent optimization algorithm for resource constrained project scheduling problem

abstract: Develop a multi-agent optimization algorithm (MAOA).Present the implement of MAOA for resource constrained project scheduling problem.Investigate the effect of parameter setting.Demonstrate the effectiveness by comparing to the existing algorithms. In this paper, a multi-agent optimization algorithm (MAOA) is proposed for solving the resource-constrained project scheduling problem (RCPSP). In the MAOA, multiple agents work in a grouped environment where each agent represents a feasible solution. The evolution of agents is achieved by using four main elements in the MAOA, including social behavior, autonomous behavior, self-learning, and environment adjustment. The social behavior includes the global one and the local one for performing exploration. Through the global social behavior, the leader agent in every group is guided by the global best leader. Through the local social behavior, each agent is guided by its own leader agent. Through the autonomous behavior, each agent exploits its own neighborhood. Through the self-learning, the best agent performs an intensified search to further exploit the promising region. Meanwhile, some agents perform migration among groups to adjust the environment dynamically for information sharing. The implementation of the MAOA for solving the RCPSP is presented in detail, and the effect of key parameters of the MAOA is investigated based on the Taguchi method of design of experiment. Numerical testing results are provided by using three sets of benchmarking instances. The comparisons to the existing algorithms demonstrate the effectiveness of the proposed MAOA for solving the RCPSP.


title: A re-examination of the community of inquiry framework: Social network and content analysis

abstract: Abstract This study provides a simultaneous examination of all components of the Community of Inquiry (CoI) framework (Garrison, Anderson & Archer, 2000; Anderson, Rourke, Garrison & Archer, 2001; and Rourke, Garrison, Anderson & Archer, 1999) and seeks to extend previous work into the nature, development, and relationships between the constructs of “presence” hypothesized in the model. To accomplish this goal we use content and social network analysis to examine the discourse produced among all participants in two semester-length online asynchronous college courses. Coding for the existence and relative intensity of forms of presence we identify patterns and relationships between instructors' and students' teaching presence and social presence and the corresponding quantity and levels of the cognitive presence that emerges. The study reveals complex relationships between these variables that have implications for the development of higher order thinking and meaningful learning in online environments. Study findings also have implications for online teaching practice and ongoing research on the CoI framework.


Personalized Recommendation of Travel Itineraries based on Tourist Interests and Preferences


title: Urdu Word Segmentation using Machine Learning Approaches

abstract: Word Segmentation is considered a basic NLP task and in diverse NLP areas, it plays a significant role. The main areas which can be benefited from Word segmentation are IR, POS, NER, sentiment analysis, etc. Urdu Word Segmentation is a challenging task. There can be a number of reasons but Space Insertion Problem and Space Omission Problems are the major ones. Compared to Urdu, the tools and resources developed for word segmentation of English and English like other western languages have record-setting performance. Some languages provide a clear indication for words just like English which having space or capitalization of the first character in a word. But there are many languages which do not have proper delimitation in between words e.g. Thai, Lao, Urdu, etc. The objective of this research work is to present a machine learning based approach for Urdu word segmentation. We adopted the use of conditional random fields (CRF) to achieve the subject task. Some other challenges faced in Urdu text are compound words and reduplicated words. In this paper, we tried to overcome such challenges in Urdu text by machine learning methodology.


title: Using Software Metrics Thresholds to Predict Fault-Prone Classes in Object-Oriented Software

abstract: Most code-based quality measurement approaches are based, at least partially, on values of multiple source code metrics. A class will often be classified as being of poor quality if the values of its metrics are above given thresholds, which are different from one metric to another. The metrics thresholds are calculated using various techniques. In this paper, we investigated two specific techniques: ROC curves and Alves rankings. These techniques are supposed to give metrics thresholds which are practical for code quality measurements or even for fault-proneness prediction. However, Alves Rankings technique has not been validated as being a good choice for fault-proneness prediction, and ROC curves only partially on few datasets. Fault-proneness prediction is an important field of software engineering, as it can be used by developers and testers as a test effort indication to prioritize tests. This will allow a better allocation of resources, reducing therefore testing time and costs, and an improvement of the effectiveness of testing by testing more intensively the components that are likely more fault-prone. In this paper, we wanted to compare empirically the selected threshold calculation methods used as part of fault-proneness prediction techniques. We also used a machine learning technique (Bayes Network) as a baseline for comparison. Thresholds have been calculated for different object-oriented metrics using four different datasets obtained from the PROMISE Repository and another one based on the Eclipse project.


title: Adaptive Population Sizing Schemes in Genetic Algorithms

abstract: Summary. This chapter presents a review of adaptive population sizing schemes used in genetic algorithms. We start by briefly revisiting theoretical models which rely on a facetwise design decomposition, and then move on to various self-adjusting population sizing schemes that have been proposed in the literature. For each method, the major advantages and disadvantages are discussed. The chapter ends with recommendations for those who design and compare self-adjusting population sizing mechanisms for genetic and evolutionary algorithms.


title: Single-layer slotted post-wall waveguide array with compact feed-line structures for 77 GHz automotive radar

abstract: A single-layer antenna composed of four subarrays and compact low-loss feed-line structures connecting to an RFmodule for 77 GHz automotive radar systems is presented. This antenna is based on a slotted post-wall waveguide array, which is integrated in a ceramic-filled PTFE substrate. The antenna gain is higher than 16 dBi relative to the maximum available directivity of 20.4 dBi at 76.5 GHz. The proposed compact feedline structures achieve the low transmission loss of less than 3.2 dB.


title: Organizational Sensemaking and Big Data Frames: Opportunity, Control, and Data Limitation

abstract: Like many organizations, electricity service providers (utilities) are facing a new era of big data as a result of the emerging smart grid. To date, much focus in practice and research has been placed on developing technical and analytical capabilities necessary for big data, with little consideration given to their organizational implications. This paper brings a theoretical perspective to understanding the implications of big data for organizations. It presents the results of a grounded study examining how one utility was able to successfully deploy smart meters and take advantage of big data. Drawing on the perspective of organizational sense making, we identify three new frames -- opportunity, control, and data limitation -- which enable utilities to effectively take advantage of big data.


Towards Improving Abstractive Summarization via Entailment Generation.


title: Achievable cases in an asynchronous environment

abstract: The paper deals with achievability of fault tolerant goals in a completely asynchronous distributed system. Fischer, Lynch, and Paterson [FLP] proved that in such a system "nontrivial agreement" cannot be achieved even in the (possible) presence of a single "benign" fault. In contrast, we exhibit two pairs of goals that are achievable even in the presence of up to t ≪ n/2 faulty processors, contradicting the widely held assumption that no nontrivial goals are attainable in such a system. The first pair deals with renaming processors so as to reduce the size of the initial name space. When only uniqueness is required of the new names, we present a lower bound of n + 1 on the size of the new name space, and a renaming algorithm which establishes an upper bound of n + t. In case the new names are required also to preserve the original order, a tight bound of 2t(n- t + 1) - 1 is obtained. The second pair of goals deals with the multi-slot critical section problem. We present algorithms for controlled access to a critical section. As for the number of slots required, a tight bound of t + 1 is proved in case the slots are identical. In the case of distinct slots the upper bound is 2t + 1.


title: Determining Usability Test Sample Size

abstract: Virzi (1992), Nielsen and Landauer (1993), and Lewis (1994) have published influential articles on the topic of sample size in usability testing. In these articles, the authors presented a mathematical model of problem discovery rates in usability testing. Using the problem discovery rate model, they showed that it was possible to determine the sample size needed to uncover a given proportion of problems in an interface during one test. The authors presented empirical evidence for the models and made several important claims:


title: Mental State Recognition via Wearable EEG

abstract: The increasing quality and affordability of consumer electroencephalogram (EEG) headsets make them attractive for situations where medical grade devices are impractical. Predicting and tracking cognitive states is possible for tasks that were previously not conducive to EEG monitoring. For instance, monitoring operators for states inappropriate to the task (e.g. drowsy drivers), tracking mental health (e.g. anxiety) and productivity (e.g. tiredness) are among possible applications for the technology. Consumer grade EEG headsets are affordable and relatively easy to use, but they lack the resolution and quality of signal that can be achieved using medical grade EEG devices. Thus, the key questions remain: to what extent are wearable EEG devices capable of mental state recognition, and what kind of mental states can be accurately recognized with these devices? In this work, we examined responses to two different types of input: instructional (logical) versus recreational (emotional) videos, using a range of machine-learning methods. We tried SVMs, sparse logistic regression, and Deep Belief Networks, to discriminate between the states of mind induced by different types of video input, that can be roughly labeled as logical vs. emotional. Our results demonstrate a significant potential of wearable EEG devices in differentiating cognitive states between situations with large contextual but subtle apparent differences.


title: Preparation, characterization, and potential application of chitosan, chitosan derivatives, and chitosan metal nanoparticles in pharmaceutical drug delivery

abstract: Naturally occurring polymers, particularly of the polysaccharide type, have been used pharmaceutically for the delivery of a wide variety of therapeutic agents. Chitosan, the second abundant naturally occurring polysaccharide next to cellulose, is a biocompatible and biodegradable mucoadhesive polymer that has been extensively used in the preparation of micro-as well as nanoparticles. The prepared particles have been exploited as a potential carrier for different therapeutic agents such as peptides, proteins, vaccines, DNA, and drugs for parenteral and nonparenteral administration. Therapeutic agent-loaded chitosan micro- or nanoparticles were found to be more stable, permeable, and bioactive. In this review, we are highlighting the different methods of preparation and characterization of chitosan micro- and nanoparticles, while reviewing the pharmaceutical applications of these particles in drug delivery. Moreover, the roles of chitosan derivatives and chitosan metal nanoparticles in drug delivery have been illustrated.


Measurement Technology for use in Active Flow Control


title: Development of Structural Health Management Technology for Aerospace Vehicles

abstract: As part of the overall goal of developing Integrated Vehicle Health Management (IVHM) systems for aerospace vehicles, NASA has focused considerable resources on the development of technologies for Structural Health Management (SHM). The motivations for these efforts are to increase the safety and reliability of aerospace structural systems, while at the same time decreasing operating and maintenance costs. Research and development of SHM technologies has been supported under a variety of programs for both aircraft and spacecraft including the Space Launch Initiative, X-33, Next Generation Launch Technology, and Aviation Safety Program. The major focus of much of the research to date has been on the development and testing of sensor technologies. A wide range of sensor technologies are under consideration including fiber-optic sensors, active and passive acoustic sensors, electromagnetic sensors, wireless sensing systems, MEMS, and nanosensors. Because of their numerous advantages for aerospace applications, most notably being extremely light weight, fiber-optic sensors are one of the leading candidates and have received considerable attention.


title: Suppression of Row Hammer Effect by Doping Profile Modification in Saddle-Fin Array Devices for Sub-30-nm DRAM Technology

abstract: The row hammer effect has become a reliability issue that cannot be ignored in sub-30-nm dynamic random-access memory (DRAM) products because of the narrow isolation spacing between the array devices. Improving the row hammer effect via fabrication process optimization is first proposed in this paper. An additional phosphorus (P) implantation with energy and dosage modification applied in the common source area between two adjacent buried word lines of the access devices and energy adjustment of the well implantation are applied to provide a doping profile modification in the array device. With the proper implantation setting of the high energy and 2X dosage in the additional P implantation, a localized shielding effect from the electric field by a depletion effect could be used to reduce the chances of hammering gate-induced electrons in the channel leaking to the adjacent access device. This proposed mechanism could be supported by the experimental results of the doping profile. Therefore, the row hammer effect can be suppressed by up to 30% in normalized fail bit counts. This proposed methodology could be used in future generations to suppress the row hammer effect in DRAMs.


title: CCSweep: automatic decomposition of multi-sweep volumes

abstract: CCSweep is a new method to automatically decompose multi-sweepable volumes into many-to-one sweepable volumes. Multi-sweepable volumes contain both multiple source and multiple target faces. In hexahedral mesh generation, most sweeping techniques handle many-to-one sweepable volumes that contain multiple source faces, but they are limited to volumes with only a single target face. Recent proposals to solve the multi-sweep problem have several disadvantages, including: indeterminate edge sizing or interval matching constraints, over-dependence on input mesh discretization, loop Boolean restrictions on creating only loops with even numbers of nodes, and unstable loop imprinting when interior holes exist. These problems are overcome through CCSweep. CCSweep decomposes multi-sweep volumes into many-to-one sweepable sub-volumes by projecting the target faces through the volume onto corresponding source faces. The projected faces are imprinted with the source faces to determine the decomposition of the solid. Interior faces are created to decompose the volume into separate new volumes. The new volumes have only single target faces and are represented in the meshing system as real, solid geometry, enabling them to be automatically meshed using existing many-to-one hexahedral sweeping approaches. The results of successful application of CCSweep to a number of problems are shown in this paper.


title: A Very Low-Profile, Omnidirectional, Ultrawideband Antenna

abstract: We present a compact, ultra-low-profile, ultrawideband (UWB) antenna with monopole-like radiation characteristics. The antenna is composed of two bent diamond-shaped loops that are fed in parallel and loaded with a common capacitive top hat to reduce their lowest frequencies of operation. A series capacitor is used at the feeding terminals of one of the loops to create an additional resonance below the lowest frequency of operation of one of the loops and, hence, further reduce the lowest frequency of operation of the antenna. At the lowest frequency of operation, the proposed antenna has electrical dimensions of L × W × H = 0.22λmin × 0.22λmin × 0.033λmin, where λmin is the free-space wavelength. The antenna demonstrates a VSWR lower than 3:1 and consistent monopole-like, omnidirectional radiation patterns over a 4:1 bandwidth. A prototype of the antenna with physical dimensions of 7 × 7 × 1 cm3, which operates in the 1.0-4.0-GHz frequency range, is fabricated and characterized using a near-field system.


title: High-Frequency PWM Buck Converters Using GaN-on-SiC HEMTs

abstract: GaN high electron mobility transistors (HEMTs) are well suited for high-frequency operation due to their lower on resistance and device capacitance compared with traditional silicon devices. When grown on silicon carbide, GaN HEMTs can also achieve very high power density due to the enhanced power handling capabilities of the substrate. As a result, GaN-on-SiC HEMTs are increasingly popular in radio-frequency power amplifiers, and applications as switches in high-frequency power electronics are of high interest. This paper explores the use of GaN-on-SiC HEMTs in conventional pulse-width modulated switched-mode power converters targeting switching frequencies in the tens of megahertz range. Device sizing and efficiency limits of this technology are analyzed, and design principles and guidelines are given to exploit the capabilities of the devices. The results are presented for discrete-device and integrated implementations of a synchronous Buck converter, providing more than 10-W output power supplied from up to 40 V with efficiencies greater than 95% when operated at 10 MHz, and greater than 90% at switching frequencies up to 40 MHz. As a practical application of this technology, the converter is used to accurately track a 3-MHz bandwidth communication envelope signal with 92% efficiency.


title: Design of a high fill factor permanent magnet integrated starter generator with compressed stator windings

abstract: This paper focuses on the improved thermal performance of electrical machines by increasing the conductor fill factor. A comparative design study of integrated starter generator (ISG) considering different pole/slot combinations and magnet topologies is used as a case study to fix slot dimensions. Compressed stator windings coupled with segmented stator structure are introduced to reduce copper losses of the optimized ISG. By performing quasi-static explicit dynamic simulations, deformation of insulation has been investigated regarding mechanical properties of the magnet wire enamels. It is noted that magnet wires at certain cross-sectional area can be compressed up to 0.72 fill factor without observing insulation failure. Thermal conductivity enhancement of the stator windings at improved slot fill factor is demonstrated by performing steady state and transient finite element thermal simulations.


title: Recognizing Humor on Twitter

abstract: In this paper, we present our work of humor recognition on Twitter, which will facilitate affect and sentimental analysis in the social network. The central question of what makes a tweet (Twitter post) humorous drives us to design humor-related features, which are derived from influential humor theories, linguistic norms, and affective dimensions. Using machine learning techniques, we are able to recognize humorous tweets with high accuracy and F-measure. More importantly, we single out features that contribute to distinguishing non-humorous tweets from humorous tweets, and humorous tweets from other short humorous texts (non-tweets). This proves that humorous tweets possess discernible characteristics that are neither found in plain tweets nor in humorous non-tweets. We believe our novel findings will inform and inspire the burgeoning field of computational humor research in the social media.


title: CAPTCHAs: An Artificial Intelligence Application to Web Security

abstract: Abstract Nowadays, it is hard to find a popular Web site with a registration form that is not protected by an automated human proof test which displays a sequence of characters in an image, and requests the user to enter the sequence into an input field. This security mechanism is based on the Turing Test—one of the oldest concepts in Artificial Intelligence—and it is most often called Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). This kind of test has been conceived to prevent the automated access to an important Web resource, for example, a Web mail service or a Social Network. There are currently hundreds of these tests, which are served millions of times a day, thus involving a huge amount of human work. On the other side, a number of these tests have been broken, that is, automated programs designed by researchers, hackers, and spammers have been able to automatically serve the correct answer. In this chapter, we present the history and the concept of CAPTCHAs, along with their applications and a wide review of their instantiations. We also discuss their evaluation, both from the user and the security perspectives, including usability, attacks, and countermeasures. We expect this chapter provides to the reader a good overview of this interesting field.


title: Driving style classification using long-term accelerometer information

abstract: Driving style can be characteristically divided into normal and aggressive. Related researches show that useful information about driving style can be extracted using vehicle's inertial measurement signals with the help of GPS. However, for public transportation the GPS sensor isn't necessary because of repetition of the route. This assumption helps to create low-cost intelligent public transport monitoring system that is capable to classify aggressive and normal driver. In this paper, we propose pattern recognition approach to classify driving style into aggressive or normal automatically without expert evaluation and knowledge using accelerometer data when driving the same route in different driving styles. 3-axis accelerometer signal statistical features were used as classifier inputs. The results show that aggressive and normal driving style classification of 100% precision is achieved using collected data when driving the same route.


title: Self-implanted artificial nodules: a computed tomography mimic of penile pathology

abstract: Self-introduction of foreign bodies along the penile shaft has been reported in several ethnic and social groups, mainly in Asia, and recently has been described in Europe. We present the case of a 34-year-old homeless Russian immigrant who had an abdominal CT performed during an emergency department visit. On the CT scan, several hyperdense, well-demarcated subcutaneous nodules along the penile shaft were noted. Following a focused history and physical examination, the nodules were found to represent artificial foreign bodies made of glass, which were self-introduced by the patient in order to allegedly increase the pleasure of sexual partners. Penile nodules may be a manifestation of diverse pathological entities including infectious, inflammatory, and neoplastic processes. It is important for the radiologist to be familiar with this social phenomenon and its radiological appearance in order to avoid erroneous diagnosis.


title: Real-time stereo vision system using semi-global matching disparity estimation: Architecture and FPGA-implementation

abstract: This paper describes a new architecture and the corresponding implementation of a stereo vision system that covers the entire stereo vision process including noise reduction, rectification, disparity estimation, and visualization. Dense disparity estimation is performed using the non-parametric rank transform and semi-global matching (SGM), which is among the top performing stereo matching methods and outperforms locally-based methods in terms of quality of disparity maps and robustness under difficult imaging conditions. Stream-based processing of the SGM despite its non-scan-aligned, complex data dependencies is achieved by a scalable, systolic-array-based architecture. This architecture fulfills the demands of real-world applications regarding frame rate, depth resolution and low resource usage. The architecture is based on a novel two-dimensional parallelization concept for the SGM. An FPGA implementation on a Xilinx Virtex-5 generates disparity maps of VGA images (640×480 pixel) with a 128 pixel disparity range under real-time conditions (30 fps) at a clock frequency as low as 39 MHz.


title: Room Temperature Control and Fire Alarm/Suppression IoT Service Using MQTT on AWS

abstract: In this paper we build an MQTT(Message Queue Telemetry Transportation) broker on Amazon Web Service(AWS). The MQTT broker has been utilized as a platform to provide the Internet of Things(IoT) services which monitor and control room temperatures, and sense, alarm, and suppress fire. Arduino was used as the IoT end device connecting sensors and actuators to the platform via Wi-Fi channel. We created smart home scenario and designed IoT massages satisfying the scenario requirement. We also implemented the smart some system in hardware and software, and verified the system operation. We show that MQTT and AWS are good technical candidates for small IoT business applications.


title: DSOD: Learning Deeply Supervised Object Detectors from Scratch

abstract: We present Deeply Supervised Object Detector (DSOD), a framework that can learn object detectors from scratch. State-of-the-art object objectors rely heavily on the off-the-shelf networks pre-trained on large-scale classification datasets like ImageNet, which incurs learning bias due to the difference on both the loss functions and the category distributions between classification and detection tasks. Model fine-tuning for the detection task could alleviate this bias to some extent but not fundamentally. Besides, transferring pre-trained models from classification to detection between discrepant domains is even more difficult (e.g. RGB to depth images). A better solution to tackle these two critical problems is to train object detectors from scratch, which motivates our proposed DSOD. Previous efforts in this direction mostly failed due to much more complicated loss functions and limited training data in object detection. In DSOD, we contribute a set of design principles for training object detectors from scratch. One of the key findings is that deep supervision, enabled by dense layer-wise connections, plays a critical role in learning a good detector. Combining with several other principles, we develop DSOD following the single-shot detection (SSD) framework. Experiments on PASCAL VOC 2007, 2012 and MS COCO datasets demonstrate that DSOD can achieve better results than the state-of-the-art solutions with much more compact models. For instance, DSOD outperforms SSD on all three benchmarks with real-time detection speed, while requires only 1/2 parameters to SSD and 1/10 parameters to Faster RCNN. Our code and models are available at: this https URL .


title: Organization and maintenance of large ordered indexes

abstract: Organization and maintenance of an index for a dynamic random access file is considered. It is assumed that the index must be kept on some pseudo random access backup store like a disc or a drum. The index organization described allows retrieval, insertion, and deletion of keys in time proportional to logk I where I is the size of the index and k is a device dependent natural number such that the performance of the scheme becomes near optimal. Storage utilization is at least 50% but generally much higher. The pages of the index are organized in a special datastructure, so-called B-trees. The scheme is analyzed, performance bounds are obtained, and a near optimal k is computed. Experiments have been performed with indexes up to 100000 keys. An index of size 15000 (100000) can be maintained with an average of 9 (at least 4) transactions per second on an IBM 360/44 with a 2311 disc.


title: Ligand specificity and ticlopidine effects distinguish three human platelet ADP receptors.

abstract: Abstract Human platelets express adenosine 5′-diphosphate (ADP)-specific purinoceptors of the P 2X and P 2Y receptor superfamily, but their structure, diversity, and precise pharmacological profile is not well understood. Here, functional assays with intact platelets and well-characterized nucleotide derivatives were performed in order to characterize the ligand specificity of these platelet-specific purinoceptors. For the signalling pathways investigated (aggregation, rapid Ca 2+ -influx, desensitization of Ca 2+ -influx, Ca 2+ -mobilization, inhibition of adenylyl cyclase), significant differences in ligand specificity were demonstrated. ADP activated all purinoceptors of human platelets, while adenosine 5′-triphosphate (ATP) was a weak agonist for the P 2X receptor and an antagonist for the P 2Y receptors. The ADP-receptor pathway-antagonist ticlopidine inhibited ADP-evoked aggregation and adenylyl cyclase inhibition but did not affect platelet purinoceptors associated with Ca 2+ -influx and Ca 2+ -mobilization. These results indicate the presence of three distinct ADP-selective purinoceptors on human platelets.


title: The need for new processes, methodologies and tools to support big data teams and improve big data project effectiveness

abstract: As data continues to be produced in massive amounts, with increasing volume, velocity and variety, big data projects are growing in frequency and importance. However, the growth in the use of big data has outstripped the knowledge of how to support teams that need to do big data projects. In fact, while much has been written in terms of the use of algorithms that can help generate insightful analysis, much less has been written about methodologies, tools and frameworks that could enable teams to more effectively and efficiently "do" big data projects. Hence, this paper discusses the key research questions relating methodologies, tools and frameworks to improve big data team effectiveness as well as the potential goals for a big data process methodology. Finally, the paper also discusses related domains, such as software development, operations research and business intelligence, since these fields might provide insight into how to define a big data process methodology.


title: Efficient Nash Equilibrium Approximation through Monte Carlo Counterfactual Regret Minimization

abstract: Recently, there has been considerable progress towards algorithms for approximating Nash equilibrium strategies in extensive games. One such algorithm, Counterfactual Regret Minimization (CFR), has proven to be effective in two-player zero-sum poker domains. While the basic algorithm is iterative and performs a full game traversal on each iteration, sampling based approaches are possible. For instance, chance-sampled CFR considers just a single chance outcome per traversal, resulting in faster but less precise iterations. While more iterations are required, chance-sampled CFR requires less time overall to converge. In this work, we present new sampling techniques that consider sets of chance outcomes during each traversal to produce slower, more accurate iterations. By sampling only the public chance outcomes seen by all players, we take advantage of the imperfect information structure of the game to (i) avoid recomputation of strategy probabilities, and (ii) achieve an algorithmic speed improvement, performing O(n2) work at terminal nodes in O(n) time. We demonstrate that this new CFR update converges more quickly than chance-sampled CFR in the large domains of poker and Bluff.


title: Clavicular facet of the coracoclavicular joint: Analysis in modern skeletons of the mapuche indigenous individuals

abstract: Abstract Introduction The coracoclavicular joint (CCJ) has been used as an anthropological marker for human migration, with a strong presence in Asian population. In South America, studies are scarce and incomplete. The aim was to determine the frequency of articular facet of CCJ in an osteological sample pertaining to Mapuche indigenous population in Chile. Methods We used 96 clavicles (48 left and 48 right), and the presence and characteristics of the articular facet on the conoid tubercle were determined by direct observation. Maximum transverse diameter (MTD) and maximum antero-posterior diameter (MAPD) were measured using a digital caliper. The frequency obtained was compared with other osteological studies on worldwide population. Results Articular facet was found in 22.9% of the cases. Twelve facets (12.5%) were present on the right side and 10 (10.4%) on the left, with 10 paired bilaterally and two paired unilaterally on the right side. Articular facets were very clear and prominent in 4 cases (4.2%), regularly prominent in 6 (6.3%), and poorly prominent in 14 (14.6%). The facets were usually oval, with an MTD and MAPD of 19.2 and 18.8 mm, respectively, without differences by side. Discussion Mapuche ethnic group showed the highest frequency of osteological material reported to date between native South American and global population. Their presence may not be related to a geographical migration, but to other causes such as genetic, environmental, or evolutionary adaptation factors. The study of CCJ as anthropological trait must be addressed through direct observation in osteological material, because radiological studies may underestimate its frequency.


title: A W-Band Power Amplifier Utilizing a Miniaturized Marchand Balun Combiner

abstract: A miniaturized Marchand balun combiner is proposed for a W-band power amplifier (PA). The proposed combiner reduces the electrical length of the transmission lines (transmission line) from about 80 $^{\circ}$ to 30 $^{\circ}$ , when compared with a conventional Marchand balun combiner. Implemented in a 1-V 65-nm CMOS process, the presented PA achieves a measured saturated output power of 11.9 dBm and a peak power-added efficiency of 9.0% at 87 GHz. The total chip area (with pads) is ${\hbox{0.77}}\times {\hbox{0.48}}\ {\hbox{mm}}^{2}$ , where the size of the balun combiner is only ${\hbox{0.36}}\times {\hbox{0.13}}\ {\hbox{mm}}^{2}$ .


title: High Level Sensor Data Fusion Approaches For Object Recognition In Road Environment

abstract: Application of high level fusion approaches demonstrate a sequence of significant advantages in multi sensor data fusion and automotive safety fusion systems are no exception to this. High level fusion can be applied to automotive sensor networks with complementary or/and redundant field of views. The advantage of this approach is that it ensures system modularity and allows benchmarking, as it does not permit feedbacks and loops inside the processing. In this paper two specific high level data fusion approaches are described including a brief architectural and algorithmic presentation. These approaches differ mainly in their data association part: (a) track level fusion approach solves it with the point to point association with emphasis on object continuity and multidimensional assignment, and (b) grid based fusion approach that proposes a generic way to model the environment and to perform sensor data fusion. The test case for these approaches is a multi sensor equipped PReVENT/ProFusion2 truck demonstrator vehicle.


title: Operation of Compressor and Electronic Expansion Valve via Different Controllers

abstract: The most critical problem in the world is to meet the energy demand, because of steadily increasing energy consumption. Refrigeration systems` electricity consumption has big portion in overall consumption. Therefore, considerable attention has been given to refrigeration capacity modulation system in order to decrease electricity consumption of these systems. Capacity modulation is used to meet exact amount of load at partial load and lowered electricity consumption by avoiding over capacity using. Variable speed refrigeration systems are the most common capacity modulation method for commercially and household purposes. Although the vapor compression refrigeration designed to satisfy the maximum load, they work at partial load conditions most of their life cycle and they are generally regulated as on/off controlled. The experimental chiller system contains four main components: compressor, condenser, expansion device, and evaporator in Fig.1 where this study deals with effects of different control methods on variable speed compressor (VSC) and electronic expansion valve (EEV). This chiller system has a scroll type VSC and a stepper motor controlled EEV.


title: Transforming Dependency Structures to Logical Forms for Semantic Parsing

abstract: The strongly typed syntax of grammar formalisms such as CCG, TAG, LFG and HPSG offers a synchronous framework for deriving syntactic structures and semantic logical forms. In contrast---partly due to the lack of a strong type system---dependency structures are easy to annotate and have become a widely used form of syntactic analysis for many languages. However, the lack of a type system makes a formal mechanism for deriving logical forms from dependency structures challenging. We address this by introducing a robust system based on the lambda calculus for deriving neo-Davidsonian logical forms from dependency trees. These logical forms are then used for semantic parsing of natural language to Freebase. Experiments on the Free917 and WebQuestions datasets show that our representation is superior to the original dependency trees and that it outperforms a CCG-based representation on this task. Compared to prior work, we obtain the strongest result to date on Free917 and competitive results on WebQuestions.


title: Petroleum Engineering Data Text Classification Using Convolutional Neural Network Based Classifier

abstract: In this paper, authors introduce a convolutional neural network (CNN) based classification approach for petroleum engineering event. Recently, text classification problem has raised substantial attention in the field of machine learning. The aim of text classification process is to assign correct label to a text on the basis of its content. However, due to the imprecise of feature representation, most of former text classification methods cannot export satisfying result. To solve this problem, authors propose a model based on convolutional neural network. Authors conduct experiments with real world petroleum engineering data text, artificially labelled into 6 categories. Beside purposed CNN-based model, the authors also implement other baseline machine learning approaches: k nearest neighbor, random forest, support vector machines, recurrent neural network, gated recurrent units, long short-term memory. Among all these models, our CNN-based model reach best performance in text classification task. In the last part of this paper, authors compare and analysis their purpose and baseline model.


title: Vision-Aided Multi-UAV Autonomous Flocking in GPS-Denied Environment

abstract: This paper presents a sophisticated vision-aided flocking system for unmanned aerial vehicles (UAVs), which is able to operate in GPS-denied unknown environments for exploring and searching missions, and also able to adopt two types of vision sensors, day and thermal cameras, to measure relative motion between UAVs in different lighting conditions without using wireless communication. In order to realize robust vision-aided flocking, an integrated framework of tracking-learning-detection on the basis of multifeature coded correlation filter has been developed. To achieve long-term tracking, a redetector is trained online to adaptively reinitialize target for global sensing. An advanced flocking strategy is developed to address the autonomous multi-UAVs' cooperative flight. Light detection and ranging (LiDAR)-based navigation modules are developed for autonomous localization, mapping, and obstacle avoidance. Flight experiments of a team of UAVs have been conducted to verify the performance of this flocking system in a GPS-denied environment. The extensive experiments validate the robustness of the proposed vision algorithms in challenging scenarios.


An Interactive Visualization Tool for Nipype Medical Image Computing Pipelines


title: Open source HEVC analyzer for rapid prototyping (HARP)

abstract: The design of new HEVC extensions comes with the need for careful analysis of internal HEVC codec decisions. Several bitstream analyzers have evolved for this purpose and provide a visualization of encoder decisions as seen from a decoder viewpoint. None of the existing solutions is able to provide actual insight into the encoder and its RDO decision process. With one exception, all solutions are closed source and make adaption of their code to specific implementation needs impossible. Overall, development with the HM code base remains a time-consuming task. Here, we present the HEVC Analyzer for Rapid Prototyping (HARP), which directly addresses the above issues and is freely available under www.lms.lnt.de/HARP.


title: Artificial Intelligence and Data Science in the Automotive Industry

abstract: Data science and machine learning are the key technologies when it comes to the processes and products with automatic learning and optimization to be used in the automotive industry of the future. This article defines the terms "data science" (also referred to as "data analytics") and "machine learning" and how they are related. In addition, it defines the term "optimizing analytics" and illustrates the role of automatic optimization as a key technology in combination with data analytics. It also uses examples to explain the way that these technologies are currently being used in the automotive industry on the basis of the major subprocesses in the automotive value chain (development, procurement; logistics, production, marketing, sales and after-sales, connected customer). Since the industry is just starting to explore the broad range of potential uses for these technologies, visionary application examples are used to illustrate the revolutionary possibilities that they offer. Finally, the article demonstrates how these technologies can make the automotive industry more efficient and enhance its customer focus throughout all its operations and activities, extending from the product and its development process to the customers and their connection to the product.


title: Athos: Efficient Authentication of Outsourced File Systems ⋆

abstract: We study the problem of authenticated storage, where we wish to construct protocols that allow to outsource any complex file system to an untrusted server and yet ensure the file-system's integrity. We introduce Athos, a new, platform-independent and user-transparent architecture for authenticated outsourced storage. Using light-weight cryptographic primitives and efficient data-structuring techniques, we design authentication schemes that allow a client to efficiently verify that the file system is fully consistent with the exact history of updates and queries requested by the client. In Athos, file-system operations are verified in time that is logarithmic in the size of the file system using optimal storage complexity--constant storage overhead at the client and asymptotically no extra overhead at the server. We provide a prototype implementation of Athos validating its performance and its authentication capabilities.


title: D³ Data-Driven Documents

abstract: Data-Driven Documents (D3) is a novel representation-transparent approach to visualization for the web. Rather than hide the underlying scenegraph within a toolkit-specific abstraction, D3 enables direct inspection and manipulation of a native representation: the standard document object model (DOM). With D3, designers selectively bind input data to arbitrary document elements, applying dynamic transforms to both generate and modify content. We show how representational transparency improves expressiveness and better integrates with developer tools than prior approaches, while offering comparable notational efficiency and retaining powerful declarative components. Immediate evaluation of operators further simplifies debugging and allows iterative development. Additionally, we demonstrate how D3 transforms naturally enable animation and interaction with dramatic performance improvements over intermediate representations.


title: Managing human error in aviation.

abstract: Crew resource management (CRM) programs were developed to address team and leadership aspects of piloting modern airplanes. The goal is to reduce errors through team work. Human factors research and social, cognitive, and organizational psychology are used to develop programs tailored for individual airlines. Flight crews study accident case histories, group dynamics, and human error. Simulators provide pilots with the opportunity to solve complex flight problems. CRM in the simulator is called line-oriented flight training (LOFT). In automated cockpits CRM promotes the idea of automation as a crew member. Cultural aspects of aviation include professional, business, and national culture. The aviation CRM model has been adapted for training surgeons and operating room staff in human factors.


title: Smoothing algorithms for state-space models

abstract: Two-filter smoothing is a principled approach for performing optimal smoothing in non-linear non-Gaussian state–space models where the smoothing distributions are computed through the combination of ‘forward’ and ‘backward’ time filters. The ‘forward’ filter is the standard Bayesian filter but the ‘backward’ filter, generally referred to as the backward information filter, is not a probability measure on the space of the hidden Markov process. In cases where the backward information filter can be computed in closed form, this technical point is not important. However, for general state–space models where there is no closed form expression, this prohibits the use of flexible numerical techniques such as Sequential Monte Carlo (SMC) to approximate the two-filter smoothing formula. We propose here a generalised two-filter smoothing formula which only requires approximating probability distributions and applies to any state–space model, removing the need to make restrictive assumptions used in previous approaches to this problem. SMC algorithms are developed to implement this generalised recursion and we illustrate their performance on various problems.


title: Privacy Risk Assessment on Email Tracking

abstract: Today's online marketing industry has widely employed email tracking techniques, such as embedding a tiny tracking pixel, to track email opens of potential customers and measure marketing effectiveness. However, email tracking could allow miscreants to collect metadata information associated with email reading without user awareness and then leverage the information for stealthy surveillance, which has raised serious privacy concerns. In this paper, we present an in-depth and comprehensive study on the privacy implications of email tracking. First, we develop an email tracking system and perform realworld tracking on hundreds of solicited crowdsourcing participants. We estimate the amount of privacy-sensitive information available from email reading, assess privacy risks of information leakage, and demonstrate how easy it is to launch a long-term targeted surveillance attack in real scenarios by simply sending an email with tracking capability. Second, we investigate the prevalence of email tracking through a large-scale measurement, which includes more than 44,000 email samples obtained over a period of seven years. Third, we conduct a user study to understand users' perception of privacy infringement caused by email tracking. Finally, we evaluate existing countermeasures against email tracking and propose guidelines for developing more comprehensive and fine-grained prevention solutions.


title: Approximation Algorithms for K-Modes Clustering

abstract: In this paper, we study clustering with respect to the k-modes objective function, a natural formulation of clustering for categorical data. One of the main contributions of this paper is to establish the connection between k- modes and k-median, i.e., the optimum of k-median is at most the twice the optimum of k-modes for the same categorical data clustering problem. Based on this observation, we derive a deterministic algorithm that achieves an approximation factor of 2. Furthermore, we prove that the distance measure in k-modes defines a metric. Hence, we are able to extend existing approximation algorithms for metric k-median to k-modes. Empirical results verify the superiority of our method.


title: Malar Augmentation With a Polymethylmethacrylate-Enhanced Filler: Assessment of a 12-Month Open-Label Pilot Study

abstract: Background: Most filler procedures in the United States are performed with hyaluronic acid (HA) derivatives. Artefill (Suneva Medical, Inc, San Diego, California), the only polymethylmethacrylate (PMMA)–enhanced dermal filler approved by the US Food and Drug Administration (FDA), has been well tolerated by patients for treatment of nasolabial folds and has a safety profile similar to other approved fillers. ::: ::: Objectives: The authors investigate the safety and efficacy of Artefill for malar augmentation. ::: ::: Methods: This prospective, multisite, open-label study included a total of 24 patients with age-related lipoatrophy. Only patients with mild to moderate lipoatrophy were considered candidates for treatment. Artefill was injected in the supraperiosteal layer of the malar region, at a maximum volume of 6 mL (3 mL/side). Touch-up injections were performed at weeks 4 and 6, up to a maximum total volume of 8.8 mL. Standardized assessments of results were made at 2, 6, and 12 months. Outcome measures included the Global Aesthetic Improvement Scale (GAIS), change in malar lipoatrophy grade, and patient satisfaction. Standardized photographs of each patient were collected. ::: ::: Results: Average patient age was 48 ± 5 years. Average volume of injections was 5.55 ± 1.87 mL. Based on both the patient- and physician-rated GAIS, 95.8% of study participants were reported as being “improved” or “very much improved.” The change in malar lipoatrophy grade was significantly improved from baseline to 1 year by 0.96 ± 0.98 ( P < .0003). Patients also reported high levels of satisfaction, with 87.5% being “satisfied” or “very satisfied.” There were no reported adverse safety events in the study. ::: ::: Conclusions: Artefill demonstrated improvement in malar atrophy with a high level of patient satisfaction and an excellent safety profile. The absence of any adverse events in our study patients was notable, and we believe this is a result of the uniform nature of the PMMA particles in the Artefill and the strict and sterile manner in which this PMMA dermal filler is produced. ::: ::: Level of Evidence: 4 ::: ::: ![Graphic][1] [1]: /embed/inline-graphic-1.gif


title: Label Distribution-Based Facial Attractiveness Computation by Deep Residual Learning

abstract: Two challenges lie in the facial attractiveness computation research: the lack of true attractiveness labels (scores), and the lack of an accurate face representation. In order to address the first challenge, this paper recasts facial attractiveness computation as a label distribution learning (LDL) problem rather than a traditional single-label supervised learning task. In this way, the negative influence of the label incomplete problem can be reduced. Inspired by the recent promising work in face recognition using deep neural networks to learn effective features, the second challenge is expected to be solved from a deep learning point of view. A very deep residual network is utilized to enable automatic learning of hierarchical aesthetics representation. Integrating these two ideas, an end-to-end deep learning framework is established. Our approach achieves the best results on a standard benchmark SCUT-FBP dataset compared with other state-of-the-art work.


Machine Learning and Knowledge Discovery in Databases


title: Inkjet printed System-in-Package design and manufacturing

abstract: Additive manufacturing technology using inkjet offers several improvements to electronics manufacturing compared to current non-additive masking technologies. Manufacturing processes can be made more efficient, straightforward and flexible compared to subtractive masking processes, several time-consuming and expensive steps can be omitted. Due to the additive process, material loss is minimal, because material is never removed as with etching processes. The amounts of used material and waste are smaller, which is advantageous in both productivity and environmental means. Furthermore, the additive inkjet manufacturing process is flexible allowing fast prototyping, easy design changes and personalization of products. Additive inkjet processing offers new possibilities to electronics integration, by enabling direct writing on various surfaces, and component interconnection without a specific substrate. The design and manufacturing of inkjet printed modules differs notably from the traditional way to manufacture electronics. In this study a multilayer inkjet interconnection process to integrate functional systems was demonstrated, and the issues regarding the design and manufacturing were considered.


title: On Using TLS to Secure In-Vehicle Networks

abstract: A trend in modern in-vehicle networks is the use of network technologies with higher bandwidth such as Automotive Ethernet. As a result, more sophisticated security technologies may be used to secure the communication. In this paper, we investigate whether the Transport Layer Security Protocol (TLS) is applicable to secure in-vehicle networks. First, we identify the security and performance requirements as well as the communication scenarios which must be supported by the TLS communication. Next, we discuss how these requirements can be realized with TLS. This also includes the discussion of the certificate management. Finally, we present and discuss our prototypical TLS implementation on a typical automotive platform and show that TLS is able to fulfill most performance requirements of the automotive industry.


title: Evaluation of Four Approaches for "Sentiment Analysis on Movie Reviews": The Kaggle Competition

abstract: In this paper we present four different approaches for automatic sentiment classification on movie reviews. The proposed approaches: (a) statistical based, (b) bag-of-words based, (c) content based and (d) lexicon based approach, were evaluated in the "Sentiment Analysis on Movie Reviews" Kaggle competition. The competition results showed that each of the first three aforementioned approaches achieved approximately 65% sentiment classification accuracy, while the latter performed poorly compared to the others. The combination scheme of (b)-(c)-(d) proved to be our best performing set up achieving our best classification accuracy 67.931%, which ranked us in the 6th position among 861 participants.