title: Who is peeping at your passwords at Starbucks? — To catch an evil twin access point

abstract: In this paper, we consider the problem of “evil twin” attacks in wireless local area networks (WLANs). An evil twin is essentially a phishing (rogue) Wi-Fi access point (AP) that looks like a legitimate one (with the same SSID name). It is set up by an adversary, who can eavesdrop on wireless communications of users' Internet access. Existing evil twin detection solutions are mostly for wireless network administrators to verify whether a given AP is in an authorized list or not, instead of for a wireless client to detect whether a given AP is authentic or evil. Such administrator-side solutions are limited, expensive, and not available for many scenarios. For example, for traveling users who use wireless networks at airports, hotels, or cafes, they need to protect themselves from evil twin attacks (instead of relying on those wireless network providers, which typically may not provide strong security monitoring/management service). Thus, a lightweight and effective solution for these users is highly desired. In this work, we propose a novel user-side evil twin detection technique that outperforms traditional administrator-side detection methods in several aspects. Unlike previous approaches, our technique does not need a known authorized AP/host list, thus it is suitable for users to identify and avoid evil twins. Our technique does not strictly rely on training data of target wireless networks, nor depend on the types of wireless networks. We propose to exploit fundamental communication structures and properties of such evil twin attacks in wireless networks and to design new active, statistical and anomaly detection algorithms. Our preliminary evaluation in real-world widely deployed 802.11b and 802.11g wireless networks shows very promising results. We can identify evil twins with a very high detection rate while keeping a very low false positive rate.


title: Body Mass Index and Quality of Life: Examining Blacks and Whites With Chronic Pain

abstract: Obesity contributes to several chronic pain conditions, negatively affecting quality of life (QOL). However, obesity's relationship with chronic pain is poorly understood. This prospective survey study examines obesity's role in chronic pain and subsequent impact on QOL. Black and white patients with chronic pain (N 183, 18 -50 years of age, 64% women, 50% black) were studied to determine predictors for the presence of body mass index (BMI) information in medical records, group BMI differences, and how BMI and pain contribute to mental/physical outcomes. BMI was calculated by using medical records nearest the enrollment date. Sociodemographic data, sleep, pain, function- ing, disability, and depression were measured. BMI data were available for 143 subjects (78%), with blacks having a higher BMI (P.002). Black (P.08), people with higher pain (P < .01), affective distress (P < .01), and post-traumatic stress disorder scores (P.07) were less likely to have their BMI recorded. Path analysis tested relationships between sociodemographics, BMI and pain with func- tioning, depression, and disability. BMI was positively associated with black race and age and predicted poorer physical functioning and greater disability. Pain was not predicted by race or age but was associated with all outcomes. These findings support assessing BMI when managing chronic pain and its negative impact on QOL, especially for minority patients. Perspective: This study examines the relationships among sociodemographic factors, BMI, and QOL in chronic pain. Our results demonstrate significant racial disparity among chronic pain patients in assessing BMI and quality of pain care. These findings support obesity's negative impact on overall health and the importance of measuring BMI in patients with chronic pain, especially racial and ethnic minorities.


title: Abstraction of 2D shapes in terms of parts

abstract: Abstraction in imagery results from the strategic simplification and elimination of detail to clarify the visual structure of the depicted shape. It is a mainstay of artistic practice and an important ingredient of effective visual communication. We develop a computational method for the abstract depiction of 2D shapes. Our approach works by organizing the shape into parts using a new synthesis of holistic features of the part shape, local features of the shape boundary, and global aspects of shape organization. Our abstractions are new shapes with fewer and clearer parts.


title: Scalable micro-service based approach to FHIR server with golang and No-SQL

abstract: Fast Health Interoperability Resource (FHIR) for Electronic Health Record (EHR) not only opens a new era for health-care systems to exchange data between themselves but also allows other various systems following the same framework to communicate between them. FHIR is engaging and evolving rapidly and it is creating the need of massive data storage, retrieval and transfer requirements. For a server that implements this framework, has to be agile to cope with the growing and massive data handling. E-health-care data also seem to grow proportional to time. So, an ideal FHIR Server has to be scalable to fit this demand over time. Several different tools could help store, anonymize, analyze and extract data because there are no single tools to get all those things done. In this work, we propose a scalable, agile, and reliable Micro-service based Architecture for FHIR server that is highly available with the help of Document Oriented Database and helps to store health-care data. The proposed architecture is fast responsive. It gives our system the flexibility to use different tools that implement FHIR Framework interface in such way that the whole system could be made vertically scalable and the system can perform better in terms of time complexity compared to a Monolithic based implementation of the framework. Experimental results show that the proposed model can be highly used as an alternative to currently available FHIR system implementation.


title: Rating Image Aesthetics Using Deep Learning

abstract: This paper investigates unified feature learning and classifier training approaches for image aesthetics assessment . Existing methods built upon handcrafted or generic image features and developed machine learning and statistical modeling techniques utilizing training examples. We adopt a novel deep neural network approach to allow unified feature learning and classifier training to estimate image aesthetics. In particular, we develop a double-column deep convolutional neural network to support heterogeneous inputs, i.e., global and local views, in order to capture both global and local characteristics of images . In addition, we employ the style and semantic attributes of images to further boost the aesthetics categorization performance . Experimental results show that our approach produces significantly better results than the earlier reported results on the AVA dataset for both the generic image aesthetics and content -based image aesthetics. Moreover, we introduce a 1.5-million image dataset (IAD) for image aesthetics assessment and we further boost the performance on the AVA test set by training the proposed deep neural networks on the IAD dataset.


title: Handwritten digit recognition: applications of neural network chips and automatic learning

abstract: Two novel methods for achieving handwritten digit recognition are described. The first method is based on a neural network chip that performs line thinning and feature extraction using local template matching. The second method is implemented on a digital signal processor and makes extensive use of constrained automatic learning. Experimental results obtained using isolated handwritten digits taken from postal zip codes, a rather difficult data set, are reported and discussed. >


title: Approximating Displacement with the Body Velocity Integral

abstract: In this paper, we present a technique for approximating the net displacement of a locomoting system over a gait without directly integrating its equations of motion. The approximation is based on a volume integral, which, among other benefits, is more open to optimization by algorithm or inspection than is the full displacement integral. Specifically, we develop the concept of a body velocity integral (BVI), which is computable over a gait as a volume integral via Stokes’s theorem. We then demonstrate that, given an appropriate choice of coordinates, the BVI for a gait approximates the displacement of the system over that gait. This consideration of coordinate choice is a new approach to locomotion problems, and provides significantly improved results over past attempts to apply Stokes’s theorem to gait analysis.


title: OpenAG: A Globally Distributed Network of Food Computing

abstract: With natural resource scarcity, flattening yields, loss of biodiversity, changing climates, environmental degradation, and booming urban populations, the current food system is rapidly approaching its natural limit. What will define the next agricultural revolution, and how will it impact and shape global societies? This is the central research question of the Open Agriculture (OpenAG) Initiative at the MIT Media Lab. The goal is an alternative distributed farming system based on new methods of communication, sensing, data collection, and automation that will enable network effect advantages in the next generation of food production. This department is part of a special issue on pervasive food.


title: Glow: Graph Lowering Compiler Techniques for Neural Networks

abstract: This paper presents the design of Glow, a machine learning compiler for heterogeneous hardware. It is a pragmatic approach to compilation that enables the generation of highly optimized code for multiple targets. Glow lowers the traditional neural network dataflow graph into a two-phase strongly-typed intermediate representation. The high-level intermediate representation allows the optimizer to perform domain-specific optimizations. The lower-level instruction-based address-only intermediate representation allows the compiler to perform memory-related optimizations, such as instruction scheduling, static memory allocation and copy elimination. At the lowest level, the optimizer performs machine-specific code generation to take advantage of specialized hardware features. Glow features a lowering phase which enables the compiler to support a high number of input operators as well as a large number of hardware targets by eliminating the need to implement all operators on all targets. The lowering phase is designed to reduce the input space and allow new hardware backends to focus on a small number of linear algebra primitives.


title: Clinical and Pathologic Features of a Suspected Selenium Deficiency in Captive Plains Zebras

abstract: Previous studies have shown that selenium (Se) deficiency is associated with nutritional myopathy, known as white muscle disease (WMD), in horses. However, correlations between Se deficiency and clinical findings, such as hematologic biochemical values and pathological features, have not been evaluated in captive plains zebras. The purpose of the present study was to investigate the clinical and pathologic features that may be caused by a Se deficiency in the captive plains zebra. Clinical findings, feed analyses, hematologic biochemical analyses, response to treatment, and pathologic examination were assessed in six affected plains zebras. The dietary concentration of Se in feed was also tested. Sudden death occurred in two cases during the first day of the onset of symptoms. Two zebras died at 4 days and two zebras survived after treatment. The clinical signs in affected animals were characterized by general weakness, astasia, and abnormal postural positions. The Se concentration in hay from the breeding stable was low, based on the reference value. Glutathione peroxidase (GSH-Px) activity was lower compared with the equine reference value. Multiple areas of subcutaneous steatitis and pale skeletal muscle and myocardium were revealed at gross necropsy. Degeneration and necrosis of myocardial and skeletal muscles, as well as congestion of the liver, lung, and kidney were found via histopathological examination. No suspected bacterial infections were found. Feed analyses, response to treatment, serum GSH-Px activity, and pathological features suggest that Se deficiency may have caused the disease in the six affected captive plains zebra.


Adaptive End-to-End Dialog Systems


title: Egocentric 6-DoF Tracking of Small Handheld Objects

abstract: Virtual and augmented reality technologies have seen significant growth in the past few years. A key component of such systems is the ability to track the pose of head mounted displays and controllers in 3D space. We tackle the problem of efficient 6-DoF tracking of a handheld controller from egocentric camera perspectives. We collected the HMD Controller dataset which consist of over 540,000 stereo image pairs labelled with the full 6-DoF pose of the handheld controller. Our proposed SSD-AF-Stereo3D model achieves a mean average error of 33.5 millimeters in 3D keypoint prediction and is used in conjunction with an IMU sensor on the controller to enable 6-DoF tracking. We also present results on approaches for model based full 6-DoF tracking. All our models operate under the strict constraints of real time mobile CPU inference.


Robot Faces that Follow Gaze Facilitate Attentional Engagement and Increase Their Likeability


title: IExM: Information Extraction System for Movies

abstract: In this demonstration, we present Information Extraction System for Movies(IExM), which helps extract relation instances from un- labeled movie articles. We have designed a new distant-supervised learning algorithm: Improved Pattern Ranking Algorithm(IPRA) to extract relation instances from unlabeled articles, which iteratively generates new patterns starting from a limited set of seed instances, and extracts new instances using high-ranking pattern in a precise and effective way. IPRA also has a special estimation for the newly generated patterns based on the quality estimation of the instances that generate the patterns and ranks patterns' quality based on var- ious factors.


Neural Network Based Face Recognition Using Matlab


title: LSTM for dynamic emotion and group emotion recognition in the wild

abstract: In this paper, we describe our work in the fourth Emotion Recognition in the Wild (EmotiW 2016) Challenge. For video based emotion recognition sub-challenge, we extract acoustic features, LBPTOP, Dense SIFT and CNN-LSTM features to recognize the emotions of film characters. For group level emotion recognition sub-challenge, we use LSTM and GEM model. We train linear SVM classifiers for these kinds of features on the AFEW6.0 and HAPPEI dataset, and use a fusion network we proposed to combine all the extracted features at the decision level. The final achievements we have gained are 51.54% accuracy on the AFEW testing set and 0.836 RMSE on the HAPPEI testing set.


title: Blockly goes to work: Block-based programming for industrial robots

abstract: The block-based approach to programming is an effective way to engage young learners in programming and the powerful ideas of computing. In this paper, we explore the potential of using this same approach in a very different programming context: industrial robotics. Using a customized language built with the Blockly library, we created a block-based interface for programming a one-armed industrial robot. This paper presents a block-based robot programming language called Robot Blockly, focusing on how the various affordances of block-based programming were utilized to make the challenge of robot programming more accessible. We also present results from a small-scale study showing adults with no prior programming experience successfully programming a virtual robot to accomplish a pick and place task. The contribution of this work is in showing the potential for block-based programming beyond young learners and classrooms.


title: Integration of multi-sensor occupancy grids into automotive ECUs

abstract: Occupancy Grids (OGs) are a popular framework for robotic perception. They were recently adopted for performing multi-sensor fusion and environment mapping for autonomous vehicles. However, high computational requirements strongly hinder their integration into less powerful automotive ECUs. To overcome this problem, we propose an algorithmic improvement for mapping range measurements into OGs. Experiments were conducted on a vehicle equipped with 16 LIDAR scans. Results demonstrate that a single-core ARM cortex A9 can build now in real-time OGs that map urban traffic scenarios of 100m-by-100m.


title: Creation of a deep convolutional auto-encoder in Caffe

abstract: The development of a deep (stacked) convolutional auto-encoder in the Caffe deep learning framework is presented in this paper. We describe simple principles which we used to create this model in Caffe. The proposed model of convolutional auto-encoder does not have pooling/unpooling layers yet. The results of our experimental research show comparable accuracy of dimensionality reduction in comparison with a classic auto-encoder on the example of MNIST dataset.


title: Dynamic refraction stereo

abstract: In this paper we consider the problem of reconstructing the 3D position and surface normal of points on an unknown, arbitrarily-shaped refractive surface. We show that two viewpoints are sufficient to solve this problem in the general case, even if the refractive index is unknown. The key requirements are 1) knowledge of a function that maps each point on the two image planes to a known 3D point that refracts to it, and 2) light is refracted only once. We apply this result to the problem of reconstructing the time-varying surface of a liquid from patterns placed below it. To do this, we introduce a novel “stereo matching” criterion called refractive disparity, appropriate for refractive scenes, and develop an optimization-based algorithm for individually reconstructing the position and normal of each point projecting to a pixel in the input views. Results on reconstructing a variety of complex, deforming liquid surfaces suggest that our technique can yield detailed reconstructions that capture the dynamic behavior of free-flowing liquids.


title: Hybrid participatory sensing for analyzing group dynamics in the largest annual religious gathering

abstract: Understanding crowd dynamics of large-scale events is crucial to deliver a pleasant experience for the participants. In this paper, we propose a novel hybrid participatory sensing approach to capture large group dynamics. Specifically, our approach is based on distributing a large number of tiny, wearable Bluetooth Low Energy (BLE) tags and few smartphones among the group members. We start by identifying the best configuration; in terms of transmit power and beaconing interval; for detecting BLE tags in indoor and outdoor environments. Then, as a case study, we deploy the system during the six main days of the Hajj pilgrimage in 2014, which is the world's largest annual religious gathering. We used the proposed hybrid participatory approach to collect the mobility data of pilgrim groups based on GPS location and co-occurring BLE tag detections. Our system provided up to 80% group-wise detectability in a single scan event. Moreover, 98% cumulative unique tags detectability is achieved in the whole event, leading to 0.74 million records. Analysis of the group dynamics revealed unexpected behavior and interesting findings. In particular, regional variations are found among different groups in the entry or exit times, duration of stay, and group cohesion; which are attributed to congestion, improper arrangements, or the nature of activities. Furthermore, we show sub-community clusters could be identified revealing clear biases based on the demographic characteristics. Based on this case study, we also give suggestions on using the proposed system for other large-scale events.


title: A Case Study on Implementing False Data Injection Attacks Against Nonlinear State Estimation

abstract: Smart grid aims to improve control and monitoring routines to ensure reliable and efficient supply of electricity. The rapid advancements in information and communication technologies of Supervisory Control And Data Acquisition (SCADA) networks, however, have resulted in complex cyber physical systems. This added complexity has broadened the attack surface of power-related applications, amplifying their susceptibility to cyber threats. A particular class of system integrity attacks against the smart grid is False Data Injection (FDI). In a successful FDI attack, an adversary compromises the readings of grid sensors in such a way that errors introduced into estimates of state variables remain undetected. This paper presents an end-to-end case study of how to instantiate real FDI attacks to the Alternating Current (AC) --nonlinear-- State Estimation (SE) process. The attack is realized through firmware modifications of the microprocessor-based remote terminal systems, falsifying the data transmitted to the SE routine, and proceeds regardless of perfect or imperfect knowledge of the current system state. The case study concludes with an investigation of an attack on the IEEE 14 bus system using load data from the New York Independent System Operator (NYISO).


title: Compact Full Duplex MIMO Radios in D2D Underlaid Cellular Networks: From System Design to Prototype Results

abstract: This paper considers the implementation and application possibilities of a compact full duplex multiple-input multiple-output (MIMO) architecture, where direct communication exists between users, e.g., device-to-device (D2D) and cellular link coexisting on the same spectrum. For the architecture of the compact full duplex radio, we combine an analog self-interference canceler-based dual polarization with high cross-polarization discrimination and long-term evolution (LTE)-based per-subcarrier digital self-interference canceler. While we consider the compactness and power efficiency of an analog solution, we focus on the digital canceler design with robustness to a frequency-selective channel and high compatibility with a conventional LTE system. For an over-the-air wireless experiment of full duplex testbed with a two-user-pair, we implement a full duplex MIMO physical layer, supporting 20-MHz bandwidth, on an Field-Programmable Gate Array-based software-defined radio platform. Furthermore, we propose a novel timing synchronization method to construct a more viable full duplex MIMO link. By having the full duplex link prototype fully operating in real time, we present the first characterization of the proposed compact full duplex MIMO performance depending on the transmit power of the full duplex node. We also show the link quality between nodes. One of the crucial insights of this paper is that the full duplex operation of a user is capable of acquiring the throughput gain if the user has self-interference capability with guaranteed performance.


title: How do changes in speed affect the perception of duration

abstract: Six experiments investigated how changes in stimulus speed influence subjective duration. Participants saw rotating or translating shapes in three conditions: constant speed, accelerating motion, and decelerating motion. The distance moved and average speed were the same in all three conditions. In temporal judgment tasks, the constant-speed objects seemed to last longer than the decelerating objects, which in turn seemed to last longer than the accelerating stimuli. In temporal reproduction tasks, the difference between accelerating and decelerating stimuli disappeared; furthermore, watching an accelerating shape lengthened the apparent duration of the subsequent (static) display. These results (a) suggest that temporal judgment and reproduction can dissociate for moving stimuli because the stimulus influences the apparent duration of the subsequent interval, and (b) constrain theories of time perception, including those which emphasize memory storage, those which emphasize the existence of a pacemaker-accumulator timing system, and those which emphasize the division of attention between temporal and non-temporal information processing.


title: Policy transfer via modularity

abstract: Non-prehensile manipulation, such as pushing, is an important function for robots to move objects and is sometimes preferred as an alternative to grasping. However, due to unknown frictional forces, pushing has been proven a difficult task for robots. We explore the use of reinforcement learning to train a robot to robustly push an object. In order to deal with the sample complexity of training such a method, we train the pushing policy in simulation and then transfer this policy to the real world. In order to ease the transfer from simulation, we propose to use modularity to separate the learned policy from the raw inputs and outputs; rather than training ``end-to-end," we decompose our system into modules and train only a subset of these modules in simulation. We further demonstrate that we can incorporate prior knowledge about the task into the state space and the reward function to speed up convergence. Finally, we introduce "reward guiding" to modify the reward function and further reduce the training time. We demonstrate, in both simulation and real-world experiments, that such an approach can be used to reliably push an object from many initial positions and orientations.


title: Robust least squares twin support vector machine for human activity recognition

abstract: Graphical abstractDisplay Omitted HighlightsIntroduces hierarchical approach to deal with multi-class activity classification problem.LS-TWSVM based classifier that deals with noise in activity recognition framework.Introduce the incremental version of RLS-TWSVM in activity recognition framework. Human activity recognition is an active area of research in Computer Vision. One of the challenges of activity recognition system is the presence of noise between related activity classes along with high training and testing time complexity of the system. In this paper, we address these problems by introducing a Robust Least Squares Twin Support Vector Machine (RLS-TWSVM) algorithm. RLS-TWSVM handles the heteroscedastic noise and outliers present in activity recognition framework. Incremental RLS-TWSVM is proposed to speed up the training phase. Further, we introduce the hierarchical approach with RLS-TWSVM to deal with multi-category activity recognition problem. Computational comparisons of our proposed approach on four well-known activity recognition datasets along with real world machine learning benchmark datasets have been carried out. Experimental results show that our method is not only fast but, yields significantly better generalization performance and is robust in order to handle heteroscedastic noise and outliers.


title: Low cost crowd counting using audio tones

abstract: With mobile devices becoming ubiquitous, collaborative applications have become increasingly pervasive. In these applications, there is a strong need to obtain a count of the number of mobile devices present in an area, as it closely approximates the size of the crowd. Ideally, a crowd counting solution should be easy to deploy, scalable, energy efficient, be minimally intrusive to the user and reasonably accurate. Existing solutions using data communication or RFID do not meet these criteria. In this paper, we propose a crowd counting solution based on audio tones, leveraging the microphones and speaker phones that are commonly available on most phones, tackling all the above criteria. We have implemented our solution on 25 Android phones and run several experiments at a bus stop, aboard a bus, within a cafeteria and a classroom. Experimental evaluations show that we are able to achieve up to 90% accuracy and consume 81% less energy than the WiFi interface in idle mode.


title: Predicting protein secondary structure using continuous wavelet transform and Chou-Fasman method

abstract: Secondary structure of a protein can be predicted efficiently by combining continuous wavelet transform (CWT) and Chou-Fasman method. We selected a protein with ID 1gca from PDB database, and substitute every amino acid of the protein with corresponding hydrophobic value. Then CWT was used to get the nucleated residues of certain type of secondary structure. The regions were extended along the protein sequence in each direction based on Chou-Fasman rules. The prediction accuracy for alpha-helix, beta-sheet (regular secondary structures), loop (irregular secondary structure) is 80.57%, 75.41%, and 92.31%, respectively


title: Design of the Hydraulically Actuated, Torque-Controlled Quadruped Robot HyQ2Max

abstract: This paper presents the design of the hydraulically actuated quadruped robot HyQ2Max . HyQ2Max is an evolution of the 80 kg agile and versatile robot HyQ. Compared to HyQ, the new robot needs to be more rugged, more powerful and extend the existing locomotion skills with self-righting capability. Since the robot's actuation system has an impact on many aspects of the overall design/specifications of the robot (e.g., payload, speed, torque, overall mass, and compactness), this paper will pay special attention to the selection and sizing of the joint actuators. To obtain meaningful joint requirements for the new machine, we simulated seven characteristic motions that cover a wide range of required behaviors of an agile rough terrain robot, including trotting on rough terrain, stair climbing, push recovery, self-righting, etc. We will describe how to use the obtained joint requirements for the selection of the hydraulic actuator types, four-bar linkage parameters, and valve size. Poorly sized actuators may lead to limited robot capabilities or higher cost, weight, energy consumption, and cooling requirements. The main contributions of this paper are: 1) a novel design of an agile quadruped robot capable of performing trotting/crawling over flat/uneven terrain, balancing, and self-righting; 2) a detailed method to find suitable hydraulic cylinder/valve properties and linkage parameters with a specific focus on optimizing the actuator areas; and 3) to the best knowledge of the authors, the most complete review of hydraulic quadruped robots.


title: mPHASiS: Mobile patient healthcare and sensor information system

abstract: Pervasive care and chronic disease management to reduce institutionalization is a priority for most western countries. The realization of next generation ubiquitous and pervasive healthcare systems will be a challenging task, as these systems are likely to involve a complex structure. Such systems will consist of various devices, ranging from resource-constrained sensors and actuators to complex multimedia devices, supporting time critical applications. This is further compounded by cultural and socio-economical factors that must be addressed for next generation healthcare systems to be widely diffused and used. In this study, the requirements for a vital sign monitoring solution space is derived and mPHASiS is developed based on these requirements. mPHASiS is an end to end solution not only providing sensor networking and vital sign monitoring but also closing the loop by signaling alert messages to the caregiver and allowing pervasive access to vital signs of a patient using smartphones over a heterogeneous network. A role based access control mechanism is developed to limit access to sensitive data. The end to end delay and delay variations for both vital sign data collection and pervasive access are analyzed. mPHASiS is developed as a complementary solution augmenting functionality of a hospital information system and can be loosely couple with the hospital information system using webservices.


title: Systematic Review: Impact of Health Information Technology on Quality, Efficiency, and Costs of Medical Care

abstract: BACKGROUND ::: Experts consider health information technology key to improving efficiency and quality of health care. ::: ::: ::: PURPOSE ::: To systematically review evidence on the effect of health information technology on quality, efficiency, and costs of health care. ::: ::: ::: DATA SOURCES ::: The authors systematically searched the English-language literature indexed in MEDLINE (1995 to January 2004), the Cochrane Central Register of Controlled Trials, the Cochrane Database of Abstracts of Reviews of Effects, and the Periodical Abstracts Database. We also added studies identified by experts up to April 2005. ::: ::: ::: STUDY SELECTION ::: Descriptive and comparative studies and systematic reviews of health information technology. ::: ::: ::: DATA EXTRACTION ::: Two reviewers independently extracted information on system capabilities, design, effects on quality, system acquisition, implementation context, and costs. ::: ::: ::: DATA SYNTHESIS ::: 257 studies met the inclusion criteria. Most studies addressed decision support systems or electronic health records. Approximately 25% of the studies were from 4 academic institutions that implemented internally developed systems; only 9 studies evaluated multifunctional, commercially developed systems. Three major benefits on quality were demonstrated: increased adherence to guideline-based care, enhanced surveillance and monitoring, and decreased medication errors. The primary domain of improvement was preventive health. The major efficiency benefit shown was decreased utilization of care. Data on another efficiency measure, time utilization, were mixed. Empirical cost data were limited. ::: ::: ::: LIMITATIONS ::: Available quantitative research was limited and was done by a small number of institutions. Systems were heterogeneous and sometimes incompletely described. Available financial and contextual data were limited. ::: ::: ::: CONCLUSIONS ::: Four benchmark institutions have demonstrated the efficacy of health information technologies in improving quality and efficiency. Whether and how other institutions can achieve similar benefits, and at what costs, are unclear.


Towards Efficient Model for Automatic Text Summarization


title: Developing a Conceptual Framework for Life Skills Interventions

abstract: The purpose of the article is to outline a comprehensive conceptual frame- work for life skills interventions by integrating aspects of Basic Needs Theory (BNT) and Life Development Intervention (LDI). In particular, we advocate the integration of (a) the three basic psychological needs of autonomy, competence, and relatedness and (b) the needs-supportive motivational climate from BNT with the LDI framework. When these basic psychological needs are satisfied, people experience positive psychological development and optimal psychological well-being—the stated outcome goals of most life skills programs. Without the development of a conceptual framework, it is difficult to determine whether individual life skills interventions achieve optimal psychologi- cal well-being. By developing this framework, we seek to identify and articulate the key underlying psychological mechanisms (i.e., basic needs of autonomy, competence, and relatedness) that contribute to optimal human functioning and positive psychosocial development in all life skill programs. The implications for counseling psychologists' research and practice are also considered.


title: Visualizing Hidden Themes of Taxi Movement with Semantic Transformation

abstract: A new methodology is developed to discover and analyze the hidden knowledge of massive taxi trajectory data within a city. This approach creatively transforms the geographic coordinates (i.e. latitude and longitude) to street names reflecting contextual semantic information. Consequently, the movement of each taxi is studied as a document consisting of the taxi traversed street names, which enables semantic analysis of massive taxi data sets as document corpora. Hidden themes, namely taxi topics, are identified through textual topic modeling techniques. The taxi topics reflect urban mobility patterns and trends, which are displayed and analyzed through a visual analytics system. The system integrates interactive visualization tools, including taxi topic maps, topic routes, street clouds and parallel coordinates, to visualize the probability-based topical information. Urban planners, administration, travelers, and drivers can conduct their various knowledge discovery tasks with direct semantic and visual assists. The effectiveness of this approach is illustrated by case studies using a large taxi trajectory data set acquired from 21, 360 taxis in a city.


title: Structurally modulated Li-rich cathode materials through cooperative cation doping and anion hybridization

abstract: High capacity Li-rich materials are mighty contenders for building rechargeable batteries that coincide with the demand in energy density. Fully realizing the extraordinary capacity involves oxygen evolution and related cation migration, resulting in phase transitions and deteriorations that would hinder their practical application. In an attempt to enhance the anodic redox participation and stabilize the structure at the same time, we proposed a structural modulation strategy with modification on anion hybridization intensifying and cation doping. Spectator ions with large ionic radius were introduced into the lattice during calcination with stannous chloride and the d-p hybridization between transition metal 3d and oxygen 2p orbitals was subsequently intensified along with expelling weakly bonded chloride species in the reheating process. Both of the reversible capacity and stability upon cycling were remarkably improved through the cooperation of bond alteration and dopant. This strategy might provide new insight into the modulation of the structure to truly fulfill the potential of Li-rich materials.


title: Generative Adversarial Source Separation

abstract: Generative source separation methods such as non-negative matrix factorization (NMF) or auto-encoders, rely on the assumption of an output probability density. Generative Adversarial Networks (GANs) can learn data distributions without needing a parametric assumption on the output density. We show on a speech source separation experiment that, a multi-layer perceptron trained with a Wasserstein-GAN formulation outperforms NMF, auto-encoders trained with maximum likelihood, and variational auto-encoders in terms of source to distortion ratio.


title: Emergency Department crowding and hospital bed shortage: is Lean a smart answer? A systematic review.

abstract: OBJECTIVE ::: Emergency Departments (EDs) worldwide face the challenges of crowding, waiting times, and cost containment. This review aims to provide a synthesis of the current literature focused on how Lean Thinking Principles and tools can be applied in an ED to address overcrowding and hospital admissions. ::: ::: ::: MATERIALS AND METHODS ::: Primary studies showing Lean interventions and implementation in ED visits, not requiring additional resources measuring specific outcomes (i.e. length of stay, patient volume, patient satisfaction, waiting times for the first visit, waiting times for diagnostic results, left without being seen) were selected. PubMed, Scopus, CINAHL, EconLit, NHS Economic Evaluation Database, Business Sources Complete, and Health Technology Assessment were used to conduct searches. Full-text articles of all potentially relevant publications were reviewed for eligibility. Discrepancies were resolved through discussion by all reviewers. Quality assessment and critical appraisal of selected studies were also evaluated by applying the Quality Improvement Minimum Quality Criteria Set. ::: ::: ::: RESULTS ::: Nine before-and-after studies met these eligibility criteria. Management of patient flow was the main intervention. Almost all studies showed EDs performance improvement: increased patient volume, decreased length of stay and number of patients left without being seen, reduced costs, and increased patient satisfaction. Only one case reported worse results after Lean intervention implementation. ::: ::: ::: CONCLUSIONS ::: Though Lean Principals have been used in healthcare for many years conclusion of their effects could still not be drawn. Surely, human-centered approach, top management support, work standardization, resources allocation and adaptation to the local context seem to be crucial for success. Furthermore, higher quality studies are needed: specific research design, appropriate statistical tests and outcome measures are needed. Before large-scale implementation, further studies are needed to evaluate the true ability of Lean interventions to improve healthcare delivery.


title: Protection of mesquite cordwood and posts from borers.

abstract: Describes the value of the mesquite tree, and the threat of the borer to it. Also describes the life cycle of the borer.


title: Omnidirectional planar folded dipole antenna

abstract: An omnidirectional planar antenna comprising two back-to-back folded dipoles printed on a dielectric substrate is presented. A constructed prototype suitable for WLAN operation in the 5-GHz band, such as the 5.2-GHz (5150-5350 MHz) and 5.8-GHz (5725-5875 GHz) bands, is demonstrated. The constructed prototype shows a wide impedance bandwidth of larger than 1.1 GHz (about 20% centered at 5.5 GHz), and a stable antenna gain level of about 4.5 dBi, with small variations (< 2 dBi) for omnidirectional radiation, is obtained across the bandwidth. Details of the antenna design and the experimental results are presented.


title: Noncontact Monitoring Breathing Pattern, Exhalation Flow Rate and Pulse Transit Time

abstract: We present optical imaging-based methods to measure vital physiological signals, including breathing frequency (BF), exhalation flow rate, heart rate (HR), and pulse transit time (PTT). The breathing pattern tracking was based on the detection of body movement associated with breathing using a differential signal processing approach. A motion-tracking algorithm was im- plemented to correct random body movements that were unre- lated to breathing. The heartbeat pattern was obtained from the color change in selected region of interest (ROI) near the subject's mouth, and the PTT was determined by analyzing pulse patterns at different body parts of the subject. The measured BF, exhaled volume flow rate and HR are consistent with those measured simul- taneously with reference technologies (r =0 .98, p< 0.001 for HR; r =0 .93, p< 0.001 for breathing rate), and the measured PTT difference (30-40 ms between mouth and palm) is comparable to the results obtained with other techniques in the literature. The imaging-based methods are suitable for tracking vital physiological parameters under free-living condition and this is the first demon- stration of using noncontact method to obtain PTT difference and exhalation flow rate.


title: Business process management for SMEs: an exploratory study of implementation factors for the Australian wine industry

abstract: Gartner EXPPremier (2005) identified Business Process Management (BPM) as the number one business priority and building Business Process Capability as a major challenge for senior executives within the coming years. The focus of BPM in practice and related research has been its application in large organisations. The general value proposition of BPM, however, is also of significance for small and medium-sized enterprises (SMEs). SMEs within the wine industry have only recently begun to apply BPM principles to their business. The main motivating factors for this business improvement effort is a need to cope with consolidation trends and the global grape glut, which are forcing wine businesses to increase operational efficiency. The wine industry has been selected as a case study for this research not only due to its local significance, its growth driven by globalisation and its contribution to the economy, but also the relative immaturity in terms of establishing concepts of a process-oriented organisation. This study aims to explore and structure the major issues of BPM adoption and implementation as the first such research initiative for SMEs in the wine industry. The research was conducted in Western Australia and involved qualitative data collection including interviews and content analysis of existing documentation. The study shows that the lack of financial resources, time, and knowledge of BPM are the major factors inhibiting BPM implementation for SMEs in the Australian wine industry.


title: Dynamic road traffic management based on krushkal's algorithm

abstract: The dynamic road traffic management is based on the dynamic vehicle routing during peak hour traffic. The real time traffic management has become a hectic problem in daily life due to the increasing traffic, sudden accidents, and bottle necks due to various reasons. In Dynamic Vehicle Navigation System (DVNS), the real time traffic junctions are mapped as nodes and the traffic rate between the signals is considered as the link weight for the selection of routes from source to destination. The selection of the route depends on various parameters such as traffic rate, speed of the vehicle, shortest path etc. The dynamic route selection is implemented using the krushkal's algorithm based on different parameters. The end-user selects the source and destination and sends the information to our system using Google maps; our system suggests the optimal route to the end-user based on the dynamic routing table. The traffic in-charge personnel have to update the traffic rates using their mobile PDA. The Dynamic routing table is updated periodically to accomplish the optimal solution.


title: Gaussian beam transformation with cylindrical lenses

abstract: We develop formulas for Gaussian beam transformation with fixed total distance between beamwaists, and consider the use of cylindrical lenses or mirrors to produce beams which are not symmetric about the axis of propagation. For antenna feeds, it is necessary that the beam waists for both planes perpendicular to the axis of propagation be coincident. This requirement is analyzed, and appropriate transformation formulas obtained. The far-field patterns of a scalar feed born cylindrical lens combination operating at a frequency of 94 GHz have been measured and found to be in quite good agreement with the elliptical patterns predicted by the transformation formulas.


title: Applications and societal benefits of plastics

abstract: This article explains the history, from 1600 BC to 2008, of materials that are today termed ‘plastics’. It includes production volumes and current consumption patterns of five main commodity plastics: polypropylene, polyethylene, polyvinyl chloride, polystyrene and polyethylene terephthalate. The use of additives to modify the properties of these plastics and any associated safety, in use, issues for the resulting polymeric materials are described. A comparison is made with the thermal and barrier properties of other materials to demonstrate the versatility of plastics. Societal benefits for health, safety, energy saving and material conservation are described, and the particular advantages of plastics in society are outlined. Concerns relating to littering and trends in recycling of plastics are also described. Finally, we give predictions for some of the potential applications of plastic over the next 20 years.


title: Evaluation-as-a-Service: Overview and Outlook

abstract: Evaluation in empirical computer science is essential to show progress and assess technologies developed. Several research domains such as information retrieval have long relied on systematic evaluation to measure progress: here, the Cranfield paradigm of creating shared test collections, defining search tasks, and collecting ground truth for these tasks has persisted up until now. In recent years, however, several new challenges have emerged that do not fit this paradigm very well: extremely large data sets, confidential data sets as found in the medical domain, and rapidly changing data sets as often encountered in industry. Also, crowdsourcing has changed the way that industry approaches problem-solving with companies now organizing challenges and handing out monetary awards to incentivize people to work on their challenges, particularly in the field of machine learning. ::: This white paper is based on discussions at a workshop on Evaluation-as-a-Service (EaaS). EaaS is the paradigm of not providing data sets to participants and have them work on the data locally, but keeping the data central and allowing access via Application Programming Interfaces (API), Virtual Machines (VM) or other possibilities to ship executables. The objective of this white paper are to summarize and compare the current approaches and consolidate the experiences of these approaches to outline the next steps of EaaS, particularly towards sustainable research infrastructures. ::: This white paper summarizes several existing approaches to EaaS and analyzes their usage scenarios and also the advantages and disadvantages. The many factors influencing EaaS are overviewed, and the environment in terms of motivations for the various stakeholders, from funding agencies to challenge organizers, researchers and participants, to industry interested in supplying real-world problems for which they require solutions.


title: A Regression Model of Adjective-Noun Compositionality in Distributional Semantics

abstract: In this paper we explore the computational modelling of compositionality in distributional models of semantics. In particular, we model the semantic composition of pairs of adjacent English Adjectives and Nouns from the British National Corpus. We build a vector-based semantic space from a lemmatised version of the BNC, where the most frequent A-N lemma pairs are treated as single tokens. We then extrapolate three different models of compositionality: a simple additive model, a pointwise-multiplicative model and a Partial Least Squares Regression (PLSR) model. We propose two evaluation methods for the implemented models. Our study leads to the conclusion that regression-based models of compositionality generally out-perform additive and multiplicative approaches, and also show a number of advantages that make them very promising for future research.


title: Effects of Optical Combiner and IPD Change for Convergence on Near-Field Depth Perception in an Optical See-Through HMD

abstract: Many error sources have been explored in regards to the depth perception problem in augmented reality environments using optical see-through head-mounted displays (OST-HMDs). Nonetheless, two error sources are commonly neglected: the ray-shift phenomenon and the change in interpupillary distance (IPD). The first source of error arises from the difference in refraction for virtual and see-through optical paths caused by an optical combiner, which is required of OST-HMDs. The second occurs from the change in the viewer's IPD due to eye convergence. In this paper, we analyze the effects of these two error sources on near-field depth perception and propose methods to compensate for these two types of errors. Furthermore, we investigate their effectiveness through an experiment comparing the conditions with and without our error compensation methods applied. In our experiment, participants estimated the egocentric depth of a virtual and a physical object located at seven different near-field distances (40∼200 cm) using a perceptual matching task. Although the experimental results showed different patterns depending on the target distance, the results demonstrated that the near-field depth perception error can be effectively reduced to a very small level (at most 1 percent error) by compensating for the two mentioned error sources.


title: ADMM based scalable machine learning on Spark

abstract: Most machine learning algorithms involve solving a convex optimization problem. Traditional in-memory convex optimization solvers do not scale well with the increase in data. This paper identifies a generic convex problem for most machine learning algorithms and solves it using the Alternating Direction Method of Multipliers (ADMM). Finally such an ADMM problem transforms to an iterative system of linear equations, which can be easily solved at scale in a distributed fashion. We implement this framework in Apache Spark and compare it with the widely used Machine Learning LIBrary (MLLIB) in Apache Spark 1.3.


title: Drawing Contour Trees in the Plane

abstract: The contour tree compactly describes scalar field topology. From the viewpoint of graph drawing, it is a tree with attributes at vertices and optionally on edges. Standard tree drawing algorithms emphasize structural properties of the tree and neglect the attributes. Applying known techniques to convey this information proves hard and sometimes even impossible. We present several adaptions of popular graph drawing approaches to the problem of contour tree drawing and evaluate them. We identify five esthetic criteria for drawing contour trees and present a novel algorithm for drawing contour trees in the plane that satisfies four of these criteria. Our implementation is fast and effective for contour tree sizes usually used in interactive systems (around 100 branches) and also produces readable pictures for larger trees, as is shown for an 800 branch example.


title: EHV transmission line capacity enhancement through increase in surge impedance loading level

abstract: The quantum of power that a given EHVAC transmission line can safely carry depends on various limits. These limits can be categorized into two types viz. thermal and stability/SIL limits. In case of long lines the capacity is limited by its SIL level only which is much below its thermal capacity due to large inductance. Decrease in line inductance and surge impedance shall increase the SIL and transmission capacity. This paper presents a mathematical model of increasing the SIL level towards thermal limit. Sensitivity of SIL on various configuration of sub-conductors in a bundle, bundle spacing, tower structure, spacing of phase conductors etc. is analyzed and presented. Various issues that need attention for application of high surge impedance loading (HSIL) line are also deliberated.


title: A Scalable Low-Cost-UAV Traffic Network (uNet)

abstract: This article proposes a new Unmanned Aerial Vehicle (UAV) operation paradigm to enable a large number of relatively low-cost UAVs to fly beyond-line-of-sight without costly sensing and communication systems or substantial human intervention in individual UAV control. Under current free-flight-like paradigm, wherein a UAV can travel along any route as long as it avoids restricted airspace and altitudes. However, this requires expensive on-board sensing and communication as well as substantial human effort in order to ensure avoidance of obstacles and collisions. The increased cost serves as an impediment to the emergence and development of broader UAV applications. The main contribution of this work is to propose the use of pre-established route network for UAV traffic management, which allows: (i) pre- mapping of obstacles along the route network to reduce the onboard sensing requirements and the associated costs for avoiding such obstacles; and (ii) use of well-developed routing algorithms to select UAV schedules that avoid conflicts. Available GPS-based navigation can be used to fly the UAV along the selected route and time schedule with relatively low added cost, which therefore, reduces the barrier to entry into new UAV-applications market. Finally, this article proposes a new decoupling scheme for conflict-free transitions between edges of the route network at each node of the route network to reduce potential conflicts between UAVs and ensuing delays. A simulation example is used to illustrate the proposed uNet approach.


title: Shadow And Highlight Invariant Colour Segmentation Algorithm For Traffic Signs

abstract: Shadows and highlights represent a challenge to the computer vision researchers due to a variance in the brightness on the surfaces of the objects under consideration. This paper presents a new colour detection and segmentation algorithm for road signs in which the effect of shadows and highlights are neglected to get better colour segmentation results. Images are taken by a digital camera mounted in a car. The RGB images are converted into HSV colour space and the shadow-highlight invariant method is applied to extract the colours of the road signs under shadow and highlight conditions. The method is tested on hundreds of outdoor images under such light conditions, and it shows high robustness; more than 95% of correct segmentation is achieved.


title: Missing Modality Transfer Learning via Latent Low-Rank Constraint

abstract: Transfer learning is usually exploited to leverage previously well-learned source domain for evaluating the unknown target domain; however, it may fail if no target data are available in the training stage. This problem arises when the data are multi-modal. For example, the target domain is in one modality, while the source domain is in another. To overcome this, we first borrow an auxiliary database with complete modalities, then consider knowledge transfer across databases and across modalities within databases simultaneously in a unified framework. The contributions are threefold: 1) a latent factor is introduced to uncover the underlying structure of the missing modality from the known data; 2) transfer learning in two directions allows the data alignment between both modalities and databases, giving rise to a very promising recovery; and 3) an efficient solution with theoretical guarantees to the proposed latent low-rank transfer learning algorithm. Comprehensive experiments on multi-modal knowledge transfer with missing target modality verify that our method can successfully inherit knowledge from both auxiliary database and source modality, and therefore significantly improve the recognition performance even when test modality is inaccessible in the training stage.


Influence of Fishing Activity on Total Species Richness and Abundance Unevenness in Reef Fish Communities: A Case Study in a Brazilian Tropical Coral Complex


title: A Tongue Image Segmentation Method Based on Enhanced HSV Convolutional Neural Network

abstract: In the procedure of the Chinese medical tongue diagnosis, it’s necessary to carry out the original tongue image segmentation to reduce interference to the tongue feature extraction caused by the non-tongue part of the face. In this paper, we propose a new method based on enhanced HSV color model convolutional neural network for tongue image segmentation. This method can get a better in tongue image segmentation results compared with others. This method also has a great advantage over other methods in the processing speed.


title: Beyond the Euler Characteristic: Approximating the Genus of General Graphs

abstract: Computing the Euler genus of a graph is a fundamental problem in graph theory and topology. It has been shown to be NP-hard by Thomassen [27] and a linear-time fixed-parameter algorithm has been obtained by Mohar [20]. Despite extensive study, the approximability of the Euler genus remains wide open. While the existence of a constant factor approximation is not ruled out, the currently best-known upper bound is a trivial O(n/g)-approximation that follows from bounds on the Euler characteristic. In this paper, we give the first non-trivial approximation algorithm for this problem. Specifically, we present a polynomial-time algorithm which given a graph G of Euler genus g outputs an embedding of G into a surface of Euler genus gO(1). Combined with the above O(n/g)-approximation, our result also implies a O(n1-α)-approximation, for some universal constant α> 0. Our approximation algorithm also has implications for the design of algorithms on graphs of small genus. Several of these algorithms require that an embedding of the graph into a surface of small genus is given as part of the input. Our result implies that many of these algorithms can be implemented even when the embedding of the input graph is unknown.


title: Noninvasive brain-actuated control of a mobile robot by human EEG

abstract: Brain activity recorded noninvasively is sufficient to control a mobile robot if advanced robotics is used in combination with asynchronous electroencephalogram (EEG) analysis and machine learning techniques. Until now brain-actuated control has mainly relied on implanted electrodes, since EEG-based systems have been considered too slow for controlling rapid and complex sequences of movements. We show that two human subjects successfully moved a robot between several rooms by mental control only, using an EEG-based brain-machine interface that recognized three mental states. Mental control was comparable to manual control on the same task with a performance ratio of 0.74.


title: A repetitive high-current pulsed accelerator—TPG700

abstract: The design and construction of a repetitive high-current pulsed accelerator-TPG700 is described in this paper. The accelerator consists of a Tesla transformer with 40 ohm build-in coaxial pulse forming line. The triggered high-pressure switch of TPG700 has the capability of conducting current of 17.5kA in 35ns' duration at 100 pps. The transformer was designed to operate at 1.4MV, when its primary capacitors were charged to approximately 1000V. Under the working state of 100pps, the jitter of breakdown of the switch voltages is lower than 1% on average. To enhance the overall efficiency of the pulser, resonant charging technology based on IGBTs was utilized. As the experimental results indicate, the total efficiency of the pulser, when measured on matched dummy load, is close to 75%. The experimental results indicate that in matching case, the output of 700kV, 17kA for 40Ω resistive load is obtained. Moreover, some experiments such as long lifetime cathode testing and high power microwave (HPM) generation using backward oscillator (BWO) have been conducted on TPG700.


title: Cyber security and artificial intelligence: from fixing the plumbing to smart water

abstract: Computer security and artificial intelligence in their early days didn't seem to have much to say to each other. AI researchers were interested in making computers do things that only humans had been able to do, while security researchers aimed to fix the leaks in the plumbing of the computing infrastructure or to design infrastructures they deemed leakproof. Further, AI researchers were often most interested in building systems with behaviors that could change over time through learning or adaptation, and hence were to some degree unpredictable. From the standpoint of security, unpredictable system behavior generally seemed undesirable. But the two fields have grown closer over the years, particularly where attacks have aimed to simulate legitimate behaviors, not only at the level of human users but also at lower system layers. This talk will cover a bit of the history of computer security and artificial intelligence, identify a few connections between them, and conclude with a bit of speculation about some directions the fields might take.


title: Achieving high navigation accuracy using inertial navigation systems in autonomous underwater vehicles

abstract: This paper presents a summary of the current state-of-the-art in INS-based navigation systems in AUVs manufactured by Bluefin Robotics Corporation. A detailed description of the successful integrations of the Kearfott T-24 Ring Laser Gyro and the IXSEA PHINS III Fiber Optic Gyro into recent Bluefin Robotics AUVs is presented. Both systems provide excellent navigation accuracy for high quality data acquisition. This paper provides a comprehensive assessment of the primary advantages and disadvantages of each INS, paying particular attention to navigation accuracy, power draw, physical size, and acoustic radiated noise. Additionally, a brief presentation of a recently integrated Synthetic Aperture Sonar system will be used to highlight how critical a high-performance INS is to hydrographic, mine countermeasures, and other SAS applications.


title: An Approach for Building Efficient and Accurate Social Recommender Systems Using Individual Relationship Networks

abstract: Social recommender system, using social relation networks as additional input to improve the accuracy of traditional recommender systems, has become an important research topic. However, most existing methods utilize the entire user relationship network with no consideration to its huge size, sparsity, imbalance, and noise issues. This may degrade the efficiency and accuracy of social recommender systems. This study proposes a new approach to manage the complexity of adding social relation networks to recommender systems. Our method first generates an individual relationship network (IRN) for each user and item by developing a novel fitting algorithm of relationship networks to control the relationship propagation and contracting. We then fuse matrix factorization with social regularization and the neighborhood model using IRN's to generate recommendations. Our approach is quite general, and can also be applied to the item-item relationship network by switching the roles of users and items. Experiments on four datasets with different sizes, sparsity levels, and relationship types show that our approach can improve predictive accuracy and gain a better scalability compared with state-of-the-art social recommendation methods.


title: Statistical Relational Artificial Intelligence: From Distributions through Actions to Optimization

abstract: Statistical Relational AI—the science and engineering of making intelligent machines acting in noisy worlds composed of objects and relations among the objects—is currently motivating a lot of new AI research and has tremendous theoretical and practical implications. Theoretically, combining logic and probability in a unified representation and building general-purpose reasoning tools for it has been the dream of AI, dating back to the late 1980s. Practically, successful statistical relational AI tools enable new applications in several large, complex real-world domains including those involving big data, natural text, social networks, the web, medicine and robotics, among others. Such domains are often characterized by rich relational structure and large amounts of uncertainty. Logic helps to faithfully model the former while probability helps to effectively manage the latter. Our intention here is to give a brief (and necessarily incomplete) overview and invitation to the emerging field of Statistical Relational AI from the perspective of acting optimally and learning to act.


title: XES, XESame, and ProM 6

abstract: Process mining has emerged as a new way to analyze business processes based on event logs. These events logs need to be extracted from operational systems and can subsequently be used to discover or check the conformance of processes. ProM is a widely used tool for process mining. In earlier versions of ProM, MXML was used as an input format. In future releases of ProM, a new logging format will be used: the eXtensible Event Stream (XES) format. This format has several advantages over MXML. The paper presents two tools that use this format - XESame and ProM 6 - and highlights the main innovations and the role of XES. XESame enables domain experts to specify how the event log should be extracted from existing systems and converted to XES. ProM 6 is a completely new process mining framework based on XES and enabling innovative process mining functionality.


title: The Eight-Fold Path of Internet Ethics: A Primer for Health Care Professionals

abstract: Anyone who has written blogs or published articles online knows that the Internet is the new “Wild West,” so far as etiquette is concerned.


title: Pyramidal directional filter banks and curvelets

abstract: A flexible multiscale and directional representation for images is proposed. The scheme combines directional filter banks with the Laplacian pyramid to provide a sparse representation for two-dimensional piecewise smooth signals resembling images. The underlying expansion is a frame and can be designed to be a tight frame. Pyramidal directional filter banks provide an effective method to implement the digital curvelet transform. The regularity issue of the iterated filters in the directional filter bank is examined.


title: Re: CAPTCHAs-Understanding CAPTCHA-Solving Services in an Economic Context

abstract: Reverse Turing tests, or CAPTCHAs, have become an ubiquitous defense used to protect open Web resources from being exploited at scale. An effective CAPTCHA resists existing mechanistic software solving, yet can be solved with high probability by a human being. In response, a robust solving ecosystem has emerged, reselling both automated solving technology and realtime human labor to bypass these protections. Thus, CAPTCHAs can increasingly be understood and evaluated in purely economic terms; the market price of a solution vs the monetizable value of the asset being protected. We examine the market-side of this question in depth, analyzing the behavior and dynamics of CAPTCHA-solving service providers, their price performance, and the underlying labor markets driving this economy.


title: Physiologic and pharmacokinetic changes in pregnancy

abstract: Physiologic changes in pregnancy induce profound alterations to the pharmacokinetic properties of many medications. These changes affect distribution, absorption, metabolism, and excretion of drugs, and thus may impact their pharmacodynamic properties during pregnancy. Pregnant women undergo several adaptations in many organ systems. Some adaptations are secondary to hormonal changes in pregnancy, while others occur to support the gravid woman and her developing fetus. Some of the changes in maternal physiology during pregnancy include, for example, increased maternal fat and total body water, decreased plasma protein concentrations, especially albumin, increased maternal blood volume, cardiac output, and blood flow to the kidneys and uteroplacental unit, and decreased blood pressure. The maternal blood volume expansion occurs at a larger proportion than the increase in red blood cell mass, which results in physiologic anemia and hemodilution. Other physiologic changes include increased tidal volume, partially compensated respiratory alkalosis, delayed gastric emptying and gastrointestinal motility, and altered activity of hepatic drug metabolizing enzymes. Understating these changes and their profound impact on the pharmacokinetic properties of drugs in pregnancy is essential to optimize maternal and fetal health.


title: Work strain, health, and absenteeism: A meta-analysis.

abstract: Work strain has been argued to be a significant cause of absenteeism in the popular and academic press. However, definitive evidence for associations between absenteeism and strain is currently lacking. A theory focused meta-analysis of 275 effects from 153 studies revealed positive but small associations between absenteeism and work strain, psychological illness, and physical illness. Structural equation modeling results suggested that the strain‐absence connection may be mediated by psychological and physical symptoms. Little support was received for the purported volitional distinction between absence frequency and time lost absence measures on the basis of illness. Among the moderators examined, common measurement, midterm and stable sources of variance, and publication year received support.


title: A method of color edge detection using mathematical morphology

abstract: It is difficult to effectively distinguish the similarity between colors in RGB color space, so color image processing is implemented in HSI space which reflects the features of human vision. An adaptive algorithm of color image edge detection based on multiple structure and multi-scale elements in HSI space is proposed. Firstly, morphological edge detection is executed by using different structure and different scale elements to hue, saturation and intensity. Then, according to the weight derived from information entropy, the color edge information is obtained by combining component's edge information. The experimental results show that proposed algorithm can make full use of the hue, saturation and intensity information to effectively eliminate the noise and adaptively extract the complete edge information.


title: Pilot clinical application of an adaptive robotic system for young children with autism

abstract: It has been argued that clinical applications of advanced technology may hold promise for addressing impairments associated with autism spectrum disorders. This pilot feasibility study evaluated the application of a novel adaptive robot-mediated system capable of both administering and automatically adjusting joint attention prompts to a small group of preschool children with autism spectrum disorders (n = 6) and a control group (n = 6). Children in both groups spent more time looking at the humanoid robot and were able to achieve a high level of accuracy across trials. However, across groups, children required higher levels of prompting to successfully orient within robot-administered trials. The results highlight both the potential benefits of closed-loop adaptive robotic systems as well as current limitations of existing humanoid-robotic platforms.


title: Learning Entity Representation for Entity Disambiguation

abstract: In this paper we present a novel disambiguation model, based on neural networks. Most existing studies focus on designing effective man-made features and complicated similarity measures to obtain better disambiguation performance. Instead, our method learns distributed representation of entity to measure similarity without man-made features. Entity representation consists of context document representation and category representation. Document representation of an entity is learned based on deep neural network (DNN), and is directly optimized for a given similarity measure. Convolutional neural network (CNN) is employed to obtain category representation, and shares deep layers with DNN. Both models are trained jointly using massive documents collected from Baike http://baike.baidu.com/. Experiment results show that our method achieves a good performance on two datasets without any manually designed features.


title: Autonomous question answering with mobile robots in human-populated environments

abstract: Autonomous mobile robots will soon become ubiquitous in human-populated environments. Besides their typical applications in fetching, delivery, or escorting, such robots present the opportunity to assist human users in their daily tasks by gathering and reporting up-to-date knowledge about the environment. In this paper, we explore this use case and present an end-to-end framework that enables a mobile robot to answer natural language questions about the state of a large-scale, dynamic environment asked by the inhabitants of that environment. The system parses the question and estimates an initial viewpoint that is likely to contain information for answering the question based on prior environment knowledge. Then, it autonomously navigates towards the viewpoint while dynamically adapting to changes and new information. The output of the system is an image of the most relevant part of the environment that allows the user to obtain an answer to their question. We additionally demonstrate the benefits of a continuously operating information gathering robot by showing how the system can answer retrospective questions about the past state of the world using incidentally recorded sensory data. We evaluate our approach with a custom mobile robot deployed in a university building, with questions collected from occupants of the building. We demonstrate our system's ability to respond to these questions in different environmental conditions.


A New Hierarchical Structure for Speech Recognition by units smaller than words , using Wavelet Packet and SVM


title: Feature selection methods for conversational recommender systems

abstract: This paper focuses on question selection methods for conversational recommender systems. We consider a scenario, where given an initial user query, the recommender system may ask the user to provide additional features describing the searched products. The objective is to generate questions/features that a user would likely reply, and if replied, would effectively reduce the result size of the initial query. Classical entropy-based feature selection methods are effective in term of result size reduction, but they select questions uncorrelated with user needs and therefore unlikely to be replied. We propose two feature-selection methods that combine feature entropy with an appropriate measure of feature relevance. We evaluated these methods in a set of simulated interactions where a probabilistic model of user behavior is exploited. The results show that these methods outperform entropy-based feature selection.


title: ZaliQL: A SQL-Based Framework for Drawing Causal Inference from Big Data

abstract: Causal inference from observational data is a subject of active research and development in statistics and computer science. Many toolkits have been developed for this purpose that depends on statistical software. However, these toolkits do not scale to large datasets. In this paper we describe a suite of techniques for expressing causal inference tasks from observational data in SQL. This suite supports the state-of-the-art methods for causal inference and run at scale within a database engine. In addition, we introduce several optimization techniques that significantly speedup causal inference, both in the online and offline setting. We evaluate the quality and performance of our techniques by experiments of real datasets.


title: A high resolution Time-to-Digital Converter (TDC) based on self-calibrated Digital-to-Time Converter (DTC)

abstract: Based on the parallel DTCs as delay cells, a 4-bit TDC with adjustable 0.7ps∼1.4ps resolution and 11ps∼22ps dynamic range is proposed in this paper. In this design, an extremely high resolution DTC is presented, achieving 15.6fs delay per LSB. By utilizing 16 DTCs which are adjusted to have the same time interval among two neighboring DTCs, a highly linear TDC is realized. To avoid the manual tuning, a self-calibration method is proposed, which allows for the calibration after the tape-out. The method utilizes a ruler DTC(RDTC) as the input signal of TDC to calibrate it. After self-calibration, the resolution of TDC is equal to the RDTC's delay step, so the resolution becomes adjustable by altering the RDTC's delay step. Setting the resolution at 1ps, the integral nonlinearity (INL) is 0.07LSB, the power consumption is 1.37mW at 50MHz with a 1.2V operating voltage and it occupies a core area of 0.018 mm2 in 0.13um CMOS process.


title: Smartphone-Based Wound Assessment System for Patients With Diabetes

abstract: Diabetic foot ulcers represent a significant health issue. Currently, clinicians and nurses mainly base their wound assessment on visual examination of wound size and healing status, while the patients themselves seldom have an opportunity to play an active role. Hence, a more quantitative and cost-effective examination method that enables the patients and their caregivers to take a more active role in daily wound care potentially can accelerate wound healing, save travel cost and reduce healthcare expenses. Considering the prevalence of smartphones with a high-resolution digital camera, assessing wounds by analyzing images of chronic foot ulcers is an attractive option. In this paper, we propose a novel wound image analysis system implemented solely on the Android smartphone. The wound image is captured by the camera on the smartphone with the assistance of an image capture box. After that, the smartphone performs wound segmentation by applying the accelerated mean-shift algorithm. Specifically, the outline of the foot is determined based on skin color, and the wound boundary is found using a simple connected region detection method. Within the wound boundary, the healing status is next assessed based on red–yellow–black color evaluation model. Moreover, the healing status is quantitatively assessed, based on trend analysis of time records for a given patient. Experimental results on wound images collected in UMASS—Memorial Health Center Wound Clinic (Worcester, MA) following an Institutional Review Board approved protocol show that our system can be efficiently used to analyze the wound healing status with promising accuracy.


title: Locating the Indian Ocean: notes on the postcolonial reconstitution of space

abstract: The networks of human relation that define the Indian Ocean region have undergone significant reconfiguration in the last half-century. More precisely, the economic insularity of the region has diminished while the postcolonial nation has both restricted movement and reoriented the political imaginations of people along the rim. At the same time, the Indian Ocean has been revivified as a unit of social exchange and analysis, particularly since the end of the Cold War. This article explores the meaning of Indian Ocean Africa in the context of a multipolar world by focusing on how the dictates of nations have transformed the region and how the petroleum economy as well as shifting means of social engagement have engendered new linkages. The essay argues that although the postcolonial era affected the closure of certain historical routes of connectivity, relationships structured by contemporary nations and air travel, among other things, have encouraged perceptions of regional coherence. What we might term b...


title: Design comparison of 6.5 kV Si-IGBT, 6.5kV SiC JBS diode, and 10 kV SiC MOSFETs in megawatt converters for shipboard power system

abstract: In this paper a comparative design study has been shown with 6.5kV Si-IGBT/Si-P IN diode, 6.5kV Si-IGBT/SiC-JBS diode, and 10kV SiC-M OSFET/SiC-JBS diode in an act ive front-end (AFE) converter for medium-voltage shipboard application. Megawatt converters based on the aforementioned technologies are being designed and compared at tw o different switching frequencies. In this regard, accurate circuit models for 5–10A die for each (a) silicon 6.5kV IGBT, (b) 6.5kV Si-IGBT incorporating a 6.5kV SiC-JB S Diode, and (c) 10kV SiC MOSFET with 10kV SiC JBS Diode, are paralleled to ma ke a 100A switch and used as conv erter switching devices in SPICE circuit simulation to perform the comparative analysis. Switching waveforms, characteristics, switching power and energy loss measurements are follo wed by an efficiency comparison of a 1MW converter with 7.5kVdc at 1kH z and 5kH z switching frequencies. It is shown that 6.5kV Si-IGBT/SiC-JBS diode, with its high efficiency performance up to 5kHz, is a strong candidate for MW ran ge converters. The 10kV SiC-MOSFET/SiC-JBS diode is an option for h igher switching frequency MW converters.


title: Behavioral Communities and the Atomic Structure of Networks

abstract: We develop a method of detecting the `behavioral communities' of a social network based on how people act when they choose their behaviors in coordination with their friends' behaviors. There can be multiple different `conventions' (equilibria) in which people in some parts of the network adopt a behavior while people in other parts of the network do not. We define atoms/communities to be groups of people who behave the same as each other in every convention. This provides a microfoundation for a method of detecting communities in social and economic networks. We characterize such behavioral communities in some random graphs as a function of how strongly the benefits of adopting the behavior depend on others' behaviors. We also discuss and illustrate applications including: optimally seeding the diffusion of behaviors involving peer influence, detecting which demographics or nodal characteristics define a society's communities, estimating the strength of peer influence on behavior, as well as identifying missing network data by observing a series of conventions.


title: Seeded region growing

abstract: We present here a new algorithm for segmentation of intensity images which is robust, rapid, and free of tuning parameters. The method, however, requires the input of a number of seeds, either individual pixels or regions, which will control the formation of regions into which the image will be segmented. In this correspondence, we present the algorithm, discuss briefly its properties, and suggest two ways in which it can be employed, namely, by using manual seed selection or by automated procedures. >


title: Joeq: a virtual machine and compiler infrastructure

abstract: Joeq is a virtual machine and compiler infrastructure designed to facilitate research in virtual machine technologies such as Just-In-Time and Ahead-Of-Time compilation, advanced garbage collection techniques, distributed computation, sophisticated scheduling algorithms, and advanced run time techniques. Joeq is entirely implemented in Java, leading to reliability, portability, maintainability, and efficiency. It is also language-independent, so code from any supported language can be seamlessly compiled, linked, and executed --- all dynamically. Each component of the virtual machine is written to be independent with a general but well-defined interface, making it easy to experiment with new ideas. Joeq is released as open source software, and is being used as a framework by researchers all over the world on topics ranging from automatic distributed virtual machines to whole-program pointer analysis.


title: Shill bidding in multi-round online auctions

abstract: The online implementation of traditional business mechanisms raises many new issues not considered in classical economic models. This partially explains why online auctions have become the most successful but also the most controversial Internet businesses in recent years. One emerging issue is that the lack of authentication over the Internet has encouraged shill bidding, the deliberate placing of bids on the seller's behalf to artificially drive up the price of the seller's auctioned item. Private-value English auctions with shill bidding can result in a higher expected seller profit than other auction formats (W. Wang et al., 2001), violating the classical revenue equivalence theory. The paper analyzes shill bidding in multi-round online English auctions and proves that there is no equilibrium without shill bidding. Taking into account the seller's shills and relistings, bidders with valuations even higher than the reserve will either wait for the next round or shield their bids in the current round. Hence, it is inevitable to redesign online auctions to deal with the "shiller's curse".


title: Specifying and Annotating Reduced Argument Span Via QA-SRL

abstract: Prominent semantic annotations take an inclusive approach to argument span annotation, marking arguments as full constituency subtrees. Some works, however, showed that identifying a reduced argument span can be beneficial for various semantic tasks. While certain practical methods do extract reduced argument spans, such as in Open-IE , these solutions are often ad-hoc and system-dependent, with no commonly accepted standards. In this paper we propose a generic argument reduction criterion, along with an annotation procedure, and show that it can be consistently and intuitively annotated using the recent QA-SRL paradigm.


title: State-of-the-Art Speech Recognition with Sequence-to-Sequence Models

abstract: Attention-based encoder-decoder architectures such as Listen, Attend, and Spell (LAS), subsume the acoustic, pronunciation and language model components of a traditional automatic speech recognition (ASR) system into a single neural network. In previous work, we have shown that such architectures are comparable to state-of-theart ASR systems on dictation tasks, but it was not clear if such architectures would be practical for more challenging tasks such as voice search. In this work, we explore a variety of structural and optimization improvements to our LAS model which significantly improve performance. On the structural side, we show that word piece models can be used instead of graphemes. We also introduce a multi-head attention architecture, which offers improvements over the commonly-used single-head attention. On the optimization side, we explore synchronous training, scheduled sampling, label smoothing, and minimum word error rate optimization, which are all shown to improve accuracy. We present results with a unidirectional LSTM encoder for streaming recognition. On a 12, 500 hour voice search task, we find that the proposed changes improve the WER from 9.2% to 5.6%, while the best conventional system achieves 6.7%; on a dictation task our model achieves a WER of 4.1% compared to 5% for the conventional system.


title: Cross-Platform Identification of Anonymous Identical Users in Multiple Social Media Networks

abstract: The last few years have witnessed the emergence and evolution of a vibrant research stream on a large variety of online social media network (SMN) platforms. Recognizing anonymous, yet identical users among multiple SMNs is still an intractable problem. Clearly, cross-platform exploration may help solve many problems in social computing in both theory and applications. Since public profiles can be duplicated and easily impersonated by users with different purposes, most current user identification resolutions, which mainly focus on text mining of users’ public profiles, are fragile. Some studies have attempted to match users based on the location and timing of user content as well as writing style. However, the locations are sparse in the majority of SMNs, and writing style is difficult to discern from the short sentences of leading SMNs such as Sina Microblog and Twitter. Moreover, since online SMNs are quite symmetric, existing user identification schemes based on network structure are not effective. The real-world friend cycle is highly individual and virtually no two users share a congruent friend cycle. Therefore, it is more accurate to use a friendship structure to analyze cross-platform SMNs. Since identical users tend to set up partial similar friendship structures in different SMNs, we proposed the Friend Relationship-Based User Identification (FRUI) algorithm. FRUI calculates a match degree for all candidate User Matched Pairs (UMPs), and only UMPs with top ranks are considered as identical users. We also developed two propositions to improve the efficiency of the algorithm. Results of extensive experiments demonstrate that FRUI performs much better than current network structure-based algorithms.


title: Analytical Solution of Air-Gap Field in Permanent-Magnet Motors Taking Into Account the Effect of Pole Transition Over Slots

abstract: We present an analytical method to study magnetic fields in permanent-magnet brushless motors, taking into consideration the effect of stator slotting. Our attention concentrates particularly on the instantaneous field distribution in the slot regions where the magnet pole transition passes over the slot opening. The accuracy in the flux density vector distribution in such regions plays a critical role in the prediction of the magnetic forces, i.e., the cogging torque and unbalanced magnetic pull. However, the currently available analytical solutions for calculating air-gap fields in permanent magnet motors can estimate only the distribution of the flux density component in the radial direction. Magnetic field and forces computed by the new analytical method agree well with those obtained by the finite-element method. The analytical method provides a useful tool for design and optimization of permanent-magnet motors.


title: Low-Profile Planar Filtering Dipole Antenna With Omnidirectional Radiation Pattern

abstract: This paper presents a low-profile planar dipole antenna with omnidirectional radiation pattern and filtering response. The proposed antenna consists of a microstrip-to-slotline transition structure as the feeding network and a planar dipole as the radiator. Filtering response is obtained by adding nonradiative elements, including a coupled U-shaped microstrip line and two I-shaped slots, to the feeding network. Within the operating passband, the added nonradiative elements do not work, and thus the in-band radiation performance of the dipole antenna is nearly not affected. However, at the side stopbands, the added elements resonate and prevent the signal passing through the feeding network to the dipole antenna, suppressing the out-of-band radiation significantly. As a result, both satisfactory filtering and radiation performances are obtained. For demonstration, an omnidirectional filtering dipole antenna is implemented. Single-band bandpass filtering responses in both the reflection coefficient and realized gain are obtained. The measured in-band gain is ~2.5 dBi, whereas the out-of-band radiation suppression is more than 15 dB.


title: Good shedder or bad shedder—the influence of skin diseases on forensic DNA analysis from epithelial abrasions

abstract: The successful analysis of weak biological stains by means of highly sensitive short tandem repeat (STR) amplification has been increased significantly over the recent years. Nevertheless, the percentage of reliably analysable samples varies considerably between different crime scene investigations even if the nature of the stains appears to be the same. It has been proposed that the amount and quality of DNA left at a crime scene may be due to individual skin conditions (among other factors). Therefore, we investigated DNA from handprints from 30 patients acutely suffering from skin diseases like atopic dermatitis, psoriasis or skin ulcer before and after therapy by STR amplification using the new and highly sensitive Powerplex® ESX17 kit in comparison to 22 healthy controls. Handprints from atopic dermatitis patients showed a correct and reliable DNA profile in 90% and 40% of patients before and after therapy, respectively. Regarding psoriasis patients, we detected full DNA profiles in only 64% and 55% of handprints before and after therapy. In contrast, in ulcus patients and controls, full DNA profiles were obtained in much lower numbers. We conclude that active skin diseases like atopic dermatitis or psoriasis have a considerable impact on the amplificable DNA left by skin contact with surfaces. Since up to 7% of adults in European countries suffer from one of these diseases, this could explain at least partially the varying quality of DNA from weak stains.


title: CANVAS: computer-assisted narrative animation synthesis

abstract: Despite the maturity in solutions for animating expressive virtual characters, innovations realizing the creative intent of story writers have yet to make the same strides. The key challenge is to provide an accessible, yet expressive interface for story authoring that enables the rapid prototyping, iteration, and deployment of narrative concepts. We present CANVAS, a computer-assisted visual authoring tool for synthesizing multi-character animations from sparsely-specified narrative events. In a process akin to storyboarding, authors lay out the key plot points in a story, and our system automatically fills in the missing details to synthesize a 3D animation that meets author constraints. CANVAS can be used by artists and directors to pre-visualize storyboards in an iterative fashion, and casual users may provide arbitrarily sparse specifications and harness automation to rapidly generate diverse narratives. CANVAS provides an accessible interface for rapidly authoring and pre-visualizing complex narratives. Automation reduces the authoring effort further without undermining creative control or interfering with the storytelling process.


title: NFV Middlebox Placement with Balanced Set-up Cost and Bandwidth Consumption

abstract: Network Function Virtualization (NFV) changes the way that we implement network services, or middleboxes, from expensive hardwares to software functions. These software middleboxes, also called Virtual Network Functions (VNFs), run on switch-connected commodity servers. Efficiently placing such middleboxes is challenging because of their traffic-changing effects and dependency relations. Private (used by one single flow) middleboxes can save more link bandwidth resources while shared (used by multiple flows) middleboxes cut down server resource expenses. This paper formulates the resource usage trade-off between bandwidth consumption and cost of middlebox placement as a combined cost minimization problem. After proving the NP-hardness of our problem in general topologies, we narrow down to a specific kind of topology: tree-structured networks. We study two kinds of constraints: traffic-chaining ratio and middlebox dependency relations. With homogeneous flows, we propose optimal greedy algorithms for the placement of a single middlebox first, and then multiple middleboxes without order. We also introduce a dynamic programming algorithm for the placement of a totally-ordered middlebox set. A performance-guaranteed algorithm is designed to handle heterogeneous flows. Extensive simulations are conducted to evaluate the performance of our proposed algorithms in various scenarios.


title: In-Vehicle Networks Outlook: Achievements and Challenges

abstract: This paper presents a comprehensive survey of five most widely used in-vehicle networks from three perspectives: system cost, data transmission capacity, and fault-tolerance capability. The paper reviews the pros and cons of each network, and identifies possible approaches to improve the quality of service (QoS). In addition, two classifications of automotive gateways have been presented along with a brief discussion about constructing a comprehensive in-vehicle communication system with different networks and automotive gateways. Furthermore, security threats to in-vehicle networks are briefly discussed, along with the corresponding protective methods. The survey concludes with highlighting the trends in future development of in-vehicle network technology and a proposal of a topology of the next generation in-vehicle network.