Query: DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing

Relevant passages:
LSTM: A Search Space Odyssey <=SEP=> Several variants of the long short-term memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995. In recent years, these networks have become the state-of-the-art models for a variety of machine learning problems. This has led to a renewed interest in understanding the role and utility of various computational components of typical LSTM variants. In this paper, we present the first large-scale analysis of eight LSTM variants on three representative tasks: speech recognition, handwriting recognition, and polyphonic music modeling. The hyperparameters of all LSTM variants for each task were optimized separately using random search, and their importance was assessed using the powerful functional ANalysis Of VAriance framework. In total, we summarize the results of 5400 experimental runs ( $\approx 15$  years of CPU time), which makes our study the largest of its kind on LSTM networks. Our results show that none of the variants can improve upon the standard LSTM architecture significantly, and demonstrate the forget gate and the output activation function to be its most critical components. We further observe that the studied hyperparameters are virtually independent and derive guidelines for their efficient adjustment.

Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning. <=SEP=> Sensors in everyday devices, such as our phones, wearables, and computers, leave a stream of digital traces. Personal sensing refers to collecting and analyzing data from sensors embedded in the context of daily life with the aim of identifying human behaviors, thoughts, feelings, and traits. This article provides a critical review of personal sensing research related to mental health, focused principally on smartphones, but also including studies of wearables, social media, and computers. We provide a layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health. Also discussed are research methods as well as challenges, including privacy and problems of dimensionality. Although personal sensing is still in its infancy, it holds great promise as a method for conducting mental health research and as a clinical tool for monitoring at-risk populations and providing the foundation for the next generation of mobile health (or mHealth) interventions.

Accurate online power estimation and automatic battery behavior based power model generation for smartphones <=SEP=> This paper describes PowerBooter, an automated power model construction technique that uses built-in battery voltage sensors and knowledge of battery discharge behavior to monitor power consumption while explicitly controlling the power management and activity states of individual components. It requires no external measurement equipment. We also describe PowerTutor, a component power management and activity state introspection based tool that uses the model generated by PowerBooter for online power estimation. PowerBooter is intended to make it quick and easy for application developers and end users to generate power models for new smartphone variants, which each have different power consumption properties and therefore require different power models. PowerTutor is intended to ease the design and selection of power efficient software for embedded systems. Combined, PowerBooter and PowerTutor have the goal of opening power modeling and analysis for more smartphone variants and their users.

Tracking drone orientation with multiple GPS receivers <=SEP=> Inertial sensors continuously track the 3D orientation of a flying drone, serving as the bedrock for maneuvers and stabilization. However, even the best inertial measurement units (IMU) are prone to various types of correlated failures. We consider using multiple GPS receivers on the drone as a fail-safe mechanism for IMU failures. The core challenge is in accurately computing the relative locations between each receiver pair, and translating these measurements into the drone's 3D orientation. Achieving IMU-like orientation requires the relative GPS distances to be accurate to a few centimeters -- a difficult task given that GPS today is only accurate to around 1-4 meters. Moreover, GPS-based orientation needs to be precise even under sharp drone maneuvers, GPS signal blockage, and sudden bouts of missing data. This paper designs SafetyNet, an off-the-shelf GPS-only system that addresses these challenges through a series of techniques, culminating in a novel particle filter framework running over multi-GNSS systems (GPS, GLONASS, and SBAS). Results from 11 sessions of 5-7 minute flights report median orientation accuracies of 2° even under overcast weather conditions. Of course, these improvements arise from an increase in cost due to the multiple GPS receivers, however, when safety is of interest, we believe that tradeoff is worthwhile.

Reading Digits in Natural Images with Unsupervised Feature Learning <=SEP=> Detecting and reading text from natural images is a hard computer vision task that is central to a variety of emerging applications. Related problems like document character recognition have been widely studied by computer vision and machine learning researchers and are virtually solved for practical applications like reading handwritten digits. Reliably recognizing characters in more complex scenes like photographs, however, is far more difficult: the best existing methods lag well behind human performance on the same tasks. In this paper we attack the problem of recognizing digits in a real application using unsupervised feature learning methods: reading house numbers from street level photos. To this end, we introduce a new benchmark dataset for research use containing over 600,000 labeled digits cropped from Street View images. We then demonstrate the difficulty of recognizing these digits when the problem is approached with hand-designed features. Finally, we employ variants of two recently proposed unsupervised feature learning methods and find that they are convincingly superior on our benchmarks.

Marijuana and Coronary Heart Disease <=SEP=> Marijuana is the most commonly abused drug in the United States.1 It mainly acts on cannabinoid receptors. There are two types of cannabinoid receptors in humans: cannabinoid receptor type 1 (CB1) and cannabinoid receptor type 2 (CB2). CB1 receptor activation is pro-atherogenic, and CB2 receptor activation is largely anti-atherogenic. Marijuana use is also implicated as a trigger for myocardial infarction (MI) in patients with stable coronary artery disease (CAD).

Active Reinforcement Learning with Monte-Carlo Tree Search <=SEP=> Active Reinforcement Learning (ARL) is a twist on RL where the agent observes reward information only if it pays a cost. This subtle change makes exploration substantially more challenging. Powerful principles in RL like optimism, Thompson sampling, and random exploration do not help with ARL. We relate ARL in tabular environments to BayesAdaptive MDPs. We provide an ARL algorithm using Monte-Carlo Tree Search that is asymptotically Bayes optimal. Experimentally, this algorithm is near-optimal on small Bandit problems and MDPs. On larger MDPs it outperforms a Q-learner augmented with specialised heuristics for ARL. By analysing exploration behaviour in detail, we uncover obstacles to scaling up simulation-based algorithms for ARL.

Health hazards and waste management. <=SEP=> Different methods of waste management emit a large number of substances, most in small quantities and at extremely low levels. Raised incidence of low birth weight births has been related to residence near landfill sites, as has the occurrence of various congenital malformations. There is little evidence for an association with reproductive or developmental effects with proximity to incinerators. Studies of cancer incidence and mortality in populations around landfill sites or incinerators have been equivocal, with varying results for different cancer sites. Many of these studies lack good individual exposure information and data on potential confounders, such as socio-economic status. The inherent latency of diseases and migration of populations are often ignored. Waste management workers have been shown to have increased incidence of accidents and musculoskeletal problems. The health impacts of new waste management technologies and the increasing use of recycling and composting will require assessment and monitoring.

Comparative Survey of Indoor Positioning Technologies, Techniques, and Algorithms <=SEP=> The user location information represents a core dimension as understanding user context is a prerequisite for providing human-centered services that generally improve quality of life. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task due in part to the expected various objects (such as walls and people) that reflect and disperse signals. In this paper, we survey the related work in the field of indoor positioning by providing a comparative analysis of the state-of-the-art technologies, techniques, and algorithms. Unlike previous studies and surveys, our survey present new taxonomies, review some major recent advances, and argue on the area open problems and future potential. We believe this paper would spur further exploration by the research community of this challenging problem space.

GraphGen: Exploring Interesting Graphs in Relational Data <=SEP=> Analyzing interconnection structures among the data through the use of graph algorithms and graph analytics has been shown to provide tremendous value in many application domains. However, graphs are not the primary choice for how most data is currently stored, and users who want to employ graph analytics are forced to extract data from their data stores, construct the requisite graphs, and then use a specialized engine to write and execute their graph analysis tasks. This cumbersome and costly process not only raises barriers in using graph analytics, but also makes it hard to explore and identify hidden or implicit graphs in the data. Here we demonstrate a system, called GRAPHGEN, that enables users to declaratively specify graph extraction tasks over relational databases, visually explore the extracted graphs, and write and execute graph algorithms over them, either directly or using existing graph libraries like the widely used NetworkX Python library. We also demonstrate how unifying the extraction tasks and the graph algorithms enables significant optimizations that would not be possible otherwise.

Facial expression recognition in peripheral versus central vision: role of the eyes and the mouth. <=SEP=> This study investigated facial expression recognition in peripheral relative to central vision, and the factors accounting for the recognition advantage of some expressions in the visual periphery. Whole faces or only the eyes or the mouth regions were presented for 150 ms, either at fixation or extrafoveally (2.5° or 6°), followed by a backward mask and a probe word. Results indicated that (a) all the basic expressions were recognized above chance level, although performance in peripheral vision was less impaired for happy than for non-happy expressions, (b) the happy face advantage remained when only the mouth region was presented, and (c) the smiling mouth was the most visually salient and most distinctive facial feature of all expressions. This suggests that the saliency and the diagnostic value of the smile account for the advantage in happy face recognition in peripheral vision. Because of saliency, the smiling mouth accrues sensory gain and becomes resistant to visual degradation due to stimulus eccentricity, thus remaining accessible extrafoveally. Because of diagnostic value, the smile provides a distinctive single cue of facial happiness, thus bypassing integration of face parts and reducing susceptibility to breakdown of configural processing in peripheral vision.

Facial Expression Recognition Using a Large Out-of-Context Dataset <=SEP=> We develop a method for emotion recognition from facial imagery. This problem is challenging in part because of the subjectivity of ground truth labels and in part because of the relatively small size of existing labeled datasets. We use the FER+ dataset [8], a dataset with multiple emotion labels per image, in order to build an emotion recognition model that encompasses a full range of emotions. Since the amount of data in the FER+ dataset is limited, we explore the use of a much larger face dataset, MS-Celeb-1M [41], in conjunction with the FER+ dataset. Specific layers within an Inception-ResNet-v1 [13, 38] model trained for facial recognition are used for the emotion recognition problem. Thus, we leverage the MS-Celeb-1M dataset in addition to the FER+ dataset and experiment with different architectures to assess the overall performance of neural networks to recognize emotion using facial imagery.

A New Nested Neutral Point-Clamped (NNPC) Converter for Medium-Voltage (MV) Power Conversion <=SEP=> In this paper, a new voltage source converter for medium voltage applications is presented which can operate over a wide range of voltages (2.4-7.2 kV) without the need for connecting the power semiconductor in series. The operation of the proposed converter is studied and analyzed. In order to control the proposed converter, a space-vector modulation (SVM) strategy with redundant switching states has been proposed. SVM usually has redundant switching state anyways. What is the main point we are trying to get to? These redundant switching states help to control the output voltage and balance voltages of the flying capacitors in the proposed converter. The performance of the converter under different operating conditions is investigated in MATLAB/Simulink environment. The feasibility of the proposed converter is evaluated experimentally on a 5-kVA prototype.

Automating Content Extraction of HTML Documents <=SEP=> Web pages often contain clutter (such as unnecessary images and extraneous links) around the body of an article that distracts a user from actual content. Extraction of “useful and relevant” content from web pages has many applications, including cell phone and PDA browsing, speech rendering for the visually impaired, and text summarization. Most approaches to making content more readable involve changing font size or removing HTML and data components such as images, which takes away from a webpage’s inherent look and feel. Unlike “Content Reformatting,” which aims to reproduce the entire webpage in a more convenient form, our solution directly addresses “Content Extraction.” We have developed a framework that employs an easily extensible set of techniques. It incorporates advantages of previous work on content extraction. Our key insight is to work with DOM trees, a W3C specified interface that allows programs to dynamically access document structure, rather than with raw HTML markup. We have implemented our approach in a publicly available Web proxy to extract content from HTML web pages. This proxy can be used both centrally, administered for groups of users, as well as by individuals for personal browsers. We have also, after receiving feedback from users about the proxy, created a revised version with improved performance and accessibility in mind.

Using argumentation logic for firewall configuration management <=SEP=> Firewalls remain the main perimeter security protection for corporate networks. However, network size and complexity make firewall configuration and maintenance notoriously difficult. Tools are needed to analyse firewall configurations for errors, to verify that they correctly implement security requirements and to generate configurations from higher-level requirements. In this paper we extend our previous work on the use of formal argumentation and preference reasoning for firewall policy analysis and develop means to automatically generate firewall policies from higher-level requirements. This permits both analysis and generation to be done within the same framework, thus accommodating a wide variety of scenarios for authoring and maintaining firewall configurations. We validate our approach by applying it to both examples from the literature and real firewall configurations of moderate size (≈ 150 rules).

Semantic Segmentation of Colorectal Polyps with DeepLab and LSTM Networks <=SEP=> In this work we attempted to use the existing deep neural network called DeepLab_v3 to detect the polyps in colonoscopy images. Due to its large structure, the location of polyps may not be preserved and transmitted effectively. To address the issue we combined Long Short-Term Memory networks and DeepLab_v3 in parallel to augment the signal of the polyps' location. The new modification was examined with the colonoscopy image database ‘CVC-ClinicDB’ from MICCAI sub-challenge 2015. After training with 267 images and testing with 345 images, we got a good performance, 93.21% mean Intersection over Union (mIOU). The average computing time is 0.023 second per image. Once the model is applied to clinical colonoscopy exam videos, it could provide effective second opinions in real time to aid the diagnosis.

Differential Mode Input Filter Design for a Three-Phase Buck-Type PWM Rectifier Based on Modeling of the EMC Test Receiver <=SEP=> For a three-phase buck-type pulsewidth modulation rectifier input stage of a high-power telecommunications power supply module, a differential-mode (DM) electromagnetic compatibility (EMC) filter is designed for compliance to CISPR 22 Class B in the frequency range of 150 kHz-30 MHz. The design is based on a harmonic analysis of the rectifier input current and a mathematical model of the measurement procedure including the line impedance stabilization network (LISN) and the test receiver. Guidelines for a successful filter design are given, and components for a 5-kW rectifier prototype are selected. Furthermore, formulas for the estimation of the quasi-peak detector output based on the LISN output voltage spectrum are provided. The damping of filter resonances is optimized for a given attenuation in order to facilitate a higher stability margin for system control. Furthermore, the dependence of the filter input and output impedances and the attenuation characteristic on the inner mains impedance are discussed. As experimentally verified by using a three-phase common-/Differential-Mode separator, this procedure allows accurate prediction of the converter DM conducted emission levels and therefore could be employed in the design process of the rectifier system to ensure compliance to relevant EMC standards

People Identification Using Gait Via Floor Pressure Sensing and Analysis <=SEP=> This paper presents an approach to people identification using gait based on floor pressure data. By using a large area high resolution pressure sensing floor, we were able to obtain 3D trajectories of the center of foot pressures over a footstep which contain both the 1D pressure profile and 2D position trajectories of the COP. Based on the 3D COP trajectories a set of features are then extracted and used for people identification together with other features such as stride length and cadence. The Fisher linear discriminant is used as the classifier. Encouraging results have been obtained using the proposed method with an average recognition rate of 94% and false alarm rate of 3% using pair-wise footstep data from 10 subjects.

The Blockchain-Based Digital Content Distribution System <=SEP=> The blockchain-based digital content distribution system was developed. Decentralized and pear-to-pear authentication mechanism can be considered as the ideal rights management mechanism. The blockchain has the potential to realize this ideal content distribution system. This is the successful model of the Superdistribution concept which was announced almost 30 years ago. The proposed system was demonstrated and got a lot of feedback for the future practical system.

The Impact of Training and Social Norms on Information Security Compliance: A Pilot Study <=SEP=> Security training has been shown to be an important factor that impacts employees’ intentions to comply with organization’s security policies. In this study, we define and then study the impact of two sub-constructs of security training, threat appraisal and policy awareness, on intentions to comply with organizational security policies. Injunctive and descriptive norms, which are standards of behavior that recommends and forbids behavior in specific circumstances, have been hypothesized as mediators between training constructs and behavioral intention to comply. We pilot-tested our proposed set of hypotheses with survey data collected from 69 employees in a higher education institute. Results supported our proposed model. Based on the findings, implications for theory and practices are discussed.

Use multiplexing to increase information in QR code <=SEP=> In this paper a novel approach to multiplex data in QR Code is presented. This technique can increase the amount of data, as the original information, in QR Code as well as keeping secret information. The original data for encoding is divided into smaller parts, each part will form QR Code pattern in its standard form. Each pattern is encoded or multiplexed and represented each module in QR Code with black and white special symbols. At the receiving end, this QR Code with special symbols (that was multiplexed) is decoded to give back the number of QR Code patterns that was multiplexed. These QR Code pattern can be read by the general QR Code reader and the data can be concatenated back to form its original information.

Patching. Restitching business portfolios in dynamic markets. <=SEP=> In turbulent markets, businesses and opportunities are constantly falling out of alignment. New technologies and emerging markets create fresh opportunities. Converging markets produce more. And of course, some markets fade. In this landscape of continuous flux, it's more important to build corporate-level strategic processes that enable dynamic repositioning than it is to build any particular defensible position. That's why smart corporate strategists use patching, a process of mapping and remapping business units to create a shifting mix of highly focused, tightly aligned businesses that can respond to changing market opportunities. Patching is not just another name for reorganizing; patchers have a distinctive mindset. Traditional managers see structure as stable; patching managers believe structure is inherently temporary. Traditional managers set corporate strategy first, but patching managers keep the organization focused on the right set of business opportunities and let strategy emerge from individual businesses. Although the focus of patching is flexibility, the process itself follows a pattern. Patching changes are usually small in scale and made frequently. Patching should be done quickly; the emphasis is on getting the patch about right and fixing problems later. Patches should have a test drive before they're formalized but then be tightly scripted after they've been announced. And patching won't work without the right infrastructure: modular business units, fine-grained and complete unit-level metrics, and companywide compensation parity. The authors illustrate how patching works and point out some common stumbling blocks.

Description of the YaCy Distributed Web Search Engine <=SEP=> Distributed web search engines have been proposed to mitigate the privacy issues that arise in centralized search systems. These issues include censorship, disclosure of sensitive queries to the search server and to any third parties with whom the search service operator might share data, as well as the lack of transparency of proprietary search algorithms. YaCy is a deployed distributed search engine that aims to provide censorship resistance and privacy to its users. Its user base has been steadily increasing and it is currently being used by several hundreds of people every day. Unfortunately, there exists no document that thoroughly describes how YaCy exactly works. We therefore investigated the source code of YaCy and summarize in this document our findings and explanation on YaCy. We confirmed with the YaCy community that our description on YaCy is accurate.

Accurate mobile malware detection and classification in the cloud <=SEP=> As the dominator of the Smartphone operating system market, consequently android has attracted the attention of s malware authors and researcher alike. The number of types of android malware is increasing rapidly regardless of the considerable number of proposed malware analysis systems. In this paper, by taking advantages of low false-positive rate of misuse detection and the ability of anomaly detection to detect zero-day malware, we propose a novel hybrid detection system based on a new open-source framework CuckooDroid, which enables the use of Cuckoo Sandbox's features to analyze Android malware through dynamic and static analysis. Our proposed system mainly consists of two parts: anomaly detection engine performing abnormal apps detection through dynamic analysis; signature detection engine performing known malware detection and classification with the combination of static and dynamic analysis. We evaluate our system using 5560 malware samples and 6000 benign samples. Experiments show that our anomaly detection engine with dynamic analysis is capable of detecting zero-day malware with a low false negative rate (1.16 %) and acceptable false positive rate (1.30 %); it is worth noting that our signature detection engine with hybrid analysis can accurately classify malware samples with an average positive rate 98.94 %. Considering the intensive computing resources required by the static and dynamic analysis, our proposed detection system should be deployed off-device, such as in the Cloud. The app store markets and the ordinary users can access our detection system for malware detection through cloud service.

Calibrate Multiple Consumer RGB-D Cameras for Low-Cost and Efficient 3D Indoor Mapping <=SEP=> Traditional indoor laser scanning trolley/backpacks with multi-laser scanner, panorama cameras, and an inertial measurement unit (IMU) installed are a popular solution to the 3D indoor mapping problem. However, the cost of those mapping suits is quite expensive, and can hardly be replicated by consumer electronic components. The consumer RGB-Depth (RGB-D) camera (e.g., Kinect V2) is a low-cost option for gathering 3D point clouds. However, because of the narrow field of view (FOV), its collection efficiency and data coverages are lower than that of laser scanners. Additionally, the limited FOV leads to an increase of the scanning workload, data processing burden, and risk of visual odometry (VO)/simultaneous localization and mapping (SLAM) failure. To find an efficient and low-cost way to collect 3D point clouds data with auxiliary information (i.e., color) for indoor mapping, in this paper we present a prototype indoor mapping solution that is built upon the calibration of multiple RGB-D sensors to construct an array with large FOV. Three time-of-flight (ToF)-based Kinect V2 RGB-D cameras are mounted on a rig with different view directions in order to form a large field of view. The three RGB-D data streams are synchronized and gathered by the OpenKinect driver. The intrinsic calibration that involves the geometry and depth calibration of single RGB-D cameras are solved by homography-based method and ray correction followed by range biases correction based on pixel-wise spline line functions, respectively. The extrinsic calibration is achieved through a coarse-to-fine scheme that solves the initial exterior orientation parameters (EoPs) from sparse control markers and further refines the initial value by an iterative closest point (ICP) variant minimizing the distance between the RGB-D point clouds and the referenced laser point clouds. The effectiveness and accuracy of the proposed prototype and calibration method are evaluated by comparing the point clouds derived from the prototype with ground truth data collected by a terrestrial laser scanner (TLS). The overall analysis of the results shows that the proposed method achieves the seamless integration of multiple point clouds from three Kinect V2 cameras collected at 30 frames per second, resulting in low-cost, efficient, and high-coverage 3D color point cloud collection for indoor mapping applications.

Multicast Outage Probability and Transmission Capacity of Multihop Wireless Networks <=SEP=> Multicast transmission, wherein the same packet must be delivered to multiple receivers, is an important aspect of sensor and tactical networks and has several distinctive traits as opposed to more commonly studied unicast networks. Specially, these include 1) identical packets must be delivered successfully to several nodes, 2) outage at any receiver requires the packet to be retransmitted at least to that receiver, and 3) the multicast rate is dominated by the receiver with the weakest link in order to minimize outage and retransmission. A first contribution of this paper is the development of a tractable multicast model and throughput metric that captures each of these key traits in a multicast wireless network. We utilize a Poisson cluster process (PCP) consisting of a distinct Poisson point process (PPP) for the transmitters and receivers, and then define the multicast transmission capacity (MTC) as the maximum achievable multicast rate per transmission attempt times the maximum intensity of multicast clusters under decoding delay and multicast outage constraints. A multicast cluster is a contiguous area over which a packet is multicasted, and to reduce outage it can be tessellated into v smaller regions of multicast. The second contribution of the paper is the analysis of several key aspects of this model, for which we develop the following main result. Assuming τ/v transmission attempts are allowed for each tessellated region in a multicast cluster, we show that the MTC is Θ(ρkxlog(k)vy) where ρ, x and y are functions of τ and v depending on the network size and intensity, and k is the average number of the intended receivers in a cluster. We derive {ρ, x, y} for a number of regimes of interest, and also show that an appropriate number of retransmissions can significantly enhance the MTC.

Half-Mode Substrate Integrated Waveguide (HMSIW) Leaky-Wave Antenna for Millimeter-Wave Applications <=SEP=> A novel leaky-wave antenna is demonstrated and developed at Ka-band in this work based on the newly proposed half-mode substrate integrated waveguide (HWSIW). This antenna is accurately simulated by using a full-wave electromagnetic simulator and then fabricated through a single-layer printed circuit board (PCB) process. Wide bandwidth and a quasi-omnidirectional radiation pattern are obtained. The proposed antenna is therefore a good candidate for millimeter-wave applications. Measured results are in good agreement with simulated results.

"One of the greatest medical success stories:" Physicians and nurses' small stories about vaccine knowledge and anxieties. <=SEP=> In recent years, the Canadian province of Alberta experienced outbreaks of measles, mumps, pertussis, and influenza. Even so, the dominant cultural narrative maintains that vaccines are safe, effective, and necessary to maintain population health. Many vaccine supporters have expressed anxieties that stories contradicting this narrative have lowered herd immunity levels because they frighten the public into avoiding vaccination. As such, vaccine policies often emphasize educating parents and the public about the importance and safety of vaccination. These policies rely on health professionals to encourage vaccine uptake and assume that all professionals support vaccination. Health professionals, however, are socially positioned between vaccine experts (such as immunologists) and non-experts (the wider public). In this article, I discuss health professionals' anxieties about the potential risks associated with vaccination and with the limitations of Alberta's immunisation program. Specifically, I address the question: If medical knowledge overwhelmingly supports vaccination, then why do some professionals continue to question certain vaccines? To investigate this topic, I interviewed twenty-seven physicians and seven nurses. With stock images and small stories that interviewees shared about their vaccine anxieties, I challenge the common assumption that all health professionals support vaccines uncritically. All interviewees provided generic statements that supported vaccination and Alberta's immunisation program, but they expressed anxieties when I asked for details. I found that their anxieties reflected nuances that the culturally dominant vaccine narrative overlooks. Particularly, they critiqued the influence that pharmaceutical companies, the perceived newness of specific vaccines, and the limitations of medical knowledge and vaccine schedules.

Fuzzy Logic-Based Natural Language Processing and Its Application to Speech Recognition <=SEP=> In this paper we describe a fuzzy logic-based language processing method, which is applied to speech recognition. Our purpose is to create a system that can learn from a linguistic corpus the fuzzy semantic relations between the concepts represented by words and use such relations to process the word sequences generated by speech recognition systems. In particular, the system will be able to predict the words failed to be recognized by a speech recognition system. This will help to increase the accuracy of a speech recognition system. This will also serve as the first stage of deep semantic processing of speech recognition results by providing “semantic relatedness” between the recognized words. We report the fuzzy inference rule learning system, which we have developed and also report the experimental results based on the system. Key-words: Fuzzy logic, Natural Language Analysis, Speech Recognition, Corpus Linguistics.

KEGGgraph: a graph approach to KEGG PATHWAY in R and bioconductor <=SEP=> MOTIVATION
KEGG PATHWAY is a service of Kyoto Encyclopedia of Genes and Genomes (KEGG), constructing manually curated pathway maps that represent current knowledge on biological networks in graph models. While valuable graph tools have been implemented in R/Bioconductor, to our knowledge there is currently no software package to parse and analyze KEGG pathways with graph theory.


RESULTS
We introduce the software package KEGGgraph in R and Bioconductor, an interface between KEGG pathways and graph models as well as a collection of tools for these graphs. Superior to existing approaches, KEGGgraph captures the pathway topology and allows further analysis or dissection of pathway graphs. We demonstrate the use of the package by the case study of analyzing human pancreatic cancer pathway.


AVAILABILITY
KEGGgraph is freely available at the Bioconductor web site (http://www.bioconductor.org). KGML files can be downloaded from KEGG FTP site (ftp://ftp.genome.jp/pub/kegg/xml).