title: Distinguishing Deceptive from Non-Deceptive Speech

abstract: To date, studies of deceptive speech have largely been confined to descriptive studies and observations from subjects, researchers, or practitioners, with few empirical studies of the specific lexical or acoustic/prosodic features which may characterize deceptive speech. We present results from a study seeking to distinguish deceptive from non-deceptive speech using machine learning techniques on features extracted from a large corpus of deceptive and non-deceptive speech. This corpus employs an interview paradigm that includes subject reports of truth vs. lie at multiple temporal scales. We present current results comparing the performance of acoustic/prosodic, lexical, and speaker-dependent features and discuss future research directions.


title: Fingerprint Classification using Fast Fourier Transform and Nonlinear Discriminant Analysis

abstract: In this paper, we present a new approach for fingerprint classification based on discrete Fourier transform (DFT) and nonlinear discriminant analysis. Utilizing the DFT and directional filters, a reliable and efficient directional image is constructed from each fingerprint image, and then nonlinear discriminant analysis is applied to the constructed directional images, reducing the dimension dramatically and extracting the discriminant features. The proposed method explores the capability of DFT and directional filtering in dealing with low-quality images and the effectiveness of nonlinear feature extraction method in fingerprint classification. Experimental results demonstrates competitive performance compared with other published results.


title: Learning User Preferences across Multiple Aspects for Merchant Recommendation

abstract: With the pervasive use of mobile devices, Location Based Social Networks(LBSNs) have emerged in past years. These LBSNs, allowing their users to share personal experiences and opinions on visited merchants, have very rich and useful information which enables a new breed of location-based services, namely, Merchant Recommendation. Existing techniques for merchant recommendation simply treat each merchant as an item and apply conventional recommendation algorithms, e.g., Collaborative Filtering, to recommend merchants to a target user. However, they do not differentiate the user's real preferences on various aspects, and thus can only achieve limited success. In this paper, we aim to address this problem by utilizing and analyzing user reviews to discover user preferences in different aspects. Following the intuition that a user rating represents a personalized rational choice, we propose a novel utility-based approach by combining collaborative and individual views to estimate user preference (i.e., rating). An optimization algorithm based on a Gaussian model is developed to train our merchant recommendation approach. Lastly we evaluate the proposed approach in terms of effectiveness, efficiency and cold-start using two real-world datasets. The experimental results show that our approach outperforms the state-of-the-art methods. Meanwhile, a real mobile application is implemented to demonstrate the practicability of our method.


1 A Leap-supported , hybrid AR interface approach


title: Representation Learning of Knowledge Graphs with Entity Descriptions

abstract: Representation learning (RL) of knowledge graphs aims to project both entities and relations into a continuous low-dimensional space. Most methods concentrate on learning representations with knowledge triples indicating relations between entities. In fact, in most knowledge graphs there are usually concise descriptions for entities, which cannot be well utilized by existing methods. In this paper, we propose a novel RL method for knowledge graphs taking advantages of entity descriptions. More specifically, we explore two encoders, including continuous bag-of-words and deep convolutional neural models to encode semantics of entity descriptions. We further learn knowledge representations with both triples and descriptions. We evaluate our method on two tasks, including knowledge graph completion and entity classification. Experimental results on real-world datasets show that, our method outperforms other baselines on the two tasks, especially under the zero-shot setting, which indicates that our method is capable of building representations for novel entities according to their descriptions. The source code of this paper can be obtained from https://github.com/xrb92/DKRL.


title: Efficient covariance matrix update for variable metric evolution strategies

abstract: Randomized direct search algorithms for continuous domains, such as evolution strategies, are basic tools in machine learning. They are especially needed when the gradient of an objective function (e.g., loss, energy, or reward function) cannot be computed or estimated efficiently. Application areas include supervised and reinforcement learning as well as model selection. These randomized search strategies often rely on normally distributed additive variations of candidate solutions. In order to efficiently search in non-separable and ill-conditioned landscapes the covariance matrix of the normal distribution must be adapted, amounting to a variable metric method. Consequently, covariance matrix adaptation (CMA) is considered state-of-the-art in evolution strategies. In order to sample the normal distribution, the adapted covariance matrix needs to be decomposed, requiring in general ?(n 3) operations, where n is the search space dimension. We propose a new update mechanism which can replace a rank-one covariance matrix update and the computationally expensive decomposition of the covariance matrix. The newly developed update rule reduces the computational complexity of the rank-one covariance matrix adaptation to ?(n 2) without resorting to outdated distributions. We derive new versions of the elitist covariance matrix adaptation evolution strategy (CMA-ES) and the multi-objective CMA-ES. These algorithms are equivalent to the original procedures except that the update step for the variable metric distribution scales better in the problem dimension. We also introduce a simplified variant of the non-elitist CMA-ES with the incremental covariance matrix update and investigate its performance. Apart from the reduced time-complexity of the distribution update, the algebraic computations involved in all new algorithms are simpler compared to the original versions. The new update rule improves the performance of the CMA-ES for large scale machine learning problems in which the objective function can be evaluated fast.


title: Context-aware Image Tweet Modelling and Recommendation

abstract: While efforts have been made on bridging the semantic gap in image understanding, the in situ understanding of social media images is arguably more important but has had less progress. In this work, we enrich the representation of images in image tweets by considering their social context. We argue that in the microblog context, traditional image features, e.g., low-level SIFT or high-level detected objects, are far from adequate in interpreting the necessary semantics latent in image tweets. To bridge this gap, we move from the images' pixels to their context and propose a context-aware image bf tweet modelling (CITING) framework to mine and fuse contextual text to model such social media images' semantics. We start with tweet's intrinsic contexts, namely, 1) text within the image itself and 2) its accompanying text; and then we turn to the extrinsic contexts: 3) the external web page linked to by the tweet's embedded URL, and 4) the Web as a whole. These contexts can be leveraged to benefit many fundamental applications. To demonstrate the effectiveness our framework, we focus on the task of personalized image tweet recommendation, developing a feature-aware matrix factorization framework that encodes the contexts as a part of user interest modelling. Extensive experiments on a large Twitter dataset show that our proposed method significantly improves performance. Finally, to spur future studies, we have released both the code of our recommendation model and our image tweet dataset.


title: Cross-language transfer learning for deep neural network based speech enhancement

abstract: In this paper, we propose a transfer learning approach to adapt a well-trained model obtained with high-resource materials of one language to another target language using a small amount of adaptation data for speech enhancement based on deep neural networks (DNNs). We investigate the performance degradation issues of enhancing noisy Mandarin speech data using DNN models already trained with only English speech materials, and vice versa. By assuming that the hidden layers of the well-trained DNN regression model as a cascade of feature extractors, we hypothesize that the first several layers should be transferable between languages. Our experimental results indicate that even with only about 1 minute of adaptation data from the resource-limited language we can achieve a considerable performance improvement over the DNN model without cross-language transfer learning.


title: MAPO: Mining and recommending API usage patterns

abstract: To improve software productivity, when constructing new software systems, programmers often reuse existing libraries or frameworks by invoking methods provided in their APIs. Those API methods, however, are often complex and not well documented. To get familiar with how those API methods are used, programmers often exploit a source code search tool to search for code snippets that use the API methods of interest. However, the returned code snippets are often large in number, and the huge number of snippets places a barrier for programmers to locate useful ones. In order to help programmers overcome this barrier, we have developed an API usage mining framework and its supporting tool called MAPO (Mining API usage Pattern from Open source repositories) for mining API usage patterns automatically. A mined pattern describes that in a certain usage scenario, some API methods are frequently called together and their usages follow some sequential rules. MAPO further recommends the mined API usage patterns and their associated code snippets upon programmers' requests. Our experimental results show that with these patterns MAPO helps programmers locate useful code snippets more effectively than two state-of-the-art code search tools. To investigate whether MAPO can assist programmers in programming tasks, we further conducted an empirical study. The results show that using MAPO, programmers produce code with fewer bugs when facing relatively complex API usages, comparing with using the two state-of-the-art code search tools.


title: Personality and compatibility: A prospective analysis of marital stability and marital satisfaction.

abstract: The antecedents of marital stability (divorce or remaining married) and marital satisfaction (within the group that remains married) were investigated with a panel of 300 couples who were followed from their engagements in the 1930s until 1980. Twenty-two of the couples broke their engagements; of the 278 couples who married, 50 got divorced at some time between 1935 and 1980. Personality characteristics (measured by acquaintance ratings made in the 1930s) were important predictors of both marital stability and marital satisfaction. The three aspects of personality most strongly related to marital outcome were the neuroticism of the husband, the neuroticism of the wife, and the impulse control of the husband. In combination, the 17 major antecedent variables were moderately predictive of a criterion variable composed of both marital stability and marital satisfaction (R = .49). The three major aspects of personality accounted for more than half of the predictable variance. The remaining variance was accounted for by attitudinal, social-environment, and sexual history variables.


title: Implementation of a Complex Event Engine for the Web

abstract: One of the key aspects in the implementation of reactive behaviour in the Web and, most importantly, in the semantic Web is the development of event detection engines. An event engine detects events occurring in a system and notifies their occurrences to its clients. Although primitive events are useful for modelling a good number of applications, certain other applications require the combination of primitive events in order to support reactive behaviour. This paper presents the implementation of an event detection engine that detects composite events specified by expressions of an illustrative sublanguage of the SNOOP event algebra


Delay Tolerant Networking- A Tutorial


Big data are coming to psychiatry: a general introduction


title: The Neurodevelopment of Empathy in Humans

abstract: Empathy, which implies a shared interpersonal experience, is implicated in many aspects of social cognition, notably prosocial behavior, morality and the regulation of aggression. The purpose of this paper is to critically examine the current knowledge in developmental and affective neuroscience with an emphasis on the perception of pain in others. It will be argued that human empathy involves several components: affective arousal, emotion understanding and emotion regulation, each with different developmental trajectories. These components are implemented by a complex network of distributed, often recursively connected, interacting neural regions including the superior temporal sulcus, insula, medial and orbitofrontal cortices, amygdala and anterior cingulate cortex, as well as autonomic and neuroendocrine processes implicated in social behaviors and emotional states. Decomposing the construct of empathy into subcomponents that operate in conjunction in the healthy brain and examining their developmental trajectory provides added value to our current approaches to understanding human development. It can also benefit our understanding of both typical and atypical development.


title: SEQUENCE BASED HIERARCHICAL CONFLICT-FREE ROUTING STRATEGY OF BI-DIRECTIONAL AUTOMATED GUIDED VEHICLES

abstract: Abstract Conflict-free routing is an important problem to be addressed in AGV Systems, in particular when they are bi-directional. This paper considers the problem of routing AGVs in the presence of contingencies. The importance of this work stems from the realization that path-planning approaches are ill suited to AGVSs subject to contingencies. In this paper, a two stage closed-loop control strategy is proposed. In the first control stage, a pre-planning method proposed by Kim and Tanchoco (1991) is used to establish the fastest conflict-free routes for AGVs. The objective of the second stage is the avoidance of deadlocks in the presence of interruptions while maintaining the planned routes.


title: Infrared image processing and data analysis

abstract: Infrared thermography in nondestructive testing provides images (thermograms) in which zones of interest (defects) appear sometimes as subtle signatures. In this context, raw images are not often appropriate since most will be missed. In some other cases, what is needed is a quantitative analysis such as for defect detection and characterization. In this paper, presentation is made of various methods of data analysis required either at preprocessing and/or processing images. References from literature are provided for briefly discussed known methods while novelties are elaborated in more details within the text which include also experimental results.


title: The Origin of Interstellar Turbulence in M33

abstract: We utilize the multi-wavelength data of M33 to study the origin of turbulence in its interstellar medium. We find that the HI turbulent energy surface density inside 8 kpc is $\sim1-3~\times~10^{46}$ erg pc$^{-2}$, and has no strong dependence on galactocentric radius because of the lack of variation in HI surface density and HI velocity dispersion. Then, we consider the energies injected by supernovae (SNe), the magneto-rotational instability (MRI), and the gravity-driven turbulence from accreted materials as the sources of turbulent energy. For a constant dissipation time of turbulence, the SNe energy can maintain turbulence inside $\sim 4$ kpc radius (equivalent to $\sim 0.5~R_{25}$), while the MRI energy is always smaller than the turbulent energy within 8 kpc radius. However, when we let the dissipation time to be equal to the crossing time of turbulence across the HI scale-height, the SNe energy is enough to maintain turbulence out to 7 kpc radius, and the sum of SNe and MRI energies is able to maintain turbulence out to 8 kpc radius. Due to lack of constraint in the mass accretion rate through the disk of M33, we can not rule out the accretion driven turbulence as a possible source of energy. Furthermore, by resolving individual Giant Molecular Clouds in M33, we also show that the SNe energy can maintain turbulence within individual molecular clouds with $\sim 1\%$ of coupling efficiency. This result strengthens the proposition that stellar feedback is an important source of energy to maintain turbulence in nearby galaxies.


title: An empirical evaluation of deep architectures on problems with many factors of variation

abstract: Recently, several learning algorithms relying on models with deep architectures have been proposed. Though they have demonstrated impressive performance, to date, they have only been evaluated on relatively simple problems such as digit recognition in a controlled environment, for which many machine learning algorithms already report reasonable results. Here, we present a series of experiments which indicate that these models show promise in solving harder learning problems that exhibit many factors of variation. These models are compared with well-established algorithms such as Support Vector Machines and single hidden-layer feed-forward neural networks.


title: Geotagging Named Entities in News and Online Documents

abstract: News sources generate constant streams of text with many references to real world entities; understanding the content from such sources often requires effectively detecting the geographic foci of the entities. We study the problem of associating geography to named entities in online documents. More specifically, given a named entity and a page (or a set of pages) where the entity is mentioned, the problem being studied is how the geographic focus of the name can be resolved at a location granularity (e.g. city or country), assuming that the name has a geographic focus. We further study dispersion, and show that the dispersion of a name can be estimated with a good accuracy, allowing a geo-centre to be detected at an exact dispersion level. Two key features of our approach are: (i) minimal assumption is made on the structure of the mentions hence the approach can be applied to a diverse and heterogeneous set of web pages, and (ii) the approach is unsupervised, leveraging shallow English linguistic features and the large volume of location data in public domain. We evaluate our methods under different task settings and with different categories of named entities. Our evaluation reveals that the geo-centre of a name can be estimated with a good accuracy based on some simple statistics of the mentions, and that the accuracy of the estimation varies with the categories of the names.


title: Using schematically heterogeneous structures

abstract: Schematic heterogeneity arises when information that is represented as data under one schema, is represented within the schema (as metadata) in another. Schematic heterogeneity is an important class of heterogeneity that arises frequently in integrating legacy data in federated or data warehousing applications. Traditional query languages and view mechanisms are insufficient for reconciling and translating data between schematically heterogeneous schemas. Higher order query languages, that permit quantification over schema labels, have been proposed to permit querying and restructuring of data between schematically disparate schemas. We extend this work by considering how these languages can be used in practice. Specifically, we consider a restricted class of higher order views and show the power of these views in integrating legacy structures. Our results provide insights into the properties of restructuring transformations required to resolve schematic discrepancies. In addition, we show how the use of these views permits schema browsing and new forms of data independence that are important for global information systems. Furthermore, these views provide a framework for integrating semi-structured and unstructured queries, such as keyword searches, into a structured querying environment. We show how these views can be used with minimal extensions to existing query engines. We give conditions under which a higher order view is usable for answering a query and provide query translation algorithms.


title: Development of Autonomous Car—Part II: A Case Study on the Implementation of an Autonomous Driving System Based on Distributed Architecture

abstract: Part I of this paper proposed a development process and a system platform for the development of autonomous cars based on a distributed system architecture. The proposed development methodology enabled the design and development of an autonomous car with benefits such as a reduction in computational complexity, fault-tolerant characteristics, and system modularity. In this paper (Part II), a case study of the proposed development methodology is addressed by showing the implementation process of an autonomous driving system. In order to describe the implementation process intuitively, core autonomous driving algorithms (localization, perception, planning, vehicle control, and system management) are briefly introduced and applied to the implementation of an autonomous driving system. We are able to examine the advantages of a distributed system architecture and the proposed development process by conducting a case study on the autonomous system implementation. The validity of the proposed methodology is proved through the autonomous car A1 that won the 2012 Autonomous Vehicle Competition in Korea with all missions completed.


title: Explanation in Recommender Systems

abstract: There is increasing awareness in recommender systems research of the need to make the recommendation process more transparent to users. Following a brief review of existing approaches to explanation in recommender systems, we focus in this paper on a case-based reasoning (CBR) approach to product recommendation that offers important benefits in terms of the ease with which the recommendation process can be explained and the system's recommendations can be justified. For example, recommendations based on incomplete queries can be justified on the grounds that the user's preferences with respect to attributes not mentioned in her query cannot affect the outcome. We also show how the relevance of any question the user is asked can be explained in terms of its ability to discriminate between competing cases, thus giving users a unique insight into the recommendation process.


title: A compact 5kV battery-capacitor seed source with rapid capacitor charger

abstract: Many pulsed power applications have demanding system requirements. Power systems for these applications are expected to provide high energy, high pulsed power and long standby times without recharge, all in a very compact package. The different properties of batteries and capacitors make them most suitable for different uses. When selecting a prime power source for compact pulsed power systems a hybrid system often provides the optimal solution, utilizing a battery for prime energy storage during standby and a capacitor for intermediate energy storage before and between operations. This system takes advantage of the best characteristics of both sources to fulfill the system requirements. The design and testing of such a compact system is discussed. The system utilizes a solid-state converter to charge a 50 µF polypropylene capacitor to 5 kV in under 500 ms from lithium-ion polymer (LiPo) batteries. Battery selection and testing is also covered. The battery and charger assembly occupies 1.25 L while the capacitor occupies an additional 1.4 L.


title: A Hybrid Book Recommender System Based on Table of Contents (ToC) and Association Rule Mining

abstract: Recommender systems are used to access appropriate items and information by personalized suggestions based on user previous preferences and their likes & dislikes. These systems are used in different domains including products, videos, images, articles, news and books. Several recommender systems have been designed for recommending books. However, the available book recommenders face several issues in making relevant book recommendations because most of these do not take into account the book contents at deeper level and process only the mere descriptions about books on web pages along with metadata and other rating information. In order to cope with this issue, we present a hybrid book recommender that recommends books by using book table of contents (TOC) along with association rule mining and opinions of similar users.


Telling a Story on a Tag: the Importance of Markers' Visual Design for Real World Applications. Mobile Interaction with the Real World (MIRW


title: D-HIP: A distributed key exchange scheme for HIP-based Internet of Things

abstract: Host Identity Protocol (HIP) emerges as the most suitable identification protocol for the Internet of Things. HIP not only provides identifier/locator split but also a key agreement procedure named HIP Base Exchange, which allows secure connections between HIP peers. However, the heterogeneous and decentralized nature of IoT architecture, coupling resource-constrained networks with powerful Internet, impedes the use of HIP on small devices due to its computationally expensive cryptographic operations. In this paper, we propose a distributed lightweight key exchange protocol designed to reduce the requirements of HIP Base Exchange, in order to be supported by resource-constrained nodes.


title: EmotionWatch: Visualizing Fine-Grained Emotions in Event-Related Tweets

abstract: Spectators are increasingly using social platforms to express their opinions and share their emotions during big public events. Those reactions reveal the subjective perception of the event and extend its understanding. This has motivated us to develop a system to explore and visualize volume, patterns, and trends of user sentiments as they evolve over time. Previous work in sentiment analysis and opinion mining has addressed these issues. But the majority of them distinguish only two polarity categories, leaving a more detailed and insightful analysis to be desired. In this paper, we suggest using a fine-grained, multi-category emotion model to classify and visualize users’ emotional reactions in public events. We describe EmotionWatch, a tool that constructs visual summaries of public emotions, and apply it to the 2012 Olympics as a test case. We report findings from a user study evaluating the usability of the tool and validating the emotion model. Results show that users prefer a more detailed inspection of public emotions over the simplified analysis. Despite its complexity, users were able to effectively grasp, understand, and interpret the emotional reactions using EmotionWatch. The same user study also pointed out few design improvements for the future development of analogous systems.


title: Checkpointing algorithms and fault prediction

abstract: This paper deals with the impact of fault prediction techniques on checkpointing strategies. We extend the classical first-order analysis of Young and Daly in the presence of a fault prediction system, characterized by its recall and its precision. In this framework, we provide optimal algorithms to decide whether and when to take predictions into account, and we derive the optimal value of the checkpointing period. These results allow us to analytically assess the key parameters that impact the performance of fault predictors at very large scale.


title: Refinement of breast cancer classification by molecular characterization of histological special types.

abstract: Most invasive breast cancers are classified as invasive ductal carcinoma not otherwise specified (IDC NOS), whereas about 25% are defined as histological 'special types'. These special-type breast cancers are categorized into at least 17 discrete pathological entities; however, whether these also constitute discrete molecular entities remains to be determined. Current therapy decision-making is increasingly governed by the molecular classification of breast cancer (luminal, basal-like, HER2+). The molecular classification is derived from mainly IDC NOS and it is unknown whether this classification applies to all histological subtypes. We aimed to refine the breast cancer classification systems by analysing a series of 11 histological special types [invasive lobular carcinoma (ILC), tubular, mucinous A, mucinous B, neuroendocrine, apocrine, IDC with osteoclastic giant cells, micropapillary, adenoid cystic, metaplastic, and medullary carcinoma] using immunohistochemistry and genome-wide gene expression profiling. Hierarchical clustering analysis confirmed that some histological special types constitute discrete entities, such as micropapillary carcinoma, but also revealed that others, including tubular and lobular carcinoma, are very similar at the transcriptome level. When classified by expression profiling, IDC NOS and ILC contain all molecular breast cancer types (ie luminal, basal-like, HER2+), whereas histological special-type cancers, apart from apocrine carcinoma, are homogeneous and only belong to one molecular subtype. Our analysis also revealed that some special types associated with a good prognosis, such as medullary and adenoid cystic carcinomas, display a poor prognosis basal-like transcriptome, providing strong circumstantial evidence that basal-like cancers constitute a heterogeneous group. Taken together, our results imply that the correct classification of breast cancers of special histological type will allow a more accurate prognostication of breast cancer patients and facilitate the identification of optimal therapeutic strategies.


title: Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis

abstract: Exploring data requires a fast feedback loop from the analyst to the system, with a latency below about 10 seconds because of human cognitive limitations. When data becomes large or analysis becomes complex, sequential computations can no longer be completed in a few seconds and data exploration is severely hampered. This article describes a novel computation paradigm called Progressive Computation for Data Analysis or more concisely Progressive Analytics, that brings at the programming language level a low-latency guarantee by performing computations in a progressive fashion. Moving this progressive computation at the language level relieves the programmer of exploratory data analysis systems from implementing the whole analytics pipeline in a progressive way from scratch, streamlining the implementation of scalable exploratory data analysis systems. This article describes the new paradigm through a prototype implementation called ProgressiVis, and explains the requirements it implies through examples.


title: CapSeat: capacitive proximity sensing for automotive activity recognition

abstract: Inattentiveness is one of the major causes of traffic accidents. Advanced car safety systems try to mitigate this by detecting potential signs of distraction or tiredness, and providing alerts to the driver. In this paper we present CapSeat - a car seat equipped with integrated capacitive proximity sensors that are used to measure a wide range of physiological parameters about the driver. This can support safety systems by detecting inattentiveness and increase passive safety by facilitating suitable seat adjustments and posture detection. We present a sensor electrode layout suitable for detecting the necessary parameters and processing methods that acquire multiple physiological parameters from sensor data, using a variety of different algorithms. A prototype of the system is presented that was evaluated for all detectable parameters in a proof-of-concept study. We achieved a classification precision between 95% and 100%.


title: HAFIX: Hardware-Assisted Flow Integrity eXtension

abstract: Code-reuse attacks like return-oriented programming (ROP) pose a severe threat to modern software on diverse processor architectures. Designing practical and secure defenses against code-reuse attacks is highly challenging and currently subject to intense research. However, no secure and practical system-level solutions exist so far, since a large number of proposed defenses have been successfully bypassed. To tackle this attack, we present H AFIX (Hardware-Assisted Flow Integrity eXtension), a defense against code-reuse attacks exploiting backward edges (returns). H AFIX provides fine-grained and practical protection, and serves as an enabling technology for future control-flow integrity instantiations. This paper presents the implementation and evaluation of HAFIX for the Intel® Siskiyou Peak and SPARC embedded system architectures, and demonstrates its security and efficiency in code-reuse protection while incurring only 2% performance overhead.


title: Fusion of Keypoint Tracking and Facial Landmark Detection for Real-Time Head Pose Estimation

abstract: In this paper, we address the problem of extreme head pose estimation from intensity images, in a monocular setup. We introduce a novel fusion pipeline to integrate into a dedicated Kalman Filter the pose estimated from a tracking scheme in the prediction stage and the pose estimated from a detection scheme in the correction stage. To that end, the measurement covariance of the Kalman Filter is updated in every frame. The tracking scheme is performed using a set of keypoints extracted in the area of the head along with a simple 3D geometric model. The detection scheme, on the other hand, relies on the alignment of facial landmarks in each frame combined with 3D features extracted on a head mesh. The head pose in each scheme is estimated by minimizing the reprojection error from the 3D-2D correspondences. By combining both frameworks, we extend the applicability of head pose estimation from facial landmarks to cases where these features are no longer visible. We compared the proposed method to other related approaches, showing that it can achieve state-of-the-art performance. We also demonstrate that our approach is suitable for cases with extreme head rotations and (self-) occlusions, besides being suitable for real time applications.


title: EC-Net: an Edge-aware Point set Consolidation Network

abstract: Point clouds obtained from 3D scans are typically sparse, irregular, and noisy, and required to be consolidated. In this paper, we present the first deep learning based edge-aware technique to facilitate the consolidation of point clouds. We design our network to process points grouped in local patches, and train it to learn and help consolidate points, deliberately for edges. To achieve this, we formulate a regression component to simultaneously recover 3D point coordinates and point-to-edge distances from upsampled features, and an edge-aware joint loss function to directly minimize distances from output points to 3D meshes and to edges. Compared with previous neural network based works, our consolidation is edge-aware. During the synthesis, our network can attend to the detected sharp edges and enable more accurate 3D reconstructions. Also, we trained our network on virtual scanned point clouds, demonstrated the performance of our method on both synthetic and real point clouds, presented various surface reconstruction results, and showed how our method outperforms the state-of-the-arts.


title: Experimental validation for N-ary error correcting output codes for ensemble learning of deep neural networks

abstract: N-ary error correcting output codes (ECOC) decompose a multi-class problem into simpler multi-class problems by splitting the classes into N subsets (meta-classes) to form an ensemble of N-class classifiers and combine them to make predictions. It is one of the most accurate ensemble learning methods for traditional classification tasks. Deep learning has gained increasing attention in recent years due to its successes on various tasks such as image classification and speech recognition. However, little is known about N-ary ECOC with deep neural networks (DNNs) as base learners, probably due to the long computation time. In this paper, we show by experiments that N-ary ECOC with DNNs as base learners generally exhibits superior performance compared with several state-of-the-art ensemble learning methods. Moreover, our work contributes to a more efficient setting of the two crucial hyperparameters of N-ary ECOC: the value of N and the number of base learners to train. We also explore valuable strategies for further improving the accuracy of N-ary ECOC.


Decision Fusion in a Wireless Sensor Network with a Large Number of Sensors


CHAPTER 3 Image Segmentation Using Deformable Models


title: A Robotic Wheelchair System: Indoor Navigation and User

abstract: Many people in wheelchairs are unable to control a powered wheelchair with the standard joystick interface. A robotic wheelchair can provide users with driving assistance, taking over low-level navigation to allow its user to travel efficiently and with greater ease. Our robotic wheelchair system, Wheelesley, consists of a standard powered wheelchair with an on-board computer, sensors and a graphical user interface. This paper describes the indoor navigation system and the customizable user interface.


title: Generalized Overlap Measures for Evaluation and Validation in Medical Image Analysis

abstract: Measures of overlap of labelled regions of images, such as the Dice and Tanimoto coefficients, have been extensively used to evaluate image registration and segmentation algorithms. Modern studies can include multiple labels defined on multiple images yet most evaluation schemes report one overlap per labelled region, simply averaged over multiple images. In this paper, common overlap measures are generalized to measure the total overlap of ensembles of labels defined on multiple test images and account for fractional labels using fuzzy set theory. This framework allows a single "figure-of-merit" to be reported which summarises the results of a complex experiment by image pair, by label or overall. A complementary measure of error, the overlap distance, is defined which captures the spatial extent of the nonoverlapping part and is related to the Hausdorff distance computed on grey level images. The generalized overlap measures are validated on synthetic images for which the overlap can be computed analytically and used as similarity measures in nonrigid registration of three-dimensional magnetic resonance imaging (MRI) brain images. Finally, a pragmatic segmentation ground truth is constructed by registering a magnetic resonance atlas brain to 20 individual scans, and used with the overlap measures to evaluate publicly available brain segmentation algorithms


title: Test-Driven Development - Still a Promising Approach?

abstract: Test-Driven Development (TDD) has been regarded as a useful practice during the last decade as well in industry as in academia. It has been suggested to have several benefits in software development process. This paper is focused on the reported empirical research on TDD. A systematic literature review was conducted in order to analyze the current empirical evidence. Based on the review data, TDD may improve external code quality, but it also leads to increase in development time. However, there are a lot of contradictory results and it raises a question about the actual factors behind them. More systematic research, specifically controlled experiments and well reported case studies, is needed in order to better understand TDD.


title: Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme

abstract: Joint extraction of entities and relations is an important task in information extraction. To tackle this problem, we firstly propose a novel tagging scheme that can convert the joint extraction task to a tagging problem. Then, based on our tagging scheme, we study different end-to-end models to extract entities and their relations directly, without identifying entities and relations separately. We conduct experiments on a public dataset produced by distant supervision method and the experimental results show that the tagging based methods are better than most of the existing pipelined and joint learning methods. What's more, the end-to-end model proposed in this paper, achieves the best results on the public dataset.


title: An Adaptable Maturity Strategy for Information Security

abstract: The lack of security in information systems has caused numerous financial and moral losses to several organizations. The organizations have a series of information security measures recommended by literature and international standards. However, the implementation of policies, actions, and adjustment to such standards is not simple and must be addressed by specific needs identified by the Information Security Governance in each organization. There are many challenges in effectively establishing, maintaining, and measuring information security in a way that adds value. Those challenges demonstrate a need for further investigations which address the problem. This paper presents a strategy to measure the maturity in information security aiming, also, to assist in the application and prioritization of information security actions in the corporate environment. For this, a survey was used as the main methodological instrument, reaching 157 distinct companies. As a result, it was possible to classify the ISO/IEC 27001 and 27002 controls in four stages according to the importance given by the companies. The COBIT maturity levels and a risk analysis matrix were also used. Finally, the adaptable strategy was successfully tested in a company


title: A fast capability extension to a RISC architecture

abstract: The concept of capability-based addressing originated in the 60's a means of promoting security and information sharing in computer systems; however, poor performance of early capability-based machines prevented it from becoming widespread. We describe a capability-based architecture implemented as a fairly straightforward extension of a conventional RISC architecture. Without compromising the security aspect, the capability mechanism was simplified to allow for an implementation that does not stretch the cycle time of the core architecture. Simulated executions of benchmark programs show that the performance penalty of using the capability mechanism is in the range of 16% to 19%, an acceptable price to pay for security. The proposed architecture can thus be a viable solution for meeting the increased demands for security as information sharing becomes pervasive.


title: Compensating Pixel Circuit Driving AMOLED Display With a-IGZO TFTs

abstract: This letter presents a novel active-matrix organic light-emitting diode (AMOLED) pixel circuit that uses amorphous indium-gallium-zinc oxide (a-IGZO) thin-film transistors (TFTs) with a bottom-gate structure to compensate for the threshold voltage shift of the TFT. An a-IGZO TFT driven AMOLED display (70 × 70 pixels) on a glass substrate is fabricated and its reliability is evaluated under electrical stress.


title: ITU-T G.729.1: AN 8-32 Kbit/S Scalable Coder Interoperable with G.729 for Wideband Telephony and Voice Over IP

abstract: This paper describes the scalable coder - G.729.1 - which has been recently standardized by ITU-T for wideband telephony and voice over IP (VoIP) applications. G.729.1 can operate at 12 different bit rates from 32 down to 8 kbit/s with wideband quality starting at 14 kbit/s. This coder is a bitstream interoperable extension of ITU-T G.729 based on three embedded stages: narrowband cascaded CELP coding at 8 and 12 kbit/s, time-domain bandwidth extension (TDBWE) at 14 kbit/s, and split-band MDCT coding with spherical vector quantization (VQ) and pre-echo reduction from 16 to 32 kbit/s. Side information - consisting of signal class, phase, and energy - is transmitted at 12, 14 and 16 kbit/s to improve the resilience and recovery of the decoder in case of frame erasures. The quality, delay, and complexity of G.729.1 are summarized based on ITU-T results.


title: An Effective Neural Network Model for Graph-based Dependency Parsing

abstract: Most existing graph-based parsing models rely on millions of hand-crafted features, which limits their generalization ability and slows down the parsing speed. In this paper, we propose a general and effective Neural Network model for graph-based dependency parsing. Our model can automatically learn high-order feature combinations using only atomic features by exploiting a novel activation function tanhcube. Moreover, we propose a simple yet effective way to utilize phrase-level information that is expensive to use in conventional graph-based parsers. Experiments on the English Penn Treebank show that parsers based on our model perform better than conventional graph-based parsers.


title: Recognizing Keystrokes Using WiFi Devices

abstract: Keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what is being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of channel state information (CSI) values, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal-based keystroke recognition system called WiKey. WiKey consists of two commercial off-the-shelf WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, WiKey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the WiKey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. WiKey achieves over 97.5% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, WiKey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%. WiKey can also recognize complete words inside a sentence with over 85% accuracy.


title: Foundations of Non-Bayesian Social Learning

abstract: In this paper, we study the problem of non-Bayesian learning over social networks by taking an axiomatic approach. As our main behavioral assumption, we postulate that agents follow social learning rules that satisfy imperfect recall, according to which they treat the current beliefs of their neighbors as sufficient statistics for all the information available to them. We establish that as long as imperfect recall represents the only point of departure from Bayesian rationality, agents’ social learning rules take a log-linear form. Our approach also enables us to provide a taxonomy of behavioral assumptions that underpin various non-Bayesian models of learning, including the canonical model of DeGroot. We then show that for a fairly large class of learning rules, the form of bounded rationality represented by imperfect recall is not an impediment to asymptotic learning, as long as agents assign weights of equal orders of magnitude to every independent piece of information. Finally, we show how the dispersion of information among different individuals in the social network determines the rate of learning.


title: Multidimensional Process Mining with PMCube Explorer

abstract: Process mining techniques allow process analysts to generate process models from recorded event logs. Typically, process mining considers the event log as a whole and creates a single model reflecting its behavior. However, the process may be influenced by several characteristics of the process instances, e.g., by the individual characteristics of a patient in the healthcare domain like age and sex. This leads to a wide range of process variations which can end up in complex and confusing models, blurring the behavior of specific process variants. Multidimensional process mining (MPM) aims to overcome this limitation by the notion of data cubes, spreading the data over multiple cells, each representing a group of cases with similar characteristics. This allows for the creation of separated process models for a homogenous set of cases. In this paper, we introduce PMCube Explorer, a novel tool for MPM, that allows for the analysis of a process from various views. It enables the analyst to specify OLAP queries to extract multiple cells from the data warehouse. Each cell contains a subset of event data which are mined separately to discover independent process models. To deal with the potentially high amount of resulting models, our tool provides some distinctive features like the visualization of model differences or the consolidation of multiple process models. We applied our tool in a case study to analyze the perioperative processes in a large German hospital.


title: 4 by 4 ultra-wideband milimeter-wave printed log-periodic dipole array antenna

abstract: 16 elements of ultra-wideband millimetre-wave printed log-periodic dipole array antenna (LPDAA) which fed by the 4-by-4 Wilkinson power distribution network is designed and fabricated for far distance chipless RFID system application. The LPDAAs are design on single side of the Taconic TLX-8 (□ r = 2.55) with 0.5 mm thickness and the feeding network is design on the same material but with 0.13 mm thickness. The antenna is designed to operate in 20–30 GHz. The total array dimension including its power distribution network is 80 × 78 × 60 mm. Radiation pattern is obtained throughout the measured bandwidth. The measured gain of the array is average at 25 dBi over the frequency range of 20–30 GHz and the cross-polarization ratio is less than −20 dB. The printable structure, low return loss, wide bandwidth and high gain of the proposed array suggest it as a suitable antenna for many applications in mm-wave range including chipless RFID systems.


title: Latest trends in hybrid machine translation and its applications

abstract: HighlightsOverview of hybrid machine translation guided by the rule-based paradigm.Overview of hybrid machine translation guided by the corpus-based paradigm.Review of works on machine translation applications with hybrid components. This survey on hybrid machine translation (MT) is motivated by the fact that hybridization techniques have become popular as they attempt to combine the best characteristics of highly advanced pure rule or corpus-based MT approaches. Existing research typically covers either simple or more complex architectures guided by either rule or corpus-based approaches. The goal is to combine the best properties of each type.This survey provides a detailed overview of the modification of the standard rule-based architecture to include statistical knowledge, the introduction of rules in corpus-based approaches, and the hybridization of approaches within this last single category. The principal aim here is to cover the leading research and progress in this field of MT and in several related applications.


title: Algorithms for Scheduling Independent Tasks

abstract: The following job sequencing problems are studied: (i) single processor job sequencing with deadlines, (ii) job sequencing on m -identical processors to minimize finish time and related problems, (iii) job sequencing on 2-identical processors to minimize weighted mean flow time. Dynamic programming type algorithms are presented to obtain optimal solutions to these problems, and three general techniques are presented to obtain approximate solutions for optimization problems solvable in this way. The techniques are applied to the problems above to obtain polynomial time algorithms that generate “good” approximate solutions.


title: Variational Intrinsic Control

abstract: In this paper we introduce a new unsupervised reinforcement learning method for discovering the set of intrinsic options available to an agent. This set is learned by maximizing the number of different states an agent can reliably reach, as measured by the mutual information between the set of options and option termination states. To this end, we instantiate two policy gradient based algorithms, one that creates an explicit embedding space of options and one that represents options implicitly. The algorithms also provide an explicit measure of empowerment in a given state that can be used by an empowerment maximizing agent. The algorithm scales well with function approximation and we demonstrate the applicability of the algorithm on a range of tasks.


title: A ping-pong ball catching and juggling robot: A real-time framework for vision guided acting of an industrial robot arm

abstract: This paper investigates the possibilities of real-time control of standard industrial robot arms by the means of a ping-pong ball juggling system that is able to catch a ball thrown by a human and to keep the ball airborne for more than 30 min. The sensors are two industrial cameras operating at 60 Hz. We discuss the image processing and the controlling algorithms used. Emphasis is placed on comparing the widespread naive linear model and a novel physically correct model of the flight trajectory of the ball. While both models are sufficient for the juggling task, it is shown that only the non-linear model is able to predict the impact parameters sufficiently early and precise enough to solve the initial catching task. The performance of the complete systems is also documented in a supplementary video [8].


title: SAMER: Spectrum Aware Mesh Routing in Cognitive Radio Networks

abstract: Cognitive radio technology holds great promises in enabling unlicensed operation in licensed bands, to meet the increasing demand for radio spectrum. The new open spectrum operation necessitates novel routing protocols to exploit the available spectrum opportunistically. In this paper we present SAMER, a routing solution for cognitive radio mesh networks. SAMER opportunistically routes traffic across paths with higher spectrum availability and quality via a new routing metric. It balances between long-term route stability and short-term opportunistic performance. SAMER builds a runtime forwarding mesh that is updated periodically and offers a set of candidate routes to the destination. The actual forwarding path opportunistically adapts to the dynamic spectrum conditions and exploits the link with the highest spectrum availability at the time. We evaluate SAMER through simulations, and show that it effectively exploits the available network spectrum and results in higher end-to-end performance.


title: Aurora: a new model and architecture for data stream management

abstract: Abstract.This paper describes the basic processing model and architecture of Aurora, a new system to manage data streams for monitoring applications. Monitoring applications differ substantially from conventional business data processing. The fact that a software system must process and react to continual inputs from many sources (e.g., sensors) rather than from human operators requires one to rethink the fundamental architecture of a DBMS for this application area. In this paper, we present Aurora, a new DBMS currently under construction at Brandeis University, Brown University, and M.I.T. We first provide an overview of the basic Aurora model and architecture and then describe in detail a stream-oriented set of operators.


title: How Do You Feel when You Can't Feel Your Body? Interoception, Functional Connectivity and Emotional Processing in Depersonalization-Derealization Disorder

abstract: Depersonalization-Derealization Disorder (DD) typically manifests as a disruption of body self-awareness. Interoception −defined as the cognitive processing of body signals− has been extensively considered as a key processing for body self-awareness. In consequence, the purpose of this study was to investigate whether there are systematic differences in interoception between a patient with DD and controls that might explain the disembodiment symptoms suffered in this disease. To assess interoception, we utilized a heartbeat detection task and measures of functional connectivity derived from fMRI networks in interoceptive/exteroceptivo/mind-wandering states. Additionally, we evaluated empathic abilities to test the association between interoception and emotional experience. The results showed patient's impaired performance in the heartbeat detection task when compared to controls. Furthermore, regarding functional connectivity, we found a lower global brain connectivity of the patient relative to controls only in the interoceptive state. He also presented a particular pattern of impairments in affective empathy. To our knowledge, this is the first experimental research that assesses the relationship between interoception and DD combining behavioral and neurobiological measures. Our results suggest that altered neural mechanisms and cognitive processes regarding body signaling might be engaged in DD phenomenology. Moreover, our study contributes experimental data to the comprehension of brain-body interactions and the emergence of self-awareness and emotional feelings.


title: The Effects of Anger Rumination and Cognitive Reappraisal on Anger-In and Anger-Control

abstract: Many studies have suggested that cognitive behavioral therapy (CBT) is efficacious for anger expression problems. However, it is still unclear which emotion regulation strategies are most effective for anger-in (a tendency to suppress anger) and anger-control (a tendency to calm angry feelings). In the present study, anger rumination and cognitive reappraisal were manipulated, and their effects on anger-in and anger-control were investigated with a within-subjects design. Participants were 46 Japanese undergraduates assigned to the three conditions; free recall (no regulation strategy), anger rumination, and cognitive reappraisal. Results indicated that, compared to free recall and anger rumination, cognitive reappraisal, taking a third-person perspective and thinking about positive aspects of an anger-related event, induced fewer angry feelings, alleviated anger-in, and facilitated anger-control. Given that CBT has been efficacious for anger expression, CBT might facilitate taking a third-person perspective and thinking about the event more flexibly in clients.


title: Articulated people detection and pose estimation: Reshaping the future

abstract: State-of-the-art methods for human detection and pose estimation require many training samples for best performance. While large, manually collected datasets exist, the captured variations w.r.t. appearance, shape and pose are often uncontrolled thus limiting the overall performance. In order to overcome this limitation we propose a new technique to extend an existing training set that allows to explicitly control pose and shape variations. For this we build on recent advances in computer graphics to generate samples with realistic appearance and background while modifying body shape and pose. We validate the effectiveness of our approach on the task of articulated human detection and articulated pose estimation. We report close to state of the art results on the popular Image Parsing [25] human pose estimation benchmark and demonstrate superior performance for articulated human detection. In addition we define a new challenge of combined articulated human detection and pose estimation in real-world scenes.


title: Popular Edges and Dominant Matchings

abstract: Given a bipartite graph G = (A u B, E) with strict preference lists and and edge e*, we ask if there exists a popular matching in G that contains the edge e*. We call this the popular edge problem. A matching M is popular if there is no matching M' such that the vertices that prefer M' to M outnumber those that prefer M to M'. It is known that every stable matching is popular; however G may have no stable matching with the edge e* in it. In this paper we identify another natural subclass of popular matchings called"dominant matchings"and show that if there is a popular matching that contains the edge e*, then there is either a stable matching that contains e* or a dominant matching that contains e*. This allows us to design a linear time algorithm for the popular edge problem. We also use dominant matchings to efficiently test if every popular matching in G is stable or not.


title: SignSynth : A Sign Language Synthesis Application Using Web 3 D and Perl

abstract: Sign synthesis (also known as text-to-sign) has recently seen a large increase in the number of projects under development. Many of these focus on translation from spoken languages, but other applications include dictionaries and language learning. I will discuss the architecture of typical sign synthesis applications and mention some of the applications and prototypes currently available. I will focus on SignSynth, a CGI-based articulatory sign synthesis prototype I am developing at the University of New Mexico. SignSynth takes as its input a sign language text in ASCII-Stokoe notation (chosen as a simple starting point) and converts it to an internal feature tree. This underlying linguistic representation is then converted into a three-dimensional animation sequence in Virtual Reality Modeling Language (VRML or Web3D), which is automatically rendered by a Web3D browser.


title: Fast and efficient median filter for removing 1-99% levels of salt-and-pepper noise in images

abstract: This paper proposes a new median filter using prior information to capture natural pixels for restoration. In addition to being very efficient in logic execution, the proposed filter restores corrupted images with 1-99% levels of salt-and-pepper impulse noise to satisfactory ones. Without any iteration for noise detection, it intuitively and simply recognizes impulse noises, while keeping the others intact as nonnoises. Depending on different noise ratios at an image, two different sets of masked pixels are employed separately for the adoption of candidates for median finding. Furthermore, no limit to the size of mask windows assures that a proper median can be found. The simple logic of the proposed algorithm achieves significant milestones on the fidelity of a restored image. Moreover, the very fast execution speed of the proposed filter is very suitable for being applied to real-time processing. Relevant experimental results on subjective visualization and objective digital measure are reported to validate the robustness of the proposed filter.


title: Hybrid-state driver/vehicle modelling, estimation and prediction

abstract: The first part of this study develops a general architecture for estimation and prediction of hybrid-state systems. The proposed system utilizes the hybrid characteristics of decision-behaviour coupling of many systems such as the driver and the vehicle; uses estimates of observable parameters to track instantaneous discrete state and predicts the most likely outcome, depending on the discrete model and the observed behaviour of the continuous subsystem. The proposed method is suitable for the scenarios that involve unknown decisions of other individuals, such as lane changes or intersection precedence/access. In the second part, this paper specifically deals with the implementation of the proposed methodology on an intersection safety system, predicting the vehicle behaviours and potential outcomes through traffic intersection scenarios. Driver intentions are tracked and predicted through vehicle behaviour, and possible combinations of intention predictions for different vehicles are interpreted for the safety of the situation.


title: Combining EEG Data with Place and Plausibility Responses as an Approach to Measuring Presence in Outdoor Virtual Environments

abstract: Outdoor virtual environments OVEs are becoming increasingly popular, as they allow a sense of presence in places that are inaccessible or protected from human intervention. These virtual environments VEs need to address physical modalities other than vision and hearing. We analyze the influence of four different physical modalities vision, hearing, haptics, and olfaction on the sense of presence on a virtual journey through the sea and the Laurissilva Forest of Funchal, Portugal. We applied Slater et al.'s 2010 method together with data gathered by the Emotiv EPOC EEG in an OVE setting. In such a setting, the combination of haptics and hearing are more important than the typical virtual environment vision and hearing in terms of place and plausibility illusions. Our analysis is particularly important for designers interested in crafting similar VEs because we classify different physical modalities according to their importance in enhancing presence.


title: An alternative approach for risk assessment in Scrum

abstract: Agile processes are widely used for the purpose of software project development sine they have overcome the limitations faced in traditional software project development. Risk management is a crucial part in project development which helps in eliminating the risks prevailing in software project. Even though risk management is associated with agile approach, still there prevails the scope for improvement. Therefore the motto of this paper is to analyze agile methodology, Scrum and highlight the extent to which risk is managed in it and propose a model to overcome its' limitations in risk analysis phase.


title: A Generalized Magnetic Circuit Modeling Approach for Design of Surface Permanent-Magnet Machines

abstract: This paper proposes a generalized equivalent magnetic circuit model for the design of permanent-magnet (PM) electric machines. Conventional approaches have been applied to PM machine design but may be insufficiently accurate or generalized without taking pole-slot counts into consideration. This would result in reduction of dimensioning accuracy at the initial design stage. Also, magnetic saturation is often ignored or compensated by correction factors in simplified models since it is difficult to determine the flux in individual stator teeth. In this paper, the flux produced by stator winding currents and PMs can be calculated accurately and rapidly using the developed model, taking saturation into account. A new modeling technique for PM poles is proposed so that the magnetic circuit is applicable to any pole-slot combinations. This aids machine dimensioning without the need for computationally expensive finite-element analysis (FEA). A 540-kW PM machine is first designed using the proposed method and then verified with FEA. Another 350-W machine is subsequently designed, manufactured, and validated by both FEA and experiments. The comparisons demonstrate the effectiveness of the proposed model.


title: Combining Supervised and Unsupervised Learning to Discover Emotional Classes

abstract: Most previous work in emotion recognition has fixed the available classes in advance, and attempted to classify samples into one of these classes using a supervised learning approach. In this paper, we present preliminary work on combining supervised and unsupervised learning to discover potential latent classes which were not initially considered. To illustrate the potential of this hybrid approach, we have used a Self-Organizing Map (SOM) to organize a large number of Electroencephalogram (EEG) signals from subjects watching videos, according to their internal structure. Results suggest that a more useful labelling scheme could be produced by analysing the resulting topology in relation to user reported valence levels (i.e., pleasantness) for each signal, refining the original set of target classes.


title: Framework for objective evaluation of privacy filters

abstract: Extensive adoption of video surveillance, affecting many aspects of our daily lives, alarms the public about the increasing invasion into personal privacy. To address these concerns, many tools have been proposed for protection of personal privacy in image and video. However, little is understood regarding the effectiveness of such tools and especially their impact on the underlying surveillance tasks, leading to a tradeoff between the preservation of privacy offered by these tools and the intelligibility of activities under video surveillance. In this paper, we investigate this privacy-intelligibility tradeoff objectively by proposing an objective framework for evaluation of privacy filters. We apply the proposed framework on a use case where privacy of people is protected by obscuring faces, assuming an automated video surveillance system. We used several popular privacy protection filters, such as blurring, pixelization, and masking and applied them with varying strengths to people's faces from different public datasets of video surveillance footage. Accuracy of face detection algorithm was used as a measure of intelligibility (a face should be detected to perform a surveillance task), and accuracy of face recognition algorithm as a measure of privacy (a specific person should not be identified). Under these conditions, after application of an ideal privacy protection tool, an obfuscated face would be visible as a face but would not be correctly identified by the recognition algorithm. The experiments demonstrate that, in general, an increase in strength of privacy filters under consideration leads to an increase in privacy (i.e., reduction in recognition accuracy) and to a decrease in intelligibility (i.e., reduction in detection accuracy). Masking also shows to be the most favorable filter across all tested datasets.


title: BRCA1 Promoter Hypermethylation Signature for Early Detection of Breast Cancer in the Vietnamese Population

abstract: Breast cancer, a leading cause of death among women in most countries worldwide, is rapidly increasing in incidence in Vietnam. One of biomarkers is the disruption of the genetic material including epigenetic changes like DNA methylation. With the aim of finding hypermethylation at CpG islands of promoter of BRCA1 gene, belonged to the tumor suppressor gene family, as the biomarker for breast cancer in Vietnamese population, sensitive methyl specific PCR (MSP) was carried out on 115 samples including 95 breast cancer specimens and 20 normal breast tissues with other diseases which were obtained from Ho Chi Minh City Medical Hospital, Vietnam. The result indicated that the frequency of BRCA1 hypermethylation reached 82.1% in the cases (p<0.001). In addition, the DNA hypermethylation of this candidate gene increased the possibility to be breast cancer with high incidence via calculated odd ratios (p<0.05). In conclusion, hypermethylation of this candidate gene could be used as the promising biomarker application with Vietnamese breast cancer patients.


title: Radiation Characterization of Commercial GaN Devices

abstract: Commercially available devices fabricated from GaN are beginning to appear from a number of different suppliers. In this initial study of the radiation tolerance of commercial GaN devices, several device types from several suppliers were chosen. Three different studies were performed: 1) a preliminary DDD/TID test of a variety of part types was performed by irradiating with 55 MeV protons, 2) a detailed DDD/TID study of one particular part type was performed by irradiating with 55 MeV protons, and 3) a SEB/SEGR test was performed on a variety of part types by irradiating with heavy ions. No significant degradation was observed in any of the tests performed in this study.


title: A Discourse-Driven Content Model for Summarising Scientific Articles Evaluated in a Complex Question Answering Task

abstract: We present a method which exploits automatically generated scientific discourse annotations to create a content model for the summarisation of scientific articles. Full papers are first automatically annotated using the CoreSC scheme, which captures 11 contentbased concepts such as Hypothesis, Result, Conclusion etc at the sentence level. A content model which follows the sequence of CoreSC categories observed in abstracts is used to provide the skeleton of the summary, making a distinction between dependent and independent categories. Summary creation is also guided by the distribution of CoreSC categories found in the full articles, in order to adequately represent the article content. Finally, we demonstrate the usefulness of the summaries by evaluating them in a complex question answering task. Results are very encouraging as summaries of papers from automatically obtained CoreSCs enable experts to answer 66% of complex content-related questions designed on the basis of paper abstracts. The questions were answered with a precision of 75%, where the upper bound for human summaries (abstracts) was 95%.


title: A survey of intrusion detection systems in wireless sensor networks

abstract: Wireless Sensor Network (WSN) is a large scale network with from dozens to thousands tiny devices. Using fields of WSNs (military, health, smart home e.g.) has a large-scale and its usage areas increasing day by day. Secure issue of WSNs is an important research area and applications of WSN have some big security deficiencies. Intrusion Detection System is a second-line of the security mechanism for networks, and it is very important to integrity, confidentiality and availability. Intrusion Detection in WSNs is somewhat different from wired and non-energy constraint wireless network because WSN has some constraints influencing cyber security approaches and attack types. This paper is a survey describing attack types of WSNs intrusion detection approaches being against to this attack types.


The Future in Ruins


title: The ESCRT Pathway

abstract: Multivesicular bodies (MVBs) deliver cargo destined for degradation to the vacuole or lysosome. The ESCRT (endosomal sorting complex required for transport) pathway is a key mediator of MVB biogenesis, but it also plays critical roles in retroviral budding and cytokinetic abscission. Despite these diverse roles, the ESCRT pathway can be simply seen as a cargo-recognition and membrane-sculpting machine viewable from three distinct perspectives: (1) the ESCRT proteins themselves, (2) the cargo they sort, and (3) the membrane they deform. Here, we review ESCRT function from these perspectives and discuss how ESCRTs may drive vesicle budding.


title: Liner shipping economics

abstract: I The Liner Shipping Industry.- 1 Characteristics of demand and supply of liner shipping.- 1.1 An aggregate picture of seaborne trade and the world fleet tonnage.- 1.2 The development of the shares of the world fleet: developed countries, flags of convenience and developing countries.- 1.3 Liner shipping, shipping for hire and 'own shipping'.- 1.4 The relative size of the liner shipping industry.- 1.5 Recent development in general cargo shipping.- 1.6 Geographical aspects of liner shipping.- 2 Market organization: the conference system.- 2.1 The scope of the conference system.- 2.2 Conference organization and main activities.- 2.3 Why conferences?.- 2.4 Concluding remarks.- 3 The level and structure of freight rates.- 3.1 The general level of freight rates.- 3.2 The structure of freight rates.- Appendix A: The construction of the CONISCON index (1975-85).- Appendix B: The liner index of the FRG (1976-85).- Appendix C: The construction of an individual line freight rate index.- 4 The art of charging what the traffic can bear.- 4.1 The main form of price discrimination in liner shipping.- 4.2 The role of commodity value for shipping demand elasticity.- 4.3 The role of competition from other sources of goods supply for shipping demand elasticity.- 4.4 Competition from 'outsiders' and other modes of transport.- 4.5 Summary and conclusions.- II Liner Service Optimization.- 5 Ship size and shipping costs.- 5.1 Sizes of ships of different categories: The statistical picture.- 5.2 Plant-size economies in general.- 5.3 The three ship capacities.- 5.4 The model.- 5.5 Estimation of ship size elasticities of handling and hauling capacities and factor costs.- 5.6 Economies of size at sea - diseconomies of size in port.- 5.7 Optimal ship size.- 5.8 Analysis of the effect on optimal ship size of parameter changes in the model.- 5.9 The optimal size of a palletized reefer ship: A case study.- 5.10 Towards a model of ship size growth.- 6 Multi-port calling versus trans-shipment.- 6.1 The general problem: Feeder-transport cost minimization in a given service range.- 6.2 The specific problem: The potential of sea-feeder transport.- 6.3 The very large container carriers and feeder services.- 7 Shippers' costs of sailings infrequency and transit time.- 7.1 Storage costs.- 7.2 Costs of sailings infrequency and transit time for goods which are not stored by importers.- 7.3 Loss of value of perishable goods.- 7.4 How important are shippers' costs?.- Appendix: Optimal ship size when both shipping company costs and the shippers' costs are accounted for.- 8 Port costs and charges and the problem of shipping and port sub-optimizations.- 8.1 'Public' general cargo transport systems versus 'private' bulk cargo transport systems.- 8.2 Bottlenecks in ports.- 8.3 Port charges as a means of coordinating shipping and port operations.- 9 A cost minimization model of a liner trade.- 9.1 A liner trade model -I The Liner Shipping Industry.- 1 Characteristics of demand and supply of liner shipping.- 1.1 An aggregate picture of seaborne trade and the world fleet tonnage.- 1.2 The development of the shares of the world fleet: developed countries, flags of convenience and developing countries.- 1.3 Liner shipping, shipping for hire and 'own shipping'.- 1.4 The relative size of the liner shipping industry.- 1.5 Recent development in general cargo shipping.- 1.6 Geographical aspects of liner shipping.- 2 Market organization: the conference system.- 2.1 The scope of the conference system.- 2.2 Conference organization and main activities.- 2.3 Why conferences?.- 2.4 Concluding remarks.- 3 The level and structure of freight rates.- 3.1 The general level of freight rates.- 3.2 The structure of freight rates.- Appendix A: The construction of the CONISCON index (1975-85).- Appendix B: The liner index of the FRG (1976-85).- Appendix C: The construction of an individual line freight rate index.- 4 The art of charging what the traffic can bear.- 4.1 The main form of price discrimination in liner shipping.- 4.2 The role of commodity value for shipping demand elasticity.- 4.3 The role of competition from other sources of goods supply for shipping demand elasticity.- 4.4 Competition from 'outsiders' and other modes of transport.- 4.5 Summary and conclusions.- II Liner Service Optimization.- 5 Ship size and shipping costs.- 5.1 Sizes of ships of different categories: The statistical picture.- 5.2 Plant-size economies in general.- 5.3 The three ship capacities.- 5.4 The model.- 5.5 Estimation of ship size elasticities of handling and hauling capacities and factor costs.- 5.6 Economies of size at sea - diseconomies of size in port.- 5.7 Optimal ship size.- 5.8 Analysis of the effect on optimal ship size of parameter changes in the model.- 5.9 The optimal size of a palletized reefer ship: A case study.- 5.10 Towards a model of ship size growth.- 6 Multi-port calling versus trans-shipment.- 6.1 The general problem: Feeder-transport cost minimization in a given service range.- 6.2 The specific problem: The potential of sea-feeder transport.- 6.3 The very large container carriers and feeder services.- 7 Shippers' costs of sailings infrequency and transit time.- 7.1 Storage costs.- 7.2 Costs of sailings infrequency and transit time for goods which are not stored by importers.- 7.3 Loss of value of perishable goods.- 7.4 How important are shippers' costs?.- Appendix: Optimal ship size when both shipping company costs and the shippers' costs are accounted for.- 8 Port costs and charges and the problem of shipping and port sub-optimizations.- 8.1 'Public' general cargo transport systems versus 'private' bulk cargo transport systems.- 8.2 Bottlenecks in ports.- 8.3 Port charges as a means of coordinating shipping and port operations.- 9 A cost minimization model of a liner trade.- 9.1 A liner trade model - purpose, scope and assumption.- 9.2 Total producer and user costs.- 9.3 Optimal ship size, multi-port diversion, and frequency of sailings.- 9.4 The minimum total cost per ton.- III Economic Evaluation of the Conference System.- 10 The charging floor reconsidered.- 10.1 Economies of scale?.- 10.2 Common cost and factor indivisibility.- Appendix: Model of profit-maximizing freight rate making.- 11 The freight rate structure is out of line with the marginal cost structure.- 11.1 Principles of marginal cost-based tariffs.- 11.2 Cross-subsidization between commodities.- 11.3 Excessive averaging of freight rates: Some suggestions for reforming the tariff construction.- 11.4 Further aspects of a cost-based freight rate structure.- Appendix: Freight rates and shipping marginal costs of Israeli imports and exports.- 12 Potential cartel profits become social costs.- 12.1 Empirical evidence of low load factors in liner shipping.- 12.2 Model of supply and demand equilibrium in a liner trade.- 12.3 Some evidence of a negative relationship between the load factor and the profit potential.- 12.4 Excessive service competition.- 13 Conclusion: price competition in liner shipping should be encouraged.- 13.1 The two types of ill effects.- 13.2 Allocative inefficiency effects.- 13.3 'Slack' effects.- 13.4 Encourage price competition and service coordination.- 13.5 Recent attempts of reforming liner conference practices.- 13.6 Problems of regulating international liner shipping.- 13.7 Hopes for the future.- References.- Author index.


title: IT Outsourcing Strategies: Universalistic, Contingency, and Configurational Explanations of Success

abstract: Focus on individual outsourcing decisions in IT research has often yielded contradictory findings and recommendations. To address these contradictions, we investigate a holistic, configurational approach with the prevailing universalistic or contingency perspectives in exploring the effects of IT outsourcing strategies on outsourcing success. Based on residual rights theory, we begin by identifying three dimensions of IT outsourcing strategies: degree of integration, allocation of control, and performance period. We then develop a model of fit-as-gestalt, drawing from literatures on strategy, governance, interorganizational relationships, and outsourcing.Next, based on data from 311 firms in South Korea, we test universalistic and contingency perspectives in explaining the relationship between IT outsourcing strategies and outsourcing success. We then identify three congruent patterns, or gestalts, of IT outsourcing strategies. We term these strategiesindependent, arm's-length, andembedded strategies. To establish the predictive validity of these gestalts and the viability of a configurational perspective, we then explore the effects of these congruent gestalts vis-A -vis noncongruent patterns on three dimensions of outsourcing success:strategic competence,cost efficiency, andtechnology catalysis. We also contrast the effects of each of the three gestalts on each of the three dimensions of outsourcing success. Our findings indicate the superiority of the configurational approach over universalistic and contingency perspectives in explaining outsourcing success.


ACOUSTİC SİGNATURE OF FOUR CUTTERS EV BURNER USİNG PİLOT İNJECTOR FUEL SHİFT


title: Optimal algorithms for smooth and strongly convex distributed optimization in networks

abstract: In this paper, we determine the optimal convergence rates for strongly convex and smooth distributed optimization in two settings: centralized and decentralized communications over a network. For centralized (i.e. master/slave) algorithms, we show that distributing Nesterov's accelerated gradient descent is optimal and achieves a precision $\varepsilon > 0$ in time $O(\sqrt{\kappa_g}(1+\Delta\tau)\ln(1/\varepsilon))$, where $\kappa_g$ is the condition number of the (global) function to optimize, $\Delta$ is the diameter of the network, and $\tau$ (resp. $1$) is the time needed to communicate values between two neighbors (resp. perform local computations). For decentralized algorithms based on gossip, we provide the first optimal algorithm, called the multi-step dual accelerated (MSDA) method, that achieves a precision $\varepsilon > 0$ in time $O(\sqrt{\kappa_l}(1+\frac{\tau}{\sqrt{\gamma}})\ln(1/\varepsilon))$, where $\kappa_l$ is the condition number of the local functions and $\gamma$ is the (normalized) eigengap of the gossip matrix used for communication between nodes. We then verify the efficiency of MSDA against state-of-the-art methods for two problems: least-squares regression and classification by logistic regression.


title: Oxidation in rheumatoid arthritis

abstract: Oxygen metabolism has an important role in the pathogenesis of rheumatoid arthritis. Reactive oxygen species (ROS) produced in the course of cellular oxidative phosphorylation, and by activated phagocytic cells during oxidative bursts, exceed the physiological buffering capacity and result in oxidative stress. The excessive production of ROS can damage protein, lipids, nucleic acids, and matrix components. They also serve as important intracellular signaling molecules that amplify the synovial inflammatory–proliferative response. Repetitive cycles of hypoxia and reoxygenation associated with changes in synovial perfusion are postulated to activate hypoxia-inducible factor-1α and nuclear factor-κB, two key transcription factors that are regulated by changes in cellular oxygenation and cytokine stimulation, and that in turn orchestrate the expression of a spectrum of genes critical to the persistence of synovitis. An understanding of the complex interactions involved in these pathways might allow the development of novel therapeutic strategies for rheumatoid arthritis.


title: ChoiceNet: Robust Learning by Revealing Output Correlations

abstract: In this paper, we focus on the supervised learning problem with corrupted training data. We assume that the training dataset is generated from a mixture of a target distribution and other unknown distributions. We estimate the quality of each data by revealing the correlation between the generated distribution and the target distribution. To this end, we present a novel framework referred to here as ChoiceNet that can robustly infer the target distribution in the presence of inconsistent data. We demonstrate that the proposed framework is applicable to both classification and regression tasks. ChoiceNet is evaluated in comprehensive experiments, where we show that it constantly outperforms existing baseline methods in the handling of noisy data. Particularly, ChoiceNet is successfully applied to autonomous driving tasks where it learns a safe driving policy from a dataset with mixed qualities. In the classification task, we apply the proposed method to the MNIST and CIFAR-10 datasets and it shows superior performances in terms of robustness to noisy labels.


title: Performance Evaluation of Bridgeless PFC Boost Rectifiers

abstract: In this paper, a systematic review of bridgeless PFC boost rectifiers, also called dual boost PFC rectifiers, is presented. Performance comparison between the conventional PFC boost rectifier and a representative member of the bridgeless PFC boost rectifier family is performed. Design considerations and experimental results in both CCM and DCM/CCM boundary operations are provided.


title: ADVICE—Educational System for Teaching Database Courses

abstract: This paper presents a Web-based educational system, ADVICE, that helps students to bridge the gap between database management system (DBMS) theory and practice. The usage of ADVICE is presented through a set of laboratory exercises developed to teach students conceptual and logical modeling, SQL, formal query languages, and normalization. While working on the exercises, students use the system to access real databases, and the system provides them with feedback about their solutions. From the perspective of an instructor, the system allows easy exercise management and continual progress monitoring. The paper also describes a practical experience with the use of ADVICE on a database course over a three-year period.


title: A distant supervised learning system for the TAC-KBP Slot Filling and Temporal Slot Filling Tasks

abstract: This paper describes the system implemented by the NLP GROUP AT UNED for our first participation in the Knowledge Base Population at the Text Analysis Conference (TACKBP). For this Slot Filling Task, our approach was to design a distant supervised learning system, which was then specialized for the Regular Slot Filling and Full Temporal Slot Filling subtasks. From the initial Knowledge Base and source document collection distributed by the organizers, we automatically gathered training data for supervised slot classifiers. We used a rich document representation, augmenting syntactic dependency trees with named entity recognition, coreference resolution, temporal events annotation and semantic graph transformations.


title: A Wideband Polarization Reconfigurable Antenna With Partially Reflective Surface

abstract: A novel partially reflective surface (PRS) antenna is proposed, which can electronically alter its polarization between linear polarization, left-hand circular polarization (CP), and right-hand CP. The antenna consists of a shorted annular patch antenna as the source, a PRS structure to enhance the gain, and a reconfigurable Wilkinson power divider as the feed network. Four p-i-n diodes are inserted into each branch of the power divider to change its electrical length. By switching the p-i-n diodes ON and OFF, a phase difference of 0°, 90°, or −90° can be realized at the two output ports of the feed network, hence enabling the antenna to radiate either a linearly polarized signal or left/right-handed circularly polarized signal. A prototype antenna with the biasing network is designed, fabricated, and measured. Good agreement between the simulated and measured results is achieved. Measurement results show that the proposed antenna has an overlapped 10-dB impedance bandwidth and 3-dB axial-ratio bandwidth of 4.7–5.36 GHz (13.1%). The average realized gains are $\sim 9$ dBi/dBic, which remain reasonably stable for different polarizations. The proposed antenna outperforms most of the reported polarization reconfigurable antennas for its wide bandwidth and relatively high gain.


title: Automatic Testing of Program Security Vulnerabilities

abstract: Vulnerabilities in applications and their widespread exploitation through successful attacks are common these days. Testing applications for preventing vulnerabilities is an important step to address this issue. In recent years, a number of security testing approaches have been proposed. However, there is no comparative study of these work that might help security practitioners select an appropriate approach for their needs. Moreover, there is no comparison with respect to automation capabilities of these approaches. In this work, we identify seven criteria to analyze program security testing work. These are vulnerability coverage, source of test cases, test generation method, level of testing, granularity of test cases, testing automation, and target applications. We compare and contrast prominent security testing approaches available in the literature based on these criteria. In particular, we focus on work that address four most common but dangerous vulnerabilities namely buffer overflow, SQL injection, format string bug, and cross site scripting. Moreover, we investigate automation features available in these work across a security testing process. We believe that our findings will provide practical information for security practitioners in choosing the most appropriate tools.


title: Emotions and heart rate while sitting on a chair

abstract: New methods for unobtrusive monitoring of computer users' emotion psychophysiology are very much needed in human-computer interaction research. The present aim was to study heart rate changes during emotionally provocative stimulation. Six-second long auditory, visual, and audiovisual emotionally negative, neutral, and positive stimuli were presented to 24 participants. Heart rate responses were measured with a regular office chair embedded with electromechanical film (the EMFi chair) and with traditional earlobe photoplethysmography (PPG). Ratings of the stimuli were also collected. The results showed that the two heart rate measurements were significantly correlated, r = 0.99. In line with other studies the results showed that, in general, heart rate decelerated in response to emotional stimulation and it decelerated the most in response to negative stimuli as compared with responses to positive and neutral stimuli. Especially, emotional stimulation caused significant changes in heart rate at the 6th second from the stimulus onset. We suggest that the EMFi chair could be used in human-computer interaction for unobtrusive measurement of the user's emotional reactions.


title: From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms

abstract: Image denoising is a well explored topic in the field of image processing. In the past several decades, the progress made in image denoising has benefited from the improved modeling of natural images. In this paper, we introduce a new taxonomy based on image representations for a better understanding of state-of-the-art image denoising techniques. Within each category, several representative algorithms are selected for evaluation and comparison. The experimental results are discussed and analyzed to determine the overall advantages and disadvantages of each category. In general, the nonlocal methods within each category produce better denoising results than local ones. In addition, methods based on overcomplete representations using learned dictionaries perform better than others. The comprehensive study in this paper would serve as a good reference and stimulate new research ideas in image denoising.


title: Smart phone based robotic arm control using raspberry pi, android and Wi-Fi

abstract: This paper proposes a method for controlling a Robotic arm using an application build in the android platform. The android phone and raspberry piboard is connected through Wi-Fi. As the name suggests the robotic arm is designed as it performs the same activity as a human hand works. A signal is generated from the android app which will be received by the raspberry pi board and the robotic arm works according to the predefined program. The android application is the command centre of the robotic arm. The program is written in the python language in the raspberry board. the different data will control the arm rotation.


title: Peak Ground Acceleration Prediction by Artificial Neural Networks for Northwestern Turkey

abstract: Three different artificial neural network (ANN) methods, namely, feed-forward back-propagation (FFBP), radial basis function (RBF), and generalized regression neural networks (GRNNs) were applied to predict peak ground acceleration (PGA). Ninety five three-component records from 15 ground motions that occurred in Northwestern Turkey between 1999 and 2001 were used during the applications. The earthquake moment magnitude, hypocentral distance, focal depth, and site conditions were used as inputs to estimate PGA for vertical (U-D), east-west (E-W), and north-south (N-S) directions. The direction of the maximum PGA of the three components was also added to the input layer to obtain the maximum PGA. Testing stage results of three ANN methods indicated that the FFBPs were superior to the GRNN and the RBF for all directions. The PGA values obtained from the FFBP were modified by linear regression analysis. The results showed that these modifications increased the prediction performances.


title: UPPER LIMB POWERED EXOSKELETON

abstract: An exoskeleton is a wearable robot with joints and links corresponding to those of the human body. With applications in rehabilitation medicine, virtual reality simulation, and teleoperation, exoskeletons offer benefits for both disabled and healthy populations. Analytical and experimental approaches were used to develop, integrate, and study a powered exoskeleton for the upper limb and its application as an assistive device. The kinematic and dynamic dataset of the upper limb during daily living activities was one among several factors guiding the development of an anthropomorphic, seven degree-of-freedom, powered arm exoskeleton. Additional design inputs include anatomical and physiological considerations, workspace analyses, and upper limb joint ranges of motion. Proximal placement of motors and distal placement of cable-pulley reductions were incorporated into the design, leading to low inertia, high-stiffness links, and back-drivable transmissions with zero backlash. The design enables full glenohumeral, elbow, and wrist joint functionality. Establishing the human-machine interface at the neural level was facilitated by the development of a Hill-based muscle model (myoprocessor) that enables intuitive interaction between the operator and the wearable robot. Potential applications of the exoskeleton as a wearable robot include (i) an assistive (orthotic) device for human power amplifications, (ii) a therapeutic and diagnostics device for physiotherapy, (iii) a haptic device in virtual reality simulation, and (iv) a master device for teleoperation.


title: Measures of Threaded Discussion Properties

abstract: In this paper, we present a set of measures to quantify certain properties of threaded discussions, which are ubiquitous in online learn-ing platforms. In particular, we address how to measure the redundancy of posts, the compactness of topics, and the degree of hierarchy in sub-threads. This preliminary work would very much benefit from discussion and serves as a starting point for ultimately creating optimal structures of threaded discussions depending on the context.