title: Enhancement of data retention and write current scaling for sub-20nm STT-MRAM by utilizing dual interfaces for perpendicular magnetic anisotropy

abstract: We investigate the sub-20nm level scalability of STT-MRAM cells possessing perpendicular magnetization induced from the interface of free layer (FL) and MgO tunnel barrier. We demonstrate that the MTJs utilizing dual interfaces of FL and MgO exhibit enhanced scalability with high thermal stability and low switching current, compared with the MTJs with a single interface. As thermal stability factor (Δ) varies as a function of MTJ dimension, MTJs with dual interfaces show Δ over 60 at 20nm node, while MTJs of single interface show Δ around 33. MTJs with dual interface also exhibit lower switching current per thermal stability (Ic/Δ), ∼1/2 level of single interface MTJs.


title: Detailed Real-Time Urban 3D Reconstruction from Video

abstract: The paper presents a system for automatic, geo-registered, real-time 3D reconstruction from video of urban scenes. The system collects video streams, as well as GPS and inertia measurements in order to place the reconstructed models in geo-registered coordinates. It is designed using current state of the art real-time modules for all processing steps. It employs commodity graphics hardware and standard CPU's to achieve real-time performance. We present the main considerations in designing the system and the steps of the processing pipeline. Our system extends existing algorithms to meet the robustness and variability necessary to operate out of the lab. To account for the large dynamic range of outdoor videos the processing pipeline estimates global camera gain changes in the feature tracking stage and efficiently compensates for these in stereo estimation without impacting the real-time performance. The required accuracy for many applications is achieved with a two-step stereo reconstruction process exploiting the redundancy across frames. We show results on real video sequences comprising hundreds of thousands of frames.


title: Conversion of LDR image to HDR-like image through high-level synthesis tool for FPGA implementation

abstract: High dynamic range (HDR) of an image will increase the visibility of the image. Due to the reflection, the image may lose its visibility in some specific area. In the image, we call it highlight. We cannot reveal the original color and information in the highlight area. By removing this highlight, we can increase the visibility of the image that expands the dynamic range of the image. In this paper, we convert our algorithm by high-level synthesis (HLS) tool to the synthesizable hardware language. Our target is to show that our algorithm for HDR-like image can be implemented on FPGA. We also compare our HLS results with simulation result.


title: The vascular territories (angiosomes) of the body: experimental study and clinical applications.

abstract: The blood supply to the skin and underlying tissues was investigated by ink injection studies, dissection, perforator mapping and radiographic analysis of fresh cadavers and isolated limbs. The results were correlated with previous regional studies done in this department. The blood supply is shown to be a continuous three-dimensional network of vessels not only in the skin but in all tissue layers. The anatomical territory of a source artery in the skin and deep tissues was found to correspond in most cases, giving rise to the angiosome concept. Arteries follow closely the connective tissue framework of the body. The primary supply to the skin is by direct cutaneous arteries which vary in calibre, length and density in different regions. This primary supply is reinforced by numerous small indirect vessels, which are "spent" terminal branches of arteries supplying the deep tissues. An average of 374 major perforators was plotted in each subject, revealing that there are still many more potential skin flaps. Our arterial roadmap of the body provides the basis for the logical planning of incisions and flaps. The angiosomes defined the tissues available for composite transfer.


title: Neural modeling for time series: A statistical stepwise method for weight elimination

abstract: Many authors use feedforward neural networks for modeling and forecasting time series. Most of these applications are mainly experimental, and it is often difficult to extract a general methodology from the published studies. In particular, the choice of architecture is a tricky problem. We try to combine the statistical techniques of linear and nonlinear time series with the connectionist approach. The asymptotical properties of the estimators lead us to propose a systematic methodology to determine which weights are nonsignificant and to eliminate them to simplify the architecture. This method (SSM or statistical stepwise method) is compared to other pruning techniques and is applied to some artificial series, to the famous Sunspots benchmark, and to daily electrical consumption data. >


title: EPIDEMIOLOGY OF OVERWEIGHT AND OBESITY AMONG THE WORKERS OF SHIRAZ HOSPITALS

abstract: Obesity is one of the leading causes of morbidity and mortality that detection of risk factors will lead to a reduction in its burden. The aim of this study was to estimate the prevalence of overweight and obesity and its determinants among Shiraz hospitals’ staff. A random sample of 1023 hospitals staff were selected with classified random sampling method. Data were collected by questions of demographic/occupational/ GHQ28/ IHS (International Headache Society) and general physical examinations. The overall prevalence of overweight and obesity were estimated 27.8% and 7.0% respectively. Prevalence of overweight and obesity were: 28.5 and 6.7% in men and 27.5 and 7.1% in women respectively. Univariate and multiple linear regressions in SPSS vs 19 were used for analysis. In multiple linear regressions, BMI had direct relationship with age, having tension headache and systolic and diastolic blood pressure and indirect relationship with irregular sleeping and widow (er) status. Smoking, shift work and exercise were among the variables that showed no association with BMI. BMI status deserves more attention, especially concerning strategies leading to primary prevention and control.


title: A hybrid intrusion detection system design for computer network security

abstract: Intrusions detection systems (IDSs) are systems that try to detect attacks as they occur or after the attacks took place. IDSs collect network traffic information from some point on the network or computer system and then use this information to secure the network. Intrusion detection systems can be misuse-detection or anomaly detection based. Misuse-detection based IDSs can only detect known attacks whereas anomaly detection based IDSs can also detect new attacks by using heuristic methods. In this paper we propose a hybrid IDS by combining the two approaches in one system. The hybrid IDS is obtained by combining packet header anomaly detection (PHAD) and network traffic anomaly detection (NETAD) which are anomaly-based IDSs with the misuse-based IDS Snort which is an open-source project. The hybrid IDS obtained is evaluated using the MIT Lincoln Laboratories network traffic data (IDEVAL) as a testbed. Evaluation compares the number of attacks detected by misuse-based IDS on its own, with the hybrid IDS obtained combining anomaly-based and misuse-based IDSs and shows that the hybrid IDS is a more powerful system.


Unsupervised morph segmentation and statistical language models for vocabulary expansion


title: Optimal Byzantine attack for distributed inference with M-ary quantized data

abstract: In many applications that employ wireless sensor networks (WSNs), robustness of distributed inference against Byzantine attacks is important. In this work, distributed inference is considered when local sensors send M-ary data to the fusion center. The optimal Byzantine attack policy is then derived under the assumption that the Byzantine adversary has the knowledge of the statistics of local quantization outputs. Our analysis indicates that the fusion center can be blinded such that the detection error is as poor as a random guess when an adequate fraction of sensors are compromised.


title: AnimaStage: Hands-on Animated Craft on Pin-based Shape Displays

abstract: In this paper, we present AnimaStage: a hands-on animated craft platform based on an actuated stage. Utilizing a pin-based shape changing display, users can animate their crafts made from various materials. Through this system, we intend to lower the barrier for artists and designers to create actuated objects and to contribute to interaction design using shape changing interfaces for inter-material interactions. We introduce a three-phase design process for AnimaStage with examples of animated crafts. We implemented the system with several control modalities that allow users to manipulate the motion of the crafts so that they could easily explore their desired motion through an iterative process. To complement the animated crafts, dynamic landscapes can also be rendered. We conducted a user study to observe the subject and process by which people make crafts using AnimaStage. We invited participants with different backgrounds to design and create crafts using multiple materials and craft techniques. A variety of outcomes and application spaces were found in this study.


title: Illustrate It! An Arabic Multimedia Text-to-Picture m-Learning System

abstract: Multimedia learning is the process of building mental representation from words associated with images. Due to the intuitiveness and vividness of visual illustration, many texts to picture systems have been proposed. However, we observe some common limitations in the existing systems, such as the retrieved pictures may not be suitable for educational purposes. Also, finding pedagogic illustrations still requires manual work, which is difficult and time-consuming. The commonly used systems based on the best keyword selection and the best sentence selection may suffer from loss of information. In this paper, we present an Arabic multimedia text-to-picture mobile learning system that is based on conceptual graph matching. Using a knowledge base, a conceptual graph is built from the text accompanied with the pictures in the multimedia repository as well as for the text entered by the user. Based on the matching scores of both conceptual graphs, matched pictures are assigned relative rankings. The proposed system demonstrated its effectiveness in the domain of Arabic stories, however, it can be easily shifted to any educational domain to yield pedagogical illustrations for organizational or institutional needs. Comparisons with the current state-of-the-art systems, based on the best keyword selection and the best sentence selection techniques, have demonstrated significant improvements in the performance. In addition, to facilitate educational needs, conceptual graph visualization and visual illustrative assessment modules are also developed. The conceptual graph visualization enables learners to discover relationships between words, and the visual illustrative assessment allows the system to automatically assess the performance of a learner. The profound user studies demonstrated the efficiency of the proposed multimedia learning system.


title: Data Center Energy Consumption Modeling: A Survey

abstract: Data centers are critical, energy-hungry infrastructures that run large-scale Internet-based services. Energy consumption models are pivotal in designing and optimizing energy-efficient operations to curb excessive energy consumption in data centers. In this paper, we survey the state-of-the-art techniques used for energy consumption modeling and prediction for data centers and their components. We conduct an in-depth study of the existing literature on data center power modeling, covering more than 200 models. We organize these models in a hierarchical structure with two main branches focusing on hardware-centric and software-centric power models. Under hardware-centric approaches we start from the digital circuit level and move on to describe higher-level energy consumption models at the hardware component level, server level, data center level, and finally systems of systems level. Under the software-centric approaches we investigate power models developed for operating systems, virtual machines and software applications. This systematic approach allows us to identify multiple issues prevalent in power modeling of different levels of data center systems, including: i) few modeling efforts targeted at power consumption of the entire data center ii) many state-of-the-art power models are based on a few CPU or server metrics, and iii) the effectiveness and accuracy of these power models remain open questions. Based on these observations, we conclude the survey by describing key challenges for future research on constructing effective and accurate data center power models.


title: On the achievable throughput of a multiantenna Gaussian broadcast channel

abstract: A Gaussian broadcast channel (GBC) with r single-antenna receivers and t antennas at the transmitter is considered. Both transmitter and receivers have perfect knowledge of the channel. Despite its apparent simplicity, this model is, in general, a nondegraded broadcast channel (BC), for which the capacity region is not fully known. For the two-user case, we find a special case of Marton's (1979) region that achieves optimal sum-rate (throughput). In brief, the transmitter decomposes the channel into two interference channels, where interference is caused by the other user signal. Users are successively encoded, such that encoding of the second user is based on the noncausal knowledge of the interference caused by the first user. The crosstalk parameters are optimized such that the overall throughput is maximum and, surprisingly, this is shown to be optimal over all possible strategies (not only with respect to Marton's achievable region). For the case of r>2 users, we find a somewhat simpler choice of Marton's region based on ordering and successively encoding the users. For each user i in the given ordering, the interference caused by users j>i is eliminated by zero forcing at the transmitter, while interference caused by users j<i is taken into account by coding for noncausally known interference. Under certain mild conditions, this scheme is found to be throughput-wise asymptotically optimal for both high and low signal-to-noise ratio (SNR). We conclude by providing some numerical results for the ergodic throughput of the simplified zero-forcing scheme in independent Rayleigh fading.


title: Differential domain analysis for non-uniform sampling

abstract: Sampling is a core component for many graphics applications including rendering, imaging, animation, and geometry processing. The efficacy of these applications often crucially depends upon the distribution quality of the underlying samples. While uniform sampling can be analyzed by using existing spatial and spectral methods, these cannot be easily extended to general non-uniform settings, such as adaptive, anisotropic, or non-Euclidean domains. ::: We present new methods for analyzing non-uniform sample distributions. Our key insight is that standard Fourier analysis, which depends on samples' spatial locations, can be reformulated into an equivalent form that depends only on the distribution of their location differentials. We call this differential domain analysis. The main benefit of this reformulation is that it bridges the fundamental connection between the samples' spatial statistics and their spectral properties. In addition, it allows us to generalize our method with different computation kernels and differential measurements. Using this analysis, we can quantitatively measure the spatial and spectral properties of various non-uniform sample distributions, including adaptive, anisotropic, and non-Euclidean domains.


COLLABORATION WITH TANGIBLE AUGMENTED REALITY INTERFACES.


title: Adverse Drug Reaction Classification With Deep Neural Networks

abstract: We study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In particular, we propose two new neural network models, Convolutional Recurrent Neural Network (CRNN) by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural Network with Attention (CNNA) by adding attention weights into convolutional neural networks. We evaluate various NN architectures on a Twitter dataset containing informal language and an Adverse Drug Effects (ADE) dataset constructed by sampling from MEDLINE case reports. Experimental results show that all the NN architectures outperform the traditional maximum entropy classifiers trained from n-grams with different weighting strategies considerably on both datasets. On the Twitter dataset, all the NN architectures perform similarly. But on the ADE dataset, CNN performs better than other more complex CNN variants. Nevertheless, CNNA allows the visualisation of attention weights of words when making classification decisions and hence is more appropriate for the extraction of word subsequences describing ADRs.


title: Bayesian models for Large-scale Hierarchical Classification

abstract: A challenging problem in hierarchical classification is to leverage the hierarchical relations among classes for improving classification performance. An even greater challenge is to do so in a manner that is computationally feasible for large scale problems. This paper proposes a set of Bayesian methods to model hierarchical dependencies among class labels using multivariate logistic regression. Specifically, the parent-child relationships are modeled by placing a hierarchical prior over the children nodes centered around the parameters of their parents; thereby encouraging classes nearby in the hierarchy to share similar model parameters. We present variational algorithms for tractable posterior inference in these models, and provide a parallel implementation that can comfortably handle large-scale problems with hundreds of thousands of dimensions and tens of thousands of classes. We run a comparative evaluation on multiple large-scale benchmark datasets that highlights the scalability of our approach and shows improved performance over the other state-of-the-art hierarchical methods.


title: Deep Relative Distance Learning: Tell the Difference between Similar Vehicles

abstract: The growing explosion in the use of surveillance cameras in public security highlights the importance of vehicle search from a large-scale image or video database. However, compared with person re-identification or face recognition, vehicle search problem has long been neglected by researchers in vision community. This paper focuses on an interesting but challenging problem, vehicle re-identification (a.k.a precise vehicle search). We propose a Deep Relative Distance Learning (DRDL) method which exploits a two-branch deep convolutional network to project raw vehicle images into an Euclidean space where distance can be directly used to measure the similarity of arbitrary two vehicles. To further facilitate the future research on this problem, we also present a carefully-organized largescale image database "VehicleID", which includes multiple images of the same vehicle captured by different realworld cameras in a city. We evaluate our DRDL method on our VehicleID dataset and another recently-released vehicle model classification dataset "CompCars" in three sets of experiments: vehicle re-identification, vehicle model verification and vehicle retrieval. Experimental results show that our method can achieve promising results and outperforms several state-of-the-art approaches.


title: Computational intelligence in sports: Challenges and opportunities within a new research domain

abstract: Computational intelligence is a branch of artificial intelligence that comprises algorithms inspired by nature. The common characteristics of all these algorithms is their collective intelligence and adaptability to a changing environment. Due to their efficiency and simplicity, these algorithms have been employed for problem solving across social and natural sciences. The aim of this paper is to demonstrate that nature-inspired algorithms are also useful within the domain of sport, in particular for obtaining safe and effective training plans targeting various aspects of performance. We outline the benefits and opportunities of applying computational intelligence in sports, and we also comment on the pitfalls and challenges for the future development of this emerging research domain.


title: User Service Innovation on Mobile Phone Platforms: Investigating Impacts of Lead Userness, Toolkit Support, and Design Autonomy

abstract: User participation is increasingly being seen as a way to mitigate the challenges that firms face in innovation, such as high costs and uncertainty of customer acceptance of their innovations. Thus, firms are establishing online platforms to support users in innovating services, such as iOS and Android platforms for mobile data service (MDS) innovation. Mobile phone platforms are characterized by technology (toolkits) and policy (rules) components that could influence user’s innovation. Additionally, attributes of user innovators (lead userness) are expected to drive their innovation behavior. Yet it is unclear how these characteristics jointly impact users’ service innovation outcomes. To address this knowledge gap, we propose a model that builds on user innovation theory and the work design literature to explain the influences of lead userness, design autonomy, toolkit support, and their interactions on user’s innovation outcomes (innovation quantity) on these platforms. We conceptualize toolkit support in terms of two constructs (i.e., ease of effort and exploration), and design autonomy in terms of three constructs (i.e., decision-making autonomy, scheduling autonomy, and work-method autonomy). The model was tested using survey and archival data from two dominant mobile phone platforms (i.e., iOS and Android). As hypothesized, lead userness, exploration through toolkits, and ease of effort through toolkits positively affect users’ innovation quantity. Additionally, decision-making autonomy and work-method autonomy influence innovation quantity, but scheduling autonomy does not. Further, the proposed three-way interactions between lead userness, toolkit support, and design autonomy constructs on users’ quantity of MDS innovation are largely supported. The findings enhance our understanding of user innovation on mobile phone platforms.


title: Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning

abstract: Learning analytics is the analysis of electronic learning data which allows teachers, course designers and administrators of virtual learning environments to search for unobserved patterns and underlying information in learning processes. The main aim of learning analytics is to improve learning outcomes and the overall learning process in electronic learning virtual classrooms and computer-supported education. The most basic unit of learning data in virtual learning environments for learning analytics is the interaction, but there is no consensus yet on which interactions are relevant for effective learning. Drawing upon extant literature, this research defines three system-independent classifications of interactions and evaluates the relation of their components with academic performance across two different learning modalities: virtual learning environment (VLE) supported face-to-face (F2F) and online learning. In order to do so, we performed an empirical study with data from six online and two VLE-supported F2F courses. Data extraction and analysis required the development of an ad hoc tool based on the proposed interaction classification. The main finding from this research is that, for each classification, there is a relation between some type of interactions and academic performance in online courses, whereas this relation is non-significant in the case of VLE-supported F2F courses. Implications for theory and practice are discussed next.


title: Semantic Softmax Loss for Zero-Shot Learning

abstract: A typical pipeline for Zero-Shot Learning (ZSL) is to integrate the visual features and the class semantic descriptors into a multimodal framework with a linear or bilinear model. However, the visual features and the class semantic descriptors locate in different structural spaces, a linear or bilinear model can not capture the semantic interactions between different modalities well. In this letter, we propose a nonlinear approach to impose ZSL as a multi-class classification problem via a Semantic Softmax Loss by embedding the class semantic descriptors into the softmax layer of multi-class classification network. To narrow the structural differences between the visual features and semantic descriptors, we further use an L2 normalization constraint to the differences between the visual features and visual prototypes reconstructed with the semantic descriptors. The results on three benchmark datasets, i.e., AwA, CUB and SUN demonstrate the proposed approach can boost the performances steadily and achieve the state-of-the-art performance for both zero-shot classification and zero-shot retrieval.


title: Armature-Reaction Magnetic Field Analysis for Interior Permanent Magnet Motor Based on Winding Function Theory

abstract: As the magnets are embedded in a rotor for the interior permanent magnet (IPM) motor, the distribution of the armature reaction magnetic field is different from that of the surface permanent magnet motor (SPM) type. Various methods have been used for the magnetic field solution, including analytical methods based on the winding function and the Laplacian-Poisson equation. However, in the IPM motor, the boundary condition is too complicated for using the Laplacian-Poisson equation. What is more, the equivalent air-gap inverse function presented in literature is not suitable for the IPM motor. A new armature reaction magnetic field model is proposed for the IPM motor, considering the effect of the embedded magnet in the rotor, which is named a pole-cap effect in this paper. The proposed model is derived from the winding function theory, and the rotor magnetic motive force (MMF) function is employed to model the so-called pole-cap effect. The proposed model is used to predict the armature reaction field under a different type of stator MMF, such as different orientation of the excitation current as well as various MMF wavelengths. The calculation result is validated by the finite element analysis (FEA) and shows remarkable advantages over the traditional method. The new model proposed in the paper is quite useful for evaluating various IPM motor performances in an accurate and time-effective manner, such as inductance, stator core losses, and magnet eddy current losses. Complete demonstration of the method to calculate the aforementioned performance indices will be presented in a separate paper, and inductance calculation of a primitive winding is given as an example at the end of this paper.


title: A new primary power regulation method for contactless power transfer

abstract: This paper presents a new power regulation method for contactless power transfer systems that combines current magnitude control of a series tuned resonant converter with energy injection concepts suitable for constant load or single pick-up applications. In the latter, the control of the primary converter operates with asymmetrical control, to regulate the magnitude of the high frequency primary track current according to the load requirement of the secondary power pickup. The feedback control algorithm is developed by monitoring the output voltage of the secondary power pickup. Switching losses and EMI are reduced due to the ZCS (Zero Current Switching) operation of the converter. Simulation and experimental results show that the primary converter has a fast and smooth start-up transient response, and the magnitudes of the primary current and the output voltage of a secondary power pickup are both fully controllable against load variations.


title: Magic sets and other strange ways to implement logic programs (extended abstract)

abstract: Several methods for implementing database queries expressed as logical rules are given and they are compared for efficiency. One method, called “magic sets,” is a general algorithm for rewriting logical rules so that they may be implemented bottomUP (= forward chaining) in a way that cuts down on the irrelevant facts that are generated. The advantage of this scheme is that by working bottom-up, we can take advantage of efficient methods for doing massive joins. Two other methods are ad hoc ways of implementing “linear” rules, i.e., rules where at most one predicate in any body is recursive. These methods are


title: A Spot Capacity Market to Increase Power Infrastructure Utilization in Multi-Tenant Data Centers

abstract: Despite the common practice of oversubscription, power capacity is largely under-utilized in data centers. A significant factor driving this under-utilization is fluctuation of the aggregate power demand, resulting in unused "spot (power) capacity". In this paper, we tap into spot capacity for improving power infrastructure utilization in multi-tenant data centers, an important but under-explored type of data center where multiple tenants house their own physical servers. We propose a novel spot capacity market, called SpotDC, to allocate spot capacity to tenants on demand. Specifically, SpotDC extracts tenants' rack-level spot capacity demand through an elastic demand function, based on which the operator sets the market price for spot capacity allocation. We evaluate SpotDC using both testbed experiments and simulations, demonstrating that SpotDC improves power infrastructure utilization and creates a "win-win" situation: the data center operator increases its profit (by nearly 10%), while tenants improve their performance (by 1.2{1.8x on average, yet at a marginal cost).


title: Malignant intravascular lymphomatosis associated with venous stenosis

abstract: ✓ This 55-year-old man presented with malignant intravascular lymphomatosis, a rare vascular disorder of the central nervous system characterized by proliferation of malignant lymphoma cells. The clinical manifestations were focal neurological signs and progressive dementia. Angiography demonstrated stenoses of the cortical veins. Postmortem examination revealed infiltration of tumor cells into the lumen and vascular wall, although the stenoses were caused primarily by fibrin thrombi. To the authors' knowledge this is the first case of malignant intravascular lymphomatosis associated with venous stenosis.


title: BBookX: Design of an Automated Web-based Recommender System for the Creation of Open Learning Content

abstract: We describe BBookX, a web-based tool that uses a human-computing approach to facilitate the creation of open source textbooks. The goal of BBookX is to create a system that can search various Open Educational Resource (OER) repositories such as Wikipedia, based on a set of user-generated criteria, and return various resources that can be combined, remixed, and re-used to support specific learning goals. As BBookX is a work-in-progress, we are in the midst of a design-based research study, where user testing guided multiple rounds of iteration in the design of the user interface (UI) as well as the query engine. From an interface perspective, the challenges we present are the matching of the UI to users' mental models from similar systems, as well as educating users how to best work with the algorithms in an iterative manner to find and refine content for inclusion into open textbooks.


title: Quantifying Skype user satisfaction

abstract: The success of Skype has inspired a generation of peer-to-peer-based solutions for satisfactory real-time multimedia services over the Internet. However, fundamental questions, such as whether VoIP services like Skype are good enough in terms of user satisfaction,have not been formally addressed. One of the major challenges lies in the lack of an easily accessible and objective index to quantify the degree of user satisfaction.In this work, we propose a model, geared to Skype, but generalizable to other VoIP services, to quantify VoIP user satisfaction based on a rigorous analysis of the call duration from actual Skype traces. The User Satisfaction Index (USI) derived from the model is unique in that 1) it is composed by objective source-and network-level metrics, such as the bit rate, bit rate jitter, and round-trip time, 2) unlike speech quality measures based on voice signals, such as the PESQ model standardized by ITU-T, the metrics are easily accessible and computable for real-time adaptation, and 3) the model development only requires network measurements, i.e., no user surveys or voice signals are necessary. Our model is validated by an independent set of metrics that quantifies the degree of user interaction from the actual traces.


title: Blockchain Based Access Control

abstract: Access Control systems are used in computer security to regulate the access to critical or valuable resources. The rights of subjects to access such resources are typically expressed through access control policies, which are evaluated at access request time against the current access context. This paper proposes a new approach based on blockchain technology to publish the policies expressing the right to access a resource and to allow the distributed transfer of such right among users. In our proposed protocol the policies and the rights exchanges are publicly visible on the blockchain, consequently any user can know at any time the policy paired with a resource and the subjects who currently have the rights to access the resource. This solution allows distributed auditability, preventing a party from fraudulently denying the rights granted by an enforceable policy. We also show a possible working implementation based on XACML policies, deployed on the Bitcoin blockchain.


title: Distributed MAC Protocol for Cognitive Radio Networks: Design, Analysis,and Optimization

abstract: In this paper, we investigate the joint optimal sensing and distributed Medium Access Control (MAC) protocol design problem for cognitive radio (CR) networks. We consider both scenarios with single and multiple channels. For each scenario, we design a synchronized MAC protocol for dynamic spectrum sharing among multiple secondary users (SUs), which incorporates spectrum sensing for protecting active primary users (PUs). We perform saturation throughput analysis for the corresponding proposed MAC protocols that explicitly capture the spectrum-sensing performance. Then, we find their optimal configuration by formulating throughput maximization problems subject to detection probability constraints for PUs. In particular, the optimal solution of the optimization problem returns the required sensing time for PUs' protection and optimal contention window to maximize the total throughput of the secondary network. Finally, numerical results are presented to illustrate developed theoretical findings in this paper and significant performance gains of the optimal sensing and protocol configuration.


title: Empirical study of PROXTONE and PROXTONE+ for fast learning of large scale sparse models

abstract: PROXTONE is a novel and fast method for optimization of large scale non-smooth convex problem \cite{shi2015large}. In this work, we try to use PROXTONE method in solving large scale \emph{non-smooth non-convex} problems, for example training of sparse deep neural network (sparse DNN) or sparse convolutional neural network (sparse CNN) for embedded or mobile device. PROXTONE converges much faster than first order methods, while first order method is easy in deriving and controlling the sparseness of the solutions. Thus in some applications, in order to train sparse models fast, we propose to combine the merits of both methods, that is we use PROXTONE in the first several epochs to reach the neighborhood of an optimal solution, and then use the first order method to explore the possibility of sparsity in the following training. We call such method PROXTONE plus (PROXTONE$^+$). Both PROXTONE and PROXTONE$^+$ are tested in our experiments, and which demonstrate both methods improved convergence speed twice as fast at least on diverse sparse model learning problems, and at the same time reduce the size to 0.5\% for DNN models. The source of all the algorithms is available upon request.


title: An expert system for the composition of formal Spanish poetry

abstract: The present paper presents an application that composes formal poetry in Spanish in a semiautomatic interactive fashion. Automatic Spanish Poetry Expert and Rewriting Application (ASPERA) is a forward reasoning rule-based system that obtains from the user basic style parameters and an intended message; applies a knowledge-based pre-processor to select the most appropriate metric structure for the user's wishes; and, by intelligent adaptation of selected examples from a corpus of verses, carries out a prose-to-poetry translation of the given message. In the composition process, ASPERA combines natural language generation and CBR techniques to apply a set of construction heuristics obtained from formal literature on Spanish poetry. If the user validates the poem draft presented by the system, the resulting verses are analysed and incorporated into the system data files.


title: Polarization reconfigurable SIW circular ring-slot antenna

abstract: In this paper a linearly polarization reconfigurable substrate integrated waveguide (SIW) circular ring slot antenna for 5.8 GHz ISM band is proposed. The antenna consists of two dielectric layers: a feeding layer and a radiator layer. In the feeding layer, an SIW operating in the fundamental mode TE 10 is accommodated. The radiator layer, which is fed by the SIW, contains etched circular ring-slot. The proposed antenna has a broadside radiation pattern and radiates linearly polarized wave which can be controlled in the range of the angles ± 45° with respect to the longitudinal axis of the antenna.


title: Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection

abstract: Most of the crowd abnormal event detection methods rely on complex hand-crafted features to represent the crowd motion and appearance. Convolutional Neural Networks (CNN) have shown to be a powerful tool with excellent representational capacities, which can leverage the need for hand-crafted features. In this paper, we show that keeping track of the changes in the CNN feature across time can facilitate capturing the local abnormality. We specifically propose a novel measure-based method which allows measuring the local abnormality in a video by combining semantic information (inherited from existing CNN models) with low-level Optical-Flow. One of the advantage of this method is that it can be used without the fine-tuning costs. The proposed method is validated on challenging abnormality detection datasets and the results show the superiority of our method compared to the state-of-the-art methods.


title: Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank

abstract: Abstract In today's economy and society, the banking industry is of great importance to every one of us. We all depend on the efficiency and quality of services that the banking industry provides. With the improvement in technology, the competition in the banking industry has become increasingly intense. Therefore, performance analyses in the banking industry attract more and more attention. This paper integrates data envelopment analysis (DEA) and neural networks (NNs) to examine the relative branch efficiency of a big Canadian bank. The results are compared with the normal DEA results. On the whole they are comparable. Furthermore, the guidance on how to improve the branch performance is given. Neural networks are also applied to do short-term efficiency prediction. Finally, the comparison between these two approaches is presented.


title: A power-on-reset circuit with precisely triggered threshold voltages

abstract: A power-on-reset (POR) circuit, which is an important block in mixed-signal integrated circuits, is used for the correct initialization of critical logic states in digital blocks of mixed-signal circuits. A POR circuit with precisely triggered threshold voltages is proposed. The circuit is designed in a 0.18 μm CMOS technology with a maximum 4 μA quiescent current. The output signal of the proposed circuit is also used to precisely detect whether the power supply is power-on or brown-out. The proposed POR circuit is insensitive to the power ramping time and tolerant to process variations. At typical corner, the proposed circuit reduces the quiescent current by more than 10x and layout area by 38% compared to the previously published designs.


title: Spam profile detection in social networks based on public features

abstract: In the context of Online Social Networks, Spam profiles are not just a source of unwanted ads, but a serious security threat used by online criminals and terrorists for various malicious purposes. Recently, such criminals were able to steal a number of accounts that belong to NatWest bank's customers. Their attack vector was based on spam tweets posted by a Twitter account which looked very close to NatWest customer support account and leaded users to a link of a phishing site. In this study, we investigate the nature of spam profiles in Twitter with a goal to improve social spam detection. Based on a set of publicly available features, we develop spam profiles detection models. At this stage, a dataset of 82 Twitter's profiles are collected and analyzed. With feature engineering, we investigate ten binary and simple features that can be used to classify spam profiles. Moreover, a feature selection process is utilized to identify the most influencing features in the process of detecting spam profiles. For feature selection, two methods are used ReliefF and Information Gain. While for classification, four classification algorithms are applied and compared: Decision Trees, Multilayer Perceptron, k-Nearest neighbors and Naive Bayes. Preliminary experiments in this work show that the promising detection rates can be obtained using such features regardless of the language of the tweets.


title: DroidLink: Automated Generation of Deep Links for Android Apps

abstract: The mobile application (app) has become the main entrance to access the Internet on handheld devices. Unlike the Web where each webpage has a global URL to reach directly, a specific "content page" of an app can be opened only by exploring the app with several operations from the landing page. The interoperability between apps is quite fixed and thus limits the value-added "linked data" between apps. Recently, deep link has been proposed to enable targeting and opening a specific page of an app externally with an accessible uniform resource identifier (URI). However, implementing deep link for mobile apps requires a lot of manual efforts by app developers, which can be very error-prone and time-consuming. In this paper, we propose DroidLink to automatically generating deep links for existing Android apps. We design a deep link model suitable for automatic generation. Then we explore the transition of pages and build a navigation graph based on static and dynamic analysis of Android apps. Next, we realize an updating mechanism that keeps on revisiting the target app and discover new pages, and thus generates deep links for every single page of the app. Finally, we repackage the app with deep link supports, but requires no additional deployment requirements. We generate deep links for some popular apps and demonstrate the feasibility of DroidLink.


title: Semi-autonomous control system of rescue crawler robot having flippers for getting Over unknown-Steps

abstract: A rescue crawler robot with flipper arms has high ability to get over rough terrain, but it is hard to control its flipper arms in remote control. The authors aim at development of a semi-autonomous control system for the solution. In this paper, the authors propose a sensor reflexive method that controls these flippers autonomously for getting over unknown steps. Our proposed method is effective in unknown and changeable environment. The authors applied the proposed method to Aladdin, and examined validity of these control rules in unknown environment.


title: The Evolution of White Matter Changes After Mild Traumatic Brain Injury: A DTI and NODDI Study

abstract: Neuroimaging biomarkers show promise for improving precision diagnosis and prognosis after mild traumatic brain injury (mTBI), but none has yet been adopted in routine clinical practice. Biophysical modeling of multishell diffusion MRI, using the neurite orientation dispersion and density imaging (NODDI) framework, may improve upon conventional diffusion tensor imaging (DTI) in revealing subtle patterns of underlying white matter microstructural pathology, such as diffuse axonal injury (DAI) and neuroinflammation, that are important for detecting mTBI and determining patient outcome. With a cross-sectional and longitudinal design, we assessed structural MRI, DTI and NODDI in 40 mTBI patients at 2 weeks and 6 months after injury and 14 matched control participants with orthopedic trauma but not suffering from mTBI at 2 weeks. Self-reported and performance-based cognitive measures assessing postconcussive symptoms, memory, executive functions and processing speed were investigated in post-acute and chronic phase after injury for the mTBI subjects. Machine learning analysis was used to identify mTBI patients with the best neuropsychological improvement over time and relate this outcome to DTI and NODDI biomarkers. In the cross-sectional comparison with the trauma control group at 2 weeks post-injury, mTBI patients showed decreased fractional anisotropy (FA) and increased mean diffusivity (MD) on DTI mainly in anterior tracts that corresponded to white matter regions of elevated free water fraction (FISO) on NODDI, signifying vasogenic edema. Patients showed decreases from 2 weeks to 6 months in white matter neurite density on NODDI, predominantly in posterior tracts. No significant longitudinal changes in DTI metrics were observed. The machine learning analysis divided the mTBI patients into two groups based on their recovery. Voxel-wise group comparison revealed associations between white matter orientation dispersion index (ODI) and FISO with degree and trajectory of improvement within the mTBI group. In conclusion, white matter FA and MD alterations early after mTBI might reflect vasogenic edema, as shown by elevated free water on NODDI. Longer-term declines in neurite density on NODDI suggest progressive axonal degeneration due to DAI, especially in tracts known to be integral to the structural connectome. Overall, these results show that the NODDI parameters appear to be more sensitive to longitudinal changes than DTI metrics. Thus, NODDI merits further study in larger cohorts for mTBI diagnosis, prognosis and treatment monitoring.


title: Gradual Learning of Deep Recurrent Neural Networks

abstract: Deep Recurrent Neural Networks (RNNs) achieve state-of-the-art results in many sequence-to-sequence tasks. However, deep RNNs are difficult to train and suffer from overfitting. We introduce a training method that trains the network gradually, and treats each layer individually, to achieve improved results in language modelling tasks. Training deep LSTM with Gradual Learning (GL) obtains perplexity of 61.7 on the Penn Treebank (PTB) corpus. As far as we know (as for the 20.05.2017), GL improves the best state-of-the-art performance by a single LSTM/RHN model on the word-level PTB dataset.


title: FACIAL EXPRESSION RECOGNITION USING ACTIVE APPEARANCE MODELS

abstract: A framework for automatic facial expression recognition combining Active Appearance Model (AAM) and Linear Discriminant Analysis (LDA) is proposed. Seven different expressions of several subjects, representing the neutral face and the facial emotions of happiness, sadness, surprise, anger, fear and disgust were analysed. The proposed solution starts by describing the human face by an AAM model, projecting the appearance results to a Fisherspace using LDA to emphasize the different expression categories. Finaly the performed classification is based on malahanobis distance.


title: Nutrition Recommendations and Interventions for Diabetes: A position statement of the American Diabetes Association

abstract: Medical nutrition therapy (MNT) is important in preventing diabetes, managing existing diabetes, and preventing, or at least slowing, the rate of development of diabetes complications. It is, therefore, important at all levels of diabetes prevention. MNT is also an integral component of diabetes self-management education (or training). This position statement provides evidence-based recommendations and interventions for diabetes MNT. The previous position statement with accompanying technical review was published in 2002 and modified slightly in 2004. This statement updates previous position statements, focuses on key references published since the year 2000, and uses grading according to the level of evidence available...


title: Fuzzy logic applications to power electronics and drives-an overview

abstract: Applications of fuzzy logic (FL) to power electronics and drives are on the rise. The paper discusses some representative applications of FL in the area, preceded by an interpretative review of fuzzy logic controller (FLC) theory. A discussion on design and implementation aspects is presented, that also considers the interaction of neural networks and fuzzy logic techniques. Finally, strengths and limitations of FLC are considered, including possible applications in the area. The applications discussed are: phase controlled converter DC motor drive, wind generation system, and slip power devices.


title: Capturing AU-Aware Facial Features and Their Latent Relations for Emotion Recognition in the Wild

abstract: The Emotion Recognition in the Wild (EmotiW) Challenge has been held for three years. Previous winner teams primarily focus on designing specific deep neural networks or fusing diverse hand-crafted and deep convolutional features. They all neglect to explore the significance of the latent relations among changing features resulted from facial muscle motions. In this paper, we study this recognition challenge from the perspective of analyzing the relations among expression-specific facial features in an explicit manner. Our method has three key components. First, we propose a pair-wise learning strategy to automatically seek a set of facial image patches which are important for discriminating two particular emotion categories. We found these learnt local patches are in part consistent with the locations of expression-specific Action Units (AUs), thus the features extracted from such kind of facial patches are named AU-aware facial features. Second, in each pair-wise task, we use an undirected graph structure, which takes learnt facial patches as individual vertices, to encode feature relations between any two learnt facial patches. Finally, a robust emotion representation is constructed by concatenating all task-specific graph-structured facial feature relations sequentially. Extensive experiments on the EmotiW 2015 Challenge testify the efficacy of the proposed approach. Without using additional data, our final submissions achieved competitive results on both sub-challenges including the image based static facial expression recognition (we got 55.38% recognition accuracy outperforming the baseline 39.13% with a margin of 16.25%) and the audio-video based emotion recognition (we got 53.80% recognition accuracy outperforming the baseline 39.33% and the 2014 winner team's final result 50.37% with the margins of 14.47% and 3.43%, respectively).


title: A physically-based particle model of woven cloth

abstract: Every time a tablecloth is draped over a table it will fold and pleat in unique ways. We report on a physically-based model and a simulation methodology, which when used together are able to reproduce many of the attributes of this characteristic behavior of cloth. Our model utilizes a microscopic particle representation that directly treats the mechanical constraints between the threads in woven material rather than using a macroscopic continuum approximation. The simulation technique is hybrid, employing force methods for gross movement and energy methods to enforce constraints within the material. The model is developed and demonstrated within a visualization environment that allows full interaction between the simulated material and conventional constructive-solid-geometry models.


title: Analyzing Worst-Case Delay Performance of IEC 61850-9-2 Process Bus Networks Using Measurements and Network Calculus

abstract: In power substation automation systems (SASs) based on IEC 61850, conventional hardwired process connections are being replaced by switched Ethernet. To ensure system reliability and responsiveness, transmission of critical information required by protection and control tasks must satisfy hard delay constraints at all times. Therefore, delay performance conformance should be taken into consideration during the design phase of an SAS project. In this paper, we propose to study the worst-case delay performance of IEC 61850-9-2 process bus networks, which generally carry non-feedforward traffic patterns, through the combination of measurements and network-calculus-based analysis. As an Ethernet switch supports dedicated interconnections between its multiple interfaces, our proposed approach converts a non-feedforward network into feedforward ones by introducing service models for its individual output interfaces instead of modeling it in its entirety with a single service model. To derive practical delay bounds that can be validated against measurement results, our approach not only constructs traffic models based on the idiosyncrasies of process bus network and switched Ethernet, but also establishes service models of networking devices by taking measurements. Results from our case studies of both feedforward and non-feedforward process bus networks show that the proposed combination of network calculus and measurement-based modeling generates accurate delay bounds for Ethernet-based substation communication networks (SCNs). The proposed approach can thus be adopted by designers and architects to analytically evaluate worst-case delay performance at miscellaneous stages of SAS design.


title: Dual-Band Circularly Polarized $S$-Shaped Slotted Patch Antenna With a Small Frequency-Ratio

abstract: A dual-band single-feed circularly polarized, S-shaped slotted patch antenna with a small frequency-ratio is proposed for GPS applications. An S-shaped slot is cut at the centre of a square patch radiator for dual-band operation. A single microstrip feed-line is underneath the center of the coupling aperture ground-plane. The frequency-ratio of the antenna can be controlled by adjusting the S-shaped slot arm lengths. The measured 10-dB return loss bandwidths for the lower and upper-bands are 16% (1.103-1.297 GHz) and 12.5% (1.444-1.636 GHz), respectively. The measured 3-dB axial-ratio (AR) bandwidth is 6.9% (1.195-1.128 GHz) for the lower-band and 0.6% (1.568-1.577 GHz) for the upper-band. The measured gain is more than 5.0 dBic over both the bands. The measured frequency-ratio is 1.28. The overall antenna size is 0.46 ?o × 0.46?o × 0.086?o at 1.2 GHz.


title: A novel scheme for 4-D Light-Field compression based on 3-D representation by multi-focus images

abstract: Light-Field enables us to observe scenes from free viewpoints. However, it generally consists of 4-D enormous data, that are not suitable for storing or transmitting without effective compression. 4-D Light-Field is very redundant because essentially it includes just 3-D scene information. Actually, although robust 3-D scene estimation such as depth recovery from Light-Field is not so easy, we successfully derived a method of reconstructing Light-Field directly from 3-D information composed of multi-focus images without any scene estimation. On the other hand, it is easy to synthesize multi-focus images from Light-Field. In this paper, based on the method, we propose novel Light-Field compression via synthesized multi-focus images as effective representation of 3-D scenes. Multi-focus images are easily compressed because they contain mostly low frequency components. We show experimental results by using synthetic and real images. Reconstruction quality of the method is robust even at very low bit-rate.


title: A transition model for cognitions about agency

abstract: Recent research in a range of fields has explored people's concepts about agency, and this issue is clearly important for understanding the conceptual basis of human-robot interaction. This research takes a wide range of approaches, but no systematic model of reasoning about agency has combined the concepts and processes involved agency-reasoning comprehensively enough to support research exploring issues such as conceptual change in reasoning about agents, and the interaction between concepts about agents and visual attention. Our goal in this paper is to develop a transition model of reasoning about agency that achieves three important goals. First, we aim to specify the different kinds of knowledge that is likely to be accessed when people reason about agents. Second, we specify the circumstances under which these different kinds of knowledge might be accessed and be changed. Finally, we discuss how this knowledge might affect basic psychological processes of attention and memory. Our approach will be to first describe the transition model, then to discuss how it might be applied in two specific domains: computer interfaces that allow a single operator to track multiple robots, and a teachable agent system currently in use assisting primary and middle school students in learning natural science concepts.


title: Flexible dv/dt and di/dt control method for insulated gate power switches

abstract: Active gate control techniques are introduced in this paper for flexibly and independently controlling the dv/dt and di/dt of insulated gate power devices during hard-switching events. In the case of dv/dt control, the output voltage dv/dt can be controlled over a wide range by electronically adjusting the effective gate-to-drain (-collector) capacitance (i.e., Miller capacitance). For di/dt control, similar techniques are applied for electronically adjusting the output current di/dt over a wide range using voltage feedback from a small inductor connected in series with the switch's source (emitter) terminal. Both techniques are designed to maximize their compatibility with power module implementations that combine the power switch and its gate drive, including integrated circuit gate drives. Simulation and experimental results are included to verify the desirable performance characteristics of the presented dv/dt and di/dt control techniques.


title: Pharmacokinetics and Bioavailability of Quercetin Glycosides in Humans

abstract: Due to its potentially beneficial impact on human health, the polyphenol quercetin has come into the focus of medicinal interest. However, data on the bioavailability of quercetin after oral intake are scarce and contradictory. Previous investigations indicate that the disposition of quercetin may depend on the sugar moiety of the glycoside or the plant matrix. To determine the influence of the sugar moiety or matrix on the absorption of quercetin, two isolated quercetin glycosides and two plant extracts were administered to 12 healthy volunteers in a four-way crossover study. Each subject received an onion supplement or quercetin-4'-O-glucoside (both equivalent to 100 mg quercetin), as well as quercetin-3-O-rutinoside and buckwheat tea (both equivalent to 200 mg quercetin). Samples were analyzed by HPLC with a 12-channel coulometric array detector. In human plasma, only quercetin glucuronides, but no free quercetin, could be detected. There was no significant difference in the bioavailability and pharmacokinetic parameters between the onion supplement and quercetin-4'-O-glucoside. Peak plasma concentrations were 2.3 +/- 1.5 microg x mL(-1) and 2.1 +/- 1.6 microg x mL(-1) (mean +/- SD) and were reached after 0.7 +/- 0.2 hours and 0.7 +/- 0.3 hours, respectively. After administration of buckwheat tea and rutin, however, peak plasma levels were--despite the higher dose-only 0.6 +/- 0.7 microg x mL(-1) and 0.3 +/- 0.3 microg x mL(-1), respectively. Peak concentrations were reached 4.3 +/- 1.8 hours after administration of buckwheat tea and 7.0 +/- 2.9 hours after ingestion of rutin. The terminal elimination half-life was about 11 hours for all treatments. Thus, the disposition of quercetin in humans primarily depends on the sugar moiety. To a minor extent, the plant matrix influences both the rate and extent of absorption in the case of buckwheat tea administration compared with the isolated compound. The site of absorption seems to be different for quercetin-4'-O-glucoside and quercetin-3-O-rutinoside. The significance of specific carriers on the absorption of quercetin glycosides, as well as specific intestinal beta-glucosidases, needs to be further evaluated.


title: CARRIER RECOVERY AND CLOCK RECOVERY FOR QPSK DEMODULATION

abstract: This paper deals with the design and implementation aspects of high data rate digital demodulators. The Existing remote sensing satellites support data rates of several hundred mega bits per second. The future trend is towards giga bit rate transmission. This necessitates for demodulators of the ground receive system to process faster and handle the ever-rising data throughput more efficiently. Different Satellites use different modulation schemes with variable data rates. In order to cater to the Multi mission /Multisatellite data reception requirements of a ground station, it is necessary to have greater flexibility and programmability features embedded in the design of demodulators. The demodulation techniques for Binary / Quadrature Phase shift Keying (BPSK/ QPSK) are well established and understood when implemented with analog circuits. The BPSK/ QPSK can be demodulated by different techniques such as squaring loop, Costas loop and others in analog domain. The Costas loop technique is adopted for developing the digital demodulator because unlike in Square Loop technique, in this the carrier recovery and data demodulation can be done simultaneously with simple blocks level design. The high data rate digital demodulator performs IF amplification, filtering and analog to digital conversion of the received IF signal followed by a Digital demodulator. The basic design strategy includes a configurable data rate BPSK/ QPSK demodulation with COSTAS loop circuitry utilizing the flexibility of FPGA implementation. The basic design considering a sampling clock from local clock oscillator operating at 125 MHz and 70 MHz carrier down converted to 30 MHz for 8 Mbps data rate (For BPSK). Later the sampling clock is increased to 250 MHz and the carrier is direct 70 MHz with data rate 42.4456 Mbps (For QPSK). The Performance of the Demodulator is evaluated using MATLAB simulation tools. The development has done with ISE implementation tools. The purpose of this paper is to evaluate the new technology by implementing a BPSK/QPSK demodulator on an ADC-FPGA board. A mathematical algorithm was developed and implemented with ISE tools for digital demodulator design. The input test signals from Modulation Simulators and signal generators have been interfaced to the FPGA board through Analog-To-Digital Converter (ADC). The recovered carrier output and I, Q demodulated data patterns have been verified through ISE tools and Wave Vision Software for FFT analysis.


title: Omnidirectional 3D reconstruction in augmented Manhattan worlds

abstract: This paper proposes a method for high-quality omnidirectional 3D reconstruction of augmented Manhattan worlds from catadioptric stereo video sequences. In contrast to existing works we do not rely on constructing virtual perspective views, but instead propose to optimize depth jointly in a unified omnidirectional space. Furthermore, we show that plane-based prior models can be applied even though planes in 3D do not project to planes in the omnidirectional domain. Towards this goal, we propose an omnidirectional slanted-plane Markov random field model which relies on plane hypotheses extracted using a novel voting scheme for 3D planes in omnidirectional space. To quantitatively evaluate our method we introduce a dataset which we have captured using our autonomous driving platform AnnieWAY which we equipped with two horizontally aligned catadioptric cameras and a Velodyne HDL-64E laser scanner for precise ground truth depth measurements. As evidenced by our experiments, the proposed method clearly benefits from the unified view and significantly outperforms existing stereo matching techniques both quantitatively and qualitatively. Furthermore, our method is able to reduce noise and the obtained depth maps can be represented very compactly by a small number of image segments and plane parameters.


title: Weighted nonnegative matrix factorization

abstract: Nonnegative matrix factorization (NMF) is a widely-used method for low-rank approximation (LRA) of a nonnegative matrix (matrix with only nonnegative entries), where nonnegativity constraints are imposed on factor matrices in the decomposition. A large body of past work on NMF has focused on the case where the data matrix is complete. In practice, however, we often encounter with an incomplete data matrix where some entries are missing (e.g., a user-rating matrix). Weighted low-rank approximation (WLRA) has been studied to handle incomplete data matrix. However, there is only few work on weighted nonnegative matrix factorization (WNMF) that is WLRA with nonnegativity constraints. Existing WNMF methods are limited to a direct extension of NMF multiplicative updates, which suffer from slow convergence while the implementation is easy. In this paper we develop relatively fast and scalable algorithms for WNMF, borrowed from well-studied optimization techniques: (1) alternating nonnegative least squares; (2) generalized expectation maximization. Numerical experiments on MovieLens and Netflix prize datasets confirm the useful behavior of our methods, in a task of collaborative prediction.


title: Perceptual Surfaces: Towards a Human and Object Sensitive Interactive Display

abstract: This paper reports on our initial results of realizing a computer augmented wall called the HoloWall and HoloTable. Using an infrared camera located behind the wall, this system allows a user to interact with this computerized wall using fingers, hands, their body, or even a physical object such as a document folder.


title: From dynamic classifier selection to dynamic ensemble selection

abstract: In handwritten pattern recognition, the multiple classifier system has been shown to be useful for improving recognition rates. One of the most important tasks in optimizing a multiple classifier system is to select a group of adequate classifiers, known as an Ensemble of Classifiers (EoC), from a pool of classifiers. Static selection schemes select an EoC for all test patterns, and dynamic selection schemes select different classifiers for different test patterns. Nevertheless, it has been shown that traditional dynamic selection performs no better than static selection. We propose four new dynamic selection schemes which explore the properties of the oracle concept. Our results suggest that the proposed schemes, using the majority voting rule for combining classifiers, perform better than the static selection method.


title: Automatic Exam Seating & Teacher Duty Allocation System

abstract: Examinations are the most crucial section of any educational system. They are intended to measure student's knowledge, skills and aptitude. At any institute, a great deal of manual effort is required to plan and arrange examination. It includes making seating arrangement for students as well as supervision duty chart for invigilators. Many institutes performs this task manually using excel sheets. This results in excessive wastage of time and manpower. Automating the entire system can help solve the stated problem efficiently saving a lot of time. This paper presents the automatic exam seating allocation. It works in two modules First as, Students Seating Arrangement (SSA) and second as, Supervision Duties Allocation (SDA). It assigns the classrooms and the duties to the teachers in any institution. An input-output data is obtained from the real system which is found out manually by the organizers who set up the seating arrangement and chalk out the supervision duties. The results obtained using the real system and these two models are compared. The application shows that the modules are highly efficient, low-cost, and can be widely used in various colleges and universities.


title: Location Detection and Tracking of Moving Targets by a 2D IR-UWB Radar System

abstract: In indoor environments, the Global Positioning System (GPS) and long-range tracking radar systems are not optimal, because of signal propagation limitations in the indoor environment. In recent years, the use of ultra-wide band (UWB) technology has become a possible solution for object detection, localization and tracking in indoor environments, because of its high range resolution, compact size and low cost. This paper presents improved target detection and tracking techniques for moving objects with impulse-radio UWB (IR-UWB) radar in a short-range indoor area. This is achieved through signal-processing steps, such as clutter reduction, target detection, target localization and tracking. In this paper, we introduce a new combination consisting of our proposed signal-processing procedures. In the clutter-reduction step, a filtering method that uses a Kalman filter (KF) is proposed. Then, in the target detection step, a modification of the conventional CLEAN algorithm which is used to estimate the impulse response from observation region is applied for the advanced elimination of false alarms. Then, the output is fed into the target localization and tracking step, in which the target location and trajectory are determined and tracked by using unscented KF in two-dimensional coordinates. In each step, the proposed methods are compared to conventional methods to demonstrate the differences in performance. The experiments are carried out using actual IR-UWB radar under different scenarios. The results verify that the proposed methods can improve the probability and efficiency of target detection and tracking.


title: Image denoising via adaptive soft-thresholding based on non-local samples

abstract: This paper proposes a new image denoising approach using adaptive signal modeling and adaptive soft-thresholding. It improves the image quality by regularizing all the patches in image based on distribution modeling in transform domain. Instead of using a global model for all patches, it employs content adaptive models to address the non-stationarity of image signals. The distribution model of each patch is estimated individually and can vary for different transform bands and for different patch locations. In particular, we allow the distribution model for each individual patch to have non-zero expectation. To estimate the expectation and variance parameters for the transform bands of a particular patch, we exploit the non-local correlation of image and collect a set of similar patches as data samples to form the distribution. Irrelevant patches are excluded so that this non-local based modeling is more accurate than global modeling. Adaptive soft-thresholding is employed since we observed that the distribution of non-local samples can be approximated by Laplacian distribution. Experimental results show that the proposed scheme outperforms the state-of-the-art denoising methods such as BM3D and CSR in both the PSNR and the perceptual quality.


Issues in the valuation of travel time changes


title: A review paper on conical corrugated horn antenna

abstract: This paper presents the review on different types of corrugated waveguides and the antennas for far field radiation pattern. This kind of antennas having a better gain and directivity and the side lobe levels are comparatively low. The side lobe levels can be further reduced by the applying the concept of corrugation to the walls. The significant disadvantage of TE 11 mode, side lobes and cross-polarization, is reduced by the HE 11 mode. Now a days, working with a new type of corrugated horn antenna, consists on generating the fundamental Gaussian beam directly from the TE 11 mode. These antennas are used when high degree of symmetry in radiation field and low cross polarization with high gain and pencil beam is required. This kind of Gaussian profile horn antenna having lower aperture in size.


title: M2E-Try On Net: Fashion from Model to Everyone

abstract: Most existing virtual try-on applications require clean clothes images. Instead, we present a novel virtual Try-On network, M2E-Try On Net, which transfers the clothes from a model image to a person image without the need of any clean product images. To obtain a realistic image of person wearing the desired model clothes, we aim to solve the following challenges: 1) non-rigid nature of clothes - we need to align poses between the model and the user; 2) richness in textures of fashion items - preserving the fine details and characteristics of the clothes is critical for photo-realistic transfer; 3) variation of identity appearances - it is required to fit the desired model clothes to the person identity seamlessly. To tackle these challenges, we introduce three key components, including the pose alignment network (PAN), the texture refinement network (TRN) and the fitting network (FTN). Since it is unlikely to gather image pairs of input person image and desired output image (i.e. person wearing the desired clothes), our framework is trained in a self-supervised manner to gradually transfer the poses and textures of the model's clothes to the desired appearance. In the experiments, we verify on the Deep Fashion dataset and MVC dataset that our method can generate photo-realistic images for the person to try-on the model clothes. Furthermore, we explore the model capability for different fashion items, including both upper and lower garments.


title: Semantic Stereo for Incidental Satellite Images

abstract: The increasingly common use of incidental satellite images for stereo reconstruction versus rigidly tasked binocular or trinocular coincident collection is helping to enable timely global-scale 3D mapping; however, reliable stereo correspondence from multi-date image pairs remains very challenging due to seasonal appearance differences and scene change. Promising recent work suggests that semantic scene segmentation can provide a robust regularizing prior for resolving ambiguities in stereo correspondence and reconstruction problems. To enable research for pairwise semantic stereo and multi-view semantic 3D reconstruction with incidental satellite images, we have established a large-scale public dataset including multi-view, multi-band satellite images and ground truth geometric and semantic labels for two large cities. To demonstrate the complementary nature of the stereo and segmentation tasks, we present lightweight public baselines adapted from recent state of the art convolutional neural network models and assess their performance.


title: Modeling of Flux Reversal Machines for direct drive applications

abstract: Flux Reversal Machines can be easy to manufacture, are reliable and well suited for direct-drive applications. They also have the advantage that they can be controlled as standard synchronous PM machines. As for most of doubly salient machines, the machine design is relatively complicated due to the highly non-linear magnetic behavior that makes it difficult to device a general optimal design procedure. A simple hybrid approach, partly analytical and partly finite element based is proposed to address the main FRM design parameters influencing its performance. Based on the modeling approach, a prototype is designed and it is currently under construction, as documented in the paper.


title: Architecture and design flow of Tele-Health Monitoring system using STM32 platform

abstract: In recent years, with the technological advancements in healthcare electronics, we see a number of digital data acquisition devices that can monitor certain body parameters of a patient and alert the concerned person in case of an emergency. However, if one desires to monitor multiple body parameters, they are forced to use multiple data acquisition devices. This determines the need for a platform that will allow users to configure their own digital data acquisition device and monitor their health parameters in real-time. This paper discusses the architecture and design flow of a Tele-Health Monitoring (THM) platform using effective usage of the computation power and various inbuilt peripherals of STM32 microcontroller that will allow users to have a reliable and interactive health monitoring system. The proposed platform provides a channel to interface various health sensors and data acquisition devices such that the individual himself or an authorized health provider can monitor and analyze the physical activity of an individual on regular basis. This platform finds great use in cases where patients are under transportation, homecare or frequent health checkup.


title: Socio-economic Factors and Children’s Walking to and from School

abstract: Walking to school is a healthy mode of travel, yet most of the children do not start their day with this activity. The cross- sectional survey used a sample of parents of children (9-12 years old) from public primary schools in Tehran; to examine if socio-economic factors moderate the relationship between parental perception of traffic safety and children travel to school. Results from Multinomial Logistic Models showed that, the numbers of cars in a household and household income are the two main moderators. The findings also showed parental perceptions of traffic safety vary across different socio-economic status areas. ::: Keywords: Socio-economic factors; Children’s travel behavior; Walking to school; Income groups ::: eISSN 2514-7528 © 2018. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.


title: Mimicry Attacks Demystified: What Can Attackers Do to Evade Detection?

abstract: Mimicry attacks have been the focus of detector research where the objective of the attacker is to generate an attack that evades detection while achieving the attackerpsilas goals. If such an attack can be found, it implies that the target detector is vulnerable against mimicry attacks. In this work, we emphasize that there are two components of a buffer overflow attack: the preamble and the exploit. Although the attacker can modify the exploit component easily, the attacker may not be able to prevent preamble from generating anomalous behavior since during preamble stage, the attacker does not have full control. Previous work on mimicry attacks considered an attack to completely evade detection, if the exploit raises no alarms. On the other hand, in this work, we investigate the source of anomalies in both the preamble and the exploit components against two anomaly detectors that monitor four vulnerable UNIX applications. Our experiment results show that preamble can be a source of anomalies, particularly if it is lengthy and anomalous.


title: The role of diet and exercise for the maintenance of fat-free mass and resting metabolic rate during weight loss.

abstract: The incidence of obesity is increasing rapidly. Research efforts for effective treatment strategies still focus on diet and exercise programmes, the individual components of which have been investigated in intervention trials in order to determine the most effective recommendations for sustained changes in bodyweight. The foremost objective of a weight-loss trial has to be the reduction in body fat leading to a decrease in risk factors for metabolic syndrome. However, a concomitant decline in lean tissue can frequently be observed. Given that fat-free mass (FFM) represents a key determinant of the magnitude of resting metabolic rate (RMR), it follows that a decrease in lean tissue could hinder the progress of weight loss. Therefore, with respect to long-term effectiveness of weight-loss programmes, the loss of fat mass while maintaining FFM and RMR seems desirable. Diet intervention studies suggest spontaneous losses in bodyweight following low-fat diets, and current data on a reduction of the carbohydrate-to-protein ratio of the diet show promising outcomes. Exercise training is associated with an increase in energy expenditure, thus promoting changes in body composition and bodyweight while keeping dietary intake constant. The advantages of strength training may have greater implications than initially proposed with respect to decreasing percentage body fat and sustaining FFM. Research to date suggests that the addition of exercise programmes to dietary restriction can promote more favourable changes in body composition than diet or physical activity on its own. Moreover, recent research indicates that the macronutrient content of the energy-restricted diet may influence body compositional alterations following exercise regimens. Protein emerges as an important factor for the maintenance of or increase in FFM induced by exercise training. Changes in RMR can only partly be accounted for by alterations in respiring tissues, and other yet-undefined mechanisms have to be explored. These outcomes provide the scientific rationale to justify further randomised intervention trials on the synergies between diet and exercise approaches to yield favourable modifications in body composition.


title: Key Performance Indicators (KPIs) and Shipping Companies Performance Evaluation: The Case of Greek Tanker Shipping Companies

abstract: If you are not sure where you are, how will you find out where to go? The aim of this paper is to examine the applicability and usefulness of performance indicators in shipping management performance and evaluation. The paper brings the importance of KPIs closer to the reader and examines their definition, purpose and role in the shipping market. The first part is devoted to literature review on KPIs. The second part is the methodology, where a questionnaire is taken across the tanker shipping companies in Greece. The third part is the analysis of the questionnaire which showed that even though many Greek tanker enterprises recognize the benefits of performance indicators, a small percentage of them use some types of KPIs. The final part is the conclusion.


title: Detecting DNA cytosine methylation using nanopore sequencing

abstract: In nanopore sequencing devices, electrolytic current signals are sensitive to base modifications, such as 5-methylcytosine (5-mC). Here we quantified the strength of this effect for the Oxford Nanopore Technologies MinION sequencer. By using synthetically methylated DNA, we were able to train a hidden Markov model to distinguish 5-mC from unmethylated cytosine. We applied our method to sequence the methylome of human DNA, without requiring special steps for library preparation.


title: Pixelated Semantic Colorization

abstract: While many image colorization algorithms have recently shown the capability of producing plausible color versions from gray-scale photographs, they still suffer from limited semantic understanding. To address this shortcoming, we propose to exploit pixelated object semantics to guide image colorization. The rationale is that human beings perceive and distinguish colors based on the semantic categories of objects. Starting from an autoregressive model, we generate image color distributions, from which diverse colored results are sampled. We propose two ways to incorporate object semantics into the colorization model: through a pixelated semantic embedding and a pixelated semantic generator. Specifically, the proposed network includes two branches. One branch learns what the object is, while the other branch learns the object colors. The network jointly optimizes a color embedding loss, a semantic segmentation loss and a color generation loss, in an end-to-end fashion. Experiments on Pascal VOC2012 and COCO-stuff reveal that our network, when trained with semantic segmentation labels, produces more realistic and finer results compared to the colorization state-of-the-art.


title: Simulation of power quality events using simulink model

abstract: This paper presents a comprehensive set of Simulink models, utilizing SimPowerSystems blockset to simulate various power quality events in distribution system. These models include distribution line fault, induction motor starting and transformer energizing models which are used to simulate various types of voltage sag. Capacitor bank switching model is also presented to simulate voltage transient at the distribution network. There are also three phase non linear load models used to simulate voltage notches and harmonic power quality events at the load side. For simplicity, these models are developed with minimum blocks and setting without scarifying the essence of power quality event. These models are also useful to simulate various power quality waveforms for power quality analysis algorithm research. These models serve as a fundamental model for analysis of complex power quality events as well as contribute to the power engineering education curriculum.


title: Drishti: an integrated indoor/outdoor blind navigation system and service

abstract: There are many navigation systems for visually impaired people but few can provide dynamic interactions and adaptability to changes. None of these systems work seamlessly both indoors and outdoors. Drishti uses a precise position measurement system, a wireless connection, a wearable computer, and a vocal communication interface to guide blind users and help them travel in familiar and unfamiliar environments independently and safely. Outdoors, it uses DGPS as its location system to keep the user as close as possible to the central line of sidewalks of campus and downtown areas; it provides the user with an optimal route by means of its dynamic routing and rerouting ability. The user can switch the system from an outdoor to an indoor environment with a simple vocal command. An OEM ultrasound positioning system is used to provide precise indoor location measurements. Experiments show an in-door accuracy of 22 cm. The user can get vocal prompts to avoid possible obstacles and step-by-step walking guidance to move about in an indoor environment. This paper describes the Drishti system and focuses on the indoor navigation design and lessons learned in integrating the indoor with the outdoor system.


title: On the privacy-utility tradeoff in participatory sensing systems

abstract: The ubiquity of sensors-equipped mobile devices has enabled citizens to contribute data via participatory sensing systems. This emergent paradigm comes with various applications to improve users' quality of life. However, the data collection process may compromise the participants' privacy when reporting data tagged or correlated with their sensitive information. Therefore, anonymization and location cloaking techniques have been designed to provide privacy protection, yet to some cost of data utility which is a major concern for queriers. Different from past works, we assess simultaneously the two competing goals of ensuring the queriers' required data utility and protecting the participants' privacy. First, we introduce a trust worthy entity to the participatory sensing traditional system. Also, we propose a general privacy-preserving mechanism that runs on this entity to release a distorted version of the sensed data in order to minimize the information leakage with its associated private information. We demonstrate how to identify a near-optimal solution to the privacy-utility tradeoff by maximizing a privacy score while considering a utility metric set by data queriers (service providers). Furthermore, we tackle the challenge of data with large size alphabets by investigating quantization techniques. Finally, we evaluate the proposed model on three different real datasets while varying the prior knowledge and the obfuscation type. The obtained results demonstrate that, for different applications, a limited distortion may ensure the participants' privacy while maintaining about 98% of the required data utility.


title: On the wafer/pad friction of chemical-mechanical planarization (CMP) processes - Part I: modeling and analysis

abstract: Friction characteristics between the wafer and the polishing pad play an important role in the chemical-mechanical planarization (CMP) process. In this paper, a wafer/pad friction modeling and monitoring scheme for the linear CMP process is presented. Kinematic analysis of the linear CMP system is investigated and a distributed LuGre dynamic friction model is utilized to capture the friction forces generated by the wafer/pad interactions. The frictional torques of both the polisher spindle and the roller systems are used to monitor in situ the changes of the friction coefficient during a CMP process. Effects of pad conditioning and patterned wafer topography on the wafer/pad friction are also analyzed and discussed. The proposed friction modeling and monitoring scheme can be further used for real-time CMP monitoring and process fault diagnosis.


Unleashing the Potential of Machine Learning and IoT in Cyber Physical Farming


title: Scheduling black-box mutational fuzzing

abstract: Black-box mutational fuzzing is a simple yet effective technique to find bugs in software. Given a set of program-seed pairs, we ask how to schedule the fuzzings of these pairs in order to maximize the number of unique bugs found at any point in time. We develop an analytic framework using a mathematical model of black-box mutational fuzzing and use it to evaluate 26 existing and new randomized online scheduling algorithms. Our experiments show that one of our new scheduling algorithms outperforms the multi-armed bandit algorithm in the current version of the CERT Basic Fuzzing Framework (BFF) by finding 1.5x more unique bugs in the same amount of time.


title: Anomaly Detection in Real-Valued Multidimensional Time Series

abstract: We present a new algorithm for detecting anomalies in realvalued multidimensional time series. Our algorithm uses an exemplar-based model that is used to detect anomalies in single dimensions of the time series and a function that predicts one dimension from a related one to detect anomalies in multiple dimensions. The algorithm is shown to work on a variety of dierent types of time series as well as to detect a variety of dierent types of anomalies. We compare our algorithm to other algorithms for both one-dimensional and multidimensional time series and demonstrate that it improves over the state-of-the-art.


Technology Ecosystem Governance


title: Understanding Web Archiving Services and Their (Mis)Use on Social Media

abstract: Either by ensuring the continuing availability of information, or by deliberately caching content that might get deleted or removed, Web archiving services play an increasingly important role in today's information ecosystem. Among these, the Wayback Machine has been proactively archiving, since 2001, versions of a large number of Web pages, while newer services like archive.is allow users to create on-demand snapshots of specific Web pages, which serve as time capsules that can be shared across the Web. ::: In this paper, we present a large-scale analysis of Web archiving services and their use on social media, aiming to shed light on the actors involved in this ecosystem, the content that gets archived, and how it is shared. To this end, we crawl and study: 1) 21M URLs, spanning almost two years, from archive.is; and 2) 356K archive.is plus 391K Wayback Machine URLs that were shared on four social networks: Reddit, Twitter, Gab, and 4chan's Politically Incorrect board (/pol/) over 14 months. We observe that news and social media posts are the most common types of content archived, likely due to their perceived ephemeral and/or controversial nature. Moreover, URLs of archiving services are extensively shared on "fringe" communities within Reddit and 4chan to preserve possibly contentious content. Lastly, we find evidence of moderators nudging or even forcing users to use archives, instead of direct links, for news sources with opposing ideologies, potentially depriving them of ad revenue.


title: High-Precision Position Control of a Linear-Switched Reluctance Motor Using a Self-Tuning Regulator

abstract: High-precision position control of linear-switched reluctance motor (LSRM) is important in motion-control industry. The static model-based controller sometimes cannot give satisfactory output performance due to the inherent nonlinearities of LSRM and the uncertainties of the system. In this paper, a self-tuning regulator (STR) based on the pole-placement algorithm is proposed for high-precision position tracking of the LSRM. Following the time-scale characteristics analysis of LSRM position-tracking system and force-characteristic investigation, the position-tracking model is treated as a second-order system. Different from the static model-based control schemes, the dynamic model of the LSRM can be obtained by online estimation. Also, some practical aspects are taken into account. Owing to the unmodeled dynamics and high-frequency measurement noises, there are some oscillations in the practical control signals, and they can be reduced by a properly designed filter. Both the simulation and experimental results demonstrate that, in the control of the proposed STR, the position-tracking system can reproduce the reference signal with the desired performance in harsh ambient. These results confirm that the method is effective and robust in the high-precision position tracking of LSRM.


title: AIsim: Functional Simulator for Analog-to-Information Perceptual Systems

abstract: Neural network accelerators have been extensively studied for artificial intelligent applications for recent years. Analogto-information systems (AIS), which could accelerate neural network computation in analog domain, are considered as one better alternative for achieving higher scalability and energy efficiency. Unlike operating in the digital domain, an AIS adopts analog computing units to construct the whole neural network for perceptual tasks. However, when designing an AIS, conventional HSPICE simulation is very time-consuming. In order to improve the design capacity, this paper presents a functional simulator, called AIsim for AIS. AIsim could efficiently simulate the noise characteristics in AIS while processing neural network algorithms. In particular, it could also guide the designers to explore the design space of analog computing units with various signal-to-noise ratio (SNR). Compared with conventional HSPICE simulator, AIsim can achieve more than 2000X simulation speedup with 2% results difference at most, for some typical benchmarks.


title: Color and uncertainty : It is not always black and white

abstract: To fully comprehend the meaning and impact of visualized data it is crucial that users are able to perceive and comprehend the inherent uncertainty of the data in a correct and intuitive way. Data uncertainty is frequently visualized through color mappings. Previous studies argued that color hue is not suitable for communicating uncertainty because most hue scales lack an intrinsic perceptual order. In this paper we examine the use of hue for communicating data uncertainty in more detail. We investigated the potential of distinct color triples (rather than the entire spectrum of colors, as used in previously studies) to represent different levels of uncertainty. We identified several color triples that reliably map to an intuitive ordering of certainty. Bipolar color scales constructed from these color triples can be used to communicate uncertainty in visualizations, particularly to audiences of nonspecialists. A ‘traffic light’ configuration (with red and green at the endpoints and either yellow or orange in the middle) communicates uncertainty most intuitively.