title: Design of an android robot head for stage performances

abstract: In this article, an android robot head is proposed for stage performances. As is well known, an android robot is a type of humanoid robot which is considered to be more like a human than many others types. An android robot has human-like joint structures and artificial skin, and so is the robot which is closest to a human in appearance. To date, several android robots have been developed, but most of them have been made for research purposes or exhibitions. In this article, attention is drawn to the more commercial value of an android robot, especially in the acting field. EveR-3, the android robot described here, has already been used in commercial plays in the theater, and through these it has been possible to learn which features of an android robot are necessary for it to function as an actor. A new 9-DOF head has been developed for stage performances. The DOF are reduced when larger motors are used to make exaggerated expressions, because exaggerated expressions are more important on the stage than detailed, complex expressions. LED lights are installed in both cheeks to emphasize emotional expressions by changes in color in the way that make-up is used to achieve a similar effect on human faces. From these trials, a new head which is more suitable for stage performances has been developed.


title: Cognitive enhancing effects of modafinil in healthy volunteers

abstract: Rationale: Modafinil, a novel wake-promoting agent, has been shown to have a similar clinical profile to that of conventional stimulants such as methylphenidate. We were therefore interested in assessing whether modafinil, with its unique pharmacological mode of action, might offer similar potential as a cognitive enhancer, without the side effects commonly experienced with amphetamine-like drugs. Objectives: The main aim of this study was to evaluate the cognitive enhancing potential of this novel agent using a comprehensive battery of neuropsychological tests. Methods: Sixty healthy young adult male volunteers received either a single oral dose of placebo, or 100 mg or 200 mg modafinil prior to performing a variety of tasks designed to test memory and attention. A randomised double-blind, between-subjects design was used. Results: Modafinil significantly enhanced performance on tests of digit span, visual pattern recognition memory, spatial planning and stop-signal reaction time. These performance improvements were complemented by a slowing in latency on three tests: delayed matching to sample, a decision-making task and the spatial planning task. Subjects reported feeling more alert, attentive and energetic on drug. The effects were not clearly dose dependent, except for those seen with the stop-signal paradigm. In contrast to previous findings with methylphenidate, there were no significant effects of drug on spatial memory span, spatial working memory, rapid visual information processing or attentional set-shifting. Additionally, no effects on paired associates learning were identified. Conclusions: These data indicate that modafinil selectively improves neuropsychological task performance. This improvement may be attributable to an enhanced ability to inhibit pre-potent responses. This effect appears to reduce impulsive responding, suggesting that modafinil may be of benefit in the treatment of attention deficit hyperactivity disorder.


title: Discriminative and consistent similarities in instance-level Multiple Instance Learning

abstract: In this paper we present a bottom-up method to instance-level Multiple Instance Learning (MIL) that learns to discover positive instances with globally constrained reasoning about local pairwise similarities. We discover positive instances by optimizing for a ranking such that positive (top rank) instances are highly and consistently similar to each other and dissimilar to negative instances. Our approach takes advantage of a discriminative notion of pairwise similarity coupled with a structural cue in the form of a consistency metric that measures the quality of each similarity. We learn a similarity function for every pair of instances in positive bags by how similarly they differ from instances in negative bags, the only certain labels in MIL. Our experiments demonstrate that our method consistently outperforms state-of-the-art MIL methods both at bag-level and instance-level predictions in standard benchmarks, image category recognition, and text categorization datasets.


title: A Hybrid Prediction Model for Moving Objects

abstract: Existing prediction methods in moving objects databases cannot forecast locations accurately if the query time is far away from the current time. Even for near future prediction, most techniques assume the trajectory of an object's movements can be represented by some mathematical formulas of motion functions based on its recent movements. However, an object's movements are more complicated than what the mathematical formulas can represent. Prediction based on an object's trajectory patterns is a powerful way and has been investigated by several work. But their main interest is how to discover the patterns. In this paper, we present a novel prediction approach, namely The Hybrid Prediction Model, which estimates an object's future locations based on its pattern information as well as existing motion functions using the object's recent movements. Specifically, an object's trajectory patterns which have ad-hoc forms for prediction are discovered and then indexed by a novel access method for efficient query processing. In addition, two query processing techniques that can provide accurate results for both near and distant time predictive queries are presented. Our extensive experiments demonstrate that proposed techniques are more accurate and efficient than existing forecasting schemes.


title: Development of genetic algorithm toolbox using MATLAB

abstract: In this study, optimization of tool path is presented through simulation material removal using Finite Element Analysis (FEA) and control via Genetic Algorithm (GA). The computer code for GA is developed and is specifically designed using MATLAB programming. The developed GA toolbox was written in m-files script together with several built-in functions in order to import the text file generated by FEA software. This paper presents the solution for cutting tool path optimization which is the interaction toolbox that consists of the integration between MATLAB and ABAQUS. This developed toolbox facilitates the optimization process to be performed successfully on the tool path machining process. The optimization technique inherently assists in producing finishing product, with minimum chatter and static deflection problem.


title: Cyber-physical-social system in intelligent transportation

abstract: A cyber-physical system (CPS) is composed of a physical system and its corresponding cyber systems that are tightly fused at all scales and levels. CPS is helpful to improve the controllability, efficiency and reliability of a physical system, such as vehicle collision avoidance and zero-net energy buildings systems. It has become a hot R&D and practical area from US to EU and other countries. In fact, most of physical systems and their cyber systems are designed, built and used by human beings in the social and natural environments. So, social systems must be of the same importance as their CPSs. The indivisible cyber, physical and social parts constitute the cyber-physical-social system (CPSS), a typical complex system and it’s a challengeable problem to control and manage it under traditional theories and methods. An artificial systems, computational experiments and parallel execution (ACP) methodology is introduced based on which data-driven models are applied to social system. Artificial systems, i.e., cyber systems, are applied for the equivalent description of physical-social system (PSS). Computational experiments are applied for control plan validation. And parallel execution finally realizes the stepwise control and management of CPSS. Finally, a CPSS-based intelligent transportation system (ITS) is discussed as a case study, and its architecture, three parts, and application are described in detail.


A reinforcement learning approach to online web system autoconfiguration


title: Slow release of growth factors and thrombospondin-1 in Choukroun's platelet-rich fibrin (PRF): a gold standard to achieve for all surgical platelet concentrates technologies

abstract: Platelet concentrates for surgical topical applications are nowadays often used, but quantification of the long-term growth factor release from these preparations in most cases is impossible. Indeed, in most protocols, platelets are massively activated and there is no significant fibrin matrix to support growth factor release and cell migration. Choukroun's platelet-rich fibrin (PRF), a second generation platelet concentrate, is a leucocyte- and platelet-rich fibrin biomaterial. Here, we show that this dense fibrin membrane releases high quantities of three main growth factors (Transforming Growth Factor b-1 (TGFbeta-1), platelet derived growth factor AB, PDGF-AB; vascular endothelial growth factor, VEGF) and an important coagulation matricellular glycoprotein (thrombospondin-1, TSP-1) during 7 days. Moreover, the comparison between the final released amounts and the initial content of the membrane (after forcible extraction) allows us to consider that the leucocytes trapped in the fibrin matrix continue to produce high quantities of TGFbeta-1 and VEGF during the whole experimental time.


title: UBH 3: an anthropomorphic hand with simplified endo-skeletal structure and soft continuous fingerpads

abstract: The paper describes work in progress at the University of Bologna concerning the design of a new anthropomorphic robot hand. The hand is based on the modular assembly of articulated fingers that adopt an original configuration of their structure, made with rigid links connected by elastic hinges that are coaxially crossed by flexible tendons. This innovative design is suitable to host distributed sensory equipment and continuous compliant cover, allowing a high level of anthropomorphism together with great structural simplification, reliability enhancement and cost reduction. Furthermore, the proposed solution is very flexible, as it can be adapted to many different hand configurations and is not dependent on a particular type of actuation, being compatible with future availability of any kind of artificial muscles.


title: Handwritten digit recognition using multilayer feedforward neural networks with periodic and monotonic activation functions

abstract: The problem of handwritten digit recognition is dealt with by multilayer feedforward neural networks with different types of neuronal activation functions. Three types of activation functions are adopted in the network, namely, the traditional sigmoid function, sinusoidal function and a periodic function that can be considered as a combination of the first two functions. To speed up the learning, as well as to reduce the network size, an extended Kalman filter algorithm with the pruning method is used to train the network. Simulation results show that periodic activation functions perform better than monotonic ones in solving multi-cluster classification problems such as handwritten digit recognition.


title: Management of occult invasive cervical cancer found after simple hysterectomy.

abstract: BACKGROUND ::: To estimate safety and efficacy of radical parametrectomy (RP) and radiation therapy (RT) or concurrent chemoradiation therapy (CCRT) for patients with occult invasive cervical cancer found after simple hysterectomy. ::: ::: ::: MATERIALS AND METHODS ::: We retrospectively evaluated outcomes in 147 patients with occult invasive cervical cancer. ::: ::: ::: RESULTS ::: Forty-eight patients with IA1 lesions (IA1 group) did not receive further treatment. Of the 99 patients with IA2-IIA lesions, 26 received no definitive treatment (observation group), 44 received RT or CCRT (RT/CCRT group), and 29 underwent RP (RP group). After a median follow-up of 116 months (range 3-235 months), recurrent disease was observed in 0%, 34.6%, 6.8%, and 0% of patients in the IA1, observation, RT/CCRT, and RP groups, respectively. In the RT/CCRT group, treatment was delayed due to severe diarrhea in 4 patients (9%) and 12 patients (27%) had late complications related to RT requiring further management (including two surgical interventions). Five patients in the RP group (17%) experienced perioperative complications which were easily managed, intraoperatively or conservatively. Late complications were not observed in the RP group. ::: ::: ::: CONCLUSION ::: Although RP and RT/CCRT had similar therapeutic efficacy, the lower rate of late complications observed with RP makes it preferable to RT/CCRT.


title: Tensor-Based Modeling of Temporal Features for Big Data CTR Estimation

abstract: In this paper we propose a simple tensor-based approach to temporal features modeling that is applicable as means for logistic regression (LR) enhancement. We evaluate experimentally the performance of an LR system based on the proposed model in the Click-Through Rate (CTR) estimation scenario involving processing of very large multi-attribute data streams. We compare our approach to the existing approaches to temporal features modeling from the perspective of the Real-Time Bidding (RTB) CTR estimation scenario. On the basis of an extensive experimental evaluation, we demonstrate that the proposed approach enables achieving an improvement of the quality of CTR estimation. We show this improvement in a Big Data application scenario of the Web user feedback prediction realized within an RTB Demand-Side Platform.


title: A low-power, 26-GHz transformer-based regulated cascode transimpedance amplifier in 0.25µm SiGe BiCMOS

abstract: A 26 GHz transimpedance amplifier (TIA) with transformer-based regulated cascode (RGC) input stage is proposed and analyzed. The transformer enhances the effective transconductance of the TIA's input common-base transistor; reducing the input resistance and providing considerable bandwidth extension. The TIA is implemented in a 0.25µm BiCMOS technology. Measurement shows the single-ended transimpedance gain of 53dBΩ with −3dB bandwidth of 26 GHz. Total chip power, including an output buffer, is 28.2mW from a 2.5V supply; while core TIA power is 8.2mW. The measured average input-referred noise current spectral density is 21.3 pA/√Hz. Total chip area, including pads, is 960µm×780µm.


title: Non invasive estimation of blood glucose using near infra red spectroscopy and double regression analysis

abstract: This paper presents a unique technique for noninvasive estimation of blood glucose concentration using near infra red spectroscopy. The spectroscopy has been performed at the second overtone of glucose which falls in the near infra red region. The near infra red spectroscopy has been performed using transmission photoplethsymography (PPG). The analog front end system has been implemented to get the PPG signal at the near infra red wavelengths of 1070nm, 950nm, 935nm. The PPG signal that has been obtained is processed and double regression analysis is carried out with the artificial neural network for estimating the glucose levels. The root mean square error of the prediction was 5.84mg/dL.


title: HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users

abstract: We introduce hand movement, orientation, and grasp (HMOG), a set of behavioral features to continuously authenticate smartphone users. HMOG features unobtrusively capture subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. We evaluated authentication and biometric key generation (BKG) performance of HMOG features on data collected from 100 subjects typing on a virtual keyboard. Data were collected under two conditions: 1) sitting and 2) walking. We achieved authentication equal error rates (EERs) as low as 7.16% (walking) and 10.05% (sitting) when we combined HMOG, tap, and keystroke features. We performed experiments to investigate why HMOG features perform well during walking. Our results suggest that this is due to the ability of HMOG features to capture distinctive body movements caused by walking, in addition to the hand-movement dynamics from taps. With BKG, we achieved the EERs of 15.1% using HMOG combined with taps. In comparison, BKG using tap, key hold, and swipe features had EERs between 25.7% and 34.2%. We also analyzed the energy consumption of HMOG feature extraction and computation. Our analysis shows that HMOG features extracted at a 16-Hz sensor sampling rate incurred a minor overhead of 7.9% without sacrificing authentication accuracy. Two points distinguish our work from current literature: 1) we present the results of a comprehensive evaluation of three types of features (HMOG, keystroke, and tap) and their combinations under the same experimental conditions and 2) we analyze the features from three perspectives (authentication, BKG, and energy consumption on smartphones).


title: Modeling and Simulation of a Lower Extremity Powered Exoskeleton

abstract: Lower extremity powered exoskeletons (LEPEs) allow people with spinal cord injury (SCI) to stand and walk. However, the majority of LEPEs walk slowly and users can become fatigued from overuse of forearm crutches, suggesting LEPE design can be enhanced. Virtual prototyping is a cost-effective way of improving design; therefore, this research developed and validated two models that simulate walking with the Bionik Laboratories' ARKE exoskeleton attached to a human musculoskeletal model. The first model was driven by kinematic data from 30 able-bodied participants walking at realistic slow walking speeds (0.2-0.8 m/s) and accurately predicted ground reaction forces (GRF) for all speeds. The second model added upper limb crutches and was driven by 3-D-marker data from five SCI participants walking with ARKE. Vertical GRF had the strongest correlations (>0.90) and root-mean-square error (RMSE) and mediolateral center of pressure trajectory had the weakest (<0.35), for both models. Strong correlations and small RMSE between predicted and measured GRFs support the use of these models for optimizing LEPE joint mechanics and improving LEPE design.


title: RIGID-FOLDABLE CYLINDERS AND CELLS

abstract: In this paper, we present newly explored families of rigid-foldable cylinders and the cellular structures constructed from these cylinders; the families include zonogon extrusion cells, bi-directionally flat-foldable cells, and a novel type of cells, i.e., woven cylinder cells. We show the geometry of such structures to demonstrate their validity, their parametric design method, and their kinetic behaviors. These types of structures exhibit continuous rigid-foldability as well as flat-foldability in one or two directions; further, they have different kinetic properties that are potentially applicable for different purposes. The newly proposed woven cylinder cellular structure is a bi-directionally flat-foldable one-DOF rigid-foldable structure and has a distinctive geometric property: structural stiffness against compression in one of three directions.


title: Statistical Syntax-Directed Translation with Extended Domain of Locality

abstract: In syntax-directed translation, the sourcelanguage input is first parsed into a parsetree, which is then recursively converted into a string in the target-language. We model this conversion by an extended treeto-string transducer that has multi-level trees on the source-side, which gives our system more expressive power and flexibility. We also define a direct probability model and use a linear-time dynamic programming algorithm to search for the best derivation. The model is then extended to the general log-linear framework in order to incorporate other features like n-gram language models. We devise a simple-yet-effective algorithm to generate non-duplicate k-best translations for ngram rescoring. Preliminary experiments on English-to-Chinese translation show a significant improvement in terms of translation quality compared to a state-of-theart phrase-based system.


title: Effects of Intensity of Rehabilitation After Stroke : A Research Synthesis

abstract: BACKGROUND AND PURPOSE ::: A research synthesis was performed to (1) critically review controlled studies evaluating effects of different intensities of stroke rehabilitation in terms of disabilities and impairments and (2) quantify patterns by calculating summary effect sizes. The influences of organizational setting of rehabilitation management, blind recording, and amount of rehabilitation on the summary effect sizes were calculated. ::: ::: ::: METHODS ::: A Medline literature search was performed for a critical review of the literature. The internal and external validity of the studies was evaluated. In addition, a meta-analysis was performed by applying the fixed (Hedges's g) effects model. ::: ::: ::: RESULTS ::: The effects of different intensities of rehabilitation were studied in nine controlled studies involving 1051 patients. Analysis of the methodological quality revealed scores varying from 14% to 47% of the maximum feasible score. Meta-analysis demonstrated a statistically significant summary effect size for activities of daily living (0.28 +/- 0.12). Lower summary effect sizes (0.19 +/- 0.17) were found for studies in which experimental and control groups were treated in the same setting compared with studies in which the two groups of patients were treated in different settings (0.40 +/- 0.19). Variables defined on a neuromuscular level (0.37 +/- 0.24) showed larger summary effect sizes than variables defined on a functional level (0.10 +/- 0.21). Weighting individual effect sizes for the difference in amount of rehabilitation between experimental and control groups resulted in larger summary effect sizes for activities of daily living and functional outcome parameters for studies that were not confounded by organizational setting. ::: ::: ::: CONCLUSIONS ::: A small but statistically significant intensity-effect relationship in the rehabilitation of stroke patients was found. Insufficient contrast in the amount of rehabilitation between experimental and control conditions, organizational setting of rehabilitation management, lack of blinding procedures, and heterogeneity of patient characteristics were major confounding factors.


title: Memory systems in the brain and localization of a memory

abstract: It is now clear that there are a number of different forms or aspects of learning and memory that involve different brain systems. Broadly, memory phenomena have been categorized as explicit or implicit. Thus, explicit memories for experience involve the hippocampus–medial temporal lobe system and implicit basic associative learning and memory involves the cerebellum, amygdala, and other systems. Under normal conditions, however, many of these brain–memory systems are engaged to some degree in learning situations. But each of these brain systems is learning something different about the situation. The cerebellum is necessary for classical conditioning of discrete behavioral responses (eyeblink, limb flexion) under all conditions; however, in the “trace” procedure where a period of no stimuli intervenes between the conditioned stimulus and the unconditioned stimulus the hippocampus plays a critical role. Trace conditioning appears to provide a simple model of explicit memory where analysis of brain substrates is feasible. Analysis of the role of the cerebellum in basic delay conditioning (stimuli overlap) indicates that the memories are formed and stored in the cerebellum. The phenomenon of cerebellar long-term depression is considered as a putative mechanism of memory storage.


Automatic Rain Drop Detection for Improved Sensing in Automotive Computer Vision Applications


title: A unified Bayesian framework for MEG/EEG source imaging

abstract: The ill-posed nature of the MEG (or related EEG) source localization problem requires the incorporation of prior assumptions when choosing an appropriate solution out of an infinite set of candidates. Bayesian approaches are useful in this capacity because they allow these assumptions to be explicitly quantified using postulated prior distributions. However, the means by which these priors are chosen, as well as the estimation and inference procedures that are subsequently adopted to affect localization, have led to a daunting array of algorithms with seemingly very different properties and assumptions. From the vantage point of a simple Gaussian scale mixture model with flexible covariance components, this paper analyzes and extends several broad categories of Bayesian inference directly applicable to source localization including empirical Bayesian approaches, standard MAP estimation, and multiple variational Bayesian (VB) approximations. Theoretical properties related to convergence, global and local minima, and localization bias are analyzed and fast algorithms are derived that improve upon existing methods. This perspective leads to explicit connections between many established algorithms and suggests natural extensions for handling unknown dipole orientations, extended source configurations, correlated sources, temporal smoothness, and computational expediency. Specific imaging methods elucidated under this paradigm include the weighted minimum l(2)-norm, FOCUSS, minimum current estimation, VESTAL, sLORETA, restricted maximum likelihood, covariance component estimation, beamforming, variational Bayes, the Laplace approximation, and automatic relevance determination, as well as many others. Perhaps surprisingly, all of these methods can be formulated as particular cases of covariance component estimation using different concave regularization terms and optimization rules, making general theoretical analyses and algorithmic extensions/improvements particularly relevant.


Tapering Off Psychotropic Drugs : Using Patient Cases to Understand Reasons for Success and Failure


title: Object Classification and Recognition From Mobile Laser Scanning Point Clouds in a Road Environment

abstract: Automatic methods are needed to efficiently process the large point clouds collected using a mobile laser scanning (MLS) system for surveying applications. Machine-learning-based object recognition from MLS point clouds in a road and street environment was studied in order to create maps from the road environment infrastructure. The developed automatic processing workflow included the following phases: the removal of the ground and buildings, segmentation, segment classification, and object location estimation. Several novel geometry-based features, which were previously applied in autonomous driving and general point cloud processing, were applied for the segment classification of MLS point clouds. The features were divided into three sets, i.e., local descriptor histograms (LDHs), spin images, and general shape and point distribution features, respectively. These were used in the classification of the following roadside objects: trees, lamp posts, traffic signs, cars, pedestrians, and hoardings. The accuracy of the object recognition workflow was evaluated using a data set that contained more than 400 objects. LDHs and spin images were applied for the first time for machine-learning-based object classification in MLS point clouds in the surveying applications of the road and street environment. The use of these features improved the classification accuracy by 9.6% (resulting in 87.9% accuracy) compared with the accuracy obtained using 17 general shape and point distribution features that represent the current state of the art in the field of MLS; therefore, significant improvement in the classification accuracy was achieved. Connected component segmentation and ground extraction were the cause of most of the errors and should be thus improved in the future.


title: A framework for recognizing the simultaneous aspects of American Sign Language

abstract: The major challenge that faces American Sign Language (ASL) recognition now is developing methods that will scale well with increasing vocabulary size. Unlike in spoken languages, phonemes can occur simultaneously in ASL. The number of possible combinations of phonemes is approximately 1.5×109, which cannot be tackled by conventional hidden Markov model-based methods. Gesture recognition, which is less constrained than ASL recognition, suffers from the same problem. In this paper we present a novel framework to ASL recognition that aspires to being a solution to the scalability problems. It is based on breaking down the signs into their phonemes and modeling them with parallel hidden Markov models. These model the simultaneous aspects of ASL independently. Thus, they can be trained independently, and do not require consideration of the different combinations at training time. We show in experiments with a 22-sign-vocabulary how to apply this framework in practice. We also show that parallel hidden Markov models outperform conventional hidden Markov models.


title: CACTI-IO: CACTI with off-chip power-area-timing models

abstract: We describe CACTI-IO, an extension to CACTI [4] that includes power, area and timing models for the IO and PHY of the off-chip memory interface for various server and mobile configurations. CACTI-IO enables design space exploration of the off-chip IO along with the DRAM and cache parameters. We describe the models added and three case studies that use CACTI-IO to study the tradeoffs between memory capacity, bandwidth and power. The case studies show that CACTI-IO helps (i) provide IO power numbers that can be fed into a system simulator for accurate power calculations, (ii) optimize off-chip configurations including the bus width, number of ranks, memory data width and off-chip bus frequency, especially for novel buffer-based topologies, and (iii) enable architects to quickly explore new interconnect technologies, including 3-D interconnect. We find that buffers on board and 3-D technologies offer an attractive design space involving power, bandwidth and capacity when appropriate interconnect parameters are deployed.


title: Torque ripple reduction in DTC of induction motor driven by 3-level inverter with low switching frequency

abstract: A torque ripple reduction technique of direct torque control (DTC) for high power induction motors driven by 3-level inverters with the inverter switching frequency limited around 0.5-l kHz level is presented. It is noted that conventional DTC algorithms to reduce torque ripple are devised for applications with relatively high switching frequency above 2-3 MHz. Such conventional algorithms cannot accomplish satisfactory torque ripple reduction for 3-level inverter systems with lower switching frequency. A new DTC algorithm, especially for low switching frequency inverter system, illustrates relatively reduced torque ripple characteristics all over the operating speed region. Simulation and experimental results show effectiveness of the proposed control algorithm.


title: Planar MS-CPS bypass balun for CPS-fed textile antennas

abstract: This paper presents the design, numerical analysis and measurements of a planar bypass balun that provides 1:4 impedance transformations between the unbalanced microstrip (MS) and balanced coplanar strip line (CPS). This type of balun is suitable for operation with small antennas fed with balanced a (parallel wire) transmission line, i.e. wire, planar dipoles and loop antennas. The balun has been applied to textile CPS-fed loop antennas, designed for operations below 1GHz. The performance of a loop antenna with the balun is described, as well as an idea of incorporating rigid circuits with flexible textile structures.


title: Employing a Qualitative Description Approach in Health Care Research

abstract: A qualitative description design is particularly relevant where information is required directly from those experiencing the phenomenon under investigation and where time and resources are limited....


title: A CAD model of triangular Split Ring Resonator based on equivalent circuit approach

abstract: In the recent decade, metamaterials have become one of the popular areas of interest. Some of the various structures of Split-Ring Resonators (SRR) we have come across are circular, square, spiral and omega shaped. All these structures produce effective permeability and permittivity as negative over a certain range of frequencies. In this paper, a new structure of triangular split ring resonator with edge-coupling is proposed. The structure consists of two equilateral concentric triangles made of metal with dielectric medium filled in the gaps. The advantage of this structure includes the reduction in size of the SRR and the wide range of resonant frequencies possible. The layout or the structure with the equivalent circuit and the Computer Aided Design model (CAD) design are presented. The variation of the resonant frequencies with respect to the conductor and dielectric width is also shown.


title: Devising the Prosthetic Prescription and Typical Examples

abstract: Limb deficiency has a significant impact on the involved person, with upper limb absence presenting a materially different set of issues than lower limb absence. The primary objectives in fitting a patient with a prosthesis are to enhance their independence in performing daily activities, and to improve their quality of life. For lower limb absence, the primary issues are safety, stability, and the ability to ambulate in a manner consistent with their overall health. The primary objectives in prescribing upper limb prosthetics are function, durability, appearance, comfort, and usability of the device. This article discusses the nuances associated with managing these patients.


title: Lateral Connections in Denoising Autoencoders Support Supervised Learning

abstract: We show how a deep denoising autoencoder with lateral connections can be used as an auxiliary unsupervised learning task to support supervised learning. The proposed model is trained to minimize simultaneously the sum of supervised and unsupervised cost functions by back-propagation, avoiding the need for layer-wise pretraining. It improves the state of the art significantly in the permutation-invariant MNIST classification task.


title: Analyzing Software Quality Evolution using Metrics: An Empirical Study on Open Source Software

abstract: The study presented in this paper aims at analyzing empirically the quality of evolving software systems using metrics. We used a synthetic metric (Quality Assurance Indicator - Qi), which captures in an integrated way different object-oriented software attributes. We wanted to investigate if the Qi metric can be used to observe how quality evolves along the evolution of software systems. We consider software quality from an internal (structural) perspective. We used various object-oriented design metrics for measuring the structural quality of a release. We performed an empirical analysis using historical data collected from successive released versions of three open source (Java) software systems. The collected data cover, for each system, a period of several years (4 years for two systems and 7 years for the third one). We focused on three issues: (1) the evolution of the Qi metric along the evolution of the subject systems, (2) the class growth of the subject systems, and (3) the quality of added classes versus the quality of removed ones. Empirical results provide evidence that the Qi metric reflects properly the quality evolution of the studied software systems.


title: Single-Stage Single-Switch PFC Flyback Converter Using a Synchronous Rectifier

abstract: A single-stage single-switch power factor correction (PFC) flyback converter with a synchronous rectifier (SR) is proposed for improving power factor and efficiency. Using a variable switching-frequency controller, this converter is continuously operated with a reduced turn-on switching loss at the boundary of the continuous conduction mode and discontinuous conduction mode (DCM). The proposed PFC circuit provides relatively low dc-link voltage in the universal line voltage, and also complies with Standard IEC 61000-3-2 Class D limits. In addition, a new driving circuit as the voltage driven-synchronous rectifier is proposed to achieve high efficiency. In particular, since a driving signal is generated according to the voltage polarity, the SR driving circuit can easily be used in DCM applications. The proposed PFC circuit and SR driving circuit in the flyback converter with the reduced switching loss are analyzed in detail and optimized for high performance. Experimental results for a 19 V/90 W adapter at the variable switching-frequency of 30~70 kHz were obtained to show the performance of the proposed converter.


title: Debugging the Internet of Things: The Case of Wireless Sensor Networks

abstract: The Internet of Things (IoT) has the strong potential to support a human society interacting more symbiotically with its physical environment. Indeed, the emergence of tiny devices that sense environmental cues and trigger actuators after consulting logic and human preferences promises a more environmentally aware and less wasteful society. However, the IoT inherently challenges software development processes, particularly techniques for ensuring software reliability. Researchers have developed debugging tools for wireless sensor networks (WSNs), which can be viewed as the enablers of perception in the IoT. These tools gather run-time information on individual sensor node executions and node interactions and then compress that information.


Pattern Matching in Trees


Understanding Mental Disorders: Your Guide to DSM-5


title: Failure Prediction with Adaptive Multi-scale Sampling and Activation Pattern Regularization

abstract: We treat failure prediction in a supervised learning framework using a convolutional neural network (CNN). Due to the nature of the problem, learning a CNN model on this kind of dataset is generally associated with three primary problems: 1) negative samples (indicating a healthy system) outnumber positives (indicating system failures) by a great margin; 2) implementation design often requires chopping an original time series into sub-sequences, defining a segmentation window size with sufficient data augmentation and avoiding serious multiple-instance learning issue is non-trivial; 3) positive samples may have a common underlying cause and thus present similar features, negative samples can have various latent characteristics which can "distract" CNN in the learning process. While the first problem has been extensively discussed in literatures, the last two issues are less explored in the context of deep learning using CNN. We mitigate the second problem by introducing a random variable on sample scaling parameters, whose distribution's parameters are jointly learnt with CNN and leads to what we call adaptive multi-scale sampling (AMS). To address the third problem, we propose activation pattern regularization (APR) on only positive samples such that the CNN focuses on learning representations pertaining to the underlying common cause. We demonstrate the effectiveness of our proposals on a past Kaggle contest dataset that predicts seizures from EEG data. Compared to the baseline method with a CNN trained in traditional scheme, we observe significant performance improvement for both proposed methods. When combined, our model without any sophisticated hyper-parameter tuning or ensemble methods shows a near 10% relative improvement on AUROC and is able to send us to the 14th place on the contest's leaderboard while the highest rank the baseline can reach is 77th.


title: RGBD Camera Based Material Recognition via Surface Roughness Estimation

abstract: Real world objects can be characterized effectively by their shape, color and material types. Material recognition of an arbitrary object at a distance is an important task for the improvement of object recognition, scene understanding, realistic rendering and various virtual and augmented reality applications. Researchers have tried to recognize material types based on color features, however material type of an object is not completely correlated with its visual appearance. In this paper, we propose a simple but effective surface roughness estimation method using single time-offlight (ToF) camera. A set of features extracted from the estimated roughness together with conventional color features are used for material type recognition. Experimental results on our material data set with 122 subjects show promising material type recognition results.


title: Contextual gaps: privacy issues on Facebook

abstract: Social networking sites like Facebook are rapidly gaining in popularity. At the same time, they seem to present significant privacy issues for their users. We analyze two of Facebooks's more recent features, Applications and News Feed, from the perspective enabled by Helen Nissenbaum's treatment of privacy as "contextual integrity." Offline, privacy is mediated by highly granular social contexts. Online contexts, including social networking sites, lack much of this granularity. These contextual gaps are at the root of many of the sites' privacy issues. Applications, which nearly invisibly shares not just a users', but a user's friends' information with third parties, clearly violates standard norms of information flow. News Feed is a more complex case, because it involves not just questions of privacy, but also of program interface and of the meaning of "friendship" online. In both cases, many of the privacy issues on Facebook are primarily design issues, which could be ameliorated by an interface that made the flows of information more transparent to users.


title: How to put probabilities on homographies

abstract: We present a family of "normal" distributions over a matrix group together with a simple method for estimating its parameters. In particular, the mean of a set of elements can be calculated. The approach is applied to planar projective homographies, showing that using priors defined in this way improves object recognition.


title: The dark side of creativity: Original thinkers can be more dishonest

abstract: Creativity is a common aspiration for individuals, organizations, and societies. Here, however, we test whether creativity increases dishonesty. We propose that a creative personality and creativity primes promote individuals' motivation to think outside the box and that this increased motivation leads to unethical behavior. In four studies, we show that participants with creative personalities who scored high on a test measuring divergent thinking tended to cheat more (Study 1); that dispositional creativity is a better predictor of unethical behavior than intelligence (Study 2); and that participants who were primed to think creatively were more likely to behave dishonestly because of their creativity motivation (Study 3) and greater ability to justify their dishonest behavior (Study 4). Finally, a field study constructively replicates these effects and demonstrates that individuals who work in more creative positions are also more morally flexible (Study 5). The results provide evidence for an association between creativity and dishonesty, thus highlighting a dark side of creativity.


title: (p, λ)-Koszul algebras and modules

abstract: In this paper, the notions of (p, λ)-Koszul algebra and (p, λ)-Koszul module are introduced. Some criteria theorems for a positively graded algebra A to be (p, λ)-Koszul are given. The notion of weakly (p, λ)-Koszul module is defined as well and let WK λ p (A) denote the category of weakly (p, λ)-Koszul modules. We show that M ∈ WK λ p (A) if and only if it can be approximated by (p, λ)-Koszul submodules, which is equivalent to that G(M) is a (p, λ)-Koszul module, where G(M) denotes the associated graded module of M. As applications, the relationships of the minimal graded projective resolutions of M, G(M) and (p, λ)-Koszul submodules are established. In particular, for a module M ∈ WK λ p (A) we prove that ⊕i≥0 Ext A i (M,A 0) ∈ gr 0(E(A)), we also get as a consequence that the finitistic dimension conjecture is valid in WK λ p (A) under certain conditions.


Using Bitcoin Pricing Data to Create a Profitable Algorithmic Trading Strategy


title: A Compact Antipodal Vivaldi Antenna for UWB Applications

abstract: A compact antipodal Vivaldi antenna (AVA) with the dimension of $42 \times 36 \times 1.6~\hbox{mm}{^3}$ is proposed in this letter. Structural modifications in the radiating fins have increased the electrical length thereby reducing the lower operating frequency from 5.2 GHz to 3.7 GHz. Proposed antenna maintains $-20~\hbox{dB}$ copolarization to cross-polarization ratio throughout the operating bandwidth. The prototype is fabricated and the measurement results are presented to validate the performance of the proposed AVA.


title: Data quality evaluation and improvement for prognostic modeling using visual assessment based data partitioning method

abstract: When developing Prognostic and Health Management (PHM) applications for manufacturing systems, data acquired frequently comes with issues which hinder further data analysis. However, there is neither a clear definition of the data quality nor evaluation methods to quantify if acquired data is suitable for these prognostic modeling tasks such as failures detection, diagnosis and prediction. Especially, during health diagnosis modeling of engineering systems, based on data-driven method, acquired data is expected to contain clusters that can be used to differentiate multiple system health conditions. So in most cases, once data is acquired, people would like to intuitively believe that data is able to cluster into subgroups. However, this bias could lead to acceptance of false information in data. Furthermore, most of the existing metrics, such as clustering tendency in statistics and cluster-ability in data mining, only individually evaluate data characteristics without considering prognostic modeling. This paper proposes a new method to evaluate and improve data quality for system health diagnosis modeling. The clusters, as critical data characteristics for modeling multiple system conditions, are first estimated by ''visualization'' on the dissimilarity spectrum from spectral analysis and then evaluated in terms of their fitness and separation with each others. A visual assessment based outlier detection method is also proposed to recognize outliers from the data, which utilizes the graphic intermediate results from previous evaluation. Finally one group of bearing testing dataset acquired from real industrial applications is used to demonstrate how proposed methods are used to evaluate and improve the data quality.


title: Exploration and Exploitation in Organizational Learning

abstract: This paper considers the relation between the exploration of new possibilities and the exploitation of old certainties in organizational learning. It examines some complications in allocating resources between the two, particularly those introduced by the distribution of costs and benefits across time and space, and the effects of ecological interaction. Two general situations involving the development and use of knowledge in organizations are modeled. The first is the case of mutual learning between members of an organization and an organizational code. The second is the case of learning and competitive advantage in competition for primacy. The paper develops an argument that adaptive processes, by refining exploitation more rapidly than exploration, are likely to become effective in the short run but self-destructive in the long run. The possibility that certain common organizational practices ameliorate that tendency is assessed.


title: System for the Measurement of Cathodic Currents in Electrorefining Processes That Employ Multicircuital Technology

abstract: This paper presents a measurement system of cathodic currents for copper electrorefining processes using multicircuital technology with optibar intercell bars. The proposed system is based on current estimation using 55 magnetic field sensors per intercell bar. Current values are sampled and stored every 5 min for seven days in a compatible SQL database. The method does not affect the normal operation of the process and does not require any structural modifications. The system for online measurement of 40 cells involving 2090 sensors is in operation in an electrorefinery site.


title: Differential Fault Analysis on PRESENT Key Schedule

abstract: PRESENT is a lightweight block cipher designed by A. Bogdanov et al. in 2007 for extremely constrained environments such as RFID tags and sensor networks, where the AES is not suitable for. In this paper, the strength of PRESENT against the differential fault attack on the key schedule is explored. Our attack adopts the nibble oriented model of random faults and assumes that the attacker can induce a single nibble fault on the round key. The attack can efficiently recover the secret key with the computational complexity of $2^{29}$, and sixty-four pairs of correct and faulty cipher texts on average.


title: A gene-coexpression network for global discovery of conserved genetic modules

abstract: To elucidate gene function on a global scale, we identified pairs of genes that are coexpressed over 3182 DNA microarrays from humans, flies, worms, and yeast. We found 22,163 such coexpression relationships, each of which has been conserved across evolution. This conservation implies that the coexpression of these gene pairs confers a selective advantage and therefore that these genes are functionally related. Many of these relationships provide strong evidence for the involvement of new genes in core biological functions such as the cell cycle, secretion, and protein expression. We experimentally confirmed the predictions implied by some of these links and identified cell proliferation functions for several genes. By assembling these links into a gene-coexpression network, we found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.


title: VEA 2012: Real-time ink simulation using a grid-particle method

abstract: This paper presents an effective method to simulate the ink diffusion process in real time that yields realistic visual effects. Our algorithm updates the dynamic ink volume using a hybrid grid-particle method: the fluid velocity field is calculated with a low-resolution grid structure, whereas the highly detailed ink effects are controlled and visualized with the particles. To facilitate user interaction and extend this method, we propose a particle-guided method that allows artists to design the overall states using the coarse-resolution particles and to preview the motion quickly. To treat coupling with solids and other fluids, we update the grid-particle representation with no-penetration boundary conditions and implicit interaction conditions. To treat moving ''ink-emitting'' objects, we introduce an extra drag-force model to enhance the particle motion effects; this force might not be physically accurate, but it proves effective for producing animations. We also propose an improved ink rendering method that uses particle sprites and motion blurring techniques. The simulation and the rendering processes are efficiently implemented on graphics hardware at interactive frame rates. Compared to traditional fluid simulation methods that treat water and ink as two mixable fluids, our method is simple but effective: it captures various ink effects, such as pinned boundaries (Nelson, 2005 [1]) and filament patterns (Shiny et al., 2010 [2]), while still running in real time, it allows easy control of the animation, it includes basic solid-fluid interactions, and it can address multiple ink sources without complex interface tracking. Our method is attractive for animation production and art design.


17 Data Mining in Web Applications


title: Automatic Steganographic Distortion Learning Using a Generative Adversarial Network

abstract: Generative adversarial network has shown to effectively generate artificial samples indiscernible from their real counterparts with a united framework of two subnetworks competing against each other. In this letter, we first propose an automatic steganographic distortion learning framework using a generative adversarial network, which is composed of a steganographic generative subnetwork and a steganalytic discriminative subnetwork. Via alternately training these two oppositional subnetworks, our proposed framework can automatically learn embedding change probabilities for every pixel in a given spatial cover image. The learnt embedding change probabilities can then be converted to embedding distortions, which can be adopted in the existing framework of minimal-distortion embedding. Under this framework, the distortion function is directly related to the undetectability against the oppositional evolving steganalyzer. Experimental results show that with adversarial learning, our proposed framework can effectively evolve from nearly naive random $\pm 1$ embedding at the beginning to much more advanced content-adaptive embedding which tries to embed secret bits in textural regions. The security performance is also steadily improved with increasing training iterations.


title: Adaptive division of labor particle swarm optimization

abstract: A new population division-based particle swarm optimization variant is proposed.Both swarm diversity and fitness are used to adaptively assign the search task of each particles.Two operators are applied on the best solution to further improve the algorithm's convergence speed.A stagnation prevention module is also proposed to mitigate the premature convergence issue.The proposed algorithm outperforms its peers in term of searching accuracy and convergence speed. Although evident progress and considerable achievements have been attained in developing a new particle swarm optimization (PSO) algorithm, successfully balancing the exploration and exploitation capabilities of PSO to determine high-quality solutions for complex optimization problems remains a fundamental challenge. In this study, we propose a new PSO variant, namely, adaptive division of labor (ADOL) PSO (ADOLPSO), to overcome the demerits of our previous work. Specifically, an ADOL module is developed in ADOLPSO to adaptively regulate the exploration and exploitation searches of swarm. To achieve this purpose, both criteria of swarm diversity and fitness are considered during the task allocation process of the ADOLPSO current swarm. Two new operators, namely, convex operator and reflectance operator, are adopted to generate new particles from the memory swarm of ADOLPSO to further enhance the searching accuracy and convergence speed of the proposed algorithm. These two operators are activated to evolve the memory swarm only if a fitness improvement is observed in the current swarm of ADOLPSO to prevent excessive computational complexity. The proposed ADOLPSO is applied to solve 18 benchmark functions with various characteristics. Simulation results of ADOLPSO are compared with those of other nine well-established PSO variants. Experimental findings reveal that ADOLPSO significantly outperforms the other PSO variants in terms of searching accuracy, reliability, and convergence speed.


DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis


title: Okazaki: New Perspectives on M-Commerce Research NEW PERSPECTIVES ON M-COMMERCE RESEARCH

abstract: ABSTRACT This paper summarizes the progress and the future directions of m-commerce research. Despite the rapid proliferation of Internet-enabled mobile handsets, empirical research has been undertaken only in a limited number of research areas. Important progress has been made in mobile Internet and SMS-based mobile advertising adoption, but other important topics, such as e- commerce and m-commerce comparison, mobile-based word-of-mouth, and methodological issues, have rarely been addressed. Future studies should explore these areas. In closing, the seven contributions to this Special Issue are introduced. Keywords: IT management; M-commerce; Marketing; Wireless communication 1. Defining M-Commerce The Internet-enabled mobile handset has rapidly achieved worldwide penetration, due to its very personal nature and sophisticated communication technologies. However, unlike e-commerce research, empirical explorations of mcommerce have seen only modest growth, because of the considerable uncertainties involved in mobile research. One major problem in m-commerce research is the lack of standards in terms, concepts, and theories. Although the infrastructure of wireless technology varies across markets, researchers tend to use the term "m-commerce" without considering the specific conditions and prerequisites of what they are examining. In addition, industry participation in academic research is at best occasional, and, in many cases, rapidly developing mobile technology may not be accepted as much as practitioners expect: either because of the higher cost per service ratio, or simply because these services can easily be replaced by the wired Internet. Current mobile Internet applications enable consumers to access a variety of services: Web information search, SMS (short message services), MMS (multimedia message service), banking, payment, gaming, emailing, chat, weather forecast, GPS (global positioning service), and so forth. Collectively, we denominate this wide array of services as "m-commerce." However, a clear distinction between m-commerce and PC-based electronic commerce has rarely been made. In a narrow sense, m-commerce can be defined as "any transaction with a monetary value that is conducted via a mobile telecommunications network" [Durlacher 1999]. Sadeh [2002] characterizes m-commerce more broadly, as "the emerging set of applications and services people can access from their Internet-enabled mobile devices." Typically, m-commerce takes place in a strategic platform called a "mobile portal." There, third-generation (3G) mobile communication systems offer a high degree of commonality of worldwide roaming capability, supporting a wide range of Internet and multimedia applications and services with higher data rates. From the perspective of marketing, Nysveen, Pedersen, and Thorbjornsen [2005] propose a grid of mobile Internet services classification that employs four primary axes: person-interactive versus machine-interactive, and goal-oriented versus experiential services. Figure 1 is an adapted version of this classification scheme. "Person interactivity" occurs between people through a medium, while "machine interactivity" refers to the interaction between people and the medium. In the latter, users can freely modify the content and form of a mediated environment. A goal-oriented process is defined by utilitarian benefits, while an experiential process provides hedonic benefits. This classification grid offers a key to understanding the future research directions of m-commerce. That is, future research should clearly define process characteristics, and the type of interactivity, in order to provide the audience with clear implications regarding a specific mobile service. In particular, very little research has addressed the question of interactivity in mobile devices, although it has been argued that mobile technology is an alternative way to connect to the Internet (i. …


title: Silicon Photonics

abstract: After dominating the electronics industry for decades, silicon is on the verge of becoming the material of choice for the photonics industry: the traditional stronghold of III-V semiconductors. Stimulated by a series of recent breakthroughs and propelled by increasing investments by governments and the private sector, silicon photonics is now the most active discipline within the field of integrated optics. This paper provides an overview of the state of the art in silicon photonics and outlines challenges that must be overcome before large-scale commercialization can occur. In particular, for realization of integration with CMOS very large scale integration (VLSI), silicon photonics must be compatible with the economics of silicon manufacturing and must operate within thermal constraints of VLSI chips. The impact of silicon photonics will reach beyond optical communication-its traditionally anticipated application. Silicon has excellent linear and nonlinear optical properties in the midwave infrared (IR) spectrum. These properties, along with silicon's excellent thermal conductivity and optical damage threshold, open up the possibility for a new class of mid-IR photonic devices


title: Robust and Reverse-Engineering Resilient PUF Authentication and Key-Exchange by Substring Matching

abstract: This paper proposes novel robust and low-overhead physical unclonable function (PUF) authentication and key exchange protocols that are resilient against reverse-engineering attacks. The protocols are executed between a party with access to a physical PUF (prover) and a trusted party who has access to the PUF compact model (verifier). The proposed protocols do not follow the classic paradigm of exposing the full PUF responses or a transformation of them. Instead, random subsets of the PUF response strings are sent to the verifier so the exact position of the subset is obfuscated for the third-party channel observers. Authentication of the responses at the verifier side is done by matching the substring to the available full response string; the index of the matching point is the actual obfuscated secret (or key) and not the response substring itself. We perform a thorough analysis of resiliency of the protocols against various adversarial acts, including machine learning and statistical attacks. The attack analysis guides us in tuning the parameters of the protocol for an efficient and secure implementation. The low overhead and practicality of the protocols are evaluated and confirmed by hardware implementation.


title: Comparative study of HEMTs for LNAs in Square Kilometre Array telescope

abstract: The paper focuses on a comparative study of transistors of different dimensions from nine prominent low noise HEMT processes around the world. The transistors include a combination of pHEMTs and mHEMTs based on GaAs and InP substrates with gate lengths ranging from 150nm to 70nm. The Square Kilometre Array telescope will require more than 30 million low noise amplifiers (LNA). Importance of power consumption and variation of ambient temperature of the amplifiers over time and also location are being recognised as two of the primary issues. Here a detailed comparative study of noise and gain indices of HEMTs of nine processes is presented with respect to power consumption. The variation of gain and minimum noise figure with respect to temperature from −50°C to 60°C is also analysed.


title: Strike (With) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects

abstract: Despite excellent performance on stationary test sets, deep neural networks (DNNs) can fail to generalize to out-of-distribution (OoD) inputs, including natural, non-adversarial ones, which are common in real-world settings. In this paper, we present a framework for discovering DNN failures that harnesses 3D renderers and 3D models. That is, we estimate the parameters of a 3D renderer that cause a target DNN to misbehave in response to the rendered image. Using our framework and a self-assembled dataset of 3D objects, we investigate the vulnerability of DNNs to OoD poses of well-known objects in ImageNet. For objects that are readily recognized by DNNs in their canonical poses, DNNs incorrectly classify 97% of their pose space. In addition, DNNs are highly sensitive to slight pose perturbations. Importantly, adversarial poses transfer across models and datasets. We find that 99.9% and 99.4% of the poses misclassified by Inception-v3 also transfer to the AlexNet and ResNet-50 image classifiers trained on the same ImageNet dataset, respectively, and 75.5% transfer to the YOLOv3 object detector trained on MS COCO.


title: REINFORCEMENT LEARNING WITH SELF-MODIFYING POLICIES

abstract: A learner''s modifiable components are called its policy. An algorithm that modifies the policy is a learning algorithm. If the learning algorithm has modifiable components represented as part of the policy, then we speak of a self-modifying policy (SMP). SMPs can modify the way they modify themselves etc. They are of interest in situations where the initial learning algorithm itself can be improved by experience - this is what we call ``learning to learn''''. How can we force some (stochastic) SMP to trigger better and better self-modifications? The success-story algorithm (SSA) addresses this question in a lifelong reinforcement learning context. During the learner''s life-time, SSA is occasionally called at times computed according to SMP itself. SSA uses backtracking to undo those SMP-generated SMP-modifications that have not been empirically observed to trigger lifelong reward accelerations (measured up until the current SSA call - this evaluates the long-term effects of SMP-modifications setting the stage for later SMP-modifications). SMP-modifications that survive SSA represent a lifelong success history. Until the next SSA call, they build the basis for additional SMP-modifications. Solely by self-modifications our SMP/SSA-based learners solve a complex task in a partially observable environment (POE) whose state space is far bigger than most reported in the POE literature.


title: Why do children with language impairment have difficulties with narrative macrostructure

abstract: Abstract Background Research has produced conflicting findings about the effects of language impairment (LI) on narrative macrostructure outcomes. Aims The present study investigated if children with LI perform weaker than typically developing (TD) controls on narrative macrostructure in different tasks, whether this changes over time and if between-group differences stem from linguistic or cognitive factors. Methods and procedures A group of monolingual Dutch children with LI (n = 84) and a TD control group (n = 45) were tested with a story comprehension and a story generation task. All children were five or six at wave 1 and six or seven at wave 2. Information was collected on vocabulary, grammar, verbal memory and sustained attention. Outcomes and results At wave 1, the LI group performed weaker than the TD group in both tasks. At wave 2, the groups performed similarly on story comprehension. On story generation, the TD group still outperformed the LI group. Sustained attention mediated the relationship between group and story generation. Conclusions and implications Effects of LI on narrative macrostructure are moderated by age and task and may stem from sustained attention weaknesses. These findings have implications for using narrative tasks in educational and diagnostic settings and may direct future interventions.


title: Embedded FIR filter design for real-time refocusing using a standard plenoptic video camera

abstract: A novel and low-cost embedded hardware architecture for real-time refocusing based on a standard plenoptic camera is presented in this study. The proposed layout design synthesizes refocusing slices directly from micro images by omitting the process for the commonly used sub-aperture extraction. Therefore, intellectual property cores, containing switch controlled Finite Impulse Response (FIR) filters, are developed and applied to the Field Programmable Gate Array (FPGA) XC6SLX45 from Xilinx. Enabling the hardware design to work economically, the FIR filters are composed of stored product as well as upsampling and interpolation techniques in order to achieve an ideal relation between image resolution, delay time, power consumption and the demand of logic gates. The video output is transmitted via High-Definition Multimedia Interface (HDMI) with a resolution of 720p at a frame rate of 60 fps conforming to the HD ready standard. Examples of the synthesized refocusing slices are presented.


title: The modified alpha power law based model of statistical fluctuation in nanometer FGMOSFET

abstract: AbstractThe modified alpha power law based model of statistical fluctuation in drain current of an unconventional Metal Oxide Semiconductor Field Effect Transistor namely Floating-Gate Metal Oxide Semiconductor Field Effect Transistor (FGMOSFET) has been proposed where the nanometer FGMOSET have been focused. Unlike the previous works, the fluctuation in drain current has been expressed in a per-unit basis which is able to show the true criticality of such fluctuation, and those previously assumed approximations on FGMOSFET’s parameters have not been adopted. The process induced device level statistical fluctuations and the related correlations have been taken into account. Nonlinearity of voltage at the floating gate and dependency on voltage at the drain terminal of the coupling factors have also been concerned. The proposed model can accurately fit the 65 nm 4th generation Berkeley Short-channel IGFET Model (BSIM4) based reference obtained from the Monte-Carlo simulation by using FGMOSFET Simulation Pr...


title: Broad-band microstrip-to-CPW transition via frequency-dependent electromagnetic coupling

abstract: An improved broad-band microstrip-to-coplanar-waveguide (CPW) transition is developed on a basis of the frequency-dependence characteristic of an electromagnetic surface-to-surface coupling. A self-calibrated method of moments is extended to model this unbounded two-port discontinuity with the two dissimilar microstrip/CPW feeding lines. Numerical results are provided to demonstrate its frequency response of transmission under varied strip/slot dimensions and further exhibit its attractive ultra-broad-band transmission with low radiation loss. Next, the back-to-back transition circuits with the two different lengths are fabricated and measured to deembed in experiment the S-parameters of two single-transition structures. Predicted and measured results show good agreement with the return loss less than -10dB over the frequency of 3.2-11.2 GHz.


title: Introducing Translation Studies: Theories and Applications

abstract: A visual tour of Introducing Translation Studies List of figures and tables Acknowledgements List of abbreviations Introduction Chapter 1 Main issues of translation studies Chapter 2 Translation theory before the twentieth century Chapter 3 Equivalence and equivalent effect Chapter 4 Studying translation product and process Chapter 5 Functional theories of translation Chapter 6 Discourse and Register analysis approaches Chapter 7 Systems theories Chapter 8 Cultural and ideological turns Chapter 9 The role of the translator: visibility, ethics and sociology Chapter 10 Philosophical approaches to translation Chapter 11 New directions from the new media Chapter 12 Research and commentary projects Notes Bibliography Index


title: Shadow Detection and Removal Based on YCbCr Color Space

abstract: Shadows in an image can reveal information about the object’s shape and orientation, and even about the light source. Thus shadow detection and removal is a very crucial and inevitable task of some computer vision algorithms for applications such as image segmentation and object detection and tracking. This paper proposes a simple framework using the luminance, chroma: blue, chroma: red (YCbCr) color space to detect and remove shadows from images. Initially, an approach based on statistics of intensity in the YCbCr color space is proposed for detecting shadows. After the shadows are identified, a shadow density model is applied. According to the shadow density model, the image is segmented into several regions that have the same density. Finally, the shadows are removed by relighting each pixel in the YCbCr color space and correcting the color of the shadowed regions in the red-green-blue (RGB) color space. The most salient feature of our proposed framework is that after removing shadows, there is no harsh transition between the shadowed parts and non-shadowed parts, and all the details in the shadowed regions remain intact. Various shadow images were used with a variety of conditions (i.e. outdoor and semi-indoor) to test the proposed framework, and results are presented to prove its effectiveness.


title: Optimizing memory performance for FPGA implementation of pagerank

abstract: Recently, FPGA implementation of graph algorithms arising in many areas such as social networks has been studied. However, the irregular memory access pattern of graph algorithms makes obtaining high performance challenging. In this paper, we present an FPGA implementation of the classic PageRank algorithm. Our goal is to optimize the overall system performance, especially the cost of accessing the off-chip DRAM. We optimize the data layout so that most of memory accesses to the DRAM are sequential. Post-place-and-route results show that our design on a state-of-the-art FPGA can achieve a high clock rate of over 200 MHz. Based on a realistic DRAM access model, we build a simulator to estimate the execution time including memory access overheads. The simulation results show that our design achieves at least 96% of the theoretically best performance of the target platform. Compared with a baseline design, our optimized design dramatically reduces the number of random memory accesses and improves the execution time by at least 70%.


title: Protecting Fragile Skin: Nursing Interventions to Decrease Development of Pressure Ulcers in Pediatric Intensive Care

abstract: BACKGROUND ::: The reported incidence of pressure ulcers in critically ill infants and children is 18% to 27%. Patients at risk for pressure ulcers and nursing interventions to prevent the development of the ulcers have not been established. ::: ::: ::: OBJECTIVES ::: To determine the incidence of pressure ulcers in critically ill children, to compare the characteristics of patients in whom pressure ulcers do and do not develop, and to identify prevention strategies associated with less frequent development of pressure ulcers. ::: ::: ::: METHODS ::: Characteristics of 5346 patients in pediatric intensive care units in whom pressure ulcers did and did not develop were compared. Multiple logistic regression was used to determine which prevention strategies were associated with less frequent development of pressure ulcers. ::: ::: ::: RESULTS ::: The overall incidence of pressure ulcers was 10.2%. Patients at greatest risk were those who were more than 2 years old; who were in the intensive care unit 4 days or longer; or who required mechanical ventilation, noninvasive ventilation, or extracorporeal membrane oxygenation. Strategies associated with less frequent development of pressure ulcers included use of specialty beds, egg crates, foam overlays, gel pads, dry-weave diapers, urinary catheters, disposable under-pads, body lotion, nutrition consultations, change in body position every 2 to 4 hours, blanket rolls, foam wedges, pillows, and draw sheets. ::: ::: ::: CONCLUSIONS ::: The overall incidence of pressure ulcers among critically ill infants and children is greater than 10%. Nursing interventions play an important role in the prevention of pressure ulcers.


title: It’s Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation

abstract: Eye gaze is an important non-verbal cue for human affect analysis. Recent gaze estimation work indicated that information from the full face region can benefit performance. Pushing this idea further, we propose an appearance-based method that, in contrast to a long-standing line of work in computer vision, only takes the full face image as input. Our method encodes the face image using a convolutional neural network with spatial weights applied on the feature maps to flexibly suppress or enhance information in different facial regions. Through extensive evaluation, we show that our full-face method significantly outperforms the state of the art for both 2D and 3D gaze estimation, achieving improvements of up to 14.3% on MPIIGaze and 27.7% on EYEDIAP for person-independent 3D gaze estimation. We further show that this improvement is consistent across different illumination conditions and gaze directions and particularly pronounced for the most challenging extreme head poses.


DBL SmartCity: An Open-Source IoT Platform for Managing Large BIM and 3D Geo-Referenced Datasets


title: Traveling path tracking algorithm in virtual coordinate system for intelligent vehicle

abstract: This paper proposes a real-time traveling path tracking algorithm for intelligent vehicle. Two encoders are installed on the left and right wheels to measure the rolling distances of the wheels. A virtual coordinate system fixed to the ground is constructed. The calculating algorithm of vehicle position and heading angle in the virtual coordinate system based on the recorded data from encoders is presented. The proposed algorithm is independent on external reference object or the global positioning system and unaffected by culverts, tall buildings, illumination, fog, rain etc. The experimental results show the efficiency of the proposed algorithm.


title: Cloudy Confidentiality: Clinical and Legal Implications of Cloud Computing in Health Care

abstract: The Internet has grown into a world of its own, and its ethereal space now offers capabilities that could aid physicians in their duties in numerous ways. In recent years software functions have moved from the individual's local hardware to a central server that operates from a remote location. This centralization is called cloud computing. Privacy laws that speak to the protection of patient confidentiality are complex and often difficult to understand in the context of an ever-growing cloud-based technology. This article is a review of the legal background of protected health records, as well as cloud technology and physician applications. An attempt is made to integrate both concepts and examine Health Insurance Portability and Accountability Act (HIPAA) compliance for each of the examples discussed. The legal regulations that may inform care and standards of practice are reviewed, and the difficulties that arise in assessment and monitoring of the current situation are analyzed. For forensic psychiatrists who may be asked to provide expert opinions regarding malpractice situations pertaining to confidentiality standards, it is important to become acquainted with the new digital language from which these questions may arise.


title: Fully convolutional networks for semantic segmentation

abstract: Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. Our key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet [20], the VGG net [31], and GoogLeNet [32]) into fully convolutional networks and transfer their learned representations by fine-tuning [3] to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes less than one fifth of a second for a typical image.


title: The Results of Partial Least Squares-Structural Equation Modelling Analyses (PLS-SEM)

abstract: The purpose of this paper is to complete prior studies’ reports related to Partial Least Square-Structural Equation Modelling (PLS-SEM, hereafter) analysis. It is also to provide more specific elements for the academic and practitioner to report the outcome analysis from PLS-SEM using six examples of the PLS-SEM type models. These are the recursive, interaction, intervening, second-order, heterogeneity and multi-group models. Each of the models will be discussed in more detail with all circumstances that ought to be reported in their analysis from other literature recommendations and suggestions. The discussion is divided into two sections using the two-step approach. The two approaches are for: (i) the PLS-SEM outer model and, (ii) the PLS-SEM inner model. For the PLS-SEM outer model, the report statement combined all the model’s types, whereas for the PLS-SEM inner model, the report discusses for each of the model’s types.


First Step Towards End-to-End Parametric TTS Synthesis: Generating Spectral Parameters with Neural Attention.


title: Keeping the Smart Home Private with Smart(er) IoT Traffic Shaping

abstract: The proliferation of smart home Internet of Things (IoT) devices presents unprecedented challenges for preserving privacy within the home. In this paper, we demonstrate that a passive network observer (e.g., an Internet service provider) can infer private in-home activities by analyzing Internet traffic from commercially available smart home devices even when the devices use end-to-end transport-layer encryption. We evaluate common approaches for defending against these types of traffic analysis attacks, including firewalls, virtual private networks, and independent link padding, and find that none sufficiently conceal user activities with reasonable data overhead. We develop a new defense, "stochastic traffic padding" (STP), that makes it difficult for a passive network adversary to reliably distinguish genuine user activities from generated traffic patterns designed to look like user interactions. Our analysis provides a theoretical bound on an adversary's ability to accurately detect genuine user activities as a function of the amount of additional cover traffic generated by the defense technique.


title: Online Model Distillation for Efficient Video Inference

abstract: High-quality computer vision models typically address the problem of understanding the general distribution of real-world images. However, most cameras observe only a very small fraction of this distribution. This offers the possibility of achieving more efficient inference by specializing compact, low-cost models to the specific distribution of frames observed by a single camera. In this paper, we employ the technique of model distillation (supervising a low-cost student model using the output of a high-cost teacher) to specialize accurate, low-cost semantic segmentation models to a target video stream. Rather than learn a specialized student model on offline data from the video stream, we train the student in an online fashion on the live video, intermittently running the teacher to provide a target for learning. Online model distillation yields semantic segmentation models that closely approximate their Mask R-CNN teacher with 7~to~17$\times$ lower inference runtime cost (11~to~26$\times$ in FLOPs), even when the target video's distribution is non-stationary. Our method requires no offline pretraining on the target video stream, achieves higher accuracy and lower cost than solutions based on flow or video object segmentation, and can exhibit better temporal stability than the original teacher. We also provide a new video dataset for evaluating the efficiency of inference over long running video streams.


title: Identification of the optic nerve head with genetic algorithms

abstract: Objective: This work proposes creating an automatic system to locate and segment the optic nerve head (ONH) in eye fundus photographic images using genetic algorithms. Methods and material: Domain knowledge is used to create a set of heuristics that guide the various steps involved in the process. Initially, using an eye fundus colour image as input, a set of hypothesis points was obtained that exhibited geometric properties and intensity levels similar to the ONH contour pixels. Next, a genetic algorithm was used to find an ellipse containing the maximum number of hypothesis points in an offset of its perimeter, considering some constraints. The ellipse thus obtained is the approximation to the ONH. The segmentation method is tested in a sample of 110 eye fundus images, belonging to 55 patients with glaucoma (23.1%) and eye hypertension (76.9%) and random selected from an eye fundus image base belonging to the Ophthalmology Service at Miguel Servet Hospital, Saragossa (Spain). Results and conclusions: The results obtained are competitive with those in the literature. The method's generalization capability is reinforced when it is applied to a different image base from the one used in our study and a discrepancy curve is obtained very similar to the one obtained in our image base. In addition, the robustness of the method proposed can be seen in the high percentage of images obtained with a discrepancy @d<5 (96% and 99% in our and a different image base, respectively). The results also confirm the hypothesis that the ONH contour can be properly approached with a non-deformable ellipse. Another important aspect of the method is that it directly provides the parameters characterising the shape of the papilla: lengths of its major and minor axes, its centre of location and its orientation with regard to the horizontal position.


title: Adapting hidden Markov models for ASL recognition by using three-dimensional computer vision methods

abstract: We present an approach to continuous American sign language (ASL) recognition, which uses as input 3D data of arm motions. We use computer vision methods for 3D object shape and motion parameter extraction and an ascension technologies 'Flock of Birds' interchangeably to obtain accurate 3D movement parameters of ASL sentences, selected from a 53-sign vocabulary and a widely varied sentence structure. These parameters are used as features for hidden Markov models (HMMs). To address coarticulation effects and improve our recognition results, we experimented with two different approaches. The first consists of training context-dependent HMMs and is inspired by speech recognition systems. The second consists of modeling transient movements between signs and is inspired by the characteristics of ASL phonology. Our experiments verified that the second approach yields better recognition results.


title: 3 D lidar imaging for detecting and understanding plant responses and canopy structure

abstract: Understanding and diagnosing plant responses to stress will benefit greatly from three-dimensional (3D) measurement and analysis of plant properties because plant responses are strongly related to their 3D structures. Light detection and ranging (lidar) has recently emerged as a powerful tool for direct 3D measurement of plant structure. Here the use of 3D lidar imaging to estimate plant properties such as canopy height, canopy structure, carbon stock, and species is demonstrated, and plant growth and shape responses are assessed by reviewing the development of lidar systems and their applications from the leaf level to canopy remote sensing. In addition, the recent creation of accurate 3D lidar images combined with natural colour, chlorophyll fluorescence, photochemical reflectance index, and leaf temperature images is demonstrated, thereby providing information on responses of pigments, photosynthesis, transpiration, stomatal opening, and shape to environmental stresses; these data can be integrated with 3D images of the plants using computer graphics techniques. Future lidar applications that provide more accurate dynamic estimation of various plant properties should improve our understanding of plant responses to stress and of interactions between plants and their environment. Moreover, combining 3D lidar with other passive and active imaging techniques will potentially improve the accuracy of airborne and satellite remote sensing, and make it possible to analyse 3D information on ecophysiological responses and levels of various substances in agricultural and ecological applications and in observations of the global biosphere.


title: Anomaly Detection Using Deep Learning Based Image Completion

abstract: Automated surface inspection is an important task in many manufacturing industries and often requires machine learning driven solutions. Supervised approaches, however, can be challenging, since it is often difficult to obtain large amounts of labeled training data. In this work, we instead perform one-class unsupervised learning on fault-free samples by training a deep convolutional neural network to complete images whose center regions are cut out. Since the network is trained exclusively on fault-free data, it completes the image patches with a fault-free version of the missing image region. The pixel-wise reconstruction error within the cut out region is an anomaly image which can be used for anomaly detection. Results on surface images of decorated plastic parts demonstrate that this approach is suitable for detection of visible anomalies and moreover surpasses all other tested methods.


A Comparison of Bottom-Up Approaches to Grounding for Templated Markov Random Fields


title: Wealth and happiness across the world: Material prosperity predicts life evaluation, whereas psychosocial prosperity predicts positive feeling.

abstract: The Gallup World Poll, the first representative sample of planet Earth, was used to explore the reasons why happiness is associated with higher income, including the meeting of basic needs, fulfillment of psychological needs, increasing satisfaction with one's standard of living, and public goods. Across the globe, the association of log income with subjective well-being was linear but convex with raw income, indicating the declining marginal effects of income on subjective well-being. Income was a moderately strong predictor of life evaluation but a much weaker predictor of positive and negative feelings. Possessing luxury conveniences and satisfaction with standard of living were also strong predictors of life evaluation. Although the meeting of basic and psychological needs mediated the effects of income on life evaluation to some degree, the strongest mediation was provided by standard of living and ownership of conveniences. In contrast, feelings were most associated with the fulfillment of psychological needs: learning, autonomy, using one's skills, respect, and the ability to count on others in an emergency. Thus, two separate types of prosperity—economic and social psychological—best predict different types of well-being.


title: Attribution of Dispositional States by 12-Month-Olds

abstract: The ability to interpret the behavior of other individuals is essential for effective social functioning. Many investigators now be- lieve that even young infants can recognize that agents act toward goals. Here we report three experiments suggesting that 12-month-old infants not only can recognize goal-related action, but also can inter- pret future actions of an actor on the basis of previously witnessed be- havior in another context. The possibility that this inference is made through the attribution of mental states is discussed.


title: Model-Based Clustering for RNA-seq Data ∗

abstract: Motivation: RNA-seq technology has been widely adopted as an attractive alternative to microarray-based methods to study global gene expression. However, robust statistical tools to analyze these complex datasets are still lacking. By grouping genes with similar expression profiles across treatments, cluster analysis provides insight into gene functions and networks, and hence is an important technique for RNAseq data analysis. Results: In this manuscript, we derive clustering algorithms based on appropriate probability models for RNA-seq data. An expectationmaximization algorithm and another two stochastic versions of expectation-maximization algorithms are described. In addition, a strategy for initialization based on likelihood is proposed to improve the clustering algorithms. Moreover, we present a model-based hybrid-hierarchical clustering method to generate a tree structure that allows visualization of relationships among clusters as well as flexibility of choosing the number of clusters. Results from both simulation studies and analysis of a maize RNA-seq dataset show that our proposed methods provide better clustering results than alternative methods such as the K-means algorithm and hierarchical clustering methods that are not based on probability models. Availability and implementation: An R package, MBCluster.Seq, has been developed to implement our proposed algorithms. This R package provides fast computation and is publicly available at http://www.


title: Privacy-Preserving Big Data Stream Mining: Opportunities, Challenges, Directions

abstract: This paper explores recent achievements and novel challenges of the annoying privacy-preserving big data stream mining problem, which consists in applying mining algorithms to big data streams while ensuring the privacy of data. Recently, the emerging big data analytics context has conferred a new light to this exciting research area. This paper follows the so-depicted research trend.


title: Circular Polarization of Periodic Leaky-Wave Antennas With Axial Asymmetry: Theoretical Proof and Experimental Demonstration

abstract: This paper includes two contributions. First, it proves that the series and shunt radiation components, corresponding to longitudinal and transversal electric fields, respectively, are always in phase quadrature in axially asymmetric periodic leaky-wave antennas (LWAs), so that these antennas are inherently elliptically polarized. This fact is theoretically proven and experimentally illustrated by two case-study examples, a composite right/left-handed (CRLH) LWA and a series-fed patch (SFP) LWA. Second, it shows (for the case of the SFP LWA) that the axial ratio is controlled and minimized by the degree of axial asymmetry.


title: Rolling of AZ31 Magnesium Alloy Thin Strip

abstract: The simulation rolling and actual rolling of AZ31 strip with a thickness of 3mm prepared by a vertical twin roll caster (Ø250×150mm) have been made by both Gleeble-1500D thermal simulation testing machine and Ø170×300mm rolling-mill. The influence of various rolling parameters such as temperature (250°C, 300°C, 350°C and 400°C) and reduction (40%, 50% and 60%) on the microstructure of the rolled AZ31B thin strip have been investigated by means of metallographic observation and grain size measurment. The results indicate that when the temperature increases, the grain size of the rolled strip increases and the uniformity decreases. When the reduction is 50% the grain size is 10~30μm and the grains are fine and uniform. When the reduction is 40% or 60%, the grain size is 5~180μm and the grains are coarse in some areas. An optimized rolling process, 350°C×50% is suggested for actual rolling and a uniform microstructure of rolled AZ31 strip is obtained.


title: A classification of citation roles for the social sciences and related fields

abstract: Abstract The paper proposes a classification scheme for the roles of citations in empirical studies from the social sciences and related fields. The use of the classification, which has eight categories, is illustrated in sociology, education, demography, epidemiology and librarianship; its association with the citations' location within the paper is presented. The question of repeated citations of the same document is discussed. Several research questions to which this classification is relevant are proposed. The need for further critique, validation and experimentation is pointed out.


title: An Intelligent SQL Tutor on the Web

abstract: The paper presents SQLT-Web, a Web-enabled intelligent tutoring system for the SQL database language. SQLT-Web is a Web-enabled version of an earlier, standalone ITS. In this paper we describe how the components of the standalone system were reused to develop the Web-enabled system. The system observes students' actions and adapts to their knowledge and learning abilities. We describe the system's architecture in comparison to the architectures of other existing Web-enabled tutors. All tutoring functions are performed on the server side, and we explain how SQLT-Web deals with multiple students. The system has been open to outside users since March 2000. SQLT-Web has been evaluated in the context of genuine teaching activities. We present the results of three evaluation studies with the University of Canterbury students taking database courses, which show that SQLT-Web is an effective system. The students have found the system a valuable asset to their learning.


title: A privacy preserving framework for RFID based healthcare systems

abstract: Abstract RFID (Radio Frequency IDentification) is anticipated to be a core technology that will be used in many practical applications of our life in near future. It has received considerable attention within the healthcare for almost a decade now. The technology’s promise to efficiently track hospital supplies, medical equipment, medications and patients is an attractive proposition to the healthcare industry. However, the prospect of wide spread use of RFID tags in the healthcare area has also triggered discussions regarding privacy, particularly because RFID data in transit may easily be intercepted and can be send to track its user (owner). In a nutshell, this technology has not really seen its true potential in healthcare industry since privacy concerns raised by the tag bearers are not properly addressed by existing identification techniques. There are two major types of privacy preservation techniques that are required in an RFID based healthcare system—(1) a privacy preserving authentication protocol is required while sensing RFID tags for different identification and monitoring purposes, and (2) a privacy preserving access control mechanism is required to restrict unauthorized access of private information while providing healthcare services using the tag ID. In this paper, we propose a framework (PriSens-HSAC) that makes an effort to address the above mentioned two privacy issues. To the best of our knowledge, it is the first framework to provide increased privacy in RFID based healthcare systems, using RFID authentication along with access control technique.