title: Predicting the determinants of the NFC-enabled mobile credit card acceptance: A neural networks approach

abstract: Abstract The main aim of this study is to determine the factors influencing the adoption of Near Field Communication (NFC)-enabled mobile credit card, an innovation in contactless payment for the future generation. Constructs from psychological science, trust-based and behavioral control theories were incorporated into the parsimonious TAM. Using empirical data and Structural Equation Modeling-Artificial Neural Networks approach together with multi group analysis, the effects of social influence, personal innovativeness in information technology, trust, perceived financial cost, perceived usefulness and perceived ease of use were examined. The significance of indirect effects was examined using the bias-corrected percentile with two-tailed significance through bootstrapping. Gender, age, experience and usage were introduced as the moderator variables with industry being the control variable in the research model. The scarcity in studies regarding the moderating effects of these variables warranted the needs to further investigate their impacts. The mediating effect of perceived usefulness was examined using the Baron–Kenny’s technique. The findings of this study have provided invaluable theoretical, methodological and managerial implications and will contribute to the decision making process by CEOs, managers, manufacturers and policy makers from the mobile manufacturing industry, businesses and financial institutions, mobile commerce, mobile telecommunication providers, mobile marketers, private or government practitioners and etc.


title: The physical and practical problems experienced by cancer survivors: a rapid review and synthesis of the literature.

abstract: PURPOSE ::: A rapid and comprehensive review to identify what is known and not known about the physical and practical problems faced by adult cancer survivors. ::: ::: ::: METHODS ::: A systematic literature review process was used. This focused on published reviews to enable a fast but rigorous identification of both the gaps and well-researched areas within survivorship. ::: ::: ::: RESULTS ::: The search identified 5121 reviews, of which 42 were screened and 9 met the quality and inclusion criteria. The majority of papers focused on physical well being (n = 6) with the remaining papers focusing on practical well being (employment and finance). The quality of the reviews varied (ranging from weak to good). Gaps identified include sexual function, lower-limb lymphoedema, peripheral neuropathy, bladder and GI problems, hormonal sequelae, older cancer survivors, work impact of cancer and context-specific unmet supportive care needs. The review found a lack of standardised nomenclature for survivorship and methodological limitations. ::: ::: ::: CONCLUSIONS ::: Four main gaps in knowledge relating to the practical and physical problems associated with cancer survivorship have been identified. These are key symptoms, unmet supportive care needs, employment and older cancer survivors, and should be addressed by future research and systematic literature reviews. Work is also needed to address the nomenclature of survivorship and to improve the methodology of research into cancer survivors (including standardised measures, theoretical frameworks, longitudinal design, inclusion of older survivors and age-matched controls for comparison). The review highlighted the need for better research within the identified areas in order to improve the experiences of cancer survivors.


title: An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks

abstract: Solving inverse problems continues to be a challenge in a wide array of applications ranging from deblurring, image inpainting, source separation etc. Most existing techniques solve such inverse problems by either explicitly or implicitly finding the inverse of the model. The former class of techniques require explicit knowledge of the measurement process which can be unrealistic, and rely on strong analytical regularizers to constrain the solution space, which often do not generalize well. The latter approaches have had remarkable success in part due to deep learning, but require a large collection of source-observation pairs, which can be prohibitively expensive. In this paper, we propose an unsupervised technique to solve inverse problems with generative adversarial networks (GANs). Using a pre-trained GAN in the space of source signals, we show that one can reliably recover solutions to under determined problems in a `blind' fashion, i.e., without knowledge of the measurement process. We solve this by making successive estimates on the model and the solution in an iterative fashion. We show promising results in three challenging applications -- blind source separation, image deblurring, and recovering an image from its edge map, and perform better than several baselines.


title: Study of Realistic Antenna Patterns in 5G mmWave Cellular Scenarios

abstract: Large antenna arrays and millimeter- wave (mmWave) frequencies have been attracting growing attention as possible candidates to meet the high requirements of future 5G mobile networks. In view of the large path loss attenuation in these bands, beamforming techniques that create a beam in the direction of the user equipment are essential to perform the transmission. For this purpose, in this paper, we aim at characterizing realistic antenna radiation patterns, motivated by the need to properly capture mmWave propagation behaviors and understand the achievable performance in 5G cellular scenarios. In particular, we highlight how the performance changes with the radiation pattern used. Consequently, we conclude that it is crucial to use an accurate and realistic radiation model for proper performance assessment and system dimensioning.


Patient safety and the Institute of Medicine (IOM) report recommendation for an 80% BSN workforce by 2020.


title: Cognitive Decision Support System for medical diagnosis

abstract: The expert medical diagnosis of infections is a difficult task where it considers numerous quantitative and qualitative values of physical factors, symptoms, laboratory test reports. The knowledge of Doctors rendering to the physical clinical inspection and medical test are the key opinion to flourish a diagnosis and treatment of a patient. This paper presents different methods of representing the knowledge for expert medical diagnosis systems and utilising it for rational decision making to predict the problem. The focal point of the research work is the real time implementation of Cognitive medical decision making systems using Machine Learning and Fuzzy-Cognitive-Map Model. Also, compare these methods for its suitability as cognitive decision-making technologies for an expert system.


title: Quality-Aware and Fine-Grained Incentive Mechanisms for Mobile Crowdsensing

abstract: Limited research efforts have been made for Mobile CrowdSensing (MCS) to address quality of the recruited crowd, i.e., quality of services/data each individual mobile user and the whole crowd are potentially capable of providing, which is the main focus of the paper. Moreover, to improve flexibility and effectiveness, we consider fine-grained MCS, in which each sensing task is divided into multiple subtasks and a mobile user may make contributions to multiple subtasks. In this paper, we first introduce mathematical models for characterizing the quality of a recruited crowd for different sensing applications. Based on these models, we present a novel auction formulation for quality-aware and fine-grained MCS, which minimizes the expected expenditure subject to the quality requirement of each subtask. Then we discuss how to achieve the optimal expected expenditure, and present a practical incentive mechanism to solve the auction problem, which is shown to have the desirable properties of truthfulness, individual rationality and computational efficiency. We conducted trace-driven simulation using the mobility dataset of San Francisco taxies. Extensive simulation results show the proposed incentive mechanism achieves noticeable expenditure savings compared to two well-designed baseline methods, and moreover, it produces close-to-optimal solutions.


AAS 10-328 NONLINEAR SYSTEM IDENTIFICATION-A CONTINUOUS-TIME BILINEAR STATE SPACE APPROACH


title: ERICA: Interaction Mining Mobile Apps

abstract: Design plays an important role in adoption of apps. App design, however, is a complex process with multiple design activities. To enable data-driven app design applications, we present interaction mining -- capturing both static (UI layouts, visual details) and dynamic (user flows, motion details) components of an app's design. We present ERICA, a system that takes a scalable, human-computer approach to interaction mining existing Android apps without the need to modify them in any way. As users interact with apps through ERICA, it detects UI changes, seamlessly records multiple data-streams in the background, and unifies them into a user interaction trace. Using ERICA we collected interaction traces from over a thousand popular Android apps. Leveraging this trace data, we built machine learning classifiers to detect elements and layouts indicative of 23 common user flows. User flows are an important component of UX design and consists of a sequence of UI states that represent semantically meaningful tasks such as searching or composing. With these classifiers, we identified and indexed more than 3000 flow examples, and released the largest online search engine of user flows in Android apps.


Straw Row Position And Orientation Reconstruction Through Image Segmentation


title: A 1.6 mW 5.4 GHz transformer-feedback gm-boosted current-reuse LNA in 0.18/μm CMOS

abstract: A fully-integrated LNA in 0.18/xm CMOS simultaneously achieves high gain, low noise figure (NF), good third-order input intercept linearity (IIP3), and low DC bias current consumption: 19 dB, 2.4 dB, −14.2 dBm and 1.3 mA, respectively, from a 1.2 V supply. The single-ended LNA uses a common-gate common-source (CG-CS) topology and operates at 5.4 GHz for WLAN applications. Using g m — boosting, current-reuse and transformer-feedback techniques, the LNA mitigates several design issues seen in the widely used common-source common-source current-reuse (CS-CS) LNAs and improves the IIP3 of CG-CS schemes by 6 dB, without increasing power and area consumption.


title: A segmented topic model based on the two-parameter Poisson-Dirichlet process

abstract: Documents come naturally with structure: a section contains paragraphs which itself contains sentences; a blog page contains a sequence of comments and links to related blogs. Structure, of course, implies something about shared topics. In this paper we take the simplest form of structure, a document consisting of multiple segments, as the basis for a new form of topic model. To make this computationally feasible, and to allow the form of collapsed Gibbs sampling that has worked well to date with topic models, we use the marginalized posterior of a two-parameter Poisson-Dirichlet process (or Pitman-Yor process) to handle the hierarchical modelling. Experiments using either paragraphs or sentences as segments show the method significantly outperforms standard topic models on either whole document or segment, and previous segmented models, based on the held-out perplexity measure.


title: Effectiveness of Osteopathic Manipulative Therapy for Managing Symptoms of Irritable Bowel Syndrome: A Systematic Review

abstract: CONTEXT ::: Irritable bowel syndrome (IBS) is a common and often lifelong functional gastrointestinal disorder. There is a scarcity of effective management options for IBS. ::: ::: ::: OBJECTIVE ::: To assess the effectiveness of osteopathic manipulative therapy (OMTh) for managing the symptoms of IBS. ::: ::: ::: DATA SOURCES ::: Articles without language or publication-date restriction were searched in PubMed, Embase, Cochrane Library, PEDro, OSTMED.DR, and Osteopathic Research Web. Search terms included irritable bowel syndrome, IBS, functional colonic disease, colon irritable, osteopath*, osteopathic manipulation, osteopathic medicine, clinical trial, and randomized clinical trial. Experts in the field of visceral osteopathy were also contacted to identify additional studies. ::: ::: ::: STUDY SELECTION ::: The authors evaluated randomized controlled trials (RCTs) of OMTh for IBS in adults in whom IBS was diagnosed using Rome (I-III) criteria. If OMTh was not the sole intervention in the intervention group and if the same additional interventions were not applied to the control group, the study was excluded. ::: ::: ::: DATA EXTRACTION ::: Citation identification, study selection, and data extraction were independently undertaken by 2 reviewers with a data extraction form from the Cochrane Collaboration. A consensus method was used to resolve disagreements concerning the assessment of the methodologic quality of the RCTs that were reviewed. ::: ::: ::: RESULTS ::: The search identified 10 studies that examined OMTh for patients with IBS; 5 studies (204 patients) met the inclusion criteria. All studies were assessed as having low risk of bias according to the Cochrane Collaboration criteria, although there was heterogeneity in the outcome measures and control interventions. Three studies used visual analog scales for abdominal pain, whereas others used the IBS severity score and the Functional Bowel Disorder Severity Index. A variety of secondary outcomes were used. All studies reported more pronounced short-term improvements with OMTh compared with sham therapy or standard care only. These differences remained statistically significant after variable lengths of follow-up in 3 studies. ::: ::: ::: CONCLUSION ::: The present systematic review provides preliminary evidence that OMTh may be beneficial in the treatment of patients with IBS. However, caution is required in the interpretation of these findings because of the limited number of studies available and the small sample sizes.


title: Dynamic Airline Pricing and Seat Availability

abstract: Airfares are determined by both intertemporal price discrimination and dynamic adjustment to stochastic demand. I estimate a model of dynamic airline pricing accounting for both forces with new flight-level data. With model estimates, I disentangle key interactions between the arrival pattern of consumer types and remaining capacity under stochastic demand. I show that the forces are complements in airline markets and lead to significantly higher revenues, as well as increased consumer surplus, compared to a more restrictive pricing regime. Finally, I show that abstracting from stochastic demand leads to a systematic bias in estimating demand elasticities.


title: Design, simulations and optimization of a tracked mobile robot manipulator with hybrid locomotion and manipulation capabilities

abstract: This paper presents a new mobile robot design based on hybridization of the mobile platform and manipulator arm as one entity for robot locomotion as well as manipulation. The novel mechanical design is described in detail. To analyse the design, a virtual prototype tool was developed with ADAMS software for multi-body dynamic motion simulations of the complete robotic system. The simulation results were used to study the robot's mobility characteristics through animations of different possible tasks that require various locomotion and manipulation capabilities. The ability to visualize and validate various robot mobility cases and to study its functionality in the early design stages aided in optimizing the design and hence dramatically reduce physical prototype development time and cost. The design optimization process also involved proper components selection. Moreover, the simulations enabled us to define motor torque requirements and maximize end-effector payload capacity for different robot configurations.


title: Capacity of multi-user MIMO systems with MMSE and ZF precoding

abstract: Spatial multiplexing can be deployed to achieve higher throughput in multi-user multiple-input multiple-output (MU-MIMO) systems. Many linear precoding techniques require each antenna to transmit its own data. The precoding techniques such as zero forcing (ZF) and minimum mean square error (MMSE) have presented for optimal capacity in the system. In this paper, we compare the channel capacity with ZF and MMSE when the relationship between the number of transmitting antennas and the number of receiving antenna changes. We apply the two types of precoding to multiple antennas in MU-MIMO downlink transmissions. We find that their impact on capacity is different, and which one provides higher capacity is dependent on the antenna numbers. The simulation results show the impact on capacity with different combinations of transmitting and receiving antennas. Finally, the simulation results demonstrate that the proposed structure can achieve the capacity-approaching performance of the precoding in different antenna environments.


title: Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures

abstract: State-of-the-art neural networks are getting deeper and wider. While their performance increases with the increasing number of layers and neurons, it is crucial to design an efficient deep architecture in order to reduce computational and memory costs. Designing an efficient neural network, however, is labor intensive requiring many experiments, and fine-tunings. In this paper, we introduce network trimming which iteratively optimizes the network by pruning unimportant neurons based on analysis of their outputs on a large dataset. Our algorithm is inspired by an observation that the outputs of a significant portion of neurons in a large network are mostly zero, regardless of what inputs the network received. These zero activation neurons are redundant, and can be removed without affecting the overall accuracy of the network. After pruning the zero activation neurons, we retrain the network using the weights before pruning as initialization. We alternate the pruning and retraining to further reduce zero activations in a network. Our experiments on the LeNet and VGG-16 show that we can achieve high compression ratio of parameters without losing or even achieving higher accuracy than the original network.


title: Domain-Specific Modeling for Robotics: From Language Construction to Ready-made Controllers and End-user Applications

abstract: We present an approach and tools implemented for model-driven development of robotics using Domain-specific Modeling (DSM). Special attention is paid to the automatic generation of robot control logic, and facilitation of the validation and certification of software components. We describe the approach using practical examples on how to construct and use domain-specific languages for modeling (1) topological properties of a robot arm, (2) robot controllers, (3) robot actions and activities as well as (4) generating and verifying end-user applications from the models. Additionally, we describe on how to build DSM tools for visual debugging and how to include generation of documents in a model-level debugging scenario. Using fast code generators and adaptive run-time systems our tool chain supports generation, incremental update and execution of native code for various hardware platforms and operating systems "on hot". Our approach is applicable to both industrial and human-like robots and is verified by developing practical robot applications.


Adaptive Query Processing for Internet Applications


A library of parameterizable floating-point cores for FPGAs and their application to scientific computing


title: Effects of Unaugmented Periphery and Vibrotactile Feedback on Proxemics with Virtual Humans in AR

abstract: In this paper, we investigate factors and issues related to human locomotion behavior and proxemics in the presence of a real or virtual human in augmented reality (AR). First, we discuss a unique issue with current-state optical see-through head-mounted displays, namely the mismatch between a small augmented visual field and a large unaugmented periphery, and its potential impact on locomotion behavior in close proximity of virtual content. We discuss a potential simple solution based on restricting the field of view to the central region, and we present the results of a controlled human-subject study. The study results show objective benefits for this approach in producing behaviors that more closely match those that occur when seeing a real human, but also some drawbacks in overall acceptance of the restricted field of view. Second, we discuss the limited multimodal feedback provided by virtual humans in AR, present a potential improvement based on vibrotactile feedback induced via the floor to compensate for the limited augmented visual field, and report results showing that benefits of such vibrations are less visible in objective locomotion behavior than in subjective estimates of co-presence. Third, we investigate and document significant differences in the effects that real and virtual humans have on locomotion behavior in AR with respect to clearance distances, walking speed, and head motions. We discuss potential explanations for these effects related to social expectations, and analyze effects of different types of behaviors including idle standing, jumping, and walking that such real or virtual humans may exhibit in the presence of an observer.


title: A Naturalness Preserved Fast Dehazing Algorithm Using HSV Color Space

abstract: Visibility of outdoor scenes is often degraded by haze. The atmosphere particles absorb and scatter the light, causing failure in various computer vision applications. In this paper, we propose a naturalness preserved fast dehazing algorithm using hue, saturation, and value (HSV) color space. First, we process hazy images in HSV color space instead of red, green, and blue (RBG) in order to preserve hue and reduce computational complexity. Second, we use the modified morphological opening operation for estimating the transmission map. In this way, the halo effects are greatly suppressed, and it costs less time than the specially designed filters. The experimental results demonstrate that the proposed algorithm can effectively remove haze. Also, our algorithm maintains naturalness by preserving hue and suppressing halo effects. Moreover, the computational complexity has been largely reduced, thus making our algorithm appropriate for real-time applications.


title: “Do You Feel in Control?”: Towards Novel Approaches to Characterise, Manipulate and Measure the Sense of Agency in Virtual Environments

abstract: While the Sense of Agency (SoA) has so far been predominantly characterised in VR as a component of the Sense of Embodiment, other communities (e.g., in psychology or neurosciences) have investigated the SoA from a different perspective proposing complementary theories. Yet, despite the acknowledged potential benefits of catching up with these theories a gap remains. This paper first aims to contribute to fill this gap by introducing a theory according to which the SoA can be divided into two components, the feeling and the judgment of agency, and relies on three principles, namely the principles of priority, exclusivity and consistency. We argue that this theory could provide insights on the factors influencing the SoA in VR systems. Second, we propose novel approaches to manipulate the SoA in controlled VR experiments (based on these three principles) as well as to measure the SoA, and more specifically its two components based on neurophysiological markers, using ElectroEncephaloGraphy (EEG). We claim that these approaches would enable us to deepen our understanding of the SoA in VR contexts. Finally, we validate these approaches in an experiment. Our results (N=24) suggest that our approach was successful in manipulating the SoA as the modulation of each of the three principles induced significant decreases of the SoA (measured using questionnaires). In addition, we recorded participants' EEG signals during the VR experiment, and neurophysiological markers of the SoA, potentially reflecting the feeling and judgment of agency specifically, were revealed. Our results also suggest that users' profile, more precisely their Locus of Control (LoC), influences their level of immersion and SoA.


title: Community Detection and Growth Potential Prediction from Patent Citation Networks

abstract: The scoring of patents is useful for technology management analysis. Therefore, a necessity of developing citation network clustering and prediction of future citations for practical patent scoring arises. In this paper, we propose a community detection method using the Node2vec. And in order to analyze growth potential we compare three ''time series analysis methods'', the Long Short-Term Memory (LSTM), ARIMA model, and Hawkes Process. The results of our experiments, we could find common technical points from those clusters by Node2vec. Furthermore, we found that the prediction accuracy of the ARIMA model was higher than that of other models.


title: Efficient and Scalable k‑Means on GPUs

abstract: k-Means is a versatile clustering algorithm widely used in practice. To cluster large data sets, state-of-the-art implementations use GPUs to shorten the data to knowledge time. These implementations commonly assign points on a GPU and update centroids on a CPU.


title: Integration of Structural Health Monitoring and Intelligent Transportation Systems for Bridge Condition Assessment: Current Status and Future Direction

abstract: Recent catastrophic bridge failures clearly indicate the urgent need for improving interval-based bridge inspection procedures that are qualitative and subjective in nature. Structural Health Monitoring (SHM) can mitigate the deficiencies of interval-based inspection techniques and provide real-time diagnostic information regarding the bridge structural health. SHM is not flawless however; the variability in the vehicle characteristics and traffic operational conditions makes it prone to false diagnosis. Recent advancements in the integration of SHM with intelligent transportation systems (ITS) demonstrate the successful use of ITS devices (e.g., traffic cameras, traffic detectors) in the analysis of bridge responses to multimodal traffic with varying loads or during the critical events that cause excess vibration beyond the normal limit. In an ITS-informed SHM system, the ITS device collected data can be integrated with SHM to increase the reliability and accuracy of the SHM system. This integration would reduce the possibility of false diagnosis of damages detected by the SHM system (e.g., vibrations caused by heavy vehicles on a bridge could be read by a SHM sensor as a structural health problem of the bridge), which would eventually decrease the bridge maintenance costs. Similarly, in SHM-informed ITS system, SHM sensors can provide data on bridge health condition for ITS applications, where ITS uses this bridge health condition information for real-time traffic management. In this paper, literature related to both ITS-informed SHM and SHM-informed ITS is reviewed. Based on the literature review, potential challenges and future research directions associated with ITS-SHM integration are also discussed.


title: The proton hyperfine interaction in HC60, signature of a potential interstellar fullerene☆

abstract: Abstract HC 60 is probably present in interstellar space. Before radioastronomers can mount an effort to detect it, its proton hyperfine interaction must be determined in the laboratory with great accuracy. By reacting C 60 with H atoms we have observed the EPR spectrum of HC 60 in both liquid and solid phases. In solid neon at 4 K,the proton hyperfine interaction is slightly anisotropic, with a |=97.8 MHz and a ⊥ =91.0 MHz. The isotropic hyperfine interaction of 93 MHz agrees with that obtained in liquid benzene, and is 10% smaller than calculated from the muonium hyperfine interaction in MuC 60 . The origin of this discreapancy is discussed.


title: Brain tumor MRI image classification with feature selection and extraction using linear discriminant analysis

abstract: Feature extraction is a method of capturing visual content of an image. The feature extraction is the process to represent raw image in its reduced form to facilitate decision making such as pattern classification. We have tried to address the problem of classification MRI brain images by creating a robust and more accurate classifier which can act as an expert assistant to medical practitioners. The objective of this paper is to present a novel method of feature selection and extraction. This approach combines the Intensity, Texture, shape based features and classifies the tumor as white matter, Gray matter, CSF, abnormal and normal area. The experiment is performed on 140 tumor contained brain MR images from the Internet Brain Segmentation Repository. The proposed technique has been carried out over a larger database as compare to any previous work and is more robust and effective. PCA and Linear Discriminant Analysis (LDA) were applied on the training sets. The Support Vector Machine (SVM) classifier served as a comparison of nonlinear techniques Vs linear ones. PCA and LDA methods are used to reduce the number of features used. The feature selection using the proposed technique is more beneficial as it analyses the data according to grouping class variable and gives reduced feature set with high classification accuracy.


title: Canonical Tensor Decomposition for Knowledge Base Completion

abstract: The problem of Knowledge Base Completion can be framed as a 3rd-order binary tensor completion problem. In this light, the Canonical Tensor Decomposition (CP) (Hitchcock, 1927) seems like a natural solution; however, current implementations of CP on standard Knowledge Base Completion benchmarks are lagging behind their competitors. In this work, we attempt to understand the limits of CP for knowledge base completion. First, we motivate and test a novel regularizer, based on tensor nuclear p-norms. Then, we present a refor-mulation of the problem that makes it invariant to arbitrary choices in the inclusion of predicates or their reciprocals in the dataset. These two methods combined allow us to beat the current state of the art on several datasets with a CP decomposition , and obtain even better results using the more advanced ComplEx model.


title: SMarTplan: a Task Planner for Smart Factories

abstract: Smart factories are on the verge of becoming the new industrial paradigm, wherein optimization permeates all aspects of production, from concept generation to sales. To fully pursue this paradigm, flexibility in the production means as well as in their timely organization is of paramount importance. AI is planning a major role in this transition, but the scenarios encountered in practice might be challenging for current tools. Task planning is one example where AI enables more efficient and flexible operation through an online automated adaptation and rescheduling of the activities to cope with new operational constraints and demands. In this paper we present SMarTplan, a task planner specifically conceived to deal with real-world scenarios in the emerging smart factory paradigm. Including both special-purpose and general-purpose algorithms, SMarTplan is based on current automated reasoning technology and it is designed to tackle complex application domains. In particular, we show its effectiveness on a logistic scenario, by comparing its specialized version with the general purpose one, and extending the comparison to other state-of-the-art task planners.


title: Identifying Factors Affecting Customer Satisfaction in Online Shopping

abstract: Online shopping is a widespread and popular phenomenon today. Both academia and practitioners are increasingly interested in identifying the factors that affect customer satisfaction in online shopping. In this study, 339 responses were received and subsequently analyzed. We determined conclusively that customer satisfaction is positively related to customer perceived value towards the shopping experience. The results also confirm that customer perceived value is positively correlated with the information quality, trustworthiness, and service quality of the e-Commerce website. Website quality is not a concern that arises unexpectedly.


title: Sound design for emotion and intention expression of socially interactive robots

abstract: The current concept of robots has been greatly influenced by the image of robots from science fiction. Since robots were introduced into human society as partners with them, the importance of human–robot interaction has grown. In this paper, we have designed seven musical sounds, five of which express intention and two that express emotion for the English teacher robot, Silbot. To identify the sound design considerations, we analyzed the sounds of robots, R2-D2 and Wall-E, from two popular movies, Star Wars and Wall-E, respectively. From the analysis, we found that intonation, pitch, and timbre are dominant musical parameters to express intention and emotion. To check the validity of these designed sounds for intention and emotion, we performed a recognition rate experiment. The experiment showed that the five designed sounds for intentions and the two for emotions are sufficient to deliver the intended emotions.


title: Active $du/dt$—New Output-Filtering Approach for Inverter-Fed Electric Drives

abstract: Active du/dt is a new output-filtering method to mitigate motor overvoltages. The inverter pulse pattern edges are broken down into narrower pulses, which control the filter LC circuit. This results in an output voltage that does not have to exhibit the overshoot typically seen in common LC circuits in output-filtering applications. Furthermore, the shape of the output-voltage edge has properties well suited for output-filtering applications. An appropriate filter rise time is selected according to the motor-cable length to eliminate the motor overvoltage. The basis of the active du/dt method is discussed in brief. Considerations on the application of the active du/dt filtering in electric drives are presented together with simulations and experimental data to verify the potential of the method.


title: An Imbalance Modified Deep Neural Network With Dynamical Incremental Learning for Chemical Fault Diagnosis

abstract: In this paper, a data-driven fault diagnosis model dealing with chemical imbalanced data streams is investigated. Different faults occur with varied frequencies by continuous arrival in chemical plants, while this issue has been hardly addressed in developing a diagnosis model. A novel incremental imbalance modified deep neural network (incremental-IMDNN) is proposed to promote the fault diagnosis to the imbalanced data stream. The first step in designing the incremental-IMDNN is the employment of an imbalance modified method combined with active learning for the extraction and generation of the most valuable information keeping in view the model feedback. DNN is utilized as a basic diagnosis model to excavate potential information. Then for the continuous arrival of new fault modes, DNN is promoted in an incremental hierarchical way. Unlike the traditional model that trained on a static snapshot of data, this model inherits the existing knowledge and hierarchically expands the diagnosis model by the similarity of faults. Similar faults that are judged by fuzzy clustering merge into a superclass, and every submodel shares the same architecture that is prevalent in previous research, which can be trained in parallel. We validate the performance of the proposed method in a Tennessee Eastman (TE) dataset, and the simulation results indicate that the proposed incremental-IM-DNN is better than the existing methods and possesses significant robustness and adaptability in chemical fault diagnosis.


title: DOVO: Mixed Visual Odometry Based on Direct Method and Orb Feature

abstract: In this paper, in order to get real-time environment information and pose estimation of robot, a novel visual odometry method called DOVO is proposed. First, the ORB feature of image frame is computed. Then, based on the number of key point that ORB feature gets, we set a threshold K to determine the reliability of pose estimation using ORB feature. If the number of key point is smaller than the threshold K, direct method is used to keep trace of the camera and estimate the camera pose by optimizing the photometric error based on luminosity of the scene is constant. If the number of key point is larger than threshold K, then pose estimation is computed by optimizing reprojection error. We use TUM dataset to make experiments to show this method guarantees pose accuracy and real-time performance.


title: Differential Power Attack on SM4 Block Cipher

abstract: This paper presents a practicable method of differential power attack on SM4 block cipher. We build the power acquisition platform based on SASEBO-G board. Through the platform, the encryption of SM4 algorithm is implemented in hardware and the power curves are obtained by the Agilent oscilloscope at the same time. The hamming weight of 8-bit output of S-box is selected as the power model to realize the DPA attack on MATLAB. Only 2000 power traces are needed to crack the sub-key byte in the first round of standard SM4 algorithm. The cost of DPA attack has been reduced more than 60% compared with the references which need at least 5000 power traces.


Stereopsis via deep learning


title: A CCG-based Approach to Fine-Grained Sentiment Analysis

abstract: In this paper, we present a Combinatory Categorial Grammar (CCG) based approach to the classification of emotion in microtext. We develop a method that makes use of the notion put forward by Ortony, Clore, and Collins (1988), that emotions are valenced reactions. This hypothesis sits central to our system, in which we adapt contextual valence shifters to infer the emotional content of a text. We integrate this with an augmented version of WordNet-Affect, which acts as our lexicon. Finally, we experiment with a corpus of headlines proposed in the 2007 SemEval Affective Task (Strapparava and Mihalcea 2007) as our microtext corpus, and by taking the other competing systems as a baseline, demonstrate that our approach to emotion categorisation performs favourably.


Identification of pesticides residues in different samples of milk


title: MEMS force sensor with DDS-based position feedback and tunable sensitivity

abstract: This paper presents a capacitive Micro ElectroMechanical System (MEMS) force sensor coupled with a properly-designed interface circuit based on Direct Digital Synthesizer (DDS) that allows tunable sensitivity exploiting an electrical servo-assisted position-feedback mechanism. The position of the probe tip is kept fixed exploiting a feedback loop that includes a variable-gap capacitive position sensor and a pair of variable-area electrostatic actuators. Signals provided by the DDS are used to excite and read the capacitive position sensor and to perform a synchronous demodulation used for the position control and the electrostatic compensation of the external applied force Fext. By adjusting specific loop parameters, the force sensitivity and the tip working point can be finely tuned electrically. Experimental results show that the proposed system is able to measure the applied force in nanonewton range, making the system promising for measuring forces generated by living biological cells.


title: Impact of Warehouse Management System in a Supply Chain Ramaa.A

abstract: In a supply chain, warehousing function is very critical as it acts as a node in linking the material flows between the supplier and customer. In today's competitive market environment companies are continuously forced to improve their warehousing operations. Many companies have also customized their value proposition to increase their customer service levels, which has led to changes in the role of warehouses. This paper highlights the findings of the study carried out to evaluate performance levels and enhance productivity of the manual warehouses by developing a WMS framework and cost benefit analysis.


title: An Introduction to Programming for Bioscientists: A Python-based Primer

abstract: Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in the biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language's usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a 'variable', the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences.


title: Hull Form Optimization for Early Stage Ship Design

abstract: Shape optimization is a growing field of interest in many areas of academic research, marine design, and manufacturing. As part of the Computational Research and Engineering Acquisition Tools and Environments Ships Hydromechanics Product, an effort is underway to develop a computational tool set and process framework that can aid the ship designer in making informed decisions regarding the influence of the planned hull shape on its hydrodynamic characteristics, even at the earliest stages where decisions can have significant cost implications. The major goal of this effort is to utilize the increasing experience gained in using these methods to assess shape optimization techniques and how they might impact design for current and future naval ships. Additionally, this effort is aimed at establishing an optimization framework within the bounds of a collaborative design environment that will result in improved performance and better understanding of preliminary ship designs at an early stage. The initial effort demonstrated here is aimed at ship resistance, and examples are shown for full ship and localized bow dome shaping related to the Joint High Speed Sealift hull concept.


The next GME accreditation system.


title: A review of audio Class D amplifiers

abstract: Class D amplifiers (CDAs) are increasingly ubiquitous as the audio power amplifier (loudspeaker driver) in audio devices due to their significantly higher power-efficiency compared to their linear counterparts. In this paper, a comprehensive review on the design of audio Class D amplifiers is provided, including a comparison of commonly used design architectures/modulation schemes. The key design parameters, including Total Harmonic Distortion, Power Supply Rejection Ratio, Intermodulation Distortion, are discussed and the design tradeoffs between these parameters delineated.


title: Aspect-oriented programming

abstract: We have found many programming problems for which neither procedural nor object-oriented programming techniques are sufficient to clearly capture some of the important design decisions the program must implement. This forces the implementation of those design decisions to be scattered throughout the code, resulting in “tangled” code that is excessively difficult to develop and maintain. We present an analysis of why certain design decisions have been so difficult to clearly capture in actual code. We call the properties these decisions address aspects, and show that the reason they have been hard to capture is that they cross-cut the system's basic functionality. We present the basis for a new programming technique, called aspect-oriented programming, that makes it possible to clearly express programs involving such aspects, including appropriate isolation, composition and reuse of the aspect code. The discussion is rooted in systems we have built using aspect-oriented programming.


title: A new Kalman filter-based power spectral density estimation for nonstationary pressure signals

abstract: This paper presents a new Kalman filter-based power spectral density estimation (PSD) algorithm for nonstationary pressure signals. The pressure signal is assumed to be an autoregressive (AR) process, and a stochastically perturbed difference equation constraint model is used to describe the dynamics of the AR coefficients. The proposed Kalman filter frame uses variable number of measurements to estimate the time-varying AR coefficients and yield the PSD estimation with better time-frequency resolution. Simulation results show that the proposed algorithm achieves a better time-frequency resolution than conventional algorithms for nonstationary pressure signals.


title: Behavior-Based Grade Prediction for MOOCs Via Time Series Neural Networks

abstract: We present a novel method for predicting the evolution of a student's grade in massive open online courses (MOOCs). Performance prediction is particularly challenging in MOOC settings due to per-student assessment response sparsity and the need for personalized models. Our method overcomes these challenges by incorporating another, richer form of data collected from each student—lecture video-watching clickstreams—into the machine learning feature set, and using that to train a time series neural network that learns from both prior performance and clickstream data. Through evaluation on two MOOC datasets, we find that our algorithm outperforms a baseline of average past performance by more than 60% on average, and a lasso regression baseline by more than 15%. Moreover, the gains are higher when the student has answered fewer questions, underscoring their ability to provide instructors with early detection of struggling and/or advanced students. We also show that despite these gains, when taken alone, none of the behavioral features are particularly correlated with performance, emphasizing the need to consider their combined effect and nonlinear predictors. Finally, we discuss how course instructors can use these predictive learning analytics to stage student interventions.


title: Inherited epidermolysis bullosa: new diagnostic criteria and classification.

abstract: Epidermolysis bullosa (EB) is a group of inherited, mechanobullous disorders caused by mutations in various structural proteins in the skin. There have been several advances in the classification of EB since it was first introduced in the late 19th century. We now recognize four major types of EB, depending on the location of the target proteins and level of the blisters: EB simplex (epidermolytic), junctional EB (lucidolytic), dystrophic EB (dermolytic), and Kindler syndrome (mixed levels of blistering). This contribution will summarize the most recent classification and discuss the molecular basis, target genes, and proteins involved. We have also included new subtypes, such as autosomal dominant junctional EB and autosomal recessive EB due to mutations in the dystonin (DST) gene, which encodes the epithelial isoform of bullouspemphigoid antigen 1. The main laboratory diagnostic techniques-immunofluorescence mapping, transmission electron microscopy, and mutation analysis-will also be discussed. Finally, the clinical characteristics of the different major EB types and subtypes will be reviewed.


title: Induction motor parameter estimation using metaheuristic methods

abstract: The steady-state equivalent circuit parameters of an induction motor can be estimated using the operation characteristics that are provided by manufacturers. The characteristics of the motor used in estimation methods are the starting, maximum, and nominal torque values; the power factor; and eciency. The operation characteristics of a motor given in data sheets are generally based on design parameters and are not suitable with real values. For this reason, in this paper, the data used in the parameter estimation for induction motors are taken from the literature. Using an optimization method for parameter estimation is useful for comparing the manufacturer values and values at the end of estimation, as well as minimizing the error in between. There are many methods in the literature for the parameter estimation of induction motors. In this study, the estimation is made using the charged system search (CSS), dierential evolution algorithm (DEA), particle swarm optimization, and genetic algorithm optimization techniques. The CSS algorithm is rst applied for estimation of the parameters of an induction motor. The results obtained from all of the methods show that the CSS algorithm is suitable with the DEA. From the obtained results, it is understood that an exact approach can be made to equivalent circuit parameters in case the values given by the manufacturer model the motor properly.


title: Int. J. Intelligent Systems Technologies and Applications, Vol. x, No. x, xxxx 1 Fusion of Stereo Vision and Time-of-Flight Imaging for Improved 3D Estimation

abstract: This paper suggests an approach in fusing two 3D estimation techniques: stereo vision and Time-Of-Flight (TOF) imaging. By mapping the TOF-depth measurements to stereo disparities, the correspondence between the images from a fast TOF-camera and standard high resolution camera pair are found, so the TOF depth measurements can be linked to the image pairs. In the same framework, a method is developed to initialise and constrain a hierarchical stereo matching algorithm. It is shown that in this way, higher spatial resolution is obtained than by only using the TOF camera and higher quality dense stereo disparity maps are the results of this data fusion.


title: Median filtering forensics in digital images based on frequency-domain features

abstract: Tampering detection has been increasingly attracting attention in the field of digital forensics. As a popular nonlinear smoothing filter, median filtering is often used as a post-processing operation after image forgeries such as copy-paste forgery (including copy-move and image splicing), which is of particular interest to researchers. To implement the blind detection of median filtering, this paper proposes a novel approach based on a frequency-domain feature coined the annular accumulated points (AAP). Experimental results obtained on widely used databases, which consists of various real-world photos, show that the proposed method achieves outstanding performance in distinguishing median-filtered images from original images or images that have undergone other types of manipulations, especially in the scenarios of low resolution and JPEG compression with a low quality factor. Moreover, our approach remains reliable even when the feature dimension decreases to 5, which is significant to save the computing time required for classification, demonstrating its great advantage to be applied in real-time processing of big multimedia data.


title: Daily administration of phosphodiesterase type 5 inhibitors for urological and nonurological indications.

abstract: OBJECTIVES ::: Although the discovery of phosphodiesterases (PDEs) was made soon after the identification of cyclic adenosine monophosphate nearly half a century ago, their true importance in medicine has taken many decades to be realised. The recognition of the important role PDE enzymes play and the impact of altering intracellular cyclic nucleotide levels became significant for most urologists and clinicians in the early 1990s with the discovery of sildenafil, a PDE5 inhibitor (PDE5-I). Once approved around the world, on-demand use of PDE5-Is became the gold standard. Recently, the potential beneficial effects of PDE5-Is on the pulmonary, vascular, and other systems has led to examination of alternative dosing regimens. In this review, we have synthesised the available published peer-reviewed literature to provide a critical contemporary view of evolving indications for PDE5-Is and how alternative dosing regimens may impact on sexual and other functions. ::: ::: ::: METHODS ::: MEDLINE search of all peer-reviewed English literature for the period 1990-2007. ::: ::: ::: RESULTS ::: The plethora of articles detailing potential uses of PDE5-I in multiple fields of medicine was uncovered. Use of alternative dosing regimens shows great promise across a number of clinical indications, including post-radical retropubic prostatectomy, pulmonary hypertension, endothelial dysfunction, and salvage of on-demand PDE5-I nonresponders. ::: ::: ::: CONCLUSIONS ::: Use of PDE5-I on a daily basis may evolve into a major form of drug administration both for men with erectile dysfunction and for those with a myriad of other conditions shown to benefit from this approach.


title: A Dual-Polarized Dual-Band Antenna With Omni-Directional Radiation Patterns

abstract: A new dual-polarized dual-band (DPDB) omni-directional antenna is presented in this communication. The proposed antenna consists of a circular patch with eight open slots, eight shorted metal pins, and a central feed coaxial probe. By utilizing TM01 mode, eight shorted metal pins, and open slots can radiate $\theta$ - and $\varphi$ -components, respectively. Omni-directional circular polarization can be generated over the lower band. When the basic TM02 mode is excited, omni-directional linear polarization can be generated over the higher band. Omni-directional circularly polarized fields and omni-directional linearly polarized fields can be achieved at both resonant frequencies. The circular patch antenna is printed on a substrate with a radius of 48 mm ( $0.24~\lambda _{\mathrm {\mathbf {0}}}$ , where $\lambda _{\mathrm {\mathbf {0}}}$ is the wavelength in free space). The antenna has a low profile of 4 mm ( $0.02~\lambda _{\mathrm {\mathbf {0}}}$ ). The antenna is fabricated and measured; the measured results show that the impedance bandwidths for VSWR <2 are 18 MHz (1566—1584 MHz) and 32 MHz (2440—2472 MHz). The axial ratio in the xoy plane is less than 3 dB over the lower band. These measured results are congruent with the simulated data. The antenna can be a good candidate for both GPS and WLAN applications. Besides, the S-parameters, radiation patterns, and some key structures are studied.


A Self-adaptation Method for Human Skin Segmentation based on Seed Growing


title: Performance of evaluation methods in image fusion

abstract: Many algorithms and software tools have been developed for fusing panchromatic and multispectral datasets in remote sensing. Also, a number of methods has been proposed and developed for the comparative evaluation of fusion results. To this date, however, no papers have been published that analyze effectiveness and quality of the evaluation techniques. In our study, methods that evaluate fusion quality are tested for different images and test sites. This analysis shows that in most cases the tested methods perform well, but are sometimes inconsistent with visual analysis results.


title: Motion tracking and gait feature estimation for recognising Parkinson’s disease using MS Kinect

abstract: BackgroundAnalysis of gait features provides important information during the treatment of neurological disorders, including Parkinson’s disease. It is also used to observe the effects of medication and rehabilitation. The methodology presented in this paper enables the detection of selected gait attributes by Microsoft (MS) Kinect image and depth sensors to track movements in three-dimensional space.Methods The experimental part of the paper is devoted to the study of three sets of individuals: 18 patients with Parkinson’s disease, 18 healthy aged-matched individuals, and 15 students. The methodological part of the paper includes the use of digital signal-processing methods for rejecting gross data-acquisition errors, segmenting video frames, and extracting gait features. The proposed algorithm describes methods for estimating the leg length, normalised average stride length (SL), and gait velocity (GV) of the individuals in the given sets using MS Kinect data.ResultsThe main objective of this work involves the recognition of selected gait disorders in both the clinical and everyday settings. The results obtained include an evaluation of leg lengths, with a mean difference of 0.004 m in the complete set of 51 individuals studied, and of the gait features of patients with Parkinson’s disease (SL: 0.38 m, GV: 0.61 m/s) and an age-matched reference set (SL: 0.54 m, GV: 0.81 m/s). Combining both features allowed for the use of neural networks to classify and evaluate the selectivity, specificity, and accuracy. The achieved accuracy was 97.2 %, which suggests the potential use of MS Kinect image and depth sensors for these applications.ConclusionsDiscussion points include the possibility of using the MS Kinect sensors as inexpensive replacements for complex multi-camera systems and treadmill walking in gait-feature detection for the recognition of selected gait disorders.


title: Eating Disorder Examination Questionnaire (EDE-Q): norms for Black women.

abstract: The current study provides Eating Disorder Examination Questionnaire (EDE-Q) norms for Black undergraduate women (N=395). EDE-Q subscale scores, clinical cutoffs, and disordered eating behavior were compared with previously published norms for primarily White undergraduate and community samples. The current sample endorsed mean EDE-Q scores similar to those of White females recruited from the community. Regarding clinical cutoffs, Black women in the current sample were most similar to White college women. Despite similar EDE-Q scores, the current sample endorsed significantly less binge eating and compensatory behaviors than White undergraduate or community women. Outcomes suggest that increased eating- and weight-related concerns among Black undergraduate women might not be associated with disordered eating behavior. Similarly, results suggest that the validity of EDE-Q cutoffs is lower in samples of Black women compared with samples of White women. Additional research is needed to enhance understanding of eating disorder risk factors for Black college women.


title: Wireless image communication system for fire-fighting robots

abstract: Recently, it has sometimes been impossible for fire-fighting personnel to access the site of a fire, even as the fire causes tremendous property damage and loss of human life, due to high temperatures or the presence of explosive materials. In such environments, fire-fighting robots can be useful for extinguishing a fire, and they should be controlled by remote operators. In order to help a remote operator who is located far away from the fire-fighting robot, wireless image communication systems have been investigated in this study. The developed system uses a High-Speed Downlink Packet Access (HSDPA) network to transmit images, showing the view from a fire-fighting robot. The fire-fighting robots and remote controllers are equipped with industrial computers, and image-transmitting and image-receiving programs function according to various protocols to allow communication between the robots and controllers


Virtual Fixture Guidance for Robot Assisted Teleoperation


title: Exploring Dynamic Redundancy to Resuscitate Faulty PCM Blocks

abstract: DRAM technology challenges have increased the necessity to adapt to the emerging memory technologies like Phase-Change Memory (PCM or PRAM). While such emerging technologies provide benefits like storage density, nonvolatility, and low energy consumption, they are constrained by limited write endurance that becomes more pronounced with process variation. In this article, we explore a novel PRAM-based main memory system which resuscitates a group of faulty pages in a cost-effective manner to significantly extend the PCM main memory lifetime while minimizing the performance impact. In particular, we explore three different dimensions of dynamic redundancy levels and group sizes, and design low-cost hardware and software support for our proposed schemes. We aim to have minimal hardware modifications (that have less than 1p on-chip and off-chip area overheads). Also, our schemes can improve the PRAM lifetime by up to 105× (times) over a chip with no error correction capabilities, and outperform prior schemes such as DRM and ECP at a small fraction of the hardware cost. The performance overhead resulting from our scheme is less than 8p on average across 21 applications from SPEC2006, Splash-2, and PARSEC benchmark suites.


title: A Secure Microservice Framework for IoT

abstract: The Internet of Things (IoT) has connected an incredible diversity of devices in novel ways, which has enabled exciting new services and opportunities. Unfortunately, IoT systems also present several important challenges to developers. This paper proposes a vision for how we may build IoT systems in the future by reconceiving IoT's fundamental unit of construction not as a "thing", but rather as a widely and finely distributed "microservice" already familiar to web service engineering circles. Since IoT systems are quite different from more established uses of microservice architectures, success of the approach depends on adaptations that enable them to met the key challenges that IoT systems present. We argue that a microservice approach to building IoT systems can combine in a mutually enforcing way with patterns for microservices, API gateways, distribution of services, uniform service discovery, containers, and access control. The approach is illustrated using two case studies of IoT systems in personal health management and connected autonomous vehicles. Our hope is that the vision of a microservices approach will help focus research that can fill in current gaps preventing more effective, interoperable, and secure IoT services and solutions in a wide variety of contexts.


title: An Introduction to Ontologies and Ontology Engineering

abstract: In the last decades, the use of ontologies in information systems has become more and more popular in various fields, such as web technologies, database integration, multi agent systems, natural language processing, etc. Artificial intelligent researchers have initially borrowed the word “ontology” from Philosophy, then the word spread in many scientific domain and ontologies are now used in several developments.


title: 3D face authentication and recognition based on bilateral symmetry analysis

abstract: We present a novel and computationally fast method for automatic human face authentication. Taking a 3D triangular facial mesh as input, the approach first automatically extracts the bilateral symmetry plane of the facial surface. The intersection between the symmetry plane and the facial surface, namely the symmetry profile, is then computed. Using both the mean curvature plot of the facial surface and the curvature plot of the symmetry profile curve, three essential points of the nose on the symmetry profile are automatically extracted. The three essential points uniquely determine a Face Intrinsic Coordinate System (FICS). Different faces are aligned based on the FICS. The symmetry profile, together with two transverse profiles, composes a compact representation, called the SFC representation, of a 3D face surface. The face authentication and recognition steps are finally performed by comparing the SFC representations of the faces. The proposed method was tested on 382 face surfaces, which come from 166 individuals and cover a wide ethnic and age variety. The equal error rate (EER) of face authentication on scans with variable facial expressions is 10.8%. For scans with normal expression, the ERR is 0.8%.


title: An augmented reality interface for visualizing and interacting with virtual content

abstract: In this paper, a novel AR interface is proposed that provides generic solutions to the tasks involved in augmenting simultaneously different types of virtual information and processing of tracking data for natural interaction. Participants within the system can experience a real-time mixture of 3D objects, static video, images, textual information and 3D sound with the real environment. The user-friendly AR interface can achieve maximum interaction using simple but effective forms of collaboration based on the combinations of human–computer interaction techniques. To prove the feasibility of the interface, the use of indoor AR techniques are employed to construct innovative applications and demonstrate examples from heritage to learning systems. Finally, an initial evaluation of the AR interface including some initial results is presented.


title: Let's Fix OpenGL

abstract: From windowing systems to virtual reality, real-time graphics code is ubiquitous. Programming models for constructing graphics software, however, have largely escaped the attention of programming languages researchers. This essay introduces the programming model of OpenGL, a ubiquitous API for real-time graphics applications, for a language-oriented audience. It highlights six broad problems with the programming model and connects them to traditions in PL research. The issues range from classic pitfalls, where established thinking can apply, to new open problems, where novel research is needed.


title: Ternary Neural Networks with Fine-Grained Quantization

abstract: We propose a novel fine-grained quantization (FGQ) method to ternarize pre-trained full precision models, while also constraining activations to 8 and 4-bits. Using this method, we demonstrate a minimal loss in classification accuracy on state-of-the-art topologies without additional training. We provide an improved theoretical formulation that forms the basis for a higher quality solution using FGQ. Our method involves ternarizing the original weight tensor in groups of $N$ weights. Using $N=4$, we achieve Top-1 accuracy within $3.7\%$ and $4.2\%$ of the baseline full precision result for Resnet-101 and Resnet-50 respectively, while eliminating $75\%$ of all multiplications. These results enable a full 8/4-bit inference pipeline, with best-reported accuracy using ternary weights on ImageNet dataset, with a potential of $9\times$ improvement in performance. Also, for smaller networks like AlexNet, FGQ achieves state-of-the-art results. We further study the impact of group size on both performance and accuracy. With a group size of $N=64$, we eliminate $\approx99\%$ of the multiplications; however, this introduces a noticeable drop in accuracy, which necessitates fine tuning the parameters at lower precision. We address this by fine-tuning Resnet-50 with 8-bit activations and ternary weights at $N=64$, improving the Top-1 accuracy to within $4\%$ of the full precision result with $<30\%$ additional training overhead. Our final quantized model can run on a full 8-bit compute pipeline using 2-bit weights and has the potential of up to $15\times$ improvement in performance compared to baseline full-precision models.


title: A current and future state of art development of hybrid energy system using wind and PV-solar: A review

abstract: The wind and solar energy are omnipresent, freely available, and environmental friendly. The wind energy systems may not be technically viable at all sites because of low wind speeds and being more unpredictable than solar energy. The combined utilization of these renewable energy sources are therefore becoming increasingly attractive and are being widely used as alternative of oil-produced energy. Economic aspects of these renewable energy technologies are sufficiently promising to include them for rising power generation capability in developing countries. A renewable hybrid energy system consists of two or more energy sources, a power conditioning equipment, a controller and an optional energy storage system. These hybrid energy systems are becoming popular in remote area power generation applications due to advancements in renewable energy technologies and substantial rise in prices of petroleum products. Research and development efforts in solar, wind, and other renewable energy technologies are required to continue for, improving their performance, establishing techniques for accurately predicting their output and reliably integrating them with other conventional generating sources. The aim of this paper is to review the current state of the design, operation and control requirement of the stand-alone PV solar-wind hybrid energy systems with conventional backup source i.e. diesel or grid. This Paper also highlights the future developments, which have the potential to increase the economic attractiveness of such systems and their acceptance by the user.


title: Circuit simulator models for the diode and IGBT with full temperature dependent features

abstract: The problems faced in generating analytical models for the insulated gate bipolar transistor (IGBT) and power diode are devising correct equations and determining realistic boundary conditions, especially for two-dimensional (2-D) features, while ensuring convergence of the models. These issues are addressed in this paper in relation to the temperature dependent modeling of NPT IGBTs and diodes. Simulation and experimental results are presented and compared to validate the modeling approach.


title: Temporal Interpretation Discourse Relations and Common Sense Entailment

abstract: This paper presents a formal account of how to determine the discourse relations between propositions introduced in a text, and the relations between the events they describe. The distinct natural interpretations of texts with similar syntax are explained in terms of defeasible rules. These characterise the effects of causal knowledge and knowledge of language use on interpretation. Patterns of defeasible entailment that are supported by the logic in which the theory is expressed are shown to underly temporal interpretation.


title: Optical Flow Estimation Using a Spatial Pyramid Network

abstract: We learn to compute optical flow by combining a classical spatial-pyramid formulation with deep learning. This estimates large motions in a coarse-to-fine approach by warping one image of a pair at each pyramid level by the current flow estimate and computing an update to the flow. Instead of the standard minimization of an objective function at each pyramid level, we train one deep network per level to compute the flow update. Unlike the recent FlowNet approach, the networks do not need to deal with large motions, these are dealt with by the pyramid. This has several advantages. First, our Spatial Pyramid Network (SPyNet) is much simpler and 96% smaller than FlowNet in terms of model parameters. This makes it more efficient and appropriate for embedded applications. Second, since the flow at each pyramid level is small (


title: Machine learning in side-channel analysis: a first study

abstract: Electronic devices may undergo attacks going beyond traditional cryptanalysis. Side-channel analysis (SCA) is an alternative attack that exploits information leaking from physical implementations of e.g. cryptographic devices to discover cryptographic keys or other secrets. This work comprehensively investigates the application of a machine learning technique in SCA. The considered technique is a powerful kernel-based learning algorithm: the Least Squares Support Vector Machine (LS-SVM). The chosen side-channel is the power consumption and the target is a software implementation of the Advanced Encryption Standard. In this study, the LS-SVM technique is compared to Template Attacks. The results show that the choice of parameters of the machine learning technique strongly impacts the performance of the classification. In contrast, the number of power traces and time instants does not influence the results in the same proportion. This effect can be attributed to the usage of data sets with straightforward Hamming weight leakages in this first study.


title: Deep Optical Flow Supervised Learning With Prior Assumptions

abstract: Traditional methods for estimating optical flow use variational model that includes data term and smoothness term, which can build a constraint relationship between two adjacent images and optical flow. However, most of them are too slow to be used in real-time applications. Recently, convolutional neural networks have been used in optical flow area successfully. Many current learning methods use large data sets that contain ground truth for network training, which can make use of prior knowledge to estimate optical flow directly. However, these methods overemphasize the factor of deep learning and ignore advantages of many traditional assumptions used in variational framework for optical flow estimation. In this paper, inspired by classical energy-based optical flow methods, we propose a novel approach for dense motion estimation, which combines traditional prior assumptions with supervised learning network. During training, the variation in image brightness, gradient and spatial smoothness are embedded in network. Our method is tested on both synthetic and real scenes. The experimental results show that employing the prior assumptions during training can obtain more detailed and smoothed flow fields and can improve the accuracy of optical flow estimation.


title: Deep Learning with S-shaped Rectified Linear Activation Units

abstract: Rectified linear activation units are important components for state-of-the-art deep convolutional networks. In this paper, we propose a novel S-shaped rectified linear activation unit (SReLU) to learn both convex and non-convex functions, imitating the multiple function forms given by the two fundamental laws, namely the Webner-Fechner law and the Stevens law, in psychophysics and neural sciences. Specifically, SReLU consists of three piecewise linear functions, which are formulated by four learnable parameters. The SReLU is learned jointly with the training of the whole deep network through back propagation. During the training phase, to initialize SReLU in different layers, we propose a "freezing" method to degenerate SReLU into a predefined leaky rectified linear unit in the initial several training epochs and then adaptively learn the good initial values. SReLU can be universally used in the existing deep networks with negligible additional parameters and computation cost. Experiments with two popular CNN architectures, Network in Network and GoogLeNet on scale-various benchmarks including CIFAR10, CIFAR100, MNIST and ImageNet demonstrate that SReLU achieves remarkable improvement compared to other activation functions.


title: Dynamic ontology evolution

abstract: Ontologies form the core of Semantic Web systems, and as such, they need to evolve to meet the changing needs of the system and its users. Most of current ontology evolution systems require user input during their processes. We propose Evolva, an ontology evolution framework, aiming to substantially reduce or even eliminate user input through exploiting various background knowledge sources. In this paper we present our ontology evolution approach, as well as our preliminary outcomes and future directions.


title: Scheduling techniques for hybrid circuit/packet networks

abstract: A range of new datacenter switch designs combine wireless or optical circuit technologies with electrical packet switching to deliver higher performance at lower cost than traditional packet-switched networks. These "hybrid" networks schedule large traffic demands via a high-rate circuits and remaining traffic with a lower-rate, traditional packet-switches. Achieving high utilization requires an efficient scheduling algorithm that can compute proper circuit configurations and balance traffic across the switches. Recent proposals, however, provide no such algorithm and rely on an omniscient oracle to compute optimal switch configurations. Finding the right balance of circuit and packet switch use is difficult: circuits must be reconfigured to serve different demands, incurring non-trivial switching delay, while the packet switch is bandwidth constrained. Adapting existing crossbar scheduling algorithms proves challenging with these constraints. In this paper, we formalize the hybrid switching problem, explore the design space of scheduling algorithms, and provide insight on using such algorithms in practice. We propose a heuristic-based algorithm, Solstice that provides a 2.9× increase in circuit utilization over traditional scheduling algorithms, while being within 14% of optimal, at scale.


Abstract Texture Preserving Variational Denoising Using an Adaptive Fidelity Term


Forty-Four Juvenile Thieves: Their Characters and Home-Life


title: Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support

abstract: A wide range of multimedia services is expected to be offered for mobile users via various wireless access networks. Even the integration of Cloud Computing in such networks does not support an adequate Quality of Experience (QoE) in areas with high demands for multimedia contents. Fog computing has been conceptualized to facilitate the deployment of new services that cloud computing cannot provide, particularly those demanding QoE guarantees. These services are provided using fog nodes located at the network edge, which is capable of virtualizing their functions/applications. Service migration from the cloud to fog nodes can be actuated by request patterns and the timing issues. To the best of our knowledge, existing works on fog computing focus on architecture and fog node deployment issues. In this article, we describe the operational impacts and benefits associated with service migration from the cloud to multi-tier fog computing for video distribution with QoE support. Besides that, we perform the evaluation of such service migration of video services. Finally, we present potential research challenges and trends.


title: A critique of cyclomatic complexity as a software metric

abstract: McCabe's cyclomatic complexity metric (1976) is widely cited as a useful predictor of various software attributes such as reliability and development effort. This critique demonstrates that it is based upon poor theoretical foundations and an inadequate model of software development. The argument that the metric provides the developer with a useful engineering approximation is not borne out by the empirical evidence. Furthermore, it would appear that for a large class of software it is no more than a proxy for, and in many cases is outperformed by, lines of code.< ::: >


title: Methodological Review: What can natural language processing do for clinical decision support?

abstract: Computerized clinical decision support (CDS) aims to aid decision making of health care providers and the public by providing easily accessible health-related information at the point and time it is needed. natural language processing (NLP) is instrumental in using free-text information to drive CDS, representing clinical knowledge and CDS interventions in standardized formats, and leveraging clinical narrative. The early innovative NLP research of clinical narrative was followed by a period of stable research conducted at the major clinical centers and a shift of mainstream interest to biomedical NLP. This review primarily focuses on the recently renewed interest in development of fundamental NLP methods and advances in the NLP systems for CDS. The current solutions to challenges posed by distinct sublanguages, intended user groups, and support goals are discussed.


title: Mobility level management for 5G network

abstract: 5G network architecture is actively discussed in 3GPP SA2 [6] where management of mobility level is one of the key study items. Mobility management of the previous generation defines seamless mobility for the always connected service without packet loss. 5G network designed to support the level of mobility based on the UE demands. This paper explains the mobility level management framework for the connected mode. Different levels of HO are defined. Architecture and control procedure to support multiple HO level is described.


title: 3-D Placement of an Unmanned Aerial Vehicle Base Station (UAV-BS) for Energy-Efficient Maximal Coverage

abstract: Unmanned aerial vehicle mounted base stations (UAV-BSs) can provide wireless services in a variety of scenarios. In this letter, we propose an optimal placement algorithm for UAV-BSs that maximizes the number of covered users using the minimum transmit power. We decouple the UAV-BS deployment problem in the vertical and horizontal dimensions without any loss of optimality. Furthermore, we model the UAV-BS deployment in the horizontal dimension as a circle placement problem and a smallest enclosing circle problem. Simulations are conducted to evaluate the performance of the proposed method for different spatial distributions of the users.


title: An Efficient Approach for Optimizing Frequency Reconfigurable Pixel Antennas Using Genetic Algorithms

abstract: In this paper we describe a method for optimizing frequency reconfigurable pixel antennas. The method utilizes a multi-objective function that is efficiently computed by using only one full electromagnetic simulation in the entire genetic algorithm optimization process. Minimization of the number of switches in the design is also attempted. The method is demonstrated using an antenna structure consisting of a rectangular grid of pixels adjacent to a ground plane and using RF MEMS switches for achieving the reconfigurability. The effects of the RF MEMS switches on the antenna performance as well as the control feed lines for them are also addressed. We provide both simulation and experimental results for a reconfigurable dual-band antenna that reconfigures the bands 820-1140 and 1720-1900 MHz to the bands 860-1160 and 1890-2300 MHz with dimensions of 39 mm × 24 mm on a ground plane of 40 mm × 65 mm with one switch only. The results demonstrate that reconfigurable antennas can be designed effectively with a minimum number of switches using an efficient optimization method.


title: Computed eigenmodes of planar regions

abstract: Recently developed numerical methods make possible the high-accuracy computation of eigenmodes of the Laplacian for a variety of "drums" in two dimensions. A number of computed examples are presented together with a discussion of their implications concerning bound and continuum states, isospectrality, symmetry and degeneracy, eigenvalue avoidance, resonance, localization, eigenvalue optimization, perturbation of eigenvalues and eigenvectors, and other matters.


title: Crisis Crowdsourcing Framework: Designing Strategic Configurations of Crowdsourcing for the Emergency Management Domain

abstract: Crowdsourcing is not a new practice but it is a concept that has gained substantial attention during recent disasters. Drawing from previous work in the crisis informatics, disaster sociology, and computer-supported cooperative work (CSCW) literature, this paper first explains recent conceptualizations of crowdsourcing and how crowdsourcing is a way of leveraging disaster convergence. The CSCW concept of "articulation work" is introduced as an interpretive frame for extracting the salient dimensions of "crisis crowdsourcing." Then, a series of vignettes are presented to illustrate the evolution of crisis crowdsourcing that spontaneously emerged after the 2010 Haiti earthquake and evolved to more established forms of public engagement during crises. The best practices extracted from the vignettes clarified the efforts to formalize crisis crowdsourcing through the development of innovative interfaces designed to support the articulation work needed to facilitate spontaneous volunteer efforts. Extracting these best practices led to the development of a conceptual framework that unpacks the key dimensions of crisis crowdsourcing. The Crisis Crowdsourcing Framework is a systematic, problem-driven approach to determining the why, who, what, when, where, and how aspects of a crowdsourcing system. The framework also draws attention to the social, technological, organizational, and policy (STOP) interfaces that need to be designed to manage the articulation work involved with reducing the complexity of coordinating across these key dimensions. An example of how to apply the framework to design a crowdsourcing system is offered with a discussion on the implications for applying this framework as well as the limitations of this framework. Innovation is occurring at the social, technological, organizational, and policy interfaces enabling crowdsourcing to be operationalized and integrated into official products and services.


Competency Based Management In Organizational Context : A Literature Review


title: Ginzburg-Landau calculations of d-wave superconducting dot in s-wave superconducting matrix

abstract: Abstract Applying the finite element method to the Ginzburg–Landau equation, we developed a numerical method that calculated superconducting states and magnetic field distributions for the composite structures of the high- T c superconductor and the conventional superconductor in arbitrary geometries. We show spontaneous magnetic flux appears at the corner of the boundary of these two superconductors. Also we propose high- T c superconducting dot embedded in conventional superconductors, which is named as d-dot, and show the spatial distribution of superconducting order parameters and the magnetic field for several d-dots.


title: Placement and Sizing of DG Using PSO&HBMO Algorithms in Radial Distribution Networks

abstract: Optimal placement and sizing of DG in distribution network is an optimization problem with continuous and discrete variables. Many researchers have used evolutionary methods for finding the optimal DG placement and sizing. This paper proposes a hybrid algorithm PSO&HBMO for optimal placement and sizing of distributed generation (DG) in radial distri- bution system to minimize the total power loss and improve the voltage profile. The proposed method is tested on a standard 13 bus radial distribution system and simulation results carried out using MATLAB software. The simulation results indicate that PSO&HBMO method can obtain better results than the simple heuristic search method and PSO algorithm. The method has a potential to be a tool for identifying the best location and rating of a DG to be installed for improving voltage profile and line losses reduction in an electrical power system. Moreover, current reduc- tion is obtained in distribution system.


title: An integrated QFD framework with multiple formatted and incomplete preferences: A sustainable supply chain application

abstract: Merging sustainable development with the business, and taking goals into account from its three dimensions (i.e., economic, environmental and social) which are derived from customer and stakeholder requirements have been a potential source of competitive differentiation for firms. Academic and corporate interest in sustainable supply chain (SSC) management has also risen considerably in recent years. This paper examines the components and elements of SSC management and how they serve as a foundation for an evaluation framework. By using quality function deployment (QFD) as a product/system planning and improvement tool, an effective SSC structure can be obtained. QFD uses a matrix called the ''House of Quality'' (HoQ), and constructing the HoQ is a critical step in the application of QFD as it translates customer requirements into engineering characteristics. However, participants of HoQ construction sessions tend to provide information about their individual judgments in multiple formats such as numerically or linguistically depending on their different knowledge, experience, culture and circumstance. Furthermore, they can generate incomplete preferences which are challenging to assess in a consistent way. Therefore, the objective of this study is to apply an extended QFD methodology in SSC by introducing a new group decision making (GDM) approach that takes multiple preference formats and incomplete information into account and fusions different formats of expressions into one uniform group decision by means of the fuzzy set theory. To assess the validity of the proposed approach, a case study conducted at HAVI Logistics-Turkey is also presented in the paper.


title: A Real-Time Audit Mechanism Based on the Compression Technique

abstract: Log management and log auditing have become increasingly crucial for enterprises in this era of information and technology explosion. The log analysis technique is useful for discovering possible problems in business processes and preventing illegal-intrusion attempts and data-tampering attacks. Because of the complexity of the dynamically changing environment, auditing a tremendous number of data is a challenging issue. We provide a real-time audit mechanism to improve the aforementioned problems in log auditing. This mechanism was developed based on the Lempel-Ziv-Welch (LZW) compression technique to facilitate effective compression and provide reliable auditing log entries. The mechanism can be used to predict unusual activities when compressing the log data according to pre-defined auditing rules. Auditors using real-time and continuous monitoring can perceive instantly the most likely anomalies or exceptions that could cause problems. We also designed a user interface that allows auditors to define the various compression and audit parameters, using real log cases in the experiment to verify the feasibility and effectiveness of this proposed audit mechanism. In summary, this mechanism changes the log access method and improves the efficiency of log analysis. This mechanism greatly simplifies auditing so that auditors must only trace the sources and causes of the problems related to the detected anomalies. This greatly reduces the processing time of analytical audit procedures and the manual checking time, and improves the log audit efficiency.


title: Extrinsic calibration of a fisheye multi-camera setup using overlapping fields of view

abstract: It is well known that the robustness of many computer vision algorithms can be improved by employing large field of view cameras, such as omnidirectional cameras. To avoid obstructions in the field of view, such cameras need to be mounted in an exposed position. Alternatively, a multicamera setup can be used. However, this requires the extrinsic calibration to be known. In the present work, we propose a method to calibrate a fisheye multi-camera rig, mounted on a mobile platform. The method only relies on feature correspondences from pairwise overlapping fields of view of adjacent cameras. In contrast to existing approaches, motion estimation or specific motion patterns are not required. To compensate for the large extent of multi-camera setups and corresponding viewpoint variations, as well as geometrical distortions caused by fisheye lenses, captured images are mapped into virtual camera views such that corresponding image regions coincide. To this end, the scene geometry is approximated by the ground plane in close proximity and by infinitely far away objects elsewhere. As a result, low complexity feature detectors and matchers can be employed. The approach is evaluated using a setup of four rigidly coupled and synchronized wide angle fisheye cameras that were attached to four sides of a mobile platform. The cameras have pairwise overlapping fields of view and baselines between 2.25 and 3 meters.


title: 1. The WinWin Approach: Using a Requirements Negotiation Tool for Rationale Capture and Use

abstract: A highly cost-effective approach for rationale capture and management is to provide automated support, and capture the resulting artifacts of the process by which software and system requirements and solutions are negotiated. The WinWin process model, equilibrium model, and collaborative negotiation tool provide capabilities for capturing the artifacts. The MBASE software process model provides an approach for using and updating the rationale artifacts and process to keep it in a win-win state. Supporting requirements negotiation with attaching rationale can have a high impact on all phases of development by enabling much better context for change impact analysis as the increasingly frequent requirements changes arrive. The WinWin approach involves having a system's success-critical stakeholders participate in a negotiation process so they can converge on a mutually satisfactory or win-win set of requirements. The WinWin framework in essence captures stakeholder-oriented objectives, options and constraints in the form of a decision rationale.


Integrating Critical Thinking into Extension Programming #2: Developing Critical Thinking Skills 1


title: High-Efficiency Modular High Step-Up Interleaved Boost Converter for DC-Microgrid Applications

abstract: In this paper, a modular interleaved boost converter is first proposed by integrating a forward energy-delivering circuit with a voltage-doubler to achieve high step-up ratio and high efficiency for dc-microgrid applications. Then, steady-state analyses are made to show the merits of the proposed converter module. For closed-loop control design, the corresponding small-signal model is also derived. It is seen that, for higher power applications, more modules can be paralleled to increase the power rating and the dynamic performance. As an illustration, closed-loop control of a 450-W rating converter consisting of two paralleled modules with 24-V input and 200-V output is implemented for demonstration. Experimental results show that the modular high step-up boost converter can achieve an efficiency of 95.8% approximately.


title: Adaptive mixed reality stroke rehabilitation: system architecture and evaluation metrics

abstract: This paper presents a novel system architecture and evaluation metrics for an Adaptive Mixed Reality Rehabilitation (AMRR) system for stroke patient. This system provides a purposeful, engaging, hybrid (visual, auditory and physical) scene that encourages patients to improve their performance of a reaching and grasping task and promotes learning of generalizable movement strategies. This system is adaptive in that it provides assistive adaptation tools to help the rehabilitation team customize the training strategy. Our key insight is to combine the patients, rehabilitation team, multimodal hybrid environments and adaptation tools together as an adaptive experiential mixed reality system. There are three major contributions in this paper: (a) developing a computational deficit index for evaluating the patient's kinematic performance and a deficit-training-improvement (DTI) correlation for evaluating adaptive training strategy, (b) integrating assistive adaptation tools that help the rehabilitation team understand the relationship between the patient's performance and training and customize the training strategy, and (c) combining the interactive multimedia environment and physical environment together to encourage patients to transfer movement knowledge from media space to physical space. Our system has been used by two stroke patients for one-month mediated therapy. They have significant improvement in their reaching and grasping performance (+48.84% and +39.29%) compared to other two stroke patients who experienced traditional therapy (-18.31% and -8.06%).


title: Vision-based state estimation for autonomous rotorcraft MAVs in complex environments

abstract: In this paper, we consider the development of a rotorcraft micro aerial vehicle (MAV) system capable of vision-based state estimation in complex environments. We pursue a systems solution for the hardware and software to enable autonomous flight with a small rotorcraft in complex indoor and outdoor environments using only onboard vision and inertial sensors. As rotorcrafts frequently operate in hover or nearhover conditions, we propose a vision-based state estimation approach that does not drift when the vehicle remains stationary. The vision-based estimation approach combines the advantages of monocular vision (range, faster processing) with that of stereo vision (availability of scale and depth information), while overcoming several disadvantages of both. Specifically, our system relies on fisheye camera images at 25 Hz and imagery from a second camera at a much lower frequency for metric scale initialization and failure recovery. This estimate is fused with IMU information to yield state estimates at 100 Hz for feedback control. We show indoor experimental results with performance benchmarking and illustrate the autonomous operation of the system in challenging indoor and outdoor environments.


Computational Thinking in Practice : How STEM Professionals Use CT in Their Work


title: Gender Identity Outcome in Female-Raised 46,XY Persons with Penile Agenesis, Cloacal Exstrophy of the Bladder, or Penile Ablation

abstract: This review addresses the long-term gender outcome of gender assignment of persons with intersexuality and related conditions. The gender assignment to female of 46,XY newborns with severe genital abnormalities despite a presumably normal-male prenatal sex-hormone milieu is highly controversial because of variations in assumptions about the role of biological factors in gender identity formation. This article presents a literature review of gender outcome in three pertinent conditions (penile agenesis, cloacal exstrophy of the bladder, and penile ablation) in infancy or early childhood. The findings clearly indicate an increased risk of later patient-initiated gender re-assignment to male after female assignment in infancy or early childhood, but are nevertheless incompatible with the notion of a full determination of core gender identity by prenatal androgens.