title: Evaluating spatiotemporal interest point features for depth-based action recognition

abstract: Human action recognition has lots of real-world applications, such as natural user interface, virtual reality, intelligent surveillance, and gaming. However, it is still a very challenging problem. In action recognition using the visible light videos, the spatiotemporal interest point (STIP) based features are widely used with good performance. Recently, with the advance of depth imaging technology, a new modality has appeared for human action recognition. It is important to assess the performance and usefulness of the STIP features for action analysis on the new modality of 3D depth map. In this paper, we evaluate the spatiotemporal interest point (STIP) based features for depth-based action recognition. Different interest point detectors and descriptors are combined to form various STIP features. The bag-of-words representation and the SVM classifiers are used for action learning. Our comprehensive evaluation is conducted on four challenging 3D depth databases. Further, we use two schemes to refine the STIP features, one is to detect the interest points in RGB videos and apply to the aligned depth sequences, and the other is to use the human skeleton to remove irrelevant interest points. These refinements can help us have a deeper understanding of the STIP features on 3D depth data. Finally, we investigate a fusion of the best STIP features with the prevalent skeleton features, to present a complementary use of the STIP features for action recognition on 3D data. The fusion approach gives significantly higher accuracies than many state-of-the-art results.


title: University Entrance Exam as a Guiding Test for Artificial Intelligence

abstract: In this paper, we propose using an University Entrance Exam (the Exame Nacional do Ensido Medio) as a proper test of success of artificial intelligence techniques, thus replacing the famous Turing Test. We argue that more importantly than measuring the ability of a system in replicating human competence, the so-called ENEM test can serve as a driver of development of new techniques. Additionally, we describe how we produced a machine-readable database of questions from previous exams, which can be used for comparing techniques for natural language processing, image processing and knowledge representation and reasoning. We then present some preliminaries to serve as baseline that are based on information retrieval techniques and Word2Vec. Experiments with previous exams show that in questions concerning Humanities and Languages these baseline methods perform in average slightly better than random guessing.


title: Characterizing Optimal DNS Amplification Attacks and Effective Mitigation

abstract: Attackers have used DNS amplification in over 34 % of high-volume DDoS attacks, with some floods exceeding 300 Gbps. The best current practices do not help victims during an attack; they are preventative measures that third-party organizations must employ in advance. Unfortunately, there are no incentives for these third parties to follow the recommendations. While practitioners have focused on reducing the number of open DNS resolvers, these efforts do not address the threat posed by authoritative DNS servers.


title: Neural Network Language Model for Chinese Pinyin Input Method Engine

abstract: Neural network language models (NNLMs) have been shown to outperform traditional ngram language model. However, too high computational cost of NNLMs becomes the main obstacle of directly integrating it into pinyin IME that normally requires a real-time response. In this paper, an efficient solution is proposed by converting NNLMs into back-off n-gram language models, and we integrate the converted NNLM into pinyin IME. Our experimental results show that the proposed method gives better decoding predictive performance for pinyin IME with satisfied efficiency.


title: Promoting general metacognitive awareness

abstract: I describe two aspects of metacognition, knowledge of cognition and regulation of cognition, and how they are related to domain-specific knowledge and cognitive abilities. I argue that metacognitive knowledge is multidimensional, domain-general in nature, and teachable. Four instructional strategies are described for promoting the construction and acquisition of metacognitive awareness. These include promoting general awareness, improving self-knowledge and regulatory skills, and promoting learning environments that are conducive to the construction and use of metacognition.


title: Recent national French food and nutrient intake data

abstract: In France, the first national dietary survey, called ASPCC, was done in 1993-1994. According to this survey, the mean fat intake in France is rather high, both for men (37.7%) and women (40%). Saturated fat intake is above 15% of energy. The intake of fruit and vegetables is particularly low for younger people and manual workers. Fruit intake is also lower for people from the north of the country. These data show the necessity of a targeted nutritional policy in France. Therefore, public health authorities are determining new dietary guidelines. The fact that people with unsatisfactory nutritional status are often not concerned with nutrition proves the importance of simple understandable food-based dietary guidelines.


title: Breast Tumor Analysis in Dynamic Contrast Enhanced MRI Using Texture Features and Wavelet Transform

abstract: Dynamic contrast enhanced MRI (DCE-MRI) is an emerging imaging protocol in locating, identifying and characterizing breast cancer. However, due to image artifacts in MR, pixel intensity alone cannot accurately characterize the tissue properties. We propose a robust method based on the temporal sequence of textural change and wavelet transform for pixel-by-pixel classification. We first segment the breast region using an active contour model. We then compute textural change on pixel blocks. We apply a three-scale discrete wavelet transform on the texture temporal sequence to further extract frequency features. We employ a progressive feature selection scheme and a committee of support vector machines for the classification. We trained the system on ten cases and tested it on eight independent test cases. Receiver-operating characteristics (ROC) analysis shows that the texture temporal sequence (Az: 0.966 and 0.949 in training and test) is much more effective than the intensity sequence (Az: 0.871 and 0.868 in training and test). The wavelet transform further improves the classification performance (Az: 0.989 and 0.984 in training and test).


title: Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series

abstract: Today's Cyber-Physical Systems (CPSs) are large, complex, and affixed with networked sensors and actuators that are targets for cyber-attacks. Conventional detection techniques are unable to deal with the increasingly dynamic and complex nature of the CPSs. On the other hand, the networked sensors and actuators generate large amounts of data streams that can be continuously monitored for intrusion events. Unsupervised machine learning techniques can be used to model the system behaviour and classify deviant behaviours as possible attacks. In this work, we proposed a novel Generative Adversarial Networks-based Anomaly Detection (GAN-AD) method for such complex networked CPSs. We used LSTM-RNN in our GAN to capture the distribution of the multivariate time series of the sensors and actuators under normal working conditions of a CPS. Instead of treating each sensor's and actuator's time series independently, we model the time series of multiple sensors and actuators in the CPS concurrently to take into account of potential latent interactions between them. To exploit both the generator and the discriminator of our GAN, we deployed the GAN-trained discriminator together with the residuals between generator-reconstructed data and the actual samples to detect possible anomalies in the complex CPS. We used our GAN-AD to distinguish abnormal attacked situations from normal working conditions for a complex six-stage Secure Water Treatment (SWaT) system. Experimental results showed that the proposed strategy is effective in identifying anomalies caused by various attacks with high detection rate and low false positive rate as compared to existing methods.


Orientation Histograms for Hand Gesture Recognition


Gesture Recognition


title: Ontology based Recruitment Process

abstract: In our research we explore the benefits resulting from the application of Semantic Web technologies in the recruitment domain. We use currently available standards and classifications to develop a human resource ontology which gives us means for semantic annotation of job postings and applications. Furthermore, we outline the process of semantic matching which improves the quality of query results. Finally, we propose an architecture of an evaluation system based on Semantic Web technologies.


title: Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market

abstract: This paper presents novel intraday session models for price forecasts (ISMPF models) for hourly price forecasting in the six intraday sessions of the Iberian electricity market (MIBEL) and the analysis of mean absolute percentage errors ( MAPEs ) obtained with suitable combinations of their input variables in order to find the best ISMPF models. Comparisons of errors from different ISMPF models identified the most important variables for forecasting purposes. Similar analyses were applied to determine the best daily session models for price forecasts (DSMPF models) for the day-ahead price forecasting in the daily session of the MIBEL, considering as input variables extensive hourly time series records of recent prices, power demands and power generations in the previous day, forecasts of demand, wind power generation and weather for the day-ahead, and chronological variables. ISMPF models include the input variables of DSMPF models as well as the daily session prices and prices of preceding intraday sessions. The best ISMPF models achieved lower MAPEs for most of the intraday sessions compared to the error of the best DSMPF model; furthermore, such DSMPF error was very close to the lowest limit error for the daily session. The best ISMPF models can be useful for MIBEL agents of the electricity intraday market and the electric energy industry.


title: Teasing, rejection, and violence: Case studies of the school shootings

abstract: Media commentators have suggested that recent school shootings were precipitated by social rejection, but no empirical research has examined this claim. Case studies were conducted of 15 school shootings between 1995 and 2001 to examine the possible role of social rejection in school violence. Acute or chronic rejection-in the form of ostracism, bullying, and/or romantic rejection-was present in all but two of the incidents. In addition, the shooters tended to be characterized by one or more of three other risk factors-an interest in firearms or bombs, a fascination with death or Satanism, or psychological problems involving depression, impulse control, or sadistic tendencies. Implications for understanding and preventing school violence are discussed.


title: A novel dual polarized aperture coupled patch element with a single layer feed network and high isolation

abstract: The aperture coupled patch geometry has been extended to dual polarization by several authors. In Tsao et al. (1988) a cross-shaped slot is fed by a balanced feed network which allows for a high degree of isolation. However, the balanced feed calls for an air-bridge which complicates both the design process and the manufacture. An alleviation to this problem is to separate the two channels onto two different substrate layers separated by the ground plane. In this case the disadvantage is increased cost. Another solution with a single layer feed is presented in Brachat and Baracco (1995) where one channel feeds a single slot centered under the patch whereas the other channel feeds two separate slots placed near the edges of the patch. Our experience is that with this geometry it is hard to achieve a well-matched broadband design since the slots near the edge of the patch present very low coupling. All the above geometries maintain symmetry with respect to the two principal planes if we ignore the small spurious coupling from feed lines in the vicinity of the aperture. We propose to reduce the symmetry to only one principal plane which turns out to be sufficient for high isolation and low cross-polarization. The advantage is that only one layer of feed network is needed, with no air-bridges required. In addition the aperture position is centered under the patch. An important application for dual polarized antennas is base station antennas. We have therefore designed and measured an element for the PCS band (1.85-1.99 GHz).


title: Edge map analysis in chest X-rays for automatic pulmonary abnormality screening

abstract: PURPOSE ::: Our particular motivator is the need for screening HIV+ populations in resource-constrained regions for the evidence of tuberculosis, using posteroanterior chest radiographs (CXRs). ::: ::: ::: METHOD ::: The proposed method is motivated by the observation that abnormal CXRs tend to exhibit corrupted and/or deformed thoracic edge maps. We study histograms of thoracic edges for all possible orientations of gradients in the range [Formula: see text] at different numbers of bins and different pyramid levels, using five different regions-of-interest selection. ::: ::: ::: RESULTS ::: We have used two CXR benchmark collections made available by the U.S. National Library of Medicine and have achieved a maximum abnormality detection accuracy (ACC) of 86.36 % and area under the ROC curve (AUC) of 0.93 at 1 s per image, on average. ::: ::: ::: CONCLUSION ::: We have presented an automatic method for screening pulmonary abnormalities using thoracic edge map in CXR images. The proposed method outperforms previously reported state-of-the-art results.


title: A vision-based vehicle identification system

abstract: This work presents a vision-based vehicle identification system which consists of object extraction, object tracking, occlusion detection and segmentation, and vehicle classification. Since the vehicles on the freeway may occlude each other, their trajectories may merge or split. To separate the occluded objects, we develop three processed: occlusion detection, motion vector calibration, and motion field clustering. Finally, the segmented objects are classified into seven different categorized vehicles.


title: Evidence for proposed ICD-11 PTSD and complex PTSD: a latent profile analysis

abstract: Background: The WHO International Classification of Diseases, 11th version (ICD-11), has proposed two related diagnoses, posttraumatic stress disorder (PTSD) and complex PTSD within the spectrum of trauma and stress-related disorders. Objective: To use latent profile analysis (LPA) to determine whether there are classes of individuals that are distinguishable according to the PTSD and complex PTSD symptom profiles and to identify potential differences in the type of stressor and severity of impairment associated with each profile. Method: An LPA and related analyses were conducted on 302 individuals who had sought treatment for interpersonal traumas ranging from chronic trauma (e.g., childhood abuse) to single-incident events (e.g., exposure to 9/11 attacks). Results: The LPA revealed three classes of individuals: (1) a complex PTSD class defined by elevated PTSD symptoms as well as disturbances in three domains of self-organization: affective dysregulation, negative selfconcept, and interpersonal problems; (2) a PTSD class defined by elevated PTSD symptoms but low scores on the three self-organization symptom domains; and (3) a low symptom class defined by low scores on all symptoms and problems. Chronic trauma was more strongly predictive of complex PTSD than PTSD and, conversely, single-event trauma was more strongly predictive of PTSD. In addition, complex PTSD was associated with greater impairment than PTSD. The LPA analysis was completed both with and without individuals with borderline personality disorder (BPD) yielding identical results, suggesting the stability of these classes regardless of BPD comorbidity. Conclusion: Preliminary data support the proposed ICD-11 distinction between PTSD and complex PTSD and support the value of testing the clinical utility of this distinction in field trials. Replication of results is necessary. Keywords: Complex PTSD; posttraumatic stress disorder; WHO; ICD-11 (Published: 15 May 2013) For the abstract or full text in other languages, please see Supplementary files in the column to the right (under Article Tools). Citation: European Journal of Psychotraumatology 2013, 4 : 20706 - http://dx.doi.org/10.3402/ejpt.v4i0.20706


APPLICATIONS OF MEMS IN ROBOTICS USING PSOC 5


title: The OpenGRASP benchmarking suite: An environment for the comparative analysis of grasping and dexterous manipulation

abstract: In this work, we present a new software environment for the comparative evaluation of algorithms for grasping and dexterous manipulation. The key aspect in its development is to provide a tool that allows the reproduction of well-defined experiments in real-life scenarios in every laboratory and, hence, benchmarks that pave the way for objective comparison and competition in the field of grasping. In order to achieve this, experiments are performed on a sound open-source software platform with an extendable structure in order to be able to include a wider range of benchmarks defined by robotics researchers. The environment is integrated into the OpenGRASP toolkit that is built upon the OpenRAVE project and includes grasp-specific extensions and a tool for the creation/integration of new robot models. Currently, benchmarks for grasp and motion planningare included as case studies, as well as a library of domestic everyday objects models, and a real-life scenario that features a humanoid robot acting in a kitchen.


title: A Mode-Matched Silicon-Yaw Tuning-Fork Gyroscope With Subdegree-Per-Hour Allan Deviation Bias Instability

abstract: In this paper, we report on the design, fabrication, and characterization of an in-plane mode-matched tuning-fork gyroscope (M2-TFG). The M2-TFG uses two high-quality-factor (Q) resonant flexural modes of a single crystalline silicon mi- crostructure to detect angular rate about the normal axis. Operating the device under mode-matched condition, i.e., zero-hertz frequency split between drive and sense modes, enables a Q -factor mechanical amplification in the rate sensitivity and also improves the overall noise floor and bias stability of the device. The M2 -TFG is fabricated on a silicon-on-insulator substrate using a combination of device and handle-layer silicon etching that precludes the need for any release openings on the proof-mass, thereby maximizing the mass per unit area. Experimental data indicate subdegree-per-hour Brownian noise floor with a measured Allan deviation bias instability of 0.15deg /hr for a 60-mum-thick 1.5 mm X 1.7 mm footprint M2-TFG prototype. The gyroscope exhibits an open-loop rate sensitivity of approximately 83 mV/deg/s in vacuum. [2007-0100].


Persistent Scatterer InSAR for Crustal Deformation Analysis , with Application to Volcán Alcedo , Galápagos


title: Large-scale brain network associated with creative insight: combined voxel-based morphometry and resting-state functional connectivity analyses

abstract: Creative insight occurs with an “Aha!” experience when solving a difficult problem. Here, we investigated large-scale networks associated with insight problem solving. We recruited 232 healthy participants aged 21–69 years old. Participants completed a magnetic resonance imaging study (MRI; structural imaging and a 10 min resting-state functional MRI) and an insight test battery (ITB) consisting of written questionnaires (matchstick arithmetic task, remote associates test, and insight problem solving task). To identify the resting-state functional connectivity (RSFC) associated with individual creative insight, we conducted an exploratory voxel-based morphometry (VBM)-constrained RSFC analysis. We identified positive correlations between ITB score and grey matter volume (GMV) in the right insula and middle cingulate cortex/precuneus, and a negative correlation between ITB score and GMV in the left cerebellum crus 1 and right supplementary motor area. We applied seed-based RSFC analysis to whole brain voxels using the seeds obtained from the VBM and identified insight-positive/negative connections, i.e. a positive/negative correlation between the ITB score and individual RSFCs between two brain regions. Insight-specific connections included motor-related regions whereas creative-common connections included a default mode network. Our results indicate that creative insight requires a coupling of multiple networks, such as the default mode, semantic and cerebral-cerebellum networks.


title: The exciton and edge emissions in CdTe crystals

abstract: Abstract A detailed analysis of photoluminescence (PL) spectra of solution grown CdTe samples with and without thermal annealing were investigated to ascertain the nature of exciton and edge emissions in the CdTe crystals. The temperature dependence of emission line intensities was also studied. In both as-grown and Te annealed CdTe samples, among other peaks, a sharp acceptor-bound exciton (A o X) line at 1.589 eV was observed. By Cd annealing, the A o X and the edge emission lines simultaneously disappeared while the intensity of a donor-bound exciton (D o X) line was increased. The experimental results suggest that the A o X line in the as-grown sample is due to excitons trapped at the complexes, each formed with a Cd-vacancy and two unidentified donors (V Cd -2D). In the Te annealed samples, we observed overlapping of edge emissions, which result from two different acceptors. In addition, the intensity of the A o X line at 1.589 eV was obviously enhanced by the Te annealing. From these results, it may be concluded that the A o X line in the Te annealed samples is due to two origins, related to V Cd -2D and an acceptor-like structural defect. The acceptor-like structural defect can be assigned to Cd-vacancy (V Cd ). The ionization energies of V Cd -2D and V Cd are about 56.4 and 50.4 meV, respectively.


title: Radio resource allocation and power control scheme to mitigate interference in device-to-device communications underlaying LTE-A uplink cellular networks

abstract: The integration of Device-to-Device (D2D) communication into a cellular network improves system spectral efficiency, increases capacity, reduces power consumption and also reduces traffic to the evolved NodeB (eNB). However, D2D communication generates interference to the conventional cellular network which deteriorates the system performance. In this paper, we propose a radio resource allocation and power control scheme to mitigate the interference using cell sectorization scheme while reusing uplink cellular resources by the D2D pairs. By simulations, we show that our proposed scheme improves the overall system performance compared with the existing methods.


title: PatternNet and PatternLRP - Improving the interpretability of neural networks.

abstract: Deep learning has significantly advanced the state of the art in machine learning. However, neural networks are often considered black boxes. There is significant effort to develop techniques that explain a classifier's decisions. Although some of these approaches have resulted in compelling visualisations, there is a lack of theory of what is actually explained. Here we present an analysis of these methods and formulate a quality criterion for explanation methods. On this ground, we propose an improved method that may serve as an extension for existing back-projection and decomposition techniques.


title: Principal Filter Analysis for Guided Network Compression.

abstract: Principal Filter Analysis (PFA), is an elegant, easy to implement, yet effective methodology for neural network compression. PFA exploits the intrinsic correlation between filter responses within network layers to recommend a smaller network footprint. We propose two compression algorithms: the first allows a user to specify the proportion of the original spectral energy that should be preserved in each layer after compression, while the second is a parameter-free approach that automatically selects the compression used at each layer. Both algorithms are evaluated against several architectures and datasets, and we show considerable compression rates without compromising accuracy, e.g., for VGG-16 on CIFAR-10 and CIFAR-100 PFA achieves a compression rate of 8x and 3x with an accuracy gain of 0.4% points and 1.4% points, respectively. In our tests we also demonstrate that networks compressed with PFA achieve an accuracy that is very close to the empirical upper bound for a given compression ratio.


title: Not Only the Lonely—How Men Explicitly and Implicitly Evaluate the Attractiveness of Sex Robots in Comparison to the Attractiveness of Women, and Personal Characteristics Influencing This Evaluation

abstract: While some theories, such as the Media Equation, suggest that men will evaluate sex robots to be attractive, other assumptions (e.g., biases of norm adherence) would contradict this hypothesis. Therefore, the present study aimed at empirically comparing men’s explicit and implicit evaluation of the (sexual) attractiveness of sex robots and women. At the same time, personal characteristics of the observer that might affect this evaluation were considered. An online survey (n = 229) and an affective priming experiment (n = 41) revealed that men rate women to be more attractive than robots if asked explicitly (=self-reported). However, this effect is not present when attractiveness is assessed implicitly (unbiased, directly). Moreover, affiliation-related traits such as loneliness, which have been assumed to be associated with the usage of sex dolls, are not related to the evaluation of attractiveness. Instead, a negative attitude towards robots is an important predictor.


title: HANDWRITTEN CHINESE CHARACTER RECOGNITION WITH DIRECTIONAL DECOMPOSITION CELLULAR FEATURES

abstract: A new feature extraction approach based on elastic meshing and directional decomposition techniques for handwritten Chinese character recognition (HCCR) is proposed in this letter. It is found that decomposing a Chinese character into horizontal, vertical stroke, left slant and right slant directional sub-patterns is very helpful for feature extraction and recognition. Three kinds of decomposition methods are proposed. A minimum distance classifier is trained by 3755 categories of characters using the new features. Testing on a total of 37,550 untrained handwritten samples produces the recognition rate of 92.36%, showing the effectiveness of the proposed approach.


title: Emotion Classification Using Facial Expression

abstract: Human emotional facial expressions play an important role in interpersonal relations. This is because humans demonstrate and convey a lot of evident information visually rather than verbally. Although humans recognize facial expressions virtually without effort or delay, reliable expression recognition by machine remains a challenge as of today. To automate recognition of emotional state, machines must be taught to understand facial gestures. In this paper we developed an algorithm which is used to identify the person's emotional state through facial expression such as angry, disgust, happy. This can be done with different age group of people with different situation. We Used a Radial Basis Function network (RBFN) for classification and Fisher's Linear Discriminant (FLD), Singular Value Decomposition (SVD) for feature selection.


title: Automatic Assessment of Singer Traits in Popular Music: Gender, Age, Height and Race

abstract: We investigate fully automatic recognition of singer traits, i. e., gender, age, height and ‘race’ of the main performing artist(s) in recorded popular music. Monaural source separation techniques are combined to simultaneously enhance harmonic parts and extract the leading voice. For evaluation the UltraStar database of 581 pop music songs with 516 distinct singers is chosen. Extensive test runs with Long Short-Term Memory sequence classification reveal that binary classification of gender, height, race and age reaches up to 89.6, 72.1, 63.3 and 57.6 % unweighted accuracy on beat level in unseen test data.


title: E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks

abstract: Recurrent Neural Networks (RNNs) are a key technology for emerging applications such as automatic speech recognition, machine translation or image description. Long Short Term Memory (LSTM) networks are the most successful RNN implementation, as they can learn long term dependencies to achieve high accuracy. Unfortunately, the recurrent nature of LSTM networks significantly constrains the amount of parallelism and, hence, multicore CPUs and many-core GPUs exhibit poor efficiency for RNN inference. In this paper, we present E-PUR, an energy-efficient processing unit tailored to the requirements of LSTM computation. The main goal of E-PUR is to support large recurrent neural networks for low-power mobile devices. E-PUR provides an efficient hardware implementation of LSTM networks that is flexible to support diverse applications. One of its main novelties is a technique that we call Maximizing Weight Locality (MWL), which improves the temporal locality of the memory accesses for fetching the synaptic weights, reducing the memory requirements by a large extent. Our experimental results show that E-PUR achieves real-time performance for different LSTM networks, while reducing energy consumption by orders of magnitude with respect to general-purpose processors and GPUs, and it requires a very small chip area. Compared to a modern mobile SoC, an NVIDIA Tegra X1, E-PUR provides an average energy reduction of 92x.


title: Link Prediction in Online Social Networks Using Group Information

abstract: Users of online social networks voluntarily participate in different user groups or communities. Researches suggest the presence of strong local community structure in these social networks, i.e., users tend to meet other people via mutual friendship. Recently, different approaches have considered communities structure information for increasing the link prediction accuracy. Nevertheless, these approaches consider that users belong to just one community. In this paper, we propose three measures for the link prediction task which take into account all different communities that users belong to. We perform experiments for both unsupervised and supervised link prediction strategies. The evaluation method considers the links imbalance problem. Results show that our proposals outperform state-of-the-art unsupervised link prediction measures and help to improve the link prediction task approached as a supervised strategy.


title: Anonymity on the Internet: why the price may be too high

abstract: By allowing anonymous Net communication, the fabric of our society is at risk.


title: AN INNOVATIVE MOVING OBJECT DETECTION AND TRACKING SYSTEM BY USING MODIFIED REGION GROWING ALGORITHM

abstract: The ultimate goal of this study is to afford enhanced video object detection and tracking by eliminating the limitations which are existing nowadays. Although high performance ratio for video object detection and tracking is achieved in the earlier work it takes more time for computation. Consequently we are in need to propose a novel video object detection and tracking technique so as to minimize the computational complexity. Our proposed technique covers five stages they are preprocessing, segmentation, feature extraction, background subtraction and hole filling. Originally the video clip in the database is split into frames. Then preprocessing is performed so as to get rid of noise, an adaptive median filter is used in this stage to eliminate the noise. The preprocessed image then undergoes segmentation by means of modified region growing algorithm. The segmented image is subjected to feature extraction phase so as to extract the multi features from the segmented image and the background image, the feature value thus obtained are compared so as to attain optimal value, consequently a foreground image is attained in this stage. The foreground image is then subjected to morphological operations of erosion and dilation so as to fill the holes and to get the object accurately as these foreground image contains holes and discontinuities. Thus the moving object is tracked in this stage. This method will be employed in MATLAB platform and the outcomes will be studied and compared with the existing techniques so as to reveal the performance of the novel video object detection and tracking technique.


title: "Your click decides your fate": Leveraging clickstream patterns from MOOC videos to infer students' information processing&attrition behavior

abstract: With an expansive and ubiquitously available gold mine of educational data, Massive Open Online courses (MOOCs) have become the an important foci of learning analytics research. The hope is that this new surge of development will bring the vision of equitable access to lifelong learning opportunities within practical reach. MOOCs offer many valuable learning experiences to students, from video lectures, readings, assignments and exams, to opportunities to connect and collaborate with others through threaded discussion forums and other Web 2.0 technologies. Nevertheless, despite all this potential, MOOCs have so far failed to produce evidence that this potential is being realized in the current instantiation of MOOCs. In this work, we primarily explore video lecture interaction in Massive Open Online Courses (MOOCs), which is central to student learning experience on these educational platforms. As a research contribution, we operationalize video lecture clickstreams of students into behavioral actions, and construct a quantitative information processing index, that can aid instructors to better understand MOOC hurdles and reason about unsatisfactory learning outcomes. Our results illuminate the effectiveness of developing such a metric inspired by cognitive psychology, towards answering critical questions regarding students' engagement, their future click interactions and participation trajectories that lead to in-video dropouts. We leverage recurring click behaviors to differentiate distinct video watching profiles for students in MOOCs. Additionally, we discuss about prediction of complete course dropouts, incorporating diverse perspectives from statistics and machine learning, to offer a more nuanced view into how the second generation of MOOCs be benefited, if course instructors were to better comprehend factors that lead to student attrition.


title: Dynamic Allocation of Pharmaceutical Detailing and Sampling for Long-Term Profitability

abstract: The U.S. pharmaceutical industry spent upwards of $18 billion on marketing drugs in 2005; detailing and drug sampling activities accounted for the bulk of this spending. To stay competitive, pharmaceutical managers need to maximize the return on these marketing investments by determining which physicians to target as well as when and how to target them. ::: ::: In this paper, we present a two-stage approach for dynamically allocating detailing and sampling activities across physicians to maximize long-run profitability. In the first stage, we estimate a hierarchical Bayesian, nonhomogeneous hidden Markov model to assess the short-and long-term effects of pharmaceutical marketing activities. The model captures physicians' heterogeneity and dynamics in prescription behavior. In the second stage, we formulate a partially observable Markov decision process that integrates over the posterior distribution of the hidden Markov model parameters to derive a dynamic marketing resource allocation policy across physicians. ::: ::: We apply the proposed approach in the context of a new drug introduction by a major pharmaceutical firm. We identify three prescription-behavior states, a high degree of physicians' dynamics, and substantial long-term effects for detailing and sampling. We find that detailing is most effective as an acquisition tool, whereas sampling is most effective as a retention tool. The optimization results suggest that the firm could increase its profits substantially while decreasing its marketing spending. Our suggested framework provides important implications for dynamically managing customers and maximizing long-run profitability.


title: Bio-Inspired Human Action Recognition using Hybrid Max-Product Neuro-Fuzzy Classifier and Quantum-Behaved PSO

abstract: Studies on computational neuroscience through functional magnetic resonance imaging (fMRI) and following biological inspired system stated that human action recognition in the brain of mammalian leads two distinct pathways in the model, which are specialized for analysis of motion (optic flow) and form information. Principally, we have defined a novel and robust form features applying active basis model as form extractor in form pathway in the biological inspired model. An unbalanced synergetic neural net-work classifies shapes and structures of human objects along with tuning its attention parameter by quantum particle swarm optimization (QPSO) via initiation of Centroidal Voronoi Tessellations. These tools utilized and justified as strong tools for following biological system model in form pathway. But the final decision has done by combination of ultimate outcomes of both pathways via fuzzy inference which increases novality of proposed model. Combination of these two brain pathways is done by considering each feature sets in Gaussian membership functions with fuzzy product inference method. Two configurations have been proposed for form pathway: applying multi-prototype human action templates using two time synergetic neural network for obtaining uniform template regarding each actions, and second scenario that it uses abstracting human action in four key-frames. Experimental results showed promising accuracy performance on different datasets (KTH and Weizmann).


title: Evolution and outlook of TSV and 3D IC/Si integration

abstract: 3D integration consists of 3D IC packaging, 3D IC integration, and 3D Si integration. They are different and in general the TSV (through-silicon via) separates 3D IC packaging from 3D IC integration and 3D Si integration since the latter two use TSV but 3D IC packaging does not. TSV (with a new concept that every chip or interposer could have two surfaces with circuits) is the heart of 3D Si integration and 3D IC integration and is the focus of this investigation. The origin of 3D Si integration and 3D IC integration is presented. Furthermore, the evolution and outlook of 3D Si integration and 3D IC integration are discussed as well as their road maps are presented. Finally, a generic, low-cost and thermal-enhanced 3D IC integration system-in-package (SiP) is proposed for high performance applications.


Clinical experience with multiplex ligation-dependent probe amplification for microdeletion syndromes in prenatal diagnosis: 7522 pregnant Korean women


title: On fully differential and complementary single-stage self-biased CMOS differential amplifiers

abstract: The paper presents and analyzes three fully differential and complementary self-biased CMOS amplifiers. The three differential amplifiers are based on standard single-stage inverter, telescopic cascode and folded cascode amplifier topologies and are truly self-biased via negative feedback in the bias loop thus eliminating the need for a voltage reference. This adaptive biasing attenuates process, supply voltage and temperature variations. The folded cascode-based amplifier possesses also an increased immunity toward common-mode components in the input signal. All three differential amplifiers are comparatively analyzed by means of low frequency calculations and circuit simulations. Finally, discussed amplifiers with high gain and PVT immunity are well-suitable for implementation in nanometer CMOS processes.


title: All-SiC power module-applied single-stage ZVS-PWM AC-AC converter for high-frequency induction heating

abstract: This paper presents a new prototype of a single-stage ac-ac converter with Silicon-Carbide (SiC)-MOSFET / SiC-SBD integrated power module for induction heating (IH) applications. Switching and conduction power losses are effectively reduced by virtue of the 1.2kV-120 A All-SiC power module under the condition of zero voltage soft-switching (ZVS), as compared to the counterpart solution using a Si-IGBT / Si-PNDiode power module. The performances of the new prototype are evaluated by experiment, thereby demonstrating the advantageous properties such as high-efficiency and low switching noise due to the wide band-gap power module.


title: An overview of Gravitational Search Algorithm utilization in optimization problems

abstract: Gravitational Search Algorithm (GSA) is one of the recent nature inspired algorithms which is capable to solve optimization problems. GSA is inspired by the Newtonian's law of gravity and the law of motion. The aim of the paper is to investigate GSA utilization in various optimization problems. The paper provides a brief explanation on GSA and also presents the previous optimization works based on GSA. Based on the literature, GSA is capable of providing more accurate, effective and robust high-quality solution for most of the optimization problems. The algorithm has been applied in various applications and has solved various optimization problems such as in power system, controller design, network routing, sensor networks, software design and many more. GSA has been adapted in the optimization of parameters, settings, strategies, cost, voltage control and also power dispatch. The algorithm also has been adapted to optimize the design of controllers, software, antenna and microgrids. Based on previous works, GSA has showed better performance in solving the optimization problems compared to other previous algorithms such as PSO, ACO and ABC. It is expected that more studies are to be done based on GSA in future as the algorithm has a high potential to solve various optimization problems in different areas.


title: Toward reference Models for Requirements Traceability

abstract: Requirements traceability is intended to ensure continued alignment between stakeholder requirements and various outputs of the system development process. To be useful, traces must be organized according to some modeling framework. Indeed, several such frameworks have been proposed, mostly based on theoretical considerations or analysis of other literature. This paper, in contrast, follows an empirical approach. Focus groups and interviews conducted in 26 major software development organizations demonstrate a wide range of traceability practices with distinct low-end and high-end users of traceability. From these observations, reference models comprising the most important kinds of traceability links for various development tasks have been synthesized. The resulting models have been validated in case studies and are incorporated in a number of traceability tools. A detailed case study on the use of the models is presented. Four kinds of traceability link types are identified and critical issues that must be resolved for implementing each type and potential solutions are discussed. Implications for the design of next-generation traceability methods and tools are discussed and illustrated.


title: Authentication by Encrypted Negative Password

abstract: Secure password storage is a vital aspect in systems based on password authentication, which is still the most widely used authentication technique, despite some security flaws. In this paper, we propose a password authentication framework that is designed for secure password storage and could be easily integrated into existing authentication systems. In our framework, first, the received plain password from a client is hashed through a cryptographic hash function (e.g., SHA-256). Then, the hashed password is converted into a negative password. Finally, the negative password is encrypted into an encrypted negative password (ENP) using a symmetric-key algorithm (e.g., AES), and multi-iteration encryption could be employed to further improve security. The cryptographic hash function and symmetric encryption make it difficult to crack passwords from ENPs. Moreover, there are lots of corresponding ENPs for a given plain password, which makes precomputation attacks (e.g., lookup table attack and rainbow table attack) infeasible. The algorithm complexity analyses and comparisons show that the ENP could resist lookup table attack and provide stronger password protection under dictionary attack. It is worth mentioning that the ENP does not introduce extra elements (e.g., salt); besides this, the ENP could still resist precomputation attacks. Most importantly, the ENP is the first password protection scheme that combines the cryptographic hash function, the negative password, and the symmetric-key algorithm, without the need for additional information except the plain password.


title: Real-World Multiobject, Multigrasp Detection

abstract: A deep learning architecture is proposed to predict graspable locations for robotic manipulation. It considers situations where no, one, or multiple object(s) are seen. By defining the learning problem to be classified with null hypothesis competition instead of regression, the deep neural network with red, green, blue and depth (RGB-D) image input predicts multiple grasp candidates for a single object or multiple objects, in a single shot. The method outperforms state-of-the-art approaches on the Cornell dataset with 96.0% and 96.1% accuracy on imagewise and object-wise splits, respectively. Evaluation on a multiobject dataset illustrates the generalization capability of the architecture. Grasping experiments achieve 96.0% grasp localization and 89.0% grasping success rates on a test set of household objects. The real-time process takes less than 0.25 s from image to plan.


title: A novel isolated electrolytic capacitor-less single-switch AC-DC offline LED driver with power factor correction

abstract: Conventional AC-DC driver circuits for Light-Emitting Diode (LED) lamps require large output capacitance across the LED load to minimize the low frequency current ripple. This large capacitance is usually achieved by using an electrolytic capacitor, which has a lifetime that is at least two times less than that of a LED device. To match the potential lifetime of the LEDs, a new isolated single switch AC-DC high power factor LED driver without any electrolytic capacitors is proposed in this paper. In the proposed circuit, the energy storage capacitor is moved to the rectifier side, with a three-winding transformer used to provide isolation; power factor correction as well as to store and provide the required energy to the output. As a result, the energy storage capacitance is significantly reduced, which allows film capacitor to replace the unreliable electrolytic capacitors. The circuit's operating principles and its characteristics are described in this paper. Simulation and experimental results confirm that a power factor of 0.96 is achieved on a 120Vrms, 12W prototype.


title: 3d printing the history of mechanisms

abstract: Physical models of machines have played an important role in the history of engineering for teaching, analyzing, and exploring mechanical concepts. Many of these models have been replaced today by computational representations, but new rapid-prototyping (RP) technologies are now allowing reintroduction of physical models as an intuitive way to demonstrate mechanical concepts. This paper reports on the use of RP to document, preserve, reproduce, and share in three dimensions, historic machines, and mechanisms. We have reproduced several preassembled, fully functional historic mechanisms from the Cornell Collection of Reuleaux Kinematic Models, and made these available as part of a new online museum of mechanism: Not only can visitors read descriptions, view pictures and videos, and interact with simulations of machines, but they can now also download and 3D-print their own physical functional replicas. We expect that this new form of “physical” preservation will become prevalent in future archives. DOI: 10.1115/1.1902999


title: CAST: A page-level FTL with compact address mapping and parallel data blocks

abstract: NAND flash memory based Solid State Drive (SSD) is increasingly popular as one of the major non-volatile storage devices. Due to the superior performance and energy efficiency properties, it becomes an important complimentary device between the main memory and the traditional mechanical Hard Disk Drive (HDD). It is also anticipated to substitute HDD as the mainstream secondary storage. Today, flash memory is widely used in embedded systems, hand-held devices, personal computers and even enterprise computer systems. To access the data on the flash, a software component called Flash Translation Layer (FTL) has to be applied to convert the file system logical address into the corresponding physical address. FTL has great impacts on the system overall performance. Numerous FTL algorithms have been proposed in the past decade. DFTL is one of the most popular page-level address mapping FTL algorithms. It has been considered to have the best flexibility. However, it has extra mapping information I/O overhead and cannot always achieve the optimal performance. In this paper, we propose CAST, a novel and efficient pagelevel FTL algorithm to relieve this issue. CAST reserves a small portion of embedded SRAM to cache most recently accessed logical-physical address mapping information. Unlike DFTL, we use a compact packing methodology. Consecutive logical-physical page mapping information is represented with only a single entry. Thus, more address mapping information can be maintained in the caching table, and the cache hit rates can be increased. To improve the garbage collection efficiency, CAST maintains multiple current data blocks simultaneously. When a new data write request comes, the system can select an appropriate one to conduct the process based on the request issuer and/or logical address information. Our simulation results show that CAST outperforms DFTL under various workloads and it can reduce the number of erase operations and decrease the I/O response time significantly.


title: Twelve tips for facilitating Millennials’ learning

abstract: Background: The current, so-called “Millennial” generation of learners is frequently characterized as having deep understanding of, and appreciation for, technology and social connectedness. This generation of learners has also been molded by a unique set of cultural influences that are essential for medical educators to consider in all aspects of their teaching, including curriculum design, student assessment, and interactions between faculty and learners.Aim: The following tips outline an approach to facilitating learning of our current generation of medical trainees.Method: The method is based on the available literature and the authors’ experiences with Millennial Learners in medical training.Results: The 12 tips provide detailed approaches and specific strategies for understanding and engaging Millennial Learners and enhancing their learning.Conclusion: With an increased understanding of the characteristics of the current generation of medical trainees, faculty will be better able to facilitate learn...


title: Can virtual reality improve anatomy education? A randomised controlled study of a computer-generated three-dimensional anatomical ear model

abstract: INTRODUCTION ::: The use of computer-generated 3-dimensional (3-D) anatomical models to teach anatomy has proliferated. However, there is little evidence that these models are educationally effective. The purpose of this study was to test the educational effectiveness of a computer-generated 3-D model of the middle and inner ear. ::: ::: ::: METHODS ::: We reconstructed a fully interactive model of the middle and inner ear from a magnetic resonance imaging scan of a human cadaver ear. To test the model's educational usefulness, we conducted a randomised controlled study in which 28 medical students completed a Web-based tutorial on ear anatomy that included the interactive model, while a control group of 29 students took the tutorial without exposure to the model. At the end of the tutorials, both groups were asked a series of 15 quiz questions to evaluate their knowledge of 3-D relationships within the ear. ::: ::: ::: RESULTS ::: The intervention group's mean score on the quiz was 83%, while that of the control group was 65%. This difference in means was highly significant (P < 0.001). ::: ::: ::: DISCUSSION ::: Our findings stand in contrast to the handful of previous randomised controlled trials that evaluated the effects of computer-generated 3-D anatomical models on learning. The equivocal and negative results of these previous studies may be due to the limitations of these studies (such as small sample size) as well as the limitations of the models that were studied (such as a lack of full interactivity). Given our positive results, we believe that further research is warranted concerning the educational effectiveness of computer-generated anatomical models.


title: Securing virtual machine orchestration with blockchains

abstract: The blockchain technology is gaining momentum because of its possible application to other systems than the cryptocurrency one. Indeed, blockchain, as a de-centralized system based on a distributed digital ledger, can be utilized to securely manage any kind of assets, constructing a system that is independent of any authorization entity. In this paper, we briefly present blockchain and our work in progress, the VMOA blockchain, to secure virtual machine orchestration operations for cloud computing and network functions virtualization systems. Using tutorial examples, we describe our design choices and draw implementation plans.


title: Generalization by Weight-Elimination with Application to Forecasting

abstract: Inspired by the information theoretic idea of minimum description length, we add a term to the back propagation cost function that penalizes network complexity. We give the details of the procedure, called weight-elimination, describe its dynamics, and clarify the meaning of the parameters involved. From a Bayesian perspective, the complexity term can be usefully interpreted as an assumption about prior distribution of the weights. We use this procedure to predict the sunspot time series and the notoriously noisy series of currency exchange rates.


title: Designing airport checked-baggage-screening strategies considering system capability and reliability

abstract: Emerging image-based technologies are critical components of airport security for screening checked baggage. Since these new technologies differ widely in cost and accuracy, a comprehensive mathematical framework should be developed for selecting technology or combination of technologies for efficient 100% baggage screening. This paper addresses the problem of setting threshold values of these screening technologies and determining the optimal combination of technologies in a two-level screening system by considering system capability and human reliability. Probability and optimization techniques are used to quantify and evaluate the cost- and risk-effectiveness of various deployment configurations, which is captured by using a system life-cycle cost model that incorporates the deployment cost, operating cost, and costs associated with system decisions. Two system decision rules are studied for a two-level screening system. For each decision rule, two different optimization approaches are formulated and investigated from practitioner's perspective. Numerical examples for different decision rules, optimization approaches and system arrangements are demonstrated.


title: Social Justice-Oriented Interaction Design: Outlining Key Design Strategies and Commitments

abstract: In recent years, many HCI designers have begun pursuing research agendas that address large scale social issues. These systemic or "wicked" problems present challenges for design practice due to their scope, scale, complexity, and political nature. In this paper, we develop a social justice orientation to designing for such challenges. We highlight a breadth of design strategies that target the goals of social justice along six dimensions -- transformation, recognition, reciprocity, enablement, distribution, and accountability -- and elaborate three commitments necessary to developing a social justice oriented design practice -- a commitment to conflict, a commitment to reflexivity, and a commitment to personal ethics and politics. Although there are no easy solutions to systemic social issues, a social justice orientation provides one way to foster an engagement with the thorny political issues that are increasingly acknowledged as crucial to a field that is not just about technological possibility, but also about political responsibility.


title: Automatic Program Repair Using Genetic Programming

abstract: Software quality is an urgent problem. There are so many bugs in industrial program source code that mature software projects are known to ship with both known and unknown bugs [1], and the number of outstanding defects typically exceeds the resources available to address them [2]. This has become a pressing economic problem whose costs in the United States can be measured in the billions of dollars annually [3]. A dominant reason that software defects are so expensive is that fixing them remains a manual process. The process of identifying, triaging, reproducing, and localizing a particular bug, coupled with the task of understanding the underlying error, identifying a set of code changes that address it correctly, and then verifying those changes, costs both time [4] and money, and the cost of repairing a defect can increase by orders of magnitude as development progresses [5]. As a result, many defects, including critical security defects [6], remain unaddressed for long periods of time [7]. Moreover, humans are error-prone, and many human fixes are imperfect, in that they are either incorrect or lead to crashes, hangs, corruption, or security problems [8]. As a result, defect repair has become a major component of software maintenance, which in turn consumes up to 90% of the total lifecycle cost of a given piece of software [9]. Although considerable research attention has been paid to supporting various aspects of the manual debugging process [10, 11], and also to preempting or dynamically addressing particular classes of vulnerabilities, such as buffer overruns [12, 13], there exist virtually no previous automated solutions that address the synthesis of patches for general bugs as they are reported in real-world software. The primary contribution of this dissertation is GenProg, one of the very first automatic solutions designed to help alleviate the manual bug repair burden by automatically and generically patching bugs in deployed and legacy software. GenProg uses a novel genetic programming algorithm, guided by test cases and domain-specific operators, to affect scalable, expressive, and high quality automated repair. We present experimental evidence to substantiate our claims that GenProg can repair multiple types of bugs in multiple types of programs, and that it can repair a large proportion of the bugs that human developers address in practice (that it is expressive); that it scales to real-world system sizes (that it is scalable); and that it produces repairs that are of sufficiently high quality. Over the course of this evaluation, we contribute new benchmark sets of real bugs in real open-source software and novel experimental frameworks for quantitatively evaluating an automated repair technique. We also contribute a novel characterization of


title: E‐commerce usage and business performance in the Malaysian tourism sector: empirical analysis

abstract: Purpose – Based upon the E‐VALUE model developed, this paper aims to investigate the impact of e‐commerce usage on business performance in the tourism sector.Design/methodology/approach – A cross‐sectional survey is carried out on 165 Malaysian firms involved in the tourism sector (hotels, resorts, and hospitals engaged in health tourism) through the use of a structured questionnaire.Findings – The structural equation modeling results indicate that technology competency, firm size, firm scope, web‐technology investment, pressure intensity, and back‐end usage have significant influence on e‐commerce usage. Among these variables, back‐end integration is found to function as a mediator. E‐commerce experience (in years) is found to moderate the relationship between e‐commerce usage and business performance.Research limitations/implications – The paper focuses on the tourism sector in Malaysia and concentrates only on the management perspective of e‐commerce adoption.Practical implications – The results provid...


title: Forward models and passive psychotic symptoms

abstract: Pickering and Clark (2014) present two ways of viewing the role of forward models in human cognition: the auxiliary forward model (AFM) account and the integral forward model (IFM) account. The AFM account “assumes a dedicated prediction mechanism implemented by additional circuitry distinct from the core mechanisms of perception and action” (p. 451). The standard AFM account exploits a corollary discharge from the motor command in order to compute the sensory consequences of the action. In contrast, on the IFM account, “perception itself involves the use of a forward (generative) model, whose role is to construct the incoming sensory signal ‘from the top down”’ (p. 453). Furthermore, within this account, motor commands are dispensed with: they are predictions that are fulfilled by movement as part of the prediction error minimization that is taken to govern all aspects of cognition (Adams et al., 2013). ::: ::: Pickering and Clark present two “testing grounds” for helping us adjudicate between IFMs and AFMs, which are committed to the idea, derived from Pickering's own work, that one predicts others in a way that is similar to the way that one predicts oneself. Although I like this “prediction by simulation” account, in this commentary, I want to emphasize that neither the IFM nor the AFM accounts are necessarily wedded to it. ::: ::: A less committal, and hence perhaps more compelling, testing ground is to be found in psychotic symptoms, and the capacity of the two frameworks to account for them. Indeed, using psychosis to illustrate forward modelling is not new: the inability to self-tickle was taken to be convincing data for the presence of forward models (viewed, by default, within an AFM account), and in particular for problems with them in patients with diagnoses of schizophrenia (Frith et al., 2000). ::: ::: The AFM account has been used more generally to explain symptoms of schizophrenia. Something goes wrong with the generation of the forward model, and so the sensory consequences of self-generated stimuli are poorly predicted, and hence fail to be attenuated, and are, ultimately, misattributed to an external source. Although most have accepted this for delusions of control, some have questioned the application of this model to passive symptoms (Stephens and Graham, 2000), namely those which do not involve action, such as auditory verbal hallucinations (AVHs). If the symptoms of schizophrenia are explainable in terms of problems with an AFM, and this is constructed out of a motor command, then non-motoric (“passive”) symptoms cannot be so explained. ::: ::: One move has been to keep working within the AFM framework but claim that “passive” symptoms merely look passive: they are actually “active.” Several theorists (e.g., Jones and Fernyhough, 2007) have attempted to explain AVHs in terms of inner speech misattribution, where inner speech is taken to involve motoric elements. This motoric involvement has been empirically supported by several electromyographical (EMG) studies (which measured muscular activity during inner speech) some of which date as far back as the early 1930s (Jacobsen, 1931). Later experiments made the connection between inner speech and AVH, showing that similar muscular activation is involved in healthy inner speech and AVH (Gould, 1948). The involvement of motoric elements in both inner speech and in AVH is further supported by findings (Gould, 1950) showing that when subjects hallucinated, subvocalizations occurred which could be picked up with a throat microphone. That these subvocalizations were causally responsible for the inner speech allegedly implicated in AVHs, and not just echoing it, was suggested by data (Bick and Kinsbourne, 1987) demonstrating that if people experiencing hallucinations opened their mouths wide, stopping vocalizations, then the majority of AVHs stopped. ::: ::: However, this does not seem to capture all AVH subtypes. For example, Dodgson and Gordon (2009) convincingly present “hypervigilance hallucinations,” which are not based on self-generated stimuli, but constitute hypervigilant boosting and molding of external stimuli. As I have argued (Wilkinson, 2014) recently, one can account for both inner speech-based and hypervigilance hallucinations, within an IFM framework (although I called it a “Predictive Processing Framework”). Since it is good practice to support models that accommodate more phenomena, assuming (as seems plausible) that hypervigilance hallucinations are a genuine subtype of AVH, the IFM account is preferable to the AFM account. ::: ::: In conclusion, although I agree with Pickering and Clark that the IFM account is preferable, I do so on the basis of a somewhat different “testing ground.” The IFM can account for both active and passive psychotic symptoms. The AFM, in tying itself to motor commands, can only explain the former.


title: Detecting Pedestrians Using Patterns of Motion and Appearance

abstract: This paper describes a pedestrian detection system that integrates image intensity information with motion information. We use a detection style algorithm that scans a detector over two consecutive frames of a video sequence. The detector is trained (using AdaBoost) to take advantage of both motion and appearance information to detect a walking person. Past approaches have built detectors based on motion information or detectors based on appearance information, but ours is the first to combine both sources of information in a single detector. The implementation described runs at about 4 frames/second, detects pedestrians at very small scales (as small as 20 × 15 pixels), and has a very low false positive rate. ::: ::: Our approach builds on the detection work of Viola and Jones. Novel contributions of this paper include: (i) development of a representation of image motion which is extremely efficient, and (ii) implementation of a state of the art pedestrian detection system which operates on low resolution images under difficult conditions (such as rain and snow).


title: Efficient Cepstral Normalization For Robust Speech Recognition

abstract: In this paper we describe and compare the performance of a series of cepstrum-based procedures that enable the CMU SPHINX-II speech recognition system to maintain a high level of recognition accuracy over a wide variety of acoustical environments. We describe the MFCDCN algorithm, an environment-independent extension of the efficient SDCN and FCDCN algorithms developed previously. We compare the performance of these algorithms with the very simple RASTA and cepstral mean normalization procedures, describing the performance of these algorithms in the context of the 1992 DARPA CSR evaluation using secondary microphones, and in the DARPA stress-test evaluation.


title: A buffer-based approach to rate adaptation: evidence from a large video streaming service

abstract: Existing ABR algorithms face a significant challenge in estimating future capacity: capacity can vary widely over time, a phenomenon commonly observed in commercial services. In this work, we suggest an alternative approach: rather than presuming that capacity estimation is required, it is perhaps better to begin by using only the buffer, and then ask when capacity estimation is needed. We test the viability of this approach through a series of experiments spanning millions of real users in a commercial service. We start with a simple design which directly chooses the video rate based on the current buffer occupancy. Our own investigation reveals that capacity estimation is unnecessary in steady state; however using simple capacity estimation (based on immediate past throughput) is important during the startup phase, when the buffer itself is growing from empty. This approach allows us to reduce the rebuffer rate by 10-20% compared to Netflix's then-default ABR algorithm, while delivering a similar average video rate, and a higher video rate in steady state.


title: A dirichlet multinomial mixture model-based approach for short text clustering

abstract: Short text clustering has become an increasingly important task with the popularity of social media like Twitter, Google+, and Facebook. It is a challenging problem due to its sparse, high-dimensional, and large-volume characteristics. In this paper, we proposed a collapsed Gibbs Sampling algorithm for the Dirichlet Multinomial Mixture model for short text clustering (abbr. to GSDMM). We found that GSDMM can infer the number of clusters automatically with a good balance between the completeness and homogeneity of the clustering results, and is fast to converge. GSDMM can also cope with the sparse and high-dimensional problem of short texts, and can obtain the representative words of each cluster. Our extensive experimental study shows that GSDMM can achieve significantly better performance than three other clustering models.


title: Let's Plan It Deductively!

abstract: Abstract The paper describes a transition logic, TL, and a deductive formalism for it. It shows how various important aspects (such as ramification, qualification, specificity, simultaneity, indeterminism etc.) involved in planning (or in reasoning about action and causality for that matter) can be modelled in TL in a rather natural way. (The deductive formalism for) TL extends the linear connection method proposed earlier by the author by embedding the latter into classical logic, so that classical and resource-sensitive reasoning coexist within TL. The attraction of a logical and deductive approach to planning is emphasized and the state of automated deduction briefly described.


title: Design, Meanings, and Radical Innovation: A Metamodel and a Research Agenda*

abstract: Recent studies on design management have helped us to better comprehend how companies can apply design to get closer to users and to better understand their needs; this is an approach usually referred to as user-centered design. Yet analysis of design-intensive manufacturers such as Alessi, Artemide, and other leading Italian firms shows that their innovation process hardly starts from a close observation of user needs and requirements. Rather, they follow a different strategy called design-driven innovation in this paper. This strategy aims at radically change the emotional and symbolic content of products (i.e., their meanings and languages) through a deep understanding of broader changes in society, culture, and technology. Rather than being pulled by user requirements, design-driven innovation is pushed by a firm's vision about possible new product meanings and languages that could diffuse in society. Design-driven innovation, which plays such a crucial role in the innovation strategy of design intensive firms, has still remained largely unexplored. This paper aims at providing a possible direction to fill this empty spot in innovation management literature. In particular, first it proposes a metamodel for investigating design-driven innovation in which a manufacturer's ability to understand, anticipate, and influence emergence of new product meanings is built by relying on external interpreters (e.g., designers, firms in other industries, suppliers, schools, artists, the media) that share its same problem: to understand the evolution of sociocultural models and to propose new visions and meanings. Managing design-driven innovation therefore implies managing the interaction with these interpreters to access, share, and internalize knowledge on product languages and to influence shifts in sociocultural models. Second, the paper proposes a possible direction to scientifically investigate the management of this networked and collective research process. In particular, it shows that the process of creating breakthrough innovations of meanings partially mirrors the process of creating breakthrough technological innovations. Studies of design-driven innovation may therefore benefit significantly from the existing body of theories in the field of technology management. The analysis of the analogies between these two types of radical innovations (i.e., meanings and technologies) allows a research agenda to be set for exploration of design-driven innovation, a relevant as well as underinvestigated phenomenon.


title: An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

abstract: A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment.


title: Immune Response in Human Pathology: Infections Caused by Bacteria, Viruses, Fungi, and Parasites

abstract: In the middle of the 19th century, it became clear that micro-organisms could cause disease. Effective treatment, however, was not possible at that time; prevention and spread of infectious diseases depended solely on proper hygienic means. At the beginning of the 20th century, passive and active vaccination procedures were developed against a number of these PATHOGENIC MICRO-ORGANISMS to prevent the diseases in question (rabies, diphtheria, tetanus, etc.). Thanks to the discovery of antimicrobial chemicals (by Paul Ehrlich) and antibiotics (by Sir Alexander Fleming), the threat of infectious diseases seemed to be minimised. Large scale vaccination programmes against childhood diseases (diphtheria, whooping cough and polio), started in the early 1950s, raised hopes of finally being able to eradicate these diseases from the planet.


title: Elephants don't play chess

abstract: There is an alternative route to Artificial Intelligence that diverges from the directions pursued under that banner for the last thirty some years. The traditional approach has emphasized the abstract manipulation of symbols, whose grounding in physical reality has rarely been achieved. We explore a research methodology which emphasizes ongoing physical interaction with the environment as the primary source of constraint on the design of intelligent systems. We show how this methodology has recently had significant successes on a par with the most successful classical efforts. We outline plausible future work along these lines which can lead to vastly more ambitious systems.


title: Crowdsourcing graphical perception: using mechanical turk to assess visualization design

abstract: Understanding perception is critical to effective visualization design. With its low cost and scalability, crowdsourcing presents an attractive option for evaluating the large design space of visualizations; however, it first requires validation. In this paper, we assess the viability of Amazon's Mechanical Turk as a platform for graphical perception experiments. We replicate previous studies of spatial encoding and luminance contrast and compare our results. We also conduct new experiments on rectangular area perception (as in treemaps or cartograms) and on chart size and gridline spacing. Our results demonstrate that crowdsourced perception experiments are viable and contribute new insights for visualization design. Lastly, we report cost and performance data from our experiments and distill recommendations for the design of crowdsourced studies.


title: Dietary emulsifiers impact the mouse gut microbiota promoting colitis and metabolic syndrome

abstract: Emulsifying agents, which are common food additives in the human diet, induce low-grade inflammation and obesity/metabolic syndrome in mice, suggesting that further investigation into the potential impact of dietary emulsifiers on the gut microbiota and human heath are warranted.


title: Accurate and efficient replaying of file system traces

abstract: Replaying traces is a time-honored method for benchmarking, stress-testing, and debugging systems--and more recently--forensic analysis. One benefit to replaying traces is the reproducibility of the exact set of operations that were captured during a specific workload. Existing trace capture and replay systems operate at different levels: network packets, disk device drivers, network file systems, or system calls. System call replayers miss memory-mapped operations and cannot replay I/O-intensive workloads at original speeds. Traces captured at other levels miss vital information that is available only at the file system level. We designed and implemented Replayfs, the first system for replaying file system traces at the VFS level. The VFS is the most appropriate level for replaying file system traces because all operations are reproduced in a manner that is most relevant to file-system developers. Thanks to the uniform VFS API, traces can be replayed transparently onto any existing file system, even a different one than the one originally traced, without modifying existing file systems. Replayfs's user-level compiler prepares a trace to be replayed efficiently in the kernel where multiple kernel threads prefetch and schedule the replay of file system operations precisely and efficiently. These techniques allow us to replay I/O-intensive traces at different speeds, and even accelerate them on the same hardware that the trace was captured on originally.


title: A steady-state equivalent model of solid state transformers for voltage regulation studies

abstract: Solid State Transformers (SSTs) are power electronic-based transformers that will be a vital component of the future smart grid with high amount of renewable energy integration and the need for improved controllability. Among its various unique features, the reactive power compensating capabilities of SSTs can be exploited in future smart distribution systems for voltage regulation under high renewable energy penetrations. SSTs are power electronic devices that show fast and non-linear dynamics which means the simulation models are often complicated and need small time steps for accurate solutions. This prevents real-time and long-term simulation of large distribution systems as the simulations become computationally prohibitive. This paper designs a simplified equivalent model of an SST using simple current and voltage sources along with simple modeling equations. These simplified models can be used to perform long-term voltage regulation studies of distribution systems where traditional transformers are replaced with SSTs.


title: Coordinated Direct Power Control of DFIG System Without Phase-Locked Loop Under Unbalanced Grid Voltage Conditions

abstract: This paper proposes a coordinated direct power control (DPC) scheme for the rotor-side converter (RSC) and the grid-side converter (GSC) of the doubly fed induction generator (DFIG) under unbalanced grid voltage conditions. In order to eliminate the coupling interactions between the phase-locked loop (PLL) and the local unbalanced network, a virtual phase angle is used to replace the actual one acquired by the PLL. Thus, the PLL is removed out of RSC and GSC in the proposed DPC scheme. During network unbalance, the RSC is controlled to reduce torque ripples, while three selectable control targets are identified for the GSC, i.e., constant total active power, constant total reactive power, and balanced currents. A single-side resonant controller with the frequency discrimination between the positive- and negative-sequence signals of the same frequency is employed in the coordinated DPC scheme to avoid the complex calculations of the power compensating components. Meanwhile, the sequential separations of the voltages and currents are also eliminated. Then, the control performance, including the limits of the dc-link voltage, the dc-capacitor power oscillations, the impacts of the frequency deviations, and the grid synchronization of the proposed DPC strategy, is discussed. Finally, the experimental results of DFIG system verify the effectiveness of the proposed DPC strategy under unbalanced grid voltage conditions.


title: Design and deployment of secure, robust, and resilient SDN controllers

abstract: The scale of the Software-Defined Network (SDN) Controller design problem has become apparent with the expansion of SDN deployments. Initial SDN deployments were small-scale, single controller environments for research and usecase testing. Today, enterprise deployments requiring multiple controllers are gathering momentum e.g. Google’s backbone network, Microsoft’s public cloud, and NTT’s edge gateway. Third-party applications are also becoming available e.g. HP SDN App Store. The increase in components and interfaces for the evolved SDN implementation increases the security challenges of the SDN controller design. In this work, the requirements of a secure, robust, and resilient SDN controller are identified, stateof- the-art open-source SDN controllers are analyzed with respect to the security of their design, and recommendations for security improvements are provided. This contribution highlights the gap between the potential security solutions for SDN controllers and the actual security level of current controller designs.


title: LED-Based Visible Light Communication and Positioning Technology and SoCs (Invited)

abstract: Rapid deployment of high-energy-efficiency, solid-state light emission diode (LED) lighting devices, replacing conventional luminaires devices, brings unprecedented opportunities for alternative optical wireless communication and navigation applications. This paper reviews the state-of-the-art of LED-based visible light communications (VLC) and positioning (VLP) technologies with a focus on system-on-a-chip (SoC) integrated circuit (IC) designs.


title: Efficient Event Log Mining with LogClusterC

abstract: Nowadays, many organizations collect large volumes of event log data on a daily basis, and the analysis of collected data is a challenging task. For this purpose, data mining methods have been suggested in past research papers, and several data clustering algorithms have been developed formining line patterns from event logs. In this paper, we introduce an open-source tool called LogClusterC which implements the LogCluster algorithm for discovering line patterns and outliers from event logs. According to our performance measurements, LogClusterC compares favorablyto other publicly available log clustering tools.


title: Cooperative Sensing among Cognitive Radios

abstract: Cognitive Radios have been advanced as a technology for the opportunistic use of under-utilized spectrum since they are able to sense the spectrum and use frequency bands if no Primary user is detected. However, the required sensitivity is very demanding since any individual radio might face a deep fade. We propose light-weight cooperation in sensing based on hard decisions to mitigate the sensitivity requirements on individual radios. We show that the "link budget" that system designers have to reserve for fading is a significant function of the required probability of detection. Even a few cooperating users (~10-20) facing independent fades are enough to achieve practical threshold levels by drastically reducing individual detection requirements. Hard decisions perform almost as well as soft decisions in achieving these gains. Cooperative gains in a environment where shadowing is correlated, is limited by the cooperation footprint (area in which users cooperate). In essence, a few independent users are more robust than many correlated users. Unfortunately, cooperative gain is very sensitive to adversarial/failing Cognitive Radios. Radios that fail in a known way (always report the presence/absence of a Primary user) can be compensated for by censoring them. On the other hand, radios that fail in unmodeled ways or may be malicious, introduce a bound on achievable sensitivity reductions. As a rule of thumb, if we believe that 1/N users can fail in an unknown way, then the cooperation gains are limited to what is possible with N trusted users.


A National Model to Supervise on Virtual Banking Systems through the Bank 2 . 0 Approach


title: Vehicle Logo Recognition System Based on Convolutional Neural Networks With a Pretraining Strategy

abstract: Since a vehicle logo is the clearest indicator of a vehicle manufacturer, most vehicle manufacturer recognition (VMR) methods are based on vehicle logo recognition. Logo recognition can be still a challenge due to difficulties in precisely segmenting the vehicle logo in an image and the requirement for robustness against various imaging situations simultaneously. In this paper, a convolutional neural network (CNN) system has been proposed for VMR that removes the requirement for precise logo detection and segmentation. In addition, an efficient pretraining strategy has been introduced to reduce the high computational cost of kernel training in CNN-based systems to enable improved real-world applications. A data set containing 11 500 logo images belonging to 10 manufacturers, with 10 000 for training and 1500 for testing, is generated and employed to assess the suitability of the proposed system. An average accuracy of 99.07% is obtained, demonstrating the high classification potential and robustness against various poor imaging situations.


title: Content-based collaborative filtering for news topic recommendation

abstract: News recommendation has become a big attraction with which major Web search portals retain their users. Two effective approaches are Content-based Filtering and Collaborative Filtering, each serving a specific recommendation scenario. The Content-based Filtering approaches inspect rich contexts of the recommended items, while the Collaborative Filtering approaches predict the interests of long-tail users by collaboratively learning from interests of related users. We have observed empirically that, for the problem of news topic displaying, both the rich context of news topics and the long-tail users exist. Therefore, in this paper, we propose a Content-based Collaborative Filtering approach (CCF) to bring both Content-based Filtering and Collaborative Filtering approaches together. We found that combining the two is not an easy task, but the benefits of CCF are impressive. On one hand, CCF makes recommendations based on the rich contexts of the news. On the other hand, CCF collaboratively analyzes the scarce feedbacks from the long-tail users. We tailored this CCF approach for the news topic displaying on the Bing front page and demonstrated great gains in attracting users. In the experiments and analyses part of this paper, we discuss the performance gains and insights in news topic recommendation in Bing.


title: View from the Top Hierarchies and Reverse Hierarchies in the Visual System

abstract: Abstract We propose that explicit vision advances in reverse hierarchical direction, as shown for perceptual learning. Processing along the feedforward hierarchy of areas, leading to increasingly complex representations, is automatic and implicit, while conscious perception begins at the hierarchy's top, gradually returning downward as needed. Thus, our initial conscious percept— vision at a glance —matches a high-level, generalized, categorical scene interpretation, identifying "forest before trees." For later vision with scrutiny , reverse hierarchy routines focus attention to specific, active, low-level units, incorporating into conscious perception detailed information available there. Reverse Hierarchy Theory dissociates between early explicit perception and implicit low-level vision, explaining a variety of phenomena. Feature search "pop-out" is attributed to high areas, where large receptive fields underlie spread attention detecting categorical differences. Search for conjunctions or fine discriminations depends on reentry to low-level specific receptive fields using serial focused attention, consistent with recently reported primary visual cortex effects.


title: RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism

abstract: Accuracy and interpretability are two dominant features of successful predictive models. Typically, a choice must be made in favor of complex black box models such as recurrent neural networks (RNN) for accuracy versus less accurate but more interpretable traditional models such as logistic regression. This tradeoff poses challenges in medicine where both accuracy and interpretability are important. We addressed this challenge by developing the REverse Time AttentIoN model (RETAIN) for application to Electronic Health Records (EHR) data. RETAIN achieves high accuracy while remaining clinically interpretable and is based on a two-level neural attention model that detects influential past visits and significant clinical variables within those visits (e.g. key diagnoses). RETAIN mimics physician practice by attending the EHR data in a reverse time order so that recent clinical visits are likely to receive higher attention. RETAIN was tested on a large health system EHR dataset with 14 million visits completed by 263K patients over an 8 year period and demonstrated predictive accuracy and computational scalability comparable to state-of-the-art methods such as RNN, and ease of interpretability comparable to traditional models.


title: From text to XML by structural information extraction

abstract: Facing tremendous volume of semi-structured XML and non-structured free text, network information retrieval is one of the most research hotspots in dealing with these data more efficiently, precisely and uniformly. Many traditional IR methods ignore text semantics and their labeling result has usually only one level, lacking of context expression as well, therefore structure extraction from free text and its conversion to XML format are studied, with a CRF based algorithm SIECRF provided. Experiment results are analyzed, showing its efficiency to extracting text structure and has a good application future.


A Universal Framework for Inductive Transfer Parsing across Multi-typed Treebanks


title: Making More from Less: Strategic Demand Reduction in the FCC Spectrum Auctions

abstract: When multiple items are sold through the use of simultaneous ascending-bid auctions, bidders can find it in their mutual interests to reduce their aggregate demand for the items while prices are still low relative to the bidders' valuations. The FCC's first broadband PCS auction provides examples of how such mutual reductions might be arranged even when the bidders are not allowed to communicate with one another outside of the auction arena. Copyright (c) 1997 Massachusetts Institute of Technology.


title: Objects as Attributes for Scene Classification

abstract: Robust low-level image features have proven to be effective representations for a variety of high-level visual recognition tasks, such as object recognition and scene classification. But as the visual recognition tasks become more challenging, the semantic gap between low-level feature representation and the meaning of the scenes increases. In this paper, we propose to use objects as attributes of scenes for scene classification. We represent images by collecting their responses to a large number of object detectors, or "object filters". Such representation carries high-level semantic information rather than low-level image feature information, making it more suitable for high-level visual recognition tasks. Using very simple, off-the-shelf classifiers such as SVM, we show that this object-level image representation can be used effectively for high-level visual tasks such as scene classification. Our results are superior to reported state-of-the-art performance on a number of standard datasets.


The Gas Chromatographic Analysis of Sub-Microgram Quantities of Barbiturates Using a Flame Ionization Detector


title: FuseMe: Classification of sMRI images by fusion of Deep CNNs in 2D+ϵ projections

abstract: The methods of Content-Based visual information indexing and retrieval penetrate into Healthcare and become popular in Computer-Aided Diagnosis. Multimedia in medical images means different imaging modalities, but also multiple views of the same physiological object, such as human brain. In this paper we propose1 a multi-projection fusion approach with CNNs for diagnostics of Alzheimer Disease. Instead of working with the whole brain volume, it fuses CNNs from each brain projection sagittal, coronal, and axial ingesting a 2D+e limited volume we have previously proposed. Three binary classification tasks are considered separating Alzheimer Disease (AD) patients from Mild Cognitive Impairment (MCI) and Normal control Subject (NC). Two fusion methods on FC-layer and on the single-projection CNN output show better performances, up to 91% and show competitive results with the SOA using heavier algorithmic chains.


title: Synthesis and Fluorescence Studies of Some New Fluorophores and Their Effect on Hybridization of Oligodeoxyribonucleotides

abstract: Some novel fluorophores, viz. 6-(6-isobutyrylamino-1,3-dioxo-1H,3H-benzo[de]isoquinolin-2-yl)-hexanoic acid (1), 6-(6-dimethylamino-1,3-dioxo-1H, 3H-benzo[de]isoquinolin-2-yl)-hexanoic acid (2), 6-(6-benzoylamino-1, 3-dioxo-1H, 3H-benzo[de]isoquinolin-2-yl)-hexanoic acid (3), 6-(6-amino-1-oxo-1H, 3H-benzo[de]isoquinolin-2-yl)-hexanoic acid (4) and 6-(6-amino-1H,3H-benzo[de] isoquinolin-2-yl)-hexanoic acid (5) have been designed, synthesized and characterized. Their comparative fluorescence has been studied in different organic solvents and aqueous solutions containing inorganic ions. Out of these, two fluorophores, 1 and 2 have been used for labelling of nucleosides which were finally converted into their phosphoramidites, and used for labelling of oligodeoxyribonucleotides through covalent attachment. These fluorophores after attachment on oligodeoxyribonucleotides showed good fluorescence signals and higher hybridisation affinity than unlabelled oligodeoxyribonucleotides.


title: A Single-Stage Single-Switch LED Driver Based on the Integrated SEPIC Circuit and Class-E Converter

abstract: A novel high-power-factor single-stage single-switch light-emitting diodes (LEDs) driver for street lighting system is proposed in this paper. By integrating the single-ended primary-inductor converter (SEPIC) power factor correction circuit and Class-E resonant dc/dc converter, the proposed converter exhibits extreme simplicity and high reliability, as there is only one active power switch. The LED driver could achieve nearly a unit power factor by operating the SEPIC circuit at discontinuous conduction mode. With careful parameters design of a single Class-E resonant converter, the proposed converter can achieve soft-switching characteristics, which could significantly reduce the switching losses and greatly improve the system efficiency. Operational principle, analytical results, and design considerations at 100 kHz are presented, and a 100-W laboratory prototype is proposed to verify the theoretical analysis, whose efficiency is as high as 91.2% in full-load state under 110 VAC input.