title: Discrete Differential-Geometry Operators for Triangulated 2-Manifolds

abstract: This paper proposes a unified and consistent set of flexible tools to approximate important geometric attributes, including normal vectors and curvatures on arbitrary triangle meshes. We present a consistent derivation of these first and second order differential properties using averaging Voronoi cells and the mixed Finite-Element/Finite-Volume method, and compare them to existing formulations. Building upon previous work in discrete geometry, these operators are closely related to the continuous case, guaranteeing an appropriate extension from the continuous to the discrete setting: they respect most intrinsic properties of the continuous differential operators. We show that these estimates are optimal in accuracy under mild smoothness conditions, and demonstrate their numerical quality. We also present applications of these operators, such as mesh smoothing, enhancement, and quality checking, and show results of denoising in higher dimensions, such as for tensor images.


title: A unified architecture for natural language processing: deep neural networks with multitask learning

abstract: We describe a single convolutional neural network architecture that, given a sentence, outputs a host of language processing predictions: part-of-speech tags, chunks, named entity tags, semantic roles, semantically similar words and the likelihood that the sentence makes sense (grammatically and semantically) using a language model. The entire network is trained jointly on all these tasks using weight-sharing, an instance of multitask learning. All the tasks use labeled data except the language model which is learnt from unlabeled text and represents a novel form of semi-supervised learning for the shared tasks. We show how both multitask learning and semi-supervised learning improve the generalization of the shared tasks, resulting in state-of-the-art-performance.


title: Zolertia Z1 energy usage simulation with Cooja simulator

abstract: Energy consumption in the embedded devices plays an essential role towards the performance of Wireless Sensor Network (WSN). Routing protocol, network topology, to the number of sensors in the network are things that highly affect the energy consumption. The research indirectly on the use of energy in WSN is quite complex for involving more hardware, software and other parameters that highly rely on the research environment involved in it. A simulator can be used as the alternative in simplifying the research environment. In this research, Cooja simulator was used to simulate the use of energy in WSN. This simulator can run the ContikiOS in a native manner in which ContikiOS is commonly used by the WSN hardware platform. Hence, the use of Cooja simulator can simplify the research environment required to obtain the expected results. The use of energy was measured based upon two scenarios: network topology and transmission ratio (Tx and Rx) from the node sensors. The parameters used to measure the use of energy included CPU Power, LPM Power, Listening Power, and Transmit Power. The result of the test showed no any significant difference in the energy usage among the different network topologies, but the significant difference of the energy usage was obtained from the difference of the transmission ratio (Tx and Rx) sensor node.


title: The happy personality: A meta-analysis of 137 personality traits and subjective well-being.

abstract: This meta-analysis used 9 literature search strategies to examine 137 distinct personality constructs as correlates of subjective well-being (SWB). Personality was found to be equally predictive of life satisfaction, happiness, and positive affect, but significantly less predictive of negative affect. The traits most closely associated with SWB were repressive-defensiveness, trust, emotional stability, locus of control-chance, desire for control, hardiness, positive affectivity, private collective selfesteem, and tension. When personality traits were grouped according to the Big Five factors, Neuroticism was the strongest predictor of life satisfaction, happiness, and negative affect. Positive affect was predicted equally well by Extraversion and Agreeableness. The relative importance of personality for predicting SWB, how personality might influence SWB, and limitations of the present review are discussed.


title: Community detection in social networks

abstract: The expansion of the web and emergence of a large number of social networking sites SNS have empowered users to easily interconnect on a shared platform. A social network can be represented by a graph consisting of a set of nodes and edges connecting these nodes. The nodes represent the individuals/entities, and the edges correspond to the interactions among them. The tendency of people with similar tastes, choices, and preferences to get associated in a social network leads to the formation of virtual clusters or communities. Detection of these communities can be beneficial for numerous applications such as finding a common research area in collaboration networks, finding a set of likeminded users for marketing and recommendations, and finding protein interaction networks in biological networks. A large number of community-detection algorithms have been proposed and applied to several domains in the literature. This paper presents a survey of the existing algorithms and approaches for the detection of communities in social networks. We also discuss some of the applications of community detection. WIREs Data Mining Knowl Discov 2016, 6:115-135. doi: 10.1002/widm.1178


title: High thermal performance lithium-ion battery pack including hybrid active–passive thermal management system for using in hybrid/electric vehicles

abstract: In this study, a novel Li-ion battery pack design including hybrid active–passive thermal management system is presented. The battery pack is suitable for using in hybrid/electric vehicles. Active part of the hybrid thermal management system uses distributed thin ducts, air flow and natural convection as cooling media while the passive part utilizes phase change material/expanded graphite composite (PCM/EG) as cooling/heating component to optimize the thermal performance of the proposed battery pack. High melting enthalpy of PCM/EG composite together with melting of PCM/EG composite at the temperature of 58.9 °C remains the temperature distribution of the battery units in the desired temperature range (below 60 °C). The temperature and voltage distributions in the proposed battery pack design consisting of battery units, distributed thin ducts and PCM/EG composite are calculated by numerical solving of the related partial differential equations. Simulation results obtained by writing M-files code in Matlab environment and plotting the numerical data are presented to validate the theoretical results. A comparison between the thermal and physical characteristics of the proposed battery pack and other latest works is presented that explicitly proves the battery pack performance.


title: Event data warehousing for Complex Event Processing

abstract: In the last few years, Complex Event Processing (CEP) has emerged as a new paradigm for event-driven applications. The research focus in this area has so far been primarily on operational issues and not on the ex-post analysis of event data. On the other side, approaches like data warehousing have proven useful in the past to extract further valuable information from the data available within an organization. In this paper, we elaborate the concept of a fully-implemented event data warehouse as an add-on for CEP that allows to efficiently archive and query valuable event data for later analysis. We outline the overall architecture and describe the relevant meta-models for an integrated data management approach. The data management itself is implemented using a RDBMS, and its schema is automatically synchronized with the CEP models. Finally, we present a real-world use case to illustrate the application of the event data warehouse in practice.


title: Development of head care robot using five-bar closed link mechanism with enhanced head shape following capability

abstract: We have developed a head care robot equipped with scrubbing fingers that washes hair and provides scalp cares in hospitals and care facilities to ease the burden on healthcare professionals and care workers. Our robot provides frequent hair washing and a higher Quality Of Life (QOL) to patients and others who need such nursing care. Its elemental technologies include the following: a cylindrical rack mechanism for self-aligning and a drive-force transmission from an electric motor to multiple fingers, a five-bar closed link mechanism to expand the area of the head that can be washed by extension motion, and rear pressure force control by a coordinated double arm motion to switch between supporting and washing the head. In addition, we introduce an orientation correction mechanism into our five-bar closed link mechanism to keep the end effector's contact face aligned along the head's surface. This paper presents several elemental technologies and the kinematics of a five-bar closed link mechanism and discusses the improvement of the head shape following capability using an orientation correction mechanism.


title: Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights

abstract: Multilayer Neural Networks (MNNs) are commonly trained using gradient descent-based methods, such as BackPropagation (BP). Inference in probabilistic graphical models is often done using variational Bayes methods, such as Expectation Propagation (EP). We show how an EP based approach can also be used to train deterministic MNNs. Specifically, we approximate the posterior of the weights given the data using a "mean-field" factorized distribution, in an online setting. Using online EP and the central limit theorem we find an analytical approximation to the Bayes update of this posterior, as well as the resulting Bayes estimates of the weights and outputs. ::: ::: Despite a different origin, the resulting algorithm, Expectation BackPropagation (EBP), is very similar to BP in form and efficiency. However, it has several additional advantages: (1) Training is parameter-free, given initial conditions (prior) and the MNN architecture. This is useful for large-scale problems, where parameter tuning is a major challenge. (2) The weights can be restricted to have discrete values. This is especially useful for implementing trained MNNs in precision limited hardware chips, thus improving their speed and energy efficiency by several orders of magnitude. ::: ::: We test the EBP algorithm numerically in eight binary text classification tasks. In all tasks, EBP outperforms: (1) standard BP with the optimal constant learning rate (2) previously reported state of the art. Interestingly, EBP-trained MNNs with binary weights usually perform better than MNNs with continuous (real) weights - if we average the MNN output using the inferred posterior.


title: Exercise motion classification from large-scale wearable sensor data using convolutional neural networks

abstract: The ability to accurately observe human motion and identify human activities is essential for developing automatic rehabilitation and sports training systems. In this paper, large-scale exercise motion data obtained from a forearm-worn wearable sensor are classified with a convolutional neural network (CNN). Time series data consisting of accelerometer and orientation measurements are formatted as "images", allowing the CNN to automatically extract discriminative features. The resulting CNN classifies 50 gym exercises with 92.14% accuracy. A comparative study on the effects of image formatting and different CNN architectures is also presented.


title: Twenty years of linear programming based portfolio optimization

abstract: Markowitz formulated the portfolio optimization problem through two criteria: the expected return and the risk, as a measure of the variability of the return. The classical Markowitz model uses the variance as the risk measure and is a quadratic programming problem. Many attempts have been made to linearize the portfolio optimization problem. Several different risk measures have been proposed which are computationally attractive as (for discrete random variables) they give rise to linear programming (LP) problems. About twenty years ago, the mean absolute deviation (MAD) model drew a lot of attention resulting in much research and speeding up development of other LP models. Further, the LP models based on the conditional value at risk (CVaR) have a great impact on new developments in portfolio optimization during the first decade of the 21st century. The LP solvability may become relevant for real-life decisions when portfolios have to meet side constraints and take into account transaction costs or when large size instances have to be solved. In this paper we review the variety of LP solvable portfolio optimization models presented in the literature, the real features that have been modeled and the solution approaches to the resulting models, in most of the cases mixed integer linear programming (MILP) models. We also discuss the impact of the inclusion of the real features.


title: An efficient deep learning hashing neural network for mobile visual search

abstract: Mobile visual search applications are emerging that enable users to sense their surroundings with smart phones. However, because of the particular challenges of mobile visual search, achieving a high recognition bitrate has becomes a consistent target of previous related works. In this paper, we propose a few-parameter, low-latency, and high-accuracy deep hashing approach for constructing binary hash codes for mobile visual search. First, we exploit the architecture of the MobileNet model, which significantly decreases the latency of deep feature extraction by reducing the number of model parameters while maintaining accuracy. Second, we add a hash-like layer into MobileNet to train the model on labeled mobile visual data. Evaluations show that the proposed system can exceed state-of-the-art accuracy performance in terms of the MAP. More importantly, the memory consumption is much less than that of other deep learning models. The proposed method requires only $13$ MB of memory for the neural network and achieves a MAP of $97.80\%$ on the mobile location recognition dataset used for testing.


title: Business-IT Alignment: Strategic Alignment Model for healthcare (case study in hospital bandung area)

abstract: Alignment of the business and IT strategies of organization required to achieve business goals. Business IT Alignment approach via communication, consider the dimensions of organizational culture, top management support and vision implementation as key success factor. Unified Strategic Alignment Model aims to improving understanding of strategic alignment business and IT, consist of business strategy, IT strategy, IT infrastructure and process, organization infrastructure and process. This paper proposed a model SAM based on organizational culture and characteristic adopter of technology in case study hospital in Bandung (West Java). Organizational culture will influence dimensions of strategic alignment model. Clan culture as organizational culture and early majority as characteristic adopter technology in hospital can help organization to achieve business IT-alignment.


title: Feature Selection in Credit Scoring Model for Credit Card Applicants in XYZ Bank: A Comparative Study

abstract: The performance of credit scoring models is determined by the used features. The relevant features for credit scoring usually are determined unsystematic and dominate by arbitrary trial. This paper presents a comparative study of four feature selection methods, which use data mining approach in reducing the feature space. The final results show that among the four feature selection methods, the Gini Index and Information Gain algorithms perform better than others with the classification accuracy of 75.46% and 75.44% respectively.


title: The influence of temperature model assumptions on the prognosis accuracy of extinction risk

abstract: Abstract For a species whose abundance is well-known to correlate on the degree of heat different temperature model assumptions may affect the prognosis accuracy of persistence. Likewise, year-to-year autocorrelations in weather fluctuations are known to decrease extinction risk. Thus, we investigated the grey bush cricket Platycleis albopunctata . For this species is known that growth and reproduction is mainly influenced by temperature. We developed a stochastic individual based model for the bush cricket. This day–degree model described the demographic growth of the species that depends on temperature. Daily temperatures were generated by five different methods: (i) temperatures were sequentially taken from a meteorological database. To analyse the influence of different levels of autocorrelation in temperature records (ii) the day-to-day correlations were reduced by randomly permuting the sequence of days within the months of successive years from the database, (iii) year-to-year correlations were reduced by randomly rearranging the sequence of the years held in the database, (iv) combined day-to-day and year-to-year correlations were reduced according to the submodel (ii) and (iii), and finally (v) temperatures were randomly generated on the basis of Gaussian normal distributions. The mean and the variance of these distributions depended on the date of the year. Distributions were derived from the above mentioned meteorological database. We estimated highly different minimum viable population sizes. These did severely depend on the chosen temperature model. Values ranged from 3000 adults to more than a million adults per generation. High amounts of autocorrelation in temperature values decreased the extinction risk of a bush cricket population. Permuting the sequence of the years in the database increased extinction risk less than reducing day-to-day correlations. A decrease in autocorrelation of temperature records can result in unrealistic phenologies of life stages. Decreasing day-to-day or year-to-year autocorrelation in temperature records resulted in an increase or a decrease of the duration of the egg development or the duration of larval development. For poikilothermic species general implications are presented that are relevant for the design of quantitative models used in conservation biology.


title: Electromyographic Activity and 6RM Strength in Bench Press on Stable and Unstable Surfaces

abstract: The purpose of the study was to compare 6-repetition maximum (6RM) loads and muscle activity in bench press on 3 surfaces, namely, stable bench, balance cushion, and Swiss ball. Sixteen healthy, resistance-trained men (age 22.5 ± 2.0 years, stature 1.82 ± 6.6 m, and body mass 82.0 ± 7.8 kg) volunteered for 3 habituation/strength testing sessions and 1 experimental session. In randomized order on the 3 surfaces, 6RM strength and electromyographic activity of pectoralis major, deltoid anterior, biceps brachii, triceps brachii, rectus abdominis, oblique external and erector spinae were assessed. Relative to stable bench, the 6RM strength was approximately 93% for balance cushion (p <= 0.001) and approximately 92% for Swiss ball (p = 0.008); the pectoralis major electromyographic (EMG) activity was approximately 90% using the balance cushion (p = 0.080) and approximately 81% using Swiss ball (p = 0.006); the triceps EMG was approximately 79% using the balance cushion (p = 0.028) and approximately 69% using the Swiss ball (p = 0.002). Relative to balance cushion, the EMG activity in pectoralis, triceps, and erector spinae using Swiss ball was approximately 89% (p = 0.016), approximately 88% (p = 0.014) and approximately 80% (p = 0.020), respectively. In rectus abdominis, the EMG activity relative to Swiss ball was approximately 69% using stable bench (p = 0.042) and approximately 65% using the balance cushion (p = 0.046). Similar EMG activities between stable and unstable surfaces were observed for deltoid anterior, biceps brachii, and oblique external. In conclusion, stable bench press had greater 6RM strength and triceps and pectoralis EMG activity compared with the unstable surfaces. These findings have implications for athletic training and rehabilitation, because they demonstrate an inferior effect of unstable surfaces on muscle activation of prime movers and strength in bench press. If an unstable surface in bench press is desirable, a balance cushion should be chosen instead of a Swiss ball.


title: Harnessing Context Incongruity for Sarcasm Detection

abstract: The relationship between context incongruity and sarcasm has been studied in linguistics. We present a computational system that harnesses context incongruity as a basis for sarcasm detection. Our statistical sarcasm classifiers incorporate two kinds of incongruity features: explicit and implicit. We show the benefit of our incongruity features for two text forms tweets and discussion forum posts. Our system also outperforms two past works (with Fscore improvement of 10-20%). We also show how our features can capture intersentential incongruity.


title: Characterization of Piezoresistive PEDOT:PSS Pressure Sensors with Inter-Digitated and Cross-Point Electrode Structures

abstract: The piezoresistive characteristics of poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) pressure sensors with inter-digitated (IDE) and cross-point electrode (CPE) structures have been investigated. A small variation of the resistance of the pressure sensors with IDE without bottom indium-tin-oxide (b-ITO) film and with CPE structures was observed owing to the single carrier-conducting pathway. For the IDE pressure sensors with b-ITO, the piezoresistive characteristics at low and high pressure were similar to those of the pressure sensors with IDE without b-ITO and with CPE structures, respectively, leading to increased piezoresistive pressure sensitivity as the PEDOT:PSS film thickness decreased. A maximum sensitivity of more than 42 kΩ/Pa was achieved. When the normal pressure was applied, the increased number of conducting points or the reduced distance between the PEDOT oligomers within the PEDOT:PSS film resulted in a decrease of the resistance. The piezoresistive pressure sensors with a single carrier-conducting pathway, i.e., IDE without b-ITO and CPE structures, exhibited a small relaxation time and a superior reversible operation, which can be advantageous for fast piezoresistive response applications.


title: Head Pose Estimation in Computer Vision: A Survey

abstract: The capacity to estimate the head pose of another person is a common human ability that presents a unique challenge for computer vision systems. Compared to face detection and recognition, which have been the primary foci of face-related vision research, identity-invariant head pose estimation has fewer rigorously evaluated systems or generic solutions. In this paper, we discuss the inherent difficulties in head pose estimation and present an organized survey describing the evolution of the field. Our discussion focuses on the advantages and disadvantages of each approach and spans 90 of the most innovative and characteristic papers that have been published on this topic. We compare these systems by focusing on their ability to estimate coarse and fine head pose, highlighting approaches that are well suited for unconstrained environments.


title: Adaptive control of systems in cascade with saturation

abstract: Some systems may be approximated as block cascades structures where each block of the cascade can be approximately feedback linearized. In such systems, the states of the lower subsystem blocks affect the dynamics of the upper subsystems. In generating an inverse for a given block, a subset of its lower subsystem states can be treated as virtual actuators in addition to actual direct actuation that may be available. The desired virtual actuator signal arising from the upper subsystem's inverse now appears as a command to the lower subsystem. This paper introduces an adaptive element that is capable of canceling modeling errors arising due to feedback linearization. It also introduces reference models that include a pseudocontrol hedging signal which protects the adaptive element from lower subsystem's dynamics.


title: 3D conditional generative adversarial networks for high‐quality PET image estimation at low dose

abstract: Abstract Positron emission tomography (PET) is a widely used imaging modality, providing insight into both the biochemical and physiological processes of human body. Usually, a full dose radioactive tracer is required to obtain high-quality PET images for clinical needs. This inevitably raises concerns about potential health hazards. On the other hand, dose reduction may cause the increased noise in the reconstructed PET images, which impacts the image quality to a certain extent. In this paper, in order to reduce the radiation exposure while maintaining the high quality of PET images, we propose a novel method based on 3D conditional generative adversarial networks (3D c-GANs) to estimate the high-quality full-dose PET images from low-dose ones. Generative adversarial networks (GANs) include a generator network and a discriminator network which are trained simultaneously with the goal of one beating the other. Similar to GANs, in the proposed 3D c-GANs, we condition the model on an input low-dose PET image and generate a corresponding output full-dose PET image. Specifically, to render the same underlying information between the low-dose and full-dose PET images, a 3D U-net-like deep architecture which can combine hierarchical features by using skip connection is designed as the generator network to synthesize the full-dose image. In order to guarantee the synthesized PET image to be close to the real one, we take into account of the estimation error loss in addition to the discriminator feedback to train the generator network. Furthermore, a concatenated 3D c-GANs based progressive refinement scheme is also proposed to further improve the quality of estimated images. Validation was done on a real human brain dataset including both the normal subjects and the subjects diagnosed as mild cognitive impairment (MCI). Experimental results show that our proposed 3D c-GANs method outperforms the benchmark methods and achieves much better performance than the state-of-the-art methods in both qualitative and quantitative measures.


title: Dynamic Simulation of Electrical Machines and Drive Systems Using MATLAB GUI

abstract: Since the first appearance, the fields of electrical machine and drive systems have been continuously enriched by introduction of many important topics. Progress in power electronics, microcontrollers, new materials and advances in numerical modeling have led to development of new types of electrical machines and in field of electrical drives to realization of complex control algorithms. Their verification is usually done by simulation during system design, thus the effort is concentrated to development of simulation models.


title: CSMA/CA channel synchronization using semaphore

abstract: Data collision in a channel leads to channel synchronization problem, which may arise due to the existence of hidden terminal node. CSMA, TDMA, ALOHA and many more protocols provide help to channel synchronization. Semaphore is a tool which deals with synchronization problem and can be used to provide channel synchronization in a communication link. In this paper a hybrid CSMA/CA-Semaphore approach is introduced to provide channel synchronization. As soon as the node gets access to the channel, it transmits its packet according to CSMA/CA protocol. This approach helps in reducing the total collisions and results in increasing the total efficiency.


title: Using Association Rules for Course Recommendation

abstract: Students often need guidance in choosing adequate courses to complete their academic degrees. Course recommender systems have been suggested in the literature as a tool to help students make informed course selections. Although a variety of techniques have been proposed in these course recommender systems, combining data mining with user ratings in order to improve the recommendation has never been done before. This paper presents RARE, a course Recommender system based on Association RulEs, which incorporates a data mining process together with user ratings in recommendation. Starting from a history of real data, it discovers significant rules that associate academic courses followed by former students. These rules are later used to infer recommendations. In order to benefit from the current students’ opinions, RARE also offers to users the possibility to rate the recommendations, thus leading to an improvement of the rules. Therefore, RARE combines the benefits of both former students’ experience and current students’ ratings in order to recommend the most relevant courses to its users.


title: X-band two-stage high-efficiency switched-mode power amplifiers

abstract: This paper presents efficiency optimization of X-band two-stage microwave power amplifiers (PAs) in which the output stage is designed to operate in class-E mode. A hybrid PA which uses the same MESFET devices in both stages achieves 16 dB of saturated gain with an output power of 20 dBm and total power added efficiency (PAE) of 52% at 10 GHz. A broadband monolithic two-stage double heterojunction bipolar transistor PA, fabricated by Northrop Grumman Space Technology, with a class-AB first stage and class-E second stage achieves 24.6 dBm of output power with 24.6-dB gain and total PAE of 52% at 8 GHz. The design is performed starting from class-E theory and using load-pull measurements and/or nonlinear simulations.


title: Aerial surveillance system using UAV

abstract: In today's world, there is a growing need for surveillance in order to maintain the decorum at a place and ensure the safety and security of its people. An aerial surveillance system will be worthwhile in this regard. This paper describes how an aerial surveillance system can be built using an unmanned aerial vehicle or a drone. We start by delineating the features of our aerial surveillance system and then discuss some of the technologies that we have used in building it. After that we mention how we have incorporated those technologies into a drone and have made them work together harmoniously in order to achieve our desired aerial surveillance system. This system will be a convenient and efficient alternative to current surveillance systems. It can be used in peace keeping activities and also real time monitoring of a place at any time of the day. The aim is to provide fast and efficient surveillance at an affordable rate so that it can be used widely at private, institutional and governmental level.


title: JPEG-HDR: a backwards-compatible, high dynamic range extension to JPEG

abstract: The transition from traditional 24-bit RGB to high dynamic range (HDR) images is hindered by excessively large file formats with no backwards compatibility. In this paper, we demonstrate a simple approach to HDR encoding that parallels the evolution of color television from its grayscale beginnings. A tone-mapped version of each HDR original is accompanied by restorative information carried in a subband of a standard output-referred image. This subband contains a compressed ratio image, which when multiplied by the tone-mapped foreground, recovers the HDR original. The tone-mapped image data is also compressed, and the composite is delivered in a standard JPEG wrapper. To naïve software, the image looks like any other, and displays as a tone-mapped version of the original. To HDR-enabled software, the foreground image is merely a tone-mapping suggestion, as the original pixel data are available by decoding the information in the subband. Our method further extends the color range to encompass the visible gamut, enabling a new generation of display devices that are just beginning to enter the market.


title: A Medical Image Steganography Method Based on Integer Wavelet Transform and Overlapping Edge Detection

abstract: Recently, there has been an increased interest in the transmission of digital medical images for e-health services. However, existing implementations of this service do not pay much attention to the confidentiality and protection of patients’ information. In this paper, we present a new medical image steganography technique for protecting patients’ confidential information through the embedding of this information in the image itself while maintaining high quality of the image as well as high embedding capacity. This technique divides the cover image into two areas, the Region of Interest (ROI) and the Region of Non-Interest (RONI), by performing Otsu’s method and then encloses ROI pixels in a rectangular shape according to the binary pixel intensities. In order to improve the security, the Electronic Patient Records (EPR) is embedded in the high frequency sub-bands of the wavelet transform domain of the RONI pixels. An edge detection method is proposed using overlapping blocks to identify and classify the edge regions. Then, it embeds two secret bits into three coefficient bits by performing an XOR operation to minimize the difference between the cover and stego images. The experimental results indicate that the proposed method provides a good compromise between security, embedding capacity and visual quality of the stego images.


Segmentation of 3D Point Clouds using a New Spectral Clustering Algorithm Without a-priori Knowledge


title: Elemental analysis of PM2.5 using PIXE and NAA in Xinzhen, Beijing

abstract: Samples collected from February 2017 to January 2018 were analyzed by Particle Induced X-ray Emission (PIXE) and Black Carbon meter, and 17 elements were determined. Among them, S, Cl, K, Mn and BC had similar temporal trends with PM2.5, and increased significantly under heavy pollution in winter, while Al、Ca、Mg、Si and Ti were the typical crustal elements. From December 19th to 30th, seven samples were re-measured by short-irradiation of Neutron Activation Analysis (NAA), which performed 15 element concentrations, Ba, Ti, I, Br, Mn, Mg, Cu, In, Na, V, K, Cl, Al, Ca and S. In, Br, V, Ba and I increased remarkably in this process of pollution, compared with those not determined by PIXE. Moreover, NAA has a high sensitivity for In, a trace element in PM2.5,which may be a key element associated with potential pollution sources. For the common elements measured by NAA and PIXE, the values of Mn, Ca, Al, S and Mg were almost equal to each other.


title: Assessing vignetting as a means to reduce VR sickness during amplified head rotations

abstract: Redirected and amplified head movements have the potential to provide more natural interaction with virtual environments (VEs) than using controller-based input, which causes large discrepancies between visual and vestibular self-motion cues and leads to increased VR sickness. However, such amplified head movements may also exacerbate VR sickness symptoms over no amplification. Several general methods have been introduced to reduce VR sickness for controller-based input inside a VE, including a popular vignetting method that gradually reduces the field of view. In this paper, we investigate the use of vignetting to reduce VR sickness when using amplified head rotations instead of controller-based input. We also investigate whether the induced VR sickness is a result of the user's head acceleration or velocity by introducing two different modes of vignetting, one triggered by acceleration and the other by velocity. Our dependent measures were pre and post VR sickness questionnaires as well as estimated discomfort levels that were assessed each minute of the experiment. Our results show interesting effects between a baseline condition without vignetting, as well as the two vignetting methods, generally indicating that the vignetting methods did not succeed in reducing VR sickness for most of the participants and, instead, lead to a significant increase. We discuss the results and potential explanations of our findings.


All that melts into air is solid: Rematerialising capital in Cube and Videodrome


title: Mobility prediction handover using user mobility pattern and guard channel assignment scheme

abstract: In the next generation of mobile communication networks, the radius of the cell will become smaller, causing more frequent handovers and disconnection of existing handover calls if the channel capacity for handover is insufficient. This paper proposes a mobility prediction handover scheme to prevent these phenomena. If the next cell into which a mobile user will move could be predicted, then the necessary bandwidth could be reserved to maintain the user’s connection. The proposed scheme predicts the next cell to which a mobile user will move, based on the user’s mobility pattern history and current mobility trace. In addition, this paper suggests a Mobility Prediction Guard Channel Reservation Scheme (MPGCRS) that adjusts guard channel size in accordance with the handover prediction ratio.


title: Internal Success Factor of Hotel Occupancy Rate

abstract: The rapid economic growth in Malaysia has had positive impact on the hospitality sector of the country. This sector is has been one of the significant contributors to the nation's GNP. To meet the growing demand of hospitality industry, owners have been actively upgrading hotels and resort. Nonetheless, to be able stay competitive in the industry, it is crucial for hotel operators to consistently improve their internal factors towards achieving successful occupancy rate, as it is the yardstick to improve revenue. The first objective of this study is to determine the internal success factors that have the greatest impact on the hotel occupancy rate. The other objective is to suggest recommendation to improve the occupancy rate. Cross-sectional survey was applied to 200 respondents where only 135 questionnaires were returned. The overall finding of this study suggests that the success of hotel operation depends on how well hotel are able to fulfill and meet customers’ expectation. Invariably, exceeding these expectation will lead to customer satisfaction. Further this study provides some insights and invaluable information in the management of the hotel industry. With this regard, hotel owners will be able to improve their current method of operation and management of hotels. It is envisaged that this study serve as reference for future research in the hotel industry. Keyword: Hotel, occupancy rate, internal success factors


Evaluation of Enterprise IT Architecture Solutions – How can an ICT consultant tell what is best for you


title: Shape-matching soft mechanical metamaterials

abstract: Architectured materials with rationally designed geometries could be used to create mechanical metamaterials with unprecedented or rare properties and functionalities. Here, we introduce “shape-matching” metamaterials where the geometry of cellular structures comprising auxetic and conventional unit cells is designed so as to achieve a pre-defined shape upon deformation. We used computational models to forward-map the space of planar shapes to the space of geometrical designs. The validity of the underlying computational models was first demonstrated by comparing their predictions with experimental observations on specimens fabricated with indirect additive manufacturing. The forward-maps were then used to devise the geometry of cellular structures that approximate the arbitrary shapes described by random Fourier’s series. Finally, we show that the presented metamaterials could match the contours of three real objects including a scapula model, a pumpkin, and a Delft Blue pottery piece. Shape-matching materials have potential applications in soft robotics and wearable (medical) devices.


title: Are Your Neighbors Swingers or Kinksters? Feeld App Forensic Analysis

abstract: Dating applications (apps) are becoming more niche and intimate, allowing users interested in alternative sexual lifestyles to discover each other in relation to their geo-location as well as mutual sexual interests. Such apps can provide a wealth of information in a forensic investigation, although research in this topic appears limited. This paper examines the extent and type of artifacts created on a device when using Feeld, an app whose user base is unconventional either by their sexual identity or by an inclination to experience alternative sexual activities, and their impact on the privacy of users sharing the most personal details of their sexual lives. Such users may be at a higher risk of victimization due to their sexual identity or orientation. Specifically, we demonstrate what types of data can be forensically recovered (e.g. messages to and from user account, and photos belonging to other dating app users), and how distance information stored by the app can be used to conduct a trilateration attack and successfully expose another user's actual location.


Deep Learning Approaches for P 300 Classification in Image Triage : Applications to the NAILS Task


title: Personality Analysis Through Handwriting

abstract: This work discusses about computer aided Graphology i.e. Personality analysis based on handwritten text, in which instead of a Graphologist, a system will be trained to perform the analysis without much human intervention. A real world dataset of handwritten text samples will be maintained and based on few features like Margins, Baseline, Size, Zones etc. depicted in each of the samples and extracted through Image processing techniques and , an approximate analysis of the writer’s personality trait will be done. These traits will then be mapped to the existing personality theories and finally a personality type, temperament and a detailed report will be given for the handwritten sample, as the result of analysis.


title: Apoptosis and cancer : mutations within caspase genes

abstract: The inactivation of programmed cell death has profound effects not only on the development but also on the overall integrity of multicellular organisms. Beside developmental abnormalities, it may l ...


title: Fast and compact hash tables for integer keys

abstract: A hash table is a fundamental data structure in computer science that can offer rapid storage and retrieval of data. A leading implementation for string keys is the cache-conscious array hash table. Although fast with strings, there is currently no information in the research literature on its performance with integer keys. More importantly, we do not know how efficient an integer-based array hash table is compared to other hash tables that are designed for integers, such as bucketized cuckoo hashing. In this paper, we explain how to efficiently implement an array hash table for integers. We then demonstrate, through careful experimental evaluations, which hash table, whether it be a bucketized cuckoo hash table, an array hash table, or alternative hash table schemes such as linear probing, offers the best performance---with respect to time and space---for maintaining a large dictionary of integers in-memory, on a current cache-oriented processor.


title: Language Games for Autonomous Robots

abstract: Integration and grounding are key AI challenges for human-robot dialogue. The author and his team are tackling these issues using language games and have experimented with them on progressively more complex platforms. A language game is a sequence of verbal interactions between two agents situated in a specific environment. Language games both integrate the various activities required for dialogue and ground unknown words or phrases in a specific context, which helps constrain possible meanings.


title: Optimal Multi-Meeting-Point Route Search

abstract: Real-time ride-sharing applications (e.g., Uber and Lyft) are very popular in recent years. Motivated by the ride-sharing application, we propose a new type of query in road networks, called the optimal multi-meeting-point route ( $\mathsf {OMMPR}$ ) query. Given a road network $G$ , a source node $s$ , a target node $t$ , and a set of query nodes $U$ , the $\mathsf {OMMPR}$ query aims at finding the best route starting from $s$ and ending at $t$ such that the weighted average cost between the cost of the route and the total cost of the shortest paths from every query node to the route is minimized. We show that the problem of computing the $\mathsf {OMMPR}$ query is NP-hard. To answer the $\mathsf {OMMPR}$ query efficiently, we propose two novel parameterized solutions based on dynamic programming (DP), with the number of query nodes $l$ (i.e., $l=|U|$ ) as a parameter, which is typically very small in practice. The two proposed parameterized algorithms run in $O(3^l\cdot m+ 2^l \cdot n\cdot (l+\log (n)))$ and $O(2^l \cdot (m + n\cdot (l+\log (n))))$ time, respectively, where $n$ and $m$ denote the number of nodes and edges in graph $G$ , thus they are tractable in practice. To reduce the search space of the DP-based algorithms, we propose two novel optimized algorithms based on bidirectional DP and a carefully-designed lower bounding technique. We conduct extensive experimental studies on four large real-world road networks, and the results demonstrate the efficiency of the proposed algorithms.


title: Biodegradable polymer microneedles: fabrication, mechanics and transdermal drug delivery

abstract: To overcome skin's barrier properties that block transdermal delivery of most drugs, we and others have microfabricated arrays of microscopic needles, primarily out of silicon or metal. This study addresses microneedles made of biocompatible and biodegradable polymers, which are expected to improve safety and manufacturability. To make biodegradable polymer microneedles with sharp tips, we adapted microelectromechanical masking and etching to produce beveled-tip and chisel tip microneedles and developed a new fabrication method to produce tapered-cone microneedles using an in-situ lens-based lithographic approach. To replicate microfabricated master structures, PDMS micromolds were generated and a novel vacuum-based method was developed to fill the molds with polylactic acid, polyglycolic acid and their copolymers. Mechanical testing of the resulting needles measured the force at which needles broke during axial loading and found that this failure force increased with Young's modulus of the material and needle base diameter and decreased with needle length. Failure forces were generally much larger than the forces needed to insert microneedles into skin, indicating that biodegradable polymers can have satisfactory mechanical properties for microneedles. Finally, arrays of polymer microneedles were shown to increase permeability of human cadaver skin to a low-molecular weight tracer, calcein, and a macromolecular protein, bovine serum albumin, by up to three orders of magnitude. Altogether, these results indicate that biodegradable polymer microneedles can be fabricated with an appropriate geometry and sufficient strength to insert into skin, and thereby dramatically increase transdermal transport of molecules.


title: Compression of Deep Convolutional Neural Networks under Joint Sparsity Constraints

abstract: We consider the optimization of deep convolutional neural networks (CNNs) such that they provide good performance while having reduced complexity if deployed on either conventional systems utilizing spatial-domain convolution or lower complexity systems designed for Winograd convolution. Furthermore, we explore the universal quantization and compression of these networks. In particular, the proposed framework produces one compressed model whose convolutional filters can be made sparse either in the spatial domain or in the Winograd domain. Hence, one compressed model can be deployed universally on any platform, without need for re-training on the deployed platform, and the sparsity of its convolutional filters can be exploited for further complexity reduction in either domain. To get a better compression ratio, the sparse model is compressed in the spatial domain which has a less number of parameters. From our experiments, we obtain $24.2\times$, $47.7\times$ and $35.4\times$ compressed models for ResNet-18, AlexNet and CT-SRCNN, while their computational cost is also reduced by $4.5\times$, $5.1\times$ and $23.5\times$, respectively.


title: High power density DC/DC-converter using coupled inductors

abstract: This paper presents a high power density coupled tape-wound inductor consisting of nano-crystalline material used in a DC/DC converter. In a first step the comparison of the 2-phase coupled inductor (CI) with the 2-phase interleaved converter (SII) is presented. Second step is designing the CI ρ/η-pareto analysis. To test the device a high power density SiC-converter is designed. The experimentally tested prototype is operating at V h = 400V, i = 100A, fs = 90kHz, aimed at an overall power density of ρ = 40kw/1.


title: A Hybrid Convolutional and Recurrent Deep Neural Network for Breast Cancer Pathological Image Classification

abstract: Hematoxylin and Eosin H&E stained breast tissue samples from biopsies are observed under microscopy for the gold standard diagnosis of breast cancer. However, a substantial workload increases and the complexity of the pathological images make this task time-consuming and may suffer from pathologist’s subjectivity. Facing this problem, the development of automatic and precise diagnosis methods is challenging but also essential for the field. In this paper, we propose a new hybrid convolutional and recurrent deep neural network for breast cancer pathological image classification. Our method considers the short-term as well as the long-term spatial correlations between patches through RNN which is directly incorporated on top of a CNN feature extractor. Experimental results showed that our method obtained an average accuracy of 90.5% for 4-class classification task, which outperforms the state-of-the-art method. At the same time, we release a bigger dataset with 1568 breast cancer pathological images to the scientific community, which are now publicly available from http://ear.ict.ac.cn/?page id=1576. In particular, our dataset covers as many different subclasses spanning different age groups as possible, thus alleviating the problem of relatively low classification accuracy of benign.


title: An expert clinical decision support system to predict disease using classification techniques

abstract: Currently in the healthcare industry different data mining methods are used to mine the interesting pattern of diseases using the statistical medical data with the help of different machine learning techniques. The conventional disease diagnosis system uses the perception and experience of doctor without using the complex clinical data. The proposed system assists doctor to predict disease correctly and the prediction makes patients and medical insurance providers benefited. This research focuses on to diagnosis diabetes disease as it is a great threat to human life worldwide. The system uses the Decision Tree and K-Nearest Neighbor (KNN) Algorithms as supervised classification model. Finally, the proposed system calculates and compares the accuracy of C4.5 and KNN and the experimental result demonstrates that the C4.5 provides better accuracy for diagnosis diabetes. For the clinical database, the Pima Indians Dataset is used in this research.


title: Outward Influence and Cascade Size Estimation in Billion-scale Networks

abstract: Estimating cascade size and nodes' influence is a fundamental task in social, technological, and biological networks. Yet this task is extremely challenging due to the sheer size and the structural heterogeneity of networks. We investigate a new influence measure, termed outward influence (OI), defined as the (expected) number of nodes that a subset of nodes S will activate, excluding the nodes in S. Thus, OI equals, the de facto standard measure, influence spread of S minus |S|. OI is not only more informative for nodes with small influence, but also, critical in designing new effective sampling and statistical estimation methods. Based on OI, we propose SIEA/SOIEA, novel methods to estimate influence spread/outward influence at scale and with rigorous theoretical guarantees. The proposed methods are built on two novel components 1) IICP an important sampling method for outward influence; and 2) RSA, a robust mean estimation method that minimize the number of samples through analyzing variance and range of random variables. Compared to the state-of-the art for influence estimation, SIEA is Ω(log4 n) times faster in theory and up to several orders of magnitude faster in practice. For the first time, influence of nodes in the networks of billions of edges can be estimated with high accuracy within a few minutes. Our comprehensive experiments on real-world networks also give evidence against the popular practice of using a fixed number, e.g. 10K or 20K, of samples to compute the ``ground truth'' for influence spread.


title: Robust photoplethysmographic (PPG) based biometric authentication for wireless body area networks and m-health applications

abstract: In this paper, we present noise-robust photoplethysmographic (PPG) based biometric authentication method for wireless body area networks and m-health applications. The method consists of four steps: (i) preprocessing of PPG signals, (ii) systolic peak detection, (iii) ensemble averaged pulsatile waveform extraction and (iv) pulsatile waveform similarity matching using a normalized cross correlation (NCC) measure. The performance of the proposed method is tested and validated using different types of PPG signals taken from the standard PPG databases. For predefined threshold of 0.997, the NCC-based PPG biometric method achieves an average false rejection rate (FRR) of 0.32 and false acceptance rate (FAR) of 0.32. Performance evaluation results show that the proposed method achieves consistent authentication results as compared to the other methods under different kinds of artifacts and noise.


title: Particle swarm based arc detection on time series in pantograph-catenary system

abstract: Pantograph-catenary system is the most important ::: component for transmitting the electric energy to the train. If the ::: faults have not detected in an early stage, energy can disrupt the ::: energy and this leads to more serious faults. The arcs occurred in ::: the contact point is the first step of a fault. When they are ::: detected in an early stage, catastrophic faults and accidents can ::: be avoided. In this study, a new approach has been proposed to ::: detect arcs in pantograph-catenary system. The proposed method ::: applies a threshold value to each video frame and the rate of ::: sudden glares are converted to time series. The phase space of the ::: obtained time series is constructed and the arc event is found by ::: using particle swarm optimization. The proposed method is ::: analyzed by using real pantograph-videos and good result have ::: been obtained.


title: An IoT based monitoring framework for software defined 5G mobile networks

abstract: This paper presents a new approach for comprehensive monitoring of software defined 5G mobile network by using IoT (Internet of Things) based framework. The proposed framework gives much easier implementation of monitoring system for mobile network operators. Moreover, the framework uses a unified IoT protocol for telemetry transport, which is light, data-agnostic, and interoperable.


title: On 3D simulators for multi-robot systems in ROS: MORSE or Gazebo?

abstract: Realistically simulating a population of robots has been an important subject to the robotics community for the last couple of decades. Multi-robot systems are often challenging to deploy in the real world due to the complexity involved, and researchers often develop and validate coordination mechanisms and collaborative robotic behavior preliminarily in simulations. Thus, choosing a useful, flexible and realistic simulator becomes an important task. In this paper, we overview several 3D multi-robot simulators, focusing on those that support the Robot Operating System (ROS). We also provide a comparative analysis, discussing two popular open-source 3D simulators compatible with ROS - MORSE and Gazebo -, using a multi-robot patrolling application, i.e. a distributed security task, as a case study.


title: A Fully Integrated Ultra-Low-Power High-Voltage Driver for Bistable LCDs

abstract: A complete ultra-low-power high-voltage driver for 80 times 104 passive-matrix bistable LCD is integrated in a 0.7mum CMOS smart-power technology. It features 100V driving capability on all row and column outputs and comprises all necessary digitally programmable high-voltage generators and multiplexers to synthesize the required complex high-voltage waveforms from a 3V battery. An original level-shifter design for the high-voltage multiplexers yields extremely low internal power consumption below 1-10mW for the entire driver chip


title: Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives

abstract: After 40 years of investigation, steady-state visually evoked potentials (SSVEPs) have been shown to be useful for many paradigms in cognitive (visual attention, binocular rivalry, working memory, and brain rhythms) and clinical neuroscience (aging, neurodegenerative disorders, schizophrenia, ophthalmic pathologies, migraine, autism, depression, anxiety, stress, and epilepsy). Recently, in engineering, SSVEPs found a novel application for SSVEP-driven brain–computer interface (BCI) systems. Although some SSVEP properties are well documented, many questions are still hotly debated. We provide an overview of recent SSVEP studies in neuroscience (using implanted and scalp EEG, fMRI, or PET), with the perspective of modern theories about the visual pathway. We investigate the steady-state evoked activity, its properties, and the mechanisms behind SSVEP generation. Next, we describe the SSVEP-BCI paradigm and review recently developed SSVEP-based BCI systems. Lastly, we outline future research directions related to basic and applied aspects of SSVEPs.


title: A Geometry-Sensitive Approach for Photographic Style Classification

abstract: Photographs are characterized by different compositional attributes like the Rule of Thirds, depth of field, vanishing-lines etc. The presence or absence of one or more of these attributes contributes to the overall artistic value of an image. In this work, we analyze the ability of deep learning based methods to learn such photographic style attributes. We observe that although a standard CNN learns the texture and appearance based features reasonably well, its understanding of global and geometric features is limited by two factors. First, the data-augmentation strategies (cropping, warping, etc.) distort the composition of a photograph and affect the performance. Secondly, the CNN features, in principle, are translation-invariant and appearance-dependent. But some geometric properties important for aesthetics, e.g. the Rule of Thirds (RoT), are position-dependent and appearance-invariant. Therefore, we propose a novel input representation which is geometry-sensitive, position-cognizant and appearance-invariant. We further introduce a two-column CNN architecture that performs better than the state-of-the-art (SoA) in photographic style classification. From our results, we observe that the proposed network learns both the geometric and appearance-based attributes better than the SoA.


title: Assuring Fully Autonomous Vehicles Safety by Design: The Autonomous Vehicle Control (AVC) Module Strategy

abstract: Massive investment in 'intelligent' vehicle technologies is going to turn autonomous vehicles into reality in a few years. The insertion of this intelligence at the road vehicles is expected to cause a reduction in traffic accidents due to the mitigation of human drivers errors and imperfections by computerized autopilots. However, autonomous vehicles shall mitigate the existing hazards at the roadway transportation systems while not creating new hazards. Thus, some critical aspects need to be better considered, such as how to ensure safety in this new vehicle paradigm. There is no specific method to analyze and assure the safety levels of the autonomous vehicle system. Despite the ISO 26262 - a new safety standard that specifies requirements and activities throughout the road vehicles development lifecycle - it cannot be applied to the autonomous road vehicles scope. This paper proposes a design strategy that may be used at the architecture design level of autonomous vehicles that may facilitate the development, analysis and, consequently, safety level assuring. The main idea is to implement an independent module - the Autonomous Vehicle Control (AVC) - that is going to both interact with the vehicle's systems and create a protection layer that is independent of the way the vehicle's system was developed. So, the AVC could be used with any autonomous vehicle system and could be tested individually. This strategy is based on both recommended practices published by Society of Automotive Engineers (SAE) and on approaches used on other transportation system domains. Another important point is that the proposed module will be intended, in principle, for fully autonomous cars (high levels of driving automation). So, it is expected that, in the future, the proposed module can be used to develop a safety software standard or to suit the existing ones to the needs of autonomous road vehicles.


title: Impact of FinFET Technology Introduction in the 3T1D-DRAM Memory Cell

abstract: In this paper, the 3T1D-DRAM cell based on FinFET devices is studied as an alternative to the bulk one. We observe an improvement in its behavior when IG and SG FinFETs are properly mixed, since together they provide a relevant increase in the memory circuit retention time. Moreover, our FinFET cell shows larger variability robustness, better performance at low supply voltage, and higher tolerance to elevated temperatures.


title: Indexing and Querying XML Data for Regular Path Expressions

abstract: With the advent of XML as a standard for data representation and exchange on the Internet, storing and querying XML data becomes more and more important. Several XML query languages have been proposed, and the common feature of the languages is the use of regular path expressions to query XML data. This poses a new challenge concerning indexing and searching XML data, because conventional approaches based on tree traversals may not meet the processing requirements under heavy access requests. In this paper, we propose a new system for indexing and storing XML data based on a numbering scheme for elements. This numbering scheme quickly determines the ancestor-descendant relationship between elements in the hierarchy of XML data. We also propose several algorithms for processing regular path expressions, namely, (1) -Join for searching paths from an element to another, (2) -Join for scanning sorted elements and attributes to find element-attribute pairs, and (3) -Join for finding Kleene-Closure on repeated paths or elements. The -Join algorithm is highly effective particularly for searching paths that are very long or whose lengths are unknown. Experimental results from our prototype system implementation show that the proposed algorithms can process XML queries with regular path expressions by up to an or


title: An optimal sampling method for web accessibility quantitative metric

abstract: When evaluating the accessibility of a website, we usually resort to sampling methods to reduce the cost of evaluation. In this kind of approaches, a small subset of pages in a website are chosen for evaluating the accessibility value of the whole website. Good sampling quality means the selected subset can represent well the accessibility level of the whole website, i.e. minimizing the accessibility evaluation difference between the whole site and the sampled subset. As existing studies show the accuracy of sampling methods depends heavily on the metric, we propose in this paper a specific sampling method OPS-WAQM that is optimized for Web Accessibility Quantitative Metric (WAQM). OPS-WAQM minimizes the sampling error by choosing the optimal sample numbers in different page depth layers. A greedy algorithm is proposed to approximately solve the optimization problem in an efficient way. We use a dataset of 20 websites, 365780 web pages to validate our method. Experimental results show that our sampling method is effective for web accessibility evaluation.


title: Cascaded multilevel inverter control considering low harmonic content based on comparison study between firefly and Newton Raphson Algorithm

abstract: This paper deals with the control design of an asymmetrical cascaded multilevel inverter. This structure therefore provides the capability to produce higher voltages at higher speeds with low switching frequency which has inherent low switching losses and high converter efficiency. Selective harmonic elimination(SHE) for asymmetrical multilevel CHB inverter control is proposed. The technique utilized in the estimation of switching angles involves Firefly Algorithm (FFA). Compared to Newton Raphson algorithm (NR), FFA is more robust and entails less computation time. Simulation results prove the effectiveness of FFA technique compared to NR algorithm. The proposed method does effectively eliminate a number of specific low order harmonics, and the output voltage is resulted in low total harmonic distortion. FFA and NR algorithm for asymmetrical cascaded H-bridge nine level inverter control are experimentally tested on a prototype using FPGA.


title: Peeking behind the curtains of serverless platforms

abstract: Serverless computing is an emerging paradigm in which an application's resource provisioning and scaling are managed by third-party services. Examples include AWS Lambda, Azure Functions, and Google Cloud Functions. Behind these services' easy-to-use APIs are opaque, complex infrastructure and management ecosystems. Taking on the viewpoint of a serverless customer, we conduct the largest measurement study to date, launching more than 50,000 function instances across these three services, in order to characterize their architectures, performance, and resource management efficiency. We explain how the platforms isolate the functions of different accounts, using either virtual machines or containers, which has important security implications. We characterize performance in terms of scalability, coldstart latency, and resource efficiency, with highlights including that AWS Lambda adopts a bin-packing-like strategy to maximize VM memory utilization, that severe contention between functions can arise in AWS and Azure, and that Google had bugs that allow customers to use resources for free.


title: Edge Computing in IoT Ecosystems for UAV-Enabled Early Fire Detection

abstract: Unmanned Aerial Vehicles (UAV) facilitate the development of Internet of Things (IoT) ecosystems for smart city and smart environment applications. This paper proposes the adoption of Edge and Fog computing principles to the UAV based forest fire detection application domain through a hierarchical architecture. This three-layer ecosystem combines the powerful resources of cloud computing, the rich resources of fog computing and the sensing capabilities of the UAVs. These layers efficiently cooperate to address the key challenges imposed by the early forest fire detection use case. Initial experimental evaluations measuring crucial performance metrics indicate that critical resources, such as CPU/RAM, battery life and network resources, can be efficiently managed and dynamically allocated by the proposed approach.


title: Deep Group-Shuffling Random Walk for Person Re-identification

abstract: Person re-identification aims at finding a person of interest in an image gallery by comparing the probe image of this person with all the gallery images. It is generally treated as a retrieval problem, where the affinities between the probe image and gallery images (P2G affinities) are used to rank the retrieved gallery images. However, most existing methods only consider P2G affinities but ignore the affinities between all the gallery images (G2G affinity). Some frameworks incorporated G2G affinities into the testing process, which is not end-to-end trainable for deep neural networks. In this paper, we propose a novel group-shuffling random walk network for fully utilizing the affinity information between gallery images in both the training and testing processes. The proposed approach aims at end-to-end refining the P2G affinities based on G2G affinity information with a simple yet effective matrix operation, which can be integrated into deep neural networks. Feature grouping and group shuffle are also proposed to apply rich supervisions for learning better person features. The proposed approach outperforms state-of-the-art methods on the Market-1501, CUHK03, and DukeMTMC datasets by large margins, which demonstrate the effectiveness of our approach.


title: SLDR-DL: A Framework for SLD-Resolution with Deep Learning

abstract: This paper introduces an SLD-resolution technique based on deep learning. This technique enables neural networks to learn from old and successful resolution processes and to use learnt experiences to guide new resolution processes. An implementation of this technique is named SLDR-DL. It includes a Prolog library of deep feedforward neural networks and some essential functions of resolution. In the SLDR-DL framework, users can define logical rules in the form of definite clauses and teach neural networks to use the rules in reasoning processes.


title: Learn + + : An Incremental Learning Algorithm for Supervised Neural Networks

abstract: We introduce Learn++, an algorithm for incremental training of neural network (NN) pattern classifiers. The proposed algorithm enables supervised NN paradigms, such as the multilayer perceptron (MLP), to accommodate new data, including examples that correspond to previously unseen classes. Furthermore, the algorithm does not require access to previously used data during subsequent incremental learning sessions, yet at the same time, it does not forget previously acquired knowledge. Learn++ utilizes ensemble of classifiers by generating multiple hypotheses using training data sampled according to carefully tailored distributions. The outputs of the resulting classifiers are combined using a weighted majority voting procedure. We present simulation results on several benchmark datasets as well as a real-world classification task. Initial results indicate that the proposed algorithm works rather well in practice. A theoretical upper bound on the error of the classifiers constructed by Learn++ is also provided.


title: Moving Object Detection using Background Subtraction, Shadow Removal and Post Processing

abstract: In many vision based application identifying moving objects is important and critical task. For different computer vision application Background subtraction is fast way to detect moving object. Background subtraction separates the foreground from background. However, background subtraction is unable to remove shadow from foreground. Moving cast shadow associated with moving object also gets detected making it challenge for video surveillance. The shadow makes it difficult to detect the exact shape of object and to recognize the object. Now days many methods are available for background subtraction. The core of background subtraction is background modeling. Gaussian Mixture model is good balance between accuracy and complexity. For better result post processing is done to output of Gaussian Mixture model. The experimental results give good performance for the proposed method.


title: Robot Assisted Physiotherapy to Support Rehabilitation of Facial Paralysis

abstract: We have been developing the Robot Mask with shape memory alloy based actuators that follows an approach of manipulating the skin through a minimally obtrusive wires, transparent strips and tapes based pulling mechanism to enhance the expressiveness of the face. For achieving natural looking facial expressions by taking the advantage of specific characteristics of the skin, the Robot Mask follows a human anatomy based criteria in selecting these manipulation points and directions. In this paper, we describe a case study of using the Robot Mask to assist physiotherapy of a hemifacial paralyzed patient. The significant differences in shape and size of the human head between different individuals demands proper customizations of the Robot Mask. This paper briefly describes the adjusting and customizing stages employed from the design level to the implementation level of the Robot Mask. We will also introduce a depth image sensor data based analysis, which can remotely evaluate dynamic characteristics of facial expressions in a continuous manner. We then investigate the effectiveness of the Robot Mask by analyzing the range sensor data. From the case study, we found that the Robot Mask could automate the physiotherapy tasks of rehabilitation of facial paralysis. We also verify that, while providing quick responses, the Robot Mask can reduce the asymmetry of a smiling face and manipulate the facial skin to formations similar to natural facial expressions.


title: Autoscanning for coupled scene reconstruction and proactive object analysis

abstract: Detailed scanning of indoor scenes is tedious for humans. We propose autonomous scene scanning by a robot to relieve humans from such a laborious task. In an autonomous setting, detailed scene acquisition is inevitably coupled with scene analysis at the required level of detail. We develop a framework for object-level scene reconstruction coupled with object-centric scene analysis. As a result, the autoscanning and reconstruction will be object-aware, guided by the object analysis. The analysis is, in turn, gradually improved with progressively increased object-wise data fidelity. In realizing such a framework, we drive the robot to execute an iterative analyze-and-validate algorithm which interleaves between object analysis and guided validations. The object analysis incorporates online learning into a robust graph-cut based segmentation framework, achieving a global update of object-level segmentation based on the knowledge gained from robot-operated local validation. Based on the current analysis, the robot performs proactive validation over the scene with physical push and scan refinement, aiming at reducing the uncertainty of both object-level segmentation and object-wise reconstruction. We propose a joint entropy to measure such uncertainty based on segmentation confidence and reconstruction quality, and formulate the selection of validation actions as a maximum information gain problem. The output of our system is a reconstructed scene with both object extraction and object-wise geometry fidelity.


title: BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment

abstract: Abstract We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local convolutions done in traditional image-based CNNs, our BrainNetCNN is composed of novel edge-to-edge, edge-to-node and node-to-graph convolutional filters that leverage the topological locality of structural brain networks. We apply the BrainNetCNN framework to predict cognitive and motor developmental outcome scores from structural brain networks of infants born preterm. Diffusion tensor images (DTI) of preterm infants, acquired between 27 and 46 weeks gestational age, were used to construct a dataset of structural brain connectivity networks. We first demonstrate the predictive capabilities of BrainNetCNN on synthetic phantom networks with simulated injury patterns and added noise. BrainNetCNN outperforms a fully connected neural-network with the same number of model parameters on both phantoms with focal and diffuse injury patterns. We then apply our method to the task of joint prediction of Bayley-III cognitive and motor scores, assessed at 18 months of age, adjusted for prematurity. We show that our BrainNetCNN framework outperforms a variety of other methods on the same data. Furthermore, BrainNetCNN is able to identify an infant's postmenstrual age to within about 2 weeks. Finally, we explore the high-level features learned by BrainNetCNN by visualizing the importance of each connection in the brain with respect to predicting the outcome scores. These findings are then discussed in the context of the anatomy and function of the developing preterm infant brain.


title: Music emotion classification: a fuzzy approach

abstract: Due to the subjective nature of human perception, classification of the emotion of music is a challenging problem. Simply assigning an emotion class to a song segment in a deterministic way does not work well because not all people share the same feeling for a song. In this paper, we consider a different approach to music emotion classification. For each music segment, the approach determines how likely the song segment belongs to an emotion class. Two fuzzy classifiers are adopted to provide the measurement of the emotion strength. The measurement is also found useful for tracking the variation of music emotions in a song. Results are shown to illustrate the effectiveness of the approach.


title: Autonomous vehicles safe-optimal trajectory selection based on big data analysis and predefined user preferences

abstract: Autonomous Vehicle (AV) or self-driving vehicle technology promises to provide many economical and societal benefits and impacts. Safety is on the top of these benefits. Trajectory or path planning is one of the essential and critical tasks in operating the autonomous vehicle. In this paper we are tackling the problem of trajectory planning for fully-autonomous vehicles. Our use cases are designed for autonomous vehicles in a cloud based connected vehicle environment. This paper presents a method for selecting safe-optimal trajectory in autonomous vehicles. Selecting the safe trajectory in our work mainly based on using Big Data mining and analysis of real-life accidents data and real-time connected vehicles' data. The decision of selecting this trajectory is done automatically without any human intervention. The human touches in this scenario could be only at defining and prioritizing the driving preferences and concerns at the beginning of the planned trip. Safety always overrides the ranked user preferences listed in this work. The output of this work is a safe trajectory that represented by the position, ETA, distance, and the estimated fuel consumption for the entire trip.


title: Pooled motion features for first-person videos

abstract: In this paper, we present a new feature representation for first-person videos. In first-person video understanding (e.g., activity recognition), it is very important to capture both entire scene dynamics (i.e., egomotion) and salient local motion observed in videos. We describe a representation framework based on time series pooling, which is designed to ab] short-term/long-term changes in feature descriptor elements. The idea is to keep track of how descriptor values are changing over time and summarize them to represent motion in the activity video. The framework is general, handling any types of per-frame feature descriptors including conventional motion descriptors like histogram of optical flows (HOF) as well as appearance descriptors from more recent convolutional neural networks (CNN). We experimentally confirm that our approach clearly outperforms previous feature representations including bag-of-visual-words and improved Fisher vector (IFV) when using identical underlying feature descriptors. We also confirm that our feature representation has superior performance to existing state-of-the-art features like local spatio-temporal features and Improved Trajectory Features (originally developed for 3rd-person videos) when handling first-person videos. Multiple first-person activity datasets were tested under various settings to confirm these findings.


title: An Empirical Comparison of Pattern Recognition, Neural Nets, and Machine Learning Classification Methods

abstract: Classification methods from statistical pattern recognition, neural nets, and machine learning were applied to four real-world data sets. Each of these data sets has been previously analyzed and reported in the statistical, medical, or machine learning literature. The data sets are characterized by statisucal uncertainty; there is no completely accurate solution to these problems. Training and testing or resampling techniques are used to estimate the true error rates of the classification methods. Detailed attention is given to the analysis of performance of the neural nets using back propagation. For these problems, which have relatively few hypotheses and features, the machine learning procedures for rule induction or tree induction clearly performed best.


title: Non-local Deep Features for Salient Object Detection

abstract: Saliency detection aims to highlight the most relevant objects in an image. Methods using conventional models struggle whenever salient objects are pictured on top of a cluttered background while deep neural nets suffer from excess complexity and slow evaluation speeds. In this paper, we propose a simplified convolutional neural network which combines local and global information through a multi-resolution 4×5 grid structure. Instead of enforcing spacial coherence with a CRF or superpixels as is usually the case, we implemented a loss function inspired by the Mumford-Shah functional which penalizes errors on the boundary. We trained our model on the MSRA-B dataset, and tested it on six different saliency benchmark datasets. Results show that our method is on par with the state-of-the-art while reducing computation time by a factor of 18 to 100 times, enabling near real-time, high performance saliency detection.


title: Semi-Supervised Fuzzy Clustering with Pairwise-Constrained Competitive Agglomeration

abstract: Traditional clustering algorithms usually rely on a pre-defined similarity measure between unlabelled data to attempt to identify natural classes of items. When compared to what a human expert would provide on the same data, the results obtained may be disappointing if the similarity measure employed by the system is too different from the one a human would use. To obtain clusters fitting user expectations better, we can exploit, in addition to the unlabelled data, some limited form of supervision, such as constraints specifying whether two data items belong to a same cluster or not. The resulting approach is called semi-supervised clustering. In this paper, we put forward a new semi-supervised clustering algorithm, pairwise-constrained competitive agglomeration: clustering is performed by minimizing a competitive agglomeration cost function with a fuzzy term corresponding to the violation of constraints. We present comparisons performed on a simple benchmark and on an image database


title: Corporate financial evaluation and bankruptcy prediction implementing artificial intelligence methods

abstract: Corporate accounting statements provide financial markets, and tax services with valuable data on the economic health of companies, although financial indices are only focused on a very limited part of the activity within the company. Useful tools in the field of processing extended financial and accounting data are the methods of Artificial Intelligence, aiming the efficient delivery of financial information to tax services, investors, and financial markets where lucrative portfolios can be created.


Strengthening Regional Evaluation Capacity-Building through Local Partnerships


title: An evaluation of the consequences of using short measures of the Big Five personality traits.

abstract: Researchers often use very abbreviated (e.g., 1-item, 2-item) measures of personality traits due to their convenience and ease of use as well as the belief that such measures can adequately capture an individual’s personality. Using data from 2 samples (N = 437 employees, N = 355 college students), we show that this practice, particularly the use of single-item measures, can lead researchers to substantially underestimate the role that personality traits play in influencing important behaviors and thereby over estimate the role played by new constructs. That is, the use of very short measures of personality may substantially increase both the Type 1 and Type 2 error rates. We argue that even slightly longer measures can substantially increase the validity of research findings without significant inconvenience to the researcher or research participants.


Efficient Emotion Recognition from Speech Using Deep Learning on Spectrograms.


Maternal Predator Odor Exposure in Mice Programs Adult Offspring Social Behavior and Increases Stress-Induced Behaviors in Semi-Naturalistic and Commonly-Used Laboratory Tasks


title: A Flexible Pinhole Camera Model for Coherent Nonuniform Sampling

abstract: The flexible pinhole camera (FPC) allows flexible modulation of the sampling rate over the field of view. The FPC is defined by a viewpoint and a map specifying the sampling locations on the image plane. The map is constructed from known regions of interest with interactive and automatic approaches. The FPC provides inexpensive 3D projection that allows rendering complex datasets quickly, in feed-forward fashion, by projection followed by rasterization. The FPC supports many types of data, including image, height field, geometry, and volume data. The resulting image is a coherent nonuniform sampling (CoNUS) of the dataset that matches the local variation of the dataset's importance. CoNUS images have been successfully implemented for remote visualization, focus-plus-context visualization, and acceleration of expensive rendering effects such as surface geometric detail and specular reflection. A video explaining and demonstrating the FPC is at http://youtu.be/kvFe5XjOPNM.


title: Understanding evolution of research themes: a probabilistic generative model for citations

abstract: Understanding how research themes evolve over time in a research community is useful in many ways (e.g., revealing important milestones and discovering emerging major research trends). In this paper, we propose a novel way of analyzing literature citation to explore the research topics and the theme evolution by modeling article citation relations with a probabilistic generative model. The key idea is to represent a research paper by a ``bag of citations'' and model such a ``citation document'' with a probabilistic topic model. We explore the extension of a particular topic model, i.e., Latent Dirichlet Allocation~(LDA), for citation analysis, and show that such a Citation-LDA can facilitate discovering of individual research topics as well as the theme evolution from multiple related topics, both of which in turn lead to the construction of evolution graphs for characterizing research themes. We test the proposed citation-LDA on two datasets: the ACL Anthology Network(AAN) of natural language research literatures and PubMed Central(PMC) archive of biomedical and life sciences literatures, and demonstrate that Citation-LDA can effectively discover the evolution of research themes, with better formed topics than (conventional) Content-LDA.


title: Semantic Segmentation of Colorectal Polyps with DeepLab and LSTM Networks

abstract: In this work we attempted to use the existing deep neural network called DeepLab_v3 to detect the polyps in colonoscopy images. Due to its large structure, the location of polyps may not be preserved and transmitted effectively. To address the issue we combined Long Short-Term Memory networks and DeepLab_v3 in parallel to augment the signal of the polyps' location. The new modification was examined with the colonoscopy image database ‘CVC-ClinicDB’ from MICCAI sub-challenge 2015. After training with 267 images and testing with 345 images, we got a good performance, 93.21% mean Intersection over Union (mIOU). The average computing time is 0.023 second per image. Once the model is applied to clinical colonoscopy exam videos, it could provide effective second opinions in real time to aid the diagnosis.


title: Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification

abstract: We consider the cross-domain sentiment classification problem, where a sentiment classifier is to be learned from a source domain and to be generalized to a target domain. Our approach explicitly minimizes the distance between the source and the target instances in an embedded feature space. With the difference between source and target minimized, we then exploit additional information from the target domain by consolidating the idea of semi-supervised learning, for which, we jointly employ two regularizations -- entropy minimization and self-ensemble bootstrapping -- to incorporate the unlabeled target data for classifier refinement. Our experimental results demonstrate that the proposed approach can better leverage unlabeled data from the target domain and achieve substantial improvements over baseline methods in various experimental settings.


title: Continuous Authentication System Using Online Activities

abstract: This paper poses the question, "Is it possible to identify users with just a set of Web activity logs?" The answer is yes: we can provide a continuous authentication system that does not require explicit actions by the users while monitoring their interactions regarding what they do for services as well as how they use their devices. We propose an activity-based authentication (ABA) system for active authentication that continuously verifies the identity of a user accessing multiple online services by means of their activity histories. ABA involves a machine-learning technique for authentication with a bagging-data-summarization approach, as it is difficult to identify users by using small logs. We assessed the performance and effect of various activity features extracted from the activity logs of 1,000 users of commercially deployed Web sites. Our findings provide valuable insights to guide the development of an authentication system utilizing the online activities of users.


title: Dual wideband printed dipole antenna with integrated balun

abstract: A printed dipole antenna with integrated balun is presented. The radiating structure is a two-element dipole with resonances at 900 and 1700 MHz, with the balun providing one extra resonance at each band. By co-designing the radiating element with a Marchand balun modified for dual-band operation, an antenna covering the cellular bands 824-960 and 1710-2170 MHz has been achieved. Measurements on a fabricated antenna on low-cost FR-4 substrate show antenna efficiencies of >80 and >70% in the lower and upper band respectively. Despite the use of a dual-element radiator, the radiation pattern that is close to omni-directional H-plane is obtained


title: Unsupervised Domain Adaptation for Zero-Shot Learning

abstract: Zero-shot learning (ZSL) can be considered as a special case of transfer learning where the source and target domains have different tasks/label spaces and the target domain is unlabelled, providing little guidance for the knowledge transfer. A ZSL method typically assumes that the two domains share a common semantic representation space, where a visual feature vector extracted from an image/video can be projected/embedded using a projection function. Existing approaches learn the projection function from the source domain and apply it without adaptation to the target domain. They are thus based on naive knowledge transfer and the learned projections are prone to the domain shift problem. In this paper a novel ZSL method is proposed based on unsupervised domain adaptation. Specifically, we formulate a novel regularised sparse coding framework which uses the target domain class labels' projections in the semantic space to regularise the learned target domain projection thus effectively overcoming the projection domain shift problem. Extensive experiments on four object and action recognition benchmark datasets show that the proposed ZSL method significantly outperforms the state-of-the-arts.


title: Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image

abstract: We propose a unified formulation for the problem of 3D human pose estimation from a single raw RGB image that reasons jointly about 2D joint estimation and 3D pose reconstruction to improve both tasks. We take an integrated approach that fuses probabilistic knowledge of 3D human pose with a multi-stage CNN architecture and uses the knowledge of plausible 3D landmark locations to refine the search for better 2D locations. The entire process is trained end-to-end, is extremely efficient and obtains state-of-the-art results on Human3.6M outperforming previous approaches both on 2D and 3D errors.


title: Off-line replay maintains declarative memories in a model of hippocampal-neocortical interactions

abstract: During sleep, neural activity in the hippocampus and neocortex seems to recapitulate aspects of its earlier, awake form. This replay may be a substrate for the consolidation of long-term declarative memories, whereby they become independent of the hippocampus and are stored in neocortex. In contrast to storage, other crucial facets of competent long-term memory, such as maintenance of access to stored traces and preservation of their correct interpretation, have received little attention. We investigate long-term episodic and semantic memory in a theoretical model of neocortical-hippocampal interaction. We find that, in the absence of regular hippocampal reactivation, even supposedly consolidated episodic memories are fragile in the face of cortical semantic plasticity. Replay allows access to episodes stored in the hippocampus to be maintained, by keeping them in appropriate register with changing neocortical representations. Hippocampal storage and replay also has a constructive role in the recall of structured, semantic information.


title: Privacy Skyline: Privacy with Multidimensional Adversarial Knowledge

abstract: Privacy is an important issue in data publishing. Many organizations distribute non-aggregate personal data for research, and they must take steps to ensure that an adversary cannot predict sensitive information pertaining to individuals with high confidence. This problem is further complicated by the fact that, in addition to the published data, the adversary may also have access to other resources (e.g., public records and social networks relating individuals), which we call external knowledge. A robust privacy criterion should take this external knowledge into consideration. ::: ::: In this paper, we first describe a general framework for reasoning about privacy in the presence of external knowledge. Within this framework, we propose a novel multidimensional approach to quantifying an adversary's external knowledge. This approach allows the publishing organization to investigate privacy threats and enforce privacy requirements in the presence of various types and amounts of external knowledge. Our main technical contributions include a multidimensional privacy criterion that is more intuitive and flexible than previous approaches to modeling background knowledge. In addition, we provide algorithms for measuring disclosure and sanitizing data that improve computational efficiency several orders of magnitude over the best known techniques.


title: Direct Torque and Indirect Flux Control of Brushless DC Motor

abstract: In this paper, the position-sensorless direct torque and indirect flux control of brushless dc (BLDC) motor with nonsinusoidal back electromotive force (EMF) has been extensively investigated. In the literature, several methods have been proposed for BLDC motor drives to obtain optimum current and torque control with minimum torque pulsations. Most methods are complicated and do not consider the stator flux linkage control, therefore, possible high-speed operations are not feasible. In this study, a novel and simple approach to achieve a low-frequency torque ripple-free direct torque control (DTC) with maximum efficiency based on dq reference frame is presented. The proposed sensorless method closely resembles the conventional DTC scheme used for sinusoidal ac motors such that it controls the torque directly and stator flux amplitude indirectly using d-axis current. This method does not require pulsewidth modulation and proportional plus integral regulators and also permits the regulation of varying signals. Furthermore, to eliminate the low-frequency torque oscillations, two actual and easily available line-to-line back EMF constants ( kba and kca) according to electrical rotor position are obtained offline and converted to the dq frame equivalents using the new line-to-line park transformation. Then, they are set up in the look-up table for torque estimation. The validity and practical applications of the proposed sensorless three-phase conduction DTC of BLDC motor drive scheme are verified through simulations and experimental results.


title: The impact of empathy on burnout in medical students: new findings

abstract: AbstractIntroduction: Research on medical students has shown they are at a higher risk for burnout and that this burnout may become more prevalent as they advance in medical school. The literature,...


title: Designing Toys with Automatic Play Characterization for Supporting the Assessment of a Child’s Development

abstract: In this paper, we describe the design considerations and implementation of the Child'sPlay system, a technology for supporting the automatic recording, recognition, and quantification of a child's object play behaviors for retrospective analysis. Our prototype system consists of six varieties of toys augmented with wireless sensing capabilities and a mobile computing platform which uses statistical pattern recognition techniques to automatically classify sensed play behaviors. This paper discusses our choices in toy design both in form factor as well as sensing capabilities. In addition, we also describe the play activities the system supports and provide an overview of our initial recognition algorithms.