title: Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce

abstract: In this paper, we present a unified end-to-end approach to build a large scale Visual Search and Recommendation system for e-commerce. Previous works have targeted these problems in isolation. We believe a more effective and elegant solution could be obtained by tackling them together. We propose a unified Deep Convolutional Neural Network architecture, called VisNet, to learn embeddings to capture the notion of visual similarity, across several semantic granularities. We demonstrate the superiority of our approach for the task of image retrieval, by comparing against the state-of-the-art on the Exact Street2Shop dataset. We then share the design decisions and trade-offs made while deploying the model to power Visual Recommendations across a catalog of 50M products, supporting 2K queries a second at Flipkart, India's largest e-commerce company. The deployment of our solution has yielded a significant business impact, as measured by the conversion-rate.


title: Multi-scale Fully Convolutional Network for Face Detection in the Wild

abstract: Face detection is a classical problem in computer vision. It is still a difficult task due to many nuisances that naturally occur in the wild. In this paper, we propose a multi-scale fully convolutional network for face detection. To reduce computation, the intermediate convolutional feature maps (conv) are shared by every scale model. We up-sample and down-sample the final conv map to approximate K levels of a feature pyramid, leading to a wide range of face scales that can be detected. At each feature pyramid level, a FCN is trained end-to-end to deal with faces in a small range of scale change. Because of the up-sampling, our method can detect very small faces (10×10 pixels). We test our MS-FCN detector on four public face detection datasets, including FDDB, WIDER FACE, AFW and PASCAL FACE. Extensive experiments show that it outperforms state-of-the-art methods. Also, MS-FCN runs at 23 FPS on a GPU for images of size 640×480 with no assumption on the minimum detectable face size.


title: Thoracic and lumbar spine trauma.

abstract: Complete thoracolumbar trauma evaluation incorporates radiographs, computed tomography, and magnetic resonance imaging. Primarily to localize the level of injury, diagnosis of thoracolumbar spine trauma begins with radiographs. Computed tomography with sagittal reformatted images is more sensitive for identifying the full extent of injury and the degree of involvement of the bony posterior elements. Magnetic resonance imaging is used for evaluating the extent of soft tissue injury, including damage to ligaments, discs, and epidural spaces. Magnetic resonance imaging is most frequently performed when radiographs and computed tomography do not explain the patients' symptoms and when there is a possibility of epidural hematoma, traumatic disc herniation, or spinal cord injury.


Iatrogenic Takotsubo Cardiomyopathy Secondary to Norepinephrine by Continuous Infusion for Shock


title: Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis

abstract: Many approaches to automatic sentiment analysis begin with a large lexicon of words marked with their prior polarity (also called semantic orientation). However, the contextual polarity of the phrase in which a particular instance of a word appears may be quite different from the word's prior polarity. Positive words are used in phrases expressing negative sentiments, or vice versa. Also, quite often words that are positive or negative out of context are neutral in context, meaning they are not even being used to express a sentiment. The goal of this work is to automatically distinguish between prior and contextual polarity, with a focus on understanding which features are important for this task. Because an important aspect of the problem is identifying when polar terms are being used in neutral contexts, features for distinguishing between neutral and polar instances are evaluated, as well as features for distinguishing between positive and negative contextual polarity. The evaluation includes assessing the performance of features across multiple machine learning algorithms. For all learning algorithms except one, the combination of all features together gives the best performance. Another facet of the evaluation considers how the presence of neutral instances affects the performance of features for distinguishing between positive and negative polarity. These experiments show that the presence of neutral instances greatly degrades the performance of these features, and that perhaps the best way to improve performance across all polarity classes is to improve the system's ability to identify when an instance is neutral.


title: Reassessing the HAROLD model: Is the hemispheric asymmetry reduction in older adults a special case of compensatory-related utilisation of neural circuits?

abstract: The HAROLD (hemispheric asymmetry reduction in older adults) model, proposed by Cabeza in 2002, suggests that age-related neurofunctional changes are characterised by a significant reduction in the functional hemispheric lateralisation in the prefrontal cortex (PFC). The supporting evidence, however, has been derived from qualitative explorations of the data rather than from explicit statistical assessments of functional lateralisation. In contrast, the CRUNCH (compensation-related utilisation of neural circuits hypothesis) model posits that elderly subjects recruit additional brain regions that do not necessarily belong to the contralateral hemisphere as much as they rely on additional strategies to solve cognitive problems. To better assess the validity and generalisability of the HAROLD model, we analysed the fMRI patterns of twenty-four young subjects (age range: 18–30 years) and twenty-four healthy elderly subjects (age range: 50–80 years) collected during the performance of two linguistic/semantic tasks (a picture-naming task and a sentence judgment task) and two episodic long-term memory (eLTM) recognition tasks for the same materials. The functional hemispheric lateralisation in each group and the ensuing between-group differences were quantitatively assessed using statistical lateralisation maps (SLMs). The number of clusters showing a genuine HAROLD effect was proportional to the level of task demand. In addition, when quantitatively significant, these effects were not restricted to the PFC. We conclude that, in its original version, the HAROLD model captures only some of the age-related brain patterns observed in graceful ageing. The results observed in our study are compatible with the more general CRUNCH model, suggesting that the former patterns can be considered a special manifestation of age-related compensatory processes.


title: Driver behavior recognition based on deep convolutional neural networks

abstract: Traffic safety is a severe problem around the world. Many road accidents are normally related with the driver's unsafe driving behavior, e.g. eating while driving. In this work, we propose a vision-based solution to recognize the driver's behavior based on convolutional neural networks. Specifically, given an image, skin-like regions are extracted by Gaussian Mixture Model, which are passed to a deep convolutional neural networks model, namely R∗CNN, to generate action labels. The skin-like regions are able to provide abundant semantic information with sufficient discriminative capability. Also, R∗CNN is able to select the most informative regions from candidates to facilitate the final action recognition. We tested the proposed methods on Southeast University Driving-posture Dataset and achieve mean Average Precision(mAP) of 97.76% on the dataset which prove the proposed method is effective in drivers's action recognition.


title: Compact 80 kW silicon carbide high voltage generator

abstract: This paper presents a concept for a compact high voltage generator based on a highly integrated power chain switching at frequencies above 500 kHz. The high frequency operation in combination with the high integration approach enable a reduction of volume and weight of HY-generators used in industrial or medical applications.


title: Patterns of Multistakeholder Recommendation

abstract: Recommender systems are personalized information systems. However, in many settings, the end-user of the recommendations is not the only party whose needs must be represented in recommendation generation. Incorporating this insight gives rise to the notion of multistakeholder recommendation, in which the interests of multiple parties are represented in recommendation algorithms and evaluation. In this paper, we identify patterns of stakeholder utility that characterize different multistakeholder recommendation applications, and provide a taxonomy of the different possible systems, only some of which have currently been implemented.


title: Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting

abstract: Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures, which enable much faster training speed with fewer parameters. Experiments show that our model STGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale traffic networks and consistently outperforms state-of-the-art baselines on various real-world traffic datasets.


title: RoadGraph - Graph based environmental modelling and function independent situation analysis for driver assistance systems

abstract: RoadGraph is a graph based environmental model for driver assistance systems. It integrates information from different sources like digital maps, onboard sensors and V2X communication into one single model about vehicle's environment. At the moment of information aggregation some function independent situation analysis is done. In this paper the concept of the RoadGraph is described in detail and first results are shown.


title: Secure semantic expansion based search over encrypted cloud data supporting similarity ranking

abstract: With the advent of cloud computing, more and more information data are outsourced to the public cloud for economic savings and ease of access. However, the privacy information has to be encrypted to guarantee the security. To implement efficient data utilization, search over encrypted cloud data has been a great challenge. The existing solutions depended entirely on the submitted query keyword and didn't consider the semantics of keyword. Thus the search schemes are not intelligent and also omit some semantically related documents. In view of the deficiency, as an attempt, we propose a semantic expansion based similar search solution over encrypted cloud data. Our solution could return not only the exactly matched files, but also the files including the terms semantically related to the query keyword. In the proposed scheme, a corresponding file metadata is constructed for each file. Then both the encrypted metadata set and file collection are uploaded to the cloud server. With the metadata set, the cloud server builds the inverted index and constructs semantic relationship library (SRL) for the keywords set. After receiving a query request, the cloud server first finds out the keywords that are semantically related to the query keyword according to SRL. Then both the query keyword and the extensional words are used to retrieve the files. The result files are returned in order according to the total relevance score. Eventually, detailed security analysis shows that our solution is privacy-preserving and secure under the previous searchable symmetric encryption (SSE) security definition. Experimental evaluation demonstrates the efficiency and effectives of the scheme.


title: Image Fusion Incorporating Parameter Estimation Optimized Gaussian Mixture Model and Fuzzy Weighted Evaluation System: A Case Study in Time-Series Plantar Pressure Data Set

abstract: The key issue in image fusion is the process of defining evaluation indices for the output image and for multi-scale image data set. This paper attempted to develop a fusion model for plantar pressure distribution images, which is expected to contribute to feature points construction based on shoe-last surface generation and modification. First, the time series plantar pressure distribution image was preprocessed, including back removing and Laplacian of Gaussian (LoG) filter. Then, discrete wavelet transform and a multi-scale pixel conversion fusion operating using a parameter estimation optimized Gaussian mixture model (PEO-GMM) were performed. The output image was used in a fuzzy weighted evaluation system, that included the following evaluation indices: mean, standard deviation, entropy, average gradient, and spatial frequency; the difference with the reference image, including the root mean square error, signal to noise ratio (SNR), and the peak SNR; and the difference with source image including the cross entropy, joint entropy, mutual information, deviation index, correlation coefficient, and the degree of distortion. These parameters were used to evaluate the results of the comprehensive evaluation value for the synthesized image. The image reflected the fusion of plantar pressure distribution using the proposed method compared with other fusion methods, such as up-down, mean-mean, and max-min fusion. The experimental results showed that the proposed LoG filtering with PEO-GMM fusion operator outperformed other methods.


title: Embedding Interactive Storytelling within Still and Video Panoramas for Cultural Heritage Sites

abstract: Still and video panoramas provide exciting opportunities for individual users to take virtual journeys through a wide range of environments. The opportunity they provide for the basis of interactive storytelling has not been substantially investigated. A combination of computer graphics and 3D modelling enriches the educational aspects of narratives created using a spherical video camera by illustrating objects that are hidden, not only physically but also due to the passage of time. Video panoramas created using a spherical video camera and within 3D modelling software are discussed in this paper employing the example of two heritage sites: Charles Church in Plymouth, UK and the town of Launceston, UK. Objects and artefacts created through the process of laser scanning and QTVR object movies can be placed within panoramic environments using XML and Flash based panoramic viewers enriching the experience of the audience by merging educational components with entertainment.


title: A Taxonomy Framework for Unsupervised Outlier Detection Techniques for Multi-Type Data Sets

abstract: The term "outlier" can generally be defined as an observation that is significantly different from ::: the other values in a data set. The outliers may be instances of error or indicate events. The ::: task of outlier detection aims at identifying such outliers in order to improve the analysis of ::: data and further discover interesting and useful knowledge about unusual events within numerous ::: applications domains. In this paper, we report on contemporary unsupervised outlier detection ::: techniques for multiple types of data sets and provide a comprehensive taxonomy framework and ::: two decision trees to select the most suitable technique based on data set. Furthermore, we ::: highlight the advantages, disadvantages and performance issues of each class of outlier detection ::: techniques under this taxonomy framework.


title: Erp Implementation Failure: A Case Study

abstract: Abstract While it is true that successful implementation of an enterprise resource planning (ERP) system is a task of Herculean proportions, it is not impossible. If your organization is to reap the benefits of ERP, it must first develop a plan for success. But “prepare to see your organization reengineered, your staff disrupted, and your productivity drop before the payoff is realized.” Implementing ERP must be viewed and undertaken as a new business endeavor and a team mission, not just a software installation. Companies must involve all employees, and unconditionally and completely sell them on the concept of ERP for it to be a success. A successful implementation means involving, supervising, recognizing, and retaining those who have worked or will work closely with the system. Without a team attitude and total backing by everyone involved, an ERP implementation will end in less than an ideal situation. This was the situation for a soft drink bottler that tried to cut corners and did not recognize the...


title: A Paradoxical Indentity-Based Signature Scheme Resulting from Zero-Knowledge

abstract: At EUROCRYPT'88, we introduced an interactive zero-knowledge protocol (Guillou and Quisquater [13]) fitted to the authentication of tamper-resistant devices (e.g. smart cards, Guillou and Ugon [14]).Each security device stores its secret authentication number, an RSA-like signature computed by an authority from the device identity. Any transaction between a tamper-resistant security device and a verifier is limited to a unique interaction: the device sends its identity and a random test number, then the verifier tells a random large question; and finally the device answers by a witness number. The transaction is successful when the test number is reconstructed from the witness number, the question and the identity according to numbers published by the authority and rules of redundancy possibly standardized.This protocol allows a cooperation between users in such a way that a group of cooperative users looks like a new entity, having a shadowed identity the product of the individual shadowed identities, while each member reveals nothing about its secret.In another scenario, the secret is partitioned between distinct devices sharing the same identity. A group of cooperative users looks like a unique user having a larger public exponent which is the greater common multiple of each individual exponent.In this paper, additional features are introduced in order to provide: firstly, a mutual interactive authentication of both communicating entities and previously exchanged messages, and, secondly, a digital signature of messages, with a non-interactive zero-knowledge protocol. The problem of multiple signature is solved here in a very smart way due to the possibilities of cooperation between users.The only secret key is the factors of the composite number chosen by the authority delivering one authentication number to each smart card. This key is not known by the user. At the user level, such a scheme may be considered as a keyless identity-based integrity scheme. This integrity has anew and important property: it cannot be misused, i.e. derived into a confidentiality scheme.


title: Generative Adversarial Networks with Inverse Transformation Unit

abstract: In this paper we introduce a new structure to Generative Adversarial Networks by adding an inverse transformation unit behind the generator. We present two theorems to claim the convergence of the model, and two conjectures to nonideal situations when the transformation is not bijection. A general survey on models with different transformations was done on the MNIST dataset and the Fashion-MNIST dataset, which shows the transformation does not necessarily need to be bijection. Also, with certain transformations that blurs an image, our model successfully learned to sharpen the images and recover blurred images, which was additionally verified by our measurement of sharpness.


title: Multi-step virtual metrology for semiconductor manufacturing: A multilevel and regularization methods-based approach

abstract: In semiconductor manufacturing, wafer quality control strongly relies on product monitoring and physical metrology. However, the involved metrology operations, generally performed by means of scanning electron microscopes, are particularly cost-intensive and time-consuming. For this reason, in common practice a small subset only of a productive lot is measured at the metrology stations and it is devoted to represent the entire lot. Virtual Metrology (VM) methodologies are used to obtain reliable predictions of metrology results at process time, without actually performing physical measurements. This goal is usually achieved by means of statistical models and by linking process data and context information to target measurements. Since semiconductor manufacturing processes involve a high number of sequential operations, it is reasonable to assume that the quality features of a given wafer (such as layer thickness and critical dimensions) depend on the whole processing and not on the last step before measurement only. In this paper, we investigate the possibilities to enhance VM prediction accuracy by exploiting the knowledge collected in the previous process steps. We present two different schemes of multi-step VM, along with dataset preparation indications. Special emphasis is placed on regression techniques capable of handling high-dimensional input spaces. The proposed multi-step approaches are tested on industrial production data.


title: A Case of Thanatophoric Dysplasia Type 2: A Novel Mutation

abstract: Thanatophoric dysplasia (TD) is a lethal form of skeletal dysplasia with short-limb dwarfism. Two types distinguished with their radiological characteristics have been defined clinically. The femur is curved in type 1, while it is straight in type 2. TD is known to be due to a mutation in the fibroblast growth factor receptor 3 (FGFR3) gene. We report a male patient who showed clinical findings congruent with TD type 2 and a new mutation in the FGFR3 gene, a finding which has not been reported previously.


UWaterloo at SemEval-2017 Task 8: Detecting Stance towards Rumours with Topic Independent Features.


title: Stochastic Resource Provisioning for Containerized Multi-Tier Web Services in Clouds

abstract: Under today's bursty web traffic, the fine-grained per-container control promises more efficient resource provisioning for web services and better resource utilization in cloud datacenters. In this paper, we present Two -stage S tochastic P rogramming R esource A llocator (2SPRA). It optimizes resource provisioning for containerized n-tier web services in accordance with fluctuations of incoming workload to accommodate predefined SLOs on response latency. In particular, 2SPRA is capable of minimizing resource over-provisioning by addressing dynamics of web traffic as workload uncertainty in a native stochastic optimization model. Using special-purpose OpenOpt optimization framework, we fully implement 2SPRA in Python and evaluate it against three other existing allocation schemes, in a Docker-based CoreOS Linux VMs on Amazon EC2. We generate workloads based on four real-world web traces of various traffic variations: AOL, WorldCup98, ClarkNet, and NASA. Our experimental results demonstrate that 2SPRA achieves the minimum resource over-provisioning outperforming other schemes. In particular, 2SPRA allocates only 6.16 percent more than application's actual demand on average and at most 7.75 percent in the worst case. It achieves 3x further reduction in total resources provisioned compared to other schemes delivering overall cost-savings of 53.6 percent on average and up to 66.8 percent. Furthermore, 2SPRA demonstrates consistency in its provisioning decisions and robust responsiveness against workload fluctuations.


title: Neuropsychological studies of mild traumatic brain injury: A meta-analytic review of research since 1995

abstract: A meta-analysis conducted by Binder, Rohling and Larrabee (1997) established a relationship between mild traumatic brain injury (TBI) and small reductions in cognitive functioning in individuals assessed more than 3 months post-injury. As a follow-up, this study summarized similar research that (1) was published since the previous meta-analysis, and (2) included data collected at any stage post-injury. An extensive literature search revealed 17 suitable studies from which effect sizes were aggregated. The overall effect size was g = 0.32, p < .001. Speed of processing measures had the largest effect, g = 0.47, p < .001. The merging of post-acute effect sizes with those reported in Binder et al.'s review yielded a nonsignificant result, g = 0.11. Time since injury was found to be a significant moderator variable, with effect sizes tending to zero with increasing time post injury. Thanks go to the authors of papers included in this analysis who willingly provided additional data and information to assist in...


title: Design and Optimization of Multipath Ring Oscillators

abstract: Multipath ring oscillators have been used to generate multiple clock phases and higher oscillation frequencies. This paper presents a modified linear analysis to find an accurate expression for the oscillation frequency and phase noise. The analysis shows that for the same power consumption, the number of phases can be increased without decreasing the oscillation frequency or degrading the phase noise. The expression further indicates factors that determine the oscillation frequency and shows that in principle, the maximum oscillation frequency can exceed that of a three-stage ring oscillator. The analytical results can be used to find the optimum coupling structure of the oscillator to achieve a desired operating frequency and phase noise performance.


title: Improving Text-to-SQL Evaluation Methodology

abstract: To be informative, an evaluation must measure how well systems generalize to realistic unseen data. We identify limitations of and propose improvements to current evaluations of text-to-SQL systems. First, we compare human-generated and automatically generated questions, characterizing properties of queries necessary for real-world applications. To facilitate evaluation on multiple datasets, we release standardized and improved versions of seven existing datasets and one new text-to-SQL dataset. Second, we show that the current division of data into training and test sets measures robustness to variations in the way questions are asked, but only partially tests how well systems generalize to new queries; therefore, we propose a complementary dataset split for evaluation of future work. Finally, we demonstrate how the common practice of anonymizing variables during evaluation removes an important challenge of the task. Our observations highlight key difficulties, and our methodology enables effective measurement of future development.


title: License Plate Detection of Myanmar Vehicle Images Captured from the Dissimilar Environmental Conditions

abstract: Due to the difficulty in defecting the images, in this paper, we proposed the method that can detect region of interest of the license plate for Myanmar vehicles captured from the dissimilar environmental conditions, e.g., different type of license plates, angle of image capturing, and real environmental conditions. In this paper, the horizontal and vertical dilation, skew angle detec-tion and automatic bounding box were proposed to detect the license number from input images. In our experiment, the ob-tained results showed that an average detection accuracy of 99% which was substantially applied for the license plate detection from the dissimilar environmental conditions.


title: Realization of embedded speech recognition module based on STM32

abstract: Speech recognition is the key to realize man-machine interface technology. In order to improve the accuracy of speech recognition and implement the module on embedded system, an embedded speaker-independent isolated word speech recognition system based on ARM is designed after analyzing speech recognition theory. The system uses DTW algorithm and improves the algorithm using a parallelogram to extract characteristic parameters and identify the results. To finish the speech recognition independently, the system uses the STM32 series chip combined with the other external circuitry. The results of speech recognition test can achieve 90%, and which meets the real-time requirements of recognition.


title: Optimizing Energy Consumption for Big Data Collection in Large-Scale Wireless Sensor Networks With Mobile Collectors

abstract: Big sensor-based data environment and the emergence of large-scale wireless sensor networks (LS-WSNs), which are spread over wide geographic areas and contain thousands of sensor nodes, require new techniques for energy-efficient data collection. Recent approaches for data collection in WSNs have focused on techniques using mobile data collectors (MDCs) or sinks. Compared to traditional methods using static sinks, the MDC techniques give two advantages for data collection in LS-WSNs. These techniques can handle data collection over spatially separated geographical regions, and have been shown to require lower node energy consumption. Two common models for data collection using MDCs have been proposed: data collection using data mule (MULE), and sensor network with mobile access point (SENMA). The MULE and SENMA approaches can be characterized as representative of the multihop and the single-hop approaches for mobile data collection in WSNs. Although the basic architectures for MULE and SENMA have been well studied, the emergence of LS-WSNs which require partitioning the network into multiple groups and clusters prior to data collection has not been particularly addressed. This paper presents analytical approaches to determine the node energy consumption for LS-WSN MDC schemes and gives models for determining the optimal number of clusters for minimizing the energy consumption. The paper also addresses the tradeoffs when the LS-WSN MULE and SENMA models perform well.


title: Neonatal Nurses' and Therapists' Perceptions of Positioning for Preterm Infants in the Neonatal Intensive Care Unit

abstract: PURPOSE ::: Determine perceptions about positioning for preterm infants in the neonatal intensive care unit (NI CU). ::: ::: ::: DESIGN ::: Twenty-item survey. ::: ::: ::: SAMPLE ::: Neonatal nurses (n = 68) and speech, physical, and occupational therapists (n = 8). ::: ::: ::: MAIN OUTCOME VARIABLE ::: Perceptions about positioning were obtained, and differences in perceptions between nurses and therapists were explored. ::: ::: ::: RESULTS ::: Ninety-nine percent of respondents agreed that positioning is important for the well-being of the infant. Sixty-two percent of nurses and 86 percent of therapists identified the Dandle ROO as the ideal method of neonatal positioning. Forty-four percent of nurses and 57 percent of therapists reported that the Dandle ROO is the easiest positioning method to use in the NICU. Some perceptions differed: Therapists were more likely to report that the SleepSack does not hold the infant in good alignment. Nurses were more likely to report that the infant does not sleep well in traditional positioning.


Robust classification of different fingerprint copies with deep neural networks for database penetration rate reduction


title: Detecting emerging research fronts based on topological measures in citation networks of scientific publications

abstract: Abstract In this paper, we performed a comparative study in two research domains in order to develop a method of detecting emerging knowledge domains. The selected domains are research on gallium nitride (GaN) and research on complex networks, which represent recent examples of innovative research. We divided citation networks into clusters using the topological clustering method, tracked the positions of papers in each cluster, and visualized citation networks with characteristic terms for each cluster. Analyzing the clustering results with the average age and parent–children relationship of each cluster may be helpful in detecting emergence. In addition, topological measures, within-cluster degree z and participation coefficient P , succeeded in determining whether there are emerging knowledge clusters. There were at least two types of development of knowledge domains. One is incremental innovation as in GaN and the other is branching innovation as in complex networks. In the domains where incremental innovation occurs, papers changed their position to large z and large P . On the other hand, in the case of branching innovation, they moved to a position with large z and small P , because there is a new emerging cluster, and active research centers shift rapidly. Our results showed that topological measures are beneficial in detecting branching innovation in the citation network of scientific publications.


title: Adapted unstructured LBM for flow simulation on curved surfaces

abstract: Flow motion on curved surfaces of arbitrary topology is an interesting visual effect but a complex dynamics to simulate. In this paper, we introduce a novel and effective way to model such dynamics. We propose a technique that adapts a recently emerged computational fluid dynamics (CFD) model, unstructured lattice Boltzmann model (Unstructured LBM), from the 2D unstructured meshes to the 3D surface meshes. Unlike previous methods in modeling flows on surfaces, which start from the macroscopic point of view and modify the Navier Stokes solvers for the curved surfaces, our method is based on the microscopic kinetic equations for discrete particle distribution functions. All computations on the surface mesh only involve the information within local neighborhoods. This model lends itself the following advantages: (i) simplicity and explicit parallelism in computation, (ii) great capability in handling complex interactions, such as the interactions between flow and boundaries and the interactions of multiple-component fluids; (iii) no need of global surface parameterization which may cause strong distortions; (iv) capability of being applied to meshes with arbitrary connectivity.


title: Finding a shortest solution for the N x N extension of the 15- Puzzle is intractable

abstract: The 8-puzzle and the 15-puzzle have been used for many years as a domain for testing heuristic search techniques. From experience it is known that these puzzles are "difficult" and therefore useful for testing search techniques. In this paper we give strong evidence that these puzzles are indeed good test problems. We extend the 8-puzzle and the 15-puzzle to a nxn board and show that finding a shortest solution for the extended puzzle is NP-hard and thus computationally infeasible. ::: ::: We also present an approximation algorithm for transforming boards that is guaranteed to use no more than c L (SP) moves, where L (SP) is the length of the shortest solution and c is a constant which is independent of the given boards and their size n.


title: Mindfulness meditation training for sport (MMTS) intervention: Impact of MMTS with division I female athletes Sporda farkindalik eğitimi (SFE) çalişmasi: SFE'nin I. lig kadin atletlerdeki etkisi

abstract: Mindfulness training has been considered an effective mode for optimizing sport performance. The purpose of this study was to examine the impact of a twelve-session, 30-minute mindfulness meditation training session for sport (MMTS) intervention. The sample included a Division I female collegiate athletes, using quantitative comparisons based on pre- and post-test ratings on the Mindfulness Attention Awareness Scale (MAAS), the Positive Affect Negative Affect Scale (PANAS), the Psychological Well-Being Scale and the Life Satisfaction Scale. Paired sample t-tests highlight significant increases in mindfulness scores for the intervention group (p < .01), while the comparison group score of mindfulness remained constant. Both groups remained stable in reported positive affect however the intervention group maintained stable reports of negative affect while the comparison group experienced a significant increase in Negative Affect (p < .001). Results are discussed in relation to existing theories on mindfulness and meditation.


title: Contents lists available at ScienceDirect Clinical Psychology Review Positive Clinical Psychology: A new vision and strategy for integrated research

abstract: Abstract This review argues for the development of a Positive Clinical Psychology, which has an integrated and equally weighted focus on both positive and negative functioning in all areas of research and practice. Positive characteristics (such as gratitude, flexibility, and positive emotions) can uniquely predict disorder beyond the predictive power of the presence of negative characteristics, and buffer the impact of negative life events, potentially preventing the development of disorder. Increased study of these characteristics can rapidly expand the knowledge base of clinical psychology and utilize the promising new interventions to treat disorder through promoting the positive. Further, positive and negative characteristics cannot logically be studied or changed in isolation as (a) they interact to predict clinical outcomes, (b) characteristics are neither “positive” or “negative”, with outcomes depending on specific situation and concomitant goals and motivations, and (c) positive and negative well-being often exist on the same continuum. Responding to criticisms of the Positive Psychology movement, we do not suggest the study of positive functioning as a separate field of clinical psychology, but rather that clinical psychology itself changes to become a more integrative discipline. An agenda for research and practice is proposed including reconceptualizing well-being, forming stronger collaborations with allied disciplines, rigorously evaluating the new positive interventions, and considering a role for clinical psychologists in promoting well-being as well as treating distress.


title: An Empirical Investigation of an Object-Oriented Software System

abstract: The paper describes an empirical investigation into an industrial object oriented (OO) system comprised of 133000 lines of C++. The system was a subsystem of a telecommunications product and was developed using the Shlaer-Mellor method (S. Shlaer and S.J. Mellor, 1988; 1992). From this study, we found that there was little use of OO constructs such as inheritance, and therefore polymorphism. It was also found that there was a significant difference in the defect densities between those classes that participated in inheritance structures and those that did not, with the former being approximately three times more defect-prone. We were able to construct useful prediction systems for size and number of defects based upon simple counts such as the number of states and events per class. Although these prediction systems are only likely to have local significance, there is a more general principle that software developers can consider building their own local prediction systems. Moreover, we believe this is possible, even in the absence of the suites of metrics that have been advocated by researchers into OO technology. As a consequence, measurement technology may be accessible to a wider group of potential users.


title: Cellular-Connected UAVs over 5G: Deep Reinforcement Learning for Interference Management

abstract: In this paper, an interference-aware path planning scheme for a network of cellular-connected unmanned aerial vehicles (UAVs) is proposed. In particular, each UAV aims at achieving a tradeoff between maximizing energy efficiency and minimizing both wireless latency and the interference level caused on the ground network along its path. The problem is cast as a dynamic game among UAVs. To solve this game, a deep reinforcement learning algorithm, based on echo state network (ESN) cells, is proposed. The introduced deep ESN architecture is trained to allow each UAV to map each observation of the network state to an action, with the goal of minimizing a sequence of time-dependent utility functions. Each UAV uses ESN to learn its optimal path, transmission power level, and cell association vector at different locations along its path. The proposed algorithm is shown to reach a subgame perfect Nash equilibrium (SPNE) upon convergence. Moreover, an upper and lower bound for the altitude of the UAVs is derived thus reducing the computational complexity of the proposed algorithm. Simulation results show that the proposed scheme achieves better wireless latency per UAV and rate per ground user (UE) while requiring a number of steps that is comparable to a heuristic baseline that considers moving via the shortest distance towards the corresponding destinations. The results also show that the optimal altitude of the UAVs varies based on the ground network density and the UE data rate requirements and plays a vital role in minimizing the interference level on the ground UEs as well as the wireless transmission delay of the UAV.


title: Distance-based Self-Attention Network for Natural Language Inference

abstract: Attention mechanism has been used as an ancillary means to help RNN or CNN. However, the Transformer (Vaswani et al., 2017) recently recorded the state-of-the-art performance in machine translation with a dramatic reduction in training time by solely using attention. Motivated by the Transformer, Directional Self Attention Network (Shen et al., 2017), a fully attention-based sentence encoder, was proposed. It showed good performance with various data by using forward and backward directional information in a sentence. But in their study, not considered at all was the distance between words, an important feature when learning the local dependency to help understand the context of input text. We propose Distance-based Self-Attention Network, which considers the word distance by using a simple distance mask in order to model the local dependency without losing the ability of modeling global dependency which attention has inherent. Our model shows good performance with NLI data, and it records the new state-of-the-art result with SNLI data. Additionally, we show that our model has a strength in long sentences or documents.


Onto Explicative Data Mining: Exploratory, Interpretable and Explainable Analysis


title: Development the Strategics and Customer Relationship Management Model for Medical Center Outpatient Services

abstract: After the implementation of national health insurance in Taiwan, the hospitals are facing severe operational competition, particularly in Taipei. Thus, to develop customer management model, hospitals have introduced Customer Relationship Management (CRM) to gain new customers, maintain the relationship with existing customers, and enhance customer satisfaction and loyalty. After analyzing the patients' needs, the hospitals can satisfy the patients who will thus revisit the hospitals. The patients' customer loyalty will be enhanced. It is also the effective approach to monitor customer relationship. This study targets on 600 outpatient service customers in a medical center in Taipei, and probes into the relationship between outpatient service customers' characteristics and satisfaction, as well as importance by correlation analysis. This study then divides outpatient service customers into four clusters by cluster analysis, and proposes different managerial strategies with regard to four clusters' characteristics as the criteria for CRM in the hospital.


title: LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices

abstract: By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices.


title: The orthodontic-periodontic interrelationship in integrated treatment challenges: a systematic review

abstract: Orthodontic treatment aims at providing an acceptable functional and aesthetic occlusion with appropriate tooth movements. These movements are strongly related to interactions of teeth with their supportive periodontal tissues. In recent years, because of the increased number of adult patients seeking orthodontic treatment, orthodontists frequently face patients with periodontal problems. Aesthetic considerations, like uneven gingival margins or functional problems resulting from inflammatory periodontal diseases should be considered in orthodontic treatment planning. Furthermore, in cases with severe periodontitis, orthodontics may improve the possibilities of saving and restoring a deteriorated dentition. In modern clinical practice, the contribution of the orthodontist, the periodontist and the general dentist is essential for optimized treatment outcomes. The purpose of this systematic review is to highlight the relationship between orthodontics and periodontics in clinical practice and to improve the level of cooperation between dental practitioners. Potentials and limitations that derive from the interdisciplinary approach of complex orthodontic-periodontal clinical problems are discussed.


Individual music therapy for agitation in dementia: an exploratory randomized controlled trial


title: Parameter generation algorithm considering Modulation Spectrum for HMM-based speech synthesis

abstract: This paper proposes a novel parameter generation algorithm for high-quality speech generation in Hidden Markov Model (HMM)-based speech synthesis. One of the biggest issues causing significant quality degradation is the over-smoothing effect often observed in generated parameter trajectories. Global Variance (GV) is known as a feature well correlated with the over-smoothing effect and a metric on the GV of the generated parameters is effectively used as a penalty term in the conventional parameter generation. However, the quality of the synthetic speech is far from that of the natural speech. Recently, we have found that a Modulation Spectrum (MS) of the generated parameters, which is also regarded as an extension of the GV, is more sensitively correlated with the over-smoothing effect than the GV. This paper incorporates a metric on the MS as a new penalty term in the proposed parameter generation algorithm. The experimental results demonstrate that the proposed parameter generation algorithm considering the MS yields significant improvements in synthetic speech quality compared to the conventional parameter generation algorithm considering the GV.


title: Efficient retrieval of recommendations in a matrix factorization framework

abstract: Low-rank Matrix Factorization (MF) methods provide one of the simplest and most effective approaches to collaborative filtering. This paper is the first to investigate the problem of efficient retrieval of recommendations in a MF framework. We reduce the retrieval in a MF model to an apparently simple task of finding the maximum dot-product for the user vector over the set of item vectors. However, to the best of our knowledge the problem of efficiently finding the maximum dot-product in the general case has never been studied. To this end, we propose two techniques for efficient search -- (i) We index the item vectors in a binary spatial-partitioning metric tree and use a simple branch and-bound algorithm with a novel bounding scheme to efficiently obtain exact solutions. (ii) We use spherical clustering to index the users on the basis of their preferences and pre-compute recommendations only for the representative user of each cluster to obtain extremely efficient approximate solutions. We obtain a theoretical error bound which determines the quality of any approximate result and use it to control the approximation. Both these simple techniques are fairly independent of each other and hence are easily combined to further improve recommendation retrieval efficiency. We evaluate our algorithms on real-world collaborative-filtering datasets, demonstrating more than ×7 speedup (with respect to the naive linear search) for the exact solution and over ×250 speedup for approximate solutions by combining both techniques.


title: Normal values of plantar pressure measurements determined using the EMED-SF system.

abstract: Plantar pressure-measurement technology is being increasingly used by podiatric physicians and surgeons in both clinical practice and research. The authors present normal reference-range values for peak pressure, mean pressure, and pressure-time integral obtained from 30 healthy subjects using a two-step recording technique and the EMED-SF system, as background for proposed clinical trials. Normative data of this type are essential for clinical practice in the comparison of plantar pressure-measurement values of individual patients with those of normal, asymptomatic feet.


Fuzzy Logic Based Edge Detection in Smooth and Noisy Clinical Images


Motivation Classification and Grade Prediction for MOOCs Learners


title: Origamizing Polyhedral Surfaces

abstract: This paper presents the first practical method for "origamizing? or obtaining the folding pattern that folds a single sheet of material into a given polyhedral surface without any cut. The basic idea is to tuck fold a planar paper to form a three-dimensional shape. The main contribution is to solve the inverse problem; the input is an arbitrary polyhedral surface and the output is the folding pattern. Our approach is to convert this problem into a problem of laying out the polygons of the surface on a planar paper by introducing the concept of tucking molecules. We investigate the equality and inequality conditions required for constructing a valid crease pattern. We propose an algorithm based on two-step mapping and edge splitting to solve these conditions. The two-step mapping precalculates linear equalities and separates them from other conditions. This allows an interactive manipulation of the crease pattern in the system implementation. We present the first system for designing three-dimensional origami, enabling a user can interactively design complex spatial origami models that have not been realizable thus far.


title: Language GANs Falling Short

abstract: Traditional natural language generation (NLG) models are trained using maximum likelihood estimation (MLE) which differs from the sample generation inference procedure. During training the ground truth tokens are passed to the model, however, during inference, the model instead reads its previously generated samples - a phenomenon coined exposure bias. Exposure bias was hypothesized to be a root cause of poor sample quality and thus many generative adversarial networks (GANs) were proposed as a remedy since they have identical training and inference. However, many of the ensuing GAN variants validated sample quality improvements but ignored loss of sample diversity. This work reiterates the fallacy of quality-only metrics and clearly demonstrate that the well-established technique of reducing softmax temperature can outperform GANs on a quality-only metric. Further, we establish a definitive quality-diversity evaluation procedure using temperature tuning over local and global sample metrics. Under this, we find that MLE models consistently outperform the proposed GAN variants over the whole quality-diversity space. Specifically, we find that 1) exposure bias appears to be less of an issue than the complications arising from non-differentiable, sequential GAN training; 2) MLE trained models provide a better quality/diversity trade-off compared to their GAN counterparts, all while being easier to train, easier to cross-validate, and less computationally expensive.


title: Cluster Ensembles for High Dimensional Clustering: An Empirical Study

abstract: This paper studies cluster ensembles for high dimensional data clustering. We examine three different approaches to constructing cluster ensembles. To address high dimensionality, we focus on ensemble construction methods that build on two popular dimension reduction techniques, random projection and principal component analysis (PCA). We present evidence showing that ensembles generated by random projection perform better than those by PCA and further that this can be attributed to the capability of random projection to produce diverse base clusterings. We also examine four different consensus functions for combining the clusterings of the ensemble. We compare their performance using two types of ensembles, each with different properties. In both cases, we show that a recent consensus function based on bipartite graph partitioning achieves the best performance.


title: Optimizing NAND flash-based SSDs via retention relaxation

abstract: As NAND Flash technology continues to scale down and more bits are stored in a cell, the raw reliability of NAND Flash memories degrades inevitably. To meet the retention capability required for a reliable storage system, we see a trend of longer write latency and more complex ECCs employed in an SSD storage system. These greatly impact the performance of future SSDs. In this paper, we present the first work to improve SSD performance via retention relaxation. NAND Flash is typically required to retain data for 1 to 10 years according to industrial standards. However, we observe that many data are over-written in hours or days in several popular workloads in datacenters. The gap between the specification guarantee and actual programs' needs can be exploited to improve write speed or ECCs' cost and performance. To exploit this opportunity, we propose a system design that allows data to be written in various latencies or protected by different ECC codes without hampering reliability. Simulation results show that via write speed optimization, we can achieve 1:8-5:7× write response time speedup. We also show that for future SSDs, retention relaxation can bring both performance and cost benefits to the ECC architecture.


title: A Dynamic and Differential CMOS Logic with Signal Independent Power Consumption to Withstand Differential Power Analysis on Smart Cards

abstract: To protect security devices such as smart cards against power attacks, we propose a dynamic and differential CMOS logic style. The logic operates with a power consumption independent of both the logic values and the sequence of the data. Consequently, it will not reveal the sensitive data in a device. We have built a set of logic gates and flip-flops needed for cryptographic functions and compared those to Static Complementary CMOS implementations.


title: DeepRT: deep learning for peptide retention time prediction in proteomics

abstract: Accurate predictions of peptide retention times (RT) in liquid chromatography have many applications in mass spectrometry-based proteomics. Herein, we present DeepRT, a deep learning based software for peptide retention time prediction. DeepRT automatically learns features directly from the peptide sequences using the deep convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model, which eliminates the need to use hand-crafted features or rules. After the feature learning, principal component analysis (PCA) was used for dimensionality reduction, then three conventional machine learning methods were utilized to perform modeling. Two published datasets were used to evaluate the performance of DeepRT and we demonstrate that DeepRT greatly outperforms previous state-of-the-art approaches ELUDE and GPTime.


title: An Internet of Things Platform for Real-World and Digital Objects

abstract: The vision of the Internet of Things (IoT) relies on the provisioning of real-world services, which are provided by smart objects that are directly related to the physical world. A structured, machine-processible approach to provision such real-world services is needed to make heterogeneous physical objects accessible on a large scale and to integrate them with the digital world. The incorporation of observation and measurement data obtained from the physical objects with the Web data, using information processing and knowledge engineering methods, enables the construction of "intelligent and interconnected things". The current research mostly focuses on the communication and networking aspects between the devices that are used for sensing amd measurement of the real world objects. There is, however, relatively less effort concentrated on creating dynamic infrastructures to support integration of the data into the Web and provide unified access to such data on service and application levels. This paper presents a semantic modelling and linked data approach to create an information framework for IoT. The paper describes a platform to publish instances of the IoT related resources and entities and to link them to existing resources on the Web. The developed platform supports publication of extensible and interoperable descriptions in the form of linked data.


title: A myosignal-based powered exoskeleton system

abstract: This paper studies the integration of a human arm with a powered exoskeleton (orthotic device) and its experimental implementation in an elbow joint, naturally controlled by the human. The human-machine interface was set at the neuromuscular level, by using the neuromuscular signal (EMG) as the primary command signal for the exoskeleton system. The EMG signal along with the joint kinematics were fed into a myoprocessor which in turn predicted the muscle moments on the elbow joint. The moment-based control system integrated myoprocessor moment prediction with feedback moments measured at the human arm/exoskeleton and external load/exoskeleton interfaces. The exoskeleton structure under study was a two-link, two-joint mechanism, corresponding to the arm limbs and joints, which was mechanically linked by the human operator. Four indices of performance were used to define the quality of the human/machine integration and to evaluate the operational envelope of the system. Experimental results indicate the feasibility of an EMG-based power exoskeleton system as an integrated human-machine system using high-level neurological signals.


title: American Sign Language Recognition Using Leap Motion Sensor

abstract: In this paper, we present an American Sign Language recognition system using a compact and affordable 3D motion sensor. The palm-sized Leap Motion sensor provides a much more portable and economical solution than Cyblerglove or Microsoft kinect used in existing studies. We apply k-nearest neighbor and support vector machine to classify the 26 letters of the English alphabet in American Sign Language using the derived features from the sensory data. The experiment result shows that the highest average classification rate of 72.78% and 79.83% was achieved by k-nearest neighbor and support vector machine respectively. We also provide detailed discussions on the parameter setting in machine learning methods and accuracy of specific alphabet letters in this paper.


What Factors Influence High School Students in Choosing Cram School in Thailand


title: Joint Motion Segmentation and Background Estimation in Dynamic Scenes

abstract: We propose a joint foreground-background mixture model (FBM) that simultaneously performs background estimation and motion segmentation in complex dynamic scenes. Our FBM consist of a set of location-specific dynamic texture (DT) components, for modeling local background motion, and set of global DT components, for modeling consistent foreground motion. We derive an EM algorithm for estimating the parameters of the FBM. We also apply spatial constraints to the FBM using an Markov random field grid, and derive a corresponding variational approximation for inference. Unlike existing approaches to background subtraction, our FBM does not require a manually selected threshold or a separate training video. Unlike existing motion segmentation techniques, our FBM can segment foreground motions over complex background with mixed motions, and detect stopped objects. Since most dynamic scene datasets only contain videos with a single foreground object over a simple background, we develop a new challenging dataset with multiple foreground objects over complex dynamic backgrounds. In experiments, we show that jointly modeling the background and foreground segments with FBM yields significant improvements in accuracy on both background estimation and motion segmentation, compared to state-of-the-art methods.


title: Teaching Object-Oriented Programming with Games

abstract: First-year students in CS/IT curriculum are often overwhelmed by the introduction to programming module, which is a mandatory component for the whole study program. In this paper, we discuss the difficulties students may encounter in this module and propose a novel approach to teaching programming to new programmer with games. We reflect on our experiences on making the programming module friendly and improving the success rate among new programmers. We present our learning theory, teaching methodology and assistive software with technical details.


title: CityBench: A Configurable Benchmark to Evaluate RSP Engines Using Smart City Datasets

abstract: With the growing popularity of Internet of Things (IoT) and IoT-enabled smart city applications, RDF stream processing (RSP) is gaining increasing attention in the Semantic Web community. As a result, several RSP engines have emerged, which are capable of processing semantically annotated data streams on the fly. Performance, correctness and technical soundness of few existing RSP engines have been evaluated in controlled settings using existing benchmarks like LSBench and SRBench. However, these benchmarks focus merely on features of the RSP query languages and engines, and do not consider dynamic application requirements and data-dependent properties such as changes in streaming rate during query execution or changes in application requirements over a period of time. This hinders wide adoption of RSP engines for real-time applications where data properties and application requirements play a key role and need to be characterised in their dynamic setting, such as in the smart city domain.


title: An evaluation of service discovery protocols in the internet of things

abstract: The IoT environment surfaces challenging requirements for service discovery, such as: services heterogeneity, mobility, scalability, security, QoS support and context management. Different protocols have been proposed to facilitate service discovery, but it is difficult to assess how well these protocols meet the IoT requirements. This paper presents an evaluation of commonly used service discovery protocols for the IoT, CoAP-SD, DNS-SD, mDNS-SD, and DDS-SD, performed against both qualitative and quantitative metrics, on a physical experimental setup. The results show the limitations and strengths of the protocols, and future research directions are discussed.


title: Uses and Gratifications of Problematic Social Media Use Among University Students: a Simultaneous Examination of the Big Five of Personality Traits, Social Media Platforms, and Social Media Use Motives

abstract: Recent studies suggest that users’ preferences of social media use differ according to their individual differences and use motives, and that these factors can lead to problematic social media use (PSMU) among a minority of users. The objectives of the present study were to investigate the influences of (i) demographics and Big Five personality dimensions on social media use motives; (ii) demographics and use motives on social media site preferences; and (iii) demographics, personality, popular social media sites, and social media use motives on PSMU. The sample comprised 1008 undergraduate students, aged between 17 and 32 years (M = 20.49, SD = 1.73; 60.5% women). The participants completed a questionnaire comprising the Social Media Use Questionnaire, Social Media Usage Aims Scale, and Ten-Item Personality Inventory. Multiple linear and hierarchical regression analyses showed that social media use motives of (i) meeting new people and socializing, (ii) expressing or presenting a more popular self, and (iii) passing time and entertainment were associated with problematic social media use. Moreover, participants that preferred Instagram, Snapchat, and Facebook reported higher scores of problematic social media use. Finally, being female, introverted, conscientious, agreeable, and neurotic were associated with PSMU. The findings offer empirical evidence for uses and gratifications theory because the findings demonstrated that (i) different personality traits predict different motives, (ii) different motives predict preference of different platforms, and (iii) different individual differences such as personality, preference of platform, and specific use motives predict PSMU.


title: Battery Energy Storage System Models for Microgrid Stability Analysis and Dynamic Simulation

abstract: With the increasing importance of battery energy storage systems (BESS) in microgrids, accurate modeling plays a key role in understanding their behavior. This paper investigates and compares the performance of BESS models with different depths of detail. Specifically, several models are examined: an average model represented by voltage sources; an ideal dc source behind a voltage source converter; a back-to-back buck/boost and bidirectional three-phase converter, with all models sharing the same control system and parameters; and two additional proposed models where the switches are replaced by dependent sources to help analyze the differences observed in the performance of the models. All these models are developed in PSCAD and their performances are simulated and compared considering various issues such as voltage and frequency stability and total harmonic distortion in a benchmark test microgrid. It is shown through simulation results and eigenvalue studies that the proposed models can exhibit a different performance, especially when the system is heavily loaded, highlighting the need for more accurate modeling under certain microgrid conditions.


title: GyrosFinger: Fingerprinting Drones for Location Tracking Based on the Outputs of MEMS Gyroscopes

abstract: Drones are widely used for various purposes such as delivery, aerial photography, and surveillance. Considering the increasing drone-related services, tracking the locations of drones can cause security threats such as escaping from drone surveillance, disturbing drone-related services, and capturing drones. For wirelessly monitoring the status of drones, telemetry is used, and this status information contains various data such as latitude and longitude, calibrated sensor outputs, and sensor offsets. Because most of the telemetry implementation supports neither authentication nor encryption, an attacker can obtain the status information of the drones by using an appropriate wireless communication device such as software-defined radio. While the attacker knows the locations of the drones from the status information, this information is not sufficient for tracking drones because the status information does not include any identity information that can bind the identity of the drone with its location. ::: ::: In this article, we propose a fingerprinting method for drones in motion for the binding of the identity of the drone with its location. Our fingerprinting method is based on the sensor outputs included in the status information, i.e., the offsets of micro-electro mechanical systems (MEMS) gyroscope, an essential sensor for maintaining the attitude of drones. We found that the offsets of MEMS gyroscopes are different from each other because of manufacturing mismatches, and the offsets of five drones obtained through their telemetry are distinguishable and constant during their flights. To evaluate the performance of our fingerprinting method on a larger scale, we collected the offsets from 70 stand-alone MEMS gyroscopes to generate fingerprints. Our experimental results show that, when using the offsets of three and two axes calculated from 128 samples of the raw outputs per axis as fingerprints, the F-scores of the proposed method reach 98.78% and 94.47%, respectively. The offsets collected after a month are also fingerprinted with F-scores of 96.58% and 78.45% under the same condition, respectively. The proposed fingerprinting method is effective, robust, and persistent. Additionally, unless the MEMS gyroscope is not replaced, our fingerprinting method can be used for drone tracking even when the target drones are flying.


title: The Extreme Value Machine

abstract: It is often desirable to be able to recognize when inputs to a recognition function learned in a supervised manner correspond to classes unseen at training time. With this ability, new class labels could be assigned to these inputs by a human operator, allowing them to be incorporated into the recognition function—ideally under an efficient incremental update mechanism. While good algorithms that assume inputs from a fixed set of classes exist, e.g. , artificial neural networks and kernel machines, it is not immediately obvious how to extend them to perform incremental learning in the presence of unknown query classes. Existing algorithms take little to no distributional information into account when learning recognition functions and lack a strong theoretical foundation. We address this gap by formulating a novel, theoretically sound classifier—the Extreme Value Machine (EVM). The EVM has a well-grounded interpretation derived from statistical Extreme Value Theory (EVT), and is the first classifier to be able to perform nonlinear kernel-free variable bandwidth incremental learning. Compared to other classifiers in the same deep network derived feature space, the EVM is accurate and efficient on an established benchmark partition of the ImageNet dataset.


title: Exploring Coherence in Visual Explanations

abstract: A wide range of communicative artifacts—perhaps the majority—involve the coordinated presentation of visual and linguistic information. We envisage computer systems that support access to information by using rich representations of the interpretation of such multimodal presentations. This paper advocates organizing such representations in terms of coherence relations [2, 19], a fundamental construct from the theory of natural language discourse that is often invoked to explain the integrated interpretation of the diverse communicative actions in face-to-face conversation [9, 25, 35]. Coherence relations come in constrained classes, such as the Explanation, Narration and Parallel relations, each of which establishes specific kinds of structural, logical, and intentional relationships among communicative actions. Representing these relationships can therefore provide a scaffold for organizing, disambiguating and integrating the interpretation of communication across modalities. This paper uses a case study of instructions presented using text and pictures to motivate and describe an analysis of multimodal discourse interpretation in terms of coherence relations and to sketch a roadmap for operationalizing the approach in computer systems.


title: Intrinsic Motivation and Employee Attitudes Role of Managerial Trustworthiness, Goal Directedness, and Extrinsic Reward Expectancy

abstract: Motivation scholars have argued that intrinsic motivation is an important driver of employee attitudes. This research tests the influence of intrinsic motivation on employee attitudes and explores three factors conditioning the effects of intrinsic motivation: managerial trustworthiness, goal directedness, and extrinsic reward expectancy. The analysis demonstrates that intrinsic motivation is substantively associated with both employee satisfaction and turnover intention. It also reveals that the three conditional factors interact with intrinsic motivation. Managerial trustworthiness and goal directedness increase the leverage of intrinsic motivation on employee satisfaction, whereas extrinsic rewards expectancy decreases the leverage. With respect to turnover intention, the factors directly influence it rather than affect it indirectly through intrinsic motivation. The implications of the results for theory and managerial strategies for employee motivation are discussed.


title: Body contouring by non-invasive transdermal focused ultrasound

abstract: BACKGROUND AND OBJECTIVES ::: The risks of currently available invasive procedures in body contouring motivate a need for safer, non-invasive technologies for improving the appearance of body silhouette. A new device has been developed that uses focused therapeutic ultrasound to reduce adipose tissue non-invasively. The aim of this study was to assess the efficacy and safety of a novel non-invasive focused ultrasound system (UltraShape Ltd, Tel Aviv, Israel) in reducing localized fat deposits to improve body contours. ::: ::: ::: STUDY DESIGN/PATIENTS AND METHODS ::: A prospective study was conducted on 30 healthy patients. All patients underwent three treatments, at 1-month intervals, and were followed for 1 month after the last treatment. Areas treated were the abdomen, inner and outer thighs, flanks, inner knees, and breasts (males only). No other body contouring procedure was used during the study. Efficacy was determined by change in fat thickness, assessed by ultrasound measurements, and by circumference measurements. Weight change was monitored to assess whether reduction in fat thickness or circumference was dependent on or independent of weight loss. Safety was determined by clinical findings, assays of serum triglycerides, and liver ultrasound evaluation for the presence of steatosis. ::: ::: ::: RESULTS ::: All patients showed significant reduction in subcutaneous fat thickness within the treated area. The mean reduction in fat thickness after three treatments was 2.28+/-0.80 cm. Circumference was reduced by a mean of 3.95+/-1.99 cm. Weight was unchanged during the treatment and follow-up period. No adverse effects were observed. ::: ::: ::: CONCLUSIONS ::: This study shows the efficacy and safety of focused ultrasound, using the UltraShape Contour I, as a non-invasive transdermal method for reducing unwanted fat deposits in the body. Multiple treatments combined with appropriate patient and treatment area selection can produce dramatic improvements in body contour.


title: Products, developers, and milestones: how should I build my N-Gram language model

abstract: Recent work has shown that although programming languages enable source code to be rich and complex, most code tends to be repetitive and predictable. The use of natural language processing (NLP) techniques applied to source code such as n-gram language models show great promise in areas such as code completion, aiding impaired developers, and code search. In this paper, we address three questions related to different methods of constructing language models in an industrial context. Specifically, we ask: (1) Do application specific, but smaller language models perform better than language models across applications? (2) Are developer specific language models effective and do they differ depending on what parts of the codebase a developer is working in? (3) Finally, do language models change over time, i.e., does a language model from early development model change later on in development? The answers to these questions enable techniques that make use of programming language models in development to choose the model training corpus more effectively. We evaluate these questions by building 28 language models across developers, time periods, and applications within Microsoft Office and present the results in this paper. We find that developer and application specific language models perform better than models from the entire codebase, but that temporality has little to no effect on language model performance.


title: MovieQA: Understanding Stories in Movies through Question-Answering

abstract: We introduce the MovieQA dataset which aims to evaluate automatic story comprehension from both video and text. The dataset consists of 14,944 questions about 408 movies with high semantic diversity. The questions range from simpler "Who" did "What" to "Whom", to "Why" and "How" certain events occurred. Each question comes with a set of five possible answers; a correct one and four deceiving answers provided by human annotators. Our dataset is unique in that it contains multiple sources of information -- video clips, plots, subtitles, scripts, and DVS. We analyze our data through various statistics and methods. We further extend existing QA techniques to show that question-answering with such open-ended semantics is hard. We make this data set public along with an evaluation benchmark to encourage inspiring work in this challenging domain.


title: Forecasting students' success in an open university

abstract: In recent years, students' performance prediction has been identified as one of the most essential and challenging research topics for educational institutions. The necessity for exploitation and analysis of data originating from several educational contexts has led to a widespread implementation of familiar machine learning methods trying to effectively analyse students' academic behaviour and predict their performance. The early detection of low performers is of major importance for open universities seeking to decrease dropout ratios, improve educational outcomes and provide high quality education. This paper introduces an ensemble of classification and regression algorithms for predicting students' performance in a distance web-based course. Several state-of-the-art machine learning methods have also been applied to compare the efficiency of our method. A plethora of experiments have been conducted for this purpose, using data provided by the Hellenic Open University. The proposed ensemble combines cl...


title: A novel SVPWM scheme for Vienna rectifier without current distortion at current zero-crossing point

abstract: Vienna rectifier is one of the most popular topologies for three-phase PFC converter. DC voltage, grid current double loop controller and three-level SVPWM are normally used to control the Vienna rectifier. Accurately detecting the current zero-crossing point is required to achieve the correct SVPWM signals since the output level of Vienna rectifier is also decided by the direction of the phase current. The error of the duty ratio is decided by the error time of the current zero-crossing point detecting and the duty ratio before and after the current zero-crossing point. It can be concluded that the impact of current zero-crossing will be eliminated if the switches near current zero-crossing point are kept “on”. To achieve this goal, a novel SVPWM scheme is proposed by using the certain state combinations of short vectors in each sector. In this method, the calculation of duty ratio will not be affected by the direction of phase current near the zero-crossing point, and therefore the distortion of current caused by current zero-crossing detection error will be eliminated. Simulations and experiments are conducted to verify the presented method.


title: Patch-based fire detection with online outlier learning

abstract: Fire detection is one of the most interesting issues for surveillance. The existing approaches for the fire detection suffer from a high false positive ratio. To solve the problems, we present a patch-based fire detection algorithm with online outlier learning. In the proposed algorithm, the candidates of fire are obtained in the form of patch, while the classical candidates have been based on pixels or blobs. Because the patches of fire have more distinctive shape than the entire fire, the shape classifier can recognize the candidates correctly from fire-like outliers. In addition, we propose an online outlier learning scheme which handles the irregularity of fire based on the repeatability of shape in time. The proposed algorithm is experimented with new challenging dataset, consisting of 50 positive videos with fire and 44 negative ones with fire-like outliers. By evaluating on the dataset, we validate the performance of our algorithm qualitatively and quantitatively.


title: A review of boosting methods for imbalanced data classification

abstract: Recently, the problem of imbalanced data classification has drawn a significant amount of interest from academia, industry and government funding agencies. The fundamental issue with imbalanced data classification is the imbalanced data has posed a significant drawback of the performance of most standard learning algorithms, which assume or expect balanced class distribution or equal misclassification costs. Boosting is a meta-technique that is applicable to most learning algorithms. This paper gives a review of boosting methods for imbalanced data classification, denoted as IDBoosting (Imbalanced-data-boosting), where conventional learning algorithms can be integrated without further modifications. The main focus is on the intrinsic mechanisms without considering implementation detail. Existing methods are catalogued and each class is displayed in detail in terms of design criteria, typical algorithms and performance analysis. The essence of two IDBoosting methods is discovered followed by experimental evidence and useful reference point for future research are also given.


title: Flexy: Shape-Customizable, Single-Layer, Inkjet Printable Patterns for 1D and 2D Flex Sensing

abstract: We contribute a new technique for fabricating highly customized 1D and 2D flex sensing surfaces on thin and flexible substrates. It enables designers and makers to easily, quickly and inexpensively realize thin physical objects in custom shapes with an embedded deformation sensor. The deformation sensor is digitally designed and then fabricated with a single layer of conductive material in a single pass, using an off-the-shelf inkjet printer. We establish a design space and investigate how to realize flex sensing surfaces of highly varied geometries. In a technical evaluation, we demonstrate the technical feasibility of such sensors and investigate their response. Lastly, we demonstrate the practical applicability for tangible interfaces by presenting five example applications.


title: Algorithms for Delta Compression and Remote File Synchronization

abstract: This chapter describes the techniques, tools, and applications for delta compression and remote file synchronization. Delta compression and remote file synchronization techniques are concerned with efficient file transfer over a slow communication link in the case where the receiving party already has a similar file (or files). This chapter discusses the techniques, software tools, and applications for delta compression, remote file synchronization, and closely related problems. Compression techniques are widely used in computer networks and data storage systems to increase the efficiency of data transfers and reduce space requirements on the receiving device and consider scenarios in networking as well as storage. This is followed by an overview of delta compression approaches and finally discusses the problems of space-constrained delta compression and choosing of good reference files. The next section focuses on the remote file synchronization problem and begins with describing a few application scenarios for file synchronization, followed by some experimental results comparing rsync and delta compressor performance. This is followed by some general theoretical bounds and a discussion on provable results for specific distance measures. The final section discusses how to estimate the distance between two files and also looks at the problem of reconciling large sets of data records.


title: Automatic inspection of pavement cracking distress

abstract: This paper presents the image-processing algorithm customized for high-speed, real-time inspection of pavement cracking. In the algorithm, a pavement image is divided into grid cells of 8x8 pixels and each cell is classified as a non-crack or crack cell using the grayscale information of the border pixels. Whether a crack cell can be regarded as a basic element (or seed) depends on its contrast to the neighboring cells. A number of crack seeds can be called a crack cluster if they fall on a linear string. A crack cluster corresponds to a dark strip in the original image that may or may not be a section of a real crack. Additional conditions to verify a crack cluster include the requirements in the contrast, width and length of the strip. If verified crack clusters are oriented in similar directions, they will be joined to become one crack. Because many operations are performed on crack seeds rather than on the original image, crack detection can be executed simultaneously when the frame grabber is forming a new image, permitting real-time, online pavement survey. The trial test results show a good repeatability and accuracy when multiple surveys were conducted at different driving conditions.


title: Protein Structure Comparison through Fuzzy Contact Maps and the Universal Similarity Metric

abstract: Comparing protein structures, either to infer biological functionality or to assess protein structure predictions is an essential component of proteomic research. In this paper we extend our previous work on the use of the Universal Similarity Metric(USM) and Generalized Fuzzy Contact maps. More specically we compare the impact that generalized fuzzy contact maps representations have on the assessment of protein similarity by means of the Universal Similarity Metric.


title: Next Item Recommendation with Self-Attention

abstract: In this paper, we propose a novel sequence-aware recommendation model. Our model utilizes self-attention mechanism to infer the item-item relationship from user's historical interactions. With self-attention, it is able to estimate the relative weights of each item in user interaction trajectories to learn better representations for user's transient interests. The model is finally trained in a metric learning framework, taking both short-term and long-term intentions into consideration. Experiments on a wide range of datasets on different domains demonstrate that our approach outperforms the state-of-the-art by a wide margin.


title: Continuity between waking activities and dream activities

abstract: Empirical studies largely support the continuity hypothesis of dreaming. Despite of previous research efforts, the exact formulation of the continuity hypothesis remains vague. The present paper focuses on two aspects: (1) the differential incorporation rate of different waking-life activities and (2) the magnitude of which interindividual differences in waking-life activities are reflected in corresponding differences in dream content. Using a correlational design, a positive, non-zero correlation coefficient will support the continuity hypothesis. Although many researchers stress the importance of emotional involvement on the incorporation rate of waking-life experiences into dreams, Hartmann (2000) formulated the hypothesis that highly focused cognitive processes such as reading, writing, etc. are rarely found in dreams due to the cholinergic activation of the brain during dreaming. The present findings based on dream diaries and the exact measurement of waking activities replicated two recent questionnaire studies. These findings indicate that it will be necessary to specify the continuity hypothesis more fully and include factors (e.g., type of waking-life experience, emotional involvement) which modulate the incorporation rate of waking-life experiences into dreams. Whether the cholinergic state of the brain during REM sleep or other alterations of brain physiology (e.g., down-regulation of the dorsolateral prefrontal cortex) are the underlying factors of the rare occurrence of highly focused cognitive processes in dreaming remains an open question. Although continuity between waking life and dreaming has been demonstrated, i.e., interindividual differences in the amount of time spent with specific waking-life activities are reflected in dream content, methodological issues (averaging over a two-week period, small number of dreams) have limited the capacity for detecting substantial relationships in all areas. Nevertheless, it might be concluded that the continuity hypothesis in its present general form is not valid and should be elaborated and tested in a more specific way.


title: An SMDP-Based Service Model for Interdomain Resource Allocation in Mobile Cloud Networks

abstract: Mobile cloud computing is a promising technique that shifts the data and computing service modules from individual devices to a geographically distributed cloud service architecture. A general mobile cloud computing system is comprised of multiple cloud domains, and each domain manages a portion of the cloud system resources, such as the Central Processing Unit, memory and storage, etc. How to efficiently manage the cloud resources across multiple cloud domains is critical for providing continuous mobile cloud services. In this paper, we propose a service decision making system for interdomain service transfer to balance the computation loads among multiple cloud domains. Our system focuses on maximizing the rewards for both the cloud system and the users by minimizing the number of service rejections that degrade the user satisfaction level significantly. To this end, we formulate the service request decision making process as a semi-Markov decision process. The optimal service transfer decisions are obtained by jointly considering the system incomes and expenses. Extensive simulation results show that the proposed decision making system can significantly improve the system rewards and decrease service disruptions compared with the greedy approach.


title: Burst deblurring: Removing camera shake through fourier burst accumulation

abstract: Numerous recent approaches attempt to remove image blur due to camera shake, either with one or multiple input images, by explicitly solving an inverse and inherently ill-posed deconvolution problem. If the photographer takes a burst of images, a modality available in virtually all modern digital cameras, we show that it is possible to combine them to get a clean sharp version. This is done without explicitly solving any blur estimation and subsequent inverse problem. The proposed algorithm is strikingly simple: it performs a weighted average in the Fourier domain, with weights depending on the Fourier spectrum magnitude. The method's rationale is that camera shake has a random nature and therefore each image in the burst is generally blurred differently. Experiments with real camera data show that the proposed Fourier Burst Accumulation algorithm achieves state-of-the-art results an order of magnitude faster, with simplicity for on-board implementation on camera phones.


title: Modified A* algorithm for safer mobile robot navigation

abstract: Mobile robot navigation is a very important task in all robot applications from domestic service cleaning machine to highly sophisticated industrial robotic applications. In this paper, we investigated, and evaluated the well-known A* path planning algorithm. The standard A* algorithm is capable of finding out the most optimal path of the robot. A grid-based map obtained from the vision-based obstacle detection system in [2] as an input to the A* algorithm. Standard A* does not take the size of the robot into consideration which could produce unsafe paths for the robot to follow and collision could be occurs. Our proposed modified A* algorithm takes the robot size as a parameter to generate a safer path for the robot and avoid sharp turns. The Direction of Arrival (DOA) is used as a restriction to the path planning process which is a requirement in some robotic applications.


title: "I need to try this"?: a statistical overview of pinterest

abstract: Over the past decade, social network sites have become ubiquitous places for people to maintain relationships, as well as loci of intense research interest. Recently, a new site has exploded into prominence: Pinterest became the fastest social network to reach 10M users, growing 4000% in 2011 alone. While many Pinterest articles have appeared in the popular press, there has been little scholarly work so far. In this paper, we use a quantitative approach to study three research questions about the site. What drives activity on Pinterest? What role does gender play in the site's social connections? And finally, what distinguishes Pinterest from existing networks, in particular Twitter? In short, we find that being female means more repins, but fewer followers, and that four verbs set Pinterest apart from Twitter: use, look, want and need. This work serves as an early snapshot of Pinterest that later work can leverage.


Black Holes, Keyholes And Brown Worms: Challenges In Sense Making


title: Miniaturized Inverted-F Antenna with capacitive loading

abstract: A miniaturization technique based on capacitive loading is introduced for antenna size reduction. The capacitive Loaded Inverted-F Antenna (CLIFA) can achieve electrical length as low as λ/8. Capacitive loading is realized with a printed parallel plate capacitor, reducing associated manufacturing time and cost. The structure exhibits great simplicity in planar form which makes it easily realized on PCB for handheld devices and mobile systems. By employing switches to control the capacitance, the device has the potential to become frequency reconfigurable. The design of the CLIFA is analyzed and the performance is compared to measured data on prototypes operating at 868 MHz. Performance figures including radiation patterns, efficiency and stored energy are discussed with respect to the size reduction.


title: Towards Scalable Real-time Analytics: An Architecture for Scale-out of OLxP Workloads

abstract: We present an overview of our work on the SAP HANA Scale-out Extension, a novel distributed database architecture designed to support large scale analytics over real-time data. This platform permits high performance OLAP with massive scale-out capabilities, while concurrently allowing OLTP workloads. This dual capability enables analytics over real-time changing data and allows fine grained user-specified service level agreements (SLAs) on data freshness. We advocate the decoupling of core database components such as query processing, concurrency control, and persistence, a design choice made possible by advances in high-throughput low-latency networks and storage devices. We provide full ACID guarantees and build on a logical timestamp mechanism to provide MVCC-based snapshot isolation, while not requiring synchronous updates of replicas. Instead, we use asynchronous update propagation guaranteeing consistency with timestamp validation. ::: ::: We provide a view into the design and development of a large scale data management platform for real-time analytics, driven by the needs of modern enterprise customers.


title: Bayesian Multi-Scale Optimistic Optimization

abstract: Bayesian optimization is a powerful global optimization technique for expensive black-box functions. One of its shortcomings is that it requires auxiliary optimization of an acquisition function at each iteration. This auxiliary optimization can be costly and very hard to carry out in practice. Moreover, it creates serious theoretical concerns, as most of the convergence results assume that the exact optimum of the acquisition function can be found. In this paper, we introduce a new technique for efficient global optimization that combines Gaussian process confidence bounds and treed simultaneous optimistic optimization to eliminate the need for auxiliary optimization of acquisition functions. The experiments with global optimization benchmarks and a novel application to automatic information extraction demonstrate that the resulting technique is more efficient than the two approaches from which it draws inspiration. Unlike most theoretical analyses of Bayesian optimization with Gaussian processes, our finite-time convergence rate proofs do not require exact optimization of an acquisition function. That is, our approach eliminates the unsatisfactory assumption that a difficult, potentially NP-hard, problem has to be solved in order to obtain vanishing regret rates.


title: KelpFusion: A Hybrid Set Visualization Technique

abstract: We present KelpFusion: a method for depicting set membership of items on a map or other visualization using continuous boundaries. KelpFusion is a hybrid representation that bridges hull techniques such as Bubble Sets and Euler diagrams and line- and graph-based techniques such as LineSets and Kelp Diagrams. We describe an algorithm based on shortest-path graphs to compute KelpFusion visualizations. Based on a single parameter, the shortest-path graph varies from the minimal spanning tree to the convex hull of a point set. Shortest-path graphs aim to capture the shape of a point set and smoothly adapt to sets of varying densities. KelpFusion fills enclosed faces based on a set of simple legibility rules. We present the results of a controlled experiment comparing KelpFusion to Bubble Sets and LineSets. We conclude that KelpFusion outperforms Bubble Sets both in accuracy and completion time and outperforms LineSets in completion time.