title: Towards More Accurate Statistical Profiling of Deployed schema.org Microdata

abstract: Being promoted by major search engines such as Google, Yahooe, Bing, and Yandex, Microdata embedded in web pages, especially using schema.org, has become one of the most important markup languages for the Web. However, deployed Microdata is very often not free from errors, which makes it difficult to estimate the data volume and create an accurate data profile. In addition, as the usage of global identifiers is not common, the real number of entities described by this format in the Web is hard to assess. In this article, we discuss how the subsequent application of data cleaning steps, such as duplicate detection and correction of common schema-based errors, leads to a more realistic view on the data, step by step. The cleaning steps applied include both heuristics for fixing errors as well as means to perform duplicate detection and duplicate elimination. Using the Web Data Commons Microdata corpus, we show that applying such quality improvement methods can essentially change the statistical profile of the dataset and lead to different estimates of both the number of entities as well as the class distribution within the data.


title: Fast, Accurate, and Scalable Method for Sparse Coupled Matrix-Tensor Factorization

abstract: How can we capture the hidden properties from a tensor and a matrix data simultaneously? Coupled matrix-tensor factorization (CMTF) is an effective method to solve this problem because it extracts latent factors from a tensor and matrices at once. Designing an efficient CMTF has become more crucial as the size and dimension of real-world data are growing explosively. However, existing methods for CMTF suffer from two problems: 1) methods for dense data are not applicable to sparse real-world data, and 2) methods based on CANDECOMP/PARAFAC (CP) decomposition suffer from high test error because they do not capture correlations between all factors. In this paper, we propose S3CMTF, a fast, accurate, and scalable CMTF method. S3CMTF achieves high speed by exploiting the sparsity of real-world tensors, and high accuracy by capturing inter-relations between factors. Also, S3CMTF accomplishes additional speed-up by lock-free parallel SGD update for multi-core shared memory systems. We present two methods, S3CMTF-naive and S3CMTF-opt. S3CMTF-naive is a basic version of S3CMTF, and S3CMTF-opt improves its speed by exploiting intermediate data. We theoretically and empirically show that S3CMTF is the fastest outperforming existing methods. Experimental results show that S3CMTF is 11~43x faster, and 2.1~4.1x more accurate than existing methods. S3CMTF shows linear scalability on the number of data entries and the number of cores. In addition, we apply S3CMTF to Yelp dataset with a 3-mode tensor coupled with 3 additional matrices to discover interesting properties.


title: Relationship of Transformational and Transactional Leadership Style with Job Satisfaction

abstract: This paper attempts to review the important concepts of human resource management (HRM),so at first it will discuss about the necessity of job satisfaction. Then this review study will present different types of leadership style (both transaction and transformational), in o order to justify the relationship between leadership style and job satisfaction. At the end, this study proposed one framework based on the different components of transformational and transactional leadership style.


title: Wireless Sensor Based Remote Monitoring System for Agriculture Using ZigBee and GPS

abstract: The advanced development in wireless sensor networks can be used in monitoring various parameters in agriculture. Due to uneven natural distribution of rain water it is very difficult for farmers to monitor and control the distribution of water to agriculture field in the whole farm or as per the requirement of the crop. There is no ideal irrigation method for all weather conditions, soil structure and variety of crops cultures. Farmers suffer large financial losses because of wrong prediciton of weather and incorrect irrigation methods. In this context, with the evolution of miniaturized sensor devices coupled with wireless technologies, it is possible remotely monitor parameters such as moisure, temperature and humidity. In this paper it is proposed to design, develop and implement a wireless sensor network connected to a central node using ZigBee, which in turn is connected to a Central Monitoring Station (CMS) through General Packet Radio Service (GPRS) or Global System for Mobile (GSM) technologies. The system also obtains Global Positionting System (GPS) parameters related to the field and sends them to a central monitoring station. This system is expected to help farmers in evaluating soil conditions and act accordingly.


title: New modulation and control scheme for phase-modular isolated matrix-type three-phase AC/DC converter

abstract: A phase-modular isolated three-phase AC/DC converter topology and its modulation and control scheme is proposed. The topology, denominated as IMY-Rectifier, is based on a star connection of single-phase mains frequency to high frequency AC/AC matrix converters at the input, feeding high-frequency isolation transformers. The transformer secondary windings are connected in series to the input of a diode rectifier with LC output filter. The converter provides a controlled output voltage and sinusoidal input currents in phase with the mains voltages. A description of the converter, its operating principle and an analytical calculation of the component stresses is presented.


title: On reception sampling region of OAM radio beams using concentric circular arrays

abstract: Orbital angular momentum (OAM) supplies a new degree of freedom for the electromagnetic (EM) wave, which can be used to increase communication capacity. The amplitude of the OAM wave is null in the center of the beam and the distribution of the electric field intensity is not uniform, so the study of the optimal receiving location of single OAM wave is necessary. Furthermore, in order to efficiently receive OAM waves with different OAM modes simultaneously on single receiving antenna, the common reception sampling region of multi-mode OAM waves should also be studied. However, few experiments study the region of multi-mode OAM waves. Here, concentric circular arrays are proposed to generate OAM waves with different OAM modes. Based on theoretical formulations of its radiation fields in the cylindrical coordinate system, its transmission and reception characteristics would be analyzed. Basically, the common reception sampling region of multi-mode OAM waves can be confirmed by calculating function formulas of the upper and lower boundaries of the region or analyzing amplitudes and phases of the axial ratio of optimal receiving location of multiple OAM waves. Numerical results show that methods of confirming the region are feasible and simple. When other parameters besides frequency are the same and the optimal receiving location of certain OAM mode wave at certain frequency is known, optimal receiving locations of certain OAM mode wave at other frequencies can be confirmed easily, according to the formula related to the optimal receiving location and frequency.


title: FlowRep: descriptive curve networks for free-form design shapes

abstract: We present FlowRep, an algorithm for extracting descriptive compact 3D curve networks from meshes of free-form man-made shapes. We infer the desired compact curve network from complex 3D geometries by using a series of insights derived from perception, computer graphics, and design literature. These sources suggest that visually descriptive networks are cycle-descriptive, i.e their cycles unambiguously describe the geometry of the surface patches they surround. They also indicate that such networks are designed to be projectable, or easy to envision when observed from a static general viewpoint; in other words, 2D projections of the network should be strongly indicative of its 3D geometry. Research suggests that both properties are best achieved by using networks dominated by flowlines, surface curves aligned with principal curvature directions across anisotropic regions and strategically extended across sharp-features and isotropic areas. Our algorithm leverages these observation in the construction of a compact descriptive curve network. Starting with a curvature aligned quad dominant mesh we first extract sequences of mesh edges that form long, well-shaped and reliable flowlines by leveraging directional similarity between nearby meaningful flowline directions We then use a compact subset of the extracted flowlines and the model's sharp-feature, or trim, curves to form a sparse, projectable network which describes the underlying surface. We validate our method by demonstrating a range of networks computed from diverse inputs, using them for surface reconstruction, and showing extensive comparisons with prior work and artist generated networks.


title: Computer-generated holography as a generic display technology

abstract: Computer-generated holography technique is a powerful technology suitable for a wide range of display types, including 2D, stereoscopic, autostereoscopic, volumetric, and true 3D imaging. Computer-generated holography is an emerging technology, made possible by increasingly powerful computers, that avoids the interferometric recording step in conventional hologram formation. Instead, a computer calculates a holographic fringe pattern that it then uses to set the optical properties of a spatial light modulator, such as a liquid crystal microdisplay. The SLM then diffracts the readout light wave, in a manner similar to a standard hologram, to yield the desired optical wavefront. Although CGH-based display systems can be built today, their high cost makes them impractical for many applications.


title: Association Rule Mining: A Survey

abstract: Data mining [Chen et al. 1996] is the process of extracting interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from large information repositories such as: relational database, data warehouses, XML repository, etc. Also data mining is known as one of the core processes of Knowledge Discovery in Database (KDD). Many people take data mining as a synonym for another popular term, Knowledge Discovery in Database (KDD). Alternatively other people treat Data Mining as the core process of KDD. The KDD processes are shown in Figure 1 [Han and Kamber 2000]. Usually there are three processes. One is called preprocessing, which is executed before data mining techniques are applied to the right data. The preprocessing includes data cleaning, integration, selection and transformation. The main process of KDD is the data mining process, in this process different algorithms are applied to produce hidden knowledge. After that comes another process called postprocessing, which evaluates the mining result according to users’ requirements and domain knowledge. Regarding the evaluation results, the knowledge can be presented if the result is satisfactory, otherwise we have to run some or all of those processes again until we get the satisfactory result. The actually processes work as follows. First we need to clean and integrate the databases. Since the data source may come from different databases, which may have some inconsistences and duplications, we must clean the data source by removing those noises or make some compromises. Suppose we have two different databases, different words are used to refer the same thing in their schema. When we try to integrate the two sources we can only choose one of them, if we know that they denote the same thing. And also real world data tend to be incomplete and noisy due to the manual input mistakes. The integrated data sources can be stored in a database, data warehouse or other repositories. As not all the data in the database are related to our mining task, the second process is to select task related data from the integrated resources and transform them into a format that is ready to be mined. Suppose we want to find which items are often purchased together in a supermarket, while the database that records the purchase history may contains customer ID, items bought, transaction time, prices, number of each items and so on, but for this specific task we only need items bought. After selection of relevant data, the database that we are going to apply our data mining techniques to will be much smaller, consequently the whole process will be


Monocular 3D Human Pose Estimation Using Transfer Learning and Improved CNN Supervision.


title: Curve-fitting with piecewise parametric cubics

abstract: Parametric piecewise-cubic functions are used throughout the computer graphics industry to represent curved shapes. For many applications, it would be useful to be able to reliably derive this representation from a closely spaced set of points that approximate the desired curve, such as the input from a digitizing tablet or a scanner. This paper presents a solution to the problem of automatically generating efficient piecewise parametric cubic polynomial approximations to shapes from sampled data. We have developed an algorithm that takes a set of sample points, plus optional endpoint and tangent vector specifications, and iteratively derives a single parametric cubic polynomial that lies close to the data points as defined by an error metric based on least-squares. Combining this algorithm with dynamic programming techniques to determine the knot placement gives good results over a range of shapes and applications.


title: Ultra-Wideband Ring-Cavity Multiple-Way Parallel Power Divider

abstract: A novel ultrawideband (UWB) ring-cavity multiple-way parallel power divider is presented in this paper. The approximated equivalent-circuit model of the power-dividing unit is presented to analyze its structural parameters and electrical performance. The overall circuit model of the proposed power divider is also given. To verify its potential to accommodate large numbers of power-dividing ports, a UWB 32-way ring-cavity power divider is developed with reasonable agreement between the simulated and measured results. The measured return loss is greater than 10 dB over the entire UWB and also greater than 15 dB from 4.2 to 9.2 GHz. The average insertion loss, amplitude imbalance, phase imbalance, and group delay are around 15.4 dB (including the 15-dB power-dividing insertion loss), ±0.7 dB, ±5°, and 0.85 ns, respectively, across the UWB. In addition, the isolations between the output ports are greater than 10 dB over the UWB except for those between the adjacent output ports.


title: The Role of Intermediaries in the Development of Trust on the Www: the Use and Prominence of Trusted Third Parties and Privacy Statements

abstract: Developing trust between suppliers and consumers is critical for the continued growth of Internet commerce. This article presents an empirical investigation into how firms promote trust by exploring the use and prominence of Trusted Third Parties (TTPs) and privacy statements. The Web sites of 102 publicly held firms with predominantly Internet based businesses were examined for their use of TTPs and privacy statements, the number of links, currency of the Web site, length of time the Web site had been operating, traffic, and financial performance. Surprisingly, only 17 of the firms utilized trusted third parties and only 45 had privacy statements. The article presents a methodology for the analysis of four propositions that explore the relationship of embeddedness and a firm's length of time online to the use and prominence of TTPs and privacy statements. The exploratory data in this article clearly supports the proposition that the use of TTPs and privacy statements increase with the embeddedness of the Web site. This article then discusses the potential reasons for this finding including how TTPs strategically solicit firms and why trusted firms may be more likely to be embedded. The remaining three propositions show mixed results but provide insight into the strategic use of TTPs and privacy statements. One key insight is that TTPs and privacy statements are actually used quite differently by firms to promote trust in Internet commerce.


title: Cognitive Stress Recognition An Approach to Stress Recognition using a Low-Cost EEG headset

abstract: This work explores using a low-cost electroencephalography (EEG) headset to quantify the human response to stressed and non-stressed states. We used a Stroop color-word interference test to elicit a mild stress response in 18 test subjects while recording scalp EEG. EEG signals were analyzed using an algorithm that computed the root mean square voltage in the beta, alpha, and theta bands immediately following the presentation of the Stroop stimuli. These features were then used as inputs to logistic regression and k-nearest neighbor classifiers. Results showed that there was a median accuracy of 73.96% for classifying mental state using the O1 sensor on the Emotiv headset.


title: Modeling software testing costs and risks using fuzzy logic paradigm

abstract: The overall lifecycle cost associated with product failures exceeds 10% of yearly corporations' turnover. A major factor contributing to this loss is ineffective performance of software and systems Verification, Validation and Testing (VVT). Given these realities, we proposed a set of quantitative probabilistic models for estimating costs and risks stemming from carrying out any given VVT strategy [Engel, A., Barad, M., 2003. A methodology for modeling VVT risks and costs. Systems Engineering Journal 6 (3), 135-151, Wiley InterScience, Online ISSN: 1520-6858, Print ISSN: 1098-1241]. We also demonstrated that quality costs in software-intensive projects are likely to consume as much as 60% of the development budget. Finally, we showed that project cost and duration could be reduced by optimizing the VVT strategy, yielding about 10-15% reduction in development costs and project schedule [Engel, A., Shachar, S., 2006. Measuring and optimizing systems' quality costs and project duration. Systems Engineering Journal 9 (3), 259-280]. A key problem associated with such cost and time estimates is that input data are imprecise by nature. Certain parameters are better captured using tuple structures (e.g. Minimum, Most-likely and Maximum values). Other parameters can be better encapsulated using linguistic terms such as ''High'' or ''Low''. This paper extends the above research by modeling the problem using the fuzzy logic paradigm. We estimate the quality cost occurring during the development of software for an avionic suite in a fighter aircraft and demonstrate that applying fuzzy logic methodology yields results comparable to estimations based on models using the probabilistic paradigm (less than 4% differences in each of the five VVT cost categories).


title: IoT based smart home management to enhance the services to the occupancies and minimized energy demand by controlling appliances using wireless motes

abstract: We have to set up a prototype model for smart Building environment with more advanced activity and event classification techniques, as well as new adaptive service provision schemes to adjust the services according to the user's behaviour model and the feedback provided by the user. Integration of the building systems with embedded sensors, actuators and microcomputers or microcontrollers allows them to communicate with one another through the building controller. We also expect to continue our work on network optimization for sensing and control, and also expand our work into cross-products of higher technological development. Examples of “things” include: People; Location (of objects); Time Information (of objects); Condition (of objects). The quality of a daily life, reduction of costs and increasing security are the main important issues when we design a smart environment for inside and outside of our building areas.


title: Brain Tumor Detection and Classification Using Deep Learning Classifier on MRI Images

abstract: Magnetic Resonance Imaging (MRI) has become an effective tool for clinical research in recent years and has found itself in applications such as brain tumour detection. In this study, tumor classification using multiple kernel-based probabilistic clustering and deep learning classifier is proposed. The proposed technique consists of three modules, namely segmentation module, feature extraction module and classification module. Initially, the MRI image is pre-processed to make it fit for segmentation and de-noising process is carried out using median filter. Then, pre-processed image is segmented using Multiple Kernel based Probabilistic Clustering (MKPC). Subsequently, features are extracted for every segment based on the shape, texture and intensity. After features extraction, important features will be selected using Linear Discriminant Analysis (LDA) for classification purpose. Finally, deep learning classifier is employed for classification into tumor or non-tumor. The proposed technique is evaluated using sensitivity, specificity and accuracy. The proposed technique results are also compared with existing technique which uses Feed-Forward Back Propagation Network (FFBN). The proposed technique achieved an average sensitivity, specificity and accuracy of 0.88, 0.80 and 0.83, respectively with the highest values as about 1, 0.85 and 0.94. Improved results show the efficiency of the proposed technique.


title: Partnering for e‐government: Challenges for public administrators

abstract: : Governments around the world are spending huge sums of money implementing electronic government. Public-private partnerships with information and communication technology firms have emerged as the vehicle of choice for implementing e-government strategies. Concerns are raised about the capacity of governments to manage these complex, multi-year, often multi-partner relationships that involve considerable sharing of authority, responsibility, financial resources, information and risks. The management challenges manifest themselves in the core partnering tasks: establishing a management framework for partnering; finding the right partners and making the right partnering arrangement; the management of relationships with partners in a network setting; and the measurement of the performance of e-government partnerships. The article reviews progress being made by governments in building capacity to deal with these core partnering tasks. It concludes that many new initiatives at the central agency and departmental/ministry level seem designed to centralize control of e-government projects and wrap them in a complex web of bureaucratic structures and processes that are, for the most part, antithetical or, at best, indifferent to the creation of strong partnerships and the business valuethat e-government public-private partnerships promise.


A case of precocious puberty in a female.


title: A Study on the Tracking Problem in Vehicular Ad Hoc Networks

abstract: Vehicles have the characteristic of high mobility which makes vehicular ad hoc networks (VANETs) different from other mobile ad hoc networks (MANETs), it is more difficult to establish an end-to-end route in VANETs, and the source and destination nodes keep moving fast from their original locations. To guarantee a data packet will finally be received by the destination node in VANETs, and the location of the destination node must be tracked constantly. Advanced Greedy Forwarding (AGF (Naumov et al. (2006))) is a good way to estimate the location of the destination node by taking advantage of the velocity vectors; however, a vehicle changes its velocity constantly, it would be very inaccurate to estimate the location of a vehicle via its initial velocity. In this paper, we study the tracking problem in VANETs and propose two solutions: area-based tracking (ABT) and parked vehicle-assisted tracking (PVAT). ABT works well when the delays in data transmission from source to destination are small, and PVAT works as a supplement of ABT and deals with the situation of which the delays are large. PVAT takes advantage of vehicular sensors to detect whether a vehicle is parking. Simulations show better results compared to AGF.


title: SOCIAL COGNITION AND THE EFFECT OF PRODUCT QUALITY ON ONLINE REPURCHASE INTENTION

abstract: An electronic commerce marketing channel is fully mediated by information technology, creating information asymmetry (i.e., limited information). Such asymmetry may impede consumers’ ability to effectively assess certain types of products, thus creating challenges for online sellers. Signaling theory can aid in the understanding of how extrinsic cues—signals—can be used by sellers to convey product quality information to consumers, reducing uncertainty and facilitating a purchase or exchange. This study proposes a model to investigate website quality as a potential signal of product quality and consider the moderating effects of product information asymmetries and signal credibility. The study also finds that perceived value and cognitive lock-in can predict consumer purchase intentions. Furthermore, personalized product recommendation (PPR) services offered by online retailers are found to influence consumer store loyalty. The results indicate that website quality influences consumers’ perceptions of product quality, and affects online purchase intentions. Website quality is found to have a greater influence on perceived product quality when consumers have higher information asymmetry. Signal credibility is found to strengthen the relationship between website quality and product quality perceptions for a high quality website. The implications of cognitive lock-in and product cues for increasing purchase intentions are discussed. Retailer learning reflected in higher quality PPRs is associated with both lower product screening cost and higher product evaluation cost. We also discuss which PPRs influence consumer repurchase intentions in electronic markets.


title: Generating Maps Using Markov Chains

abstract: In this paper we outline a method of procedurally generating maps using Markov Chains. Our method attempts to learn what makes a “good” map from a set of given human-authored maps, and then uses those learned patterns to generate new maps. We present an empirical evaluation using the game Super Mario Bros., showing encouraging results.


title: Design and analysis of Slimjim dual band VHF and UHF antenna with crossarm variation

abstract: Most wireless communications need more than one band frequency to operate due to many reasons. Amateur radio, military operators and commercial mostly use either multiple antennas for different bands, that operates on any number of frequency bands. This situation also occurs in very high- frequency (VHF) and ultra high-frequency (UHF) bands. In this research, Slimjim antenna has been implemented on the VHF and UHF bands. Slimjim antenna was designed to work at 146 MHz for VHF band and at 443 MHz for UHF band, by adding a crossarm compensating element. This element was made by aluminum with 5 mm diameter. The antenna was measured in order to obtain some parameters such as resonant frequency, S11, bandwith, gain and radiation pattern. The measured results show that Slimjim-cross antenna resonates at frequency 154 MHz with S11 −32.65 on the VHF band, and at the frequency 457 MHz with S11 −36.82 on the UHF band. The qualitative test by implementing Slimjim-cross antenna with handy-transceiver radio shows that Slimjim-cross antennas can be used for communication as far as 390 meters on VHF band and 1100 meters on UHF band. By comparison, the original Slimjim antenna has 350 meters range for communication and −11.74 for S11 on the VHF band.


NetCDF User's Guide - An Interface for Data Access Version


title: A 4-to-10.5 Gb/s Continuous-Rate Digital Clock and Data Recovery With Automatic Frequency Acquisition

abstract: A continuous-rate digital clock and data recovery (CDR) with automatic frequency acquisition is presented. The proposed automatic frequency acquisition scheme implemented using a conventional bang–bang phase detector (BBPD) requires minimum additional hardware, is immune to input data transition density, and is applicable to subrate CDRs. A ring-oscillator-based two-stage fractional-N phase-locked loop (PLL) is used as a digitally controlled oscillator (DCO) to achieve wide frequency range, low noise, and to decouple the tradeoff between jitter transfer (JTRAN) bandwidth and ring oscillator noise suppression in conventional CDRs. The CDR is implemented using a digital D/PLL architecture to decouple JTRAN bandwidth from jitter tolerance (JTOL) corner frequency, eliminate jitter peaking, and remove JTRAN dependence on BBPD gain. Fabricated in a 65 nm CMOS process, the prototype CDR achieves error-free operation (BER ${\rm{ ) from 4 to 10.5 Gb/s with pseudorandom binary sequence (PRBS) data sequences ranging from PRBS7 to PRBS31. The proposed automatic frequency acquisition scheme always locks the CDR loop within 1000 ppm residual frequency error in worst case. At 10 Gb/s, the CDR consumes 22.5 ${\rm{\,mW}}$ power and achieves a recovered clock long-term jitter of 2.2 ${\rm ps_{rms}}$ /24.0 ${\rm{ ps_{pp}}}$ with PRBS31 input data. The measured JTRAN bandwidth and JTOL corner frequencies are 0.2 and 9 MHz, respectively.


title: Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions

abstract: Disembodied conversational agents in the form of chatbots are increasingly becoming a reality on social media and messaging applications, and are a particularly pressing topic for service encounters with companies. Adopting an experimental design with actual chatbots powered with current technology, this study explores the extent to which human-like cues such as language style and name, and the framing used to introduce the chatbot to the consumer can influence perceptions about social presence as well as mindful and mindless anthropomorphism. Moreover, this study investigates the relevance of anthropomorphism and social presence to important company-related outcomes, such as attitudes, satisfaction and the emotional connection that consumers feel with the company after interacting with the chatbot.


title: Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks

abstract: Variational Autoencoders (VAEs) are expressive latent variable models that can be used to learn complex probability distributions from training data. However, the quality of the resulting model crucially relies on the expressiveness of the inference model. We introduce Adversarial Variational Bayes (AVB), a technique for training Variational Autoencoders with arbitrarily expressive inference models. We achieve this by introducing an auxiliary discriminative network that allows to rephrase the maximum-likelihood-problem as a two-player game, hence establishing a principled connection between VAEs and Generative Adversarial Networks (GANs). We show that in the nonparametric limit our method yields an exact maximum-likelihood assignment for the parameters of the generative model, as well as the exact posterior distribution over the latent variables given an observation. Contrary to competing approaches which combine VAEs with GANs, our approach has a clear theoretical justification, retains most advantages of standard Variational Autoencoders and is easy to implement.


title: A radio resource management framework for TVWS exploitation under an auction-based approach

abstract: This paper elaborates on the design, implementation and performance evaluation of a prototype Radio Resource Management (RRM) framework for TV white spaces (TVWS) exploitation, under an auction-based approach. The proposed RRM framework is applied in a centralised Cognitive Radio (CR) network architecture, where exploitation of the available TVWS by Secondary Systems is orchestrated via a Spectrum Broker. Efficient RRM framework performance, as a matter of maximum-possible resources utilization and benefit of Spectrum Broker, is achieved by proposing and evaluating an auction-based algorithm. This auction-based algorithm considers both frequency and time domain during TVWS allocation process which was defined as an optimization problem, where maximum payoff of Spectrum Broker is the optimization goal. Experimental tests that were carried-out under controlled conditions environment, verified the validity of the proposed framework, besides identifying fields for further research.


title: Measuring Negative and Positive Thoughts in Children: An Adaptation of the Children’s Automatic Thoughts Scale (CATS)

abstract: The aim of this study is to describe the factor structure and psychometric properties of an extended version of the Children’s Automatic Thoughts Scale (CATS), the CATS-Negative/Positive (CATS-N/P). The CATS was originally designed to assess negative self-statements in children and adolescents. However, positive thoughts also play a major role in childhood disorders such as anxiety and depression. Therefore, positive self-statements were added to the CATS. The CATS-N/P was administered to a community sample of 554 children aged 8–18 years. The results of a confirmatory factor analysis revealed that the positive self-statements formed a separate and psychometrically sound factor. Internal and short-term test–retest reliability was good. Boys reported more hostile and positive thoughts than girls; and younger children reported more negative thoughts concerning physical threat, social threat, and failure than older children. In conclusion, the results of the current study support the use of the CATS-N/P for the measurement of positive and negative thoughts in children. The application of the CATS-N/P can facilitate further research on cognitive factors in different childhood disorders.


title: Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks

abstract: A low precision deep neural network training technique for producing sparse, ternary neural networks is presented. The technique incorporates hard- ware implementation costs during training to achieve significant model compression for inference. Training involves three stages: network training using L2 regularization and a quantization threshold regularizer, quantization pruning, and finally retraining. Resulting networks achieve improved accuracy, reduced memory footprint and reduced computational complexity compared with conventional methods, on MNIST and CIFAR10 datasets. Our networks are up to 98% sparse and 5 & 11 times smaller than equivalent binary and ternary models, translating to significant resource and speed benefits for hardware implementations.


title: Random Bits Regression: a Strong General Predictor for Big Data

abstract: Data-based modeling is becoming practical in predicting outcomes. In the era of big data, two practically conflicting challenges are eminent: (1) the prior knowledge on the subject is largely insufficient; (2) computation and storage cost of big data is unaffordable. To improve accuracy and speed of regressions and classifications, we present a data-based prediction method, Random Bits Regression (RBR). This method first generates a large number of random binary intermediate/derived features based on the original input matrix, and then performs regularized linear/logistic regression on those intermediate/derived features to predict the outcome. Benchmark analyses on a simulated dataset, UCI machine learning repository datasets and a GWAS dataset showed that RBR outperforms other popular methods in accuracy and robustness. RBR (available on https://sourceforge.net/projects/rbr/ ::: ) is very fast and requires reasonable memories, therefore, provides a strong, robust and fast predictor in the big data era.


title: The All Relevant Feature Selection using Random Forest

abstract: In this paper we examine the application of the random forest classifier for the all relevant feature selection problem. To this end we first examine two recently proposed all relevant feature selection algorithms, both being a random forest wrappers, on a series of synthetic data sets with varying size. We show that reasonable accuracy of predictions can be achieved and that heuristic algorithms that were designed to handle the all relevant problem, have performance that is close to that of the reference ideal algorithm. Then, we apply one of the algorithms to four families of semi-synthetic data sets to assess how the properties of particular data set influence results of feature selection. Finally we test the procedure using a well-known gene expression data set. The relevance of nearly all previously established important genes was confirmed, moreover the relevance of several new ones is discovered.


title: YADING: Fast Clustering of Large-Scale Time Series Data

abstract: Fast and scalable analysis techniques are becoming increasingly important in the era of big data, because they are the enabling techniques to create real-time and interactive experiences in data analysis. Time series are widely available in diverse application areas. Due to the large number of time series instances (e.g., millions) and the high dimensionality of each time series instance (e.g., thousands), it is challenging to conduct clustering on large-scale time series, and it is even more challenging to do so in real-time to support interactive exploration. ::: ::: In this paper, we propose a novel end-to-end time series clustering algorithm, YADING, which automatically clusters large-scale time series with fast performance and quality results. Specifically, YADING consists of three steps: sampling the input dataset, conducting clustering on the sampled dataset, and assigning the rest of the input data to the clusters generated on the sampled dataset. In particular, we provide theoretical proof on the lower and upper bounds of the sample size, which not only guarantees YADING's high performance, but also ensures the distribution consistency between the input dataset and the sampled dataset. We also select L1 norm as similarity measure and the multi-density approach as the clustering method. With theoretical bound, this selection ensures YADING's robustness to time series variations due to phase perturbation and random noise. ::: ::: Evaluation results have demonstrated that on typical-scale (100,000 time series each with 1,000 dimensions) datasets, YADING is about 40 times faster than the state-of-the-art, sampling-based clustering algorithm DENCLUE 2.0, and about 1,000 times faster than DBSCAN and CLARANS. YADING has also been used by product teams at Microsoft to analyze service performance. Two of such use cases are shared in this paper.


Stereo Vision Based Depth Estimation Algorithm In Uncalibrated Rectification


title: Sparse Overcomplete Word Vector Representations

abstract: Current distributed representations of words show little resemblance to theories of lexical semantics. The former are dense and uninterpretable, the latter largely based on familiar, discrete classes (e.g., supersenses) and relations (e.g., synonymy and hypernymy). We propose methods that transform word vectors into sparse (and optionally binary) vectors. The resulting representations are more similar to the interpretable features typically used in NLP, though they are discovered automatically from raw corpora. Because the vectors are highly sparse, they are computationally easy to work with. Most importantly, we find that they outperform the original vectors on benchmark tasks.


"This Website Uses Cookies": Users' Perceptions and Reactions to the Cookie Disclaimer


Near-Duplicates Detection and Elimination Based on Web Provenance for Effective Web Search


title: Neural net pruning-why and how

abstract: A continuing question in neural net research is the size of network needed to solve a particular problem. If training is started with too small a network for the problem no learning can occur. The researcher must then go through a slow process of deciding that no learning is taking place, increasing the size of the network and training again. If a network that is larger than required is used, then processing is slowed, particularly on a conventional von Neumann computer. An approach to this problem is discussed that is based on learning with a net which is larger than the minimum size network required to solve the problem and then pruning the solution network. The result is a small, efficient network that performs as well or better than the original which does not give a complete answer to the question, since the size of the initial network is still largely based on guesswork but it gives a very useful partial answer and sheds some light on the workings of a neural network in the process. >


title: Smart Configuration of Smart Environments

abstract: One of the central research challenges in the Internet of Things and Ubiquitous Computing domains is how users can be enabled to “program” their personal and industrial smart environments by combining services that are provided by devices around them. We present a service composition system that enables the goal-driven configuration of smart environments for end users by combining semantic metadata and reasoning with a visual modeling tool. In contrast to process-driven approaches where service mashups are statically defined, we make use of embedded semantic API descriptions to dynamically create mashups that fulfill the user’s goal. The main advantage of our system is its high degree of flexibility, as service mashups can adapt to dynamic environments and are fault-tolerant with respect to individual services becoming unavailable. To support users in expressing their goals, we integrated a visual programming tool with our system that allows to model the desired state of a smart environment graphically, thereby hiding the technicalities of the underlying semantics. Possible applications of the presented system include the management of smart homes to increase individual well-being, and reconfigurations of smart environments, for instance in the industrial automation or healthcare domains.


title: The Impact of Home Computer Use on Children's Activities and Development

abstract: The increasing amount of time children are spending on computers at home and school has raised questions about how the use of computer technology may make a difference in their lives—from helping with homework to causing depression to encouraging violent behavior. This article provides an overview of the limited research on the effects of home computer use on children’s physical, cognitive, and social development. Initial research suggests, for example, that access to computers increases the total amount of time children spend in front of a television or computer screen at the expense of other activities, thereby putting them at risk for obesity. At the same time, cognitive research suggests that playing computer games can be an important building block to computer literacy because it enhances children’s ability to read and visualize images in three-dimensional space and track multiple images simultaneously. The limited evidence available also indicates that home computer use is linked to slightly better academic performance. The research findings are more mixed, however, regarding the effects on children’s social development. Although little evidence indicates that the moderate use of computers to play games has a negative impact on children’s friendships and family relationships, recent survey data show that increased use of the Internet may be linked to increases in loneliness and depression. Of most concern are the findings that playing violent computer games may increase aggressiveness and desensitize a child to suffering, and that the use of computers may blur a child’s ability to distinguish real life from simulation. The authors conclude that more systematic research is needed in these areas to help parents and policymakers maximize the positive effects and to minimize the negative effects of home computers in children’s lives.


title: On Deep Multi-View Representation Learning

abstract: We consider learning representations (features) in the setting in which we have access to multiple unlabeled views of the data for representation learning while only one view is available at test time. Previous work on this problem has proposed several techniques based on deep neural networks, typically involving either autoencoder-like networks with a reconstruction objective or paired feedforward networks with a correlation-based objective. We analyze several techniques based on prior work, as well as new variants, and compare them experimentally on visual, speech, and language domains. To our knowledge this is the first head-to-head comparison of a variety of such techniques on multiple tasks. We find an advantage for correlation-based representation learning, while the best results on most tasks are obtained with our new variant, deep canonically correlated autoencoders (DCCAE).


title: Does neuropsychological performance impact on real-life functional achievements in obsessive-compulsive disorder? A preliminary study.

abstract: AbstractObjective: We investigated the association between neuropsychological performance and real-life functioning in obsessive-compulsive disorder (OCD). As a secondary aim, we investigated the association between neuropsychological performance and self-reported quality of life (QoL).Methods: We retrospectively selected 68 of 240 inpatients with OCD, who had been hospitalised for a 4-week psychiatric rehabilitation programme. We used clinical information recorded in the patients’ electronic medical records. We considered the following variables: neuropsychological performance (verbal/visual memory, sustained attention, visual-constructive ability, and language fluency; in a sub-sample of 37 subjects, divided attention, working memory, and attentional shifting were also available); real-life functional achievements (social/vocational outcomes and independent living); subjectively reported QoL (WHOQOL-BREF); obsessive-compulsive (OC) symptoms severity (DY-BOCS).Results: We found significant associations b...


title: Author Topic Model-Based Collaborative Filtering for Personalized POI Recommendations

abstract: From social media has emerged continuous needs for automatic travel recommendations. Collaborative filtering (CF) is the most well-known approach. However, existing approaches generally suffer from various weaknesses. For example , sparsity can significantly degrade the performance of traditional CF. If a user only visits very few locations, accurate similar user identification becomes very challenging due to lack of sufficient information for effective inference. Moreover, existing recommendation approaches often ignore rich user information like textual descriptions of photos which can reflect users’ travel preferences. The topic model (TM) method is an effective way to solve the “sparsity problem,” but is still far from satisfactory. In this paper, an author topic model-based collaborative filtering (ATCF) method is proposed to facilitate comprehensive points of interest (POIs) recommendations for social users. In our approach, user preference topics, such as cultural, cityscape, or landmark, are extracted from the geo-tag constrained textual description of photos via the author topic model instead of only from the geo-tags (GPS locations). Advantages and superior performance of our approach are demonstrated by extensive experiments on a large collection of data.


title: Localized Multiple Kernel Learning With Dynamical Clustering and Matrix Regularization

abstract: Localized multiple kernel learning (LMKL) is an attractive strategy for combining multiple heterogeneous features with regard to their discriminative power for each individual sample. However, the learning of numerous local solutions may not scale well even for a moderately sized training set, and the independently learned local models may suffer from overfitting. Hence, in existing local methods, the distributed samples are typically assumed to share the same weights, and various unsupervised clustering methods are applied as preprocessing. In this paper, to enable the learner to discover and benefit from the underlying local coherence and diversity of the samples, we incorporate the clustering procedure into the canonical support vector machine-based LMKL framework. Then, to explore the relatedness among different samples, which has been ignored in a vector $\ell _{p}$ -norm analysis, we organize the cluster-specific kernel weights into a matrix and introduce a matrix-based extension of the $\ell _{p}$ -norm for constraint enforcement. By casting the joint optimization problem as a problem of alternating optimization, we show how the cluster structure is gradually revealed and how the matrix-regularized kernel weights are obtained. A theoretical analysis of such a regularizer is performed using a Rademacher complexity bound, and complementary empirical experiments on real-world data sets demonstrate the effectiveness of our technique.


title: The Effect of Quercetin on Inflammatory Factors and Clinical Symptoms in Women with Rheumatoid Arthritis: A Double-Blind, Randomized Controlled Trial.

abstract: OBJECTIVE ::: Previous studies have shown that the bioflavonoid quercetin has anti-inflammatory and anti-nociceptive effects. We investigated the effect of quercetin supplementation on inflammation, disease severity, and clinical symptoms in women with rheumatoid arthritis (RA). ::: ::: ::: METHODS ::: The present study was a randomized, double-blind, placebo-controlled clinical trial in which 50 women with RA were allocated into a quercetin (500 mg/day) or placebo group for 8 weeks. Plasma levels of high-sensitivity tumor necrosis factor-α (hs-TNFα), erythrocyte sedimentation rate (ESR), clinical symptoms including early morning stiffness (EMS), morning and after-activity pain, and tender (TSC) and swollen joint counts (SJC) were determined. Disease activity and functional disability were assessed by Disease Activity Score 28 (DAS-28), physician global assessment (PGA), and a health assessment questionnaire (HAQ) at the beginning and end of the study. ::: ::: ::: RESULTS ::: Quercetin supplementation for 8 weeks significantly reduced EMS, morning pain, and after-activity pain (p < 0.05). DAS-28 and HAQ scores decreased in the quercetin group compared to placebo and the number of patients with active disease significantly decreased in the quercetin group. Plasma hs-TNFα level was significantly reduced in the quercetin group compared to placebo (p < 0.05). There were no significant differences in TJC and SJC between groups but TJC significantly decreased in the quercetin group after the intervention. Supplementation had an effect on ESR but it was not significant (p > 0.05). ::: ::: ::: CONCLUSIONS ::: Five hundred milligrams per day quercetin supplementation for 8 weeks resulted in significant improvements in clinical symptoms, disease activity, hs-TNFα, and HAQ in women with RA.


title: Language Generation with Recurrent Generative Adversarial Networks without Pre-training

abstract: Generative Adversarial Networks (GANs) have shown great promise recently in image generation. Training GANs for language generation has proven to be more difficult, because of the non-differentiable nature of generating text with recurrent neural networks. Consequently, past work has either resorted to pre-training with maximum-likelihood or used convolutional networks for generation. In this work, we show that recurrent neural networks can be trained to generate text with GANs from scratch using curriculum learning, by slowly teaching the model to generate sequences of increasing and variable length. We empirically show that our approach vastly improves the quality of generated sequences compared to a convolutional baseline.


title: A Small-Footprint Accelerator for Large-Scale Neural Networks

abstract: Machine-learning tasks are becoming pervasive in a broad range of domains, and in a broad range of systems (from embedded systems to data centers). At the same time, a small set of machine-learning algorithms (especially Convolutional and Deep Neural Networks, i.e., CNNs and DNNs) are proving to be state-of-the-art across many applications. As architectures evolve toward heterogeneous multicores composed of a mix of cores and accelerators, a machine-learning accelerator can achieve the rare combination of efficiency (due to the small number of target algorithms) and broad application scope. Until now, most machine-learning accelerator designs have been focusing on efficiently implementing the computational part of the algorithms. However, recent state-of-the-art CNNs and DNNs are characterized by their large size. In this study, we design an accelerator for large-scale CNNs and DNNs, with a special emphasis on the impact of memory on accelerator design, performance, and energy. We show that it is possible to design an accelerator with a high throughput, capable of performing 452 GOP/s (key NN operations such as synaptic weight multiplications and neurons outputs additions) in a small footprint of 3.02mm2 and 485mW; compared to a 128-bit 2GHz SIMD processor, the accelerator is 117.87 × faster, and it can reduce the total energy by 21.08 ×. The accelerator characteristics are obtained after layout at 65nm. Such a high throughput in a small footprint can open up the usage of state-of-the-art machine-learning algorithms in a broad set of systems and for a broad set of applications.


title: Tunable Inter-Resonator Coupling Structure With Positive and Negative Values and Its Application to the Field-Programmable Filter Array (FPFA)

abstract: In this paper, we show a tunable inter-resonator coupling structure capable of varying the coupling coefficient between resonators from positive to negative values, including values that approach zero. The presented inter-resonator coupling structure can be tuned to generate large isolation between two resonators as well as the required coupling for filter responses. Using the inter-resonator coupling structure, this paper demonstrates the concept of a field-programmable filter array (FPFA). The proposed array is composed of tunable resonators and can have multiple functionalities by routing signals from input ports to output ports. Signal routing can be achieved by controlling the inter-resonator coupling with a wide tuning ratio of coupling coefficients. A unit cell of the proposed FPFA was fabricated to prove the proposed concept. It is shown that the unit cell can be adjusted to have filter array responses with two second-order bandpass responses, third-order bandpass responses, and fourth-order bandpass responses. The unit cell can also be operated as a switchable filter bank without a switch. All operation modes are verified by measurements. This paper also demonstrates, for the first time, a reconfigurable filter that can be tuned to have both elliptic and self-equalized responses using the presented inter-resonator coupling structure.


title: The UJI librarian robot

abstract: This paper describes the UJI librarian robot, a mobile manipulator that is able to autonomously locate a book in an ordinary library, and grasp it from a bookshelf, by using eye-in-hand stereo vision and force sensing. The robot is only provided with the book code, a library map and some knowledge about its logical structure and takes advantage of the spatio-temporal constraints and regularities of the environment by applying disparate techniques such as stereo vision, visual tracking, probabilistic matching, motion estimation, multisensor-based grasping, visual servoing and hybrid control, in such a way that it exhibits a robust and dependable performance. The system has been tested, and experimental results show how it is able to robustly locate and grasp a book in a reasonable time without human intervention.


title: A Comparison of Single Channel Fetal ECG Extraction Methods

abstract: The abdominal electrocardiogram (ECG) provides a non-invasive method for monitoring the fetal cardiac activity in pregnant women. However, the temporal and frequency overlap between the fetal ECG (FECG), the maternal ECG (MECG) and noise results in a challenging source separation problem. This work seeks to compare temporal extraction methods for extracting the fetal signal and estimating fetal heart rate. A novel method for MECG cancelation using an echo state neural network (ESN) based filtering approach was compared with the least mean square (LMS), the recursive least square (RLS) adaptive filter and template subtraction (TS) techniques. Analysis was performed using real signals from two databases composing a total of 4 h 22 min of data from nine pregnant women with 37,452 reference fetal beats. The effects of preprocessing the signals was empirically evaluated. The results demonstrate that the ESN based algorithm performs best on the test data with an F1 measure of 90.2% as compared to the LMS (87.9%), RLS (88.2%) and the TS (89.3%) techniques. Results suggest that a higher baseline wander high pass cut-off frequency than traditionally used for FECG analysis significantly increases performance for all evaluated methods. Open source code for the benchmark methods are made available to allow comparison and reproducibility on the public domain data.


title: An Empirical Analysis of Vulnerabilities in OpenSSL and the Linux Kernel

abstract: Vulnerabilities are one of the main concerns faced by practitioners when working with security critical applications. Unfortunately, developers and security teams, even experienced ones, fail to identify many of them with severe consequences. Vulnerabilities are hard to discover since they appear in various forms, caused by many different issues and their identification requires an attacker's mindset. In this paper, we aim at increasing the understanding of vulnerabilities by investigating their characteristics on two major open-source software systems, i.e., the Linux kernel and OpenSSL. In particular, we seek to analyse and build a profile for vulnerable code, which can ultimately help researchers in building automated approaches like vulnerability prediction models. Thus, we examine the location, criticality and category of vulnerable code along with its relation with software metrics. To do so, we collect more than 2,200 vulnerable files accounting for 863 vulnerabilities and compute more than 35 software metrics. Our results indicate that while 9 Common Weakness Enumeration (CWE) types of vulnerabilities are prevalent, only 3 of them are critical in OpenSSL and 2 of them in the Linux kernel. They also indicate that different types of vulnerabilities have different characteristics, i.e., metric profiles, and that vulnerabilities of the same type have different profiles in the two projects we examined. We also found that the file structure of the projects can provide useful information related to the vulnerabilities. Overall, our results demonstrate the need for making project specific approaches that focus on specific types of vulnerabilities.


title: Characteristics of canopy and air temperature in tropical seasonal rainforests in Xishuangbanna

abstract: Based on the observation data in 2005, this paper analyzed the seasonal variations of the canopy temperature and the air temperature above and below the canopy in a tropical seasonal rainforest in Xishuangbanna of Southwest China. The results showed that at the same layers, the canopy temperature was higher than the air temperature except at nights of foggy-cool season. At most time of foggy-cool and dry-heat seasons, the air temperature above the canopy was higher than that below the canopy, while there was an opposite trend at noon time. In the rain season with higher mean air temperature, the air temperature above the canopy was all along higher than that below the canopy. The differences in the variations of the air temperature above and below the canopy presented the feedback effect of the seasonal tropical rainforest canopy to the microclimate in the forest. At the night time of foggy-cool season, the canopy temperature and air temperature differed with those at other time, suggesting that the low temperature at the nights of foggy-cool season was the key limiting factor for the existence of tropical seasonal rainforests in Xishuangbanna.


title: Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition

abstract: Abstract Tasks that demand externalized attention reliably suppress default network activity while activating the dorsal attention network. These networks have an intrinsic competitive relationship; activation of one suppresses activity of the other. Consequently, many assume that default network activity is suppressed during goal-directed cognition. We challenge this assumption in an fMRI study of planning. Recent studies link default network activity with internally focused cognition, such as imagining personal future events, suggesting a role in autobiographical planning. However, it is unclear how goal-directed cognition with an internal focus is mediated by these opposing networks. A third anatomically interposed ‘frontoparietal control network’ might mediate planning across domains, flexibly coupling with either the default or dorsal attention network in support of internally versus externally focused goal-directed cognition, respectively. We tested this hypothesis by analyzing brain activity during autobiographical versus visuospatial planning. Autobiographical planning engaged the default network, whereas visuospatial planning engaged the dorsal attention network, consistent with the anti-correlated domains of internalized and externalized cognition. Critically, both planning tasks engaged the frontoparietal control network. Task-related activation of these three networks was anatomically consistent with independently defined resting-state functional connectivity MRI maps. Task-related functional connectivity analyses demonstrate that the default network can be involved in goal-directed cognition when its activity is coupled with the frontoparietal control network. Additionally, the frontoparietal control network may flexibly couple with the default and dorsal attention networks according to task domain, serving as a cortical mediator linking the two networks in support of goal-directed cognitive processes.


title: Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55

abstract: We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation of 3D shapes and 3D reconstruction from single view images. Ten teams have participated in the challenge and the best performing teams have outperformed state-of-the-art approaches on both tasks. A few novel deep learning architectures have been proposed on various 3D representations on both tasks. We report the techniques used by each team and the corresponding performances. In addition, we summarize the major discoveries from the reported results and possible trends for the future work in the field.


title: An ultra high density pseudo dual-port SRAM in 16nm FINFET process for graphics processors

abstract: In recent times, graphic, audio and video definition has improved due to significant advancement in complex algorithms and video processing techniques. These techniques require heterogeneous and multi-core processors because of their complex computation abilities. Dual-port memories have become an essential component of CPUs because multi-core processors require significant data transfer. However, dual-port memories come at a cost of increased area and leakage. In this paper, an ultra-high-density dual-port SRAM (RADPUHD) architecture is proposed which addresses area and leakage challenges. It is designed and fabricated in 16nm technology. This paper presents use of a single-port bitcell to achieve functionality of dual-port SRAM thus improving area efficiency. The use of latches for Port B signals instead of full flip-flops further reduces area. The proposed design is a bolt-on wrapper around a 6T single-port SRAM. This design achieved a memory density of 8.1Mb/mm2 chip area and achieved 53% area savings and approximately 60% leakage savings when compared to an 8T dual-port SRAM that was also fabricated in 16nm. Silicon results show that the proposed circuit is functional down to a minimum operating voltage of 520mV.


title: Context-aware role-based access control in pervasive computing systems

abstract: In this paper we present a context-aware RBAC (CARBAC) model for pervasive computing applications. The design of this model has been guided by the context-based access control requirements of such applications. These requirements are related to users' memberships in roles, permission executions by role members, and context-based dynamic integration of services in the environment with an application. Context information is used in role admission policies, in policies related to permission executions by role members, and in policies related to accessing of dynamically interfaced services by role members. The dynamic nature of context information requires model-level support for revocations of role memberships and permission activations when certain context conditions fail to hold. Based on this model we present a programming framework for building context-aware applications, providing mechanisms for specifying and enforcing context-based access control requirements.


Unsupervised Lesion Detection in Brain CT using Bayesian Convolutional Autoencoders


title: The Flash ADC [A Circuit for All Seasons]

abstract: Flash analog-to-digital converters (ADCs) find wide application both as stand-alone components and as building blocks of more complex systems. This architecture dates back to at least the early 1960s. For example, in a patent filed in 1963, Stephenson [1] describes the "parallel" ADC as a known technique. In this article, we study the properties and design issues of this topology.


title: Tight Prediction Intervals Using Expanded Interval Minimization

abstract: Prediction intervals are a valuable way of quantifying uncertainty in regression problems. Good prediction intervals should be both correct, containing the actual value between the lower and upper bound at least a target percentage of the time; and tight, having a small mean width of the bounds. Many prior techniques for generating prediction intervals make assumptions on the distribution of error, which causes them to work poorly for problems with asymmetric distributions. ::: This paper presents Expanded Interval Minimization (EIM), a novel loss function for generating prediction intervals using neural networks. This loss function uses minibatch statistics to estimate the coverage and optimize the width of the prediction intervals. It does not make the same assumptions on the distributions of data and error as prior work. We compare to three published techniques and show EIM produces on average 1.37x tighter prediction intervals and in the worst case 1.06x tighter intervals across two large real-world datasets and varying coverage levels.


title: Implementation of Multipath Network Virtualization With SDN and NFV

abstract: Multipath networks have been extensively studied to improve throughput and alleviate network congestion. However, multipath networks, with the exception of equal-cost multi-path networks, are difficult to implement because they schedule network resources at cross layers and require complex signaling mechanisms to monitor network status (e.g., round trip time, bandwidths, and link state). Virtualization, in contrast, provides network isolation with protocols and resources and simplifies the process of coupling the interaction between signaling mechanisms, which makes multipath implementation practical. This paper proposes a multipath network virtualization scheme that implements software-defined networking (SDN) and network function virtualization (NFV). Multipath networks can be deployed in the virtualized environment. The proposed scheme attains a summary of network resources, such as network topology and link bandwidth, and can schedule these resources for selecting and spreading flow over multiple paths. In addition, virtualization provides computing and storage resources for flow splitting, tag adding, and packet reordering, among other applications. Specifically, the tags are designed for forwarding and packet reordering. Our experiment builds the platform based on open platform for NFV and SDN on the practice testbed. To verify its functionalities, we first evaluate the overhead of each component and then implement and compare five state-of-the-art multipath models on the proposed platform. Our scheme is compatible with these multipath models. Moreover, the experimental results show that the model with flow splitting has superior performance with respect to load balancing.


title: An Efficient Data Warehouse for Crop Yield Prediction

abstract: The 14th International Conference on Precision Agriculture, Montreal, Canada, 24-27 June 2018


title: An Adaptable Surface Parameterization Method

abstract: Parameterizations of triangulated surfaces are used in an increasing number of mesh processing applications for various purposes. Although demands vary, they are often required to preserve the surface metric and thus minimize angle, area and length deformation. However, most of the existing techniques primarily target at angle preservation while disregarding global area deformation. In this paper an energy functional is proposed, that quantifies angle and global area deformations simultaneously, while the relative importance between angle and area preservation can be controlled by the user through a parameter. We show how this parameter can be chosen to obtain parameterizations, that are optimized for an uniform sampling of the surface of a model. Maps obtained by minimizing this energy are well suited for applications that desire an uniform surface sampling, like re-meshing or mapping regularly patterned textures. Besides being invariant under rotation and translation of the domain, the energy is designed to prevent face flips during minimization and does not require a fixed boundary in the parameter domain. Although the energy is nonlinear, we show how it can be minimized efficiently using non-linear conjugate gradient methods in a hierarchical optimization framework and prove the convergence of the algorithm. The ability to control the tradeoff between the degree of angle and global area preservation is demonstrated for several models of varying complexity.


title: Minimizing area and energy of deep learning hardware design using collective low precision and structured compression

abstract: Deep learning algorithms have shown tremendous success in many recognition tasks; however, these algorithms typically include a deep neural network (DNN) structure and a large number of parameters, which makes it challenging to implement them on power/area-constrained embedded platforms. To reduce the network size, several studies investigated compression by introducing element-wise or row-/column-/block-wise sparsity via pruning and regularization. In addition, many recent works have focused on reducing precision of activations and weights with some reducing down to a single bit. However, combining various sparsity structures with binarized or very-low-precision (2-3 bit) neural networks have not been comprehensively explored. In this work, we present design techniques for minimum-area/-energy DNN hardware with minimal degradation in accuracy. During training, both binarization/low-precision and structured sparsity are applied as constraints to find the smallest memory footprint for a given deep learning algorithm. The DNN model for CIFAR-10 dataset with weight memory reduction of 50X exhibits accuracy comparable to that of the floating-point counterpart. Area, performance and energy results of DNN hardware in 40nm CMOS are reported for the MNIST dataset. The optimized DNN that combines 8X structured compression and 3-bit weight precision showed 98.4% accuracy at 20nJ per classification.


title: HHSRP: a cluster based hybrid hierarchical secure routing protocol for wireless sensor networks

abstract: Wireless sensor networks happen to be fundamentally networks with energy restrictions. This is limited over longer period of time proves to be the challenge in clustering algorithms. We suggested a new method related to co-ordinator head (CNH) selection by using a mixed hierarchical cluster based algorithm that includes selecting the CNH happens to be the greatest value of the co-ordinator node (CN) and fitness value. This analysis put forward two different algorithms; mixed hierarchical cluster oriented routing program and hybrid hierarchical secure algorithm. A mixed hierarchal cluster based algorithm has been suggested for generating CNH and CN, for identifying the harmful node and packet is securely delivered. The source node transmits details about packet to the CN and then it chooses the pathway which is the shortest, depending upon the trust value among intermediate node and sensor node. The behavior activities of all the nodes are analyzed by the CNH, if anyone of the node’s behavior found to be incorrect, the organizer who finds the harmful node and CNH will drop those particular node behaviors. The CNH then changes to other path way immediately and transmits the delivery packet securely to the target destination under short time.


title: S.: How smart are our environments? An updated look at the state of the art

abstract: In this paper we take a look at the state of the art in smart environments research. The survey is motivated by the recent dramatic increase of activity in the field, and summarizes work in a variety of supporting disciplines. We also discuss the application of smart environments research to health monitoring and assistance, followed by ongoing challenges for continued research.


title: MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs

abstract: Chest radiography is an extremely powerful imaging modality, allowing for a detailed inspection of a patient's thorax, but requiring specialized training for proper interpretation. With the advent of high performance general purpose computer vision algorithms, the accurate automated analysis of chest radiographs is becoming increasingly of interest to researchers. However, a key challenge in the development of these techniques is the lack of sufficient data. Here we describe MIMIC-CXR-JPG v2.0.0, a large dataset of 377,110 chest x-rays associated with 227,827 imaging studies sourced from the Beth Israel Deaconess Medical Center between 2011 - 2016. Images are provided with 14 labels derived from two natural language processing tools applied to the corresponding free-text radiology reports. MIMIC-CXR-JPG is derived entirely from the MIMIC-CXR database, and aims to provide a convenient processed version of MIMIC-CXR, as well as to provide a standard reference for data splits and image labels. All images have been de-identified to protect patient privacy. The dataset is made freely available to facilitate and encourage a wide range of research in medical computer vision.


The stages of political development


title: Compact CPW-Fed Tri-Band Printed Antenna With Meandering Split-Ring Slot for WLAN/WiMAX Applications

abstract: A novel compact tri-band printed antenna for WLAN and WiMAX applications is presented. The proposed antenna consists of a modified rectangular slot, a pair of symmetrical inverted-L strips, and a Y-shaped monopole radiator with a meandering split-ring slot. Tuning the locations and the sizes of these structures, three distinct current paths can be produced at three independent frequency bands, respectively. Based on this concept, a prototype of the tri-band antenna is further fabricated and measured. The experimental and numerical results show that the antenna has impedance bandwidth (for return loss less than 10 dB) of 430 MHz (2.33-2.76 GHz), 730 MHz (3.05-3.88 GHz), and 310 MHz (5.57-5.88 GHz), which can cover both the WLAN 2.4/5.8-GHz bands and the WiMAX 2.5/3.5-GHz bands.


title: Rainy weather recognition from in-vehicle camera images for driver assistance

abstract: We propose a weather recognition method from in-vehicle camera images that uses a subspace method to judge rainy weather by detecting raindrops on the windshield. "Eigendrops" represent the principal components extracted from raindrop images in the learning stage. Then the method detects raindrops by template matching. In experiments using actual video sequences, our method showed good detection ability of raindrops and promising results for rainfall judgment from detection results.


title: Adaptive optimal control for continuous-time linear systems based on policy iteration

abstract: In this paper we propose a new scheme based on adaptive critics for finding online the state feedback, infinite horizon, optimal control solution of linear continuous-time systems using only partial knowledge regarding the system dynamics. In other words, the algorithm solves online an algebraic Riccati equation without knowing the internal dynamics model of the system. Being based on a policy iteration technique, the algorithm alternates between the policy evaluation and policy update steps until an update of the control policy will no longer improve the system performance. The result is a direct adaptive control algorithm which converges to the optimal control solution without using an explicit, a priori obtained, model of the system internal dynamics. The effectiveness of the algorithm is shown while finding the optimal-load-frequency controller for a power system.


title: An adaptive energy-efficient and low-latency MAC for data gathering in wireless sensor networks

abstract: Summary form only given. In many sensor network applications the major traffic pattern consists of data collected from several source nodes to a sink through a unidirectional tree. We propose DMAC, an energy efficient and low latency MAC that is designed and optimized for such data gathering trees in wireless sensor networks. We first show that previously proposed MAC protocols for sensor networks that utilize activation/sleep duty cycles suffer from a data forwarding interruption problem, whereby not all nodes on a multihop path to the sink are notified of data delivery in progress, resulting in significant sleep delay. DMAC is designed to solve the interruption problem and allow continuous packet forwarding by giving the sleep schedule of a node an offset that depends upon its depth on the tree. DMAC also adjusts the duty cycles adaptively according to the traffic load in the network. We further propose a data prediction mechanism and the use of more-to-send (MTS) packets in order to alleviate problems pertaining to channel contention and collisions. Our simulation results show that by exploiting the application-specific structure of data gathering trees in sensor networks, DMAC provides significant energy savings and latency reduction while ensuring high data reliability.


title: Adolescent Physical Activities as Predictors of Young Adult Weight

abstract: OBJECTIVE ::: To examine the relationship between increased physical activity in adolescence and adult weight status. ::: ::: ::: DESIGN ::: Cohort study based on data from the National Longitudinal Study of Adolescent Health. ::: ::: ::: SETTING ::: In-home interviews. ::: ::: ::: PARTICIPANTS ::: A total of 3345 adolescents in grades 8 to 12 with body mass index (calculated as weight in kilograms divided by height in meters squared) data available at baseline and 5 years later. Main Exposures Days per week of curricular and extracurricular physical activity. ::: ::: ::: MAIN OUTCOME MEASURE ::: Overweight status (body mass index > or =25) 5 years after baseline. ::: ::: ::: RESULTS ::: Increasing participation in certain extracurricular physical activities and physical education decreased the likelihood of young adulthood overweight. Regarding extracurricular physical activities, the likelihood of being an overweight adult was reduced most (ie, 48%) by performing certain wheel-related activities (ie, rollerblading, roller skating, skateboarding, or bicycling) more than 4 times per week. Each weekday that adolescents participated in physical education decreased the odds of being an overweight adult by 5%, with participation in all 5 weekdays of physical education decreasing the odds by 28%. In general, physical activity predicted normal-weight maintenance better than weight loss. ::: ::: ::: CONCLUSION ::: These data underscore the important role that school-based and extracurricular physical activity play in reducing the likelihood of transitioning to overweight as young adults.


title: Group development and group maturity when building agile teams: A qualitative and quantitative investigation at eight large companies

abstract: The agile approach to projects focuses more on close-knit teams than traditional waterfall projects, which means that aspects of group maturity become even more important. This psychological aspect is not much researched in connection to the building of an "agile team." The purpose of this study is to investigate how building agile teams is connected to a group development model taken from social psychology. We conducted ten semi-structured interviews with coaches, Scrum Masters, and managers responsible for the agile process from seven different companies, and collected survey data from 66 group-members from four companies (a total of eight different companies). The survey included an agile measurement tool and the one part of the Group Development Questionnaire. The results show that the practitioners define group developmental aspects as key factors to a successful agile transition. Also, the quantitative measurement of agility was significantly correlated to the group maturity measurement. We conclude that adding these psychological aspects to the description of the "agile team" could increase the understanding of agility and partly help define an "agile team." We propose that future work should develop specific guidelines for how software development teams at different maturity levels might adopt agile principles and practices differently.


title: Path planning using lazy PRM

abstract: Describes an approach to probabilistic roadmap planners (PRMs). The overall theme of the algorithm, called Lazy PRM, is to minimize the number of collision checks performed during planning and hence minimize the running time of the planner. Our algorithm builds a roadmap in the configuration space, whose nodes are the user-defined initial and goal configurations and a number of randomly generated nodes. Neighboring nodes are connected by edges representing paths between the nodes. In contrast with PRMs, our planner initially assumes that all nodes and edges in the roadmap are collision-free, and searches the roadmap at hand for a shortest path between the initial and the goal node. The nodes and edges along the path are then checked for collision. If a collision with the obstacles occurs, the corresponding nodes and edges are removed from the roadmap. Our planner either finds a new shortest path, or first updates the roadmap with new nodes and edges, and then searches for a shortest path. The above process is repeated until a collision-free path is returned. Lazy PRM is tailored to efficiently answer single planning queries, but can also be used for multiple queries. Experimental results presented in the paper show that our lazy method is very efficient in practice.


title: Protection of distributed generation connected networks with coordination of overcurrent relays

abstract: The disconnection of distributed generators (DGs) from a distribution network for every abnormal condition drastically reduces the DG benefits and system reliability when DG penetration level is high. The DGs can be used to supply the load demand in the absence of grid supply if DGs are allowed to operate in islanded mode. In this paper, protection issues associated with disconnection of DGs are addressed in the context of a radial distribution feeder. Protection strategies are proposed to allow islanded operation and to restore the system performing auto-reclosing maintaining as many DG connections as possible. An overcurrent relay based protection scheme is proposed for a converter based DG connected radial feeder to operate either in grid-connected or islanded mode maximizing the DG benefits to customers. Moreover, an effective method is proposed to restore the system with DGs using auto-reclosers. The proposals are verified through PSCAD simulation and MATLAB calculations.


title: Infusing Collaborative Recommenders with Distributed Representations

abstract: Recommender systems assist users in navigating complex information spaces and focus their attention on the content most relevant to their needs. Often these systems rely on user activity or descriptions of the content. Social annotation systems, in which users collaboratively assign tags to items, provide another means to capture information about users and items. Each of these data sources provides unique benefits, capturing different relationships. In this paper, we propose leveraging multiple sources of data: ratings data as users report their affinity toward an item, tagging data as users assign annotations to items, and item data collected from an online database. Taken together, these datasets provide the opportunity to learn rich distributed representations by exploiting recent advances in neural network architectures. We first produce representations that subjectively capture interesting relationships among the data. We then empirically evaluate the utility of the representations to predict a user's rating on an item and show that it outperforms more traditional representations. Finally, we demonstrate that traditional representations can be combined with representations trained through a neural network to achieve even better results.


title: Inception-v3 for flower classification

abstract: The study of flower classification system is a very important subject in the field of Botany. A classifier of flowers with high accuracy will also bring a lot of fun to people's lives. However, because of the complex background of flowers, the similarity between the different species of flowers, and the differences among the same species of flowers, there are still some challenges in the recognition of flower images. The traditional flower classification is mainly based on the three features: color, shape and texture, this classification requires people to select features for classification, and the accuracy is not very high. In this paper, based on Inception-v3 model of TensorFlow platform, we use the transfer learning technology to retrain the flower category datasets, which can greatly improve the accuracy of flower classification.


Mindfulness Meditation Improves Attention in Novices


title: Spin transfer nano-oscillators

abstract: The use of spin transfer nano-oscillators (STNOs) to generate microwave signals in nanoscale devices has aroused tremendous and continuous research interest in recent years. Their key features are frequency tunability, nanoscale size, broad working temperature, and easy integration with standard silicon technology. In this feature article, we give an overview of recent developments and breakthroughs in the materials, geometry design and properties of STNOs. We focus in more depth on our latest advances in STNOs with perpendicular anisotropy, showing a way to improve the output power of STNO towards the μW range. Challenges and perspectives of the STNOs that might be productive topics for future research are also briefly discussed.


title: Constrained Deep Weak Supervision for Histopathology Image Segmentation

abstract: In this paper, we develop a new weakly supervised learning algorithm to learn to segment cancerous regions in histopathology images. This paper is under a multiple instance learning (MIL) framework with a new formulation, deep weak supervision (DWS); we also propose an effective way to introduce constraints to our neural networks to assist the learning process. The contributions of our algorithm are threefold: 1) we build an end-to-end learning system that segments cancerous regions with fully convolutional networks (FCNs) in which image-to-image weakly-supervised learning is performed; 2) we develop a DWS formulation to exploit multi-scale learning under weak supervision within FCNs; and 3) constraints about positive instances are introduced in our approach to effectively explore additional weakly supervised information that is easy to obtain and enjoy a significant boost to the learning process. The proposed algorithm, abbreviated as DWS-MIL, is easy to implement and can be trained efficiently. Our system demonstrates the state-of-the-art results on large-scale histopathology image data sets and can be applied to various applications in medical imaging beyond histopathology images, such as MRI, CT, and ultrasound images.


title: Cell sorter immunofluorescence detection of human erythrocytes labelled in suspension with antibodies specific for hemoglobin S and C.

abstract: We have developed an immunochemical method for labeling human red blood cells in suspension with hemoglobin-specific antibodies. A membrane permeable cross-linking reagent, dimethyl suberimidate, is used to covalently bind, in situ, a fraction of the intracellular hemoglobin to integral membrane proteins. Hypotonic lysis and washing of the cells removes the unbound hemoglobin resulting in red blood cell ghosts which are permeable to macromolecules. Fluorescein-labeled antibodies for the hemoglobin variants S and C bind specifically to hemoglobin AS and AC ghosts, respectively, and not to normal hemoglobin AA ghosts. This technique can be used to prepare ghost suspensions for cell sorter analysis in which large numbers (10(9)--10(10)) of normal ghosts can be rapidly screened for the presence of rare anti-hemoglobin S and anti-hemoglobin C binding ghosts. In reconstruction experiments using mixtures of AS and AA cells and anti-hemoglobin S, AS ghosts as rare as 3 X 10(-5) were quantitatively recovered. Fluorescence artifacts prevented direct enumeration of AS ghosts at lower frequencies, but a two-step flow sorting-fluorescence microscope visual scanning procedure allows semiquantitative detection of anti-hemoglobin S-labeled ghosts as low as 10(--7). This method can be used for rapidly screening blood samples from individuals of normal hemoglobin A genotype for the presence of rare anti-hemoglobin S and anti-hemoglobin C binding ghosts.


title: Care about you: towards large-scale human-centric visual relationship detection

abstract: Visual relationship detection aims to capture interactions between pairs of objects in images. Relationships between objects and humans represent a particularly important subset of this problem, with implications for challenges such as understanding human behaviour, and identifying affordances, amongst others. In addressing this problem we first construct a large-scale human-centric visual relationship detection dataset (HCVRD), which provides many more types of relationship annotation (nearly 10K categories) than the previous released datasets. ::: This large label space better reflects the reality of human-object interactions, but gives rise to a long-tail distribution problem, which in turn demands a zero-shot approach to labels appearing only in the test set. This is the first time this issue has been addressed. We propose a webly-supervised approach to these problems and demonstrate that the proposed model provides a strong baseline on our HCVRD dataset.


title: Conditions for the convergence of one-layer networks under reinforcement learning

abstract: An extension to the convergence theorem for single neurons learning under the AR-P algorithm is proved. The extension shows that if the conditions of the single-neuron theorem are satisfied and if the environment satisfies one of two sufficient conditions, the weights in an arbitrarily large one-layer network will converge with probability one to values with which the network correctly classifies the training input set. One condition requires that for all output vectors, the probability of reinforcement being one (success) has one of two values: the output vectors having at least l k correct elements have the higher probability, whereas the output vectors having less than l k correct elements have the lower probability. The alternative condition requires that the reinforcement have a higher probability of being one for output vectors having a higher number of correct elements. The extension and its proof are significant because they further the understanding of the factors affecting the convergence of multilayer networks under reinforcement learning


title: Modeling and design analysis of 3D vertical resistive memory — A low cost cross-point architecture

abstract: Resistive Random Access Memory (ReRAM) is one of the most promising emerging non-volatile memory (NVM) candidates due to its fast read/write speed, excellent scalability and low-power operation. Recently proposed 3D vertical cross-point ReRAM (3D-VRAM) architecture attracts a lot of attention because it offers a cost-competitive solution as NAND Flash replacement. In this work, we first develop an array-level model which includes the geometries and properties of all the components in the 3D structure. The model is capable of analyzing the read/write noise margin of a 3D-VRAM array in the presence of the sneak leakage current and voltage drop. Then we build a system-level design tool that is able to explore the design space with specified constraints and find the optimal design points with different targets. We also study the impact of different design parameters on the array size, bit density, and overall cost-per-bit. Compared to the state-of-the-art 3D horizontal ReRAM (3D-HRAM), the 3D-VRAM shows great cost advantage when stacking more than 16 layers.


title: Active Learning For Outdoor Obstacle Detection

abstract: Real-world applications of mobile robotics call for increased autonomy, requiring reliable perception systems. Since manually tuned perception algorithms are difficult to adapt to new operating environments, systems based on supervised learning are necessary for future progress in autonomous navigation. Data labeling is a major concern when supervised learning is applied to the large-scale problems occuring in realistic robotics applications. We believe that algorithms for automatically selecting important data for labeling are necessary, and propose to employ active learning techniques to reduce the amount of labeling required to learn from a data set. In this paper we show that several standard active learning algorithms can be adapted to meet specific constraints characteristic to our domain, such as the need to learn from data with severely unbalanced class priors. We validate the solutions we propose by extensive experimentation on multiple realistic data sets captured with a robotic vehicle. Based on our results for the task of obstacle detection, we conclude that active learning techniques are applicable to our domain, and they can lead to significant reductions in the labeling effort required to use supervised learning in outdoor perception.


title: Music, Cognition, Culture, and Evolution

abstract: Abstract: We seem able to define the biological foundations for our musicality within a clear and unitary framework, yet music itself does not appear so clearly definable. Music is different things and does different things in different cultures; the bundles of elements and functions that are music for any given culture may overlap minimally with those of another culture, even for those cultures where “music” constitutes a discrete and identifiable category of human activity in its own right. The dynamics of culture, of music as cultural praxis, are neither necessarily reducible, nor easily relatable, to the dynamics of our biologies. Yet music appears to be a universal human competence. Recent evolutionary theory, however, affords a means for exploring things biological and cultural within a framework in which they are at least commensurable. The adoption of this perspective shifts the focus of the search for the foundations of music away from the mature and particular expression of music within a specific culture or situation and on to the human capacity for musicality. This paper will survey recent research that examines that capacity and its evolutionary origins in the light of a definition of music that embraces music's multifariousness. It will be suggested that music, like speech, is a product of both our biologies and our social interactions; that music is a necessary and integral dimension of human development; and that music may have played a central role in the evolution of the modern human mind.