title: Impact of single layer, double layer and four layer windings on the performance of AFPMSMs

abstract: In this paper, the impact of an increased number of layers on the performance of axial flux permanent magnet synchronous machines (AFPMSMs) is studied. The studied parameters are the inductance, terminal voltages, PM losses, iron losses, the mean value of torque, and the ripple torque. It is shown that increasing the number of layers reduces the fundamental winding factor. In consequence, the rated torque for the same current reduces. However, the reduction of harmonics associated with a higher number of layers reduces the ripple torque, PM losses, and iron losses. Besides studying the performance of the AFPMSMs for the rated conditions, the study is broadened for the field weakening (FW) region. During the FW region, the flux of the PMs is weakened by an injection of a reversible d-axis current. This keeps the terminal voltage of the machine fixed at the rated value. The inductance plays an important role in the FW study. A complete study for the FW shows that the two layer winding has the optimum performance compared to machines with an other number of winding layers.


The Long-Term Benefits of Positive Self-Presentation via Profile Pictures, Number of Friends and the Initiation of Relationships on Facebook for Adolescents’ Self-Esteem and the Initiation of Offline Relationships


title: Music Emotion Classification of Chinese Songs based on Lyrics Using TF*IDF and Rhyme

abstract: This paper presents the outcomes of research into an automatic classification system based on the lingual part of music. Two novel kinds of short features are extracted from lyrics using tf*idf and rhyme. Meta-learning algorithm is adapted to combine these two sets of features. Results show that our features promote the accuracy of classification and meta-learning algorithm is effective in fusing the two features.


The agile professional culture: A source of agile quality


title: Matching properties of MOS transistors

abstract: The matching properties of the threshold voltage, substrate factor and current factor of MOS transistors have been analysed and measured. Improvements of the existing theory are given, as well as extensions for long distance matching and rotation of devices. The matching results have been verified by measurements and calculations on a band-gap reference circuit.


title: A Matlab mesh generator for the two-dimensional finite element method

abstract: A new Matlab code for the generation of unstructured (3-node or 6-node) triangular meshes in two dimensions is proposed. The method is based on the Matlab mesh generator distmesh of Persson and Strang (2004). As input, the code takes a signed distance function for the domain geometry. A mesh size function, for the spatial node distribution, is constructed using an approximate medial axis. As outputs, the code generates a 3-node or a 6-node triangular mesh with boundary data (edges and nodes). The approach presented consists of three steps: (1) an initial nodes placement is obtained using a probabilistic node distribution, (2) an iterative smoothing is performed assuming the presence of an attractive/repulsive internode force, and (3) a fast refinement procedure is performed for 6-node triangular meshes or large scale meshes.


title: Cost Benefit Analysis for condition based maintenance

abstract: Investment in maintenance of machines is significant for industries. In this paper, we suggest a Cost Benefit Analysis model and review most commonly used maintenance strategies like corrective maintenance and preventive maintenance with the model. Here, we presented a method to calculate net revenue generated by machine for optimal and cost effective alternative for machine maintenance. Method used for calculating net revenue is improved using factors such as labor cost, failure rate, utilization factor etc. Formulated net revenue provides satisfactory results when compared with the industrial information on machine maintenance strategies. Realistic data are used to analyze revenue with different maintenance strategies. From the results it is concluded that though condition based maintenance as a maintenance strategy has high initial investment cost with time it has high revenue generation compared to other maintenance polices.


title: Transfer Learning via Dimensionality Reduction

abstract: Transfer learning addresses the problem of how to utilize plenty of labeled data in a source domain to solve related but different problems in a target domain, even when the training and testing problems have different distributions or features. In this paper, we consider transfer learning via dimensionality reduction. To solve this problem, we learn a low-dimensional latent feature space where the distributions between the source domain data and the target domain data are the same or close to each other. Onto this latent feature space, we project the data in related domains where we can apply standard learning algorithms to train classification or regression models. Thus, the latent feature space can be treated as a bridge of transferring knowledge from the source domain to the target domain. The main contribution of our work is that we propose a new dimensionality reduction method to find a latent space, which minimizes the distance between distributions of the data in different domains in a latent space. The effectiveness of our approach to transfer learning is verified by experiments in two real world applications: indoor WiFi localization and binary text classification.


title: Testosterone, sexuality and antisocial personality in rapists and child molesters: a pilot study

abstract: Abstract Morning and afternoon levels of saliva testosterone levels in Finnish imprisoned rapists ( n =10) and child molesters ( n =10) were compared to those in randomly selected control subjects ( n =31). The associations of saliva testosterone with sexual behavior and antisocial personality traits were explored in all groups. The sexual offenders and control subjects did not differ in the between-subjects main effect estimated for the averaged morning and afternoon testosterone levels. Seven rapists and three child molesters met the criteria for antisocial personality disorder (ASP). In the sexual offenders, a summed ASP index was positively correlated with mean saliva testosterone. Sexual activity as estimated from self-reports of sexual intercourse and masturbation was significantly related to testosterone in both rapists and child molesters but not in the control males. The implications of these results are discussed.


title: Wireless SDN mobile ad hoc network: From theory to practice

abstract: A promising approach for dealing with the increasing demand of data traffic is the use of device-to-device (D2D) technologies, in particular when the destination can be reached directly, or though few retransmissions by peer devices. Thus, the cellular network can offload local traffic that is transmitted by an ad hoc network, e.g., a mobile ad hoc network (MANET), or a vehicular ad hoc network (VANET). The cellular base station can help coordinate all the devices in the ad hoc network by reusing the software tools developed for software-defined networks (SDNs), which divide the control and the data messages, transmitted in two separate interfaces. In this paper, we present a practical implementation of an SDN MANET, describe in detail the software components that we adopted, and provide a repository for all the new components that we developed. This work can be a starting point for the wireless networking community to design new testbeds with SDN capabilities that can have the advantages of D2D data transmissions and the flexibility of a centralized network management. In order to prove the feasibility of such a network, we also showcase the performance of the proposed network implemented in real devices, as compared to a distributed ad hoc network.


title: On learning optimized reaction diffusion processes for effective image restoration

abstract: For several decades, image restoration remains an active research topic in low-level computer vision and hence new approaches are constantly emerging. However, many recently proposed algorithms achieve state-of-the-art performance only at the expense of very high computation time, which clearly limits their practical relevance. In this work, we propose a simple but effective approach with both high computational efficiency and high restoration quality. We extend conventional nonlinear reaction diffusion models by several parametrized linear filters as well as several parametrized influence functions. We propose to train the parameters of the filters and the influence functions through a loss based approach. Experiments show that our trained nonlinear reaction diffusion models largely benefit from the training of the parameters and finally lead to the best reported performance on common test datasets for image restoration. Due to their structural simplicity, our trained models are highly efficient and are also well-suited for parallel computation on GPUs.


title: A broadband and low-loss Ruthroff 4∶9 transmission-line transformer using coupled Microstrip lines

abstract: This paper presents a Ruthroff type spiral un-to-un transmission-line transformer (TLT) with impedance transformation ratio of 4∶9. Our TLT is fabricated in Microstrip lines technology and covers a chip area of 1.02 mm × 1.12 mm. The simulated minimum insertion loss is 0.19 dB at 5.4 GHz, and the correspondent bandwidth of this TLT is 5.4 GHz (when the insertion loss exceeds 1 dB).


DESIGN AND SIMULATION OF A 32-BIT RISC BASED MIPS PROCESSOR USING VERILOG


title: Cache craftiness for fast multicore key-value storage

abstract: We present Masstree, a fast key-value database designed for SMP machines. Masstree keeps all data in memory. Its main data structure is a trie-like concatenation of B+-trees, each of which handles a fixed-length slice of a variable-length key. This structure effectively handles arbitrary-length possiblybinary keys, including keys with long shared prefixes. +-tree fanout was chosen to minimize total DRAM delay when descending the tree and prefetching each tree node. Lookups use optimistic concurrency control, a read-copy-update-like technique, and do not write shared data structures; updates lock only affected nodes. Logging and checkpointing provide consistency and durability. Though some of these ideas appear elsewhere, Masstree is the first to combine them. We discuss design variants and their consequences. On a 16-core machine, with logging enabled and queries arriving over a network, Masstree executes more than six million simple queries per second. This performance is comparable to that of memcached, a non-persistent hash table server, and higher (often much higher) than that of VoltDB, MongoDB, and Redis.


title: IPython: A System for Interactive Scientific Computing

abstract: Python offers basic facilities for interactive work and a comprehensive library on top of which more sophisticated systems can be built. The IPython project provides on enhanced interactive environment that includes, among other features, support for data visualization and facilities for distributed and parallel computation


title: Multimodal Attention for Neural Machine Translation

abstract: The attention mechanism is an important part of the neural machine translation (NMT) where it was reported to produce richer source representation compared to fixed-length encoding sequence-to-sequence models. Recently, the effectiveness of attention has also been explored in the context of image captioning. In this work, we assess the feasibility of a multimodal attention mechanism that simultaneously focus over an image and its natural language description for generating a description in another language. We train several variants of our proposed attention mechanism on the Multi30k multilingual image captioning dataset. We show that a dedicated attention for each modality achieves up to 1.6 points in BLEU and METEOR compared to a textual NMT baseline.


title: Not-So-CLEVR: learning same–different relations strains feedforward neural networks

abstract: The advent of deep learning has recently led to great successes in various engineering applications. As a prime example, convolutional neural networks, a type of feedforward neural network, now approach human accuracy on visual recognition tasks like image classification and face recognition. However, here we will show that feedforward neural networks struggle to learn abstract visual relations that are effortlessly recognized by non-human primates, birds, rodents and even insects. We systematically study the ability of feedforward neural networks to learn to recognize a variety of visual relations and demonstrate that same-different visual relations pose a particular strain on these networks. Networks fail to learn same-different visual relations when stimulus variability makes rote memorization difficult. Further, we show that learning same-different problems becomes trivial for a feedforward network that is fed with perceptually grouped stimuli. This demonstration and the comparative success of biological vision in learning visual relations suggests that feedback mechanisms such as attention, working memory and perceptual grouping may be the key components underlying human-level abstract visual reasoning.


title: Detection of scintillation light of liquid xenon with a LAAPD

abstract: Abstract First observation of liquid xenon scintillation due to α-particles and γ-rays with a large area avalanche photodiode immersed in the liquid is reported. An energy resolution of 10% (FWHM) and a coincidence time resolution of λ =178 nm) is estimated to be ∼100%.


European Miniatures in the Metropolitan Museum of Art


title: Small sets supporting fary embeddings of planar graphs

abstract: Answering a question of Rosenstiehl and Tarjan, we show that every plane graph with n vertices has a Fary embedding (i.e., straight-line embedding) on the 2 n - 4 by n - 2 grid and provide an O( n ) space, O( n log n ) time algorithm to effect this embedding. The grid size is asymptotically optimal and it had been previously unknown whether one can always find a polynomial sized grid to support such an embedding. On the other hand we show that any set F , which can support a Fary embedding of every planar graph of size n , has cardinality at least n + (1 - o (1)) √ n which settles a problem of Mohar.


title: Playing Atari with Deep Reinforcement Learning

abstract: We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them.


title: A test data generation tool for testing RFID middleware

abstract: This paper presents a noble RFID data generation tool to enable efficient and low cost testing of the RFID middleware. RFID systems have been widely used in many business areas such as supply-chain management and yard management. As a key component of RFID system, the RFID middleware should be carefully evaluated from various environments. However, taking a variety of tests for RFID middleware is very complex and costly process because it requires construction of test environments such as deploying RFID devices, tagging of product items, and so on. For instance, the stress testing of RFID middleware needs to deploy millions of tags which take lots of money, time and human recourses. To solve these problems, we design virtual readers and tags for constructing virtual environment instead of real devices. Using virtual devices, we implement a test data generation tool which generates tag stream data for testing the RFID middleware. To simulate real RFID environment, we first define several parameters for representing real environments. By configuring the parameters, this tool can generate test data set which emulates various business scenarios. We also define several measurement metrics which are related to four factors like group, redundancy, noisy as well as route to verify the correctness of the generated data set. The experimental results show that our testing tool is cable for facilitating RFID middleware test.


title: Harnessing the power of hashtags in tweet analytics

abstract: Twitter is one of the most popular microblogging platforms where users can interact with each other by posting texts of up to 140 characters called tweets. Because of the large and fast growing number of tweets being generated daily, tweet analytics is viewed as one of the fundamental problems of Big data stream. Recently, hashtags, hyperlinked words, in tweets have been applied for tweet retrieval, trend/event detection and advertisement. However, using hashtags for tweet classification remains challenging because we have to cope with context dependent words, slangs, abbreviations, and emoticons with a limited small number of words and an evolving use of hashtags. Most existing approaches deal with classifying tweet sentiments by using the lexicon and meaning of hashtags. Our research aims to classify tweets by topics. Unlike sentiment analytics, hashtags for describing a topic need to be more diverse to cover various aspects of a topic. This paper presents a tweet analytics approach that uses domain-specific knowledge to create a set of strong hashtag predictors for tweet topic classification. The paper describes the approach and preliminary experiments that show promising results toward Big data tweet analytics.


title: Estimating Trade Flows: Trading Partners and Trading Volumes

abstract: We develop a simple model of international trade with heterogeneous firms that is consistent with a number of stylized features of the data. In particular, the model predicts positive as well as zero trade flows across pairs of countries, and it allows the number of exporting firms to vary across destination countries. As a result, the impact of trade frictions on trade flows can be decomposed into the intensive and extensive margins, where the former refers to the trade volume per exporter and the latter refers to the number of exporters. This model yields a generalized gravity equation that accounts for the self-selection of firms into export markets and their impact on trade volumes. We then develop a two-stage estimation procedure that uses a selection equation into trade partners in the first stage and a trade flow equation in the second. We implement this procedure parametrically, semi-parametrically, and non-parametrically, showing that in all three cases the estimated effects of trade frictions are similar. Importantly, our method provides estimates of the intensive and extensive margins of trade. We show that traditional estimates are biased, and that most of the bias is not due to selection but rather due to the omission of the extensive margin. Moreover, the effect of the number of exporting firms varies across country pairs according to their characteristics. This variation is large, and particularly so for trade between developed and less developed countries and between pairs of less developed countries.


title: A Collective Bayesian Poisson Factorization Model for Cold-start Local Event Recommendation

abstract: Event-based social networks (EBSNs), in which organizers publish events to attract other users in local city to attend offline, emerge in recent years and grow rapidly. Due to the large volume of events in EBSNs, event recommendation is essential. A few recent works focus on this task, while almost all the methods need that each event to be recommended should have been registered by some users to attend. Thus they ignore two essential characteristics of events in EBSNs: (1) a large number of new events will be published every day which means many events have few participants in the beginning, (2) events have life cycles which means outdated events should not be recommended. Overall, event recommendation in EBSNs inevitably faces the cold-start problem. In this work, we address the new problem of cold-start local event recommendation in EBSNs. We propose a collective Bayesian Poisson factorization (CBPF) model for handling this problem. CBPF takes recently proposed Bayesian Poisson factorization as its basic unit to model user response to events, social relation, and content text separately. Then it further jointly connects these units by the idea of standard collective matrix factorization model. Moreover, in our model event textual content, organizer, and location information are utilized to learn representation of cold-start events for predicting user response to them. Besides, an efficient coordinate ascent algorithm is adopted to learn the model. We conducted comprehensive experiments on real datasets crawled from EBSNs and the results demonstrate our proposed model is effective and outperforms several alternative methods.


title: Diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization techniques

abstract: Magnetic Resonance Imaging (MRI) is one of the best technologies currently being used for diagnosing brain tumor. Brain tumor is diagnosed at advanced stages with the help of the MRI image. Segmentation is an important process to extract suspicious region from complex medical images. Automatic detection of brain tumor through MRI can provide the valuable outlook and accuracy of earlier brain tumor detection. In this paper an intelligent system is designed to diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization tools, such as Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The detection of tumor is performed in two phases: Preprocessing and Enhancement in the first phase and segmentation and classification in the second phase


title: Big Data: Challenges, Opportunities and Realities

abstract: With the advent of Internet of Things (IoT) and Web 2.0 technologies, there has been a tremendous growth in the amount of data generated. This chapter emphasizes on the need for big data, technological advancements, tools and techniques being used to process big data are discussed. Technological improvements and limitations of existing storage techniques are also presented. Since, the traditional technologies like Relational Database Management System (RDBMS) have their own limitations to handle big data, new technologies have been developed to handle them and to derive useful insights. This chapter presents an overview of big data analytics, its application, advantages, and limitations. Few research issues and future directions are presented in this chapter.


title: ApLeaf: An efficient android-based plant leaf identification system

abstract: To automatically identify plant species is very useful for ecologists, amateur botanists, educators, and so on. The Leafsnap is the first successful mobile application system which tackles this problem. However, the Leafsnap is based on the IOS platform. And to the best of our knowledge, as the mobile operation system, the Android is more popular than the IOS. In this paper, an Android-based mobile application designed to automatically identify plant species according to the photographs of tree leaves is described. In this application, one leaf image can be either a digital image from one existing leaf image database or a picture collected by a camera. The picture should be a single leaf placed on a light and untextured background without other clutter. The identification process consists of three steps: leaf image segmentation, feature extraction, and species identification. The demo system is evaluated on the ImageCLEF2012 Plant Identification database which contains 126 tree species from the French Mediterranean area. The outputs of the system to users are the top several species which match the query leaf image the best, as well as the textual descriptions and additional images about plant leaves, flowers, etc. Our system works well with state-of-the-art identification performance.


title: Stochastic Expectation Propagation

abstract: Expectation propagation (EP) is a deterministic approximation algorithm that is often used to perform approximate Bayesian parameter learning. EP approximates the full intractable posterior distribution through a set of local approximations that are iteratively refined for each datapoint. EP can offer analytic and computational advantages over other approximations, such as Variational Inference (VI), and is the method of choice for a number of models. The local nature of EP appears to make it an ideal candidate for performing Bayesian learning on large models in large-scale dataset settings. However, EP has a crucial limitation in this context: the number of approximating factors needs to increase with the number of data-points, N, which often entails a prohibitively large memory overhead. This paper presents an extension to EP, called stochastic expectation propagation (SEP), that maintains a global posterior approximation (like VI) but updates it in a local way (like EP). Experiments on a number of canonical learning problems using synthetic and real-world datasets indicate that SEP performs almost as well as full EP, but reduces the memory consumption by a factor of N. SEP is therefore ideally suited to performing approximate Bayesian learning in the large model, large dataset setting.


title: ChoreoGraphics: An Authoring Tool for Dance Shows

abstract: This work describes a computer system for authoring dance shows. The system can be used both as a mechanism to synthesize virtual group performances and as a tool for interactive design and visualization of real choreographies. To this end, we use motion capture of dance sequences that are synchronized with an underlying musical composition as input. We adapt motion synthesis methods for use in combination with a musical track. The authoring system provides methods for controlling group movement of dancers on stage, such as creating formations and following trajectories. Our integrated platform supports collaboration among artists, allowing the show to naturally evolve from iterative contributions from dancers, musicians, and choreographers.


title: An Overview to Customer Relationship Management

abstract: Marketing historically has undergone various shifts in emphasis from production through sales to marketing orientation. However, the various orientations have failed to engage customers in meaningful relationship mutually beneficial to organisations and customers, with all forms of the shift still exhibiting the transactional approach inherit in traditional marketing (Kubil & Doku, 2010). However, Coltman (2006) indicates that in strategy and marketing literature, scholars have long suggested that a customer centred strategy is fundamental to competitive advantage and that customer relationship management (CRM) programmes are increasingly being used by organisations to support the type of customer understanding and interdepartmental connectedness required to effectively execute a customer strategy.


On the Complexity of Scheduling University Courses


title: Applying Modern Control Techniques to a Nonlinear Bacterial Growth System

abstract: A robust multivariable control design methodology is applied to a nonlinear bacterial growth system. The bacterial system is modeled in the time domain with white noises present in both the state dynamics and the measurement equations. The robust design is based on the properties of linear quadratic Gaussian (LQG) and loop transfer recovery (LTR) techniques. The feasibility of this control design approach is evaluated.


title: Tackling business intelligence with bioinspired deep learning

abstract: To tackle the complex problem of providing business intelligence solutions based on business data, bioinspired deep learning has to be considered. This paper focuses on the application of artificial metaplasticity learning in business intelligence systems as an alternative paradigm of achieving a deeper information extraction and learning from arbitrary size data sets. As a case study, artificial metaplasticity multilayer perceptron applied to the automation of credit approval decision based on collected client data is analyzed, showing its potential and improvements over the state-of-the-art techniques. This paper successfully introduces the relevant novelty that the artificial neural network itself estimates the pdf of the input data to be used in the metaplasticity learning, so it is much closer to the biologic reality than previous implementations of artificial metaplasticity.


title: DeXpression: Deep Convolutional Neural Network for Expression Recognition

abstract: We propose a convolutional neural network (CNN) architecture for facial expression recognition. The proposed architecture is independent of any hand-crafted feature extraction and performs better than the earlier proposed convolutional neural network based approaches. We visualize the automatically extracted features which have been learned by the network in order to provide a better understanding. The standard datasets, i.e. Extended Cohn-Kanade (CKP) and MMI Facial Expression Databse are used for the quantitative evaluation. On the CKP set the current state of the art approach, using CNNs, achieves an accuracy of 99.2%. For the MMI dataset, currently the best accuracy for emotion recognition is 93.33%. The proposed architecture achieves 99.6% for CKP and 98.63% for MMI, therefore performing better than the state of the art using CNNs. Automatic facial expression recognition has a broad spectrum of applications such as human-computer interaction and safety systems. This is due to the fact that non-verbal cues are important forms of communication and play a pivotal role in interpersonal communication. The performance of the proposed architecture endorses the efficacy and reliable usage of the proposed work for real world applications.


title: Optimal loop unrolling for GPGPU programs

abstract: Graphics Processing Units (GPUs) are massively parallel, many-core processors with tremendous computational power and very high memory bandwidth. With the advent of general purpose programming models such as NVIDIA's CUDA and the new standard OpenCL, general purpose programming using GPUs (GPGPU) has become very popular. However, the GPU architecture and programming model have brought along with it many new challenges and opportunities for compiler optimizations. One such classical optimization is loop unrolling. Current GPU compilers perform limited loop unrolling. In this paper, we attempt to understand the impact of loop unrolling on GPGPU programs. We develop a semi-automatic, compile-time approach for identifying optimal unroll factors for suitable loops in GPGPU programs. In addition, we propose techniques for reducing the number of unroll factors evaluated, based on the characteristics of the program being compiled and the device being compiled to. We use these techniques to evaluate the effect of loop unrolling on a range of GPGPU programs and show that we correctly identify the optimal unroll factors. The optimized versions run up to 70% faster than the unoptimized versions.


title: Orientation and Pose recovery from Spherical Panoramas

abstract: This paper addresses the problem of camera pose recovery from spherical images. The 3D information is extracted from a set of panoramas sparsely distributed over a scene of interest. We present an algorithm to recover the position of omni-directional cameras in a scene using pair-wise essential matrices. First, all rotations with respect to the world frame are found using an incremental bundle adjustment procedure, thus achieving what we called cube alignment. The structure of the scene is then computed using a full bundle adjustment. During this step, the previously computed panorama orientations, used to feed the global optimization process, are further refined. Results are shown for indoor and outdoor panorama sets.


title: Cost- and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds

abstract: Large-scale applications expressed as scientific workflows are often grouped into ensembles of inter-related workflows. In this paper, we address a new and important problem concerning the efficient management of such ensembles under budget and deadline constraints on Infrastructure as a Service (IaaS) clouds. IaaS clouds are characterized by on-demand resource provisioning capabilities and a pay-per-use model. We discuss, develop, and assess novel algorithms based on static and dynamic strategies for both task scheduling and resource provisioning. We perform the evaluation via simulation using a set of scientific workflow ensembles with a broad range of budget and deadline parameters, taking into account task granularity, uncertainties in task runtime estimations, provisioning delays, and failures. We find that the key factor determining the performance of an algorithm is its ability to decide which workflows in an ensemble to admit or reject for execution. Our results show that an admission procedure based on workflow structure and estimates of task runtimes can significantly improve the quality of solutions. We define the problem of scheduling prioritized workflow ensembles on IaaS clouds.We analyze and develop several dynamic (online) and static (offline) algorithms.We address both problems of task scheduling and resource provisioning.Our simulator models uncertainties in estimates, provisioning delays, and failures.We use synthetic workflow ensembles based on important, real scientific applications.


title: Security and privacy considerations for Wireless Sensor Networks in smart home environments

abstract: Wireless Sensor Network (WSN) has emerged as a dependable technology to improve the quality of life in smart homes through offering various automated, interactive and comfortable services. Sensors integrated at different places in homes, offices, and even in clothes, equipment, and utilities are used to sense and monitor occupants' positions, movements, vital signs, utility usage, temperature and humidity levels of rooms, etc. Along with sensing and monitoring capabilities, sensors cooperate and communicate with themselves to deliver, share and process sensed information and assist real-time decision-making procedures through triggering appropriate alerts and actions. However, ensuring privacy and providing adequate security in these crucial services provided by WSNs is a major issue in smart home environments. In this paper, we examine the privacy and security challenges of WSNs and survey its practicality for smart home environments. We discuss the unique characteristics that distinguish a smart environment from the rest, elaborate on security and privacy issues and their respective solution measures. A number of challenges and interesting research issues emerging from this study have been reported for further investigation.


title: The use of NARX neural networks to predict chaotic time series

abstract: The prediction of chaotic time series with neural networks is a traditional practical problem of dynamic systems. This paper is not intended for proposing a new model or a new methodology, but to study carefully and thoroughly several aspects of a model on which there are no enough communicated experimental data, as well as to derive conclusions that would be of interest. The recurrent neural networks (RNN) models are not only important for the forecasting of time series but also generally for the control of the dynamical system. A RNN with a sufficiently large number of neurons is a nonlinear autoregressive and moving average (NARMA) model, with "moving average" referring to the inputs. The prediction can be assimilated to identification of dynamic process. An architectural approach of RNN with embedded memory, "Nonlinear Autoregressive model process with eXogenous input" (NARX), showing promising qualities for dynamic system applications, is analyzed in this paper. The performances of the NARX model are verified for several types of chaotic or fractal time series applied as input for neural network, in relation with the number of neurons, the training algorithms and the dimensions of his embedded memory. In addition, this work has attempted to identify a way to use the classic statistical methodologies (R/S Rescaled Range analysis and Hurst exponent) to obtain new methods of improving the process efficiency of the prediction chaotic time series with NARX.


IMPROVING SEARCH ENGINE PERFORMANCE WITH CONTEXT EXTRACTION USING LUCENE, DBPEDIA-SPOTLIGHT, AND WORDNET


title: Time-of-Flight sensor calibration for accurate range sensing

abstract: Over the past years Time-of-Flight (ToF) sensors have become a considerable alternative to conventional distance sensing techniques like laser scanners or image based stereo-vision. Due to the ability to provide full-range distance information at high frame-rates, ToF sensors achieve a significant impact onto current research areas like online object recognition, collision prevention or scene and object reconstruction. Nevertheless, ToF-cameras like the Photonic Mixer Device (PMD) still exhibit a number of error sources that affect the accuracy of measured distance information. For this reason, major error sources for ToF-cameras will be discussed, along with a new calibration approach that combines intrinsic, distance as well as a reflectivity related error calibration in an overall, easy to use system and thus significantly reduces the number of necessary reference images. The main contribution, in this context, is a new intensity-based calibration model that requires less input data compared to other models and thus significantly contributes to the reduction of calibration data.


title: A Harmonic Mean Linear Discriminant Analysis for Robust Image Classification

abstract: Linear Discriminant Analysis (LDA) is a widely-used supervised dimensionality reduction method in computer vision and pattern recognition. In null space based LDA (NLDA), a well-known LDA extension, between-class distance is maximized in the null space of the within-class scatter matrix. However, there are some limitations in NLDA. Firstly, for many data sets, null space of within-class scatter matrix does not exist, thus NLDA is not applicable to those datasets. Secondly, NLDA uses arithmetic mean of between-class distances and gives equal consideration to all between-class distances, which makes larger between-class distances can dominate the result and thus limits the performance of NLDA. In this paper, we propose a harmonic mean based Linear Discriminant Analysis, Multi-Class Discriminant Analysis (MCDA), for image classification, which minimizes the reciprocal of weighted harmonic mean of pairwise between-class distance. More importantly, MCDA gives higher priority to maximize small between-class distances. MCDA can be extended to multi-label dimension reduction. Results on 7 single-label data sets and 4 multi-label data sets show that MCDA has consistently better performance than 10 other single-label approaches and 4 other multi-label approaches in terms of classification accuracy, macro and micro average F1 score.


title: Approximating prediction uncertainty for random forest regression models

abstract: Abstract Machine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as inputs to other modeling applications such as fire modeling. Here we use a Monte Carlo approach to quantify prediction uncertainty for random forest regression models. We test the approach by simulating maps of dependent and independent variables with known characteristics and comparing actual errors with prediction errors. Our approach produced conservative prediction intervals across most of the range of predicted values. However, because the Monte Carlo approach was data driven, prediction intervals were either too wide or too narrow in sparse parts of the prediction distribution. Overall, our approach provides reasonable estimates of prediction uncertainty for random forest regression models.


title: Retrieving Real Time Data Through IOT Devices and Storing Securely on Cloud Using Li-Fi

abstract: Internet of things is an accumulation of physical objects which are able to send information over the network. Nowaday, an association utilizes IoT devices to gather real time and continuous data from sensors. This information can be utilized to enhance the customer satisfaction and to make better business decisions. The cloud has various advantages over on-premises storage for storing IoT information. But there are apprehensions with using the cloud for IoT data storage. The real one is security. To exchange the information over the cloud IoT devices utilizes WiFi technology. However, it has a few limits that confine the potential outcomes of the Internet of Things. On the off chance that more devices or clients that will be associated with the internet utilizing WiFi, the transfer speed gets separated among the clients hence the outcome will be slower network. Consequently, there is a necessity of a speedier and a solid internet administration to the Internet of Things to be completely operational. This paper shows the strategy which permits exchanging gathered loT information over the cloud safely utilizing LiFi innovation by applying role based access control approaches and the cryptography techniques.


title: AGA: Attribute-Guided Augmentation

abstract: We consider the problem of data augmentation, i.e., generating artificial samples to extend a given corpus of training data. Specifically, we propose attributed-guided augmentation (AGA) which learns a mapping that allows to synthesize data such that an attribute of a synthesized sample is at a desired value or strength. This is particularly interesting in situations where little data with no attribute annotation is available for learning, but we have access to a large external corpus of heavily annotated samples. While prior works primarily augment in the space of images, we propose to perform augmentation in feature space instead. We implement our approach as a deep encoder-decoder architecture that learns the synthesis function in an end-to-end manner. We demonstrate the utility of our approach on the problems of (1) one-shot object recognition in a transfer-learning setting where we have no prior knowledge of the new classes, as well as (2) object-based one-shot scene recognition. As external data, we leverage 3D depth and pose information from the SUN RGB-D dataset. Our experiments show that attribute-guided augmentation of high-level CNN features considerably improves one-shot recognition performance on both problems.


title: Quality-of-service in cloud computing: modeling techniques and their applications

abstract: Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management.


title: Continuous Requirements Engineering and Human-Centered Agile Software Development

abstract: The idea of Continuous Requirements Engineering in relation to a Human-Centered Agile Development Process is discussed. First, it is argued that Continuous Requirements Engineering has to cover design-time and runtime aspects. In this way maintenance is covered as well. Second, arguments are provided for integrating aspects of usability and user experience into requirements specifications. This has to be done continuously. Therefore, the term Continuous Human-Centered Development is introduced and discussed. Based on a process model for SCRUM some aspects of integrating HCD into the development process are discussed.


title: LIBSVX: A Supervoxel Library and Benchmark for Early Video Processing

abstract: Supervoxel segmentation has strong potential to be incorporated into early video analysis as superpixel segmentation has in image analysis. However, there are many plausible supervoxel methods and little understanding as to when and where each is most appropriate. Indeed, we are not aware of a single comparative study on supervoxel segmentation. To that end, we study seven supervoxel algorithms, including both off-line and streaming methods, in the context of what we consider to be a good supervoxel: namely, spatiotemporal uniformity, object/region boundary detection, region compression and parsimony. For the evaluation we propose a comprehensive suite of seven quality metrics to measure these desirable supervoxel characteristics. In addition, we evaluate the methods in a supervoxel classification task as a proxy for subsequent high-level uses of the supervoxels in video analysis. We use six existing benchmark video datasets with a variety of content-types and dense human annotations. Our findings have led us to conclusive evidence that the hierarchical graph-based (GBH), segmentation by weighted aggregation (SWA) and temporal superpixels (TSP) methods are the top-performers among the seven methods. They all perform well in terms of segmentation accuracy, but vary in regard to the other desiderata: GBH captures object boundaries best; SWA has the best potential for region compression; and TSP achieves the best undersegmentation error.


title: Preliminary, Open-Label, Pilot Study of Add-On Oral Δ9-Tetrahydrocannabinol in Chronic Post-Traumatic Stress Disorder

abstract: Background and Objectives ::: Many patients with post-traumatic stress disorder (PTSD) achieve but partial remission with current treatments. Patients with unremitted PTSD show high rates of substance abuse. Marijuana is often used as compassion add-on therapy for treatment-resistant PTSD. This open-label study evaluates the tolerance and safety of orally absorbable Δ9-tetrahydrocannabinol (THC) for chronic PTSD.


title: Taming THC: potential cannabis synergy and phytocannabinoid-terpenoid entourage effects

abstract: Tetrahydrocannabinol (THC) has been the primary focus of cannabis research since 1964, when Raphael Mechoulam isolated and synthesized it. More recently, the synergistic contributions of cannabidiol to cannabis pharmacology and analgesia have been scientifically demonstrated. Other phytocannabinoids, including tetrahydrocannabivarin, cannabigerol and cannabichromene, exert additional effects of therapeutic interest. Innovative conventional plant breeding has yielded cannabis chemotypes expressing high titres of each component for future study. This review will explore another echelon of phytotherapeutic agents, the cannabis terpenoids: limonene, myrcene, α-pinene, linalool, β-caryophyllene, caryophyllene oxide, nerolidol and phytol. Terpenoids share a precursor with phytocannabinoids, and are all flavour and fragrance components common to human diets that have been designated Generally Recognized as Safe by the US Food and Drug Administration and other regulatory agencies. Terpenoids are quite potent, and affect animal and even human behaviour when inhaled from ambient air at serum levels in the single digits ng·mL(-1) . They display unique therapeutic effects that may contribute meaningfully to the entourage effects of cannabis-based medicinal extracts. Particular focus will be placed on phytocannabinoid-terpenoid interactions that could produce synergy with respect to treatment of pain, inflammation, depression, anxiety, addiction, epilepsy, cancer, fungal and bacterial infections (including methicillin-resistant Staphylococcus aureus). Scientific evidence is presented for non-cannabinoid plant components as putative antidotes to intoxicating effects of THC that could increase its therapeutic index. Methods for investigating entourage effects in future experiments will be proposed. Phytocannabinoid-terpenoid synergy, if proven, increases the likelihood that an extensive pipeline of new therapeutic products is possible from this venerable plant. http://dx.doi.org/10.1111/bph.2011.163.issue-7.


title: Kinematic Design of a Dynamic Brace for Measurement of Head/Neck Motion

abstract: Head drop is a symptom commonly seen in patients with amyotrophic lateral sclerosis. These patients usually experience neck pain and have difficulty in swallowing and breathing. Static neck braces are used in current treatment. These braces, however, immobilize the head in a single configuration, which causes muscle atrophy. This letter presents the design of a dynamic neck brace for the first time in the literature, which can both measure and potentially assist in the head motion of the human user. This letter introduces the brace design method and validates its capability to perform measurements. The brace is designed based on kinematics data collected from a healthy individual via a motion capture system. A pilot study was conducted to evaluate the wearability of the brace and the accuracy of measurements with the brace. This study recruited ten participants who performed a series of head motions. The results of this human study indicate that the brace is wearable by individuals who vary in size, the brace allows nearly $70\%$ of the overall range of head rotations, and the sensors on the brace give accurate motion of the head with an error of under $5^{\circ }$ when compared to a motion capture system. We believe that this neck brace can be a valid and accurate measurement tool for human head motion. This brace will be a big improvement in the available technologies to measure head motion as these are currently done in the clinic using hand-held protractors in two orthogonal planes.


title: Efficient Gender Classification Using a Deep LDA-Pruned Net

abstract: Many real-time tasks, such as human-computer interaction, require fast and efficient facial gender classification. Although deep CNN nets have been very effective for a multitude of classification tasks, their high space and time demands make them impractical for personal computers and mobile devices without a powerful GPU. In this paper, we develop a 16-layer, yet lightweight, neural network which boosts efficiency while maintaining high accuracy. Our net is pruned from the VGG-16 model starting from the last convolutional (conv) layer where we find neuron activations are highly uncorrelated given the gender. Through Fisher's Linear Discriminant Analysis (LDA), we show that this high decorrelation makes it safe to discard directly last conv layer neurons with high within-class variance and low between-class variance. Combined with either Support Vector Machines (SVM) or Bayesian classification, the reduced CNNs are capable of achieving comparable (or even higher) accuracies on the LFW and CelebA datasets than the original net with fully connected layers. On LFW, only four Conv5_3 neurons are able to maintain a comparably high recognition accuracy, which results in a reduction of total network size by a factor of 70X with a 11 fold speedup. Comparisons with a state-of-the-art pruning method as well as two smaller nets in terms of accuracy loss and convolutional layers pruning rate are also provided.


title: Design, Development, and Evaluation of a Noninvasive Autonomous Robot-Mediated Joint Attention Intervention System for Young Children With ASD

abstract: Research indicates that human–robot interaction can help children with autism spectrum disorder (ASD). While most early robot-mediated interaction studies were based on free interactions, recent studies have shown that robot-mediated interventions that focus on the core impairments of ASD such as joint attention deficit tend to produce better outcomes. Joint attention impairment is one of the core deficits in ASD that has an important impact in the neuropsychological development of these children. In this paper, we propose a novel joint attention intervention system for children with ASD that overcomes several existing limitations in this domain such as the need to use body-worn sensors, nonautonomous robot operation requiring human involvement and lack of a formal model for robot-mediated joint attention interaction. We present a fully autonomous robotic system, called noncontact-responsive robot-mediated intervention system, that can infer attention through a distributed noncontact gaze inference mechanism with an embedded least-to-most (LTM) robot-mediated interaction model to address the current limitations. The system was tested in a multisession user study with 14 young children with ASD. The results showed that participants’ joint attention skills improved significantly, their interest in the robot remained consistent throughout the sessions, and the LTM interaction model was effective in promoting the children's performance.


title: Applications of Soft Computing in Civil Engineering: A Review

abstract: field of engineering is a creative one. The problems encountered in this field are generally unstructured and imprecise influenced by intuitions and past experiences of a designer. The conventional methods of computing relying on analytical or empirical relations become time consuming and labor intensive when posed with real life problems. To study, model and analyze such problems, approximate computer based Soft Computing techniques inspired by the reasoning, intuition, consciousness and wisdom possessed by a human beings are employed. In contrast to conventional computing techniques which rely on exact solutions, soft computing aims at exploiting given tolerance of imprecision, the trivial and uncertain nature of the problem to yield an approximate solution to a problem in quick time. Soft Computing being a multi-disciplinary field uses a variety of statistical, probabilistic and optimization tools which complement each other to produce its three main branches viz., Neural Networks, Genetic Algorithms and Fuzzy Logic. The review paper presents the applications of two major Soft Computing techniques viz., Artificial Neural Networks and Genetic Algorithms in the field of Civil Engineering, which to some extent has replaced the time consuming conventional techniques of computing with intelligent and time saving computing tools.


Cellulosic ethanol production via consolidated bioprocessing at 75 °C by engineered Caldicellulosiruptor bescii


title: Object-Aware Identification of Microservices

abstract: Microservices is an architectural style inspired by service-oriented computing that structures an application as a collection of cohesive and loosely coupled components, which implement business capabilities. One of today’s problems in designing microservice architectures is to decompose a system into cohesive, loosely coupled, and fine-grained microservices. Identification of microservices is usually performed intuitively, based on the experience of the system designers, however, if the functionalities of a system are highly interconnected, it is a challenging task to decompose the system into appropriate microservices. To tackle this challenge, we present a microservice identification method that decomposes a system using clustering technique. To this end, we model a system as a set of business processes and take two aspects of structural dependency and data object dependency of functionalities into account. Furthermore, we conduct a study to evaluate the effect of process characteristics on the accuracy of identification approaches.


title: Real-time implementation of robust face detection on mobile platforms

abstract: Although many face detection algorithms have been introduced in the literature, only a handful of them can meet the real-time constraints of mobile devices. This paper presents the real-time implementation of our previously introduced face detection algorithm on a mobile device. The steps taken to achieve such a real-time implementation are discussed. Real-time comparison results with the widely used Viola-Jones face detection algorithm in terms of detection rate and processing speed are presented to demonstrate the robustness of our real-time solution.


title: Advanced and authenticated marking schemes for IP traceback

abstract: Defending against distributed denial-of-service attacks is one of the hardest security problems on the Internet today. One difficulty to thwart these attacks is to trace the source of the attacks because they often use incorrect, or spoofed IP source addresses to disguise the true origin. In this paper, we present two new schemes, the advanced marking scheme and the authenticated marking scheme, which allow the victim to trace-back the approximate origin of spoofed IP packets. Our techniques feature low network and router overhead, and support incremental deployment. In contrast to previous work, our techniques have significantly higher precision (lower false positive rate) and fewer computation overhead for the victim to reconstruct the attack paths under large scale distributed denial-of-service attacks. Furthermore the authenticated marking scheme provides efficient authentication of routers' markings such that even a compromised router cannot forge or tamper markings from other uncompromised routers.


title: The Generality of Working Memory Capacity: A Latent-Variable Approach to Verbal and Visuospatial Memory Span and Reasoning.

abstract: A latent-variable study examined whether verbal and visuospatial working memory (WM) capacity measures reflect a primarily domain-general construct by testing 236 participants in 3 span tests each of verbal WM, visuospatial WM, verbal short-term memory (STM), and visuospatial STM, as well as in tests of verbal and spatial reasoning and general fluid intelligence (Gf). Confirmatory factor analyses and structural equation models indicated that the WM tasks largely reflected a domain-general factor, whereas STM tasks, based on the same stimuli as the WM tasks, were much more domain specific. The WM construct was a strong predictor of Gf and a weaker predictor of domain-specific reasoning, and the reverse was true for the STM construct. The findings support a domain-general view of WM capacity, in which executive-attention processes drive the broad predictive utility of WM span measures, and domain-specific storage and rehearsal processes relate more strongly to domain-specific aspects of complex cognition.


title: Vacuum induction heating furnace temperature control system based on smith fuzzy-PID

abstract: Mathematical model of vacuum induction furnace temperature control system was established. In view of the heating furnace temperature control system of the pure lag, the Smith predictive compensation combined with fuzzy PID control method is used to compensate for it. Software simulation is implemented, and the results show that temperature control system has many good characteristics, such as small overshoot, short response time, stability, etc. The simulation verifies that smith predictive compensation combined with fuzzy PID control algorithm has good stability and robustness.


title: Trust based recommender system using ant colony for trust computation

abstract: Collaborative Filtering (CF) technique has proven to be promising for implementing large scale recommender systems but its success depends mainly on locating similar neighbors. Due to data sparsity of the user-item rating matrix, the process of finding similar neighbors does not often succeed. In addition to this, it also suffers from the new user (cold start) problem as finding possible neighborhood and giving recommendations to user who has not rated any item or rated very few items is difficult. In this paper, our proposed Trust based Ant Recommender System (TARS) produces valuable recommendations by incorporating a notion of dynamic trust between users and selecting a small and best neighborhood based on biological metaphor of ant colonies. Along with the predicted ratings, displaying additional information for explanation of recommendations regarding the strength and level of connectedness in trust graph from where recommendations are generated, items and number of neighbors involved in predicting ratings can help active user make better decisions. Also, new users can highly benefit from pheromone updating strategy known from ant algorithms as positive feedback in the form of aggregated dynamic trust pheromone defines ''popularity'' of a user as recommender over a period of time. The performance of TARS is evaluated using two datasets of different sparsity levels viz. Jester dataset and MovieLens dataset (available online) and compared with traditional Collaborative Filtering based approach for generating recommendations.


title: Hybrid digital and analog beamforming design for large-scale MIMO systems

abstract: Large-scale multiple-input multiple-output (MIMO) systems enable high spectral efficiency by employing large antenna arrays at both the transmitter and the receiver of a wireless communication link. In traditional MIMO systems, full digital beamforming is done at the baseband; one distinct radio-frequency (RF) chain is required for each antenna, which for large-scale MIMO systems can be prohibitive from either cost or power consumption point of view. This paper considers a two-stage hybrid beamforming structure to reduce the number of RF chains for large-scale MIMO systems. The overall beamforming matrix consists of analog RF beamforming implemented using phase shifters and baseband digital beamforming of much smaller dimension. This paper considers precoder and receiver design for maximizing the spectral efficiency when the hybrid structure is used at both the transmitter and the receiver. On the theoretical front, bounds on the minimum number of transmit and receive RF chains that are required to realize the theoretical capacity of the large-scale MIMO system are presented. It is shown that the hybrid structure can achieve the same performance as the fully-digital beamforming scheme if the number of RF chains at each end is greater than or equal to twice the number of data streams. On the practical design front, this paper proposes a heuristic hybrid beamforming design strategy for the critical case where the number of RF chains is equal to the number of data streams, and shows that the performance of the proposed hybrid beamforming design can achieve spectral efficiency close to that of the fully-digital solution.


MovieScope : Movie trailer classification using Deep Neural Networks


title: A Monocular Vision Sensor-Based Efficient SLAM Method for Indoor Service Robots

abstract: This paper presents a new implementation method for efficient simultaneous localization and mapping using a forward-viewing monocular vision sensor. The method is developed to be applicable in real time on a low-cost embedded system for indoor service robots. In this paper, the orientation of a robot is directly estimated using the direction of the vanishing point. Then, the estimation models for the robot position and the line landmark are derived as simple linear equations. Using these models, the camera poses and landmark positions are efficiently corrected by a local map correction method. The performance of the proposed method is demonstrated under various challenging environments using dataset-based experiments using a desktop computer and real-time experiments using a low-cost embedded system. The experimental environments include a real home-like setting. These conditions contain low-textured areas, moving people, or changing environments. The proposed method is also tested using the robotics advancement through web publishing of sensorial and elaborated extensive datasets benchmark dataset.


title: IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection

abstract: As an important tool in security, the intrusion detection system bears the responsibility of the defense to network attacks performed by malicious traffic. Nowadays, with the help of machine learning algorithms, the intrusion detection system develops rapidly. However, the robustness of this system is questionable when it faces the adversarial attacks. To improve the detection system, more potential attack approaches should be researched. In this paper, a framework of the generative adversarial networks, IDSGAN, is proposed to generate the adversarial attacks, which can deceive and evade the intrusion detection system. Considering that the internal structure of the detection system is unknown to attackers, adversarial attack examples perform the black-box attacks against the detection system. IDSGAN leverages a generator to transform original malicious traffic into adversarial malicious traffic. A discriminator classifies traffic examples and simulates the black-box detection system. More significantly, we only modify part of the attacks' nonfunctional features to guarantee the validity of the intrusion. Based on the dataset NSL-KDD, the feasibility of the model is demonstrated to attack many detection systems with different attacks and the excellent results are achieved. Moreover, the robustness of IDSGAN is verified by changing the amount of the unmodified features.


title: Non-invasive heart rate monitoring system using giant magneto resistance sensor

abstract: A simple heart rate (HR) monitoring system designed and developed using the Giant Magneto-Resistance (GMR) sensor is presented in this paper. The GMR sensor is placed on the wrist of the human and it provides the magneto-plethysmographic signal. This signal is processed by the simple analog and digital instrumentation stages to render the heart rate indication. A prototype of the system has been built and test results on 26 volunteers have been reported. The error in HR estimation of the system is merely 1 beat per minute. The performance of the system when layer of cloth is present between the sensor and the human body is investigated. The capability of the system as a HR variability estimator has also been established through experimentation. The proposed technique can be used as an efficient alternative to conventional HR monitors and is well suited for remote and continuous monitoring of HR.


Setting Goals and Choosing Metrics for Recommender System Evaluations


Fast algorithms for volume ray tracing


title: Introducing A Public Stereoscopic 3D High Dynamic Range (SHDR) Video Database

abstract: High Dynamic Range (HDR) displays and cameras are paving their ways through the consumer market at a rapid growth rate. Thanks to TV and camera manufacturers, HDR systems are now becoming available commercially to end users. This is taking place only a few years after the blooming of 3D video technologies. MPEG/ITU are also actively working towards the standardization of these technologies. However, preliminary research efforts in these video technologies are hammered by the lack of sufficient experimental data. In this paper, we introduce a Stereoscopic 3D HDR (SHDR) database of videos that is made publicly available to the research community. We explain the procedure taken to capture, calibrate, and post-process the videos. In addition, we provide insights on potential use-cases, challenges, and research opportunities, implied by the combination of higher dynamic range of the HDR aspect, and depth impression of the 3D aspect.


title: You are what you say: privacy risks of public mentions

abstract: In today's data-rich networked world, people express many aspects of their lives online. It is common to segregate different aspects in different places: you might write opinionated rants about movies in your blog under a pseudonym while participating in a forum or web site for scholarly discussion of medical ethics under your real name. However, it may be possible to link these separate identities, because the movies, journal articles, or authors you mention are from a sparse relation space whose properties (e.g., many items related to by only a few users) allow re-identification. This re-identification violates people's intentions to separate aspects of their life and can have negative consequences; it also may allow other privacy violations, such as obtaining a stronger identifier like name and address.This paper examines this general problem in a specific setting: re-identification of users from a public web movie forum in a private movie ratings dataset. We present three major results. First, we develop algorithms that can re-identify a large proportion of public users in a sparse relation space. Second, we evaluate whether private dataset owners can protect user privacy by hiding data; we show that this requires extensive and undesirable changes to the dataset, making it impractical. Third, we evaluate two methods for users in a public forum to protect their own privacy, suppression and misdirection. Suppression doesn't work here either. However, we show that a simple misdirection strategy works well: mention a few popular items that you haven't rated.


title: Victim Selection in the Jeffrey Dahmer Slayings: An Example of Repetition in the Paraphilias?

abstract: It has been suggested that Jeffrey Dahmer, who was convicted for the serial murders of 15 people, carefully selected most of his victims on the basis of their external phenotype. Although the sample size is small, both univariate and multivariate methods support the possibility that similarity in craniofacial architecture strongly influenced his choice of victims. Because necrophilia played a key role during expert testimony at his trial, and because Dahmer retained most of the skulls and one postcranial skeleton, the results suggest that his actions represent an example of repetition in the paraphilias.


Strategically Leveraging Corporate Social Responsibility: A CORPORATE BRANDING PERSPECTIVE


Anticipatory Artificial Intelligence


Examining Trolls and Polarization with a Retweet Network


title: An electrolytic-capacitorless and inductorless AC direct LED driver with power compensation

abstract: In this paper, a novel system for driving LEDs directly from AC supply without the use of electrolytic capacitors and inductors is proposed. The system includes a power compensation module, enabling the output power of LED approximately constant if there is a fluctuation in input voltage. Unlike the conventional LED drivers, the proposed LED driver only employs one rectifier, one controller IC, four LED strings and a few chip resistors and capacitors. The four LED strings will automatically conduct according to the level of input voltage, thus creating a step-shaped input current proportional to the sinusoidal voltage waveform. By this means, high power factor is achieved. The controller IC is designed by using CSMC's 0.8-µm 650V process. Simulation results show that the LED driver has a high power factor of 97.46% and efficiency of 90.64% under the 220V|rms utility input voltage.


title: 1 Spatial registration and normalization of images

abstract: This paper concerns the spatial and intensity transformations that map one image onto another. We present a general technique that facilitates nonlinear spatial (stereotactic) normalization and image realignment. This technique minimizes the sum of squares between two images following nonlinear spatial deformations and transformations of the voxel (intensity) values. The spatial and intensity transformations are obtained simultaneously, and explicitly, using a least squares solution and a series of linearising devices. The approach is completely noninteractive (automatic), nonlinear, and noniterative. It can be applied in any number of dimensions. ::: ::: Various applications are considered, including the realignment of functional magnetic resonance imaging (MRI) time-series, the linear (affine) and nonlinear spatial normalization of positron emission tomography (PET) and structural MRI images, the coregistration of PET to structural MRI, and, implicitly, the conjoining of PET and MRI to obtain high resolution functional images. © 1995 Wiley-Liss, Inc.


Embedding Senses via Dictionary Bootstrapping


title: Big data's end run around procedural privacy protections

abstract: Recognizing the inherent limitations of consent and anonymity.


title: Variational shape approximation

abstract: A method for concise, faithful approximation of complex 3D datasets is key to reducing the computational cost of graphics applications. Despite numerous applications ranging from geometry compression to reverse engineering, efficiently capturing the geometry of a surface remains a tedious task. In this paper, we present both theoretical and practical contributions that result in a novel and versatile framework for geometric approximation of surfaces. We depart from the usual strategy by casting shape approximation as a variational geometric partitioning problem. Using the concept of geometric proxies, we drive the distortion error down through repeated clustering of faces into best-fitting regions. Our approach is entirely discrete and error-driven, and does not require parameterization or local estimations of differential quantities. We also introduce a new metric based on normal deviation, and demonstrate its superior behavior at capturing anisotropy.


title: Social and Cultural Factors Affecting Uptake of Interventions for Malaria in Pregnancy in Africa: A Systematic Review of the Qualitative Research

abstract: Background ::: Malaria during pregnancy (MiP) results in adverse birth outcomes and poor maternal health. MiP-related morbidity and mortality is most pronounced in sub-Saharan Africa, where recommended MiP interventions include intermittent preventive treatment, insecticide-treated bednets and appropriate case management. Besides their clinical efficacy, the effectiveness of these interventions depends on the attitudes and behaviours of pregnant women and the wider community, which are shaped by social and cultural factors. Although these factors have been studied largely using quantitative methods, qualitative research also offers important insights. This article provides a comprehensive overview of qualitative research on social and cultural factors relevant to uptake of MiP interventions in sub-Saharan Africa. ::: ::: ::: Methods and Findings ::: A systematic search strategy was employed: literature searches were undertaken in several databases (OVID SP, IS Web of Knowledge, MiP Consortium library). MiP-related original research, on social/cultural factors relevant to MiP interventions, in Africa, with findings derived from qualitative methods was included. Non-English language articles were excluded. A meta-ethnographic approach was taken to analysing and synthesizing findings. Thirty-seven studies were identified. Fourteen concentrated on MiP. Others focused on malaria treatment and prevention, antenatal care (ANC), anaemia during pregnancy or reproductive loss. Themes identified included concepts of malaria and risk in pregnancy, attitudes towards interventions, structural factors affecting delivery and uptake, and perceptions of ANC. ::: ::: ::: Conclusions ::: Although malaria risk is associated with pregnancy, women's vulnerability is often considered less disease-specific and MiP interpreted in locally defined categories. Furthermore, local discourses and health workers' ideas and comments influence concerns about MiP interventions. Understandings of ANC, health worker-client interactions, household decision-making, gender relations, cost and distance to health facilities affect pregnant women's access to MiP interventions and lack of healthcare infrastructure limits provision of interventions. Further qualitative research is however required: many studies were principally descriptive and an in-depth comparative approach is recommended.


title: PageNet: Page Boundary Extraction in Historical Handwritten Documents

abstract: When digitizing a document into an image, it is common to include a surrounding border region to visually indicate that the entire document is present in the image. However, this border should be removed prior to automated processing. In this work, we present a deep learning based system, PageNet, which identifies the main page region in an image in order to segment content from both textual and non-textual border noise. In PageNet, a Fully Convolutional Network obtains a pixel-wise segmentation which is post-processed into the output quadrilateral region. We evaluate PageNet on 4 collections of historical handwritten documents and obtain over 94% mean intersection over union on all datasets and approach human performance on 2 of these collections. Additionally, we show that PageNet can segment documents that are overlayed on top of other documents.


title: Bio-inspired computational paradigm for feature investigation and malware detection: interactive analytics

abstract: Recently, people rely on mobile devices to conduct their daily fundamental activities. Simultaneously, most of the people prefer devices with Android operating system. As the demand expands, deceitful authors develop malware to compromise Android for private and money purposes. Consequently, security analysts have to conduct static and dynamic analyses to counter malware violation. In this paper, we adopt static analysis which only requests minimal resource consumption and rapid processing. However, finding a minimum set of features in the static analysis are vital because it removes irrelevant data, reduces the runtime of machine learning detection and reduces the dimensionality of datasets. Therefore, in this paper, we investigate three categories of features, which are permissions, directory path, and telephony. This investigation considers the features frequency as well as repeatedly used in each application. Subsequently, this study evaluates the proposed features in three bio-inspired machine learning classifiers in artificial neural network (ANN) category to signify the usefulness of ANN type in uncovering unknown malware. The classifiers are multilayer perceptron (MLP), voted perceptron (VP) and radial basis function network (RBFN). Among all these three classifiers, the outstanding outcomes acquire is the MLP, which achieves 90% in accuracy and 87% in true positive rate (TPR), as well as 97% accuracy in our Bio Analyzer prediction system.


title: Reactions to Discrimination, Stigmatization, Ostracism, and Other Forms of Interpersonal Rejection: A Multimotive Model

abstract: This article describes a new model that provides a framework for understanding people’s reactions to threats to social acceptance and belonging as they occur in the context of diverse phenomena such as rejection, discrimination, ostracism, betrayal, and stigmatization. People’s immediate reactions are quite similar across different forms of rejection in terms of negative affect and lowered self-esteem. However, following these immediate responses, people’s reactions are influenced by construals of the rejection experience that predict 3 distinct motives for prosocial, antisocial, and socially avoidant behavioral responses. The authors describe the relational, contextual, and dispositional factors that affect which motives determine people’s reactions to a rejection experience and the ways in which these 3 motives may work at cross-purposes. The multimotive model accounts for the myriad ways in which responses to rejection unfold over time and offers a basis for the next generation of research on interpersonal rejection.


title: Visual perceptual and handwriting skills in children with Developmental Coordination Disorder

abstract: This study was funded by the Doctoral Training Programme for Children and Young People at Oxford Brookes University.


title: On the Role of Lexical and World Knowledge in RTE3

abstract: To score well in RTE3, and even more so to create good justifications for entailments, substantial lexical and world knowledge is needed. With this in mind, we present an analysis of a sample of the RTE3 positive entailment pairs, to identify where and what kinds of world knowledge are needed to fully identify and justify the entailment, and discuss several existing resources and their capacity for supplying that knowledge. We also briefly sketch the path we are following to build an RTE system (Our implementation is very preliminary, scoring 50.9% at the time of RTE). The contribution of this paper is thus a framework for discussing the knowledge requirements posed by RTE and some exploration of how these requirements can be met.


title: Interleaved-MIMO DAS for Indoor Radio Coverage: Concept and Performance Assessment

abstract: Distributed antenna systems (DAS) represent a suitable method for extending multiple-operator radio coverage into indoor premises, tunnels, etc. With the advent of 4G (and beyond) mobile communications systems and of multiple input–multiple output (MIMO) transmission techniques, a common problem for wireless operators is how to upgrade existing DAS systems to the MIMO technology in a cost-effective way. In this paper novel indoor solutions based on the combination of DAS and MIMO transmission techniques (Interleaved-MIMO DAS solutions) are proposed, and their performance is investigated in realistic cases with the aid of measurements, ray tracing simulations, and LTE link-level simulations.


title: Extending Graphics Hardware For Occlusion Queries In OpenGL

abstract: For interactive rendering of large polygonal objects, fast visibility queries are necessary to quickly decide whether polygonal objects are visible and need to be rendered. None of the numerous published algorithms provide visibility performance for interactive rendering of large models. In this paper, we propose an OpenGL extension for fast occlusion queries. Added after the depth test stage of the OpenGL rendering pipeline, our algorithm provides fast queries to establish the occlusion of polygonal objects. Furthermore, hardware aspects of this proposal are discussed and possible implementations on two different graphics architectures are presented.


title: Bundled Object Context for Referring Expressions

abstract: Referring expressions are natural language descriptions of objects within a given scene. Context is of crucial importance for a referring expression, as the description not only depicts the properties of the object but also involves the relationships of the referred object with other ones. Most of previous work uses either the whole image or one particular contextual object as the context. However, the context of these approaches is holistic and insufficient, as a referring expression often describes relationships of multiple objects in an image. To leverage rich context information from all objects in an image, in this paper, we propose a novel scheme that is composed of a visual context long short-term memory (LSTM) module and a sentence LSTM module to model bundled object context for referring expressions. All contextual objects are arranged with their spatial locations and progressively fed into the visual context LSTM module to acquire and aggregate the context features. Then the concatenation of the learned context features and the features of the referred object are put into the sentence LSTM module to learn the probability of a referring expression. The feedback connections and internal gating mechanism of the LSTM cells enable our model to selectively propagate relevant contextual information through the whole network. Experiments on three benchmark datasets show that our methods can achieve promising results compared to state-of-the-art methods. Moreover, visualization of the internal states of the visual context LSTM cells also shows that our method can automatically select the pertinent context objects.


title: Data warehouse state of the art and future challenges

abstract: The evolution of the computer world and the proliferation of storage devices has led to an explosion of available information, so that made their exploitation difficult. In addition, it has been found that a large part of this data will not be stored or used; however, it constitutes a precious raw material to understand the activity and anticipate its evolution., To cope with these problems, professionals implemented a decision making support systems (or decision support systems) and which allow a synthetic and multidimensional treatment of all available and stored information, hence the birth of the data warehouse and data mining techniques. This paper introduces the data warehousing, gives researchers terminologies necessary for understanding the process, and summarizes its evolution. It also presents an analysis of the latest research in the field, and explores the future trends. And therefore, this study provides a reference for researchers wishing to contribute in this area.


title: Realization of three-dimensional walking of a cheetah-modeled bio-inspired quadruped robot

abstract: Adaptability of quadruped animals is not solely reached by brain control, but by the interaction between its body, environment, and control. Especially, morphology of the body is supposed to contribute largely to the adaptability. We have tried to understand quadrupedal locomotion by building a bio-inspired quadruped robot named “Pneupard”, which has a feline-like muscular-skeletal structure. In our previous study, we successfully realized alternative gait of hindlimbs by reflex control based on the sole touch information, which is called an unloading rule, and that of forelimbs as well. In this paper, we finally connect forelimbs and hindlimbs by a rigid spine, and conduct 3D walking experiments only with the simple unloading rule. Through several preliminary experiments, we realize that the touch information on the sole is the most critical for stable 3D walking.


title: Goal-based action priors

abstract: Robots that interact with people must flexibly respond to requests by planning in stochastic state spaces that are often too large to solve for optimal behavior. In this work, we develop a framework for goal and state dependent action priors that can be used to prune away irrelevant actions based on the robot's current goal, thereby greatly accelerating planning in a variety of complex stochastic environments. Our framework allows these goal-based action priors to be specified by an expert or to be learned from prior experience in related problems. We evaluate our approach in the video game Minecraft, whose complexity makes it an effective robot simulator. We also evaluate our approach in a robot cooking domain that is executed on a two-handed manipulator robot. In both cases, goal-based action priors enhance baseline planners by dramatically reducing the time taken to find a near-optimal plan.


Application of Single Tuned Passive Filters in Distribution Networks at the Point of Common Coupling


title: Low-Power High-Efficiency Class D Audio Power Amplifiers

abstract: The architecture, design, and implementation of two clock-free analog class D audio power amplifiers using 0.5 ?m CMOS standard technology are introduced. The amplifiers are designed to consume significantly less power than former implementations. Both designs operate with a 2.7 V single voltage supply and deliver a maximum output power of 250 mW into an 8 ? speaker. The two class-D amplifiers are based on a hysteretic sliding mode controller, which avoids the complex task of generating the highly linear triangle carrier signal used in conventional architectures. The first design generates a two-level modulated signal, known as binary modulated amplifier (BMA); the second topology implements a three-level modulated signal, also called ternary modulated amplifier (TMA). The architectures and implementations are simple and compact, providing very low quiescent power consumption. Experimental results of the BMA/TMA yield an efficiency of 89/90% and a total harmonic distortion plus noise (THD+N) of 0.02/0.03%, respectively. The efficiency and linearity is comparable to state-of-the-art amplifiers but the static power consumption is less than one tenth of previous proposed architectures. Both class D amplifiers achieve a power supply rejection ratio greater than 75 dB at 217 Hz, and a signal-to-noise ratio (SNR) higher than 90 dB within the whole audio band. Each amplifier occupies less than 1.5 mm2.


title: Image Restoration from Patch-based Compressed Sensing Measurement

abstract: A series of methods have been proposed to reconstruct an image from compressively sensed random measurement, but most of them have high time complexity and are inappropriate for patch-based compressed sensing capture, because of their serious blocky artifacts in the restoration results. In this paper, we present a non-iterative image reconstruction method from patch-based compressively sensed random measurement. Our method features two cascaded networks based on residual convolution neural network to learn the end-to-end full image restoration, which is capable of reconstructing image patches and removing the blocky effect with low time cost. Experimental results on synthetic and real data show that our method outperforms state-of-the-art compressive sensing (CS) reconstruction methods with patch-based CS measurement. To demonstrate the effectiveness of our method in more general setting, we apply the de-block process in our method to JPEG compression artifacts removal and achieve outstanding performance as well.


title: Priming, Replication, and the Hardest Science:

abstract: Concerns have been raised recently about the replicability of behavioral priming effects, and calls have been issued to identify priming methodologies with effects that can be obtained in any context and with any population. I argue that such expectations are misguided and inconsistent with evolutionary understandings of the brain as a computational organ. Rather, we should expect priming effects to be highly sensitive to variations in experimental features and subject populations. Such variation does not make priming effects frivolous or capricious but instead can be predicted a priori. However, absent theories specifying the precise contingencies that lead to such variation, failures to replicate another researcher’s findings will necessarily be ambiguous with respect to the inferences that can be made. Priming research is not yet at the stage where such theories exist, and therefore failures are uninformative at the current time. Ultimately, priming researchers themselves must provide direct replicatio...


title: Challenges and opportunities of the Internet for medical oncology

abstract: PURPOSEThe internet, and in particular the world wide web (www), has a rapidly increasing potential to provide information for oncologists and their patients about cancer biology and treatment. A brief overview of this environment is given along with examples of how easily the information is accessed as a means of introducing the web page of the American Society of Clinical Oncology (ASCO), ASCO OnLine.METHODSOncology information sources on the www were accessed from the author's home using a 14.4 kbs modem, Netscape browser (Netscape communications Corp, Mountain View, CA), and the locations recorded for tabulation and discussion.RESULTSOverwhelming amounts of oncology-related information are now available via the Internet.CONCLUSIONOncology as a subspecialty is ideally suited to apply the newest information technology to traditional needs in areas of education, research, and patient care. Oncologists will increasingly act as information guides rather than information resources for patients and their fam...


title: Hybrid Petri Net scheduling model of household appliances for optimal renewable energy dispatching

abstract: Abstract Renewable energies represent one of the most promising and eco-friendly solutions for energy generation, which guarantees the sustainable development of society. Compared to fuel-based generators, the photovoltaic/Battery (PV/B) system is more suitable to supply domestic loads in sunny, isolated regions. However, given weather disturbances, a storage element is required so as to ensure the load supply autonomy. This work proposes an energy flow scheduling algorithm for a PV/B chalet installation in order to guarantee continuous supply of loads. The aim consists in the satisfaction of the loads demand, the maximization of the PV generation use, the limitation of the battery access time, and the battery protection against deep/over charge. The Timed Hybrid Petri Net (THPN) formalism is used to model the system operation considering the classification of loads into two priority levels. The developed algorithm computes several starting operation times of the secondary load, then selects the optimal one in order to satisfy the criteria cited above and guarantee continuous supply of the primary load. A comparative study of the calculated starting operation times demonstrates the efficiency of the optimal solution for a best PV/B power exploitation with a maximum battery lifetime and safety. The lost PV energy is nearly 26% with respect to an appropriate range of the battery State of Charge (SoC) from 100% to 21.76%. Such evaluation offers the opportunity to users to install more loads operating only during daylight in order to consume the PV lost energy.


title: Reliable stair climbing in the simple hexapod 'RHex'

abstract: RHex is a hexapod with compliant legs and only six actuated degrees of freedom. Its ability to traverse highly fractured and unstable terrain, as well ascend and descend a particular flight of stairs has already been documented. In this paper, we describe an open loop controller that enables our small robot (length: 51 cm, width: 20 cm, height: 12.7 cm, leg length: 16 cm) to reliably climb a wide range of regular, full-size stairs with no operator input during stair climbing. Experimental data of energy efficiency in a form of specific resistance during stair climbing is given. The results presented in this paper are based on a new half circle leg design that implements a passive, effective leg length change.


title: Deep Learning Stock Volatility with Google Domestic Trends

abstract: We have applied a Long Short-Term Memory neural network to model S&P 500 volatility, incorporating Google domestic trends as indicators of the public mood and macroeconomic factors. In a held-out test set, our Long Short-Term Memory model gives a mean absolute percentage error of 24.2%, outperforming linear Ridge/Lasso and autoregressive GARCH benchmarks by at least 31%. This evaluation is based on an optimal observation and normalization scheme which maximizes the mutual information between domestic trends and daily volatility in the training set. Our preliminary investigation shows strong promise for better predicting stock behavior via deep learning and neural network models.


Temporal Filtering of Depth Images using Optical Flow


title: Towards linking buyers and sellers: detecting commercial Intent on twitter

abstract: Since more and more people use the micro-blogging platform Twitter to convey their needs and desires, it has become a particularly interesting medium for the task of identifying commercial activities. Potential buyers and sellers can be contacted directly thereby opening up novel perspectives and economic possibilities. By detecting commercial intent in tweets, this work is considered a first step to bring together buyers and sellers. In this work, we present an automatic method for detecting commercial intent in tweets where we achieve reasonable precision 57% and recall 77% scores. In addition, we provide insights into the nature and characteristics of tweets exhibiting commercial intent thereby contributing to our understanding of how people express commercial activities on Twitter.


title: Situational Context for Ranking in Personal Search

abstract: Modern search engines leverage a variety of sources, beyond the conventional query-document content similarity, to improve their ranking performance. Among them, query context has attracted attention in prior work. Previously, query context was mainly modeled by user search history, either long-term or short-term, to help the ranking of future queries. In this paper, we focus on situational context, i.e., the contextual features of the current search request that are independent from both query content and user history. As an example, situational context can depend on search request time and location. We propose two context-aware ranking models based on neural networks. The first model learns a low-dimensional deep representation from the combination of contextual features. The second model extends the first one by leveraging binarized contextual features in addition to the high-level abstractions learned using a deep network. ::: ::: The existing context-aware ranking models are mainly based on search history, especially click data that can be gathered from the search engine logs. Although context-aware models have been widely explored in web search, their influence on search scenarios where click data is highly sparse is relatively unstudied. The focus of this paper, personal search (e.g., email search or on-device search), is one of such scenarios. We evaluate our models using the click data collected from one of the world's largest personal search engines. The experiments demonstrate that the proposed models significantly outperform the baselines which do not take context into account. These results indicate the importance of situational context for personal search, and open up a venue for further exploration of situational context in other search scenarios.