title: Development of Optical Flow Based Moving Object Detection and Tracking System on an Embedded DSP Processor

abstract: Moving object detection and tracking has attracted a great interest from computer vision researchers due to its promising applications in areas, like video surveillance, traffic monitoring and image recognition. Moving object detection in digital image sequence involves identification of the presence of an object in consecutive frames whereas object tracking is used to monitor the movements with respect to the region of interest. In this project, Optical Flow based Lucas-Kanade algorithm using different smoothing techniques for single and multiple moving object detection and tracking have been developed and compared for their performance. Optical Flow is the distribution of apparent velocities of movement of brightness patterns in an image. Lucas-Kanade algorithm with Sobel, Horn and Guassian smoothing techniques is used in this work for computation of Optical Flow vectors. Single and multiple object movements with respect to the computed vectors are segmented using thresholding. The extracted movements are tracked using edge and centroid information. Suitable image enhancement techniques are applied to the segmented results to avoid the unwanted information present in the image. Real and virtual image data with static and dynamic environment are used as test sequences to validate the developed algorithms. The tracking performance, in terms of their accuracy and computation time, of the different algorithms with and without image pyramid is analyzed and compared. Lucas-Kanade algorithm with Sobel smoothing and image pyramid provides the best accuracy as well as computation time and hence selected for implementation in C. The processing times of developed algorithm is compared for its MATLAB, C on Intel Core2 Duo processor and C on embedded processor (the latter both under Linux environment) implementations. The average computation time to process consecutive image sequence of resolution 180x120 pixels is 0.12. The computation time can further be reduced by parallelizing the developed algorithm and using a high end embedded processor


title: Management of a remote backup copy for disaster recovery

abstract: A remote backup database system tracks the state of a primary system, taking over transaction processing when disaster hits the primary site. The primary and backup sites are physically isolated so that failures at one site are unlikely to propogate to the other. For correctness, the execution schedule at the backup must be equivalent to that at the primary. When the primary and backup sites contain a single processor, it is easy to achieve this property. However, this is harder to do when each site contains multiple processors and sites are connected via multiple communication lines. We present an efficient transaction processing mechanism for multiprocessor systems that guarantees this and other important properties. We also present a database initialization algorithm that copies the database to a backup site while transactions are being processed.


title: User Experience Evaluation Methods in Academic and Industrial Contexts

abstract: In this paper, we investigate 30user experience (UX) evaluation methods that were collected during a special interest group session at the CHI2009 Conference. We present a categorization of the collected UX evaluation methods and discuss the range of methods from both academic and industrial perspectives.


title: Academic Procrastinators and Their Self-Regulation

abstract: Previous procrastination research has provided considerable support for procrastination as a failure of self-regulation. However, procrastination has rarely been examined in relation to models of self-regulated learning. The purpose of this study was to understand the motives and reasons for academic procrastination from a self-regulated learning perspective. The current study employed a mixed-methods design in which participants completed several survey instruments of academic procrastination, self-regulation, and academic motivation and participated in semi-structured interviews. Findings indicated that academic procrastination was related to poor self-regulatory skills and defensive behaviors including self-handica- pping strategies. Only limited support for students’ demonstration of procrastination as an adaptive beha- vior (or, active procrastination) was also indicated. Limitations and implications for future research are discussed.


title: The Importance of Photography as an Information Support in Event Management (Case Event of Anugerah Bambu Indonesia 2013)

abstract: This study aims to investigate the potential of media photography as a means of support in an event practice, analyze photography as data source in identifying the event practice, explore photography as documentation that helps in event execution, examine photography as visual language that supports the event management. From the case of an event organized by Anugerah Bambu Indonesia 2013, results from the study finds that photography has an added value as a data source, documentation, and visual language. This study proves that the theory EMBOK by Silvers, who categorizes photography as information management for event management, a theory which can be accounted for.


title: Snap & Play: Auto-Generated Personalized Find-the-Difference Game

abstract: In this article, by taking a popular game, the Find-the-Difference (FiDi) game, as a concrete example, we explore how state-of-the-art image processing techniques can assist in developing a personalized, automatic, and dynamic game. Unlike the traditional FiDi game, where image pairs (source image and target image) with five different patches are manually produced by professional game developers, the proposed Personalized FiDi (P-FiDi) electronic game can be played in a fully automatic Snap & Play mode. Snap means that players first take photos with their digital cameras. The newly captured photos are used as source images and fed into the P-FiDi system to autogenerate the counterpart target images for users to play. Four steps are adopted to autogenerate target images: enhancing the visual quality of source images, extracting some changeable patches from the source image, selecting the most suitable combination of changeable patches and difference styles for the image, and generating the differences on the target image with state-of-the-art image processing techniques. In addition, the P-FiDi game can be easily redesigned for the im-game advertising. Extensive experiments show that the P-FiDi electronic game is satisfying in terms of player experience, seamless advertisement, and technical feasibility.


title: Creative Writing with a Machine in the Loop: Case Studies on Slogans and Stories

abstract: As the quality of natural language generated by artificial intelligence systems improves, writing interfaces can support interventions beyond grammar-checking and spell-checking, such as suggesting content to spark new ideas. To explore the possibility of machine-in-the-loop creative writing, we performed two case studies using two system prototypes, one for short story writing and one for slogan writing. Participants in our studies were asked to write with a machine in the loop or alone (control condition). They assessed their writing and experience through surveys and an open-ended interview. We collected additional assessments of the writing from Amazon Mechanical Turk crowdworkers. Our findings indicate that participants found the process fun and helpful and could envision use cases for future systems. At the same time, machine suggestions do not necessarily lead to better written artifacts. We therefore suggest novel natural language models and design choices that may better support creative writing.


title: Learning Knowledge-guided Pose Grammar Machine for 3D Human Pose Estimation

abstract: In this paper, we propose a knowledge-guided pose grammar network to tackle the problem of 3D human pose estimation. Our model directly takes 2D poses as inputs and learns the generalized 2D-3D mapping function, which renders high applicability. The proposed network consists of a base network which efficiently captures pose-aligned features and a hierarchy of Bidirectional RNNs on top of it to explicitly incorporate a set of knowledge (e.g., kinematics, symmetry, motor coordination) and thus enforce high-level constraints over human poses. In learning, we develop a pose-guided sample simulator to augment training samples in virtual camera views, which further improves the generalization ability of our model. We validate our method on public 3D human pose benchmarks and propose a new evaluation protocol working on cross-view setting to verify the generalization ability of different methods. We empirically observe that most state-of-the-arts face difficulty under such setting while our method obtains superior performance.


title: Automatic Target Recognition of Aircraft using Inverse Synthetic Aperture Radar

abstract: Along with the improvement of radar technologies, Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) and Inverse SAR (ISAR) has come to be an active research area. SAR/ISAR are radar techniques to generate a two-dimensional high-resolution image of a target. Unlike other similar experiments using Convolutional Neural Networks (CNN) to solve this problem, we utilize an unusual approach that leads to better performance and faster training times. Our CNN uses complex values generated by a simulation to train the network; additionally, we utilize a multi-radar approach to increase the accuracy of the training and testing processes, thus resulting in higher accuracies than the other papers working on SAR/ISAR ATR. We generated our dataset with 7 different aircraft models with a radar simulator we developed called RadarPixel; it is a Windows GUI program implemented using Matlab and Java programming, the simulator is capable of accurately replicating a real SAR/ISAR configurations. Our objective is to utilize our multi-radar technique and determine the optimal number of radars needed to detect and classify targets.


title: Multi-Modal Multi-Task Deep Learning for Autonomous Driving

abstract: Several deep learning approaches have been applied to the autonomous driving task, many employing end-to-end deep neural networks. Autonomous driving is complex, utilizing multiple behavioral modalities ranging from lane changing to turning and stopping. However, most existing approaches do not factor in the different behavioral modalities of the driving task into the training strategy. This paper describes a technique for using Multi-Modal Multi-Task Learning that considers multiple behavioral modalities as distinct modes of operation for an end-to-end autonomous deep neural network utilizing the insertion of modal information as secondary input data. Using labeled data from hours of driving our fleet of 1/10th scale model cars, we trained multiple neural networks to imitate the steering angle and driving speed of human control of a car. We show that in each case, our models trained with MTL can match or outperform multiple networks trained on individual tasks, while using a fraction of the parameters and having more distinct modes of operation than a network trained without MTL on the same multi-modal data. These results should encourage Multi-Modal MTL-style training with the insertion of Modal Information for tasks with related behaviors.


title: Efficient Methods for Overlapping Group Lasso

abstract: The group Lasso is an extension of the Lasso for feature selection on (predefined) non-overlapping groups of features. The non-overlapping group structure limits its applicability in practice. There have been several recent attempts to study a more general formulation, where groups of features are given, potentially with overlaps between the groups. The resulting optimization is, however, much more challenging to solve due to the group overlaps. In this paper, we consider the efficient optimization of the overlapping group Lasso penalized problem. We reveal several key properties of the proximal operator associated with the overlapping group Lasso, and compute the proximal operator by solving the smooth and convex dual problem, which allows the use of the gradient descent type of algorithms for the optimization. We have performed empirical evaluations using both synthetic and the breast cancer gene expression data set, which consists of 8,141 genes organized into (overlapping) gene sets. Experimental results show that the proposed algorithm is more efficient than existing state-of-the-art algorithms.


title: Scalable Graph-based Bug Search for Firmware Images

abstract: Because of rampant security breaches in IoT devices, searching vulnerabilities in massive IoT ecosystems is more crucial than ever. Recent studies have demonstrated that control-flow graph (CFG) based bug search techniques can be effective and accurate in IoT devices across different architectures. However, these CFG-based bug search approaches are far from being scalable to handle an enormous amount of IoT devices in the wild, due to their expensive graph matching overhead. Inspired by rich experience in image and video search, we propose a new bug search scheme which addresses the scalability challenge in existing cross-platform bug search techniques and further improves search accuracy. Unlike existing techniques that directly conduct searches based upon raw features (CFGs) from the binary code, we convert the CFGs into high-level numeric feature vectors. Compared with the CFG feature, high-level numeric feature vectors are more robust to code variation across different architectures, and can easily achieve realtime search by using state-of-the-art hashing techniques. We have implemented a bug search engine, Genius, and compared it with state-of-art bug search approaches. Experimental results show that Genius outperforms baseline approaches for various query loads in terms of speed and accuracy. We also evaluated Genius on a real-world dataset of 33,045 devices which was collected from public sources and our system. The experiment showed that Genius can finish a search within 1 second on average when performed over 8,126 firmware images of 420,558,702 functions. By only looking at the top 50 candidates in the search result, we found 38 potentially vulnerable firmware images across 5 vendors, and confirmed 23 of them by our manual analysis. We also found that it took only 0.1 seconds on average to finish searching for all 154 vulnerabilities in two latest commercial firmware images from D-LINK. 103 of them are potentially vulnerable in these images, and 16 of them were confirmed.


Deep Belief Networks for phone recognition


title: Supporting Microservice Evolution

abstract: Microservices have become a popular pattern for deploying scale-out application logic and are used at companies like Netflix, IBM, and Google. An advantage of using microservices is their loose coupling, which leads to agile and rapid evolution, and continuous re-deployment. However, developers are tasked with managing this evolution and largely do so manually by continuously collecting and evaluating low-level service behaviors. This is tedious, error-prone, and slow. We argue for an approach based on service evolution modeling in which we combine static and dynamic information to generate an accurate representation of the evolving microservice-based system. We discuss how our approach can help engineers manage service upgrades, architectural evolution, and changing deployment trade-offs.


title: Push vs. Pull-Based Loop Fusion in Query Engines

abstract: Database query engines use pull-based or push-based approaches to avoid the materialization of data across query operators. In this paper, we study these two types of query engines in depth and present the limitations and advantages of each engine. Similarly, the programming languages community has developed loop fusion techniques to remove intermediate collections in the context of collection programming. We draw parallels between the DB and PL communities by demonstrating the connection between pipelined query engines and loop fusion techniques. Based on this connection, we propose a new type of pull-based engine, inspired by a loop fusion technique, which combines the benefits of both approaches. Then we experimentally evaluate the various engines, in the context of query compilation, for the first time in a fair environment, eliminating the biasing impact of ancillary optimizations that have traditionally only been used with one of the approaches. We show that for realistic analytical workloads, there is no considerable advantage for either form of pipelined query engine, as opposed to what recent research suggests. Also, by using microbenchmarks we show that our proposed engine dominates the existing engines by combining the benefits of both.


title: Modality-Dependent Cross-Media Retrieval

abstract: In this article, we investigate the cross-media retrieval between images and text, that is, using image to search text (I2T) and using text to search images (T2I). Existing cross-media retrieval methods usually learn one couple of projections, by which the original features of images and text can be projected into a common latent space to measure the content similarity. However, using the same projections for the two different retrieval tasks (I2T and T2I) may lead to a tradeoff between their respective performances, rather than their best performances. Different from previous works, we propose a modality-dependent cross-media retrieval (MDCR) model, where two couples of projections are learned for different cross-media retrieval tasks instead of one couple of projections. Specifically, by jointly optimizing the correlation between images and text and the linear regression from one modal space (image or text) to the semantic space, two couples of mappings are learned to project images and text from their original feature spaces into two common latent subspaces (one for I2T and the other for T2I). Extensive experiments show the superiority of the proposed MDCR compared with other methods. In particular, based on the 4,096-dimensional convolutional neural network (CNN) visual feature and 100-dimensional Latent Dirichlet Allocation (LDA) textual feature, the mAP of the proposed method achieves the mAP score of 41.5p, which is a new state-of-the-art performance on the Wikipedia dataset.


title: E-Commerce Barriers in South China: The Broader Perspective

abstract: In the middle of the last decade China began to develop the use of the Internet. Within a few years, the World Wide Web (WWW) had experienced rapid growth in terms of network infrastructure, shopping behaviour and numbers of Internet users. China currently maintains annual economic growth higher than anywhere else in the world. Consequently an increasing number of businesses are seeing China as an attractive market opportunity. Many such businesses are seeking to use the Internet as their point of access. This study identifies the major barriers to e-commerce adoption as perceived by both Internet users and businesses in the South China region. South China was chosen as the target population for this study because of the prevailing economic conditions that exist in that region. The study commences with secondary research that identifies the main impediments to ecommerce. These include lack of a nationwide credit card system and high Internet access costs. To compliment this research the results of a unique online survey of Chinese Internet users is presented. Over two thousand responses were received and analysed. These data provided deeper insights into the main impediments to e-commerce as perceived by Chinese Internet users. The views of businesses were also investigated through a series of interviews. Many of the views from users as well as businesses were found to be similar, with concerns about government policy, security and privacy. The paper summarizes the main findings of this research and concludes with ways in which these barriers may be removed or their impacts lessened.


title: Corporate Finance and Corporate Governance

abstract: A combined treatment of corporate finance and corporate governance is herein proposed. Debt and equity are treated not mainly as alternative financial instruments, but rather as alternative governance structures. Debt governance works mainly out of rules, while equity governance allows much greater discretion. A project-financing approach is adopted. I argue that whether a project should be financed by debt or by equity depends principally on the characteristics of the assets. Transaction-cost reasoning supports the use of debt (rules) to finance redeployable assets, while non-redeployable assets are financed by equity (discretion). Experiences with leasing and leveraged buyouts are used to illustrate the argument. The article also compares and contrasts the transaction-cost approach with the agency approach to the study of economic organization.


title: DeepSIC : a deep model For satellite image classification

abstract: Satellite Image Classification is the problem of classifying satellite images into their corresponding classes. Using supervised deep learning, we provide an efficient classification mechanism of earth observation imagery captured by satellite. Promising classification results were obtained through Convolution Neural Network. Two sets of experiments were carried out. In the first set, the traditional pre-deep learning era approach was followed: features were extracted first which were then given as input to Support Vector Machines (SVM). In the second set of experiments, the images were directly provided as input to Convolution Neural Networks and the output features were then used as input to the SVM. The proposed schemes were tested on satellite images datasets AID, UC-merced and WHU-RS, having varied classes and heterogeneous image dimensions. We show that our model, with Convolution Neural Network, achieves 92% accuracy on the AID dataset and outperforms the previously reported best accuracy of 89.64% (Xia et al. in CoRR, arXiv:1608.05167 ::: , 2016).


title: Innovative public-private partnership to support Smart City: the case of “Chaire REVES”

abstract: Purpose – The purpose of this paper is to discuss how French universities can play a key role in generating Smart City (SC) through an innovative Public-Private Partnership (PPP) dedicated to urban transformation. Design/methodology/approach – The authors led an action-research study for five years with several research and pedagogic projects, including users or citizens. Findings – The paper points out main factors of SC development. It also presents shared demonstrators’ characteristics, including industrial scale, sustainability, and citizens’ participation. Research limitations/implications – This research is experimented with only one regional multi-case study. Practical implications – Practical implications of this paper include a University of Lorraine diversification strategy through the “Chaire REVES” supported by public and private partners. Social implications – At the regional level, industrial-university-territorial partnerships could tackle both societal and economical issues “with,” “for,” ...


title: Functional imaging of human crossmodal identification and object recognition

abstract: The perception of objects is a cognitive function of prime importance. In everyday life, object perception benefits from the coordinated interplay of vision, audition, and touch. The different sensory modalities provide both complementary and redundant information about objects, which may improve recognition speed and accuracy in many circumstances. We review crossmodal studies of object recognition in humans that mainly employed functional magnetic resonance imaging (fMRI). These studies show that visual, tactile, and auditory information about objects can activate cortical association areas that were once believed to be modality-specific. Processing converges either in multisensory zones or via direct crossmodal interaction of modality-specific cortices without relay through multisensory regions. We integrate these findings with existing theories about semantic processing and propose a general mechanism for crossmodal object recognition: The recruitment and location of multisensory convergence zones varies depending on the information content and the dominant modality.


title: Convolutional neural network architecture for hand gesture recognition

abstract: This paper presents the design of a convolutional neural network architecture using the MatConvNet library for MATLAB in order to achieve the recognition of 2 classes of hand gestures: ”open” and ”closed”. Six architectures were implemented to which their hyperparameters and depth were varied to observe their behavior through the validation error in the training and accuracy in the estimation of each one of the set classes, which was evaluated by a matrix of confusion. Given each of these results, the neural network with the best performance was chosen.


title: New developments in burst strength predictions for locally corroded pipelines

abstract: Existing industry standards for the integrity assessment of corroded pipelines are based on semi-empirical burst capacity formulae that have poor accuracy. To improve upon this, and to provide a better understanding of the phenomena that govern the maximum pressure capacity of corroded pipelines, analytical models based on sound principles of plasticity theory and large strain continuum mechanics are being progressed within Shell. An accurate burst model for pipelines with corrosion over a considerable length has already been developed (and published) and now the focus is on localized corrosion in the form of a patch or pit. The main objective of the work presented is to investigate the possibility of representing a locally corroded region in a pipe as a plane-strain membrane arch. It is shown that for a shallow arch the pressure capacity is governed by a bifurcation/localization instability.


title: Intrinsic Depth: Improving Depth Transfer with Intrinsic Images

abstract: We formulate the estimation of dense depth maps from video sequences as a problem of intrinsic image estimation. Our approach synergistically integrates the estimation of multiple intrinsic images including depth, albedo, shading, optical flow, and surface contours. We build upon an example-based framework for depth estimation that uses label transfer from a database of RGB and depth pairs. We combine this with a method that extracts consistent albedo and shading from video. In contrast to raw RGB values, albedo and shading provide a richer, more physical, foundation for depth transfer. Additionally we train a new contour detector to predict surface boundaries from albedo, shading, and pixel values and use this to improve the estimation of depth boundaries. We also integrate sparse structure from motion with our method to improve the metric accuracy of the estimated depth maps. We evaluate our Intrinsic Depth method quantitatively by estimating depth from videos in the NYU RGB-D and SUN3D datasets. We find that combining the estimation of multiple intrinsic images improves depth estimation relative to the baseline method.


Mean – Median Filtering For Impulsive Noise Removal


title: On-Load Field Component Separation in Surface-Mounted Permanent-Magnet Motors Using an Improved Conformal Mapping Method

abstract: This paper introduces an improved conformal mapping (ICM) method for the separation of the on-load air-gap magnetic field components in surface-mounted permanent-magnet (SPM) motors under loading conditions. The ICM method can model the magnetic induction inside a permanent magnet (PM) due to the armature reaction and the relative recoil permeability. The ICM method can also consider the simultaneous influences of the armature reaction, the slotting effect, the magnetic saturation, and the relative recoil permeability in the determination of operating points of the PM in its different parts. Therefore, this proposed method can be useful for an on-load performance analysis of SPM motors. Three conformal mappings are used to reach the main canonical domain: two logarithmic complex functions and the Schwarz–Christoffel (SC) mapping. The field solution in the slotless domain is then mapped back into the slotted domain using the complex air-gap permeance. The field solution in the slotted domain is used to calculate the on-load air-gap field components due to the PMs and the armature reaction. These on-load field components allow the calculation of the on-load PM flux linkage, the on-load PM back-EMF, and the on-load torque components. The influences of electric loading on the on-load torque components and the on-load PM back-EMF are studied using the ICM method. The on-load cogging torque periodicity is confirmed to change due to the magnetic saturation, and the harmonic content of torque components and the PM back-EMF is also altered due to the electric loading. The accuracy of this developed model is verified by comparing the results obtained through the ICM method with the respective results obtained through the finite-element analysis and the frozen permeability method.


title: Incremental semi-supervised kernel construction with self-organizing incremental neural network and application in intrusion detection

abstract: The semi-supervised learning (SSL) problems are often solved by graph based algorithms, semi-definite program- mings etc. These methods always require high space complexities, and thus are not efficient for network intrusion detection systems. Apart from the space complexity challenge, a network intrusion detection system should be able to handle the distribution drifting of data flow as well. A common solution for this concept drift problem is by SSL. In this paper, an incre- mental SSL training framework is proposed to combine the low space complexity advantage of topology learning and SSL for network intrusion detection. First, the unsupervised self-organizing incremental neural network is extended to process labeled and unlabeled information incrementally. Second, a kernel function is constructed from the training results of the previous step. Finally, a kernel machine is trained with the constructed kernel function. The proposed method reduces the space complexity of SSL to the magnitude similar to supervised learning. The experiments are carried out on the NSL-KDD datasets, and the results show that the proposed method outperforms the mainstream methods such as Transductive Support Vector Machine and Label Propagation.


title: Applying Deep Learning Techniques to Cultural Heritage Images Within the INCEPTION Project

abstract: The digital documentation of cultural heritage (CH) often requires interpretation and classification of a huge amount of images. The INCEPTION European project focuses on the development of tools and methodologies for obtaining 3D models of cultural heritage assets, enriched by semantic information and integration of both parts on a new H-BIM (Heritage - Building Information Modeling) platform. In this sense, the availability of automated techniques that allow the interpretation of photos and the search using semantic terms would greatly facilitate the work to develop the project. In this article the use of deep learning techniques, specifically the convolutional neural networks (CNNs) for analyzing images of cultural heritage is assessed. It is considered that the application of these techniques can make a significant contribution to the objectives sought in the INCEPTION project and, more generally, the digital documentation of cultural heritage.


Survey on Software Defect Prediction Using Machine Learning Techniques


title: Modelling stars using XML

abstract: We suppose collections of XML data described by Document Type Definitions (DTDs). This data has been generated by applications and plays a role of OLTP database(s). A star schema, a well-known technique used in data warehousing, can be applied. Then dimension information is supposed to be contained in XML data. We will use the notions of subDTD and view, and formulate referential integrity constraints in XML environment. We use simple pattern matching capabilities of current XML query languages for XML view specification and tree embedding algorithms for these purposes. A dimension hierarchy is defined as a set of logically connected collections of XML data. Facts may be also conceived as elements of an XML document. Due to the structural complexity of XML data the approach requires subtler formal model than it is done with conventional dimension and fact tables described by classical star schemes. In consequence, our approach captures more from heterogeneity of source databases than it is done in classical relational approaches to data warehousing.


title: Operational TomoSAR processing using TerraSAR-X high resolution spotlight stacks from multiple view angles

abstract: With the availability of meter resolution space-borne SAR systems, urban monitoring using SAR Tomography (TomoSAR) becomes increasingly popular, because of its layover separation capability. However, compared to Persistent Scatterer Interferometry (PSI), TomoSAR applications are much more computationally expensive. This article introduces a TomoSAR processing system for long-term large urban area mapping and monitoring. Two new features were introduced: 1. PSI was integrated into the currently available TomoSAR algorithms (e.g. TSVD, SVD-Wiener, and SL1MMER) to increase the overall computational efficiency; 2. results from multiple view angles were fused to provide full coverage of each building facade. This processing system handles an entire TerraSAR-X high resolution spotlight scene in an affordable time, achieving scatterer density up to 1 million scatterer/km2 from a single stack, comparing to 60 thousand to 100 thousand scatterer/km2 for PSI.


title: Biodegradable polymeric nanoparticles as drug delivery devices

abstract: This review presents the most outstanding contributions in the field of biodegradable polymeric nanoparticles used as drug delivery systems. Methods of preparation, drug loading and drug release are covered. The most important findings on surface modification methods as well as surface characterization are covered from 1990 through mid-2000.


title: Accurate Vehicle Location System Using RFID, an Internet of Things Approach

abstract: Modern infrastructure, such as dense urban areas and underground tunnels, can effectively block all GPS signals, which implies that effective position triangulation will not be achieved. The main problem that is addressed in this project is the design and implementation of an accurate vehicle location system using radio-frequency identification (RFID) technology in combination with GPS and the Global system for Mobile communication (GSM) technology, in order to provide a solution to the limitation discussed above. In essence, autonomous vehicle tracking will be facilitated with the use of RFID technology where GPS signals are non-existent. The design of the system and the results are reflected in this paper. An extensive literature study was done on the field known as the Internet of Things, as well as various topics that covered the integration of independent technology in order to address a specific challenge. The proposed system is then designed and implemented. An RFID transponder was successfully designed and a read range of approximately 31 cm was obtained in the low frequency communication range (125 kHz to 134 kHz). The proposed system was designed, implemented, and field tested and it was found that a vehicle could be accurately located and tracked. It is also found that the antenna size of both the RFID reader unit and RFID transponder plays a critical role in the maximum communication range that can be achieved.


title: ON-LINE MONITORING AND ANALYSIS OF FAULTS IN TRANSMISSION AND DISTRIBUTION LINES USING GSM TECHNIQUE

abstract: Increase in demand of electricity for entire applications in any country, need to produce consistently with advanced protection system. Many special protection systems are available based on volume of power distributed and often the load changes without prediction required an advanced and special communication based systems to control the electrical parameters of the generation. Most of the existing systems are reliable on various applications but not perfect for electrical applications. Electrical environment will have lots of disturbance in nature, Due to natural disasters like storms, cyclones or heavy rains transmission and distribution lines may lead to damage. The electrical wire may cut and fall on ground, this leads to very harmful for human beings and may become fatal. So, a rigid, reliable and robust communications like GSM technology instead of many communication techniques used earlier. This enhances speed of communication with distance independenncy. This technology saves human life from this electrical danger by providing the fault detection and automatically stops the electricity to the damaged line and also conveys the message to the electricity board to clear the fault. An Embedded based hardware design is developed and must acquire data from electrical sensing system. A powerful GSM networking is designed to send data from a network to other network. Any change in parameters of transmission is sensed to protect the entire transmission and distribution.


title: Verifying liveness by multiple experts in face biometrics

abstract: Resisting spoofing attempts via photographs and video playbacks is a vital issue for the success of face biometrics. Yet, the ldquolivenessrdquo topic has only been partially studied in the past. In this paper we are suggesting a holistic liveness detection paradigm that collaborates with standard techniques in 2D face biometrics. The experiments show that many attacks are avertible via a combination of anti-spoofing measures. We have investigated the topic using real-time techniques and applied them to real-life spoofing scenarios in an indoor, yet uncontrolled environment.


title: Study of Power System load shedding scheme based on dynamic simulation

abstract: An adaptive load shedding scheme is designed, modeled and simulated in Power System Simulator for Engineers (PSS/E) and compared with conventional under-frequency load shedding scheme (UFLS). In this paper a new distributed load shedding scheme based on real time synchronized frequency measurement is proposed. This scheme improves the load shedding operation and sheds optimal amount of load taking into account simultaneous frequency measurements from various buses along with operating conditions and system topology. Modified New England 39 bus system is used for evaluating the results. The simulation results show that the adaptive scheme has improved the system performance under disturbance. The new distributed load shedding scheme sheds less amount of load as compared to adaptive load shedding.


title: A 28-GHz phased-array transceiver with series-fed dual-vector distributed beamforming

abstract: This paper presents a 28-GHz four-element phased-array transceiver in 130-nm SiGe BiCMOS technology for 5G cellular application. The array employs scalar-only weighting functions within each front-end and a global quadrature function, enabling small footprint for each element. A dual-vector series feed network also reduces size of the array. Measurements show that each receive front-end achieves 8.7 to 11.5 dB gain, 4.5 to 6.9 dB noise figure, −25.4 to −18.4 dBm input 1-dB compression point, and < 0.5dB/2.1° RMS gain/phase error at 24 to 28 GHz. Each transmit front-end achieves 9.4 to 14.3 dB gain, 5.5 to 10.6 dBm output 1-dB compression point, and < 0.4dB/4.2° RMS gain/phase error at 24 to 28 GHz. The four-element transceiver array occupies 2.9 mm2 area and consumes 1.08 W in transmit mode and 0.68 W in receive mode.


Nurses’ perceptions of their professional practice environment in relation to job satisfaction


title: The Receptive Fields of Inferior Temporal Cortex Neurons in Natural Scenes

abstract: Inferior temporal cortex neurons have generally been found to have large visual receptive fields that typically include the fovea and extend throughout much of the visual field. However, a problem of such a large receptive field is that it does not easily support object selection by subsequent processing areas, in that all objects within such a large receptive field might activate inferior temporal cortex cells. To clarify this, we recorded from inferior temporal cortex neurons while macaques searched for objects in complex natural scenes or in plain backgrounds, as normally used. Inferior temporal cortex neuron receptive fields were much smaller in natural scenes (mean radius, 11 degrees) than in plain backgrounds (39 degrees). With two objects in a scene, one of which was a target for action (a touch), the firing rates were equally high during foveation of the effective stimulus when it was the target and when it was the distractor in both the plain and the complex scenes. With a plain background and two objects present, the receptive fields were much larger (24 degrees ) for the stimulus when it was the target than when it was the distractor (9 degrees ). This effect of object-based attention was much less evident in the complex scene, when the receptive fields were small both when the stimulus was a distractor and when it was a target. The results show that the temporal visual cortex provides an unambiguous representation in natural scenes by responding to the object shown at or close to the fixation point.


title: Erratum to: Design and pilot validation of A-gear: a novel wearable dynamic arm support

abstract: Unfortunately, the original version of this article [1] contained an error. Equation 6 was included incorrectly: in the original equation variable slinks3 was missing.The correct Equation 6 can be found below:


The Second PASCAL Recognising Textual Entailment Challenge


title: Forensic Attacks Analysis and the Cyber Security of Safety-Critical Industrial Control Systems

abstract: Industrial Control Systems (ICS) and SCADA (Supervisory Control And Data Acquisition) applications monitor ::: and control a wide range of safety-related functions. These include energy generation where failures could have ::: significant, irreversible consequences. They also include the control systems that are used in the manufacture of ::: safety-related products. In this case bugs in an ICS/SCADA system could introduce flaws in the production of ::: components that remain undetected before being incorporated into safety-related applications. Industrial Control ::: Systems, typically, use devices and networks that are very different from conventional IP-based infrastructures. ::: These differences prevent the re-use of existing cyber-security products in ICS/SCADA environments; the ::: architectures, file formats and process structures are very different. This paper supports the forensic analysis of ::: industrial control systems in safety-related applications. In particular, we describe how forensic attack analysis is ::: used to identify weaknesses in devices so that we can both protect components but also determine the information ::: that must be analyzed during the aftermath of a cyber-incident. Simulated attacks detect vulnerabilities; a risk-based ::: approach can then be used to assess the likelihood and impact of any breach. These risk assessments are then used ::: to justify both immediate and longer-term countermeasures.


title: Development of the MACARM - a novel cable robot for upper limb neurorehabilitation

abstract: This paper describes the design and operation of the multi-axis cartesian-based arm rehabilitation machine (MACARM), a new cable (wire) robot for upper limb rehabilitation. The prototype configuration is comprised of an array of 8 motors mounted at the corners of a cubic support frame that provides, via cables, 6 degree of freedom (DOF) control of a centrally located end-effector. A 6 DOF load cell mounted on the end-effector provides force measurement. Given its relatively simple architecture, the MACARM may provide an attractive alternative to serial robots for use in neurorehabilitation.


title: MDM tool: A data mining framework integrated into Moodle

abstract: The interest in developing Learning Analytics tools that can be integrated into the well-known Moodle course management systems is increasing nowadays. These tools generally provide some type of basic analytics and graphs about users' interaction in the course. However, they do not enable a varied set of Data Mining techniques to be applied, such as approaches for classification, clustering, or association. To address this issue, a new and freely available Moodle Data Mining tool, named MDM, has been proposed in this paper. The proposed tool eases the whole knowledge discovery process, including tasks such as selection, data pre-processing, and data mining from Moodle courses. The proposed MDM tool has been developed in PHP programming language, so it can be easily integrated into Moodle as a module for a specific course. Its main features and architecture are described in depth, and a tutorial is also provided as a practical way of using the MDM interface. Finally, some experimental results using a real-life sample dataset of mechanical engineering students are analyzed.


title: Labeled RTDP: Improving the convergence of real-time dynamic programming

abstract: RTDP is a recent heuristic-search DP algorithm for solving non-deterministic planning problems with full observability. In relation to other dynamic programming methods, RTDP has two benefits: first, it does not have to evaluate the entire state space in order to deliver an optimal policy, and second, it can often deliver good policies pretty fast. On the other hand, RTDP final convergence is slow. In this paper we introduce a labeling scheme into RTDP that speeds up its convergence while retaining its good anytime behavior. The idea is to label a state s as solved when the heuristic values, and thus, the greedy policy defined by them, have converged over s and the states that can be reached from s with the greedy policy. While due to the presence of cycles, these labels cannot be computed in a recursive, bottom-up fashion in general, we show nonetheless that they can be computed quite fast, and that the overhead is compensated by the recomputations avoided. In addition, when the labeling procedure cannot label a state as solved, it improves the heuristic value of a relevant state. This results in the number of Labeled RTDP trials needed for convergence, unlike the number of RTDP trials, to be bounded. From a practical point of view, Labeled RTDP (LRTDP) converges orders of magnitude faster than RTDP, and faster also than another recent heuristic-search DP algorithm, LAO*. Moreover, LRTDP often converges faster than value iteration, even with the heuristic h = 0, thus suggesting that LRTDP has a quite general scope.


title: Development of the Coping Strategies Questionnaire 24, a Clinically Utilitarian Version of the Coping Strategies Questionnaire

abstract: Objective: To use principal-components analysis to obtain a shorter and therefore more clinically useful version of the Coping Strategies Questionnaire (CSQ). Subjects: A British sample of 214 chronic back pain patients attending outpatient spinal assessment. Results: A 4-factor solution was discovered that incorporates Catastrophizing, Diversion, Cognitive Coping, and Reinterpreting factors. Apart from the absence of a Praying and Hoping factor, this solution is similar to those previously found by researchers in this field. Conclusions: The new questionnaire (the CSQ24) is a valid utilitarian version of the CSQ that is easy to score and is appropriate for clinical use. In recent years, the effective management of chronic musculoskeletal pain, especially chronic low back pain (CLBP), has become a topical area of research. This is primarily because of a significant increase in the number of people seeking treatment for this condition (Clinical Standards Advisory Group, 1994) and a realization that the majority of common treatment options are unsuccessful (Barton, 2000; Clinical Standards Advisory Group, 1994; Evans & Richards, 1996). Specifically, the observation that physically and pathologically similar patients may react very differently to their condition has led to great interest in the cognitive and behavioral coping mechanisms such patients demonstrate. It was through the work of authors such as Lazarus and Folkman


Parental Overprotection and Emotional Abuse Predict the Presence of Psychopathic Traits in Adulthood


title: The Use of Immersive Virtual Reality in the Learning Sciences: Digital Transformations of Teachers, Students, and Social Context

abstract: This article illustrates the utility of using virtual environments to transform social interaction via behavior and context, with the goal of improving learning in digital environments. We first describe the technology and theories behind virtual environments and then report data from 4 empirical studies. In Experiment 1, we demonstrated that teachers with augmented social perception (i.e., receiving visual warnings alerting them to students not receiving enough teacher eye gaze) were able to spread their attention more equally among students than teachers without augmented perception. In Experiments 2 and 3, we demonstrated that by breaking the rules of spatial proximity that exist in physical space, students can learn more by being in the center of the teacher's field of view (compared to the periphery) and by being closer to the teacher (compared to farther away). In Experiment 4, we demonstrated that inserting virtual co-learners who were either model students or distracting students changed the learn...


title: Pwm control of dual active bridge: Comprehensive analysis and experimental verification

abstract: The dual active bridge (DAB) topology is ideally suited for high power dc-dc conversion. This paper presents a comprehensive analysis and experimental results with PWM control of the DAB. The PWM control is in addition to phase shift modulation between the two H-bridges. The analysis includes PWM of one bridge at a time and of both bridges simultaneously. In the latter, five distinct modes arise based on the choice of PWM and load condition. The possibilities are analyzed for optimizing power density and efficiency for low load operation. An optimum PWM scheme is proposed that extends the soft-switching range, reduces rms and peak currents at low loads, and results in significant size reduction of the transformer. Experimental results are presented with a 10 kW prototype.


title: Sparse Learning of the Disease Severity Score for High-Dimensional Data

abstract: Learning disease severity scores automatically from collected measurements may aid in the quality of both healthcare and scientific understanding. Some steps in that direction have been taken and machine learning algorithms for extracting scoring functions from data have been proposed. Given the rapid increase in both quantity and diversity of data measured and stored, the large amount of information is becoming one of the challenges for learning algorithms. In this work, we investigated the direction of the problem where the dimensionality of measured variables is large. Learning the severity score in such cases brings the issue of which of measured features are relevant. We have proposed a novel approach by combining desirable properties of existing formulations, which compares favorably to alternatives in accuracy and especially in the robustness of the learned scoring function. The proposed formulation has a nonsmooth penalty that induces sparsity. This problem is solved by addressing a dual formulation which is smooth and allows an efficient optimization. The proposed approach might be used as an effective and reliable tool for both scoring function learning and biomarker discovery, as demonstrated by identifying a stable set of genes related to influenza symptoms’ severity, which are enriched in immune-related processes.


title: Business Intelligence for Enterprise Systems: A Survey

abstract: Business intelligence (BI) is the process of transforming raw data into useful information for more effective strategic, operational insights, and decision-making purposes so that it yields real business benefits. This new emerging technique can not only improve applications in enterprise systems and industrial informatics, respectively, but also play a very important role to bridge the connection between enterprise systems and industrial informatics. This paper was intended as a short introduction to BI with the emphasis on the fundamental algorithms and recent progress. In addition, we point out the challenges and opportunities to smoothly connect industrial informatics to enterprise systems for BI research.


title: Automatic categorization of medical images for content-based retrieval and data mining

abstract: Categorization of medical images means selecting the appropriate class for a given image out of a set of pre-defined categories. This is an important step for data mining and content-based image retrieval (CBIR). So far, published approaches are capable to distinguish up to 10 categories. In this paper, we evaluate automatic categorization into more than 80 categories describing the imaging modality and direction as well as the body part and biological system examined. Based on 6231 reference images from hospital routine, 85.5% correctness is obtained combining global texture features with scaled images. With a frequency of 97.7%, the correct class is within the best ten matches, which is sufficient for medical CBIR applications. q 2004 Elsevier Ltd. All rights reserved.


title: Design of a low power 0.25 µm CMOS comparator for sigma-delta Analog-to-Digital Converter

abstract: A CMOS comparator using dynamic latch, suitable for high-speed Analog-to-Digital Converter (ADC) with high speed and low power dissipation is presented. The design is intended to be implemented in Sigma-delta ADC. This circuit combines the good features of the resistive dividing comparator and the differential current sensing comparator. The design has been carried out in Tanner EDA tools, the schematic simulation is done using Schematic Editor (S-Edit) and layout simulation of the design is verified using Layout Editor (L-Edit) using 0.25µm CMOS technology. Simulation results are done with supply voltages of 1.6V, 1.8V and 2.0V respectively. It is found that the power is least dissipated in 1.6V which is 0.7899 mW, but it has the longest propagation delay of 0.715 ns. In contrast, the 2.0V supply produced 1.471 mW and a shorter delay of 0.550 ns.


title: Brain Tumor Detection Using Artificial Neural Network Fuzzy Inference System (ANFIS)

abstract: Manual classification of brain tumor is time devastating and bestows ambiguous results. Automatic image classification is emergent thriving research area in medical field. In the proposed methodology, features are extracted from raw images which are then fed to ANFIS (Artificial neural fuzzy inference system).ANFIS being neuro-fuzzy system harness power of both hence it proves to be a sophisticated framework for multiobject classification. A comprehensive feature set and fuzzy rules are selected to classify an abnormal image to the corresponding tumor type. This proposed technique is fast in execution, efficient in classification and easy in implementation.


M.: Incremental free-space carving for real-time 3d reconstruction. In: 3DPVT


title: Joint Semantic Segmentation and Depth Estimation with Deep Convolutional Networks

abstract: Multi-scale deep CNNs have been used successfully for problems mapping each pixel to a label, such as depth estimation and semantic segmentation. It has also been shown that such architectures are reusable and can be used for multiple tasks. These networks are typically trained independently for each task by varying the output layer(s) and training objective. In this work we present a new model for simultaneous depth estimation and semantic segmentation from a single RGB image. Our approach demonstrates the feasibility of training parts of the model for each task and then fine tuning the full, combined model on both tasks simultaneously using a single loss function. Furthermore we couple the deep CNN with fully connected CRF, which captures the contextual relationships and interactions between the semantic and depth cues improving the accuracy of the final results. The proposed model is trained and evaluated on NYUDepth V2 dataset outperforming the state of the art methods on semantic segmentation and achieving comparable results on the task of depth estimation.


title: The Peopling of the New World: Perspectives from Molecular Anthropology

abstract: ▪ Abstract A number of important insights into the peopling of the New World have been gained through molecular genetic studies of Siberian and Native American populations. These data indicate that the initial migration of ancestral Amerindian originated in south-central Siberia and entered the New World between 20,000–14,000 calendar years before present (cal yr BP). These early immigrants probably followed a coastal route into the New World, where they expanded into all continental regions. A second migration that may have come from the same Siberian region entered the Americas somewhat later, possibly using an interior route, and genetically contributed to indigenous populations from North and Central America. In addition, Beringian populations moved into northern North America after the last glacial maximum (LGM) and gave rise to Aleuts, Eskimos, and Na-Dene Indians.


title: An Assessment of Long-Term Changes in Anthropometric Dimensions: Secular Trends of U.S. Army Males

abstract: Abstract : This report describes long-term changes in the body dimensions within the Army population for 22 body dimensions in four racial/cultural groups: Whites, Blacks, Hispanics and Asian/Pacific Islanders. Individuals were grouped by birth year into 12 five-year cohorts, which span the years 1911 to 1970. Rates of change were calculated by regressing age-adjusted dimensions against cohort. Analyses of these relationships showed that almost all dimensions sustained statistically significant linear trends, the few exceptions being found within the Asian/Pacific Islanders group. Furthermore, except for the Asian/Pacific Islanders, the greatest relative rates of change were found in dimensions related to soft tissue development rather than skeletal dimensions. This pattern is consistent with the most recent American cultural emphasis on health and physical fitness. The markedly different patterns seen in Asian/ Pacific Islanders were best explained by immigration: they have experienced a linear increase in the proportion of foreign born members.


On the Representation and Estimation of


title: Digital forensic research: current state of the art

abstract: Digital forensics is the process of employing scientific principles and processes to analyze electronically stored information and determine the sequence of events which led to a particular incident. In this digital age, it is important for researchers to become aware of the recent developments in this dynamic field and understand scope for the future. The past decade has witnessed significant technological advancements to aid during a digital investigation. Many methodologies, tools and techniques have found their way into the field designed on forensic principles. Digital forensics has also witnessed many innovative approaches that have been explored to acquire and analyze digital evidence from diverse sources. In this paper, we review the research literature since 2000 and categorize developments in the field into four major categories. In recent years the exponential growth of technological has also brought with it some serious challenges for digital forensic research which is elucidated. Within each category, research is sub-classified into conceptual and practical advancements. We highlight the observations made by previous researchers and summarize the research directions for the future.


title: STEAP: simultaneous trajectory estimation and planning

abstract: We present a unified probabilistic framework for simultaneous trajectory estimation and planning. Estimation and planning problems are usually considered separately, however, within our framework we show that solving them simultaneously can be more accurate and efficient. The key idea is to compute the full continuous-time trajectory from start to goal at each time-step. While the robot traverses the trajectory, the history portion of the trajectory signifies the solution to the estimation problem, and the future portion of the trajectory signifies a solution to the planning problem. Building on recent probabilistic inference approaches to continuous-time localization and mapping and continuous-time motion planning, we solve the joint problem by iteratively recomputing the maximum a posteriori trajectory conditioned on all available sensor data and cost information. Our approach can contend with high-degree-of-freedom trajectory spaces, uncertainty due to limited sensing capabilities, model inaccuracy, the stochastic effect of executing actions, and can find a solution in real-time. We evaluate our framework empirically in both simulation and on a mobile manipulator.


title: A Reliable PUF in a Dual Function SRAM

abstract: An SRAM Physical Unclonable Function (SRAM-PUF) is a potential solution for lightweight secure key generation, and is particularly suitable for resource-constrained security devices. An SRAM-PUF is able to generate random and unique cryptographic keys based on start-up values (SUVs) by exploiting intrinsic manufacturing process variations. For cost efficiency the available on-chip SRAM in a system can be reused as a PUF. As CMOS technology scales down, ageing-induced Negative Bias Temperature Instability (NBTI) becomes more pronounced, resulting in asymmetric degradation of memory bit cells after prolonged storage of the same bit values. This causes unreliable SUVs for an SRAM-PUF. In this paper, we investigate the bit probabilities in an instruction cache and the effect on long-term reliability. We show that the signal probability in a 32-bit ARM instruction cache has a predictable pattern. Hence, we propose a bit selection technique to mitigate the NBTI effect when an instruction cache is used as a PUF. We show that this technique can reduce the predicted bit error in an SRAM-PUF from 14.18% to 5.58% over 5 years. Consequently, as the bit error reduces, the area overhead of the error-correction is about $6 \times$ smaller compared to that without a bit selection technique.


title: Enriching process models for business process compliance checking in ERP environments

abstract: In enterprise resource planning (ERP) environments the audit of business process compliance is a complex task as audit relevant context information about the ERP system like application controls (ACs) need to be considered to derive comprehensive audit results. Current compliance checking approaches neglect such information as it is not readily available in process models. Even if ACs are automatically analysed with audit software, the results still need to be linked to related processes. By now, this linking is not methodically supported. To address this gap this paper presents a method to automatically enrich process models with audit relevant information about ACs. The method consists of three phases: process model construction, automated analysis of ACs, and model enrichment. It utilizes two existing artefacts and combines them to provide a comprehensive basis for compliance checking. Moreover, the enriched process models can support auditors in conducting process audits in ERP environments.


title: Towards a scalable resource-driven approach for detecting repackaged Android applications

abstract: Repackaged Android applications (or simply apps) are one of the major sources of mobile malware and also an important cause of severe revenue loss to app developers. Although a number of solutions have been proposed to detect repackaged apps, the majority of them heavily rely on code analysis, thus suffering from two limitations: (1) poor scalability due to the billion opcode problem; (2) unreliability to code obfuscation/app hardening techniques. In this paper, we explore an alternative approach that exploits core resources, which have close relationships with codes, to detect repackaged apps. More precisely, we define new features for characterizing apps, investigate two kinds of algorithms for searching similar apps, and propose a two-stage methodology to speed up the detection. We realize our approach in a system named ResDroid and conduct large scale evaluation on it. The results show that ResDroid can identify repackaged apps efficiently and effectively even if they are protected by obfuscation or hardening systems.


title: Combined low-dose imatinib mesylate and paclitaxel lack synergy in an experimental model of extra-osseous hormone-refractory prostate cancer

abstract: OBJECTIVE ::: ::: To determine the efficacy of low-dose imatinib mesylate (STI571) alone or combined with a taxane (paclitaxel) in inhibiting the growth of experimental extra-osseous hormone-refractory prostrate cancer. ::: ::: ::: ::: MATERIALS AND METHODS ::: ::: Orthotopic PC3 prostate tumours were established in male severe combined-immunodeficient mice; on day 3 the mice were randomly assigned to one of four groups: paclitaxel 10 mg/kg intraperitoneally once a week; STI571 50 mg/kg once a day for 6/7 weekdays; combined paclitaxel and STI571; and vehicle-treated controls. On day 40, the primary prostate tumour and metastatic lymphadenopathy were removed and measured. Effects were correlated with tumour cell proliferation and microvessel density. ::: ::: ::: ::: RESULTS ::: ::: Paclitaxel reduced the mean tumour weight and volume by 21.3% (not significant) and 73.7% (P < 0.05), respectively, compared to controls, and reduced the number of lymph node metastases by 49.1% (P < 0.05) and mean lymph node size by 13.5% (not significant). Adding low-dose STI571 had a small additive effect on tumour weight and the incidence of lymph node metastases, but this was not significant compared to paclitaxel alone. STI571 alone did not inhibit tumour progression. Antitumour effects were associated with parallel changes in tumour cell proliferation with no significant changes in neo-angiogenesis. ::: ::: ::: ::: CONCLUSION ::: ::: Combined low-dose STI571 and paclitaxel had little synergy in this experimental model. Low-dose STI571 monotherapy was not effective in extra-osseous disease, apparently due to a site-specific failure to up-regulate β-platelet-derived growth factor receptor expression in prostate cancer cells and associated tumour stroma.


title: Information systems citation patterns from International Conference on Information Systems articles

abstract: Research patterns could enhance understanding of the Information Systems (IS) field. Citation analysis is the methodology commonly used to determine such research patterns. In this study, the citation methodology is applied to one of the top-ranked Information Systems conferences—International Conference on Information Systems (ICIS). Information is extracted from papers in the proceedings of ICIS 2000 to 2002. A total of 145 base articles and 4,226 citations are used. Research patterns are obtained using total citations, citations per journal or conference, and overlapping citations. We then provide the citation ranking of journals and conferences. We also examine the difference between the citation ranking in this study and the ranking of IS journals and IS conferences in other studies. Based on the comparison, we confirm that IS research is a multidisciplinary research area. We also identify the most cited papers and authors in the IS research area, and the organizations most active in producing papers in the top-rated IS conference. We discuss the findings and implications of the study.


title: Consolidative Technique Otoplasty: Comprehensive Approach in Combining Techniques

abstract: BACKGROUND ::: This study aimed to present the long-term results of the consolidative technique otoplasty (CTO). ::: ::: ::: METHODS ::: Patient age, sex, duration of follow-up, and how the deformity (bilateral-unilateral) occurred were determined. The kind of anesthesia, duration of surgery, surgical techniques used during the operation were reported, and the preoperative and postoperative distances between the ear and head were measured at four points (SUP: The highest point of the helix; SCA: the concha superior sticking points; ICA: stick point of Concha at the inferior; and lobular). Early- and late-term complications were recorded in the postoperative period. ::: ::: ::: RESULTS ::: A total of 63 (21M-42FM) patients whose average age of 21:41 ± 8.96 (7-41), 119 ears (Right: 58/Left: 61) were operated on with this technique between 2013 and 2015. Mean follow-up was 18:22 ± 9:42 months. Anterior scoring was performed for three patients; concha resection was performed in 17 patients. Fifty-five patients were operated on as primary otoplasties and eight were secondary otoplasties. When the preoperative and postoperative SUP, SCA, ICA, and lobule measurements for both ears were compared, the postoperative values were found to be significantly decreased (p < 0.001). ::: ::: ::: CONCLUSIONS ::: This technique may be an alternative in the repairs of prominent ears, and it provides an intervention opportunity for all the anatomical structures. ::: ::: ::: LEVEL OF EVIDENCE V ::: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .


title: An improved algorithm for basis path testing

abstract: Basis path testing is an important test method in white box testing. This method generates a set of linearly independent paths, which we called basis paths, from Control Flow Graph (CFG) and all the other paths can be expressed by them. However, when applying these basis paths to designing test cases, we will always find that some of them are infeasible when understanding from business logic. In this paper we put forward a new idea to simply CFG, at the same time to avoid the generation of infeasible paths in the set of basis paths.


title: A retrosynthetic biology approach to metabolic pathway design for therapeutic production

abstract: BackgroundSynthetic biology is used to develop cell factories for production of chemicals by constructively importing heterologous pathways into industrial microorganisms. In this work we present a retrosynthetic approach to the production of therapeutics with the goal of developing an in situ drug delivery device in host cells. Retrosynthesis, a concept originally proposed for synthetic chemistry, iteratively applies reversed chemical transformations (reversed enzyme-catalyzed reactions in the metabolic space) starting from a target product to reach precursors that are endogenous to the chassis. So far, a wider adoption of retrosynthesis into the manufacturing pipeline has been hindered by the complexity of enumerating all feasible biosynthetic pathways for a given compound.ResultsIn our method, we efficiently address the complexity problem by coding substrates, products and reactions into molecular signatures. Metabolic maps are represented using hypergraphs and the complexity is controlled by varying the specificity of the molecular signature. Furthermore, our method enables candidate pathways to be ranked to determine which ones are best to engineer. The proposed ranking function can integrate data from different sources such as host compatibility for inserted genes, the estimation of steady-state fluxes from the genome-wide reconstruction of the organism's metabolism, or the estimation of metabolite toxicity from experimental assays. We use several machine-learning tools in order to estimate enzyme activity and reaction efficiency at each step of the identified pathways. Examples of production in bacteria and yeast for two antibiotics and for one antitumor agent, as well as for several essential metabolites are outlined.ConclusionsWe present here a unified framework that integrates diverse techniques involved in the design of heterologous biosynthetic pathways through a retrosynthetic approach in the reaction signature space. Our engineering methodology enables the flexible design of industrial microorganisms for the efficient on-demand production of chemical compounds with therapeutic applications.


title: Cue-invariant detection of centre-surround discontinuity by V1 neurons in awake macaque monkey

abstract: Visual perception of an object depends on the discontinuity between the object and its background, which can be defined by a variety of visual features, such as luminance, colour and motion. While human object perception is largely cue invariant, the extent to which neural mechanisms in the primary visual cortex contribute to cue-invariant perception has not been examined extensively. Here we report that many VI neurons in the awake monkey are sensitive to the stimulus discontinuity between their classical receptive field (CRF) and non-classical receptive field (nCRF) regardless of the visual feature that defines the discontinuity. The magnitude of this sensitivity is strongly dependent on the strength of nCRF suppression of the cell. These properties of V1 neurons may contribute significantly to cue-invariant object perception.


title: 3D generic object categorization, localization and pose estimation

abstract: We propose a novel and robust model to represent and learn generic 3D object categories. We aim to solve the problem of true 3D object categorization for handling arbitrary rotations and scale changes. Our approach is to capture a compact model of an object category by linking together diagnostic parts of the objects from different viewing points. We emphasize on the fact that our "parts" are large and discriminative regions of the objects that are composed of many local invariant features. Instead of recovering a full 3D geometry, we connect these parts through their mutual homographic transformation. The resulting model is a compact summarization of both the appearance and geometry information of the object class. We propose a framework in which learning is done via minimal supervision compared to previous works. Our results on categorization show superior performances to state-of-the-art algorithms such as (Thomas et al., 2006). Furthermore, we have compiled a new 3D object dataset that consists of 10 different object categories. We have tested our algorithm on this dataset and have obtained highly promising results.


Dynamic bayesian networks: representation, inference and learning


title: Transformation of relational model to RDF model

abstract: The vision of the semantic Web is to give data on the Web a well-defined meaning by representing it in RDF and linking it to commonly accepted ontologies. Today, most data are stored in relational databases. To be able to use these data on the semantic Web we need a flexible but easy to use mechanism to convert relational data to RDF data. In this paper, we present an approach on transformation of relational model to RDF model. It goes through two steps including extracting the semantics of relational model and mapping to RDF model. Finally, we consider the quality of transformation and give the implementation results.


title: Rectenna performance under power-optimized waveform excitation

abstract: Power-optimized waveforms (POWs) have been shown to increase the power-conversion efficiency in energy harvesting circuits. This paper is the first attempt to study the effects of a POW on these circuits and determine how its design can affect circuit performance. As an example, a 5.8 GHz single-shunt rectenna was designed to investigate the variation of POW parameters. It was shown that there is an optimal range of subcarrier spacing to maximize POW gain and minimize voltage ripple. Furthermore, a relationship between the maximum number of equal energy subcarriers, ripple voltage, and circuit parameters has been determined.


title: Exposure: A Passive DNS Analysis Service to Detect and Report Malicious Domains

abstract: A wide range of malicious activities rely on the domain name service (DNS) to manage their large, distributed networks of infected machines. As a consequence, the monitoring and analysis of DNS queries has recently been proposed as one of the most promising techniques to detect and blacklist domains involved in malicious activities (e.g., phishing, spam, botnets command-and-control, etc.). EXPOSURE is a system we designed to detect such domains in real time, by applying 15 unique features grouped in four categories. We conducted a controlled experiment with a large, real-world dataset consisting of billions of DNS requests. The extremely positive results obtained in the tests convinced us to implement our techniques and deploy it as a free, online service. In this article, we present the Exposure system and describe the results and lessons learned from 17 months of its operation. Over this amount of time, the service detected over 100K malicious domains. The statistics about the time of usage, number of queries, and target IP addresses of each domain are also published on a daily basis on the service Web page.


A Minimalist ’ s Implementation of the 3-d Divide-and-Conquer Convex Hull Algorithm


title: Unleashing Tabular Content to Open Data: A Survey on PDF Table Extraction Methods and Tools

abstract: Portable Document Format (PDF) has been a popular way to exchange data in documents since Adobe introduced the format in 1993. Its report-like characteristic which preserves and prioritizes graphical visualization was part of the main publishing concerns among several segments including government agencies. In this way, tabular data started to be enclosed within PDF documents and disclosed in government portals. This situation, apart being surprisingly contradictory to data openness, is still found even in the major open data initiatives. It is estimated that roughly 13% of published files in some main open data portals around the world have their data made available in PDF. Thus, there is a need for effective tools capable of extracting tabular content (a main placeholder for data) from PDF to allow its data to be published in more open formats such as the well-known CSV which complies with accessible and machine processable open data principles. This paper aims at providing a structured and comprehensive overview of the research in tabular content extraction specifically from PDF documents as well as to provide an overview of most recent practical results in the literature. The contribution of this work goes beyond theoretical discussions by helping data practitioners to understand to what extent methods and tools regarding tabular content extraction from PDF can benefit the open data initiatives in practical and effective ways.


title: ECG Measurement Using Capacitive Coupling Electrodes for Man-Machine Emotional Communication

abstract: We have studied the capacitive coupling ECG measurement as a new emotional communication between man and machine. In this capacitive coupling measurement a metal plate is placed outside the body and this plate and the electric signal source inside body forms a condenser and the electric biosignal can be derived through this condenser. In this paper, we studied ECG measurement using two or only one capacitive coupling electrodes without any ground electrode for more convenient ECG monitoring. Three ECG measurement methods different in the setting of capacitive coupling electrodes and ground electrode were tested as follows; two isolated signal electrodes with an isolated ground electrode, two isolated signal electrodes without any ground electrode and one isolated signal electrode without any ground electrode. As a result, two isolated electrodes are enough to measure ECG whether with a ground electrode or not. Moreover ECG can be measured with only one signal electrode. Of course ECG waveform was noisy but "R" peak of ECG could be detected. Same tendencies were confirmed in all five subjects participated in experiments.


title: Robust nonrigid ICP using outlier-sparsity regularization

abstract: We show how to incorporate a statistical shape model into the nonrigid ICP framework, and propose a robust nonrigid ICP algorithm. In the nonrigid ICP framework, a template surface is represented by a set of points, and the shape of the template is parametrized by a transformation matrix per one template point. In the proposed method, the statistics of the matrices are estimated based on a set of training surfaces, and the statistical shape model is incorporated into the nonrigid ICP framework by modifying the representation of the stiffness of the template. The statistical shape model and a noise model make it possible to discriminate outliers from inliers in given targets. Our proposed method detects the outliers, which are not represented by the models appropriately, based on their sparseness. The detected outliers are automatically excluded from the target to be registered, and the template is deformed to fit the inliers only. As the result, the accuracy of the registration is improved. The performance of the proposed method is evaluated qualitatively and quantitatively using synthetic data and clinical CT images.


title: Implementing WebGIS on Hadoop: A case study of improving small file I/O performance on HDFS

abstract: Hadoop framework has been widely used in various clusters to build large scale, high performance systems. However, Hadoop distributed file system (HDFS) is designed to manage large files and suffers performance penalty while managing a large amount of small files. As a consequence, many web applications, like WebGIS, may not take benefits from Hadoop. In this paper, we propose an approach to optimize I/O performance of small files on HDFS. The basic idea is to combine small files into large ones to reduce the file number and build index for each file. Furthermore, some novel features such as grouping neighboring files and reserving several latest version of data are considered to meet the characteristics of WebGIS access patterns. Preliminary experiment results show that our approach achieves better performance.


title: Medicinal mushrooms in supportive cancer therapies: an approach to anti-cancer effects and putative mechanisms of action

abstract: Medicinal mushrooms have been valued as natural sources of bioactive compounds since times immemorial and have been recognized as potential immunomodulating and anti-cancer agents. Their consumption has consistently been shown to have beneficial effects on human health. Cancer is a generic term for several types of diseases that can be chronic and are responsible for a large number of deaths worldwide. Although there has been considerable progress in modern cancer therapy research, difficulties in understanding the molecular behavior of various types of cancers and the numerous side effects experienced by patients from treatments means that this whole subject area is still problematic. Thus, biological immunotherapy using natural bioactive compounds as supportive treatments in conventional cancer therapies has become in vogue. Bioactive metabolites isolated from medicinal mushrooms have shown potential successes in cancer treatment as biological immunotherapeutic agents that stimulate the immune system against cancer cells. They also act as an effective source of anti-cancer agents, capable of interfering with cellular signal transduction pathways linked to cancer development and progression. In this review we compile available data on the characteristics of medicinal mushrooms that appear to be particularly effective as biological immunotherapeutic agents. Major consideration is given to biological constituents and the putative mechanisms of action by which bioactive compounds act on the human body. Consideration is also given to the benefits that have been claimed for the use of mushrooms in treating cancer and the future prospects of using medicinal mushrooms as potent supportive candidate bioagents for treatment of cancers is discussed.


title: Cloud Federations in the Sky: Formation Game and Mechanism

abstract: The amount of computing resources required by current and future data-intensive applications is expected to increase dramatically, creating high demands for cloud resources. The cloud providers’ available resources may not be sufficient enough to cope with such demands. Therefore, the cloud providers need to reshape their business structures and seek to improve their dynamic resource scaling capabilities. Federated clouds offer a practical platform for addressing this service management issue. We introduce a cloud federation formation game that considers the cooperation of the cloud providers in offering cloud IaaS services. Based on the proposed federation formation game, we design a cloud federation formation mechanism that enables the cloud providers to dynamically form a cloud federation maximizing their profit. In addition, the proposed mechanism produces a stable cloud federation structure, that is, the participating cloud providers in the federation do not have incentives to break away from the federation. We analyze the performance of the proposed mechanism by performing extensive experiments. The results of the experiments show that the cloud federation obtained by our proposed mechanism is stable, yielding high profit for the participating cloud providers.


title: Privacy Capsules: Preventing Information Leaks by Mobile Apps

abstract: Preventing the leakage of user information via untrusted third-party apps is a key challenge in mobile privacy. We propose and evaluate privacy capsules (PCs), a platform execution model for mobile apps that prevents the flow of private information to untrusted parties by design. With PCs, apps execute in two sequential phases. In the unsealed phase, the app has no access to sensitive input but full access to untrusted network resources. In the sealed state, the untrusted app has access to sensitive input, but can no longer communicate with untrusted resources. Privacy capsules are implemented by the mobile platform, are language independent, and require few changes to apps. Using a prototype PC implementation in Android, we show that PCs have low performance and energy overhead, and are suitable for a large class of apps.


Some Connectivity Concepts in Bipolar Fuzzy Graphs


title: The dark side of social media: Associations between the Dark Triad of personality, self-disclosure online and selfie-related behaviours

abstract: The present study investigated the relations between the Dark Triad personality traits (i.e., Machiavellianism, narcissism, and psychopathy), distinct dimensions of self-disclosure online (i.e., honesty, amount, positive valence, and intent) and selfie related-behaviours (taking, posting and editing selfies). The results indicated, that all three Dark Triad components were positively correlated with posting and editing selfies on social networking sites (SNSs). However, multiple regression analysis demonstrated that only narcissism predicted selfie-related behaviours. Narcissism and Machiavellianism were positively related to the amount of personal information disclosed online and the tendency to self-disclose intentionally in computer-mediated communication (CMC). Moreover, we found no significant correlations between the perceived controllability of Internet communication and two types of self-promotion in the Internet (self-disclosure online and selfie-related behaviours). Our findings demonstrate the importance of analysing the "dark" personality traits in the context of self-promotional behaviours in social media.


NITE: A Neural Inductive Teaching Framework for Domain Specific NER


title: Analysis and simulation of a three-phase push-pull/flyback interleaved bidirectional dc-dc converter

abstract: This paper presents a new bidirectional dc/dc converter topology, which is composed of three-phase push-pull and interleaved flyback converter. The two directions operation of the proposed converters is analyzed and explained even as the DC transfer function of the circuit versus the duty ratio has been presented. The main advantages of this topology are high switching frequency, lower input current ripple, smaller output capacitor, less number of switches and the frequency of output voltage and input current ripple are three times the switching frequency. A simulation model is developed in the OrCAD Capture simulator. Then, the simulation results will verify the validity of the mathematical and theoretical analysis.


title: A Framework in CRM Customer Lifecycle: Identify Downward Trend and Potential Issues Detection

abstract: Customer retention is one of the primary goals in the area of customer relationship management. A mass of work exists in which machine learning models or business rules are established to predict churn. However, targeting users at an early stage when they start to show a downward trend is a better strategy. In downward trend prediction, the reasons why customers show a downward trend is of great interest in the industry as it helps the business to understand the pain points that customers suffer and to take early action to prevent them from churning. A commonly used method is to collect feedback from customers by either aggressively reaching out to them or by passively hearing from them. However, it is believed that there are a large number of customers who have unpleasant experiences and never speak out. In the literature, there is limited research work that provides a comprehensive and scientific approach to identify these "silent suffers". In this study, we propose a novel two-part framework: developing the downward prediction process and establishing the methodology to identify the reasons why customers are in the downward trend. In the first prediction part, we focus on predicting the downward trend, which is an earlier stage of the customer lifecycle compared to churn. In the second part, we propose an approach to figuring out the cause (of the downward trend) based on a causal inference method and semi-supervised learning. The proposed approach is capable of identifying potential silent sufferers. We take bad shopping experiences as inputs to develop the framework and validate it via a marketing A/B test in the real world. The test readout demonstrates the effectiveness of the framework by driving 88.5% incremental lift in purchase volume.


title: Adaptive vibrotactile flow rendering of 2.5D surface features on touch screen with multiple fingertip interfaces

abstract: This study presents a new vibrotactile fingertip interface and a rendering algorithm for 2.5D surface features on a touch screen. The fingertip device has four piezoelectric actuators surrounding both sides of a user's finger. The proposed algorithm generates a time-varying vibration flow in-between actuators, in which their intensities and durations adaptively changes according to local geometry under touch point. The proposed approach can provide separate effects on individual fingers by using multiple interfaces. The experiments revealed that a user can reliably distinguish height of virtual bump by the levels of 2.5+ out of 4, for both single and multi-touch interactions.


title: Object Calibration for Augmented Reality

abstract: Augmented reality involves the use of models and their associated renderings to supplement information in a real scene. In order for this information to be relevant or meaningful, the models must be positioned and displayed in such a way that they align with their corresponding real objects. For practical reasons this alignment cannot be known a priori, and cannot be hard-wired into a system. Instead a simple, reliable alignment or calibration process is performed so that computer models can be accurately registered with their real-life counterparts. We describe the design and implementation of such a process and we show how it can be used to create convincing interactions between real and virtual objects.


title: Natural Scene Character Recognition Using Robust PCA and Sparse Representation

abstract: Natural scene character recognition is challenging due to the cluttered background, which is hard to separate from text. In this paper, we propose a novel method for robust scene character recognition. Specifically, we first use robust principal component analysis (PCA) to denoise character image by recovering the missing low-rank component and filtering out the sparse noise term, and then use a simple Histogram of oriented Gradient (HOG) to perform image feature extraction, and finally, use a sparse representation based classifier for recognition. In experiments on four public datasets, namely the Char74K dataset, ICADAR 2003 robust reading dataset, Street View Text (SVT) dataset and IIIT5K-word dataset, our method was demonstrated to be competitive with the state-of-the-art methods.


title: Benchmarking State-of-the-Art Deep Learning Software Tools

abstract: Deep learning has been shown as a successful machine learning method for a variety of tasks, and its popularity results in numerous open-source deep learning software tools. Training a deep network is usually a very time-consuming process. To address the computational challenge in deep learning, many tools exploit hardware features such as multi-core CPUs and many-core GPUs to shorten the training time. However, different tools exhibit different features and running performance when training different types of deep networks on different hardware platforms, which makes it difficult for end users to select an appropriate pair of software and hardware. In this paper, we aim to make a comparative study of the state-of-the-art GPU-accelerated deep learning software tools, including Caffe, CNTK, MXNet, TensorFlow, and Torch. We first benchmark the running performance of these tools with three popular types of neural networks on two CPU platforms and three GPU platforms. We then benchmark some distributed versions on multiple GPUs. Our contribution is two-fold. First, for end users of deep learning tools, our benchmarking results can serve as a guide to selecting appropriate hardware platforms and software tools. Second, for software developers of deep learning tools, our in-depth analysis points out possible future directions to further optimize the running performance.


title: Simultaneous tracking & activity recognition (star) using many anonymous, binary sensors

abstract: In this paper we introduce the simultaneous tracking and activity recognition (STAR) problem, which exploits the synergy between location and activity to provide the information necessary for automatic health monitoring. Automatic health monitoring can potentially help the elderly population live safely and independently in their own homes by providing key information to caregivers. Our goal is to perform accurate tracking and activity recognition for multiple people in a home environment. We use a “bottom-up” approach that primarily uses information gathered by many minimally invasive sensors commonly found in home security systems. We describe a Rao-Blackwellised particle filter for room-level tracking, rudimentary activity recognition (i.e., whether or not an occupant is moving), and data association. We evaluate our approach with experiments in a simulated environment and in a real instrumented home.


title: Tweets and Votes: A Study of the 2011 Singapore General Election

abstract: This study focuses on the uses of Twitter during the elections, examining whether the messages posted online are reflective of the climate of public opinion. Using Twitter data obtained during the official campaign period of the 2011 Singapore General Election, we test the predictive power of tweets in forecasting the election results. In line with some previous studies, we find that during the elections the Twitter sphere represents a rich source of data for gauging public opinion and that the frequency of tweets mentioning names of political parties, political candidates and contested constituencies could be used to make predictions about the share of votes at the national level, although the accuracy of the predictions was significantly lower that in the studies done in Germany and the UK. At the level of constituency the predictive power of tweets was much weaker, although still better than chance. The findings suggest that the context in which the elections take place also matters, and that issues like media freedoms, competitiveness of the elections and specifics of the electoral system may lead to certain over- and under-estimations of voting sentiment. The implications for future research are discussed.


title: Workflow scheduling in cloud: a survey

abstract: To program in distributed computing environments such as grids and clouds, workflow is adopted as an attractive paradigm for its powerful ability in expressing a wide range of applications, including scientific computing, multi-tier Web, and big data processing applications. With the development of cloud technology and extensive deployment of cloud platform, the problem of workflow scheduling in cloud becomes an important research topic. The challenges of the problem lie in: NP-hard nature of task-resource mapping; diverse QoS requirements; on-demand resource provisioning; performance fluctuation and failure handling; hybrid resource scheduling; data storage and transmission optimization. Consequently, a number of studies, focusing on different aspects, emerged in the literature. In this paper, we firstly conduct taxonomy and comparative review on workflow scheduling algorithms. Then, we make a comprehensive survey of workflow scheduling in cloud environment in a problem---solution manner. Based on the analysis, we also highlight some research directions for future investigation.