title: Acoustic feature extraction by statistics based local binary pattern for environmental sound classification

abstract: Classification of environmental sounds is a fundamental procedure for a wide range of real-world applications. In this paper, we propose a novel acoustic feature extraction method for classifying the environmental sounds. The proposed method is motivated from the image processing technique, local binary pattern (LBP), and works on a spectrogram which forms two-dimensional (time-frequency) data like an image. Since the spectrogram contains noisy pixel values, for improving classification performance, it is crucial to extract the features which are robust to the fluctuations in pixel values. We effectively incorporate the local statistics, mean and standard deviation on local pixels, to establish robust LBP. In addition, we provide the technique of L2-Hellinger normalization which is efficiently applied to the proposed features so as to further enhance the discriminative power while increasing the robustness. In the experiments on environmental sound classification using RWCP dataset that contains 105 sound categories, the proposed method produces the superior performance (98.62%) compared to the other methods, exhibiting significant improvements over the standard LBP method as well as robustness to noise and low computation time.


Reproductive performance of marmosets.


title: Designing of Robust Image Steganography Technique Based on LSB Insertion and Encryption

abstract: This paper discusses the design of a Robust image steganography technique based on LSB (Least Significant Bit) insertion and RSA encryption technique. Steganography is the term used to describe the hiding of data in images to avoid detection by attackers. Steganalysis is the method used by attackers to determine if images have hidden data and to recover that data. The application discussed in this paper ranks images in a users library based on their suitability as cover objects for some data. By matching data to an image, there is less chance of an attacker being able to use steganalysis to recover the data. Before hiding the data in an image the application first encrypts it. The steganography method proposed in this paper and illustrated by the application is superior to that used by current steganography tools.


title: Item Response Modeling of Forced-Choice Questionnaires

abstract: Multidimensional forced-choice formats can significantly reduce the impact of numerous response biases typically associated with rating scales. However, if scored with classical methodology, these questionnaires produce ipsative data, which lead to distorted scale relationships and make comparisons between individuals problematic. This research demonstrates how item response theory (IRT) modeling may be applied to overcome these problems. A multidimensional IRT model based on Thurstone’s framework for comparative data is introduced, which is suitable for use with any forced-choice questionnaire composed of items fitting the dominance response model, with any number of measured traits, and any block sizes (i.e., pairs, triplets, quads, etc.). Thurstonian IRT models are normal ogive models with structured factor loadings, structured uniquenesses, and structured local dependencies. These models can be straightforwardly estimated using structural equation modeling (SEM) software Mplus. A number of simulation studies are performed to investigate how latent traits are recovered under various forced-choice designs and provide guidelines for optimal questionnaire design. An empirical application is given to illustrate how the model may be applied in practice. It is concluded that when the recommended design guidelines are met, scores estimated from forced-choice questionnaires with the proposed methodology reproduce the latent traits well.


title: Labeled Faces in the Wild: A Survey

abstract: In 2007, Labeled Faces in the Wild was released in an effort to spur research in face recognition, specifically for the problem of face verification with unconstrained images. Since that time, more than 50 papers have been published that improve upon this benchmark in some respect. A remarkably wide variety of innovative methods have been developed to overcome the challenges presented in this database. As performance on some aspects of the benchmark approaches 100 % accuracy, it seems appropriate to review this progress, derive what general principles we can from these works, and identify key future challenges in face recognition. In this survey, we review the contributions to LFW for which the authors have provided results to the curators (results found on the LFW results web page). We also review the cross cutting topic of alignment and how it is used in various methods. We end with a brief discussion of recent databases designed to challenge the next generation of face recognition algorithms.


title: Some Simple Economics of Crowdfunding

abstract: Executive SummaryIt is not surprising that the financing of early-stage creative projects and ventures is typically geographically localized since these types of funding decisions are usually predicated on personal relationships and due diligence requiring face-to-face interactions in response to high levels of risk, uncertainty, and information asymmetry. So, to economists, the recent rise of crowdfunding—raising capital from many people through an online platform—which offers little opportunity for careful due diligence and involves not only friends and family but also many strangers from near and far, is initially startling. On the eve of launching equity-based crowdfunding, a new market for early-stage finance in the United States, we provide a preliminary exploration of its underlying economics. We highlight the extent to which economic theory, in particular transaction costs, reputation, and market design, can explain the rise of nonequity crowdfunding and offer a framework for speculating on how eq...


title: Use of Computer Technology To Help Students with Special Needs

abstract: Millions of students across the United States cannot benefit fully from a traditional educational program because they have a disability that impairs their ability to participate in a typical classroom environment. For these students, computer-based technologies can play an especially important role. Not only can computer technology facilitate a broader range of educational activities to meet a variety of needs for students with mild learning disorders, but adaptive technology now exists than can enable even those students with severe disabilities to become active learners in the classroom alongside their peers who do not have disabilities. This article provides an overview of the role computer technology can play in promoting the education of children with special needs within the regular classroom. For example, use of computer technology for word processing, communication, research, and multimedia projects can help the three million students with specific learning and emotional disorders keep up with their nondisabled peers. Computer technology has also enhanced the development of sophisticated devices that can assist the two million students with more severe disabilities in overcoming a wide range of limitations that hinder classroom participation––from speech and hearing impairments to blindness and severe physical disabilities. However, many teachers are not adequately trained on how to use technology effectively in their classrooms, and the cost of the technology is a serious consideration for all schools. Thus, although computer technology has the potential to act as an equalizer by freeing many students from their disabilities, the barriers of inadequate training and cost must first be overcome before more widespread use can become a reality.


title: Just Enough Requirements Traceability

abstract: Even though traceability is legally required in most safety critical software applications and is a recognized component of many software process improvement initiatives, organizations continue to struggle to implement it in a cost-effective manner. This panel addresses the problems and challenges of requirements traceability and asks questions such as "How much traceability is enough?" and "What kinds of traceability provide cost effective solutions?" Traditional, automated, and lean traceability methods are all discussed.


title: ImageNet pre-trained models with batch normalization

abstract: Convolutional neural networks (CNN) pre-trained on ImageNet are the backbone of most state-of-the-art approaches. In this paper, we present a new set of pre-trained models with popular state-of-the-art architectures for the Caffe framework. The first release includes Residual Networks (ResNets) with generation script as well as the batch-normalization-variants of AlexNet and VGG19. All models outperform previous models with the same architecture. The models and training code are available at this http URL and this https URL


title: Using Multiple Segmentations to Discover Objects and their Extent in Image Collections

abstract: Given a large dataset of images, we seek to automatically determine the visually similar object and scene classes together with their image segmentation. To achieve this we combine two ideas: (i) that a set of segmented objects can be partitioned into visual object classes using topic discovery models from statistical text analysis; and (ii) that visual object classes can be used to assess the accuracy of a segmentation. To tie these ideas together we compute multiple segmentations of each image and then: (i) learn the object classes; and (ii) choose the correct segmentations. We demonstrate that such an algorithm succeeds in automatically discovering many familiar objects in a variety of image datasets, including those from Caltech, MSRC and LabelMe.


title: Flow-based fabrication: An integrated computational workflow for design and digital additive manufacturing of multifunctional heterogeneously structured objects

abstract: Structural hierarchy and material organization in design are traditionally achieved by combining discrete homogeneous parts into functional assemblies where the shape or surface is the determining factor in achieving function. In contrast, biological structures express higher levels of functionality on a finer scale through volumetric cellular constructs that are heterogeneous and complex. Despite recent advancements in additive manufacturing of functionally graded materials, the limitations associated with computational design and digital fabrication of heterogeneous materials and structures frame and limit further progress. Conventional computer-aided design tools typically contain geometric and topologic data of virtual constructs, but lack robust means to integrate material composition properties within virtual models. We present a seamless computational workflow for the design and direct digital fabrication of multi-material and multi-scale structured objects. The workflow encodes for and integrates domain-specific meta-data relating to local, regional and global feature resolution of heterogeneous material organizations. We focus on water-based materials and demonstrate our approach by additively manufacturing diverse constructs associating shape-informing variable flow rates and material properties to mesh-free geometric primitives. The proposed workflow enables virtual-to-physical control of constructs where structural, mechanical and optical gradients are achieved through a seamless design-to-fabrication tool with localized control. An enabling technology combining a robotic arm and a multi-syringe multi nozzle deposition system is presented. Proposed methodology is implemented and full-scale demonstrations are included. Display Omitted


title: Cognitive load theory: implications of cognitive load theory on the design of learning

abstract: Abstract C ognitive load theory (CLT) can provide guidelines to assist in the presentation of information in a manner that encourages learner activities that optimise intellectual performance. It is based on a cognitive architecture that consists of a limited working memory, with partly independent processing units for visual and audio information, which interacts with an unlimited long-term memory. According to the theory, the limitations of working memory can be circumvented by coding multiple elements of information as one element in cognitive schemata, by automating rules, and by using more than one presentation modality. This special issue consists of six articles from four countries and three continents on the instructional implications of CLT. The articles cover presenting instructional techniques for increasing germane CL in studying worked examples (van Merrienboer, Schuurman, De Croock, & Paas), effects of example elaboration training on decreasing cognitive interference and overload (Stark, Mandl, Gruber, & Renkl), CLT-based instructional design when dealing with very high element interactivity material (Pollock, Chandler, & Sweller), effects of worked examples on CL in older learners (Van Gerven, Paas, & Schmidt), a cognitive theory of multimedia learning (Mayer & Moreno), and the use of external representations to help manage CL in Computer Supported Collaborative Learning environments (Van Bruggen, Kirschner, & Jochems).


title: A Software Architecture for Distributed Geospatial Decision Support Systems

abstract: The National Agricultural Decision Support System (NADSS) is a joint project with the U.S. Department of Agriculture and the University of Nebraska-Lincoln. The initial focus of the NADSS project is to improve the quality and accessibility of drought related knowledge, information, and data for drought risk management. At the core of the NADSS project is a software architecture for distributed geospatial decision support systems that consists of four distributed layers: the data layer, the information layer, the knowledge layer, and the presentation layer. This work presents that architecture as well as the benefits and impact of the architecture and the NADSS project.


title: Neural Network-Based Face Detection

abstract: We present a neural network-based face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We use a bootstrap algorithm for training the networks, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting non-face training examples, which must be chosen to span the entire space of non-face images. Comparisons with other state-of-the-art face detection systems are presented; our system has better performance in terms of detection and false-positive rates.


title: Medication-related osteonecrosis of the jaw: Clinical and practical guidelines

abstract: Medication-related osteonecrosis of the jaw (MRONJ) is a severe adverse drug reaction, consisting of progressive bone destruction in the maxillofacial region of patients. ONJ can be caused by two pharmacological agents: Antiresorptive (including bisphosphonates (BPs) and receptor activator of nuclear factor kappa-B ligand inhibitors) and antiangiogenic. MRONJ pathophysiology is not completely elucidated. There are several suggested hypothesis that could explain its unique localization to the jaws: Inflammation or infection, microtrauma, altered bone remodeling or over suppression of bone resorption, angiogenesis inhibition, soft tissue BPs toxicity, peculiar biofilm of the oral cavity, terminal vascularization of the mandible, suppression of immunity, or Vitamin D deficiency. Dental screening and adequate treatment are fundamental to reduce the risk of osteonecrosis in patients under antiresorptive or antiangiogenic therapy, or before initiating the administration. The treatment of MRONJ is generally difficult and the optimal therapy strategy is still to be established. For this reason, prevention is even more important. It is suggested that a multidisciplinary team approach including a dentist, an oncologist, and a maxillofacial surgeon to evaluate and decide the best therapy for the patient. The choice between a conservative treatment and surgery is not easy, and it should be made on a case by case basis. However, the initial approach should be as conservative as possible. The most important goals of treatment for patients with established MRONJ are primarily the control of infection, bone necrosis progression, and pain. The aim of this paper is to represent the current knowledge about MRONJ, its preventive measures and management strategies.


title: Relationship-based access control: protection model and policy language

abstract: Social Network Systems pioneer a paradigm of access control that is distinct from traditional approaches to access control. Gates coined the term Relationship-Based Access Control (ReBAC) to refer to this paradigm. ReBAC is characterized by the explicit tracking of interpersonal relationships between users, and the expression of access control policies in terms of these relationships. This work explores what it takes to widen the applicability of ReBAC to application domains other than social computing. To this end, we formulate an archetypical ReBAC model to capture the essence of the paradigm, that is, authorization decisions are based on the relationship between the resource owner and the resource accessor in a social network maintained by the protection system. A novelty of the model is that it captures the contextual nature of relationships. We devise a policy language, based on modal logic, for composing access control policies that support delegation of trust. We use a case study in the domain of Electronic Health Records to demonstrate the utility of our model and its policy language. This work provides initial evidence to the feasibility and utility of ReBAC as a general-purpose paradigm of access control.


title: Mobility Management for Femtocells in LTE-Advanced: Key Aspects and Survey of Handover Decision Algorithms

abstract: Support of femtocells is an integral part of the Long Term Evolution - Advanced (LTE-A) system and a key enabler for its wide adoption in a broad scale. Femtocells are short-range, low-power and low-cost cellular stations which are installed by the consumers in an unplanned manner. Even though current literature includes various studies towards understanding the main challenges of interference management in the presence of femtocells, little light has been shed on the open issues of mobility management (MM) in the two-tier macrocell-femtocell network. In this paper, we provide a comprehensive discussion on the key aspects and research challenges of MM support in the presence of femtocells, with the emphasis given on the phases of a) cell identification, b) access control, c) cell search, d) cell selection/reselection, e) handover (HO) decision, and f) HO execution. A detailed overview of the respective MM procedures in the LTE-A system is also provided to better comprehend the solutions and open issues posed in real-life systems. Based on the discussion for the HO decision phase, we subsequently survey and classify existing HO decision algorithms for the two-tier macrocell-femtocell network, depending on the primary HO decision criterion used. For each class, we overview up to three representative algorithms and provide detailed flowcharts to describe their fundamental operation. A comparative summary of the main decision parameters and key features of selected HO decision algorithms concludes this work, providing insights for future algorithmic design and standardization activities.


title: Full-Coverage and k-Connectivity (k=14,6) Three Dimensional Networks

abstract: In this paper, we study the problem of constructing full-coverage three dimensional networks with multiple connectivity. We design a set of patterns for full coverage and two representative connectivity requirements, i.e. 14- and 6-connectivity. We prove their optimality under any ratio of the communication range over the sensing range among regular lattice deployment patterns. We also conduct a study on the proposed patterns under practical settings. To our knowledge, our work is the first one that provides deployment patterns with proven optimality that achieve both coverage and connectivity in three dimensional networks.


title: Throughput optimization for streaming applications on CPU-FPGA heterogeneous systems

abstract: Streaming processing is an important technology that finds applications in networking, multimedia, signal processing, etc. However, it is very challenging to design and implement streaming applications as they impose complex constraints. First, the tasks involved in the streaming applications must complete the computation under a latency constraint. Second, streaming systems are built under more and more stringent power budget. Hence, power capping technique is employed to manage the power consumption for streaming systems. To accommodate these needs, heterogeneous systems that consist of CPUs and FPGAs are becoming increasingly popular due to their performance and power benefits. In this paper, we optimize the throughput for streaming applications on CPU-FPGA heterogeneous system under latency and power constraints. We develop two algorithms to map the tasks onto the heterogeneous system and order their execution by exploiting the heterogeneity in architectural capabilities and task characteristics. We also employ pipelining to improve the throughput by overlapping the execution of different frames and use frequency scaling to adjust the execution of tasks for power saving. Experiments using a variety of streaming applications show that our heterogeneous solution can successfully meet the latency and power constraints for the cases where the CPU implementation fails. Furthermore, our technique can improve the throughput by 37.32% on average.


title: Novel capacitor-isolated power converter

abstract: This paper presented a novel power conversion method of realizing the galvanic isolation by dual safety capacitors (Y-cap) instead of conventional transformer. With limited capacitance of the Y capacitor, series resonant is proposed to achieve the power transfer. The basic concept is to control the power path impedance, which blocks the dominant low-frequency part of touch current and let the high-frequency power flow freely. Conceptual analysis, simulation and design considerations are mentioned in this paper. An 85W AC/AC prototype is designed and verified to substitute the isolation transformer of a CCFL LCD TV backlight system. Compared with the conventional transformer isolation, the new method is proved to meet the function and safety requirements of its specification while has higher efficiency and smaller size.


title: Asynchronous indoor positioning system based on visible light communications

abstract: Indoor positioning has become an attractive research topic within the past two decades. However, no satisfying solution has been found with consideration of both accuracy and system complexity. Recently, research on visible light communications (VLC) offer new opportunities in realizing accurate indoor positioning with relatively simple system configuration. An indoor positioning system based on VLC technology is intro- duced, with no synchronization requirement on the transmitters. Simulation results show that, with over 95% confidence, the target receiver can be located with an accuracy of 5.9 cm, assuming indirect sunlight exposure and proper installation of light-emitting diode bulbs. © 2014 Society of Photo-Optical Instrumentation Engineers (SPIE) (DOI: 10.1117/ 1.OE.53.4.045105)


title: The multiscale competitive code via sparse representation for palmprint verification

abstract: Palm lines are the most important features for palmprint recognition. They are best considered as typical multiscale features, where the principal lines can be represented at a larger scale while the wrinkles at a smaller scale. Motivated by the success of coding-based palmprint recognition methods, this paper investigates a compact representation of multiscale palm line orientation features, and proposes a novel method called the sparse multiscale competitive code (SMCC). The SMCC method first defines a filter bank of second derivatives of Gaussians with different orientations and scales, and then uses the l 1 -norm sparse coding to obtain a robust estimation of the multiscale orientation field. Finally, a generalized competitive code is used to encode the dominant orientation. Experimental results show that the SMCC achieves higher verification accuracy than state-of-the-art palmprint recognition methods, yet uses a smaller template size than other multiscale methods.


title: Quality versus efficiency in document scoring with learning-to-rank models

abstract: Characterization the quality versus cost trade-off of Learning-to-Rank models.QuickRank: a public-domain Learning-to-Rank learning and evaluation framework.a new measure, named AuQC, for the evaluation of LtR algorithms. Learning-to-Rank (LtR) techniques leverage machine learning algorithms and large amounts of training data to induce high-quality ranking functions. Given a set of documents and a user query, these functions are able to precisely predict a score for each of the documents, in turn exploited to effectively rank them. Although the scoring efficiency of LtR models is critical in several applications - e.g., it directly impacts on response time and throughput of Web query processing - it has received relatively little attention so far.The goal of this work is to experimentally investigate the scoring efficiency of LtR models along with their ranking quality. Specifically, we show that machine-learned ranking models exhibit a quality versus efficiency trade-off. For example, each family of LtR algorithms has tuning parameters that can influence both effectiveness and efficiency, where higher ranking quality is generally obtained with more complex and expensive models. Moreover, LtR algorithms that learn complex models, such as those based on forests of regression trees, are generally more expensive and more effective than other algorithms that induce simpler models like linear combination of features.We extensively analyze the quality versus efficiency trade-off of a wide spectrum of state-of-the-art LtR, and we propose a sound methodology to devise the most effective ranker given a time budget. To guarantee reproducibility, we used publicly available datasets and we contribute an open source C++ framework providing optimized, multi-threaded implementations of the most effective tree-based learners: Gradient Boosted Regression Trees (GBRT), Lambda-Mart (λ-MART), and the first public-domain implementation of Oblivious Lambda-Mart (źλ-MART), an algorithm that induces forests of oblivious regression trees.We investigate how the different training parameters impact on the quality versus efficiency trade-off, and provide a thorough comparison of several algorithms in the quality-cost space. The experiments conducted show that there is not an overall best algorithm, but the optimal choice depends on the time budget.


title: Frequency Tunability and Spectral Control in Terahertz Quantum Cascade Lasers With Phase-Adjusted Finite-Defect-Site Photonic Lattices

abstract: This dataset contains experimental data presented in the paper titled 'Frequency tunability and spectral control in terahertz quantum cascade lasers with phase-adjusted finite-defect-site photonic lattices'. The effect of finite-defect-site photonic lattices (PLs) on the spectral emission of terahertz (THz) frequency quantum cascade lasers (QCLs), is studied both theoretically and experimentally. A central π-phase adjusted defect is incorporated into the PL to slectively favor emission within the photonic bandgap. The effect of the PL duty cycle and the longitudinal position of such PLs relative the QCL cavity is also investigated, and is used to demonstrate three distinct spectral behaviors: single mode emission from QCLs in the range 2.2−5 THz, with a side-mode suppression ratio of 40 dB and exhibiting continuous frequency tuning over >8 GHz; discrete tuning between two engineered emission modes separated by ~40 GHz; and multiple-mode emission with an engineered logitudinal frequency spacing between the emission lines.


title: Mining Version Histories for Detecting Code Smells

abstract: Code smells are symptoms of poor design and implementation choices that may hinder code comprehension, and possibly increase change- and fault-proneness. While most of the detection techniques just rely on structural information, many code smells are intrinsically characterized by how code elements change over time. In this paper, we propose H istorical I nformation for S mell de T ection (HIST), an approach exploiting change history information to detect instances of five different code smells, namely Divergent Change, Shotgun Surgery, Parallel Inheritance, Blob, and Feature Envy. We evaluate HIST in two empirical studies. The first, conducted on 20 open source projects, aimed at assessing the accuracy of HIST in detecting instances of the code smells mentioned above. The results indicate that the precision of HIST ranges between 72 and 86 percent, and its recall ranges between 58 and 100 percent. Also, results of the first study indicate that HIST is able to identify code smells that cannot be identified by competitive approaches solely based on code analysis of a single system’s snapshot. Then, we conducted a second study aimed at investigating to what extent the code smells detected by HIST (and by competitive code analysis techniques) reflect developers’ perception of poor design and implementation choices. We involved 12 developers of four open source projects that recognized more than 75 percent of the code smell instances identified by HIST as actual design/implementation problems.


title: ERC -- A Theory of Equity, Reciprocity and Competition

abstract: We demonstrate that a simple model, constructed on the premise that people are motivated by both their pecuniary payoff and their relative payoff standing, organizes a large and seemingly disparate set of laboratory observations as one consistent pattern. The model is incomplete information but nevertheless posed entirely in terms of directly observable variables. The model explains observations from games where equity is thought to be a factor, such as ultimatum and dictator, games where reciprocity is thought to play a role, such as the prisoner's dilemma and gift exchange, and games where competitive behavior is observed, such as Bertrand markets.


title: Omega Model for Human Detection and Counting for application in Smart Surveillance System

abstract: Driven by the significant advancements in technology and social issues such as security management, there is a strong need for Smart Surveillance System in our society today. One of the key features of a Smart Surveillance System is efficient human detection and counting such that the system can decide and label events on its own. In this paper we propose a new, novel and robust model, The Omega Model, for detecting and counting human beings present in the scene. The proposed model employs a set of four distinct descriptors for identifying the unique features of the head, neck and shoulder regions of a person. This unique head neck shoulder signature given by the Omega Model exploits the challenges such as inter person variations in size and shape of peoples head, neck and shoulder regions to achieve robust detection of human beings even under partial occlusion, dynamically changing background and varying illumination conditions. After experimentation we observe and analyze the influences of each of the four descriptors on the system performance and computation speed and conclude that a weight based decision making system produces the best results. Evaluation results on a number of images indicate the validation of our method in actual situation.


title: Joint sizing and adaptive independent gate control for FinFET circuits operating in multiple voltage regimes using the logical effort method

abstract: FinFET has been proposed as an alternative for bulk CMOS in current and future technology nodes due to more effective channel control, reduced random dopant fluctuation, high ON/OFF current ratio, lower energy consumption, etc. Key characteristics of FinFET operating in the sub/near-threshold region are very different from those in the strong-inversion region. This paper first introduces an analytical transregional FinFET model with high accuracy in both sub- and near-threshold regimes. Next, the paper extends the well-known and widely-adopted logical effort delay calculation and optimization method to FinFET circuits operating in multiple voltage (sub/near/super-threshold) regimes. More specifically, a joint optimization of gate sizing and adaptive independent gate control is presented and solved in order to minimize the delay of FinFET circuits operating in multiple voltage regimes. Experimental results on a 32nm Predictive Technology Model for FinFET demonstrate the effectiveness of the proposed logical effort-based delay optimization framework.


title: Visual-Interactive Preprocessing of Time Series Data

abstract: Time series data is an important data type in many different application scenarios. Consequently, there are a great variety of approaches for analyzing time series data. Within these approaches different strategies for cleaning, segmenting, representing, normalizing, comparing, and aggregating time series data can be found. When combining these operations, the time series analysis preprocessing workflow has many degrees of freedom. To define an appropriate preprocessing pipeline, the knowledge of experts coming from the application domain has to be included into the design process. Unfortunately, these experts often cannot estimate the effects of the chosen preprocessing algorithms and their parameterizations on the time series. We introduce a system for the visual-interactive exploitation of the preprocessing parameter space. In contrast to ‘black box’-driven approaches designed by computer scientists based on the requirements of domain experts, our system allows these experts to visual-interactively compose time series preprocessing pipelines by themselves. Visual support is provided to choose the right order and parameterization of the preprocessing steps. We demonstrate the usability of our approach with a case study from the digital library domain, in which time-oriented scientific research data has to be preprocessed to realize a visual search and analysis application.


title: Iterative Hessian sketch: Fast and accurate solution approximation for constrained least-squares

abstract: We study randomized sketching methods for approximately solving least-squares problem with a general convex constraint. The quality of a least-squares approximation can be assessed in different ways: either in terms of the value of the quadratic objective function (cost approximation), or in terms of some distance measure between the approximate minimizer and the true minimizer (solution approximation). Focusing on the latter criterion, our first main result provides a general lower bound on any randomized method that sketches both the data matrix and vector in a least-squares problem; as a surprising consequence, the most widely used least-squares sketch is sub-optimal for solution approximation. We then present a new method known as the iterative Hessian sketch, and show that it can be used to obtain approximations to the original least-squares problem using a projection dimension proportional to the statistical complexity of the least-squares minimizer, and a logarithmic number of iterations. We illustrate our general theory with simulations for both unconstrained and constrained versions of least-squares, including l1-regularization and nuclear norm constraints. We also numerically demonstrate the practicality of our approach in a real face expression classification experiment.


title: On Performance Evaluation Metrics for Lane Estimation

abstract: Accurate and efficient lane estimation is a critical component of active safety systems in automobiles such as lane departure warning systems. A substantial number of lane estimation methods have been proposed and evaluated in literature. However, a common set of evaluation metrics that assess different components of lane estimation process has not been addressed. This paper proposes a set of performance evaluation metrics for lane estimation process, that can be deployed to evaluate different kinds of lane estimation algorithms. Evaluation by applying the proposed metrics is demonstrated using a recent lane estimation method.


title: The Spectrum Kernel: A String Kernel for SVM Protein Classification

abstract: We introduce a new sequence-similarity kernel, the spectrum kernel, for use with support vector machines (SVMs) in a discriminative approach to the protein classification problem. Our kernel is conceptually simple and efficient to compute and, in experiments on the SCOP database, performs well in comparison with state-of-the-art methods for homology detection. Moreover, our method produces an SVM classifier that allows linear time classification of test sequences. Our experiments provide evidence that string-based kernels, in conjunction with SVMs, could offer a viable and computationally efficient alternative to other methods of protein classification and homology detection.


title: Hybrid Systems for Knowledge Representation in Artificial Intelligence

abstract: There are few knowledge representation (KR) techniques available for efficiently representing knowledge. However, with the increase in complexity, better methods are needed. Some researchers came up with hybrid mechanisms by combining two or more methods. In an effort to construct an intelligent computer system, a primary consideration is to represent large amounts of knowledge in a way that allows effective use and efficiently organizing information to facilitate making the recommended inferences. There are merits and demerits of combinations, and standardized method of KR is needed. In this paper, various hybrid schemes of KR were explored at length and details presented.


title: Semantic Modelling of Smart City Data

abstract: Recent advancements in communication technologies for providing ubiquitous Internet access as well as advancements on reduction of cost and form-factor of mobile devices and sensors are seen as an enabler for the Internet of Things (IoT). The industry predicts an interconnected world of 50 billion devices by 20201. The Web of Things (WoT) relies on the connectivity service of IoT to create services and applications exploiting the IoT data [1]. Cities present an opportunity for rendering WoT-enabled services. According to the World Health Organization, population in cities will double by the middle of this century2, while cities deal with increasingly pressing issues such as environmental sustainability, economic growth and citizen mobility. In this paper, we propose a discussion around the need for common semantic descriptions for smart city data to facilitate future services in “smart cities”. We present examples of data that can be collected from cities, discuss issues around this data and put forward some preliminary thoughts for creating a semantic description model to describe and help discover, index and query smart city data.


title: Investigation of pole and slot numbers in axial-flux PM BLDC motors with single-layer windings for electric vehicles

abstract: Axial-flux permanent-magnet brushless-dc (PM BLDC) machines with concentrated windings are excellent candidates for direct drive applications regarding their torque-density, efficiency and required size. They can be placed in the wheel of electric vehicles as direct drive motors to eliminate mechanical power transmission system and increase efficiency. Choice of pole number and slot number has dominant effect on machine performance. In this paper an extensive investigation of axial-flux PM BLDC machines having different pole and slot combination has been carried out. As axial-flux machines are three-dimensional electric machines, three-dimensional finite-element (3-D FE) analysis is used to achieve the performance of axial-flux machines accurately.


A Stream Channel Stability Assessment Methodology


title: Decoding with Finite-State Transducers on GPUs

abstract: Weighted finite automata and transducers (including hidden Markov models and conditional random fields) are widely used in natural language processing (NLP) to perform tasks such as morphological analysis, part-of-speech tagging, chunking, named entity recognition, speech recognition, and others. Parallelizing finite state algorithms on graphics processing units (GPUs) would benefit many areas of NLP. Although researchers have implemented GPU versions of basic graph algorithms, limited previous work, to our knowledge, has been done on GPU algorithms for weighted finite automata. We introduce a GPU implementation of the Viterbi and forward-backward algorithm, achieving decoding speedups of up to 5.2x over our serial implementation running on different computer architectures and 6093x over OpenFST.


title: Prosthetic advances.

abstract: Much of the current prosthetic technology is based on developments that have taken place during or directly following times of war. These developments have evolved and improved over the years, and now there are many more available options to provide a comfortable, cosmetic, and highly functional prosthesis. Even so, problems with fit and function persist. Recent developments have addressed some of the limitations faced by some military amputees. On-board microprocessor-controlled joints are making prosthetic arms and legs more responsive to environmental barriers and easier to control by the user. Advances in surgical techniques will allow more intuitive control and secure attachment to the prosthesis. As surgical techniques progress and permeate into standard practice, more sophisticated powered prosthetic devices will become commonplace, helping to restore neuromuscular loss of function. Prognoses following amputation will certainly rise, factoring into the surgeon's decision to attempt to save a limb versus perform an amputation.


title: Summarizing Unconstrained Videos Using Salient Montages

abstract: We present a novel method to summarize unconstrained videos using salient montages (i.e., a “melange” of frames in the video as shown in Fig. 1 , by finding “montageable moments” and identifying the salient people and actions to depict in each montage. Our method aims at addressing the increasing need for generating concise visualizations from the large number of videos being captured from portable devices. Our main contributions are (1) the process of finding salient people and moments to form a montage, and (2) the application of this method to videos taken “in the wild” where the camera moves freely. As such, we demonstrate results on head-mounted cameras, where the camera moves constantly, as well as on videos downloaded from YouTube. In our experiments, we show that our method can reliably detect and track humans under significant action and camera motion. Moreover, the predicted salient people are more accurate than results from state-of-the-art video salieny method [1] . Finally, we demonstrate that a novel “montageability” score can be used to retrieve results with relatively high precision which allows us to present high quality montages to users.


title: Some Empirical Criteria for Attributing Creativity to a Computer Program

abstract: Over recent decades there has been a growing interest in the question of whether computer programs are capable of genuinely creative activity. Although this notion can be explored as a purely philosophical debate, an alternative perspective is to consider what aspects of the behaviour of a program might be noted or measured in order to arrive at an empirically supported judgement that creativity has occurred. We sketch out, in general abstract terms, what goes on when a potentially creative program is constructed and run, and list some of the relationships (for example, between input and output) which might contribute to a decision about creativity. Specifically, we list a number of criteria which might indicate interesting properties of a program's behaviour, from the perspective of possible creativity. We go on to review some ways in which these criteria have been applied to actual implementations, and some possible improvements to this way of assessing creativity.


title: Deep neural networks-based vehicle detection in satellite images

abstract: Vehicle detection in satellite images is an Challenging task, but meaningful at the same time. This paper propose a vehicle detection method in satellite images using Deep Convolutional Neural Network(DNN). DNN is a model of deep learning and it has a high learning capacity when dealing with images. DNN consist of several convolution layers and pooling layers, the last layer is full connection with output(this can be considered as neural network). DNN can automatically learn rich features from trainning dataset, and has achieved excellent performance in many applications such as image classification and object recognition. To benefit from this method, we propose a vehicle detection framework. Firstly we use a graph-based superpixel segmentation to extract a set of image patches, which can help us locate vehicle effectively. And then we train a DNN network to classify these pathes into vehicle and non-vehicle. Experimental results indicate that the proposed method has a good performance, with high detection rates and very few false alarms for all test road segment.


title: An Analysis of the Mobile Payment Problem in Europe

abstract: Mobile payment is crucial for, but not limited to mobile commerce. But a role as an established payment system still seems to be a distant prospect for it. In this paper we examine the basic conditions to mobile payment with special regard to the European market. Based on this, we analyze the current deadlock on the mobile payment market in order to develop a set of requirements to an integrative solution in the form of a Universal Mobile Payment System (UMPS). Finally, applications and constrictions of the results are shown and an outlook on the future of mobile payment is given.


title: Exact Top-k Feature Selection via ` 2 , 0-Norm Constraint

abstract: In this paper, we propose a novel robust and pragmatic feature selection approach. Unlike those sparse learning based feature selection methods which tackle the approximate problem by imposing sparsity regularization in the objective function, the proposed method only has one l2,1-norm loss term with an explicit l2,0-Norm equality constraint. An efficient algorithm based on augmented Lagrangian method will be derived to solve the above constrained optimization problem to find out the stable local solution. Extensive experiments on four biological datasets show that although our proposed model is not a convex problem, it outperforms the approximate convex counterparts and state-of-art feature selection methods evaluated in terms of classification accuracy by two popular classifiers. What is more, since the regularization parameter of our method has the explicit meaning, i.e. the number of feature selected, it avoids the burden of tuning the parameter, making it a pragmatic feature selection method.


title: Long-term expansion of epithelial organoids from human colon, adenoma, adenocarcinoma, and Barrett's epithelium.

abstract: BACKGROUND & AIMS ::: We previously established long-term culture conditions under which single crypts or stem cells derived from mouse small intestine expand over long periods. The expanding crypts undergo multiple crypt fission events, simultaneously generating villus-like epithelial domains that contain all differentiated types of cells. We have adapted the culture conditions to grow similar epithelial organoids from mouse colon and human small intestine and colon. ::: ::: ::: METHODS ::: Based on the mouse small intestinal culture system, we optimized the mouse and human colon culture systems. ::: ::: ::: RESULTS ::: Addition of Wnt3A to the combination of growth factors applied to mouse colon crypts allowed them to expand indefinitely. Addition of nicotinamide, along with a small molecule inhibitor of Alk and an inhibitor of p38, were required for long-term culture of human small intestine and colon tissues. The culture system also allowed growth of mouse Apc-deficient adenomas, human colorectal cancer cells, and human metaplastic epithelia from regions of Barrett's esophagus. ::: ::: ::: CONCLUSIONS ::: We developed a technology that can be used to study infected, inflammatory, or neoplastic tissues from the human gastrointestinal tract. These tools might have applications in regenerative biology through ex vivo expansion of the intestinal epithelia. Studies of these cultures indicate that there is no inherent restriction in the replicative potential of adult stem cells (or a Hayflick limit) ex vivo.


title: Application identification via network traffic classification

abstract: Recent developments in Internet technology have led to an increased importance of network traffic classification. In this study, we used machine-learning methods for the identification of applications using network traffic classification. Contrary to existing studies, which classify applications into categories like FTP, Instant Messaging, etc., we tried to identify popular end-user applications such as Facebook, Twitter, Skype and many more individually. We are motivated by the fact that individual identification of applications is of high importance for network security, QoS enforcement, and trend analysis. For our tests, we used UNB ISCX Network Traffic dataset and our internal dataset, consisting of 14 and 13 well-known applications respectively. In our experiments, we evaluated four classification algorithms, namely J48, Random Forest, k-NN, and Bayes Net. With the complete set of 111 features, k-NN gave the best result for the ISCX Dataset as 93.94% of accuracy using the value of k as 1, and Random Forest gave the best result for the internal dataset as 90.87% of accuracy. During the course of this study, the initial numbers of features were successfully reduced to two sets of 12 features specific to each dataset without a compromise to the success. Moreover, we observed a 2% increase in the success rate for the internal dataset. We believe that individual application identification by applying machine-learning methods is a viable solution and currently we are investigating a two-tier approach to make it more resilient to in category confusion.


title: Adoption of internet banking: an empirical study in Hong Kong

abstract: This study investigates how customers perceive and adopt internet banking (IB) in Hong Kong. We developed a theoretical model based on the Technology Acceptance Model (TAM) with an added construct Perceived Web Security, and empirically tested its ability in predicting customers' behavioral intention of adopting IB. We designed a questionnaire and used it to survey a randomly selected sample of customers of IB from the Yellow Pages, and obtained 203 usable responses. We analyzed the data using Structured Equation Modeling (SEM) to evaluate the strength of the hypothesized relationships, if any, among the constructs, which include Perceived Ease of Use and Perceived Web Security as independent variables, Perceived Usefulness and Attitude as intervening variables, and Intention to Use as the dependent variable. The results provide support of the extended TAM model and confirm its robustness in predicting customers' intention of adoption of IB. This study contributes to the literature by formulating and validating TAM to predict IB adoption, and its findings provide useful information for bank management in formulating IB marketing strategies.


title: Real time contextual collective anomaly detection over multiple data streams

abstract: Anomaly detection has always been a critical and challenging problem in many application areas such as industry, healthcare, environment and finance. This problem becomes more dicult in the Big Data era as the data scale increases dramatically and the type of anomalies gets more complicated. In time sensitive applications like real time monitoring, data are often fed in streams and anomalies are required to be identified online across multiple streams with a short time delay. The new data characteristics and analysis requirements make existing solutions no longer suitable. In this paper, we propose a framework to discover a new type of anomaly called contextual collective anomaly over a collection of data streams in real time. A primary advantage of this solution is that it can be seamlessly integrated with real time monitoring systems to timely and accurately identify the anomalies. Also, the proposed framework is designed in a way with a low computational intensity, and is able to handle large scale data streams. To demonstrate the e↵ectiveness and eciency of our framework, we empirically validate it on two real world applications.


title: Modality-constrained statistical learning of tactile, visual, and auditory sequences

abstract: The authors investigated the extent to which touch, vision, and audition mediate the processing of statistical regularities within sequential input. Few researchers have conducted rigorous comparisons across sensory modalities; in particular, the sense of touch has been virtually ignored. The current data reveal not only commonalities but also modality constraints affecting statistical learning across the senses. To be specific, the authors found that the auditory modality displayed a quantitative learning advantage compared with vision and touch. In addition, they discovered qualitative learning biases among the senses: Primarily, audition afforded better learning for the final part of input sequences. These findings are discussed in terms of whether statistical learning is likely to consist of a single, unitary mechanism or multiple, modality-constrained ones. The world is temporally bounded: Events do not occur all at once but rather are distributed in time. Therefore, it is crucial for organisms to be able to encode and represent temporal order information. One potential method for encoding temporal order is to learn the statistical relationships of elements within sequential input. This process appears to be important in a diverse set of learning situations, including speech segmentation (Saffran, Newport, & Aslin, 1996), learning orthographic regularities of written


title: Towards network-wide QoE fairness using openflow-assisted adaptive video streaming

abstract: Video streaming is an increasingly popular way to consume media content. Adaptive video streaming is an emerging delivery technology which aims to increase user QoE and maximise connection utilisation. Many implementations naively estimate bandwidth from a one-sided client perspective, without taking into account other devices in the network. This behaviour results in unfairness and could potentially lower QoE for all clients. We propose an OpenFlow-assisted QoE Fairness Framework that aims to fairly maximise the QoE of multiple competing clients in a shared network environment. By leveraging a Software Defined Networking technology, such as OpenFlow, we provide a control plane that orchestrates this functionality. The evaluation of our approach in a home networking scenario introduces user-level fairness and network stability, and illustrates the optimisation of QoE across multiple devices in a network.


title: Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Subimage Classification

abstract: This paper presents a novel three-stage blood vessel segmentation algorithm using fundus photographs. In the first stage, the green plane of a fundus image is preprocessed to extract a binary image after high-pass filtering, and another binary image from the morphologically reconstructed enhanced image for the vessel regions. Next, the regions common to both the binary images are extracted as the major vessels . In the second stage, all remaining pixels in the two binary images are classified using a Gaussian mixture model (GMM) classifier using a set of eight features that are extracted based on pixel neighborhood and first and second-order gradient images. In the third postprocessing stage, the major portions of the blood vessels are combined with the classified vessel pixels. The proposed algorithm is less dependent on training data, requires less segmentation time and achieves consistent vessel segmentation accuracy on normal images as well as images with pathology when compared to existing supervised segmentation methods. The proposed algorithm achieves a vessel segmentation accuracy of 95.2%, 95.15%, and 95.3% in an average of 3.1, 6.7, and 11.7 s on three public datasets DRIVE, STARE, and CHASE_DB1, respectively.


title: Integration of it governance and security risk management: A systematic literature review

abstract: GRC is an umbrella acronym covering the three disciplines of governance, risk management and compliance. In this context, IT GRC is the subset of GRC dealing with IT aspects of GRC. The main challenge of GRC is to have an approach as integrated as possible of the three domains. The objective of our paper is to study one facet of IT GRC: the links and integration between IT governance and risk management that we consider today as the least integrated. To do so, the method followed in this paper is a systematic literature review, in order to identify the existing research works in this field. The resulting contribution of the paper is a set of recommendations established for practitioners and for researchers on how better deal with the integration between IT governance and risk management.


title: Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty

abstract: Data mining (DM) methodology has a tremendous contribution for researchers to extract the hidden knowledge and information which have been inherited in the data used by researchers. This study has proposed a new procedure, based on expanded RFM model by including one additional parameter, joining WRFM-based method to K-means algorithm applied in DM with K-optimum according to Davies-Bouldin Index, and then classifying customer product loyalty in under B2B concept. The developed methodology has been implemented for SAPCO Co. in Iran. The result shows a tremendous capability to the firm to assess his customer loyalty in marketing strategy designed by this company in comparing with random selection commonly used by most companies in Iran.


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title: Development and whirl tower test of the SMART active flap rotor

abstract: A full scale Smart Material Actuated Rotor Technology (SMART) system with piezoelectric actuated blade flaps was developed and whirl tower tested. The development effort included design, fabrication, and component testing of rotor blades, trailing edge flaps, piezoelectric actuators, switching power amplifiers, and the data/power system. ::: Simulations and model scale wind tunnel tests have shown that this system can provide 80% vibration reduction, 10dB noise reduction for a helicopter passing overhead, and substantial aerodynamic performance gains. Whirl tower testing of the 34-foot diameter rotor demonstrated the functionality, robustness, and required authority of the active flap system. ::: The program involved extensive development work and risk reduction tests which resulted in a robust, high performance actuator and a tightly integrated actuator, flap, and blade system. The actuator demonstrated excellent performance during bench testing and has accumulated over 60 million cycles under a spectrum of loading ::: conditions. The flight worthy active flap rotor blades were based on a modified design of the FAA certified MD900 Explorer production rotor blade. Whirl tower testing was conducted with full rotor instrumentation and a 5-component balance. The rotor was tested for 13 hours under a range of conditions, including 7 hours of flap ::: operation. Flap inputs included open loop static and dynamic commands. The flaps showed excellent authority with oscillatory thrust greater than 10% of the steady baseline thrust. Various flap actuation frequency sweeps were run to investigate the dynamics of the rotor and the flap system. Limited closed loop tests used hub accelerations and hub loads for feedback. ::: Proving the integration, robust operation, and authority of the flap system were the key objectives met by the whirl tower test. This success depended on tailoring the piezoelectric materials and actuator to the application and meeting actuator/blade integration requirements. Test results demonstrate the feasibility and practicality of applying smart materials for limited authority, active control on a helicopter rotor. Follow-on forward flight demonstrations are needed to quantify the expected significant improvements in vibrations, noise, and aerodynamic performance. ::: Extensions of this technology are a prime candidate for on-blade flight control, i.e. elimination of the swashplate. ::: This program was performed as part of DARPA's Smart Materials and Structures Demonstrations. Funding was provided by DARPA, The Boeing Company, NASA, and the U.S. Army. Additional cost share funds were provided by the University of Maryland, MIT, and UCLA.


title: A fully automated greedy square jigsaw puzzle solver

abstract: In the square jigsaw puzzle problem one is required to reconstruct the complete image from a set of non-overlapping, unordered, square puzzle parts. Here we propose a fully automatic solver for this problem, where unlike some previous work, it assumes no clues regarding parts' location and requires no prior knowledge about the original image or its simplified (e.g., lower resolution) versions. To do so, we introduce a greedy solver which combines both informed piece placement and rearrangement of puzzle segments to find the final solution. Among our other contributions are new compatibility metrics which better predict the chances of two given parts to be neighbors, and a novel estimation measure which evaluates the quality of puzzle solutions without the need for ground-truth information. Incorporating these contributions, our approach facilitates solutions that surpass state-of-the-art solvers on puzzles of size larger than ever attempted before.


title: A comparative evaluation of 3D keypoint detectors in a RGB-D Object Dataset

abstract: When processing 3D point cloud data, features must be extracted from a small set of points, usually called keypoints. This is done to avoid the computational complexity required to extract features from all points in a point cloud. There are many keypoint detectors and this suggests the need of a comparative evaluation. When the keypoint detectors are applied to 3D objects, the aim is to detect a few salient structures which can be used, instead of the whole object, for applications like object registration, retrieval and data simplification. In this paper, we propose to do a description and evaluation of existing keypoint detectors in a public available point cloud library with real objects and perform a comparative evaluation on 3D point clouds. We evaluate the invariance of the 3D keypoint detectors according to rotations, scale changes and translations. The evaluation criteria used are the absolute and the relative repeatability rate. Using these criteria, we evaluate the robustness of the detectors with respect to changes of point-of-view. In our experiments, the method that achieved better repeatability rate was the ISS3D method.


title: An Automatic Contextual Analysis and Clustering Classifiers Ensemble approach to Sentiment Analysis

abstract: Products reviews are one of the major resources to determine the public sentiment. The existing literature on reviews sentiment analysis mainly utilizes supervised paradigm, which needs labeled data to be trained on and suffers from domain-dependency. This article addresses these issues by describes a completely automatic approach for sentiment analysis based on unsupervised ensemble learning. The method consists of two phases. The first phase is contextual analysis, which has five processes, namely (1) data preparation; (2) spelling correction; (3) intensifier handling; (4) negation handling and (5) contrast handling. The second phase comprises the unsupervised learning approach, which is an ensemble of clustering classifiers using a majority voting mechanism with different weight schemes. The base classifier of the ensemble method is a modified k-means algorithm. The base classifier is modified by extracting initial centroids from the feature set via using SentWordNet (SWN). We also introduce new sentiment analysis problems of Australian airlines and home builders which offer potential benchmark problems in the sentiment analysis field. Our experiments on datasets from different domains show that contextual analysis and the ensemble phases improve the clustering performance in term of accuracy, stability and generalization ability.


title: Multi-label Music Genre Classification from Audio, Text, and Images Using Deep Features

abstract: Music genres allow to categorize musical items that share common characteristics. Although these categories are not mutually exclusive, most related research is traditionally focused on classifying tracks into a single class. Furthermore, these categories (e.g., Pop, Rock) tend to be too broad for certain applications. In this work we aim to expand this task by categorizing musical items into multiple and fine-grained labels, using three different data modalities: audio, text, and images. To this end we present MuMu, a new dataset of more than 31k albums classified into 250 genre classes. For every album we have collected the cover image, text reviews, and audio tracks. Additionally, we propose an approach for multi-label genre classification based on the combination of feature embeddings learned with state-of-the-art deep learning methodologies. Experiments show major differences between modalities, which not only introduce new baselines for multi-label genre classification, but also suggest that combining them yields improved results.


title: Specification-Based Monitoring of Cyber-Physical Systems: A Survey on Theory, Tools and Applications

abstract: The term Cyber-Physical Systems (CPS) typically refers to engineered, physical and biological systems monitored and/or controlled by an embedded computational core. The behaviour of a CPS over time is generally characterised by the evolution of physical quantities, and discrete software and hardware states. In general, these can be mathematically modelled by the evolution of continuous state variables for the physical components interleaved with discrete events. Despite large effort and progress in the exhaustive verification of such hybrid systems, the complexity of CPS models limits formal verification of safety of their behaviour only to small instances. An alternative approach, closer to the practice of simulation and testing, is to monitor and to predict CPS behaviours at simulation-time or at runtime. In this chapter, we summarise the state-of-the-art techniques for qualitative and quantitative monitoring of CPS behaviours. We present an overview of some of the important applications and, finally, we describe the tools supporting CPS monitoring and compare their main features.


Exploiting partial replication in unbalanced parallel loop scheduling on multicomputer.


title: Artificial vision for the blind by connecting a television camera to the visual cortex.

abstract: Blindness is more feared by the public than any ailment with the exception of cancer and AIDS. We report the development of the first visual prosthesis providing useful "artificial vision" to a blind volunteer by connecting a digital video camera, computer, and associated electronics to the visual cortex of his brain. This device has been the objective of a development effort begun by our group in 1968 and represents realization of the prediction of an artificial vision system made by Benjamin Franklin in his report on the "kite and key" experiment, with which he discovered electricity in 1751.


title: Parallel MMF: a Multiresolution Approach to Matrix Computation

abstract: Multiresolution Matrix Factorization (MMF) was recently introduced as a method for finding multiscale structure and defining wavelets on graphs/matrices. In this paper we derive pMMF, a parallel algorithm for computing the MMF factorization. Empirically, the running time of pMMF scales linearly in the dimension for sparse matrices. We argue that this makes pMMF a valuable new computational primitive in its own right, and present experiments on using pMMF for two distinct purposes: compressing matrices and preconditioning large sparse linear systems.


title: The human connectome: Origins and challenges

abstract: Abstract The human connectome refers to a map of the brain's structural connections, rendered as a connection matrix or network. This article attempts to trace some of the historical origins of the connectome, in the process clarifying its definition and scope, as well as its putative role in illuminating brain function. Current efforts to map the connectome face a number of significant challenges, including the issue of capturing network connectivity across multiple spatial scales, accounting for individual variability and structural plasticity, as well as clarifying the role of the connectome in shaping brain dynamics. Throughout, the article argues that these challenges require the development of new approaches for the statistical analysis and computational modeling of brain network data, and greater collaboration across disciplinary boundaries, especially with researchers in complex systems and network science.


title: Fuzzy Finite-state Automata Can Be Deterministically Encoded into Recurrent Neural Networks

abstract: There has been an increased interest in combining fuzzy systems with neural networks because fuzzy neural systems merge the advantages of both paradigms. On the one hand, parameters in fuzzy systems have clear physical meanings and rule-based and linguistic information can be incorporated into adaptive fuzzy systems in a systematic way. On the other hand, there exist powerful algorithms for training various neural network models. However, most of the proposed combined architectures are only able to process static input-output relationships; they are not able to process temporal input sequences of arbitrary length. Fuzzy finite-state automats (FFAs) can model dynamical processes whose current state depends on the current input and previous states. Unlike in the case of deterministic finite-state automats (DFAs), FFAs are not in one particular state, rather each state is occupied to some degree defined by a membership function. Based on previous work on encoding DFAs in discrete-time second-order recurrent neural networks, we propose an algorithm that constructs an augmented recurrent neural network that encodes a FFA and recognizes a given fuzzy regular language with arbitrary accuracy. We then empirically verify the encoding methodology by correct string recognition of randomly generated FFAs. In particular, we examine how the networks' performance varies as a function of synaptic weight strengths.


title: Mechanical design and control of inflatable robotic arms for high positioning accuracy

abstract: AbstractIn recent years, inflatable robotic arms have been developed so that physical contact with humans and working environments could be performed safety. In industry, there is a growing demand for safe industrial robots to collaborate with people and work in various environments. In general, however, the positioning accuracy of inflatable robotic arms has not been discussed. This paper proposes an inflatable link structure and a non-inflatable joint structure that could realize a high positioning accuracy for such robotic arms. This paper experimentally demonstrates that these structures can improve positioning accuracy. In addition to these structures, the joint torque characteristics of the inflatable robotic arms were investigated. In order to perform accurate motion control, a visual feedback control method was introduced for inflatable robotic arms. The mechanism and control system used in this paper can improve the positioning accuracy performance of inflatable robots. A 2-DOF inflatable robotic...


title: Congestion Management Using Multiobjective Particle Swarm Optimization

abstract: Summary form only given. This paper presents an effective method of congestion management in power systems. Congestions or overloads in transmission network are alleviated by generation rescheduling and/or load shedding of participating generators and loads. The two conflicting objectives (1) alleviation of overload and (2) minimization of cost of operation are optimized to provide Pareto-optimal solutions. A multiobjective particle swarm optimization (MOPSO) method is used to solve this complex nonlinear optimization problem. A realistic frequency and voltage dependent load flow method which considers the voltage and frequency dependence of loads and generator regulation characteristics is used to solve this problem. The proposed algorithm is tested on IEEE 30-bus system, IEEE 118-bus system, and Northern Region Electricity Board, India (NREB) 390-bus system with smooth as well as nonsmooth cost functions due to valve point loading effect.


title: The Role of Instructional Design in Persuasion: A Comics Approach for Improving Cybersecurity

abstract: AbstractAlthough computer security technologies are the first line of defense to secure users, their success is dependent on individuals’ behavior. It is therefore necessary to persuade users to practice good computer security. This interview analysis of users’ conceptualization of security password guessing attacks, antivirus protection, and mobile online privacy shows that poor understanding of security threats influences users’ motivation and ability to practice safe behaviors. An online interactive comic series called Secure Comics was designed and developed based on instructional design principles to address this problem. An eye-tracking experiment suggests that the graphical and interactive components of the comics direct users’ attention and facilitate comprehension of the information. In the evaluations of Secure Comics, results from several user studies show that the comics improve understanding and motivate positive changes in security management behavior. The implication of the findings to bett...


title: Experimental investigation on the flexural behavior of steel-polypropylene hybrid fiber reinforced concrete

abstract: Abstract This paper presents an experimental study on the flexural behavior of steel-polypropylene hybrid fiber reinforced concrete (HFRC) using four-point bending tests on 51 samples. Three types of steel fibers, i.e., straight, hooked-end and corrugated fiber, and a type of monofilament polypropylene fiber are considered. The flexural behavior in terms of load-deflection curves, load and deflection characteristics, toughness, cracking properties as well as the synergetic effect of hybrid fibers is studied. The results show that synergetic effect is observed for all the three types of steel fiber with the combination of polypropylene fiber on improving the flexural behavior of HFRC. Specimens with hooked-end fibers show the best flexural performance. However, the combination of straight steel and polypropylene fibers presents the most obvious synergy. Moreover, an increase in volume fractions of both steel and polypropylene fibers leads to an increase in the compressive, splitting tensile and flexural strengths of concrete. The post-peak ductility of concrete is improved and the strength degradation is alleviated with increasing fiber volume fraction and steel fiber aspect ratio. In addition, specimens with corrugated and hooked-end fibers exhibit a better failure behavior than specimens with straight fibers, with multiply micro-cracks induced by mechanical interlocks of deformed steel fibers observed at the main cracks. Finally, based on a comprehensive fiber reinforcing index, analytical equations for flexural loads, deflections, and toughness of HFRC are developed with varying fiber parameters taken into consideration.


title: Contextual information retrieval in research articles: Semantic publishing tools for the research community

abstract: In recent years, the dramatic increase in academic research publications has gained significant research attention. Research has been carried out exploring novel ways of providing information services using this research content. However, the task of extracting meaningful information from research documents remains a challenge. This paper presents our research work on developing intelligent information systems that exploit online article databases. We present in this paper, a linked data application which uses a new semantic publishing model for providing value added information services for the research community. The paper presents a conceptual framework for modelling contexts associated with sentences in research articles and discusses the Sentence Context Ontology, which is used to convert the information extracted from research documents into machine-understandable data. The paper reports supervised learning experiments carried out using conditional probabilistic models for achieving automatic context identification. The paper also describes a Semantic Web Application that provides various citation context based information services.


title: A graph kernel based on context vectors for extracting drug-drug interactions

abstract: Display Omitted Graph representation of a sentence obtains relations among different distance words.Context vectors adequately capture the direct and indirect information of a word.The graph kernel considering the distance between context vectors is derived.Achieve the best detection and classification F-score (81.8 and 68.4, respectively).For Medline-2013, F-score outperforms the best systems (10.7 and 12.2, respectively). The clinical recognition of drug-drug interactions (DDIs) is a crucial issue for both patient safety and health care cost control. Thus there is an urgent need that DDIs be extracted automatically from biomedical literature by text-mining techniques. Although the top-ranking DDIs systems explore various features of texts, these features can't yet adequately express long and complicated sentences. In this paper, we present an effective graph kernel which makes full use of different types of contexts to identify DDIs from biomedical literature. In our approach, the relations among long-range words, in addition to close-range words, are obtained by the graph representation of a parsed sentence. Context vectors of a vertex, an iterative vectorial representation of all labeled nodes adjacent and nonadjacent to it, adequately capture the direct and indirect substructures' information. Furthermore, the graph kernel considering the distance between context vectors is used to detect DDIs. Experimental results on the DDIExtraction 2013 corpus show that our system achieves the best detection and classification performance (F-score) of DDIs (81.8 and 68.4, respectively). Especially for the Medline-2013 dataset, our system outperforms the top-ranking DDIs systems by F-scores of 10.7 and 12.2 in detection and classification, respectively.


title: Question Answering on Freebase via Relation Extraction and Textual Evidence

abstract: Existing knowledge-based question answering systems often rely on small annotated training data. While shallow methods like relation extraction are robust to data scarcity, they are less expressive than the deep meaning representation methods like semantic parsing, thereby failing at answering questions involving multiple constraints. Here we alleviate this problem by empowering a relation extraction method with additional evidence from Wikipedia. We first present a neural network based relation extractor to retrieve the candidate answers from Freebase, and then infer over Wikipedia to validate these answers. Experiments on the WebQuestions question answering dataset show that our method achieves an F_1 of 53.3%, a substantial improvement over the state-of-the-art.


title: The GeoLink Framework for Pattern-based Linked Data Integration

abstract: GeoLink is one of the building block projects within EarthCube, a major effort of the National Science Foundation to establish a next-generation knowledge infrastructure for geosciences. Specifically, GeoLink aims to improve data reuse and integration of seven geoscience data repositories through the use of ontologies. In this paper, we present the approach taken by this project, which combines linked data publishing and modular ontology engineering based on ontology design patterns to realize integration while respecting existing heterogeneity within the participating repositories.


title: Countering antivaccination attitudes.

abstract: Three times as many cases of measles were reported in the United States in 2014 as in 2013. The reemergence of measles has been linked to a dangerous trend: parents refusing vaccinations for their children. Efforts have been made to counter people’s antivaccination attitudes by providing scientific evidence refuting vaccination myths, but these interventions have proven ineffective. This study shows that highlighting factual information about the dangers of communicable diseases can positively impact people’s attitudes to vaccination. This method outperformed alternative interventions aimed at undercutting vaccination myths.


title: Novel hemostatic biomolecules based on elastin-like polypeptides and the self-assembling peptide RADA-16

abstract: Safe and effective hemostatic materials are important for reducing mortality resulting from excessive hemorrhage. In this work, new biomaterials with hemostatic effects were created by fusing the gene coding for RADA-16, a self-assembling peptide with the sequence RADARADARADARADA, to the 3′-end of the open reading frame (ORF) encoding elastin-like polypeptides through gene recombination. The fusion proteins, termed 36R, 60R and 96R, were solubly over-expressed in Escherichia coli BL21 (DE3) based on genetic manipulation of the high-efficiency prokaryotic expression vector pET28a (+) and bacterial transformation. Western Blot analysis showed that the over-expressed proteins were the target fusion proteins. The target proteins 36R with 94.72% purity, 60R with 96.91% purity and 96R with 96.37% purity were prepared using an inverse phase transition cycle at 65 °C followed by His-tag affinity chromatography. The proliferation results of the mouse fibroblast cell line L929 and hippocampus neuron cell line HT22 indicated that the fusion proteins did not cause obvious cell toxicity. The lyophilized spongy film of the purified 36R, 60R and 96R could stop the hemorrhage of a 2 × 2 mm bleeding wound in the mouse liver after 27.21 ± 1.92 s, 18.65 ± 1.97 s and 15.85 ± 1.21 s, respectively. The hemostasis time was 21.23 ± 1.84 s for rat-tail collagen and 14.44 ± 1.33 s for RADA-16 lyophilized on gauze. The hemostatic time of three treated groups were all significantly superior to that of the negative control without any hemostasis treatment, which spontaneously stopped bleeding after 37.64 ± 1.34 s. Statistical analysis showed that the spongy film with purified 96R exhibited an exciting hemostatic effect that was superior to rat-tail collagen and close to that of RADA-16 lyophilized on gauze. These results revealed that the fusion proteins achieved by gene recombination technology could serve as a promising hemostatic material.


title: Face-MagNet: Magnifying Feature Maps to Detect Small Faces

abstract: In this paper, we introduce the Face Magnifier Network (Face-MageNet), a face detector based on the Faster-RCNN framework which enables the flow of discriminative information of small scale faces to the classifier without any skip or residual connections. To achieve this, Face-MagNet deploys a set of ConvTranspose, also known as deconvolution, layers in the Region Proposal Network (RPN) and another set before the Region of Interest (RoI) pooling layer to facilitate detection of finer faces. In addition, we also design, train, and evaluate three other well-tuned architectures that represent the conventional solutions to the scale problem: context pooling, skip connections, and scale partitioning. Each of these three networks achieves comparable results to the state-of-the-art face detectors. With extensive experiments, we show that Face-MagNet based on a VGG16 architecture achieves better results than the recently proposed ResNet101-based HR [7] method on the task of face detection on WIDER [25] dataset and also achieves similar results on the hard set as our other method SSH [17].


title: Neural Embeddings of Graphs in Hyperbolic Space

abstract: Neural embeddings have been used with great success in Natural Language Processing (NLP). They provide compact representations that encapsulate word similarity and attain state-of-the-art performance in a range of linguistic tasks. The success of neural embeddings has prompted significant amounts of research into applications in domains other than language. One such domain is graph-structured data, where embeddings of vertices can be learned that encapsulate vertex similarity and improve performance on tasks including edge prediction and vertex labelling. For both NLP and graph based tasks, embeddings have been learned in high-dimensional Euclidean spaces. However, recent work has shown that the appropriate isometric space for embedding complex networks is not the flat Euclidean space, but negatively curved, hyperbolic space. We present a new concept that exploits these recent insights and propose learning neural embeddings of graphs in hyperbolic space. We provide experimental evidence that embedding graphs in their natural geometry significantly improves performance on downstream tasks for several real-world public datasets.


title: Evaluating Faces on Trustworthiness An Extension of Systems for Recognition of Emotions Signaling Approach / Avoidance Behaviors

abstract: People routinely make various trait judgments from facial appearance, and such judgments affect important social outcomes. These judgments are highly correlated with each other, reflecting the fact that valence evaluation permeates trait judgments from faces. Trustworthiness judgments best approximate this evaluation, consistent with evidence about the involvement of the amygdala in the implicit evaluation of face trustworthiness. Based on computer modeling and behavioral experiments, I argue that face evaluation is an extension of functionally adaptive systems for understanding the communicative meaning of emotional expressions. Specifically, in the absence of diagnostic emotional cues, trustworthiness judgments are an attempt to infer behavioral intentions signaling approach/avoidance behaviors. Correspondingly, these judgments are derived from facial features that resemble emotional expressions signaling such behaviors: happiness and anger for the positive and negative ends of the trustworthiness continuum, respectively. The emotion overgeneralization hypothesis can explain highly efficient but not necessarily accurate trait judgments from faces, a pattern that appears puzzling from an evolutionary point of view and also generates novel predictions about brain responses to faces. Specifically, this hypothesis predicts a nonlinear response in the amygdala to face trustworthiness, confirmed in functional magnetic resonance imaging (fMRI) studies, and dissociations between processing of facial identity and face evaluation, confirmed in studies with developmental prosopagnosics. I conclude with some methodological implications for the study of face evaluation, focusing on the advantages of formally modeling representation of faces on social dimensions.


title: The Mediation Formula: A guide to the assessment of causal pathways in nonlinear models

abstract: Me diation analysis aims to uncover causal pathways along which changes are transmitted from stimulus to response. Recent advances in causal inference have given rise to a general and easy-to-use estimator for assessing the extent to which the effect of one variable on another is mediated by a third, thus setting a causally-sound standard for mediation analysis of empirical data. This estimator, called Mediation Formula, is applicable to nonlinear models with both discrete and continuous variables, and permits the evaluation of path-specific effects with minimal assumptions regarding the data-generating process. We demonstrate the use of the Mediation Formula in simple examples and illustrate why parametric methods of analysis yield distorted results, even when parameters are known precisely. We stress the importance of distinguishing between the necessary and sufficient interpretations


How Does Cyber Crime Affect Firms? The Effect of Information Security Breaches on Stock Returns


Facial mimicry in its social setting


title: Good Practice in CNN Feature Transfer

abstract: The objective of this paper is the effective transfer of the Convolutional Neural Network (CNN) feature in image search and classification. Systematically, we study three facts in CNN transfer. 1) We demonstrate the advantage of using images with a properly large size as input to CNN instead of the conventionally resized one. 2) We benchmark the performance of different CNN layers improved by average/max pooling on the feature maps. Our observation suggests that the Conv5 feature yields very competitive accuracy under such pooling step. 3) We find that the simple combination of pooled features extracted across various CNN layers is effective in collecting evidences from both low and high level descriptors. Following these good practices, we are capable of improving the state of the art on a number of benchmarks to a large margin.


title: Annotation-based empirical performance tuning using Orio

abstract: For many scientific applications, significant time is spent in tuning codes for a particular high-performance architecture. Tuning approaches range from the relatively nonintrusive (e.g., by using compiler options) to extensive code modifications that attempt to exploit specific architecture features. Intrusive techniques often result in code changes that are not easily reversible, and can negatively impact readability, maintainability, and performance on different architectures. We introduce an extensible annotation-based empirical tuning system called Orio that is aimed at improving both performance and productivity. It allows software developers to insert annotations in the form of structured comments into their source code to trigger a number of low-level performance optimizations on a specified code fragment. To maximize the performance tuning opportunities, the annotation processing infrastructure is designed to support both architecture-independent and architecture-specific code optimizations. Given the annotated code as input, Orio generates many tuned versions of the same operation and empirically evaluates the alternatives to select the best performing version for production use. We have also enabled the use of the Pluto automatic parallelization tool in conjunction with Orio to generate efficient OpenMP-based parallel code. We describe our experimental results involving a number of computational kernels, including dense array and sparse matrix operations.


title: Is it a bug or an enhancement?: a text-based approach to classify change requests

abstract: Bug tracking systems are valuable assets for managing maintenance activities. They are widely used in open-source projects as well as in the software industry. They collect many different kinds of issues: requests for defect fixing, enhancements, refactoring/restructuring activities and organizational issues. These different kinds of issues are simply labeled as "bug" for lack of a better classification support or of knowledge about the possible kinds. This paper investigates whether the text of the issues posted in bug tracking systems is enough to classify them into corrective maintenance and other kinds of activities. We show that alternating decision trees, naive Bayes classifiers, and logistic regression can be used to accurately distinguish bugs from other kinds of issues. Results from empirical studies performed on issues for Mozilla, Eclipse, and JBoss indicate that issues can be classified with between 77% and 82% of correct decisions.


title: An exact mapping between the Variational Renormalization Group and Deep Learning

abstract: Deep learning is a broad set of techniques that uses multiple layers of representation to automatically learn relevant features directly from structured data. Recently, such techniques have yielded record-breaking results on a diverse set of difficult machine learning tasks in computer vision, speech recognition, and natural language processing. Despite the enormous success of deep learning, relatively little is understood theoretically about why these techniques are so successful at feature learning and compression. Here, we show that deep learning is intimately related to one of the most important and successful techniques in theoretical physics, the renormalization group (RG). RG is an iterative coarse-graining scheme that allows for the extraction of relevant features (i.e. operators) as a physical system is examined at different length scales. We construct an exact mapping from the variational renormalization group, first introduced by Kadanoff, and deep learning architectures based on Restricted Boltzmann Machines (RBMs). We illustrate these ideas using the nearest-neighbor Ising Model in one and two-dimensions. Our results suggests that deep learning algorithms may be employing a generalized RG-like scheme to learn relevant features from data.


title: Exogenous (automatic) attention to emotional stimuli: a review

abstract: Current knowledge on the architecture of exogenous attention (also called automatic, bottom-up, or stimulus-driven attention, among other terms) has been mainly obtained from studies employing neutral, anodyne stimuli. Since, from an evolutionary perspective, exogenous attention can be understood as an adaptive tool for rapidly detecting salient events, reorienting processing resources to them, and enhancing processing mechanisms, emotional events (which are, by definition, salient for the individual) would seem crucial to a comprehensive understanding of this process. This review, focusing on the visual modality, describes 55 experiments in which both emotional and neutral irrelevant distractors are presented at the same time as ongoing task targets. Qualitative and, when possible, meta-analytic descriptions of results are provided. The most conspicuous result is that, as confirmed by behavioral and/or neural indices, emotional distractors capture exogenous attention to a significantly greater extent than do neutral distractors. The modulatory effects of the nature of distractors capturing attention, of the ongoing task characteristics, and of individual differences, previously proposed as mediating factors, are also described. Additionally, studies reviewed here provide temporal and spatial information—partially absent in traditional cognitive models—on the neural basis of preattention/evaluation, reorienting, and sensory amplification, the main subprocesses involved in exogenous attention. A model integrating these different levels of information is proposed. The present review, which reveals that there are several key issues for which experimental data are surprisingly scarce, confirms the relevance of including emotional distractors in studies on exogenous attention.


title: Review of Three-Dimensional Human-Computer Interaction with Focus on the Leap Motion Controller

abstract: Modern hardware and software development has led to an evolution of user interfaces from command-line to natural user interfaces for virtual immersive environments. Gestures imitating real-world interaction tasks increasingly replace classical two-dimensional interfaces based on Windows/Icons/Menus/Pointers (WIMP) or touch metaphors. Thus, the purpose of this paper is to survey the state-of-the-art Human-Computer Interaction (HCI) techniques with a focus on the special field of three-dimensional interaction. This includes an overview of currently available interaction devices, their applications of usage and underlying methods for gesture design and recognition. Focus is on interfaces based on the Leap Motion Controller (LMC) and corresponding methods of gesture design and recognition. Further, a review of evaluation methods for the proposed natural user interfaces is given.


title: Ball-Sport Endurance and Sprint Test (BEAST90): validity and reliability of a 90-minute soccer performance test.

abstract: The aim of this study was to determine the validity and reliability of a 90-minute soccer performance test: Ball-sport Endurance and Sprint Test (BEAST90). Fifteen healthy male amateur soccer players participated and attended 5 testing sessions over a 10-day period to perform physiologic and soccer-specific assessments. This included familiarization sessions and 2 full trials of the BEAST90, separated by 7 days. The total 90-minute distance, mean percent peak heart rate (HRpeak), and estimated percent peak oxygen uptake of the BEAST90 were 8,097 ± 458 m, 85 ± 5% and 82 ± 14%, respectively. Measures obtained from trial 1 and trial 2 were not significantly different (p > 0.05). Reliability of measures over 90 minutes ranged from 0.9-25.5% (% typical error). The BEAST90 protocol replicated soccer match play in terms of time, movement patterns, physical demands (volume and intensity), distances, and mean and HRpeak values, as well as having an aerobic load similar to that observed during a soccer match. Reproducibility of key physical measures during the BEAST90 were mostly high, suggesting good reliability. The BEAST90 could be used in studies that wish to determine the effects of training or nutritional interventions on prolonged intermittent physical performance.