title: Journal of Autism and Developmental Disorders (Ó 2006) DOI 10.1007/s10803-005-0039-0 The Weak Coherence Account: Detail-focused Cognitive Style in Autism Spectrum Disorders

abstract: “Weak central coherence” refers to the detail-focused processing style proposed to characterise autism spectrum disorders (ASD). The original suggestion of a core deficit in central processing resulting in failure to extract global form/meaning, has been challenged in three ways. First, it may represent an outcome of superiority in local processing. Second, it may be a processing bias, rather than deficit. Third, weak coherence may occur alongside, rather than explain, deficits in social cognition. A review of over 50 empirical studies of coherence suggests robust findings of local bias in ASD, with mixed findings regarding weak global processing. Local bias appears not to be a mere side-effect of executive dysfunction, and may be independent of theory of mind deficits. Possible computational and neural models are discussed.


Issues of Data Security and Privacy in Cloud Computing


title: Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach

abstract: The Internet and the World Wide Web in particular provide a unique platform to connect learners with educational resources. Educational material in hypermedia form in a Web-based educational system makes learning a task-driven process. It motivates learners to explore alternative navigational paths through the domain knowledge and from different resources around the globe. Consequently, many researchers have focused on developing e-learning systems with personalized learning mechanisms to assist on-line Web-based learning and to adaptively provide learning paths. However, although most personalized systems consider learner preferences, interests and browsing behaviors when providing personalized curriculum sequencing services, these systems usually neglect to consider whether learner ability and the difficulty level of the recommended curriculums are matched to each other. Therefore, our proposed approach is based on the evolvement technique through computerized adaptive testing (CAT). Then the genetic algorithm (GA) and case-based reasoning (CBR) are employed to construct an optimal learning path for each learner. This paper makes three critical contributions: (1) it presents a genetic-based curriculum sequencing approach that will generate a personalized curriculum sequencing; (2) it illustrates the case-based reasoning to develop a summative examination or assessment analysis; and (3) it uses empirical research to indicate that the proposed approach can generate the appropriate course materials for learners, based on individual learner requirements, to help them to learn more effectively in a Web-based environment.


title: Toward Scalable Social Alt Text: Conversational Crowdsourcing as a Tool for Refining Vision-to-Language Technology for the Blind

abstract: The access of visually impaired users to imagery in social media is constrained by the availability of suitable alt text. It is unknown how imperfections in emerging tools for automatic caption generation may help or hinder blind users' understanding of social media posts with embedded imagery. In this paper, we study how crowdsourcing can be used both for evaluating the value provided by existing automated approaches and for enabling workflows that provide scalable and useful alt text to blind users. Using real-time crowdsourcing, we designed experiences that varied the depth of interaction of the crowd in assisting visually impaired users at caption interpretation, and measured trade-offs in effectiveness, scalability, and reusability. We show that the shortcomings of existing AI image captioning systems frequently hinder a user's understanding of an image they cannot see to a degree that even clarifying conversations with sighted assistants cannot correct. Our detailed analysis of the set of clarifying conversations collected from our studies led to the design of experiences that can effectively assist users in a scalable way without the need for real-time interaction. They also provide lessons and guidelines that human captioners and the designers of future iterations of AI captioning systems can use to improve labeling of social media imagery for blind users.


title: Convolution neural network based syntactic and semantic aware paraphrase identification

abstract: Paraphrase identification is a fundamental task in natural language process areas. During the process of fulfilling this challenge, different features are exploited. Semantically equivalence and syntactic similarity are of the most importance. Apart from advance feature extraction, deep learning based models are also proven their promising in natural language process jobs. As a result in this research, we adopted an interactive representation to modelling the relationship between two sentences not only on word level, but also on phrase and sentence level by employing convolution neural network to conduct paraphrase identification by using semantic and syntactic features at the same time. The experimental study on commonly used MSRP has shown the proposed method's promising potential.


title: Learning Deep Generative Models With Doubly Stochastic Gradient MCMC

abstract: Deep generative models (DGMs), which are often organized in a hierarchical manner, provide a principled framework of capturing the underlying causal factors of data. Recent work on DGMs focussed on the development of efficient and scalable variational inference methods that learn a single model under some mean-field or parameterization assumptions. However, little work has been done on extending Markov chain Monte Carlo (MCMC) methods to Bayesian DGMs, which enjoy many advantages compared with variational methods. We present doubly stochastic gradient MCMC, a simple and generic method for (approximate) Bayesian inference of DGMs in a collapsed continuous parameter space. At each MCMC sampling step, the algorithm randomly draws a mini-batch of data samples to estimate the gradient of log-posterior and further estimates the intractable expectation over hidden variables via a neural adaptive importance sampler, where the proposal distribution is parameterized by a deep neural network and learnt jointly along with the sampling process. We demonstrate the effectiveness of learning various DGMs on a wide range of tasks, including density estimation, data generation, and missing data imputation. Our method outperforms many state-of-the-art competitors.


title: Design and Analysis of an Axial Flux Magnetically Geared Generator

abstract: Axial flux magnetically geared machines offer the potential to achieve exceptional torque densities at high diameters. This work proposes a topology for an axial flux magnetically geared machine, in which the electric machine is placed in the bore of the axial flux magnetic gear. This new topology offers the advantage of correctly matching the size of the electric machine to its required torque without increasing the volume of the system beyond that of the magnetic gear. A proof of concept prototype with a torque density of 7.8 kNm/m3 was designed, fabricated, and tested to evaluate the novel topology. A less conservative alternate design capable of achieving 60.6 kNm/m3 was also simulated to demonstrate the topology's potential for high torque densities, and even higher torque densities can be reached at larger scale designs.


title: Multiple Access and Waveforms for 5G: IDMA and Universal Filtered Multi-Carrier

abstract: In this paper we investigate multiple access schemes and multi-carrier waveforms in the context of future 5th Generation (5G) wireless communication systems. We compare classical Frequency Division Multiple Access (FDMA) to Interleave-Division Multiple Access (IDMA) on top of two different multicarrier waveforms: Orthogonal Frequency Division Multiplexing (OFDM) and a new approach called Universal Filtered Multi-Carrier (UFMC). A relaxation of timing and frequency alignment requirements is taken into account for supporting applications like Machine Type Communications (MTC) and the Internet of Things (IoT). This paper contains a first uplink comparison scenario where traffic with Relaxed Synchronicity (RS) is embedded into synchronous traffic. Two main users of interest are either using IDMA or FDMA on top of either OFDM or UFMC modulation. Simulation results give first suitability indications for 5G for the combination of waveform and multiple access scheme. The numerical results reveal that IDMA brings in significant enhancement for low rate users, and UFMC introduces additional protection to high-rate users. Both schemes can be combined well.


title: Credit scoring in banks and financial institutions via data mining techniques: A literature review

abstract: This paper presents a comprehensive review of the studies conducted in the application of data mining techniques focus on credit scoring from 2000 to 2012. Yet, there isn‟t adequate literature reviews in the field of data mining applications in credit scoring. Using a novel research approach, this paper investigates academic and systematic literature review and includes all of the journals in the Science direct online journal database. The studies are categorized and classified into enterprise, individual and small and midsized (SME) companies credit scoring. Data mining techniques are also categorized to single classifier, Hybrid methods and Ensembles. Variable selection methods are also investigated separately because there is a major issue in a credit scoring problem. The findings of this literature review reveals that data mining techniques are mostly applied to an individual credit score and there is inadequate research on enterprise and SME credit scoring. Also ensemble methods, support vector machines and neural network methods are the most favorite techniques used recently. Hybrid methods are investigated in four categories and two of the frequently used combinations are “classification and classification” and “clustering and classification”. This review of literature analysis provides scope for future research and concludes with some helpful suggestions for further research.


title: Making concrete construals mindful: A novel approach for developing mindfulness and self-compassion to assist weight loss

abstract: Research on the usefulness of mindfulness and self-compassion for dieting has focused on meditative practices. However, meditation can be difficult to maintain, especially while dieting. Thus, the present research attempted to induce mindfulness and self-compassion by using food diaries that required the participant to either focus on concrete (i.e. how they are eating) construals or abstract (i.e. why they are eating) construals. The concrete construals were expected to increase mindfulness and self-compassion, as well as decrease avoidance and negative thoughts (which would further aid the development of mindfulness and self-compassion). Study 1 found that mindfulness and self-compassion mediated the inverse relationship of avoidance and negative thoughts with weight loss. Study 2 showed that concrete construal diaries increased mindfulness and self-compassion, decreased avoidance and negative thoughts, and supported weight loss significantly more than the abstract construal diaries. Study 3, then, compared the concrete construal diaries with a mindful self-compassionate meditation programme. There was no difference in weight loss at the end of the intervention, but at a three-month follow-up, the diaries performed better at weight maintenance. Thus, the concrete construal diaries may promote mindfulness and self-compassion and potentially promote long-term weight loss.


title: Visual field constriction in children with shunt-treated hydrocephalus

abstract: Object Many ophthalmological abnormalities are described in conjunction with hydrocephalus. The results of visual field diagnosis remain a matter of further discussion. The aim of this study was to investigate visual field deficits in children with shunt-treated hydrocephalus. Methods All children over 6 years of age treated for hydrocephalus at the authors' institute between December 2007 and December 2008 were included in the study. The children underwent an ophthalmological investigation for strabismus and binocular function, ophthalmoscopy, visual acuity, and refraction. The special focus was the visual field diagnosis, which the authors established in all children with cognitive conditions. The investigation was made by using the Goldmann visual field examination (kinetic perimetry). Children with and without visual field defects were compared concerning age at the time of ophthalmological examination, genesis of hydrocephalus, and frontooccipital horn ratio measured on current CT or MR images. Resul...


title: High Power, Thermally Efficient, X-band 3D T/R Module With Calibration Capability for Space Radar

abstract: Earth observation from space radar is based on the active electronically steerable antenna (AESA) whose performances count on reliable and powerful transmit/receive modules (TRM). To this aim, as a follow-up of the successful demonstration of a low-footprint transmit/receive hybrid module concept based on 3-D packaging and interconnect technologies (3DTRM), the interest for carrying out additional development work took a further step aiming at achieving a three-fold module performance improvement as well as at consolidating the novel proposed 3-D technology for space applications. First, the possibility of performing an AESA highly accurate calibration was implemented by embedding a wide band, high directivity directional coupler in the module circuitry without any total module footprint increase. Second, the heat extraction capability of the metal-ceramic hermetic package was enhanced through a re-design of the monolithic microwave integrated circuit (MMIC)-to-sink interface. And finally, the gallium arsenide (GaAs) MMIC high power amplifier (HPA) of the first 3DTRM version was replaced by a gallium nitride (GaN) HPA MMIC, obtaining both a higher transmit output power at 5 dB of compressed gain and an improved power added efficiency (PAE) at module level.


title: Deep Multimodal Representation Learning from Temporal Data

abstract: In recent years, Deep Learning has been successfully applied to multimodal learning problems, with the aim of learning useful joint representations in data fusion applications. When the available modalities consist of time series data such as video, audio and sensor signals, it becomes imperative to consider their temporal structure during the fusion process. In this paper, we propose the Correlational Recurrent Neural Network (CorrRNN), a novel temporal fusion model for fusing multiple input modalities that are inherently temporal in nature. Key features of our proposed model include: (i) simultaneous learning of the joint representation and temporal dependencies between modalities, (ii) use of multiple loss terms in the objective function, including a maximum correlation loss term to enhance learning of cross-modal information, and (iii) the use of an attention model to dynamically adjust the contribution of different input modalities to the joint representation. We validate our model via experimentation on two different tasks: video-and sensor-based activity classification, and audio-visual speech recognition. We empirically analyze the contributions of different components of the proposed CorrRNN model, and demonstrate its robustness, effectiveness and state-of-the-art performance on multiple datasets.


title: A Compact Acoustic Communication Module for Remote Control Underwater

abstract: This paper describes an end-to-end compact acoustic communication system designed for easy integration into remotely controlled underwater operations. The system supports up to 2048 commands that are encoded as 16 bit words. We present the design, hardware, and supporting algorithms for this system. A pulse-based FSK modulation scheme is presented, along with a method of demodulation requiring minimal processing power that leverages the Goertzel algorithm and dynamic peak detection. We packaged the system together with an intuitive user interface for remotely controlling an autonomous underwater vehicle. We evaluated this system in the pool and in the open ocean. We present the communication data collected during experiments using the system to control an underwater robot.


title: Power Management of Inverter Interfaced Autonomous Microgrid Based on Virtual Frequency-Voltage Frame

abstract: This paper presents the power management scheme for a power electronics based low voltage microgrid in islanding operation. The proposed real and reactive power control is based on the virtual frequency and voltage frame, which can effectively decouple the real and reactive power flows and improve the system transient and stability performance. Detailed analysis of the virtual frame operation range is presented, and a control strategy to guarantee that the microgrid can be operated within the predetermined voltage and frequency variation limits is also proposed. Moreover, a reactive power control with adaptive voltage droop method is proposed, which automatically updates the maximum reactive power limit of a DG unit based on its current rating and actual real power output and features enlarged power output range and further improved system stability. Both simulation and experimental results are provided in this paper.


title: ShieldFS: a self-healing, ransomware-aware filesystem

abstract: Preventive and reactive security measures can only partially mitigate the damage caused by modern ransomware attacks. Indeed, the remarkable amount of illicit profit and the cyber-criminals' increasing interest in ransomware schemes suggest that a fair number of users are actually paying the ransoms. Unfortunately, pure-detection approaches (e.g., based on analysis sandboxes or pipelines) are not sufficient nowadays, because often we do not have the luxury of being able to isolate a sample to analyze, and when this happens it is already too late for several users! We believe that a forward-looking solution is to equip modern operating systems with practical self-healing capabilities against this serious threat. Towards such a vision, we propose ShieldFS, an add-on driver that makes the Windows native filesystem immune to ransomware attacks. For each running process, ShieldFS dynamically toggles a protection layer that acts as a copy-on-write mechanism, according to the outcome of its detection component. Internally, ShieldFS monitors the low-level filesystem activity to update a set of adaptive models that profile the system activity over time. Whenever one or more processes violate these models, their operations are deemed malicious and the side effects on the filesystem are transparently rolled back. We designed ShieldFS after an analysis of billions of low-level, I/O filesystem requests generated by thousands of benign applications, which we collected from clean machines in use by real users for about one month. This is the first measurement on the filesystem activity of a large set of benign applications in real working conditions. We evaluated ShieldFS in real-world working conditions on real, personal machines, against samples from state of the art ransomware families. ShieldFS was able to detect the malicious activity at runtime and transparently recover all the original files. Although the models can be tuned to fit various filesystem usage profiles, our results show that our initial tuning yields high accuracy even on unseen samples and variants.


title: System level concurrency control for distributed database systems

abstract: A distributed database system is one in which the database is spread among several sites and application programs “move” from site to site to access and update the data they need. The concurrency control is that portion of the system that responds to the read and write requests of the application programs. Its job is to maintain the global consistency of the distributed database while ensuring that the termination of the application programs is not prevented by phenomena such as deadlock. We assume each individual site has its own local concurrency control which responds to requests at that site and can only communicate with concurrency controls at other sites when an application program moves from site to site, terminates, or aborts. This paper presents designs for several distributed concurrency controls and demonstrates that they work correctly. It also investigates some of the implications of global consistency of a distributed database and discusses phenomena that can prevent termination of application programs.


title: Array Designs for Long-Distance Wireless Power Transmission: State-of-the-Art and Innovative Solutions

abstract: The concept of long-range wireless power transmission (WPT) has been formulated shortly after the invention of high power microwave amplifiers. The promise of WPT, energy transfer over large distances without the need to deploy a wired electrical network, led to the development of landmark successful experiments, and provided the incentive for further research to increase the performances, efficiency, and robustness of these technological solutions. In this framework, the key-role and challenges in designing transmitting and receiving antenna arrays able to guarantee high-efficiency power transfer and cost-effective deployment for the WPT system has been soon acknowledged. Nevertheless, owing to its intrinsic complexity, the design of WPT arrays is still an open research field whose importance is growing as the possibility to transfer energy by means of electromagnetic waves gathers more and more interest from the applicative viewpoint. This paper is aimed at reviewing the array design approaches proposed in the state of the art for long-range wireless power transmission, highlighting the latest advances and innovative solutions as well as envisaging possible future trends of the research in this area.


title: The Survey of Data Mining Applications And Feature Scope

abstract: In this paper we have focused a variety of techniques, approaches and different areas of the research which are helpful and marked as the important field of data mining Technologies. As we are aware that many Multinational companies and large organizations are operated in different places of the different countries.Each place of operation may generate large volumes of data. Corporate decision makers require access from all such sources and take strategic decisions.The data warehouse is used in the significant business value by improving the effectiveness of managerial decision-making. In an uncertain and highly competitive business environment, the value of strategic information systems such as these are easily recognized however in todays business environment,efficiency or speed is not the only key for competitiveness.This type of huge amount of data are available in the form of tera-topeta-bytes which has drastically changed in the areas of science and engineering.To analyze,manage and make a decision of such type of huge amount of data we need techniques called the data mining which will transforming in many fields.This paper imparts more number of applications of the data mining and also focuses scope of the data mining which will helpful in the further research.


title: Networking Models in Flying Ad-Hoc Networks (FANETs): Concepts and Challenges

abstract: In recent years, the capabilities and roles of Unmanned Aerial Vehicles (UAVs) have rapidly evolved, and their usage in military and civilian areas is extremely popular as a result of the advances in technology of robotic systems such as processors, sensors, communications, and networking technologies. While this technology is progressing, development and maintenance costs of UAVs are decreasing relatively. The focus is changing from use of one large UAV to use of multiple UAVs, which are integrated into teams that can coordinate to achieve high-level goals. This level of coordination requires new networking models that can be set up on highly mobile nodes such as UAVs in the fleet. Such networking models allow any two nodes to communicate directly if they are in the communication range, or indirectly through a number of relay nodes such as UAVs. Setting up an ad-hoc network between flying UAVs is a challenging issue, and requirements can differ from traditional networks, Mobile Ad-hoc Networks (MANETs) and Vehicular Ad-hoc Networks (VANETs) in terms of node mobility, connectivity, message routing, service quality, application areas, etc. This paper identifies the challenges with using UAVs as relay nodes in an ad-hoc manner, introduces network models of UAVs, and depicts open research issues with analyzing opportunities and future work.


title: Enhanced Autofocus Algorithm Using Robust Focus Measure and Fuzzy Reasoning

abstract: A new passive autofocus algorithm consisting of a robust focus measure for object detection and fuzzy reasoning for target selection is presented. The proposed algorithm first detects objects distributed in the image using a mid-frequency discrete cosine transform focus measure and then selects the target object through fuzzy reasoning with three fuzzy membership functions. The proposed algorithm is designed as full digital blocks and fabricated using 0.35-mum CMOS technology. Experimental results obtained from various samples show that the present method is insensitive to Gaussian and impulsive noises and able to improve the quality of the image by focusing to the appropriate target object.


title: Role of Lormetazepam in the Treatment of Insomnia in the Elderly

abstract: Background and objective: Sleep architecture changes with age, both in terms of efficiency and total duration of sleep. Hypnotic benzodiazepines promote rapid onset of sleep, uninterrupted sleep and longer duration of sleep in the absence of carryover sedation the following morning; therefore, these may be appropriate for use in older patients. This study was performed to evaluate the efficacy and safety of lormetazepam in elderly patients with primary insomnia when used in association with sleep hygiene training (SHT). The impact of restored sleep on daily sleepiness was also investigated.


title: Adaptive mixture density estimation

abstract: Abstract A recursive, nonparametric method is developed for performing density estimation derived from mixture models, kernel estimation and stochastic approximation. The asymptotic performance of the method, dubbed “adaptive mixtures” (Priebe and Marchette, Pattern Recognition 24 , 1197–1209 (1991)) for its data-driven development of a mixture model approximation to the true density, is investigated using the method of sieves. Simulations are included indicating convergence properties for some simple examples.


title: Indoor Tracking: Theory, Methods, and Technologies

abstract: In the last decade, the research on and the technology for outdoor tracking have seen an explosion of advances. It is expected that in the near future, we will witness similar trends for indoor scenarios where people spend more than 70% of their lives. The rationale for this is that there is a need for reliable and high-definition real-time tracking systems that have the ability to operate in indoor environments, thus complementing those based on satellite technologies, such as the Global Positioning System (GPS). The indoor environments are very challenging, and as a result, a large variety of technologies have been proposed for coping with them, but no legacy solution has emerged. This paper presents a survey on indoor wireless tracking of mobile nodes from a signal processing perspective. It can be argued that the indoor tracking problem is more challenging than the problem on indoor localization. The reason is simple: From a set of measurements, one has to estimate not one location but a series of correlated locations of a mobile node. The paper illustrates the theory, the main tools, and the most promising technologies for indoor tracking. New directions of research are also discussed.


title: An Artificial Intelligence Approach to Financial Fraud Detection under IoT Environment: A Survey and Implementation

abstract: Financial fraud under IoT environment refers to the unauthorized use of mobile transaction using mobile platform through identity theft or credit card stealing to obtain money fraudulently. Financial fraud under IoT environment is the fast-growing issue through the emergence of smartphone and online transition services. In the real world, a highly accurate process of financial fraud detection under IoT environment is needed since financial fraud causes financial loss. Therefore, we have surveyed financial fraud methods using machine learning and deep learning methodology, mainly from 2016 to 2018, and proposed a process for accurate fraud detection based on the advantages and limitations of each research. Moreover, our approach proposed the overall process of detecting financial fraud based on machine learning and compared with artificial neural networks approach to detect fraud and process large amounts of financial data. To detect financial fraud and process large amounts of financial data, our proposed process includes feature selection, sampling, and applying supervised and unsupervised algorithms. The final model was validated by the actual financial transaction data occurring in Korea, 2015.


title: Ballistic Projectile Tracking Using CW Doppler Radar

abstract: The theory of operation is presented, together with experimental results, of a projectile target scoring system that determines highly accurate hit locations of small arms fire by processing constant frequency CW radar Doppler phase content into a trajectory estimate. We describe a new algorithm for extracting trajectory parameters from Doppler time series to obtain time, distance, and velocity at closest approach. Vector trajectories are derived by combining multiple scalar trajectories from three radar units. Live fire test results from a local range and Aberdeen Proving Ground are provided to show that a hit location accuracy of 4 cm can be achieved reliably on small arms.


title: Learning High-Level Judgments of Urban Perception

abstract: Human observers make a variety of perceptual inferences about pictures of places based on prior knowledge and experience. In this paper we apply computational vision techniques to the task of predicting the perceptual characteristics of places by leveraging recent work on visual features along with a geo-tagged dataset of images associated with crowd-sourced urban perception judgments for wealth, uniqueness, and safety. We perform extensive evaluations of our models, training and testing on images of the same city as well as training and testing on images of different cities to demonstrate generalizability. In addition, we collect a new densely sampled dataset of streetview images for 4 cities and explore joint models to collectively predict perceptual judgments at city scale. Finally, we show that our predictions correlate well with ground truth statistics of wealth and crime.


title: Common Lisp Object System specification

abstract: Introduction The Common Lisp Object System is an object-oriented extension toCommon Lisp as defined in Common Lisp: The Language, by GuyL. Steele Jr. It is based on generic functions, multipleinheritance, declarative method combination, and a meta-objectprotocol. The first two chapters of this specification present adescription of the standard Programmer Interface for the CommonLisp Object System. The first chapter contains a description of theconcepts of the Common Lisp Object System, and the second containsa description of the functions and macros in the Common Lisp ObjectSystem Programmer Interface. The chapter "The Common Lisp ObjectSystem Meta-Object Protocol" describes how the Common Lisp ObjectSystem can be customized. The fundamental objects of the Common Lisp Object System areclasses, instances, generic functions, and methods. A class object determines the structure and behavior of aset of other objects, which are called its instances. EveryCommon Lisp object is an instance of a class. The class ofan object determines the set of operations that can be performed onthe object. A generic function is a function whose behavior dependson the classes or identities of the arguments supplied to it. Ageneric function object contains a set of methods, a lambda-list, amethod combination type, and other information. The methodsdefine the class-specific behavior and operations of the genericfunction; a method is said to specialize a generic function.When invoked, a generic function executes a subset of its methodsbased on the classes of its arguments. A generic function can be used in the same ways that an ordinaryfunction can be used in Common Lisp; in particular, a genericfunction can be used as an argument to funcall andapply and can be given a global or a local name. A method is an object that contains a method function, asequence of parameter speclalizers that specify when thegiven method is applicable, and a sequence of qualifiersthat is used by the method combination facility todistinguish among methods. Each required formal parameter of eachmethod has an associated parameter specializer, and the method willbe invoked only on arguments that satisfy its parameterspecializers. The method combination facility controls the selection ofmethods, the order in which they are run, and the values that arereturned by the generic function. The Common Lisp Object Systemoffers a default method combination type and provides a facilityfor declaring new types of method combination.


Transliteration Better than Translation? Answering Code-mixed Questions over a Knowledge Base


title: On the perimeter of k pairwise disjoint convex bodies contained in a convex set in the plane

abstract: We prove the following isoperimetric inequality in R2, conjectured by Glazyrin and Moric. Given a convex body S and k pairwise disjoint convex bodies C1,...,Ck that are contained in S, then Σi=1kPer(Ci)≤Per(S)+2(k--1)Diam(S). Here Per(.) denotes the perimeter of a set and Diam(.) is the diameter of a set.


title: Beneficial Effects of Astragaloside IV for Hair Loss via Inhibition of Fas/Fas L-Mediated Apoptotic Signaling

abstract: Apoptosis with premature termination of hair follicle growth induces several types of hair loss and is one of the crucial factors of hair loss. Astragaloside IV, which is a major component of Astragalus membranaceus, is a cycloartane triterpene saponin. Although an anti-apoptotic effect of Astragaloside IV has been reported, its effects against hair loss have not been investigated. To explore the underlying mechanisms of Astragaloside IV on apoptotic signaling in hair follicle, the dorsal skin of depilated C57BL/6 mice was topically treated with 1 and 100 μM Astragaloside IV for 14 days. In Astragaloside IV-treated group, TUNEL-positive cells were reduced. We found that Astragaloside IV blocked the procaspase-8, resulting in the inhibition of caspase-3 and procaspase-9 activities. The changes were accompanied with down-regulation of Bax and p53, and up-regulation of Bcl-2 and Bcl-xL by Astragaloside IV treatment. In addition, activation of NF-κB and phosphorylation of IκB-α were inhibited, along with decreases in three MAPKs: ERK, SAPK/JNK and p38 by Astragaloside IV. The expressions of KGF, p21, TNF-α and IL-1β, which are keratinocyte terminal differentiation markers associated with catagen, were modulated by treatment with Astragaloside IV. These results demonstrated that Astragaloside IV is concerned with blocking the Fas/Fas L-mediated apoptotic pathway, which would be an alternative therapy for hair loss.


title: Kernel Entropy Component Analysis

abstract: We introduce kernel entropy component analysis (kernel ECA) as a new method for data transformation and dimensionality reduction. Kernel ECA reveals structure relating to the Renyi entropy of the input space data set, estimated via a kernel matrix using Parzen windowing. This is achieved by projections onto a subset of entropy preserving kernel principal component analysis (kernel PCA) axes. This subset does not need, in general, to correspond to the top eigenvalues of the kernel matrix, in contrast to the dimensionality reduction using kernel PCA. We show that kernel ECA may produce strikingly different transformed data sets compared to kernel PCA, with a distinct angle-based structure. A new spectral clustering algorithm utilizing this structure is developed with positive results. Furthermore, kernel ECA is shown to be an useful alternative for pattern denoising.


title: MR‐based synthetic CT generation using a deep convolutional neural network method

abstract: Purpose ::: Interests have been rapidly growing in the field of radiotherapy to replace CT with magnetic resonance imaging (MRI), due to superior soft tissue contrast offered by MRI and the desire to reduce unnecessary radiation dose. MR-only radiotherapy also simplifies clinical workflow and avoids uncertainties in aligning MR with CT. Methods, however, are needed to derive CT-equivalent representations, often known as synthetic CT (sCT), from patient MR images for dose calculation and DRR-based patient positioning. Synthetic CT estimation is also important for PET attenuation correction in hybrid PET-MR systems. We propose in this work a novel deep convolutional neural network (DCNN) method for sCT generation and evaluate its performance on a set of brain tumor patient images. ::: ::: Methods ::: The proposed method builds upon recent developments of deep learning and convolutional neural networks in the computer vision literature. The proposed DCNN model has 27 convolutional layers interleaved with pooling and unpooling layers and 35 million free parameters, which can be trained to learn a direct end-to-end mapping from MR images to their corresponding CTs. Training such a large model on our limited data is made possible through the principle of transfer learning and by initializing model weights from a pretrained model. Eighteen brain tumor patients with both CT and T1-weighted MR images are used as experimental data and a sixfold cross-validation study is performed. Each sCT generated is compared against the real CT image of the same patient on a voxel-by-voxel basis. Comparison is also made with respect to an atlas-based approach that involves deformable atlas registration and patch-based atlas fusion. ::: ::: Results ::: The proposed DCNN method produced a mean absolute error (MAE) below 85 HU for 13 of the 18 test subjects. The overall average MAE was 84.8 ± 17.3 HU for all subjects, which was found to be significantly better than the average MAE of 94.5 ± 17.8 HU for the atlas-based method. The DCNN method also provided significantly better accuracy when being evaluated using two other metrics: the mean squared error (188.6 ± 33.7 versus 198.3 ± 33.0) and the Pearson correlation coefficient(0.906 ± 0.03 versus 0.896 ± 0.03). Although training a DCNN model can be slow, training only need be done once. Applying a trained model to generate a complete sCT volume for each new patient MR image only took 9 s, which was much faster than the atlas-based approach. ::: ::: Conclusions ::: A DCNN model method was developed, and shown to be able to produce highly accurate sCT estimations from conventional, single-sequence MR images in near real time. Quantitative results also showed that the proposed method competed favorably with an atlas-based method, in terms of both accuracy and computation speed at test time. Further validation on dose computation accuracy and on a larger patient cohort is warranted. Extensions of the method are also possible to further improve accuracy or to handle multi-sequence MR images.


title: The moderating role of psychosocial well-being on the relationship between escapism and excessive online gaming

abstract: The purpose of this study was to empirically test the proposed theory of compensatory internet use, suggesting that people who play online games excessively are motivated to do so because they need to cope with psychosocial problems (Kardefelt-Winther, 2014a; Kardefelt-Winther, 2014b). The study used survey data from players of World of Warcraft (WoW), a popular MMO game. The indicators of psychosocial problems were high stress and low self-esteem and the motivation was escapism. The empirical analysis investigated interaction effects between indicators of psychosocial well-being and motivations. It was hypothesized that the relationship between escapism and negative outcomes would be positive for individuals with high stress or low self-esteem, which would be indicative of escapist online gaming as a coping strategy. However, this was only expected for individuals who experience more negative outcomes from their online gaming, which would highlight an important difference between those who experience many problems and those who experience few. The results showed that both stress and self-esteem moderated the relationship between escapism and negative outcomes as expected. In both cases, the relationship between escapism and negative outcomes was positive in the presence of more psychosocial problems (i.e. high stress or low self-esteem) for those who experience many negative outcomes, but not for those who experience few. The results support the theory of compensatory internet use and suggest that excessive online gaming may be a coping strategy for life problems rather than a mental disorder as proposed in DSM-V.


title: Frameworks to Support E-Business Growth Strategy

abstract: ABSTRACT It has been almost three decades since Nolan proposed his stages hypothesis. Since then several studies on the stages of growth theory appeared in the academic literature. Despite the criticism it received, the s-tages theory is acknowledged by both the academic community and the professional as one of the most popular models to chart the progression of the IS/IT within an organisation. Although the stages model is claimed to have a descriptive as well as prescriptive nature, little has been written on the transition strategy to a more mature stage. Planning an e-business initiative involves consideration of several important issues. The decisions and considerations regarding these issues or dimensions of e-business should be integrated to form a consistent, coherent and complete strategy for the e-business initiative. Using the stages of growth for e-business (SOGe) framework and the e-business support framework, this paper argued that a balanced, holistic and integrated consideration of these issues will lead to an effective and complete e-business strategy.


title: An integrated value-risk investigation of contactless mobile payments adoption

abstract: Display Omitted A model integrating perceived risk into a multi-sided perceived value is constructed.Broader view of the duality Gain-Cost in the value perception of an ICT is achieved.Psychological and privacy risks undermine adoption of contactless mobile payments.Value perception of contactless mobile payments is not influenced by media reports.Consumers see more gains than costs in the use of payments with NFC smartphones. The objective of this study is to investigate empirically consumer adoption views on credit card contactless payments with smartphones. Contactless Near Field Communication (NFC) mobile payments have unquestionable advantages but may also face some user doubts. To investigate consumer possible dual views about this new mobile service, a third-order innovative factor integrating value and risk perceptions was developed and validated empirically in an adoption model through a survey involving 289 participants. Findings indicate that the integrated value-risk perception is a significant factor of adoption of NFC payments with smartphones having utilitarian and enjoyment values as the main user motivators, and psychological and privacy risks as the most important deterrents. Overall, this study proposes a broader view of the consumer perceptions of value by integrating value and risk into an unbiased model of technology adoption.


title: Kinematics and Application of a Hybrid Industrial Robot - Delta-RST

abstract: Serial robots and parallel robots have their own pros and cons. While hybrid robots consisting of both of them are possible and expected to retain their merits and minimize the disadvantages. The Delta-RST presented here is such a hybrid robot built up by integrating a 3-DoFs traditional Delta parallel structure and a 3-DoFs RST robotic wrist. In this paper, we focus on its kinematics analysis and its applications in industry. Firstly, the robotic system of the Delta-RST will be described briefly. Then the complete and systemic kinematics of this kind of robot will be presented in detail, followed by simulations and applications to demonstrate the correctness of the analysis, as well as the effectiveness of the developed robotic system. The closed-form kinematic analysis results are universal for similar hybrid robots constructing with the Delta parallel mechanism and serial chains. Copyright © 2014 IFSA Publishing, S. L.


title: SpEnD: Linked Data SPARQL Endpoints Discovery Using Search Engines

abstract: In this study, a novel metacrawling method is proposed for discovering and monitoring linked data sources on the Web. We implemented the method in a prototype system, named SPARQL Endpoints Discovery (SpEnD). SpEnD starts with a "search keyword" discovery process for finding relevant keywords for the linked data domain and specifically SPARQL endpoints. Then, these search keywords are utilized to find linked data sources via popular search engines (Google, Bing, Yahoo, Yandex). By using this method, most of the currently listed SPARQL endpoints in existing endpoint repositories, as well as a significant number of new SPARQL endpoints, have been discovered. Finally, we have developed a new SPARQL endpoint crawler (SpEC) for crawling and link analysis.


title: A Survey of Face Morphing Techniques

abstract: Nowadays, face morphing is used in various fields of works such as computer animations, games and movies. Face morphing is an effect that shows a transition from one face image to another face image smoothly. Research on face morphing is as vast as the many interests and needs that can be found in the general public, television or film production. In this paper, we thus review the different morphing techniques that can be used to generate and manipulate faces. Due to the advantages in applying face morphing in various kinds of work, there are several works on face morphing and we can categorize them into three groups, based on their corresponding fields of works as Face Transfer, Facial Animation and Enhancement of Facial Attractiveness.


title: A Mutual Authentication and Key Establishment Scheme for M2M Communication in 6LoWPAN Networks

abstract: The machine-to-machine (M2M) communication, which plays a vital role in the Internet of Things (IoT), allows wireless and wired systems to monitor environments and exchange the information among various machines automatically without human interventions. In order to promote the development of the IoT and exploit the M2M applications, the Internet Engineering Task Force (IETF) has been developing a standard named Internet Protocol version 6 (IPv6) over low-power wireless personal area networks (6LoWPAN) to enable IP-based M2M devices to connect to the open Internet. Although the 6LoWPAN standard has specified the important issues in the M2M communications, various security issues have not been addressed. In this paper, an enhanced mutual authentication and key establishment scheme is designed for the M2M communications in 6LoWPAN networks. The proposed scheme enables a 6LoWPAN device to securely authenticate with the remote server with a session key established between them. The security proof by the protocol composition logic can prove the logic correctness of the proposed scheme. The formal verification and the simulation show that the proposed scheme in 6LoWPAN could not only enhance the security functionality with the ability to prevent various malicious attacks, but also incur less computational and transmission overhead.


title: Workflow Management and Databases

abstract: Workflow management systems are among the most interesting concepts for supporting modern organizations with a focus on processes rather than on structure. Workflow management systems offer different degrees of automation of business processes. We classify workflow management systems according to the features they provide and the types of processes they support. Database systems facilitate the realization of workflow management systems in several ways. They can provide the necessary functionality to keep the workflow relevant data, business data as well as process data. The dynamic execution of workflows can be handled by triggers of active database systems. Furthermore, the transaction concept can be extended to develop workflow transactions for consistent execution of workflows and intelligent treatment of exceptions and errors.


title: A Cross Tenant Access Control (CTAC) Model for Cloud Computing: Formal Specification and Verification

abstract: Sharing of resources on the cloud can be achieved on a large scale, since it is cost effective and location independent. Despite the hype surrounding cloud computing, organizations are still reluctant to deploy their businesses in the cloud computing environment due to concerns in secure resource sharing. In this paper, we propose a cloud resource mediation service offered by cloud service providers, which plays the role of trusted third party among its different tenants. This paper formally specifies the resource sharing mechanism between two different tenants in the presence of our proposed cloud resource mediation service. The correctness of permission activation and delegation mechanism among different tenants using four distinct algorithms (activation, delegation, forward revocation, and backward revocation) is also demonstrated using formal verification. The performance analysis suggests that the sharing of resources can be performed securely and efficiently across different tenants of the cloud.


title: Comparison of Pharmaceutical Equivalence for Compounded Preparations of Pergolide Mesylate

abstract: Compounding is any manipulation of a drug formulation that produces a dosage form other than that provided for in the directions for use on the labeling of the Food and Drug Administration (FDA)-approved drug product. Formulations produced by compounding pharmacies are not subject to consistent regulatory oversight. While the guidelines for safety, efficacy, potency, stability, and purity testing of compounded preparations are largely described in the United States Pharmacopeia (USP), enforcement of these guidelines is left up to individual state pharmacy boards and can vary from state to state. Also, although many compounding pharmacies may have internal standards and guidelines for their preparations, these requirements are not uniform across the industry. The compounding pharmacist is required to maintain various records including a reference for the formula used to prepare the compound and stability data testing to support the assigned beyond-use dating. Pharmacists should use evidence-based formulas for preparations to be used in veterinary species. Although some formula sources are pro


The Simplest Protocol for Oblivious Transfer


Virtual reality: software and technology


title: Cannabinoids in the management of difficult to treat pain

abstract: This article reviews recent research on cannabinoid analgesia via the endocannabinoid system and non-receptor mechanisms, as well as randomized clinical trials employing cannabinoids in pain treatment. Tetrahydrocannabinol (THC, Marinol®) and nabilone (Cesamet®) are currently approved in the United States and other countries, but not for pain indications. Other synthetic cannabinoids, such as ajulemic acid, are in development. Crude herbal cannabis remains illegal in most jurisdictions but is also under investigation. Sativex®, a cannabis derived oromucosal spray containing equal proportions of THC (partial CB1 receptor agonist ) and cannabidiol (CBD, a non-euphoriant, anti-inflammatory analgesic with CB1 receptor antagonist and endocannabinoid modulating effects) was approved in Canada in 2005 for treatment of central neuropathic pain in multiple sclerosis, and in 2007 for intractable cancer pain. Numerous randomized clinical trials have demonstrated safety and efficacy for Sativex in central and peripheral neuropathic pain, rheumatoid arthritis and cancer pain. An Investigational New Drug application to conduct advanced clinical trials for cancer pain was approved by the US FDA in January 2006. Cannabinoid analgesics have generally been well tolerated in clinical trials with acceptable adverse event profiles. Their adjunctive addition to the pharmacological armamentarium for treatment of pain shows great promise.


title: Intruder Detection Using Deep Learning and Association Rule Mining

abstract: With the upsurge of internet popularity, nowadays there are millions of online transactions that are being processed per minute thus increasing the possibilities of intruder attacks over the recent times. There have been various intruder detection techniques such as using traditional machine learning based algorithms. These algorithms were widely used to identify and prevent intruder activities in the recent past. Furthermore, multilayer neural networks[5] were also used in this regard to perform the detection. Hence multi-layer neural networks inherit fundamental drawbacks due to its inability to perform training due the problems such as overfitting, etc. In contrast, deep learning algorithms were introduced to overcome these issues effectively. We propose a novel framework to perform intruder detection and analysis using deep learning nets and association rule mining. We utilize a recurrent network to predict intruder activities and FP-Growth to perform the analysis. Our results show the effectiveness of our framework in detail.


title: Effects of intermediate coil on power transfer capability and efficiency in three-coil wireless power transfer system

abstract: Intermediate coils are used in wireless power transfer to improve system performance, such as power transfer capability and efficiency. However, they can have adverse effects if used improperly. This paper focuses on the coil coupling stage of three-coil series-compensated wireless power transfer systems and analyses the effects of the intermediate coil under different conditions. When the coupling between the transmitter and the receiver is high, the intermediate coil usually brings negative effects; only when the coupling is weak can it possibly improve power transfer capability or efficiency. Then, the criteria in deciding the necessity of adopting an intermediate coil and the conditions to be met to improve output power and efficiency are proposed. Simulation is carried out for verification. Experiments are done on an EV-based wireless power transfer prototype. When the coupling coefficient is as low as 0.05 between the transmitter and receiver, the coil coupling efficiency is improved from 84.7% to 91.0% with the help of a carefully-designed intermediate coil.


title: From road distraction to safe driving: Evaluating the effects of boredom and gamification on driving behaviour, physiological arousal, and subjective experience

abstract: Boredom and low levels of task engagement while driving can pose road safety risks, e.g., inattention during low traffic, routine trips, or semi-automated driving. Digital technology interventions that increase task engagement, e.g., through performance feedback, increased challenge, and incentives (often referred to as gamification), could therefore offer safety benefits. To explore the impact of such interventions, we conducted experiments in a high-fidelity driving simulator with thirty-two participants. In two counterbalanced conditions (control and intervention), we compared driving behaviour, physiological arousal, and subjective experience. Results indicate that the gamified boredom intervention reduced unsafe coping mechanisms such as speeding while promoting anticipatory driving. We can further infer that the intervention not only increased one's attention and arousal during the intermittent gamification challenges, but that these intermittent stimuli may also help sustain one's attention and arousal in between challenges and throughout a drive. At the same time, the gamified condition led to slower hazard reactions and short off-road glances. Our contributions deepen our understanding of driver boredom and pave the way for engaging interventions for safety critical tasks. Our driving simulator study addresses safety risks posed by boredom and low task engagement.A gamified intervention is studied as a means to increase engagement in the safe driving task.The intervention reduced speeding while promoting anticipatory driving.We infer that gamification may increase and sustain attention and arousal throughout a drive.


title: Simple and Minimum-Cost Satisfiability for Goal Models

abstract: Goal models have been used in Computer Science in order to represent software requirements, business objectives and design qualities. In previous work we have presented a formal framework for reasoning with goal models, in a qualitative or quantitative way, and we have introduced an algorithm for forward propagating values through goal models. In this paper we focus on the qualitative framework and we propose a technique and an implemented tool for addressing two much more challenging problems: (1) find an initial assignment of labels to leaf goals which satisfies a desired final status of root goals by upward value propagation, while respecting some given constraints; and (2) find an minimum cost assignment of labels to leaf goals which satisfies root goals. The paper also presents preliminary experimental results on the performance of the tool using the goal graph generated by a case study involving the Public Transportation Service of Trentino (Italy).


title: Cell segmentation in 3D confocal images using supervoxel merge-forests with CNN-based hypothesis selection

abstract: Automated segmentation approaches are crucial to quantitatively analyze large-scale 3D microscopy images. Particularly in deep tissue regions, automatic methods still fail to provide error-free segmentations. To improve the segmentation quality throughout imaged samples, we present a new supervoxel-based 3D segmentation approach that outperforms current methods and reduces the manual correction effort. The algorithm consists of gentle preprocessing and a conservative super-voxel generation method followed by supervoxel agglomeration based on local signal properties and a postprocessing step to fix under-segmentation errors using a Convolutional Neural Network. We validate the functionality of the algorithm on manually labeled 3D confocal images of the plant Arabidopis thaliana and compare the results to a state-of-the-art meristem segmentation algorithm.


title: A review on synchronization methods for grid-connected three-phase VSC under unbalanced and distorted conditions

abstract: The requirements for grid-connected voltage source converters (VSC) are increasing with the evolution towards smartgrids. In this context, synchronization methods for VSC have become a key feature for a fast and reliable control. The first synchronization methods, which were originally dedicated for communication applications have been gradually adapted to power systems applications and, today, an extensive collection of techniques exist. Although partial reviews and comparisons exist, a comprehensive classification of these approaches is needed. This paper presents an all-embracing survey of synchronization methods for grid-connected series and shunt VSC applications.


title: R3F: RDF triple filtering method for efficient SPARQL query processing

abstract: With the rapid growth in the amount of graph-structured Resource Description Framework (RDF) data, SPARQL query processing has received significant attention. The most important part of SPARQL query processing is its method of subgraph pattern matching. For this, most RDF stores use relation-based approaches, which can produce a vast number of redundant intermediate results during query evaluation. In order to address this problem, we propose an RDF Triple Filtering (R3F) method that exploits the graph-structural information of RDF data. We design a path-based index called the RDF Path index (RP-index) to efficiently provide filter data for the triple filtering. We also propose a relational operator called the RDF Filter (RFLT) that can conduct the triple filtering with little overhead compared to the original query processing. Through comprehensive experiments on large-scale RDF datasets, we demonstrate that R3F can effectively and efficiently reduce the number of redundant intermediate results and improve the query performance.


title: Evaluating NoSQL Document Oriented Data Model

abstract: Exabytes of data are created everyday based on the generated user information over Internet. Social networks, mobile devices, emails, blogs, videos, banking transactions and others, are now establishing a new digital channel between the brands and their audiences. Powerful tools are needed to store and explore this daily expanding data, in order to submit an easy and reliable processing of user information. Traditional modeling tools face their limits in this challenge, as the information keeps growing in volume and variety. That can be handled only by non-relational data models. From 10 years, document oriented data model is shaking up the standard relation data model. We will present in this paper an evaluation of this model using a set of predefined criteria.


title: ALGORITHM FOR MAXIMUM MATCHINGS IN BIPARTITE GRAPHS*

abstract: The present paper shows how to construct a maximum matching in a bipartite graph with n vertices and m edges in a number of computation steps proportional to $(m + n)\sqrt n $.


title: 3D Slicer as an image computing platform for the Quantitative Imaging Network

abstract: Abstract Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer.


title: Deep learning of binary hash codes for fast image retrieval

abstract: Approximate nearest neighbor search is an efficient strategy for large-scale image retrieval. Encouraged by the recent advances in convolutional neural networks (CNNs), we propose an effective deep learning framework to generate binary hash codes for fast image retrieval. Our idea is that when the data labels are available, binary codes can be learned by employing a hidden layer for representing the latent concepts that dominate the class labels. The utilization of the CNN also allows for learning image representations. Unlike other supervised methods that require pair-wised inputs for binary code learning, our method learns hash codes and image representations in a point-wised manner, making it suitable for large-scale datasets. Experimental results show that our method outperforms several state-of-the-art hashing algorithms on the CIFAR-10 and MNIST datasets. We further demonstrate its scalability and efficacy on a large-scale dataset of 1 million clothing images.


title: Recent Progress in Synthesis and Functionalization of Multimodal Fluorescent-Magnetic Nanoparticles for Biological Applications

abstract: There is a great interest in the development of new nanomaterials for multimodal imaging applications in biology and medicine. Multimodal fluorescent-magnetic based nanomaterials deserve particular attention as they can be used as diagnostic and drug delivery tools, which could facilitate the diagnosis and treatment of cancer and many other diseases. This review focuses on the recent developments of magnetic-fluorescent nanocomposites and their biomedical applications. The recent advances in synthetic strategies and approaches for the preparation of fluorescent-magnetic nanocomposites are presented. The main biomedical uses of multimodal fluorescent-magnetic nanomaterials, including biological imaging, cancer therapy and drug delivery, are discussed, and prospects of this field are outlined.


title: Multi-camera platform for panoramic real-time HDR video construction and rendering

abstract: High dynamic range (HDR) images are usually obtained by capturing several images of the scene at different exposures. Previous HDR video techniques adopted the same principle by stacking HDR frames in time domain. We designed a new multi-camera platform which is able to construct and render HDR panoramic video in real time, with $$1{,}024 \times 256$$1,024?256 resolution and a frame rate of 25 fps. We exploit the overlapping fields of view between the cameras with different exposures to create an HDR radiance map. We propose a method for HDR frame reconstruction which merges the previous HDR imaging techniques with the algorithms for panorama reconstruction. The developed FPGA-based processing system is able to reconstruct the HDR frame using the proposed method and tone map the resulting image using a hardware-adapted global operator. The measured throughput of the system is 245 MB/s, which is, up to our knowledge, among the fastest HDR video processing system.


title: Major technical advancements in apache hive

abstract: Apache Hive is a widely used data warehouse system for Apache Hadoop, and has been adopted by many organizations for various big data analytics applications. Closely working with many users and organizations, we have identified several shortcomings of Hive in its file formats, query planning, and query execution, which are key factors determining the performance of Hive. In order to make Hive continuously satisfy the requests and requirements of processing increasingly high volumes data in a scalable and efficient way, we have set two goals related to storage and runtime performance in our efforts on advancing Hive. First, we aim to maximize the effective storage capacity and to accelerate data accesses to the data warehouse by updating the existing file formats. Second, we aim to significantly improve cluster resource utilization and runtime performance of Hive by developing a highly optimized query planner and a highly efficient query execution engine. In this paper, we present a community-based effort on technical advancements in Hive. Our performance evaluation shows that these advancements provide significant improvements on storage efficiency and query execution performance. This paper also shows how academic research lays a foundation for Hive to improve its daily operations.


title: High Dynamic Range Image Display With Halo and Clipping Prevention

abstract: The dynamic range of an image is defined as the ratio between the highest and the lowest luminance level. In a high dynamic range (HDR) image, this value exceeds the capabilities of conventional display devices; as a consequence, dedicated visualization techniques are required. In particular, it is possible to process an HDR image in order to reduce its dynamic range without producing a significant change in the visual sensation experienced by the observer. In this paper, we propose a dynamic range reduction algorithm that produces high-quality results with a low computational cost and a limited number of parameters. The algorithm belongs to the category of methods based upon the Retinex theory of vision and was specifically designed in order to prevent the formation of common artifacts, such as halos around the sharp edges and clipping of the highlights, that often affect methods of this kind. After a detailed analysis of the state of the art, we shall describe the method and compare the results and performance with those of two techniques recently proposed in the literature and one commercial software.


title: Modified H-bridge multilevel inverter for Photovoltaic micro-grid systems

abstract: A modified multilevel inverter (MLI) structure has been presented for Photovoltaic (PV) fed 2.3 kV micro-grid applications. To feed the cascaded multilevel micro-grid connected inverter (CM-MGCI) voltage, a PV array is designed considering the environmental effects of Bangladesh. We have estimated the performance of the classical and a modified cascaded H-bridge (CHB) MLI with level-shifted carrier sinusoidal pulse width modulation (LSC-SPWM) technique in MATLAB/Simulink environment. The voltage and total harmonic distortion (THD) profile for the proposed eleven-level modified H-bridge MLI topology has been compared with the conventional CHB topology. The harmonic profile has also been compared with other modified MLI topologies. Compared to the conventional CHB and other recent modified MLI topologies, the proposed H-bridge CM-MGCI shows better THD profile.


title: The effect of site quality on repurchase intention in Internet shopping through mediating variables: The case of university students in South Korea

abstract: We performed a study to determine the influence that site quality has on repurchase intention of Internet shopping through customer satisfaction, customer trust, and customer commitment. Appropriate measures were developed and tested on 230 university students of Gyeongnam province in South Korea with a cross-sectional questionnaire survey. The results of the empirical analysis confirmed that site quality can be conceptualized as a composite of six dimensions of shopping convenience, site design, information usefulness, transaction security, payment system, and customer communication. Second, site quality positively affected customer satisfaction and customer trust, but did not affect customer commitment and repurchase intention. Third, site quality can affect repurchase intention by enhancing or attenuating customer satisfaction, customer trust, and customer commitment in online transaction situation. The mediating effect of customer satisfaction, customer trust, and customer commitment between site quality and repurchase intention is identified. Fourth, site quality indirectly affected customer commitment through customer satisfaction. Customer satisfaction indirectly affected repurchase intention through customer trust and customer commitment. Thus, it is found that site quality can be a very important factor to enhance repurchase intention in the customer perspective.


title: Fast burst images denoising

abstract: This paper presents a fast denoising method that produces a clean image from a burst of noisy images. We accelerate alignment of the images by introducing a lightweight camera motion representation called homography flow. The aligned images are then fused to create a denoised output with rapid per-pixel operations in temporal and spatial domains. To handle scene motion during the capture, a mechanism of selecting consistent pixels for temporal fusion is proposed to "synthesize" a clean, ghost-free image, which can largely reduce the computation of tracking motion between frames. Combined with these efficient solutions, our method runs several orders of magnitude faster than previous work, while the denoising quality is comparable. A smartphone prototype demonstrates that our method is practical and works well on a large variety of real examples.


title: Diode-triggered SCR (DTSCR) for RF-ESD protection of BiCMOS SiGe HBTs and CMOS ultra-thin gate oxides

abstract: A novel diode-triggered SCR (DTSCR) ESD protection element is introduced for low-voltage application (signal, supply voltage /spl les/1.8 V) and extremely narrow ESD design margins. Trigger voltage engineering in conjunction with fast and efficient SCR voltage clamping is applied for the protection of ultra-sensitive circuit nodes, such as SiGe HBT bases (e.g. f/sub Tmax/=45 GHz in BiCMOS-0.35 /spl mu/m LNA input) and thin gate-oxides (e.g. tox=1.7 nm in CMOS-0.09 /spl mu/m input). SCR integration is possible based on CMOS devices or can alternatively be formed by high-speed SiGe HBTs.


Similitude of ice-sheet dynamics against scaling of geometry and physical parameters


Description and evaluation of the tropospheric aerosol scheme in the Integrated Forecasting System ( IFS-AER , cycle 45 R 1 ) of ECMWF


title: Behavioral Analysis of Android Applications Using Automated Instrumentation

abstract: Google's Android operating system has become one the most popular operating system for hand-held devices. Dueto its ubiquitous use, open source nature and wide-spread popularity, it has become the target of recent mobile malware. In this paper, we present our efforts on effective security inspection mechanisms for identification of malicious applications for Android mobile applications. To achieve that, we developed a comprehensive software inspection framework. Moreover, to identify potential software reliability flaws and to triggermalware, we develop a transparent instrumentation system for automating user interactions with an Android application that does not require source code. Additionally, for run-time behavior analysis of an application, we monitor the I/O system calls generated the by application under monitoring to the underlying Linux kernel. As a case study, we present two Android malware samples found in the wild to experimentally evaluate the applicability of our proposed system for uncovering potential malicious activities.


title: Stable fuzzy self-tuning PID control of robot manipulators

abstract: In this paper we present a fuzzy self-tuning algorithm to select the Proportional, Integral and Derivative gains of a PID controller according to the actual state of a robotic manipulator. The stability via Lyapunov theory for the closed loop control system is also analyzed and shown that is locally asymptotically stable for a class of gain matrices depending on the manipulator states. This feature increases the potential of the PID control scheme to handle practical constraints in actual robots such as presence of actuators with limited torque capabilities. Experimental results on a two degrees of freedom robot arm shown the usefulness of the proposed approach.


title: A variable neighborhood search for graph coloring C

abstract: Descent methods for combinatorial optimization proceed by performing a sequence of local changes on an initial solution which improve each time the value of an objective function until a local optimum is found. Several metaheuristics have been proposed which extend in various ways this scheme and avoid being trapped in local optima. For example, Hansen and Mladenovic have recently proposed the variable neighborhood search method which has not yet been applied to many combinatorial optimization problems. The aim of this paper is to propose an adaptation of this new method to the graph coloring problem. � 2003 Published by Elsevier B.V.


title: Generalized Multiview Analysis: A discriminative latent space

abstract: This paper presents a general multi-view feature extraction approach that we call Generalized Multiview Analysis or GMA. GMA has all the desirable properties required for cross-view classification and retrieval: it is supervised, it allows generalization to unseen classes, it is multi-view and kernelizable, it affords an efficient eigenvalue based solution and is applicable to any domain. GMA exploits the fact that most popular supervised and unsupervised feature extraction techniques are the solution of a special form of a quadratic constrained quadratic program (QCQP), which can be solved efficiently as a generalized eigenvalue problem. GMA solves a joint, relaxed QCQP over different feature spaces to obtain a single (non)linear subspace. Intuitively, GMA is a supervised extension of Canonical Correlational Analysis (CCA), which is useful for cross-view classification and retrieval. The proposed approach is general and has the potential to replace CCA whenever classification or retrieval is the purpose and label information is available. We outperform previous approaches for textimage retrieval on Pascal and Wiki text-image data. We report state-of-the-art results for pose and lighting invariant face recognition on the MultiPIE face dataset, significantly outperforming other approaches.


title: Antibiotic resistance in Enterobacteriaceae: mechanisms and clinical implications.

abstract: Resistance of the Enterobacteriaceae to antibiotics, especially of the β lactam type, is increasingly dominated by the mobilization of continuously expressed single genes that encode efficient drug modifying enzymes. Strong and ubiquitous selection pressure has seemingly been accompanied by a shift from "natural" resistance, such as inducible chromosomal enzymes, membrane impermeability, and drug efflux, to the modern paradigm of mobile gene pools that largely determine the epidemiology of modern antibiotic resistance. In this way, antibiotic resistance is more available than ever before to organisms such as Escherichia coli and Klebsiella pneumoniae that are important causes of major sepsis. Modulation of the phenotype by host bacteria makes gene transmission less obvious and may in part explain why tracking and control of carbapenem resistance has been particularly problematic in the Enterobacteriaceae. This review discusses the underlying principles and clinical implications of the mobility and fixation of resistance genes and the exploitable opportunities and potential threats arising from apparent limitations on diversity in these mobile gene pools. It also provides some illustrative paradoxes and clinical corollaries, as well as a summary of future options.


title: Evolutionary Robotics for Legged Machines: From Simulation to Physical Reality

abstract: This talk will outline challenges and opportunities in translating evolutionary learning of autonomous robotics from simulation to reality. It covers evolution and adaptation of both morphology and control, hybrid co-evolution of reality and simulation, handling noise and uncertainty, and morphological adaptation in hardware.


title: Research on Dynamic Game between Government and Corporation Environmental Behavior and Optimal Strategies Based on Environmental Regulation

abstract: With the rapid development of industrialization,environmental pollution problem is getting worse.Environmental pollution regulation is an important part of the social regulation.Based on Regulatory Economics,environmental regulation and game analysis,the paper studies game between government and enterprise when government choosing emission tax as environmental regulation measure.By constructing a game model,the result indicates that: emission tax rate,penalty of enterprise violations,and green technology and reputation losses of company have effects on enterprise’s environmental strategy including reported pollution quantity and environment management,while different environmental regulation cannot affect enterprise’s pollution level directly.Therefore,improving the social responsibility consciousness,perfecting the environment regulation policy,inspiring technology innovation incentive is required to carry out pollution control work actively,and to enhance our country’s environmental regulation efficiency.


title: Remote Sensing of Irrigated Agriculture: Opportunities and Challenges

abstract: Over the last several decades, remote sensing has emerged as an effective tool to monitor irrigated lands over a variety of climatic conditions and locations. The objective of this review, which summarizes the methods and the results of existing remote sensing studies, is to synthesize principle findings and assess the state of the art. We take a taxonomic approach to group studies based on location, scale, inputs, and methods, in an effort to categorize different approaches within a logical framework. We seek to evaluate the ability of remote sensing to provide synoptic and timely coverage of irrigated lands in several spectral regions. We also investigate the value of archived data that enable comparison of images through time. This overview of the studies to date indicates that remote sensing-based monitoring of irrigation is at an intermediate stage of development at local scales. For instance, there is overwhelming consensus on the efficacy of vegetation indices in identifying irrigated fields. Also, single date imagery, acquired at peak growing season, may suffice to identify irrigated lands, although to multi-date image data are necessary for improved classification and to distinguish different crop types. At local scales, the mapping of irrigated lands with remote sensing is also strongly affected by the timing of image acquisition and the number of images used. At the regional and global scales, on the other hand, remote sensing has not been fully operational, as methods that work in one place and time are not necessarily transferable to other locations and periods. Thus, at larger scales, more work is required to indentify the best spectral indices, best time periods, and best classification methods under different climatological and cultural environments. Existing studies at regional scales also establish the fact that both remote sensing and national statistical approaches require further refinement with a substantial investment of time and resources for ground-truthing. An additional challenge in mapping irrigation across large areas occurs in fragmented landscapes with small irrigated and cultivated fields, where the spatial scale of observations is pitted against the need for high frequency temporal acquisitions. Finally, this review identifies passive and active microwave observations, advanced image classification methods, and data fusion including optical and radar sensors or with information from sources with multiple spatial and temporal characteristics as key areas where additional research is needed.


title: User pattern based online fraud detection and prevention using big data analytics and self organizing maps

abstract: Online banking is the one most common service availed by almost all banking customers in the current era. Every second the banking organization, generate enormous amount of valuable data from their customers and their transactions. These valuable data need to be saved and analysed effectively using big data analytic techniques so as to get the necessary insights for the banking organizations. In today's market trend, analysing large data sets comprising of variety of data is of high importance to discover hidden patterns, market tendencies, customer likings and other business insights. The purpose of this research paper is to suggest a machine learning and big data analytics technique to detect and prevent any fraudulent online transactions. The model allows storage of the huge volume of online transaction data, which is then cleaned and features were extracted and reduced using the principal component analysis method. The reduced features are used to train the machine learning model, which is used to identify and recognize the user patterns related to e-transactions. Any e-transactions carried out by the user, the algorithm first checks for the matching user patterns, if there is a match, then the transaction will be successful otherwise the transaction will be reported as fraudulent. Thus the stored patterns created by the self-organizing map algorithm will detect and prevent the unauthorized access on banking transactions.


title: Gravitational Field Equations and Theory of Dark Matter and Dark Energy

abstract: The main objective of this article is to derive a new set of gravitational field equations and to establish a new unified theory for dark energy and dark matter. The new gravitational field equations with scalar potential $\varphi$ are derived using the Einstein-Hilbert functional, and the scalar potential $\varphi$ is a natural outcome of the divergence-free constraint of the variational elements. Gravitation is now described by the Riemannian metric $g_{ij}$, the scalar potential $\varphi$ and their interactions, unified by the new gravitational field equations. Associated with the scalar potential $\varphi$ is the scalar potential energy density $\frac{c^4}{8\pi G} \Phi=\frac{c^4}{8\pi G} g^{ij}D_iD_j \varphi$, which represents a new type of energy caused by the non-uniform distribution of matter in the universe. The negative part of this potential energy density produces attraction, and the positive part produces repelling force. This potential energy density is conserved with mean zero: $\int_M \Phi dM=0$. The sum of this new potential energy density $\frac{c^4}{8\pi G} \Phi$ and the coupling energy between the energy-momentum tensor $T_{ij}$ and the scalar potential field $\varphi$ gives rise to a new unified theory for dark matter and dark energy: The negative part of this sum represents the dark matter, which produces attraction, and the positive part represents the dark energy, which drives the acceleration of expanding galaxies. In addition, the scalar curvature of space-time obeys $R=\frac{8\pi G}{c^4} T + \Phi$. Furthermore, the new field equations resolve a few difficulties encountered by the classical Einstein field equations.


Security Issues in Internet of Things (IoT): A Survey


title: Bilingual Word Embeddings from Parallel and Non-parallel Corpora for Cross-Language Text Classification

abstract: In many languages, sparse availability of resources causes numerous challenges for textual analysis tasks. Text classification is one of such standard tasks that is hindered due to limited availability of label information in lowresource languages. Transferring knowledge (i.e. label information) from high-resource to low-resource languages might improve text classification as compared to the other approaches like machine translation. We introduce BRAVE (Bilingual paRAgraph VEctors), a model to learn bilingual distributed representations (i.e. embeddings) of words without word alignments either from sentencealigned parallel or label-aligned non-parallel document corpora to support cross-language text classification. Empirical analysis shows that classification models trained with our bilingual embeddings outperforms other stateof-the-art systems on three different crosslanguage text classification tasks.


title: TheanoLM - An Extensible Toolkit for Neural Network Language Modeling

abstract: We present a new tool for training neural network language models (NNLMs), scoring sentences, and generating text. The tool has been written using Python library Theano, which allows researcher to easily extend it and tune any aspect of the training process. Regardless of the flexibility, Theano is able to generate extremely fast native code that can utilize a GPU or multiple CPU cores in order to parallelize the heavy numerical computations. The tool has been evaluated in difficult Finnish and English conversational speech recognition tasks, and significant improvement was obtained over our best back-off n-gram models. The results that we obtained in the Finnish task were compared to those from existing RNNLM and RWTHLM toolkits, and found to be as good or better, while training times were an order of magnitude shorter.


title: Management and orchestration challenges in network functions virtualization

abstract: NFV continues to draw immense attention from researchers in both industry and academia. By decoupling NFs from the physical equipment on which they run, NFV promises to reduce CAPEX and OPEX, make networks more scalable and flexible, and lead to increased service agility. However, despite the unprecedented interest it has gained, there are still obstacles that must be overcome before NFV can advance to reality in industrial deployments, let alone delivering on the anticipated gains. While doing so, important challenges associated with network and function MANO need to be addressed. In this article, we introduce NFV and give an overview of the MANO framework that has been proposed by ETSI. We then present representative projects and vendor products that focus on MANO, and discuss their features and relationship with the framework. Finally, we identify open MANO challenges as well as opportunities for future research.


title: Self-Driving Cars: A Survey

abstract: We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving cars is typically organized into the perception system and the decision-making system. The perception system is generally divided into many subsystems responsible for tasks such as self-driving-car localization, static obstacles mapping, moving obstacles detection and tracking, road mapping, traffic signalization detection and recognition, among others. The decision-making system is commonly partitioned as well into many subsystems responsible for tasks such as route planning, path planning, behavior selection, motion planning, and control. In this survey, we present the typical architecture of the autonomy system of self-driving cars. We also review research on relevant methods for perception and decision making. Furthermore, we present a detailed description of the architecture of the autonomy system of the self-driving car developed at the Universidade Federal do Esp\'irito Santo (UFES), named Intelligent Autonomous Robotics Automobile (IARA). Finally, we list prominent self-driving car research platforms developed by academia and technology companies, and reported in the media.


title: A Performance Comparison of Event Calendar Algorithms: an Empirical Approach

abstract: We propose a suite of tests based on two-state Markov chains for experimentally assessing the dynamic performance of a variety of simulation event calendar implementations. In contrast to previous studies based on the standard hold model for evaluation of performance statically, the proposed Markov hold model is more general and can be used to examine how different implementations respond dynamically to dependent sequences of insertion and deletion requests. The Markov hold model is used to conduct tests based on random, stressed, and correlated input sequences of requests, with performance measures including completion times, sensitivity to correlations, sensitivity to duplication, and efficiency of data-handling. We apply these tests to fourteen different event calendar implementations. To demonstrate the utility of the proposed model, we also include a comparison of the event calendar algorithms on a token ring protocol with bursty Markovian packet-traffic.


title: Effect of different packaging systems on the quality of tomato (Lycopersicon esculentum var. Rio Grande) fruits during storage

abstract: Summary ::: A packaging system is designed in our laboratory and evaluated for its suitability to extend storage life and improve the quality of tomato (var. Rio Grande) fruits at ambient temperature (Patent App. No. 484/2005). Freshly harvested mature green tomatoes were packed in polyethylene packaging with or without treating with calcium chloride, boric acid and potassium permanganate. The fruits were then evaluated for changes in quality parameters within the different stages of ripening. The treatments improved the storage life up to 96 days when compared with that of control (32 days). The results showed that within each ripening stage, the treated fruits remained better than that of control and all the ripening stages and treatments are significantly different (P < 0.05) from each other. It can also be deduced that treated fruits showed lower weight loss (%), total soluble solids (TSS) contents, acidity and spoilage incidence while ascorbic acid contents, sugar to acid ratio, flavour, texture, colour and overall acceptability were higher in treated fruits when compared with control at the red stage of ripening. Total sugars (%) were low in fruits treated with calcium chloride and boric acid with or without potassium permanganate.


title: Twitter spammer detection using data stream clustering

abstract: The rapid growth of Twitter has triggered a dramatic increase in spam volume and sophistication. The abuse of certain Twitter components such as ''hashtags'', ''mentions'', and shortened URLs enables spammers to operate efficiently. These same features, however, may be a key factor in identifying new spam accounts as shown in previous studies. Our study provides three novel contributions. Firstly, previous studies have approached spam detection as a classification problem, whereas we view it as an anomaly detection problem. Secondly, 95 one-gram features from tweet text were introduced alongside the user information analyzed in previous studies. Finally, to effectively handle the streaming nature of tweets, two stream clustering algorithms, StreamKM++ and DenStream, were modified to facilitate spam identification. Both algorithms clustered normal Twitter users, treating outliers as spammers. Each of these algorithms performed well individually, with StreamKM++ achieving 99% recall and a 6.4% false positive rate; and DenStream producing 99% recall and a 2.8% false positive rate. When used in conjunction, these algorithms reached 100% recall and a 2.2% false positive rate, meaning that our system was able to identify 100% of the spammers in our test while incorrectly detecting only 2.2% of normal users as spammers.


title: Cognitive Radio Wireless Sensor Networks: Applications, Challenges and Research Trends

abstract: A cognitive radio wireless sensor network is one of the candidate areas where cognitive techniques can be used for opportunistic spectrum access. Research in this area is still in its infancy, but it is progressing rapidly. The aim of this study is to classify the existing literature of this fast emerging application area of cognitive radio wireless sensor networks, highlight the key research that has already been undertaken, and indicate open problems. This paper describes the advantages of cognitive radio wireless sensor networks, the difference between ad hoc cognitive radio networks, wireless sensor networks, and cognitive radio wireless sensor networks, potential application areas of cognitive radio wireless sensor networks, challenges and research trend in cognitive radio wireless sensor networks. The sensing schemes suited for cognitive radio wireless sensor networks scenarios are discussed with an emphasis on cooperation and spectrum access methods that ensure the availability of the required QoS. Finally, this paper lists several open research challenges aimed at drawing the attention of the readers toward the important issues that need to be addressed before the vision of completely autonomous cognitive radio wireless sensor networks can be realized.