title: A survey on visual analysis approaches for financial data

abstract: Market participants and businesses have made tremendous efforts to make the best decisions in a timely manner under varying economic and business circumstances. As such, decision-making processes based on financial data have been a popular topic in industries. However, analyzing financial data is a non-trivial task due to large volume, diversity and complexity, and this has led to rapid research and development of visualizations and visual analytics systems for financial data exploration. Often, the development of such systems requires researchers to collaborate with financial domain experts to better extract requirements and challenges in their tasks. Work to systematically study and gather the task requirements and to acquire an overview of existing visualizations and visual analytics systems that have been applied in financial domains with respect to real-world data sets has not been completed. To this end, we perform a comprehensive survey of visualizations and visual analytics. In this work, we categorize financial systems in terms of data sources, applied automated techniques, visualization techniques, interaction, and evaluation methods. For the categorization and characterization, we utilize existing taxonomies of visualization and interaction. In addition, we present task requirements extracted from interviews with domain experts in order to help researchers design better systems with detailed goals.


title: Novel Sum-of-Sinusoids Simulation Models for Rayleigh and Rician Fading Channels

abstract: The statistical properties of Clarke's fading model with a finite number of sinusoids are analyzed, and an improved reference model is proposed for the simulation of Rayleigh fading channels. A novel statistical simulation model for Rician fading channels is examined. The new Rician fading simulation model employs a zero-mean stochastic sinusoid as the specular (line-of-sight) component, in contrast to existing Rician fading simulators that utilize a non-zero deterministic specular component. The statistical properties of the proposed Rician fading simulation model are analyzed in detail. It is shown that the probability density function of the Rician fading phase is not only independent of time but also uniformly distributed over [-pi, pi). This property is different from that of existing Rician fading simulators. The statistical properties of the new simulators are confirmed by extensive simulation results, showing good agreement with theoretical analysis in all cases. An explicit formula for the level-crossing rate is derived for general Rician fading when the specular component has non-zero Doppler frequency


title: Tomato plant disease classification in digital images using classification tree

abstract: The applications based on image processing for plant disease recognition and classification is the wide area of research these days. These applications are useful for timely recognition of plant disease. The disease like fungal, bacterial and virus are the destructive disease for any plant. In the study, five types of tomato diseases i.e. tomato late blight, Septoria spot, bacterial spot, bacterial canker, tomato leaf curl and healthy tomato plant leaf and stem images are classified. The classification conducted by extracting color, shape and texture features from healthy and unhealthy tomato plant image. The feature extraction process is done after the segmentation process. Extracted features from segmented images fed to classification tree. Finally, the disease classification was based on these six different types of classes. The classification of six types of tomato images yielded overall 97.3% of classification accuracy.


title: Improving the performance of GIS polygon overlay computation with MapReduce for spatial big data processing

abstract: As one of the important operations in Geographic Information System (GIS) spatial analysis, polygon overlay processing is a time-consuming task in many big data cases. In this paper, a specially designed MapReduce algorithm with grid index is proposed to decrease the running time. Our proposed algorithm can reduce the times of calling intersection computation by the aid of grid index. The experiment is carried out on the cloud framework based on Hadoop built by ourselves. Experimental results show that our algorithm with spatial grid index consumes less time than its peer without spatial index. Moreover, the proposed algorithm has an upward speed-up ratio when more nodes of Hadoop framework are used. Nevertheless, with the increase of nodes, the upward trend of speed-up ratio slows down.


title: Online robust image alignment via iterative convex optimization

abstract: In this paper we study the problem of online aligning a newly arrived image to previously well-aligned images. Inspired by recent advances in batch image alignment using low rank decomposition [ ], we treat the newly arrived image, after alignment, as being linearly and sparsely reconstructed by the well-aligned ones. The task is accomplished by a sequence of convex optimization that minimizes the l\-norm. After that, online basis updating is pursued in two different ways: (1) a two-stage incremental alignment for joint registration of a large image dataset which is known a prior, and (2) a greedy online alignment of dynamically increasing image sequences, such as in the tracking scenario. In (1), we first sequentially collect basis images that are easily aligned by checking their reconstruction residuals, followed by the second stage where all images are re-aligned one-by-one using the collected basis set. In (2), during the tracking process, we dynamically enrich the image basis set by the new target if it significantly distinguishes itself from existing basis images. While inheriting the benefits of sparsity, our method enjoys the great time efficiency and therefore be capable of dealing with large image set and real time tasks such as visual tracking. The efficacy of the proposed online robust alignment algorithm is verified with extensive experiments on image set alignment and visual tracking, in reference with state-of-the-art methods.


Long-Lived Planar Lipid Bilayer Membranes Anchored to an In Situ Polymerized Hydrogel


title: Evaluation of the DSC algorithm and the BSS color scheme in dense cellular-like IEEE 802.11ax deployments

abstract: Coping with the extreme growth of the number of users is one of the main challenges for the future IEEE 802.11 networks. The high interference level, along with the conventional standardized carrier sensing approaches, will degrade the network performance. To tackle these challenges, the Dynamic Sensitivity Control (DSC) and the BSS Color scheme are considered in IEEE 802.11ax and IEEE 802.11ah, respectively. The main purpose of these schemes is to enhance the network throughput and improve the spectrum efficiency in dense networks. In this paper, we evaluate the DSC and the BSS Color scheme along with the PARTIAL-AID (PAID) feature introduced in IEEE 802.11ac, in terms of throughput and fairness. We also, exploit the performance when the aforementioned techniques are combined. The simulations show a significant gain in total throughput when these techniques are applied.


title: Graph embedding: a general framework for dimensionality reduction

abstract: In the last decades, a large family of algorithms - supervised or unsupervised; stemming from statistic or geometry theory - have been proposed to provide different solutions to the problem of dimensionality reduction. In this paper, beyond the different motivations of these algorithms, we propose a general framework, graph embedding along with its linearization and kernelization, which in theory reveals the underlying objective shared by most previous algorithms. It presents a unified perspective to understand these algorithms; that is, each algorithm can be considered as the direct graph embedding or its linear/kernel extension of some specific graph characterizing certain statistic or geometry property of a data set. Furthermore, this framework is a general platform to develop new algorithm for dimensionality reduction. To this end, we propose a new supervised algorithm, Marginal Fisher Analysis (MFA), for dimensionality reduction by designing two graphs that characterize the intra-class compactness and inter-class separability, respectively. MFA measures the intra-class compactness with the distance between each data point and its neighboring points of the same class, and measures the inter-class separability with the class margins; thus it overcomes the limitations of traditional Linear Discriminant Analysis algorithm in terms of data distribution assumptions and available projection directions. The toy problem on artificial data and the real face recognition experiments both show the superiority of our proposed MFA in comparison to LDA.


title: A Compact Fractal Loop Rectenna for RF Energy Harvesting

abstract: This letter presents a compact fractal loop rectenna for RF energy harvesting at GSM1800 bands. First, a fractal loop antenna with novel in-loop ground-plane impedance matching is proposed for the rectenna design. Also, a high-efficiency rectifier is designed in the loop antenna to form a compact rectenna. Measured results show that an efficiency of 61% and an output dc voltage of 1.8 V have been achieved over 12-kΩ resistor for 10 μ W/cm2 power density at 1.8 GHz. The rectenna is able to power up a battery-less LCD watch at a distance of 10 m from the cell tower. The proposed rectenna is compact, easy to fabricate, and useful for various energy harvesting applications.


title: The SecretBlaze: A Configurable and Cost-Effective Open-Source Soft-Core Processor

abstract: Owing to the flexibility they offer, soft-core processors are becoming an attractive alternative for embedded system designs. Through the use of reconfigurable computing devices, such flexibility provides designers the opportunity to quickly uncover best design trade-offs and to achieve specific application goals. The present work introduces the Secret Blaze, a highly configurable and open-source RISC soft-core processor. Details about its modular approach that aims to strike the balance between the design quality and the efficiency of the data path are particularly discussed. Synthesis and performance results also demonstrates that the Secret Blaze is a very promising processor.


title: An Open-Source Research Platform for Embedded Visible Light Networking

abstract: Despite the growing interest in visible light communication, a reference networking platform based on commercial off-the-shelf components is not available yet. An open source platform would lower the barriers to entry of VLC network research and help the VLC community gain momentum. We introduce OpenVLC, an open source VLC research platform based on softwaredefined implementation. Built around a creditcard- sized embedded Linux platform with a simple opto-electronic transceiver front-end, OpenVLC offers a basic physical layer, a set of essential medium access primitives, as well as interoperability with Internet protocols. We investigate the performance of OpenVLC and show examples of how it can be used along with standard network diagnostics tools. Our software-defined implementation can currently reach throughput on the order of the basic rate of the IEEE 802.15.7 standard. We discuss several techniques that researchers and engineers could introduce to improve the performance of OpenVLC and envision several directions that can benefit from OpenVLC by adopting it as a reference platform.


title: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise

abstract: Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases. The well-known clustering algorithms offer no solution to the combination of these requirements. In this paper, we present the new clustering algorithm DBSCAN relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. We performed an experimental evaluation of the effectiveness and efficiency of DBSCAN using synthetic data and real data of the SEQUOIA 2000 benchmark. The results of our experiments demonstrate that (1) DBSCAN is significantly more effective in discovering clusters of arbitrary shape than the well-known algorithm CLAR-ANS, and that (2) DBSCAN outperforms CLARANS by a factor of more than 100 in terms of efficiency.


title: Earliness-Aware Deep Convolutional Networks for Early Time Series Classification

abstract: We present Earliness-Aware Deep Convolutional Networks (EA-ConvNets), an end-to-end deep learning framework, for early classification of time series data. Unlike most existing methods for early classification of time series data, that are designed to solve this problem under the assumption of the availability of a good set of pre-defined (often hand-crafted) features, our framework can jointly perform feature learning (by learning a deep hierarchy of \emph{shapelets} capturing the salient characteristics in each time series), along with a dynamic truncation model to help our deep feature learning architecture focus on the early parts of each time series. Consequently, our framework is able to make highly reliable early predictions, outperforming various state-of-the-art methods for early time series classification, while also being competitive when compared to the state-of-the-art time series classification algorithms that work with \emph{fully observed} time series data. To the best of our knowledge, the proposed framework is the first to perform data-driven (deep) feature learning in the context of early classification of time series data. We perform a comprehensive set of experiments, on several benchmark data sets, which demonstrate that our method yields significantly better predictions than various state-of-the-art methods designed for early time series classification. In addition to obtaining high accuracies, our experiments also show that the learned deep shapelets based features are also highly interpretable and can help gain better understanding of the underlying characteristics of time series data.


title: Applying K-harmonic means clustering to the part-machine classification problem

abstract: Cellular manufacturing system (CMS) is an application of group technology (GT) to the production environment. There are many advantages of CMS over traditional manufacturing systems like reduction in setup-time, throughput time, etc. The grouping of machine cells and their associated part families so as to minimize the cost of material handling is a major step in CMS and it is called as cell formation (CF) problem. Cell formation is important to the effective performance of manufacturing. In this paper, an attempt has been made to effectively apply the K-harmonic means clustering technique to form machine cells and part families simultaneously, which we call K-harmonic means cell formation (KHM-CF). A set of 20 test problems with various sizes drawn from the literature are used to test the performance of the proposed algorithm. Then, the results are compared with the optimal solution, and the efficacy of the proposed algorithms is discussed. The comparative study shows that the proposed KHM-CF algorithm improves the grouping efficacy for 70% of the test problems, and gives the same results for 30% of the test problems.


Coding Dialogs with the DAMSL Annotation Scheme


title: Automatic Objects Removal for Scene Completion

abstract: With the explosive growth of web-based cameras and mobile devices, billions of photographs are uploaded to the internet. We can trivially collect a huge number of photo streams for various goals, such as 3D scene reconstruction and other big data applications. However, this is not an easy task due to the fact the retrieved photos are neither aligned nor calibrated. Furthermore, with the occlusion of unexpected foreground objects like people, vehicles, it is even more challenging to find feature correspondences and reconstruct realistic scenes. In this paper, we propose a structure based image completion algorithm for object removal that produces visually plausible content with consistent structure and scene texture. We use an edge matching technique to infer the potential structure of the unknown region. Driven by the estimated structure, texture synthesis is performed automatically along the estimated curves. We evaluate the proposed method on different types of images: from highly structured indoor environment to the natural scenes. Our experimental results demonstrate satisfactory performance that can be potentially used for subsequent big data processing: 3D scene reconstruction and location recognition.


title: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power

abstract: Experimental analysis of the performance of a proposed method is a crucial and necessary task in an investigation. In this paper, we focus on the use of nonparametric statistical inference for analyzing the results obtained in an experiment design in the field of computational intelligence. We present a case study which involves a set of techniques in classification tasks and we study a set of nonparametric procedures useful to analyze the behavior of a method with respect to a set of algorithms, such as the framework in which a new proposal is developed. Particularly, we discuss some basic and advanced nonparametric approaches which improve the results offered by the Friedman test in some circumstances. A set of post hoc procedures for multiple comparisons is presented together with the computation of adjusted p-values. We also perform an experimental analysis for comparing their power, with the objective of detecting the advantages and disadvantages of the statistical tests described. We found that some aspects such as the number of algorithms, number of data sets and differences in performance offered by the control method are very influential in the statistical tests studied. Our final goal is to offer a complete guideline for the use of nonparametric statistical procedures for performing multiple comparisons in experimental studies.


title: New Learning—Old Methods? How E-research Might Change Technology-Enhanced Learning Research

abstract: That computer have changed how students learn and teachers teach—for better or worse—is well documented, amongst other sources by research reported in this book. In this chapter, we look at how computers (more generally, information technology) are beginning to change how learning research itself is conducted. An important driver of this change is the nature of the data that learning researchers analyze. While data used to be textual or numerical, they are becoming increasingly multimodal. And while data used to be gathered at one point in time or at a few distinct measurement points, learning researchers have now access to continuous streams of data. This raises for instance the basic question of when a research project is “finished.” Furthermore, IT is changing the methods of data analysis, the social orchestration of research, and the dissemination of research results—what used to be called publishing. For instance, research on a single project can now be distributed easily to involve many people with multiple specializations in many locations. Technology affords also to blur the distinction between “subjects” and researchers, enabling all kinds of new forms of participatory research. The ease of recording and storing data is the main driver of all these changes, but this convenience is a mixed blessing: an immediate problem is data deluge. Already today, only a small percentage of the data recorded in the modal design-based research study gets analyzed. Based on a review of how IT has been used to harness the deluge of data in other sciences, we describe new approaches to deal with massive amounts of complex data in the learning sciences (stemming, e.g., from log file recordings or from video recordings). Then, we will outline some aspects of research dissemination that are particularly relevant and important for the learning science research. We will conclude by discussing how new methods might affect the notion of theory in learning research and by outlining key challenges and a research agenda for the application of e-research methods in the learning sciences.


Importance of Cuminum cyminum L. and Carum carvi L. in traditional medicaments – a review


title: An extended Kalman filter for quaternion-based orientation estimation using MARG sensors

abstract: Presents an extended Kalman filter for real-time estimation of rigid body orientation using the newly developed MARG (magnetic, angular rate, and gravity) sensors. Each MARG sensor contains a three-axis magnetometer, a three-axis angular rate sensor, and a three-axis accelerometer. The filter represents rotations using quaternions rather than Euler angles, which eliminates the long-standing problem of singularities associated with attitude estimation. A process model for rigid body angular motions and angular rate measurements is defined. The process model converts angular rates into quaternion rates, which are integrated to obtain quaternions. The Gauss-Newton iteration algorithm is utilized to find the best quaternion that relates the measured accelerations and earth magnetic field in the body coordinate frame to calculated values in the earth coordinate frame. The best quaternion is used as part of the measurements for the Kalman filter. As a result of this approach, the measurement equations of the Kalman filter become linear, and the computational requirements are significantly reduced, making it possible to estimate orientation in real time. Extensive testing of the filter with synthetic data and actual sensor data proved it to be satisfactory. Test cases included the presence of large initial errors as well as high noise levels. In all cases the filter was able to converge and accurately track rotational motions.


title: Mobile Edge Computing: Opportunities, solutions, and challenges

abstract: Abstract The emergence of several new computing applications, such as virtual reality and smart environments, has become possible due to availability of large pool of cloud resources and services. However, the delay-sensitive applications pose strict delay requirements that transforms euphoria into a problem. The cloud computing paradigm is unable to meet the requirements of low latency, location awareness, and mobility support. In this context, Mobile Edge Computing (MEC) was introduced to bring the cloud services and resources closer to the user proximity by leveraging the available resources in the edge networks. In this paper, we present the definitions of the MEC given by researchers. Further, motivation of the MEC is highlighted by discussing various applications. We also discuss the opportunities brought by the MEC and some of the important research challenges are highlighted in MEC environment. A brief overview of accepted papers in our Special Issue on MEC is presented. Finally we conclude this paper by highlighting the key points and summarizing the paper.


title: The BGP visibility scanner

abstract: By tweaking the BGP configurations, the network operators are able to express their interdomain routing preferences, designed to accommodate a myriad goals. Given the complex interactions between policies in the Internet, the origin AS by itself cannot ensure that only by configuring a routing policy it can also achieve the anticipated results. Moreover, the definition of routing policies is a complicated process, involving a number of subtle tuning operations prone to errors. In this paper, we propose the BGP Visibility Scanner which allows network operators to validate the correct implementation of their routing policies, by corroborating the BGP routing information from approximatively 130 independent observation points in the Internet. We exemplify the use of the proposed methodology and also perform an initial validation for the BGP Visibility Scanner capabilities through various real operational use cases.


title: Psychophysical evidence for separate channels for the perception of form, color, movement and depth

abstract: Physiological and anatomical findings in the primate visual system, as well as clinical evidence in humans, suggest that different components of visual information processing are segregated into largely independent parallel pathways. Such a segregation leads to certain predictions about human vision. In this paper we describe psychophysical experiments on the interactions of color, form, depth, and movement in human perception, and we attempt to correlate these aspects of visual perception with the different subdivisions of the visual system.


title: Practical End-to-End Web Content Integrity

abstract: Widespread growth of open wireless hotspots has made it easy to carry out man-in-the-middle attacks and impersonate web sites. Although HTTPS can be used to prevent such attacks, its universal adoption is hindered by its performance cost and its inability to leverage caching at intermediate servers (such as CDN servers and caching proxies) while maintaining end-to-end security. To complement HTTPS, we revive an old idea from SHTTP, a protocol that offers end-to-end web integrity without confidentiality. We name the protocol HTTPi and give it an efficient design that is easy to deploy for today's web. In particular, we tackle several previously-unidentified challenges, such as supporting progressive page loading on the client's browser, handling mixed content, and defining access control policies among HTTP, HTTPi, and HTTPS content from the same domain. Our prototyping and evaluation experience show that HTTPi incurs negligible performance overhead over HTTP, can leverage existing web infrastructure such as CDNs or caching proxies without any modifications to them, and can make many of the mixed-content problems in existing HTTPS web sites easily go away. Based on this experience, we advocate browser and web server vendors to adopt HTTPi.


title: Detection of Alzheimer's disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC

abstract: Abstract Background We proposed a novel classification system to distinguish among elderly subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal controls (NC), based on 3D magnetic resonance imaging (MRI) scanning. Methods The method employed 3D data of 178 subjects consisting of 97 NCs, 57 MCIs, and 24 ADs. First, all these 3D MR images were preprocessed with atlas-registered normalization to form an averaged volumetric image. Then, 3D discrete wavelet transform (3D-DWT) was used to extract wavelet coefficients the volumetric image. The triplets (energy, variance, and Shannon entropy) of all subbands coefficients of 3D-DWT were obtained as feature vector. Afterwards, principle component analysis (PCA) was applied for feature reduction. On the basic of the reduced features, we proposed nine classification methods: three individual classifiers as linear SVM, kernel SVM, and kernel SVM trained by PSO with time-varying acceleration-coefficient (PSOTVAC), with three multiclass methods as Winner-Takes-All (WTA), Max-Wins-Voting, and Directed Acyclic Graph. Results The 5-fold cross validation results showed that the “WTA-KSVM + PSOTVAC” performed best over the OASIS benchmark dataset, with overall accuracy of 81.5% among all proposed nine classifiers. Moreover, the method “WTA-KSVM + PSOTVAC” exceeded significantly existing state-of-the-art methods (accuracies of which were less than or equal to 74.0%). Conclusion We validate the effectiveness of 3D-DWT. The proposed approach has the potential to assist in early diagnosis of ADs and MCIs.


title: Chabot: Retrieval from a Relational Database of Images

abstract: Selecting from a large, expanding collection of images requires carefully chosen search criteria. We present an approach that integrates a relational database retrieval system with a color analysis technique. The Chabot project was initiated at our university to study storage and retrieval of a vast collection of digitized images. These images are from the State of California Department of Water Resources. The goal was to integrate a relational database retrieval system with content analysis techniques that would give our querying system a better method for handling images. Our simple color analysis method, if used in conjunction with other search criteria, improves our ability to retrieve images efficiently. The best result is obtained when text-based search criteria are combined with content-based criteria and when a coarse granularity is used for content analysis. >


title: SpinLight: A High Accuracy and Robust Light Positioning System for Indoor Applications

abstract: This paper presents SpinLight, an indoor positioning system that uses infrared LED lamps as signal transmitters, and light sensors as receivers. The main idea is to divide the space into spatial beams originating from the light source, and identify each beam with a unique timed sequence of light signals. This sequence is created by a coded shade that covers and rotates around the LED, blocking the light or allowing it to pass through according to pre-defined patterns. The receiver, equipped with a light sensor, is able to determine its spatial beam by detecting the light signals, followed by optimization schemes to refine its location within that beam. We present both 2D and 3D localization designs, demonstrated by a prototype implementation. Experiments show that SpinLight produces a median location error of 3.8 cm, with a 95th percentile of 6.8 cm. The receiver design is very low power and thus can operate for months to years from a button coin battery.


title: Digital LCR meter using arduino

abstract: Measurement of electrical parameters such as Inductance (L), Capacitance (C) and Resistance (R)plays an integral role in the real world implementation of the circuits. This paper is focused on design and development of a LCR meter using simple techniques that leads a low cost device at the beginner level. The techniques are implemented on TINA software along with the hardware implementations using Arduino UNO. Results obtained from developed device are confirmed with commercial LCR meter.


title: Financial Market Prediction using Google Trends

abstract: Financial decisions are among the most significant life-changing decisions that individuals make. There is a strong correlation between financial decision making and human behavior. In this research the relationship between what people think and how stock market moves is investigated. The data from 2010 to 2015 of some of business, political and financial events which directly impact the local stock market in Pakistan is analyzed. The data was collected from search engine Google via Google trends. The association between internet searches regarding the political or business events and how the subsequent stock market moves is established. It was found that increase in search of these topics may lead to stock market fall or rise. The overall objective of this research is to predict Karachi Stock Exchange (now known as Pakistan stock exchange) 100 index by quantifying the semantics of international market. In addition to that, the relation between what an individual thinks while searching on Google which affects the local market is also investigated. The collected data has been mined by Multiclass Neural Network and Multiclass Decision Trees. The result shows that Multiclass Decision Trees performed best with an accuracy of 94%.


title: Voice Activity Detection based on Multi-Dilated Convolutional Neural Network

abstract: Voice activity detection (VAD) is important frontend of audio signal processing. Therefore, VAD system is required to have high performance with low computation cost. Contextual information (CI). CI is useful for improving performance in low signal-to-noise scenarios. We presents to apply Convolutional neural networks (CNN) for exploiting CI. Because CNN can efficiently control the duration of CI with convolution filter. Conventional CNN needs need a large number of parameters and computation cost when employing long-term CI. We apply the dilated convolution to utilize the long-term CI using few numbers of parameters. Dilated convolution is a method configuring subsampled large filter. Since the speech signal has a high correlation between adjacent frames, it is effective to use subsampled input signal with applying dilated convolution. We propose multi-dilated convolution to employing multiple CI. The multi-Dilated convolution layer extracts features of multiple CI by consisting filters having different filter size. We used TIMIT dataset for the speech data. We used 'A sound effect library' and Noise-X for noise data. Experimental result show the VAD system applied multi-dilated convolution layer has higher average performance than reference systems


title: Author-level metrics in the new academic profile platforms: The online behaviour of the Bibliometrics community

abstract: Abstract The new web-based academic communication platforms do not only enable researchers to better advertise their academic outputs, making them more visible than ever before, but they also provide a wide supply of metrics to help authors better understand the impact their work is making. This study has three objectives: a) to analyse the uptake of some of the most popular platforms (Google Scholar Citations, ResearcherID, ResearchGate, Mendeley and Twitter) by a specific scientific community (bibliometrics, scientometrics, informetrics, webometrics, and altmetrics); b) to compare the metrics available from each platform; and c) to determine the meaning of all these new metrics. To do this, the data available in these platforms about a sample of 811 authors (researchers in bibliometrics for whom a public profile Google Scholar Citations was found) were extracted. A total of 31 metrics were analysed. The results show that a high number of the analysed researchers only had a profile in Google Scholar Citations (159), or only in Google Scholar Citations and ResearchGate (142). Lastly, we find two kinds of metrics of online impact. First, metrics related to connectivity (followers), and second, all metrics associated to academic impact. This second group can further be divided into usage metrics (reads, views), and citation metrics. The results suggest that Google Scholar Citations is the source that provides more comprehensive citation-related data, whereas Twitter stands out in connectivity-related metrics.


title: Air-arm: A new kind of flying manipulator

abstract: We introduce a new kind of aerial manipulator based on multirotors. This device exploits the rarely used yaw movement which characterizes these aircrafts in order to generate and transmit forces and displacements trough a serial-chain of rigid links and other drones, as if the whole system was a flying robot arm with UAVS as rotational joints. Also we provide comparisons with the existing technologies and a condensed review concerning them. Finally, this paper proposes many open problems in order to develop the final system. Due to the scientific and social impact of this new approach, we provide some application examples.


title: Migration of a relational databases to NoSQL: The way forward

abstract: NoSQL databases are designed to solve data processing problems in volume. Growth data today pushes us to think about new concepts and approaches for converting data from a source database to a NoSQL database. This paper describes some approaches, methods of migration, database reverse engineering, and offers a migration approach from a relational database to a Document-oriented database NoSQL (MongoDB). The approach based on the achievement of a data model, from which we proceed to the migration of schema and data mapping, while ensuring the integrity constraints between collections.


title: Lightweight container-based OpenEPC deployment and its evaluation

abstract: The Network Function Virtualization (NFV) paradigm has become mature enough for network operators to start migrating their core architectures to it. NFV presents numerous advantages over the traditional hardware-centric approach in terms of cost, versatility, time-to-market and ease of deployment. Another technology that is experiencing rapid adoption is virtualization using containers. This technology allows the implementation of a virtualized environment which is very close to that obtained from a virtual machine (VM) hypervisor but without the overhead of running multiple operating systems (OSs) on a single machine, resulting in a significant performance boost. Given this background it is natural to seek solutions combining these two approaches. In our case we are interested in deploying OpenEPC, an EPC implementation developed by Core Network Dynamics, using containers. This is, as far as we know, the first container-based deployment of a virtualized LTE core network. In this paper we give a step-by-step description of the process we followed, perform some tests of the resulting platform and analyse its performance.


title: Comet assay in reconstructed 3D human epidermal skin models—investigation of intra- and inter-laboratory reproducibility with coded chemicals

abstract: Reconstructed 3D human epidermal skin models are being used increasingly for safety testing of chemicals. Based on EpiDerm™ tissues, an assay was developed in which the tissues were topically exposed to test chemicals for 3h followed by cell isolation and assessment of DNA damage using the comet assay. Inter-laboratory reproducibility of the 3D skin comet assay was initially demonstrated using two model genotoxic carcinogens, methyl methane sulfonate (MMS) and 4-nitroquinoline-n-oxide, and the results showed good concordance among three different laboratories and with in vivo data. In Phase 2 of the project, intra- and inter-laboratory reproducibility was investigated with five coded compounds with different genotoxicity liability tested at three different laboratories. For the genotoxic carcinogens MMS and N-ethyl-N-nitrosourea, all laboratories reported a dose-related and statistically significant increase (P < 0.05) in DNA damage in every experiment. For the genotoxic carcinogen, 2,4-diaminotoluene, the overall result from all laboratories showed a smaller, but significant genotoxic response (P < 0.05). For cyclohexanone (CHN) (non-genotoxic in vitro and in vivo, and non-carcinogenic), an increase compared to the solvent control acetone was observed only in one laboratory. However, the response was not dose related and CHN was judged negative overall, as was p-nitrophenol (p-NP) (genotoxic in vitro but not in vivo and non-carcinogenic), which was the only compound showing clear cytotoxic effects. For p-NP, significant DNA damage generally occurred only at doses that were substantially cytotoxic (>30% cell loss), and the overall response was comparable in all laboratories despite some differences in doses tested. The results of the collaborative study for the coded compounds were generally reproducible among the laboratories involved and intra-laboratory reproducibility was also good. These data indicate that the comet assay in EpiDerm™ skin models is a promising model for the safety assessment of compounds with a dermal route of exposure.


title: Effects of alternative electron donors, acceptors, and inhibitors on 2,4‐dichlorophenoxyacetic acid and 2,4,5‐trichlorophenoxyacetic acid dechlorination in soil

abstract: Abstract The potential for dechlorinating 2,4‐dichlorophenoxyacetic acid (2,4‐D) and 2,4,5‐trichlorophenoxyacetic acid (2,4,5‐T) in soil with a consortium showing stable dechlorinating activity was investigated. The effects of adding electron donors and/or acceptors under three anaerobic reducing conditions was compared. Results show that both 2,4‐D and 2,4,5‐T dechlorination rates were enhanced in methanogenic conditions, delayed in sulfate‐reducing conditions, and inhibited in denitrifying conditions. Also under the same three conditions dechlorination was be enhanced by the addition of lactate, pyruvate, and acetate, delayed by the addition of manganese oxide and vitamin B12, and inhibited by the addition of ferric chloride. Response to treatment with such microbial inhibitors as bromoethane sulfonic acid (BESA), vancomycin, and molybdate suggests that the major bacteria involved in 2,4‐D and 2,4,5‐T dechlonnation is methanogen followed joined by sulfate‐reducing bacteria and eubacteria.


Morality and the Professional Life: Values at Work


title: Application characteristics of permanent magnet synchronous and brushless DC motors for servo drives

abstract: The permanent magnet synchronous motor (PMSM) and the brushless DC motor (BDCM) have many similarities; they both have permanent magnets on the rotor and require alternating stator currents to produce constant torque. For application considerations, these two motor drives have to be differentiated on the basis of known engineering criteria. Some of the criteria used to assess these two machines include power density, torque per unit current, speed range, feedback devices, inverter rating, cogging torque, ripple torque, and parameter sensitivity. Guidelines for the appropriate machine to be used for a given application are given based on the results of the criteria. >


title: Total variation regularization for manifold-valued data

abstract: We consider total variation minimization for manifold valued data. We propose a cyclic proximal point algorithm and a parallel proximal point algorithm to minimize TV functionals with $\ell^p$-type data terms in the manifold case. These algorithms are based on iterative geodesic averaging which makes them easily applicable to a large class of data manifolds. As an application, we consider denoising images which take their values in a manifold. We apply our algorithms to diffusion tensor images, interferometric SAR images as well as sphere and cylinder valued images. For the class of Cartan-Hadamard manifolds (which includes the data space in diffusion tensor imaging) we show the convergence of the proposed TV minimizing algorithms to a global minimizer.


title: TabletGaze: Unconstrained Appearance-based Gaze Estimation in Mobile Tablets

abstract: We study gaze estimation on tablets, our key design goal is uncalibrated gaze estimation using the front-facing camera during natural use of tablets, where the posture and method of holding the tablet is not constrained. We collected the first large unconstrained gaze dataset of tablet users, labeled Rice TabletGaze dataset. The dataset consists of 51 subjects, each with 4 different postures and 35 gaze locations. Subjects vary in race, gender and in their need for prescription glasses, all of which might impact gaze estimation accuracy. Driven by our observations on the collected data, we present a TabletGaze algorithm for automatic gaze estimation using multi-level HoG feature and Random Forests regressor. The TabletGaze algorithm achieves a mean error of 3.17 cm. We perform extensive evaluation on the impact of various factors such as dataset size, race, wearing glasses and user posture on the gaze estimation accuracy and make important observations about the impact of these factors.


title: Irrelevant speech , articulatory suppression , and phonological similarity : A test of the phonological loop model and the feature model

abstract: Two experiments tested competing predictions about the nature of the irrelevant speech effect that were derived from Neath’s (2000) feature model and from Salame and Baddeley’s (1982) phonological loop model. The first experiment examined the combined effects of irrelevant speech and articulatory suppression when target items were presented auditorily. Contrary to the suggestions of Neath, but consistent with the phonological loop model, the effects of articulatory suppression and irrelevant speech were additive even when the irrelevant speech was presented during the retention interval. The second experiment examined the combined effects of irrelevant speech and phonological similarity when target items were presented visually. Consistent with the phonological loop model, the effects of phonological similarity and irrelevant speech were additive when participants were specifically instructed to use articulatory/phonological rehearsal to remember the list items. The results therefore contradicted Neath’s claim that irrelevant speech abolishes the phonological similarity effect when list items are presented visually. However, the effect of phonological similarity was abolished in the irrelevant speech conditions when no instructions were given concerning rehearsal. It is argued that the phonological similarity effect disappears in some experiments because participants sometimes employ a semantic rehearsal strategy, consistent with the views of Salame and Baddeley (1986).


title: Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model

abstract: We propose a model structure with a double-layer hidden Markov model (HMM) to recognise driving intention and predict driving behaviour. The upper-layer multi-dimensional discrete HMM (MDHMM) in the double-layer HMM represents driving intention in a combined working case, constructed according to the driving behaviours in certain single working cases in the lower-layer multi-dimensional Gaussian HMM (MGHMM). The driving behaviours are recognised by manoeuvring the signals of the driver and vehicle state information, and the recognised results are sent to the upper-layer HMM to recognise driving intentions. Also, driving behaviours in the near future are predicted using the likelihood-maximum method. A real-time driving simulator test on the combined working cases showed that the double-layer HMM can recognise driving intention and predict driving behaviour accurately and efficiently. As a result, the model provides the basis for pre-warning and intervention of danger and improving comfort performance.


title: Improved driver modeling for human-in-the-loop vehicular control

abstract: In order to develop provably safe human-in-the-loop systems, accurate and precise models of human behavior must be developed. Driving is a good example of such a system because the driver has full control of the vehicle, and her likely actions are highly dependent on her mental state and the context of the current situation. This paper presents a testbed for collecting driver data that allows us to collect realistic data, while maintaining safety and control of the environmental surroundings. We extend previous work that focuses on set predictions consisting of trajectories observed from the nonlinear dynamics and behaviors of the human driven car, accounting for the driver mental state, the context or situation that the vehicle is in, and the surrounding environment in both highway and intersection scenarios. This allows us to predict driving behavior over long time horizons with extremely high accuracy. By using this realistic data and flexible algorithm, a precise and accurate driver model can be developed that is tailored to an individual and usable in semi-autonomous frameworks.


title: Deep Predictive Models for Collision Risk Assessment in Autonomous Driving

abstract: In this paper, we investigate a predictive approach for collision risk assessment in autonomous and assisted driving. A deep predictive model is trained to anticipate imminent accidents from traditional video streams. In particular, the model learns to identify cues in RGB images that are predictive of hazardous upcoming situations. In contrast to previous work, our approach incorporates (a) temporal information during decision making, (b) multi-modal information about the environment, as well as the proprioceptive state and steering actions of the controlled vehicle, and (c) information about the uncertainty inherent to the task. To this end, we discuss Deep Predictive Models and present an implementation using a Bayesian Convolutional LSTM. Experiments in a simple simulation environment show that the approach can learn to predict impending accidents with reasonable accuracy, especially when multiple cameras are used as input sources.


title: Diagnosing wind turbine faults using machine learning techniques applied to operational data

abstract: Unscheduled or reactive maintenance on wind turbines due to component failures incurs significant downtime and, in turn, loss of revenue. To this end, it is important to be able to perform maintenance before it's needed. By continuously monitoring turbine health, it is possible to detect incipient faults and schedule maintenance as needed, negating the need for unnecessary periodic checks. To date, a strong effort has been applied to developing Condition monitoring systems (CMSs) which rely on retrofitting expensive vibration or oil analysis sensors to the turbine. Instead, by performing complex analysis of existing data from the turbine's Supervisory Control and Data Acquisition (SCADA) system, valuable insights into turbine performance can be obtained at a much lower cost. In this paper, data is obtained from the SCADA system of a turbine in the South-East of Ireland. Fault and alarm data is filtered and analysed in conjunction with the power curve to identify periods of nominal and fault operation. Classification techniques are then applied to recognise fault and fault-free operation by taking into account other SCADA data such as temperature, pitch and rotor data. This is then extended to allow prediction and diagnosis in advance of specific faults. Results are provided which show success in predicting some types of faults.


title: Dermatologist-level classification of skin cancer with deep neural networks

abstract: An artificial intelligence trained to classify images of skin lesions as benign lesions or malignant skin cancers achieves the accuracy of board-certified dermatologists.


title: Artistic Data Visualization : Beyond Visual Analytics

abstract: Information visualization is traditionally viewed as a tool for data exploration and hypothesis formation. Because of its roots in scientific reasoning, visualization has traditionally been viewed as an analytical tool for sensemaking. In recent years, however, both the mainstreaming of computer graphics and the democratization of data sources on the Internet have had important repercussions in the field of information visualization. With the ability to create visual representations of data on home computers, artists and designers have taken matters into their own hands and expanded the conceptual horizon of infovis as artistic practice. This paper presents a brief survey of projects in the field of artistic information visualization and a preliminary examination of how artists appropriate and repurpose "scientific" techniques to create pieces that actively guide analytical reasoning and encourage a contextualized reading of their subject matter.


title: Pose-based human action recognition with Extreme Gradient Boosting

abstract: This Paper investigate action recognition by using Extreme Gradient Boosting (XGBoost). XGBoost is a supervised classification technique using an ensemble of decision trees. In this study, we also compare the performance of Xboost using another machine learning techniques Support Vector Machine (SVM) and Naive Bayes (NB). The experimental study on the human action dataset shows that XGBoost better as compared to SVM and NB in classification accuracy. Although takes more computational time the XGBoost performs good classification on action recognition.


title: Action and Interaction Recognition in First-Person Videos

abstract: In this work, we evaluate the performance of the popular dense trajectories approach on first-person action recognition datasets. A person moving around with a wearable camera will actively interact with humans and objects and also passively observe others interacting. Hence, in order to represent real-world scenarios, the dataset must contain actions from first-person perspective as well as third-person perspective. For this purpose, we introduce a new dataset which contains actions from both the perspectives captured using a head-mounted camera. We employ a motion pyramidal structure for grouping the dense trajectory features. The relative strengths of motion along the trajectories are used to compute different bag-of-words descriptors and concatenated to form a single descriptor for the action. The motion pyramidal approach performs better than the baseline improved trajectory descriptors. The method achieves 96.7% on the JPL interaction dataset and 61.8% on our NUS interaction dataset. The same is used to detect actions in long video sequences and achieves average precision of 0.79 on JPL interaction dataset.


title: Communication and networking of UAV-based systems: Classification and associated architectures☆

abstract: Abstract Many advancements have been taking place in unmanned aerial vehicle (UAV) technology lately. This is leading towards the design and development of UAVs with various sizes that possess increased on-board processing, memory, storage, and communication capabilities. Consequently, UAVs are increasingly being used in a vast amount of commercial, military, civilian, agricultural, and environmental applications. However, to take full advantages of their services, these UAVs must be able to communicate efficiently with each other using UAV-to-UAV (U2U) communication and with existing networking infrastructures using UAV-to-Infrastructure (U2I) communication. In this paper, we identify the functions, services and requirements of UAV-based communication systems. We also present networking architectures, underlying frameworks, and data traffic requirements in these systems as well as outline the various protocols and technologies that can be used at different UAV communication links and networking layers. In addition, the paper discusses middleware layer services that can be provided in order to provide seamless communication and support heterogeneous network interfaces. Furthermore, we discuss a new important area of research, which involves the use of UAVs in collecting data from wireless sensor networks (WSNs). We discuss and evaluate several approaches that can be used to collect data from different types of WSNs including topologies such as linear sensor networks (LSNs), geometric and clustered WSNs. We outline the benefits of using UAVs for this function, which include significantly decreasing sensor node energy consumption, lower interference, and offers considerably increased flexibility in controlling the density of the deployed nodes since the need for the multihop approach for sensor-to-sink communication is either eliminated or significantly reduced. Consequently, UAVs can provide good connectivity to WSN clusters.


title: A framework for three-dimensional simulation of morphogenesis

abstract: We present COMPUCELL3D, a software framework for three-dimensional simulation of morphogenesis in different organisms. COMPUCELL3D employs biologically relevant models for cell clustering, growth, and interaction with chemical fields. COMPUCELL3D uses design patterns for speed, efficient memory management, extensibility, and flexibility to allow an almost unlimited variety of simulations. We have verified COMPUCELL3D by building a model of growth and skeletal pattern formation in the avian (chicken) limb bud. Binaries and source code are available, along with documentation and input files for sample simulations, at http:// compucell.sourceforge.net.


title: Comparing Graph Representations of Protein Structure for Mining Family-Specific Residue-Based Packing Motifs

abstract: We find recurring amino-acid residue packing patterns, or spatial motifs, that are characteristic of protein structural families, by applying a novel frequent subgraph mining algorithm to graph representations of protein three-dimensional structure. Graph nodes represent amino acids, and edges are chosen in one of three ways: first, using a threshold for contact distance between residues; second, using Delaunay tessellation; and third, using the recently developed almost-Delaunay edges. For a set of graphs representing a protein family from the Structural Classification of Proteins (SCOP) database, subgraph mining typically identi- fies several hundred common subgraphs corresponding to spatial motifs that are frequently found in proteins in the family but rarely found outside of it. We find that some of the large motifs map onto known functional regions in two protein families explored in this study, i.e., serine proteases and kinases. We find that graphs based on almost-Delaunay edges significantly reduc...


title: Automatic Assessment of Open Ended Questions with a Bleu -Inspired Algorithm and Shallow NLP

abstract: This paper compares the accuracy of several variations of the Bleu algorithm when applied to automatically evaluating student essays. The different configurations include closed-class word removal, stemming, two baseline word-sense disambiguation procedures, and translating the texts into a simple semantic representation. We also prove empirically that the accuracy is kept when the student answers are translated automatically. Although none of the representations clearly outperform the others, some conclusions are drawn from the results.


title: An Overview of Perception Methods for Horticultural Robots: From Pollination to Harvest

abstract: Horticultural enterprises are becoming more sophisticated as the range of the crops they target expands. Requirements for enhanced efficiency and productivity have driven the demand for automating on-field operations. However, various problems remain yet to be solved for their reliable, safe deployment in real-world scenarios. This paper examines major research trends and current challenges in horticultural robotics. Specifically, our work focuses on sensing and perception in the three main horticultural procedures: pollination, yield estimation, and harvesting. For each task, we expose major issues arising from the unstructured, cluttered, and rugged nature of field environments, including variable lighting conditions and difficulties in fruit-specific detection, and highlight promising contemporary studies.


title: Monitoring the transaction selection policy of Bitcoin mining pools

abstract: Mining pools are collection of workers that work together as a group in order to collaborate in the proof of work and reduce the variance of their rewards when mining. In order to achieve this, Mining pools distribute amongst the workers the task of finding a block so that each worker works on a different subset of the candidate solutions. In most mining pools the selection of transactions to be part of the next block is performed by the pool manager and thus becomes more centralized. A mining Pool is expected to give priority to the most lucrative transactions in order to increase the block reward however changes to the transaction policy done without notification of workers would be difficult to detect. In this paper we treat the transaction selection policy performed by miners as a classification problem; for each block we create a dataset, separate them by mining pool and apply feature selection techniques to extract a vector of importance for each feature. We then track variations in feature importance as new blocks arrive and show using a generated scenario how a change in policy by a mining pool could be detected.


title: Formal worst-case timing analysis of Ethernet TSN's time-aware and peristaltic shapers

abstract: Ethernet is considered as a future communication standard for distributed embedded systems in the automotive and industrial domains. A key challenge is the deterministic low-latency transport of Ethernet frames, as many safety-critical real-time applications in these domains have tight timing requirements. Time-sensitive networking (TSN) is an upcoming set of Ethernet standards, which (among other things) address these requirements by specifying new quality of service mechanisms in the form of different traffic shapers. In this paper, we consider TSN's time-aware and peristaltic shapers and evaluate whether these shapers are able to fulfill these strict timing requirements. We present a formal timing analysis, which is a key requirement for the adoption of Ethernet in safety-critical real-time systems, to derive worst-case latency bounds for each shaper. We use a realistic automotive Ethernet setup to compare these shapers to each other and against Ethernet following IEEE 802.1Q.


title: The effects of paranodal myelin damage on action potential depend on axonal structure

abstract: Biophysical computational models of axons provide an important tool for quantifying the effects of injury and disease on signal conduction characteristics. Several studies have used generic models to study the average behavior of healthy and injured axons; however, few studies have included the effects of normal structural variation on the simulated axon’s response to injury. The effects of variations in physiological characteristics on axonal function were mapped by altering the structure of the nodal, paranodal, and juxtaparanodal regions across reported values in three different caliber axons (1, 2, and 5.7 μm). Myelin detachment and retraction were simulated to quantify the effects of each injury mechanism on signal conduction. Conduction velocity was most affected by axonal fiber diameter (89%), while membrane potential amplitude was most affected by nodal length (86%) in healthy axons. Postinjury axonal functionality was most affected by myelin detachment in the paranodal and juxtaparanodal regions when retraction and detachment were modeled simultaneously. The efficacy of simulated potassium channel blockers on restoring membrane potential and velocity varied with axonal caliber and injury type. The structural characteristics of axons affect their functional response to myelin retraction and detachment and their subsequent response to potassium channel blocker treatment.


title: Deep Learning with Energy-efficient Binary Gradient Cameras

abstract: Power consumption is a critical factor for the deployment of embedded computer vision systems. We explore the use of computational cameras that directly output binary gradient images to reduce the portion of the power consumption allocated to image sensing. We survey the accuracy of binary gradient cameras on a number of computer vision tasks using deep learning. These include object recognition, head pose regression, face detection, and gesture recognition. We show that, for certain applications, accuracy can be on par or even better than what can be achieved on traditional images. We are also the first to recover intensity information from binary spatial gradient images--useful for applications with a human observer in the loop, such as surveillance. Our results, which we validate with a prototype binary gradient camera, point to the potential of gradient-based computer vision systems.


title: Hardening Quantum Machine Learning Against Adversaries

abstract: Security for machine learning has begun to become a serious issue for present day applications. An important question remaining is whether emerging quantum technologies will help or hinder the security of machine learning. Here we discuss a number of ways that quantum information can be used to help make quantum classifiers more secure or private. In particular, we demonstrate a form of robust principal component analysis that, under some circumstances, can provide an exponential speedup relative to robust methods used at present. To demonstrate this approach we introduce a linear combinations of unitaries Hamiltonian simulation method that we show functions when given an imprecise Hamiltonian oracle, which may be of independent interest. We also introduce a new quantum approach for bagging and boosting that can use quantum superposition over the classifiers or splits of the training set to aggregate over many more models than would be possible classically. Finally, we provide a private form of $k$--means clustering that can be used to prevent an all powerful adversary from learning more than a small fraction of a bit from any user. These examples show the role that quantum technologies can play in the security of ML and vice versa. This illustrates that quantum computing can provide useful advantages to machine learning apart from speedups.


title: Adaptive segmentation of MRI data

abstract: Intensity-based classification of MR images has proven problematic, even when advanced techniques are used. Intrascan and interscan intensity inhomogeneities are a common source of difficulty. While reported methods have had some success in correcting intrascan inhomogeneities, such methods require supervision for the individual scan. This paper describes a new method called adaptive segmentation that uses knowledge of tissue intensity properties and intensity inhomogeneities to correct and segment MR images. Use of the expectation-maximization (EM) algorithm leads to a method that allows for more accurate segmentation of tissue types as well as better visualization of magnetic resonance imaging (MRI) data, that has proven to be effective in a study that includes more than 1000 brain scans. Implementation and results are described for segmenting the brain in the following types of images: axial (dual-echo spin-echo), coronal [three dimensional Fourier transform (3-DFT) gradient-echo T1-weighted] all using a conventional head coil, and a sagittal section acquired using a surface coil. The accuracy of adaptive segmentation was found to be comparable with manual segmentation, and closer to manual segmentation than supervised multivariant classification while segmenting gray and white matter.


title: Optimal Resource Allocation for Network Protection: A Geometric Programming Approach.

abstract: We study the problem of containing spreading processes in arbitrary directed networks by distributing protection resources throughout the nodes of the network. We consider two types of protection resources are available: (i) Preventive resources able to defend nodes against the spreading (such as vaccines in a viral infection process), and (ii) corrective resources able to neutralize the spreading after it has reached a node (such as antidotes). We assume that both preventive and corrective resources have an associated cost and study the problem of finding the cost-optimal distribution of resources throughout the nodes of the network. We analyze these questions in the context of viral spreading processes in directed networks. We study the following two problems: (i) Given a fixed budget, find the optimal allocation of preventive and corrective resources in the network to achieve the highest level of containment, and (ii) when a budget is not specified, find the minimum budget required to control the spreading process. We show that both resource allocation problems can be solved in polynomial time using Geometric Programming (GP) for arbitrary directed graphs of nonidentical nodes and a wide class of cost functions. Furthermore, our approach allows to optimize simultaneously over both preventive and corrective resources, even in the case of cost functions being nodedependent. We illustrate our approach by designing optimal protection strategies to contain an epidemic outbreak that propagates through an air transportation network.


title: Catastrophic Importance of Catastrophic Forgetting

abstract: This paper describes some of the possibilities of artificial neural networks that open up after solving the problem of catastrophic forgetting. A simple model and reinforcement learning applications of existing methods are also proposed.


title: Surgical anatomy of the ligamentous attachments in the temple and periorbital regions.

abstract: This study documents the anatomy of the deep attachments of the superficial fasciae within the temporal and periorbital regions. A highly organized and consistent three-dimensional connective tissue framework supports the overlying skin and soft tissues in these areas. The regional nerves and vessels display constant and predictable relationships with both the fascial planes and their ligamentous attachments. Knowledge of these relationships allows the surgeon to use the tissue planes and soft-tissue ligaments as intraoperative landmarks for the vital neurovascular structures. This results in improved efficiency and safety for aesthetic procedures in these regions.


title: The corrugator supercilii muscle revisited.

abstract: BACKGROUND ::: Few studies have examined the anatomy of the central forehead as it pertains to vertical glabellar furrows. ::: ::: ::: OBJECTIVE ::: This study sought to critically examine the corrugator supercilii muscle (CSM) in situ, in relation to surrounding bony and soft tissue landmarks. ::: ::: ::: METHODS ::: Anatomic dissection of 10 fresh cadaveric hemi-heads was performed, focusing on the CSM origin and insertion, with emphasis on regional fascial relations, neurovascular structures, and osseous topography. ::: ::: ::: RESULTS ::: The CSM originates along the supraciliary arch. The muscle is attached at its medial and superior margin, whereas the lateral and inferior margins are free. The muscle originates from a bony plateau on the supraciliary arch. The CSM travels laterally, with most of the muscle passing through the fibers of the orbicularis oculi and the frontalis. The dermal insertion of the muscle is under the central portion of the eyebrow. The nerve supply enters at the lateral aspect of the muscle approximately 5 mm cephalic to the lateral brow. The action of the CSM is to elevate the medial aspect of the brow and depress the lateral segment of the brow. ::: ::: ::: CONCLUSION ::: The CSM does not appear to be the primary determinant of vertical glabellar frown lines. (Aesthetic Surg J 2001;21:209-215.).


title: Drone-Based External Calibration of a Fully Synchronized Ku-Band Heterodyne FMCW Radar

abstract: This paper discusses a novel drone-based external radar calibration technique, which utilizes a metal sphere hanging from a drone. The advantages of drone-based calibration are introduced and applied to a dual intermediate-frequency frequency-modulation continuous-wave (FMCW) radar system, which was developed to detect a low radar cross section (RCS) target like a drone with high sensitivity. The developed radar system operates in 150-MHz bandwidth with a frequency sweeping time of $500~\mu \text{m}$ at Ku-band frequencies. A 75- $\Omega $ coaxial cable was utilized in the radar system to physically separate the transmitter (Tx) and receiver (Rx) modules for better isolation. The physical separation with a coaxial cable helps in reducing Tx leakage signal effect, which is a critical concern of typical FMCW radar system, and also it helps the proposed radar system to have higher sensitivity. The designed radar system is a wired system that is fully synchronized with an internal reference clock generator. Offset frequency and other compensation factors were calculated for the calibration of the radar system using a big size metal sphere hanging from a drone. We observed that the theoretical calculations using the standard radar equation and the measurement results on calibration are in good agreement with each other. Furthermore, the RCS value of a small size drone is also investigated using the calibrated radar system. The RCS value estimated for the drone was about −22 dBsm.


title: An Effective Anomaly Detection Method in SMTP Traffic

abstract: We investigate an effective and robust mechanism for detecting SMTP traffic anomaly. Our detection method cumulates the deviation of current delivering status from history behavior based on the leaky integrate-and-fire model to detect anomaly. The simplicity of our detection method is that the method requires neither the set of anomalies to be detected, nor the thresholds to be supplied by the user. Furthermore the proposed method need not store history profile and has low computation overhead, which makes the detection method itself immune to attacks. The performance evaluation results show that leaky integrate-and-fire method is quite effective at detecting constant intensity attacks and increasing intensity attacks in the SMTP traffic. Compared with other anomaly detection method, our detection method has better detecting performance.


title: On product uncertainty in online markets: theory and evidence

abstract: Online markets pose a difficulty for evaluating products, particularly experience goods, such as used cars, that cannot be easily described online. This exacerbates product uncertainty, the buyer's difficulty in evaluating product characteristics, and predicting how a product will perform in the future. However, the IS literature has focused on seller uncertainty and ignored product uncertainty. To address this void, this study conceptualizes product uncertainty and examines its effects and antecedents in online markets for used cars (eBay Motors). ::: ::: Extending the information asymmetry literature from the seller to the product, we first theorize the nature and dimensions (description and performance) of product uncertainty. Second, we propose product uncertainty to be distinct from, yet shaped by, seller uncertainty. Third, we conjecture product uncertainty to negatively affect price premiums in online markets beyond seller uncertainty. Fourth, based on the information signaling literature, we describe how information signals (diagnostic product descriptions and third-party product assurances) reduce product uncertainty. ::: ::: The structural model is validated by a unique dataset comprised of secondary transaction data from used cars on eBay Motors matched with primary data from 331 buyers who bid on these used cars. The results distinguish between product and seller uncertainty, show that product uncertainty has a stronger effect on price premiums than seller uncertainty, and identify the most influential information signals that reduce product uncertainty. ::: ::: The study's implications for the emerging role of product uncertainty in online markets are discussed.


title: DASH2M: Exploring HTTP/2 for Internet Streaming to Mobile Devices

abstract: Today HTTP/1.1 is the most popular vehicle for delivering Internet content, including streaming video. Standardized in 2015 with a few new features, HTTP/2 is gradually replacing HTTP 1.1 to improve user experience. Yet, how HTTP/2 can help improve the video streaming delivery has not been thoroughly investigated. In this work, we set to investigate how to utilize the new features offered by HTTP/2 for video streaming over the Internet, focusing on the streaming delivery to mobile devices as, today, more and more users watch video on their mobile devices. For this purpose, we design DASH2M, Dynamic Adaptive Streaming over HTTP/2 to Mobile Devices. DASH2M deliberately schedules the streaming content delivery by comprehensively considering the user's Quality of Experience (QoE), the dynamics of the network resources, and the power efficiency on the mobile devices. Experiments based on an implemented prototype show that DASH2M can outperform prior strategies for users' QoE while minimizing the battery power consumption on mobile devices.


title: Fitting procedural yarn models for realistic cloth rendering

abstract: Fabrics play a significant role in many applications in design, prototyping, and entertainment. Recent fiber-based models capture the rich visual appearance of fabrics, but are too onerous to design and edit. Yarn-based procedural models are powerful and convenient, but too regular and not realistic enough in appearance. In this paper, we introduce an automatic fitting approach to create high-quality procedural yarn models of fabrics with fiber-level details. We fit CT data to procedural models to automatically recover a full range of parameters, and augment the models with a measurement-based model of flyaway fibers. We validate our fabric models against CT measurements and photographs, and demonstrate the utility of this approach for fabric modeling and editing.


title: Detecting (Un)Important Content for Single-Document News Summarization

abstract: We present a robust approach for detecting intrinsic sentence importance in news, by training on two corpora of document-summary pairs. When used for single-document summarization, our approach, combined with the"beginning of document"heuristic, outperforms a state-of-the-art summarizer and the beginning-of-article baseline in both automatic and manual evaluations. These results represent an important advance because in the absence of cross-document repetition, single document summarizers for news have not been able to consistently outperform the strong beginning-of-article baseline.


title: An intelligent fault diagnosis method of high voltage circuit breaker based on improved EMD energy entropy and multi-class support vector machine

abstract: Abstract Targeting the characteristics of machinery vibration signals of high voltage circuit breaker (CB), a new method based on improved empirical mode decomposition (EMD) energy entropy and multi-class support vector machine (MSVM) to diagnose fault for high voltage CB is proposed. In the fault diagnosis for the high voltage CB, the feature extraction based on improved EMD energy entropy is detailedly analyzed. A new multi-layered classification of SVM named ‘one against others’ algorithm approach is proposed and applied to machinery fault diagnosis of high voltage CB. The extracted features are applied to MSVM for estimating fault type. Compared with back-propagation network (BPN), the test results of MSVM demonstrate that the applying of improved EMD energy entropy to vibration signals is superior to that based on wavelet packet analysis (WPT) and hence estimating fault type on machinery condition of high voltage CB accurately and quickly.


title: Chronic sinusitis: a surgical perspective.

abstract: Rhinosinusitis is one of the most common chronic diseases with significant symptoms and impact on the quality of life. Surgery is indicated when appropriate medical treatment fails. A review of the clinical presentations of rhinosinusitis is presented, as well as an overview of current medical and surgical treatment options. Although external approaches are still mandated in some selected instances, endoscopic sinus surgery has become the standard for surgical treatment of sinonasal pathologies, by aiming at restoring the physiological functions of the nasal and paranasal cavities. This technique presents numerous advantages with a minimally invasive approach, better visualization of structures, camera magnification of the surgical field, and allows thorough exploration of difficult-to-reach areas. The risks and complications of endoscopic sinus surgery are well identified and may be reduced significantly by the adjunct use of computer-assisted image guidance and intraoperative fluoroscopy. Surgical simulation also is a promising tool to help reduce surgical errors and improve surgeon training in the future.


title: A snapshot of public web services

abstract: Web Service Technology has been developing rapidly as it provides a flexible application-to-application interaction mechanism. Several ongoing research efforts focus on various aspects of web service technology, including the modeling, specification, discovery, composition and verification of web services. The approaches advocated are often conflicting---based as they are on differing expectations on the current status of web services as well as differing models of their future evolution. One way of deciding the relative relevance of the various research directions is to look at their applicability to the currently available web services. To this end, we took a snapshot of the currently publicly available web services. Our aim is to get an idea of the number, type, complexity and composability of these web services and see if this analysis provides useful information about the near-term fruitful research directions.


Querying standardized EHRs by a Search Ontology XML extension (SOX)


title: Towards an open question answering architecture

abstract: Billions of facts pertaining to a multitude of domains are now available on the Web as RDF data. However, accessing this data is still a difficult endeavour for non-expert users. In order to meliorate the access to this data, approaches imposing minimal hurdles to their users are required. Although many question answering systems over Linked Data have being proposed, retrieving the desired data is still significantly challenging. In addition, developing and evaluating question answering systems remains a very complex task. To overcome these obstacles, we present a modular and extensible open-source question answering framework. We demonstrate how the framework can be used by integrating two state-of-the-art question answering systems. As a result our evaluation shows that overall better results can be achieved by the use of combination rather than individual stand-alone versions.


title: Fully automatic coloring of grayscale images

abstract: This paper introduces a methodology for adding color to grayscale images in a way that is completely automatic. Towards this goal, we build on a technique that was recently developed to transfer colors from a user-selected source image to a target grayscale image. More specifically, in order to eliminate the need for manual selection of the source image, we use content-based image retrieval methods to find suitable source images in an image database. To assess the merit of our methodology, we performed a survey where volunteers were asked to rate the plausibility of the colorings generated automatically for grayscale images. In most cases, automatically-colored images were rated either as totally plausible or as


title: Why was china trapped in an agrarian society? An economic geographical approach to the needham puzzle

abstract: This paper argues that before the world started to globalize, the differences in the geographical endowments that different population faced were the most important constraints to their long-term production and consumption. The paper uses this central hypothesis to explain the sharp contrast between the flourishing Song and the stagnant Ming and Qing. During the Song dynasty, despite the fact that China lost a significant amount of arable land to invading nomads as its population peaked, China witnessed a higher urbanization level, more prosperous commerce and international trade, and an explosion of technical inventions and institutional innovations. However, after having significantly improved its man-to-land ratio in the period after the Song China only found itself induced deeper into the agrarian trap, resulting in reduced urbanization, withering foreign trade, a declining division of labor, and stagnant in technology.


title: The Consciousness Prior

abstract: A new prior is proposed for representation learning, which can be combined with other priors in order to help disentangling abstract factors from each other. It is inspired by the phenomenon of consciousness seen as the formation of a low-dimensional combination of a few concepts constituting a conscious thought, i.e., consciousness as awareness at a particular time instant. This provides a powerful constraint on the representation in that such low-dimensional thought vectors can correspond to statements about reality which are true, highly probable, or very useful for taking decisions. The fact that a few elements of the current state can be combined into such a predictive or useful statement is a strong constraint and deviates considerably from the maximum likelihood approaches to modelling data and how states unfold in the future based on an agent's actions. Instead of making predictions in the sensory (e.g. pixel) space, the consciousness prior allows the agent to make predictions in the abstract space, with only a few dimensions of that space being involved in each of these predictions. The consciousness prior also makes it natural to map conscious states to natural language utterances or to express classical AI knowledge in the form of facts and rules, although the conscious states may be richer than what can be expressed easily in the form of a sentence, a fact or a rule.


title: Performance comparison of classifiers on reduced phishing website dataset

abstract: The Internet is becoming a necessary and important tool in everyday life. However, Internet users might have poor security for different kinds of web threats, which may lead to financial loss or clients lacking trust in online trading and banking. Phishing is described as a skill of impersonating a trusted website aiming to obtain private and secret information such as a username and password or social security and credit card number. In this paper, phising website dataset taken from UCI was investigated. Its dimension was reduced and the performance comparison of classification algorithms is studied on reduced phishing website dataset. Phishing website dataset was taken from UCI machine learning repository. This dataset consists of 11055 records and 31 features. Feature selection algorithms were applied to reduce the dimension of phishing website dataset and to obtain higher classification performance. Then, the performance of classification algorithms is compared to other data mining classification algorithms. Finally, a comparative classification performance on the reduced dataset by using the common classification algorithms is given.


title: A survey on ARP cache poisoning and techniques for detection and mitigation

abstract: Network security has become a major concern in any industry. Address resolution protocol (ARP) is a basic protocol used by all hosts in a network. When ARP is compromised, it leads to several security attacks. In this survey, we present a deep investigation of existing ARP cache poisoning or ARP spoofing solutions. We first introduce Address resolution protocol and its operation. We then dwell on ARP spoofing, with an emphasis on its impact on network security. Then we provide an overview on existing solutions to detect and mitigate ARP spoofing in both traditional networking and software-defined networking (SDN) platforms. Finally, we compare the techniques discussed and conclude that SDN based solutions are more effective in detecting and eliminating any kind of ARP spoofing attack.


Challenges and Opportunities with Big Data 2011-1


title: The Applicability of Porter’s Generic Strategies in the Digital Age: Assumptions, Conjectures, and Suggestions

abstract: Because current management theories evolved in the context of brick-and-mortar firms, this paper examines three key questions raised by the advent of e-business: (1) Will the strategy types found among e-business firms resemble Porter’s (1980) generic strategies? (2) Will we find performance differences among e-business firms pursuing different types of strategies? (3) Will we find differences in the strategy-performance relationships of pure online firms (pure plays) and firms with both online and offline operations (clicks-and-bricks)? We conclude that integrated strategies that combine elements of cost leadership and differentiation will outperform cost leadership or differentiation strategies. We also argue that, regardless of business strategy type, clicks-and-bricks firms that closely integrate their on-and offline operations will enjoy performance advantages over their pure play counterparts.


title: Transcranial Doppler correlation with angiography in detection of intracranial stenosis.

abstract: BACKGROUND AND PURPOSE ::: The purpose of this study was to evaluate the use of velocity criteria applied to transcranial Doppler (TCD) signals in the detection of stenosis of the middle cerebral (MCA), distal vertebral, and basilar arteries. ::: ::: ::: METHODS ::: Sixty-five patients who underwent both cerebral angiography and transcranial Doppler examinations in the workup of acute cerebral ischemia were reviewed. Angiography was performed a mean of 7 +/- 5 days (range, 1 to 28 days) after TCD. Interpretation of the angiogram was performed without input regarding the TCD findings. TCD interpretation was performed according to standard criteria. ::: ::: ::: RESULTS ::: When we used a mean velocity (MV) cutoff of > or = 80 cm/s in the MCA as the criterion for stenosis, 10 of 12 stenoses of any degree were detected by TCD, with 11 of 87 false-positives. Nine of 12 MCA stem (M1) stenoses were detected when a cutoff of > or = 90 cm/s was used, with 7 of 87 false-positives. When we used an MV cutoff of > or = 70 cm/s as the criterion for > or = 50% stenosis of the vertebrobasilar system, 5 of 6 stenoses were detected, with 15 of 85 false-positives. The most important confounding factor was the presence of > or = 75% stenosis of the extracranial internal carotid artery, resulting in both false-positive (from collateral flow) and false-negative (decreased volume flow from the proximal stenosis without adequate collateral flow) errors in TCD interpretation. When patients with > or = 75% stenosis of the cervical internal carotid artery were excluded from analysis, a TCD MV cutoff of > or = 80 cm/s identified 9 of 10 M1 lesions with 7 of 61 false-positives, and an MV of > or = 70 cm/s identified 3 of 4 vertebrobasilar lesions causing > or = 50% stenosis with 7 of 56 false-positives. ::: ::: ::: CONCLUSIONS ::: TCD may be an effective screening test for M1 stenosis when velocity criteria alone are used. TCD may less reliably detect intracranial vertebral and basilar artery stenosis.


title: Efficient algorithms for Web services selection with end-to-end QoS constraints

abstract: Service-Oriented Architecture (SOA) provides a flexible framework for service composition. Using standard-based protocols (such as SOAP and WSDL), composite services can be constructed by integrating atomic services developed independently. Algorithms are needed to select service components with various QoS levels according to some application-dependent performance requirements. We design a broker-based architecture to facilitate the selection of QoS-based services. The objective of service selection is to maximize an application-specific utility function under the end-to-end QoS constraints. The problem is modeled in two ways: the combinatorial model and the graph model. The combinatorial model defines the problem as a multidimension multichoice 0-1 knapsack problem (MMKP). The graph model defines the problem as a multiconstraint optimal path (MCOP) problem. Efficient heuristic algorithms for service processes of different composition structures are presented in this article and their performances are studied by simulations. We also compare the pros and cons between the two models.


title: A class of dynamic memory allocation algorithms

abstract: A new dynamic memory allocation algorithm, the Fibonacci system, is introduced. This algorithm is similar to, but seems to have certain advantages over, the “buddy” system. A generalization is mentioned which includes both of these systems as special cases.


title: Class Activation Map Generation by Representative Class Selection and Multi-Layer Feature Fusion

abstract: Existing method generates class activation map (CAM) by a set of fixed classes (i.e., using all the classes), while the discriminative cues between class pairs are not considered. Note that activation maps by considering different class pair are complementary, and therefore can provide more discriminative cues to overcome the shortcoming of the existing CAM generation that the highlighted regions are usually local part regions rather than global object regions due to the lack of object cues. In this paper, we generate CAM by using a few of representative classes, with aim of extracting more discriminative cues by considering each class pair to obtain CAM more globally. The advantages are twofold. Firstly, the representative classes are able to obtain activation regions that are complementary to each other, and therefore leads to generating activation map more accurately. Secondly, we only need to consider a small number of representative classes, making the CAM generation suitable for small networks. We propose a clustering based method to select the representative classes. Multiple binary classification models rather than a multiple class classification model are used to generate the CAM. Moreover, we propose a multi-layer fusion based CAM generation method to simultaneously combine high-level semantic features and low-level detail features. We validate the proposed method on the PASCAL VOC and COCO database in terms of segmentation groundtruth. Various networks such as classical network (Resnet-50, Resent-101 and Resnet-152) and small network (VGG-19, Resnet-18 and Mobilenet) are considered. Experimental results show that the proposed method improves the CAM generation obviously.


title: Predicting Visual Exemplars of Unseen Classes for Zero-Shot Learning

abstract: Leveraging class semantic descriptions and examples of known objects, zero-shot learning makes it possible to train a recognition model for an object class whose examples are not available. In this paper, we propose a novel zero-shot learning model that takes advantage of clustering structures in the semantic embedding space. The key idea is to impose the structural constraint that semantic representations must be predictive of the locations of their corresponding visual exemplars. To this end, this reduces to training multiple kernel-based regressors from semantic representation-exemplar pairs from labeled data of the seen object categories. Despite its simplicity, our approach significantly outperforms existing zero-shot learning methods on standard benchmark datasets, including the ImageNet dataset with more than 20,000 unseen categories.


title: Random Formulas, Monotone Circuits, and Interpolation

abstract: We prove new lower bounds on the sizes of proofs in the Cutting Plane proof system, using a concept that we call unsatisfiability certificate. This approach is, essentially, equivalent to the well-known feasible interpolation method, but is applicable to CNF formulas that do not seem suitable for interpolation. Specifically, we prove exponential lower bounds for random k-CNFs, where k is the logarithm of the number of variables, and for the Weak Bit Pigeon Hole Principle. Furthermore, we prove a monotone variant of a hypothesis of Feige [12]. We give a superpolynomial lower bound on monotone real circuits that approximately decide the satisfiability of k-CNFs, where k = ω(1). For k ≈ logn, the lower bound is exponential.


title: A Comparison Framework and Review of Service Brokerage Solutions for Cloud Architectures

abstract: Cloud service brokerage has been identified as a key concern for future cloud technology development and research. We compare service brokerage solutions. A range of specific concerns like architecture, programming and quality will be looked at. We apply a 2-pronged classification and comparison framework. We will identify challenges and wider research objectives based on an identification of cloud broker architecture concerns and technical requirements for service brokerage solutions. We will discuss complex cloud architecture concerns such as commoditisation and federation of integrated, vertical cloud stacks.


title: Recruiters’ Inferences of Applicant Personality Based on Resume Screening: Do Paper People have a Personality?

abstract: Research shows recruiters infer dispositional characteristics from job applicants’ resumes and use these inferences in evaluating applicants’ employability. However, the reliability and validity of these inferences have not been empirically tested. Using data collected from 244 recruiters, we found low levels of estimated interrater reliability when they reviewed entry-level applicants’ resumes and made inferences regarding applicants’ personality traits. Moreover, when recruiters’ inferences of applicant personality were correlated with applicants’ actual Big Five personality scores, results indicated that recruiters’ inferences lacked validity, with the possible exceptions of extraversion and openness to experience. Finally, despite being largely unreliable and invalid, recruiters’ inferences of applicants’ extraversion, openness to experience, and conscientiousness predicted the recruiters’ subsequent employability assessments of the applicants.


title: NTUA-SLP at SemEval-2018 Task 2: Predicting Emojis using RNNs with Context-aware Attention

abstract: In this paper we present a deep-learning model that competed at SemEval-2018 Task 2 "Multilingual Emoji Prediction". We participated in subtask A, in which we are called to predict the most likely associated emoji in English tweets. The proposed architecture relies on a Long Short-Term Memory network, augmented with an attention mechanism, that conditions the weight of each word, on a "context vector" which is taken as the aggregation of a tweet's meaning. Moreover, we initialize the embedding layer of our model, with word2vec word embeddings, pretrained on a dataset of 550 million English tweets. Finally, our model does not rely on hand-crafted features or lexicons and is trained end-to-end with back-propagation. We ranked 2nd out of 48 teams.