Reviewerly/exHarmony huggingface dataset:
Integrating AI Capabilities into Existing Technology Platforms: Drones as a Case in Point

Breakthroughs in artificial intelligence promise to supercharge technology platforms, and give them added capabilities like inference, real-time decision making, contextual awareness, and autonomous operation. In this paper, we explore this phenomenon using the case of drones. Drones have been used for many years in military applications, and have now found their way into the commercial sector. Several industries such as agriculture, construction, industrial infrastructure, security and surveillance, and entertainment, to name a few, operate drones on a regular basis. Using advanced sensors, drones can not only continuously collect and store data, but also send them back to base or the cloud for further analysis, and then act on the insights generated. The combination of large datasets, complex algorithms, and the ability to access machine learning on powerful cloud servers mean that drones are no longer passive functionaries. In fact, they can act as sophisticated and intelligent machines using advanced AI, and respond dynamically to challenges in real time. In this paper, we describe how this newfound capability is being harnessed for new classes of applications and business opportunities. From a strategic perspective, we argue that AI needs to be conceptualized as a meta layer on top of existing technology platforms, that in turn gives the platform new capabilities that were previously out of reach.
A Framework for Preserving Location Privacy for Continuous Queries

The growth of the location-based services (LBSs) market in recent years was motivated by the widespread use of mobile devices equipped with positioning capability and Internet accessibility. To preserve the location privacy of LBS users, many mechanisms have been proposed to provide a partial disclosure by decreasing or blurring or the accuracy of the shared location. While these Location Privacy Preserving Mechanisms (LPPMs) have demonstrated effective performance with snapshot queries, this work shows that preserving location privacy for continuous queries should be addressed differently. In this paper, MOPROPLS framework is proposed with the aim to preserve location privacy in the specific case of continuous queries. As part of the proposed framework, a novel set of six requirements that any LPPM should meet in order to provide location privacy for continuous queries is proposed. In addition, a novel location privacy leakage metric and a novel two-phased probabilistic candidate selection algorithm are proposed. Comparing the performance of MOPROPLS framework with the geo-indistinguishability LPPM in terms of privacy (adversary estimation error) shows that the average of MOPROPLS framework improvement is 34%.
Perspectives for VHR Big Data Image Processing and Analytics Toward a Dedicated Framework for Major Disaster and Environment Monitoring from Space

Through various initiatives, CNES, the French space agency, has been involved in major disaster and environment monitoring from Space for many years and in particular in the International Charter which delivers satellite data to nations affected by major disasters and in the THEIA organization which promotes the use of satellite data to monitor human and climate impacts on environment.Thus, CNES developed in 2014 a reference framework to generate value-added products from satellite data. Since 2017, considering the increase of data and processing requests, CNES has decided to move to a new framework based on big data and cloud technologies and extended to data analytics.This paper will first introduce THEIA and Charter initiatives. It will present the current framework in operation and its limitations. It will then focus on the innovative approach to handle big data and analytics needs and finally presents the first results and perspectives.
TEAMwISE

The recent availability of affordable and lightweight tracking sensors allows researchers to collect large and complex movement datasets. These datasets require applications that are capable of handling them whilst providing an environment that enables the analyst(s) to focus on the task of analysing the movement in the context of the geographic environment it occurred in. We present a framework for collaborative analysis of geospatial-temporal movement data with a use-case in collective behavior analysis. It supports the concurrent usage of several program instances, allowing to have different perspectives on the same data in collocated or remote setups. The implementation can be deployed in a variety of immersive environments, e.g. on a tiled display wall or mobile VR devices.
On the existence and number of invariant polynomials

This paper explores a natural action of the group PGL2(Fq) on the set of monic irreducible polynomials of degree at least two over a finite field Fq. Our main results deal with the existence and number of fixed points and, in particular, we provide some improvements of previous works.
Exploring the effect of national cultural disposition on drone technology adoption and development

The 21st century has witnessed a paradigm shift of drone technology from military operations to civil operations sparking off a growing interest in all the three main sectors of the economy; primary sector, secondary sector and service or tertiary sector. Culture is known to influence the diffusion and adoption of certain technologies in any given nation. As such, this study uses a binomial logistic regression technique on Hofstede’s cultural dimensions and drone regulation existence in a selected number of countries to statistically assess whether national culture influences the adoption of drone regulations.
Modeling and Development of AUTOSAR Software Components

Theoretical background and development methodology of AUTOSAR software components is distributed over several different standards. For a reader that is unfamiliar with AUTOSAR concepts it is a tedious task to derive concrete and structured knowledge about software component development. This paper systematically goes through theoretical background and the main functional concepts of software component development, from modeling to source code generation.
Credit Card Transaction Based on Face Recognition Technology

Abstract This paper proposes a method for credit card transaction system which will make use of face recognition and face detection technology, using Haar Cascade and GLCM algorithm. The main problem faced by credit card users is attack to lot of privacy issues such as credit card. This generally happens when users give their credit card number to unknown people or when the card is lost. So, we are proposing a system that will reduce the risk of credit card frauds. The system we are proposing will match the image of user’s face with dataset of respective user. A database will be maintained for authentication purpose. If the image matches, that means user is genuine and he will be allowed to proceed otherwise, the user will be denied to do the transaction.
Diminished Reality Based on 3D-Scanning

In this paper a new method for diminished reality is explored, which uses a 3D model of a room to fill in missing regions when removing objects in real time on a video. The room is scanned before, so it is possible to recreate missing pieces in all their detail. The proposed method allows freedom in movement and rotation and is demonstrated with an application on a mobile device, detailed results are shown.
Combining Evidences from Variable Teager Energy Source and Mel Cepstral Features for Classification of Normal vs. Pathological Voices

In this paper, novel Variable length Teager Energy Operator (VTEO) based Mel frequency cepstral coefficients, namely, VTMFCC are proposed for automatic classification of normalvs. pathological voices. Experiments have been carried out using this proposed feature set, Mel Frequency Cepstral Coefficients (MFCC), and their score-level fusion. Classification was primarily performed using a discriminatively-trained 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> order polynomial classifier on a subset of the MEEI database for a feature dimension of 12. The equal error rate (EER) on fusion was reduced by 3.2% than MFCC alone which was used as the baseline. The classification accuracy was analyzed for different dimensions of feature vector. Furthermore, results obtained for the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> order classifier were compared with the results obtained from the 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> order polynomial classifier for different feature dimensions. In addition, the effectiveness of dynamic features, in particular, delta, delta-delta, and shifted delta cepstral features have been investigated for this particular problem. It has been observed that the score-level fusion (with equal weights) of proposed feature set and state-of-the-art MFCC gave better classification performance than MFCC alone for various evaluation factors considered in this paper.
Precise Photon Correlation Measurement of a Chaotic Laser

The second order photon correlation g(2)(τ) of a chaotic optical-feedback semiconductor laser is precisely measured using a Hanbury Brown–Twiss interferometer. The accurate g(2)(τ) with non-zero delay time is obtained experimentally from the photon pair time interval distribution through a ninth-order self-convolution correction. The experimental results agree well with the theoretical analysis. The relative error of g(2)(τ) is no more than 5‰ within 50 ns delay time. The bunching effect and coherence time of the chaotic laser are measured via the precise photon correlation technique. This technique provides a new tool to improve the accuracy of g(2)(τ) measurement and boost applications of quantum statistics and correlation.
Spatial and Hierarchical Riemannian Dimensionality Reduction and Dictionary Learning for Segmenting Multichannel Images

In this paper, we proposed an automated method for segmenting objects of weak boundaries and similar intensities on volumetric multichannel images. This method relied on a multiresolution classifier that tackled class overlaps by using the Riemannian geometry of the RCDs of the multiscale patches of every multichannel image and reducing the dimensionality of these RCDs through a novel method that incorporated the intra-and inter-class neighborhoods of the RCDs in the Riemannian space and the spatial and hierarchical relationships between their corresponding patches. The reduced dimensional RCDs were then used to learn resolution-specific dictionaries for coding and classifications. To speed up the optimizations and to avoid convergence to local extrema, the dictionaries and the codes got initialized by a novel scheme that used the Riemannian geometry of the RCDs. This method was evaluated on the challenging task of segmenting cardiac adipose tissues on fat-water MR images.
A Localized No-Reference Blurriness Measure for Omnidirectional Images and Video

Blurriness is a defect commonly occurring in conventional video but also in omnidirectional video. In this work, we propose a novel no-reference blurriness measure for images captured with omnidirectional video cameras. These images present unique challenges for quality measures due to their size and due to the equirectangular projection which is commonly employed for them. We base upon a state of the art algorithm and adapt it for the specifics of omnidirectional images. Furthermore, we extend it with a coarse-scale blurriness map for measuring spatially varying blur. We present a novel ground truth dataset which was generated by adding spatially varying gaussian blur of different magnitude in a viewport-centric way. Experiments with the proposed algorithm on this dataset show a strong correlation of the localized blurriness measure with the ground truth.
Research on Quality Modeling of Information Education in Colleges and Universities Based on Web Data Mining

In order to remedy the problem of poor practicability and effectiveness of traditional teaching quality model, this paper proposes a research model of information-based education quality in Colleges and Universities Based on Web data mining. Firstly, through the method of feature acquisition, the teaching information data of colleges and universities are mined, and the characteristic parameters of the mining data are evaluated, the data evaluation grades are divided, and the quality of education is optimized according to different levels, so as to realize the effective modeling research on the quality of information-based education in Colleges and universities. The experimental results show that the research model of university information education quality based on Web data mining is more practical than the traditional model.
Modelling the Social Interactions in Ant Colony Optimization

Ant Colony Optimization (ACO) is a swarm-based algorithm inspired by the foraging behavior of ants. Despite its success, the efficiency of ACO has depended on the appropriate choice of parameters, requiring deep knowledge of the algorithm. A true understanding of ACO is linked to the (social) interactions between the agents given that it is through the interactions that the ants are able to explore-exploit the search space. We propose to study the social interactions that take place as artificial agents explore the search space and communicate using stigmergy. We argue that this study bring insights to the way ACO works. The interaction network that we model out of the social interactions reveals nuances of the algorithm that are otherwise hard to notice. Examples include the ability to see whether certain agents are more influential than others, the structure of communication, to name a few. We argue that our interaction-network approach may lead to a unified way of seeing swarm systems and in the case of ACO, remove part of the reliance on experts for parameter choice.
Multi-scale Deep Residual Network for Satellite Image Super-Resolution Reconstruction

Satellite images are used in all aspects of human life, and the demand for high-resolution satellite images is increasing dramatically as human technology advances. The most straightforward way to improve imaging resolution is to improve hardware design or reduce satellite flight altitude, but at a higher cost and with unbreakable physical limits. Super-resolution reconstruction is a way to improve image resolution. Satellite imagery has a wide imaging range. The scale of the ground target varies greatly and the texture information is diversified, which brings new challenges to the existing image super-resolution technology. A multi-scale residual deep neural network is proposed for the multi-scale characteristics of satellite imagery in this paper. In the middle of the residual body, the series-parallel combined dilated convolution is used to obtain different sizes of receptive fields which can achieve different scale information, and finally generate high-resolution satellite images after pixel shuffle. The experimental results on the Airbus satellite ship image dataset prove the superiority of the proposed algorithm.
V4Ann: Representation and Interlinking of Atom-Based Annotations of Digital Content

There is a great potential in creative industries, such as architecture and video game design, for re-using and re-purposing of digital content. Paintings, archival footage, documentaries, movies, reviews or catalogues, and various other forms of artwork can serve as sources of inspiration and design direction towards innovative designs and new concepts. In this paper, we present V4Ann, an ontology-based framework for semantically representing, aggregating and combining annotations (atoms) coming from visual and textual analysis of digital content. The aim is to structure and link data in such a way so as to facilitate the systematic process, integration and organisation of information and establish innovative value chains and end-user applications. The framework is part of the V4Design platform that aims to re-use and re-purpose existing heterogeneous multimedia content by semantically enriching and transforming assets into a 3D representation, so as to inspire and support the design, architecture, as well as 3D and VR game industries.
Multi-Scale Deformable CNN for Answer Selection

The answer selection task is one of the most important issues within the automatic question answering system, and it aims to automatically find accurate answers to questions. Traditional methods for this task use manually generated features based on tf-idf and n-gram models to represent texts, and then select the right answers according to the similarity between the representations of questions and the candidate answers. Nowadays, many question answering systems adopt deep neural networks such as convolutional neural network (CNN) to generate the text features automatically, and obtained better performance than traditional methods. CNN can extract consecutive n-gram features with fixed length by sliding fixed-length convolutional kernels over the whole word sequence. However, due to the complex semantic compositionality of the natural language, there are many phrases with variable lengths and be composed of non-consecutive words in natural language, such as these phrases whose constituents are separated by other words within the same sentences. But the traditional CNN is unable to extract the variable length n-gram features and non-consecutive n-gram features. In this paper, we propose a multi-scale deformable convolutional neural network to capture the non-consecutive n-gram features by adding offset to the convolutional kernel, and also propose to stack multiple deformable convolutional layers to mine multi-scale n-gram features by the means of generating longer n-gram in higher layer. Furthermore, we apply the proposed model into the task of answer selection. Experimental results on public dataset demonstrate the effectiveness of our proposed model in answer selection.
Adaptive Feature Redundancy Minimization

Most existing feature selection methods select the top-ranked features according to certain criterion. However, without considering the redundancy among the features, the selected ones are frequently highly correlated with each other, which is detrimental to the performance. To tackle this problem, we propose a framework regarding adaptive redundancy minimization (ARM) for the feature selection. Unlike other feature selection methods, the proposed model has the following merits: (1) The redundancy matrix is adaptively constructed instead of presetting it as the priori information. (2) The proposed model could pick out the discriminative and non-redundant features via minimizing the global redundancy of the features. (3) ARM can reduce the redundancy of the features from both supervised and unsupervised perspectives.
Deep learning for rectification of radial distortion image

Fisheye lens and telephoto lens for long distance are becoming more widely used because of their larger field of view or much longer focal length than normal pinhole camera. The larger field of view is obtained by the image radial distortion - the effect which should be rectified not only for visualization but for use by manifold applications using image based measurements. This research article applies deep learning to address the computer vision problem of image rectification using the single image acquired by the unknown camera. A large scale of radial distortion image dataset is synthesized following the general optical ray projection model in angular form to train the proposed network. The experimental results verify that the constructed network learns distortion information from input images with high accuracy and performs the image rectification without explicit use of image semantic information. It was possible due to generating of good training distortion models and their inversion by Newton optimization and pseudo-inverse of resulting Vandermonde matrix.
Precise Reentry Maneuver Estimation Using Radar Trilateration

An iterated recursive filter is formulated for radar trilateration of reentry maneuvers. Using range and range rate measurements from three radars in a regional network, trilateration provides very accurate three-dimensional position and velocity measurements, which improves drag and lift acceleration estimates. A new first-order, nonlinear vector differential equation specifies the rate of change of acceleration (or jerk). Acceleration process noise is determined from dynamic pressure estimates and mean-square area-to-mass covariances, which are derived from probability density functions that statistically characterize a family of expected maneuvers. Trilateration accuracies are demonstrated for demanding reentry maneuvers. Monte Carlo simulations assess accuracy sensitivity to modeling assumptions and to off-nominal reentry trajectories.
Taming Social Bots

Social bots have been around for over a decade since 2008. Social bots are capable of swaying political opinion, spreading false information, and recruiting for terrorist organizations. Social bots use various sophisticated techniques by adopting emotions, sympathy following, synchronous deletions, and profile molting. There are several approaches proposed in the literature for detection, exploration, and measuring social bots. We provide a comprehensive overview of the existing work from data mining and machine learning perspective, discuss relative strengths and weaknesses of various methods, make recommendations for researchers and practitioners, and propose novel directions for future research in taming the social bots. The tutorial also discusses pitfalls in collecting and sharing data on social bots.
ROS-CBT: Communication Benchmarking Tool for the Robot Operating System: Extended Abstract

Simulation plays a critical role in robotics as it allows verification and it provides evaluations of robot software before real world deployment [1]. One prevalent limitation of robotic simulators that support the Robot Operating System, ROS [2] - a de facto standard for developing robotic software - is the absence of communication simulators. This poses a serious challenge in testing software for multi-robot systems (MRS) that use explicit communication, hence limiting the development of this technology. Figure 1 illustrates the problem tackled in this paper.
Enabling Text-Line Segmentation in Run-Length Encoded Handwritten Document Image Using Entropy-Driven Incremental Learning

In today’s digital era, archival and transmission of document images are generally carried out in a compressed form in order to avoid wastage of storage space and bandwidth. In the case of CCITT Group 3 and Group 4, the compressed representation is a stream of white and black pixel intensity values called runs, correspondingly indicating background and foreground regions of the document image. In this research paper, we propose a novel entropy-driven incremental learning technique that directly works on the compressed stream of runs, and subsequently facilitates text-line segmentation in handwritten document images using entropy and connected component analysis. Spatial Entropy Quantifier (SEQ) is extracted from the stream of runs based on a suitable window. Further, incremental entropy and connected component analysis are carried out thus separating text and non-text regions leading to automatic text-line segmentation. The proposed method is validated with the compressed dataset of handwritten document images and performance is reported.