Reviewerly/exHarmony huggingface dataset:
Opportunistic use of GNSS Signals to Characterize the Environment by Means of Machine Learning Based Processing

GNSS is widely used to provide positions in an absolute reference frame in Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles (UGV), where GNSS is merged with the information provided by other sensors. Even if the main goal of GNSS signal processing is the positioning, multifrequency signals are a rich source of information about the propagation environment surrounding the mobile vehicle. In urban and harsh environment, situational awareness is essential to tailor the operations and take proper countermeasure to harsh propagation conditions. Given this framework the present paper will describe the use of GNSS as signals of opportunity for the characterization of the operative environment by processing the GNSS observables through Machine Learning (ML) algorithms that can be used as efficient features extractors. The paper will present some case studies of operational scenarios for UGVs and for a static monitoring station, showing how through combining DSP techniques with both unsupervised and supervised ML algorithms (K-means classes, Support Vector Machines) it is possible to retrieve the information about the propagation scenario for multipath, interference and atmospheric limitations.
Language Specific Information from LP Residual Signal Using Linear Sub Band Filters

In this work, an analysis and comparison of the parameterization methods of excitation source information is demonstrated for the spoken language recognition task. The excitation source information is represented by the features called residual mel frequency cepstral coefficients (RMFCC) and residual linear frequency cepstral coefficients (RLFCC), both derived from the linear prediction residual signal. In general, inspired from the speaker recognition task, perceptually inspired mel-sub band filters are used for the parameterization of LP residual signal (known as RMFCC). In this study, as the LP residual signal is impulsive in nature (i.e. having a flat spectrum) a uniform triangular sub-band filter based parameterization method, called as RLFCC is proposed. From the experimental results, it has been observed that the RLFCC features perform better than RMFCC features. The RLFCC features combined with the MFCC features provide a relative improvement of 20% in terms of EERavg over the combined system of MFCC and RMFCC features using DNN-WA architecture.
Management System for Publication Activity of Academic and Teaching Personnel at Russian Flagship University

In 2015, a government program was approved in Russia, the purpose of which was to create favorable conditions for the effective development of education that meets the requirements of the modern innovative socially oriented development of the Russian Federation. As an essential part of this program, a comprehensive project was implemented to form a network of regional flagship universities in Russia. One of the key indicators of research activities effectiveness for a modern Russian higher education institution is its academic publication activity. The chapter discusses a problem of building an efficient management system for academic and teaching personnel publication activity at a Russian higher education institution using the example of Togliatti State University (TSU). To improve publication activity, a mechanism controlling motivation and stimulation of the academic and teaching personnel of the university is used. A procedure of making incentive payments for academic and teaching personnel publication activity of TSU is developed and approved. Thus, all types of publications included in the international bibliographic databases Scopus and Web of Science Core Collection are subject to payment. Information and analytical tools of the management system for academic publication activity are implemented within the framework of the TSU corporate portal developed on the basis of a Russian collaboration platform Bitrix24. To automate the calculation of the payments according to the established method, a subsystem for incentive control is used. An algorithm for managing academic publication activity at TSU is developed. The prospects for the further development of the management system for academic publication activity are considered. Utilizing the presented management system at TSU allowed the university to increase the number of publications included in the Scopus and Web of Science Core Collection databases by 151 and 202%, respectively.
Compressive Adaptive Bilateral Filtering

We propose a fast algorithm for an adaptive variant of the classical bilateral filter, where the range kernel is allowed to vary from pixel to pixel. Several fast and accurate algorithms have been proposed for bilateral filtering, but they assume that the same range kernel is used at each pixel and hence cannot be used for adaptive bilateral filtering (ABF). Only recently, it was shown that fast algorithms for ABF can be developed by approximating the local histogram around each pixel using polynomials. The present algorithm is derived using an entirely different approximation, namely, the range kernels across all pixels are jointly approximated (compressed) using singular value decomposition (SVD). The SVD involves a very large matrix and cannot be computed exactly; however, we are able to get a sufficiently accurate approximation using the Nyström method (without populating/storing the entire matrix). We show that this SVD-type decomposition allows us to approximate the adaptive bilateral filter using fast convolutions. To demonstrate the speed and accuracy of the proposed algorithm in relation to existing algorithms, we use it for texture filtering, JPEG deblocking, and detail enhancement.
Angular Discriminative Deep Feature Learning for Face Verification

Thanks to the development of deep Convolutional Neural Network (CNN), face verification has achieved great success rapidly. Specifically, Deep Distance Metric Learning (DDML), as an emerging area, has achieved great improvements in computer vision community. Softmax loss is widely used to supervise the training of most available CNN models. Whereas, feature normalization is often used to compute the pair similarities when testing. In order to bridge the gap between training and testing, we require that the intra-class cosine similarity of the inner-product layer before softmax loss is larger than a margin in the training step, accompanied by the supervision signal of softmax loss. To enhance the discriminative power of the deeply learned features, we extend the intra-class constraint to force the intra-class cosine similarity larger than the mean of nearest neighboring inter-class ones with a margin in the normalized exponential feature projection space. Extensive experiments on Labeled Face in the Wild (LFW) and Youtube Faces (YTF) datasets demonstrate that the proposed approaches achieve competitive performance for the open-set face verification task.
Measuring non-Markovianity via incoherent mixing with Markovian dynamics

We introduce a measure of non-Markovianity based on the minimal amount of extra Markovian noise we have to add to the process via incoherent mixing, in order to make the resulting transformation Markovian too at all times. We show how to evaluate this measure by considering the set of depolarizing evolutions in arbitrary dimension and the set of dephasing evolutions for qubits.
Comparative Analysis of OpenCV Recognisers for Face Recognition

In today's world, face recognition has turned out to be one of the key aspects of Computer Vision. People are truly adept at perceiving faces and computer complex figures. Indeed, even an entry of time doesn't influence this ability and along these lines, it would help become as hearty as people in face acknowledgment. Machine acknowledgment of human countenances from still or video pictures has pulled in a lot of consideration in the brain research, picture handling, design acknowledgment, neural science, computer security, and computer vision networks. Face recognition is presumably a standout amongst the most non-meddlesome and easy to use biometric validation techniques right now accessible; a screensaver furnished with face recognition innovation can naturally open the screen at whatever point the approved client approaches the machine. Tech organizations are utilizing these uncommon advances in their items nowadays in all respects now and again. The face is a significant piece of our identity and how individuals recognize us. Face recognition has been one of the fast-growing, exacting and very keen areas in real-time applications. It is seemingly an individual's most extraordinary physical trademark. While people have had the intrinsic capacity to perceive and recognize various faces for many years, computers are a little difficult to perform so while it's getting up to speed. Facial recognition programming is intended to pinpoint a face and measure its highlights or various components. Each face has a certain breakthrough, which makes up the distinctive facial highlights. These milestones are implied as nodal focuses. There are around 80 nodal focuses on a human face.
Forward-Backward Splitting for Optimal Transport Based Problems

Optimal transport aims to estimate a transportation plan that minimizes a displacement cost. This is realized by optimizing the scalar product between the sought plan and the given cost, over the space of doubly stochastic matrices. When the entropy regularization is added to the problem, the transportation plan can be efficiently computed with the Sinkhorn algorithm. Thanks to this breakthrough, optimal transport has been progressively extended to machine learning and statistical inference by introducing additional application-specific terms in the problem formulation. It is however challenging to design efficient optimization algorithms for optimal transport based extensions. To overcome this limitation, we devise a general forward-backward splitting algorithm based on Bregman distances for solving a wide range of optimization problems involving a differentiable function with Lipschitz-continuous gradient and a doubly stochastic constraint. We illustrate the efficiency of our approach in the context of continuous domain adaptation. Experiments show that the proposed method leads to a significant improvement in terms of speed and performance with respect to the state of the art for domain adaptation on a continually rotating distribution coming from the standard two moon dataset.
Cell-Phone Classification: A Convolutional Neural Network Approach Exploiting Electromagnetic Emanations

In this paper, we propose a methodology to identify both the brand of a cell-phone, and the status of its camera by exploiting electromagnetic (EM) emanations. The method is composed of two parts: Feature extraction and Convolutional Neural Network (CNN). We first extract features by averaging magnitudes of short-time Fourier transform (STFT) of the measured EM signal, which helps to reduce input dimension of the neural network, and to filter spurious emissions. The extracted features are fed into the proposed CNN, which contains two convolutional layers (followed by max-pooling layers), and four fully-connected layers. Finally, we provide experimental results which exhibit more than 99% classification accuracy for the test signals.
Circumventing the “true threat” and “viewpoint” protection tests to deal with persistent campus hate speech

ABSTRACT This essay suggests using harassment law as a mechanism for remedying defects in “true threat” and “viewpoint protection” language which places an undue burden of proof on those trying to quell hate speech on campuses. The essay reviews noted failures and their causes in “true threat” and “viewpoint protection” rulings. Currently, the First Amendment of the Constitution protects freedom of expression unless it presents “a clear and present danger,” is treasonous, obscene, libelous or slanderous. The initial “clear and present danger” standard laid out by Justice Holmes in the Schenck decision has been refined in several cases to create an even heavier burden of proof. Currently, based particularly on the Brandenburg v. Ohio and Virginia v. Black rulings, to be prosecutable, speech must be a “true threat,” which means person-specific, imminent, and possible. However, the Supreme Court has also protected hate speech by defining it as “viewpoint” in such case as Indiana v. Hess, Snyder v. Phelps, and Matal v. Tam. This essay concludes by exploring ways to circumvent the “true threat” and “viewpoint” standards by relying on harassment rulings and extending these precedents from the workplace to campus learning environments.
Object Tracking Framework for Unmanned Aerial Vehicles

Object tracking is a computer vision problem that gained attention in the past few decades. Numerous algorithms targeting various challenges associated with object tracking have been proposed to address issues related to multiple objects tracking, handling complex scenarios like fog and occlusions, and other subproblems within the object tracking area. On the other hand, the extensive use of Unmanned Aerial Vehicles (UAVs) or Drones is increasing exponentially all around the world for a variety of applications. Object tracking using drones has an enormous number of applications, especially in smart transportation system. In this paper, we present a lightweight infrastructure-less object tracking system and assess its performance using the VICON motion capture system. The performance results obtained was very close to that of the optimal infrastructure-based solution.
Fair Byzantine Agreements for Blockchains

The Byzantine general problem is the core problem that consensus algorithms are trying to solve, which is at the heart of the design of blockchains. As a result, we have seen numerous proposals of consensus algorithms in recent years, trying to improve the level of decentralization, performance, and security of blockchains. In our opinion, there are two most challenging issues when we consider the design of such algorithms in the context of powering blockchains in practice. First, the outcome of a consensus algorithm usually depends on the underlying incentive model, so each participant should have an equal probability of receiving rewards for its work. Secondly, the protocol should be able to resist network failures, such as cloud services shutdown, while maintaining high performance otherwise. We address these two critical issues in this paper. First, we propose a new metric, called fair validity, for measuring the performance of Byzantine agreements. Intuitively, fair validity provides a lower bound for the probability of acceptances of honest nodes' proposals. This is a strong notion of fairness, and we argue that it is crucial for the success of a blockchain in practice. We then show that any Byzantine agreement could not achieve fair validity in an asynchronous network, so we will focus on synchronous protocols. This leads to our second contribution: we propose a fair, responsive, and partition-resilient Byzantine agreement protocol able to tolerate up to 1/3 corruptions. As we will show in the paper, our protocol achieves fair validity and is responsive in the sense that the termination time only depends on actual network delay, as opposed to arbitrary, pre-determined time-bound. Furthermore, our proposal is partition-resilient. Last but not least, experimental results show that our Byzantine agreement protocol outperforms a wide variety of state-of-art synchronous protocols, combining the best from both theoretic and practical worlds.
Refugees’ loss of self-determination in UNHCR operations through the gaining of identity in blockchain technology

Through the experimental use of blockchain technology with biometric enrollment, the United Nations High Commissioner for Refugees (UNHCR) is seeking ways to give refugees control over their own individual identities and greater forms of self-determination within contexts of assistance and protection. The point is to empower refugees with identity-sovereignty that may greatly enhance their autonomy and dignity as persons. In this regard, UNHCR addresses core problems in refugee identification that have always undermined its institutional objectives. However, regardless of its successes in this regard, UNHCR's use of blockchain with these aims demonstrates a great misunderstanding of identity, as if identity were a mere property and disregards the social and political relational contexts in which identity is effectively mobilized with autonomy. Moreover, UNHCR ignores how refugees’ identities are already richly formed for them. UNHCR's development of blockchain systems of identification have the effect of displacing the relational and social qualities of identity with a system of fixed coding over which refugees have no actual control. The uses of blockchain in this context serve the function of rendering the identities of persons seeking assistance and protection as refugees merely as “refugees” within a manageable system over which UNHCR maintains sovereignty.
Who thinks removing Confederate icons violates free speech?

The belief that removing Confederate icons from public spaces violates free expression rights occasionally makes its way into the national discourse. Because rights-based claims represent ostensibly race-neutral justification for supporting Confederate symbols, we field an original, nationally representative survey to ascertain how pervasive this belief is among the general public, as well as what motivates it. We find that while this is a decidedly minority position, this view is strongly correlated with racial attitudes. Our findings highlight an important form of popular constitutionalism and have implications for policymakers and others who might view rights-based claims as inherently race neutral.
Mobile Learning Adoption: An Empirical Study

Regardless of the occupation, in order to acquire knowledge, skills and competences training and education plays a vital role. Mobile learning brings the concept of using mobile devices such as mobile phones, smartphones, etc. for learning purpose. This document brings out the subject of mobile learning for educational purposes and presents prospects and opportunities of M-Learning; it also explores various implications and challenges faced in its implementation. A survey is conducted in Government and Private Institutes in India to determine both instructors and students cognizance and perception towards the trend of M-learning, to assess the productiveness, and to analyze social and cultural challenges that affects the adoption of M-learning in India. A questionnaire was distributed to 390 students and 57 instructors from some educational institution in India. From the result we came to know that instructors and students have positive perception towards M-learning, and accepted that M-learning improves the learning and teaching process. However, there are few challenges that will act as an obstacle to M-Learning implementation.
Adversarial Mixup Synthesis Training for Unsupervised Domain Adaptation

Domain adversarial training is a popular approach for Unsupervised Domain Adaptation (DA). However, the transferability of adversarial training framework may drop greatly on the adaptation tasks with a large distribution divergence between source and target domains. In this paper, we propose a new approach termed Adversarial Mixup Synthesis Training (AMST) to alleviate the issue. The AMST augments the training with synthesis samples by linearly interpolating between pairs of hidden representations and their domain labels. By this means, AMST encourages the model to make consistency domain prediction less confidently on interpolations points, which learn domain-specific representations with fewer directions of variance. Based on the previous work, we conduct a theoretical analysis on this phenomenon under ideal conditions and show that AMST could improve generalization ability. Finally, experiments on benchmark dataset demonstrate the effectiveness and practicability of AMST. We will publicly release our code on github soon.
Machine-Actionable Data Management Plans: A Knowledge Retrieval Approach to Automate the Assessment of Funders’ Requirements

Funding bodies and other policy-makers are increasingly more concerned with Research Data Management (RDM). The Data Management Plan (DMP) is one of the tools available to perform RDM tasks, however it is not a perfect concept. The Machine-Actionable Data Management Plan (maDMP) is a concept that aims to make the DMP interoperable, automated and increasingly standardised. In this paper we showcase that through the usage of semantic technologies, it is possible to both express and exploit the features of the maDMP. In particular, we focus on showing how a maDMP formalised as an ontology can be used automate the assessment of a funder's requirements for a given organisation.
Gray-Scale Image Colorization Using Cycle-Consistent Generative Adversarial Networks with Residual Structure Enhancer

The colorization of gray-scale images has always been a challenging task in computer vision. Recently, novel approaches have been introduced for unsupervised image translation between two domains using Generative Adversarial Networks (GANs). Since one can consider the gray-scale and colorful images as two separate domains, we propose a two-stage cycle-consistent network architecture to produce convincible images. First, an intermediate image is generated with a relatively uncomplicated objective function at the output. Next, at the second stage, the intermediate image is enhanced via a residual network structure with a more complicated objective function. Furthermore, by employing two inverse networks, a cycle-consistent architecture is formed at both stages. The proposed model is trained on the ImageNet dataset, and the achieved outcomes demonstrate exceptional performance comparing with the state-of-the-art models.
Forensic Analysis of Colorized Grayscale Images Using Local Binary Pattern

Versatile existence of the digital images creates the need of its forensic analysis. Basically, the goal of forensic analysis is to find the malicious manipulations in the images. However, it can be divided into many categories like splicing and copy move. Sometimes various operations like contrast enhancement, histogram equalization, resampling, etc. need to be detected in forensic analysis. Also there is need of forensic analysis to distinguish between computer generated images and natural images. Now a day's various techniques are available to colorize the grayscale images. The purpose of these techniques is to color the historic grayscale images. However, to decide the credibility of images it is necessary to differentiate between natural color and colorized images. In this paper, we have applied Local Binary Pattern feature extractor method on colorized images and natural images. We have found good results on two techniques of image colorization.
Rate Assignment in 360-Degree Video Tiled Streaming Using Random Forest Regression

Streaming of high-resolution 360-degree video is typically done in a viewport-dependent fashion such as in the tile-based viewport-dependent profile of MPEG OMAF wherein clients continuously adapt their tile selection according to the user viewport. From the perspective of a streaming service operator, tile rate assignment is crucial to ensure that a given target bitrate is obeyed while quality distribution among tiles leads to a favorable user experience. This paper addresses rate assignment in a distributed tile encoding system for such multi-resolution tiled streaming services based on the emerging Versatile Video Coding Standard. A model for rate assignment is derived based on random forest regression using spatio-temporal activity and encodings of a learning dataset.