Reviewerly/exHarmony huggingface dataset:
Frame-Correlation Transfers Trigger Economical Attacks on Deep Reinforcement Learning Policies

Adversarial attack can be deemed as a necessary prerequisite evaluation procedure before the deployment of any reinforcement learning (RL) policy. Most existing approaches for generating adversarial attacks are gradient based and are extensive, viz., perturbing every pixel of every frame. In contrast, recent advances show that gradient-free selective perturbations (i.e., attacking only selected pixels and frames) could be a more realistic adversary. However, these attacks treat every frame in isolation, ignoring the relationship between neighboring states of a Markov decision process; thus resulting in high computational complexity that tends to limit their real-world plausibility due to the tight time constraint in RL. Given the above, this article showcases the first study of how transferability across frames could be exploited for boosting the creation of minimal yet powerful attacks in image-based RL. To this end, we introduce three types of frame-correlation transfers (FCTs) (i.e., anterior case transfer, random projection-based transfer, and principal components-based transfer) with varying degrees of computational complexity in generating adversaries via a genetic algorithm. We empirically demonstrate the tradeoff between the complexity and potency of the transfer mechanism by exploring four fully trained state-of-the-art policies on six Atari games. Our FCTs dramatically speed up the attack generation compared to existing methods, often reducing the computation time required to nearly zero; thus, shedding light on the real threat of real-time attacks in RL.
RETRACTED CHAPTER: Implementation of Hand Gesture Recognition System to Aid Deaf-Dumb People

In recent years, the population of deaf-dumb victims has increased because of birth defects and other issues. Since a deaf and mute person cannot talk with an ordinary person in order that they ought to rely on some kind of communication system. The gesture shows some physical movements of the hand that convey a piece of information. Gesture recognition is the analytical interpretation of the movement of an individual through an information processing system. Linguistic communication provides the most effective conversation platform for the mute person to speak with an ordinary person. The aim of this paper is to build up a time system for hand gesture recognition that acknowledges hand gestures and then converts them into text and voice. In this paper, efforts have been done to detect 8 different gestures. Each gesture has assigned unique sound and text output. In experimental results, 800 samples were taken into the consideration out of which 760 samples were detected correctly and 40 samples were detected wrongly. Hence, the proposed system gives accuracy of 95%.
Research and Application of Blockchain Technology in Smart Grid

Smart grid, as a new paradigm of the traditional grid, was proposed to find an effective way to integrate green and renewable energy technologies. In this model, the Internet-connected smart grid is also known as the Internet of Energy, which is also an innovative method to ensure the sharing of energy data anytime and anywhere, and ultimately build a sustainable society. However, integrating and coordinating the large and growing number of connections is a challenging problem for the traditional centralized grid system. Therefore, the smart grid architecture is changing from the centralized structure to the decentralized structure. Besides, due to its excellent characteristics, blockchain technology has been widely used in various scenarios of Smart Grid. Therefore, this article aims to review the research and application of blockchain technology in smart grid industry. Firstly, we identify the major security challenges in smart grid scenarios that the blockchain can deal with. Then, we introduce some of the latest research work based on blockchain and propose security issues in the field of smart grid in different works of literature. Finally, we discuss the main research challenges and future research directions for applying blockchain to smart grid.
Cops and robbers on oriented toroidal grids

The game of cops and robbers is a well-known game played on graphs. In this paper we consider the straight-ahead orientations of 4-regular quadrangulations of the torus and the Klein bottle and we prove that their cop number is bounded by a constant. We also show that the cop number of every k-regularly oriented toroidal grid is at most 13.
An Overview of Control Strategy and Trajectory Planning of Visual Servoing

Visual servoing is a hot topic in the field of Robotics. Compared with other sensors, visual servo has the characteristics of high flexibility, high precision and strong robustness of calibration. In this paper, visual servoing are classified according to different types, and the research progress of visual servoing is reviewed from position-based visual servoing, image-based visual servoing, and hybrid visual servoing. Secondly, the feature extraction in the visual servoing process is introduced, and the current development situation is introduced from several feature extraction algorithms Then, the trajectory planning based on image interpolation, the trajectory planning based on optimization and the trajectory planning based on potential field are analyzed and discussed. Finally, based on the current research progress, the future research direction is prospected.
Two-Way Recursive Filtering

We present a new way of recursive image filtering called the two-way recursive filtering. Different from most recursive image filters that pose the 2D image filtering as iterative 1D row/column filtering, we consider the propagation on a 2D image plane directly. To compute the filtered value of a pixel, our method takes feedback from two neighboring pixels, one connected horizontally and the other connected vertically. In this way, the proposed filter has a dense receptive field, leading to fewer iterations needed to filter an image. We demonstrate its ability in high-quality edge-aware image smoothing without compromising the efficiency compared to 1D filters. We show that the proposed filter can also be formulated as a layer in a deep neural network. The feedback coefficients of the recursive filter are learned via backpropagating through the proposed filter. Our experiments on segmentation refinement show that our method produces favorable results against the three-way connection scheme in a previous segmentation refinement algorithm. To the best of our knowledge, this is the first work that explores the two-way pixel connectivity for recursive filtering.
Fast Kernelized Correlation Filter without Boundary Effect

In recent years, correlation filter based trackers (CF trackers) have attracted much attention from the vision community because of their top performance in both localization accuracy and efficiency. The society of visual tracking, however, still needs to deal with the following difficulty on CF trackers: avoiding or eliminating the boundary effect completely, in the meantime, exploiting non-linear kernels and running efficiently. In this paper, we propose a fast kernelized correlation filter without boundary effect (nBEKCF) to solve this problem. To avoid the boundary effect thoroughly, a set of real and dense patches is sampled through the traditional sliding window and used as the training samples to train nBEKCF to fit a Gaussian response map. Non-linear kernels can be applied naturally in nBEKCF due to its different theoretical foundation from the existing CF trackers'. To achieve the fast training and detection, a set of cyclic bases is introduced to construct the filter. Two algorithms, ACSII and CCIM, are developed to significantly accelerate the calculation of kernel correlation matrices. ACSII and CCIM fully exploit the density of training samples and cyclic structure of bases, and totally run in space domain. The efficiency of CCIM exceeds that of the FFT counterpart remarkably in our task. Extensive experiments on six public datasets, OTB-2013, OTB-2015, NfS, VOT2018, GOT10k, and TrackingNet, show that compared to the CF trackers designed to relax the boundary effect, BACF and SRDCF, our nBEKCF achieves higher localization accuracy without tricks, in the meanwhile, runs at higher FPS.
WITHDRAWN: Web based optical character recognition application using flask and tesseract

Optical Character Recognition (OCR) is a methodology utilized to transform over checked pictures into editable content configuration. OCR is basically the mechanical or electronic interpretation of manually written pictures or typewritten content into machine-editable content. Many antithetic kinds of Optical Character Recognition (OCR) implementations are commercially accessible today and it is a tract of investigation in pattern recognition, man-made reasoning and machine visual sense. In the proposed work images processed independently (.png, .jpg, and .gif files). Manually written acknowledgement is utilized regularly to depict the capacity of a PC to translate human composition into content. This may take in one of the two different ways, either by examining written content or by composing straightforwardly on fringe input gadgets. This work also assembles something comparative utilizing an tool of Optical Character Recognition (OCR) available in Google Tesseract OCR Engine alongside Python, Open-CV to specific fictional characters present in pictures. Primarily, in this proposed work a website is being set up using a web framework called Flask which is written in Python. Finally, the objective of this to deploy a web based Optical Character Recognition (OCR) using Python and Python based web tool i.e. Flask and the Tesseract engine. The communication protocol thus used is HTTP. The maximum accuracy obtained by authors is 94% for handwritten black & white images and 87% for handwritten color images. Authors continued their research for digitized images and the results obtained are 98% for black and white and 93% color images.
An Efficient Viewport-Dependent 360 VR System Based on Adaptive Tiled Streaming

Recent advances in 360 video streaming technologies have enhanced the immersive experience of video streaming services.Particularly, there is immense potential for the application of 360 video encoding formats to achieve highly immersive virtual reality (VR) systems.However, 360 video streaming requires considerable bandwidth, and its performance depends on several factors.Consequently, the optimization of 360 video bitstreams according to viewport texture is crucial.Therefore, we propose an adaptive solution for VR systems using viewport-dependent tiled 360 video streaming.To increase the degrees of freedom of users, the moving picture experts group (MPEG) recently defined three degrees plus of freedom (3DoF+) and six degrees of freedom (6DoF) to support free user movement within camera-captured scenes.The proposed method supports 6DoF to allow users to move their heads freely.Herein, we propose viewport-dependent tiled 360 video streaming based on users' head movements.The proposed system generates an adaptive bitstream using tile sets that are selected according to a parameter set of user's viewport area.This extracted bitstream is then transmitted to the user's computer.After decoding, the user's viewport is generated and rendered on VR head-mounted display (HMD).Furthermore, we introduce certain approaches to reduce the motion-to-photon latency.The experimental results demonstrated that, in contrast with non-tiled streaming, the proposed method achieved high-performance 360 video streaming for VR systems, with a 25.89% BD-rate saving for Y-PSNR and 61.16% for decoding time.
Learning Fast Converging, Effective Conditional Generative Adversarial Networks with a Mirrored Auxiliary Classifier

Training conditional generative adversarial networks (GANs) has been remaining as a challenging task, though standard GANs have developed substantially and gained huge successes in recent years. In this paper, we propose a novel conditional GAN architecture with a mirrored auxiliary classifier (MAC-GAN) in its discriminator for the purpose of label conditioning. Unlike existing works, our mirrored auxiliary classifier contains both a real and a fake node for each specific class to distinguish real samples from generated samples that are assigned into the same category by previous models. Comparing with previous auxiliary classifier-based conditional GANs, our MAC-GAN learns a fast converging model for high-quality image generation, taking benefits from its robust, newly designed auxiliary classifier. Experiments on multiple benchmark datasets illustrate that our proposed model improves the quality of image synthesis compared with state-of-the-art approaches. Moreover, much better classification performance can be achieved with the mirrored auxiliary classifier, which can in turn promote the use of MAC-GAN in various transfer learning tasks.
Challenges in Integrating SCRUM and the User-Centered Design Framework: A Systematic Review

This paper presents the results of a systematic review conducted in order to identify the challenges integrating user-centered design and Scrum, as well as the techniques and methodologies that would allow the Scrum Team to overcome these challenges. A total of 416 studies where identified, out of which only 29 studies where selected for this review. According to the analysis, most challenges are related to the insufficient importance assigned to Usability and the User Experience in general, insufficient communication between designers and developers, insufficient resources assigned to Scrum for upfront activities, and clients trying to represent final users without being aware of their real needs. Additionally, 30 techniques and methodologies have been identified as possible solutions to these challenges, which should be further analyzed to determine whether they should be adapted and incorporated into the Scrum framework.
Recommending Question-Answers for Enriching Textbooks

The students often use the community question-answering (cQA) systems along with textbooks to gain more knowledge. The millions of question-answers (QA) already accessible in these cQA forums. The huge amount of QA makes it hard for the students to go through all possible QA for a better understanding of the concepts. To address this issue, this paper provides a technological solution “Topic-based text enrichment process” for a textbook with relevant QA pairs from cQA. We used techniques of natural language processing, topic modeling, and data mining to extract the most relevant QA sets and corresponding links of cQA to enrich the textbooks. This work provides all the relevant QAs for the important topics of the textbook to the students, therefore it helps them to learn the concept more quickly. Experiments were carried out on a variety of textbooks such as Pattern Recognition and Machine Learning by Christopher M Bishop, Data Mining Concepts & techniques by Jiawei Han, Information Theory, Inference, & Learning Algorithms by David J.C. MacKay, and National Council of Educational Research and Training (NCERT). The results prove that we are effective in learning enhancement by enhancing the textbooks on various subjects and across different grades with the relevant QA pairs using automated techniques. We also present the results of quiz sessions which were conducted to evaluate the effectiveness of the proposed model in establishing relevance to learning among students.
Analysis of spectral irradiance distribution for PV applications at high latitude

The spectral distribution of global tilted irradiance measured in Grimstad, Norway, is analyzed in terms of the average photon energy ( APE) blue fraction ( BF) and useful fraction ( UF). The results based on eight months of recorded data show a general shift of the APE, BF, and UF values to higher values than reference AM1.5G spectrum, with 1.63 eV, 0.45, and 0.88 being the average indices, respectively. The statistical coefficient of variation ( CV) is used to determine the variation of indices. The CV for APE, BF, and UF are calculated to be 20.1%, 26.1%, and 25.7%, respectively confirming the earlier findings on APE being a more accurate index for the quantification of spectral distribution. Also, the spectral indices were classified in terms of sky conditions using the clearness index and CV. The results show a higher variation of indices in the cloudy sky conditions compared to clear and partially sunny sky conditions.
Imitation Learning for UAS Navigation in Cluttered Environments

Autonomous navigation is a critical component for the use of unmanned aerial systems (UAS) in complex tasks such as package delivery and disaster response. In recent years, these systems have seen increased usage in harsh tasks such as search and rescue and disaster relief, however there remains challenges for efficient and safe operation in a fully autonomous mission. This work seeks to provide a data-driven, vision-based method to navigating and searching through a clustered environment, which is high-speed, low-cost and vehicle-agnostic. This is done by first assuming obstacle avoidance as a two-dimensional navigation task that can be solved by knowing the relative location of the goal and the 2D image of the obstacle in the camera frame. Imitation learning is used to train a deep neural network from an expert planning policy, while a model predictive controller tracks the target. All the processing is capable onboard the vehicle, with the assumption that the general target direction is forward of the camera-frame, and that the global state estimation error is low. The framework and trained model are tested in simulation, with a quadcopter conducting search scenarios in different environments. The resulting framework is quicker to avoid obstacles and can be applied on small, low-cost systems with a single monocular camera.
Social explorative attention based recommendation for content distribution platforms

In modern social media platforms, an effective content recommendation should benefit both creators to bring genuine benefits to them and consumers to help them get really interesting content. To address the limitations of existing methods for social recommendation, we propose Social Explorative Attention Network (SEAN), a social recommendation framework that uses a personalized content recommendation model to encourage personal interests driven recommendation. SEAN has two versions: (1) SEAN-END2END allows user's attention vector to attend their personalized interested points in the documents. (2) SEAN-KEYWORD extracts keywords from users' historical readings to capture their long-term interests. It is much faster than the first version, more suitable for practical usage, while SEAN-END2END is more effective. Both versions allow the personalization factors to attend to users' higher-order friends on the social network to improve the accuracy and diversity of recommendation results. Constructing two datasets in two languages, English and Spanish, from a popular decentralized content distribution platform, Steemit, we compare SEAN models with state-of-the-art collaborative filtering (CF) and content based recommendation approaches. Experimental results demonstrate the effectiveness of SEAN in terms of both Gini coefficients for recommendation equality and F1 scores for recommendation accuracy.
Polarizations of Abelian Varieties Over Finite Fields via Canonical Liftings

Abstract We describe all polarizations for all abelian varieties over a finite field in a fixed isogeny class corresponding to a squarefree Weil polynomial, when one variety in the isogeny class admits a canonical lifting to characteristic zero, that is, a lifting for which the reduction morphism induces an isomorphism of endomorphism rings. Categorical equivalences between abelian varieties over finite fields and fractional ideals in étale algebras enable us to explicitly compute isomorphism classes of polarized abelian varieties satisfying some mild conditions. We also implement algorithms to perform these computations.
Modeling and Design of a Lift-Augmented Quadcopter

In this paper, we discuss the modeling and design of a new VTOL aircraft configuration - namely the lift-augmented quadcopter. As the name suggests, this aircraft is a quadcopter that is augmented with small, lifting wings. However, these wings do not entirely support the aircraft's weight during flight. Rather, the aircraft flies like a standard quadcopter even during cruise, but with a portion of the lift being provided more efficiently by the wings. The advantage compared to other hybrid multicopter configurations is that this configuration requires no additional moving parts, or complex transition maneuvers. This paper presents two models to predict vehicle cruise performance with wing lift augmentation, and compares one of these models with an additional empirical model that predicts cruise power based on body forces. The results show that by adding wings, the cruise thrust can be substantially reduced in order to reduce cruise power consumption. When applying this to our quadcopter, this thrust and power reduction increases the range by 25% compared to the baseline quadcopter.
Chaotic spin-photonic quantum states in an open periodically modulated cavity

When applied to dynamical systems, both classical and quantum, time periodic modulations can produce complex non-equilibrium states which are often termed “chaotic.” Being well understood within the unitary Hamiltonian framework, this phenomenon is less explored in open quantum systems. Here, we consider quantum chaotic states emerging in a leaky cavity when the intracavity photonic mode is coherently pumped with the pumping intensity varying periodically in time. We show that a single spin when placed inside the cavity and coupled to the mode can moderate transitions between regular and chaotic regimes—that are identified by using quantum Lyapunov exponents or features of photon emission statistics—and thus can be used to control the degree of chaos.
JUST System for WMT20 Chat Translation Task

Machine Translation (MT) is a sub-field of Artificial Intelligence and Natural Language Processing that investigates and studies the ways of automatically translating a text from one language to another. In this paper, we present the details of our submission to the WMT20 Chat Translation Task, which consists of two language directions, English –> German and German –> English. The major feature of our system is applying a pre-trained BERT embedding with a bidirectional recurrent neural network. Our system ensembles three models, each with different hyperparameters. Despite being trained on a very small corpus, our model produces surprisingly good results.
Robust Texture Features for Breast Density Classification in Mammograms

This paper proposes an effective and robust texture feature descriptor to classify mammographic images into different breast density categories. Accurate breast density based categorization of images plays an important role in the risk assessment at early stages of breast cancer. Based on the commonly used local binary patterns (LBP), we investigate its variant method local quinary patterns (LQP) for considering more details of texture features. The rotation invariant approach with different concerned numbers of spatial transitions is used to extend LQP to rotation invariant LQP (RILQP). The proposed method recognizes more texture patterns and reduces the high dimensionality of its feature vector significantly, which make it a robust texture descriptor. In addition, in the breast density classification task, this paper also investigates the influence to classifying results by using resized mammogram images. Two mammogram datasets, INBreast and MIAS, are used in our experiments to test the proposed method. Comparing to state-of-the-art methods, competitive classifying results are observed using the RILQP method, with classification accuracies of 82.50% and 80.30% on INBreast and MIAS respectively. Comparative analysis also indicates that the proposed method outperforms other methods statistically.