Reviewerly/exHarmony huggingface dataset:
A Solution for the Challenges Presented by the 2020 AUVSI SUAS Competition

A suite of solutions was developed by the University of Cincinnati Aerial Vehicles (UCAV) team to address the challenges presented by the 2020 AUVSI SUAS Competition. Competition tasks are reflective of current topics in Unmanned Aerial System (UAS) research including autonomous flight, object detection classification and localization (ODLC), obstacle avoidance, coverage path planning (CPP), and aerial payload delivery. A custom designed, autonomous hexacopter Unmanned Aerial Vehicle (UAV) named Xelaya was developed, having a gross takeoff weight (GTOW) of 22kg and an endurance of more than 30 minutes, allowing for the transport of additional vehicle subsystems. A second vehicle, a custom autonomous Unmanned Ground Vehicle (UGV), was manufactured and tested to be integrated into the UAV platform for the delivery objective. A modular approach to software design was used, taking advantage of the features of Robot Operating System (ROS) for managing data flow and handling a distributed workload across multiple systems and vehicles. Both an autonomous and manual system were implemented for ODLC. The autonomous system implements a custom convolutional neural network (CNN), while the manual system is composed of two web-based graphical user interfaces (GUIs) for operator input. For obstacle avoidance, a geometry-based method is compared to a node-based A* algorithm approach in order to find the more effective way to minimize both travel distance and execution time. Several methods typically used for solving NP-hard problems, including a genetic algorithm, 2-opt heuristic, and nearest neighbor are investigated for their application to a CPP problem through the competition’s search area.
Gradient Bias to Solve the Generalization Limit of Genetic Algorithms Through Hybridization with Reinforcement Learning

Genetic Algorithms have recently been successfully applied to the Machine Learning framework, being able to train autonomous agents and proving to be valid alternatives to state-of-the-art Reinforcement Learning techniques. Their attractiveness relies on the simplicity of their formulation and stability of their procedure, making them an appealing choice for Machine Learning applications where the complexity and instability of Deep Reinforcement Learning techniques is still an issue. However, despite their apparent potential, the classic formulation of Genetic Algorithms is unable to solve Machine Learning problems in the presence of high variance of the fitness function, which is common in realistic applications. To the best of our knowledge, the presented research is the first study about this limit, introduced as the Generalization Limit of Genetic Algorithms, which causes the solutions that Genetic Algorithms return to be not robust and in general low-performing. A solution is proposed based on the Gradient Bias effect, which is obtained by artificially injecting more robust individuals into the genetic population, therefore biasing the evolutionary process towards this type of solutions. This Gradient Bias effect is obtained by hybridising the Generic Algorithm with the Deep Reinforcement Learning technique DDPG, resulting in the Explorative-DDPG algorithm (X-DDPG). X-DDPG will be shown to solve the Generalization Limit of its genetic component via Gradient Bias, while outperforming its DDPG baseline in terms of agent return and speed of learning.
The detection of isomorphism of networks described by Reference Graphs

This paper includes the analysis and the results of the study of the possibility to utilize the unevenness coefficients of the use of individual graph edges for the assessment of the isomorphism of ICT networks described with Reference Graphs. These parameters are used to confirm or reject the presence of the isomorphism of the analyzed graphs. In the first part of the paper the terms chord graph, Reference Graph and isomorphism of graphs are defined. Further, the analysis of the possibility to utilize the unevenness coefficients for the evaluation of the isomorphism of RG structures is described. The term unevenness coefficient is defined and the method of their calculation is discussed. The examples of the application of the aforementioned coefficients for the assessment of the isomorphism of networks modelled with RG graphs are given.
Longest Common Subsequence based Multistage Collaborative Filtering for Recommender Systems

The contemporary recommender systems are facing challenges such as noise in user's choice, scalability, cold-start problem, availability of ample choices and handling of sparse data sets. In this paper, a multistage collaborative filtering is proposed to address the issues of noise in user's choice and ample choices availability. The two-stage filtering at first stage, filtering is performed using Pearson coefficient as a similarity measure and in the second stage, the longest common subsequence (LCS) is used to do filtering. The experiments are performed using benchmark 100k movielense datasets. The performance of multistage collaborative filtering is evaluated using accuracy, precision, recall, and f-measure. The results are also compared with single stage filtering and performance of multistage collaborative filtering is significantly improved over the used datasets.
Digital Watermarking Method for Printed Matters Using Deep Learning for Detecting Watermarked Areas

There are some technologies like QR codes to obtain digital information from printed matters. Digital watermarking is one of such techniques. Compared with other techniques, digital watermarking is suitable for adding information to images without spoiling their design. For such purposes, digital watermarking methods for printed matters using detection markers or image registration techniques for detecting watermarked areas are proposed. However, the detection markers themselves can damage the appearance such that the advantages of digital watermarking, which do not lose design, are not fully utilized. On the other hand, methods using image registration techniques are not able to work for non-registered images. In this paper, we propose a novel digital watermarking method using deep learning for the detection of watermarked areas instead of using detection markers or image registration. The proposed method introduces a semantic segmentation based on deep learning model for detecting watermarked areas from printed matters. We prepare two datasets for training the deep learning model. One is constituted of geometrically transformed non-watermarked and watermarked images. The number of images in this dataset is relatively large because the images can be generated based on image processing. This dataset is used for pre-training. The other is obtained from actually taken photographs including non-watermarked or watermarked printed matters. The number of this dataset is relatively small because taking the photographs requires a lot of effort and time. However, the existence of pre-training allows a fewer training images. This dataset is used for fine-tuning to improve robustness for print-cam attacks. In the experiments, we investigated the performance of our method by implementing it on smartphones. The experimental results show that our method can carry 96 bits of information with watermarked printed matters.
Comparison of Sampling-based Path Planners for Robocup Small Size League

The path planning problem is an important issue in robotics. In the context of the Small Size Soccer league, a widely used algorithm for path planning is the Rapidly-Exploring Random Tree (RRT), which is a single-query planner. However, properties such as asymptotic optimality and anytime resolution may not be achieved by some of the algorithms used in the competition. Some of the possible path planning solvers with some of these properties are the Batch Informed Trees (BIT*), Fast Marching Trees (FMT*), and other RRT implementations (such as Extended Execution RRT - ERRT and RRT*). The contribution of this paper is to carry out a benchmark comparative investigation with a selected set of path planners found in the literature, evaluating their performance in the context of Robocup Small Size League Competition. The results show good convergence properties of BIT* when the planning time requirement is larger. ERRT performs well to find a solution, but it falls short concerning the solution quality. RRT* did not have a good convergence to optimal paths, but succeeded to find paths with short planning time. Since FMT* does not present anytime resolution, the algorithm did not present a satisfactory convergence with larger planning time. To improve the performance of the planners, it may be interesting to conceive heuristic strategies to allow trivial problems to be solved faster.
Augmentation based Convolutional Neural Network for recognition of Handwritten Gujarati Characters

The aim of this research is to construct handwritten Gujarati characters' dataset as well as their recognition using Convolutional Neural Network (CNN). Handwritten Character Recognition (HCR) is an electronic translation of handwritten text to editable machine text. It is more challenging due to lots of variation in writing style, characters' thickness, and curves of different age groups. The dataset is collected from the primary school in Gujarat, India and preprocessed in MATLAB using various techniques, such as Segmentation, Equalization, Skeletonization, Dilation, and Merging. Deep Learning techniques can be utilized to overcome the various challenges which are faced while recognizing handwritten characters. Therefore, CNN with Dropouts, Augmentation and Multi-Layer Perceptron (MLP) is employed as classifier. The proposed system has achieved maximum training accuracy of 98.6% and testing accuracy of 94.8%.
A Systems Theory Approach to the Synthesis of Minimum Noise Non-Reciprocal Phase-Insensitive Quantum Amplifiers

We present a systems theory approach to finding the minimum required level of added quantum noise in a nonreciprocal phase-insensitive quantum amplifier. We also present a synthesis procedure for constructing a quantum optical nonreciprocal phase-insensitive quantum amplifier which adds the minimum level of quantum noise and achieves a required gain and bandwidth. This synthesis procedure is based on a singularly perturbed quantum system involving the broadband approximation of a Bogoliubov transformation and leads to an amplifier involving three squeezers and six beamsplitters.
Reliable Time Propagation Algorithms for PMF and RBPMF

This paper addresses the reliable time propagation algorithms for Point Mass Filter (PMF) and Rao–Blackwellized PMF (RBPMF) for the nonlinear estimaton problem. The conventional PMF and RBPMF process the probability diffusion for the time propagation with the direct sampled-values of the process noise. However, if the grid interval is not dense enough, it fails to represent the statistical characteristics of the noise accurately so the performance might deteriorate. To overcome that problem, we propose time propagation convolution algorithms adopting Moment Matched Gaussian Kernel (MMGK) on regular grids through mass linear interpolation. To extend the dimension of the MMGK that can accurately describe the noise moments up to the kernel length, we propose the extended MMGK based on the outer tensor product. The proposed time propagation algorithms using one common kernel through the mass linear interpolation not only improve the performance of the filter but also significantly reduce the computational load. The performance improvement and the computational load reduction of the proposed algorithms are verified through numerical simulations for various nonlinear models.
Privacy Preserving Deep Learning Framework in Fog Computing

Nowadays, the widespread use of mobile devices has raised serious cybersecurity challenges. Mobile services and applications use deep learning (DL) models for the modelling, classification and recognition of complex data, such as images, audio, video or text. Users benefit from the wide range of services and applications offered by these devices but pay an enormous price, the privacy of their personal data. Mobile services collect all different types of users’ data, including sensitive personal data, photos, videos, clinical data, banking data, etc. All this data is pooled to the Cloud to train global DL models, and big companies benefit from all the collected users’ data, posing obvious serious privacy issues. This paper proposes a privacy preserving framework for Fog computing environments, which adopts a distributed deep learning approach. Internet of Things (IoT) end nodes never reveals their sensitivity to the Cloud server; instead, they share a fraction of the users’ data, blurred with Gaussian noise, with a nearby Fog node. The DL methods considered in this work are Multilayer Perceptron (MLP) and Convolutional Neural Networks (CNN), and for both cases the accuracy is similar to the centralized and privacy violating approach, obtaining the best results for the CNN model.
Study of the Possibility of Using Foreign Experience in the Formation of the Russian Model of Personnel Motivation

The article discusses related to both the justification of the Russian model of personnel motivation, taking into account international experience. The urgency of solving this problem in modern Russian conditions has been substantiated. The main goal of the formation of a motivation model for Russian organizations is formulated - ensuring the achievement of the organization's goals by attracting and retaining professionally trained personnel and, above all, through effective, strong and sustainable motivation, formed on the basis of complex incentives, which is the subject of interest for all groups of the organization's personnel. The reasons are disclosed that prevent a change in the traditional approach to personnel motivation in Russian organizations, the main among which are: the adherence of Russian companies to traditional methods of incentives and other organization management technologies, as well as the economic situation in the country and the resulting needs of workers in Russia. The characteristics of the main foreign models of motivation, including Japanese, American, French, English, German and Swedish models, are given. The comparison of these models with the Russian ones is carried out and the prospects for their adaptation and use in the Russian practice of personnel motivation and incentives are substantiated.
Adversarial Audio Attacks that Evade Temporal Dependency

As the real-world applications (image segmentation, speech recognition, machine translation, etc.) are increasingly adopting Deep Neural Networks (DNNs), DNN’s vulnerabilities in a malicious environment have become an increasingly important research topic in adversarial machine learning. Adversarial machine learning (AML) focuses on exploring vulnerabilities and defensive techniques for machine learning models. Recent work has shown that most adversarial audio generation methods fail to consider audios’ temporal dependency (TD) (i.e., adversarial audios exhibit weaker TD than benign audios). As a result, the adversarial audios are easily detectable by examining their TD. Therefore, one area of interest in the audio AML community is to develop a novel attack that evades a TD-based detection model. In this contribution, we revisit the LSTM model for audio transcription and propose a new audio attack algorithm that evades the TD-based detection by explicitly controlling the TD in generated adversarial audios. The experimental results show that the detectability of our adversarial audio is significantly reduced compared to the state-of-the-art audio attack algorithms. Furthermore, experiments also show that our adversarial audios remain nearly indistinguishable from benign audios with only negligible perturbation magnitude.
Fingerprinting User Privacy in Web Applications

Information leakage by side channels in web applications is a major security threat, even when web traffic is encrypted. In this paper, we describe an automated system to analyse leakages of user privacy. Previous works focus on communications directly interacting with sensitive information. We show how, in real-world web applications, user information can be leaked through "non-intuitive" communications, which do not contain direct interactions with sensitive information. Unexpectedly, we found that user privacy can be leaked more from this kind of "non-intuitive" communication than direct interactions. This work also discloses user identities can be inferred by traffic analysis. In this work, we combine machine learning to recognize traffic pattern, i.e., Hidden Markov model is used to fingerprint web traffic of user privacy.
THE IMPACT OF SOCIAL MEDIA ON LANGUAGE

This paper is aimed at exploring the impact of social networks on English language proficiency, whether it affects the language knowledge and usage negatively or positively. The paper is focused on investigating various key components like grammar, vocabulary, and 4 crucial language skills: listening, speaking, reading and writing, keeping in mind the effects of numerous elements of Social Media on each of them. Based on the results of the present study, it can confidently be stated that social networks impact our writing more than any other skill. Constant software enhancements have made constructing a message or statement, of any length, a child’s play. Ironically, our smartphones have become smarter than us, even when it comes to our natural communication skills. The basic building blocks of any Language lies in its roots and sentence construction. In this generation, those building blocks are replaced or modified by the inclusion of several elements like abbreviations, emoticons and autofill. This paper explores both positive and negative aspects of the effects of social media on Language and presents a critical evaluation.
Automatic Text Summarization for Marine Natural Products Literature

Abstract Based on the Encoder-Decoder framework, an automatic summarization model MNPAS for marine natural products literature is proposed. Instead of directly using the structure of the common encoder in the past, a certain degree of innovation has been made to make Gru model complete the coding work. This advantage is that data information is not so easy to lose. In decoder, attention mechanism is integrated to improve the quality of predicted output word sequence. Compared with the traditional methods, the method proposed in this paper can complete the automatic generation of marine natural product literature abstracts better.
Estimating Evaluation of Cosmetics Reviews with Machine Learning Methods

This paper presents methods for estimating evaluation of cosmetics reviews, i.e., for assigning scores to cosmetics reviews, by using three kinds of machine learning methods: support vector machine (SVM), stacked denoising autoencoder (SdA), and convolutional neural network (CNN). The experimental results show that (1) using words with various parts of speech (POSs), not only nouns, as features in vectorizing review text is effective, (2) selecting features on the basis of dependency analysis is effective, (3) the three machine learning methods have almost the same estimation precision and are much higher than a rule-based baseline method, and (4) the training cost of SVM is extremely lower than the other two methods, and SVM therefore performs the best among the three methods.
A Self-correction Based Algorithm for Single-Shot Camera Calibration

Camera calibration is a fundamental task in photogrammetry and computer vision. In view of the requirements in live camera characteristics, we present a novel calibration approach to obtain all the camera optimal parameters and the distortion rectification by using only a single image. The existing automatic calibration approaches inspired by the vanishing point theory or the homography matrix are not capable of entirely deal with the internal, external and the len distortion parameters simultaneously. Compared to the previous works, our approach solves the problem by applying the linear features extracted from the image and thus improves the accuracy and efficiency. Results of experiments in different scenes demonstrate the comparable performance to the traditional methods.
Annual Solar Irradiance Anomaly Features Over the USA During 1998–2017 Using NSRDB V3

Annual solar irradiance anomalies (or departures from the long-term mean annual value) have a direct impact on various phases of solar energy projects, from prefeasibility studies to technical deployment decisions. Anomalies can happen because of normal climate variability or exceptional weather patterns. This study investigates such anomalies for both global horizontal irradiance (GHI) and direct normal irradiance (DNI) using Version 3 of the National Solar Radiation Database (NSRDB V3) and surface irradiance measurements at eight U.S. locations. At each site, the annual anomaly is analyzed here by evaluating the irradiance deviation from the long-term average for each specific year from 1998-2017. A positive/negative anomaly indicates that the solar resource was higher/lower than the long-term average during that specific year. The results show that in most cases the anomaly is within ±5% for GHI and ±10% for DNI using either ground-based irradiance measurements or modeled data from the NSRDB.
FGAN: Fan-Shaped GAN for Racial Transformation

Racial bias in face recognition has recently been concerned by both general public and research community. Most face recognition systems have a strong bias in recognition accuracy for different races mainly because of the unbalanced ethnic distribution in their datasets. In this paper, we propose a novel generative adversarial network, which transfer the facial images of one race to corresponding images of other races, to facilitate the data augmentation to balance the ethnic distribution. Our approach can generate more realistic results and make the training process more stable than other image-to-image translation methods such as StarGAN and CycleGAN. Experiments results show the superiority of FGAN to the previous methods on the racial transformation task in terms of visual effects and quantitative results. Besides, we perform extensive experiments to show our data augmentation is beneficial to reduce the racial bias, improving the face recognition rate of non-Caucasian people. Finally, we show the possibility to generate the ethnic independent facial image by the average of various races.
Shot Boundary Detection Using Artificial Neural Network

As of late video is the most utilized information type on the Internet. Content, sound, and pictures are consolidated to establish a video, so recordings are enormous in size. The human mind can process visual media quicker than it can process message. This expansion in information has required the investigation of powerful strategies to process and store information content. In this paper, we have suggested a hybrid video shot boundary detection process using feature extraction by mean log difference which is combined with artificial neural network. We devised two-step method for automatic shot boundary detection. Firstly, features are extracted using H, V, S procedure along with histogram distribution technique, and then, this mean log difference array is applied as an input to ANN which identifies video shots based on probability function. We have incorporated feed-forward network structure which processes nonlinear factual information to calculate shot boundary detection considering probability function. Finally, we have evaluated the results using precision, recall, and F1 measure. An experimental result indicates that ANN along with mean log difference, it offers efficient representation of shot boundaries and the results are satisfactory. Comparing the proposed method with improved block color feature method, there is a sort of trade-off relation between the two algorithms, and it is observed that for fast characteristic variations, ANN performs moderately better while for complex videos improved block color feature method is suited in better way.