Reviewerly/exHarmony huggingface dataset:
Wave Scale, Speed and Direction from Airborne Video of Maritime Scene

Ocean surfaces and large water bodies are commonly monitored by aircraft. While water features are visually non-static, they do include information that allows determination of water motion which has applications in navigation, assessing sub-surface changes and the estimation of drift and size of objects within the scene. This study presents an enhancement of state of the art methods to extract water surface features from imagery acquired by an overhead aircraft and assesses its performance on a real world maritime scene.
Deep-Learning from Mistakes: Automating Cloud Class Refinement for Sky Image Segmentation

There is considerable research effort directed toward ground based cloud detection due to its many applications in Air traffic control, Cloud-track wind data monitoring, and Solar-power forecasting to name a few. There are key challenges that have been identified consistently in the literature being primarily: glare, varied illumination, poorly defined boundaries, and thin wispy clouds. At this time there is one significant research database for use in Cloud Segmentation; the SWIMSEG database [1] which consists of 1013 Images and the corresponding Ground Truths. While investigating the limitations around detecting thin cloud, we found significant ambiguity even within this high quality hand labelled research dataset. This is to be expected, as the task of tracing cloud boundaries is subjective. We propose capitalising on these inconsistencies by utilising robust deep-learning techniques, which have been recently shown to be effective on this data. By implementing a two-stage training strategy, validated on the smaller HYTA dataset, we plan to leverage the mistakes in the first stage of training to refine class features in the second. This approach is based on the assumption that the majority of mistakes made in the first stage will correspond to thin cloud pixels. The results of our experimentation indicate that this assumption is true, with this two-stage process producing quality results, while also proving to be robust when extended to unseen data.
Identifying WeChat Message Types without Using Traditional Traffic

Attackers can eavesdrop and exploit user privacy by classifying traffic into different types of in-app service usage to identify user actions. WeChat is the largest social messaging platform, which is a popular application in China. When WeChat is shut down, it is unable to generate traffic; that is, traditional traffic. However, the traffic still can be generated by system. How to identify the message types within WeChat with traffic generated by a system instead of traditional traffic becomes a new challenge. To deal with this challenge, we designed a system to identify and analyze the traffic of the Apple Push Notification service (APNs) to identify the message types of WeChat. In detail, we designed a system to identify and analyze the traffic of the APNs. First, the system clusters the traffic based on the session and divides it into multiple bursts. Then, it extracts the features of each burst and sends these features to the learning-based classifier to extract APNs’s traffic from the background traffic. Finally, it uses a hash-based lookup table method to analyze message types from APNs traffic. Extensive evaluation results show that we can accurately identify the six message types of APN and WeChat. In addition, we propose two coping strategies for the method proposed in this article.
Innovative Review on Artificial Bee Colony Algorithm and Its Variants

The popular swarm-based algorithm is being inspired by the intelligent behavior of the honeybees that helps in finding the optimal solutions for getting the best food source. This paper is focused on highlighting the concept of the swan intelligence and the concept of the ABC algorithm, its variant, and also about its applications.
Scenario Analysis in Control of Complex Geopolitical Information Confrontation

The paper considers methodological and applied problems of increasing the efficiency of information control processes in the context of globalization. The tasks of organizing effective counteraction to information aggression have been set and solved. The results of the scenario analysis study of the developed multigraph models for controlling of information threats counteraction are provided.
Assessment of the Dynamics of Publication Activity in the Field of Natural Language Processing and Deep Learning

Natural language processing (NLP) is a rapidly developing field of research. In solving the problems of NLP, along with traditional methods based on a statistical model of language, machine learning methods (ML) are used. The paper considers bibliometric indicators NLP and ML. Dynamic indicators are evaluated and areas of research with the highest growth rates are identified. The indicators were calculated for the following NLP applications: Grammar Checking, Information Extraction, Text Categorization, Dialog Systems, Speech Recognition, Machine Translation, Information Retrieval, Question Answering, Opinion Mining, Smart advisors, etc. The greatest values of dynamic indicators are demonstrated by: Grammar Checking, Information Extraction, Machine Translation, Question Answering. At the same time, the following methods of solving NLP problems are developing most dynamically: Machine Learning, Deep Learning, Neural Networks and Supervised Learning. In turn, Deep Learning is used to solve a wide range of applications. Its feature is the increased requirements for the volume of data processed. The paper assesses the growth dynamics of bibliometric indicators for some Deep Learning applications. The most dynamically developing research is the field of deep learning applications to solve healthcare problems.
DatAR: Your Brain, Your Data, On Your Desk - A Research Proposal

We present a research proposal that investigates the use of 3D representations in Augmented Reality (AR) to allow neuroscientists to explore literature they wish to understand for their own scientific purposes. Neuroscientists need to identify potential real-life experiments they wish to perform that provide the most information for their field with the minimum use of limited resources. This requires understanding both the already known relationships among concepts and those that have not yet been discovered. Our assumption is that by providing overviews of the correlations among concepts through the use of linked data, these will allow neuroscientists to better understand the gaps in their own literature and more quickly identify the most suitable experiments to carry out. We will identify candidate visualizations and improve upon these for a specific information need. We describe our planned prototype 3D AR implementation and directions we intend to explore.
ADS-B Data Attack Detection Based on Generative Adversarial Networks

With the requirements on accuracy, coverage and reliability, the air traffic surveillance is being developed into the next generation. In 2020, ADS-B data is becoming the foundation to establish air traffic situation awareness capabilities. However, ADS-B is designed without sufficient security guarantees, which results in diverse attack threats. Hence, it is in demand of effective attack detections to keep attack data away from decision flows. To improve the accuracy and robustness, attack detection based on generative adversarial network for ADS-B data is proposed. The LSTM networks are the core components to set up the generator and discriminator to make the most of temporal spatial correlations. Utilizing the reconstruction error and discriminative loss, the comprehensive detection metric is obtained to identify attack behaviors. To enhance the robustness, the analysis threshold for detection decision is determined in terms of normal data intrinsic features. By experimental analysis on real ADS-B data, the accuracy and robustness of the proposed method is validated.
MRI image compression using multiple wavelets at different levels of discrete wavelets transform

Abstract Medicine is benefited in large amount with the utilization of imaging. Magnetic Resonance Imaging (MRI) is an important imaging modality that is used for better diagnosis. Since the number of images generated by MRI is in large number per patient, the requirement for storage space and transmission bandwidth will be larger. In the area of image processing, image compression finds an important application that reduces storage space and transmission bandwidth. One way to compress the image is by using transform based techniques. In this work, we have used DWT (Discrete Wavelet Transform) with multiple wavelets at different levels to transform the MRI images. In the first few levels CDF (Cohen–Daubechies–Feauveau) 9/7 wavelet is used for transformation and Haar wavelet is used for remaining levels. After transformation SPIHT (Set Partitioning In Hierarchical Trees) algorithm is used for compression of the images. We have observed better compression in terms of MSE (Mean squared Error) and PSNR (Peak Signal to Noise Ratio) with the proposed method in comparison with the single wavelet transformation.
A multi-feature bipartite graph ensemble for image segmentation

Image segmentation benefits from using multi-feature ensembles. In this paper, we propose a novel multi-layer bipartite graph model for more effective feature fusion. This model employs multiple graph layers, each representing a feature space. They share common vertices but have individual edge sets that are obtained from different feature spaces. The features are fused by the regularization defined on a Grassmann manifold, which compresses the graph layers into subspace representations and merges them into one. The merged graph is then fed into a graph-cut algorithm to generate the final segmentation. Experiments carried out using the Berkeley segmentation benchmark show that our model is effective in feature fusion and image segmentation, outperforming the state-of-the-art superpixel-based methods.
Deep Learning Approach for Extracting Catch Phrases from Legal Documents

Catch phrases are the important phrases that precisely explain the document. They represent the context of the whole document. They can also be used to retrieve relevant prior cases by the judges and lawyers for assuring justice in the domain of law. Currently, catch phrases are extracted using statistical methods, machine learning techniques, and deep learning techniques. The authors propose a sequence to sequence (Seq2Seq) deep neural network to extract catch phrases from legal documents. They have employed several layers, namely embedding layer, encoder-decoder layer, projection layer, and loss layer to build the deep neural network. The methodology is evaluated on IRLeD@FIRE-2017 dataset and the method has obtained 0.787 and 0.607 as mean average precision and recall scores respectively. Results show that the proposed method outperforms the existing systems.
Implementation of Encryption and Decryption Algorithms for Security of Mobile Devices

Progress of mobile communication and VLSI technology has aided in development of smart devices. These devices process the information of various formats and sizes in a limited amount of time. This information will be either stored in the devices or in cloud, hence there is a need for some kind of methodology to process and secure the data. Implementation of new algorithms to secure the information is always of immense interest. These algorithms will improve the performance of smart devices and helps for better human-machine interaction. Generally, symmetric and asymmetric approaches are used to secure the data from unauthorized users or attacks. Considering the amount of delay and complexity involved in processing the data, various forms of algorithms are used. In this paper, we propose a novel algorithm to secure the data from vulnerable attacks. These algorithms can be implemented on various platforms. The experimental results demonstrate an improvement of 10% for contacts and 15% for the encryption of images as compared to other conventional approaches.
Un-VDNet: unsupervised network for visual odometry and depth estimation

Monocular visual odometry and depth estimation play an important role in augmented reality and robot applications. Recently, deep learning technologies have been widely used in these areas. However, most existing works utilize supervised learning, which requires large amounts of labeled data and assumes that the scene is static. We propose a framework, called Un-VDNet, based on unsupervised convolutional neural networks to predict camera ego-motion and depth maps from image sequences. The framework includes three subnetworks (PoseNet, DepthNet, and FlowNet) and learns temporal motion and spatial association information in an end-to-end network. We propose a pose-consistency loss to penalize errors about the translation and rotation drifts of the pose estimated from the PoseNet. Furthermore, a geometric consistency loss, between the structure flow and scene flow learned from the FlowNet, is proposed to deal with dynamic objects in the real-world scene, which is combined with spatial and temporal photometric consistency constraints. Extensive experiments on the KITTI and TUM datasets demonstrate that our proposed Un-VDNet outperforms the state-of-the-art methods for visual odometry and depth estimation in dealing with dynamic objects of outdoor and indoor scenes.
Automatic Clustering of Hyperspectral Images Using Qutrit Based Particle Swarm Optimization

Hyperspectral Images (HSI) contain a lot of data channels. Due to their high dimensionality, it is difficult to design systems which are able to find optimal number of clusters to segment them. A qutrit based particle swarm optimization (PSO) for automatic clustering of hyperspectral images is introduced in this paper. A Band Fusion Technique is implemented by improving the Improved Subspace Decomposition Algorithm using SF Index as the fitness function. A new method for maintaining the superposition state of the qutrits is also successfully designed. A comparison with classical PSO is performed to clearly establish the supremacy of the proposed technique with respect to Peak signal-to-noise ratio (PSNR), Jaccard Index, Sørensen-Dice Similarity Index and the computational time. Finally, the unpaired two-tailed t test is conducted between the proposed technique and classical PSO, which renders better results for proposed qutrit based technique. The experiments are carried out on the Salinas Dataset. The proposed technique yields better results in all the tests conducted in comparison to the classical PSO.
Bayesian Residual Policy Optimization: : Scalable Bayesian Reinforcement Learning with Clairvoyant Experts

Informed and robust decision making in the face of uncertainty is critical for robots operating in unstructured environments. We formulate this as Bayesian Reinforcement Learning over latent Markov Decision Processes (MDPs). While Bayes-optimality is theoretically the gold standard, existing algorithms scale poorly to continuous state and action spaces. We build on the following insight: in the absence of uncertainty, each latent MDP is easier to solve. We first obtain an ensemble of experts, one for each latent MDP, and fuse their advice to compute a baseline policy. Next, we train a Bayesian residual policy to improve upon the ensemble’s recommendation and learn to reduce uncertainty. Our algorithm, Bayesian Residual Policy Optimization (BRPO), imports the scalability of policy gradient methods and task-specific expert skills. BRPO significantly improves the ensemble of experts and drastically outperforms existing adaptive RL methods, both in simulated and physical robot experiments.
Higher Education Virtual Classrooms Applied to Learning Processes in Latin America

Virtual classrooms in Latin America facilitate Higher Education learning processes in Latin America. This qualitative research is based on the writing of some bibliographical analysis and conceptualization methods found in several scientific articles which cover both from the importance and understanding of the impact that virtual classrooms have on the academic context. A guided bibliographical technique applied through four questions such as: What kind of impact do virtual classrooms have on Higher Education didactic processes in Latin America? To what extent, do virtual classrooms have an impact on administrative and operative processes in regards of Higher Education learning processes in Latin America? Are virtual classrooms quality indicator tools used in Higher Education evaluations and accreditation in Latin America? On the whole, TICs contribute to administrative, financial and educational procedures therefore, minimizing time and resources while offering students learning environments within virtual class-rooms containing cutting-age technology that will certainly meet their technological needs.
A General Feature Abstraction Method for Clustering Algorithm

Clustering algorithms are one of the most important methods in data mining, which can be used in real applications to detect the similarity of objects. To improve the performance, we use a general feature abstraction method, the Auto-Encoder[9], which can compress the data features so that the noises of data can be filtered. Our extensive experimental results show that when we use a more complicated neural networks with certain activation functions, we can significantly improve the performance, both the computing cost and the clustering accuracy, of the clustering methods.
A Deep Quadruplet Network for Local Descriptor Learning

Generating a distinguishable feature for local patch is a main task in computer vision which aims at matching local patches. Recently, local patch descriptors from deep convolutional neural network (CNN) with a triplet loss have achieved promising performance. In this paper, we design a quadruplet loss, which can achieve a better result than other pairwise loss and triplet loss methods. Our loss is inspired by the thoughts of uniform distribution. It separates non-matching examples by using the hard sampled non-matching pairs in a batch, and simultaneously uses the random sampled non-matching examples to keep non-matching pairs to obey uniform distribution. A compact descriptor named QuadrupletNet is generated by combining the proposed quadruplet loss and L2Net CNN architecture. From our experiment, QuadrupletNet shows better performance on the Brown dataset and Hpatches dataset than Triplet loss methods on the same training set. The pre-trained QuadrupletNet is publicly available.
Vehicle Detection using Road Lines

In video surveillance systems the first step is motion detection. So, we can use a threshold to detect moving objects. To not change the threshold for each sequence, we propose a vehicle detection system, which automatically generates the threshold according to the road lines. To do that, several techniques must be used. Generally, we start by the generation of road lines to define our threshold, we subtract the background using the k-nearest neighbor (KNN) method. Finally, we use the calculated threshold to select moving objects.
Distributed Adaptive Neuro Intuitionistic Fuzzy Architecture for prediction of the dose in gamma irradiated milk products

In this paper, a Distributed Adaptive Neuro Intuitionistic Fuzzy Architecture (DANIFA) with a second order Takagi-Sugeno inference is presented. The architecture represents a layered set of simple fuzzy inferences connected in a distributed way, thus minimizing the number of the interconnected fuzzy rules and their associated parameters. The flexibility of the designed structure to handle uncertain data variations is complemented, by embedding an Intuitionistic fuzzification approach. A simple two-step gradient descent algorithm with a fixed learning rate is used as a learning algorithm of the proposed architecture. To test the prediction abilities of the designed model a biological case for estimation of the low gamma irradiation dose to destruct the protein fractions in milk products with potential uncertain data variations is studied.
Wrapper-Based Feature Selection Using Self-adaptive Differential Evolution

Knowledge discovery in databases is a comprehensive procedure which enables researchers to explore knowledge and information from raw sample data usefully. Some problems may arise during this procedure, for example the Curse of Dimensionality, where the reduction of database is desired to avoid feature redundancy or irrelevancy. In this paper, we propose a wrapper-based feature selection algorithm, consisting of an artificial neural network and self-adaptive differential evolution optimization algorithm. We test performance of the feature selection algorithm on a case study of bank marketing and show that this feature selection algorithm reduces the size of the database and simultaneously improves prediction performance on the observed problem.
Regulatory Sandboxes and Experimental Legislation as the Main Instruments of Regulation in the Digital Transformation

Digital transformation poses an intrinsic challenge for regulators. With the rapid development of digital technology, there is a need for new regulatory tools. Russia, like many other countries, is trying to improve its regulatory policy for digitalization and digital transformation. This paper aims comparing the possibilities and prospects for the use of regulatory sandboxes in developed countries, in Russia and in the EAEU. The analysis demonstrates that the transition from experimental legislation to regulatory sandboxes is associated with the need for rapid adaptation of regulation to digitalization conditions. Moreover, in Russia, the full implementation of regulatory sandboxes for digital transformation is complicated by the peculiarities of the legal system and the role of the law as the main regulatory tool. In the EAEU, the introduction of regulatory sandboxes is complicated by the different approaches of states to protecting their digital sovereignty and attitudes towards virtual jurisdictions. Based on the analysis, the new regulatory specific mechanism for the relationship between strategic planning, legal forecasting, experimentation and the formation of proactive regulation is proposed. Implementation of these recommendations would help improve legal regulation of the development of digital technologies in Russia and the EAEU. It has been demonstrated that regulatory sandboxes are one early step in a new smart—digitized and datafied—process regulatory systems.