Reviewerly/exHarmony huggingface dataset:
Leveraging a Federation of Knowledge Graphs to Improve Faceted Search in Digital Libraries

Scientists always look for the most accurate and relevant answers to their queries in the literature. Traditional scholarly digital libraries list documents in search results, and therefore are unable to provide precise answers to search queries. In other words, search in digital libraries is metadata search and, if available, full-text search. We present a methodology for improving a faceted search system on structured content by leveraging a federation of scholarly knowledge graphs. We implemented the methodology on top of a scholarly knowledge graph. This search system can leverage content from third-party knowledge graphs to improve the exploration of scholarly content. A novelty of our approach is that we use dynamic facets on diverse data types, meaning that facets can change according to the user query. The user can also adjust the granularity of dynamic facets. An additional novelty is that we leverage third-party knowledge graphs to improve exploring scholarly knowledge.
A new classification method for encrypted internet traﬀic using machine learning

The rate of internet usage in the world is over 62% and this rate is increasing day by day. With this increase, it becomes important to ensure the confidentiality of the information in the traffic flowing over the internet. Encryption algorithms and protocols are used for this purpose. This situation, which is beneficial for normal users, is also used by attackers to hide. Cyber attackers or hackers gain the ability to bypass security precautions such as IDS/IPS and antivirus systems with using encrypted traffic. Since payload analysis cannot be performed without deciphering the encrypted traffic, existing commercial security solutions fall short in this situation. In this study, it is aimed to classify the network traffic by analysing the outgoing and incoming data over the encrypted traffic using extreme gradient boosting (XGBoost), decision tree and random forest classification methods. Thus, without deciphering, it is possible to classify packets passing through encrypted traffic using some metadata like size and duration and to take precautions against attacks. ISCX VPN-NonVPN dataset was used to test the proposed model in this study. With the created framework, encrypted traffic was classified with a high success rate and 94.53% success was achieved by using the XGBoost classification method.
A new binary representation method for shape convexity and application to image segmentation

We present a novel and computable characterization method for convex shapes. We prove that the shape convexity is equivalent to a quadratic constraint on the associated indicator function. Such a simple characterization method allows us to design efficient algorithms for various applications with convex shape prior. In order to show the effectiveness of the proposed approach, this method is incorporated with a probability-based model to extract an object with convexity prior. The Lagrange multiplier method is used to solve the proposed model. Numerical results on various images show the superiority of the proposed method.
The exact worst-case convergence rate of the gradient method with fixed step lengths for L-smooth functions

Abstract In this paper, we study the convergence rate of the gradient (or steepest descent) method with fixed step lengths for finding a stationary point of an L -smooth function. We establish a new convergence rate, and show that the bound may be exact in some cases, in particular when all step lengths lie in the interval (0, 1/ L ]. In addition, we derive an optimal step length with respect to the new bound.
OpenUAV Cloud Testbed: a Collaborative Design Studio for Field Robotics

Simulations play a crucial role in robotics research and education. This paper presents the OpenUAV testbed, an open-source, easy-to-use, web-based, and reproducible software system that enables students and researchers to run robotic simulations on the cloud. We have built upon our previous work and have addressed some of the educational and research challenges associated with the prior work. The critical contributions of the paper to the robotics and automation community are threefold: First, OpenUAV saves students and researchers from tedious and complicated software setups by providing web-browser-based Linux desktop sessions with standard robotics software like Gazebo, ROS, and flight autonomy stack. Second, a method for saving an individual's research work with its dependencies for the work's future reproducibility. Third, the platform provides a mechanism to support photorealistic robotics simulations by combining Unity game engine-based camera rendering and Gazebo physics. The paper addresses a research need for photorealistic simulations and describes a methodology for creating a photorealistic aquatic simulation. We also present the various academic and research use-cases of this platform to improve robotics education and research, especially during times like the COVID-19 pandemic, when virtual collaboration is necessary. GitHub https://github.com/Open-UAV/openuav-turbovnc Webpage: https://openuav.us
Partially Occluded Skeleton Action Recognition Based on Multi-stream Fusion Graph Convolutional Networks

Skeleton-based action recognition methods have been widely developed in recent years. However, the occlusion problem is still a difficult problem at present. Existing skeleton action recognition methods are usually based on complete skeleton data, and their performance is greatly reduced in occluded skeleton action recognition tasks. In order to improve the recognition accuracy on occluded skeleton data, a multi-stream fusion graph convolutional network (MSFGCN) is proposed. The proposed multi-stream fusion network consists of multiple streams, and different streams can handle different occlusion cases. In addition, joint coordinates, relative coordinates, small-scale temporal differences and large-scale temporal differences are extracted simultaneously to construct more discriminative multimodal features. In particular, to the best of our knowledge, we are the first to propose the simultaneous extraction of temporal difference features at different scales, which can more effectively distinguish between actions with different motion amplitude. Experimental results show that the proposed MSFGCN obtains state-of-the-art performance on occluded skeleton datasets.
Effect of Target Size, Location, and Input Method on Interaction in Immersive Virtual Reality

Although new virtual reality (VR) devices and their contents are actively being released, there are still not enough studies to prepare its interface/interaction standard. In this study, it was investigated whether specific interaction factors influenced task performance and the degree of virtual reality sickness when performing pointing tasks in immersive virtual reality. A smartphone-based VR device was used, and twenty-five targets were placed in a 5 × 5 layout on the VR experimental area that extended to a range similar to the human viewing angle. Task completion time (TCT) was significantly affected by target selection method (p &lt; 0.001) and target size (p &lt; 0.001), whereas the error rate (ER) significantly differed for the target selection method (p &lt; 0.001) and not for the target size (p = 0.057). Target location was observed to be a factor affecting TCT (p &lt; 0.001), but it did not affect the ER (p = 0.876). VR sickness was more severe when the target size was smaller. Gaze selection was found to be more efficient when accuracy is demanded, and manual selection is more efficient for quick selection. Moreover, applying these experimental data to Fitts’ Law showed that the movement time was found to be less affected by the device when using the gaze-selection method. Virtual reality provides a three-dimensional visual environment, but a one-dimensional formula can sufficiently predict the movement time. The result of this study is expected to be a reference for preparing interface/interaction design standards for virtual reality.
SAR Image Super-Resolution Reconstruction Based on an Optimize Iterative Method for Regularization

SAR image enhancement plays an important role in the process of SAR image processing and information interpretation. Super-resolution reconstruction is a widely adopted enhancement method. However, it is difficult to achieve a decent tradeoff between reconstruction effectiveness and the convergence speed for existing methods. To combat such problem, this paper proposed a novel solution to it, we started from the modeling of SAR image degradation principle, applying the adaptive line search strategy to the SAR image super-resolution reconstruction process, and redefined the step size selection in the reconstruction process, made it possible to achieve both reconstruction effectiveness and convergence speed. Compared with the existing empirical setting or iterative selection, the proposed method can reduce the number of iterations while guarantee the reconstruction results.
A Novel Deep Transfer Learning Method for SAR and Optical Fusion Imagery Semantic Segmentation

Synthetic Aperture Radar (SAR) imagery have been one of the important tools to support earth observations and topographic measurements. It means SAR imagery are essentially rich in structures and some important target categories are difficult to recognize. Optical imagery contains rich and clear spectral information which has a good influence on semantic image segmentation. The success of deep neural networks for semantic segmentation heavily depends on large-scale and well-labeled data sets, which are hard to collect in practice. In this paper, we consider deep transfer learning for semantic segmentation, we propose a deep novel transfer learning method, which transfers a semantically segment model from SAR imagery to SAR and optical fusion imagery. The experimental results show that the method proposed achieves higher mean Intersection over Union (mIoU) with less training time compared with other methods.
Learning to See in the Rain via Disentangled Representation

Image de-raining is an important task in many robot vision applications since rain effects and hazy air largely threaten the performance of visual analytics. While deep neural de-raining models have greatly boosted performance by learning rich representations of rainy input data, they are still likely to indicate incongruent information to spoil de-raining. We find that the process of extracting the feature representations in the rainy image space is surprisingly important for perceptual de-raining performance. We further probe the limitations of the universal restoration learning method and formulate explicitly relevant and irrelevant information onto feature representations. To handle this issue, we employ an information-theoretic concept to define disentangled representation which is divided into shared and excluded characteristics. Our key idea is to remove excluded feature representations from a set of co-occurrence features while preserving details using mutual information. To achieve this, we propose a novel stage-wise training strategy that captures a more discriminative and pure factor that preserves details. Specifically, we utilize an adversarial objective that explicitly defines each representation to enforce disentanglement. Extensive computational experiments on six benchmark datasets show the superiority of our new model against state-of-the-art methods.
On The Ingredients of an Effective Zero-shot Semantic Parser

Semantic parsers map natural language utterances into meaning representations (e.g.programs).Such models are typically bottlenecked by the paucity of training data due to the laborious annotation efforts.Recent studies have performed zero-shot learning by synthesizing training examples of canonical utterances and programs from a grammar, and further paraphrasing these utterances to improve linguistic diversity.However, such synthetic examples cannot fully capture patterns in real data.In this paper we analyze zero-shot parsers through the lenses of the language and logical gaps (Herzig and Berant, 2019), which quantify the discrepancy of language and programmatic patterns between the synthetic canonical examples and real-world user-issued ones.We propose bridging these gaps using improved grammars, stronger paraphrasers, and efficient learning methods using canonical examples that most likely reflect real user intents.Our model achieves strong results on the SCHOLAR and GEO benchmarks with zero labeled data.
Dual Architecture Deep Learning Based Object Detection System for Autonomous Driving

Object detection of autonomous vehicles presents a big challenge for researchers due to the requirements of accuracy and precision in real-time. This work presents a deep learning approach based on a dual architecture design of the network. A highly accurate multi-class network of convolutional neural networks (CNN) is presented for input data classification. A Region-Based Convolutional Neural Networks (Faster R-CNN) network with a modified Feature Pyramid Networks (FPN) is used for better detection of tiny objects and You Only Look Once (YOLOv3) network is used for general detection. Each network independently detects the existence of an object. The decision maps are then fused and compared to decide whether an object is present or not. Faster R-CNN with FPN model reported a higher intersection over Union (IoU) and mean average precision (mAP) than the YOLOv3. This approach is reliable demonstrating an upgrade on the existing state-of-the-art methods of fully connected networks. Index Terms— autonomous driving, computer vision, deep learning, object detection
A Motion Image Pose Contour Extraction Method Based on B-Spline Wavelet

In order to improve the accuracy of traditional motion image pose contour extraction and shorten the extraction time, a motion image pose contour extraction method based on B-spline wavelet is proposed. Moving images are acquired through the visual system, the information fusion process is used to perform statistical analysis on the images containing motion information, the location of the motion area is determined, convolutional neural network technology is used to preprocess the initial motion image pose contour, and B-spline wavelet theory is used. The preprocessed motion image pose contour is detected, combined with the heuristic search method to obtain the pose contour points, and the motion image pose contour extraction is completed. The simulation results show that the proposed method has higher accuracy and shorter extraction time in extracting motion image pose contours.
Improvements on Making BKW Practical for Solving LWE

The learning with errors (LWE) problem is one of the main mathematical foundations of post-quantum cryptography. One of the main groups of algorithms for solving LWE is the Blum–Kalai–Wasserman (BKW) algorithm. This paper presents new improvements of BKW-style algorithms for solving LWE instances. We target minimum concrete complexity, and we introduce a new reduction step where we partially reduce the last position in an iteration and finish the reduction in the next iteration, allowing non-integer step sizes. We also introduce a new procedure in the secret recovery by mapping the problem to binary problems and applying the fast Walsh Hadamard transform. The complexity of the resulting algorithm compares favorably with all other previous approaches, including lattice sieving. We additionally show the steps of implementing the approach for large LWE problem instances. We provide two implementations of the algorithm, one RAM-based approach that is optimized for speed, and one file-based approach which overcomes RAM limitations by using file-based storage.
IOT based Portable Image Dehazing Machine

Inspired by the practical significance of image-dehazing problem, an emerging research topic in computer vision field, several researchers have proposed various image-dehazing methods over the past decade. This research work carries out real time implementation of a modified dark channel based fast dehazing algorithm that captures weather degraded images in any location and produces a restored image of that scene employing Broadcom BCM2711, Quad core Cortex-A72 (ARM v8) 64-bit SoC @ 1.5GHz. basd Raspberry Pi4 Model B. The dark channel prior based method is modified for implementation in Raspbian OS with in-built Python language. As this setup is small and light, it is portable and the output of the system is competitive w.r.t. existing methods.
Prospects for Using Blockchain Technology in Healthcare: Supply Chain Management

Blockchain technology radically transforms resource management mechanisms at any level and scope of activity, making them highly productive, perfect, and efficient. The purpose of this paper is to consider the prospects for the application of blockchain technology in healthcare for the supply chain management. The essence of the blockchain as a systematized database stored on more than one server corresponds to the tasks of modern healthcare. An additional advantage is the accumulation of inform
Optimal Communication Complexity of Authenticated Byzantine Agreement

Byzantine Agreement (BA) is one of the most fundamental problems in distributed computing, and its communication complexity is an important efficiency metric. It is well known that quadratic communication is necessary for BA in the worst case due to a lower bound by Dolev and Reischuk. This lower bound has been shown to be tight for the unauthenticated setting with f < n/3 by Berman et al. but a considerable gap remains for the authenticated setting with n/3 ≤ f < n/2.This paper provides two results towards closing this gap. Both protocols have a quadratic communication complexity and have different trade-offs in resilience and assumptions. The first protocol achieves the optimal resilience of f < n/2 but requires a trusted setup for threshold signature. The second protocol achieves near optimal resilience f ≤ (1/2 - e)n in the standard PKI model.
MELD: Meta-Reinforcement Learning from Images via Latent State Models

Meta-reinforcement learning algorithms can enable autonomous agents, such as robots, to quickly acquire new behaviors by leveraging prior experience in a set of related training tasks. However, the onerous data requirements of meta-training compounded with the challenge of learning from sensory inputs such as images have made meta-RL challenging to apply to real robotic systems. Latent state models, which learn compact state representations from a sequence of observations, can accelerate representation learning from visual inputs. In this paper, we leverage the perspective of meta-learning as task inference to show that latent state models can \emph{also} perform meta-learning given an appropriately defined observation space. Building on this insight, we develop meta-RL with latent dynamics (MELD), an algorithm for meta-RL from images that performs inference in a latent state model to quickly acquire new skills given observations and rewards. MELD outperforms prior meta-RL methods on several simulated image-based robotic control problems, and enables a real WidowX robotic arm to insert an Ethernet cable into new locations given a sparse task completion signal after only $8$ hours of real world meta-training. To our knowledge, MELD is the first meta-RL algorithm trained in a real-world robotic control setting from images.
Mining Software Entities in Scientific Literature

We present a comprehensive information extraction system dedicated to software entities in scientific literature. This task combines the complexity of automatic reading of scientific documents (PDF processing, document structuring, styled/rich text, scaling) with challenges specific to mining software entities: high heterogeneity and extreme sparsity of mentions, document-level cross-references, disambiguation of noisy software mentions and poor portability of Machine Learning approaches between highly specialized domains. While NER is a key component to recognize new and unseen software, considering this task as a simple NER application fails to address most of these issues.
Architectural Features of Distributed Computing Systems

Recently, there has been an increasing penetration of information technology in almost all areas of human life. The development of information technology is associated with the emergence of new tasks that require significant computing resources and can not be solved on a conventional computer. A large amount of computing requires the creation of so-called supercomputers, which is not always technically possible. But there is another way to solve this problem, when a complex task is divided into a number of subtasks that run in parallel. And here come in handy distributed computing system. In general, a distributed computing system is a virtual machine that consists of several nodes connected by a network. That is, a certain three-dimensional problem is divided into several simple subtasks and connections are established between them. But such a system will be operational only when the tasks between the nodes are distributed correctly, and the sequence of their execution will take place according to a given algorithm. The article analyzes the architectural features of distributed computing systems. The main task of distributed computing technologies is to provide access to globally distributed resources and solve problems that require significant computing power and can not be implemented on a conventional computer. The complexity of global tasks is due to the fact that the necessary data can be accessed on different computers. In addition, distributed computing systems, which are formed from autonomous resources, can change their architecture dynamically. Management of such distributed computer systems requires the search for new computational models and the search for architectural solutions to build new systems that would meet the current level of development of information technology.
Learning Hybrid Control Barrier Functions from Data

Motivated by the lack of systematic tools to obtain safe control laws for hybrid systems, we propose an optimization-based framework for learning certifiably safe control laws from data. In particular, we assume a setting in which the system dynamics are known and in which data exhibiting safe system behavior is available. We propose hybrid control barrier functions for hybrid systems as a means to synthesize safe control inputs. Based on this notion, we present an optimization-based framework to learn such hybrid control barrier functions from data. Importantly, we identify sufficient conditions on the data such that feasibility of the optimization problem ensures correctness of the learned hybrid control barrier functions, and hence the safety of the system. We illustrate our findings in two simulations studies, including a compass gait walker.