Reviewerly/exHarmony huggingface dataset:
UAV swarm path planning with reinforcement learning for field prospecting

Abstract There has been steady growth in the adoption of Unmanned Aerial Vehicle (UAV) swarms by operators due to their time and cost benefits. However, this kind of system faces an important problem, which is the calculation of many optimal paths for each UAV. Solving this problem would allow control of many UAVs without human intervention while saving battery between recharges and performing several tasks simultaneously. The main aim is to develop a Reinforcement Learning based system capable of calculating the optimal flight path for a UAV swarm. This method stands out for its ability to learn through trial and error, allowing the model to adjust itself. The aim of these paths is to achieve full coverage of an overflight area for tasks such as field prospection, regardless of map size and the number of UAVs in the swarm. It is not necessary to establish targets or to have any previous knowledge other than the given map. Experiments have been conducted to determine whether it is optimal to establish a single control for all UAVs in the swarm or a control for each UAV. The results show that it is better to use one control for all UAVs because of the shorter flight time. In addition, the flight time is greatly affected by the size of the map. The results give starting points for future research, such as finding the optimal map size for each situation.
Gradient-free optimization of chaotic acoustics with reservoir computing

The suppression of chaotic acoustic oscillations is a challenging problem in optimization. This is because gradient-based optimization struggles to optimize chaotic systems. In this paper, we develop a Bayesian approach based on reservoir computing to suppress chaotic oscillations without calculating the gradient.
Citus

Citus is an open source distributed database engine for PostgreSQL that is implemented as an extension. Citus gives users the ability to distribute data, queries, and transactions in PostgreSQL across a cluster of PostgreSQL servers to handle the needs of data-intensive applications. The development of Citus has largely been driven by conversations with companies looking to scale PostgreSQL beyond a single server and their workload requirements. This paper describes the requirements of four common workload patterns and how Citus addresses those requirements. It also shares benchmark results demonstrating the performance and scalability of Citus in each of the workload patterns and describes how Microsoft uses Citus to address one of its most challenging data problems.
Absolute phase retrieval for a single-shot fringe projection profilometry based on deep learning

A deep learning-based method is proposed to recover the absolute phase value from a single fringe pattern. We propose a deep neural network architecture that includes two subnetworks used for wrapping phase calculation and phase unwrapping, respectively. The training set is generated with the absolute phase obtained by the combination of phase shifting and gray coding. In addition, a reference plane is adopted to provide periodic range information for phase unwrapping. Then according to the output of the well-trained network, a high-quality absolute phase is obtained through only a single fringe pattern of the measured object. Experiments on the test set verify that high accuracy for complex texture objects is acquired using the proposed method, which indicates its potential in high-speed measurement.
Intellectual Property Protection for Deep Learning Models: Taxonomy, Methods, Attacks, and Evaluations

The training and creation of deep learning model is usually costly, thus it can be regarded as an intellectual property (IP) of the model creator. However, malicious users who obtain high-performance models may illegally copy, redistribute, or abuse the models without permission. To deal with such security threats, a few deep neural networks (DNN) IP protection methods have been proposed in recent years. This paper attempts to provide a review of the existing DNN IP protection works and also an outlook. First, we propose the first taxonomy for DNN IP protection methods in terms of six attributes: scenario, mechanism, capacity, type, function, and target models. Then, we present a survey on existing DNN IP protection works in terms of the above six attributes, especially focusing on the challenges these methods face, whether these methods can provide proactive protection, and their resistances to different levels of attacks. After that, we analyze the potential attacks on DNN IP protection methods from the aspects of model modifications, evasion attacks, and active attacks. Besides, a systematic evaluation method for DNN IP protection methods with respect to basic functional metrics, attack-resistance metrics, and customized metrics for different application scenarios is given. Lastly, future research opportunities and challenges on DNN IP protection are presented.
Channel Split Convolutional Neural Network (ChaSNet) for Thermal Image Super-Resolution

The ability of thermal sensors to penetrate smoke, mist, dust and aerosol makes them attractive for deployment in essential applications in military, medical, agricultural and animal ecology over the regular optical cameras. However, unlike optical imaging devices, the most sophisticated commercial thermal imaging sensor does not match the megapixel imaging ability. The Low-Resolution (LR) images from thermal sensors can be enhanced through a software-driven solution called Super-Resolution (SR). A number of works have been proposed to employ deep networks for SR task; however, they are overloaded with redundant features due to the deep architecture. This paper introduces a Channel Splitting-based Convolutional Neural Network (ChasNet) for thermal image SR eliminating the redundant features in the network. The use of channel splitting extracts the versatile features from Low-Resolution (LR) thermal image, helping to preserve high-frequency details in the SR images. We demonstrate the applicability proposed network for SR task in two different scenarios organized in the PBVS-2021 Thermal SR Challenge, consisting of noise elimination (Track-1) and domain shifting (Track-2). The efficacy is justified by comparing the SR results with other state-of-the-art thermal SR techniques in qualitative and quantitative metrics. A set of extensive experiments separately analyzes the importance of each block in the proposed architecture. The code of this work is also published online <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
QKD Based on Symmetric Entangled Bernstein-Vazirani

This paper introduces a novel entanglement-based QKD protocol, that makes use of a modified symmetric version of the Bernstein-Vazirani algorithm, in order to achieve secure and efficient key distribution. Two variants of the protocol, one fully symmetric and one semi-symmetric, are presented. In both cases, the spatially separated Alice and Bob share multiple EPR pairs, each one qubit of the pair. The fully symmetric version allows both parties to input their tentative secret key from their respective location and acquire in the end a totally new and original key, an idea which was inspired by the Diffie-Hellman key exchange protocol. In the semi-symmetric version, Alice sends her chosen secret key to Bob (or vice versa). The performance of both protocols against an eavesdroppers attack is analyzed. Finally, in order to illustrate the operation of the protocols in practice, two small scale but detailed examples are given.
Document image layout analysis via explicit edge embedding network

Layout analysis from a document image plays an important role in document content understanding and information extraction systems. While many existing methods focus on learning knowledge with convolutional networks directly from color channels, we argue the importance of high-frequency structures in document images, especially edge information. In this paper, we present a novel document layout analysis framework with the Explicit Edge Embedding Network (E3 Net). Specifically, the proposed network contains the edge embedding block and dynamic skip connection block to produce detailed features, as well as a lightweight fully convolutional subnet as the backbone for the effectiveness of the framework. The edge embedding block is designed to explicitly incorporate the edge information from the document images. The dynamic skip connection block aims to learn both color and edge representations with learnable weights. In contrast to the previous methods, we harness the model by using a synthetic document approach to overcome data scarcity. The combination of data augmentation and edge embedding is important toward a more compact representation than directly using the training images with only color channels. We conduct experiments using the proposed framework on three document layout analysis benchmarks and demonstrate its superiority in terms of effectiveness and efficiency over previous approaches.
ViewpointCovid-19 digital test certificates and blockchain

Purpose The purpose of this study is to investigate how healthcare and public organizations can control and monitor digital health test certificates with citizens or other stakeholders using Blockchain platforms. The paper reviews and analyses the literature by focusing on keywords like “Blockchain AND COVID-19”. In response to the 2019 pandemic, most local governments closed their borders and imposed movement restrictions, impacting the global economy, peoples' mobility and everyday life. This study aims to provide a solution to how Blockchain technology can improve the socioeconomic impacts of coronavirus disease 2019 (COVID-19) by enhancing people's mobility and achieving a balance between protecting individuals' rights and public health safety. Design/methodology/approach This research utilized machine learning bibliometric tools for investigating the normative literature in the area of blockchain and COVID-19. The article conducts a systematic literature review and develops a bibliometric map based on Plevris et al.’s (2017) method. Findings This study indicates that there is limited literature on the use of blockchain technology in issuing and validating COVID-19 tests. The development of such solutions can be done through the utilization of smart contracts, and it is expected to increase mobility in a secure and trusted environment that will help in monitoring and slow down the spread of the pandemic. Research limitations/implications This analysis is done during the first ten months of the pandemic outbreak, and there is still limited scientific literature investigating blockchain and COVID-19 concepts. Practical Implications Organizations are rethinking their information management due to the COVID-19 pandemic for creating better value for the enterprise and all associate stakeholders. Blockchain technology helps organizations to move from a centralized to a decentralized way of information managing. The decentralization of information in the health-care sector will create a better value for all involved stakeholders and radical change in how health-care data are managed and controlled. The implementation of blockchain applications in the health-care industry will result in a more secure, visible, auditable environment accessible by all the parties involved. Originality/value It was identified that there is currently limited research done on aligning smart contracts structure within the health-care sector. Therefore, while the current literature demonstrates the importance of aligning the key concepts, little research is done on considering people’s mobility and cross-country communication.
On the Potential of Augmented Reality for Mathematics Teaching with the Application cleARmaths

Learning content in mathematics, such as vector geometry, is still predominantly taught in an abstract manner, as the visualization and interaction of three-dimensional problems are limited with classical forms of teaching such as blackboard lessons or exercise sheets. This research article proposes the use of augmented reality (AR) in mathematics education. The proposed approach aims at easing the learning process related to vector geometry currently taught in senior mathematics classes by using intuitive visualization. The article introduces the concept of AR and presents the didactic foundations and the influence on the learning process based on an extensive literature review. Although studies see great potential in the use of AR for teaching mathematics, the method has so far hardly been used in schools. This can be mainly explained by the technological entry barrier of AR and the lack of simple, robust AR applications, in particular for vector geometry. To fill this gap, the authors developed “cleARmaths”, a developed android application for augmented reality-based teaching in vector geometry that allows widespread use. As a didactical concept, some example exercises sessions with the app are proposed, demonstrating how the app could be used in a mathematics classroom. Finally, the app was evaluated in a mathematics class and the results analyzed in a detailed study. It was found by the teacher and students to be beneficial and amusing, demonstrating the potential for AR in mathematics classes.
A Comparative Survey: Benchmarking for Pool-based Active Learning

Active learning (AL) is a subfield of machine learning (ML) in which a learning algorithm aims to achieve good accuracy with fewer training samples by interactively querying the oracles to label new data points. Pool-based AL is well-motivated in many ML tasks, where unlabeled data is abundant, but their labels are hard or costly to obtain. Although many pool-based AL methods have been developed, some important questions remain unanswered such as how to: 1) determine the current state-of-the-art technique; 2) evaluate the relative benefit of new methods for various properties of the dataset; 3) understand what specific problems merit greater attention; and 4) measure the progress of the field over time. In this paper, we survey and compare various AL strategies used in both recently proposed and classic highly-cited methods. We propose to benchmark pool-based AL methods with a variety of datasets and quantitative metric, and draw insights from the comparative empirical results.
Learning an Augmented RGB Representation with Cross-Modal Knowledge Distillation for Action Detection

In video understanding, most cross-modal knowledge distillation (KD) methods are tailored for classification tasks, focusing on the discriminative representation of the trimmed videos. However, action detection requires not only categorizing actions, but also localizing them in untrimmed videos. Therefore, transferring knowledge pertaining to temporal relations is critical for this task which is missing in the previous cross-modal KD frameworks. To this end, we aim at learning an augmented RGB representation for action detection, taking advantage of additional modalities at training time through KD. We propose a KD framework consisting of two levels of distillation. On one hand, atomic-level distillation encourages the RGB student to learn the sub-representation of the actions from the teacher in a contrastive manner. On the other hand, sequence-level distillation encourages the student to learn the temporal knowledge from the teacher, which consists of transferring the Global Contextual Relations and the Action Boundary Saliency. The result is an Augmented-RGB stream that can achieve competitive performance as the two-stream network while using only RGB at inference time. Extensive experimental analysis shows that our proposed distillation framework is generic and outperforms other popular cross-modal distillation methods in action detection task.
Applied Self-Supervised Learning: Review of the State-of-the-Art and Implementations in Medicine

Machine learning has become an increasingly ubiquitous technology, as big data continues to inform and influence everyday life and decision-making. Currently in healthcare, as well as in most other industries, the two most prevalent machine learning paradigms are supervised learning and transfer learning. Both practices rely on large-scale, manually annotated datasets to train increasingly complex models. However, the requirement of data to be manually labeled leaves an excess of unused, unlabeled data available in both public and private data repositories. Self-supervised learning (SSL) is a growing area of machine learning that has the ability to take advantage of unlabeled data. Contrary to other machine learning paradigms, SSL algorithms create artificial supervisory signals from unlabeled data and pretrain algorithms on these signals. The aim of this review is two-fold: firstly, we provide a formal definition of SSL, divide SSL algorithms into their four unique subsets, and review the state-of-the-art published in each of those subsets between the years of 2014-2020. Second, this work surveys recent SSL algorithms published in healthcare, in order to provide medical experts with a clearer picture of how they can integrate SSL into their research, with the objective of leveraging unlabeled data.
Big Data-Driven Pedestrian Analytics: Unsupervised Clustering and Relational Query Based on Tencent Street View Photographs

Recent technological advancements in geomatics and mobile sensing have led to various urban big data, such as Tencent street view (TSV) photographs; yet, the urban objects in the big dataset have hitherto been inadequately exploited. This paper aims to propose a pedestrian analytics approach named vectors of uncountable and countable objects for clustering and analysis (VUCCA) for processing 530,000 TSV photographs of Hong Kong Island. First, VUCCA transductively adopts two pre-trained deep models to TSV photographs for extracting pedestrians and surrounding pixels into generalizable semantic vectors of features, including uncountable objects such as vegetation, sky, paved pedestrian path, and guardrail and countable objects such as cars, trucks, pedestrians, city animals, and traffic lights. Then, the extracted pedestrians are semantically clustered using the vectors, e.g., for understanding where they usually stand. Third, pedestrians are semantically indexed using relations and activities (e.g., walking behind a guardrail, road-crossing, carrying a backpack, or walking a pet) for queries of unstructured photographic instances or natural language clauses. The experiment results showed that the pedestrians detected in the TSV photographs were successfully clustered into meaningful groups and indexed by the semantic vectors. The presented VUCCA can enrich eye-level urban features into computational semantic vectors for pedestrians to enable smart city research in urban geography, urban planning, real estate, transportation, conservation, and other disciplines.
Towards Defeating Mass Surveillance and SARS-CoV-2: The Pronto-C2 Fully Decentralized Automatic Contact Tracing System

Automatic contact tracing is currently used in several countries in order to limit the spread of SARS-CoV-2.Many governments decided to develop smartphone apps based on the "Exposure Notifications" designed by Apple and Google according to a decentralized approach previously proposed by the DP-3T team.Decentralization was pushed as a key feature to protect privacy in contrast to centralized approaches that could leverage automatic contact tracing to realize mass-surveillance programs.In this work, taking into account the privacy and integrity vulnerabilities of DP-3T systems, we show the design of a decentralized contact tracing system named Pronto-C2 that has better resilience against various attacks.We also discuss the significant overhead of Pronto-C2 when used in real-world scenarios.
Haptic-enabled collaborative learning in virtual reality for schools

Abstract This paper reports on a study which designed and developed a multi-fingered haptic interface in conjunction with a three-dimensional (3D) virtual model of a section of the cell membrane in order to enable students to work collaboratively to learn cell biology. Furthermore, the study investigated whether the addition of haptic feedback to the 3D virtual reality (VR) simulation affected learning of key concepts in nanoscale cell biology for students aged 12 to 13. The haptic interface was designed so that the haptic feedback could be turned on or switched off. Students (N = 64), in two secondary schools, worked in pairs, on activities designed to support learning of specific difficult concepts. Findings from observation of the activities and interviews revealed that students believed that being immersed in the 3D VR environment and being able to feel structures and movements within the model and work collaboratively assisted their learning. More specifically, the pilot/co-pilot model that we developed was successful for enabling collaborative learning and reducing the isolating effects of immersion with a 3D headset. Results of pre and post-tests of conceptual knowledge showed significant knowledge gains but addition of haptic feedback did not affect the knowledge gains significantly. The study enabled identification of important issues to consider when designing and using haptic-enabled 3D VR environments for collaborative learning.
Comparative Analysis of Detectors and Feature Descriptors for Multispectral Image Matching in Rice Crops

Precision agriculture has greatly benefited from advances in machine vision and image processing techniques. The use of feature descriptors and detectors allows to find distinctive keypoints in an image and the use of this approach for agronomical applications has become a widespread field of study. By combining near infrared (NIR) images, acquired with a modified Nikon D80 camera, and visible spectrum (VIS) images, acquired with a Nikon D300s, a proper crop identification could be obtained. Still, the use of different sensors brings an image matching challenge due to the difference between cameras and the possible distortions from each imaging technique. The aim of this paper is to compare the performance of several feature descriptors and detectors by comparing near infrared and visual spectral bands in rice crop images. Therefore, a group of 20 different scenes with different cameras and growth stages in a rice crop were evaluated. Thus, red, green, blue (RGB) and L, a, b (CIE L*a*b*) channels were extracted from VIS images in order to compare the matches obtained between each of them and the corresponding NIR image. The BRISK, SURF, SIFT, ORB, KAZE, and AKAZE methods were implemented, which act as descriptors and detectors. Additionally, a combination was made between the FAST algorithm for the detection of keypoints with the BRIEF, BRISK, and FREAK methods for features description. BF and FLANN matching methods were used. The algorithms were implemented in Python using OpenCV library. The green channel presented the highest number of correct matches in all methods. In turn, the method that presented the highest performance both in time and in the number of correct matches was the combination of the FAST feature detector and the BRISK descriptor.
New Galois Hulls of GRS Codes and Application to EAQECCs

Galois hulls of linear codes have important applications in quantum coding theory. In this paper, we construct some new classes of (extended) generalized Reed-Solomon (GRS) codes with Galois hulls of arbitrary dimensions. We also propose a general method on constructing GRS codes with Galois hulls of arbitrary dimensions from special Euclidean orthogonal GRS codes. Finally, we construct several new families of entanglement-assisted quantum error-correcting codes (EAQECCs) and MDS EAQECCs by utilizing the above results.