Reviewerly/exHarmony huggingface dataset:
A multi-task learning framework for end-to-end aspect sentiment triplet extraction

Aspect sentiment triplet extraction (ASTE) is a significant and challenging task in aspect-based sentiment analysis, which aims to summarize people’s opinions by extracting triplets consisting of opinion targets, opinion expressions, and sentiment polarities. In this paper, we propose a novel multi-task learning framework to achieve end-to-end ASTE. We decompose ASTE into three subtasks, namely target tagging, opinion tagging, and sentiment tagging. In target tagging and opinion tagging, we adopt the BIO tagging scheme to detect the boundaries of opinion targets and opinion expressions. In sentiment tagging, we introduce a target-aware tagging scheme, which utilizes a series of target-specific tag sequences to identify the correspondences between opinion targets and opinion expressions, and determine their sentiment polarities. We conduct extensive experiments on four benchmark datasets. The experimental results show that our framework achieves consistently superior results. Compared with existing methods, our method has better performance in extracting overlapping triplets and identifying long-range correspondences. Further analysis demonstrates the effectiveness of our framework.
Multi Robot Space Exploration : A Modified Frequency Whale Optimization Approach

Multi-robots configuration provides an effective means for space exploration in an obstacle cluttered environment. Recent progression in research involves a significant work in the field of robotics path planning. Advanced algorithms compute these trajectories utilizing two or more techniques. One such approach involves integration of deterministic Coordinated Multi-robot Exploration and meta-heuristic Whale Optimizer, collectively referred as stochastic method/exploration. This research presents an improved version of stochastic exploration namely Frequency Modified Hybrid Whale Optimization Algorithm (FMH-WOA), in which instead of using conventional whale algorithm, the stochastic parameters are dynamically tuned utilizing a frequency parameter. The frequency is adjusted to tune and optimize both the Exploitation and Exploration operators. Deterministic method derives the cost and utility determines the precedence of grid cell around robot. Stochastic optimizer then helps in improving the overall solution. The effectivity of the proposed FMH-WOA is validated under different environmental conditions. The results are then compared with conventional whale optimizer to demonstrate the improvements achieved in terms of enhanced area coverage in considerably less exploration time.
A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19

Coronavirus disease 2019 (COVID-19) continues to pose a great challenge to the world since its outbreak. To fight against the disease, a series of artificial intelligence (AI) techniques are developed and applied to real-world scenarios such as safety monitoring, disease diagnosis, infection risk assessment, lesion segmentation of COVID-19 CT scans,etc. The coronavirus epidemics have forced people wear masks to counteract the transmission of virus, which also brings difficulties to monitor large groups of people wearing masks. In this paper, we primarily focus on the AI techniques of masked facial detection and related datasets. We survey the recent advances, beginning with the descriptions of masked facial detection datasets. Thirteen available datasets are described and discussed in details. Then, the methods are roughly categorized into two classes: conventional methods and neural network-based methods. Conventional methods are usually trained by boosting algorithms with hand-crafted features, which accounts for a small proportion. Neural network-based methods are further classified as three parts according to the number of processing stages. Representative algorithms are described in detail, coupled with some typical techniques that are described briefly. Finally, we summarize the recent benchmarking results, give the discussions on the limitations of datasets and methods, and expand future research directions. To our knowledge, this is the first survey about masked facial detection methods and datasets. Hopefully our survey could provide some help to fight against epidemics.
Drone-vs-Bird Detection Challenge at IEEE AVSS2021

This paper presents the 4-th edition of the “drone-vs-bird” detection challenge, launched in conjunction with the the 17-th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS). The objective of the challenge is to tackle the problem of detecting the presence of one or more drones in video scenes where birds may suddenly appear, taking into account some important effects such as the background and foreground motion. The proposed solutions should identify and localize drones in the scene only when they are actually present, without being confused by the presence of birds and the dynamic nature of the captured scenes. The paper illustrates the results of the challenge on the 2021 dataset, which has been further extended compared to the previous edition run in 2020.
Online learning during COVID19 and beyond: a human right based approach to internet access in Africa

Teaching and learning were disrupted due to lockdown imposed as part of efforts to curb the spread of the COVID19 virus that hit the world in 2020. As a result, many national governments requested educational establishments to migrate their teachings online. In Africa, internet penetration has improved in the last decade. However, the continent still lags in integrating the Internet into learning. Besides, there is unequal access to technologies used in online education and unequal access to data and connectivity. Regarding access to quality learning, the COVID-19 pandemic has widened the gap between the rich and the poor and has exposed society's fragility. This paper evaluates the strategies of African leaders in sustaining access to learning and the experience of learners during COVID19 lockdown. It argues that most African countries’ educational systems are unprepared for the sudden switch to online learning. Finally, it investigates future policy strategies in bringing more Africans out of the digital desert.
The automatic design of parameter adaptation techniques for differential evolution with genetic programming

This study proposes a technique aimed at the automatic search for parameter adaptation strategies in a differential evolution algorithm with genetic programming symbolic regression. Genetic programming is applied to find the symbolic expression for scaling factor control during the optimization process of differential evolution based on the current computational resource, ratio of successful solutions and adapted scaling factor value. The design of the parameter adaptation technique is performed by a computational experiment, which consisted in solving several complex optimization problems. Better symbolic expressions are selected with regards to the Friedman ranking procedure, and the best solutions are additionally evaluated to compare them to the existing parameter adaptation techniques. The experimental results show that the automatically designed parameter adaptation techniques described by symbolic expressions are capable of outperforming existing parameter adaptation methods, while using different information sources. The analysis of automatically generated solutions shows that the proposed technique can be considered an automatic knowledge extraction method. This is due to the results showing that well-performing parameter adaptation can behave differently from state-of-the-art methods, thereby revealing previously unknown algorithm properties.
Breast Tumor Classification Based on MRI-US Images by Disentangling Modality Features

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and ultrasound (US), which are two common modalities for clinical breast tumor diagnosis besides Mammograms, can provide different and complementary information for the same tumor regions. Although many machine learning methods have been proposed for breast tumor classification based on either single modality, it remains unclear how to further boost the classification performance by utilizing paired multi-modality information with different dimensions. In this paper, we propose MRI-US multi-modality network (MUM-Net) to classify breast tumor into different subtypes based on 3D MR and 2D US images. The key insight of MUM-Net is that we explicitly distill modality-agnostic features for tumor classification. Specifically, we first adopt a discrimination-adaption module to decompose features into modality-agnostic and modality-specific ones with min-max training strategies. Then, we propose a feature fusion module to increase the compactness of the modality-agnostic features by utilizing an affinity matrix with nearest neighbour selection. We build a paired MRI-US breast tumor classification dataset containing 502 cases with three clinical indicators to validate the proposed method. In three tasks including lymph node metastasis, histological grade and Ki-67 level, MUM-Net achieves AUC scores of 0.8581, 0.8965 and 0.8577, outperforming other counterparts which are based on single task or single modality by a wide margin. In addition, we find that the extracted modality-agnostic features can help the network focus on the tumor regions in both modalities.
Observing crossover between quantum speed limits

Quantum mechanics sets fundamental limits on how fast quantum states can be transformed in time. Two well-known quantum speed limits are the Mandelstam-Tamm and the Margolus-Levitin bounds, which relate the maximum speed of evolution to the system’s energy uncertainty and mean energy, respectively. Here, we test concurrently both limits in a multilevel system by following the motion of a single atom in an optical trap using fast matter wave interferometry. We find two different regimes: one where the Mandelstam-Tamm limit constrains the evolution at all times, and a second where a crossover to the Margolus-Levitin limit occurs at longer times. We take a geometric approach to quantify the deviation from the speed limit, measuring how much the quantum evolution deviates from the geodesic path in the Hilbert space of the multilevel system. Our results are important to understand the ultimate performance of quantum computing devices and related advanced quantum technologies.
A privacy-preserving and verifiable federated learning method based on blockchain

As a novel distributed learning mechanism, federated learning has drawn widespread attention by allowing multiple parties to train an accurate model collaboratively without collecting their raw data. However, it relies on a trustworthy central server that still suffers from severe security challenges, such as model inversion attack and single point of failure. Thereby, a privacy-preserving and verifiable federated learning method based on blockchain is proposed to enable fully decentralized and reliable federated learning in untrusted network. In our scheme, we propose a secure aggregation protocol to guarantee the confidentiality of gradients while supporting clients dropping out during the workflow, and design a novel blockchain structure enabling global gradient verification to defend against potential tampering attack. In addition, a gradient compression method is proposed to reduce the communication overhead. Security analysis shows that our scheme can preserve the privacy by adding pairwise random masks to the gradients, and prevent Sybil attack by reasonable threshold setting in verifiable secret sharing. Experimental results on two real-world datasets illustrate that when the clients’ dropout rate is less than 20%, our scheme can achieve almost the same accuracy as original federated learning, and performs better than similar blockchain-based federated learning methods in terms of computation overhead and communication overhead.
Online Review based Sentiment Classification on Bangladesh Airline Service using Supervised Learning

Still leading main common modes of transportation we use on a daily basis is flying by airplane. Online reviews are becoming increasingly popular as a way for travelers to discuss their experiences with airplanes. Sentiment analysis may be used to understand people’s attitudes or sentiments by utilizing sites that provide opinion-rich data. To conduct this research, we generated a customized dataset including online reviews for 4 major Bangladesh Airlines, conducted a multiclass sentiment analysis, and compared the classifiers. This method begins with pre-processing procedures used to clean the reviews and balance the review data using the Pegasus model’s oversampling mechanism. System choices use various machine learning techniques to integrate feature engineering and word embedding for deep learning. The analysis was carried out 3 different machine learning (Decision Tree, Random Forest, XGBoost) and 3 different deep learning classification strategies (CNN, LSTM, BERT). Using a three-class dataset, the testing output is the online review sentiment (positive/negative/mixed) and the accuracy is determined. Based on the results, we have achieved the best accuracy 83% in terms of BERT. The accuracies were determined to compare each categorization technique, and the total sentiment count for all 4 airlines of Bangladesh have been displayed.
FPGA implementation of a chaos-based image encryption algorithm

With the rapid development of communication networks and the establishment of 5G networks, there is a growing demand for the transmission of images and videos with different sizes and formats. Since these can contain sensitive and personal information, security is of great importance when transferring the contents of such files. Therefore, the use of powerful encryption mechanisms is an absolute requirement to protect the confidentiality of the exchanged data. In this study, we propose an image encryption algorithm with a high level of security. Thus, a chaotic system is proposed, and its state variables have been used to create a new substitution matrix. The importance of this matrix lies both in its use to generate encryption keys and in the encryption algorithm itself. In formulating the proposed encryption algorithm, we adopted the Feistel network in addition to some steps of the Advanced Encryption Standard (AES). The results of the various tests prove that the proposed algorithm guarantees high resistance to statistical and differential attacks and can therefore be used safely in image encryption. The incorporation of various algorithms suggested in this article in the form of IPcore for FPGA platforms amply promotes the use of our cryptosystem in various embedded systems or IoT environments.
RiceChain: secure and traceable rice supply chain framework using blockchain technology

The increasing number of rice product safety issues and the potential for contamination have established an enormous need for an effective strategy for the traceability of the rice supply chain. Tracing the origins of a rice product from raw materials to end customers is very complex and costly. Existing food supply chain methods (for example, rice) do not provide a scalable and cost-effective means of agricultural food supply. Besides, consumers lack the capability and resources required to check or report on the quality of agricultural goods in terms of defects or contamination. Consequently, customers are forced to decide whether to utilize or discard the goods. However, blockchain is an innovative framework capable of offering a transformative solution for the traceability of agricultural products and food supply chains. The aim of this paper is to propose a framework capable of tracking and monitoring all interactions and transactions between all stakeholders in the rice chain ecosystem through smart contracts. The model incorporates a system for customer satisfaction feedback, which enables all stakeholders to get up-to-date information on product quality, enabling them to make more informed supply chain decisions. Each transaction is documented and stored in the public ledger of the blockchain. The proposed framework provides a safe, efficient, reliable, and effective way to monitor and track rice products safety and quality especially during product purchasing. The security and performance analysis results shows that the proposed framework outperform the benchmark techniques in terms of cost-effectiveness, security and scalability with low computational overhead.
An optimal control-based path planning method for unmanned surface vehicles in complex environments

This paper proposes an optimal control-based method named the rapid-Gauss pseudospectral method (RGPM) to obtain a smooth time-optimal path for unmanned surface vehicles (USVs). It is a general global path planning method, improving the Gauss pseudospectral method (GPM) which cannot be applied to complex ocean scenarios. In addition, we optimize the representation of obstacles and add a Maklink algorithm to reduce the computaitonal cost. More specifically, a unified obstacle modeling approach is first introduced, then a continuous-time-optimal-control path planning problem considering USV kinematics is formulated. The orthogonal collocation method is used to discretize the continuous-time-optimal control problem into a nonlinear programming (NLP) problem. Furthermore, we introduce a mesh refinement technique to achieve collision avoidance between adjacent discrete points. To enhance computational efficiency, an initialization and iteration approach is further proposed. Herein, a MAKLINK graph is established to obtain a suboptimal path as the initial guess for the NLP problem. Obstacle-avoidance constraints are iteratively updated according to whether or not there are collisions in the current path. The proposed method was compared with the traditional GPM under two different scenarios. The simulation results show that the proposed method is superior to the traditional GPM in terms of path quality and convergence speed and can obtain quasi-time-optimal paths under complex conditions simultaneously.
Asymmetric Hash Code Learning for Remote Sensing Image Retrieval

Remote sensing image retrieval (RSIR), aiming at searching for a set of similar items to a given query image, is a very important task in remote sensing applications. Deep hashing learning as the current mainstream method has achieved satisfactory retrieval performance. On one hand, various deep neural networks are used to extract semantic features of remote sensing images. On the other hand, the hashing techniques are subsequently adopted to map the high-dimensional deep features to the low-dimensional binary codes. This kind of method attempts to learn one hash function for both the query and database samples in a symmetric way. However, with the number of database samples increasing, it is typically time-consuming to generate the hash codes of large-scale database images. In this article, we propose a novel deep hashing method, named asymmetric hash code learning (AHCL), for RSIR. The proposed AHCL generates the hash codes of query and database images in an asymmetric way. In more detail, the hash codes of query images are obtained by binarizing the output of the network, while the hash codes of database images are directly learned by solving the designed objective function. In addition, we combine the semantic information of each image and the similarity information of pairs of images as supervised information to train a deep hashing network, which improves the representation ability of deep features and hash codes. The experimental results on three public datasets demonstrate that the proposed method outperforms symmetric methods in terms of retrieval accuracy and efficiency. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/weiweisong415/Demo_AHCL_for_TGRS2022</uri> .
Joint Classification and Regression for Visual Tracking with Fully Convolutional Siamese Networks

Abstract Visual tracking of generic objects is one of the fundamental but challenging problems in computer vision. Here, we propose a novel fully convolutional Siamese network to solve visual tracking by directly predicting the target bounding box in an end-to-end manner. We first reformulate the visual tracking task as two subproblems: a classification problem for pixel category prediction and a regression task for object status estimation at this pixel. With this decomposition, we design a simple yet effective Siamese architecture based classification and regression framework, termed SiamCAR, which consists of two subnetworks: a Siamese subnetwork for feature extraction and a classification-regression subnetwork for direct bounding box prediction. Since the proposed framework is both proposal- and anchor-free, SiamCAR can avoid the tedious hyper-parameter tuning of anchors, considerably simplifying the training. To demonstrate that a much simpler tracking framework can achieve superior tracking results, we conduct extensive experiments and comparisons with state-of-the-art trackers on a few challenging benchmarks. Without bells and whistles, SiamCAR achieves leading performance with a real-time speed. Furthermore, the ablation study validates that the proposed framework is effective with various backbone networks, and can benefit from deeper networks. Code is available at https://github.com/ohhhyeahhh/SiamCAR .
MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets

Recently, there has been a wealth of development in motion planning for robotic manipulation—new motion planners are continuously proposed, each with their own unique strengths and weaknesses. However, evaluating new planners is challenging and researchers often create their own ad-hoc problems for benchmarking, which is time-consuming, prone to bias, and does not directly compare against other state-of-the-art planners. We present <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MotionBenchMaker</small> , an open-source tool to generate benchmarking datasets for realistic robot manipulation problems. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MotionBenchMaker</small> is designed to be an extensible, easy-to-use tool that allows users to both generate datasets and benchmark them by comparing motion planning algorithms. Empirically, we show the benefit of using <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MotionBenchMaker</small> as a tool to procedurally generate datasets which helps in the fair evaluation of planners. We also present a suite of 40 prefabricated datasets, with 5 different commonly used robots in 8 environments, to serve as a common ground to accelerate motion planning research.