Reviewerly/exHarmony huggingface dataset:
The Role of Transparency, Trust, and Social Influence on Uncertainty Reduction in Times of Pandemics: Empirical Study on the Adoption of COVID-19 Tracing Apps

Background Contact tracing apps are an essential component of an effective COVID-19 testing strategy to counteract the spread of the pandemic and thereby avoid overburdening the health care system. As the adoption rates in several regions are undesirable, governments must increase the acceptance of COVID-19 tracing apps in these times of uncertainty. Objective Building on the Uncertainty Reduction Theory (URT), this study aims to investigate how uncertainty reduction measures foster the adoption of COVID-19 tracing apps and how their use affects the perception of different risks. Methods Representative survey data were gathered at two measurement points (before and after the app’s release) and analyzed by performing covariance-based structural equation modeling (n=1003). Results We found that uncertainty reduction measures in the form of the transparency dimensions disclosure and accuracy, as well as social influence and trust in government, foster the adoption process. The use of the COVID-19 tracing app in turn reduced the perceived privacy and performance risks but did not reduce social risks and health-related COVID-19 concerns. Conclusions This study contributes to the mass adoption of health care technology and URT research by integrating interactive communication measures and transparency as a multidimensional concept to reduce different types of uncertainty over time. Furthermore, our results help to derive communication strategies to promote the mass adoption of COVID-19 tracing apps, thus detecting infection chains and allowing intelligent COVID-19 testing.
Word embedding-based Part of Speech tagging in Tamil texts

This paper proposes a word embedding-based Part of Speech (POS) tagger for Tamil language. The experiments are conducted with different word embeddings BoW, TF-IDF, Word2vec, fastText and GloVe that are created using UJ-Tamil corpus. Different combinations of eight features with three classifiers linear SVM, Extreme Gradient Boosting and k-Nearest Neighbor are used to build the POS tagger. The results are compared against Viterbi algorithm-based POS tagger. The results show that word embedding can be used for POS tagging with good performance. BoW, TF-IDF and fastText give an impressive performance compared with Word2vec and GloVe. The accuracy of 99% is obtained with word embedding of BoW and TF-IDF with unigrams as well as bigrams and with linear SVM classifier. POS tag of a given word can be identified with 99% of accuracy using word embeddings based POS tagger in Tamil.
Trajectory planning for multi-robot systems: Methods and applications

In the multiple fields covered by Artificial Intelligence (AI), path planning is undoubtedly one of the issues that cover a wide range of research lines. To be able to find an optimal solution, which allows one or several vehicles to establish a safe and effective way to reach a final state from an initial state, is a challenge that continues to be studied today. The increasingly widespread use of autonomous vehicles, both aerial and ground-based, make path planning an essential aspect for incorporating these systems into an endless number of applications. Besides, in recent years, the use of Multi-Robot Systems (MRS) has spread, consisting of both Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs), gaining versatility and robustness in their operation. The possibility of using heterogeneous robotic teams allows tackling, autonomously, and simultaneously, a wide range of tasks with different characteristics in the same environment. For this purpose, path planning becomes a crucial aspect and, for this reason, this work aims to offer a general vision of trajectory planning, to establish a comparison between the methods and algorithms present in the literature for the resolution of this problem within MRS, and finally, to show the applicability of these methods in different areas, together with the importance of these methods for achieving autonomous and safe navigation of different types of vehicles.
Hierarchical Graph Neural Networks for Few-Shot Learning

Recent graph neural network (GNN) based methods for few-shot learning (FSL) represent the samples of interest as a fully-connected graph and conduct reasoning on the nodes flatly, which ignores the hierarchical correlations among nodes. However, real-world categories may have hierarchical structures, and for FSL, it is important to extract the distinguishing features of the categories from individual samples. To explore this, we propose a novel hierarchical graph neural network (HGNN) for FSL, which consists of three parts, i.e., bottom-up reasoning, top-down reasoning, and skip connections, to enable the efficient learning of multi-level relationships. For the bottom-up reasoning, we design intra-class k-nearest neighbor pooling (intra-class knnPool) and inter-class knnPool layers, to conduct hierarchical learning for both the intra- and inter-class nodes. For the top-down reasoning, we propose to utilize graph unpooling (gUnpool) layers to restore the down-sampled graph into its original size. Skip connections are proposed to fuse multi-level features for the final node classification. The parameters of HGNN are learned by episodic training with the signal of node losses, which aims to train a well-generalizable model for recognizing unseen classes with few labeled data. Experimental results on benchmark datasets have demonstrated that HGNN outperforms other state-of-the-art GNN based methods significantly, for both transductive and non-transductive FSL tasks. The dataset as well as the source code can be downloaded online <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>1</sup></xref>
Unto the breach: What the COVID-19 pandemic exposes about digitalization

Much recent scholarly investigation has been focused on the promise of digitalization and the new ways of working and organizing it makes possible. In this paper, we analyze how the COVID-19 pandemic has acted as a natural breaching experiment that has challenged taken-for-granted expectations about digitalization and revealed four important issues: uneven access to digital infrastructures, the persistence of the analog in digitalization, the brittleness of unchecked digitalization, and panoptical surveillance. The sudden shift to digital work has exposed taken-for-granted assumptions about the universality of digital access. The crisis has also revealed that many highly digitalized processes still rely on analog elements. The pandemic has also exposed that many algorithms used in digitalized inter-organizational processes are brittle due to overreliance on historic patterns. Finally, the pandemic has breached fundamental expectations of privacy when organizational surveillance was extended into private and public spaces. Thus, the pandemic has laid bare fundamental challenges in digitalization and has exposed the limits of rose‑tinted thinking about the relation between technology and organizing.
Semantic Segmentation of Underwater Imagery: Dataset and Benchmark

In this paper, we present the first large-scale dataset for semantic Segmentation of Underwater IMagery (SUIM). It contains over 1500 images with pixel annotations for eight object categories: fish (vertebrates), reefs (invertebrates), aquatic plants, wrecks/ruins, human divers, robots, and sea-floor. The images have been rigorously collected during oceanic explorations and human-robot collaborative experiments, and annotated by human participants. We also present a comprehensive benchmark evaluation of several state-of-the-art semantic segmentation approaches based on standard performance metrics. Additionally, we present SUIM-Net, a fully-convolutional deep residual model that balances the trade-off between performance and computational efficiency. It offers competitive performance while ensuring fast end-to-end inference, which is essential for its use in the autonomy pipeline by visually-guided underwater robots. In particular, we demonstrate its usability benefits for visual servoing, saliency prediction, and detailed scene understanding. With a variety of use cases, the proposed model and benchmark dataset open up promising opportunities for future research in underwater robot vision.
Machine learning algorithms for social media analysis: A survey

Social Media (SM) are the most widespread and rapid data generation applications on the Internet increase the study of these data. However, the efficient processing of such massive data is challenging, so we require a system that learns from these data, like machine learning. Machine learning methods make the systems to learn itself. Many papers are published on SM using machine learning approaches over the past few decades. In this paper, we provide a comprehensive survey of multiple applications of SM analysis using robust machine learning algorithms. Initially, we discuss a summary of machine learning algorithms, which are used in SM analysis. After that, we provide a detailed survey of machine learning approaches to SM analysis. Furthermore, we summarize the challenges and benefits of Machine Learning usages in SM analysis. Finally, we presented open issues and consequences in SM analysis for further research.
A survey of consensus algorithms in public blockchain systems for crypto-currencies

In recent years, crypto-currencies (a form of decentralised digital currencies) have been quite popular as an alternative form of payments. They are underpinned by a breakthrough technology called Blockchain which extensively use a number of cryptographic mechanisms and other advanced techniques from the domain of distributed computing. This blockchain technology has received unparalleled attention from academia, industry, and governments worldwide and is considered to have the potential to disrupt several application domains, other than currencies, touching all spheres of our lives. The sky-rocket anticipation of its potential has caused a wide-scale exploration of its usage in different application domains. This has resulted in a plethora of blockchain systems for various purposes. However, many of these blockchain systems suffer from serious shortcomings related to their performance and security, which need to be addressed before any wide-scale adoption can be achieved. A crucial component of any blockchain system is its underlying consensus algorithm, which determines its performance and security in many ways. Therefore, to address the limitations of different blockchain systems, several existing as well novel consensus algorithms have been introduced. A systematic analysis of these algorithms will help to understand how and why any particular blockchain performs the way it functions. Towards this aim, there are a number of existing works that have surveyed and reviewed a number of consensus algorithms. However, all these works have some major shortcomings. For example, the factors upon which the consensus algorithms have been analysed are not comprehensive. Importantly, a wide range of consensus algorithms utilised in public blockchain systems supporting mainly crypto-currencies have different variants. Such variants and their internal mechanisms utilised in many existing crypto-currencies have not been considered at all. This article fills these gaps by analysing a wide range of consensus algorithms leveraged in different public blockchain systems using a comprehensive taxonomy of properties. We have also analysed more than a hundred top crypto-currencies belonging to different categories of consensus algorithms to understand their properties and implicate different trends in these crypto-currencies. Finally, we have presented a decision tree of the reviewed algorithms to be used as a tool to test the suitability of consensus algorithms for a particular application under different criteria.
ByT5: Towards a Token-Free Future with Pre-trained Byte-to-Byte Models

Abstract Most widely used pre-trained language models operate on sequences of tokens corresponding to word or subword units. By comparison, token-free models that operate directly on raw text (bytes or characters) have many benefits: They can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by removing complex and error-prone text preprocessing pipelines. Because byte or character sequences are longer than token sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences. We characterize the trade-offs in terms of parameter count, training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our experiments.1
Representative Forgery Mining for Fake Face Detection

Although vanilla Convolutional Neural Network (CNN) based detectors can achieve satisfactory performance on fake face detection, we observe that the detectors tend to seek forgeries on a limited region of face, which reveals that the detectors is short of understanding of forgery. Therefore, we propose an attention-based data augmentation framework to guide detector refine and enlarge its attention. Specifically, our method tracks and occludes the Top-N sensitive facial regions, encouraging the detector to mine deeper into the regions ignored before for more representative forgery. Especially, our method is simple-to-use and can be easily integrated with various CNN models. Extensive experiments show that the detector trained with our method is capable to separately point out the representative forgery of fake faces generated by different manipulation techniques, and our method enables a vanilla CNN-based detector to achieve state-of-the-art performance without structure modification. Our code is available at https://github.com/crywang/RFM.
Video anomaly detection with spatio-temporal dissociation

Anomaly detection in videos remains a challenging task due to the ambiguous definition of anomaly and the complexity of visual scenes from real video data. Different from the previous work which utilizes reconstruction or prediction as an auxiliary task to learn the temporal regularity, in this work, we explore a novel convolution autoencoder architecture that can dissociate the spatio-temporal representation to separately capture the spatial and the temporal information, since abnormal events are usually different from the normality in appearance and/or motion behavior. Specifically, the spatial autoencoder models the normality on the appearance feature space by learning to reconstruct the input of the first individual frame (FIF), while the temporal part takes the first four consecutive frames as the input and the RGB difference as the output to simulate the motion of optical flow in an efficient way. The abnormal events, which are irregular in appearance or in motion behavior, lead to a large reconstruction error. To improve detection performance on fast moving outliers, we exploit a variance-based attention module and insert it into the motion autoencoder to highlight large movement areas. In addition, we propose a deep K-means cluster strategy to force the spatial and the motion encoder to extract a compact representation. Extensive experiments on some publicly available datasets have demonstrated the effectiveness of our method which achieves the state-of-the-art performance. The code is publicly released at the link1.
An enhanced genetic algorithm for path planning of autonomous UAV in target coverage problems

Unmanned aerial vehicles (UAV) have become an important and integral part of military and civilian operations in recent years. In many UAV missions, the main purpose is to visit some predetermined checkpoints in operational space. If the number of checkpoints and constraints increases, finding a feasible solution may take up too much time. In this paper; the path planning problem of autonomous UAV in target coverage problems is solved by using artificial intelligent methods including genetic algorithm (GA), ant colony optimizer (ACO), Voronoi diagram, and clustering methods. The main contribution of this article is to propose initial population enhancement methods in GA, and thus accelerate convergence process. The first common enhancement to basic GA structure is to generate a sub-optimal path by implementing ACO. A sub-optimal path can be used to generate initial individuals. However, sub-optimal paths may have the problem that is collision with terrain. To avoid a UAV from any crash three approaches are integrated into an initial population phase of genetic algorithm. The first approach includes Voronoi vertices as additional waypoints to keep clear of trouble. The second approach consists of cluster centers which forms Voronoi vertices as supplemental waypoints. The final proposal comprises again cluster centers but based on a set of collision points. The proposed methods are tested in different three dimensional (3D) environments and the results are compared. Performance results show that collision with terrain surface is a local phenomenon and solving this issue by using the cluster center of collision points provides the best result including at least 70% or much more decrease in the required number of objective function evaluations.
An automated essay scoring systems: a systematic literature review

Assessment in the Education system plays a significant role in judging student performance. The present evaluation system is through human assessment. As the number of teachers' student ratio is gradually increasing, the manual evaluation process becomes complicated. The drawback of manual evaluation is that it is time-consuming, lacks reliability, and many more. This connection online examination system evolved as an alternative tool for pen and paper-based methods. Present Computer-based evaluation system works only for multiple-choice questions, but there is no proper evaluation system for grading essays and short answers. Many researchers are working on automated essay grading and short answer scoring for the last few decades, but assessing an essay by considering all parameters like the relevance of the content to the prompt, development of ideas, Cohesion, and Coherence is a big challenge till now. Few researchers focused on Content-based evaluation, while many of them addressed style-based assessment. This paper provides a systematic literature review on automated essay scoring systems. We studied the Artificial Intelligence and Machine Learning techniques used to evaluate automatic essay scoring and analyzed the limitations of the current studies and research trends. We observed that the essay evaluation is not done based on the relevance of the content and coherence.The online version contains supplementary material available at 10.1007/s10462-021-10068-2.
Introduction to the Pontryagin Maximum Principle for Quantum Optimal Control

Optimal control theory is a powerful mathematical tool, which has known a rapid development since the 1950s, mainly for engineering applications. More recently, it has become a widely used method to improve process performance in quantum technologies by means of highly efficient control of quantum dynamics. This tutorial aims at providing an introduction to key concepts of optimal control theory that is accessible to physicists and engineers working in quantum control or in related fields. The different mathematical results are introduced intuitively, before being rigorously stated. This tutorial describes modern aspects of optimal control theory, with a particular focus on the Pontryagin maximum principle, which is the main tool for determining open-loop control laws without experimental feedback. The different steps to solve an optimal control problem are discussed, before moving on to more advanced topics such as the existence of optimal solutions or the definition of the different types of extremals, namely normal, abnormal, and singular. The tutorial covers various quantum control issues and describes their mathematical formulation suitable for optimal control. The connection between the Pontryagin maximum principle and gradient-based optimization algorithms used for high-dimensional quantum systems is described. The optimal solution of different low-dimensional quantum systems is presented in detail, illustrating how the mathematical tools are applied in a practical way.2 MoreReceived 10 December 2020Revised 21 May 2021DOI:https://doi.org/10.1103/PRXQuantum.2.030203Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasCoherent controlOptimization problemsQuantum controlQuantum Information
The Emerging Trends of Multi-Label Learning

Exabytes of data are generated daily by humans, leading to the growing needs for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classification is an active and rapidly growing research area that deals with classification tasks with extremely large number of classes or labels; utilizing massive data with limited supervision to build a multi-label classification model becomes valuable for practical applications, etc. Besides these, there are tremendous efforts on how to harvest the strong learning capability of deep learning to better capture the label dependencies in multi-label learning, which is the key for deep learning to address real-world classification tasks. However, it is noted that there have been a lack of systemic studies that focus explicitly on analyzing the emerging trends and new challenges of multi-label learning in the era of big data. It is imperative to call for a comprehensive survey to fulfil this mission and delineate future research directions and new applications.
Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey

Federated learning (FL) is a promising decentralized deep learning technology, which allows users to update models cooperatively without sharing their data. FL is reshaping existing industry paradigms for mathematical modeling and analysis, enabling an increasing number of industries to build privacy-preserving, secure distributed machine learning models. However, the inherent characteristics of FL have led to problems such as privacy protection, communication cost, systems heterogeneity, and unreliability model upload in actual operation. Interestingly, the integration with Blockchain technology provides an opportunity to further improve the FL security and performance, besides increasing its scope of applications. Therefore, we denote this integration of Blockchain and FL as the Blockchain-based federated learning (BCFL) framework. This paper introduces an in-depth survey of BCFL and discusses the insights of such a new paradigm. In particular, we first briefly introduce the FL technology and discuss the challenges faced by such technology. Then, we summarize the Blockchain ecosystem. Next, we highlight the structural design and platform of BCFL. Furthermore, we present the attempts ins improving FL performance with Blockchain and several combined applications of incentive mechanisms in FL. Finally, we summarize the industrial application scenarios of BCFL.