Reviewerly/exHarmony huggingface dataset:
Predicting Patient Readmission Risk from Medical Text via Knowledge Graph Enhanced Multiview Graph Convolution

Unplanned intensive care unit (ICU) readmission rate is an important metric for evaluating the quality of hospital care. Efficient and accurate prediction of ICU readmission risk can not only help prevent patients from inappropriate discharge and potential dangers, but also reduce associated costs of healthcare. In this paper, we propose a new method that uses medical text of Electronic Health Records (EHRs) for prediction, which provides an alternative perspective to previous studies that heavily depend on numerical and time-series features of patients. More specifically, we extract discharge summaries of patients from their EHRs, and represent them with multiview graphs enhanced by an external knowledge graph. Graph convolutional networks are then used for representation learning. Experimental results prove the effectiveness of our method, yielding state-of-the-art performance for this task.
<i>Just Words: On Speech and Hidden Harm:</i>An Overview and an Application

This paper argues for a hidden way in which speech constitutes harm by enacting harmful norms. The paper then explores the potential legal consequences of uncovering such instances of harm constitution. In particular, the paper argues that some public racist speech constitutes harm and is thus harmful enough to warrant legal remedy. Such utterances are actionable, it is contended, because they enact discriminatory norms in public spaces.
HACCS: Heterogeneity-Aware Clustered Client Selection for Accelerated Federated Learning

Federated Learning is a machine learning paradigm where a global model is trained in-situ across a large number of distributed edge devices. While this technique avoids the cost of transferring data to a central location and achieves a strong degree of privacy, it presents additional challenges due to the heterogeneous hardware resources available for training. Furthermore, data is not independent and identically distributed (IID) across all edge devices, resulting in statistical heterogeneity across devices. Due to these constraints, client selection strategies play an important role for timely convergence during model training. Existing strategies ensure that each individual device is included, at least periodically, in the training process. In this work, we propose HACCS, a Heterogeneity-Aware Clustered Client Selection system that identifies and exploits the statistical heterogeneity by representing all distinguishable data distributions instead of individual devices in the training process. HACCS is robust to individual device dropout, provided other devices in the system have similar data distributions. We propose privacy-preserving methods for estimating these client distributions and clustering them. We also propose strategies for leveraging these clusters to make scheduling decisions in a federated learning system. Our evaluation on real-world datasets suggests that our framework can provide 18% −38% reduction in time to convergence compared to the state of the art without any compromise in accuracy.
Document Dewarping with Control Points

Document images are now widely captured by handheld devices such as mobile phones. The OCR performance on these images are largely affected due to geometric distortion of the document paper, diverse camera positions and complex backgrounds. In this paper, we propose a simple yet effective approach to rectify distorted document image by estimating control points and reference points. After that, we use interpolation method between control points and reference points to convert sparse mappings to backward mapping, and remap the original distorted document image to the rectified image. Furthermore, control points are controllable to facilitate interaction or subsequent adjustment. We can flexibly select post-processing methods and the number of vertices according to different application scenarios. Experiments show that our approach can rectify document images with various distortion types, and yield state-of-the-art performance on real-world dataset. This paper also provides a training dataset based on control points for document dewarping. Both the code and the dataset are released at https://github.com/gwxie/Document-Dewarping-with-Control-Points.
Efficient time-optimal path planning of AUV under the ocean currents based on graph and clustering strategy

Path planning is particularly important for Autonomous Underwater Vehicles (AUVs) in the marine environments. Due to the existence of flow fields, many conventional path planning methods are not effective. As the flow distributions vary slowly in large spatial scales, this paper considers the influence of static non-uniform vector field and studies the time-optimal path planning of AUV. The method proposed in this paper is called GK-OPM (Graph and K-means based Optimization Method). GK-OPM generates initial paths for minimum-time path optimization using digraph and K-means method, which benefit optimality and efficiency, respectively. To discuss the parameters in GK-OPM and compare the performance with genetic algorithm (GA) and particle swarm optimization (PSO), Monte Carlo simulation is carried out under the ocean currents simulated by the Lamb-vertex function. The feasibility is verified on discrete ocean data and three-dimensional environments, too. The results show that GK-OPM has about 6–8 times better efficiency than GA and PSO and maintains optimality at the same time.
A robust and secure watermarking algorithm based on DWT and SVD in the fractional order fourier transform domain

The present paper aims to develop and validate a robust and secure watermarking algorithm. Firstly, the algorithm transforms the cover image and the watermark image separately by FRFT to obtain the amplitudes of both the cover image and watermark image, and then the two-level DWT transformation only carries out for the amplitude of the cover image. Secondly, the algorithm applies the SVD to the low-frequency sub-band of the second level DWT of the cover image and the amplitude of the watermark image. Thirdly, a new matrix is constructed based on the singular values from both the cover and watermark images to embed watermark information. Lastly, the algorithm takes the preliminary numerical calculation to determine an appropriate transformation order of FRFT for the cover image and to provide an outstanding balance between the imperceptibility and robustness in the proposed watermarking scheme. Moreover, it guarantees the security of watermarking scheme by the transformation order of FRFT. Experimental results demonstrate that the proposed watermarking scheme provides better performance in imperceptibility and resistance against traditional signal processing and geometric attacks, especially in image rotation, image cropping, average filtering, median filtering, and Gaussian filtering in comparison with the existing methods in the literature.
Bots Amplify and Redirect Hate Speech in Online Discourse About Racism During the COVID-19 Pandemic

Online talk about racism has been salient throughout the COVID-19 pandemic. Yet while such social media conversations reflect existing tensions in the offline world, the same discourse has also become a target for information operations aiming to heighten social divisions. This article examines Twitter discussions of racism in the first and sixth months since COVID-19 was accorded pandemic status by the World Health Organization and uncovers dynamic associations with bot activity and hate speech. Humans initially constituted the most hateful accounts in online conversations about racism in March, but in August, bots dominated hate speech. Over time, greater bot activity likewise amplified levels of hate speech a week later. Moreover, while discourse about racism in March primarily featured an organic focus on racial identities like Asian and Chinese, we further observed a bot-dominated focus in August toward political identities like president, Democrat, and Republican. Although hate speech targeting Asian groups remained present among racism discussions in August, these findings suggest a bot-fueled redirection from focusing on racial groups at the onset of the pandemic to targeting politics closer to the 2020 US elections. This work enhances understanding of the complexity of racism discussions during the pandemic, its vulnerability to manipulation through information operations, and the large-scale quantitative study of inorganic hate campaigns in online social networks.
A systematic exploration of reservoir computing for forecasting complex spatiotemporal dynamics

A reservoir computer (RC) is a type of recurrent neural network architecture with demonstrated success in the prediction of spatiotemporally chaotic dynamical systems. A further advantage of RC is that it reproduces intrinsic dynamical quantities essential for its incorporation into numerical forecasting routines such as the ensemble Kalman filter-used in numerical weather prediction to compensate for sparse and noisy data. We explore here the architecture and design choices for a "best in class" RC for a number of characteristic dynamical systems. Our analysis points to the importance of large scale parameter optimization. We also note in particular the importance of including input bias in the RC design, which has a significant impact on the forecast skill of the trained RC model. In our tests, the use of a nonlinear readout operator does not affect the forecast time or the stability of the forecast. The effects of the reservoir dimension, spinup time, amount of training data, normalization, noise, and the RC time step are also investigated. Finally, we detail how our investigation leads to optimal design choices for a parallel RC scheme applied to the 40 dimensional spatiotemporally chaotic Lorenz 1996 dynamics.
An efficient IoT group association and data sharing mechanism in edge computing paradigm

Despite its benefits and promising future, security and privacy challenges for the IoT wireless communication of edge computing environment remain unaddressed. As a result, proper authentication mechanisms are critical, especially in the extreme scenario where some edge facilities are not functional. For the above consideration, in this paper we develop an efficient IoT group association and updating mechanism in edge computing paradigm. The proposed scheme can provide data transmission and communication guarantees for special practical scenarios. The group key updating process in our architecture only necessitates minor changes on the EI side, whereas the decryption information of some IoT devices remains constant if the devices have not been revoked. The proposed strategy can accomplish the desired security features, according to the security analysis.
All-Photonic Architecture for Scalable Quantum Computing with Greenberger-Horne-Zeilinger States

Linear optical quantum computing is beset by the lack of deterministic entangling operations besides photon loss. Motivated by advancements at the experimental front in deterministic generation of various kinds of multiphoton entangled states, we propose an architecture for linear-optical quantum computing that harnesses the availability of three-photon Greenberger-Horne-Zeilinger (GHZ) states. Our architecture and its subvariants use polarized photons in GHZ states, polarization beam splitters, delay lines, optical switches and on-off detectors. We concatenate topological quantum error correction code with three-qubit repetition codes and estimate that our architecture can tolerate remarkably high photon-loss rate of $11.5 \%$; this makes a drastic change that is at least an order higher than those of known proposals. Further, considering three-photon GHZ states as resources, we estimate the resource overheads to perform gate operations with an accuracy of $10^{-6}~(10^{-15})$ to be $2.0\times10^6~(5.6\times10^7)$. Compared to other all-photonic schemes, our architecture is also resource-efficient. In addition, the resource overhead can be even further improved if larger GHZ states are available. Given its striking enhancement in the photon loss threshold and the recent progress in generating multiphoton entanglement, our scheme will make scalable photonic quantum computing a step closer to reality.
Protect Nature and Reduce the Carbon Footprint With an Application of Blockchain for IIoT

The cumulative amount of greenhouse gases that are shaped by our actions is a carbon footmark. In the US, the total carbon footmark of a humanoid is 16 tonnes, one of the largest amounts in the world. The average is closer to 4 tonnes worldwide. The average universal carbon footmark per year requirements is to drop below 3 tonnes by 2050 to have the utmost chance of stopping a 2°C point rise in worldwide temperature. Rahul et al. already predicted that the carbon footprint reduced by 17% with the use of IoT-enabled services. In this research study a novel approach to reduce carbon footprint using IoT with reinforcement AI learning is presented, which further reduced carbon footprint by 5% when using and nearly 7% when it is done using Q-Learning. The detailed findings are included to demonstrate the result.
JGEA

We present the Java General Evolutionary Algorithm (JGEA) framework, a modular Java framework for experimenting with Evolutionary Computation (EC). We designed JGEA to be (a) aimed at providing a general interface to potentially all Evolutionary Algorithms (EAs), yet (b) solid and easy to use for people who rely on EC as a tool. To this extent, we developed JGEA including a range of ready-to-use EAs, backed by a modular architecture, comprising diverse layers of abstraction, which simplifies the implementation of new EAs and the addition of new features. Here, we detail the general structure of JGEA, focusing on its high-level components, and present the use case of the introduction of a new EA in the framework. To complete the picture, we illustrate the application of JGEA for solving a real world problem, from its formal definition in the framework to the saving and processing of results. The source code of JGEA is available at https://github.com/ericmedvet/jgea.
Joint operation and attention block search for lightweight image restoration

Recently, block-based design methods have shown effectiveness in image restoration tasks, which are usually designed in a handcrafted manner and have computation and memory consumption challenges in practice. In this paper, we propose a joint operation and attention block search algorithm for image restoration, which focuses on searching for optimal combinations of operation blocks and attention blocks. Specifically, we first construct two search spaces: operation block search space and attention block search space. The former is used to explore the suitable operation of each layer and aims to construct a lightweight and effective operation search module (OSM). The latter is applied to discover the optimal connection of various attention mechanisms and aims to enhance the feature expression. The searched structure is called the attention search module (ASM). Then we combine OSM and ASM to construct a joint search module (JSM), which serves as the basic module to build the final network. Moreover, we propose a cross-scale fusion module (CSFM) to effectively integrate multiple hierarchical features from JSMs, which helps to mine feature corrections of intermediate layers. Extensive experiments on image super-resolution, gray image denoising, and JPEG image deblocking tasks demonstrate that our proposed network can achieve competitive performance. The source code is available on https://github.com/it-hao/JSNet.
Bi-Modal Transformer-Based Approach for Visual Question Answering in Remote Sensing Imagery

Recently, vision-language models based on transformers are gaining popularity for joint modeling of visual and textual modalities. In particular, they show impressive results when transferred to several downstream tasks such as zero and few-shot classification. In this paper, we propose a visual question answering (VQA) approach for remote sensing images based on these models. The VQA task attempts to provide answers to image-related questions. While VQA has gained popularity in computer vision, in remote sensing it is not widespread. First, we use the contrastive language image pre-training (CLIP) network for embedding the image patches and question words into a sequence of visual and textual representations. Then, we learn attention mechanisms to capture the intra-and-inter dependencies within and between these representations. Afterward, we generate the final answer by averaging the predictions of two classifiers mounted on the top of the resulting contextual representations. In the experiments, we study the performance of the proposed approach on two datasets acquired with Sentinel-2 and aerial sensors. In particular, we demonstrate that our approach can achieve better results with reduced training size compared to the recent state-of-the-art.
Comparison of single image HDR reconstruction methods — the caveats of quality assessment

As the problem of reconstructing high dynamic range (HDR) images from a single exposure has attracted much research effort, it is essential to provide a robust protocol and clear guidelines on how to evaluate and compare new methods. In this work, we compared six recent single image HDR reconstruction (SI-HDR) methods in a subjective image quality experiment on an HDR display. We found that only two methods produced results that are, on average, more preferred than the unprocessed single exposure images. When the same methods are evaluated using image quality metrics, as typically done in papers, the metric predictions correlate poorly with subjective quality scores. The main reason is a significant tone and color difference between the reference and reconstructed HDR images. To improve the predictions of image quality metrics, we propose correcting for the inaccuracies of the estimated camera response curve before computing quality values. We further analyze the sources of prediction noise when evaluating SI-HDR methods and demonstrate that existing metrics can reliably predict only large quality differences.
MP-GCN: A Phishing Nodes Detection Approach via Graph Convolution Network for Ethereum

Blockchain is making a big impact in various applications, but it is also attracting a variety of cybercrimes. In blockchain, phishing transfers the victim’s virtual currency to make huge profits through fraud, which poses a threat to the blockchain ecosystem. To avoid greater losses, Ethereum, one of the blockchain platforms, can provide information to detect phishing fraud. In this study, to effectively detect phishing nodes, we propose a phishing node detection approach as message passing based graph convolution network. We first form a transaction network through the transaction records of Ethereum and then extract the information of nodes effectively via message passing. Finally, we use a graph convolution network to classify the normal and phishing nodes. Experiments show that our method is effective and superior to other existing methods.
Can Solana be the Solution to the Blockchain Scalability Problem?

Solana is a public blockchain platform, launched in April 2018, that aims to increase scalability when compared to other blockchains without compromising decentralization and security. It supports smart contracts and the creation of decentralized applications (DApps). The study aims to collect data from the Solana blockchain and verify some of its properties such as its transactions&#x0027; throughput, i.e. the rate at which valid transactions are committed into a block by the Solana blockchain during a one second interval of time (TPS). The data were collected over the period of two months (14 October - 15 December) and made public on a GitHub repository. The results of our data analysis show how the average transactions&#x0027; throughput is about 2812 TPS and that the fees paid by users to have the transactions confirmed are on average much lower than the fees users pay for other blockchains that support the same functions, such as smart contract and the creation of DApps. The paper sheds light on the mechanisms of Solana blockchain that, according to their founders, promises to solve the scalability problem without sacrificing decentralization and security.
Safety-Critical Manipulation for Collision-Free Food Preparation

Recent advances allow for the automation of food preparation in high-throughput environments, yet the successful deployment of these robots requires the planning and execution of quick, robust, and ultimately collision-free behaviors. In this work, we showcase a novel framework for modifying previously generated trajectories of robotic manipulators in highly detailed and dynamic collision environments using Control Barrier Functions (CBFs). This method dynamically re-plans previously validated behaviors in the presence of changing environments—and does so in a computationally efficient manner. Moreover, the approach provides rigorous safety guarantees of the resulting trajectories, factoring in the true underlying dynamics of the manipulator. This methodology is extensively validated on a full-scale robotic manipulator in a real-world cooking environment, and has resulted in substantial improvements in computation time and robustness over re-planning.
Algorithms, data, and platforms: the diverse challenges of governing AI

Artificial intelligence (AI) poses a set of interwoven challenges. A new general purpose technology likened to steam power or electricity, AI must first be clearly defined before considering its global governance. In this context, a useful definition is technology that uses advanced computation to perform at human cognitive capacity in some task area. Like electricity, AI cannot be governed in isolation, but in the context of a broader digital technology toolbox. Establishing national and community priorities on how to reap AI’s benefits, while managing its social and economic risks, will be an evolving debate. A fundamental driver of the development and deployment of AI tools, of the algorithms and data, are the dominant Digital Platform Firms (DPFs). Unless specifically regulated, DPF's set de facto rules for use of data and algorithms. That can shift the borderline between public and private, and result in priorities that differ from those of the public sector or civil society. Governance of AI and the toolbox is a critical component of national success in the coming decades, as governments recognize opportunities and geopolitical risks posed by the suite of technologies. However, AI pries open a Pandora's box of questions that sweep across the economy and society engaging diverse communities. Rather than strive towards global agreement on a single set of market and social rules, one must consider how to pursue objectives of interoperability amongst nations with quite different political economies. Even such limited agreements are complicated following the Russian invasion of Ukraine.