Reviewerly/exHarmony huggingface dataset:
Personalized Federated Learning With Differential Privacy and Convergence Guarantee

Personalized federated learning (PFL), as a novel federated learning (FL) paradigm, is capable of generating personalized models for heterogenous clients. Combined with with a meta-learning mechanism, PFL can further improve the convergence performance with few-shot training. However, meta-learning based PFL has two stages of gradient descent in each local training round, therefore posing a more serious challenge in information leakage. In this paper, we propose a differential privacy (DP) based PFL (DP-PFL) framework and analyze its convergence performance. Specifically, we first design a privacy budget allocation scheme for inner and outer update stages based on the Rényi DP composition theory. Then, we develop two convergence bounds for the proposed DP-PFL framework under convex and non-convex loss function assumptions, respectively. Our developed convergence bounds reveal that 1) there is an optimal size of the DP-PFL model that can achieve the best convergence performance for a given privacy level, and 2) there is an optimal tradeoff among the number of communication rounds, convergence performance and privacy budget. Evaluations on various real-life datasets demonstrate that our theoretical results are consistent with experimental results. The derived theoretical results can guide the design of various DP-PFL algorithms with configurable tradeoff requirements on the convergence performance and privacy levels.
Enhancing Remote Sensing Image Super-Resolution with Efficient Hybrid Conditional Diffusion Model

Recently, optical remote-sensing images have been widely applied in fields such as environmental monitoring and land cover classification. However, due to limitations in imaging equipment and other factors, low-resolution images that are unfavorable for image analysis are often obtained. Although existing image super-resolution algorithms can enhance image resolution, these algorithms are not specifically designed for the characteristics of remote-sensing images and cannot effectively recover high-resolution images. Therefore, this paper proposes a novel remote-sensing image super-resolution algorithm based on an efficient hybrid conditional diffusion model (EHC-DMSR). The algorithm applies the theory of diffusion models to remote-sensing image super-resolution. Firstly, the comprehensive features of low-resolution images are extracted through a transformer network and CNN to serve as conditions for guiding image generation. Furthermore, to constrain the diffusion model and generate more high-frequency information, a Fourier high-frequency spatial constraint is proposed to emphasize high-frequency spatial loss and optimize the reverse diffusion direction. To address the time-consuming issue of the diffusion model during the reverse diffusion process, a feature-distillation-based method is proposed to reduce the computational load of U-Net, thereby shortening the inference time without affecting the super-resolution performance. Extensive experiments on multiple test datasets demonstrated that our proposed algorithm not only achieves excellent results in quantitative evaluation metrics but also generates sharper super-resolved images with rich detailed information.
A hybrid path planning algorithm considering AUV dynamic constraints based on improved A* algorithm and APF algorithm

Path planning is crucial for (autonomous underwater vehicles)AUVs to achieve autonomy. A proper path-planning method can obtain the optimal path while ensuring the safety of the path. This paper aims to address the issue of significant path-tracking errors arising from the independent research of traditional path-planning algorithms and path-tracking techniques. To this end, a path-planning algorithm based on A* + APF(AplusPF) is proposed for AUVs. This algorithm incorporates AUV kinetics into the iterative process of the A* algorithm and plans a safe and trackable path while fully considering AUV dynamic constraints and position and attitude information. Additionally, the algorithm adds the prediction of AUV trajectory to the heuristic function of the A* algorithm so that the heuristic function is closer to the actual value, thereby improving the algorithm's efficiency. The algorithm combines the APF algorithm to achieve fast and effective obstacle avoidance, thereby solving the problem of the A* algorithm's slow running speed in complex multi-obstacle environments. The effectiveness of the AplusPF algorithm is verified by the simulation results.
Reviewing methods of deep learning for intelligent healthcare systems in genomics and biomedicine

The advancements in genomics and biomedical technologies have generated vast amounts of biological and physiological data, which present opportunities for understanding human health. Deep learning (DL) and machine learning (ML) are frontiers and interdisciplinary fields of computer science that consider comprehensive computational models and provide integral roles for disease diagnosis and therapy investigation. DL-based algorithms can discover the intrinsic hierarchies in the training data to show great promise for extracting features and learning patterns from complex datasets and performing various analytical tasks. This review comprehensively discusses the wide-ranging DL approaches for intelligent healthcare systems (IHS) in genomics and biomedicine. This paper explores advanced concepts in deep learning (DL) and discusses the workflow of utilizing role-based algorithms in genomics and biomedicine to integrate intelligent healthcare systems (IHS). The aim is to overcome biomedical obstacles like patient disease classification, core biomedical processes, and empowering patient-disease integration. The paper also highlights how DL approaches are well-suited for addressing critical challenges in these domains, offering promising solutions for improved healthcare outcomes. We also provided a concise concept of DL architectures and model optimization in genomics and bioinformatics at the molecular level to deal with biomedicine classification, genomic sequence analysis, protein structure classification, and prediction. Finally, we discussed DL's current challenges and future perspectives in genomics and biomedicine for future directions.
SoK: Not Quite Water Under the Bridge: Review of Cross-Chain Bridge Hacks

The blockchain ecosystem has evolved into a multi-chain world with various blockchains vying for use. Although each blockchain may have its own native cryptocurrency or digital assets, there are use cases to transfer these assets between blockchains. Systems that bring these digital assets across blockchains are called bridges, and have become important parts of the ecosystem. The designs of bridges vary and range from quite primitive to extremely complex. However, they typically consist of smart contracts holding and releasing digital assets, as well as nodes that help facilitate user interactions between chains. In this paper we first provide a high level break-down of components in a bridge and the different processes for some bridge designs. Then, we analyse past exploits in the blockchain ecosystem that specifically targeted bridges. In doing this, we identify risks associated with bridge components.
Detect occluded items in X-ray baggage inspection

X-ray baggage inspection automatically determines whether there are prohibited items in passenger luggage and has recently achieved good progress due to the development of deep convolutional neural networks (DCNNs). However, the performance of X-ray baggage inspection degrades with heavily occluded and cluttered baggage owing to the interaction of the imaged objects. Inspired by the idea that discriminant cues may still exist in multiscale perceptions, discrepancies among different classes, and associations among different tasks, we propose a novel approach that exploits pseudo semantic masks to enhance the discriminant ability of feature representations. Moreover, the features at neighboring scales interact to further explore context information. We verify our method on the publicly available SIXray, OPIXray, and HIXray datasets. The results show that our method outperforms other state-of-the-art X-ray baggage inspection methods by 1.02% and 0.85% in terms of mean average precision (mAP).
Creating a large language model of a philosopher

Can large language models produce expert‐quality philosophical texts? To investigate this, we fine‐tuned GPT‐3 with the works of philosopher Daniel Dennett. To evaluate the model, we asked the real Dennett 10 philosophical questions and then posed the same questions to the language model, collecting four responses for each question without cherry‐picking. Experts on Dennett's work succeeded at distinguishing the Dennett‐generated and machine‐generated answers above chance but substantially short of our expectations. Philosophy blog readers performed similarly to the experts, while ordinary research participants were near chance distinguishing GPT‐3's responses from those of an “actual human philosopher”.
Finding and Evaluating Parameters for BGV

Fully Homomorphic Encryption (FHE) is a groundbreaking technology that allows for arbitrary computations to be performed on encrypted data. State-of-the-art schemes such as Brakerski Gentry Vaikuntanathan (BGV) are based on the Learning with Errors over rings (RLWE) assumption where each ciphertext has an associated error that grows with each homomorphic operation. For correctness, the error needs to stay below a certain threshold, requiring a trade-off between security and error margin for computations in the parameters. Choosing the parameters accordingly, for example, the polynomial degree or the ciphertext modulus, is challenging and requires expert knowledge specific to each scheme. In this work, we improve the parameter generation across all steps of its process. We provide a comprehensive analysis for BGV in the Double Chinese Remainder Theorem (DCRT) representation providing more accurate and better bounds than previous work on the DCRT, and empirically derive a closed formula linking the security level, the polynomial degree, and the ciphertext modulus. Additionally, we introduce new circuit models and combine our theoretical work in an easy-to-use parameter generator for researchers and practitioners interested in using BGV for secure computation. Our formula results in better security estimates than previous closed formulas while our DCRT analysis results in reduced prime sizes of up to 42% compared to previous work.
Testing Graph Database Engines via Query Partitioning

Graph Database Management Systems (GDBMSs) store data as graphs and allow the efficient querying of nodes and their relationships. Logic bugs are bugs that cause a GDBMS to return an incorrect result for a given query (e.g., by returning incorrect nodes or relationships). The impact of such bugs can be severe, as they often go unnoticed. The core insight of this paper is that Query Partitioning, a test oracle that has been proposed to test Relational Database Systems, is applicable to testing GDBMSs as well. The core idea of Query Partitioning is that, given a query, multiple queries are derived whose results can be combined to reconstruct the given query's result. Any discrepancy in the result indicates a logic bug. We have implemented this approach as a practical tool named GDBMeter and evaluated GDBMeter on three popular GDBMSs and found a total of 40 unique, previously unknown bugs. We consider 14 of them to be logic bugs, the others being error or crash bugs. Overall, 27 of the bugs have been fixed, and 35 confirmed. We compared our approach to the state-of-the-art approach to testing GDBMS, which relies on differential testing; we found that it results in a high number of false alarms, while Query Partitioning reported actual logic bugs without any false alarms. Furthermore, despite the previous efforts in testing Neo4j and JanusGraph, we found 18 additional bugs. The developers appreciate our work and plan to integrate GDBMeter into their testing process. We expect that this simple, yet effective approach and the practical tool will be used to test other GDBMSs.
Learning knowledge graph embedding with multi-granularity relational augmentation network

Knowledge graph embedding (KGE) aims to complete link prediction tasks effectively by learning the representation of entity and relation. Recently, deep neural networks have achieved prominent results for learning KGE. However, knowledge graphs typically contain massive information about entities and relations, and existing deep neural network-based KGE models exploit semantic information from simple explicit feature concatenation and reshaping without considering bidirectional implicit interactions, which cannot accentuate relevant predictive information. Additionally, these models simply extract nonlinear features from embedded matrices of relations, resulting in neglecting the distinctive semantic connotation of heterogeneous relations. To overcome these two challenges, a multi-granularity relational augmentation network (MRAN) to learn KGE is proposed. Specifically, multi-granularity implicit interaction embeddings are generated to facilitate relevant predictive information selection, which model bidirectional implicit interactions between entities and relations in Euclidean space, complex space, and quaternion space. Moreover, relational augmentation convolution module utilizes relation-aware filters at the convolutional layer to preserve the distinctive relational properties for dealing with heterogeneous relations. Subsequently, the augmentation mechanism is injected to focus on informative features and restrain useless features of neural networks, which boosts the quality of KGE. Experimental results demonstrate the outstanding link prediction results of MRAN on five benchmark datasets, revealing the benefits of considering bidirectional implicit interactions and heterogeneous relations. Our model substantially improves efficiency while achieving excellent performance, which is superior to state-of-the-art baseline models in 2 out of 4 metrics on WN18 and in 3 out of 4 metrics on FB15k-237 and YAGO3-10 datasets.
Can we improve meta-learning model in few-shot learning by aligning data distributions?

Meta-learning becomes a promising way to solve the few-shot learning problem in recent research. This paradigm mainly relies on hierarchical architecture and episodic training to achieve good generalization on the new learning task. However, as we know, support and query sets in each training task in episodic training only contain a few data points. Too limited training data can lead to data discrepancy between the training and test data which further hurts the performance of learning models in classical supervised and semi-supervised learning. Hence, there is an interesting question: Can we also improve the meta-learning model in few-shot learning by aligning the data distributions of support and query sets? In this work, we try to answer this question. Firstly, we give a theoretical analysis of the generalization ability of meta-learning to show that eliminating the data-distribution discrepancy between the support and query sets in each training task can improve the meta-learning model in few-shot learning. Next, we design an experiment to further verify our analysis. The experimental results show that aligning the data distributions is crucial and even can help the learning model trained by the classical batch-based training achieve better performance on unseen few-shot learning tasks. Furthermore, we also propose two simple meta-batch normalization methods following our result which can be integrated into recent meta-learning methods to improve their performance in few-shot learning.
Accurate fringe order recognition with adaptive morphological operations for 3D measurement

Temporal phase unwrapping (TPU) techniques are vulnerable to inaccurate recognition of fringe order due to various factors, including noise, motion blur and defocusing effects. The misalignment between the fringe order and wrapped phase can lead to abnormal jump errors and compromise measuring accuracy. To address this issue, an accurate fringe order recognition method with adaptive morphological operations for 3D measurement is proposed. Differs from the traditional half-period fringe order recognition method, a complete full-flow algorithm is constructed to accurately separate adhesive connected domains, which includes: segmentation and erosion of connected domains, localization of fully eroded connected regions, clustering of separated valid points, and adaptive selection of radius. In contrast to a simplistic morphological open arithmetic, the proposed method has the capability to perform clustering on all separated valid points, thereby achieving lossless fringe order recognition of the full field. And its row-correlation property gives it a larger correctable range than the previous row-independent approach. The experimental results demonstrate the generality, advancement, adaptivity, and precision of our method, built upon the traditional TPUs for obtaining fringe orders.
The path toward equal performance in medical machine learning

To ensure equitable quality of care, differences in machine learning model performance between patient groups must be addressed. Here, we argue that two separate mechanisms can cause performance differences between groups. First, model performance may be worse than theoretically achievable in a given group. This can occur due to a combination of group underrepresentation, modeling choices, and the characteristics of the prediction task at hand. We examine scenarios in which underrepresentation leads to underperformance, scenarios in which it does not, and the differences between them. Second, the optimal achievable performance may also differ between groups due to differences in the intrinsic difficulty of the prediction task. We discuss several possible causes of such differences in task difficulty. In addition, challenges such as label biases and selection biases may confound both learning and performance evaluation. We highlight consequences for the path toward equal performance, and we emphasize that leveling up model performance may require gathering not only more data from underperforming groups but also better data. Throughout, we ground our discussion in real-world medical phenomena and case studies while also referencing relevant statistical theory.
RSI-YOLO: Object Detection Method for Remote Sensing Images Based on Improved YOLO

With the continuous development of deep learning technology, object detection has received extensive attention across various computer fields as a fundamental task of computational vision. Effective detection of objects in remote sensing images is a key challenge, owing to their small size and low resolution. In this study, a remote sensing image detection (RSI-YOLO) approach based on the YOLOv5 target detection algorithm is proposed, which has been proven to be one of the most representative and effective algorithms for this task. The channel attention and spatial attention mechanisms are used to strengthen the features fused by the neural network. The multi-scale feature fusion structure of the original network based on a PANet structure is improved to a weighted bidirectional feature pyramid structure to achieve more efficient and richer feature fusion. In addition, a small object detection layer is added, and the loss function is modified to optimise the network model. The experimental results from four remote sensing image datasets, such as DOTA and NWPU-VHR 10, indicate that RSI-YOLO outperforms the original YOLO in terms of detection performance. The proposed RSI-YOLO algorithm demonstrated superior detection performance compared to other classical object detection algorithms, thus validating the effectiveness of the improvements introduced into the YOLOv5 algorithm.
Shall androids dream of genocides? How generative AI can change the future of memorialization of mass atrocities

Abstract The memorialization of mass atrocities such as war crimes and genocides facilitates the remembrance of past suffering, honors those who resisted the perpetrators, and helps prevent the distortion of historical facts. Digital technologies have transformed memorialization practices by enabling less top-down and more creative approaches to remember mass atrocities. At the same time, they may also facilitate the spread of denialism and distortion, attempt to justify past crimes and attack the dignity of victims. The emergence of generative forms of artificial intelligence (AI), which produce textual and visual content, has the potential to revolutionize the field of memorialization even further. AI can identify patterns in training data to create new narratives for representing and interpreting mass atrocities—and do so in a fraction of the time it takes for humans. The use of generative AI in this context raises numerous questions: For example, can the paucity of training data on mass atrocities distort how AI interprets some atrocity-related inquiries? How important is the ability to differentiate between human- and AI-made content concerning mass atrocities? Can AI-made content be used to promote false information concerning atrocities? This article addresses these and other questions by examining the opportunities and risks associated with using generative AIs for memorializing mass atrocities. It also discusses recommendations for AIs integration in memorialization practices to steer the use of these technologies toward a more ethical and sustainable direction.
Federated Edge Intelligence and Edge Caching Mechanisms

Federated learning (FL) has emerged as a promising technique for preserving user privacy and ensuring data security in distributed machine learning contexts, particularly in edge intelligence and edge caching applications. Recognizing the prevalent challenges of imbalanced and noisy data impacting scalability and resilience, our study introduces two innovative algorithms crafted for FL within a peer-to-peer framework. These algorithms aim to enhance performance, especially in decentralized and resource-limited settings. Furthermore, we propose a client-balancing Dirichlet sampling algorithm with probabilistic guarantees to mitigate oversampling issues, optimizing data distribution among clients to achieve more accurate and reliable model training. Within the specifics of our study, we employed 10, 20, and 40 Raspberry Pi devices as clients in a practical FL scenario, simulating real-world conditions. The well-known FedAvg algorithm was implemented, enabling multi-epoch client training before weight integration. Additionally, we examined the influence of real-world dataset noise, culminating in a performance analysis that underscores how our novel methods and research significantly advance robust and efficient FL techniques, thereby enhancing the overall effectiveness of decentralized machine learning applications, including edge intelligence and edge caching.