Reviewerly/exHarmony huggingface dataset:
When the Curious Abandon Honesty: Federated Learning Is Not Private

In federated learning (FL), data does not leave personal devices when they are jointly training a machine learning model. Instead, these devices share gradients, parameters, or other model updates, with a central party (e.g., a company) coordinating the training. Because data never "leaves" personal devices, FL is often presented as privacy-preserving. Yet, recently it was shown that this protection is but a thin facade, as even a passive, honest-but-curious attacker observing gradients can reconstruct data of individual users contributing to the protocol.In this work, we show a novel data reconstruction attack which allows an active and dishonest central party to efficiently extract user data from the received gradients. While prior work on data reconstruction in FL relies on solving computationally expensive optimization problems or on making easily detectable modifications to the shared model’s architecture or parameters, in our attack the central party makes inconspicuous changes to the shared model’s weights before sending them out to the users. We call the modified weights of our attack trap weights.Our active attacker is able to recover user data perfectly, i.e., with zero error, even when this data stems from the same class. Recovery comes with near-zero costs: the attack requires no complex optimization objectives. Instead, our attacker exploits inherent data leakage from model gradients and simply amplifies this effect by maliciously altering the weights of the shared model through the trap weights. These specificities enable our attack to scale to fully-connected and convolutional deep neural networks trained with large mini-batches of data. For example, for the high-dimensional vision dataset ImageNet, we perfectly reconstruct more than 50% of the training data points from mini-batches as large as 100 data points. In textual tasks, such as IMDB sentiment analysis, more than 65% of data points from mini-batches containing 100 data points can be perfectly reconstructed.
Robust Few-Shot Aerial Image Object Detection via Unbiased Proposals Filtration

Few-shot aerial image object detection aims to rapidly detect object instances of novel category in aerial images by using few labeled samples. However, due to the complex background of aerial images, few labeled samples of novel categories, and the model trained with the few-shot learning paradigm is biased towards the base categories, it greatly increases the difficulty of identifying foreground objects of novel categories. In addition to this, tiny object detection is always a hot potato in aerial image object detection, and it is even more difficult for few-shot object detection. To this end, we propose a Few-shot aerial image object detection with Confidence-Iou collaborative proposal filtration and Tiny object constraint loss (FsCIT). Specifically, we first introduce a new confidence-iou collaborative proposal filtration scheme to RPN, which combines the unbiased IoU scores between the two bounding boxes with foreground-background confidence scores to filter redundant region proposals and rescue more foreground proposals for the novel categories in RPN. Then, we design a new tiny object loss constraint term to attempt at overcoming the challenge of tiny object detection in few-shot aerial image object detection. This term considers the central point distance, the size of ground-truth bounding boxes, and the distances between the four edges of the ground-truth bounding box and the predicted bounding box. Experiments on DIOR, AI-TOD and HRRSD datasets show that FsCIT is effective and can improve the performance of few-shot aerial image object detection.
A Novel Conservative Chaotic System Involved in Hyperbolic Functions and Its Application to Design an Efficient Colour Image Encryption Scheme

It is well known that, compared to low-dimension chaotic systems, three-dimensional chaotic systems have a wider parameter range, more complicated behaviour, and better unpredictability. This fact motivated us to introduce a novel image encryption method that employs a three-dimensional chaotic system. We proposed a novel three-dimensional conservative system that can exhibit chaotic behaviour involving hyperbolic functions. The dynamical behaviours of the proposed system are discovered by calculating Lyapunov exponents and bifurcation diagrams. Thereafter, we designed an image encryption method based on the proposed system and a 4×4 self-invertible matrix. A modified Diffie–Hellman key exchange protocol was utilised to generate the self-invertible key matrix Km employed in the diffusion stage. Our approach has three main stages. In the first stage, the proposed three-dimensional system utilises the original image to create three sequences, two of which are chosen for confusion and diffusion processes. The next stage involves confusing the image’s pixels by changing the positions of pixels using these sequences. In the third stage, the confused image is split into sub-blocks of size 4×4, and each block is encrypted by multiplying it with Km. Simulation findings demonstrated that the proposed image scheme has a high level of security and is resistant to statistical analysis, noise, and other attacks.
Early Detection of Earthquakes Using IoT and Cloud Infrastructure: A Survey

Earthquake early warning systems (EEWS) are crucial for saving lives in earthquake-prone areas. In this study, we explore the potential of IoT and cloud infrastructure in realizing a sustainable EEWS that is capable of providing early warning to people and coordinating disaster response efforts. To achieve this goal, we provide an overview of the fundamental concepts of seismic waves and associated signal processing. We then present a detailed discussion of the IoT-enabled EEWS, including the use of IoT networks to track the actions taken by various EEWS organizations and the cloud infrastructure to gather data, analyze it, and send alarms when necessary. Furthermore, we present a taxonomy of emerging EEWS approaches using IoT and cloud facilities, which includes the integration of advanced technologies such as machine learning (ML) algorithms, distributed computing, and edge computing. We also elaborate on a generic EEWS architecture that is sustainable and efficient and highlight the importance of considering sustainability in the design of such systems. Additionally, we discuss the role of drones in disaster management and their potential to enhance the effectiveness of EEWS. Furthermore, we provide a summary of the primary verification and validation methods required for the systems under consideration. In addition to the contributions mentioned above, this study also highlights the implications of using IoT and cloud infrastructure in early earthquake detection and disaster management. Our research design involved a comprehensive survey of the existing literature on early earthquake warning systems and the use of IoT and cloud infrastructure. We also conducted a thorough analysis of the taxonomy of emerging EEWS approaches using IoT and cloud facilities and the verification and validation methods required for such systems. Our findings suggest that the use of IoT and cloud infrastructure in early earthquake detection can significantly improve the speed and effectiveness of disaster response efforts, thereby saving lives and reducing the economic impact of earthquakes. Finally, we identify research gaps in this domain and suggest future directions toward achieving a sustainable EEWS. Overall, this study provides valuable insights into the use of IoT and cloud infrastructure in earthquake disaster early detection and emphasizes the importance of sustainability in designing such systems.
Approximate Autonomous Quantum Error Correction with Reinforcement Learning

Autonomous quantum error correction (AQEC) protects logical qubits by engineered dissipation and thus circumvents the necessity of frequent, error-prone measurement-feedback loops. Bosonic code spaces, where single-photon loss represents the dominant source of error, are promising candidates for AQEC due to their flexibility and controllability. While existing proposals have demonstrated the in-principle feasibility of AQEC with bosonic code spaces, these schemes are typically based on the exact implementation of the Knill-Laflamme conditions and thus require the realization of Hamiltonian distances $d\ensuremath{\ge}2$. Implementing such Hamiltonian distances requires multiple nonlinear interactions and control fields, rendering these schemes experimentally challenging. Here, we propose a bosonic code for approximate AQEC by relaxing the Knill-Laflamme conditions. Using reinforcement learning (RL), we identify the optimal bosonic set of code words (denoted here by RL code), which, surprisingly, is composed of the Fock states $|2⟩$ and $|4⟩$. As we show, the RL code, despite its approximate nature, successfully suppresses single-photon loss, reducing it to an effective dephasing process that well surpasses the break-even threshold. It may thus provide a valuable building block toward full error protection. The error-correcting Hamiltonian, which includes ancilla systems that emulate the engineered dissipation, is entirely based on the Hamiltonian distance $d=1$, significantly reducing model complexity. Single-qubit gates are implemented in the RL code with a maximum distance ${d}_{g}=2$.
Design of pseudorandom number generator based on a controllable multi-double-scroll chaotic system

The generation, implementation, and application of multi-scroll chaotic systems have been extensively studied due to their complex dynamic behaviors. This paper introduces a new controlled multi-double-scroll chaotic system and its corresponding implementation on a digital platform, the Field Programmable Gate Array (FPGA). The proposed system can generate 2N+1 and 2N+2 numbers of double-scroll attractors. In particular, the system has controllable multistability and multi-double-scroll complete amplitude control characteristics. The numerical analyses of Lyapunov exponents, bifurcation diagrams, phase diagrams, and basin of attraction show that the system exhibits complex dynamic behaviors. Furthermore, a new pseudo-random number generator (PRNG) is designed based on the multi-double-scroll chaotic system (MDSCS), in which a post-processing circuit is used to improve the quality of the generator. The PRNG has a high data throughput and uses only a small amount of resources in the target FPGA. The proposed PRNG uses less than 1% of the resources of the target FPGA and delivers a data bit throughput of 7.2 Gbps/s. To our best knowledge, it reaches the highest data throughput of PRNG based on continuous chaotic systems. The evaluation sequences are tested using generic randomness tests such as NIST SP800-22, TestU01, and Histograms. The experimental results show that the random sequences generated by our design have passed all the test items, which means that they will have excellent performance in the application.
Improvement of a prediction model for heart failure survival through explainable artificial intelligence

Cardiovascular diseases and their associated disorder of heart failure (HF) are major causes of death globally, making it a priority for doctors to detect and predict their onset and medical consequences. Artificial Intelligence (AI) allows doctors to discover clinical indicators and enhance their diagnoses and treatments. Specifically, "eXplainable AI" (XAI) offers tools to improve the clinical prediction models that experience poor interpretability of their results. This work presents an explainability analysis and evaluation of two HF survival prediction models using a dataset that includes 299 patients who have experienced HF. The first model utilizes survival analysis, considering death events and time as target features, while the second model approaches the problem as a classification task to predict death. The model employs an optimization data workflow pipeline capable of selecting the best machine learning algorithm as well as the optimal collection of features. Moreover, different post hoc techniques have been used for the explainability analysis of the model. The main contribution of this paper is an explainability-driven approach to select the best HF survival prediction model balancing prediction performance and explainability. Therefore, the most balanced explainable prediction models are Survival Gradient Boosting model for the survival analysis and Random Forest for the classification approach with a c-index of 0.714 and balanced accuracy of 0.74 (std 0.03) respectively. The selection of features by the SCI-XAI in the two models is similar where "serum_creatinine", "ejection_fraction", and "sex" are selected in both approaches, with the addition of "diabetes" for the survival analysis model. Moreover, the application of post hoc XAI techniques also confirm common findings from both approaches by placing the "serum_creatinine" as the most relevant feature for the predicted outcome, followed by "ejection_fraction". The explainable prediction models for HF survival presented in this paper would improve the further adoption of clinical prediction models by providing doctors with insights to better understand the reasoning behind usually "black-box" AI clinical solutions and make more reasonable and data-driven decisions.
Efficient multiparty quantum secret sharing based on a novel structure and single qubits

Abstract Quantum secret sharing (QSS) is a significant branch of quantum cryptography and can be widely used in various applications. Quantum secret sharing schemes can be developed by utilizing different features of quantum mechanics, and quantum secure direct communication (QSDC) is an effective way to achieve secret sharing using single qubits. The utilization of QSDC offers certain benefits, such as low cost, high security, and great potential for implementation with current technologies. However, the purpose of QSDC is different from that of QSS, which causes some vulnerabilities, such as dishonest participant attacks. We discover two critical factors that affect the security of traditional protocols. Firstly, they skip a few steps from the QSDC protocol to the QSS protocol. Secondly, the participants have different privileges. This can lead to participants with more privileges engaging in potential attack behavior. In light of these issues, this study proposes a new multiparty QSS scheme to address these vulnerabilities. The proposed protocol ensures the independence of each participant and grants them equal privileges. Analysis results demonstrate that it can defend against malicious attackers, retain the advantages of the QSDC protocol, and further reduce transmission costs. It achieves an excellent balance between security and performance.
Photo tourism: exploring photo collections in 3D

We present a system for interactively browsing and exploring large unstructured collections of photographs of a scene using a novel 3D interface. Our system consists of an image-based modeling front end that automatically computes the viewpoint of each photograph as well as a sparse 3D model of the scene and image to model correspondences. Our photo explorer uses image-based rendering techniques to smoothly transition between photographs, while also enabling full 3D navigation and exploration of the set of images and world geometry, along with auxiliary information such as overhead maps. Our system also makes it easy to construct photo tours of scenic or historic locations, and to annotate image details, which are automatically transferred to other relevant images. We demonstrate our system on several large personal photo collections as well as images gathered from Internet photo sharing sites.
Warning: Humans cannot reliably detect speech deepfakes

Speech deepfakes are artificial voices generated by machine learning models. Previous literature has highlighted deepfakes as one of the biggest security threats arising from progress in artificial intelligence due to their potential for misuse. However, studies investigating human detection capabilities are limited. We presented genuine and deepfake audio to n = 529 individuals and asked them to identify the deepfakes. We ran our experiments in English and Mandarin to understand if language affects detection performance and decision-making rationale. We found that detection capability is unreliable. Listeners only correctly spotted the deepfakes 73% of the time, and there was no difference in detectability between the two languages. Increasing listener awareness by providing examples of speech deepfakes only improves results slightly. As speech synthesis algorithms improve and become more realistic, we can expect the detection task to become harder. The difficulty of detecting speech deepfakes confirms their potential for misuse and signals that defenses against this threat are needed.
Revolutionizing Agriculture Through Blockchain

Blockchain is a promising technology that has the potential to revolutionize agriculture by enabling transparent, secure, and efficient data sharing and management. This chapter presents a bibliometric analysis of emerging trends and applications related to the use of blockchain in agriculture. The study uses a systematic search of relevant databases and applies bibliometric methods, including citation analysis, co-occurrence analysis, and keyword analysis, to identify key research themes, influential authors, and emerging trends. The findings suggest that the application of blockchain in agriculture is a rapidly growing field, with increasing interest and investment from stakeholders across the industry. The study highlights several emerging trends and applications, such as supply chain management, traceability, food safety, and sustainable agriculture, that have the potential to transform the agriculture industry. The chapter concludes by discussing the implications of these findings for future research and practice in the field of agriculture and blockchain.
Dynamic DNA cryptography-based image encryption scheme using multiple chaotic maps and SHA-256 hash function

In today's world, a substantial number of digital images are conserved and transmitted through diverse online platforms on a daily basis. Preserving the confidentiality of images is a paramount issue in the current digital environment. To safeguard the privacy of digital images on internet platforms, a robust, effective, and reliable encryption technique is required. This paper proposes a robust and efficient image encryption scheme based on a dynamic DNA encoding and DNA operations associated with chaotic maps with simple structures and highly chaotic behavior, such as the Logistic map, the Henon map, and the Lorenz system, to provide substantial security for digital images. Furthermore, the SHA-256 hash technique and zigzag traversal are used in the algorithm to reinforce the system. This paper defines an improved DNA encoding scheme that can encode four bits at once instead of two bits. Besides, unique and randomized keys are generated for each encryption and decryption session. The proposed scheme exhibits several admirable characteristics, including low processing cost, high randomness, large key space, flexible parameter space, high sensitivity to both keys and plaintext, and fast execution speed. As a result, the proposed scheme effectively safeguards sensitive digital images from a broad range of cryptographic attacks. Numerous evaluation results have demonstrated that the proposed algorithm is more secure and efficient than state-of-the-art methods against a huge assortment of cryptographic attacks.
Digital real estate in the metaverse: An empirical analysis of retail investor motivations

This paper investigates retail investor motivations for digital real estate ownership in the crypto-metaverse. Utilizing a detailed financial behavior survey of metaverse landowners' intrinsic and extrinsic motivations, we apply principal components analysis to uncover four distinct motivational groups: (1) Aesthetics and Identity, (2) Social and Community, (3) Speculation and Investment, and (4) Innovation and Technology. Our findings reveal that age, education, investment knowledge, risk-taking, and impulsivity significantly influence investor group membership. This research provides valuable insights to investors and developers, underscoring the potential of a platform to attract retail investors with speculative intentions, engagement longevity, and passive or active trading characteristics, contingent on unique crypto-metaverse attributes.
Artificial neural networks for photonic applications—from algorithms to implementation: tutorial

This tutorial–review on applications of artificial neural networks in photonics targets a broad audience, ranging from optical research and engineering communities to computer science and applied mathematics. We focus here on the research areas at the interface between these disciplines, attempting to find the right balance between technical details specific to each domain and overall clarity. First, we briefly recall key properties and peculiarities of some core neural network types, which we believe are the most relevant to photonics, also linking the layer’s theoretical design to some photonics hardware realizations. After that, we elucidate the question of how to fine-tune the selected model’s design to perform the required task with optimized accuracy. Then, in the review part, we discuss recent developments and progress for several selected applications of neural networks in photonics, including multiple aspects relevant to optical communications, imaging, sensing, and the design of new materials and lasers. In the following section, we put a special emphasis on how to accurately evaluate the complexity of neural networks in the context of the transition from algorithms to hardware implementation. The introduced complexity characteristics are used to analyze the applications of neural networks in optical communications, as a specific, albeit highly important example, comparing those with some benchmark signal-processing methods. We combine the description of the well-known model compression strategies used in machine learning, with some novel techniques introduced recently in optical applications of neural networks. It is important to stress that although our focus in this tutorial–review is on photonics, we believe that the methods and techniques presented here can be handy in a much wider range of scientific and engineering applications.
EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain

Existing summarization datasets come with two main drawbacks: (1) They tend to focus on overly exposed domains, such as news articles or wiki-like texts, and (2) are primarily monolingual, with few multilingual datasets.In this work, we propose a novel dataset, called EUR-Lex-Sum, based on manually curated document summaries of legal acts from the European Union law platform (EUR-Lex).Documents and their respective summaries exist as cross-lingual paragraph-aligned data in several of the 24 official European languages, enabling access to various cross-lingual and lower-resourced summarization setups.We obtain up to 1,500 document/summary pairs per language, including a subset of 375 crosslingually aligned legal acts with texts available in all 24 languages.In this work, the data acquisition process is detailed and key characteristics of the resource are compared to existing summarization resources.In particular, we illustrate challenging sub-problems and open questions on the dataset that could help the facilitation of future research in the direction of domain-specific cross-lingual summarization.Limited by the extreme length and language diversity of samples, we further conduct experiments with suitable extractive monolingual and cross-lingual baselines for future work.Code for the extraction as well as access to our data and baselines is available online at: https://github.com/achouhan93/eur-lex-sum.