Reviewerly/exHarmony huggingface dataset:
Mixed-State Long-Range Order and Criticality from Measurement and Feedback

Utilizing midcircuit measurement and unitary feedback, a general framework is proposed to realize mixed states with a variety of quantum orders, criticality, and long-range entanglement in finite time.
Information Retrieval meets Large Language Models: A strategic report from Chinese IR community

The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Models (LLMs) have demonstrated exceptional capabilities in text understanding, generation, and knowledge inference, opening up exciting avenues for IR research. LLMs not only facilitate generative retrieval but also offer improved solutions for user understanding, model evaluation, and user-system interactions. More importantly, the synergistic relationship among IR models, LLMs, and humans forms a new technical paradigm that is more powerful for information seeking. IR models provide real-time and relevant information, LLMs contribute internal knowledge, and humans play a central role of demanders and evaluators to the reliability of information services. Nevertheless, significant challenges exist, including computational costs, credibility concerns, domain-specific limitations, and ethical considerations. To thoroughly discuss the transformative impact of LLMs on IR research, the Chinese IR community conducted a strategic workshop in April 2023, yielding valuable insights. This paper provides a summary of the workshop's outcomes, including the rethinking of IR's core values, the mutual enhancement of LLMs and IR, the proposal of a novel IR technical paradigm, and open challenges.
ProxyMix: Proxy-based Mixup training with label refinery for source-free domain adaptation

Due to privacy concerns and data transmission issues, Source-free Unsupervised Domain Adaptation (SFDA) has gained popularity. It exploits pre-trained source models, rather than raw source data for target learning, to transfer knowledge from a labeled source domain to an unlabeled target domain. Existing methods solve this problem typically with additional parameters or noisy pseudo labels, and we propose an effective method named Proxy-based Mixup training with label refinery (ProxyMix) to avoid these drawbacks. To avoid additional parameters and leverages information in the source model, ProxyMix defines classifier weights as class prototypes and creates a class-balanced proxy source domain using nearest neighbors of the prototypes. To improve the reliability of pseudo labels, we further propose the frequency-weighted aggregation strategy to generate soft pseudo labels for unlabeled target data. Our strategy utilizes target features' internal structure, increases weights of low-frequency class samples, and aligns the proxy and target domains using inter- and intra-domain mixup regularization. This mitigates the negative impact of noisy labels. Experiments on three 2D image and 3D point cloud object recognition benchmarks demonstrate that ProxyMix yields state-of-the-art performance for source-free UDA tasks.
Weakly supervised joint whole-slide segmentation and classification in prostate cancer

The identification and segmentation of histological regions of interest can provide significant support to pathologists in their diagnostic tasks. However, segmentation methods are constrained by the difficulty in obtaining pixel-level annotations, which are tedious and expensive to collect for whole-slide images (WSI). Though several methods have been developed to exploit image-level weak-supervision for WSI classification, the task of segmentation using WSI-level labels has received very little attention. The research in this direction typically require additional supervision beyond image labels, which are difficult to obtain in real-world practice. In this study, we propose WholeSIGHT, a weakly-supervised method that can simultaneously segment and classify WSIs of arbitrary shapes and sizes. Formally, WholeSIGHT first constructs a tissue-graph representation of WSI, where the nodes and edges depict tissue regions and their interactions, respectively. During training, a graph classification head classifies the WSI and produces node-level pseudo-labels via post-hoc feature attribution. These pseudo-labels are then used to train a node classification head for WSI segmentation. During testing, both heads simultaneously render segmentation and class prediction for an input WSI. We evaluate the performance of WholeSIGHT on three public prostate cancer WSI datasets. Our method achieves state-of-the-art weakly-supervised segmentation performance on all datasets while resulting in better or comparable classification with respect to state-of-the-art weakly-supervised WSI classification methods. Additionally, we assess the generalization capability of our method in terms of segmentation and classification performance, uncertainty estimation, and model calibration. Our code is available at: https://github.com/histocartography/wholesight.
LaBRADOR: Compact Proofs for R1CS from Module-SIS

The most compact quantum-safe proof systems for large circuits are PCP-type systems such as Ligero, Aurora, and Shockwave, that only use weak cryptographic assumptions, namely hash functions modeled as random oracles. One would expect that by allowing for stronger assumptions, such as the hardness of Module-SIS, it should be possible to design more compact proof systems. But alas, despite considerable progress in lattice-based proofs, no such proof system was known so far. We rectify this situation by introducing a Lattice-Based Recursively Amortized Demonstration Of R1CS (LaBRADOR), with more compact proof sizes than known hash-based proof systems. At the 128 bits security level, LaBRADOR proves knowledge of a solution for an R1CS mod $$2^{64}+1$$ with $$2^{20}$$ constraints, with a proof size of only 58 KB, an order of magnitude more compact than previous quantum-safe proofs.
Fast Practical Lattice Reduction Through Iterated Compression

We introduce a new lattice basis reduction algorithm with approximation guarantees analogous to the LLL algorithm and practical performance that far exceeds the current state of the art. We achieve these results by iteratively applying precision management techniques within a recursive algorithm structure and show the stability of this approach. We analyze the asymptotic behavior of our algorithm, and show that the heuristic running time is $$O(n^{\omega }(C+n)^{1+\varepsilon })$$ for lattices of dimension n, $$\omega \in (2,3]$$ bounding the cost of size reduction, matrix multiplication, and QR factorization, and C bounding the log of the condition number of the input basis B. This yields a running time of $$O\left( n^\omega (p + n)^{1 + \varepsilon }\right) $$ for precision $$p = O(\log \Vert B\Vert _{max})$$ in common applications. Our algorithm is fully practical, and we have published our implementation. We experimentally validate our heuristic, give extensive benchmarks against numerous classes of cryptographic lattices, and show that our algorithm significantly outperforms existing implementations.
Does the Dual-Sieve Attack on Learning with Errors Even Work?

Guo and Johansson (ASIACRYPT 2021), and MATZOV (tech. report 2022) have independently claimed improved attacks against various NIST lattice candidates by adding a Fast Fourier Transform (FFT) trick on top of the so-called Dual-Sieve attack. Recently, there was more follow up work in this line adding new practical improvements. However, from a theoretical perspective, all of these works are painfully specific to Learning with Errors, while the principle of the Dual-Sieve attack is more general (Laarhoven & Walter, CT-RSA 2021). More critically, all of these works are based on heuristics that have received very little theoretical and experimental attention. This work attempts to rectify the above deficiencies of the literature. We first propose a generalization of the FFT trick by Guo and Johansson to arbitrary Bounded Distance Decoding instances. This generalization offers a new improvement to the attack. We then theoretically explore the underlying heuristics and show that these are in contradiction with formal, unconditional theorems in some regimes, and with well-tested heuristics in other regimes. The specific instantiations of the recent literature fall into this second regime. We confirm these contradictions with experiments, documenting several phenomena that are not predicted by the analysis, including a "waterfall-floor" phenomenon, reminiscent of Low-Density Parity-Check decoding failures. We conclude that the success probability of the recent Dual-Sieve-FFT attacks are presumably significantly overestimated. We further discuss the adequate way forward towards fixing the attack and its analysis.
Virtual Reality for Criminologists: A Road Map

Virtual reality (VR) technology offers unique and as yet largely untapped potential for criminology. It can address problems that have traditionally plagued the field, provide an ecologically valid alternative for conventional research methods, and create novel possibilities for theory testing, and it allows for the study of phenomena that are difficult to research in the real world for ethical, safety, or practical reasons. This essay reviews the budding research literature using VR in criminogenic contexts, as well as relevant research from other disciplines, and explains the technology's basic features, current limitations, and ethical challenges. It concludes that within the foreseeable future, VR may become the criminological equivalent of the petri dish, offering the possibility to study the unfolding of highly complex behavioral processes in very detailed ways and help achieve step changes in our understanding of crime.
The Growing Significance of Cyber Law Professionals in Higher Education: Effective Learning Strategies and Innovative Approaches

The article discusses the prospects and problems in cyber law education, concentrating on successful learning techniques and cutting-edge methods that can improve the caliber of teaching and student outcomes. The analysis uncovers various problems faced by higher education institutions, such as the lack of a standardized curriculum, a lack of faculty knowledge, and a lack of practical exposure for students, through a qualitative research approach that includes case studies and literature reviews. In order to improve cyber law education, the study underlines the value of using collaborative learning, simulations, real-world case studies, and technology integration. Innovations like gamification, virtual reality simulations, online and mixed learning models, and interdisciplinary approaches are also covered. The results imply that for the development of knowledgeable professionals capable of navigating the complicated legal environment of the digital era, cyber law education must continually be improved.
Quantum Computation of Hydrogen Bond Dynamics and Vibrational Spectra

Calculating observable properties of chemical systems is often classically intractable and widely viewed as a promising application of quantum information processing. Here, we introduce a new framework for solving generic quantum chemical dynamics problems using quantum logic. We experimentally demonstrate a proof-of-principle instance of our method using the QSCOUT ion-trap quantum computer, where we experimentally drive the ion-trap system to emulate the quantum wavepacket dynamics corresponding to the shared-proton within an anharmonic hydrogen bonded system. Following the experimental creation and propagation of the shared-proton wavepacket on the ion-trap, we extract measurement observables such as its time-dependent spatial projection and its characteristic vibrational frequencies to spectroscopic accuracy (3.3 cm-1 wavenumbers, corresponding to >99.9% fidelity). Our approach introduces a new paradigm for studying the chemical dynamics and vibrational spectra of molecules and opens the possibility to describe the behavior of complex molecular processes with unprecedented accuracy.
Blockchain-Based Secure Storage and Access Control Scheme for Supply Chain Ecological Business Data: A Case Study of the Automotive Industry

The reliable circulation of automotive supply chain data is crucial for automotive manufacturers and related enterprises as it promotes efficient supply chain operations and enhances their competitiveness and sustainability. However, with the increasing prominence of privacy protection and information security issues, traditional data sharing solutions are no longer able to meet the requirements for highly reliable secure storage and flexible access control. In response to this demand, we propose a secure data storage and access control scheme for the supply chain ecosystem based on the enterprise-level blockchain platform Hyperledger Fabric. The design incorporates a dual-layer attribute-based auditable access control model for access control, with four smart contracts aimed at coordinating and implementing access policies. The experimental results demonstrate that the proposed approach exhibits significant advantages under large-scale data and multi-attribute conditions. It enables fine-grained, dynamic access control under ciphertext and maintains high throughput and security in simulated real-world operational scenarios.
Bridged-U-Net-ASPP-EVO and Deep Learning Optimization for Brain Tumor Segmentation

Brain tumor segmentation from Magnetic Resonance Images (MRI) is considered a big challenge due to the complexity of brain tumor tissues, and segmenting these tissues from the healthy tissues is an even more tedious challenge when manual segmentation is undertaken by radiologists. In this paper, we have presented an experimental approach to emphasize the impact and effectiveness of deep learning elements like optimizers and loss functions towards a deep learning optimal solution for brain tumor segmentation. We evaluated our performance results on the most popular brain tumor datasets (MICCAI BraTS 2020 and RSNA-ASNR-MICCAI BraTS 2021). Furthermore, a new Bridged U-Net-ASPP-EVO was introduced that exploits Atrous Spatial Pyramid Pooling to enhance capturing multi-scale information to help in segmenting different tumor sizes, Evolving Normalization layers, squeeze and excitation residual blocks, and the max-average pooling for down sampling. Two variants of this architecture were constructed (Bridged U-Net_ASPP_EVO v1 and Bridged U-Net_ASPP_EVO v2). The best results were achieved using these two models when compared with other state-of-the-art models; we have achieved average segmentation dice scores of 0.84, 0.85, and 0.91 from variant1, and 0.83, 0.86, and 0.92 from v2 for the Enhanced Tumor (ET), Tumor Core (TC), and Whole Tumor (WT) tumor sub-regions, respectively, in the BraTS 2021validation dataset.
Survey on Explainable AI: From Approaches, Limitations and Applications Aspects

Abstract In recent years, artificial intelligence (AI) technology has been used in most if not all domains and has greatly benefited our lives. While AI can accurately extract critical features and valuable information from large amounts of data to help people complete tasks faster, there are growing concerns about the non-transparency of AI in the decision-making process. The emergence of explainable AI (XAI) has allowed humans to better understand and control AI systems, which is motivated to provide transparent explanations for the decisions made by AI. This article aims to present a comprehensive overview of recent research on XAI approaches from three well-defined taxonomies. We offer an in-depth analysis and summary of the status and prospects of XAI applications in several key areas where reliable explanations are urgently needed to avoid mistakes in decision-making. We conclude by discussing XAI’s limitations and future research directions.
Accessibility and inclusiveness of new information and communication technologies for disabled users and content creators in the Metaverse

The study critically reassesses existing Metaverse concepts and proposes a novel framework for inclusiveness of physically disabled artists. The purpose is to enable and inspire physically disabled users and content creators to participate in the evolving concept of the Metaverse. The article also highlights the need for standards and regulations governing the inclusion of people with disabilities in Metaverse projects.The study examines current information technologies and their relevance to the inclusion of physically disabled individuals in the Metaverse. We analyse existing Metaverse concepts, exploring emerging information technologies such as Virtual and Augmented Reality, and the Internet of Things. The emerging framework in the article is based on the active involvement of disabled creatives in the development of solutions for inclusivity.The review reveals that despite the proliferation of Blockchain Metaverse projects, the inclusion of physically disabled individuals in the Metaverse remains distant, with limited standards and regulations in place. The article proposes a concept of the Metaverse that leverages emerging technologies, to enable greater engagement of disabled creatives. This approach is designed to enhance inclusiveness in the Metaverse landscape.Active involvement of physically disabled individuals in the design and development of Metaverse platforms is crucial for promoting inclusivity. The framework for accessibility and inclusiveness in decentralised Metaverses provides a basis for the meaningful participation of disabled creatives. The article emphasises the importance of addressing the mechanisms for art production by individuals with disabilities in the emerging Metaverse landscape.IMPLICATIONS FOR REHABILITATIONThis article addresses a global challenge related to helping disabled people operate in the modern society, targeting new and emerging technologies, and enabling early understanding of required actions for inclusiveness of people with disabilities in the Metaverse.The increased use of advanced technologies (e.g., AI and IoT) in the Metaverse, amplified the importance of this research being conducted.The aggregate impact from this research for science and society is a more inclusive, and unbiassed Metaverses that are compliant with regulations on anti-disability discrimination. This is followed by the secondary values, related to increased technological opportunities from a breakthrough in designing new, more inclusive, and autonomous devices.The research study presents a new framework for integrating new technologies in existing Metaverses, resulting with a stronger accessibility and inclusiveness of the Metaverse. This creates a new understanding on how new technologies can be used for disability discrimination prevention and early understanding of disability requirements. We also highlighted normative constraints and the need for further reflection and weighing to avoid dystopian futures for the physically disabled in relation to the Metaverse.
Augmented Reality in a Lean Workplace at Smart Factories: A Case Study

The last few years have seen a massive transformation of the global industrial landscape, thanks to the emergence of Industry 4.0 and the disruptive technologies it enables, such as Augmented Reality (AR). This paper presents the result of a project with the primary focus on enhancing the operators’ working conditions and the further definition of the most suitable AR for each material handling and motion process. To achieve this, a methodology called Risk Assessment for Ergonomics and Safety in Logistics (RAES-Log) was developed in order to analyse and define AR implementation requirements, in order to mitigate existing risks and improve ergonomic conditions. Utilizing a human-centric approach consistent with Lean Thinking and Industry 5.0 vision, the main aim was to reduce human effort during task performance. Furthermore, the potential for creating waste-free and more efficient workspaces was explored, as well as the possibility of Human Augmentation (HA) to enhance workers’ capabilities and senses. The workers’ opinions and acceptance of the proposed AR solutions resulting from the RAES-Log methodology in a case study were collected and analysed. The overall feedback was positive and it is expected a lower prevalence of work-related Musculoskeletal Disorders (MSD), less lost time days, and lower injury severity, as well as increased process efficiency, operator motivation, well-being and engagement in continuous improvement processes.
Drone-YOLO: An Efficient Neural Network Method for Target Detection in Drone Images

Object detection in unmanned aerial vehicle (UAV) imagery is a meaningful foundation in various research domains. However, UAV imagery poses unique challenges, including large image sizes, small sizes detection objects, dense distribution, overlapping instances, and insufficient lighting impacting the effectiveness of object detection. In this article, we propose Drone-YOLO, a series of multi-scale UAV image object detection algorithms based on the YOLOv8 model, designed to overcome the specific challenges associated with UAV image object detection. To address the issues of large scene sizes and small detection objects, we introduce improvements to the neck component of the YOLOv8 model. Specifically, we employ a three-layer PAFPN structure and incorporate a detection head tailored for small-sized objects using large-scale feature maps, significantly enhancing the algorithm’s capability to detect small-sized targets. Furthermore, we integrate the sandwich-fusion module into each layer of the neck’s up–down branch. This fusion mechanism combines network features with low-level features, providing rich spatial information about the objects at different layer detection heads. We achieve this fusion using depthwise separable evolution, which balances parameter costs and a large receptive field. In the network backbone, we employ RepVGG modules as downsampling layers, enhancing the network’s ability to learn multi-scale features and outperforming traditional convolutional layers. The proposed Drone-YOLO methods have been evaluated in ablation experiments and compared with other state-of-the-art approaches on the VisDrone2019 dataset. The results demonstrate that our Drone-YOLO (large) outperforms other baseline methods in the accuracy of object detection. Compared to YOLOv8, our method achieves a significant improvement in mAP0.5 metrics, with a 13.4% increase on the VisDrone2019-test and a 17.40% increase on the VisDrone2019-val. Additionally, the parameter-efficient Drone-YOLO (tiny) with only 5.25 M parameters performs equivalently or better than the baseline method with 9.66M parameters on the dataset. These experiments validate the effectiveness of the Drone-YOLO methods in the task of object detection in drone imagery.
Towards Evaluating Explanations of Vision Transformers for Medical Imaging

As deep learning models increasingly find applications in critical domains such as medical imaging, the need for transparent and trustworthy decision-making becomes paramount.Many explainability methods provide insights into how these models make predictions by attributing importance to input features.As Vision Transformer (ViT) becomes a promising alternative to convolutional neural networks for image classification, its interpretability remains an open research question.This paper investigates the performance of various interpretation methods on a ViT applied to classify chest X-ray images.We introduce the notion of evaluating faithfulness, sensitivity, and complexity of ViT explanations.The obtained results indicate that Layerwise relevance propagation for transformers outperforms Local interpretable model-agnostic explanations and Attention visualization, providing a more accurate and reliable representation of what a ViT has actually learned.Our findings provide insights into the applicability of ViT explanations in medical imaging and highlight the importance of using appropriate evaluation criteria for comparing them.