Reviewerly/exHarmony huggingface dataset:
Collaborative Edge Intelligence Service Provision in Blockchain Empowered Urban Rail Transit Systems

With the advancement of Urban Rail Transits (URTs), the demand for artificial intelligence (AI) based URTs services grows exponentially. Edge intelligence (EI) leverages computing resources on the network edge to provide realtime intelligent services in close proximity. As it enables fast distributed learning, EI is envisioned to be a potential component of URTs, and ideal EI service provision is a critical concern for the intelligent development of URTs. The existing EI-related research concentrates on the computation offloading of general AI-based tasks, whereas both the edge server deployment and AI model training process are not explicitly designed for URTs. The URTs AI service characteristics such as model training demand, priority, and security are largely ignored. In this paper, we propose a novel collaborative EI service provision framework for URTs. Blockchain is used along with the EI server to construct a trusted computing infrastructure. To address the EI service credit crisis, a blockchain-based trust management mechanism including short-term reward incentives and long-term reputation evaluation is designed in the trusted computing infrastructure. An HRL-based collaborative training service optimization model is proposed to improve the learning efficiency and edge resource utilization rate in URTs. Specifically, the proposed two-stage collaborative optimization model jointly considers high-level service scheduling and low-level task offloading. In addition, we present an intelligent train control model based on the state-ofthe-art decision transformer (DT), with the training service as a case study to demonstrate the effectiveness of the proposed collaborative EI service provision. Extensive simulation results show that the proposed EI service provision framework can provide trusted, efficient, and high-quality AI training services, simultaneously improving URTs operational efficiency.
Bidirectional hierarchical quantum teleportation based on an eight-qubit entangled state

Abstract This article proposes an asymmetric bidirectional hierarchical quantum teleportation protocol, which is based on a quantum channel composed of a maximum of eight-qubit entangled state. With this method, Alice can transmit an unknown single-qubit state to Bob, meanwhile, Bob can transmit an arbitrary unknown two-qubit quantum state to Alice. As a sender with high authority, Bob can send any unknown two-qubit quantum state to Alice only under the sole control of the controller Charlie; Alice, as a sender with low-level permissions, if Bob intends to receive the unknown single-qubit quantum state sent by Alice, he must get the permission and assistance of Charlie and David to complete the communication.
A Preprocessing Manifold Learning Strategy Based on t-Distributed Stochastic Neighbor Embedding

In machine learning and data analysis, dimensionality reduction and high-dimensional data visualization can be accomplished by manifold learning using a t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm. We significantly improve this manifold learning scheme by introducing a preprocessing strategy for the t-SNE algorithm. In our preprocessing, we exploit Laplacian eigenmaps to reduce the high-dimensional data first, which can aggregate each data cluster and reduce the Kullback–Leibler divergence (KLD) remarkably. Moreover, the k-nearest-neighbor (KNN) algorithm is also involved in our preprocessing to enhance the visualization performance and reduce the computation and space complexity. We compare the performance of our strategy with that of the standard t-SNE on the MNIST dataset. The experiment results show that our strategy exhibits a stronger ability to separate different clusters as well as keep data of the same kind much closer to each other. Moreover, the KLD can be reduced by about 30% at the cost of increasing the complexity in terms of runtime by only 1–2%.
Convolutional neural network application on a new middle Eocene radiolarian dataset

A new radiolarian image database was used to train a Convolutional Neural Network (CNN) for automatic image classification. The focus was on 39 commonly occurring nassellarian species, which are important for biostratigraphy. The database consisted of tropical radiolarian assemblages from 129 middle Eocene samples retrieved from ODP Holes 1258A, 1259A, and 1260A (Demerara Rise). A total of 116 taxonomic classes were established, with 96 classes used for training a ResNet50 CNN. To represent the diverse radiolarian assemblage, some classes were formed by grouping forms based on external morphological criteria. This approach resulted in an 86.6% training accuracy. A test set of 800 images from new samples obtained from Hole 1260A was used to validate the CNN, achieving a 75.69% accuracy. The focus then shifted to 39 well-known nassellarian species, using a total of 15,932 images from the new samples. The goal was to determine if the targeted species were correctly classified and explore potential real-world applications of the trained CNN. Different prediction threshold values were experimented with. In most cases, a lower threshold value was preferred to ensure that all species were captured in the correct groups, even if it resulted in lower accuracies within the classes.
Fine-Tuning Strategies for Faster Inference Using Speech Self-Supervised Models: A Comparative Study

Self-supervised learning (SSL) has allowed substantial progress in Automatic Speech Recognition (ASR) performance in low-resource settings. In this context, it has been demonstrated that larger self-supervised feature extractors are crucial for achieving lower downstream ASR error rates. Thus, better performance might be sanctioned with longer inferences. This article explores different approaches that may be deployed during the fine-tuning to reduce the computations needed in the SSL encoder, leading to faster inferences. We adapt a number of existing techniques to common ASR settings and benchmark them, displaying performance drops and gains in inference times. Interestingly, we found that given enough downstream data, a simple downsampling of the input sequences outperforms the other methods with both low performance drops and high computational savings, reducing computations by 61.3% with an WER increase of only 0. 81. Finally, we analyze the robustness of the comparison to changes in dataset conditions, revealing sensitivity to dataset size.
Weakly Supervised Semantic Segmentation via Box-Driven Masking and Filling Rate Shifting

Semantic segmentation has achieved huge progress via adopting deep Fully Convolutional Networks (FCN). However, the performance of FCN-based models severely rely on the amounts of pixel-level annotations which are expensive and time-consuming. Considering that bounding boxes also contain abundant semantic and objective information, an intuitive solution is to learn the segmentation with weak supervisions from the bounding boxes. How to make full use of the class-level and region-level supervisions from bounding boxes to estimate the uncertain regions is the critical challenge for the weakly supervised learning task. In this paper, we propose a mixture model to address this problem. First, we introduce a box-driven class-wise masking model (BCM) to remove irrelevant regions of each class. Moreover, based on the pixel-level segment proposal generated from the bounding box supervision, we calculate the mean filling rates of each class to serve as an important prior cue to guide the model ignoring the wrongly labeled pixels in proposals. To realize the more fine-grained supervision at instance-level, we further propose the anchor-based filling rate shifting module. Unlike previous methods that directly train models with the generated noisy proposals, our method can adjust the model learning dynamically with the adaptive segmentation loss. Thus it can help reduce the negative impacts from wrongly labeled proposals. Besides, based on the learned high-quality proposals with above pipeline, we explore to further boost the performance through two-stage learning. The proposed method is evaluated on the challenging PASCAL VOC 2012 benchmark and achieves 74.9 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and 76.4 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> mean IoU accuracy under weakly and semi-supervised modes, respectively. Extensive experimental results show that the proposed method is effective and is on par with, or even better than current state-of-the-art methods.
A mathematical framework for improved weight initialization of neural networks using Lagrange multipliers

A good weight initialization is crucial to accelerate the convergence of the weights in a neural network. However, training a neural network is still time-consuming, despite recent advances in weight initialization approaches. In this paper, we propose a mathematical framework for the weight initialization in the last layer of a neural network. We first derive analytically a tight constraint on the weights that accelerates the convergence of the weights during the back-propagation algorithm. We then use linear regression and Lagrange multipliers to analytically derive the optimal initial weights and initial bias of the last layer, that minimize the initial training loss given the derived tight constraint. We also show that the restrictive assumption of traditional weight initialization algorithms that the expected value of the weights is zero is redundant for our approach. We first apply our proposed weight initialization approach to a Convolutional Neural Network that predicts the Remaining Useful Life of aircraft engines. The initial training and validation loss are relatively small, the weights do not get stuck in a local optimum, and the convergence of the weights is accelerated. We compare our approach with several benchmark strategies. Compared to the best performing state-of-the-art initialization strategy (Kaiming initialization), our approach needs 34% less epochs to reach the same validation loss. We also apply our approach to ResNets for the CIFAR-100 dataset, combined with transfer learning. Here, the initial accuracy is already at least 53%. This gives a faster weight convergence and a higher test accuracy than the benchmark strategies.
SliDL: A toolbox for processing whole-slide images in deep learning

The inspection of stained tissue slides by pathologists is essential for the early detection, diagnosis and monitoring of disease. Recently, deep learning methods for the analysis of whole-slide images (WSIs) have shown excellent performance on these tasks, and have the potential to substantially reduce the workload of pathologists. However, WSIs present a number of unique challenges for analysis, requiring special consideration of image annotations, slide and image artefacts, and evaluation of WSI-trained model performance. Here we introduce SliDL, a Python library for performing pre- and post-processing of WSIs. SliDL makes WSI data handling easy, allowing users to perform essential processing tasks in a few simple lines of code, bridging the gap between standard image analysis and WSI analysis. We introduce each of the main functionalities within SliDL: from annotation and tile extraction to tissue detection and model evaluation. We also provide ‘code snippets’ to guide the user in running SliDL. SliDL has been designed to interact with PyTorch, one of the most widely used deep learning libraries, allowing seamless integration into deep learning workflows. By providing a framework in which deep learning methods for WSI analysis can be developed and applied, SliDL aims to increase the accessibility of an important application of deep learning.
Fixing and Mechanizing the Security Proof of Fiat-Shamir with Aborts and Dilithium

We extend and consolidate the security justification for the Dilithium signature scheme. In particular, we identify a subtle but crucial gap that appears in several ROM and QROM security proofs for signature schemes that are based on the Fiat-Shamir with aborts paradigm, including Dilithium. The gap lies in the CMA-to-NMA reduction and was uncovered when trying to formalize a variant of the QROM security proof by Kiltz, Lyubashevsky, and Schaffner (Eurocrypt 2018). The gap was confirmed by the authors, and there seems to be no simple patch for it. We provide new, fixed proofs for the affected CMA-to-NMA reduction, both for the ROM and the QROM, and we perform a concrete security analysis for the case of Dilithium to show that the claimed security level is still valid after addressing the gap. Furthermore, we offer a fully mechanized ROM proof for the CMA-security of Dilithium in the EasyCrypt proof assistant. Our formalization includes several new tools and techniques of independent interest for future formal verification results.
Case Review of Untact Online Courses based on Video Recording vs. Synchronous Video Conferencing

The purpose of this study is to contribute to the improvement of the quality of online courses delivered as the mode of non-face-to-face class situation during the spring semester in 2020. The subject courses were ‘Chemistry Logic and Essay’ and Artificial Intelligence targeted at the junior or senior students. The number of participating students were 8 and 20 students in ‘Chemistry Logic and Essay’ (mixed video and video conference) and Artificial Intelligence (video conference class), respectively. They filled out questionnaires about their perception on taking non-face-to-face courses and also submitted reflection notes at the end of the semester. The characteristics of the non-face-to-face class and the students’ perception were analyzed and compared with each other.&#x0D; By adopting a case study method, we obtained the following results. For the course of ‘Chemistry Logic and Essay’, there were the following benefits: 1) the online teaching materials were systematically accumulated, 2) the students could turn out to increase their learning efficiency and 3) the lecturer could systematically deal with the students’ learning outcomes. However, the following disadvantages were also revealed: 1) the lecture could not grasp the students’ learning progress easily and 2) the students with less self-directed learning skills were not able to obtain the efficient learning progress. On the other hand, the Artificial Intelligence course, took real-time video conferencing method, which showed several benefits such as the presentation of learning materials through screen sharing, the cultivation of logical/computing thinking through carrying out coding tasks and presenting her own individual project. On the other side of coin, it called for flexible adjustment of individualized learning, etc. Overall, the case comparison study indicated several factors that might enhance the next round of online course delivery for per-service teachers.
ViGT: proposal-free video grounding with a learnable token in the transformer

The video grounding (VG) task aims to locate the queried action or event in an untrimmed video based on rich linguistic descriptions. Existing proposal-free methods are trapped in the complex interaction between video and query, overemphasizing cross-modal feature fusion and feature correlation for VG. In this paper, we propose a novel boundary regression paradigm that performs regression token learning in a transformer. Particularly, we present a simple but effective proposal-free framework, namely video grounding transformer (ViGT), which predicts the temporal boundary using a learnable regression token rather than multi-modal or cross-modal features. In ViGT, the benefits of a learnable token are manifested as follows. (1) The token is unrelated to the video or the query and avoids data bias toward the original video and query. (2) The token simultaneously performs global context aggregation from video and query features. First, we employed a sharing feature encoder to project both video and query into a joint feature space before performing cross-modal co-attention (i.e., video-to-query attention and query-to-video attention) to highlight discriminative features in each modality. Furthermore, we concatenated a learnable regression token [REG] with the video and query features as the input of a vision-language transformer. Finally, we utilized the token [REG] to predict the target moment and visual features to constrain the foreground and background probabilities at each timestamp. The proposed ViGT performed well on three public datasets: ANet-Captions, TACoS, and YouCookII. Extensive ablation studies and qualitative analysis further validated the interpretability of ViGT.
Target detection of substation electrical equipment from infrared images using an improved faster regions with convolutional neural network features algorithm

The failure of substation equipment can cause incalculable losses to the economy and power consumption of the whole country. The use of infrared images is a powerful tool to obtain equipment temperature, which can then be used directly to diagnose substation equipment without stopping the operation of the equipment. In this paper, the authors focus on the correct identification of different types of electrical equipment from the infrared images. An improved faster regions with convolutional neural network features (faster R-CNN) algorithm is proposed, which shows very high detection accuracy for substation equipment. Firstly, the backbone of the faster R-CNN is optimised. A new network, the ResNet-30 network, is designed to reduce the redundancy of the ResNet-34 network and increases the proportion of residual blocks in the network in the previous stages. Next, the deep receptive field is combined with the shallow receptive field of the network and a double-shortcut structure with a large convolutional kernel is proposed. This enhances the ability of network feature extraction. A cross-channel shortcut is proposed at the channel transition of the network based on the channel number relationship between the dual-shortcut structures. Finally, the proposed method is compared with faster R-CNNs whose backbones are ResNet-50 plus a feature pyramid network (ResNet-50+FPN), you only look once v3 plus spatial pyramid pooling (YOLOv3+SPP) and a single-shot multibox detector (SSD). The results show that the improved model not only has a smaller number of parameters and low requirements for graphics processing unit (GPU) equipment, but also has the highest mean average precision (mAP) for mostly substation equipment in the test-set. This lays a foundation for fault diagnosis of substation equipment in the future.
Uncovering the Disentanglement Capability in Text-to-Image Diffusion Models

Generative models have been widely studied in computer vision. Recently, diffusion models have drawn substantial attention due to the high quality of their generated images. A key desired property of image generative models is the ability to disentangle different attributes, which should enable modification towards a style without changing the semantic content, and the modification parameters should generalize to different images. Previous studies have found that generative adversarial networks (GANs) are inherently endowed with such disentanglement capability, so they can perform disentangled image editing without re-training or fine-tuning the network. In this work, we explore whether diffusion models are also inherently equipped with such a capability. Our finding is that for stable diffusion models, by partially changing the input text embedding from a neutral description (e.g., “a photo of person”) to one with style (e.g., “a photo of person with smile”) while fixing all the Gaussian random noises introduced during the denoising process, the generated images can be modified towards the target style without changing the semantic content. Based on this finding, we further propose a simple, light-weight image editing algorithm where the mixing weights of the two text embeddings are optimized for style matching and content preservation. This entire process only involves optimizing over around 50 parameters and does not fine-tune the diffusion model itself. Experiments show that the proposed method can modify a wide range of attributes, with the performance outperforming diffusion-model-based image-editing algorithms that require fine-tuning. The optimized weights generalize well to different images. Our code is publicly available at https://github.com/UCSB-NLP-Chang/DiffusionDisentanglement.
DSCNet: Deep Skip Connections-Based Dense Network for ALL Diagnosis Using Peripheral Blood Smear Images

Acute lymphoblastic leukemia (ALL) is a life-threatening hematological malignancy that requires early and accurate diagnosis for effective treatment. However, the manual diagnosis of ALL is time-consuming and can delay critical treatment decisions. To address this challenge, researchers have turned to advanced technologies such as deep learning (DL) models. These models leverage the power of artificial intelligence to analyze complex patterns and features in medical images and data, enabling faster and more accurate diagnosis of ALL. However, the existing DL-based ALL diagnosis suffers from various challenges, such as computational complexity, sensitivity to hyperparameters, and difficulties with noisy or low-quality input images. To address these issues, in this paper, we propose a novel Deep Skip Connections-Based Dense Network (DSCNet) tailored for ALL diagnosis using peripheral blood smear images. The DSCNet architecture integrates skip connections, custom image filtering, Kullback–Leibler (KL) divergence loss, and dropout regularization to enhance its performance and generalization abilities. DSCNet leverages skip connections to address the vanishing gradient problem and capture long-range dependencies, while custom image filtering enhances relevant features in the input data. KL divergence loss serves as the optimization objective, enabling accurate predictions. Dropout regularization is employed to prevent overfitting during training, promoting robust feature representations. The experiments conducted on an augmented dataset for ALL highlight the effectiveness of DSCNet. The proposed DSCNet outperforms competing methods, showcasing significant enhancements in accuracy, sensitivity, specificity, F-score, and area under the curve (AUC), achieving increases of 1.25%, 1.32%, 1.12%, 1.24%, and 1.23%, respectively. The proposed approach demonstrates the potential of DSCNet as an effective tool for early and accurate ALL diagnosis, with potential applications in clinical settings to improve patient outcomes and advance leukemia detection research.
Decentralized Platforms: Governance, Tokenomics, and ICO Design

Traditional two-sided platforms (e.g., Amazon, Uber) rely primarily on commission contracts to generate revenues and fuel growth, whereas their decentralized counterparts (e.g., Uniswap, Filecoin) often forego these in favor of token retention. What economics underpin this choice? We show that with properly designed initial coin offerings (ICOs), both mechanisms can independently alleviate market failures at the initial fundraising stage and incentivize long-term platform building. However, they achieve this in different ways. Although commission contracts often lead to higher profits for founders, token retention leads to higher service levels, benefiting the users and service providers. In essence, token retention surrenders a fraction of earnings to better align with the tenets of decentralized governance. Combining both mechanisms can add value, but only in relatively limited cases. These findings offer guidance and a possible rationale for why platforms may want to favor one mechanism over the other or use both. This paper was accepted by Will Cong, Special Section of Management Science: Blockchains and Crypto Economics. Funding: This work is funded by the Mack Institute at the Wharton School, University of Pennsylvania. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2021.02076 .