Reviewerly/exHarmony huggingface dataset:
A Unified Cryptoprocessor for Lattice-Based Signature and Key-Exchange

We propose design methodologies for building a compact, unified and programmable cryptoprocessor architecture that computes post-quantum key agreement and digital signature. Synergies in the two types of cryptographic primitives are used to make the cryptoprocessor compact. As a case study, the cryptoprocessor architecture has been optimized targeting the signature scheme ’CRYSTALS-Dilithium’ and the key encapsulation mechanism (KEM) ’Saber,’ both finalists in the NIST's post-quantum cryptography standardization project. The programmable cryptoprocessor executes key generations, encapsulations, decapsulations, signature generations, and signature verifications for all the security levels of Dilithium and Saber. On a Xilinx Ultrascale+ FPGA, the proposed cryptoprocessor consumes 18,406 LUTs, 9,323 FFs, 4 DSPs, and 24 BRAMs. It achieves 200 MHz clock frequency and finishes CCA-secure key-generation/encapsulation/decapsulation operations for LightSaber in 29.6/40.4/ 58.3 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> s; for Saber in 54.9/69.7/94.9 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> s; and for FireSaber in 87.6/108.0/139.4 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> s, respectively. It finishes key-generation/sign/verify operations for Dilithium-2 in 70.9/151.6/75.2 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> s; for Dilithium-3 in 114.7/237/127.6 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> s; and for Dilithium-5 in 194.2/342.1/228.9 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> s, respectively, for the best-case scenario. On UMC 65 nm library for ASIC the latency is improved by a factor of two due to a 2× increase in clock frequency.
Dimension Embeddings for Monocular 3D Object Detection

Most existing deep learning-based approaches for monocular 3D object detection directly regress the dimensions of objects and overlook their importance in solving the illposed problem. In this paper, we propose a general method to learn appropriate embeddings for dimension estimation in monocular 3D object detection. Specifically, we consider two intuitive clues in learning the dimension-aware embeddings with deep neural networks. First, we constrain the pair-wise distance on the embedding space to reflect the similarity of corresponding dimensions so that the model can take advantage of inter-object information to learn more discriminative embeddings for dimension estimation. Second, we propose to learn representative shape templates on the dimension-aware embedding space. Through the attention mechanism, each object can interact with the learnable templates and obtain the attentive dimensions as the initial estimation, which is further refined by the combined features from both the object and the attentive templates. Experimental results on the well-established KITTI dataset demonstrate the proposed method of dimension embeddings can bring consistent improvements with negligible computation cost overhead. We achieve new state-of-the-art performance on the KITTI 3D object detection benchmark.
Practical Non-malleable Codes from Symmetric-Key Primitives in 2-Split-State Model

Non-malleable codes (NMC) are introduced as a relaxation of error correcting codes to protect message against tampering attacks. It is guaranteed that a message encoded with non-malleable codes, if tampered by some classes of tampering functions, produces either completely unrelated message or the original message, when tampering has no effect. Kiayias et al. [19] have proposed a NMC construction based on leakage resilient authenticated encryption (AE) and 1-more extractable hash function. They obtain a codeword of length $$|m|+18n$$ in common reference string (CRS) model. In this paper, we propose a construction of computationally secure non-malleable code in 2-split-state model from an authenticated encryption scheme with close to optimal codeword length $$|m|+2n$$ . Specifically we use an AE based on triple M-DES and CBC-MAC. The security of our NMC reduces to related-key and pseudorandom permutation security of the underlying block cipher under leakage, and also to the unforgeability of the CBC-MAC under leakage.
High-Fidelity Image Inpainting with GAN Inversion

Image inpainting seeks a semantically consistent way to recover the corrupted image in the light of its unmasked content. Previous approaches usually reuse the well-trained GAN as effective prior to generate realistic patches for missing holes with GAN inversion. Nevertheless, the ignorance of hard constraint in these algorithms may yield the gap between GAN inversion and image inpainting. Addressing this problem, in this paper we devise a novel GAN inversion model for image inpainting, dubbed InvertFill, mainly consisting of an encoder with a pre-modulation module and a GAN generator with $$ \mathcal {F} \& \mathcal {W}^+$$ latent space. Within the encoder, the pre-modulation network leverages multi-scale structures to encode more discriminative semantic into style vectors. In order to bridge the gap between GAN inversion and image inpainting, $$ \mathcal {F} \& \mathcal {W}^+$$ latent space is proposed to eliminate glaring color discrepancy and semantic inconsistency. To reconstruct faithful and photorealistic images, a simple yet effective Soft-update Mean Latent module is designed to capture more diverse in-domain patterns that synthesize high-fidelity textures for large corruptions. Comprehensive experiments on four challenging dataset, including Places2, CelebA-HQ, MetFaces, and Scenery, demonstrate that our InvertFill outperforms the advanced approaches qualitatively and quantitatively and supports the completion of out-of-domain images well. The code is available at https://github.com/yeates/InvertFill .
Fuzzy Rule-Based Local Surrogate Models for Black-Box Model Explanation

Understanding the rationale behind the predictions produced by machine learning models is a necessary prerequisite for human to build confidence and trust for the intelligent systems. To tackle the problem of interpretability faced by black-box models, a fuzzy local surrogate model is proposed in this study to articulate the rationale for predictions to enhance the interpretability of the results of machine learning models. Fuzzy rule-based model comes with high interpretability since it is composed of a collection of readable rules, and thus is suitable for prediction interpretation. The general scheme of fuzzy local surrogate model is composed of the following phases: i) select data points around the instance of interest, for which we wish to explain the prediction result produced by the predictive model; ii) generate predictions for these newly selected data and weight the selected data based on the distance from the instance of interest; and iii) a fuzzy rule-based model composed a collection of interpretable is constructed to approximate the weighted data and offer meaningful interpretation to the prediction result of the given instance. The proposed fuzzy model for explaining predictions is model-agnostic and could provide high estimation accuracy. The proposed methodology offers a significant original contribution to the interpretation of machine learning models. Experimental studies demonstrate the usefulness of the proposed fuzzy local surrogate model in providing local explanations.
Skeleton-Free Pose Transfer for Stylized 3D Characters

We present the first method that automatically transfers poses between stylized 3D characters without skeletal rigging. In contrast to previous attempts to learn pose transformations on fixed or topology-equivalent skeleton templates, our method focuses on a novel scenario to handle skeleton-free characters with diverse shapes, topologies, and mesh connectivities. The key idea of our method is to represent the characters in a unified articulation model so that the pose can be transferred through the correspondent parts. To achieve this, we propose a novel pose transfer network that predicts the character skinning weights and deformation transformations jointly to articulate the target character to match the desired pose. Our method is trained in a semi-supervised manner absorbing all existing character data with paired/unpaired poses and stylized shapes. It generalizes well to unseen stylized characters and inanimate objects. We conduct extensive experiments and demonstrate the effectiveness of our method on this novel task.
Collaborative Product Recommendation System for E-commerce Websites

Product advice are the important thing elements for convincing the patron in a proper way, to shop for the goods in any e-trade internet site. The online patron score are completely based on every patron real enjoy which could comprise significant and greater person-orientated information. The advice is probably important on cost, feature, formerly bought emblem and plenty of greater parameters. In this paper we purpose to decorate the advice provider by comparing trendy score out of all of the rankings to be had in numerous e-trade internet site for any precise product. The end result is that the quantity of merchandise could be decreased upto atleast one-third of the general quantity of merchandise to have the below class and the person is inclined to buy product. Finally, only a few merchandise is advocated to the person by means of making use of person-supplied filtering capabilities and suggested single five-scale score.
Hybrid Attention and Motion Constraint for Anomaly Detection in Crowded Scenes

Crowds often appear in surveillance videos in public places, from which anomaly detection is of great importance to public safety. Since the abnormal cases are rare, variable and unpredictable, autoencoders with encoder and decoder structures using only normal samples have become a hot topic among various approaches for anomaly detection. However, since autoencoders have excessive generalization ability, they can sometimes still reconstruct abnormal cases very well. Recently, some researchers construct memory modules under normal conditions and use these normal memory items to reconstruct test samples during inference to increase the reconstruction errors for anomalies. However, in practice, the errors of reconstructing normal samples with the memory items often increase as well, which makes it still difficult to distinguish between normal and abnormal cases. In addition, the memory-based autoencoder is usually available only in the specific scene where the memory module is constructed and almost loses the prospect of cross-scene applications. We mitigate the overgeneralization of autoencoders from a different perspective, namely, by reducing the prediction errors for normal cases rather than increasing the prediction errors for abnormal cases. To this end, we propose an autoencoder based on hybrid attention and motion constraint for anomaly detection. The hybrid attention includes the channel attention used in the encoding process and spatial attention added to the skip connection between the encoder and decoder. The hybrid attention is introduced to reduce the weight of the feature channels and regions representing the background in the feature matrix, which makes the autoencoder features more focused on optimizing the representation of the normal targets during training. Furthermore, we introduce motion constraint to improve the autoencoder’s ability to predict normal activities in crowded scenes. We conduct experiments on real-world surveillance videos, UCSD, CUHK Avenue, and ShanghaiTech datasets. The experimental results indicate that the prediction errors of the proposed method for frequent normal crowd activities are smaller than those of other approaches, which increases the gap between the prediction errors for normal frames and the prediction errors for abnormal frames. In addition, the proposed method does not depend on a specific scene. Therefore, it balances good anomaly detection performance and strong cross-scene capability.
Indy528 — Federated Learning Model Tokenization with Non-Fungible Tokens(NFT) and Model Cards

Non-Fungible Tokens (NFTs) are used to represent ownership of unique items. The tokens provide means to validate integrity. That is, the item is unique and cannot be replaced with something else. NFTs are securely stored in the blockchain-based decentralized storage in the format of ERC721 tokens, so it is technically difficult to modify the record of ownership or duplicate a new NFT token. Currently, NFTs are used to tokenise objects such as arts, collectables, and even real estate. In this research, we propose an open platform “Indy528” and utilize NFT tokens to represent federated machine learning (FML) models. To the best of our knowledge, this is the very first research that tries to represent machine learning models as NFTs. Different peers in the blockchain network can build FML models using a blockchain-enabled coordinator-less FML system. The data provenance information of the FML models, which records the model data changes from the origin, is recorded as Model Cards. The FML model ownership, storage locations of the FML models and data provenance information(with Model Cards) are encoded into NFT tokens and stored in the decentralized ledger. We design a novel NFT token schema i528 (which is an extension of ERC721) to represent machine learning models as NFTs. As a use-case of Indy528, we propose a concept of the NFT-based decentralized FML model marketplace. Model creators can train the FML models and publish them in the marketplace as NFTs. Parties who want to use the models can buy them from the model creators as NFTs. The buyers can view model information, accuracy, training process etc via the model card objects stored with the NFT. This design methodology provides enhanced transparency and auditability to the federated learning process while providing an open platform to share and trade machine learning models in the marketplace.
A Simple Episodic Linear Probe Improves Visual Recognition in the Wild

Understanding network generalization and feature discrimination is an open research problem in visual recognition. Many studies have been conducted to assess the quality of feature representations. One of the simple strategies is to utilize a linear probing classifier to quantitatively evaluate the class accuracy under the obtained features. The typical linear probe is only applied as a proxy at the inference time, but its efficacy in measuring features' suitability for linear classification is largely neglected in training. In this paper, we propose an episodic linear probing (ELP) classifier to reflect the generalization of visual rep-resentations in an online manner. ELP is trained with detached features from the network and re-initialized episodically. It demonstrates the discriminability of the visual representations in training. Then, an ELP-suitable Regularization term (ELP-SR) is introduced to reflect the distances of probability distributions between the ELP classifier and the main classifier. ELP-SR leverages are-scaling factor to regularize each sample in training, which modulates the loss function adaptively and encourages the features to be discriminative and generalized. We observe significant improvements in three real-world visual recognition tasks: fine-grained visual classification, long-tailed visual recognition, and generic object recognition. The performance gains show the effectiveness of our method in im-proving network generalization and feature discrimination.
FedTAR: Task and Resource-Aware Federated Learning for Wireless Computing Power Networks

In the 6G era, the proliferation of data and data-intensive applications poses unprecedented challenges on the current communication and computing networks. The collaboration among cloud computing, edge computing, and networking is imperative to process such massive data, eventually realizing ubiquitous computing and intelligence. In this article, we propose a wireless computing power network (WCPN) by orchestrating the computing and networking resources of heterogeneous nodes toward specific computing tasks. To enable intelligent service in WCPN, we design a task and resource-aware federated learning model, coined FedTAR, which minimizes the sum energy consumption of all computing nodes by the joint optimization of the computing strategies of individual computing nodes and their collaborative learning strategy. Based on the solution of the optimization problem, the neural network depth of computing nodes and the collaboration frequency among nodes are adjustable according to specific computing task requirements and resource constraints. To further adapt to heterogeneous computing nodes, we then propose an energy-efficient asynchronous aggregation algorithm for FedTAR, which accelerates the convergence speed of federated learning in WCPN. Numerical results show that the proposed scheme outperforms the existing studies in terms of learning accuracy, convergence rate, and energy saving.
Hybrid Approaches (ABAC and RBAC) Toward Secure Access Control in Smart Home IoT

Smart homes are interconnected homes in which a wide variety of digital devices with limited resources communicate with multiple users and among themselves using multiple protocols. The deployment of resource-limited devices and the use of a wide range of technologies expand the attack surface and position the smart home as a target for many potential security threats. Access control is among the top security challenges in smart home IoT. Several access control models have been developed or adapted for IoT in general, with a few specifically designed for the smart home IoT domain. Most of these models are built on the role-based access control (RBAC) model or the attribute-based access control (ABAC) model. However, recently some researchers demonstrated that the need arises for a hybrid model combining ABAC and RBAC, thereby incorporating the benefits of both models to better meet IoT access control challenges in general and smart homes requirements in particular. In this paper, we used two approaches to develop two different hybrid models for smart home IoT. We followed a role-centric approach and an attribute-centric approach to develop HyBAC <inline-formula><tex-math notation="LaTeX">$_{RC}$</tex-math></inline-formula> and HyBAC <inline-formula><tex-math notation="LaTeX">$_{AC}$</tex-math></inline-formula> , respectively. We formally define these models and illustrate their features through a use case scenario demonstration. We further provide a proof-of-concept implementation for each model in Amazon Web Services (AWS) IoT platform. Finally, we conduct a theoretical comparison between the two models proposed in this paper in addition to the EGRBAC model (RBAC model for smart home IoT) and HABAC model (ABAC model for smart home IoT), which were previously developed to meet smart homes’ challenges.
Human Perceptual Quality Driven Underwater Image Enhancement Framework

Underwater images suffer from severe color casts, low contrast, and blurriness, which greatly degrade the visibility and color fidelity of underwater images. Recently, numerous underwater image enhancement (UIE) algorithms have been proposed. Existing synthetic datasets-based deep learning methods employ synthetic datasets to train UIE models. However, there is a gap between synthetic datasets and real underwater images, leading to poor generalization of synthetic datasets-based UIE methods. Besides, existing real datasets-based deep learning methods largely focus on minimizing the mean squared reconstruction error between UIE results and corresponding ground-truth on the real datasets, but do not take human visual perception into account. Thus, although they achieve high PSNR between UIE results and corresponding ground-truth obtained by user study on the real datasets, they often achieve unsatisfactory perceptual quality. To address these problems, we propose a Human Perceptual Quality Driven Underwater Image Enhancement Framework (HPQ-UIEF) to achieve better results in human perceptual quality and maintain satisfactory PSNR, which is trained on a real underwater enhancement quality assessment database (UEQAB). Specifically, an Underwater Image Quality Assessment Network (UIQAN) for UIE images is first proposed to assist UIE task, in which a novel depth map prior spatial attention block (DPPAB) is embedded into UIQAN. The DPPAB can adaptively recalibrate the quality-aware feature maps and model human visual attention in a data-driven manner. Then, the UIE model is proposed, in which the UIQAN is introduced as the loss function to optimize our UIE model in the direction of perceptual metrics. Moreover, since the confidence map acquired by UIQAN can effectively reflect the sensitivity of human perceptual of local area in an UIE image, the confidence map is introduced to our UIEF to help our UIEF to perceive the perceptually important regions. Thus, the confidence map is down-sampled and then concatenated into the decoder module of the UIE model, which can further improve the perceptual quality of the UIE results. Extensive experimental results show that the proposed HPQ-UIEF outperforms state-of-the-art UIE methods qualitatively and quantitatively.
A Feature Divide-and-Conquer Network for RGB-T Semantic Segmentation

Similar to other multi-modal pixel-level prediction tasks, existing RGB-T semantic segmentation methods usually employ a two-stream structure to extract RGB and thermal infrared (TIR) features, respectively, and adopt the same fusion strategies to integrate different levels of unimodal features. This will result in inadequate extraction of unimodal features and exploitation of cross-modal information from the paired RGB and TIR images. Alternatively, in this paper, we present a novel RGB-T semantic segmentation model, i.e., FDCNet, where a feature divide-and-conquer strategy performs unimodal feature extraction and cross-modal feature fusion in one go. Concretely, we first employ a two-stream structure to extract unimodal low-level features, followed by a Siamese structure to extract unimodal high-level features from the paired RGB and TIR images. This concise but efficient structure enables to take into account both the modality discrepancies of low-level features and the underlying semantic consistency of high-level features across the paired RGB and TIR images. Furthermore, considering the characteristics of different layers of features, a Cross-modal Spatial Activation (CSA) module and a Cross-modal Channel Activation (CCA) module are presented for the fusion of low-level RGB and TIR features and for the fusion of high-level RGB and TIR features, respectively, thus facilitating the capture of cross-modal information. On top of that, with an embedded Cross-scale Interaction Context (CIC) module for mining multi-scale contextual information, our proposed model (i.e., FDCNet) for RGB-T semantic segmentation achieves new state-of-the-art experimental results on MFNet dataset and PST900 dataset.