Reviewerly/exHarmony huggingface dataset:
Welcome to the Metaverse: Social Media, the Phantasmatic Big Other, and the Anxiety of the Prosthetic Gods

In Algorithmic Desire: Toward a New Structuralist Theory of Social Media, Matthew Flisfeder develops an alternative structuralist theory of social media. As a Žižekian analysis of social media, this book is an important contribution to the field of Žižekian studies. While Flisfeder theorizes social media in relation to Slavoj Žižek’s idea of the big Other, he nonetheless proposes an unorthodox understanding, importantly choosing not to engage Žižek’s analysis of technology, which develops in the context of his discussion of artificial intelligence and virtual reality in relation to Freud’s “prosthetic God” and Lacan’s lathouses. The present essay suggests that Žižek’s analysis of technology has radical implications for understanding social media, providing an important perspective that shifts the emphasis of theorizing social media from desire to anxiety in a zone “beyond the pleasure principle,” an analysis that Žižek situates in the context of the global COVID-19 pandemic and its impact on the big Other.
Modulation Classifier: A Few-Shot Learning Semi-Supervised Method Based on Multimodal Information and Domain Adversarial Network

This letter addresses the issue of underfitting or failure of deep learning models caused by insufficient training samples. Unlike previous supervised methods, a new few-shot learning semi-supervised automatic modulation recognition method based on multimodal information and domain adversarial network is proposed herein. The fusion input of multimodal information realizes the joint utilization of modulated signal modal features in the time and frequency domains. Domain adversarial training mines the potential knowledge information of a large number of unlabeled target domain data and introduces the convolutional block attention module (CBAM) to enhance the ability of the network to represent the key features of data. Numerical results validate the high-average classification accuracy of the proposed scheme compared to that of state-of-the-art schemes using fewer samples, particularly in high-order modulation classification. Alternative network structures are compared to confirm the applicability of multimodal information, domain adversarial training, and CBAM.
Feature Relevance Evaluation using Grad-CAM, LIME and SHAP for Deep Learning SAR Data Classification

For predictive analysis and automatic classification, Deep Neural Networks (DNNs) are investigated and visualized. All the DNNs used for Automatic Target Recognition (ATR) have inbuilt feature extraction and classification abilities, but the inner working gets more opaque rendering them a black box as the networks get deeper and more complex. The main goal of this paper is to get a glimpse of what the network perceives in order to classify Moving and Stationary Target Acquisition and Recognition (MSTAR) targets. However, past works have shown that classification of targets was performed solely based on clutter within the MSTAR data. Here we show that the DNN trained on the MSTAR dataset classifies only based on target information and the clutter plays no role in it. To demonstrate this, heatmaps are generated using the Gradient-weighted Class Activation Mapping (Grad-CAM) method to highlight the areas of attention in each input Synthetic Aperture Radar (SAR) image. To further probe into the interpretability of classifiers, reliable post hoc explanation techniques are used such as Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to approximate the behaviour of a black box by extracting relationships between feature value and prediction.
Blockchain-Based Crypto Management for Reliable Real-Time Decision-Making

Crypto management is proposed to tackle the management decision-making challenges under data asymmetry and trust asymmetry that cannot be solved merely by technical means. It emphasizes the novel management model for the real-time generation of reliable, trustworthy, and usable management decisions based on blockchain and blockchain-driven technologies. First, the framework model of crypto management with detailed descriptions of each technique is introduced, where blockchain is the underlying technology, decentralized autonomous organization (DAO) is the management structure, federated data is the decision basis, smart contract is the decision method, and non-fungible token (NFT) is the main decision incentive. Then, its collaboration mechanisms of on-blockchain DAO and off-blockchain organization as well as intra-organization and extra-organization nodes are discussed. Moreover, the potential applications of crypto management are addressed, and a case of task-oriented performance management is given to state how crypto management works to generate the real-time management decisions. Toward the end, the future research directions are pointed out in this emerging new area.
Dressing in the Wild by Watching Dance Videos

While significant progress has been made in garment transfer, one of the most applicable directions of human-centric image generation, existing works overlook the in-the-wild imagery, presenting severe garment-person mis-alignment as well as noticeable degradation in fine texture details. This paper, therefore, attends to virtual try-on in real-world scenes and brings essential improvements in authenticity and naturalness especially for loose garment (e.g., skirts, formal dresses), challenging poses (e.g., cross arms, bent legs), and cluttered backgrounds. Specifically, we find that the pixel flow excels at handling loose gar-ments whereas the vertex flow is preferred for hard poses, and by combining their advantages we propose a novel generative network called wFlow that can effectively push up garment transfer to in-the-wild context. Moreover, former approaches require paired images for training. Instead, we cut down the laboriousness by working on a newly constructed large-scale video dataset named Dance50k with self-supervised cross-frame training and an online cycle op-timization. The proposed Dance50k can boost real-world virtual dressing by covering a wide variety of garments under dancing poses. Extensive experiments demonstrate the superiority of our w Flow in generating realistic garment transfer results for in-the-wild images without resorting to expensive paired datasets. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Xiaodan Liang is the corresponding author. The project page of wFlow is https://awesome-wflow.github.io.
Self-distilled Feature Aggregation for Self-supervised Monocular Depth Estimation

Self-supervised monocular depth estimation has received much attention recently in computer vision. Most of the existing works in literature aggregate multi-scale features for depth prediction via either straightforward concatenation or element-wise addition, however, such feature aggregation operations generally neglect the contextual consistency between multi-scale features. Addressing this problem, we propose the Self-Distilled Feature Aggregation (SDFA) module for simultaneously aggregating a pair of low-scale and high-scale features and maintaining their contextual consistency. The SDFA employs three branches to learn three feature offset maps respectively: one offset map for refining the input low-scale feature and the other two for refining the input high-scale feature under a designed self-distillation manner. Then, we propose an SDFA-based network for self-supervised monocular depth estimation, and design a self-distilled training strategy to train the proposed network with the SDFA module. Experimental results on the KITTI dataset demonstrate that the proposed method outperforms the comparative state-of-the-art methods in most cases. The code is available at https://github.com/ZM-Zhou/SDFA-Net_pytorch.
Modified Double U-Net Architecture for Medical Image Segmentation

Semantic segmentation is the process of labeling each pixel of an input image with its appropriate class. In biomedical image processing, semantic segmentation is a critical preprocessing step. After the evolution of deep learning, specifically, the U-Net, which is based on the encoder–decoder model, researchers have extensively used several modified versions of U-Net for semantic segmentation. This manuscript presents a novel architecture called modified Double U-Net for various biomedical image segmentation tasks. The modified Double U-Net takes full advantage of Double U-Net and ensemble learning. It is the combination of two U-Net architectures stacked on top of each other. The first U-Net uses an ensemble of pretrained Xception, DenseNet, and VGG-19. We have deliberately used a pretrained model so that the features learned from the ImageNet classification task can be reused for our semantic segmentation task. Another U-Net is stacked at the bottom to capture more information that can be used in our semantic segmentation task. The performance of our proposed model has been evaluated on five different medical datasets, namely, the Data Science Bowl Challenge-2018, the CVC-ClinicDB dataset, which has complex images, such as smaller and flat polyps, the ISIC-2018 challenge dataset, the Kvasir-Instrument dataset, and the INBreast dataset achieving state-of-the-art performance. Conducting fivefold cross-validation on the five datasets mentioned above intersection over union of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$89.13~\pm ~2.88$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$85.22~\pm ~1.26$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$83.13~\pm ~3.56$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$85.22~\pm ~1.26$ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$95.22~\pm ~2.56$ </tex-math></inline-formula> were obtained.
Calibrating Deep Neural Networks by Pairwise Constraints

It is well known that deep neural networks (DNNs) pro-duce poorly calibrated estimates of class-posterior prob-abilities. We hypothesize that this is due to the limited calibration supervision provided by the cross-entropy loss, which places all emphasis on the probability of the true class and mostly ignores the remaining. We consider how each example can supervise all classes and show that the calibration of a C-way classification problem is equivalent to the calibration of C(C - 1) /2 pairwise binary classifi-cation problems that can be derived from it. This suggests the hypothesis that DNN calibration can be improved by providing calibration supervision to all such binary prob-lems. An implementation of this calibration by pairwise constraints (CPC) is then proposed, based on two types of binary calibration constraints. This is finally shown to be implementable with a very minimal increase in the complex-ity of cross-entropy training. Empirical evaluations of the proposed CPC method across multiple datasets and DNN architectures demonstrate state-of-the-art calibration per-formance.
3D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive Selection

3D visual grounding aims to locate the referred target object in 3D point cloud scenes according to a free-form language description. Previous methods mostly follow a two-stage paradigm, i.e., language-irrelevant detection and cross-modal matching, which is limited by the isolated architecture. In such a paradigm, the detector needs to sample keypoints from raw point clouds due to the inherent properties of 3D point clouds (irregular and large-scale), to generate the corresponding object proposal for each keypoint. However, sparse proposals may leave out the target in detection, while dense proposals may confuse the matching model. Moreover, the language-irrelevant detection stage can only sample a small proportion of keypoints on the target, deteriorating the target prediction. In this paper, we propose a 3D Single-Stage Referred Point Progressive Selection (3D-SPS) method, which progressively selects keypoints with the guidance of language and directly locates the target. Specifically, we propose a Description-aware Keypoint Sampling (DKS) module to coarsely focus on the points of language-relevant objects, which are significant clues for grounding. Besides, we devise a Target-oriented Progressive Mining (TPM) module to finely concentrate on the points of the target, which is enabled by progressive intra-modal relation modeling and inter-modal target mining. 3D-SPS bridges the gap between detection and matching in the 3D visual grounding task, localizing the target at a single stage. Experiments demonstrate that 3D-SPS achieves state-of-the-art performance on both ScanRe-fer and Nr3D/Sr3D datasets.
Locally Verifiable Signature and Key Aggregation

Aggregate signatures (Boneh, Gentry, Lynn, Shacham, Eurocrypt 2003) enable compressing a set of N signatures on N different messages into a short aggregate signature. This reduces the space complexity of storing the signatures from linear in N to a fixed constant (that depends only on the security parameter). However, verifying the aggregate signature requires access to all N messages, resulting in the complexity of verification being at least $$\varOmega (N)$$ . In this work, we introduce the notion of locally verifiable aggregate signatures that enable efficient verification: given a short aggregate signature $$\sigma $$ (corresponding to a set $$\mathcal {M}$$ of N messages), the verifier can check whether a particular message m is in the set, in time independent of N. Verification does not require knowledge of the entire set $$\mathcal {M}$$ . We demonstrate many natural applications of locally verifiable aggregate signature schemes: in the context of certificate transparency logs; in blockchains; and for redacting signatures, even when all the original signatures are produced by a single user. We provide two constructions of single-signer locally verifiable aggregate signatures, the first based on the RSA assumption and the second on the bilinear Diffie-Hellman inversion assumption, both in the random oracle model. As an additional contribution, we introduce the notion of compressing cryptographic keys in identity-based encryption (IBE) schemes, show applications of this notion, and construct an IBE scheme where the secret keys for N identities can be compressed into a single aggregate key, which can then be used to decrypt ciphertexts sent to any of the N identities.
Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation

This paper studies a new, practical but challenging problem, called Class-Incremental Unsupervised Domain Adaptation (CI-UDA), where the labeled source domain contains all classes, but the classes in the unlabeled target domain increase sequentially. This problem is challenging due to two difficulties. First, source and target label sets are inconsistent at each time step, which makes it difficult to conduct accurate domain alignment. Second, previous target classes are unavailable in the current step, resulting in the forgetting of previous knowledge. To address this problem, we propose a novel Prototype-guided Continual Adaptation (ProCA) method, consisting of two solution strategies. 1) Label prototype identification: we identify target label prototypes by detecting shared classes with cumulative prediction probabilities of target samples. 2) Prototype-based alignment and replay: based on the identified label prototypes, we align both domains and enforce the model to retain previous knowledge. With these two strategies, ProCA is able to adapt the source model to a class-incremental unlabeled target domain effectively. Extensive experiments demonstrate the effectiveness and superiority of ProCA in resolving CI-UDA. The @scut.edu.cnsource code is available at https://github.com/Hongbin98/ProCA.git .
Practical Statistically-Sound Proofs of Exponentiation in Any Group

A proof of exponentiation (PoE) in a group $${\mathbb {G}}$$ of unknown order allows a prover to convince a verifier that a tuple $$(x,q,T,y)\in {\mathbb {G}}\times {\mathbb {N}}\times {\mathbb {N}}\times {\mathbb {G}}$$ satisfies $$x^{q^T}=y$$ . This primitive has recently found exciting applications in the constructions of verifiable delay functions and succinct arguments of knowledge. The most practical PoEs only achieve soundness either under computational assumptions, i.e., they are arguments (Wesolowski, Journal of Cryptology 2020), or in groups that come with the promise of not having any small subgroups (Pietrzak, ITCS 2019). The only statistically-sound PoE in general groups of unknown order is due to Block et al. (CRYPTO 2021), and can be seen as an elaborate parallel repetition of Pietrzak's PoE: to achieve $$\lambda $$ bits of security, say $$\lambda =80$$ , the number of repetitions required (and thus the blow-up in communication) is as large as $$\lambda $$ . In this work, we propose a statistically-sound PoE for the case where the exponent q is the product of all primes up to some bound B. We show that, in this case, it suffices to run only $$\lambda /\log (B)$$ parallel instances of Pietrzak's PoE, which reduces the concrete proof-size compared to Block et al. by an order of magnitude. Furthermore, we show that in the known applications where PoEs are used as a building block such structured exponents are viable. Finally, we also discuss batching of our PoE, showing that many proofs (for the same $$\mathbb {G}$$ and q but different x and T) can be batched by adding only a single element to the proof per additional statement.
An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation

Off-road semantic segmentation with fine-grained labels is necessary for autonomous vehicles to understand driving scenes, as the coarse-grained road detection cannot satisfy off-road vehicles with various mechanical properties. Pixel-wise annotation of fine-grained labels in off-road scenes is very hard because a large part of the pixels could suffer from severe semantic ambiguity. Furthermore, semantic properties of off-road scenes can be very changeable due to various precipitations, temperature, defoliation, etc. To address these challenges, this research proposes an active and contrastive learning-based method. A few image patches are annotated mainly to distinguish semantic differences rather than semantic categories, which can greatly reduce the burden of manual annotation. A feature representation is learnt using the contrastive pairs of image patches, and semantic categories are adaptively modeled from the data. To actively adapt to new scenes, a risk evaluation method is developed to discover and select hard frames with high-risk predictions for supplementary labeling, to update the model efficiently. Extensive experiments and analyses are conducted on self-developed and public datasets. Experimental results demonstrate that fine-grained semantic segmentation can be learned with only dozens of weakly labeled frames, and the model can efficiently adapt across scenes by weak supervision, while achieving competitive performance with the typical fully supervised ones.
Unleashing Potential of Unsupervised Pre-Training with Intra-Identity Regularization for Person Re-Identification

Existing person reidentification (ReID) methods typically load the pretrained ImageNet weights for initialization directly. However, as a fine-grained classification task, ReID is more challenging and there exists a large domain gap between ImageNet classification. Inspired by the great success of self-supervised representation learning with contrastive objectives, in this paper, we design an Unsupervised Pretraining framework for reidentification (UP-ReID) based on the contrastive learning (CL) pipeline. During the pre-training, we attempt to address two critical issues for learning fine-grained ReID features: (1) the augmentations in the CL pipeline usually distort the discriminative clues in person images, and (2) the fine-grained local features of person images are not fully-explored. Therefore, we introduce an intra-identity (1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -) regularization in the UP-ReID, which is instantiated as two constraints coming from the global image and local patch aspects, respectively. A global consistency constraint is enforced between augmented and original person images to increase robustness to augmentation, while an intrinsic contrastive constraint among local patches of each image is employed to fully explore the local discriminative clues. Extensive experiments on multiple popular reid datasets, PersonX, Market1501, CUHK03, and MSMT17, demonstrate that our UP-ReID pretrained model can significantly benefit the downstream ReID fine-tuning and achieve state-of-the-art performance.
Benchmark Performance of a New Quantum-Safe Multivariate Polynomial Digital Signature Algorithm

Kuang et al. introduced the new quantum-safe algorithm Multivariate Polynomial Public Key Digital Signature (MPPK DS). To create a signature, the MPPK DS scheme’s private key consists of univariate polynomials used as exponents of a secret randomly generated base. For signature verification, the verifier leverages public key multivariate polynomials and a modular arithmetic property. The verification procedure is probabilistic. The verifier uses noise variables and evaluates the public key polynomials. For a genuine signature, the verification procedure is successful for any evaluation of the public key polynomials. In this paper, we report the results of benchmarking MPPK DS on a 16-core Intel®Core™i7-10700 CPU system at 2.90 GHz using the SUPERCOP toolkit. SUPERCOP has been widely used to analyze the performance of post-quantum public-key encryption and key-establishment algorithms. We provide a side-by-side comparison of the NIST PQC third-round digital signature schemes with MPPK DS. With respect to the PQC schemes, the MPPK DS cryptosystem achieves small size public keys, private keys, and signatures. Moreover, compared with the NIST PQC digital signature algorithms, the performance of the MPPK DS algorithm is outstanding with fast procedures for key generation, signing, and verifying.
Cross-Stage Multi-Scale Interaction Network for RGB-D Salient Object Detection

Salient object detection (SOD) aims to detect the most prominent objects and regions in the human vision. Since the RGB and depth modalities contain discrepant characteristics and convey the clues of different domains, how to explore the fusion of multi-modal information and the interaction of cross-stage features remain the key problems in RGB-D SOD. In this letter, we propose a cross-stage multi-scale interaction network (CMINet), consisting of a multi-scale spatial pooling (MSP) module and a cross-stage pyramid interaction (CPI) module to interweave the feature maps of different stages in a bottom-up and top-down way. In addition, we also design an adaptive weight fusion (AWF) module to weigh the importance of multimodality features and fuse them. Extensive experiments are conducted on 4 widely used datasets to validate the effectiveness of the proposed CMINet. The results demonstrate that our approach achieves state-of-the-art performance against other 11 methods under 4 evaluation metrics.