Reviewerly/exHarmony huggingface dataset:
F2RPC: Fake to Real Portrait Control From a Virtual Character

Existing methods for generating virtual character videos focus on improving either appearance or motion.However, achieving both photo-and motion-realistic characters is critical in real services.To address both aspects, we propose Fake to Real Portrait Control (F2RPC), a unified framework for image destylization and face reenactment.F2RPC employs a blind face restoration model to circumvent GAN inversion limitations, such as identity loss and alignment sensitivity, while preserving GAN's generation quality.This framework includes two novel sub-modules, AdaGPEN for destylization and PCGPEN for reenactment, both leveraging the same restoration model as a backbone.AdaGPEN exploits GAN prior of the restoration model via blending features from the original and its blurred image using the AdaMix block.PCGPEN reenacts the input image to follow the input motion condition via flow-based feature editing.These components function in an end-to-end manner, enhancing efficiency and lowering computational overhead.We evaluate F2RPC using synthetic character dataset and high-resolution talking face datasets for destylization and reenactment, respectively.The results show that F2RPC outperforms the combined use of state-of-the-art methods for destylization (i.e., DualStyleGAN) and reenactment (i.e., StyleHEAT).F2RPC improves the FID by 26.4% and preserves identity similarity by 95% more at a resolution of 512 × 512 video.This evidences F2RPC's efficacy and superiority in the photo-and motion-realistic virtual character video generation task.
Side-Channel Free Measurement-Device- Independent Quantum Key Distribution Based on Source Monitoring

Measurement-device-independent quantum key dis-tribution (MDI-QKD) can remove all detector side-channel attacks. However, there are still some assumptions on source preparations in MDI-QKD protocols, which are too strict to achieve in real-life implementations. To remove those assump-tions, here we construct a scheme on characterizing the source modulation errors with Hong-Ou-Mandel (HOM) interferences. Furthermore, we combine it with the decoy-state method and present the security analysis. Besides, finite data-size effects are taken into account as well. Simulation results verify the feasibility and practicability of this scheme. It thus seems a very promising candidate for constructing high-security network in the near future.
Adv-Eye: A Transfer-Based Natural Eye Makeup Attack on Face Recognition

Deep face recognition models are vulnerable to adversarial samples generated by adversarial attack methods. However, current attack methods do not adequately represent the security problems of the deep FR models, because they either produce adversarial samples which are unnatural and easily perceived by human or have poor attack capabilities with low attack success rates on the black-box victim FR model. To achieve a good trade-off between the imperceptibility and attack capability, we propose Adv-Eye, a novel method for constructing adversarial facial images by adding natural eyeshadow to the orbital region. Adv-Eye consists of Makeup Generation Module, Makeup Blending Module, and Attack Module. Makeup Generation Module develops pre-makeup strategy to help adversarial generative networks (GANs) to accurately generate eyeshadow on the orbital image. Makeup Blending Module develops a multi-view image visual similarity evaluation method to improve the imperceptibility of the generated eyeshadow. In Attack Module, an ensemble attack strategy based on fine-grained meta-learning and input decay, is applied to improve attack capability under query-free black-box setting. From the experimental results under LADN dataset and MT dataset, compared with existing techniques, the adversarial samples generated by Adv-Eye not only significantly improve the visual quality, but also get 1.63% and 1.05% average success rates improving on the local black-box FR model, 5.33 and 5.22 average confidence point improving on the online commercial FR plateform respectively. The above results demonstrate that pre-makeup strategy and multi-view image visual similarity evaluation method effectively improve the imperceptibility of generated adversarial perturbations, and Attack Module effectively improves attack success rate while maintaining high image quality.
Generative Adversarial Networks Assist Missing Data Imputation: A Comprehensive Survey and Evaluation

Missing data imputation is a technique to deal with incomplete datasets. Since many models and algorithms cannot be applied to data containing missing values, a pre-processing step needs to be performed to remove incomplete data or to estimate the missing values. This is a well-known problem referred to as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data imputation problem</i> . Several approaches have been designed for data imputation. These algorithms can be divided into two main categories: statistical and machine learning-based algorithms. As machine learning algorithms are optimized, they usually have better performance compared with statistical ones. In this paper, we review the most recent literature related to missing data imputation based on generative adversarial networks (GANs) that have gained tremendous attention in dealing with missing values. We examine the structures of GANs for missing data imputation and discuss the commonly used datasets and metrics for evaluation. We also cover the influence of the missing datatype, the effect of the missing data fraction, and the algorithm-related problems on data imputation performance. We conduct experiments on two publicly available datasets and evaluate the performance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GAIN</i> , a missing data imputation algorithm to that of existing state-of-the-art approaches, demonstrating that the GAN-based algorithm outperforms the others in terms of RMSE and FID.
A Novel Multi-Objective Evolutionary Algorithm to Address Turnover in the Software Project Scheduling Problem Based on Best Fit Skills Criterion

As a typical scheduling problem, the software project scheduling (SPS) under uncertain and dynamic events has gathered attention of academic researchers and practitioners because it causes tangible and intangible loss and will also influence the project schedule and cost if not addressed in time. In this paper, a SPS model based on skills acquired by employees and required by tasks criterion is proposed to tackle the issue of ‘employee turnover’ along with novel multi-objective evolutionary optimization algorithm to handle such dynamic events by employing the domain knowledge for population initialization. This is critical for the success of a software project as when an employee leaves, the project is rescheduled with remaining resources to assess if the project can be completed on time and under budget; otherwise, an alternative resource is hired. The proposed model and algorithm are evaluated on 18 dynamic benchmark and 6 real-world problem instances. The experimental results indicate that if existing employees are reassigned to the tasks, the project cost and duration increases on average 22% and 4% respectively. However, hiring new employees with recommended skills and proficiency based on proposed approach provides 9% decrease in project cost with no increase in duration and 15% decrease in cost against 11% referenced increase in project duration, in comparison to classical rescheduling results. The results demonstrate that proposed model and algorithm handles dynamic event effectively and help in identifying the right set of skills for hiring of an alternate resource while optimizing project duration and cost.
Radio-Frequency Identification for Drones With Nonstandard Waveforms Using Deep Learning

Identifying non-cooperative drones has attracted much attentions in recent years, which is critical to public safety. Extensive research has demonstrated that state-of-the-art deep learning (DL) is well suited to address this problem. This paper aims to improve the performance of the DL-based radio frequency identification (RFI) systems for drones considering some major challenges faced in practical applications, including the unknown drone operating channels, the applicability of drone signal representations to the DL models, and the variation of wireless channels. Firstly, a morphological-filtering based carrier frequency offset estimation and compensation is proposed to adapt to the switchable operating channel of drones. Secondly, the applicability of typical drone signal representations revealing different signal features and typical DL models possessing different learning capabilities is investigated, in terms of classification accuracy, training time, and number of parameters. Thirdly, a data augmentation scheme is proposed to generate training signals by injecting various channel distortions, including additive white Gaussian noise, multi-path, and Doppler shift. Finally, we set up a universal software radio peripheral based experimental platform to identify 12 types of drones in a real wireless environment. Experimental results validate the improvement of the proposed schemes. The spectrogram combined with the real-valued CNN is recommended for drone classification, which achieves an accuracy of 97% with the least training time and parameters.
High-Accurate Robust Total Variation Denoising Algorithm With Adjustable Exponential Upper Bound Function for Micro-Thrust Measurement

Micro-Newton thrusters are widely utilized in the field of astronautics. Typically, micro-newton thrust signal processing aims at the restoration of piecewise constant signals. Improving the amplitude accuracy of the denoised signal remains a major difficulty. In this research, we proposed a new nonlinear total variation denoising (TVD) algorithm with an adjustable majorization-minimization (MM) upper bound function for optimizing the iterative solution process of TVD. The influences of the upper bound function of the TVD on denoising are analyzed in detail. It is demonstrated that the proposed aTVD method shows extraordinary robustness for denoising signals with different noise levels. In addition, this algorithm can also adjust the sensitivity of the jumps in signals for optimized amplitude accuracy effectively. Numerical examples show that the proposed method has 40.4% and 18.2% accuracy improvement over conventional TVD using traditional quadratic upper bound and enhanced TVD using exponential upper bound function. Moreover, the advantages of the proposed method are validated in an experiment of measured thrust signal processing. The proposed method can be further adopted to improve the performance of signal denoising algorithms developed from the TVD method.
Semantically Consistent Visual Representation for Adversarial Robustness

Deep neural networks have been widely used in various domains owing to the success of deep learning. However, recent studies have shown that these models are vulnerable to adversarial examples, leading to inaccurate predictions. In this paper, we focus on the issue of adversarial robustness by examining it through the lens of semantic information, which drives us to propose a new perspective, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., adversarial attacks destroy the correlation between visual representations and semantic word vectors, while adversarial training restores it. Additionally, we discover that the correlation among robust representations of different categories aligns with the correlation among the corresponding semantic word vectors. Based on these empirical observations, we incorporate the semantic information into the model training process and propose Semantic Constraint Adversarial Robust Learning (SCARL). Firstly, inspired by the information-theoretical perspective, we maximize mutual information to bridge the information gap between the visual representations and the corresponding semantic word vectors in the embedding space. We further provide a differentiable lower bound to optimize such mutual information efficiently. Secondly, we introduce a novel semantic structure constraint that maintains the structure of visual representations consistent with that of semantic word vectors. Finally, we integrate these techniques with adversarial training to learn robust visual representations. We conduct extensive experiments on several datasets (such as CIFAR and TinyImageNet) and evaluate the robustness against various adversarial attacks (such as PGD-attack and AutoAttack), demonstrating the benefits of incorporating semantic information for improving model robustness. Our code is available at https://github.com/SkyKuang/SCARL.
Blockchain Based M+1st-Price Auction With Exponential Bid Upper Bound

An auction is commonly used to sell limited resources in modern society. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> + 1st-price auction sells <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> identical goods to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</i> bidders. The top <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> winners can buy the goods at the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> +1st-price. Each bidder sends their bids secretly as a bit-slice bidding vector to a trusted manager. Bit-slice is commonly used to compare secret values without revealing them. However, the bit-slice bidding vector also limits the upper bound of a bid as the length of the bidding vector. A binary format bidding vector was proposed to increase the bid upper bound to an exponential scale. For example, given a bidding vector with length 32, a binary format bidding vector can increase the bid upper bound from 32 to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">32</sup> . However, previous protocols that use binary format bidding vectors require a somewhat homomorphic encryption (SHE) and a trusted manager. To make sure no party except the bidder itself knows its bid, our protocol does not have any managers. Instead, each bidder interacts with the Smart Contract independently. We propose a zero-knowledge proof that allows our protocol only requires partially homomorphic encryption such as an ElGamal encryption. To our best knowledge, our protocol is the first secure <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> + 1st-price auction protocol that can reach an exponential bid upper bound without a manager and SHE.
A Complementary Gray Code Like Double N-Step Phase Shift Method Without Gray Code Fringe Images

The complementary Gray code double N-step phase shift method is widely used because of its high detection accuracy and good robustness.However, phase unwrapping methods have the problem of low detection efficiency.In order to solve above problem, this paper proposes a complementary Gray code like double N-step phase shift method without Gray code fringe images.Firstly, the wrapped phases of the different groups of phase shift fringe images are obtained, then we get the phase difference and the fused wrapped phase, and the phase difference is converted into Gray code series.Finally, the unwrapped phase of the measured target is obtained by combining the fused wrapped phase and the Gray code series.This method in this paper improves the detection efficiency.To verify the effectiveness of method proposed in this paper, the method is compared with the complementary Gray code double N-step phase shift method, the complementary Gray code 2N-step phase shift method and the phase derivative variance method.The experimental results show that the method in this paper could effectively obtain the highprecision unwrapped phase, and the efficiency is 33.3% higher than the complementary Gray code double N-step phase shift method and the complementary Gray code 2N-step phase shift method.Index Terms-Double N-step
Keyword Search Shareable Encryption for Fast and Secure Data Replication

It has become a trend for clients to outsource their encrypted databases to remote servers and then leverage the Searchable Encryption technique to perform secure data retrieval. However, the method has yet to be considered a crucial need for replication on searchable encrypted data. It calls for challenging works on Dynamic Searchable Symmetric Encryption (DSSE) since clients must share the search capability of the encrypted data replicas and guarantee forward and backward privacy. We define a new notion called “Keyword Search Shareable Encryption” (KS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> E) and the corresponding security model capturing forward and backward privacy. In our notion, data owners are allowed to share search indexes of the encrypted data with users. A search index will be updated with a new search key before sharing to guarantee the data privacy of the source database. The target database also inherits data search efficiency along with the shared data. We further construct an instance of KS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> E called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Branch</i> , prove its security, and use real-world datasets to evaluate Branch. The evaluation results show that Branch’s performance is comparable to classical DSSE schemes on search efficiency and demonstrate the effectiveness on searching encrypted data replicas from multiple owners.
Non-Orthogonal Multiple Access Enhanced Multi-User Semantic Communication

Semantic communication has served as a novel paradigm and attracted a broad interest from researchers.One critical aspect of it is the multi-user semantic communication theory, which can further enhance its application for the practical network environment.While most existing works have focused on the design of end-to-end single-user semantic transmission, a novel non-orthogonal multiple access (NOMA)-based multi-user semantic communication system named NOMASC is proposed in this study.The proposed system can support semantic transmission of multiple users with diverse modalities of source information.To avoid high demand for the hardware, an asymmetric quantizer is employed at the end of the semantic encoder for discretizing the continuous full-resolution semantic feature.In addition, a neural network model is used for mapping the discrete feature into self-learned symbols and accomplishing intelligent multi-user detection (MUD) at the receiver side.Simulation results demonstrate that the proposed system attains sound performance in non-orthogonal transmission of multiple user signals and outperforms the other methods, particularly with low-to-medium signal-to-noise ratios (SNRs).Moreover, it has shown high robustness under various simulation settings and mismatched testing scenarios.
Solving Data Imbalance in Text Classification With Constructing Contrastive Samples

Contrastive learning (CL) has been successfully applied in Natural Language Processing (NLP) as a powerful representation learning method and has shown promising results in various downstream tasks. Recent research has highlighted the importance of constructing effective contrastive samples through data augmentation. However, current data augmentation methods primarily rely on random word deletion, substitution, and cropping, which may introduce noisy samples and hinder representation learning. In this article, we propose a novel approach to address data imbalance in text classification by constructing contrastive samples. Our method involves the use of a Label-indicative Component to generate high-quality positive samples for the minority class, along with the introduction of a Hard Negative Mixing strategy to synthesize challenging negative samples at the feature level. By applying supervised contrastive learning to these samples, we are able to obtain superior text representations, which significantly benefit text classification tasks with imbalanced data. Our approach effectively mitigates distributional biases and promotes noise-resistant representation learning. To validate the effectiveness of our method, we conducted experiments on benchmark datasets (THUCNews, AG’s News, 20NG) as well as the imbalanced FDCNews dataset. The code for our method is publicly available at the following GitHub repository: https://github.com/hanggun/CLDMTC.
Priest: Adversarial Attack Detection Techniques for Signal Injection Attacks

Machine learning is widely used for autonomous driving because it can recognize surrounding circumstances feasibly from sensor and determine appropriate actions. Most of these sensors are based on micro-electro-mechanical systems (MEMS), which enable autonomous vehicles to judge objects in conjunction with object-detection algorithms. However, recent studies have shown that MEMS are vulnerable to signal-injection attacks, in which the input images are manipulated to force the object detection algorithms to misclassify the results. These attacks can be critical in the wild because they deteriorate state-of-the-art detection techniques, dropping their detection rates until the objects would no longer be detected at all. In this paper, we propose Priest, a novel detection method against prior signal-injection attacks. Priest uses the similarity of pixel values between two consecutive images. Using only two images ensures a low computational cost. According to our performance analysis, Priest detects state-of-the-art signal-injection attacks in real-time with 99% accuracy on average, achieving practical autonomous driving security.
KINA: Karatsuba Initiated Novel Accelerator for Ring-Binary-LWE (RBLWE)-Based Post-Quantum Cryptography

Along with the National Institute of Standards and Technology (NIST) post-quantum cryptography (PQC) standardization process, lightweight PQC-related research, and development have also gained substantial attention from the research community. Ring-binary-learning-with-errors (RBLWE), a ring variant of binary-LWE (BLWE), has been used to build a promising lightweight PQC scheme for emerging Internet-of-Things (IoT) and edge computing applications, namely the RBLWE-based encryption scheme (RBLWE-ENC). The parameter settings of RBLWE-ENC, however, are not in favor of deploying typical fast algorithms like number theoretic transform (NTT). Following this direction, in this work, we propose a Karatsuba initiated novel accelerator (KINA) for efficient implementation of RBLWE-ENC. Overall, we have made several coherent interdependent stages of efforts to carry out the proposed work: 1) we have innovatively used the Karatsuba algorithm (KA) to derive the major arithmetic operation of RBLWE-ENC into a new form for high-performance operation; 2) we have then effectively mapped the proposed algorithm into an efficient hardware accelerator with the help of a number of optimization techniques; and 3) we have also provided detailed complexity analysis and implementation comparison to demonstrate the superior performance of the proposed KINA, e.g., the proposed design with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$u=2$</tex-math> </inline-formula> involves 64.71% higher throughput and 15.37% less area-delay product (ADP) than the state-of-the-art design for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n=512$</tex-math> </inline-formula> (Virtex-7). The proposed KINA offers flexible processing speed and is suitable for high-performance applications like IoT servers. This work is expected to be useful for lightweight PQC development.