Reviewerly/exHarmony huggingface dataset:
Rethinking Unsupervised Domain Adaptation for Nighttime Tracking

Despite the considerable progress that has been achieved in visual object tracking, it remains a challenge to track in low-light circumstances. Prior nighttime tracking methods suffer from either weak collaboration of cascade structures or the lack of pseudo supervision, and thus fail to bring out satisfactory results. In this paper, we develop a novel unsupervised domain adaptation framework for nighttime tracking. Specifically, we benefit from the establishment of pseudo supervision in the mean teacher network, and further extend it with three components at the input level and the optimization level. For the unlabeled target domain dataset, we first present an assignment-based object discovery strategy to generate suitable training patches. Additionally, a low-light enhancer is embedded to improve the pseudo labels that facilitate the following consistency learning. Finally, with the aid of better training data and pseudo labels, we replace the common mean square error with two stricter losses, which are entropy-decreasing classification consistency loss and confidence-weighted regression consistency loss, for better convergence. Experiments demonstrate that our proposed method achieves significant performance gains on multiple nighttime tracking benchmarks, and even brings slight enhancement on the source domain.
A Three-Stage Framework for Event-Event Relation Extraction with Large Language Model

Expanding the parameter count of a large language model (LLM) alone is insufficient to achieve satisfactory outcomes in natural language processing tasks, specifically event extraction (EE), event temporal relation extraction (ETRE), and event causal relation extraction (ECRE). To tackle these challenges, we propose a novel three-stage extraction framework (ThreeEERE) that integrates an improved automatic chain of thought prompting (Auto-CoT) with LLM and is tailored based on a golden rule to maximize event and relation extraction precision. The three stages include constructing examples in each category, federating local knowledge to extract relationships between events, and selecting the best answer. By following these stages, we can achieve our objective. Although supervised models dominate for these tasks, our experiments on three types of extraction tasks demonstrate that utilizing these three stages approach yields significant results in event extraction and event relation extraction, even surpassing some supervised model methods in the extraction task.
TRFN: Triple-Receptive-Field Network for Regional-Texture and Holistic-Structure Image Inpainting

Image inpainting is a challenging task and has become a hot issue in recent years. Despite the significant progress of modern methods, it is still difficult to fill arbitrary missing regions with both vivid textures and coherent structures. Because of the limited receptive fields, methods centered on convolution neural networks only deal with regular textures but lose holistic structures. To this end, we propose a Triple-Receptive-Field Network (TRFN) that fuses local convolution features, global attention mechanism, and frequency domain learning in this study. TRFN roots in a concurrent structure that enables different receptive fields and retains local features and global representations to the maximum extent. TRFN captures effective representations and generates simultaneously detailed textures and holistic structures by using the concurrent structure. Experiments demonstrate the efficacy of TRFN and the proposed method achieves outstanding performance over the competitors.
Topic-Aware Two-Layer Context-Enhanced Model for Chinese Discourse Parsing

In the past decade, Chinese Discourse Parsing has drawn much attention due to its fundamental role in document-level Natural Language Processing (NLP). In this work, we propose a topic-aware two-layer context-enhanced model based on transition system. Specifically, in one hand, we first adopt a two-layer context-enhanced Chinese discourse parser as a strong baseline, where the Star-Transformer with star topology is employed to enhance the EDU representation. On the other hand, we split the document into multiple sub-topics based on the change of nuclearity of discourse relations. Then we implicitly incorporate topic boundary information via joint learning framework. Experimental results on the Chinese CDTB corpus indicate that, the proposed approach can contribute much to Chinese discourse parsing.
A Data-Free Substitute Model Training Method for Textual Adversarial Attacks

BERT and other pre-trained language models are vulnerable to textual adversarial attacks. While current transfer-based textual adversarial attacks in black-box environments rely on real datasets to train substitute models, obtaining the datasets can be challenging for attackers. To address this issue, we propose a data-free substitute training method (DaST-T) for textual adversarial attacks, which can train substitute models without the assistance of real data. DaST-T consists of two major steps. Firstly, DaST-T creates a special Generative Adversarial Network (GAN) to train substitute models without any real data. The training procedure utilizes samples synthesized at random by the generative model, where labels are generated by the attacked model. In particular, DaST-T designs a data augmenter for the generative model to facilitate rapid exploration of the entire sample space, thereby accelerating the performance of substitute model training. Secondly, DaST-T applies existing white-box textual adversarial attack methods to the substitute model to generate adversarial text, which is then migrated to the attacked model. DaST-T can effectively address the issue of limited access to real datasets in black-box textual adversarial attacks. Experimental results on text classification tasks in NLP show that DaST-T can achieve superior attack performance compared to other baselines of black-box textual adversarial attacks while requiring fewer sample queries.
Weakening Assumptions for Publicly-Verifiable Deletion

We develop a simple compiler that generically adds publicly-verifiable deletion to a variety of cryptosystems. Our compiler only makes use of one-way functions (or one-way state generators, if we allow the public verification key to be quantum). Previously, similar compilers either relied on indistinguishability obfuscation along with any one-way function (Bartusek et al., ePrint:2023/265), or on almost-regular one-way functions (Bartusek, Khurana and Poremba, CRYPTO 2023).
Distributed-Prover Interactive Proofs

Interactive proof systems enable a verifier with limited resources to decide an intractable language (or compute a hard function) by communicating with a powerful but untrusted prover. Such systems guarantee soundness: the prover can only convince the verifier of true statements. This is a central notion in computer science with far-reaching implications. One key drawback of the classical model is that the data on which the prover operates must be held by a single machine. In this work, we initiate the study of distributed-prover interactive proofs (dpIPs): an untrusted cluster of machines, acting as a distributed prover, interacts with a single verifier. The machines in the cluster jointly store and operate on a massive data-set that no single machine can store. The goal is for the machines in the cluster to convince the verifier of the validity of some statement about its data-set. We formalize the communication and space constraints via the massively parallel computation (MPC) model, a widely accepted analytical framework capturing the computational power of massive data-centers. Our main result is a compiler that generically augments any verification algorithm in the MPC model with a (computational) soundness guarantee. Concretely, for any language L for which there is an MPC algorithm verifying whether $$x \in L$$ , we design a new MPC protocol capable of convincing a verifier of the validity of $$x \in L$$ and where if $$x\not \in L$$ , the verifier rejects with overwhelming probability. The new protocol requires only slightly more rounds, i.e., a $$\textsf{poly}(\log N)$$ blowup, and a slightly bigger memory per machine, i.e., $$\textsf{poly}(\lambda )$$ blowup, where N is the total size of the dataset and $$\lambda $$ is a security parameter independent of N. En route, we introduce distributed-prover interactive oracle proofs (dpIOPs), a natural adaptation of the (by now classical) IOP model to the distributed prover setting. We design a dpIOP for verification algorithms in the MPC model and then translate them to “plain model” dpIPs via an adaptation of existing polynomial commitment schemes into the distributed prover setting.
DeFusion: Aerial Image Matching Based on Fusion of Handcrafted and Deep Features

Machine vision has become a crucial method for drones to perceive their surroundings, and image matching, as a fundamental task in machine vision, has also gained widespread attention. However, due to the complexity of aerial images, traditional matching methods based on handcrafted features lack the ability to extract high-level semantics and unavoidably suffer from low robustness. Although deep learning has potential to improve matching accuracy, it comes with the high cost of requiring specific samples and computing resources, making it infeasible for many scenarios. To fully leverage the strengths of both approaches, we introduce DeFusion, a novel image matching scheme with a fine-grained decision-level fusion algorithm that effectively combines handcrafted and deep features. We train generic features on public datasets, enabling us to handle unseen scenarios. We use RootSIFT as prior knowledge to guide the extraction of deep features, significantly reducing computational overhead. We also carefully design preprocessing steps by incorporating drone attitude information. Eventually, as evidenced by our experimental results, the proposed scheme achieves an overall 2.5–6x more correct matches with improved robustness when compared to existing methods.
On Black-Box Verifiable Outsourcing

We study verifiable outsourcing of computation in a model where the verifier has black-box access to the function being computed. We introduce the problem of oracle-aided batch verification of computation (OBVC) for a function class $$\mathcal {F}$$ . This allows a verifier to efficiently verify the correctness of any $$f \in \mathcal {F}$$ evaluated on a batch of n instances $$x_1, \ldots , x_n$$ , while only making $$\lambda $$ calls to an oracle for f (along with $$O(n \lambda )$$ calls to low-complexity helper oracles), for security parameter $$\lambda $$ . We obtain the following positive and negative results:
Agile Cryptography: A Universally Composable Approach

Being capable of updating cryptographic algorithms is an inevitable and essential practice in cryptographic engineering. This cryptographic agility, as it has been called, is a fundamental desideratum for long term cryptographic system security that still poses significant challenges from a modeling perspective. For instance, current formulations of agility fail to express the fundamental security that is expected to stem from timely implementation updates, namely the fact that the system retains some of its security properties provided that the update is performed prior to the deprecated implementation becoming exploited. In this work we put forth a novel framework for expressing updateability in the context of cryptographic primitives within the universal composition model. Our updatable ideal functionality framework provides a general template for expressing the security we expect from cryptographic agility capturing in a fine grained manner all the properties that can be retained across implementation updates. We exemplify our framework over two basic cryptographic primitives, digital signatures and non-interactive zero-knowledge (NIZK), where we demonstrate how to achieve updateability with consistency and backwards-compatibility across updates in a composable manner. We also illustrate how our notion is a continuation of a much broader scope of the concept of agility introduced by Acar, Belenkiy, Bellare, and Cash in Eurocrypt 2010 in the context of symmetric cryptographic primitives.
Locally Verifiable Distributed SNARGs

The field of distributed certification is concerned with certifying properties of distributed networks, where the communication topology of the network is represented as an arbitrary graph; each node of the graph is a separate processor, with its own internal state. To certify that the network satisfies a given property, a prover assigns each node of the network a certificate, and the nodes then communicate with one another and decide whether to accept or reject. We require soundness and completeness: the property holds if and only if there exists an assignment of certificates to the nodes that causes all nodes to accept. Our goal is to minimize the length of the certificates, as well as the communication between the nodes of the network. Distributed certification has been extensively studied in the distributed computing community, but it has so far only been studied in the information-theoretic setting, where the prover and the network nodes are computationally unbounded. In this work we introduce and study computationally bounded distributed certification: we define locally verifiable distributed $$\textsf{SNARG}$$ s ( s), which are an analog of $$\textsf{SNARG}$$ s for distributed networks, and are able to circumvent known hardness results for information-theoretic distributed certification by requiring both the prover and the verifier to be computationally efficient (namely, PPT algorithms). We give two constructions: the first allows us to succinctly certify any network property in $${\textsf{P}}$$ , using a global prover that can see the entire network; the second construction gives an efficient distributed prover, which succinctly certifies the execution of any efficient distributed algorithm. Our constructions rely on non-interactive batch arguments for $${\textsf{NP}}$$ ( $$\textsf{BARG}$$ s) and on $$\textsf{RAM}~\textsf{SNARG}$$ s, which have recently been shown to be constructible from standard cryptographic assumptions.
All You See Is the Tip of the Iceberg: Distilling Latent Interactions Can Help You Find Treasures

Recommender systems suffer from data sparsity problem severely, which can be attributed to the combined action of various possible causes like: gradually strengthened privacy protection policies, exposure bias, etc. In these cases, the unobserved items do not always refer to the items that users are not interested in; they could also be imputed to the inaccessibility of interaction data or users' unawareness over items. Thus, blindly fitting all unobserved interactions as negative interactions in the training stage leads to the incomplete modeling of user preferences. In this work, we propose a novel training strategy to distill latent interactions for recommender systems (shorted as DLI). Latent interactions refer to the possible links between users and items that can reflect user preferences but not happened. We first design a False-negative interaction selecting module to dynamically distill latent interactions along the training process. After that, we devise two types of loss paradigms: Truncated Loss and Reversed Loss. The former one can reduce the detrimental effect of False-negative interactions by discarding the False-negative samples in the loss computing stage, while the latter turning them into positive ones to enrich the interaction data. Meanwhile, both loss functions can be further detailed into full mode and partial mode to discriminate different confidence levels of False-negative interactions. Extensive experiments on three benchmark datasets demonstrate the effectiveness of DLI in improving the recommendation performance of backbone models.
Public-Key Encryption, Local Pseudorandom Generators, and the Low-Degree Method

The low-degree method postulates that no efficient algorithm outperforms low-degree polynomials in certain hypothesis-testing tasks. It has been used to understand computational indistinguishability in high-dimensional statistics. We explore the use of the low-degree method in the context of cryptography. To this end, we apply it in the design and analysis of a new public-key encryption scheme whose security is based on Goldreich’s pseudorandom generator. The scheme is a combination of two proposals of Applebaum, Barak, and Wigderson, and inherits desirable features from both.
Category-Wise Meal Recommendation

Meal recommender system, as an application of bundle recommendation, aims to provide courses from specific categories (e.g., appetizer, main dish) that are enjoyed as a meal for a user. Existing bundle recommendation methods work on learning user preferences from user-bundle interactions to satisfy users' information need. However, users in food scenarios may have different preferences for different course categories. It is a challenge to effectively consider course category constraints when predicting meals for users. To this end, we propose a model CMRec: Category-wise Meal Recommendation model. Specifically, our model first decomposes interactions and affiliations between users, meals, and courses according to category. Secondly, graph neural networks are utilized to learn category-wise user/meal representations. Then, the likelihood of user-meal interactions is estimated category by category. Finally, our model is trained by a category-wise enhanced Bayesian Personalized Ranking loss. CMRec outperforms state-of-the-art methods in terms of Recall@K and NDCG@K on two public datasets.
Publicly Verifiable Deletion from Minimal Assumptions

We present a general compiler to add the publicly verifiable deletion property for various cryptographic primitives including public key encryption, attribute-based encryption, and quantum fully homomorphic encryption. Our compiler only uses one-way functions, or more generally hard quantum planted problems for $$\textsf{NP}$$ , which are implied by one-way functions. It relies on minimal assumptions and enables us to add the publicly verifiable deletion property with no additional assumption for the above primitives. Previously, such a compiler needs additional assumptions such as injective trapdoor one-way functions or pseudorandom group actions [Bartusek-Khurana-Poremba, CRYPTO 2023]. Technically, we upgrade an existing compiler for privately verifiable deletion [Bartusek-Khurana, CRYPTO 2023] to achieve publicly verifiable deletion by using digital signatures.
Pseudorandomness with Proof of Destruction and Applications

Two fundamental properties of quantum states that quantum information theory explores are pseudorandomness and provability of destruction. We introduce the notion of quantum pseudorandom states with proofs of destruction (PRSPD) that combines both these properties. Like standard pseudorandom states (PRS), these are efficiently generated quantum states that are indistinguishable from random, but they can also be measured to create a classical string. This string is verifiable (given the secret key) and certifies that the state has been destructed. We show that, similarly to PRS, PRSPD can be constructed from any post-quantum one-way function. As far as the authors are aware, this is the first construction of a family of states that satisfies both pseudorandomness and provability of destruction. We show that many cryptographic applications that were shown based on PRS variants using quantum communication can be based on (variants of) PRSPD using only classical communication. This includes symmetric encryption, message authentication, one-time signatures, commitments, and classically verifiable private quantum coins.
From Polynomial IOP and Commitments to Non-malleable zkSNARKs

We study sufficient conditions to compile simulation-extractable zkSNARKs from information-theoretic interactive oracle proofs (IOP) using a simulation-extractable commit-and-prove system for its oracles. Specifically, we define simulation extractability for opening and evaluation proofs of polynomial commitment schemes, which we then employ to prove the security of zkSNARKS obtained from polynomial IOP proof systems. To instantiate our methodology, we additionally prove that KZG commitments satisfy our simulation extractability requirement, despite being naturally malleable. To this end, we design a relaxed notion of simulation extractability that matches how KZG commitments are used and optimized in real-world proof systems. The proof that KZG satisfies this relaxed simulation extractability property relies on the algebraic group model and random oracle model.
MelMAE-VC: Extending Masked Autoencoders to Voice Conversion

Voice conversion is a technique that generates speeches with text contents identical to source speeches and timbre features similar to reference speeches. This paper proposes MelMAE-VC, a neural network for non-parallel many-to-many voice conversion that utilizes pre-trained Masked Autoencoders (MAEs) for representation learning. Our neural network mainly consists of transformer layers and no recurrent units, aiming to achieve better scalability and parallel computing capability. We follow a similar scheme of image-based MAE in the pre-training phase that conceals a portion of the input spectrogram; then we set up a vanilla autoencoding task for training. The encoder yields latent representation from the visible subset of the full spectrogram; then the decoder reconstructs the full spectrogram from the representation of only visible patches. To achieve voice conversion, we adopt the pre-trained encoder to extract preliminary features, and then use a speaker embedder to control timbre information of synthesized spectrograms. The style transfer decoder could be either a simple autoencoder or a conditional variational autoencoder (CVAE) that mixes timbre and text information from different utterances. The optimization goal of voice conversion model training is a hybrid loss function that combines reconstruction loss, style loss, and stochastic similarity. Results show that our model speeds up and simplifies the training process, and has better modularity and scalability while achieving similar performance compared with other models.
Depth Normalized Stable View Synthesis

Novel view synthesis (NVS) aims to synthesize photo-realistic images depicting a scene by utilizing existing source images. The synthesized images are supposed to be as close as possible to the scene content. We present Deep Normalized Stable View Synthesis (DNSVS), an NVS method for large-scale scenes based on the pipeline of Stable View Synthesis (SVS). SVS combines neural networks with the 3D scene representation obtained from structure-from-motion and multi-view stereo, where the view rays corresponding to each surface point of the scene representation and the source view feature vector together yield a value of each pixel in the target view. However, it weakens geometric information in the refinement stage, resulting in blur and artifacts in novel views. To address this, we propose DNSVS that leverages the depth map to enhance the rendering process via a normalization approach. The proposed method is evaluated on the Tanks and Temples dataset, as well as the FVS dataset. The average Learned Perceptual Image Patch Similarity (LPIPS) of our results is better than state-of-the-art NVS methods by 0.12%, indicating the superiority of our method.