Reviewerly/exHarmony huggingface dataset:
Modeling Chinese Ancient Book Catalog

Chinese ancient book catalogs are a unique type of Chinese ancient books, which are essential resources for Chinese humanities research. They are distinguished by their systematic arrangement and provision of organization, classification, review, and generalization of ancient books. However, the lack of knowledge bases that support the discovery and representation of knowledge interrelationships poses a significant challenge for utilizing these catalogs. Identifying, extracting, standardizing, and analyzing the entities and relationships in Chinese ancient book catalogs remains difficult. Ontology can serve as a potent tool for facilitating knowledge discovery and representation, addressing the organization of catalog resources in the digital environment. This paper develops the CABC ontology, which aims to formally represent the concepts present in Chinese ancient book catalogs. By drawing on academic tradition and research needs in this area, the CABC ontology offers a reference conceptual model and opens new avenues for comprehending Chinese ancient books, thereby expanding the application scope of traditional Chinese ancient book catalogs. This study provides a powerful tool for knowledge organization and retrieval, empowering scholars to explore and analyze knowledge interrelationships in Chinese ancient culture, humanities, and history with greater ease.
Malicious Secure, Structure-Aware Private Set Intersection

Structure-Aware private set intersection (sa-PSI) is a variant of PSI where Alice’s input set A has some publicly known structure, Bob’s input B is an unstructured set of points, and Alice learns the intersection $$A \cap B$$ . sa-PSI was recently introduced by Garimella et al. (Crypto 2022), who described a semi-honest protocol with communication that scales with the description size of Alice’s set, instead of its cardinality. In this paper, we present the first sa-PSI protocol secure against malicious adversaries. sa-PSI protocols are built from function secret sharing (FSS) schemes, and the main challenge in our work is ensuring that multiple FSS sharings encode the same structured set. We do so using a cut-and-choose approach. In order to make FSS compatible with cut-and-choose, we introduce a new variant of function secret sharing, called derandomizable FSS (dFSS). We show how to construct dFSS for union of geometric balls, leading to a malicious-secure sa-PSI protocol where Alice’s input is a union of balls. We also improve prior FSS constructions, giving asymptotic improvements to semi-honest sa-PSI.
On Linear Communication Complexity for (Maximally) Fluid MPC

Secure multiparty computation protocols with dynamic parties, which assume that honest parties do not need to be online throughout the whole execution of the protocol, have recently gained a lot of traction for computations of large scale distributed protocols, such as blockchains. More specifically, in Fluid MPC, introduced in (Choudhuri et al. CRYPTO 2021), parties can dynamically join and leave the computation from round to round. The best known Fluid MPC protocol in the honest majority setting communicates $$O(n^2)$$ elements per gate where n is the number of parties online at a time. While Le Mans (Rachuri and Scholl, CRYPTO 2022) extends Fluid MPC to the dishonest majority setting with preprocessing, it still communicates $$O(n^2)$$ elements per gate. In this work we present alternative Fluid MPC solutions that require O(n) communication per gate for both the information-theoretic honest majority setting and the information-theoretic dishonest majority setting with preprocessing. Our solutions also achieve maximal fluidity where parties only need to be online for a single communication round. Additionally, we show that a protocol in the information-theoretic dishonest majority setting with sub-quadratic $$o(n^2)$$ overhead per gate requires for each of the N parties who may ever participate in the (later) execution phase, $$\varOmega (N)$$ preprocessed data per gate.
Tighter QCCA-Secure Key Encapsulation Mechanism with Explicit Rejection in the Quantum Random Oracle Model

Hofheinz et al. (TCC 2017) proposed several key encapsulation mechanism (KEM) variants of Fujisaki-Okamoto (FO) transformation, including and $$\textsf {QFO}_m^\bot $$ , and they are widely used in the post-quantum cryptography standardization launched by NIST. These transformations are divided into two types, the implicit and explicit rejection type, including and $$\{\textsf {FO}^{\bot }$$ , $$\textsf {FO}_m^\bot $$ , $$\textsf {QFO}_m^\bot \}$$ , respectively. The decapsulation algorithm of the implicit (resp. explicit) rejection type returns a pseudorandom value (resp. an abort symbol $$\bot $$ ) for an invalid ciphertext. For the implicit rejection type, the IND-CCA security reduction of in the quantum random oracle model (QROM) can avoid the quadratic security loss, as shown by Kuchta et al. (EUROCRYPT 2020). However, for the explicit rejection type, the best known IND-CCA security reduction in the QROM presented by Hövelmanns et al. (ASIACRYPT 2022) for $$\textsf {FO}_m^\bot $$ still suffers from a quadratic security loss. Moreover, it is not clear until now whether the implicit rejection type is more secure than the explicit rejection type. In this paper, a QROM security reduction of $$\textsf {FO}_m^\bot $$ without incurring a quadratic security loss is provided. Furthermore, our reduction achieves IND-qCCA security, which is stronger than the IND-CCA security. To achieve our result, two steps are taken: The first step is to prove that the IND-qCCA security of $$\textsf {FO}_m^\bot $$ can be tightly reduced to the IND-CPA security of $$\textsf {FO}_m^\bot $$ by using the online extraction technique proposed by Don et al. (EUROCRYPT 2022). The second step is to prove that the IND-CPA security of $$\textsf {FO}_m^\bot $$ can be reduced to the IND-CPA security of the underlying public key encryption (PKE) scheme without incurring quadratic security loss by using the Measure-Rewind-Measure One-Way to Hiding Lemma (EUROCRYPT 2020). In addition, we prove that (at least from a theoretic point of view), security is independent of whether the rejection type is explicit ( $$\textsf {FO}_m^\bot $$ ) or implicit ( ) if the underlying PKE scheme is weakly $$\gamma $$ -spread.
Tennis Action Recognition Based on Multi-Branch Mixed Attention

Tennis action recognition is a challenging problem due to its inherent characteristics such as high speed and large amplitude of actions. In this paper, an end-to-end, Multi-Branch Mixed Attention-based network model (MBMA-Net) is proposed to tackle those challenges. Specifically, the model is designed to: (a) capture the inter-dependencies between channel features using channel attention; (b) improve the spatial receptive field to better filter spatial features by spatial attention;and (c) model the actions between consecutive frames using motion excitation to improve the accuracy of action feature extraction and recognition. The experimental results on dataset THETIS show that MBMA-Net achieves an accuracy of 0.8698, 5.35% higher than other baseline models for tennis action recognition and the model is further validated through various ablation experiments.
Machine-Checked Security for $$\textrm{XMSS} $$ as in RFC 8391 and $$\mathrm {SPHINCS^{+}} $$

This work presents a novel machine-checked tight security proof for $$\textrm{XMSS} $$ —a stateful hash-based signature scheme that is (1) standardized in RFC 8391 and NIST SP 800-208, and (2) employed as a primary building block of $$\mathrm {SPHINCS^{+}} $$ , one of the signature schemes recently selected for standardization as a result of NIST's post-quantum competition. In 2020, Kudinov, Kiktenko, and Fedoro pointed out a flaw affecting the tight security proofs of $$\mathrm {SPHINCS^{+}} $$ and $$\textrm{XMSS} $$ . For the case of $$\mathrm {SPHINCS^{+}} $$ , this flaw was fixed in a subsequent tight security proof by Hülsing and Kudinov. Unfortunately, employing the fix from this proof to construct an analogous tight security proof for $$\textrm{XMSS} $$ would merely demonstrate security with respect to an insufficient notion. At the cost of modeling the message-hashing function as a random oracle, we complete the tight security proof for $$\textrm{XMSS} $$ and formally verify it using the EasyCrypt proof assistant. (Note that this merely extends the use of the random oracle model, as this model is already required in other parts of the security analysis to justify the currently standardized parameter values). As part of this endeavor, we formally verify the crucial step common to the security proofs of $$\mathrm {SPHINCS^{+}} $$ and $$\textrm{XMSS} $$ that was found to be flawed before, thereby confirming that the core of the aforementioned security proof by Hülsing and Kudinov is correct. As this is the first work to formally verify proofs for hash-based signature schemes in EasyCrypt, we develop several novel libraries for the fundamental cryptographic concepts underlying such schemes—e.g., hash functions and digital signature schemes—establishing a common starting point for future formal verification efforts. These libraries will be particularly helpful in formally verifying proofs of other hash-based signature schemes such as $$\textrm{LMS} $$ or $$\mathrm {SPHINCS^{+}} $$ .
Unifying Freedom and Separation for Tight Probing-Secure Composition

The masking countermeasure is often analyzed in the probing model. Proving the probing security of large circuits at high masking orders is achieved by composing gadgets that satisfy security definitions such as non-interference (NI), strong non-interference (SNI) or free SNI. The region probing model is a variant of the probing model, where the probing capabilities of the adversary scale with the number of regions in a masked circuit. This model is of interest as it allows better reductions to the more realistic noisy leakage model. The efficiency of composable region probing secure masking has been recently improved with the introduction of the input-output separation (IOS) definition. In this paper, we first establish equivalences between the non-interference framework and the IOS formalism. We also generalize the security definitions to multiple-input gadgets and systematically show implications and separations between these notions. Then, we study which gadgets from the literature satisfy these. We give new security proofs for some well-known arbitrary-order gadgets, and also some automated proofs for fixed-order, special-case gadgets. To this end, we introduce a new automated formal verification algorithm that solves the open problem of verifying free SNI, which is not a purely simulation-based definition. Using the relationships between the security notions, we adapt this algorithm to further verify IOS. Finally, we look at composition theorems. In the probing model, we use the link between free SNI and the IOS formalism to generalize and improve the efficiency of the tight private circuit (Asiacrypt 2018) construction, also fixing a flaw in the original proof. In the region probing model, we relax the assumptions for IOS composition (TCHES 2021), which allows to save many refresh gadgets, hence improving the efficiency.
Are GPT Embeddings Useful for Ads and Recommendation?

Advertisement (ads) and recommendation are important for companies to drive their business objectives and improve user loyalty. A key strategy for these services is semantic modeling, which involves extracting useful knowledge or information from text. Large language models (LLMs) such as GPT-3 and LaMDA have incredible natural language understanding capabilities and their text embeddings have achieved excellent performance in various NLP tasks. Despite their potential, the discussion about whether text embeddings of LLMs can help ads and recommendation services is limited. In order to explore the utilization of GPT embeddings for ads and recommendation, we propose three strategies to integrate LLMs’ knowledge into basic PLMs and improve their performance. These strategies consider GPT embedding as a feature (EaaF) to enrich text semantics, as a regularization (EaaR) to guide text token embedding aggregation, and as a pre-training task (EaaP) to replicate the capability of LLMs, respectively. Our experiments demonstrate that, by incorporating GPT embeddings, basic PLMs can improve their performance in both ads and recommendation tasks. Our code is available at https://github.com/Wenjun-Peng/GPT4SM
A Cybersecurity Knowledge Graph Completion Method for Scalable Scenarios

Cybersecurity knowledge graph can well organize and manage cybersecurity data. However, there are still some challenges in the utilization of cybersecurity knowledge graph. Due to the limited cognitive ability and cyberspace exploration ability of human beings, the data in cybersecurity knowledge graph is incomplete. With the continuous changes of cyberspace, there are many unseen entities in the newly added data, and it is difficult to use this kind of data. In order to realize knowledge graph completion in scalable scenarios, we propose a knowledge graph completion method, which uses meta-learning to transfer knowledge from seen entities to unseen entities. It also utilizes a new scoring function to model the relationships between entities from multiple perspectives such as spatial rotation and angle transformation. For improving the robust expression of samples, it uses the correlation matrix and multi-head attention mechanism to explore the relationships between samples. To mitigate the catastrophic forgetting problem, a new self-distillation algorithm is designed to enhance the robustness of the trained model. We construct knowledge graph based on cybersecurity data, and conduct knowledge graph completion experiments. The experiments show that our method is effective in dealing with the problem of cybersecurity knowledge graph completion in scalable scenarios.
Chinese Relation Extraction with Bi-directional Context-Based Lattice LSTM

Chinese entity relation extraction (Chinese RE) is a crucial task for various NLP applications. It aims to automatically extract relationships between entities in Chinese texts, thereby enhancing the accuracy of natural language understanding. Although existing hybrid methods can overcome some of the shortcomings of character-based and word-based methods, they still suffer from polysemy ambiguity, which results in inaccuracy when representing the relationships between entities in text. To address the issue, we propose a Bi-directional Context-based Lattice (BC-Lattice) model for Chinese RE task. In detail, our BC-Lattice consists of: (1) A context-based polysemy weighting (CPW) module allocates weights to multiple senses of polysemous words from external knowledge base by modeling context-level information, thus obtaining more accurate representations of polysemous words; (2) A cross-attention semantic interaction-enhanced (CSI) classifier promotes exchange of semantic information between hidden states from forward and backward perspectives for more comprehensive representations of relation types. In experiments conducted on two public datasets from distinct domains, our method yields improved F1 score by up to 3.17%.
Degree-Aware Embedding and Interactive Feature Fusion-Based Graph Convolution Collaborative Filtering

The graph convolution network(GCN), as an advanced deep learning technique, has been effectively implemented in the collaborative filtering recommendation system. However, existing graph convolution-based collaborative filtering models neglect a pervasive issue, which is the distinction between users’ activity and sensitivity for items’ popularity, while the traditional neighbor aggregations in GCN have addressed this issue through Graph Laplacian Normalization, which produces suboptimal personalized outcomes. To solve this problem, this paper proposes a Degree-aware embedding and Interaction feature fusion-based Graph Convolution Collaborative Filtering (Di-GCCF). Firstly, the degree-aware embedding utilizes the degree of nodes in the user-item graph to reflect users’ activity and items’ popularity. Moreover, it combines users’ activity and popularity preferences to generate feature representations of users and items, which enables the model to capture their personalized preferences better. Secondly, the interaction feature fusion merges shallow and high-order features during propagation to improve the representation ability of high-order GCN. Finally, this paper performs comprehensive experiments on four public datasets to validate the effectiveness of our proposed model, and the results demonstrate the superior performance of our model from both analytical and empirical perspectives.
HERMES: Efficient Ring Packing Using MLWE Ciphertexts and Application to Transciphering

Most of the current fully homomorphic encryption (FHE) schemes are based on either the learning-with-errors (LWE) problem or on its ring variant (RLWE) for storing plaintexts. During the homomorphic computation of FHE schemes, RLWE formats provide high throughput when considering several messages, and LWE formats provide a low latency when there are only a few messages. Efficient conversion can bridge the advantages of each format. However, converting LWE formats into RLWE format, which is called ring packing, has been a challenging problem. We propose an efficient solution for ring packing for FHE. The main improvement of this work is twofold. First, we accelerate the existing ring packing methods by using bootstrapping and ring switching techniques, achieving practical runtimes. Second, we propose a new method for efficient ring packing, HERMES, by using ciphertexts in Module-LWE (MLWE) formats, to also reduce the memory. To this end, we generalize the tools of LWE and RLWE formats for MLWE formats. On a single-thread implementation, HERMES consumes 10.2s for the ring packing of $$2^{15}$$ LWE-format ciphertexts into an RLWE-format ciphertext. This gives 41x higher throughput compared to the state-of-the-art ring packing for FHE, PEGASUS [S &P'21], which takes 51.7s for packing $$2^{12}$$ LWE ciphertexts with similar homomorphic capacity. We also illustrate the efficiency of HERMES by using it for transciphering from LWE symmetric encryption to CKKS fully homomorphic encryption, significantly outperforming the recent proposals HERA [Asiacrypt'21] and Rubato [Eurocrypt'22].
Learning Graph Neural Networks on Feature-Missing Graphs

Graph neural networks have demonstrated state-of-the-art performance in many graph analysis tasks. However, relying on both node features and topology completeness can be challenging, especially as node features may be completely missing. Existing efforts that direct node feature completion suffer from several limitations on feature-missing graphs. In this paper, we propose a novel and general extension for running graph neural networks on feature-missing graphs via complete missing node feature information in the embedding space, called GNN-FIC. Specifically, it utilizes a Feature Information Generator to simulate missing feature information in the embedding space and then completes the node embedding using the predicted missing feature information. Additionally, GNN-FIC introduces two alignment mechanisms and a relation constraint mechanism, aiding in generating high-quality missing feature information. Extensive experiments on four benchmark datasets have shown that our proposed method provides consistent performance gains compared with several advanced methods.
Practical Schnorr Threshold Signatures Without the Algebraic Group Model

Threshold signatures are digital signature schemes in which a set of n signers specify a threshold t such that any subset of size t is authorized to produce signatures on behalf of the group. There has recently been a renewed interest in this primitive, largely driven by the need to secure highly valuable signing keys, e.g., DNSSEC keys or keys protecting digital wallets in the cryptocurrency ecosystem. Of special interest is FROST, a practical Schnorr threshold signature scheme, which is currently undergoing standardization in the IETF and whose security was recently analyzed at CRYPTO'22. We continue this line of research by focusing on FROST's unforgeability combined with a practical distributed key generation (DKG) algorithm. Existing proofs of this setup either use non-standard heuristics, idealized group models like the AGM, or idealized key generation. Moreover, existing proofs do not consider all practical relevant optimizations that have been proposed. We close this gap between theory and practice by presenting the Schnorr threshold signature scheme $$\textsf{Olaf} $$ , which combines the most efficient known FROST variant $$\textsf{FROST3} $$ with a variant of Pedersen's DKG protocol (as commonly used for FROST), and prove its unforgeability. Our proof relies on the AOMDL assumption (a weaker and falsifiable variant of the OMDL assumption) and, like proofs of regular Schnorr signatures, on the random oracle model.
Efficient Hash Coding for Image Retrieval Based on Improved Center Generation and Contrastive Pre-training Knowledge Model

In image retrieval tasks, effective hash coding methods can achieve better similarity preservation, so as to gain efficient retrieval results. Existing methods are usually divided into constructing global center constraints and utilizing local feature constraints. These methods mainly use the category labels of images as numbers, and do not fully exploit the value of classification labels as textual knowledge information. In this paper, we propose a novel and efficient hash coding framework. In the proposed method, the generation method of hash centers is improved, so that each image can correspond to multiple hash centers. In this way, the loss function for local similarity constraint is proposed, and the joint loss function for deep model is designed. Furthermore, based on the text-image pre-trained large model, we extract the text of the category label from the image as knowledge information, which is used to construct better hash codes for the image retrieval query, so as to improve the image retrieval evaluation effect. Extensive experiments on several large-scale datasets verify that the proposed method achieves better results than the state-of-the-art hash coding methods.
Reinforcement Learning-Based Recommendation with User Reviews on Knowledge Graphs

Introducing knowledge graphs (KGs) into recommendation systems can improve their performance, while reinforcement learning (RL) methods can help utilize graph data for recommendation. We investigate existing RL-based methods for recommendation on KGs, and find that such approaches do not make full use of information from user reviews. Introducing user reviews into a recommendation system can reveal user preferences more deeply and equip a RL agent with a stronger ability to distinguish users’ preferences for an item or not, which in turn improves the accuracy of recommendation results. We propose Reinforced Knowledge Graph Reasoning with User Reviews (RKGR-UR) by introducing user reviews into a RL-based recommendation model, which combines a rating prediction task to transform predicted ratings into rewards feedback for the RL agent. Experiments on three real datasets demonstrate the effectiveness of our method.