Reviewerly/exHarmony huggingface dataset:
Cryptanalysis of Symmetric Primitives over Rings and a Key Recovery Attack on Rubato

Symmetric primitives are a cornerstone of cryptography, and have traditionally been defined over fields, where cryptanalysis is now well understood. However, a few symmetric primitives defined over rings $$\mathbb Z _q$$ for a composite number $$q$$ have recently been proposed, a setting where security is much less studied. In this paper we focus on studying established algebraic attacks typically defined over fields and the extent of their applicability to symmetric primitives defined over the ring of integers modulo a composite $$q$$ . Based on our analysis, we present an attack on full Rubato, a family of symmetric ciphers proposed by Ha et al. at Eurocrypt 2022 designed to be used in a transciphering framework for approximate fully homomorphic encryption. We show that at least 25 $$\%$$ of the possible choices for $$q$$ satisfy certain conditions that lead to a successful key recovery attack with complexity significantly lower than the claimed security level for five of the six ciphers in the Rubato family.
EACCNet: Enhanced Auto-Cross Correlation Network for Few-Shot Classification

Few-shot classification, which belongs to a popular direction in few-shot learning, can be said to bridge the gap between human intelligence and machine models at present, aiming at rapid classification of new classes encountered in downstream tasks with only a small number of samples after the model has learned a certain class of data. However, the current approach suffers from two fatal problems, which are a very complicated training process and the presence of irrelevant information in the captured semantic features, resulting in low accuracy. We propose two core modules that can be learned: the enhanced auto-correlation representation (EACR) module and the enhanced cross-correlation attention (ECCA) module. The EACR module takes the representations computed by enhanced auto-correlation and learns the auto-correlation representation by convolutional operations to reveal the structural patterns of the images. The ECCA module works together from both meta-learning and representation cosine similarity to construct the query image and support enhanced cross-correlation attention maps between images. In the experiments, the EACCNet consisting of these two modules performs quit well on three standard benchmarks, achieving end-to-end few-shot classification with guaranteed high accuracy.
SNARGs for Monotone Policy Batch NP

We construct a succinct non-interactive argument ( $$\textsf{SNARG} $$ ) for the class of monotone policy batch $$\textsf{NP} $$ languages, under the Learning with Errors ( $$\textsf{LWE} $$ ) assumption. This class is a subclass of $$\textsf{NP} $$ that is associated with a monotone function $$f:\{0,1\}^k\rightarrow \{0,1\}$$ and an $$\textsf{NP} $$ language $$\mathcal {L} $$ , and contains instances $$(x_1,\ldots ,x_k)$$ such that $$f(b_1,\ldots ,b_k)=1$$ where $$b_j=1$$ if and only if $$x_j\in \mathcal {L} $$ . Our $$\textsf{SNARG} $$ s are arguments of knowledge in the non-adaptive setting, and satisfy a new notion of somewhere extractability against adaptive adversaries. This is the first $$\textsf{SNARG} $$ under standard hardness assumptions for a sub-class of $$\textsf{NP} $$ that is not known to have a (computational) non-signaling $$\textsf{PCP} $$ with parameters compatible with the standard framework for constructing $$\textsf{SNARG} $$ s dating back to [Kalai-Raz-Rothblum, STOC '13]. Indeed, our approach necessarily departs from this framework. Our construction combines existing quasi-arguments for $$\textsf{NP} $$ (based on batch arguments for $$\textsf{NP}$$ ) with a new type of cryptographic encoding of the instance and a new analysis going from local to global soundness. The main novel ingredient used in our encoding is a predicate-extractable hash ( $$\textsf{PEHash}$$ ) family, which is a primitive that generalizes the notion of a somewhere extractable hash. Whereas a somewhere extractable hash allows to extract a single input coordinate, our $$\textsf{PEHash}$$ extracts a global property of the input. We view this primitive to be of independent interest, and believe that it will find other applications.
Automatic Gaussian Bandwidth Selection for Kernel Principal Component Analysis

Kernel principal component analysis (KPCA) has been proved to be useful in many applications as a dimension reduction technique. Compared with PCA, KPCA better exploits the complicated spatial structure of high-dimensional features. Because of the high computational cost of exact KPCA, however, research has focused on the fast computation of KPCA, through techniques such as matrix sketching, the Nyström method, and random Fourier features. There has been little discussion of the selection of kernel bandwidth, which is critical in practice. In this paper, we propose a new data-driven bandwidth selection method, called the criterion of the maximum sum of eigenvalues (CMSE) method and a scalable variation (SCMSE) to handle big data. Both feature high time efficiency and achieve performance better than or comparable to that of the existing methods. We conduct both simulation study and real-world data analyses to support our conclusions.
PRACM: Predictive Rewards for Actor-Critic with Mixing Function in Multi-Agent Reinforcement Learning

Inspired by the centralised training with decentralised execution (CTDE) paradigm, the field of multi-agent reinforcement learning (MARL) has made significant progress in tackling cooperative problems with discrete action spaces. Nevertheless, many existing algorithms suffer from significant performance degradation when faced with large numbers of agents or more challenging tasks. Furthermore, some specific scenarios, such as cooperative environments with penalties, pose significant challenges to these algorithms , which often lack sufficient cooperative behavior to converge successfully. A new approach, called PRACM, based on the Actor-Critic framework is proposed in this study to address these issues. PRACM employs a monotonic mixing function to generate a global action value function, $$Q_{tot}$$ , which is used to compute the loss function for updating the critic network. To handle the discrete action space, PRACM uses Gumbel-Softmax. And to promote cooperation among agents and to adapt to cooperative environments with penalties, the predictive rewards is introduced. PRACM was evaluated against several baseline algorithms in “Cooperative Predator-Prey” and the challenging “SMAC” scenarios. The results of this study illustrate that PRACM scales well as the number of agents and task difficulty increase, and performs better in cooperative tasks with penalties, demonstrating its usefulness in promoting collaboration among agents.
Debiased Contrastive Loss for Collaborative Filtering

Collaborative filtering (CF) is the most fundamental technique in recommender systems, which reveals user preference by implicit feedback. Generally, binary cross-entropy or bayesian personalized ranking are usually employed as the loss function to optimize model parameters. Recently, the sampled softmax loss has been proposed to enhance the sampling efficiency, which adopts an in-batch sample strategy. However, it suffers from the sample bias issue, which unavoidably introduces false negative instances, resulting inaccurate representations of users’ genuine interests. To address this problem, we propose a debiased contrastive loss, incorporating a bias correction probability to alleviate the sample bias. We integrate the proposed method into several matrix factorizations (MF) and graph neural network-based (GNN) recommendation models. Besides, we theoretically analyze the effectiveness of our methods in automatically mining the hard negative instances. Experimental results on three public benchmarks demonstrate that the proposed debiased contrastive loss can augment several existing MF and GNN-based CF models and outperform popular learning objectives in the recommendation. Additionally, we demonstrate that our method substantially enhances training efficiency.
On Concurrent Multi-party Quantum Computation

Recently, significant progress has been made toward quantumly secure multi-party computation (MPC) in the stand-alone setting. In sharp contrast, the picture of concurrently secure MPC (or even 2PC), for both classical and quantum functionalities, still remains unclear. Quantum information behaves in a fundamentally different way, making the job of adversaries harder and easier at the same time. Thus, it is unclear if the positive or negative results from the classical setting still apply. This work initiates a systematic study of concurrent secure computation in the quantum setting. We obtain a mix of positive and negative results. We first show that assuming the existence of post-quantum one-way functions (PQ-OWFs), concurrently secure 2PC (and thus MPC) for quantum functionalities is impossible. Next, we focus on the bounded-concurrent setting, where we obtain simulation-sound zero-knowledge arguments for both $$\textbf{NP} $$ and $$\textbf{QMA}$$ , assuming PQ-OWFs. This is obtained by a new design of simulation-sound gadget, relying on the recent post-quantum non-malleable commitments by Liang, Pandey, and Yamakawa [arXiv:2207.05861], and the quantum rewinding strategy recently developed by Ananth, Chung, and La Placa [CRYPTO'21] for bounded-concurrent post-quantum ZK. Moreover, we show that our technique is general enough—It also leads to quantum-secure bounded-concurrent coin-flipping protocols, and eventually general-purpose 2PC and MPC, for both classical and quantum functionalities. All these constructions can be based on the quantum hardness of Learning with Errors.
Improved Multi-user Security Using the Squared-Ratio Method

Proving security bounds in contexts with a large number of users is one of the central problems in symmetric-key cryptography today. This paper introduces a new method for information-theoretic multi-user security proofs, called "the Squared-Ratio method". At its core, the method requires the expectation of the square of the ratio of observing the so-called good transcripts (from Patarin's H-coefficient technique) in the real and the ideal world. Central to the method is the observation that for information-theoretic adversaries, the KL-divergence for the multi-user security bound can be written as a summation of the KL-divergence of every single user. We showcase the Squared-Ratio method on three examples: the Xor of two Permutations by Bellare et al. (EUROCRYPT '98) and Hall et al. (CRYPTO '98), the Encrypted Davies-Mayer by Cogliati and Seurin (CRYPTO '16), and the two permutation variant of the nEHtM MAC algorithm by Dutta et al. (EUROCRYPT '19). With this new tool, we provide improved bounds for the multi-user security of these constructions. Our approach is modular in the sense that the multi-user security can be obtained directly from single-user results.
Snowblind: A Threshold Blind Signature in Pairing-Free Groups

Both threshold and blind signatures have, individually, received a considerable amount of attention. However little is known about their combination, i.e., a threshold signature which is also blind, in that no coalition of signers learns anything about the message being signed or the signature being produced. Several applications of blind signatures (e.g., anonymous tokens) would benefit from distributed signing as a means to increase trust in the service and hence reduce the risks of key compromise. This paper builds the first blind threshold signatures in pairing-free groups. Our main contribution is a construction that transforms an underlying blind non-threshold signature scheme with a suitable structure into a threshold scheme, preserving its blindness. The resulting signing protocol proceeds in three rounds, and produces signatures consisting of one group element and two scalars. The underlying non-threshold blind signature schemes are of independent interest, and improve upon the current state of the art (Tessaro and Zhu, EUROCRYPT '22) with shorter signatures (three elements, instead of four) and simpler proofs of security. All of our schemes are proved secure in the Random Oracle and Algebraic Group Models, assuming the hardness of the discrete logarithm problem.
Graph-Theoretic Algorithms for the Alternating Trilinear Form Equivalence Problem

At Eurocrypt'22 Tang, Duong, Joux, Plantard, Qiao, and Susilo proposed a digital signature algorithm based on the hardness of the isomorphism problem of alternating trilinear forms. They propose three concrete parameters in dimensions 9, 10, and 11 respectively. We give new heuristic algorithms that solve this problem more efficiently. With our new algorithms, the first parameter set can be broken in less than a day on a laptop. For the second parameter set, we show there is a $$2^{-17}$$ fraction of the public keys that can also be broken in less than a day. We do not break the third parameter set in practice, but we claim it falls short of the target security level of 128 bits.
Some topics in English newsmagazines in 2022, with special reference to mineral resources, minerals matter, and regional geology

Information collected from geological newsmagazines in 2022 is reviewed. First topic is mineral resources such as mineral commodity summary in USA, geospatial analyses of lode gold potential in Alaska, and mineralogy of Eudialyte. Second topic is mineral matters, that is, attribution of minerals to science, technology, and society, and notation history from alchemy to modern mineralogy. Third topic is regional geology such as large igneous provinces across the Archean-Proterozoic transition and a giant meteorite impact in Scotland. In addition, Anthropogenic data question for a new geological epoch is reviewed.
ParaSum: Contrastive Paraphrasing for Low-Resource Extractive Text Summarization

Existing extractive summarization methods achieve state-of-the-art (SOTA) performance with pre-trained language models (PLMs) and sufficient training data. However, PLM-based methods are known to be data-hungry and often fail to deliver satisfactory results in low-resource scenarios. Constructing a high-quality summarization dataset with human-authored reference summaries is a prohibitively expensive task. To address these challenges, this paper proposes a novel paradigm for low-resource extractive summarization, called ParaSum. This paradigm reformulates text summarization as textual paraphrasing, aligning the text summarization task with the self-supervised Next Sentence Prediction (NSP) task of PLMs. This approach minimizes the training gap between the summarization model and PLMs, enabling a more effective probing of the knowledge encoded within PLMs and enhancing the summarization performance. Furthermore, to relax the requirement for large amounts of training data, we introduce a simple yet efficient model and align the training paradigm of summarization to textual paraphrasing to facilitate network-based transfer learning. Extensive experiments over two widely used benchmarks (i.e., CNN/DailyMail, Xsum) and a recent open-sourced high-quality Chinese benchmark (i.e., CNewSum) show that ParaSum consistently outperforms existing PLM-based summarization methods in all low-resource settings, demonstrating its effectiveness over different types of datasets.
An Evaluation Metric for Prediction Stability with Imprecise Data

Application of machine learning techniques for analysis and prediction in healthcare heavily relies on patient data. Lab tests, medical instruments, and other sources generate a significant portion of patient healthcare data, which may contain imprecision ranges of up to 20%. However, previous studies have shown that prediction models built from such imprecise data tend to be “brittle” or unstable. Even a minor deviation within the normal imprecision range can lead to varying prediction results. Measuring the stability of such models is a crucial challenge for public health agencies in large cities in China. In this paper, we report our preliminary results on measuring stability, specifically, develop a “voting” based metric to assess the predictive stability of a model. We also formulate an effective method to calculate this metric that allows us to observe, understand, and compare the predictive stability of a model at a finer granularity. We conducted experiments on the MIMIC dataset and a predefined dataset to test the baseline method and two commonly used improved methods for handling noisy and imprecise data. The results showed that the improved methods demonstrated lower instability than the original method, highlighting the soundness of our evaluation metrics. Notably, when two models had similar accuracy for the same task, our evaluation metric identifies varying levels of stability among the models, suggesting that our proposed method provides a valuable perspective for model selection that deserves further explanation.
Exploring Decryption Failures of BIKE: New Class of Weak Keys and Key Recovery Attacks

Code-based cryptography has received a lot of attention recently because it is considered secure under quantum computing. Among them, the QC-MDPC based scheme is one of the most promising due to its excellent performance. QC-MDPC based schemes are usually subject to a small rate of decryption failure, which can leak information about the secret key. This raises two crucial problems: how to accurately estimate the decryption failure rate and how to use the failure information to recover the secret key. However, the two problems are challenging due to the difficulty of geometrically characterizing the bit-flipping decoder employed in QC-MDPC, such as using decoding radius. In this work, we introduce the gathering property and show it is strongly connected with the decryption failure rate of QC-MDPC. Based on this property, we present two results for QC-MDPC based schemes. The first is a new construction of weak keys obtained by extending the keys that have gathering property via ring isomorphism. For the set of weak keys, we present a rigorous analysis of the probability, as well as experimental simulation of the decryption failure rates. Considering BIKE's parameter set targeting 128-bit security, our result eventually indicates that the average decryption failure rate is lower bounded by $$\text {DFR}_{\text {avg}} \ge 2^{-116.61}$$ . The second entails two key recovery attacks against CCA secure QC-MDPC schemes using decryption failures in a multi-target setting. The two attacks consider whether or not it is allowed to reuse ciphertexts respectively. In both cases, we show the decryption failures can be used to identify whether a target's secret key satisfies the gathering property. Then using the gathering property as an extra information, we present a modified information set decoding algorithm that efficiently retrieves the target's secret key. For BIKE's parameter set targeting 128-bit security, we show a key recovery attack with complexity $$2^{116.61}$$ can be mounted if ciphertexts reusing is not permitted, and the complexity can be reduced to $$2^{98.77}$$ when ciphertexts reusing is permitted.
Importance-Based Neuron Selective Distillation for Interference Mitigation in Multilingual Neural Machine Translation

Multilingual neural machine translation employs a single model to translate multiple languages, enabling efficient cross-lingual transferability through shared parameters. However, multilingual training suffers from negative language interference, especially interference with high-resource languages. Existing approaches generally use language-specific modules to distinguish heterogeneous characteristics among different languages but suffer from the parameter explosion problem. In this paper, we propose a “divide and conquer” multilingual translation training method based on the importance of neurons that can mitigate negative language interference effectively without adding additional parameters. The key technologies can be summarized as estimation, pruning, distillation, and fine-tuning. Specifically, we estimate the importance of existing pre-trained model neurons, dividing them into the important ones representing general knowledge of each language and the unimportant ones representing individual knowledge of each low-resource language. Then, we prune the pre-trained model, retaining only the important neurons, and train the pruned model supervised by the original complete model via selective distillation to compensate for some performance loss due to unstructured pruning. Finally, we restore the pruned neurons and only fine-tune them. Experimental results on several language pairs demonstrate the effectiveness of the proposed method.
Reductions from Module Lattices to Free Module Lattices, and Application to Dequantizing Module-LLL

In this article, we give evidence that free modules (i.e., modules which admit a basis) are no weaker than arbitrary modules, when it comes to solving cryptographic algorithmic problems (and when the rank of the module is at least 2). More precisely, we show that for three algorithmic problems used in cryptography, namely the shortest vector problem, the Hermite shortest vector problem and a variant of the closest vector problem, there is a reduction from solving the problem in any module of rank $$n \ge 2$$ to solving the problem in any free module of the same rank n. As an application, we show that this can be used to dequantize the LLL algorithm for module lattices presented by Lee et al. (Asiacrypt 2019).
Contextualized Hybrid Prompt-Tuning for Generation-Based Event Extraction

Recent years have witnessed the wide attention of event extraction task, which highly benefits various downstream applications. Traditionally, prior arts attempt to model this task in the perspective of sequence generation problem with discrete prompts. However, these methods typically construct templates for each event type without considering unique instance characteristics, which may lead to the suboptimal performance and unwanted noise. To address this issue, in this paper, we propose a novel solution for event extraction based on structure generation with contextualized hybrid prompt tuning, called CHPT-EE, in terms of both discrete and continuous prompts. Specifically, CHPT-EE unifies the encoding of event mentions from various event types via a structured event extraction language. Along this line, CHPT-EE could effectively exploit the complementary advantages of different prompts to mitigate the issues encountered by prior methods. Afterwards, for each context, we employ a semantically similar strategy to retrieve answered prompts as discrete prompt DISC-P, and obtain context-aware continuous prompt CONT-P by aggregating specific event type information and contextual semantic information. Experiments on ACE 2005 and CASIE show that CHPT-EE achieved competitive performance compared with state-of-the-art methods.