Reviewerly/exHarmony huggingface dataset:
MA-TGNN: Multiple Aggregators Graph-Based Model for Text Classification

In recent years, graph neural network (GNN) has performed well in processing non-Euclidean structural data and saving global co-occurrence information. Researchers are exploring the application of GNN in the field of text classification. However, some existing GNN-based methods employ corpus-level graph structures, which can result in high memory consumption. Additionally, a single-node aggregation method may only partially extract semantic features. We propose a graph-based text classification model called the Multi-Aggregator GNN model to address these limitations. Specifically, we utilize multiple aggregation methods to obtain the distributional characteristics of the text comprehensively. And we incorporate dimensionality reduction pooling to preserve crucial information in the text representation. Finally, we use the updated node representations as document embeddings. Experimental results on seven benchmark datasets demonstrate that our proposed model significantly improves the performance of text classification tasks.
Joint Extraction of Nested Entities and Relations Based on Multi-task Learning

Nested named entity recognition and relation extraction are two crucial tasks in information extraction. Traditional systems often treat them as separate, sequential tasks, which can lead to error propagation. To mitigate this issue, we present a joint extraction model for nested named entities and relations based on a two-level structure, which facilitates joint learning of these subtasks through parameter sharing. Initially, we employ a hierarchical network to identify nested entities. Then, to extract relationships between the nested entities identified at different layers, we introduce multiple rounds of hierarchical relation extraction, creating a dual-dynamic hierarchical network structure. Moreover, as there is a current lack of suitable tagging schemes, we propose a novel tagging scheme grounded in a hierarchical structure. Utilizing this approach, we relabel three datasets: Genia, KBP, and NYT. Experimental results indicate that our proposed joint extraction model significantly outperforms traditional methods in both tasks.
Tracing Quantum State Distinguishers via Backtracking

We show the following results: Traitor tracing and differing inputs obfuscation with quantum decoders/auxiliary input arises naturally when considering the post-quantum security of these primitives. We obtain our results by abstracting out a core algorithmic model, which we call the Back One Step (BOS) model. We prove a general theorem, reducing many quantum results including ours to designing classical algorithms in the BOS model. We then provide simple algorithms for the particular instances studied in this work.
A 2D Entity Pair Tagging Scheme for Relation Triplet Extraction

It is essential for large-scale knowledge graph construction to extract relation triplets composed of entity pairs and relations from unstructured texts. Although recent researches have made considerable progress in joint extraction of entities and relations, they are still confronted with some problems. Specifically, existing methods usually decompose the task of relation triplet extraction into several subtasks or different modules, which inevitably suffers from error propagation or poor information interaction. In this paper, a novel end-to-end relation extraction model named 2DEPT is proposed, which can effectively address the problems mentioned above. The proposed model comprises a 2D entity pair tagging scheme that provides a simple but effective decoding method and a token-pair classifier based on scoring which determines whether a token pair belongs to the specific relationship. Besides, extensive experiments on two public datasets widely used by many researchers are conducted, and the experimental results perform better than the state-of-the-art baselines overall and deliver consistent performance gains on complex scenarios of various overlapping patterns and multiple triplets. The source code and dataset are available at: https://github.com/Oliverstars/Code42DEPT .
The Power of Undirected Rewindings for Adaptive Security

Existing proofs of adaptive security (e.g., in settings in which decryption keys are adaptively revealed) often rely on guessing arguments. Such guessing arguments can be simple (and, e.g., just involve guessing which keys are revealed), or more complex "partitioning" arguments. Since guessing directly and negatively impacts the loss of the corresponding security reduction, this leads to black-box lower bounds for a number of cryptographic scenarios that involve adaptive security. In this work, we provide an alternative to such guessing arguments: instead of guessing in a security reduction which adaptive choices an adversary $$\mathcal {A} $$ makes, we rewind $$\mathcal {A} $$ many times until we can successfully embed a given computational challenge. The main benefit of using rewindings is that these rewindings can be arranged sequentially, and the corresponding reduction loss only accumulates additively (instead of multiplicatively, as with guessing). The main technical challenge is to show that $$\mathcal {A} $$ 's success is not negatively affected after (potentially many) rewindings. To this end, we develop a machinery for "undirected " rewindings that preserve $$\mathcal {A} $$ 's success across (potentially many) rewindings. We use this strategy to show In both cases, we provide the first polynomial reductions to standard assumptions (i.e., to IND-CPA and PRG security, respectively), and in case of the GGM PRF, we also circumvent an existing lower bound.
Enhancing Adversarial Robustness via Anomaly-aware Adversarial Training

Adversarial training (AT) is one of the most promising solutions for defending adversarial attacks. By exploiting the adversarial examples generated in the maximization step of AT, a large improvement on the robustness can be brought. However, by analyzing the original natural examples and the corresponding adversarial examples, we observe that a certain part of them are abnormal. In this paper, we propose a novel AT framework called anomaly-aware adversarial training (A $$^3$$ T), which utilizes different learning strategies for handling the one normal case and two abnormal cases of generating adversarial examples. Extensive experiments on three publicly available datasets with classifiers in three major network architectures demonstrate that A $$^3$$ T is effective in robustifying networks to adversarial attacks in both white/black-box settings and outperforms the state-of-the-art AT methods.
Citation Recommendation Based on Knowledge Graph and Multi-task Learning

The Citation Recommendation aims to address the problem of academic information overload by filtering and suggesting relevant references for researchers. Traditional content-based citation recommendation methods may not be comprehensive enough to extract paper attributes that are essential for evaluating paper content similarity. To better use the abundant attributes and interaction information, the knowledge graph is introduced to recommendation system recently. We construct a multi-task learning-based model for citation recommendation that incorporates a knowledge graph, consisting of two primary tasks: citation recommendation and knowledge graph link prediction. To identify the interactions between papers, we propose a pseudo-interaction matrix in the citation recommendation task. The knowledge graph link prediction task aids in identifying paper attribute information and enhancing representation. By automatically merging and sharing low-level features, exploring feature similarity, and enhancing the performance of both tasks, the multi-task learning framework can improve the final recommendation result significantly. Multiple experiments on the academic paper datasets AMiner and DBLP verify the effectiveness of our proposed model.
DBA: An Efficient Approach to Boost Transfer-Based Adversarial Attack Performance Through Information Deletion

In practice, deep learning models are easy to be fooled by input images with subtle perturbations, and those images are called adversarial examples. Regarding one model, the crafted adversarial examples can successfully fool other models with varying architectures but the same task, which is referred to as adversarial transferability. Nevertheless, in practice, it is hard to get information about the model to be attacked, transfer-based adversarial attacks have developed rapidly. Later, different techniques are proposed to promote adversarial transferability. Different from existing input transformation attacks based on spatial transformation, our approach is a novel one on the basis of information deletion. By deleting squares of the input images by channels, we mitigate overfitting on the surrogate model of the adversarial examples and further enhance adversarial transferability. The corresponding performance of our method is superior to the existing input transformation attacks on different models (here, we consider unsecured models and defense ones), as demonstrated by extensive evaluations on ImageNet.
PAC Privacy: Automatic Privacy Measurement and Control of Data Processing

We propose and study a new privacy definition, termed Probably Approximately Correct (PAC) Privacy. PAC Privacy characterizes the information-theoretic hardness to recover sensitive data given arbitrary information disclosure/leakage during/after any processing. Unlike the classic cryptographic definition and Differential Privacy (DP), which consider the adversarial (input-independent) worst case, PAC Privacy is a simulatable metric that quantifies the instance-based impossibility of inference. A fully automatic analysis and proof generation framework is proposed: security parameters can be produced with arbitrarily high confidence via Monte-Carlo simulation for any black-box data processing oracle. This appealing automation property enables analysis of complicated data processing, where the worst-case proof in the classic privacy regime could be loose or even intractable. Moreover, we show that the produced PAC Privacy guarantees enjoy simple composition bounds and the automatic analysis framework can be implemented in an online fashion to analyze the composite PAC Privacy loss even under correlated randomness. On the utility side, the magnitude of (necessary) perturbation required in PAC Privacy is not lower bounded by $${\varTheta }(\sqrt{d})$$ for a d-dimensional release but could be O(1) for many practical data processing tasks, which is in contrast to the input-independent worst-case information-theoretic lower bound. Example applications of PAC Privacy are included with comparisons to existing works.
Application of the VAR model in examining the determinants of returns of selected cryptocurrencies

The increase in the value of cryptocurrencies, market capitalization, and volume of trading on crypto exchanges resulted in a significant increase in the interest of researchers in this decentralized financial system. The two most popular cryptocurrencies today - bitcoin and ethereum - have captured the greatest attention of researchers. Given that cryptocurrency trading is similar to stock trading, the author's assumption is that their returns are determined by the price of gold and the volatility index - VIX, representing this paper's research hypothesis. Testing through vector autoregression (VAR) models, Granger causality tests, and impulse response function (IRF) shows that gold returns do not impact, unlike the VIX volatility index and Ethereum, indicating a significant relationship between cryptocurrencies bitcoin and US stock markets. On the other hand, Bitcoin returns and the volatility index cause ethereum returns, while gold returns do not.
A Flexible Generative Model for Joint Label-Structure Estimation from Multifaceted Graph Data

Learning an optimal graph structure for graph neural networks (GNNs) is crucial for solving graph-based node classification, while current methods mainly apply in the scenario that there is only one or no observed graph structure, ignoring a real and important multifaceted scenario. In the multifaceted scenario, a set of graphs are derived from different measurements and trade-off considerations applied to the same complex system. In this paper, we proposed a Flexible Generative model for joint label-structure Estimation from Multifaceted graph data (FGEM), which formulated a joint probabilistic distribution of the observations including node features, labels, and multifaceted graph structures. To explore the general relationship among these observations, our model took a flexible setting by considering various prior distributions over graphs, rather than assuming implicit label independence in GNNs. We derived a learning objective with a scalable variational inference algorithm, which considered the posterior inference of missing labels and underlying optimal graph, along with the parameter optimization of GNNs. We conducted thorough experiments on one real and three pseudo multifaceted graph datasets, and the results demonstrated the state-of-the-art performance of our model in both settings.
A Framework for Statistically Sender Private OT with Optimal Rate

Statistical sender privacy (SSP) is the strongest achievable security notion for two-message oblivious transfer (OT) in the standard model, providing statistical security against malicious receivers and computational security against semi-honest senders. In this work we provide a novel construction of SSP OT from the Decisional Diffie-Hellman (DDH) and the Learning Parity with Noise (LPN) assumptions achieving (asymptotically) optimal amortized communication complexity, i.e. it achieves rate 1. Concretely, the total communication complexity for k OT instances is $$2k(1+o(1))$$ , which (asymptotically) approaches the information-theoretic lower bound. Previously, it was only known how to realize this primitive using heavy rate-1 FHE techniques [Brakerski et al., Gentry and Halevi TCC'19]. At the heart of our construction is a primitive called statistical co-PIR, essentially a a public key encryption scheme which statistically erases bits of the message in a few hidden locations. Our scheme achieves nearly optimal ciphertext size and provides statistical security against malicious receivers. Computational security against semi-honest senders holds under the DDH assumption.
A Lower Bound for Proving Hardness of Learning with Rounding with Polynomial Modulus

Regev's Learning with Errors (LWE) problem (STOC 2005) is a fundamental hardness assumption for modern cryptography. The Learning with Rounding (LWR) Problem was put forth by Banerjee, Peikert and Rosen (Eurocrypt 2012) as an alternative to LWE, for use in cryptographic situations which require determinism. The only method we currently have for proving hardness of LWR is the so-called "rounding reduction" which is a specific reduction from an analogous LWE problem. This reduction works whenever the LWE error is small relative to the noise introduced by rounding, but it fails otherwise. For this reason, all prior work on establishing hardness of LWR forces the LWE error to be small, either by setting other parameters extremely large (which hurts performance), or by limiting the number of LWR samples seen by the adversary (which rules out certain applications). Hardness of LWR is poorly understood when the LWE modulus (q) is polynomial and when the number of LWE samples (m) seen by the adversary is an unbounded polynomial. This range of parameters is the most relevant for practical implementations, so the lack of a hardness proof in this situation is not ideal. In this work, we identify an obstacle for proving the hardness of LWR from LWE in the above framework when q is polynomial and m is an unbounded polynomial. Specifically, we show that any "pointwise" reduction from LWE to LWR (i.e., any reduction which maps LWE samples independently to LWR samples) admits an efficient algorithm which directly solves LWE (without the use of an LWR solver). Consequently, LWE cannot be reduced to LWR in our pointwise reduction model with our setting of q and m, unless LWE is easy. Our model of a pointwise reduction from LWE to LWR captures all prior reductions from LWE to LWR except the rejection sampling reduction of Bogdanov et al. (TCC 2016); while their reduction still operates in a pointwise manner, it can reject an LWE sample instead of mapping it to an LWR sample. However we conjecture that our result still holds in this setting. Our argument proceeds roughly as follows. First, we show that any pointwise reduction from LWE to LWR must have good agreement with some affine map. Then, we use the affine agreement of a pointwise reduction together with a type of Goldreich-Levin "prediction-implies-inversion" argument to extract the LWE secret from LWE input samples. Both components may be of independent interest.
Twin Graph Attention Network with Evolution Pattern Learner for Few-Shot Temporal Knowledge Graph Completion

Recent years have witnessed a growing number of studies on few-shot knowledge graph completion (FSKGC), which aims to infer new facts for relations given its few-shot observed samples. Despite current research’s great success in static knowledge graphs, few-shot temporal knowledge graph completion (FSTKGC) has not been well explored yet. Existing FSTKGC solutions mainly face two challenges. First, these models fail to distinguish the contribution of neighbors and model the difference between recurring and ever-changing facts. Second, they ignore the latent evolution patterns from observed temporal samples when learning relation representations. In this paper, we propose a novel framework named TwinGAT-VEDA with twin graph attention and an evolution pattern learner to address the above issues. First, our model devises two graph attention network (the twins) to aggregate most relative signals from recurring and dynamic neighbors separately and automatically fuses these futures based on the interaction between the subject and object. Secondly, we inject the time-differences to encode entity pairs and learn evolution patterns from few-shot reference sequence to represent few-shot relations. Comprehensive experiments on two benchmark datasets ICEWS-few-intp and GDELT-few-intp demonstrate that TwinGAT-VEDA achieves the state-of-the-art results.
SNAFA-Net: Squared Normalization Attention and Feature Alignment for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) aims to match images of the same individual across visible and infrared image sets. Due to the modality discrepancy between different modalities and the inter-modality discrepancy caused by occlusions and postures, make this a challenge task. In this paper, we propose a novel network, called SNAFA-Net, that uses innovative techniques to address the key challenges in VI-ReID. The squared normalization attention (SNA) module addresses the modality disparity by attending to the invariant features of pedestrians in each modality. The feature alignment-enriched part-based convolutional block (FA-PCB) module allows us to partition the human body image into multiple parts and extract corresponding features for each part of the body. These features are then aligned using the shortest path method, reducing the impact of posture and viewpoint changes. This alignment process ultimately reduces feature redundancy and improves the feature representation and discrimination accuracy. The experiments performed on the SYSU-MM01 [18] and RegDB [14] datasets demonstrate that the proposed approach achieves state-of-the-art performance.
Arithmetic Sketching

This paper introduces arithmetic sketching, an abstraction of a primitive that several previous works use to achieve lightweight, low-communication zero-knowledge verification of secret-shared vectors. An arithmetic sketching scheme for a language $$\mathcal {L}\subseteq \mathbb {F}^n$$ consists of (1) a randomized linear function compressing a long input x to a short "sketch," and (2) a small arithmetic circuit that accepts the sketch if and only if $$x \in \mathcal {L}$$ , up to some small error. If the language $$\mathcal {L}$$ has an arithmetic sketching scheme with short sketches, then it is possible to test membership in $$\mathcal {L}$$ using an arithmetic circuit with few multiplication gates. Since multiplications are the dominant cost in protocols for computation on secret-shared, encrypted, and committed data, arithmetic sketching schemes give rise to lightweight protocols in each of these settings. Beyond the formalization of arithmetic sketching, our contributions are: We also prove the first nontrivial lower bounds showing limits on the sketch size for certain languages (e.g., vectors of Hamming-weight one) and proving the non-existence of arithmetic sketching schemes for others (e.g., the language of all vectors that contain a specific value).
Cluster Robust Inference for Embedding-Based Knowledge Graph Completion

Knowledge Graphs (KGs) are able to structure and represent knowledge in complex systems, whereby their completeness impacts the quality of any further application. Real world KGs are notoriously incomplete, which is why KG Completion (KGC) methods emerged. Most KGC methods rely on Knowledge Graph Embedding (KGE) based link prediction models, which provide completion candidates for a sparse KG. Metrics like the Mean Rank, the Mean Reciprocal Rank or Hits@K evaluate the quality of those models, like TransR, DistMult or ComplEx. Based on the principle of supervised learning, these metrics evaluate a KGC model trained on a training dataset, based on the partition of true completion candidates it achieves on a test dataset. Dealing with real world, complex KGs, we found that sparsity is not equally distributed across a KG, but rather grouped in clusters. We use modularity-based KG clustering, to approximate sparsity levels in a KG. Furthermore, we postulate that prediction errors of an embedding-based KGC model are correlated within clusters of a KG but uncorrelated between them and formalize a new, cluster-robust KGC evaluation metric. We test our metric using six benchmark dataset and one real-world industrial example dataset. Our experiments show its superiority to existing metrics with regards to the prediction of cluster-robust triplets $$^{1}$$ ( $$^{1}$$ The code is available at https://github.com/simoncharmms/crmrr .).
A Cognitive Knowledge Enriched Joint Framework for Social Emotion and Cause Mining

Social emotion mining and its cause identification are important tasks in Web-based social media analysis and text mining with many domain applications, which focus on analyzing the emotions evoked to the reader and their corresponding causes. Previously, they are conducted as separate tasks. Based on the cognitive emotion theory, there is a deep coupling relationship between social emotion and its cause identification. In this paper, we propose a cognitive knowledge enriched joint framework (JointPSEC) for predicting social emotion and its causes. Specifically, we formulate the rules based on the cognitive emotion model and utilize this knowledge together with the lexicon-based knowledge to improve emotion-clause relation learning for social emotion mining. Meanwhile, we utilize the predicted emotion to enhance dimension-clause relation learning for cause identification. Our method is mediated by cognitive knowledge to mutually facilitate the joint prediction task. Experimental results on the benchmark dataset verify the effectiveness of our framework.