Label Enhanced Event Detection with Heterogeneous Graph Attention Networks

Event Detection (ED) aims to recognize instances of specified types of event triggers in text. Different from English ED, Chinese ED suffers from the problem of word-trigger mismatch due to the uncertain word boundaries. Existing approaches injecting word information into character-level models have achieved promising progress to alleviate this problem, but they are limited by two issues. First, the interaction between characters and lexicon words is not fully exploited. Second, they ignore the semantic information provided by event labels. We thus propose a novel architecture named Label enhanced Heterogeneous Graph Attention Networks (L-HGAT). Specifically, we transform each sentence into a graph, where character nodes and word nodes are connected with different types of edges, so that the interaction between words and characters is fully reserved. A heterogeneous graph attention networks is then introduced to propagate relational message and enrich information interaction. Furthermore, we convert each label into a trigger-prototype-based embedding, and design a margin loss to guide the model distinguish confusing event labels. Experiments on two benchmark datasets show that our model achieves significant improvement over a range of competitive baseline methods

Federated Learning over a Wireless Network: Distributed User Selection through Random Access

User selection has become crucial for decreasing the communication costs of federated learning (FL) over wireless networks. However, centralized user selection causes additional system complexity. This study proposes a network intrinsic approach of distributed user selection that leverages the radio resource competition mechanism in random access. Taking the carrier sensing multiple access (CSMA) mechanism as an example of random access, we manipulate the contention window (CW) size to prioritize certain users for obtaining radio resources in each round of training. Training data bias is used as a target scenario for FL with user selection. Prioritization is based on the distance between the newly trained local model and the global model of the previous round. To avoid excessive contribution by certain users, a counting mechanism is used to ensure fairness. Simulations with various datasets demonstrate that this method can rapidly achieve convergence similar to that of the centralized user selection approach

Adaptive Data Augmentation for Aspect Sentiment Quad Prediction

Aspect sentiment quad prediction (ASQP) aims to predict the quad sentiment elements for a given sentence, which is a critical task in the field of aspect-based sentiment analysis. However, the data imbalance issue has not received sufficient attention in ASQP task. In this paper, we divide the issue into two-folds, quad-pattern imbalance and aspect-category imbalance, and propose an Adaptive Data Augmentation (ADA) framework to tackle the imbalance issue. Specifically, a data augmentation process with a condition function adaptively enhances the tail quad patterns and aspect categories, alleviating the data imbalance in ASQP. Following previous studies, we also further explore the generative framework for extracting complete quads by introducing the category prior knowledge and syntax-guided decoding target. Experimental results demonstrate that data augmentation for imbalance in ASQP task can improve the performance, and the proposed ADA method is superior to naive data oversampling.Comment: Accepted by ICASSP 2024, 5 page

Enhancing Multimodal Entity and Relation Extraction with Variational Information Bottleneck

This paper studies the multimodal named entity recognition (MNER) and multimodal relation extraction (MRE), which are important for multimedia social platform analysis. The core of MNER and MRE lies in incorporating evident visual information to enhance textual semantics, where two issues inherently demand investigations. The first issue is modality-noise, where the task-irrelevant information in each modality may be noises misleading the task prediction. The second issue is modality-gap, where representations from different modalities are inconsistent, preventing from building the semantic alignment between the text and image. To address these issues, we propose a novel method for MNER and MRE by Multi-Modal representation learning with Information Bottleneck (MMIB). For the first issue, a refinement-regularizer probes the information-bottleneck principle to balance the predictive evidence and noisy information, yielding expressive representations for prediction. For the second issue, an alignment-regularizer is proposed, where a mutual information-based item works in a contrastive manner to regularize the consistent text-image representations. To our best knowledge, we are the first to explore variational IB estimation for MNER and MRE. Experiments show that MMIB achieves the state-of-the-art performances on three public benchmarks

FFT: Towards Harmlessness Evaluation and Analysis for LLMs with Factuality, Fairness, Toxicity

The widespread of generative artificial intelligence has heightened concerns about the potential harms posed by AI-generated texts, primarily stemming from factoid, unfair, and toxic content. Previous researchers have invested much effort in assessing the harmlessness of generative language models. However, existing benchmarks are struggling in the era of large language models (LLMs), due to the stronger language generation and instruction following capabilities, as well as wider applications. In this paper, we propose FFT, a new benchmark with 2116 elaborated-designed instances, for LLM harmlessness evaluation with factuality, fairness, and toxicity. To investigate the potential harms of LLMs, we evaluate 9 representative LLMs covering various parameter scales, training stages, and creators. Experiments show that the harmlessness of LLMs is still under-satisfactory, and extensive analysis derives some insightful findings that could inspire future research for harmless LLM research.Comment: Work in progres

New Automatic Search Tool for Impossible Differentials and Zero-Correlation Linear Approximations

Impossible differential and zero-correlation linear cryptanalysis are two of the most powerful cryptanalysis methods in the field of symmetric key cryptography. There are several automatic tools to search such trails for ciphers with S-boxes. These tools focus on the properties of linear layers, and idealize the underlying S-boxes, i.e., assume any input and output difference pairs are possible. In reality, such S-box never exists, and the possible output differences with any fixed input difference can be at most half of the entire space. Hence, some of the possible differential trails under the ideal world become impossible in reality, possibly resulting in impossible differential trails for more rounds. In this paper, we firstly take the differential and linear properties of non-linear components such as S-box into consideration and propose a new automatic tool to search impossible differential trails for ciphers with S-box. We then generalize the tool to modulo addition, and apply it to ARX ciphers. To demonstrate the usefulness of the tool, we apply it to HIGHT, SHACAL-2, LEA, LBlock. As a result, it improves the best existing results of each cipher

Tuning Oxygen Redox Reaction through the Inductive Effect with Proton Insertion in Li-Rich Oxides.

As a parent compound of Li-rich electrodes, Li2MnO3 exhibits high capacity during the initial charge; however, it suffers notoriously low Coulombic efficiency due to oxygen and surface activities. Here, we successfully optimize the oxygen activities toward reversible oxygen redox reactions by intentionally introducing protons into lithium octahedral vacancies in the Li2MnO3 system with its original structural integrity maintained. Combining structural probes, theoretical calculations, and resonant inelastic X-ray scattering results, a moderate coupling between the introduced protons and lattice oxygen at the oxidized state is revealed, which stabilizes the oxygen activities during charging. Such a coupling leads to an unprecedented initial Coulombic efficiency (99.2%) with a greatly improved discharge capacity of 302 mAh g-1 in the protonated Li2MnO3 electrodes. These findings directly demonstrate an effective concept for controlling oxygen activities in Li-rich systems, which is critical for developing high-energy cathodes in batteries

Oxidation of Enrofloxacin with Permanganate: Kinetics, Multivariate Effects, Identification of Oxidation Products, and Determination of Residual Antibacterial Activity

Permanganate [Mn(VII)] chemistry oxidation of fluoroquinolone (FQ) antibiotic enrofloxacin (ENR) in water is investigated with respect to the kinetics, pH effect, buffer effect, and the evaluation of residual antibacterial activity after oxidative treatment. The degradation of ENR by Mn(VII) obeyed a secondary-order kinetics. Modern high-resolution tandem mass spectrometry coupled with high performance liquid chromatography was used to analyze the structures of degradation products. Four main oxidation products were identified at different pH values. Several influencing factors, pH value, and buffer obviously affect reaction rate and products relative abundance. Autocatalysis taking place at slightly acidic pH promotes the reaction but has no effect on the product types. A plausible oxidation pathway for enrofloxacin with Mn(VII) was proposed. The oxidation took place at the piperazine ring. Structural changes to the piperazine ring include N-dealkylation, hydroxylation, and hydrolysis. Residual antibacterial activity of the oxidative reaction solutions against nonresistant Escherichia coli reference strain DH5α is evaluated by means of quantitative bioassays. It is noticed that the oxidation products exhibited negligible antibacterial activity just when the structures of the products changed