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Relational Prompt-Based Pre-Trained Language Models for Social Event Detection

ACM TRANSACTIONS ON INFORMATION SYSTEMS 2025年第1期43卷 1-43页

作者： Li, Pu Yu, Xiaoyan Peng, Hao Xian, Yantuan Wang, Linqin Sun, Li Zhang, Jingyun Yu, Philip S. Kunming Univ Sci & Technol Kunming Peoples R China Beijing Inst Technol Beijing Peoples R China Beihang Univ Beijing Peoples R China North China Elect Power Univ Beijing Peoples R China Univ Illinois Chicago IL USA

Social Event Detection (SED) aims to identify significant events from social streams, and has a wide application ranging from public opinion analysis to risk management. In recent years, Graph Neural Network (GNN) based solutions have achieved state-of-the-art performance. However, GNN-based methods often struggle with missing and noisy edges between messages, affecting the quality of learned message embedding. Moreover, these methods statically initialize node embedding before training, which, in turn, limits the ability to learn from message texts and relations simultaneously. In this article, we approach social event detection from a new perspective based on Pre-trained Language Models (PLMs), and present RPLMSED (Relational prompt-based Pre-trained Language Models for Social Event Detection). We first propose a new pairwise message modeling strategy to construct social messages into message pairs with multi-relational sequences. Secondly, a new multi-relational prompt-based pairwise message learning mechanism is proposed to learn more comprehensive message representation from message pairs with multi-relational prompts using PLMs. Thirdly, we design a new clustering constraint to optimize the encoding process by enhancing intra-cluster compactness and inter-cluster dispersion, making the message representation more distinguishable. We evaluate the RPLMSED on three real-world datasets, demonstrating that the RPLMSED model achieves state-of-the-art performance in offline, online, low-resource, and long-tail distribution scenarios for social event detection tasks.

关键词： Social networks computing methodologies Artificial intelligence Neural networks news reports sudden public incidents social trends market dynamics Phrases Social event detection pre-trained language models prompt learning clustering constraint

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Controllable Human Trajectory Generation Using Profile-Guided Latent Diffusion

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2025年第1期19卷 1-25页

作者： Song, Yiwen Ding, Jingtao Yuan, Jian Liao, Qingmin Li, Yong Tsinghua Univ Shenzhen Int Grad Sch Dept Elect Engn Beijing Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol BNRist Dept Elect Engn Beijing Peoples R China

Trajectory generation is a vital element in AI applications. Firstly, it enables simulation such as traffic simulation and epidemic spreading modeling. Secondly, it can provide synthetic privacy-preserving data for training AI models. Notably, trajectory generation featuring controllable user profiles holds substantial value in generating customized mobility trajectories tailored to diverse requirements. However, relevant work is still lacking. On the one hand, traditional deep generative models fall short in guiding controllable trajectory generation due to the statistical nature of human mobility patterns and the corresponding insufficient control mechanisms. On the other hand, though the diffusion model has demonstrated strong generative capabilities in many fields, to achieve controllable generation on discrete trajectory data, we still need to redesign the structure of the continuous diffusion model. In this article, we introduce a controllable trajectory generation framework that leverages a continuous diffusion model and classifier guidance for more robust condition control. Our proposed framework comprises two modules: a latent trajectory diffusion model and a trajectory classifier for profile guidance. Experiments on two real-world mobility datasets consistently demonstrate its capability of generating trajectories matching given user profiles and conforming to human mobility patterns. Our source code and trained models are released at https://***/tsinghua-fib-lab/User-Profile-Guided-Latent-Diffusion.

关键词： computing methodologies Knowledge representation and reasoning Information systems Data mining

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KeYric: Unsupervised Keywords Extraction and Expansion from Music for Coherent Lyrics Generation

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ACM TRANSACTIONS ON MULTIMEDIA computing COMMUNICATIONS AND APPLICATIONS 2025年第1期21卷 1-28页

作者： Ma, Xichu Sharma, Varun Kan, Min-yen Lee, Wee sun Wang, Ye Natl Univ Singapore Sch Comp Singapore Singapore

We address the challenge of enhancing coherence in generated lyrics from symbolic music, particularly for creating singing-based language learning materials. Coherence, defined as the quality of being logical and consistent, forming a unified whole, is crucial for lyrics at multiple levels-word, sentence, and full-text. Additionally, it involves lyrics' musicality-matching of style and sentiment of the music. To tackle this, we introduce KeYric, a novel system that leverages keyword skeletons to strengthen both coherence and musicality in lyrics generation. KeYric employs an innovative approach with an unsupervised keyword skeleton extractor and a graph-based skeleton expander, designed to produce a style-appropriate keyword skeleton from input music. This framework integrates the skeleton with the input music via a three-layer coherence mechanism, significantly enhancing lyric coherence by 5% in objective evaluations. Subjective assessments confirm that KeYric-generated lyrics are perceived as 19% more coherent and suitable for language learning through singing compared to existing models. Our analyses indicate that integrating genre-relevant elements, such as pitch, into music encoding is crucial, as musical genres significantly affect lyric coherence.

关键词： Applied computing Sound and music computing Education computing methodologies Natural language processing

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Efficient Latent-based Scoring Function Search for N-ary Relational Knowledge Bases

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2025年第2期19卷 1-26页

作者： Di, Shimin Zhang, Yongqi Yao, Quanming Zhou, Xiaofang Chen, Lei Hong Kong Univ Sci & Technol Hong Kong Peoples R China Southeast Univ Nanjing Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China Tsinghua Univ Beijing Peoples R China

Designing a proper scoring function is the key to ensuring the excellent performance of knowledge base (KB) embedding. Recently, the scoring function search method introduces the automated machine learning technique to design the data-aware scoring function for the given binary relational data (a.k.a. knowledge graph, KG), which can consistently achieve good performance on different data sets. However, the current data- aware search method is still not as good as desired. First, the existing model can only search scoring functions on the given binary relational data, which is a special form of N-ary relational KBs. Second, observing that existing scoring functions can exhibit distinct performance on different semantic patterns, we are motivated to explore such semantics by searching pattern-aware scoring functions. Unfortunately, it is hard to extend existing search approaches to the scenarios of N-ary and pattern-aware due to the search efficiency and effectiveness issues. In this paper, we propose latent-based factors to model relational patterns and an efficient search algorithm on the N-ary scenario, i.e., efficient LAtent-based SCO ring function search for N-ary relational KBs (LASCO). The empirical results of LASCO on binary and N-ary relational data sets demonstrate that the proposed method can efficiently search pattern-aware scoring functions and achieve better embedding performance than advanced baselines.

关键词： computing methodologies Knowledge representation and reasoning Machine learning Information systems

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A Comprehensive Survey on Rare Event Prediction

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ACM computing SURVEYS 2025年第3期57卷 1-39页

作者： Shyalika, Chathurangi Wickramarachchi, Ruwan Sheth, Amit P. Univ South Carolina Artificial Intelligence Inst Columbia SC 29208 USA

Rare event prediction involves identifying and forecasting events with a low probability using machine learning (ML) and data analysis. Due to the imbalanced data distributions, where the frequency of common events vastly outweighs that of rare events, it requires using specialized methods within each step of the ML pipeline, that is, from data processing to algorithms to evaluation protocols. Predicting the occurrences of rare events is important for real-world applications, such as Industry 4.0, and is an active research area in statistical and ML. This article comprehensively reviews the current approaches for rare event prediction along four dimensions: rare event data, data processing, algorithmic approaches, and evaluation approaches. Specifically, we consider 73 datasets from different modalities (i.e., numerical, image, text, and audio), four major categories of data processing, five major algorithmic groupings, and two broader evaluation approaches. This article aims to identify gaps in the current literature and highlight the challenges of predicting rare events. It also suggests potential research directions, which can help guide practitioners and researchers.

关键词： computing methodologies Artificial intelligence

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Large language models for software vulnerability detection: a guide for researchers on models, methods, techniques, datasets, and metrics

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INTERNATIONAL JOURNAL OF INFORMATION SECURITY 2025年第2期24卷 1-66页

作者： Far, Seyed Mohammad Taghavi Feyzi, Farid Univ Guilan Comp Engn Rasht Iran

Large language models (LLMs) have emerged as transformative tools in the domain of software vulnerability detection and management, offering sophisticated capabilities in identifying, analyzing, and mitigating security risks. This article delves into the utilization of LLMs, examining their role in revolutionizing traditional approaches to software vulnerability detection. We explore the various categories of LLMs, such as bidirectional encoder representations from transformers (BERT) and generative pre-trained transformer (GPT), and how these models are being leveraged to improve the accuracy and efficiency of vulnerability detection. This article reviews how LLMs are being integrated into existing software security frameworks, synthesizing research findings on their performance in various contexts. It includes insights into how LLM-based methods complement traditional techniques like static analysis and fuzz testing, without engaging in a direct comparative analysis of these approaches. The comparison highlights the strengths of LLMs, such as their ability to generalize across diverse codebases and programming languages, while also addressing their limitations, such as susceptibility to biases from training data and the hallucination. The article synthesizes findings from recent research, showcasing how LLMs have been successfully employed to detect a range of vulnerabilities, from buffer overflows to SQL injections, and outlines how these models enhance productivity by automating the detection and reporting of security flaws. Additionally, we discuss the inherent challenges in applying LLMs to software vulnerability detection, such as the need for high-quality datasets, and the ethical implications related to the deployment of LLM-based systems in security-critical applications. Addressing these challenges is crucial for the future advancement of LLM technologies in the cybersecurity domain. A comprehensive introduction to foundational and specialized datasets is

关键词： Security and privacy Software and application security Software security engineering computing methodologies Machine learning Machine learning approaches Large language models (LLMs) Software vulnerabilities Software vulnerability detection Software vulnerability datasets Vulnerability detection metrics Fine-tuning techniques

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Margin-aware Noise-robust Contrastive Learning for Partially View-aligned Problem

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2025年第1期19卷 1-20页

作者： Qin, Yalan Pu, Nan Wu, Hanzhou Sebe, Nicu Shanghai Univ Shanghai Peoples R China Univ Trento Trento Italy

In this article, we study a challenging problem in contrastive learning when just a portion of data is aligned in multi-view dataset due to temporal, spatial, or spatio-temporal asynchronism across views. It is important to study partially view-aligned data since this type of data is common in real-world application and easily leads to data inconsistency among different views. Such a Partially View-aligned Problem (PVP) in contrastive learning has been relatively less touched so far, especially in downstream tasks, i.e., classification and clustering. In order to solve this problem, we introduce a flexible margin and propose margin-aware noise-robust contrastive learning to simultaneously identify the within-category counterparts from the other view of one data point based on the established cross-view correspondence and learn a shared representation. To be specific, the proposed learning framework is built on a novel margin-aware noise-robust contrastive loss. Since data pairs are used as input for the proposed margin-aware noise-robust contrastive learning, we build positive pairs according to the known correspondences and negative pairs in the manner of random sampling. Our margin-aware noise-robust contrastive learning framework is able to effectively reduce or remove the impacts caused by the possible existing noise for the constructed pairs in a margin-aware manner, i.e., false negative pairs led by random sampling in PVP. We relax the proposed margin-aware noise-robust contrastive loss and then give a detailed mathematical analysis for the effectiveness of our loss. As an instantiation, we construct an example under the proposed margin-aware noise-robust contrastive learning framework for validation in this work. To the best of our knowledge, this is the first attempt of extending contrastive learning to a margin-aware noise-robust version for dealing with PVP. We also enrich the learning paradigm when there is noise in the data. Extensive experiments on diff

关键词： computing methodologies Machine learning

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A Survey on Deep Learning for Design and Generation of Virtual Architecture

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ACM computing SURVEYS 2025年第2期57卷 1-41页

作者： Wang, Anqi Dong, Jiahua Lee, Lik-hang Shen, Jiachuan Hui, Pan Hong Kong Univ Sci & Technol Hong Kong Peoples R China Hong Kong Univ Sci & Technol Guangzhou Campus Guangzhou Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China UCL London England

Three-dimensional (3D) shape generation techniques leveraging deep learning have garnered significant interest from both computer vision and architectural design communities, promising to enrich the content in the virtual environment. However, research on virtual architectural design remains limited, particularly regarding designer-AI collaboration and deep learning-assisted design. In our survey, we reviewed 149 related articles (81.2% of articles published between 2019 and 2023) covering architectural design, 3D shape techniques, and virtual environments. Through scrutinizing the literature, we first identify the principles of virtual architecture and illuminate its current production challenges, including datasets, multimodality, design intuition, and generative frameworks. We then introduce the latest approaches to designing and generating virtual buildings leveraging 3D shape generation and summarize four characteristics of various approaches to virtual architecture. Based on our analysis, we expound on four research agendas, including agency, communication, user consideration, and integrating tools. Additionally, we highlight four important enablers of ubiquitous interaction with immersive systems in deep learning-assisted architectural generation. Our work contributes to fostering understanding between designers and deep learning techniques, broadening access to designer-AI collaboration. We advocate for interdisciplinary efforts to address this timely research topic, facilitating content designing and generation in the virtual environment.

关键词： Human-centered computing Interaction design process and methods Virtual reality computing methodologies Machine learning Applied computing Architecture buildings

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Exploring Generative Adversarial Networks for Augmenting Network Intrusion Detection Tasks

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ACM TRANSACTIONS ON MULTIMEDIA computing COMMUNICATIONS AND APPLICATIONS 2025年第1期21卷 1-19页

作者： Constantin, Mihai gabriel Stanciu, Dan-cristian Stefan, Liviu-daniel Dogariu, Mihai Mihailescu, Dan Cioban, George Bergeron, Matt Liu, Winston Belov, Konstan tin Radu, Octavian Ionescu, Bogdan Univ Politehn Bucuresti Bucharest Romania Keysight Technol Bucharest Romania Keysight Technol Santa Rosa CA USA

The advent of generative networks and their adoption in numerous domains and communities have led to a wave of innovation and breakthroughs in AI and machine learning. Generative Adversarial Networks (GANs) have expanded the scope of what is possible with machine learning, allowing for new applications in areas such as computer vision, natural language processing, and creative AI. GANs, in particular, have been used for a wide range of tasks, including image and video generation, data augmentation, style transfer, and anomaly detection. They have also been used for medical imaging and drug discovery, where they can generate synthetic data to augment small datasets, reduce the need for expensive experiments, and lower the number of real patients that must be included in medical trials. Given these developments, we propose using the power of GANs to create and augment flow-based network traffic datasets. We evaluate a series of GAN architectures, including Wasserstein, conditional, energy-based, gradient penalty, and LSTM-GANs. We evaluate their performance on a set of flow-based network traffic data collected from 16 subjects who used their computers for home, work, and study purposes. The performance of these GAN architectures is described according to metrics that involve networking principles, data distribution among a collection of flows, and temporal data distribution. Given the tendency of network intrusion detection datasets to have a very imbalanced data distribution, i.e., a large number of samples in the "normal traffic" category and a comparatively low number of samples assigned to the "intrusion" categories, we test our GANs by augmenting

关键词： CCS Concepts: computing methodologies Machine learning Networks Network simulations

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Demo2Test: Transfer Testing of Agent in Competitive Environment with Failure Demonstrations

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ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2025年第2期34卷 1-28页

作者： Chen, Jianming Wang, Yawen Wang, Junjie Xie, Xiaofei Wang, Dandan Wang, Qing Xu, Fanjiang Chinese Acad Sci Inst Software Beijing Peoples R China Singapore Management Univ Singapore Singapore

The competitive game between agents exists in many critical applications, such as military unmanned aerial vehicles. It is urgent to test these agents to reduce the significant losses caused by their failures. Existing studies mainly are to construct a testing agent that competes with the target agent to induce its failures. These approaches usually focus on a single task, requiring much more time for multi-task testing. However, if the previously tested tasks (source tasks) and the task to be tested (target task) share similar agents or task objectives, the transferable knowledge in source tasks can potentially increase the effectiveness of testing in the target task. We propose Demo2Test for conducting transfer testing of agents in the competitive environment, i.e., leveraging the demonstrations of failure scenarios from the source task to boost the testing effectiveness in the target task. It trains a testing agent with demonstrations and incorporates the action perturbation at key states to balance the number of revealed failures and their diversity. We conduct experiments in the simulated robotics competitive environments of MuJoCo. The results indicate that Demo2Test outperforms the best-performing baseline with improvements ranging from 22.38% to 87.98%, and 12.69% to 60.98%, in terms of the number and diversity of discovered failure scenarios, respectively.

关键词： Computer systems organization Reliability Software and its engineering Software testing and debugging computing methodologies Adversarial learning

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