Bin Guo

AAMAS Conference 2026 Conference Paper

Bridging Expertise and Data: Multi-Label Disease Detection via Causal Learning and Decision Fusion

• Xin Zhang
• Minhui Zhang
• Jiaqi Liu
• Zhiwen Yu
• Bin Guo

Recent multi-label disease detection methods exploit disease causality and disease–image feature interactions, but causal learning is ofteninaccurateandcomputationallycostly. Meanwhile, human–AI collaboration in diagnosis can outperform either clinicians or models alone. We propose a framework that combines expert-guided causal learning with Bayesian human–AI decision fusion. First, we learn an expert causal matrix via a GCN from expert labels and authoritative medical knowledge, and use it to regularize inter-disease causallearning. Second, weconvertper-labelprobabilitiesintojoint label-set probabilities and fuse them with expert decisions using a Bayesian scheme. Experiments on three medical datasets show that our method outperforms state-of-the-art multi-label disease detection models by up to 13. 18%.

AAMAS Conference 2026 Conference Paper

CentaurMD: Confidence-Aware Human-AI Decision Fusion for Multi-Label Disease Diagnosis via Label-Specific MoE

• Youcheng Zhang
• Hui Wang
• Jiaqi Liu
• Yao Zhang
• Zhiwen Yu
• Bin Guo

Multi-label disease diagnosis is prevalent in clinical applications, such as chest X-rays that may indicate multiple coexisting diseases. Despite advances in AI, current models remain insufficient for reliably addressing such complexity. Human–AI synergy thus emerges as both a necessary and promising approach, motivating our focus on effective decision fusion for multi-label disease diagnosis. There are two challenges. Confidence, a key factor in decision fusion, is often unrecorded in human annotations, making its estimation nontrivial. Moreover, label-specific variations in human and model expertise must be considered to achieve effective fusion. To address these challenges, we propose CentaurMD, a confidence-aware human–AI decision fusion framework based on label-specific Mixture-of-Experts (MoE). We first present a novel multi-label confusion matrix construction method that employs maximum entropy modeling to capture label correlations, enabling more accurate confidence estimation and weight allocation. Then, we develop a label-specific MoE module with dedicated gating networks and thresholds, which dynamically adjust expert weights using information extracted from the confusion matrix via a Transformer encoder. Extensive experiments on three real-world clinical datasets demonstrate that our method reduces Hamming loss by 39. 14% and improves MMR (missed-misdiagnosis reduction) by 17. 38%, achieving substantial diagnostic improvements.

AAMAS Conference 2026 Conference Paper

MeCo: Enhancing LLM-Empowered Multi-Robot Collaboration via Similar Task Memoization

• Baiqing Wang
• Helei Cui
• Bo Zhang
• Xiaolong Zheng
• Bin Guo
• Zhiwen Yu

Multi-robot systems have been widely deployed in real-world applications, providing significant improvements in efficiency and reductions in labor costs. However, most existing multi-robot collaboration methods rely on extensive task-specific training, which limitstheiradaptabilitytonewordiversescenarios. Recentresearch leverages the language understanding and reasoning capabilities of large language models (LLMs) to enable more flexible collaboration without specialized training. Yet, current LLM-empowered approaches remain inefficient: when confronted with identical or similar tasks, they must replan from scratch because they omit tasklevel similarities. To address this limitation, we propose MeCo, a similarity-aware multi-robot collaboration framework that applies the principle of “cache and reuse” (a. k. a. , memoization) to reduce redundant computation. Unlike simple task repetition, identifying and reusing solutions for similar but not identical tasks is far more challenging, particularly in multi-robot settings. To this end, MeCo introduces a new similarity testing method that retrieves previouslysolvedtaskswithhighrelevance, enablingeffectiveplanreuse without re-invoking LLMs. Furthermore, we present MeCoBench, the first benchmark designed to evaluate performance on similartask collaboration scenarios. Experimental results show that MeCo substantially reduces planning costs and improves success rates compared with state-of-the-art approaches.

AAMAS Conference 2026 Conference Paper

Mind-Inference for Multi-Turn Emotional Support: Distinguishing Personal and Factual with Adaptive Memory

• Peilin Li
• Qiuyun Zhang
• Yukun Lian
• Chengxuan Li
• Bin Guo
• Mengqi Chen
• Han Wang
• Yuyang Fang

Emotional Support Conversation (ESC) requires models to track a user’s evolving psychological state and produce supportive responsesacrossmultipleturns. ExistingESCmethodsimproveempathy with emotion clues, strategy planning, or commonsense reasoning, but they often treat personal and factual information uniformly and therefore struggle with stale memories in long interactions. We present MIA, a compact framework for multi-turn ESC with three coordinated modules: EToM for hierarchical mind inference, PFD for adaptive Personal–Factual weighting, and ORM for removing obsolete psychological inferences during memory updates. Evaluations on ESConv and CPsyCounD show consistent gains over strong baselines, while ablations confirm that both adaptive factor weightingandobsolescence-awarememoryarenecessary. Theseresults indicate that explicit Personal/Factual separation is a practical design principle for robust multi-turn emotional support. ∗These authors contributed equally to this work. †Corresponding author. This work is licensed under a Creative Commons Attribution International 4. 0 License. Proc. of the 25th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2026), C. Amato, L. Dennis, V. Mascardi, J. Thangarajah (eds.), May 25 – 29, 2026, Paphos, Cyprus. © 2026 International Foundation for Autonomous Agents and Multiagent Systems (www. ifaamas. org). https: //doi. org/10. 65109/CIMN4023

AAAI Conference 2026 Conference Paper

VIL2C: Value-of-Information Aware Low-Latency Communication for Multi-Agent Reinforcement Learning

• Qian Zhang
• Zhuo Sun
• Yao Zhang
• Zhiwen Yu
• Bin Guo
• Jun Zhang

Inter-agent communication serves as an effective mechanism for enhancing performance in collaborative multi-agent reinforcement learning (MARL) systems. However, the inherent communication latency in practical systems induces both action decision delays and outdated information sharing, impeding MARL performance gains, particularly in time-critical applications like autonomous driving. In this work, we propose a Value-of-Information aware Low-latency Communication (VIL2C) scheme that proactively adjusts the latency distribution to mitigate its effects in MARL systems. Specifically, we define a Value of Information (VoI) metric to quantify the importance of delayed messages on the recipient agent's decision. We then design a VoI aware resource allocation method that dynamically prioritizes message transmission based on each delayed message's importance. Moreover, we propose a progressive message reception mechanism to adaptively adjust the reception duration based on received messages. We derive the optimized VoI aware resource allocation and theoretically prove the performance advantage of the proposed VIL2C scheme. Extensive experiments demonstrate that VIL2C outperforms existing approaches under various communication conditions. These gains are attributed to the low-latency transmission of high-VoI messages via resource allocation and the elimination of unnecessary waiting periods via adaptive reception duration.

JBHI Journal 2025 Journal Article

A Novel Recognition and Classification Approach for Motor Imagery Based on Spatio-Temporal Features

• Renjie Lv
• Wenwen Chang
• Guanghui Yan
• Wenchao Nie
• Lei Zheng
• Bin Guo
• Muhammad Tariq Sadiq

Motor imagery, as a paradigm of brain-computer interface, holds vast potential in the field of medical rehabilitation. Addressing the challenges posed by the non-stationarity and low signal-to-noise ratio of EEG signals, the effective extraction of features from motor imagery signals for accurate recognition stands as a key focus in motor imagery brain-computer interface technology. This paper proposes a motor imagery EEG signal classification model that combines functional brain networks with graph convolutional networks. First, functional brain networks are constructed using different brain functional connectivity metrics, and graph theory features are calculated to deeply analyze the characteristics of brain networks under different motor tasks. Then, the constructed functional brain networks are combined with graph convolutional networks for the classification and recognition of motor imagery tasks. The analysis based on brain functional connectivity reveals that the functional connectivity strength during the both fists task is significantly higher than that of other motor imagery tasks, and the functional connectivity strength during actual movement is generally superior to that of motor imagery tasks. In experiments conducted on the Physionet public dataset, the proposed model achieved a classification accuracy of 88. 39% under multi-subject conditions, significantly outperforming traditional methods. Under single-subject conditions, the model effectively addressed the issue of individual variability, achieving an average classification accuracy of 99. 31%. These results indicate that the proposed model not only exhibits excellent performance in the classification of motor imagery tasks but also provides new insights into the functional connectivity characteristics of different motor tasks and their corresponding brain regions.

IJCAI Conference 2025 Conference Paper

ActiveHAI: Active Collection Based Human-AI Diagnosis with Limited Expert Predictions

• Xuehan Zhao
• Jiaqi Liu
• Xin Zhang
• Zhiwen Yu
• Bin Guo

Recent studies indicate that human-AI collaboration performs better than either alone, particularly in medical diagnosis. Beyond collaboration methods that focus on assigning tasks to humans or AI, like deferral, combining human and AI decisions with their confidence scores is emerging as a promising strategy. Due to high cognitive load, doctors often struggle to provide confidence assessments, necessitating explicit human uncertainty evaluation through a limited number of additional expert predictions. There are two challenges. (1) how to actively collect limited yet representative expert predictions? (2) how to accurately evaluate human uncertainty with limited expert predictions? To address the challenges, we propose ActiveHAI, an active human-AI diagnosis method that reduces expert costs through a median-window sampling strategy that actively selects representative samples near the estimated median; and evaluate expert confidence through an evaluator module that integrates sample features and expert predictions, converting them into probability distributions. Experiments on three real-world datasets show that ActiveHAI surpasses doctor and other human-AI methods by 16. 3% and 3. 6% in accuracy, respectively. Furthermore, ActiveHAI reaches 97. 2% relative accuracy, even with just eight expert predictions per class.

TIST Journal 2025 Journal Article

Balancing Cooperation and Competition: Selfish Worker Coalition Formation in Spatial Crowdsourcing

• Liang Wang
• Shan Su
• Rongchang Cheng
• Dingqi Yang
• Lianbo Ma
• Fei Xiong
• Bin Guo
• Zhiwen Yu

Spatial Crowdsourcing (SC), which outsources location-dependent tasks to workers for physical completion, is gaining popularity. Recently, more complex tasks have emerged that require a group of workers collaborating in a coalition. Several pioneering studies have examined this issue using the server assigned tasks mode from an overall perspective, such as maximizing the total benefits of all workers. Unfortunately, maximizing the overall benefit does not necessarily align with maximizing individual benefits. In practice, crowd workers are often self-interested and autonomous, making decisions based on their personal perspectives. In this article, under the worker selected tasks mode, we investigate an important problem: Selfish Workers Coalition Formation (SWCF) problem in SC. Here, selfish workers autonomously form coalitions to accomplish tasks to maximize their individual benefits. Achieving a stable coalition formation for SWCF problem requires balancing cooperation and competition. First, we transform the SWCF problem into a hedonic coalition formation game using a devised exploited skills-based reward distribution model. Subsequently, we propose a distributed algorithm HCFTA and prove its Nash stability and performance bounds. Additionally, to enhance coalition formation efficiency, we propose a Markov blanket coloring parallel optimization algorithm MCPHCF. Extensive experiments demonstrate the superiority of the proposed methods on both synthetic and real-world datasets.

YNIMG Journal 2025 Journal Article

Brain development during the lifespan of cynomolgus monkeys

• Zhiqiang Tan
• Binbin Nie
• Huanhua Wu
• Bang Li
• Jingjie Shang
• Tianhao Zhang
• Zeyu Xiao
• Chenchen Dong

F]FDG PET-MRI data from 228 healthy cynomolgus monkeys spanning the age range of 0.5-29.5 years to construct an age-specific multimodal image brain template toolset tailored to cynomolgus monkeys. Their brain volume and glucose metabolism were quantitatively analyzed by utilizing an individualized spatial segmentation algorithm. Our findings encapsulated the growth and development trends, sex differences, and asymmetrical variations in brain volume and glucose metabolism in cynomolgus monkeys, and analyzed the correlation between the brain volume and glucose metabolism. This endeavor enhances our capacity to leverage the cynomolgus monkey model in neuroscience research by providing a valuable resource for researchers. The age-specific brain template toolset and associated data offer a robust foundation for future investigations, facilitating a nuanced understanding of brain development in this primate species and, consequently, informing and advancing neuroscience research employing cynomolgus monkeys.

AAAI Conference 2025 Conference Paper

CollageNoter: Real-Time and Adaptive Collage Layout Design for Screenshot-Based E-Note-Taking

• Qiuyun Zhang
• Bin Guo
• Lina Yao
• Xiaotian Qiao
• Ying Zhang
• Zhiwen Yu

To enhance the processing of complex multi-modal documents (e.g. e-books, long web pages, etc.), it is an efficient way for users to take digital screenshots of key parts and reorganize them into a new collage E-Note. Existing methods for assisting collage layout design primarily employ a semantic relevance-first strategy, with arranging related contents together. Though capable, it can not ensure the visual readability of screenshots and may conflict with human natural reading patterns. In this paper, we introduce CollageNoter for real-time collage layout design that adapts to various devices (e.g. laptop, tablet, phone, etc.), offering users with visually and cognitively well-organized screenshot-based E-Notes. Specifically, we construct a novel two-stage pipeline for collage design, including 1) readability-first layout generation and 2) cognitive-driven layout adjustment. In addition, to achieve real-time response and adaptive model training, we propose a cascade transformer-based layout generator named CollageFormer and a size-aware collage layout builder for automatic dataset construction. Extensive experimental results have confirmed the effectiveness of our CollageNoter.

IJCAI Conference 2025 Conference Paper

Tree-of-AdEditor: Heuristic Tree Reasoning for Automated Video Advertisement Editing with Large Language Model

• Yuqi Zhang
• Bin Guo
• Nuo Li
• Ying Zhang
• Shijie Wang
• Zhiwen Yu
• Qing Li

Video advertising has become a popular marketing strategy on e-commerce platforms, requiring high-level semantic reasoning like selling point discovery, narrative organization. Previous rule-based methods struggle with these complex tasks, and learning-based approaches demand large datasets and high training costs. Recently, Large Language Models have opened incredible opportunities for advancing intelligent video advertisement editing. However, Input-output (IO) prompting and Chain-of-Thought (CoT) struggle to adapt to the nonlinear thinking hierarchy of video editing, where editors iteratively select shots or revert them to explore potential editing solutions. While Tree-of-Thought (ToT) offers a conceptual structure that mirrors this hierarchy, it falls short in aligning with effective video advertising strategies and lacks robust fact-checking mechanisms. To address these, we propose a novel framework, Tree-of-AdEditor (ToAE), which constructs a reasoning tree to mimic human editors, and incorporates domain-specific theories and heuristic fact-checking to identify optimal editing solutions. Specifically, motivated by effective advertisement principles, we develop a "local-global" mechanism to guide LLM in both the shot level and sequence level decision-making. We introduce a visual incoherence pruning module to provide external heuristic fact-checking, ensuring visual attractiveness and reducing computation costs. Quantitative experiments and expert evaluation demonstrate the superiority of our method compared to baselines.

NeurIPS Conference 2024 Conference Paper

HAWK: Learning to Understand Open-World Video Anomalies

• Jiaqi Tang
• Hao Lu
• Ruizheng Wu
• Xiaogang Xu
• Ke Ma
• Cheng Fang
• Bin Guo
• Jiangbo Lu

Video Anomaly Detection (VAD) systems can autonomously monitor and identify disturbances, reducing the need for manual labor and associated costs. However, current VAD systems are often limited by their superficial semantic understanding of scenes and minimal user interaction. Additionally, the prevalent data scarcity in existing datasets restricts their applicability in open-world scenarios. In this paper, we introduce HAWK, a novel framework that leverages interactive large Visual Language Models (VLM) to interpret video anomalies precisely. Recognizing the difference in motion information between abnormal and normal videos, HAWK explicitly integrates motion modality to enhance anomaly identification. To reinforce motion attention, we construct an auxiliary consistency loss within the motion and video space, guiding the video branch to focus on the motion modality. Moreover, to improve the interpretation of motion-to-language, we establish a clear supervisory relationship between motion and its linguistic representation. Furthermore, we have annotated over 8, 000 anomaly videos with language descriptions, enabling effective training across diverse open-world scenarios, and also created 8, 000 question-answering pairs for users' open-world questions. The final results demonstrate that HAWK achieves SOTA performance, surpassing existing baselines in both video description generation and question-answering. Our codes/dataset/demo will be released at https: //github. com/jqtangust/hawk.

TIST Journal 2024 Journal Article

Learning Cross-modality Interaction for Robust Depth Perception of Autonomous Driving

• Yunji Liang
• Nengzhen Chen
• Zhiwen Yu
• Lei Tang
• Hongkai Yu
• Bin Guo
• Daniel Dajun Zeng

As one of the fundamental tasks of autonomous driving, depth perception aims to perceive physical objects in three dimensions and to judge their distances away from the ego vehicle. Although great efforts have been made for depth perception, LiDAR-based and camera-based solutions have limitations with low accuracy and poor robustness for noise input. With the integration of monocular cameras and LiDAR sensors in autonomous vehicles, in this article, we introduce a two-stream architecture to learn the modality interaction representation under the guidance of an image reconstruction task to compensate for the deficiencies of each modality in a parallel manner. Specifically, in the two-stream architecture, the multi-scale cross-modality interactions are preserved via a cascading interaction network under the guidance of the reconstruction task. Next, the shared representation of modality interaction is integrated to infer the dense depth map due to the complementarity and heterogeneity of the two modalities. We evaluated the proposed solution on the KITTI dataset and CALAR synthetic dataset. Our experimental results show that learning the coupled interaction of modalities under the guidance of an auxiliary task can lead to significant performance improvements. Furthermore, our approach is competitive against the state-of-the-art models and robust against the noisy input. The source code is available at https://github.com/tonyFengye/Code/tree/master.

AAMAS Conference 2023 Conference Paper

Learning to Self-Reconfigure for Freeform Modular Robots via Altruism Multi-Agent Reinforcement Learning

• Lei Wu
• Bin Guo
• Qiuyun Zhang
• Zhuo Sun
• Jieyi Zhang
• Zhiwen Yu

Modular robots can change between different configurations to adapt to complex and dynamic environments. Therefore, performing accurate and efficient changes to modular robot system, known as the self-reconfiguration problem, is essential. Existing reconfiguration algorithms are based on discrete motion primitives. However, freeform modular robots are connected without alignment and their motion space is continuous, making existing reconfiguration methods infeasible. In this work, we design a parallel distributed self-reconfiguration algorithm based on multi-agent reinforcement learning for freeform modular robots. We introduce a collaboration mechanism into the reinforcement learning to avoid conflicts in continuous action spaces. Simulations show that our algorithm reduces conflicts and improves effectiveness compared to the baselines.

IJCAI Conference 2023 Conference Paper

Learning to Self-Reconfigure for Freeform Modular Robots via Altruism Proximal Policy Optimization

• Lei Wu
• Bin Guo
• Qiuyun Zhang
• Zhuo Sun
• Jieyi Zhang
• Zhiwen Yu

The advantages of modular robot systems stem from their ability to change between different configurations, enabling them to adapt to complex and dynamic real-world environments. Then, how to perform the accurate and efficient change of the modular robot system, i. e. , the self-reconfiguration problem, is essential. Existing reconfiguration algorithms are based on discrete motion primitives and are suitable for lattice-type modular robots. The modules of freeform modular robots are connected without alignment, and the motion space is continuous. It renders existing reconfiguration methods infeasible. In this paper, we design a parallel distributed self-reconfiguration algorithm for freeform modular robots based on multi-agent reinforcement learning to realize the automatic design of conflict-free reconfiguration controllers in continuous action spaces. To avoid conflicts, we incorporate a collaborative mechanism into reinforcement learning. Furthermore, we design the distributed termination criteria to achieve timely termination in the presence of limited communication and local observability. When compared to the baselines, simulations show that the proposed method improves efficiency and congruence, and module movement demonstrates altruism.

TIST Journal 2023 Journal Article

Modeling Within-Basket Auxiliary Item Recommendation with Matchability and Ubiquity

• En Xu
• Zhiwen Yu
• Zhuo Sun
• Bin Guo
• Lina Yao

Within-basket recommendation is to recommend suitable items for the current basket with some already known items. The within-basket auxiliary item recommendation ( WBAIR ) is to recommend auxiliary items based on the primary items in the basket. Such a task exists in many real-life scenarios. Unlike the associations between items that can be transmitted in both directions, primary and auxiliary relationships are unidirectional. Then, the suitable matching patterns between primary and auxiliary items cannot be explored by traditional directionless methods. Therefore, we design the Matc4Rec algorithm to integrate the primary and auxiliary factors, and finally recommend items that not only match the interests of users but also satisfy the primary and auxiliary relationships between items. Specifically, we capture the pattern from three aspects: matchability within-basket, matchability between baskets, and ubiquity. By exploiting this pattern, the designed algorithm not only achieves good results on real-world datasets but also improves the interpretability of recommendations. As a result, we can know which commodities are suitable as auxiliary items. The experiment results demonstrate that our algorithm can also alleviate the cold start problem.

TIST Journal 2022 Journal Article

Data-driven Targeted Advertising Recommendation System for Outdoor Billboard

• Liang Wang
• Zhiwen Yu
• Bin Guo
• Dingqi Yang
• Lianbo Ma
• Zhidan Liu
• Fei Xiong

In this article, we propose and study a novel data-driven framework for Targeted Outdoor Advertising Recommendation (TOAR) with a special consideration of user profiles and advertisement topics. Given an advertisement query and a set of outdoor billboards with different spatial locations and rental prices, our goal is to find a subset of billboards, such that the total targeted influence is maximum under a limited budget constraint. To achieve this goal, we are facing two challenges: (1) it is difficult to estimate targeted advertising influence in physical world; (2) due to NP hardness, many common search techniques fail to provide a satisfied solution with an acceptable time, especially for large-scale problem settings. Taking into account the exposure strength, advertisement matching degree, and advertising repetition effect, we first build a targeted influence model that can characterize that the advertising influence spreads along with users mobility. Subsequently, based on a divide-and-conquer strategy, we develop two effective approaches, i.e., a master–slave-based sequential optimization method, TOAR-MSS, and a cooperative co-evolution-based optimization method, TOAR-CC, to solve our studied problem. Extensive experiments on two real-world datasets clearly validate the effectiveness and efficiency of our proposed approaches.

TIST Journal 2022 Journal Article

DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Prediction

• Siyuan Ren
• Bin Guo
• Longbing Cao
• Ke Li
• Jiaqi Liu
• Zhiwen Yu

The prediction of express delivery sequence, i.e., modeling and estimating the volumes of daily incoming and outgoing parcels for delivery, is critical for online business, logistics, and positive customer experience, and specifically for resource allocation optimization and promotional activity arrangement. A precise estimate of consumer delivery requests has to involve sequential factors such as shopping behaviors, weather conditions, events, business campaigns, and their couplings. Despite that various methods have integrated external features to enhance the effects, extant works fail to address complex feature-sequence couplings in the following aspects: weaken the inter-dependencies when processing heterogeneous data and ignore the cumulative and evolving situation of coupling relationships. To address these issues, we propose DeepExpress—a deep-learning-based express delivery sequence prediction model, which extends the classic seq2seq framework to learn feature-sequence couplings. DeepExpress leverages an express delivery seq2seq learning, a carefully designed heterogeneous feature representation, and a novel joint training attention mechanism to adaptively handle heterogeneity issues and capture feature-sequence couplings for accurate prediction. Experimental results on real-world data demonstrate that the proposed method outperforms both shallow and deep baseline models.

TIST Journal 2022 Journal Article

Dynamic Probabilistic Graphical Model for Progressive Fake News Detection on Social Media Platform

• Ke Li
• Bin Guo
• Jiaqi Liu
• Jiangtao Wang
• Haoyang Ren
• Fei Yi
• Zhiwen Yu

Recently, fake news has been readily spread by massive amounts of users in social media, and automatic fake news detection has become necessary. The existing works need to prepare the overall data to perform detection, losing important information about the dynamic evolution of crowd opinions, and usually neglect the issue of uneven arrival of data in the real world. To address these issues, in this article, we focus on a kind of approach for fake news detection, namely progressive detection, which can be achieved by the dynamic Probabilistic Graphical Model. Based on the observation on real-world datasets, we adaptively improve the Kalman Filter to the Labeled Variable Dimension Kalman Filter (LVDKF) that learns two universal patterns from true and fake news, respectively, which can capture the temporal information of time-series data that arrive unevenly. It can take sequential data as input, distill the dynamic evolution knowledge regarding a post, and utilize crowd wisdom from users’ responses to achieve progressive detection. Then we derive the formulas using the Forward, Backward, and EM Algorithm, and we design a dynamic detection algorithm using Bayes’ theorem. Finally, we design experimental scenarios simulating progressive detection and evaluate LVDKF on two public datasets. It outperforms the baseline methods in these experimental scenarios, which indicates that it is adequate for progressive detection.

TIST Journal 2022 Journal Article

MetaDetector: Meta Event Knowledge Transfer for Fake News Detection

• Yasan Ding
• Bin Guo
• Yan Liu
• Yunji Liang
• Haocheng Shen
• Zhiwen Yu

The blooming of fake news on social networks has devastating impacts on society, the economy, and public security. Although numerous studies are conducted for the automatic detection of fake news, the majority tend to utilize deep neural networks to learn event-specific features for superior detection performance on specific datasets. However, the trained models heavily rely on the training datasets and are infeasible to apply to upcoming events due to the discrepancy between event distributions. Inspired by domain adaptation theories, we propose an end-to-end adversarial adaptation network, dubbed as MetaDetector, to transfer meta knowledge (event-shared features) between different events. Specifically, MetaDetector pushes the feature extractor and event discriminator to eliminate event-specific features and preserve required meta knowledge by adversarial training. Furthermore, the pseudo-event discriminator is utilized to evaluate the importance of news records in historical events to obtain partial knowledge that are discriminative for detecting fake news. Under the coordinated optimization among all the submodules, MetaDetector accurately transfers the meta knowledge of historical events to the upcoming event for fact checking. We conduct extensive experiments on two real-world datasets collected from Sina Weibo and Twitter. The experimental results demonstrate that MetaDetector outperforms the state-of-the-art methods, especially when the distribution discrepancy between events is significant.

TIST Journal 2022 Journal Article

Utility-aware and Privacy-preserving Trajectory Synthesis Model that Resists Social Relationship Privacy Attacks

• Zhirun Zheng
• Zhetao Li
• Jie Li
• Hongbo Jiang
• Tong Li
• Bin Guo

For academic research and business intelligence, trajectory data has been widely collected and analyzed. Releasing trajectory data to a third party may lead to serious privacy leakage, which has spawned considerable researches on trajectory privacy protection technology. However, existing work suffers from several shortcomings. They either focus on point-based location privacy, ignoring the spatio-temporal correlations among locations within a trajectory, or they protect the privacy of each user separately without considering privacy leakage of the social relationship between trajectories of different users. Besides, they fail to balance privacy protection and data utility. Motivated by these limitations, in this article, we propose S 3 T -Trajectory, which is a utility-aware and privacy-preserving trajectory synthesis model that Resists social relationship privacy attacks. Specifically, we first develop a time-dependent Markov chain based on an adaptive spatio-temporal discrete grid to efficiently and accurately capture human mobility behavior. Then, we propose three mobility feature metrics from spatio-temporal, semantic, and social dimensions. On the basis of the metrics, we construct a bi-level optimization problem to accomplish the utility-aware and privacy-preserving trajectory synthesizing. The upper-level objective guarantees data utility and the lower-level optimization problems (or upper-level constraints) provides two-layer privacy protection for S 3 T -Trajectory, i.e., resisting location inference attacks and social relationship privacy attacks. We conduct extensive experiments on large-scale real-world datasets loc-Gowalla and loc-Brightkite. The experimental results demonstrate the effectiveness and robustness of S 3 T Trajectory. Compared with the baseline models, S 3 T Trajectory achieves between 7.8% and 23.8% performance improvement in resisting social relationship privacy attacks and achieves at least 5.19% improvement regarding data utility.

TIST Journal 2021 Journal Article

Conditional Text Generation for Harmonious Human-Machine Interaction

• Bin Guo
• Hao Wang
• Yasan Ding
• Wei Wu
• Shaoyang Hao
• Yueqi Sun
• Zhiwen Yu

In recent years, with the development of deep learning, text-generation technology has undergone great changes and provided many kinds of services for human beings, such as restaurant reservation and daily communication. The automatically generated text is becoming more and more fluent so researchers begin to consider more anthropomorphic text-generation technology, that is, the conditional text generation, including emotional text generation, personalized text generation, and so on. Conditional Text Generation (CTG) has thus become a research hotspot. As a promising research field, we find that much attention has been paid to exploring it. Therefore, we aim to give a comprehensive review of the new research trends of CTG. We first summarize several key techniques and illustrate the technical evolution route in the field of neural text generation, based on the concept model of CTG. We further make an investigation of existing CTG fields and propose several general learning models for CTG. Finally, we discuss the open issues and promising research directions of CTG.

TIST Journal 2021 Journal Article

MetaStore: A Task-adaptative Meta-learning Model for Optimal Store Placement with Multi-city Knowledge Transfer

• Yan Liu
• Bin Guo
• Daqing Zhang
• Djamal Zeghlache
• Jingmin Chen
• Sizhe Zhang
• Dan Zhou
• Xinlei Shi

Optimal store placement aims to identify the optimal location for a new brick-and-mortar store that can maximize its sale by analyzing and mining users’ preferences from large-scale urban data. In recent years, the expansion of chain enterprises in new cities brings some challenges because of two aspects: (1) data scarcity in new cities, so most existing models tend to not work (i.e., overfitting), because the superior performance of these works is conditioned on large-scale training samples; (2) data distribution discrepancy among different cities, so knowledge learned from other cities cannot be utilized directly in new cities. In this article, we propose a task-adaptative model-agnostic meta-learning framework, namely, MetaStore, to tackle these two challenges and improve the prediction performance in new cities with insufficient data for optimal store placement, by transferring prior knowledge learned from multiple data-rich cities. Specifically, we develop a task-adaptative meta-learning algorithm to learn city-specific prior initializations from multiple cities, which is capable of handling the multimodal data distribution and accelerating the adaptation in new cities compared to other methods. In addition, we design an effective learning strategy for MetaStore to promote faster convergence and optimization by sampling high-quality data for each training batch in view of noisy data in practical applications. The extensive experimental results demonstrate that our proposed method leads to state-of-the-art performance compared with various baselines.

IJCAI Conference 2019 Conference Paper

Multi-agent Attentional Activity Recognition

• Kaixuan Chen
• Lina Yao
• Dalin Zhang
• Bin Guo
• Zhiwen Yu

Multi-modality is an important feature of sensor based activity recognition. In this work, we consider two inherent characteristics of human activities, the spatially-temporally varying salience of features and the relations between activities and corresponding body part motions. Based on these, we propose a multi-agent spatial-temporal attention model. The spatial-temporal attention mechanism helps intelligently select informative modalities and their active periods. And the multiple agents in the proposed model represent activities with collective motions across body parts by independently selecting modalities associated with single motions. With a joint recognition goal, the agents share gained information and coordinate their selection policies to learn the optimal recognition model. The experimental results on four real-world datasets demonstrate that the proposed model outperforms the state-of-the-art methods.

IJCAI Conference 2019 Conference Paper

Neural Network based Continuous Conditional Random Field for Fine-grained Crime Prediction

• Fei Yi
• Zhiwen Yu
• Fuzhen Zhuang
• Bin Guo

Crime prediction has always been a crucial issue for public safety, and recent works have shown the effectiveness of taking spatial correlation, such as region similarity or interaction, for fine-grained crime modeling. In our work, we seek to reveal the relationship across regions for crime prediction using Continuous Conditional Random Field (CCRF). However, conventional CCRF would become impractical when facing a dense graph considering all relationship between regions. To deal with it, in this paper, we propose a Neural Network based CCRF (NN-CCRF) model that formulates CCRF into an end-to-end neural network framework, which could reduce the complexity in model training and improve the overall performance. We integrate CCRF with NN by introducing a Long Short-Term Memory (LSTM) component to learn the non-linear mapping from inputs to outputs of each region, and a modified Stacked Denoising AutoEncoder (SDAE) component for pairwise interactions modeling between regions. Experiments conducted on two different real-world datasets demonstrate the superiority of our proposed model over the state-of-the-art methods.

IJCAI Conference 2018 Conference Paper

Extracting Job Title Hierarchy from Career Trajectories: A Bayesian Perspective

• Huang Xu
• Zhiwen Yu
• Bin Guo
• Mingfei Teng
• Hui Xiong

A job title usually implies the responsibility and the rank of a job position. While traditional job title analysis has been focused on studying the responsibilities of job titles, this paper attempts to reveal the rank of job titles. Specifically, we propose to extract job title hierarchy from employees' career trajectories. Along this line, we first quantify the Difficulty of Promotion (DOP) from one job title to another by a monotonic transformation of the length of tenure based on the assumption that a longer tenure usually implies a greater difficulty to be promoted. Then, the difference of two job title ranks is defined as a mapping of the DOP observed from job transitions. A Gaussian Bayesian Network (GBN) is adopted to model the joint distribution of the job title ranks and the DOPs in a career trajectory. Furthermore, a stochastic algorithm is developed for inferring the posterior job title rank by a given collection of DOPs in the GBN. Finally, experiments on more than 20 million job trajectories show that the job title hierarchy can be extracted precisely by the proposed method.

TIST Journal 2015 Journal Article

An Introduction to the Special Issue on Participatory Sensing and Crowd Intelligence

• Bin Guo
• Alvin Chin
• Zhiwen Yu
• Runhe Huang
• Daqing Zhang