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Previous researches on multimedia fake news detection include a series of complex feature extraction and fusion networks to gather useful information from the news. However, how cross-modal consistency relates to the fidelity of news and how features from different modalities affect the decision-making are still open questions. This paper presents a novel scheme of Bootstrapping Multi-view Representations (BMR) for fake news detection. Given a multi-modal news, we extract representations respectively from the views of the text, the image pattern and the image semantics. Improved Multi-gate Mixture-of-Expert networks (iMMoE) are proposed for feature refinement and fusion. Representations from each view are separately used to coarsely predict the fidelity of the whole news, and the multimodal representations are able to predict the cross-modal consistency. With the prediction scores, we reweigh each view of the representations and bootstrap them for fake news detection. Extensive experiments conducted on typical fake news detection datasets prove that BMR outperforms state-of-the-art schemes.

AAAI 2023

Bootstrapping Multi-view Representations for Fake News Detection

fake news detection

AIAI 2020 (16th International Conference on Artificial Intelligence Applications and Innovations) should be held in Halkidiki, Greece. Due to current Covid-19 situation the conference will be held virtually. Underline will host a repository of video talks and preserve them for the future. Videos will be released on Underline one day after the conference.

AIAI 2020

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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

The increased integration of artificial intelligence (AI) technologies in human workflows has resulted in a new paradigm of AI-assisted decision making,
in which an AI model provides decision recommendations while humans make the final decisions. To best support humans in decision making, it is critical to obtain a quantitative understanding of how humans interact with and rely on AI. Previous studies often model humans' reliance on AI as an analytical process, i.e., reliance decisions are made based on cost-benefit analysis. However, theoretical models in psychology suggest that the reliance decisions can often be driven by {\em emotions} like humans' {\em trust} in AI models. In this paper, we propose a {\em hidden Markov model} to capture the affective process underlying the human-AI interaction in AI-assisted decision making, by characterizing how decision makers adjust their trust in AI over time and make reliance decisions based on their trust. Evaluations on real human behavior data collected from human-subject experiments show that the proposed model outperforms various baselines in accurately predicting humans' reliance behavior in AI-assisted decision making. Based on the proposed model, we further provide insights into how humans' trust and reliance dynamics in AI-assisted decision making is influenced by contextual factors like decision stakes and their interaction experiences. 

Modeling Human Trust and Reliance in AI-assisted Decision Making: A Markovian Approach

Weisfeiler-Lehman (WL) test is widely applied as a basic algorithm for graph machine learning models, such as graph kernels, graph metrics, and graph neural networks. However, its ability to describe structural information is insufficient because it focuses only on the consistency of graphs. Therefore, this limits the performance of existing methods based on the WL test. For example, the similarity and distance between graphs defined using the WL test are coarse measures that do not accurately describe the differences in structure. This paper clarifies these facts and proposes a novel graph metric called Wasserstein WL subtree (WWLS) distance. First, we adopt the WL subtree as the structural information of the node neighborhood. Then, we define the node metric using L1-approximated tree edit distance (L1-TED) between WL subtrees of nodes. Finally, we define the WWLS by combining the Wasserstein distance and the L1-TED. The WWLS can capture slight structural differences that are difficult with conventional metrics. We demonstrate its superior performance in metric validation and graph classification experiments.

Wasserstein Graph Distance based on L1‚ÄìApproximated Tree Edit Distance between Weisfeiler‚ÄìLehman Subtrees

This paper presents a rewriting method for Boolean circuits that minimizes small subcircuits with exact synthesis. Individual synthesis tasks are encoded as Quantified Boolean Formulas (QBFs) that capture the full flexibility for the implementation of multi-output subcircuits.
This is in contrast to SAT-based resynthesis, where "don't cares" are computed for an individual gate, and replacements are confined to circuitry that is used exclusively by that gate.
An implementation of our method achieved substantial size reductions compared to state-of-the-art methods across a wide range of benchmark circuits.

Circuit Minimization with QBF-Based Exact Synthesis

We address the following mechanism design problem: Given a multi-player Normal-Form Game (NFG) with a continuous action space, find a non-discriminatory (i.e., identical for all players) restriction of the action space which maximizes the resulting Nash Equilibrium with respect to a fixed social utility function. First, we propose a formal model of a Restricted Game and the corresponding restriction optimization problem. We then present an algorithm to find optimal non-discriminatory restrictions under some assumptions. Our experimental results with Braess' Paradox and the Cournot Game show that this method leads to an optimized social utility of the Nash Equilibria, even when the assumptions are not guaranteed to hold. Finally, we outline a generalization of our approach to the much wider scope of Stochastic Games.

Socially Optimal Non-Discriminatory Restrictions for Continuous-Action Games

While deep learning models have achieved the state-of-the-art performance on single-image rain removal, most methods only consider learning fixed mapping rules on the single synthetic dataset for lifetime. This limits the real-life application as iterative optimization may change mapping rules and training samples. However, when models learn a sequence of datasets in multiple incremental steps, they are susceptible to catastrophic forgetting that adapts to new incremental episodes while failing to preserve previously acquired mapping rules. In this paper, we argue the importance of sample diversity in the episodes on the iterative optimization, and propose a novel memory management method, Associative Memory, to achieve incremental image de-raining. It bridges connections between current and past episodes for feature reconstruction by sampling domain mappings of past learning steps, and guides the learning to trace the current pathway back to the historical environment without storing extra data. Experiments demonstrate that our method can achieve better performance than existing approaches on both inhomogeneous and incremental datasets within the spectrum of highly compact systems.

Incremental Image De-raining via Associative Memory

Contrastive learning is a form of distance learning that aims to learn invariant features from two related representations. In this paper, we explore the bold hypothesis that an image and its caption can be simply regarded as two different views of the underlying mutual information, and train a model to learn a unified vision-language representation space that encodes both modalities at once in a modality-agnostic manner. We first identify difficulties in learning a generic one-tower model for vision-language pretraining (VLP), and propose OneR as a simple yet effective framework for our goal. We discover intriguing properties that distinguish OneR from the previous works that learn modality-specific representation spaces such as zero-shot object localization, text-guided visual reasoning and multi-modal retrieval, and present analyses to provide insights into this new form of multi-modal representation learning. Thorough evaluations demonstrate the potential of a unified modality-agnostic VLP framework.

Unifying Vision-Language Representation Space with Single-tower Transformer

This paper addresses the issue of adversarial attacks on ethical AI systems. We investigate using moral axioms and rules of deontic logic in a norm learning framework to mitigate adversarial norm training. This model of moral intuition and construction provides AI systems with moral guard rails yet still allows for learning conventions. We evaluate our approach by drawing inspiration from a study commonly used in moral development research. This questionnaire aims to test an agent's ability to reason to moral conclusions despite opposed testimony. Our findings suggest that our model can still correctly evaluate moral situations and learn conventions in an adversarial training environment. We conclude that adding axiomatic moral prohibitions and deontic inference rules to a norm learning model makes it less vulnerable to adversarial attacks.

Mitigating Adversarial Norm Training with Moral Axioms

Bayesian neural networks (BNNs) retain NN structures with a probability distribution placed over their weights. With the introduced uncertainties and redundancies, BNNs are proper choices of robust controllers for safety-critical control systems. This paper considers the problem of verifying the safety of nonlinear closed-loop systems with BNN controllers over unbounded-time horizon. In essence, we compute a safe weight set such that as long as the BNN controller is always applied with weights sampled from the safe weight set, the controlled system is guaranteed to be safe. We propose a novel two-phase method for the safe weight set computation. First, we construct a reference safe control set that constraints the control inputs, through polynomial approximation to the BNN controller followed by polynomial-optimization-based barrier certificate generation. Then, the computation of safe weight set is reduced to a range inclusion problem of the BNN on the system domain w.r.t. the safe control set, which can be solved incrementally and the set of safe weights can be extracted. Compared with the existing method based on invariant learning and mixed-integer linear programming, we could compute safe weight sets with larger radii on a series of linear benchmarks. Moreover, experiments on a series of widely used nonlinear control tasks show that our method can synthesize large safe weight sets with probability measure as high as 95\% even for a large-scale system of dimension 7.

Safety Verification of Nonlinear Systems with Bayesian Neural Network Controllers

Achieving gender equality is an important pillar for humankind’s sustainable future. Pioneering data-driven gender bias research is based on large-scale public records such as scientific papers, patents, and company registrations, covering female researchers, inventors and entrepreneurs, and so on. Since gender information is often missing in relevant datasets, studies rely on tools to infer genders from names. However, available open-sourced Chinese gender-guessing tools are not yet suitable for scientific purposes, which may be partially responsible for female Chinese being underrepresented in mainstream gender bias research and affect their universality. Specifically, these tools focus on character-level information while overlooking the fact that the combinations of Chinese characters in multi-character names, as well as the components and pronunciations of characters, convey important messages. As a first effort, we design a Chinese Heterogeneous Graph Attention (CHGAT) model to capture the heterogeneity in component relationships and incorporate the pronunciations of characters. Our model largely surpasses current tools and also outperforms the state-of-the-art algorithm. Last but not least, the most popular Chinese name-gender dataset is single-character based with far less female coverage from an unreliable source, naturally hindering relevant studies. We open-source a more balanced multi-character dataset from an official source together with our code, hoping to help future research promoting gender equality.

Bootstrapping Multi-view Representations for Fake News Detection

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