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Artificial Intelligence Generated Content (AIGC) technology development has facilitated the creation of rumors with misinformation, impacting societal, economic, and political ecosystems, challenging democracy. Current rumor detection efforts fall short by merely labeling potentially misinformation (classification task), inadequately addressing the issue, and it is unrealistic to have authoritative institutions debunk every piece of information on social media. Our proposed comprehensive debunking process not only detects rumors but also provides explanatory generated content to refute the authenticity of the information. The Expert-Citizen Collective Wisdom (ECCW) module we designed aensures high-precision assessment of the credibility of information and the retrieval module is responsible for retrieving relevant knowledge from a Real-time updated debunking database based on information keywords. By using prompt engineering techniques, we feed results and knowledge into a LLM (Large Language Model), achieving satisfactory discrimination and explanatory effects while eliminating the need for fine-tuning, saving computational costs, and contributing to debunking efforts., Comment: 8 pages

Computational imaging~(CI) has been attracting a lot of interest in recent years for its superiority over traditional imaging in various applications. In CI systems, information is generally acquired in an encoded form and subsequently decoded via processing algorithms, which is quite in line with the information transmission mode of modern communication, and leads to emerging studies from the viewpoint of information optical imaging. Currently, one of the most important issues to be theoretically studied for CI is to quantitatively evaluate the fundamental ability of information acquisition, which is essential for both objective performance assessment and efficient design of imaging system. In this paper, by incorporating the Bayesian filtering paradigm, we propose a framework for CI that enables quantitative evaluation and design of the imaging system, and demonstate it based on ghost imaging. In specific, this framework can provide a quantitative evaluation on the acquired information through Fisher information and Cram\'er-Rao Lower Bound (CRLB), and the intrinsic performance of the imaging system can be accessed in real-time. With simulation and experiments, the framework is validated and compared with existing linear unbiased algorithms. In particular, the image retrieval can reach the CRLB. Furthermore, information-driven adaptive design for optimizing the information acquisition procedure is also achieved. By quantitative describing and efficient designing, the proposed framework is expected to promote the practical applications of CI techniques.

Variances in ad impression outcomes across demographic groups are increasingly considered to be potentially indicative of algorithmic bias in personalized ads systems. While there are many definitions of fairness that could be applicable in the context of personalized systems, we present a framework which we call the Variance Reduction System (VRS) for achieving more equitable outcomes in Meta's ads systems. VRS seeks to achieve a distribution of impressions with respect to selected protected class (PC) attributes that more closely aligns the demographics of an ad's eligible audience (a function of advertiser targeting criteria) with the audience who sees that ad, in a privacy-preserving manner. We first define metrics to quantify fairness gaps in terms of ad impression variances with respect to PC attributes including gender and estimated race. We then present the VRS for re-ranking ads in an impression variance-aware manner. We evaluate VRS via extensive simulations over different parameter choices and study the effect of the VRS on the chosen fairness metric. We finally present online A/B testing results from applying VRS to Meta's ads systems, concluding with a discussion of future work. We have deployed the VRS to all users in the US for housing ads, resulting in significant improvement in our fairness metric. VRS is the first large-scale deployed framework for pursuing fairness for multiple PC attributes in online advertising., Comment: 11 pages, 7 figure, KDD 2023

Underwater ghost imaging is an effective means of underwater detection. In this paper, a theoretical and experimental study of underwater ghost imaging is carried out by combining the description of underwater optical field transmission with the inherent optical parameters of the water body. This paper utilizes the Wells model and the approximate S-S scattering phase function to create a model for optical transmission underwater. The second-order Glauber function of the optical field is then employed to analyze the scattering field's degradation during the transmission process. This analysis is used to evaluate the impact of the water body on ghost imaging. The simulation and experimental results verify that the proposed underwater ghost imaging model can better describe the degradation effect of water bodies on ghost imaging. A series of experiments comparing underwater ghost imaging at different detection distances are also carried out in this paper. In the experiments, cooperative targets can be imaged up to 65.2m (9.3AL, at attenuation coefficient c=0.1426m-1 and the scattering coefficient b=0.052m-1) and non-cooperative targets up to 41.2m (6.4AL, at c=0.1569m-1 and b=0.081m-1) . By equating the experimental maximum imaged attenuation length for cooperative targets to Jerlov-I water (b=0.002m-1 and a=0.046m-1), the system will have a maximum imaging distance of 193m. Underwater ghost imaging is expected to achieve longer-range imaging by optimizing the system emission energy and detection sensitivity., Comment: 16 pages, 9 figures

High resolution imaging is achieved using increasingly larger apertures and successively shorter wavelengths. Optical aperture synthesis is an important high-resolution imaging technology used in astronomy. Conventional long baseline amplitude interferometry is susceptible to uncontrollable phase fluctuations, and the technical difficulty increases rapidly as the wavelength decreases. The intensity interferometry inspired by HBT experiment is essentially insensitive to phase fluctuations, but suffers from a narrow spectral bandwidth which results in a lack of detection sensitivity. In this study, we propose optical synthetic aperture imaging based on spatial intensity interferometry. This not only realizes diffraction-limited optical aperture synthesis in a single shot, but also enables imaging with a wide spectral bandwidth. And this method is insensitive to the optical path difference between the sub-apertures. Simulations and experiments present optical aperture synthesis diffraction-limited imaging through spatial intensity interferometry in a 100 $nm$ spectral width of visible light, whose maximum optical path difference between the sub-apertures reach $69.36\lambda$. This technique is expected to provide a solution for optical aperture synthesis over kilometer-long baselines at optical wavelengths.

We propose new tools for policy-makers to use when assessing and correcting fairness and bias in AI algorithms. The three tools are: - A new definition of fairness called "controlled fairness" with respect to choices of protected features and filters. The definition provides a simple test of fairness of an algorithm with respect to a dataset. This notion of fairness is suitable in cases where fairness is prioritized over accuracy, such as in cases where there is no "ground truth" data, only data labeled with past decisions (which may have been biased). - Algorithms for retraining a given classifier to achieve "controlled fairness" with respect to a choice of features and filters. Two algorithms are presented, implemented and tested. These algorithms require training two different models in two stages. We experiment with combinations of various types of models for the first and second stage and report on which combinations perform best in terms of fairness and accuracy. - Algorithms for adjusting model parameters to achieve a notion of fairness called "classification parity". This notion of fairness is suitable in cases where accuracy is prioritized. Two algorithms are presented, one which assumes that protected features are accessible to the model during testing, and one which assumes protected features are not accessible during testing. We evaluate our tools on three different publicly available datasets. We find that the tools are useful for understanding various dimensions of bias, and that in practice the algorithms are effective in starkly reducing a given observed bias when tested on new data.

Effective machine learning models can automatically learn useful information from a large quantity of data and provide decisions in a high accuracy. These models may, however, lead to unfair predictions in certain sense among the population groups of interest, where the grouping is based on such sensitive attributes as race and gender. Various fairness definitions, such as demographic parity and equalized odds, were proposed in prior art to ensure that decisions guided by the machine learning models are equitable. Unfortunately, the "fair" model trained with these fairness definitions is threshold sensitive, i.e., the condition of fairness may no longer hold true when tuning the decision threshold. This paper introduces the notion of threshold invariant fairness, which enforces equitable performances across different groups independent of the decision threshold. To achieve this goal, this paper proposes to equalize the risk distributions among the groups via two approximation methods. Experimental results demonstrate that the proposed methodology is effective to alleviate the threshold sensitivity in machine learning models designed to achieve fairness., Comment: Accepted to UAI 2020

In the field of information forensics, many emerging problems involve a critical step that estimates and tracks weak frequency components in noisy signals. It is often challenging for the prior art of frequency tracking to i)achieve a high accuracy under noisy conditions, ii)detect and track multiple frequency components efficiently, or iii)strike a good trade-off of the processing delay versus the resilience and the accuracy of tracking. To address these issues, we propose Adaptive Multi-Trace Carving (AMTC), a unified approach for detecting and tracking one or more subtle frequency components under very low signal-to-noise ratio (SNR) conditions and in near real time. AMTC takes as input a time-frequency representation of the system's preprocessing results (such as the spectrogram), and identifies frequency components through iterative dynamic programming and adaptive trace compensation. The proposed algorithm considers relatively high energy traces sustaining over a certain duration as an indicator of the presence of frequency/oscillation components of interest and track their time-varying trend. Extensive experiments using both synthetic data and real-world forensic data of power signatures and physiological monitoring reveal that the proposed method outperforms representative prior art under low SNR conditions, and can be implemented in near real-time settings. The proposed AMTC algorithm can empower the development of new information forensic technologies that harness very small signals.

Vital signs, such as heart rate (HR), heart rate variability (HRV), respiratory rate (RR), are important indicators for a person's health. Vital signs are traditionally measured with contact sensors, and may be inconvenient and cause discomfort during continuous monitoring. Commercial cameras are promising contact-free sensors, and remote photoplethysmography (rPPG) have been studied to remotely monitor heart rate from face videos. For remote RR measurement, most prior art was based on small periodical motions of chest regions caused by breathing cycles, which are vulnerable to subjects' voluntary movements. This paper explores remote RR measurement based on rPPG obtained from face videos. The paper employs motion compensation, two-phase temporal filtering, and signal pruning to capture signals with high quality. The experimental results demonstrate that the proposed framework can obtain accurate RR results and can provide HR, HRV and RR measurement synergistically in one framework.