Transparency of Machine Learning models used for decision support in various industries becomes essential for ensuring their ethical use. To that end, feature attribution methods such as SHAP (SHapley Additive exPlanations) are widely used to explain the predictions of black-box machine learning models to customers and developers. However, a parallel trend has been to train machine learning models in collaboration with other data holders without accessing their data. Such models, trained over horizontally or vertically partitioned data, present a challenge for explainable AI because the explaining party may have a biased view of background data or a partial view of the feature space. As a result, explanations obtained from different participants of distributed machine learning might not be consistent with one another, undermining trust in the product. This paper presents an Explainable Data Collaboration Framework based on a model-agnostic additive feature attribution algorithm (KernelSHAP) and Data Collaboration method of privacy-preserving distributed machine learning. In particular, we present three algorithms for different scenarios of explainability in Data Collaboration and verify their consistency with experiments on open-access datasets. Our results demonstrated a significant (by at least a factor of 1.75) decrease in feature attribution discrepancies among the users of distributed machine learning.

多源数据融合，共同分析了多个数据源以获得改进的信息，引起了广泛的研究关注。对于多个医疗机构的数据集，数据机密性和跨机构沟通至关重要。在这种情况下，数据协作（DC）分析通过共享维数减少的中间表示，而无需迭代跨机构通信可能是合适的。在分析包括个人信息在内的数据时，共享数据的可识别性至关重要。在这项研究中，研究了DC分析的可识别性。结果表明，共享的中间表示很容易识别为原始数据以进行监督学习。然后，这项研究提出了一个非可读性可识别的直流分析，仅共享多个医疗数据集（包括个人信息）的非可读数据。所提出的方法基于随机样本排列，可解释的直流分析的概念以及无法重建的功能的使用来解决可识别性问题。在医学数据集的数值实验中，提出的方法表现出非可读性可识别性，同时保持了常规DC分析的高识别性能。对于医院的数据集，提出的方法在仅使用本地数据集的本地分析的识别性能方面表现出了9个百分点的改善。

Diagnostic radiologists need artificial intelligence (AI) for medical imaging, but access to medical images required for training in AI has become increasingly restrictive. To release and use medical images, we need an algorithm that can simultaneously protect privacy and preserve pathologies in medical images. To develop such an algorithm, here, we propose DP-GLOW, a hybrid of a local differential privacy (LDP) algorithm and one of the flow-based deep generative models (GLOW). By applying a GLOW model, we disentangle the pixelwise correlation of images, which makes it difficult to protect privacy with straightforward LDP algorithms for images. Specifically, we map images onto the latent vector of the GLOW model, each element of which follows an independent normal distribution, and we apply the Laplace mechanism to the latent vector. Moreover, we applied DP-GLOW to chest X-ray images to generate LDP images while preserving pathologies.

Artificial life is a research field studying what processes and properties define life, based on a multidisciplinary approach spanning the physical, natural and computational sciences. Artificial life aims to foster a comprehensive study of life beyond "life as we know it" and towards "life as it could be", with theoretical, synthetic and empirical models of the fundamental properties of living systems. While still a relatively young field, artificial life has flourished as an environment for researchers with different backgrounds, welcoming ideas and contributions from a wide range of subjects. Hybrid Life is an attempt to bring attention to some of the most recent developments within the artificial life community, rooted in more traditional artificial life studies but looking at new challenges emerging from interactions with other fields. In particular, Hybrid Life focuses on three complementary themes: 1) theories of systems and agents, 2) hybrid augmentation, with augmented architectures combining living and artificial systems, and 3) hybrid interactions among artificial and biological systems. After discussing some of the major sources of inspiration for these themes, we will focus on an overview of the works that appeared in Hybrid Life special sessions, hosted by the annual Artificial Life Conference between 2018 and 2022.

Our team, Hibikino-Musashi@Home (the shortened name is HMA), was founded in 2010. It is based in the Kitakyushu Science and Research Park, Japan. We have participated in the RoboCup@Home Japan open competition open platform league every year since 2010. Moreover, we participated in the RoboCup 2017 Nagoya as open platform league and domestic standard platform league teams. Currently, the Hibikino-Musashi@Home team has 20 members from seven different laboratories based in the Kyushu Institute of Technology. In this paper, we introduce the activities of our team and the technologies.

Mutation-based fuzzing has become one of the most common vulnerability discovery solutions over the last decade. Fuzzing can be optimized when targeting specific programs, and given that, some studies have employed online optimization methods to do it automatically, i.e., tuning fuzzers for any given program in a program-agnostic manner. However, previous studies have neither fully explored mutation schemes suitable for online optimization methods, nor online optimization methods suitable for mutation schemes. In this study, we propose an optimization framework called SLOPT that encompasses both a bandit-friendly mutation scheme and mutation-scheme-friendly bandit algorithms. The advantage of SLOPT is that it can generally be incorporated into existing fuzzers, such as AFL and Honggfuzz. As a proof of concept, we implemented SLOPT-AFL++ by integrating SLOPT into AFL++ and showed that the program-agnostic optimization delivered by SLOPT enabled SLOPT-AFL++ to achieve higher code coverage than AFL++ in all of ten real-world FuzzBench programs. Moreover, we ran SLOPT-AFL++ against several real-world programs from OSS-Fuzz and successfully identified three previously unknown vulnerabilities, even though these programs have been fuzzed by AFL++ for a considerable number of CPU days on OSS-Fuzz.

We propose "factor matting", an alternative formulation of the video matting problem in terms of counterfactual video synthesis that is better suited for re-composition tasks. The goal of factor matting is to separate the contents of video into independent components, each visualizing a counterfactual version of the scene where contents of other components have been removed. We show that factor matting maps well to a more general Bayesian framing of the matting problem that accounts for complex conditional interactions between layers. Based on this observation, we present a method for solving the factor matting problem that produces useful decompositions even for video with complex cross-layer interactions like splashes, shadows, and reflections. Our method is trained per-video and requires neither pre-training on external large datasets, nor knowledge about the 3D structure of the scene. We conduct extensive experiments, and show that our method not only can disentangle scenes with complex interactions, but also outperforms top methods on existing tasks such as classical video matting and background subtraction. In addition, we demonstrate the benefits of our approach on a range of downstream tasks. Please refer to our project webpage for more details: https://factormatte.github.io

从出生到死亡，由于老化，我们都经历了令人惊讶的无处不在的变化。如果我们可以预测数字领域的衰老，即人体的数字双胞胎，我们将能够在很早的阶段检测病变，从而提高生活质量并延长寿命。我们观察到，没有一个先前开发的成年人体数字双胞胎在具有深层生成模型的体积医学图像之间明确训练的纵向转换规则，可能导致例如心室体积的预测性能不佳。在这里，我们建立了一个新的成人人体的数字双胞胎，该数字双胞胎采用纵向获得的头部计算机断层扫描（CT）图像进行训练，从而从一个当前的体积头CT图像中预测了未来的体积头CT图像。我们首次采用了三维基于流动的深层生成模型之一，以实现这种顺序的三维数字双胞胎。我们表明，我们的数字双胞胎在相对较短的程度上优于预测心室体积的最新方法。

本文解决了解释黑框回归模型异常预测的任务。当使用黑框模型（例如从许多传感器测量值中预测能源消耗的一个模型）时，我们通常会有某些观察到的样品可能会显着偏离其预测的情况。这可能是由于亚最佳黑盒模型，或仅仅​​是因为这些样品是异常值。无论哪种情况，理想情况下都希望计算``责任分数''，以指示输入变量负责异常输出的程度。在这项工作中，我们将此任务形式化为一个统计逆问题：给定模型偏离预期值，推断每个输入变量的责任分数。我们提出了一种称为似然补偿（LC）的新方法，该方法基于可能性原理，并计算对每个输入变量的校正。据我们所知，这是第一个计算实际有价值异常模型偏差的责任分数的原则性框架。我们将方法应用于现实世界中的建筑能源预测任务，并根据专家反馈确认其实用性。

我们为时间事件数据提出了一个新的稀疏Granger-Causal学习框架。我们专注于一种称为Hawkes流程的特定点过程。我们首先指出，霍克斯工艺的大多数现有稀疏因果学习算法在最大似然估计中都具有奇异性。结果，它们的稀疏溶液只能显示为数值伪像。在本文中，我们提出了一个基于基于基数规范化的霍克斯过程的数学定义明确的稀疏因果学习框架，该过程可以纠正现有方法的病理问题。我们利用提出的算法来完成实例因果事件分析的任务，其中稀疏性起着至关重要的作用。我们使用两个真实用例验证了所提出的框架，一个来自电网，另一个来自云数据中心管理域。