汇总数据通常出现在社会经济和公共安全等各个领域。汇总数据与点不关联,而与支持(例如,城市中的空间区域)相关联。由于支撑物可能取决于属性(例如贫困率和犯罪率),因此对此类数据进行建模并不直接。本文提供了一个多输出高斯流程(MOGP)模型,该模型使用各自粒度的多个聚合数据集侵入属性的功能。在提出的模型中,每个属性的函数被认为是建模为独立潜在GPS的线性混合的依赖GP。我们设计一个具有每个属性聚合过程的观察模型;该过程是GP在相应支持上的组成部分。我们还引入了混合权重的先验分布,该分布可以通过共享先验来跨域(例如城市)进行知识转移。在这种情况下,这是有利的,因为城市中的空间汇总数据集太粗糙而无法插值。提出的模型仍然可以通过利用其他城市中的聚合数据集来准确地预测属性。提出的模型的推断是基于变异贝叶的,它使人们能够使用来自多个域的聚合数据集学习模型参数。该实验表明,所提出的模型在改善现实世界数据集上的粗粒骨料数据的任务中胜过:北京的空气污染物的时间序列以及来自纽约市和芝加哥的各种空间数据集。
translated by 谷歌翻译
Text-to-speech synthesis (TTS) is a task to convert texts into speech. Two of the factors that have been driving TTS are the advancements of probabilistic models and latent representation learning. We propose a TTS method based on latent variable conversion using a diffusion probabilistic model and the variational autoencoder (VAE). In our TTS method, we use a waveform model based on VAE, a diffusion model that predicts the distribution of latent variables in the waveform model from texts, and an alignment model that learns alignments between the text and speech latent sequences. Our method integrates diffusion with VAE by modeling both mean and variance parameters with diffusion, where the target distribution is determined by approximation from VAE. This latent variable conversion framework potentially enables us to flexibly incorporate various latent feature extractors. Our experiments show that our method is robust to linguistic labels with poor orthography and alignment errors.
translated by 谷歌翻译
End-to-end text-to-speech synthesis (TTS) can generate highly natural synthetic speech from raw text. However, rendering the correct pitch accents is still a challenging problem for end-to-end TTS. To tackle the challenge of rendering correct pitch accent in Japanese end-to-end TTS, we adopt PnG~BERT, a self-supervised pretrained model in the character and phoneme domain for TTS. We investigate the effects of features captured by PnG~BERT on Japanese TTS by modifying the fine-tuning condition to determine the conditions helpful inferring pitch accents. We manipulate content of PnG~BERT features from being text-oriented to speech-oriented by changing the number of fine-tuned layers during TTS. In addition, we teach PnG~BERT pitch accent information by fine-tuning with tone prediction as an additional downstream task. Our experimental results show that the features of PnG~BERT captured by pretraining contain information helpful inferring pitch accent, and PnG~BERT outperforms baseline Tacotron on accent correctness in a listening test.
translated by 谷歌翻译
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.
translated by 谷歌翻译
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.
translated by 谷歌翻译
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.
translated by 谷歌翻译
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。
translated by 谷歌翻译
根据有关批准药物的信息预测药物的新作用可以被视为推荐系统。矩阵分解是最常用的推荐系统之一,为其设计了各种算法。用于预测药物效应的现有算法的文献调查和摘要表明,大多数此类方法,包括邻里正规逻辑矩阵分解,这是基准测试中最佳性能的最佳性能,它使用了仅考虑存在或不存在相互作用的二进制矩阵。但是,已知药物作用具有两个相反的方面,例如副作用和治疗作用。在本研究中,我们建议使用邻域正规化双向基质分解(NRBDMF)通过纳入双向性来预测药物作用,这是药物效应的特征。我们使用这种建议的方法使用矩阵来预测副作用,该基质考虑了药物效应的双向,其中已知的副作用被分配为阳性标签(加1),并为已知的治疗效应分配了阴性(负1)标签。使用药物双向信息的NRBDMF模型在预测列表的底部达到了副作用的富集和指示。第一次尝试使用NRBDMF来考虑药物效应的双向性质的尝试表明,它降低了假阳性并产生了高度可解释的输出。
translated by 谷歌翻译
预计到2023年,物联网设备的数量将达到1,250亿。物联网设备的增长将加剧设备之间的碰撞,从而降低通信性能。选择适当的传输参数,例如通道和扩展因子(SF),可以有效地减少远程(LORA)设备之间的碰撞。但是,当前文献中提出的大多数方案在具有有限的计算复杂性和内存的物联网设备上都不容易实现。为了解决此问题,我们提出了一种轻巧的传输参数选择方案,即使用用于低功率大区域网络(Lorawan)的增强学习的联合通道和SF选择方案。在拟议的方案中,可以仅使用确认(ACK)信息来选择适当的传输参数。此外,我们从理论上分析了我们提出的方案的计算复杂性和记忆要求,该方案验证了我们所提出的方案可以选择具有极低计算复杂性和内存要求的传输参数。此外,在现实世界中的洛拉设备上实施了大量实验,以评估我们提出的计划的有效性。实验结果证明了以下主要现象。 (1)与其他轻型传输参数选择方案相比,我们在Lorawan中提出的方案可以有效避免Lora设备之间的碰撞,而与可用通道的变化无关。 (2)可以通过选择访问通道和使用SFS而不是仅选择访问渠道来提高帧成功率(FSR)。 (3)由于相邻通道之间存在干扰,因此可以通过增加相邻可用通道的间隔来改善FSR和公平性。
translated by 谷歌翻译
我们提出了一项对基于自我监督的语音表示(S3R)语音转换(VC)的大规模比较研究。在识别合成VC的背景下,S3RS由于其替代昂贵的监督表示的潜力,例如语音后验(PPG),因此很有吸引力,这些表示是由最先进的VC系统采用的。使用先前开发的开源VC软件S3PRL-VC,我们在三种VC设置下提供了一系列深入的目标和主观分析:内部/跨语义的任何一对一(A2O)和任何对象 - 使用语音转换挑战2020(VCC2020)数据集。我们在各个方面研究了基于S3R的VC,包括模型类型,多语言和监督。我们还研究了通过K-均值聚类的滴定过程的效果,并展示了其在A2A设置中的改进。最后,与最先进的VC系统的比较证明了基于S3R的VC的竞争力,并阐明了可能的改进方向。
translated by 谷歌翻译