联合学习（FL）已成为协作分布式学习的隐私解决方案，客户直接在其设备上训练AI模型，而不是与集中式（潜在的对手）服务器共享数据。尽管FL在某种程度上保留了本地数据隐私，但已显示有关客户数据的信息仍然可以从模型更新中推断出来。近年来，已经制定了各种隐私计划来解决这种隐私泄漏。但是，它们通常以牺牲模型性能或系统效率为代价提供隐私，而在实施FL计划时，平衡这些权衡是一个至关重要的挑战。在本手稿中，我们提出了一个保护隐私的联合学习（PPFL）框架，该框架建立在控制理论中的矩阵加密和系统沉浸工具的协同作用上。这个想法是将学习算法（随机梯度体面（SGD））浸入更高维度的系统（所谓的目标系统）中，并设计目标系统的动力学，以便：浸入原始SGD的轨迹： /嵌入其轨迹中，并在加密数据上学习（在这里我们使用随机矩阵加密）。矩阵加密是在服务器上重新重新格式化的，作为将原始参数映射到更高维的参数空间的坐标的随机更改，并强制执行目标SGD收敛到原始SGD Optiral解决方案的加密版本。服务器使用浸入式地图的左侧逆汇总模型解密。我们表明，我们的算法提供与标准FL相同的准确性和收敛速度，而计算成本可忽略不计，同时却没有透露有关客户数据的信息。

Persuasion modeling is a key building block for conversational agents. Existing works in this direction are limited to analyzing textual dialogue corpus. We argue that visual signals also play an important role in understanding human persuasive behaviors. In this paper, we introduce the first multimodal dataset for modeling persuasion behaviors. Our dataset includes 199 dialogue transcriptions and videos captured in a multi-player social deduction game setting, 26,647 utterance level annotations of persuasion strategy, and game level annotations of deduction game outcomes. We provide extensive experiments to show how dialogue context and visual signals benefit persuasion strategy prediction. We also explore the generalization ability of language models for persuasion modeling and the role of persuasion strategies in predicting social deduction game outcomes. Our dataset, code, and models can be found at https://persuasion-deductiongame.socialai-data.org.

Despite the remarkable success achieved by graph convolutional networks for functional brain activity analysis, the heterogeneity of functional patterns and the scarcity of imaging data still pose challenges in many tasks. Transferring knowledge from a source domain with abundant training data to a target domain is effective for improving representation learning on scarce training data. However, traditional transfer learning methods often fail to generalize the pre-trained knowledge to the target task due to domain discrepancy. Self-supervised learning on graphs can increase the generalizability of graph features since self-supervision concentrates on inherent graph properties that are not limited to a particular supervised task. We propose a novel knowledge transfer strategy by integrating meta-learning with self-supervised learning to deal with the heterogeneity and scarcity of fMRI data. Specifically, we perform a self-supervised task on the source domain and apply meta-learning, which strongly improves the generalizability of the model using the bi-level optimization, to transfer the self-supervised knowledge to the target domain. Through experiments on a neurological disorder classification task, we demonstrate that the proposed strategy significantly improves target task performance by increasing the generalizability and transferability of graph-based knowledge.

Perceptual speech quality is an important performance metric for teleconferencing applications. The mean opinion score (MOS) is standardized for the perceptual evaluation of speech quality and is obtained by asking listeners to rate the quality of a speech sample. Recently, there has been increasing research interest in developing models for estimating MOS blindly. Here we propose a multi-task framework to include additional labels and data in training to improve the performance of a blind MOS estimation model. Experimental results indicate that the proposed model can be trained to jointly estimate MOS, reverberation time (T60), and clarity (C50) by combining two disjoint data sets in training, one containing only MOS labels and the other containing only T60 and C50 labels. Furthermore, we use a semi-supervised framework to combine two MOS data sets in training, one containing only MOS labels (per ITU-T Recommendation P.808), and the other containing separate scores for speech signal, background noise, and overall quality (per ITU-T Recommendation P.835). Finally, we present preliminary results for addressing individual rater bias in the MOS labels.

Millions of people participate in online peer-to-peer support sessions, yet there has been little prior research on systematic psychology-based evaluations of fine-grained peer-counselor behavior in relation to client satisfaction. This paper seeks to bridge this gap by mapping peer-counselor chat-messages to motivational interviewing (MI) techniques. We annotate 14,797 utterances from 734 chat conversations using 17 MI techniques and introduce four new interviewing codes such as chit-chat and inappropriate to account for the unique conversational patterns observed on online platforms. We automate the process of labeling peer-counselor responses to MI techniques by fine-tuning large domain-specific language models and then use these automated measures to investigate the behavior of the peer counselors via correlational studies. Specifically, we study the impact of MI techniques on the conversation ratings to investigate the techniques that predict clients' satisfaction with their counseling sessions. When counselors use techniques such as reflection and affirmation, clients are more satisfied. Examining volunteer counselors' change in usage of techniques suggest that counselors learn to use more introduction and open questions as they gain experience. This work provides a deeper understanding of the use of motivational interviewing techniques on peer-to-peer counselor platforms and sheds light on how to build better training programs for volunteer counselors on online platforms.

从积极和未标记的数据（又称PU学习）中学习的问题已在二进制（即阳性与负面）分类设置中进行了研究，其中输入数据包括（1）从正类别及其相应标签的观察结果，（（（ 2）来自正面和负面类别的未标记观察结果。生成对抗网络（GAN）已被用来将问题减少到监督环境中，其优势是，监督学习在分类任务中具有最新的精度。为了生成\ textIt {pseudo}阴性观察，甘恩（GAN）接受了正面和未标记的观测值的培训，并修改了损失。同时使用正面和\ textit {pseudo} - 阴性观察会导致监督的学习设置。现实到足以替代缺失的负类样品的伪阴性观察的产生是当前基于GAN的算法的瓶颈。通过在GAN体系结构中加入附加的分类器，我们提供了一种基于GAN的新方法。在我们建议的方法中，GAN歧视器指示发电机仅生成掉入未标记的数据分布中的样品，而第二分类器（观察者）网络将GAN训练监视为：（i）防止生成的样品落入正分布中; （ii）学习正面观察和负面观测之间的关键区别的特征。四个图像数据集的实验表明，我们训练有素的观察者网络在区分实际看不见的正和负样本时的性能优于现有技术。

大气效应（例如湍流和背景热噪声）抑制了在开关键控自由空间光学通信中使用的相干光的传播。在这里，我们介绍并实验验证了卷积神经网络，以降低后处理中自由空间光学通信的位错误率，而自由空间光学通信的位比基于高级光学器件的现有解决方案明显简单，更便宜。我们的方法由两个神经网络组成，这是第一个确定在热噪声和湍流中存在相干位序列以及第二个解调相干位序列的存在。通过生成连贯的光线，将它们与热灯结合在一起，并通过湍流的水箱将其结合起来，通过生成开关的键入键流，可以通过实验获得我们网络的所有数据，从而获得了模拟的湍流，并将其传递给了最终的光线。高度准确性。我们的卷积神经网络提高了与阈值分类方案相比的检测准确性，并具有与当前解调和误差校正方案集成的能力。

细分是MRI医学图像分析中最重要的任务之一，通常是许多临床应用中的第一步也是最关键的步骤。在大脑MRI分析中，头部分割通常用于测量和可视化大脑的解剖结构，也是其他应用的必要步骤，例如电脑摄影和磁脑摄影（EEG/MEG）中的电流源重建。在这里，我们提出了一个深度学习框架，该框架可以仅使用T1加权MRI作为输入来分割大脑，头骨和颅外组织。此外，我们描述了一种在嘈杂标签的存在下训练模型的强大方法。

人们通过他们写的文本的语言风格来传达他们的意图和态度。在这项研究中，我们在整个两个镜头上调查讲述型号的Lexicon用法：人类感知和机器的重要性，因为词语在他们提供的风格线索的力量中不同。收集人类感知标签，我们策划了一个新的数据集，蜂鸟，在基准标记的样式数据集之上。我们有人群工人突出了文本中的代表词，使他们认为文本具有以下样式：礼貌，情绪，冒险性和五种情绪类型。然后，我们将这些人类词标签与来自像BERT这样的流行的微调样式分类器派生的单词重要性。我们的结果表明，伯特通常会发现与目标风格无关的内容词作为风格预测中使用的重要词语，但即使对于某些风格（例如，积极情绪和喜悦）人类和机器，人类也不会相同地察觉。已识别的单词为某些风格共享显着重叠。