Deep learning methods have gained increased attention in various applications due to their outstanding performance. For exploring how this high performance relates to the proper use of data artifacts and the accurate problem formulation of a given task, interpretation models have become a crucial component in developing deep learning-based systems. Interpretation models enable the understanding of the inner workings of deep learning models and offer a sense of security in detecting the misuse of artifacts in the input data. Similar to prediction models, interpretation models are also susceptible to adversarial inputs. This work introduces two attacks, AdvEdge and AdvEdge$^{+}$, that deceive both the target deep learning model and the coupled interpretation model. We assess the effectiveness of proposed attacks against two deep learning model architectures coupled with four interpretation models that represent different categories of interpretation models. Our experiments include the attack implementation using various attack frameworks. We also explore the potential countermeasures against such attacks. Our analysis shows the effectiveness of our attacks in terms of deceiving the deep learning models and their interpreters, and highlights insights to improve and circumvent the attacks.

Functionality and dialogue experience are two important factors of task-oriented dialogue systems. Conventional approaches with closed schema (e.g., conversational semantic parsing) often fail as both the functionality and dialogue experience are strongly constrained by the underlying schema. We introduce a new paradigm for task-oriented dialogue - Dialog2API - to greatly expand the functionality and provide seamless dialogue experience. The conversational model interacts with the environment by generating and executing programs triggering a set of pre-defined APIs. The model also manages the dialogue policy and interact with the user through generating appropriate natural language responses. By allowing generating free-form programs, Dialog2API supports composite goals by combining different APIs, whereas unrestricted program revision provides natural and robust dialogue experience. To facilitate Dialog2API, the core model is provided with API documents, an execution environment and optionally some example dialogues annotated with programs. We propose an approach tailored for the Dialog2API, where the dialogue states are represented by a stack of programs, with most recently mentioned program on the top of the stack. Dialog2API can work with many application scenarios such as software automation and customer service. In this paper, we construct a dataset for AWS S3 APIs and present evaluation results of in-context learning baselines.

Privacy protection and nonconvexity are two challenging problems in decentralized optimization and learning involving sensitive data. Despite some recent advances addressing each of the two problems separately, no results have been reported that have theoretical guarantees on both privacy protection and saddle/maximum avoidance in decentralized nonconvex optimization. We propose a new algorithm for decentralized nonconvex optimization that can enable both rigorous differential privacy and saddle/maximum avoiding performance. The new algorithm allows the incorporation of persistent additive noise to enable rigorous differential privacy for data samples, gradients, and intermediate optimization variables without losing provable convergence, and thus circumventing the dilemma of trading accuracy for privacy in differential privacy design. More interestingly, the algorithm is theoretically proven to be able to efficiently { guarantee accuracy by avoiding} convergence to local maxima and saddle points, which has not been reported before in the literature on decentralized nonconvex optimization. The algorithm is efficient in both communication (it only shares one variable in each iteration) and computation (it is encryption-free), and hence is promising for large-scale nonconvex optimization and learning involving high-dimensional optimization parameters. Numerical experiments for both a decentralized estimation problem and an Independent Component Analysis (ICA) problem confirm the effectiveness of the proposed approach.

Misinformation spread over social media has become an undeniable infodemic. However, not all spreading claims are made equal. If propagated, some claims can be destructive, not only on the individual level, but to organizations and even countries. Detecting claims that should be prioritized for fact-checking is considered the first step to fight against spread of fake news. With training data limited to a handful of languages, developing supervised models to tackle the problem over lower-resource languages is currently infeasible. Therefore, our work aims to investigate whether we can use existing datasets to train models for predicting worthiness of verification of claims in tweets in other languages. We present a systematic comparative study of six approaches for cross-lingual check-worthiness estimation across pairs of five diverse languages with the help of Multilingual BERT (mBERT) model. We run our experiments using a state-of-the-art multilingual Twitter dataset. Our results show that for some language pairs, zero-shot cross-lingual transfer is possible and can perform as good as monolingual models that are trained on the target language. We also show that in some languages, this approach outperforms (or at least is comparable to) state-of-the-art models.

我们考虑在平均场比赛中在线加强学习。与现有作品相反，我们通过开发一种使用通用代理的单个样本路径来估算均值场和最佳策略的算法来减轻对均值甲骨文的需求。我们称此沙盒学习为其，因为它可以用作在多代理非合作环境中运行的任何代理商的温暖启动。我们采用了两种时间尺度的方法，在该方法中，平均场的在线固定点递归在较慢的时间表上运行，并与通用代理更快的时间范围内的控制策略更新同时进行。在足够的勘探条件下，我们提供有限的样本收敛保证，从平均场和控制策略融合到平均场平衡方面。沙盒学习算法的样本复杂性为$ \ Mathcal {o}（\ epsilon^{ - 4}）$。最后，我们从经验上证明了沙盒学习算法在交通拥堵游戏中的有效性。

通过使多个代理在缺乏中央协调员的情况下合作解决全球优化问题，分散的随机优化在像机器学习，控制和传感器网络这样的多种多样的领域中，人们的注意力越来越多。由于相关数据通常包含敏感信息，例如用户位置和个人身份，因此在实施分散的随机优化时，隐私保护已成为至关重要的需求。在本文中，我们提出了一种分散的随机优化算法，即使在存在与量化幅度成正比的积极量化误差的情况下，该算法也能够保证可证明的收敛精度。该结果同时适用于凸面和非凸目标函数，使我们能够利用积极的量化方案来混淆共享信息，因此可以在不失去可证明的优化精度的情况下进行隐私保护。实际上，通过使用将任何值量化为三个数值级别的任何值的{随机}三元量化方案，我们在分散的随机优化中实现了基于量化的严格差异隐私，以前尚未报告。结合提出的量化方案，提出的算法首次确保了分散的随机优化中的严格差异隐私，而不会失去可证明的收敛精度。分布式估计问题以及基准计算机学习数据集上分散学习的数值实验的仿真结果证实了所提出方法的有效性。

自然语言处理的最新进展在文本分析和语言理解模型中产生了许多令人兴奋的发展。但是，这些模型也可以用于跟踪人们，引起严重的隐私问题。在这项工作中，我们调查了个人可以在使用社交媒体平台时避免被这些模型检测到的事情。我们将调查在两项曝光危险任务，立场检测和地理标记中进行。我们探索了各种用于修改文本的简单技术，例如用显着词，​​释义和添加虚拟社交媒体帖子插入错别字。我们的实验表明，基于BERT的模型的性能因错别字而被罚款以进行立场检测，但不受释义的影响。此外，我们发现错别字对最先进的地理参考模型的影响最小，因为它们对社交网络的依赖增加了。但是，我们表明用户可以通过与不同的用户互动来欺骗这些模型，从而将其绩效降低了近50％。

观察到通过使用深度学习方法，实现高分类性能和二维信号来实现。在这种情况下，大多数研究人员已经尝试通过使用深度学习方法来分类高光谱图像，并且对这些图像实现了90％以上的分类成功。深度神经网络（DNN）实际上包括两个部分：i）卷积神经网络（CNN）和II）完全连接的神经网络（FCNN）。虽然CNN确定了特征，但FCNN用于分类。在高光谱图像的分类中，观察到几乎所有的研究人员在光谱数据（特征）旁边的空间数据上使用了2D或3D卷积滤波器。在图像或时间信号上使用卷积过滤器可以方便。在高光谱图像中，每个像素由签名矢量表示，该签名矢量包括彼此独立的单个特征。由于可以改变向量中的特征的顺序，因此在这些功能上根据时间信号使用卷积滤波器没有有意义。同时，由于高光谱图像没有纹理结构，因此不需要除光谱数据之外使用空间数据。在这项研究中，通过仅使用完全连接的神经网络和具有一维的光谱数据，对印度松树，萨利纳斯，帕维亚中心，帕维亚大学和博茨瓦纳的高光谱图像进行分类。为所有高光谱图像的测试集实现了97.5％的平均精度。

我们考虑一个多代理网络，其中每个节点具有随机（本地）成本函数，这取决于该节点的决策变量和随机变量，并且进一步的相邻节点的判定变量是成对受约束的。网络具有总体目标函数，其在节点处的本地成本函数的预期值ack，以及网络的总体目标是将该聚合目标函数的最小化解决方案最小化为所有成对约束。这将在节点级别使用分散的信息和本地计算来实现，其中仅具有相邻节点允许的压缩信息的交换。该文件开发算法，并在节点上获得两个不同型号的本地信息可用性模型的性能界限：（i）样本反馈，其中每个节点可以直接访问局部随机变量的样本，以评估其本地成本，（ii）babrit反馈，其中无随机变量的样本不可用，但只有每个节点可用的两个随机点处的本地成本函数的值可用。对于两种模型，具有邻居之间的压缩通信，我们开发了分散的骑马点算法，从没有通信压缩的那些没有不同（符号意义）的表现;具体而言，我们表明，与全局最小值和违反约束的偏差是由$ \ mathcal {o}的大约限制（t ^ { - \ frac {1} {2}}）$和$ \ mathcal {o} （t ^ { - \ frac {1} {4}}）分别为$ t $是迭代次数。本文中提供的数值例子证实了这些界限并证明了所提出的方法的通信效率。

随机游戏的学习可以说是多功能钢筋学习（MARL）中最标准和最基本的环境。在本文中，我们考虑在非渐近制度的随机游戏中分散的Marl。特别是，我们在大量的一般总和随机游戏（SGS）中建立了完全分散的Q学习算法的有限样本复杂性 - 弱循环SGS，包括对所有代理商的普通合作MARL设置具有相同的奖励（马尔可夫团队问题是一个特例。我们专注于实用的同时具有挑战性地设置完全分散的Marl，既不奖励也没有其他药剂的作用，每个试剂都可以观察到。事实上，每个特工都完全忘记了其他决策者的存在。表格和线性函数近似情况都已考虑。在表格设置中，我们分析了分散的Q学习算法的样本复杂性，以收敛到马尔可夫完美均衡（NASH均衡）。利用线性函数近似，结果用于收敛到线性近似平衡 - 我们提出的均衡的新概念 - 这描述了每个代理的策略是线性空间内的最佳回复（到其他代理）。还提供了数值实验，用于展示结果。