收集的数据量不断增长，其分析以提供更好的服务正在引起人们对数字隐私的担忧。为了解决隐私问题并提供实用的解决方案，文献依赖于安全的多方计算。但是，最近的研究主要集中在多达四个政党的小党诚实造成的设置上，并指出了效率的问题。在这项工作中，我们扩展了策略，以在中心舞台上以效率为诚实的多数参与者。在预处理范式中，我们的半冬季协议改善了Damg \ aa Rd和Nielson（Crypto'07）十年最先进的协议的在线复杂性。除了提高在线沟通成本外，我们还可以在在线阶段关闭几乎一半的各方，从而节省了系统的运营成本高达50％。我们恶意安全的协议也享有类似的好处，除了一次性验证外，只需要一半的当事方。为了展示设计协议的实用性，我们基准了使用原型实现的深度神经网络，图形神经网络，基因组序列匹配以及生物识别匹配等流行应用程序。我们改进的协议有助于在先前的工作中节省高达60-80％的货币成本。

在现代化的计算时代，机器学习工具已经证明了他们在重要的部门（如医疗保健和金融）中的潜力，以获得适当的推论。这些部门中数据的敏感和机密性质对数据隐私提出了真正的担忧。这激发了隐私保留机学习（PPML）的区域，其中保证了数据的隐私。在本文中，我们使用安全的多方计算（MPC）技术在安全的外包计算（SOC）设置中为PPML设计一个有效的平台MPCleague。 MPC，安全分布式计算的圣杯问题​​，使一组N个相互不信任的各方能够在他们的私人投入上执行联合计算，以便没有T派对的联盟可以了解更多信息，而不是产出（隐私）或影响计算的真实输出（正确性）。虽然MPC一般来说，一般来说是一个广泛的研究主题，但MPC的面积具有少数各方的潜在普及，主要是由于其应用于实时场景，效率和简单性。本文侧重于为2,3和4个缔约方设计高效的MPC框架，最多一个腐败并支持环形结构。本论文的核心是四个框架 - Astra，Swift，Tetrad，Aby2.0 - 迎合不同的环境。我们的框架的实用性是通过改进的广泛使用ML算法 - 线性回归，逻辑回归，神经网络和支持向量机的基准。我们为每个框架提出了两个变体，其中一个变体旨在最大限度地减少执行时间，而另一个变体集中于货币成本。我们的框架的具体效率收益加上强大的安全保证使我们的平台成为PPML技术实时部署的理想选择。

The COVID-19 pandemic created a deluge of questionable and contradictory scientific claims about drug efficacy -- an "infodemic" with lasting consequences for science and society. In this work, we argue that NLP models can help domain experts distill and understand the literature in this complex, high-stakes area. Our task is to automatically identify contradictory claims about COVID-19 drug efficacy. We frame this as a natural language inference problem and offer a new NLI dataset created by domain experts. The NLI framing allows us to create curricula combining existing datasets and our own. The resulting models are useful investigative tools. We provide a case study of how these models help a domain expert summarize and assess evidence concerning remdisivir and hydroxychloroquine.

This volume contains revised versions of the papers selected for the third volume of the Online Handbook of Argumentation for AI (OHAAI). Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument. Argumentation, as a field within artificial intelligence (AI), is highly relevant for researchers interested in symbolic representations of knowledge and defeasible reasoning. The purpose of this handbook is to provide an open access and curated anthology for the argumentation research community. OHAAI is designed to serve as a research hub to keep track of the latest and upcoming PhD-driven research on the theory and application of argumentation in all areas related to AI.

Deep learning models operating in the complex domain are used due to their rich representation capacity. However, most of these models are either restricted to the first quadrant of the complex plane or project the complex-valued data into the real domain, causing a loss of information. This paper proposes that operating entirely in the complex domain increases the overall performance of complex-valued models. A novel, fully complex-valued learning scheme is proposed to train a Fully Complex-valued Convolutional Neural Network (FC-CNN) using a newly proposed complex-valued loss function and training strategy. Benchmarked on CIFAR-10, SVHN, and CIFAR-100, FC-CNN has a 4-10% gain compared to its real-valued counterpart, maintaining the model complexity. With fewer parameters, it achieves comparable performance to state-of-the-art complex-valued models on CIFAR-10 and SVHN. For the CIFAR-100 dataset, it achieves state-of-the-art performance with 25% fewer parameters. FC-CNN shows better training efficiency and much faster convergence than all the other models.

Building segmentation in high-resolution InSAR images is a challenging task that can be useful for large-scale surveillance. Although complex-valued deep learning networks perform better than their real-valued counterparts for complex-valued SAR data, phase information is not retained throughout the network, which causes a loss of information. This paper proposes a Fully Complex-valued, Fully Convolutional Multi-feature Fusion Network(FC2MFN) for building semantic segmentation on InSAR images using a novel, fully complex-valued learning scheme. The network learns multi-scale features, performs multi-feature fusion, and has a complex-valued output. For the particularity of complex-valued InSAR data, a new complex-valued pooling layer is proposed that compares complex numbers considering their magnitude and phase. This helps the network retain the phase information even through the pooling layer. Experimental results on the simulated InSAR dataset show that FC2MFN achieves better results compared to other state-of-the-art methods in terms of segmentation performance and model complexity.

Object detection and classification using aerial images is a challenging task as the information regarding targets are not abundant. Synthetic Aperture Radar(SAR) images can be used for Automatic Target Recognition(ATR) systems as it can operate in all-weather conditions and in low light settings. But, SAR images contain salt and pepper noise(speckle noise) that cause hindrance for the deep learning models to extract meaningful features. Using just aerial view Electro-optical(EO) images for ATR systems may also not result in high accuracy as these images are of low resolution and also do not provide ample information in extreme weather conditions. Therefore, information from multiple sensors can be used to enhance the performance of Automatic Target Recognition(ATR) systems. In this paper, we explore a methodology to use both EO and SAR sensor information to effectively improve the performance of the ATR systems by handling the shortcomings of each of the sensors. A novel Multi-Modal Domain Fusion(MDF) network is proposed to learn the domain invariant features from multi-modal data and use it to accurately classify the aerial view objects. The proposed MDF network achieves top-10 performance in the Track-1 with an accuracy of 25.3 % and top-5 performance in Track-2 with an accuracy of 34.26 % in the test phase on the PBVS MAVOC Challenge dataset [18].

Memes are powerful means for effective communication on social media. Their effortless amalgamation of viral visuals and compelling messages can have far-reaching implications with proper marketing. Previous research on memes has primarily focused on characterizing their affective spectrum and detecting whether the meme's message insinuates any intended harm, such as hate, offense, racism, etc. However, memes often use abstraction, which can be elusive. Here, we introduce a novel task - EXCLAIM, generating explanations for visual semantic role labeling in memes. To this end, we curate ExHVV, a novel dataset that offers natural language explanations of connotative roles for three types of entities - heroes, villains, and victims, encompassing 4,680 entities present in 3K memes. We also benchmark ExHVV with several strong unimodal and multimodal baselines. Moreover, we posit LUMEN, a novel multimodal, multi-task learning framework that endeavors to address EXCLAIM optimally by jointly learning to predict the correct semantic roles and correspondingly to generate suitable natural language explanations. LUMEN distinctly outperforms the best baseline across 18 standard natural language generation evaluation metrics. Our systematic evaluation and analyses demonstrate that characteristic multimodal cues required for adjudicating semantic roles are also helpful for generating suitable explanations.

Over the recent twenty years, argumentation has received considerable attention in the fields of knowledge representation, reasoning, and multi-agent systems. However, argumentation in dynamic multi-agent systems encounters the problem of significant arguments generated by agents, which comes at the expense of representational complexity and computational cost. In this work, we aim to investigate the notion of abstraction from the model-checking perspective, where several arguments are trying to defend the same position from various points of view, thereby reducing the size of the argumentation framework whilst preserving the semantic flow structure in the system.

This paper addresses the problem of position estimation in UAVs operating in a cluttered environment where GPS information is unavailable. A model learning-based approach is proposed that takes in the rotor RPMs and past state as input and predicts the one-step-ahead position of the UAV using a novel spectral-normalized memory neural network (SN-MNN). The spectral normalization guarantees stable and reliable prediction performance. The predicted position is transformed to global coordinate frame which is then fused along with the odometry of other peripheral sensors like IMU, barometer, compass etc., using the onboard extended Kalman filter to estimate the states of the UAV. The experimental flight data collected from a motion capture facility using a micro-UAV is used to train the SN-MNN. The PX4-ECL library is used to replay the flight data using the proposed algorithm, and the estimated position is compared with actual ground truth data. The proposed algorithm doesn't require any additional onboard sensors, and is computationally light. The performance of the proposed approach is compared with the current state-of-art GPS-denied algorithms, and it can be seen that the proposed algorithm has the least RMSE for position estimates.