Federated Learning (FL) has become a key choice for distributed machine learning. Initially focused on centralized aggregation, recent works in FL have emphasized greater decentralization to adapt to the highly heterogeneous network edge. Among these, Hierarchical, Device-to-Device and Gossip Federated Learning (HFL, D2DFL \& GFL respectively) can be considered as foundational FL algorithms employing fundamental aggregation strategies. A number of FL algorithms were subsequently proposed employing multiple fundamental aggregation schemes jointly. Existing research, however, subjects the FL algorithms to varied conditions and gauges the performance of these algorithms mainly against Federated Averaging (FedAvg) only. This work consolidates the FL landscape and offers an objective analysis of the major FL algorithms through a comprehensive cross-evaluation for a wide range of operating conditions. In addition to the three foundational FL algorithms, this work also analyzes six derived algorithms. To enable a uniform assessment, a multi-FL framework named FLAGS: Federated Learning AlGorithms Simulation has been developed for rapid configuration of multiple FL algorithms. Our experiments indicate that fully decentralized FL algorithms achieve comparable accuracy under multiple operating conditions, including asynchronous aggregation and the presence of stragglers. Furthermore, decentralized FL can also operate in noisy environments and with a comparably higher local update rate. However, the impact of extremely skewed data distributions on decentralized FL is much more adverse than on centralized variants. The results indicate that it may not be necessary to restrict the devices to a single FL algorithm; rather, multi-FL nodes may operate with greater efficiency.

Data trading is essential to accelerate the development of data-driven machine learning pipelines. The central problem in data trading is to estimate the utility of a seller's dataset with respect to a given buyer's machine learning task, also known as data valuation. Typically, data valuation requires one or more participants to share their raw dataset with others, leading to potential risks of intellectual property (IP) violations. In this paper, we tackle the novel task of preemptively protecting the IP of datasets that need to be shared during data valuation. First, we identify and formalize two kinds of novel IP risks in visual datasets: data-item (image) IP and statistical (dataset) IP. Then, we propose a novel algorithm to convert the raw dataset into a sanitized version, that provides resistance to IP violations, while at the same time allowing accurate data valuation. The key idea is to limit the transfer of information from the raw dataset to the sanitized dataset, thereby protecting against potential intellectual property violations. Next, we analyze our method for the likely existence of a solution and immunity against reconstruction attacks. Finally, we conduct extensive experiments on three computer vision datasets demonstrating the advantages of our method in comparison to other baselines.

Point-of-Care Ultrasound (POCUS) refers to clinician-performed and interpreted ultrasonography at the patient's bedside. Interpreting these images requires a high level of expertise, which may not be available during emergencies. In this paper, we support POCUS by developing classifiers that can aid medical professionals by diagnosing whether or not a patient has pneumothorax. We decomposed the task into multiple steps, using YOLOv4 to extract relevant regions of the video and a 3D sparse coding model to represent video features. Given the difficulty in acquiring positive training videos, we trained a small-data classifier with a maximum of 15 positive and 32 negative examples. To counteract this limitation, we leveraged subject matter expert (SME) knowledge to limit the hypothesis space, thus reducing the cost of data collection. We present results using two lung ultrasound datasets and demonstrate that our model is capable of achieving performance on par with SMEs in pneumothorax identification. We then developed an iOS application that runs our full system in less than 4 seconds on an iPad Pro, and less than 8 seconds on an iPhone 13 Pro, labeling key regions in the lung sonogram to provide interpretable diagnoses.

Handling and digesting a huge amount of information in an efficient manner has been a long-term demand in modern society. Some solutions to map key points (short textual summaries capturing essential information and filtering redundancies) to a large number of arguments/opinions have been provided recently (Bar-Haim et al., 2020). To complement the full picture of the argument-to-keypoint mapping task, we mainly propose two approaches in this paper. The first approach is to incorporate prompt engineering for fine-tuning the pre-trained language models (PLMs). The second approach utilizes prompt-based learning in PLMs to generate intermediary texts, which are then combined with the original argument-keypoint pairs and fed as inputs to a classifier, thereby mapping them. Furthermore, we extend the experiments to cross/in-domain to conduct an in-depth analysis. In our evaluation, we find that i) using prompt engineering in a more direct way (Approach 1) can yield promising results and improve the performance; ii) Approach 2 performs considerably worse than Approach 1 due to the negation issue of the PLM.

The extent to which men and women use language differently has been questioned previously. Finding clear and consistent gender differences in language is not conclusive in general, and the research is heavily influenced by the context and method employed to identify the difference. In addition, the majority of the research was conducted in written form, and the sample was collected in writing. Therefore, we compared the word choices of male and female presenters in public addresses such as TED lectures. The frequency of numerous types of words, such as parts of speech (POS), linguistic, psychological, and cognitive terms were analyzed statistically to determine how male and female speakers use words differently. Based on our data, we determined that male speakers use specific types of linguistic, psychological, cognitive, and social words in considerably greater frequency than female speakers.

Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text. The task not only includes the correction of grammatical errors, such as missing prepositions and mismatched subject-verb agreement, but also orthographic and semantic errors, such as misspellings and word choice errors respectively. The field has seen significant progress in the last decade, motivated in part by a series of five shared tasks, which drove the development of rule-based methods, statistical classifiers, statistical machine translation, and finally neural machine translation systems which represent the current dominant state of the art. In this survey paper, we condense the field into a single article and first outline some of the linguistic challenges of the task, introduce the most popular datasets that are available to researchers (for both English and other languages), and summarise the various methods and techniques that have been developed with a particular focus on artificial error generation. We next describe the many different approaches to evaluation as well as concerns surrounding metric reliability, especially in relation to subjective human judgements, before concluding with an overview of recent progress and suggestions for future work and remaining challenges. We hope that this survey will serve as comprehensive resource for researchers who are new to the field or who want to be kept apprised of recent developments.

孟加拉国手语（BDSL）与其他标志语言一样 - 对于普通人来说很难学习，尤其是在表达信件时。在这张海报中，我们提出了Persign，该系统可以通过引入标志手势来重现人的形象。我们使此操作个性化，这意味着生成的图像可以保持人的初始图像轮廓 - 脸部，肤色，服装，背景 - 不变，同时适当地改变了手，手掌和手指位置。我们使用图像到图像翻译技术并构建相应的唯一数据集来完成任务。我们认为，翻译的图像可以减少签名者（使用手语的人）和非签名者之间的沟通差距，而无需事先了解BDSL。

红外成像系统的非均匀光电响应导致固定图案条纹噪声叠加在红外图像上，从而严重降低了图像质量。由于降级红外图像的应用有限，因此有效保留原始细节至关重要。现有的图像破坏方法难以同时消除所有条纹噪声伪影，保留图像细节和结构，并平衡实时性能。在本文中，我们提出了一种用于破坏退化图像的新型算法，该算法利用相邻的列信号相关性去除独立的列条纹噪声。这是通过一种迭代深度展开算法来实现的，其中一种网络迭代的估计噪声被用作下一个迭代的输入。该进展大大减少了可能的功能近似的搜索空间，从而可以在较大的数据集上进行有效的培训。提出的方法允许对条纹噪声进行更精确的估计，以更准确地保留场景细节。广泛的实验结果表明，所提出的模型在定量和定性评估上都超过了人为损坏的图像上的现有破坏方法。

文本到图像合成的最新进展导致了较大的经过验证的变压器，具有出色的能力，可以从给定文本产生可视化。但是，这些模型不适合专门的任务，例如故事可视化，该任务要求代理商制作一系列图像，给定相应的字幕序列，形成叙述。此外，我们发现故事可视化任务无法适应新叙事中看不见的情节和角色的概括。因此，我们首先提出了故事延续的任务，其中生成的视觉故事是在源图像上进行的，从而可以更好地对具有新角色的叙述进行更好的概括。然后，我们使用特定于（a）顺序图像生成的任务特定模块和（b）从初始帧复制相关元素的任务特定模块来增强或“复古”文本对图像合成模型。然后，我们探讨了预训练模型的全模型芬太尼以及对参数适应的及时调整。我们在两个现有数据集（PororoSV和FlintStonessV）上评估了我们的方法storydall-e，并介绍了从视频吸引数据集收集的新数据集DIDEMOSV。我们还基于生成的对抗网络（GAN）开发了一个模型故事游戏，以进行故事的延续，并将其与StoryDall-E模型进行比较，以展示我们方法的优势。我们表明，我们的复古拟合方法优于基于GAN的模型，用于故事延续，并促进从源图像中复制视觉元素，从而改善了生成的视觉故事中的连续性。最后，我们的分析表明，经过审计的变压器努力理解包含几个角色的叙述。总体而言，我们的工作表明，可以验证的文本对图像合成模型可以适应复杂和低资源的任务，例如故事延续。

点云压缩（PCC）是各种3-D应用程序的关键推动器，这是由于点云格式的通用性。理想情况下，3D点云努力描绘了连续的对象/场景表面。实际上，作为一组离散样本，点云是局部断开连接并稀疏分布的。这种稀疏的性质阻碍了在压缩点之间发现局部相关性的发现。通过分形维度的分析，我们提出了一种异质方法，并深入学习有损耗的点云几何压缩。在压缩输入的粗表示的基础层的顶部上，增强层的设计旨在应对具有挑战性的几何残差/详细信息。具体而言，应用基于点的网络将不稳定的本地详细信息转换为位于粗点云上的潜在特征。然后启动了在粗点云上运行的稀疏卷积神经网络。它利用粗糙几何形状的连续性/平滑度来压缩潜在特征，作为增强的位流，极大地使重建质量受益。当此位流不可用时，例如，由于数据包丢失，我们支持具有相同体系结构的跳过模式，该模式直接从粗点云中生成几何细节。对密度和稀疏点云的实验证明了我们的提案实现的最新压缩性能。我们的代码可在https://github.com/interdigitalinc/grasp-net上找到。