弥补联邦学习（FL）模型的分散培训中所涉及的成本的激励措施是客户长期参与的关键刺激。但是，由于缺乏以下信息，请说服客户在FL上进行质量参与：（i）有关客户数据质量和属性的完整信息； （ii）客户数据贡献的价值； （iii）货币奖励优惠的可信赖机制。这通常会导致培训和沟通效率较差。尽管有几项工作着重于战略激励设计和客户选择以克服这个问题，但就针对预见的数字经济（包括Web 3.0）量身定制的总体设计存在一个重大的知识差距，同时同时实现了学习目标。为了解决这一差距，我们提出了一个基于贡献的令牌化激励方案，即\ texttt {fedToken}，并得到区块链技术的支持，可确保在模型培训期间与其数据估值相对应的客户之间的公平分配。利用工程设计的基于Shapley的计划，我们首先近似模型聚合过程中本地模型的贡献，然后战略性地安排客户降低沟通循环的融合和锚定方式，以分配\ emph {负担得起的}代币在受限的货币预算下。广泛的模拟证明了我们提出的方法的功效。

本文考虑了用于训练机器学习模型的物联网（IoT）数据的市场。数据通过网络提供给市场平台，并根据其给机器学习模型带来的价值来控制数据的价格。我们在游戏理论环境中探索数据的相关性属性，最终为数据交易机制提供了简化的分布解决方案，该解决方案强调了设备和市场的共同利益。关键建议是针对市场的有效算法，该算法共同解决了参与中的可用性和异质性的挑战，以及信任的转移以及物联网网络中数据交换的经济价值。提出的方法通过通过相关数据加强设备之间的协作机会来建立数据市场，以避免信息泄漏。在其中，我们开发了一个整个网络优化问题，可最大程度地提高相似数据类型的IoT设备之间联盟的社会价值；同时，它最大程度地减少了由于网络外部性而引起的成本，即由于数据相关性而引起的信息泄漏的影响以及机会成本。最后，我们揭示了该法式问题作为分布式联盟游戏的结构，并根据简化的分裂和合并算法解决了它。仿真结果表明，我们提出的机制设计对值得信赖的物联网数据市场的功效，每个卖方的平均收益高达32.72％。

在联合学习（FL）设置中，许多设备有助于培训通用模型。我们提出了一种选择提供更新的设备，以实现改进的概括，快速收敛和更好的设备级别性能。我们制定了最低 - 最大优化问题，并将其分解为原始偶的设置，在该设置中，双重性差距用于量化设备级的性能。我们的策略通过\ emph {exploitation}的随机设备选择，通过简化的设备贡献来结合数据新鲜度。这在概括和个性化方面都改善了受过训练的模型的性能。在开发阶段，应用了修改的截短蒙特卡洛（TMC）方法，以估计设备的贡献并降低开销的通信。实验结果表明，所提出的方法具有竞争性能，对基线方案的沟通开销和竞争性个性化绩效较低。

由于机器学习（ML）模型变得越来越复杂，其中一个中央挑战是它们在规模的部署，使得公司和组织可以通过人工智能（AI）创造价值。 ML中的新兴范式是一种联合方法，其中学习模型部分地将其交付给一组异构剂，允许代理与自己的数据一起培训模型。然而，模型的估值问题，以及数据/模型的协作培训和交易的激励问题，在文献中获得了有限的待遇。本文提出了一种在基于信任区块基网络上交易的ML模型交易的新生态系统。买方可以获得ML市场的兴趣模型，兴趣的卖家将本地计算花在他们的数据上，以增强该模型的质量。在这样做时，考虑了本地数据与训练型型号的质量之间的比例关系，并且通过分布式数据福价（DSV）估计了销售课程中的训练中的数据的估值。同时，通过分布式分区技术（DLT）提供整个交易过程的可信度。对拟议方法的广泛实验评估显示出具有竞争力的运行时间绩效，在参与者的激励方面下降了15 \％。

Our paper aims to analyze political polarization in US political system using Language Models, and thereby help candidates make an informed decision. The availability of this information will help voters understand their candidates views on the economy, healthcare, education and other social issues. Our main contributions are a dataset extracted from Wikipedia that spans the past 120 years and a Language model based method that helps analyze how polarized a candidate is. Our data is divided into 2 parts, background information and political information about a candidate, since our hypothesis is that the political views of a candidate should be based on reason and be independent of factors such as birthplace, alma mater, etc. We further split this data into 4 phases chronologically, to help understand if and how the polarization amongst candidates changes. This data has been cleaned to remove biases. To understand the polarization we begin by showing results from some classical language models in Word2Vec and Doc2Vec. And then use more powerful techniques like the Longformer, a transformer based encoder, to assimilate more information and find the nearest neighbors of each candidate based on their political view and their background.

Object movement identification is one of the most researched problems in the field of computer vision. In this task, we try to classify a pixel as foreground or background. Even though numerous traditional machine learning and deep learning methods already exist for this problem, the two major issues with most of them are the need for large amounts of ground truth data and their inferior performance on unseen videos. Since every pixel of every frame has to be labeled, acquiring large amounts of data for these techniques gets rather expensive. Recently, Zhao et al. [1] proposed one of a kind Arithmetic Distribution Neural Network (ADNN) for universal background subtraction which utilizes probability information from the histogram of temporal pixels and achieves promising results. Building onto this work, we developed an intelligent video surveillance system that uses ADNN architecture for motion detection, trims the video with parts only containing motion, and performs anomaly detection on the trimmed video.

Attention mechanisms form a core component of several successful deep learning architectures, and are based on one key idea: ''The output depends only on a small (but unknown) segment of the input.'' In several practical applications like image captioning and language translation, this is mostly true. In trained models with an attention mechanism, the outputs of an intermediate module that encodes the segment of input responsible for the output is often used as a way to peek into the `reasoning` of the network. We make such a notion more precise for a variant of the classification problem that we term selective dependence classification (SDC) when used with attention model architectures. Under such a setting, we demonstrate various error modes where an attention model can be accurate but fail to be interpretable, and show that such models do occur as a result of training. We illustrate various situations that can accentuate and mitigate this behaviour. Finally, we use our objective definition of interpretability for SDC tasks to evaluate a few attention model learning algorithms designed to encourage sparsity and demonstrate that these algorithms help improve interpretability.

Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing the COD has led us to solving high-dimensional PDEs. This has resulted in opening doors to solving a variety of real-world problems ranging from mathematical finance to stochastic control for industrial applications. Although feasible, these deep learning methods are still constrained by training time and memory. Tackling these shortcomings, Tensor Neural Networks (TNN) demonstrate that they can provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. Besides TNN, we also introduce Tensor Network Initializer (TNN Init), a weight initialization scheme that leads to faster convergence with smaller variance for an equivalent parameter count as compared to a DNN. We benchmark TNN and TNN Init by applying them to solve the parabolic PDE associated with the Heston model, which is widely used in financial pricing theory.

As language models have grown in parameters and layers, it has become much harder to train and infer with them on single GPUs. This is severely restricting the availability of large language models such as GPT-3, BERT-Large, and many others. A common technique to solve this problem is pruning the network architecture by removing transformer heads, fully-connected weights, and other modules. The main challenge is to discern the important parameters from the less important ones. Our goal is to find strong metrics for identifying such parameters. We thus propose two strategies: Cam-Cut based on the GradCAM interpretations, and Smooth-Cut based on the SmoothGrad, for calculating the importance scores. Through this work, we show that our scoring functions are able to assign more relevant task-based scores to the network parameters, and thus both our pruning approaches significantly outperform the standard weight and gradient-based strategies, especially at higher compression ratios in BERT-based models. We also analyze our pruning masks and find them to be significantly different from the ones obtained using standard metrics.

The paper presents a cross-domain review analysis on four popular review datasets: Amazon, Yelp, Steam, IMDb. The analysis is performed using Hadoop and Spark, which allows for efficient and scalable processing of large datasets. By examining close to 12 million reviews from these four online forums, we hope to uncover interesting trends in sales and customer sentiment over the years. Our analysis will include a study of the number of reviews and their distribution over time, as well as an examination of the relationship between various review attributes such as upvotes, creation time, rating, and sentiment. By comparing the reviews across different domains, we hope to gain insight into the factors that drive customer satisfaction and engagement in different product categories.