社交媒体和数字技术的广泛使用促进了有关事件和活动的各种新闻和信息。尽管分享了积极的信息误导和虚假信息，但社交媒体也正在传播。在确定人类专家和自动工具手动的这种误导性信息方面，已经做出了努力。由于包含事实主张的大量信息正在网上出现，手动努力并不能很好地扩展。因此，自动确定值得支票的主张对于人类专家来说非常有用。在这项研究中，我们描述了我们参与子任务-1a：checkthat的推文（英语，荷兰语和西班牙语）的值得检查！在CLEF 2022的实验室。我们执行了标准的预处理步骤，并应用了不同的模型来确定给定文本是否值得事实检查。我们使用过度采样技术来平衡数据集和应用SVM和随机森林（RF）和TF-IDF表示。我们还将BERT多语言（BERT-M）和XLM-ROBERTA-BASE预培训模型用于实验。我们将BERT-M用于官方提交，我们的系统分别在西班牙语，荷兰语和英语中分别排名第三，第五和第十二。在进一步的实验中，我们的评估表明，变压器模型（Bert-M和XLM-Roberta-bas）在荷兰语和英语语言中优于SVM和RF，在荷兰语和英语中，对于西班牙来说，观察到不同的情况。

我们提出了一个新颖的框架概念，以分析在预训练的语言模型中学习的潜在概念如何编码。它使用聚类来发现编码的概念，并通过与大量的人类定义概念对齐来解释它们。我们对七个变压器语言模型的分析揭示了有趣的见解：i）学习表示形式中的潜在空间与不同程度的不同语言概念重叠，ii）ii）模型中的下层以词汇概念（例如附加物为附加物）为主，而却是核心语言概念（例如形态学或句法关系）在中间和更高层中更好地表示，iii）一些编码的概念是多方面的，无法使用现有的人类定义的概念来充分解释。

最近在灾害信息学的研究证明了人工智能的实用而重要的用例，以拯救人类生命和基于社交媒体内容（文本和图像）的自然灾害期间的痛苦。虽然使用文本的显着进度，但利用图像的研究仍然相对较低。要提前基于图像的方法，我们提出了Medic（可用于：https://crisisnlp.qcri.org/medic/index.html），这是人道主义响应的最大社交媒体图像分类数据集，由71,198个图像组成在多任务学习设置中的四个不同任务。这是它的第一个数据集：社交媒体图像，灾难响应和多任务学习研究。该数据集的一个重要属性是它的高潜力，可以为多任务学习进行贡献，该研究最近从机器学习界获得了很多兴趣，并在内存，推理速度，性能和泛化能力方面显示出显着的结果。因此，所提出的数据集是用于推进基于图像的灾害管理和多任务机器学习研究的重要资源。

静态嵌入的后处理已成为提高其在词汇和序列级任务上的性能。但是，在上下文化嵌入的后处理是一个研究不足的问题。在这项工作中，我们质疑从不同训练的语言模型获得的上下文化嵌入的后处理的有用性。更具体地说，我们使用Z分数，Min-Max归一化以及使用全而top方法来删除顶部原理组件，将单个神经元激活标准化。此外，我们将单位长度标准化应用于单词表示。在各种预训练的模型集中，我们表明，在表示两个词汇任务（例如单词相似性和类比）和序列分类任务的表示后处理中存在重要信息。我们的发现提出了有关使用上下文表示表示的研究研究的有趣点，并建议在应用程序中使用Z分数归一化作为要考虑的重要步骤。

近年来，在网上见证了令人反感的内容的泛滥，例如假新闻，宣传，错误信息和虚假信息。虽然最初这主要是关于文本内容，但随着时间的流逝，图像和视频越来越受欢迎，因为它们更容易消费，吸引更多的关注并比文本更广泛地传播。结果，研究人员开始利用不同的方式及其组合来解决在线多模式进攻内容。在这项研究中，我们提供了有关最新的多模式虚假信息检测的调查，该检测涵盖了各种模式组合：文本，图像，语音，视频，社交媒体网络结构和时间信息。此外，尽管有些研究集中于事实，但其他研究调查了内容的有害性。尽管虚假信息定义中的这两个组成部分（i）事实和（ii）有害性同样重要，但通常会孤立地研究它们。因此，我们主张在同一框架中考虑多种方式以及事实和有害性来解决虚假信息检测。最后，我们讨论当前的挑战和未来的研究方向

Handwritten character recognition is a hot topic for research nowadays. If we can convert a handwritten piece of paper into a text-searchable document using the Optical Character Recognition (OCR) technique, we can easily understand the content and do not need to read the handwritten document. OCR in the English language is very common, but in the Bengali language, it is very hard to find a good quality OCR application. If we can merge machine learning and deep learning with OCR, it could be a huge contribution to this field. Various researchers have proposed a number of strategies for recognizing Bengali handwritten characters. A lot of ML algorithms and deep neural networks were used in their work, but the explanations of their models are not available. In our work, we have used various machine learning algorithms and CNN to recognize handwritten Bengali digits. We have got acceptable accuracy from some ML models, and CNN has given us great testing accuracy. Grad-CAM was used as an XAI method on our CNN model, which gave us insights into the model and helped us detect the origin of interest for recognizing a digit from an image.

Generative models have been very successful over the years and have received significant attention for synthetic data generation. As deep learning models are getting more and more complex, they require large amounts of data to perform accurately. In medical image analysis, such generative models play a crucial role as the available data is limited due to challenges related to data privacy, lack of data diversity, or uneven data distributions. In this paper, we present a method to generate brain tumor MRI images using generative adversarial networks. We have utilized StyleGAN2 with ADA methodology to generate high-quality brain MRI with tumors while using a significantly smaller amount of training data when compared to the existing approaches. We use three pre-trained models for transfer learning. Results demonstrate that the proposed method can learn the distributions of brain tumors. Furthermore, the model can generate high-quality synthetic brain MRI with a tumor that can limit the small sample size issues. The approach can addresses the limited data availability by generating realistic-looking brain MRI with tumors. The code is available at: ~\url{https://github.com/rizwanqureshi123/Brain-Tumor-Synthetic-Data}.

Most cross-device federated learning (FL) studies focus on the model-homogeneous setting where the global server model and local client models are identical. However, such constraint not only excludes low-end clients who would otherwise make unique contributions to model training but also restrains clients from training large models due to on-device resource bottlenecks. In this work, we propose FedRolex, a partial training (PT)-based approach that enables model-heterogeneous FL and can train a global server model larger than the largest client model. At its core, FedRolex employs a rolling sub-model extraction scheme that allows different parts of the global server model to be evenly trained, which mitigates the client drift induced by the inconsistency between individual client models and server model architectures. We show that FedRolex outperforms state-of-the-art PT-based model-heterogeneous FL methods (e.g. Federated Dropout) and reduces the gap between model-heterogeneous and model-homogeneous FL, especially under the large-model large-dataset regime. In addition, we provide theoretical statistical analysis on its advantage over Federated Dropout and evaluate FedRolex on an emulated real-world device distribution to show that FedRolex can enhance the inclusiveness of FL and boost the performance of low-end devices that would otherwise not benefit from FL. Our code is available at https://github.com/MSU-MLSys-Lab/FedRolex.

Unmanned air vehicles (UAVs) popularity is on the rise as it enables the services like traffic monitoring, emergency communications, deliveries, and surveillance. However, the unauthorized usage of UAVs (a.k.a drone) may violate security and privacy protocols for security-sensitive national and international institutions. The presented challenges require fast, efficient, and precise detection of UAVs irrespective of harsh weather conditions, the presence of different objects, and their size to enable SafeSpace. Recently, there has been significant progress in using the latest deep learning models, but those models have shortcomings in terms of computational complexity, precision, and non-scalability. To overcome these limitations, we propose a precise and efficient multiscale and multifeature UAV detection network for SafeSpace, i.e., \textit{MultiFeatureNet} (\textit{MFNet}), an improved version of the popular object detection algorithm YOLOv5s. In \textit{MFNet}, we perform multiple changes in the backbone and neck of the YOLOv5s network to focus on the various small and ignored features required for accurate and fast UAV detection. To further improve the accuracy and focus on the specific situation and multiscale UAVs, we classify the \textit{MFNet} into small (S), medium (M), and large (L): these are the combinations of various size filters in the convolution and the bottleneckCSP layers, reside in the backbone and neck of the architecture. This classification helps to overcome the computational cost by training the model on a specific feature map rather than all the features. The dataset and code are available as an open source: github.com/ZeeshanKaleem/MultiFeatureNet.

This paper introduces and presents a new language named MAIL (Malware Analysis Intermediate Language). MAIL is basically used for building malware analysis and detection tools. MAIL provides an abstract representation of an assembly program and hence the ability of a tool to automate malware analysis and detection. By translating binaries compiled for different platforms to MAIL, a tool can achieve platform independence. Each MAIL statement is annotated with patterns that can be used by a tool to optimize malware analysis and detection.