人类堕落是非常关键的健康问题之一，尤其是对于长老和残疾人而言。在全球范围内，老年人口的数量正在稳步增加。因此，人类的跌倒发现已成为为这些人辅助生活的有效技术。为了辅助生活，大量使用了深度学习和计算机视觉。在这篇评论文章中，我们讨论了基于深度学习（DL）的最先进的非侵入性（基于视觉的）秋季检测技术。我们还提出了有关秋季检测基准数据集的调查。为了清楚理解，我们简要讨论用于评估秋季检测系统性能的不同指标。本文还为基于视觉的人类跌落检测技术提供了未来的指导。

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.

Along with the springing up of semantics-empowered communication (SemCom) researches, it is now witnessing an unprecedentedly growing interest towards a wide range of aspects (e.g., theories, applications, metrics and implementations) in both academia and industry. In this work, we primarily aim to provide a comprehensive survey on both the background and research taxonomy, as well as a detailed technical tutorial. Specifically, we start by reviewing the literature and answering the "what" and "why" questions in semantic transmissions. Afterwards, we present corresponding ecosystems, including theories, metrics, datasets and toolkits, on top of which the taxonomy for research directions is presented. Furthermore, we propose to categorize the critical enabling techniques by explicit and implicit reasoning-based methods, and elaborate on how they evolve and contribute to modern content \& channel semantics-empowered communications. Besides reviewing and summarizing the latest efforts in SemCom, we discuss the relations with other communication levels (e.g., reliable and goal-oriented communications) from a holistic and unified viewpoint. Subsequently, in order to facilitate the future developments and industrial applications, we also highlight advanced practical techniques for boosting semantic accuracy, robustness, and large-scale scalability, just to mention a few. Finally, we discuss the technical challenges that shed light on future research opportunities.

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.

The ongoing amalgamation of UAV and ML techniques is creating a significant synergy and empowering UAVs with unprecedented intelligence and autonomy. This survey aims to provide a timely and comprehensive overview of ML techniques used in UAV operations and communications and identify the potential growth areas and research gaps. We emphasise the four key components of UAV operations and communications to which ML can significantly contribute, namely, perception and feature extraction, feature interpretation and regeneration, trajectory and mission planning, and aerodynamic control and operation. We classify the latest popular ML tools based on their applications to the four components and conduct gap analyses. This survey also takes a step forward by pointing out significant challenges in the upcoming realm of ML-aided automated UAV operations and communications. It is revealed that different ML techniques dominate the applications to the four key modules of UAV operations and communications. While there is an increasing trend of cross-module designs, little effort has been devoted to an end-to-end ML framework, from perception and feature extraction to aerodynamic control and operation. It is also unveiled that the reliability and trust of ML in UAV operations and applications require significant attention before full automation of UAVs and potential cooperation between UAVs and humans come to fruition.

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.

作为世界上口语最广泛的语言之一，孟加拉国的使用在社交媒体世界中也在增加。讽刺是一种积极的陈述或言论，其基本的负面动机在当今的社交媒体平台中广泛使用。在过去的许多年中，英语的讽刺检测有了显着改善，但是有关孟加拉讽刺检测的情况仍然没有改变。结果，仍然很难识别孟加拉国中的讽刺，缺乏高质量的数据是主要因素。本文提出了Banglasarc，该数据集是专门为孟加拉文本数据讽刺检测的数据集。该数据集包含5112条评论/状态和从各种在线社交平台（例如Facebook，YouTube）以及一些在线博客中收集的内容。由于孟加拉语中分类评论的数据收集数量有限，因此该数据集将有助于确定讽刺的研究，认识到人们的情绪，检测到各种类型的孟加拉语表达式和其他领域。该数据集可在https://www.kaggle.com/datasets/sakibapon/banglasarc上公开获得。

小型模块化反应堆的概念改变了解决未来能源危机的前景。考虑到其较低的投资要求，模块化，设计简单性和增强的安全功能，这种新的反应堆技术非常有希望。人工智能驱动的多尺度建模（中子，热液压，燃料性能等）在小型模块化反应堆的研究中纳入了数字双胞胎和相关的不确定性。在这项工作中，进行了一项关于耐亡燃料的多尺度建模的全面研究。探索了这些燃料在轻水的小型模块化反应堆中的应用。本章还重点介绍了机器学习和人工智能在设计优化，控制和监视小型模块反应器中的应用。最后，简要评估了有关人工智能在高燃烧复合事故耐受燃料的发展中的研究差距。还讨论了实现这些差距的必要行动。