知识图（kgs）中的实体类型信息（例如DBPEDIA，FREEBASE等）通常由于自动产生或人类策划而通常不完整。实体键入是在kg中分配或推断实体的语义类型的任务。本文介绍了\ textit {grand {grand}，这是一种实体键入的新方法，利用RDF2VEC中的不同图形步行策略以及文本实体描述。 RDF2VEC首先生成图形步行，然后使用语言模型来获取图中每个节点的嵌入。这项研究表明，步行生成策略和嵌入模型对实体打字任务的性能有重大影响。所提出的方法的表现优于基准数据集DBPedia和Figer在kgs中的实体和小颗粒类别的实体。结果表明，订单感知RDF2VEC变体的组合以及文本实体描述的上下文嵌入可实现最佳结果。

隐喻检测的最先进方法比较他们的文字或核心 - 使用基于神经网络的顺序隐喻分类器的含义及其语境含义。表示字面含义的信号通常由（非语境）字嵌入式表示。然而，隐喻表达由于各种原因，例如文化和社会影响，随着时间的推移而发展。已知隐喻表达式通过语言和文字词含义，甚至在某种程度上驾驶这一进化。这升起了对文字含义不同，可能是特定于特定的，可能影响隐喻检测任务的问题。据我们所知，这是第一项研究，该研究在详细的探索性分析中检查了隐喻检测任务，其中使用不同的时间和静态字嵌入来占对字面意义的不同表示。我们的实验分析基于用于隐喻检测的三个流行基准，并从不同的Corpora中提取的单词嵌入式，并在时间上对齐到不同的最先进的方法。结果表明，不同的单词嵌入对隐喻检测任务的影响和一些时间字嵌入略高于一些性能措施的静态方法。然而，结果还表明，时间字嵌入可以提供单词“核心意义的表示，即使太接近其隐喻意义，因此令人困惑的分类器。总的来说，时间语言演化和隐喻检测之间的相互作用在我们的实验中使用的基准数据集中出现了微小。这表明对这种重要语言现象的计算分析的未来工作应该首先创建一个新的数据集，其中这个交互是更好的代表。

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.

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。

基于自我监督的基于学习的预科可以使用小标签的数据集开发可靠和广义的深度学习模型，从而减轻了标签生成的负担。本文旨在评估基于CL的预处理对可转介的性能与非转介糖尿病性视网膜病（DR）分类的影响。我们已经开发了一个基于CL的框架，具有神经风格转移（NST）增强，以生成具有更好表示和初始化的模型，以检测颜色底面图像中的DR。我们将CL预估计的模型性能与用成像网权重预测的两个最先进的基线模型进行了比较。我们通过减少标记的训练数据（降至10％）进一步研究模型性能，以测试使用小标签数据集训练模型的鲁棒性。该模型在EYEPACS数据集上进行了培训和验证，并根据芝加哥伊利诺伊大学（UIC）的临床数据进行了独立测试。与基线模型相比，我们的CL预处理的基础网模型具有更高的AUC（CI）值（0.91（0.898至0.930），在UIC数据上为0.80（0.783至0.820）和0.83（0.783至0.820）（0.801至0.853）。在10％标记的培训数据时，在UIC数据集上测试时，基线模型中的FoldusNet AUC为0.81（0.78至0.84），比0.58（0.56至0.64）和0.63（0.56至0.64）和0.63（0.60至0.66）。基于CL的NST预处理可显着提高DL分类性能，帮助模型良好（可从Eyepacs转移到UIC数据），并允许使用小的带注释的数据集进行培训，从而减少临床医生的地面真相注释负担。