基于变压器的大型语言模型在自然语言处理中表现出色。通过考虑这些模型在一个领域中获得的知识的可传递性，以及自然语言与高级编程语言（例如C/C ++）的亲密关系，这项工作研究了如何利用（大）基于变压器语言模型检测软件漏洞以及这些模型在漏洞检测任务方面的良好程度。在这方面，首先提出了一个系统的（凝聚）框架，详细介绍了源代码翻译，模型准备和推理。然后，使用具有多个漏洞的C/C ++源代码的软件漏洞数据集进行经验分析，该数据集对应于库功能调用，指针使用，数组使用情况和算术表达式。我们的经验结果证明了语言模型在脆弱性检测中的良好性能。此外，这些语言模型具有比当代模型更好的性能指标，例如F1得分，即双向长期记忆和双向封闭式复发单元。由于计算资源，平台，库和依赖项的要求，对语言模型进行实验始终是具有挑战性的。因此，本文还分析了流行的平台，以有效地微调这些模型并在选择平台时提出建议。

This article presents a novel review of Active SLAM (A-SLAM) research conducted in the last decade. We discuss the formulation, application, and methodology applied in A-SLAM for trajectory generation and control action selection using information theory based approaches. Our extensive qualitative and quantitative analysis highlights the approaches, scenarios, configurations, types of robots, sensor types, dataset usage, and path planning approaches of A-SLAM research. We conclude by presenting the limitations and proposing future research possibilities. We believe that this survey will be helpful to researchers in understanding the various methods and techniques applied to A-SLAM formulation.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Accurate recognition of food items along with quality assessment is of paramount importance in the agricultural industry. Such automated systems can speed up the wheel of the food processing sector and save tons of manual labor. In this connection, the recent advancement of Deep learning-based architectures has introduced a wide variety of solutions offering remarkable performance in several classification tasks. In this work, we have exploited the concept of Densely Connected Convolutional Neural Networks (DenseNets) for fruit quality assessment. The feature propagation towards the deeper layers has enabled the network to tackle the vanishing gradient problems and ensured the reuse of features to learn meaningful insights. Evaluating on a dataset of 19,526 images containing six fruits having three quality grades for each, the proposed pipeline achieved a remarkable accuracy of 99.67%. The robustness of the model was further tested for fruit classification and quality assessment tasks where the model produced a similar performance, which makes it suitable for real-life applications.

In this paper, deep-learning-based approaches namely fine-tuning of pretrained convolutional neural networks (VGG16 and VGG19), and end-to-end training of a developed CNN model, have been used in order to classify X-Ray images into four different classes that include COVID-19, normal, opacity and pneumonia cases. A dataset containing more than 20,000 X-ray scans was retrieved from Kaggle and used in this experiment. A two-stage classification approach was implemented to be compared to the one-shot classification approach. Our hypothesis was that a two-stage model will be able to achieve better performance than a one-shot model. Our results show otherwise as VGG16 achieved 95% accuracy using one-shot approach over 5-fold of training. Future work will focus on a more robust implementation of the two-stage classification model Covid-TSC. The main improvement will be allowing data to flow from the output of stage-1 to the input of stage-2, where stage-1 and stage-2 models are VGG16 models fine-tuned on the Covid-19 dataset.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

数据驱动的优化和基于机器学习的无线电访问网络的性能诊断不仅需要源于基本数据源的性质，而且还归因于复杂的时空关系以及由于用户移动性和不同流量模式而引起的单元格之间的相互依赖性。我们讨论如何使用多元分析来研究这些配置和性能管理数据集以及在关键性能指标方面识别细胞之间的关系。为此，我们利用了基于规范相关分析（CCA）的新框架，这不仅是降低维度的高效方法，而且还用于分析跨不同多元数据集的关系。作为一个案例研究，我们讨论了基于商业蜂窝网络中细胞关闭的节能用例，在该案例中，我们将CCA应用于分析容量细胞关闭对同一部门覆盖电池KPI的影响。来自LTE网络的数据用于分析示例案例。我们得出的结论是，CCA是一种可行的方法，用于识别网络计划和配置数据之间的关键关系，还可以动态绩效数据，为诸如降低维度降低，绩效分析和性能诊断的根本原因分析等努力铺平道路。

作物疾病是对粮食安全的主要威胁，其快速识别对于防止产量损失很重要。由于缺乏必要的基础设施，因此很难迅速识别这些疾病。计算机视觉的最新进展和智能手机渗透的渗透为智能手机辅助疾病识别铺平了道路。大多数植物疾病在植物的叶面结构上留下了特定的文物。这项研究于2020年在巴基斯坦拉合尔工程技术大学计算机科学与工程系进行，以检查基于叶片的植物疾病识别。这项研究为叶面疾病鉴定提供了基于神经网络的深度解决方案，并纳入了图像质量评估，以选择执行识别所需质量的图像，并将其命名为农业病理学家（AGRO PATH）。新手摄影师的捕获图像可能包含噪音，缺乏结构和模糊，从而导致诊断失败或不准确。此外，Agropath模型具有99.42％的叶面疾病鉴定精度。拟议的添加对于在农业领域的叶面疾病鉴定的应用特别有用。

当今智能城市中产生的大型视频数据从其有目的的用法角度引起了人们的关注，其中监视摄像机等是最突出的资源，是为大量数据做出贡献的最突出的资源，使其自动化分析成为计算方面的艰巨任务。和精确。暴力检测（VD）在行动和活动识别域中广泛崩溃，用于分析大型视频数据，以了解由于人类而引起的异常动作。传统上，VD文献基于手动设计的功能，尽管开发了基于深度学习的独立模型的进步用于实时VD分析。本文重点介绍了深度序列学习方法以及检测到的暴力的本地化策略。该概述还介入了基于机器学习的初始图像处理和基于机器学习的文献及其可能具有的优势，例如针对当前复杂模型的效率。此外，讨论了数据集，以提供当前模型的分析，并用对先前方法的深入分析得出的VD域中的未来方向解释了他们的利弊。

在本文中，我们分析了具有基于视觉导航的无人机（UAV）的时间延迟动力学对控制器设计的影响。时间延迟是网络物理系统中不可避免的现象，并且对无人机的控制器设计和轨迹产生具有重要意义。时间延迟对无人机动态的影响随着基于视力较慢的导航堆栈的使用而增加。我们表明，文献中的现有模型不包括时间延迟，不适合控制器调整，因为一个微不足道的解决方案始终存在错误的解决方案。我们确定的微不足道的解决方案表明，使用无限控制器的利益来实现最佳性能，这与实际发现相矛盾。我们通过引入无人机的新型非线性时间延迟模型来避免这种缺点，然后获得与每个UAV控制回路相对应的一组线性解耦模型。分析了角度和高度动力学的线性时间延迟模型的成本函数，与无延迟模型相反，我们显示了有限的最佳控制器参数的存在。由于使用了时间延迟模型，我们在实验上表明，所提出的模型准确地表示系统稳定性限制。由于时间延迟的考虑，我们使用基于视觉探视的无人机（VO）导航，在跟踪峰值速度为2.09 m/s的lemsistate轨迹时，我们实现了RMSE 5.01 cm的跟踪结果，这与最新-艺术。