仇恨言论等攻击性内容的广泛构成了越来越多的社会问题。 AI工具是支持在线平台的审核过程所必需的。为了评估这些识别工具，需要与不同语言的数据集进行连续实验。 HASOC轨道（仇恨语音和冒犯性内容识别）专用于为此目的开发基准数据。本文介绍了英语，印地语和马拉地赛的Hasoc Subtrack。数据集由Twitter组装。此子系统有两个子任务。任务A是为所有三种语言提供的二进制分类问题（仇恨而非冒犯）。任务B是三个课程（仇恨）仇恨言论，令人攻击和亵渎为英语和印地语提供的细粒度分类问题。总体而言，652名队伍提交了652次。任务A最佳分类算法的性能分别为Marathi，印地语和英语的0.91,0.78和0.83尺寸。此概述介绍了任务和数据开发以及详细结果。提交竞争的系统应用了各种技术。最好的表演算法主要是变压器架构的变种。

In a rapidly flourishing country like Bangladesh, accidents in unmanned level crossings are increasing daily. This study presents a deep learning-based approach for automating level crossing junctions, ensuring maximum safety. Here, we develop a fully automated technique using computer vision on a microcontroller that will reduce and eliminate level-crossing deaths and accidents. A Raspberry Pi microcontroller detects impending trains using computer vision on live video, and the intersection is closed until the incoming train passes unimpeded. Live video activity recognition and object detection algorithms scan the junction 24/7. Self-regulating microcontrollers control the entire process. When persistent unauthorized activity is identified, authorities, such as police and fire brigade, are notified via automated messages and notifications. The microcontroller evaluates live rail-track data, and arrival and departure times to anticipate ETAs, train position, velocity, and track problems to avoid head-on collisions. This proposed scheme reduces level crossing accidents and fatalities at a lower cost than current market solutions. Index Terms: Deep Learning, Microcontroller, Object Detection, Railway Crossing, Raspberry Pi

In this research work, we have demonstrated the application of Mask-RCNN (Regional Convolutional Neural Network), a deep-learning algorithm for computer vision and specifically object detection, to semiconductor defect inspection domain. Stochastic defect detection and classification during semiconductor manufacturing has grown to be a challenging task as we continuously shrink circuit pattern dimensions (e.g., for pitches less than 32 nm). Defect inspection and analysis by state-of-the-art optical and e-beam inspection tools is generally driven by some rule-based techniques, which in turn often causes to misclassification and thereby necessitating human expert intervention. In this work, we have revisited and extended our previous deep learning-based defect classification and detection method towards improved defect instance segmentation in SEM images with precise extent of defect as well as generating a mask for each defect category/instance. This also enables to extract and calibrate each segmented mask and quantify the pixels that make up each mask, which in turn enables us to count each categorical defect instances as well as to calculate the surface area in terms of pixels. We are aiming at detecting and segmenting different types of inter-class stochastic defect patterns such as bridge, break, and line collapse as well as to differentiate accurately between intra-class multi-categorical defect bridge scenarios (as thin/single/multi-line/horizontal/non-horizontal) for aggressive pitches as well as thin resists (High NA applications). Our proposed approach demonstrates its effectiveness both quantitatively and qualitatively.

近年来，深神经网络（DNN）应用的流行和成功促使对DNN压缩的研究，例如修剪和量化。这些技术加速了模型推断，减少功耗，并降低运行DNN所需的硬件的大小和复杂性，而准确性几乎没有损失。但是，由于DNN容易受到对抗输入的影响，因此重要的是要考虑压缩和对抗性鲁棒性之间的关系。在这项工作中，我们研究了几种不规则修剪方案和8位量化产生的模型的对抗性鲁棒性。此外，尽管常规修剪消除了DNN中最不重要的参数，但我们研究了一种非常规修剪方法的效果：根据对抗输入的梯度去除最重要的模型参数。我们称这种方法称贪婪的对抗修剪（GAP），我们发现这种修剪方法会导致模型可抵抗从其未压缩的对应物转移攻击的模型。

智能基础设施中的人类活动从个人穿着的可穿戴设备中产生大量IMU数据。许多现有的研究依赖于人类活动识别（HAR）的这种感觉数据；但是，主要的瓶颈之一是它们依赖预先注销或标记的数据。手动人工驱动的注释既不是可扩展的，也不是有效的，而现有的自动通量技术在很大程度上取决于视频签名。尽管如此，基于视频的自动保管仍需要高度的计算资源，并且当将来自智能家庭（智能家庭）的数据转移到云中时，仍存在隐私问题。本文利用了人类活动产生的声学标志，以标记可穿戴设备的IMU数据，从而减轻资源需求和数据隐私问题。即使两个人在相同的环境环境下执行同时但不同的活动，我们也利用基于声学的预训练的HAR模型来对IMU数据进行跨模式标记。我们观察到，在环境声学环境中两个人执行的同时活动中，存在非重叠的声学差距，这有助于我们解决重叠的活动签名以单独标记它们。对两个现实生活中的内部数据集的拟议方法的原则评估进一步增强以创建双重乘员设置，表明该框架可以正确注释来自两个人的大量未标记的IMU数据，这些数据具有$ \ mathbf { 82.59 \％} $（$ \ Mathbf {\ pm 17.94 \％} $）和$ \ Mathbf {98.32 \％} $（$ \ Mathbf {\ Mathbf {\ PM 3.68 \％} $）环境。