The increasing number of surveillance cameras and security concerns have made automatic violent activity detection from surveillance footage an active area for research. Modern deep learning methods have achieved good accuracy in violence detection and proved to be successful because of their applicability in intelligent surveillance systems. However, the models are computationally expensive and large in size because of their inefficient methods for feature extraction. This work presents a novel architecture for violence detection called Two-stream Multi-dimensional Convolutional Network (2s-MDCN), which uses RGB frames and optical flow to detect violence. Our proposed method extracts temporal and spatial information independently by 1D, 2D, and 3D convolutions. Despite combining multi-dimensional convolutional networks, our models are lightweight and efficient due to reduced channel capacity, yet they learn to extract meaningful spatial and temporal information. Additionally, combining RGB frames and optical flow yields 2.2% more accuracy than a single RGB stream. Regardless of having less complexity, our models obtained state-of-the-art accuracy of 89.7% on the largest violence detection benchmark dataset.

Cashews are grown by over 3 million smallholders in more than 40 countries worldwide as a principal source of income. As the third largest cashew producer in Africa, Benin has nearly 200,000 smallholder cashew growers contributing 15% of the country's national export earnings. However, a lack of information on where and how cashew trees grow across the country hinders decision-making that could support increased cashew production and poverty alleviation. By leveraging 2.4-m Planet Basemaps and 0.5-m aerial imagery, newly developed deep learning algorithms, and large-scale ground truth datasets, we successfully produced the first national map of cashew in Benin and characterized the expansion of cashew plantations between 2015 and 2021. In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations, which can fully capture texture information from discriminative time steps during a growing season. We further developed a Clustering Augmented Self-supervised Temporal Classification (CASTC) model to distinguish high-density versus low-density cashew plantations by automatic feature extraction and optimized clustering. Results show that the STCA model has an overall accuracy of 80% and the CASTC model achieved an overall accuracy of 77.9%. We found that the cashew area in Benin has doubled from 2015 to 2021 with 60% of new plantation development coming from cropland or fallow land, while encroachment of cashew plantations into protected areas has increased by 70%. Only half of cashew plantations were high-density in 2021, suggesting high potential for intensification. Our study illustrates the power of combining high-resolution remote sensing imagery and state-of-the-art deep learning algorithms to better understand tree crops in the heterogeneous smallholder landscape.

Dengue fever is a virulent disease spreading over 100 tropical and subtropical countries in Africa, the Americas, and Asia. This arboviral disease affects around 400 million people globally, severely distressing the healthcare systems. The unavailability of a specific drug and ready-to-use vaccine makes the situation worse. Hence, policymakers must rely on early warning systems to control intervention-related decisions. Forecasts routinely provide critical information for dangerous epidemic events. However, the available forecasting models (e.g., weather-driven mechanistic, statistical time series, and machine learning models) lack a clear understanding of different components to improve prediction accuracy and often provide unstable and unreliable forecasts. This study proposes an ensemble wavelet neural network with exogenous factor(s) (XEWNet) model that can produce reliable estimates for dengue outbreak prediction for three geographical regions, namely San Juan, Iquitos, and Ahmedabad. The proposed XEWNet model is flexible and can easily incorporate exogenous climate variable(s) confirmed by statistical causality tests in its scalable framework. The proposed model is an integrated approach that uses wavelet transformation into an ensemble neural network framework that helps in generating more reliable long-term forecasts. The proposed XEWNet allows complex non-linear relationships between the dengue incidence cases and rainfall; however, mathematically interpretable, fast in execution, and easily comprehensible. The proposal's competitiveness is measured using computational experiments based on various statistical metrics and several statistical comparison tests. In comparison with statistical, machine learning, and deep learning methods, our proposed XEWNet performs better in 75% of the cases for short-term and long-term forecasting of dengue incidence.

Predicting stock market movements has always been of great interest to investors and an active area of research. Research has proven that popularity of products is highly influenced by what people talk about. Social media like Twitter, Reddit have become hotspots of such influences. This paper investigates the impact of social media posts on close price prediction of stocks using Twitter and Reddit posts. Our objective is to integrate sentiment of social media data with historical stock data and study its effect on closing prices using time series models. We carried out rigorous experiments and deep analysis using multiple deep learning based models on different datasets to study the influence of posts by executives and general people on the close price. Experimental results on multiple stocks (Apple and Tesla) and decentralised currencies (Bitcoin and Ethereum) consistently show improvements in prediction on including social media data and greater improvements on including executive posts.

数据文章介绍了路线损坏数据集RDD2022，其中包括来自六个国家，日本，印度，捷克共和国，挪威，美国和中国的47,420条道路图像。图像已注释了超过55,000个道路损坏的实例。数据集中捕获了四种类型的道路损坏，即纵向裂缝，横向裂纹，鳄鱼裂纹和坑洼。设想注释的数据集用于开发基于深度学习的方法以自动检测和对道路损害进行分类。该数据集已作为基于人群传感的道路伤害检测挑战（CRDDC2022）的一部分发布。 CRDDC2022挑战邀请了来自全球的研究人员提出解决方案，以在多个国家 /地区自动道路损害检测。市政当局和道路机构可以使用RDD2022数据集，并使用RDD2022培训的模型用于低成本自动监测道路状况。此外，计算机视觉和机器学习研究人员可能会使用数据集对其他类型的其他基于图像的应用程序（分类，对象检测等）进行不同算法的性能。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

在这项研究中，要求各种印度生物的听众倾听并认识到美国扬声器所说的速度话语。我们识别出一个话语时，我们有三种来自每个听众的回应：1。句子难度评级，2.扬声器难度评级，以及讲话的转录。从这些转录中，计算并用作标准以评估识别和原始句子之间的相似性。本研究中选择的句子分为三组：简单，中和硬，基于此研究它们中的单词的频率。我们观察到句子，扬声器难度评级和行动从易于难以句子的句子增加。我们还使用以下三种自动语音识别（ASR）进行人类语音识别性能，在声学模型（AM）和语言模型（LM）（LM）（LM）：ASR1）训练中，录制了印度源头和LM的录音Timit Text，ASR2）我正在使用来自Libli语音语料库的本地美国扬声器和LM的录音，以及ASR3）正在使用来自美国原住民扬声器和LM构建的录音在Libli语音和Timit文本上。我们观察到HSR性能类似于ASR1的性能，而ASR3则实现最佳性能。扬声器诞生明智的分析表明，与少数其他生命神相比，印度听众的扬声器的话语更难以识别

机器学习开始在一系列环境应用中提供最先进的性能，例如水文流域中的流量预测。但是，由于主要的水文工艺的可变性，在实践中建立准确的大规模模型在实践中仍然具有挑战性，这是通过一组与过程相关的盆地特征捕获的。现有的盆地特征遭受了噪音和不确定性的影响，以及许多其他事情，这会对模型性能产生不利影响。为了应对上述挑战，在本文中，我们提出了一种新颖的知识引导的自学学习（KGSSL）逆框架，以从驱动程序和响应数据中提取系统特征。即使特征被损坏，这个首先的框架即使在特征被损坏的情况下也达到了强大的性能。我们表明，KGSSL为骆驼的流量建模（大型研究的流域属性和气象学）实现了最新的结果，这是一个广泛使用的水文基准数据集。具体而言，KGSSL在重建特性中最多优于其他方法16 \％。此外，我们表明KGSSL比基线方法相对强大，并且在插入KGSSL推断的特征时，基线模型的表现优于35 \％。

非凸优化的马鞍点避免问题在大规模分布式学习框架中非常具有挑战性，例如联邦学习，特别是在拜占庭工作者的存在。 「庆祝的立方规范化牛顿方法\ Cite {Nest}是避免标准集中（非分布式）设置中的马鞍点的最优雅方式之一。在本文中，我们将立方正规化的牛顿方法扩展到分布式框架，同时解决了几种实际挑战，如通信瓶颈和拜占庭攻击。请注意，由于流氓机器可以在丢失功能的鞍点附近创建\ emph {假本地最小值}，因此在丢失函数的鞍点附近，尚未创建拜占机器的存在，避免问题在拜占庭机器的情况下变得更加重要。作为二阶算法，我们的迭代复杂性远低于第一订单对应物。此外，我们使用像$ \ delta $类似的压缩（或稀疏）技术，以便进行通信效率。我们在包括近似（子采样）梯度和黑森州的若干环境下获得理论担保。此外，我们通过使用标准数据集和几种类型的拜占庭攻击进行实验验证了我们的理论调查结果，并在迭代复杂性中获得了25 \％$ 25 \％$的提高。

In many applications of classifier learning, training data suffers from label noise. Deep networks are learned using huge training data where the problem of noisy labels is particularly relevant. The current techniques proposed for learning deep networks under label noise focus on modifying the network architecture and on algorithms for estimating true labels from noisy labels. An alternate approach would be to look for loss functions that are inherently noise-tolerant. For binary classification there exist theoretical results on loss functions that are robust to label noise. In this paper, we provide some sufficient conditions on a loss function so that risk minimization under that loss function would be inherently tolerant to label noise for multiclass classification problems. These results generalize the existing results on noise-tolerant loss functions for binary classification. We study some of the widely used loss functions in deep networks and show that the loss function based on mean absolute value of error is inherently robust to label noise. Thus standard back propagation is enough to learn the true classifier even under label noise. Through experiments, we illustrate the robustness of risk minimization with such loss functions for learning neural networks.