视频预测模型的研究被认为是对视频学习的基本方法。虽然存在用于预测过去几帧的未来帧像素值的多种生成模型，但已经发现预测帧的定量评估非常具有挑战性。在这种情况下，我们研究了预测视频的质量评估问题。我们创建了印度科学研究所预测视频质量评估（IISC PVQA）数据库，该数据库由300个视频组成，通过在不同的数据集上应用不同的预测模型，并伴随着人类观察分数。我们收集了这些视频的50名人类参与者的主观评级。我们的主观研究表明，人类观察者在预测视频的质量判断中非常一致。我们基准评估视频预测的几种普遍使用的措施，并表明它们与这些主观评分没有充分相关。我们介绍了两个新功能，以有效地捕获预测视频的质量，具有过去的帧的预测帧的深度特征的运动补偿余弦相似之处，以及从重新置于帧差异中提取的深度特征。我们表明，我们的特色设计导致了根据ISC PVQA数据库的人类判断的艺术质量预测的状态。数据库和代码在我们的项目网站上公开提供：https://nagabhushansn95.github.io/publications/2020/pvqa

Edge computing is changing the face of many industries and services. Common edge computing models offload computing which is prone to security risks and privacy violation. However, advances in deep learning enabled Internet of Things (IoTs) to take decisions and run cognitive tasks locally. This research introduces a decentralized-control edge model where most computation and decisions are moved to the IoT level. The model aims at decreasing communication to the edge which in return enhances efficiency and decreases latency. The model also avoids data transfer which raises security and privacy risks. To examine the model, we developed SAFEMYRIDES, a scene-aware ridesharing monitoring system where smart phones are detecting violations at the runtime. Current real-time monitoring systems are costly and require continuous network connectivity. The system uses optimized deep learning that run locally on IoTs to detect violations in ridesharing and record violation incidences. The system would enhance safety and security in ridesharing without violating privacy.

Cognitive Computing (COC) aims to build highly cognitive machines with low computational resources that respond in real-time. However, scholarly literature shows varying research areas and various interpretations of COC. This calls for a cohesive architecture that delineates the nature of COC. We argue that if Herbert Simon considered the design science is the science of artificial, cognitive systems are the products of cognitive science or 'the newest science of the artificial'. Therefore, building a conceptual basis for COC is an essential step into prospective cognitive computing-based systems. This paper proposes an architecture of COC through analyzing the literature on COC using a myriad of statistical analysis methods. Then, we compare the statistical analysis results with previous qualitative analysis results to confirm our findings. The study also comprehensively surveys the recent research on COC to identify the state of the art and connect the advances in varied research disciplines in COC. The study found that there are three underlaying computing paradigms, Von-Neuman, Neuromorphic Engineering and Quantum Computing, that comprehensively complement the structure of cognitive computation. The research discuss possible applications and open research directions under the COC umbrella.

Explainability has been widely stated as a cornerstone of the responsible and trustworthy use of machine learning models. With the ubiquitous use of Deep Neural Network (DNN) models expanding to risk-sensitive and safety-critical domains, many methods have been proposed to explain the decisions of these models. Recent years have also seen concerted efforts that have shown how such explanations can be distorted (attacked) by minor input perturbations. While there have been many surveys that review explainability methods themselves, there has been no effort hitherto to assimilate the different methods and metrics proposed to study the robustness of explanations of DNN models. In this work, we present a comprehensive survey of methods that study, understand, attack, and defend explanations of DNN models. We also present a detailed review of different metrics used to evaluate explanation methods, as well as describe attributional attack and defense methods. We conclude with lessons and take-aways for the community towards ensuring robust explanations of DNN model predictions.

This paper revisits the work of Rauch et al. (1965) and develops a novel method for recursive maximum likelihood particle filtering for general state-space models. The new method is based on statistical analysis of incomplete observations of the systems. Score function and conditional observed information of the incomplete observations/data are introduced and their distributional properties are discussed. Some identities concerning the score function and information matrices of the incomplete data are derived. Maximum likelihood estimation of state-vector is presented in terms of the score function and observed information matrices. In particular, to deal with nonlinear state-space, a sequential Monte Carlo method is developed. It is given recursively by an EM-gradient-particle filtering which extends the work of Lange (1995) for state estimation. To derive covariance matrix of state-estimation errors, an explicit form of observed information matrix is proposed. It extends Louis (1982) general formula for the same matrix to state-vector estimation. Under (Neumann) boundary conditions of state transition probability distribution, the inverse of this matrix coincides with the Cramer-Rao lower bound on the covariance matrix of estimation errors of unbiased state-estimator. In the case of linear models, the method shows that the Kalman filter is a fully efficient state estimator whose covariance matrix of estimation error coincides with the Cramer-Rao lower bound. Some numerical examples are discussed to exemplify the main results.

在软件开发过程中，开发人员需要回答有关代码语义方面的查询。即使已经用神经方法进行了广泛的自然语言研究，但尚未探索使用神经网络对代码回答语义查询的问题。这主要是因为没有现有的数据集，具有提取性问答和答案对，涉及复杂概念和较长推理的代码。我们通过构建一个名为Codequeries的新的，策划的数据集并提出了一种关于代码的神经问题方法来弥合这一差距。我们基于最先进的预训练的代码模型，以预测答案和支持事实跨度。给定查询和代码，只有一些代码可能与回答查询有关。我们首先在理想的环境下进行实验，其中仅给出了模型的相关代码，并表明我们的模型做得很好。然后，我们在三个务实的考虑因素下进行实验：（1）扩展到大尺寸的代码，（2）从有限数量的示例中学习，（3）代码中对次要语法错误的鲁棒性。我们的结果表明，虽然神经模型可以抵御代码中的次要语法错误，代码的大小增加，与查询无关的代码的存在以及减少的培训示例数量限制了模型性能。我们正在释放数据和模型，以促进未来关于回答代码语义查询的问题的工作。

蚊子传播的疾病（MBD），例如登革热病毒，基孔肯雅病毒和西尼罗河病毒，每年在全球造成超过100万人死亡。由于许多这样的疾病都被伊蚊和库氏蚊子传播，因此跟踪这些幼虫对于缓解MBD的传播至关重要。即使公民科学成长并获得了较大的蚊子图像数据集，蚊子图像的手动注释变得越来越耗时且效率低下。先前的研究使用计算机视觉识别蚊子物种，卷积神经网络（CNN）已成为图像分类的事实。但是，这些模型通常需要大量的计算资源。这项研究介绍了视觉变压器（VIT）在比较研究中的应用，以改善伊蚊和库尔克斯幼虫的图像分类。在蚊子幼虫图像数据上对两个VIT模型，Vit-Base和CVT-13以及两个CNN模型进行了RESNET-18和CORVNEXT的培训，并比较确定最有效的模型，以将蚊子幼虫区分为AEDES或CULEX。测试表明，Convnext获得了所有分类指标的最大值，证明了其对蚊子幼虫分类的生存能力。基于这些结果，未来的研究包括通过结合CNN和Transformer架构元素来创建专门为蚊子幼虫分类设计的模型。

引入逻辑混淆是针对集成电路（IC）的多个硬件威胁的关键防御，包括反向工程（RE）和知识产权（IP）盗窃。逻辑混淆的有效性受到最近引入的布尔满意度（SAT）攻击及其变体的挑战。还提出了大量对策，以挫败SAT袭击。不论针对SAT攻击的实施防御，大型权力，性能和领域的开销是必不可少的。相比之下，我们提出了一种认知解决方案：基于神经网络的UNSAT子句翻译器Satconda，它会造成最小的区域和开销，同时以无法穿透的安全性保留原始功能。 SATCONDA与UNSAT子句生成器一起孵育，该生成器通过最小的扰动（例如包含一对逆变器或缓冲液）转换现有的结合性正常形式（CNF），或者根据提供的CNF添加新的轻巧UNSAT块。为了有效的Unsat子句生成，Satconda配备了多层神经网络，该网络首先了解特征（文字和条款）的依赖性，然后是一个长期 - 长期内存（LSTM）网络，以验证和回溯SAT-硬度，以更好地学习和翻译。我们拟议的Satconda在ISCAS85和ISCAS89基准上进行了评估，并被认为可以防御为硬件RE设计的多个最先进的SAT攻击。此外，我们还评估了针对Minisat，Lingeling和葡萄糖SAT求解器的拟议SATCONDAS经验性能，这些溶剂构成了许多现有的Deobfuscation SAT攻击。

如今，瑜伽因现代生活方式的压力增加而受到全世界的关注，并且学习瑜伽有很多方法或资源。瑜伽一词意味着思想和身体之间的深厚联系。今天，有大量的医学和科学证据表明，我们大脑活动的基本面，我们的化学甚至可以通过练习不同的瑜伽系统来改变我们的化学。 Suryanamaskar，也被称为“向太阳致敬”，是一种瑜伽练习，结合了八种不同的形式和12个体式（4个Asana重复），专门介绍了印度太阳神Surya。 Suryanamaskar提供了许多健康益处，例如增强肌肉和帮助控制血糖水平。在这里，MediaPipe库用于分析Surya Namaskar的情况。高级软件可以实时检测到站立，因为人们在相机前表演了Surya Namaskar。班级分隔器将该表格识别为以下一项：pranamasana，hasta padasana，hasta uttanasana，ashwa -Sanchalan Asana，Ashtanga Namaskar，Dandasana或Bhujangasana和Svanasana。基于深度学习的技术（CNN）用于开发该模型，模型精度为98.68％，精度得分为0.75，以检测正确的瑜伽（Surya Namaskar）姿势。使用此方法，用户可以练习所需的姿势，并可以检查该人所做的姿势是否正确。它将有助于正确地做Surya Namaskar的所有不同姿势，并提高瑜伽从业者的效率。本文描述了将在模型中实现的整个框架。

对象检测是各种关键计算机视觉任务的基础，例如分割，对象跟踪和事件检测。要以令人满意的精度训练对象探测器，需要大量数据。但是，由于注释大型数据集涉及大量劳动力，这种数据策展任务通常被外包给第三方或依靠志愿者。这项工作揭示了此类数据策展管道的严重脆弱性。我们提出MACAB，即使数据策展人可以手动审核图像，也可以将干净的图像制作清洁的图像将后门浸入对象探测器中。我们观察到，当后门被不明确的天然物理触发器激活时，在野外实现了错误分类和披肩的后门效应。与带有清洁标签的现有图像分类任务相比，带有清洁通道的非分类对象检测具有挑战性，这是由于每个帧内有多个对象的复杂性，包括受害者和非视野性对象。通过建设性地滥用深度学习框架使用的图像尺度函数，II结合了所提出的对抗性清洁图像复制技术，以及在考虑到毒品数据选择标准的情况下，通过建设性地滥用图像尺度尺度，可以确保MACAB的功效。广泛的实验表明，在各种现实世界中，MacAB在90％的攻击成功率中表现出超过90％的攻击成功率。这包括披肩和错误分类后门效应，甚至限制了较小的攻击预算。最先进的检测技术无法有效地识别中毒样品。全面的视频演示位于https://youtu.be/ma7l_lpxkp4上，该演示基于yolov4倒置的毒药率为0.14％，yolov4 clokaking后门和更快的速度R-CNN错误分类后门。