在现实世界中，扬声器身份系统的任务是在一组注册的扬声器中识别出一个只有几个注册扬声器的示例中的扬声器。本文展示了该用例的元学习和关系网络的有效性。我们提出了改进的关系网络，用于说话者验证和很少的射击者（看不见）的说话者识别。关系网络的使用促进了前端扬声器编码器和后端模型的联合培训。受到使用典型网络在扬声器验证中使用原型网络并增加说话者嵌入的可区分性的启发，我们训练该模型以在训练集中存在的所有扬声器中对当前情节进行分类。此外，我们通过从给定的元学习插曲中提取更多信息，并提出一种新的培训方式，以使用可忽略不计的额外计算，从而提出了更快的模型收敛性。我们在Voxceleb，SITW和VCTK数据集上评估了有关说话者验证的任务和看不见的说话者识别的提议技术。所提出的方法在这两个任务上始终如一地优于现有方法。

The core of the computer business now offers subscription-based on-demand services with the help of cloud computing. We may now share resources among multiple users by using virtualization, which creates a virtual instance of a computer system running in an abstracted hardware layer. It provides infinite computing capabilities through its massive cloud datacenters, in contrast to early distributed computing models, and has been incredibly popular in recent years because to its continually growing infrastructure, user base, and hosted data volume. This article suggests a conceptual framework for a workload management paradigm in cloud settings that is both safe and performance-efficient. A resource management unit is used in this paradigm for energy and performing virtual machine allocation with efficiency, assuring the safe execution of users' applications, and protecting against data breaches brought on by unauthorised virtual machine access real-time. A secure virtual machine management unit controls the resource management unit and is created to produce data on unlawful access or intercommunication. Additionally, a workload analyzer unit works simultaneously to estimate resource consumption data to help the resource management unit be more effective during virtual machine allocation. The suggested model functions differently to effectively serve the same objective, including data encryption and decryption prior to transfer, usage of trust access mechanism to prevent unauthorised access to virtual machines, which creates extra computational cost overhead.

Predicting emotions expressed in text is a well-studied problem in the NLP community. Recently there has been active research in extracting the cause of an emotion expressed in text. Most of the previous work has done causal emotion entailment in documents. In this work, we propose neural models to extract emotion cause span and entailment in conversations. For learning such models, we use RECCON dataset, which is annotated with cause spans at the utterance level. In particular, we propose MuTEC, an end-to-end Multi-Task learning framework for extracting emotions, emotion cause, and entailment in conversations. This is in contrast to existing baseline models that use ground truth emotions to extract the cause. MuTEC performs better than the baselines for most of the data folds provided in the dataset.

A real-world application or setting involves interaction between different modalities (e.g., video, speech, text). In order to process the multimodal information automatically and use it for an end application, Multimodal Representation Learning (MRL) has emerged as an active area of research in recent times. MRL involves learning reliable and robust representations of information from heterogeneous sources and fusing them. However, in practice, the data acquired from different sources are typically noisy. In some extreme cases, a noise of large magnitude can completely alter the semantics of the data leading to inconsistencies in the parallel multimodal data. In this paper, we propose a novel method for multimodal representation learning in a noisy environment via the generalized product of experts technique. In the proposed method, we train a separate network for each modality to assess the credibility of information coming from that modality, and subsequently, the contribution from each modality is dynamically varied while estimating the joint distribution. We evaluate our method on two challenging benchmarks from two diverse domains: multimodal 3D hand-pose estimation and multimodal surgical video segmentation. We attain state-of-the-art performance on both benchmarks. Our extensive quantitative and qualitative evaluations show the advantages of our method compared to previous approaches.

Human activity recognition (HAR) using IMU sensors, namely accelerometer and gyroscope, has several applications in smart homes, healthcare and human-machine interface systems. In practice, the IMU-based HAR system is expected to encounter variations in measurement due to sensor degradation, alien environment or sensor noise and will be subjected to unknown activities. In view of practical deployment of the solution, analysis of statistical confidence over the activity class score are important metrics. In this paper, we therefore propose XAI-BayesHAR, an integrated Bayesian framework, that improves the overall activity classification accuracy of IMU-based HAR solutions by recursively tracking the feature embedding vector and its associated uncertainty via Kalman filter. Additionally, XAI-BayesHAR acts as an out of data distribution (OOD) detector using the predictive uncertainty which help to evaluate and detect alien input data distribution. Furthermore, Shapley value-based performance of the proposed framework is also evaluated to understand the importance of the feature embedding vector and accordingly used for model compression

AI-powered Medical Imaging has recently achieved enormous attention due to its ability to provide fast-paced healthcare diagnoses. However, it usually suffers from a lack of high-quality datasets due to high annotation cost, inter-observer variability, human annotator error, and errors in computer-generated labels. Deep learning models trained on noisy labelled datasets are sensitive to the noise type and lead to less generalization on the unseen samples. To address this challenge, we propose a Robust Stochastic Knowledge Distillation (RoS-KD) framework which mimics the notion of learning a topic from multiple sources to ensure deterrence in learning noisy information. More specifically, RoS-KD learns a smooth, well-informed, and robust student manifold by distilling knowledge from multiple teachers trained on overlapping subsets of training data. Our extensive experiments on popular medical imaging classification tasks (cardiopulmonary disease and lesion classification) using real-world datasets, show the performance benefit of RoS-KD, its ability to distill knowledge from many popular large networks (ResNet-50, DenseNet-121, MobileNet-V2) in a comparatively small network, and its robustness to adversarial attacks (PGD, FSGM). More specifically, RoS-KD achieves >2% and >4% improvement on F1-score for lesion classification and cardiopulmonary disease classification tasks, respectively, when the underlying student is ResNet-18 against recent competitive knowledge distillation baseline. Additionally, on cardiopulmonary disease classification task, RoS-KD outperforms most of the SOTA baselines by ~1% gain in AUC score.

道路车辙是严重的道路障碍，可能导致早期和昂贵的维护成本的道路过早失败。在过去的几年中，正在积极进行使用图像处理技术和深度学习的道路损害检测研究。但是，这些研究主要集中在检测裂缝，坑洼及其变体上。很少有关于探测道路的研究。本文提出了一个新颖的道路车辙数据集，其中包括949张图像，并提供对象级别和像素级注释。部署了对象检测模型和语义分割模型，以检测所提出的数据集上的道路插道，并对模型预测进行了定量和定性分析，以评估模型性能并确定使用拟议方法检测道路插道时面临的挑战。对象检测模型Yolox-S实现了61.6％的Map@iou = 0.5，语义分割模型PSPNET（RESNET-50）达到54.69，精度为72.67，从而为将来的类似工作提供了基准的准确性。拟议的道路车辙数据集和我们的研究结果将有助于加速使用深度学习发现道路车辙的研究。

抑郁症的心理运动迟缓与二元临床访谈中的语音时机变化有关。在这项工作中，我们研究了自由生活二元相互作用的语音定时特征。除了进行连续监测以补充临床就诊的可能性外，在自由生活条件下进行的研究还可以推断社交特征，例如与抑郁症有关的二元相互作用频率。我们将扬声器计数估计量调整为二元相互作用检测器，特异性为89.5％，在Dihard数据集中的灵敏度为86.1％。使用探测器，我们从32名参与者的多天音频记录中获得了语音定时特征，该记录由13位健康个体，11个患有抑郁症的人和8个患有精神疾病的人组成。没有或轻度抑郁的参与者的二元相互作用频率随着抑郁的严重程度而增加，表明潜在的抑郁症发作标记。但是，中度或重度抑郁症的参与者的二元相互作用频率随着抑郁严重程度的增加而降低。在语音时序特征方面，响应时间与抑郁严重程度有显着的正相关。我们的工作表明了自由生活的音频记录的二元相互作用分析的潜力，以获得抑郁严重程度的标记。

分布式能源的广泛采用以及智能电网技术的出现使传统上被动的电力系统用户得以积极参与能源交易。认识到传统的集中式网格驱动能源市场为这些用户提供最低盈利能力的事实，最近的研究已将重点转移到分散的点对点（P2P）能源市场上。在这些市场中，用户彼此交易能源，比买卖网格的收益更高。但是，假设持续的可用性，参与和完全合规性，大多数P2P能源交易中的研究很大程度上忽略了交易过程中用户的看法。结果，这些方法可能会导致负面态度和随着时间的推移参与度的减少。在本文中，我们设计了一个自动化的P2P能源市场，该市场将用户感知考虑在内。我们采用前景理论来对用户的看法进行建模并制定优化框架，以最大程度地提高买方的感知，同时匹配需求和生产。鉴于优化问题的非线性和非凸性性质，我们提出了基于差分进化的算法，用于交易能源，称为辩论。此外，我们推出了一种具有风险敏感的Q学习算法，该算法名为“ Q-学习和风险敏感性”（PQR），该算法（PQR）得知考虑其感知到的实用程序的卖家的最佳价格。基于真正的能耗和生产的真实痕迹以及现实的前景理论的功能，表明我们的方法可为买家带来26％的感知价值，并为卖方产生7％的奖励，与最新的最新状态相比。

近年来，在全球范围内解决了强大的智能运输系统（ITS）的开发，以通过减少频繁的交通问题来提高交通效率。作为其应用，车辆的重新识别对计算机视觉和机器人技术的领域产生了充足的兴趣。开发了基于卷积的神经网络（CNN）方法来执行车辆重新识别，以应对诸如遮挡，照明变化，规模等的关键挑战。计算机视觉中变形金刚的进步已经为进一步探索重新识别流程提供了机会提高性能。在本文中，开发了一个框架来执行跨CCTV摄像机的车辆的重新识别。为了进行重新识别，提出的框架将使用CNN和变压器模型学习的车辆表示。该框架在一个数据集上进行了测试，该数据集包含在20个CCTV摄像机上观察到的81个独特的车辆身份。从实验中，融合的车辆重新识别框架的地图为61.73％，与独立的CNN或变压器模型相比，该框架的地图明显更好。