Visual域适应挑战2021称为无监督域适配方法，可以通过将从源数据集的知识传输到分发外目标数据集来改善模型的性能。在本文中，我们利用Beit [1]并展示其从源数据集中捕获密钥属性的能力，并以半监督方式将其应用于目标数据集。我们的方法能够优于最新的最先进（SOTA）技术，并且能够在Visda领域适应挑战中实现第1位，ACC为56.29％，Auroc为69.79％。

This paper presents our solutions for the MediaEval 2022 task on DisasterMM. The task is composed of two subtasks, namely (i) Relevance Classification of Twitter Posts (RCTP), and (ii) Location Extraction from Twitter Texts (LETT). The RCTP subtask aims at differentiating flood-related and non-relevant social posts while LETT is a Named Entity Recognition (NER) task and aims at the extraction of location information from the text. For RCTP, we proposed four different solutions based on BERT, RoBERTa, Distil BERT, and ALBERT obtaining an F1-score of 0.7934, 0.7970, 0.7613, and 0.7924, respectively. For LETT, we used three models namely BERT, RoBERTa, and Distil BERTA obtaining an F1-score of 0.6256, 0.6744, and 0.6723, respectively.

Large language models have recently attracted significant attention due to their impressive performance on a variety of tasks. ChatGPT developed by OpenAI is one such implementation of a large, pre-trained language model that has gained immense popularity among early adopters, where certain users go to the extent of characterizing it as a disruptive technology in many domains. Understanding such early adopters' sentiments is important because it can provide insights into the potential success or failure of the technology, as well as its strengths and weaknesses. In this paper, we conduct a mixed-method study using 10,732 tweets from early ChatGPT users. We first use topic modelling to identify the main topics and then perform an in-depth qualitative sentiment analysis of each topic. Our results show that the majority of the early adopters have expressed overwhelmingly positive sentiments related to topics such as Disruptions to software development, Entertainment and exercising creativity. Only a limited percentage of users expressed concerns about issues such as the potential for misuse of ChatGPT, especially regarding topics such as Impact on educational aspects. We discuss these findings by providing specific examples for each topic and then detail implications related to addressing these concerns for both researchers and users.

Deep Neural Networks (DNNs) have been widely used to perform real-world tasks in cyber-physical systems such as Autonomous Driving Systems (ADS). Ensuring the correct behavior of such DNN-Enabled Systems (DES) is a crucial topic. Online testing is one of the promising modes for testing such systems with their application environments (simulated or real) in a closed loop taking into account the continuous interaction between the systems and their environments. However, the environmental variables (e.g., lighting conditions) that might change during the systems' operation in the real world, causing the DES to violate requirements (safety, functional), are often kept constant during the execution of an online test scenario due to the two major challenges: (1) the space of all possible scenarios to explore would become even larger if they changed and (2) there are typically many requirements to test simultaneously. In this paper, we present MORLOT (Many-Objective Reinforcement Learning for Online Testing), a novel online testing approach to address these challenges by combining Reinforcement Learning (RL) and many-objective search. MORLOT leverages RL to incrementally generate sequences of environmental changes while relying on many-objective search to determine the changes so that they are more likely to achieve any of the uncovered objectives. We empirically evaluate MORLOT using CARLA, a high-fidelity simulator widely used for autonomous driving research, integrated with Transfuser, a DNN-enabled ADS for end-to-end driving. The evaluation results show that MORLOT is significantly more effective and efficient than alternatives with a large effect size. In other words, MORLOT is a good option to test DES with dynamically changing environments while accounting for multiple safety requirements.

全球一百多个国家的主食是大米（Oryza sativa）。大米的种植对于全球经济增长至关重要。但是，农业产业面临的主要问题是水稻疾病。农作物的质量和数量下降了，这是主要原因。由于任何国家的农民对水稻疾病都没有太多了解，因此他们无法正确诊断稻叶疾病。这就是为什么他们不能适当照顾米叶的原因。结果，生产正在减少。从文献调查中，Yolov5表现出更好的结果与其他深度学习方法相比。由于对象检测技术的不断发展，Yolo家族算法具有非常高的精度和更好的速度，已在各种场景识别任务中使用，以构建稻叶疾病监测系统。我们已经注释了1500个收集的数据集，并提出了基于Yolov5深学习的水稻疾病分类和检测方法。然后，我们训练并评估了Yolov5模型。模拟结果显示了本文提出的增强Yolov5网络的对象检测结果的改进。所需的识别精度，召回，MAP值和F1得分的水平分别为90 \％，67 \％，76 \％和81 \％\％被视为性能指标。

声音是现实世界中最有用，最丰富的方式之一，同时可以通过可以放置在移动设备上的小型和便宜的传感器来感知不接触。尽管深度学习能够从多个感官输入中提取信息，但很少有声音控制和学习机器人动作。对于无监督的强化学习，预计代理人将积极地收集经验，并以一种自制的方式共同学习代表和政策。我们使用基于物理的声音模拟来构建逼真的机器人操作场景，并提出内在的好奇模块（ISCM）。 ISCM向加强学习者提供反馈，以学习强大的表示并奖励更有效的探索行为。我们在适应过程中对启用声音进行了启用的声音实验，并表明ISCM所学的表示形式优于仅视力基线的基本线和预训练的策略，可以在应用于下游任务时加速学习过程。

持续（渐进或终身学习）学习的最新进展集中在预防遗忘可能导致灾难性后果的预防上，但是必须解决两项重大挑战。首先是评估所提出方法的鲁棒性。第二个是确保学习任务的安全性在很大程度上没有探索。本文介绍了一项关于持续学习的任务（包括当前和以前学到的任务）的敏感性的全面研究，这些任务容易忘记。对抗攻击的任务的这种脆弱性引发了数据完整性和隐私方面的深刻问题。我们考虑任务增量学习（任务-IL）方案，并探索三个基于正则化的实验，三个基于重播的实验以及一种基于答复和示例方法的混合技术。我们检查了这些方法的鲁棒性。特别是，我们考虑了我们证明属于当前或先前学习的任务的任何类都容易出现错误分类的情况。我们的观察结果突出了现有任务IL方法的潜在局限性。我们的实证研究建议，研究界考虑了拟议的持续学习方法的鲁棒性，并投入了大量努力来减轻灾难性的遗忘。

深度强化学习（DRL）使用多样化的非结构化数据，并使RL能够在高维环境中学习复杂的策略。基于自动驾驶汽车（AVS）的智能运输系统（ITS）为基于政策的DRL提供了绝佳的操场。深度学习体系结构解决了传统算法的计算挑战，同时帮助实现了AV的现实采用和部署。 AVS实施的主要挑战之一是，即使不是可靠和有效地管理的道路上的交通拥堵可能会加剧交通拥堵。考虑到每辆车的整体效果并使用高效和可靠的技术可以真正帮助优化交通流量管理和减少拥堵。为此，我们提出了一个智能的交通管制系统，该系统处理在交叉路口和交叉点后面的复杂交通拥堵场景。我们提出了一个基于DRL的信号控制系统，该系统根据当前交叉点的当前拥塞状况动态调整交通信号。为了应对交叉路口后面的道路上的拥堵，我们使用重新穿线技术来加载道路网络上的车辆。为了实现拟议方法的实际好处，我们分解了数据筒仓，并将所有来自传感器，探测器，车辆和道路结合使用的数据结合起来，以实现可持续的结果。我们使用Sumo微型模拟器进行模拟。我们提出的方法的重要性从结果中体现出来。

我们得出了基于等效路径问题的平均成本决策过程的Q学习算法的浓度，并将其与基于相对价值迭代的替代方案进行比较。

深度学习模型已使高维功能MRI（fMRI）数据的分析能够跃升。然而，许多以前的方法对各种时间尺度的上下文表示次优敏感。在这里，我们提出了螺栓，这是一种血氧级依赖性变压器模型，用于分析多变量fMRI时间序列。螺栓利用了一系列具有新型融合窗户注意机制的变压器编码器。编码是在时间序列中在时间重叠的窗口上执行的，以捕获本地表示。为了暂时地集成信息，在每个窗口中的基本令牌和来自附近窗口的边缘令牌之间计算交叉窗口的注意力。要逐渐从本地表示，窗口重叠的程度以及边缘令牌的数量在整个级联反应中逐渐增加。最后，采用了一种新颖的跨窗口正则化来使整个时间序列之间的高级分类特征对齐。大规模公共数据集的全面实验证明了螺栓与最先进方法的出色性能。此外，解释性分析以确定具有里程碑意义的时间点和区域，这些时间点和区域最大程度地促进模型的决策证实了文献中突出的神经科学发现。