In this paper, a complete framework for Autonomous Self Driving is implemented. LIDAR, Camera and IMU sensors are used together. The entire data communication is managed using Robot Operating System which provides a robust platform for implementation of Robotics Projects. Jetson Nano is used to provide powerful on-board processing capabilities. Sensor fusion is performed on the data received from the different sensors to improve the accuracy of the decision making and inferences that we derive from the data. This data is then used to create a localized map of the environment. In this step, the position of the vehicle is obtained with respect to the Mapping done using the sensor data.The different SLAM techniques used for this purpose are Hector Mapping and GMapping which are widely used mapping techniques in ROS. Apart from SLAM that primarily uses LIDAR data, Visual Odometry is implemented using a Monocular Camera. The sensor fused data is then used by Adaptive Monte Carlo Localization for car localization. Using the localized map developed, Path Planning techniques like "TEB planner" and "Dynamic Window Approach" are implemented for autonomous navigation of the vehicle. The last step in the Project is the implantation of Control which is the final decision making block in the pipeline that gives speed and steering data for the navigation that is compatible with Ackermann Kinematics. The implementation of such a control block under a ROS framework using the three sensors, viz, LIDAR, Camera and IMU is a novel approach that is undertaken in this project.

Conversational AI has become an increasingly prominent and practical application of machine learning. However, existing conversational AI techniques still suffer from various limitations. One such limitation is a lack of well-developed methods for incorporating auxiliary information that could help a model understand conversational context better. In this paper, we explore how persona-based information could help improve the quality of response generation in conversations. First, we provide a literature review focusing on the current state-of-the-art methods that utilize persona information. We evaluate two strong baseline methods, the Ranking Profile Memory Network and the Poly-Encoder, on the NeurIPS ConvAI2 benchmark dataset. Our analysis elucidates the importance of incorporating persona information into conversational systems. Additionally, our study highlights several limitations with current state-of-the-art methods and outlines challenges and future research directions for advancing personalized conversational AI technology.

Cement is the most used construction material. The performance of cement hydrate depends on the constituent phases, viz. alite, belite, aluminate, and ferrites present in the cement clinker, both qualitatively and quantitatively. Traditionally, clinker phases are analyzed from optical images relying on a domain expert and simple image processing techniques. However, the non-uniformity of the images, variations in the geometry and size of the phases, and variabilities in the experimental approaches and imaging methods make it challenging to obtain the phases. Here, we present a machine learning (ML) approach to detect clinker microstructure phases automatically. To this extent, we create the first annotated dataset of cement clinker by segmenting alite and belite particles. Further, we use supervised ML methods to train models for identifying alite and belite regions. Specifically, we finetune the image detection and segmentation model Detectron-2 on the cement microstructure to develop a model for detecting the cement phases, namely, Cementron. We demonstrate that Cementron, trained only on literature data, works remarkably well on new images obtained from our experiments, demonstrating its generalizability. We make Cementron available for public use.

The pattern of pedestrian crashes varies greatly depending on lighting circumstances, emphasizing the need of examining pedestrian crashes in various lighting conditions. Using Louisiana pedestrian fatal and injury crash data (2010-2019), this study applied Association Rules Mining (ARM) to identify the hidden pattern of crash risk factors according to three different lighting conditions (daylight, dark-with-streetlight, and dark-no-streetlight). Based on the generated rules, the results show that daylight pedestrian crashes are associated with children (less than 15 years), senior pedestrians (greater than 64 years), older drivers (>64 years), and other driving behaviors such as failure to yield, inattentive/distracted, illness/fatigue/asleep. Additionally, young drivers (15-24 years) are involved in severe pedestrian crashes in daylight conditions. This study also found pedestrian alcohol/drug involvement as the most frequent item in the dark-with-streetlight condition. This crash type is particularly associated with pedestrian action (crossing intersection/midblock), driver age (55-64 years), speed limit (30-35 mph), and specific area type (business with mixed residential area). Fatal pedestrian crashes are found to be associated with roadways with high-speed limits (>50 mph) during the dark without streetlight condition. Some other risk factors linked with high-speed limit related crashes are pedestrians walking with/against the traffic, presence of pedestrian dark clothing, pedestrian alcohol/drug involvement. The research findings are expected to provide an improved understanding of the underlying relationships between pedestrian crash risk factors and specific lighting conditions. Highway safety experts can utilize these findings to conduct a decision-making process for selecting effective countermeasures to reduce pedestrian crashes strategically.

至于其他形式的AI，最近已经对不同用户同伙的性能差异进行了研究。在语音识别方面实现公平性的一种方法是（1）确定遭受低标准表现的说话者队列，以及（2）采取针对发现同类的公平性缓解措施。在本文中，我们使用产品规模的AI助手语音识别系统的数据报告了发现和缓解性能差异的初步发现。我们将基于地理和人口统计学信息的队列发现与一种更可扩展的方法进行比较，该方法将使用扬声器嵌入技术分组没有人类标签的说话者。为了缓解公平性，我们发现对代表性不足的队列的过度采样，以及通过其他输入变量对扬声器队列的建模，从而减少了表现和底部性能队列之间的差距，而不会降低整体识别精度。

增量学习是一种范式，可以通过流数据大规模构建模型构建和更新。对于端到端的自动语音识别（ASR）任务，缺乏人类注释的标签，以及需要保留模型建设政策的隐私政策，这使其成为艰巨的挑战。受这些挑战的激励，在本文中，我们使用基于云的框架为生产系统展示了从隐私保存自动语音识别（ILASR）的增量学习中的见解。我们的意思是，通过保留隐私性，对没有人类注释的短暂数据使用。该系统是用于增量/持续学习的生产LevelAsASR模型的一步，该模型提供了接近实时测试床，以在云中进行端到端ASR实验，同时遵守保留隐私的政策。我们表明，即使在没有人类注释的标签的情况下，拟议的系统也可以在六个月的新时间内显着改善生产模型（3％），而在增量学习中，较弱的监督和大批量大小。在新时期，这种改进比测试集的新单词和短语相比为20％。我们在ASR的同时进一步探讨了拥有有效的教师模型和使用大批量大小的实用性的同时，以保护隐私的增量方式展示了模型构建的有效性。

机器学习（ML）是人工智能（AI）的子场，其放射学中的应用正在以不断加速的速度增长。研究最多的ML应用程序是图像的自动解释。但是，可以将自然语言处理（NLP）与文本解释任务组合的ML结合使用，在放射学中也具有许多潜在的应用。一种这样的应用是放射学原始胶体的自动化，涉及解释临床放射学转介并选择适当的成像技术。这是一项必不可少的任务，可确保执行正确的成像。但是，放射科医生必须将专门用于原始胶片的时间进行报告，与推荐人或教学进行报告，交流。迄今为止，很少有使用临床文本自动选择协议选择的ML模型的出版物。本文回顾了该领域的现有文献。参考机器学习公约建议的最佳实践对已发布模型进行系统评估。讨论了在临床环境中实施自动质胶的进展。

心电图（ECG）监测心脏产生的电活动，用于检测致命的心血管疾病（CVD）。从传统上讲，为了捕获精确的电活动，临床专家使用多铅的心电图（通常为12条线索）。但是最近，大尺寸的深度学习模型已被用于检测这些疾病。但是，这样的模型需要大量的计算资源，例如巨大的记忆和漫长的推理时间。为了减轻这些缺点，我们提出了一个低参数模型，称为低资源心脏网络（LRH-NET），该模型使用较少的潜在客户在资源受限的环境中检测ECG异常。除此之外，还使用多层次知识蒸馏过程，以在我们提出的模型上获得更好的概括性能。多层次知识蒸馏过程将知识提炼成经过培训的LRH-NET，以减少在多个线索中训练的高级参数（教师）模型减少铅的铅，以减少性能差距。在Physionet-2020挑战数据集上评估了所提出的模型，输入受限。 LRH-NET的参数比检测CVD的教师模型小106倍。与教师模型相比，LRH-NET的性能缩放高达3.2％，推理时间降低了75％。与计算和参数密集的深度学习技术相反，提出的方法使用了使用低资源LRH-NET的ECG铅的子集，使其非常适合在边缘设备上部署。

无线电贴图在无线通信和移动机器人任务中找到了许多应用，包括资源分配，干扰协调和任务规划。尽管已经提出了许多技术来构造来自空间分布测量的无线电映射，但是预先假定了这种测量的位置的位置。相反，本文提出了频谱测量，其中诸如无人航空车辆（UAV）的移动机器人在主动选择的一组位置处收集测量以在短测量时间内获得高质量地图估计。这是以两步执行的。首先，设计了两种新颖的算法，基于模型的在线贝叶斯估计器和数据驱动的深度学习算法，以更新地图估计和指示每个可能位置的测量信息的信息性。这些算法提供互补的益处，并且每次测量都具有恒定的复杂性。其次，不确定度量用于规划无人机的轨迹，以在最具信息地的位置收集测量。为了克服这个问题的组合复杂性，提出了一种动态编程方法，以通过线性时间的大不确定性的区域获取航路点列表。在现实数据集上进行的数值实验证实了所提出的方案快速构建精确的无线电贴图。

在这项努力中，我们考虑一种加强学习（RL）技术，用于解决具有复杂奖励信号的个性化任务。特别是，我们的方法是基于状态空间聚类，使用简单的$ k $ -means算法以及网络架构和优化算法的传统选择。数值示例展示了不同RL程序的效率，并用于说明该技术加速了代理的学习能力，并不限制代理商的性能。