question:so find the all research paper which in mention in the below text - ``` ACTIVE LEARNING IN MACHINE LEARNING 1. INTRODUCTION : Active learning is a subfield of machine learning that revolves around the selection of useful datapoints for inclusion in the training dataset. The central goal of active learning is to minimize the amount of labeled data needed to achieve a desirable predictive performance of the machine learning model. This is crucial considering that acquiring labeled data can often be expensive, time-consuming, and require the expertise of domain experts. Active sampling strategies aim to identify unlabeled instances for which obtaining labels would add the most value to the training set. In other words, an active learner asks the oracle (e.g., a human expert) to label only the most informative examples. In this paper, we summarize the active learning paradigm and discuss its application in various machine learning contexts. We will explore the necessity and relevance of active learning, and provide a brief literature survey on the development and latest improvements in techniques and applications of active learning. 2. Necessity and Relevance of Active Learning : The necessity and relevance of Active Learning can be attributed to several factors. Firstly, the amount of unlabeled data available is often much larger than the amount of labeled data. Acquiring labeled data can be expensive, time-consuming, and sometimes even impossible. Active Learning provides a solution to this problem by selecting the most informative samples for labeling, thus reducing the amount of labeled data required to train a model. Secondly, in many real-world scenarios, the distribution of the data is non-uniform, and certain regions of the feature space may be more important than others. Active Learning enables the selection of informative samples from these important regions, which can lead to more accurate models. Thirdly, Active Learning can improve the efficiency of the labeling process. Instead of labeling all samples in the dataset, Active Learning enables the selection of the most informative samples, which can reduce the number of samples that need to be labeled. Fourthly, Active Learning can improve the accuracy of the model. By selecting the most informative samples for labeling, the model can be trained on a more representative subset of the data, which can lead to better generalization performance. Fifthly, Active Learning can enable the development of models in domains where labeled data is scarce. In many domains, acquiring labeled data can be a bottleneck in the development of machine learning models. Active Learning can reduce the amount of labeled data required, which can enable the development of models in these domains. Finally, Active Learning can enable the development of more interpretable models. By selecting the most informative samples for labeling, Active Learning can help to identify the most important features in the data, which can lead to more interpretable models. In conclusion, Active Learning is a necessary and relevant paradigm in machine learning due to its ability to reduce the amount of labeled data required, improve the efficiency of the labeling process, improve the accuracy of the model, enable the development of models in domains where labeled data is scarce, and enable the development of more interpretable models. As the amount of data continues to grow, and the demand for accurate and interpretable models increases, the importance of Active Learning is likely to grow. 3. A Literature Survey on Active Learning : The concept of active learning has its roots in the works of researchers from as early as the 1960s. It gained more attention in the 1990s, thanks to the seminal paper by Lewis and Gale (1994), where the authors introduced a simple method for text classification, employing an uncertainty sampling strategy. Since then, active learning has been the subject of considerable interest and research. In this section, we will delve into the development of active learning from its early stages up to the modern era. 3.1. Early Stage Development : In the early years, active learning techniques focused mainly on uncertainty sampling, query-by-committee (QBC), and expected error reduction. Uncertainty sampling, introduced by Lewis and Gale (1994), explores the idea of querying instances that are the most uncertain for a given model, meaning that instances near the decision boundary are selected for annotation. The uncertainty of an instance can be measured in different ways, such as least confidence, maximum margin or entropy-based methods. The QBC approach proposed by Seung et al. (1992) relies on querying instances that have the most disagreement among an ensemble of models, promoting diversity of opinions. Initially, the committee is trained on a small labeled dataset, and then each member makes predictions on the unlabeled dataset. Instances with the highest disagreement among the committee members are considered informative and selected for annotation. Expected error reduction methods, such as query-by-expected-error-reduction (Roy and McCallum, 2001), take a more direct approach by estimating the expected reduction of the model’s error if a specific data point were to be labeled. These methods aim at minimizing the expected error over the entire dataset rather than focusing on individual instances. 3.2. Intermediate Stage Development : Throughout the 2000s, active learning research tackled new challenges and considered alternative frameworks for selecting informative samples. Some of these developments include: Exploiting the structure of the data: In some contexts, the structure of the data provides additional information that can be used for active learning. For example, queries can be constructed that exploit the relationships between instances, as used in graph-based approaches (Zhu et al., 2003), or consider the structure of class densities in minimum expected risk approaches (Zhang and Chen, 2015). Pool-based and stream-based active learning: Pool-based active learning assumes that the learner has access to a large pool of unlabeled data, from which it can request labels for the most informative samples. In contrast, stream-based active learning (Cohn et al., 1996) deals with data arriving sequentially, and the learner must decide whether or not to query the label of an instance immediately upon observing it. Different strategies have been developed for each active learning setting. Multi-label and multi-class active learning: As machine learning tasks became more complex, active learning had to adapt to accommodate multiple classes or the presence of multiple labels for each instance. Binary classification methods, such as uncertainty sampling, were modified to handle multi-class or multi-label problems (Brinker, 2003). 3.3. Latest Improvement and Application : In recent years, active learning has been applied to many different types of machine learning tasks, often achieving state-of-the-art performance. Deep learning: With the rise of deep learning, active learning has been applied to various complex problems. For instance, Gal et al. (2017) proposed an approach based on Bayesian deep learning for selecting informative samples, showing that active learning can significantly reduce the amount of labeled data required for achieving high performance in tasks such as image classification and object detection. Natural language processing: Active learning has been successfully used in a variety of NLP tasks, like named entity recognition and sentiment analysis (Settles, 2009). The primary challenges in this domain involve the complexity of text data and the dependence of instances on each other (e.g., words in sentences, sentences in documents). Bioinformatics: In the field of bioinformatics, active learning techniques have been used for tasks such as drug discovery, biomarker identification, and protein structure prediction (Warmuth et al., 2002). These tasks often involve high-dimensional data with a large number of possible feature combinations, making it challenging to identify the most informative samples. Computer vision: Active Learning has been used in computer vision tasks such as object detection, image segmentation, and image classification. For instance, in the work by Wang et al. (2020), active learning was used to improve the performance of a deep learning model for object detection in satellite images. Reinforcement Learning: Active Learning has also been used in the context of reinforcement learning, where it is used to select the most informative and relevant samples for the agent to learn from. For example, in the work by Azizzadenesheli et al. (2018), active learning was used to improve the performance of a reinforcement learning agent in a complex navigation task. Anomaly detection: Active Learning has been used for anomaly detection in various domains such as cybersecurity, medical diagnosis, and fraud detection. In the work by Liu et al. (2020), active learning was used to improve the performance of a deep learning-based anomaly detection system for cybersecurity. Active Learning has also been combined with other machine learning techniques to improve their performance, such as semi-supervised learning, transfer learning, and domain adaptation. For example, in the work by Konyushkova et al. (2017), active learning was used to improve the performance of a semi-supervised learning algorithm for image classification. 4. Conclusion : Active learning is a promising paradigm for effectively addressing the scarcity of labeled data in many machine learning applications. Over the years, the development of novel techniques that accommodate various learning scenarios, structures of data, and types of models has broadened the scope and applicability of active learning. As a result, active learning has proven to be indispensable in many domains, such as computer vision, natural language processing, and bioinformatics. In the future, the convergence of active learning with other advances in machine learning, such as reinforcement learning or transfer learning, is expected to deepen our understanding of the field while refining and expanding its applications further. ```

question:采用STM32F103C8T6芯片作为指纹和密码识别的超市存储柜的主微控制器，本设计的电路原理图模块较少，相对简单，且不需要较高的工作频率，STM32F103C8T6最高工作频率72MHz，拥有丰富的外设接口和IO，SRAM和FLASH容量也足够使用，而且STM32F103C8T6使用极为广泛，有很多成熟的参考例程和资料，STM32F103C8T6价格也可以接受，所以本次项目使用STM32F103C8T6芯片作为主微控制器非常合适。所以综上所述，本次火灾报警系统采取STC89C52单片机用作主控制器。请补充一下

question:STM32F103C8T6最高工作频率72MHz，拥有丰富的外设接口和IO，SRAM和FLASH容量也足够使用，而且STM32F103C8T6使用极为广泛，有很多成熟的参考例程和资料，STM32F103C8T6价格也可以接受，所以本次项目使用STM32F103C8T6芯片作为主微控制器非常合适。补充到500字

question:From now on write an interactive story about a young dragon named Sarion hiking alone through the mountains while he really needs to pee. After writing part of it stop writing and ask me “What happens next or would you like me to change something?”. If my response begins with "Continue: ", continue the story based on my response. If my response begins with "Change: ", redo the part of the story you just wrote with the changes I ask.