排序方式： 共有455条查询结果，搜索用时 31 毫秒
61.
《Information processing & management》2023,60(2):103194
The application of natural language processing (NLP) to financial fields is advancing with an increase in the number of available financial documents. Transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT) have been successful in NLP in recent years. These cutting-edge models have been adapted to the financial domain by applying financial corpora to existing pre-trained models and by pre-training with the financial corpora from scratch. In Japanese, by contrast, financial terminology cannot be applied from a general vocabulary without further processing. In this study, we construct language models suitable for the financial domain. Furthermore, we compare methods for adapting language models to the financial domain, such as pre-training methods and vocabulary adaptation. We confirm that the adaptation of a pre-training corpus and tokenizer vocabulary based on a corpus of financial text is effective in several downstream financial tasks. No significant difference is observed between pre-training with the financial corpus and continuous pre-training from the general language model with the financial corpus. We have released our source code and pre-trained models. 相似文献
62.
《Information processing & management》2023,60(2):103192
The replies of people seeking support in online mental health communities can be analyzed to discover if they feel better after receiving support; feeling better indicates a cognitive change. Most research uses key phrase matching and word frequency statistics to identify psychological cognitive change, methods that result in omissions and inaccuracy. This study constructs an intelligent method for identifying psychological cognitive change based on natural language processing technology. It incorporates information related to emotions that appears in reply text to help identify whether psychological cognitive change has occurred. The model first encodes the emotion information based on rule matching and manual annotation, then adds the encoded emotion lexicon and a cognitive change lexicon to a word2vec high-dimensional semantic word vector training, converts the annotated cognitive change recognition text into a vector matrix using the trained model, and train in the annotated text using TextCNN. To compare the results with those of the traditional methods (key phrase matching and sentiment word frequency statistics), this study uses a semi-automated approach to construct a lexicon of psychological cognitive change, as well as a keyword lexicon without cognitive change, based on word vectors and similarity. We compare the performance of the classifier before and after the fusion of the graphical emotion information, compare the LSTM and Transformer as baselines, and compare traditional word frequency statistics methods. The experimental results show that our proposed classification model performs better than the others; it achieves 84.38% precision, an 84.09% recall rate, and an 84.17% F1 value. Our work bears methodological implications for online mental health platforms. 相似文献
63.
《Information processing & management》2023,60(2):103212
The massive number of Internet of Things (IoT) devices connected to the Internet is continuously increasing. The operations of these devices rely on consuming huge amounts of energy. Power limitation is a major issue hindering the operation of IoT applications and services. To improve operational visibility, Low-power devices which constitute IoT networks, drive the need for sustainable sources of energy to carry out their tasks for a prolonged period of time. Moreover, the means to ensure energy sustainability and QoS must consider the stochastic nature of the energy supplies and dynamic IoT environments. Artificial Intelligence (AI) enhanced protocols and algorithms are capable of predicting and forecasting demand as well as providing leverage at different stages of energy use to supply. AI will improve the efficiency of energy infrastructure and decrease waste in distributed energy systems, ensuring their long-term viability. In this paper, we conduct a survey to explore enhanced AI-based solutions to achieve energy sustainability in IoT applications. AI is relevant through the integration of various Machine Learning (ML) and Swarm Intelligence (SI) techniques in the design of existing protocols. ML mechanisms used in the literature include variously supervised and unsupervised learning methods as well as reinforcement learning (RL) solutions. The survey constitutes a complete guideline for readers who wish to get acquainted with recent development and research advances in AI-based energy sustainability in IoT Networks. The survey also explores the different open issues and challenges. 相似文献
64.
《Information processing & management》2023,60(2):103233
Zero-shot object classification aims to recognize the object of unseen classes whose supervised data are unavailable in the training stage. Recent zero-shot learning (ZSL) methods usually propose to generate new supervised data for unseen classes by designing various deep generative networks. In this paper, we propose an end-to-end deep generative ZSL approach that trains the data generation module and object classification module jointly, rather than separately as in the majority of existing generation-based ZSL methods. Due to the ZSL assumption that unseen data are unavailable in the training stage, the distribution of generated unseen data will shift to the distribution of seen data, and subsequently causes the projection domain shift problem. Therefore, we further design a novel meta-learning optimization model to improve the proposed generation-based ZSL approach, where the parameters initialization and the parameters update algorithm are meta-learned to assist model convergence. We evaluate the proposed approach on five standard ZSL datasets. The average accuracy increased by the proposed jointly training strategy is 2.7% and 23.0% for the standard ZSL task and generalized ZSL task respectively, and the meta-learning optimization further improves the accuracy by 5.0% and 2.1% on two ZSL tasks respectively. Experimental results demonstrate that the proposed approach has significant superiority in various ZSL tasks. 相似文献
65.
《Information processing & management》2023,60(3):103247
Recently, models that based on Transformer (Vaswani et al., 2017) have yielded superior results in many sequence modeling tasks. The ability of Transformer to capture long-range dependencies and interactions makes it possible to apply it in the field of portfolio management (PM). However, the built-in quadratic complexity of the Transformer prevents its direct application to the PM task. To solve this problem, in this paper, we propose a deep reinforcement learning-based PM framework called LSRE-CAAN, with two important components: a long sequence representations extractor and a cross-asset attention network. Direct Policy Gradient is used to solve the sequential decision problem in the PM process. We conduct numerical experiments in three aspects using four different cryptocurrency datasets, and the empirical results show that our framework is more effective than both traditional and state-of-the-art (SOTA) online portfolio strategies, achieving a 6x return on the best dataset. In terms of risk metrics, our framework has an average volatility risk of 0.46 and an average maximum drawdown risk of 0.27 across the four datasets, both of which are lower than the vast majority of SOTA strategies. In addition, while the vast majority of SOTA strategies maintain a poor turnover rate of approximately greater than 50% on average, our framework enjoys a relatively low turnover rate on all datasets, efficiency analysis illustrates that our framework no longer has the quadratic dependency limitation. 相似文献
66.
67.
[目的/意义]对社会情景感知计算研究对象、特征及关键技术进行总结和分析。[方法/过程]基于文献调研,以社会情景的概念为出发点,从社会情景感知系统的视角,对社会情景获取、社会情景建模、社会情景推理、安全与隐私四个模块的关键理论和技术进行探讨。[结果/结论]总结社会情景的五个描述维度;论述社会情景感知计算与传统情景感知计算的区别与联系;对比分析社会情景感知计算关键技术的特点和适用性,为社会情景感知计算研究关键技术的选择与应用提供理论依据和参考。 相似文献
68.
69.
[目的/意义] 大数据时代下,迫切需要数据分析人才来从海量的数据中发掘信息、揭示规律并帮助人们决策。作为专业教育的补充,数据分析类MOOC可以在较短时间内满足人们对数据分析课程内容的学习需求,通过分析其发展现状,将有助于更好地培养数据分析人才和发展数据科学。[方法/过程] 主要采用网络调查法对国内外数据分析MOOC的开设情况进行调查,在此基础上从教学内容、教学方式和教学效果三个方面进行分析。[结果/结论] 调查发现,数据分析类MOOC存在着教学内容上传统与新兴知识并存,教学方式上理论知识与实践技能并重的特点。为了更好地适应大数据时代人才需求的特点,要积极探索合作机制,通过不同形式的合作促进数据分析MOOC的发展。 相似文献
70.
[目的/意义]推荐系统已经成为电子商务网站的重要组成部分之一,为用户提供多种形式的信息推荐服务。国内以淘宝、京东和亚马逊为代表的电子商务网站的推荐系统采用不同的技术架构和多种热点推荐技术,并且越来越重视信息服务的质量。对推荐系统服务质量进行比较研究,能够进一步推动电子商务推荐系统的发展。[方法/过程]首先,从准确性、时效性、新颖性三个技术指标对比以上推荐系统的技术架构对于推荐服务质量的影响;其次,以用户体验作为信息服务质量评价的基础,对182名受访者进行热点技术的认可度调查,研究热点技术对推荐服务质量的影响;最后,对功能模块的用户体验情况进行调查和比较分析。[结果/结论]在这些研究、调查和分析的基础上,给出电子商务推荐系统使用的技术架构和热点技术,以及改进功能模块设计的对策,以进一步提升推荐系统的信息服务质量。 相似文献