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排序方式： 共有371条查询结果，搜索用时 15 毫秒
251.
《Information processing & management》2022,59(2):102795
This paper presents an approach to measuring business sentiment based on textual data. Business sentiment has been measured by traditional surveys, which are costly and time-consuming to conduct. To address the issues, we take advantage of daily newspaper articles and adopt a self-attention-based model to define a business sentiment index, named S-APIR, where outlier detection models are investigated to properly handle various genres of news articles. Moreover, we propose a simple approach to temporally analyzing how much any given event contributed to the predicted business sentiment index. To demonstrate the validity of the proposed approach, an extensive analysis is carried out on 12 years’ worth of newspaper articles. The analysis shows that the S-APIR index is strongly and positively correlated with established survey-based index (up to correlation coefficient ) and that the outlier detection is effective especially for a general newspaper. Also, S-APIR is compared with a variety of economic indices, revealing the properties of S-APIR that it reflects the trend of the macroeconomy as well as the economic outlook and sentiment of economic agents. Moreover, to illustrate how S-APIR could benefit economists and policymakers, several events are analyzed with respect to their impacts on business sentiment over time. 相似文献
252.
《Information processing & management》2022,59(1):102740
One of the most time-critical challenges for the Natural Language Processing (NLP) community is to combat the spread of fake news and misinformation. Existing approaches for misinformation detection use neural network models, statistical methods, linguistic traits, fact-checking strategies, etc. However, the menace of fake news seems to grow more vigorous with the advent of humongous and unusually creative language models. Relevant literature reveals that one major characteristic of the virality of fake news is the presence of an element of surprise in the story, which attracts immediate attention and invokes strong emotional stimulus in the reader. In this work, we leverage this idea and propose textual novelty detection and emotion prediction as the two tasks relating to automatic misinformation detection. We re-purpose textual entailment for novelty detection and use the models trained on large-scale datasets of entailment and emotion to classify fake information. Our results correlate with the idea as we achieve state-of-the-art (SOTA) performance (7.92%, 1.54%, 17.31% and 8.13% improvement in terms of accuracy) on four large-scale misinformation datasets. We hope that our current probe will motivate the community to explore further research on misinformation detection along this line. The source code is available at the GitHub.2 相似文献
253.
随着教育的不断深入革新,人们对小学数学教育提出了更高的要求。小学生在学校除了要接受教育,掌握必要的数学知识以外,还要进行深度学习,并在深度学习的过程中进行良好的数学交流,以此让学生掌握更多的数学知识以及与数学有关的学习方法、学习思维,通过这样的方式全方面提高小学生的数学综合素养。 相似文献
254.
Objective: There is a remarkable lack of scientific evidence to support the option to use alpha-stat or pH-stat management, as to which is more beneficial to brain protection during deep hypothermic CPB. This study examined cortical blood flow (CBF), cerebral oxygenation, and brain oxygen consumption in relation to deep hypothermic CPB with alpha-stat or pH-stat management. Methods: Twenty-two pigs were cooled with alpha-stat or pH-stat during CPB to 15℃ esophageal temperature. CBF and cerebral oxygenation were measured continuously with a laser flowmeter and near-infrared spectroscopy, respectively. Brain oxygen consumption was measured with standard laboratory techniques. Results: During CPB cooling, CBF was significantly decreased, about 52.2%±6.3% (P＜0.01 vs 92.6%±6.5% of pH-stat) at 15℃ in alpha-stat,whereas there were no significant changes in CBF in pH-stat. While cooling down, brain oxygen extraction (OER) progressively decreased, about 9.5%±0.9% and 10.9%±1.5% at 15 ℃ in alpha-stat and pH-stat, respectively. At 31℃ the decreased value in pH-stat was lower than in alpha-stat (29.9%±2.7% vs 22.5%±1.9%; P＜0.05). The ratio of CBF/OER were 2.0±0.3 in alpha-stat and pH-stat, respectively; it was kept in constant level in alpha-stat, and significantly increased by 19℃ to 15℃ in pH-stat (4.9±0.9 vs 2.3±0.4; P＜0.01). In mild hypothermia, cerebral oxyhemoglobin and oxygen saturation in alpha-stat were greater than that in pH-stat (102.5%±1.4% vs 99.1%±0.7%; P＜0.05). In deep hypothermia, brain oxygen saturation in pH-stat was greater than that in alpha-stat (99.2%±1.0% vs 93.8%±1.0%; P＜0.01), and deoxyhemoglobin in pH-stat decreased more greatly than that in alpha-stat (28.7%±6.8% vs 54.1%±4.7%; P＜0.05). Conclusions: In mild hypothermic CPB, brain tissue oxygen saturation was greater in alpha-stat than in pH-stat. However, cerebral oxygenation and brain tissue oxygen saturation were better in pH-stat than in alpha-stat during profound hypothermia. PH-stat strategyprovided much more oxygen to brain tissue before deep hypothermic circulatory arrest. 相似文献
255.
《Information processing & management》2023,60(3):103284
Deep multi-view clustering (MVC) is to mine and employ the complex relationships among views to learn the compact data clusters with deep neural networks in an unsupervised manner. The more recent deep contrastive learning (CL) methods have shown promising performance in MVC by learning cluster-oriented deep feature representations, which is realized by contrasting the positive and negative sample pairs. However, most existing deep contrastive MVC methods only focus on the one-side contrastive learning, such as feature-level or cluster-level contrast, failing to integrating the two sides together or bringing in more important aspects of contrast. Additionally, most of them work in a separate two-stage manner, i.e., first feature learning and then data clustering, failing to mutually benefit each other. To fix the above challenges, in this paper we propose a novel joint contrastive triple-learning framework to learn multi-view discriminative feature representation for deep clustering, which is threefold, i.e., feature-level alignment-oriented and commonality-oriented CL, and cluster-level consistency-oriented CL. The former two submodules aim to contrast the encoded feature representations of data samples in different feature levels, while the last contrasts the data samples in the cluster-level representations. Benefiting from the triple contrast, the more discriminative representations of views can be obtained. Meanwhile, a view weight learning module is designed to learn and exploit the quantitative complementary information across the learned discriminative features of each view. Thus, the contrastive triple-learning module, the view weight learning module and the data clustering module with these fused features are jointly performed, so that these modules are mutually beneficial. The extensive experiments on several challenging multi-view datasets show the superiority of the proposed method over many state-of-the-art methods, especially the large improvement of 15.5% and 8.1% on Caltech-4V and CCV in terms of accuracy. Due to the promising performance on visual datasets, the proposed method can be applied into many practical visual applications such as visual recognition and analysis. The source code of the proposed method is provided at https://github.com/ShizheHu/Joint-Contrastive-Triple-learning. 相似文献
256.
深度学习是当前学习科学和教育教学领域重点关注的热点话题。国内外学者对“如何促进深度学习发生”进行了系列研究,但是现有研究忽视了教师对学生深度学习状态的识别,因而实践中无法采用精准的教学干预。运用扎根理论对159篇教师发表的深度学习文献进行三级编码,逐步构建出教师视野中的学生深度学习三维状态表征体系。该表征体系由认知、行为和情感三个维度构成,其中认知状态包括分析解释、推理质疑、概括论证等9种表征,行为状态包括主动交互、自主学习、执行计划等9种表征,情感状态包括产生学习动机、养成学习态度、形成学习意指等8种表征。对学生深度学习状态表征的进一步分析发现,深度学习的概念内涵实操性较弱、对学习层次区分的敏感性较低以及将中等水平的深度学习当作学习目标是造成教师对学生课堂深度学习出现认知偏误的深层原因。因此,教师不仅要学习和实践深度学习的发生机制和促进策略,更要从学习者的多维发展视角精准科学地理解课堂深度学习的本真。该表征体系扩宽和深化了已有研究成果,为教师有效开展促进学生深度学习的教学实践提供了方向指引。 相似文献
257.
With the development of information technology and economic growth, the Internet of Things (IoT) industry has also entered the fast lane of development. The IoT industry system has also gradually improved, forming a complete industrial foundation, including chips, electronic components, equipment, software, integrated systems, IoT services, and telecom operators. In the event of selective forwarding attacks, virus damage, malicious virus intrusion, etc., the losses caused by such security problems are more serious than those of traditional networks, which are not only network information materials, but also physical objects. The limitations of sensor node resources in the Internet of Things, the complexity of networking, and the open wireless broadcast communication characteristics make it vulnerable to attacks. Intrusion Detection System (IDS) helps identify anomalies in the network and takes the necessary countermeasures to ensure the safe and reliable operation of IoT applications. This paper proposes an IoT feature extraction and intrusion detection algorithm for intelligent city based on deep migration learning model, which combines deep learning model with intrusion detection technology. According to the existing literature and algorithms, this paper introduces the modeling scheme of migration learning model and data feature extraction. In the experimental part, KDD CUP 99 was selected as the experimental data set, and 10% of the data was used as training data. At the same time, the proposed algorithm is compared with the existing algorithms. The experimental results show that the proposed algorithm has shorter detection time and higher detection efficiency. 相似文献
258.
《Information processing & management》2022,59(2):102850
Edge computing has recently gained momentum as it provides computing services for mobile devices through high-speed networks. In edge computing system optimization, deep reinforcement learning(DRL) enhances the quality of services(QoS) and shorts the age of information(AoI). However, loosely coupled edge servers saturate a noisy data space for DRL exploration, and learning a reasonable solution is enormously costly. Most existing works assume that the edge is an exact observation system and harvests well-labeled data for the pretraining of DRL neural networks. However, this assumption stands in opposition to the motivation of driving DRL to explore unknown information and increases the scheduling and computing costs in large-scale dynamic systems. This article leverages DRL with a distillation module to drive learning efficiency for edge computing with partial observation. We formulate the deadline-aware offloading problem as a decentralized partially observable Markov decision process (Dec-POMDP) with distillation, called fast decentralized reinforcement distillation(Fast-DRD). Each edge server decides makes offloading decisions in accordance with its own observations and learning strategies in a decentralized manner. By defining trajectory observation history(TOH) distillation and trust distillation to avoid overfitting, Fast-DRD learns a suitable offloading model in a noisy partially observed edge system and reduces the cost for communication among servers. Finally, experimental simulations are presented to evaluate and compare the effectiveness and complexity of Fast-DRD. 相似文献
259.
心脏是全身耗氧量最多的器官,安静状态下心肌以FFA氧化供能为主,其耗氧量是其他组织的2-3倍,随着运动强度的增加,心肌能量代谢转变为以乳酸有氧供能为主,耗氧量增加,为了满足心肌在运动状态下对氧的需求,机体常通过舒张冠状动脉来实现供氧平衡,实验证明某些中药如党参等能有效舒张冠状动脉,增加心肌的血流量,从而提高心肌有氧代谢的能力。 相似文献
260.
数据库驱动的Web站点根据查询产生的Web页结构布局都是极其相似的;现有的Web提取方法忽视或者忽略了这种相似性,因而在提取效率性能和通用性上都有较大的限制。本文提出一种基于标签树相似度的模板自动学习方法;进而根据模板来提取这类网页的数据;并利用Eclipse和开源HTML Parser对算法进行了实现;实验结果表明该算法具有较快的提取速度和较好的准确率。 相似文献