基于前景检测的视差优化算法

目前立体匹配算法分两类,传统的匹配算法通过计算两幅图的像素点相似程度,采用的是一种局部优先的方法。而当前的策略主要将问题转化为求解能量方程,进而对全局空间进行优化,提高匹配精度,获得更好的视差图。但是在实际应用过程中,由于光学失真和噪声,平滑表面的镜面反射,投影缩减,透视失真,低纹理和重复纹理等影响,导致误匹配或者找不到匹配点,从而得不到有效视差。然而在特定的场合,可以利用有限的有效视差,基于前景检测以及最小二乘法,优化得到较为完整的前景视差图。     

超高压变电站设备配置与检修

超高压变电设备的运行可靠性是电力系统中非常重要的条件。为了保障超高压变电设备的正常运行,延长设备的使用寿命,避免资源浪费,必须改进并加强对超高压变电设备的日常维护技术。对设备进行状态维修,其目标主要是：确保电气设备运行的安全性、稳定性,在一定程度上防止设备运行时出现损坏。对设备进行状态监测,熟练掌握设备的运行状态是重点,只有这样才能起到理想的维修效果,进一步提高设备的使用效率。     

Users’ ability to perceive misinformation: An information quality assessment approach

Digital information exchange enables quick creation and sharing of information and thus changes existing habits. Social media is becoming the main source of news for end-users replacing traditional media. This also enables the proliferation of fake news, which misinforms readers and is used to serve the interests of the creators. As a result, automated fake news detection systems are attracting attention. However, automatic fake news detection presents a major challenge; content evaluation is increasingly becoming the responsibility of the end-user. Thus, in the present study we used information quality (IQ) as an instrument to investigate how users can detect fake news. Specifically, we examined how users perceive fake news in the form of shorter paragraphs on individual IQ dimensions. We also investigated which user characteristics might affect fake news detection. We performed an empirical study with 1123 users, who evaluated randomly generated stories with statements of various level of correctness by individual IQ dimensions. The results reveal that IQ can be used as a tool for fake news detection. Our findings show that (1) domain knowledge has a positive impact on fake news detection; (2) education in combination with domain knowledge improves fake news detection; and (3) personality trait conscientiousness contributes significantly to fake news detection in all dimensions.     

Fake news detection via knowledgeable prompt learning

The spread of fake news has become a significant social problem, drawing great concern for fake news detection (FND). Pretrained language models (PLMs), such as BERT and RoBERTa can benefit this task much, leading to state-of-the-art performance. The common paradigm of utilizing these PLMs is fine-tuning, in which a linear classification layer is built upon the well-initialized PLM network, resulting in an FND mode, and then the full model is tuned on a training corpus. Although great successes have been achieved, this paradigm still involves a significant gap between the language model pretraining and target task fine-tuning processes. Fortunately, prompt learning, a new alternative to PLM exploration, can handle the issue naturally, showing the potential for further performance improvements. To this end, we propose knowledgeable prompt learning (KPL) for this task. First, we apply prompt learning to FND, through designing one sophisticated prompt template and the corresponding verbal words carefully for the task. Second, we incorporate external knowledge into the prompt representation, making the representation more expressive to predict the verbal words. Experimental results on two benchmark datasets demonstrate that prompt learning is better than the baseline fine-tuning PLM utilization for FND and can outperform all previous representative methods. Our final knowledgeable model (i.e, KPL) can provide further improvements. In particular, it achieves an average increase of 3.28% in F1 score under low-resource conditions compared with fine-tuning.     

Transfer language selection for zero-shot cross-lingual abusive language detection

We study the selection of transfer languages for automatic abusive language detection. Instead of preparing a dataset for every language, we demonstrate the effectiveness of cross-lingual transfer learning for zero-shot abusive language detection. This way we can use existing data from higher-resource languages to build better detection systems for low-resource languages. Our datasets are from seven different languages from three language families. We measure the distance between the languages using several language similarity measures, especially by quantifying the World Atlas of Language Structures. We show that there is a correlation between linguistic similarity and classifier performance. This discovery allows us to choose an optimal transfer language for zero shot abusive language detection.     

深海极端环境原位探测技术研究现状与对策

深海极端环境塑造了特殊的生命过程,资源潜力巨大,对其探测与研究是国际地球科学前沿,但深海严苛的高压环境极大地限制了深海采样及探测技术的应用。深海原位探测技术可以在不改变被测物位置及状态的条件下,获取深海样品的组分及含量信息,因此被越来越广泛地应用到深海极端环境的研究工作中。深海极端环境原位探测技术拥有广阔的前景,但作为一种新兴的探测技术仍需解决诸多科学难题。文章总结了国际和国内深海极端环境原位探测技术的研究进展,在分析当前我国深海极端环境研究现状基础上,提出未来我国深海极端环境原位探测技术发展对策。     

循证决策视角下的患者健康咨询主题分析

[目的/意义]为了将大数据更好地用于循证决策,文章构建了循证决策视角下的患者健康主题分析模型。[方法/过程]首先使用LDA主题模型对高血压问答数据进行主题探测,并结合文献提炼出高血压八大主题;再次使用问卷调查法和层次分析法对主题探测结果进行评估;最后对得到的证据进行实践讨论。[结果/结论]文章对大数据和循证决策的结合做了尝试性探索,结果表明循证决策视角下的患者健康主题分析模型,具有一定的实用性和创新性。     

循证决策视角下的患者健康咨询主题分析

[目的/意义]为了将大数据更好地用于循证决策,文章构建了循证决策视角下的患者健康主题分析模型。[方法/过程]首先使用LDA主题模型对高血压问答数据进行主题探测,并结合文献提炼出高血压八大主题;再次使用问卷调查法和层次分析法对主题探测结果进行评估;最后对得到的证据进行实践讨论。[结果/结论]文章对大数据和循证决策的结合做了尝试性探索,结果表明循证决策视角下的患者健康主题分析模型,具有一定的实用性和创新性。     

浅谈冬瓜山铜矿充填自动化控制技术的应用

冬瓜山充填自动化系统以实时数据平台为基础集成Profibus、Modbus现场总线,3＋2的网络结构组成分布式集散系统;通过嵌入系统的多媒体、先进控制策略及设备管理等基本模块为一体的技术设备,实现远程监控和管理,使冬瓜山尾砂充填过程控制系统从DCS上升到FCS系统。     

浅析奥斯汀和塞尔的言语行为理论

语用学是语言学各分支中一个以语言意义为研究对象的新兴学科领域,是专门研究语言的理解和使用的学问。奥斯汀第一个在语用学范围内研究言语行为理论。经塞尔的完善与发展,已成为哲学、语言学的重要研究课题,更是现代语用学的核心内容之一。塞尔的间接言语行为理论从使用和交际的角度研究语言的意义,为认识言外之力的本质提供了独到的解释方法。