Object detection based on visual memory: a feature learning and feature imagination process

ABSTRACT

Visual memory plays an important role for the human’s visual system to detect objects. The features of an object stored in the visual memory have much lower dimensions than the features contained within an image. We simulate the visual memory as a feature learning and feature imagination (FLFI) process to build an object detection algorithm. The method is constructed by a bottom-up feature learning and a top-down feature imagination. The proposed object detection method is tested using publicly available benchmark data sets, and the result indicates that it is fast and more robust.     

Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets

Bankruptcy prediction is still important topic receiving notable attention. Information about an imminent bankruptcy threat is a crucial aspect of the decision-making process of managers, financial institutions, and government agencies. In this paper, we utilize a newly acquired dataset comprising financial parameters derived from the annual reports of small- and medium-sized companies. The data, which reveal the true ratio between bankrupt and non-bankrupt companies, are severely imbalanced and only contain a small fraction of bankrupt companies. Our solution to overcome this challenging scenario of imbalanced learning was to adopt three one-class classification methods: a least-squares approach to anomaly detection, an isolation forest, and one-class support vector machines for comparison with conventional support vector machines. We provide a comprehensive analysis of the financial attributes and identify those that are most relevant to bankruptcy prediction. The highest prediction performance in terms of the geometric mean score is 91%. The results are validated on two datasets from the manufacturing and construction industries.     

好的表演需要好的观众——顾客察觉准确性在员工情绪劳动影响中的作用

基于385对顾客和员工的问卷调查的配对数据，考察了员工的情绪劳动如何影响顾客的信任和忠诚，以及顾客察觉准确性在其中的调节作用。研究结果表明员工的深层表演会促进顾客的信任，进而促进顾客忠诚；顾客对深层扮演的高察觉准确性会加强员工的深层扮演与顾客信任之间的正向关系，而当顾客对表面扮演察觉准确性低时，员工的表面扮演会对顾客信任有正向的影响；顾客的深层/表面扮演察觉准确性加强了深层/表面扮演、顾客信任和忠诚之间的中介效应。研究帮助从情绪的视角更好地理解服务情境中信任的前因变量，发现顾客的察觉准确性在情绪劳动影响过程中的重要作用，有助于服务企业从情绪劳动的角度来建立顾客信任和忠诚。     

环境检测报告质量的提升策略分析

环境检测工作对于环境保护具有重要影响,这要求全社会稳步提升对于环境检测工作的认识水平,积极地参与相关工作实践,为环境保护提供可靠的参考依据。论文重点分析了环境检测报告质量的提升策略,结合提升环境检测报告质量的意义,提出对应的方案,以期推动环境检测报告的质量提升至新的高度。     

用螺纹量规检测产品产生争议时的解决方法

本文分析了检测结果合格的螺纹量规检测螺纹产品时出现的疑问,并解答这些疑问。     

食品安全检测技术对食品质量安全的影响

食物的品质安全是保证人们生命健康的关键因素,是人们赖以生存与发展的最主要的前提条件.而随着社会主义市场经济的日益发展,人们的生活水平也在逐步改善,对食物的安全品质问题也越来越关注.目前,在我国的食品安全监督管理检验中,不论是在技术上,还是在产品质量安全监督管理方面,都仍面临着许多不足和问题,并不断影响检验技术的深入发展与完善.因此,本文重点对食品安全监督管理检验技术进行了论述与研究,期望可以通过了解该技术进展状况,为我国食品领域发展提出合理的指导意见,实现我国食品安全监督管理检验技术水平的进一步提高,进而促进我国食品领域的健康发展.     

5种不同类型饮用水水质检测及分析

目的:通过微生物和理化指标检测,对比分析不同类型水净化前后的水质,为居民选择合理健康的饮用水提供参考.方法:取5种不同处理方式的水进行微生物、理化指标检测.具体项目为菌落总数、总大肠菌群、大肠埃希氏菌、耐热大肠菌群、pH、溶解性总固体、总硬度、砷、汞、铅、镉、硫酸盐、氯化物、氟化物和硝酸盐.结果:自来水、净化后的自来水15项指标都合格;井水共有6项指标不合格,分别是菌落总数、总大肠菌群、大肠埃希氏菌、耐热大肠菌群、溶解性总固体和总硬度;净化后井水、直饮水的菌落总数不合格.结论:井水水质卫生质量差,被粪便污染,水硬度高.净化后的水水质较好,但在净化、运输等过程中要防止二次污染.水质达到标准规定的前提下,在日常生活中人们可以根据自身对各类物质的需求,交替饮用自来水、净化水、直饮水和凉白开.     

苹果中腐霉利残留快检测定及方法评估

本研究以阿克苏苹果样品中腐霉利的残留为研究对象,采用行业标准《蔬菜和水果中有机磷、有机氯、拟除虫菊酯和氨基甲酸酯类农药多残留的测定》(NY/T 761—2008)为参比方法,分别选取3家快检试剂盒测定苹果样品中添加的不同浓度腐霉利标品,根据《食品快速检测方法评价技术规范》对其快检适用性进行评估.结果显示,试剂盒A与试剂盒C在0.3 mg·kg-1浓度以上呈阳性结果,灵敏度为75％、特异性为100％、假阴性率为25％、假阳性率为0;试剂盒B在0.1 mg·kg-1以上呈阳性结果,其灵敏度为100％、特异性为50％、假阴性率为0、假阳性率为50％;3家试剂盒的显著性差异均为83.33％、相对准确度为8.1％.所选试剂盒适用于苹果中腐霉利残留量的快速筛查.     

Imperfect Accounting and Reporting Bias

Errors and bias are both inherent features of accounting. In theory, while errors discourage bias by lowering the value relevance of accounting, they can also facilitate bias by providing camouflage. Consistent with theory, we find a hump‐shaped relation between a firm's propensity to engage in intentional misstatement and the prevalence of unintentional misstatements in the firm's industry for the whole economy and a majority of the industries. The result is robust to using firms’ number of items in financial statements and exposure to complex accounting rules as alternative proxies for errors and to using the restatement amount in net income to quantify the magnitude of bias and errors. To directly test for the two effects of errors, we show that when errors are more prevalent, the market reacts less to firms’ earnings surprises and bias is more difficult to detect. Our results highlight the imperfectness of accounting, advance understanding of firms’ reporting incentives, and shed light on accounting standard setting.