The use of response patterns to improve item scoring.

This study compared the validity of 2 methods of scoring the Minnesota Vocational Interest Inventory. One method used keys developed in the usual manner by selecting items that differentiated between specific occupational groups and a reference group of tradesmen-in-general. The other method used keys developed by selecting items that were part of response patterns that differentiated between criterion and reference groups. 3 occupational keys were developed and cross-validated: painter, printer, and electrician. Results showed that the keys were about equal in their ability to separate criterion from reference groups, but the key developed from response patterns used far fewer items. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

对修改其他债务条件进行债务重组规定的异议

债务重组是指债权人按照其与债务人达成的协议或法院的裁决,同意债务人修改债务条件的事项,债务重组共有五种方式,分别是：以低于债务帐面价值的现金清偿债务;以非现金资产清偿债务;债务转为资本;修改其他债务条件;以上两种或两种以上方式的组合,本文对其中第四种修改其他债务条件进行债务重组的帐务处理规定提出了不同意见,认为将来应付金额（将来应收金额）不应包括将来应计利息,并认为重组债务（债权）等于或小于将来应付金额（将来应收金额）时,债务（债权）人也要进行帐务处理,最后给出了具体的帐务处理方法。     

高职生英语学习兴趣缺失原因探析

分析了高职生英语学习兴趣缺失的原因,提出了培养并激发高职生英语学习兴趣的对策。     

基于用户兴趣子类的协作推荐算法

随着电子商务规模的进一步扩大,用户数目和文档资源急剧增加,导致用户数据的极端稀疏性.传统协作推荐算法都无法很好地解决数据稀疏性问题.本文提出一种基于兴趣子类的协作推荐算法,通过子类处理思想的引入,使得某两个用户即使整体不相似而因为“局部点”的相似产生有用的推荐,“最近邻居”的发现变得更容易更准确.实验结果表明,该算法能有效地解决用户数据的极端稀疏问题,在同等条件下,相对于传统协作推荐算法有更好的推荐质量.     

基于感兴趣区的MPEG-4 FGS增强层码率分配算法

该文提出了一种基于感兴趣区的MPEG-4 FGS增强层码率分配算法。该算法在建立一种针对感兴趣区的率失真模型的基础上,在等质量的约束条件下,可以实现选择性增强后FGS增强层码率的优化分配。实验结果表明,与平均码率分配算法相比,该算法可以显著降低视频图像中人眼感兴趣区域重建质量的波动。同时,该算法的计算复杂度低,完全能满足视频服务器实时码率分配的要求。     

基于FasterMDNet的视频目标跟踪算法

多域卷积神经网络（MDNet）算法在卷积层采用选择性搜索的方式来提取候选框，因此它没有共享完整图像特征，从而导致在线视频目标跟踪速度慢。针对这个问题，提出一种快速多域卷积神经网络（FasterMDNet）视频目标跟踪算法。FasterMDNet是建立在MDNet基础上的一种模型，在卷积层后面引入RPN（Region Proposal Network）网络，优化了损失函数，共享完整图像卷积特征，加快候选区域建议框（ROI）更高效的生成；为了更好地获得目标和背景信息表示，在RPN网络后加入ROIAlign层，对提取的候选区域建议框特征图用双线性插值方法来提高感受野的分辨率。该算法对目标跟踪基准数据集OTB2013、OTB2015、VOT2016进行了评估，并与前沿的跟踪算法做对比，实验结果证明，该算法跟踪准确率优于其他对比方法，并且对比相同实验环境下MDNet算法，在线跟踪速度提高了近12倍。     

A design method for an intelligent manufacturing and service system for rehabilitation assistive devices and special groups

The maturity of Industrial 4.0 technologies (smart wearable sensors, Internet of things [IoT], cloud computing, etc.) has facilitated the iteration and digitization of rehabilitation assistive devices (RADs) and the innovative development of intelligent manufacturing systems of RADs, expanding the value-added component of smart healthcare services. The intelligent manufacturing service mode, based on the concept of the product life cycle, completes the multi-source data production process analysis and the optimization of manufacturing, operation, and maintenance through intelligent industrial Internet of things and other means and improves the product life cycle management and operation mechanism. The smart product-service system (PSS) realizes the value-added of products by providing users with personalized products and value-added services, service efficiency, and sustainable development and gradually forms an Internet-product-service ecosystem. However, research on the PSS of RADs for special populations is relatively limited. Thus, this paper provides an overview of an IoT-based production model for RADs and a smart PSS-based development method of multimodal healthcare value-added services for special people. Taking the hand rehabilitation training devices for autistic children as a case, this paper verifies the effectiveness and availability of the proposed method. Compared with the traditional framework, the method used in this paper primarily helps evaluate rehabilitation efficacy, personalizes schemes for patients, provides auxiliary intelligent manufacturing service data and digital rehabilitation data for RAD manufacturers, and optimizes the product iteration development procedures by combining user-centered product interaction, multimodal evaluation, and value-added design. This study incorporates the iterative design of RADs into the process of smart PSS to provide some guidance to the RADs design manufacturers.     

Affective Tutoring System for Built Environment Management

There are quite a lot of researche performed in the world, which prove that interest, stress and learning productivity of a learner to a large extent determine study results. Moreover, learning emotionality, pleasurableness and attractiveness, which help to increase its efficiency, are stressed by various researchers. Researchers have noticed, that a student's interest, stress, learning productivity and academic achievement are quite closely related. Experts have noticed that in order to cause interest, increase learning productivity and maintain stress in a rational level of a learner it is necessary to constantly change the learning subject with regard to situational and individual interest. For this reason, an Affective Tutoring System for Built ENvironment Management (ATEN), developed by the authors of the article is very suitable. The ATEN was developed and fine-tuned in the course of the TEMPUS project “Reformation of the Curricula on Built Environment in the Eastern Neighbouring Area”. One of the ATEN innovations is that the System integrates the self-assessment and self-esteem measurement of students with multimodal biometric and intelligent methodologies and technologies. Affective Tutoring System for Built Environment Management can create a rational version of a learning process tailored to a specific student, taking into account such factors as how much the studies are interesting or difficult and the level of stress (with the help of biometric technologies). The System includes an automatic function that takes module topics and compiles an optimal set of personalised materials for a specific student. The case study submitted in this article partly demonstrates this developed System.     

生产实习中学生心理活动的研究

人的一切实践活动都是在心理活动调节下进行的．．生产实习是工科的重要实践性教学环节,指导教师应该从对学生心理活动发生、发展规律的研究入手,调节、控制实习过程中学生的心理状态,使在实习中发挥最佳效果。