课堂有效评价:激励性 启发性 机智性

当前课堂教学．大量流于形式的无效评价充斥着我们的课堂。那么，什么样的课堂评价才是有效评价呢？

Self-supervised learning artificial intelligence noise reduction technology based on the nearest adjacent layer in ultra-low dose CT of urinary calculi

Objective To observe the value of self-supervised deep learning artificial intelligence(AI)noise reduction technology based on the nearest adjacent layer applicated in ultra-low dose CT(ULDCT)for urinary calculi.Methods Eighty-eight urinary calculi patients were prospectively enrolled.Low dose CT(LDCT)and ULDCT scanning were performed,and the effective dose(ED)of each scanning protocol were calculated.The patients were then randomly divided into training set(n=75)and test set(n=13),and a self-supervised deep learning AI noise reduction system based on the nearest adjacent layer constructed with ULDCT images in training set was used for reducing noise of ULDCT images in test set.In test set,the quality of ULDCT images before and after AI noise reduction were compared with LDCT images,i.e.Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)scores,image noise(SD ROI)and signal-to-noise ratio(SNR).Results The tube current,the volume CT dose index and the dose length product of abdominal ULDCT scanning protocol were all lower compared with those of LDCT scanning protocol(all P<0.05),with a decrease of ED for approximately 82.66%.For 13 patients with urinary calculi in test set,BRISQUE score showed that the quality level of ULDCT images before AI noise reduction reached 54.42%level but raised to 95.76%level of LDCT images after AI noise reduction.Both ULDCT images after AI noise reduction and LDCT images had lower SD ROI and higher SNR than ULDCT images before AI noise reduction(all adjusted P<0.05),whereas no significant difference was found between the former two(both adjusted P>0.05).Conclusion Self-supervised learning AI noise reduction technology based on the nearest adjacent layer could effectively reduce noise and improve image quality of urinary calculi ULDCT images,being conducive for clinical application of ULDCT.

坚持机智、复杂、审慎的反思性评价

张淑琼、贾志国在《教师博览》（科研版）2018年第1期中撰文指出,杜绝随意性、功利性与算计性的教学评价,离不开理性的教学反思。通过对自身的评价根据、评价行为与评价态度进行反思,幼儿教师能够摈弃自身出于个人喜好、个人利益得失与个人主观臆断而造成的幼儿言语表达与行为表现的积极性的挫伤。坚持机智性评价,就是鼓励教师发挥创造性、因人因时因地制宜.

论幽默的特征和功能

幽默是口才的最高技巧,它使口才艺术的运用尽显奥妙,它使人际交往变得神奇无比。幽默具有三大特征,即机智性、含蓄性和趣味性。它的功能在于协调人际关系,激励我们乐观进取,轻装前进,对前进充满信心。在现实生活中,擅于幽默,能把许多不可能变成可能,从而改善我们的生活处境,在广阔的人生舞台上展示自己的魅力风采,在欢笑中谱写自己精彩的人生篇章。

语文教师口语的特点

语文教师教学口语的特点是示范性、情感性和机智性.这也是较高的教师语言艺术.教师应在这些方面勤加 揣摩、自我进修、自我提高.

浅谈幽默教学

运用幽默是创设愉快教学情境的一种方法,它能使沉闷的课堂气氛变得活跃起来,使枯燥的说教变得生动有趣,并能融洽师生感情,激发学生的学习兴趣,如何才能做到幽默教学呢?(一)领会幽默的实质众多研究表明:教学中体现出的幽默既具有一般幽默的特性——感染性、高雅性、机智性、含蓄性、启发性,又具有自身特殊的规定性即教育性,且实质在教育性。

教学艺术的特点

教学艺术是教师在科学观和审美原理的指导下,旨在省时高效地达到教育目的所使用的具有个性独特性、创造性的教学方法。教学艺术的特点是: 1.科学性和创造性结合而成的灵活性 遵循科学规律是办任何事情的基础,去之,一切行为都会失去信度。求异创新方成艺术,因此,创造性是教学艺术的心理前提。以科学性为基础,以创造性为前提必然导致灵活性。 2.经验积累与理论素养融合而成的机智性 如果说灵活性是艺术化教学活动的日常行为特征的话(也含有思维的灵活性),机智性则是处理突发事件的心理特征。教学机智的培养既要有丰富的教学实践经验。

数学教学艺术初探

教学不仅是一门科学,而且是一门艺术。优秀的教学之所以并不容易,正在于它不仅是知识的传授、能力的培养,而且是情感的交流、艺术的享受。教学艺术和教学原则存在着深刻的内在联系,它是达到最佳教学效果的必要条件。教学论若不包括对教学艺术的研究则是不完备的。在当前的教学改革中,开展对教学艺术的深入探讨,无论在理论上还是实践上,都有重要意义。教学艺术包括教学的形象性、情感性、机智性三个主要方面。本文试图结合数学教学的实践,对数学教学艺术作一些粗浅的探讨。

公开课的十大通病

近年来．公开课似乎已经积淀下了一些固定的不良模型．列举如下；①为活动而活动——受课学生缺少自主性．学生成为老师的思想“玩偶”．被动地跟从老师完成一个个活动。②为多媒体而多媒体——教学手段缺少实用性．多媒体使用的“量”与“度”没有被充分考虑。③为完成任务而完成任务———授课教师缺少机智性，

浅谈数学教学中评价性语言的使用原则

本文从六个方面简要阐述评价性语言在数学教学中使用的原则。

生活体验式教学中小学美术教师语言的应用

在新课改背景下，小学美术教师也要打破传统的教学模式，以生动形象的语言激发学生学习兴趣，以潜移默化的方式教会学生发现美、创造美，提高学生的审美情趣。生活体验式教学是一种全新的教学模式，教学中教师丰富而机智的语言不仅可以使学生在快乐中学习，还可以使学生在快乐中陶冶情操，塑造健全人格。

课堂评价语言的“艺术”

教师的评价语言在课堂教学中的地位十分重要，教师评价的恰当与否，直接关系到课堂气氛和教学效果。因此，课堂上教师的评价语言，应讲求准确性、启发性和机智性，让课堂成为学生流连忘返的殿堂。

Scene-adaptive hierarchical data association and depth-invariant part-based appearance model for indoor multiple objects tracking

Indoor multi-tracking is more challenging compared with outdoor tasks due to frequent occlusion, view-truncation, severe scale change and pose variation, which may bring considerable unreliability and ambiguity to target representation and data association. So discriminative and reliable target representation is vital for accurate data association in multi-tracking. Pervious works always combine bunch of features to increase the discriminative power, but this is prone to error accumulation and unnecessary computational cost, which may increase ambiguity on the contrary. Moreover, reliability of a same feature in different scenes may vary a lot, especially for currently widespread network cameras, which are settled in various and complex indoor scenes, previous fixed feature selection schemes cannot meet general requirements. To properly handle these problems, first, we propose a scene-adaptive hierarchical data association scheme, which adaptively selects features with higher reliability on target representation in the applied scene, and gradually combines features to the minimum requirement of discriminating ambiguous targets; second, a novel depth-invariant part-based appearance model using RGB-D data is proposed which makes the appearance model robust to scale change, partial occlusion and view-truncation. The introduce of RGB-D data increases the diversity of features, which provides more types of features for feature selection in data association and enhances the final multi-tracking performance. We validate our method from several aspects including scene-adaptive feature selection scheme, hierarchical data association scheme and RGB-D based appearance modeling scheme in various indoor scenes, which demonstrates its effectiveness and efficiency on improving multi-tracking performances in various indoor scenes.

Perceived Indoor Discomfort, Performance and Productivity

Even all indoor environmental standards are met the users are usually not satisfied and perceived discomfort is occurred in the smart office buildings. The most frequently cause of discomfort in smart buildings is overrun of intelligence. There are physical and psychological factors that influenced building users＇ comfort. An indoor air quality seems to be one of the main problems of smart office buildings. In Slovakia the office buildings relating to indoor environment European standard are mostly evaluated as the non-low polluting buildings. The pollution from building as well as the pollution from occupancy and using was respected. The odor intensity and indoor air acceptability were assessed by a sensory panel. The concentrations of total volatile organic compounds and carbon dioxide were measured. The odors from building materials studied under different air change rate are presented in this paper. The case study of indoor air acceptability concerning to indoor odors under occupancy and its affect on perceived air quality influenced by air change rate are also presented in this paper.

Improved control of intelligent excavator using proportional-integral-plus gain scheduling

Consider the design and implementation of an electro-hydraulic control system for a robotic excavator, namely the Lancaster University computerized and intelligent excavator (LUCIE). The excavator was developed to autonomously dig trenches without human intervention. One stumbling block is the achievement of adequate, accurate, quick and smooth movement under automatic control, which is difficult for traditional control algorithm, e.g. PI/PID. A gain scheduling design, based on the true digital proportional-integral-plus (PIP) control methodology, was utilized to regulate the nonlinear joint dynamics. Simulation and initial field tests both demonstrated the feasibility and robustness of proposed technique to the uncertainties of parameters, time delay and load disturbances, with the excavator arm directed along specified trajectories in a smooth, fast and accurate manner. The tracking error magnitudes for oblique straight line and horizontal straight line are less than 20 mm and 50 mm, respectively, while the velocity reaches 9 m/min.

Generation Reliability Assessment in Electricity Markets Using Cooperative and Non-cooperative Concepts and Intelligent Systems

Deregulation policy has caused some changes in the concepts of power systems reliability assessment and enhancement. In the present research, generation reliability is considered, and a method for its assessment is proposed using game theory and NN （neural networks）. Also, due to the stochastic behavior of power markets and generators＇ forced outages, MCS （monte carlo simulation） is used for reliability evaluation. Generation reliability merely focuses on the interaction between generation complex and load. Therefore, in the research, based on the behavior of players in the market and using game theory, two outcomes are considered： cooperation and non-cooperation. The proposed method is assessed on IEEE-Reliability Test System with satisfactory results. LOLE （loss of load expectation） is used as the reliability index and it will be shown that generation reliability in cooperation market is better than non-cooperation outcome.

The Application of a Smartphone in Ship Stability Experiment

The inclining experiment is the only regulatory tool to assess ship stability. This experiment is a time consuming process for both real-life tests and ship model experiments. The difficulty is mainly due to a bias in the measurement of heel angle. Nowadays, digital inclinometers are available, but they are expensive. In this study, the use of a smartphone application is presented for ship inclination and rolling-period tests. The idea consists of using accelerometer and gyroscope sensors built into the current smartphones for the measurements. Therefore, some experiments are carried out on an example trawler model to exhibit the uses and advantages of this method. The obtained results are in good agreement with those provided from the pendulum method and natural roll-period test. This application is new, easy, and more accurately assesses metacentric height during the inclining and rolling-period tests.

Smart phone-based context-aware augmentative and alternative communications system

A smartphone-based context-aware augmentative and alternative communication(AAC) was applied was in order to enhance the user's experience by providing simple, adaptive, and intuitive interfaces. Various potential context-aware technologies and AAC usage scenarios were studied, and an efficient communication system was developed by combining smartphone's multimedia functions and its optimized sensor technologies. The experimental results show that context-awareness accuracy is achieved up to 97%.

Mobile Business Process Personalization for End Users

Many specified business needs in enterprise context cannot be effectively satisfied using current business process technology.This phenomenon is called the "long tail" of business processes.In addition,more and more business applications need to be accessed from mobile devices such as smartphones by enterprise end users.This paper attempts to solve both two challenges above.A lightweight event-driven process model is proposed aiming at satisfying the spontaneous business needs in enterprise.And we design an innovative wizard,which works like a tutorial,guiding end users in creating this lightweight process model.Moreover,end users are allowed to interact with the process created by themselves on smartphones.Finally,the usability of our approach was evaluated on a small set of users in a real business scenario.The results show that end users can effectively build their personalized business processes using our approach and interact with them in mobile environment.