Complex adaptive filtering user profile using graphical models

This article explores how to develop complex data driven user models that go beyond the bag of words model and topical relevance. We propose to learn from rich user specific information and to satisfy complex user criteria under the graphical modelling framework. We carried out a user study with a web based personal news filtering system, and collected extensive user information, including explicit user feedback, implicit user feedback and some contextual information. Experimental results on the data set collected demonstrate that the graphical modelling approach helps us to better understand the complex domain. The results also show that the complex data driven user modelling approach can improve the adaptive information filtering performance. We also discuss some practical issues while learning complex user models, including how to handle data noise and the missing data problem.     

自适应软件设计模式探讨

根据移动终端应用需求,以自适应软件开发需求为背景,通过对诸多学者关于自适应软件研究与开发成果进行比较,分析其中的优、缺点,展望了自适应软件设计模式的发展趋势,提出了一种新的、基于设计模式组合的自适应软件开发模式可行性设计方法。     

“五位一体”促进大学生就业的路径研究--基于CAS理论

大学生就业既是人才工程也是民生工程,促进大学生就业与政府、高校、用人单位、就业中介机构及大学生等多重主体息息相关。促进大学生就业体系具有一般复杂适应系统多样性、开放性、非线性、涌现性等特征。基于CAS理论,政府通过政策、法律手段搞好宏观调控,高校在课程设置、就业指导方面与用人单位搞好对接,企事业单位要摒弃就业歧视、降低门槛和就业中介、非营利机构相配合搭建公开、公正的就业平台,大学生自身也应秉承务实的就业态度,从而实现“五位一体”,促进大学生就业。     

物理与机电工程学院新型适应型人才培养模式中的教学分析

文章以教育学者舒尔曼的动态的学科教学认识理论为基本理论依据,以同等条件下班级学生在期中考试的自我预期为参数,统计分析了物理与机电工程学院在合并前后不同教学背景下的相关的教学效果与学生自我评估的关系,发现:①班级学生自我评估的合理度与平均成绩成正相关关系.②两系合并之后的教学环境发生了变化,理工科相互结合.工科的大学物理课程班级学生自我评估合理度较好,过高估计自我比率降低,平均成绩高.③同一个班级在一到四年级的自我评估能力逐渐提高.说明大学的教育氛围对于学生的自我评估和定位能力的培养有非常重要的作用.     

高校突发事件心理弹性干预研究——基于复杂适应系统的视角

校园突发事件的频发,使心理危机干预成为高校学生工作的重点和难点。心理弹性作为大学生重要的积极品质,能够激发个体潜在的正能量,是运用内外资源积极调适或修补心理危机的过程和结果。而复杂适应系统理论的＂适应产生复杂性＂理念为提升大学生心理弹性品质提供了方法论指导,高校可以从大学生个体主动性、学校内主客体互动性、心理危机的动态预测性等方面着手,构建具有高度适应性的突发事件心理弹性干预系统,提高高校应对心理危机的实效性。     

自适应加权中值滤波图像去噪算法

中值滤波是常用的降噪算法，本文提出了一种基于窗口拟合曲线的斜率信息的自适应加权算法。该算法具有较好的边缘保持特性及去噪性能。     

复杂适应系统理论视域下徽剧的海外译介研究

在复杂适应系统理论视域下,对新历史语境下译介主体、译介过程及翻译标准的复杂性和适应性进行了分析,在对徽剧的海外译介史进行介绍的基础上,具体考察了徽剧译介主体、译介过程及翻译标准,以期构建更加科学、动态、多元及适宜的徽剧译介观,来弥补当代徽剧海外译介的不足,并为中国传统戏曲文化的对外传播铺路搭桥。     

Line clipping against polygonal window algorithm based on the multiple virtual boxes rejecting

This paper presents a new algorithm for line clipping against a polygonal window by exploiting the local relationship between each line segment and the polygon. Firstly, a minimal enclosing box (MEB) of the polygon is adopted to reject the invisible line segments located outside the MEB. Secondly, a 45° rotated box is used to encode the endpoint of the line segment, and then reject a portion of the invisible segments crossing polygon corners. Finally, instead of encoding the endpoints of all line segments with respect to the polygonal window, each vertex of the polygon is encoded, taking the line segment to be clipped as reference. For efficient encoding of the polygon vertices, a new concept, termed with slope adaptive virtual box, is introduced regarding each line segment. Such a box can not only conveniently reject all totally invisible lines lying outside the MEB conveniently, but also precisely identify the edges of the polygon with which the line segment potentially intersects. With the summation of the vertex codes, it can be verified whether the line segment is separated from or potentially intersects the polygon window. Based on the product of the codes of adjacent vertices, singular cases of intersection can be solved accurately. Experimental results demonstrate the efficiency and stability of the new algorithm.     

A3CRank: An adaptive ranking method based on connectivity,content and click-through data

Due to the proliferation and abundance of information on the web, ranking algorithms play an important role in web search. Currently, there are some ranking algorithms based on content and connectivity such as BM25 and PageRank. Unfortunately, these algorithms have low precision and are not always satisfying for users. In this paper, we propose an adaptive method, called A3CRank, based on the content, connectivity, and click-through data triple. Our method tries to aggregate ranking algorithms such as BM25, PageRank, and TF-IDF. We have used reinforcement learning to incorporate user behavior and find a measure of user satisfaction for each ranking algorithm. Furthermore, OWA, an aggregation operator is used for merging the results of the various ranking algorithms. A3CRank adapts itself with user needs and makes use of user clicks to aggregate the results of ranking algorithms. A3CRank is designed to overcome some of the shortcomings of existing ranking algorithms by combining them together and producing an overall better ranking criterion. Experimental results indicate that A3CRank outperforms other combinational ranking algorithms such as Ranking SVM in terms of P@n and NDCG metrics. We have used 130 queries on University of California at Berkeley’s web to train and evaluate our method.     

An evaluation of adaptive filtering in the context of realistic task-based information exploration

Exploratory search increasingly becomes an important research topic. Our interests focus on task-based information exploration, a specific type of exploratory search performed by a range of professional users, such as intelligence analysts. In this paper, we present an evaluation framework designed specifically for assessing and comparing performance of innovative information access tools created to support the work of intelligence analysts in the context of task-based information exploration. The motivation for the development of this framework came from our needs for testing systems in task-based information exploration, which cannot be satisfied by existing frameworks. The new framework is closely tied with the kind of tasks that intelligence analysts perform: complex, dynamic, and multiple facets and multiple stages. It views the user rather than the information system as the center of the evaluation, and examines how well users are served by the systems in their tasks. The evaluation framework examines the support of the systems at users’ major information access stages, such as information foraging and sense-making. The framework is accompanied by a reference test collection that has 18 tasks scenarios and corresponding passage-level ground truth annotations. To demonstrate the usage of the framework and the reference test collection, we present a specific evaluation study on CAFÉ, an adaptive filtering engine designed for supporting task-based information exploration. This study is a successful use case of the framework, and the study indeed revealed various aspects of the information systems and their roles in supporting task-based information exploration.