共查询到20条相似文献，搜索用时 243 毫秒
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源模型和目标模型描述之间的异构性是实现模型映射的主要困难.本文通过对模型描述语言的语法结构和语义表达特性进行抽象分析,提出了一种基于语义一致性的模型映射方法.该方法不仅可为模型转换的具体实现提供理论指导,还为验证不同抽象层次模型之间映射关系的正确性提供依据. 相似文献
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针对软件模型的时间约束能力不强以及形式化验证复杂的问题,本文提出一种基于Petri网的形式化模型调度方法,从时间层次上对模型的合理性进行验证与分析.该方法通过构建系统领域模型到Petri网模型的转换规则,利用Petri网的分析验证技术,实现对软件模型的正确性验证,解决了系统建模时存在的问题.应用实例和实验结果验证了该方... 相似文献
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研究一种改进的土地集约利用评价模型,通过低碳节能区域规划,设计土地集约利用数学控制模型,提高土地利用效益。传统方法的模型无法定量刻画土地利用的特定时间和空间上的属性特征,利用效益不高。本文提出一种基于改进反馈约束分析的土地集约利用评价模型,根据不同区域内不同的自然环境特征和环境容量,实现功能分区调整,有效预测和规划今后一定时期内土地资源的开发利用及其发展趋势。对集约利用要素进行有机结合,设计反馈约束算法,进行土地物质、能量和价值信息交流控制模型设计,从经济发展的需要和可能出发,以土地利用数据的测定为基础,按利用现状进行分类调查统计,实现模型改进。仿真结果表明,该模型能有效提高土地利用收益,对降低生成成本,改善生态等方面具有促进作用。 相似文献
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人眼模型及其运动控制 总被引：3，自引：0，他引：3
描述了一种人眼建模及其运动控制的方法 .人眼采用三维网格模型 ,并结合纹理匹配 ,获得了较逼真的人眼模型 .人眼眼皮上下边界用抛物线近似 ,其运动通过眼皮上的特征点控制 ,并在模型中考虑了眼皮皮肤运动的关联作用 ,达到了较好的动态效果 .该方法已在微型计算机上进行了模拟 ,人眼模型逼真 ,通过帧间时间的非线性分配 ,可较为形象的模拟出人眼不同的眨眼动作 . 相似文献
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TPM虚拟域安全模型 总被引：1，自引：0，他引：1
针对TPM访问控制机制无法直接应用于虚拟计算、云计算等环境的问题,重点分析TPM内部对象间依赖关系,并结合虚拟域的安全需求,建立TPM虚拟域安全模型.该模型对TPM对象的访问请求增加了虚拟域的完整性、机密性等安全约束,解决了多虚拟域环境下的TPM对象的创建、使用、销毁等问题.还进一步对该模型的安全规则进行了相关逻辑分析,并通过实际原型系统的测试,证明了TPM虚拟域安全模型的实施对可信虚拟平台的性能影响非常小. 相似文献
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柔性激励机制设计及其模型研究 总被引：1，自引：0，他引：1
提出了与传统激励机制不同的柔性激励机制,具有敏捷性、适应不同情况的能力及灵活性、柔韧性等优势;包括经济激励等正向激励和以威胁激励为代表的负向激励;构建的柔性激励模型包括柔性激励工资和工资模型、威胁激励模型、柔性激励机制的约束模型及有效性检验模型。研究结果表明:柔性激励对企业员工具有诱导性作用,非诱导型员工在一定条件下也会转变成可诱导型员工。企业激励越高,员工维持激励的积极性就越高。由此可以得出柔性激励是一种非常有效的激励策略、方式和方法。 相似文献
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本文以辽阳首山水源地作为研究对象,在地下水动力场模拟基础上,采用情景分析方法,将3种情景的地下水生态水位作为1项重要的约束条件纳入到地下水管理模型中,建立了基于生态约束的地下水管理模型,采用线性规划方法求得3种不同情景的地下水新增优化开采量分别为21 986m3/d、12 934m3/d、22 602m3/d.管理模型结果表明,通过优化调整开采方案,在增加全区开采量的同时能够保证首山地下水漏斗的逐渐恢复,同时有利于解决研究区内潜在的土壤盐渍化威胁,实现经济效益与生态效益的统一. 相似文献
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基于模型驱动的测试架构 总被引：1，自引：0，他引：1
如何在确保软件质量的前提下有效缩短上市周期的问题日益显得重要.笔者基于MDT的思路研究出了一种基于模型驱动的自动化测试架构.该架构通过算法直接将UML系统设计模型转换成U2TP测试设计模型,然后由测试设计模型根据测试策略和测试工程方法自动生成测试用例,实现了测试资源重利用和测试活动的前移,从而有效缩短了测试周期. 相似文献
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为了更好地适应现实业务流程的动态性,从工作流过程定义入手,建立了一种动态工作流元模型.该元模型具备较好的可操作性和动态适应性,通过实例分析说明该工作流元模型为动态工作流的具体实现提供了一种新的方法参考. 相似文献
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《Information processing & management》2022,59(1):102719
When a recommender system suggests items to the end-users, it gives a certain exposure to the providers behind the recommended items. Indeed, the system offers a possibility to the items of those providers of being reached and consumed by the end-users. Hence, according to how recommendation lists are shaped, the experience of under-recommended providers in online platforms can be affected. To study this phenomenon, we focus on movie and book recommendation and enrich two datasets with the continent of production of an item. We use this data to characterize imbalances in the distribution of the user–item observations and regarding where items are produced (geographic imbalance). To assess if recommender systems generate a disparate impact and (dis)advantage a group, we divide items into groups, based on their continent of production, and characterize how represented is each group in the data. Then, we run state-of-the-art recommender systems and measure the visibility and exposure given to each group. We observe disparities that favor the most represented groups. We overcome these phenomena by introducing equity with a re-ranking approach that regulates the share of recommendations given to the items produced in a continent (visibility) and the positions in which items are ranked in the recommendation list (exposure), with a negligible loss in effectiveness, thus controlling fairness of providers coming from different continents. A comparison with the state of the art shows that our approach can provide more equity for providers, both in terms of visibility and of exposure. 相似文献
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Traditionally, recommender systems for the web deal with applications that have two dimensions, users and items. Based on access data that relate these dimensions, a recommendation model can be built and used to identify a set of N items that will be of interest to a certain user. In this paper we propose a multidimensional approach, called DaVI (Dimensions as Virtual Items), that consists in inserting contextual and background information as new user–item pairs. The main advantage of this approach is that it can be applied in combination with several existing two-dimensional recommendation algorithms. To evaluate its effectiveness, we used the DaVI approach with two different top-N recommender algorithms, Item-based Collaborative Filtering and Association Rules based, and ran an extensive set of experiments in three different real world data sets. In addition, we have also compared our approach to the previously introduced combined reduction and weight post-filtering approaches. The empirical results strongly indicate that our approach enables the application of existing two-dimensional recommendation algorithms in multidimensional data, exploiting the useful information of these data to improve the predictive ability of top-N recommender systems. 相似文献
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《The Journal of High Technology Management Research》2023,34(2):100477
Increasing numbers of devices that output large amounts of geographically referenced data are being deployed as the Internet of Things (IoT) continues to expand. Partly as a result of the IoT's dynamic, decentralized, and heterogeneous architecture. These are all examples of the Internet of items (IoT), despite the fact that we might be thinking that one of these items is different from the others. The physical and digital worlds are connected by the Internet of Things (IoT). Nowadays, one of the key goals of the Internet is its own development. This paper provides an in-depth analysis of IoT-based data quality and data preparation strategies developed with multinational corporations in mind. The goal is to make IoT data more trustworthy and practical so that MNCs may use it to their advantage in making educated business decisions. The proposed structure consists of three distinct actions: gathering data, evaluating data quality, and cleaning up raw data. Data preprocessing research is essential since it decides and significantly affects the accuracy of predictions made in later stages. Thus, the recommendation for a special and useful combination in the framework of different data preprocessing task types, which includes the following four technical elements and is briefly justified, is made. The Internet of Things (IoT) is a design pattern in which commonplace items can be equipped with classification, sensing, networking, and processing capabilities that will enable them to communicate with one another over the Internet to fulfill a specific function. The Internet of Things will eventually change physical objects into virtual objects with intelligence. In addition to a detailed analysis of the IoT layer, this article gives an overview of the existing Internet of Things (IoT), technical specifics, and applications in this recently growing field. However, this publication will provide future scholars who desire to conduct study in this area of Internet of Things with a better knowledge. 相似文献
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针对查新报告中各种有效信息未被整理、利用的现状,提出了一种利用VBA技术和EndNote软件建立查新报告数据库的方法。首先,利用Word VBA技术,依据文本特征从Word文档里抽取查新报告元数据项,之后利用Excel VBA技术将Word中的元数据项变为Excel数据,同时将Excel数据转换为EndNote可识别的文本文档从而建立了基于EndNote软件的查新报告数据库。 相似文献
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随着电子商务的迅速发展，推荐系统与算法已经成为理论研究的热点。支持向量机是一种强大的分类工具，由其衍生出的支持向量机回归方法能很好地解决非线性回归问题。文中以电影推荐为例，引入支持向量机回归方法来分析项目的内容，构建用户模型，进而给出推荐。实验结果和理论分析表明这种推荐算法与传统协同过滤算法相比，能够明显提高推荐精度，并显著缩短了推荐所需时间；在大样本量情况下也能同样高效。 相似文献
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Organizational knowledge exists in different types of knowledge retainers. Efforts are being made to preserve this knowledge because of its value to the organization. In this paper we present a methodology for codifying the knowledge of a domain. This methodology is based on an ontology for the domain in question, from which different types of knowledge items are extracted. These knowledge items represent the different types of knowing that are embedded in the organization's structure and its processes. From an analysis of a process instance described in the ontology, different knowledge items can be extracted and represented as knowledge maps. These maps represent the internal competencies of the organization as they relate to certain processes and hence they can be used to provide inputs in the decision-making process, for example, knowledge process outsourcing decisions. The purpose of this paper is to present an ontology-driven methodology for extracting different knowledge items and representing them as knowledge maps. 相似文献
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互联网背景下的平台经济时代重构了人与组织之间的关系,需要创新性地建立一种基于平台隐喻的领导范式和实践途径。本文在文献内容分析基础上对6位管理学领域专家和21位企业管理者进行结构化访谈,经过质性研究过程归纳38个题项,进行606份有效问卷的实证分析并通过信度和效度检验,开发出平台型领导量表的22个测量题项以及分享利他、交互协作、孵化创客、度己化人、赋能平等5个构成维度,并给出五个构成维度的内涵释义。该研究较为完备地刻画了平台型领导的行为特征,弥补了平台型领导研究缺乏测量量表和维度界定的理论缺口,所确定的5维度22题项具有系统性、涵盖性和内在区别性,具与有互联网、大数据背景下企业平台化经营和平台化升级的契合性,为践行平台型领导提供了理论依据和行为导向,对推动平台领导力的进一步研究具有积极作用。 相似文献
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Recommender systems learn from historical users’ feedback that is often non-uniformly distributed across items. As a consequence, these systems may end up suggesting popular items more than niche items progressively, even when the latter would be of interest for users. This can hamper several core qualities of the recommended lists (e.g., novelty, coverage, diversity), impacting on the future success of the underlying platform itself. In this paper, we formalize two novel metrics that quantify how much a recommender system equally treats items along the popularity tail. The first one encourages equal probability of being recommended across items, while the second one encourages true positive rates for items to be equal. We characterize the recommendations of representative algorithms by means of the proposed metrics, and we show that the item probability of being recommended and the item true positive rate are biased against the item popularity. To promote a more equal treatment of items along the popularity tail, we propose an in-processing approach aimed at minimizing the biased correlation between user-item relevance and item popularity. Extensive experiments show that, with small losses in accuracy, our popularity-mitigation approach leads to important gains in beyond-accuracy recommendation quality. 相似文献
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Analyzing review sentiments and product images by parallel deep nets for personalized recommendation
《Information processing & management》2023,60(1):103166
Recommendation is an effective marketing tool widely used in the e-commerce business, and can be made based on ratings predicted from the rating data of purchased items. To improve the accuracy of rating prediction, user reviews or product images have been used separately as side information to learn the latent features of users (items). In this study, we developed a hybrid approach to analyze both user sentiments from review texts and user preferences from item images to make item recommendations more personalized for users. The hybrid model consists of two parallel modules to perform a procedure named the multiscale semantic and visual analyses (MSVA). The first module is designated to conduct semantic analysis on review documents in various aspects with word-aware and scale-aware attention mechanisms, while the second module is assigned to extract visual features with block-aware and visual-aware attention mechanisms. The MSVA model was trained, validated and tested using Amazon Product Data containing sampled reviews varying from 492,970 to 1 million records across 22 different domains. Three state-of-the-art recommendation models were used as the baselines for performance comparisons. Averagely, MSVA reduced the mean squared error (MSE) of predicted ratings by 6.00%, 3.14% and 3.25% as opposed to the three baselines. It was demonstrated that combining semantic and visual analyses enhanced MSVA's performance across a wide variety of products, and the multiscale scheme used in both the review and visual modules of MSVA made significant contributions to the rating prediction. 相似文献
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David Nicholas Paul Huntington Hamid R. Jamali Anthony Watkinson 《Information processing & management》2006
The article employs deep log analysis (DLA) techniques, a more sophisticated form of transaction log analysis, to demonstrate what usage data can disclose about information seeking behaviour of virtual scholars – academics, and researchers. DLA works with the raw server log data, not the processed, pre-defined and selective data provided by journal publishers. It can generate types of analysis that are not generally available via proprietary web logging software because the software filters out relevant data and makes unhelpful assumptions about the meaning of the data. DLA also enables usage data to be associated with search/navigational and/or user demographic data, hence the name ‘deep’. In this connection the usage of two digital journal libraries, those of EmeraldInsight, and Blackwell Synergy are investigated. The information seeking behaviour of nearly three million users is analyzed in respect to the extent to which they penetrate the site, the number of visits made, as well as the type of items and content they view. The users are broken down by occupation, place of work, type of subscriber (“Big Deal”, non-subscriber, etc.), geographical location, type of university (old and new), referrer link used, and number of items viewed in a session. 相似文献