Automatic cataloguing of advertisement in magazines

Advertising activities are one of the main aspects of companies. Its global evaluation is an important index, not only within economic sectors, but also for public economic policies. The evaluation of the adverts is carried out by specialised companies, which deliver advertisement expenditure information. Their work consist in an exhaustive collection of adverts (e.g., all adverts from any media), and deliver information (integrated from every media) relevant for their clients. The delivered information can range from statistical data to bear witness of an advertisement campaign. Important quality indicators for the delivered information are the exhaustiveness, the delay of delivering and a correct identification of different versions of an advert. In this article, we present an advanced computer-aided system for collecting and delivering advertising information from magazines and daily press. The system provides computer-aided data validation and exploitation resources. Using computerised document image analysis and image database indexing and retrieval, the system is able to locate an advert in a page, extract relevant quality indicators and search the advert (or similar ones) in a database. This tool is configured as an intranet and offers resources for image data acquisition, storage/retrieval and advert quality indicators extraction, however, the key of the system is the underlying idea of incorporating computer-aided visual information management.The EU has funded the system construction within the program: European Take-up of essential Information Society Technologies, Integrated Machine Vision (EUTIST-IMV), task CV-AdControl. .     

An ontology-based personalized target advertisement system on interactive TV

We design and implement a personalized target advertisement (PTA) system in IPTV using ontology-based semantic relations between IPTV program and advertisement. In the PTA system, we focus on the development of an ontology reasoning technique of improving advertisement recommendation ability and exploiting efficient information reuse. For this purpose, we first build the IPTV program ontology and viewer profiling ontology based on the IPTV program and viewers’ content consumption behavior, using OWL (Web Ontology Language) standardized by W3C. We then classify each viewer into his/her corresponding reference group using a similarity metric. After that, we define the semantic relations between advertisement and shopping products consumed by the prototype through shopping sites in IPTV. Based on these semantic relations, we infer the preferred advertisements for each viewer through our reasoning process. In the experimental section, we demonstrate a prototype of our system and the algorithm of the PTA system using various cases.     

工程教育学习风格分析及因材施教策略研究

在教学中尊重学习风格差异性,对贯彻执行因材施教原则及体现工程教育OBE理念具有重要作用.基于Felder-Silverman学习模型,对90名研究生的学习风格进行调查分析,获得受测试学生在信息的加工、感知、输入、理解四个维度上的学习行为偏好.针对学习风格差异提出相应教学方法、教学设计改进策略,以提高课程教学质量和学习成效.以研究生学位课程"最优控制"为例,设计了具体的教学改进策略,为探究个性化教学策略提供了支持.     

An evolving museum metaphor applied to cultural heritage for personalized content delivery

User Modeling and User-Adapted Interaction - The aim of this article concerns adaptive and personalized navigation in a cultural heritage database. The theoretical grounding of the proposition...     

Mapping inland water carbon content with Landsat 8 data

Landsat 8 is the first Earth observation satellite with sufficient radiometric and spatial resolution to allow global mapping of lake CDOM and DOC (coloured dissolved organic matter and dissolved organic carbon, respectively) content. Landsat 8 is a multispectral sensor however, the number of potentially usable band ratios, or more sophisticated indices, is limited. In order to test the suitability of the ratio most commonly used in lake carbon content mapping, the green–red band ratio, we carried out fieldwork in Estonian and Brazilian lakes. Several atmospheric correction methods were also tested in order to use image data where the image-to-image variability due to illumination conditions would be minimal. None of the four atmospheric correction methods tested, produced reflectance spectra that matched well with in situ measured reflectance. Nevertheless, the green–red band ratio calculated from the reflectance data was in correlation with measured CDOM values. In situ data show that there is a strong correlation between CDOM and DOC concentrations in Estonian and Brazilian lakes. Thus, mapping the global CDOM and DOC content from Landsat 8 is plausible but more data from different parts of the world are needed before decisions can be made about the accuracy of such global estimation.     

A semi-automatic strategy for achieving learning content and practice repurposing

The development of instructional content using Information Technologies is an expensive, time-consuming and complex process that requires new methodologies. It was in this context that the concept of Learning Objects (LOs) was proposed in order to promote reuse. However, this goal is not yet fully attained and new contributions to increase reuse are still welcome. Besides, if content is conveyed in LOs that are easier to reuse, they must be combined and sequenced in order to build more elaborated and complex content. This paper presents a strategy to deal with these problems based on the definition of small LOs here called Component Objects (COs). These COs are structured and combined according to a conceptual metamodel, which is the basis for the definition of conceptual schemas representing the existing material, including not only content but also practice. This strategy for searching, extracting, and sequencing COs, supports a teacher to better control the implementation of complex content, reducing errors in the authoring process. This approach includes a specification language and an algorithm for semi-automatic sequencing learning content and practice. Finally, a case study that shows the proposed approach and some results of using the algorithm are presented.     

Focusing on content reusability and interoperability in a personalized hypermedia assessment tool

This paper presents the development of a modularized hypermedia testing tool, called iAdaptTest, based entirely on e-learning specifications and discusses how this architecture improves the reusability and the interoperability of the learning data. All the categories of data,—that is—topics, user profiles, testing data, adaptive rules, and testing results are coded in XML format complying with Topic Maps, IMS LIP and IMS QTI. The data are stored in distinct files and can be independently shared across different educational applications. The paper concludes with an evaluation study concerning the creation of formative and summative assessments for adult seminars. Through focused interviews, the participants of the study identified the ability to share information and the multi-criteria adaptation options as the most important features of the system. Further, in the second phase of the evaluation the files produced were shared with other educational applications and thus it was verified that the learning data could be imported and rendered correctly.     

Towards a predictive cache replacement strategy for multimedia content

In recent years, telecom operators have been moving away from traditional broadcast-driven television, towards IP-based interactive and on-demand multimedia services. Consequently, multicast is no longer sufficient to limit the amount of generated traffic in the network. In order to prevent an explosive growth in traffic, caches can be strategically placed throughout the content delivery infrastructure. As the size of caches is usually limited to only a small fraction of the total size of all content items, it is important to accurately predict future content popularity. Traditional caching strategies only take into account the past when deciding what content to cache. Recently, a trend towards novel strategies that actually try to predict future content popularity has arisen. In this paper, we ascertain the viability of using popularity prediction in realistic multimedia content caching scenarios. The proposed generic popularity prediction algorithm is capable of predicting future content popularity, independent of specific content and service characteristics. Additionally, a novel cache replacement strategy, which employs the popularity prediction algorithm when making its decisions, is introduced. A detailed evaluation, based on simulation results using trace files from an actual deployed Video on Demand service, was performed. The evaluation results are used to determine the merits of popularity-based caching compared to traditional strategies. Additionally, the synergy between several parameters, such as cache size and prediction window, is investigated. Results show that the proposed prediction-based caching strategy has the potential to significantly outperform state-of-the-art traditional strategies. Specifically, the evaluated Video on Demand scenario showed a performance increase of up to 20% in terms of cache hit rate.     

Hybrid storage-based caching strategy for content delivery network services

This study proposes a novel caching scheme for content delivery network services. In general, video content users often watch the first part of video clips and then switch to other content. Therefore, a caching scheme is proposed, in which the first part of the frequently referenced content is stored on a solid state drive (SSD) while the remaining video content is stored on a hard disk drive (HDD),. The proposed hybrid (SSD/HDD) caching scheme offers several benefits, such as an improved average data output capacity due to the high average data rate and average hit capacity of the SSD. That is, performance can be significantly improved at a low extra cost with the cache server of a content delivery network (CDN).     

An effective procedure for the distribution of magazines

The article provides theoretical and empirical findings on developing an effective procedure for magazine single copy distribution. Considerations of determinants of demand are provided, emphasizing the importance of price and seasonality. A relative adjustment formula for stockout corrections is introduced, applicable in connection with normally distributed demand. The main contribution is a new demand forecasting and supply decision model, taking explicitly into account price and seasonality. The applicability and potential economic benefits are shown by giving results from an extensive empirical test in the Finnish single copy market.     

A new matching strategy for content based image retrieval system

Adopting effective model to access the desired images is essential nowadays with the presence of a huge amount of digital images. The present paper introduces an accurate and rapid model for content based image retrieval process depending on a new matching strategy. The proposed model is composed of four major phases namely: features extraction, dimensionality reduction, ANN classifier and matching strategy. As for the feature extraction phase, it extracts a color and texture features, respectively, called color co-occurrence matrix (CCM) and difference between pixels of scan pattern (DBPSP). However, integrating multiple features can overcome the problems of single feature, but the system works slowly mainly because of the high dimensionality of the feature space. Therefore, the dimensionality reduction technique selects the effective features that jointly have the largest dependency on the target class and minimal redundancy among themselves. Consequently, these features reduce the calculation work and the computation time in the retrieval process. The artificial neural network (ANN) in our proposed model serves as a classifier so that the selected features of query image are the input and its output is one of the multi classes that have the largest similarity to the query image. In addition, the proposed model presents an effective feature matching strategy that depends on the idea of the minimum area between two vectors to compute the similarity value between a query image and the images in the determined class. Finally, the results presented in this paper demonstrate that the proposed model provides accurate retrieval results and achieve improvement in performance with significantly less computation time compared with other models.     

Mapping verbal AHP scale to numerical scale for cloud computing strategy selection

Analytic Hierarchy Process (AHP) is an established multi-criteria decision making method based on pairwise comparisons. Evaluations are given on a verbal scale and then converted into quantitative values for calculating the priorities of the criteria and alternatives. Several conversion scales have been proposed, which confuses the decision-maker. In order to select the best matching scale according to the mental representation of the verbal scale of each individual decision-maker, verbal scales are first used to compare alternatives with known measures, e.g. surface of figures. The best matching scale representing the real values is then selected. This AHP with individualised scales has been applied in a real case study to select cloud computing strategies.     

用于虚拟广告系统的视频对象分割算法

在虚拟广告系统中,视频对象分割是其中最为关键的技术之一。在兼顾分割精度和实时性的原则上,提出了一种基于置信传播的视频运动对象分割算法。算法先建立背景、阴影和前景的统计模型,再结合马尔可夫随机场对像素空间相关性建模,最后利用置信传播算法完成有效的视频对象分割。实验结果表明算法具有良好的性能,并在虚拟广告系统中得到成功应用。     

基于动态内容流行度的NDN缓存决策和替换策略研究

命名数据网络(NDN)中的路由器节点具有缓存能力,这就极大地提高了网络中的数据发送与检索效率。然而,由于路由器的缓存能力是有限的,设计有效的缓存策略仍然是一项紧迫的任务。为了解决这个问题,提出了一种动态内容流行度缓存决策和替换策略(DPDR)。DPDR综合考虑内容流行度和缓存能力,利用一个和式增加、积式减少(AIMD)的算法动态调节流行度阈值,并将超过流行度阈值的内容存入缓存空间;同时提出了一个缓存替换算法,综合考虑了缓存空间中内容的流行度和内容最后被访问时间等因素,将替换值最小的内容移出内容缓存。大量仿真结果显示,与其他算法相比,本文所提的算法能够有效提高缓存命中率,缩短平均命中距离和网络吞吐量。     

The key role of relevance in personalized advertisement: Examining its impact on perceptions of privacy invasion,self-awareness,and continuous use intentions

Internet advertising has become increasingly personalized as advertisers tailor content to individual users. However, this has led consumers to be concerned about their privacy. Based on rational choice theory and self-awareness theory, the current research explores the role of relevance in personalized advertisements and examines its impact on perceptions of privacy invasion, self-awareness, and subsequent continuous use intentions of personalized advertising. Analysis of survey data from 386 online users found that although privacy invasion perceptions are negatively related to continuous use intentions, perceived advertisement relevance mitigates consumer's privacy concerns. Perceived relevance was also found to be positively related to consumer's continuous use intentions through the mediation of self-awareness. This research identifies and highlights the importance of relevance in the tension between privacy concerns and personalized advertisements.     

DRM数字内容包装与分发策略研究

在分析关联信息模型、分离信息模型基础上,提出了数字内容包装的综合信息模型,并基于此模型设计了打包内容容器的文件头数据结构和文件模型,从而为数字内容包装和分发创造了条件。在分析数字内容的超级分发模式基础上,同时考虑到数字内容分发过程中内容使用者地位的缺失,设计了内容使用者为中心的用户定制内容分发模式。之后,在数字内容密钥分解的基础上,详细地说明了数字内容包装的关键过程,并进一步实现了内容包装工具和内容分发系统。     

支持局部更新的双向内容适配策略

内容适配旨在解决融合环境下多种终端兼容问题,现有的内容适配策略在用户通过受限终端进行更新时会造成信息丢失。提出一种支持局部更新的双向内容适配策略,该策略基于DOM树建立统一的存储模型对元数据和适配实例进行存储,当受限终端发起更新时,通过比较DOM树中的节点对元数据进行局部更新。实验结果表明,相比现有策略,支持局部更新的内容适配策略在大部分情况下可以减少60%以上的信息丢失。