Search advertising placement strategy: Exploring the efficacy of the conventional wisdom

Search advertising is one of the most important forms of electronic commerce. While click-through rates are considered a key measure of search advertising effectiveness by search providers, brand attention can also be a valuable objective. Our paper reports on an experiment that investigated how search advertisement placement affected search users’ brand recall and recognition. The results showed that semantically associated search ads generated significantly higher levels of brand attention than contextually associated ones. Significant interaction effects were found among search ad position, keyword association, and search result quality. Implications for decision makers are discussed.     

Synthesis of quantum size ZnO crystals and their gas sensing properties for NO2

Quantum size ZnO crystals have been synthesized successfully by a room temperature sol-gel process. Oleic acid (OA) has been used as capping agent to control the particle size of ZnO. The crystal structure and size of the ZnO are characterized by the X-ray diffraction (XRD) and transmission electron microscope (TEM). The XRD results show the as-synthesized ZnO has hexagonal wurtzite structure and the average crystallite size is 5.7 nm which is little less than TEM result. It is testified by photoluminescence (PL) and Raman spectra that the quantum size ZnO keeps the crystal structure of the bulk ZnO and possesses more surface defects. The quantum size ZnO has the highest response of 280 to NO₂ and the highest selectivity of 31 and 49 corresponding to CO and CH₄ at operating temperature of 290 °C. The effect of calcination temperatures on sensing property and transient response of the ZnO sensor are also investigated.     

Development of a self-propelled microflotilla

In this work, we initiated the effort of developing a so-called water microflotilla, which was capable of moving on a water surface. We explored its design, fabrication, propulsion, and motions in straight and circular channels. A water microflotilla comprised five microboats connected with a Nylon rope, and was fabricated using ultra-violet lithographic techniques. It was actuated by the difference between fore-and-aft surface tensions. The water microflotilla was tested in three straight and one circular channels at water levels of 3, 6 and 9?mm, respectively. In all the tests, the microflotilla moved like a rigid object, and no collisions were observed between neighboring microboats. Travel distances were above 0.44?m, and average speeds varied from 40.2 to 103.2?mm/s. Both the travel distances and the average speeds were found to increase with the increasing water depth and channel width. The two ends of the microflotilla had direct contact with the outer sidewall of the circular channel during a circular motion. The support forces of the outer sidewall at these contact points provided the centripetal force needed for the circular motion. Compared with a single microboat, such a microflotilla can be potentially employed to transport a large amount of supply in small-scale fluidic applications.     

基于相对熵的直方图差异与JPEG隐写的定量分析

针对图像隐写分析中常用的直方图特征,给出了一类基于相对熵的直方图差异计算方法,并提出了一种基于相对熵的JPEG隐写的定量分析方法.首先根据两假设检验中的最优检验——似然比检验,分析了相对熵在衡量2个直方图间的距离时的优越性,并给出了2种基于相对熵的直方图差异计算方法.然后,以新的直方图差异特征为基础,训练支持向量回归分析器,以估计隐写对DCT系数的更改比率.针对JSteg和改进的F5隐写的实验结果表明:与其他的直方图差异特征相比,根据所提出的基于相对熵的直方图差异特征所训练的定量隐写分析器具有更高的估计精度和稳定性.     

电阻抗扫描成像计算机辅助诊断方法研究

为了提高电阻抗扫描成像EISI(Electrical Impedance Scanning Imaging)在乳腺疾病检查方面的准确性,将独立分量方法(ICA)应用于EISI数据的特征提取,并结合支持向量机(SVM)进行乳腺疾病计算机辅助诊断(CAD)。实验结果表明:ICA方法所提取的特征对于乳腺疾病分类是有效的。对于良性肿瘤和恶性肿瘤的鉴别,提出的CAD方法灵敏度为74.2%,特异度为82.8%,准确度为80%。结合所提出的CAD方法和传统的EISI检查方法,可以提高EISI在乳腺疾病检查方面的可靠性,降低假阳性率,避免对良性肿瘤患者进行活检。     

基于OWL本体的UML类图推理

分析UML模型一致性检测的研究意义,以及UML类图推理的研究现状,提出一种基于OWL本体的UML类图推理方案.研究UML类图向OWL本体转换的映射规则,给出UML类图包含关系、可满足性、等价关系和相离关系的判断方法及其推理规则.利用Jena推理机制实现了UML类图包含关系、可满足性、等价关系和相离关系的推理.     

Automatic Cage Building with Quadric Error Metrics

Modern computer graphics applications usually require high resolution object models for realistic rendering.However,it is expensive and difficult to deform such models in real time.In order to reduce the computational cost during deformations,a dense model is often manipulated through a simplified structure,called cage,which envelops the model.However,cages are usually built interactively by users,which is tedious and time-consuming.In this paper,we introduce a novel method that can build cages automatically for both 2D polygons and 3D triangular meshes.The method consists of two steps:1) simplifying the input model with quadric error metrics and quadratic programming to build a coarse cage;2) removing the self-intersections of the coarse cage with Delaunay partitions.With this new method,a user can build a cage to envelop an input model either entirely or partially with the approximate vertex number the user specifies.Experimental results show that,compared to other cage building methods with the same number of vertex,cages built by our method are more similar to the input models.Thus,the dense models can be manipulated with higher accuracy through our cages.     

Multiscale computation for bioartificial soft tissues with complex geometries

The mechanical function of soft collagenous tissues is inherently multiscale, with the tissue dimension being in the centimeter length scale and the underlying collagen network being in the micrometer length scale. This paper uses a volume averaging multiscale model to predict the collagen gel mechanics. The model is simulated using a multiscale component toolkit that is capable of dealing with any 3D geometries. Each scale in the multiscale model is treated as an independent component that exchanges the deformation and average stress information through a scale-linking operator. An arterial bifurcation was simulated using the multiscale model, and the results demonstrated that the mechanical response of the soft tissues is strongly sensitive to the network orientation and fiber-to-fiber interactions.     

A distributed approach to enabling privacy-preserving model-based classifier training

This paper proposes a novel approach for privacy-preserving distributed model-based classifier training. Our approach is an important step towards supporting customizable privacy modeling and protection. It consists of three major steps. First, each data site independently learns a weak concept model (i.e., local classifier) for a given data pattern or concept by using its own training samples. An adaptive EM algorithm is proposed to select the model structure and estimate the model parameters simultaneously. The second step deals with combined classifier training by integrating the weak concept models that are shared from multiple data sites. To reduce the data transmission costs and the potential privacy breaches, only the weak concept models are sent to the central site and synthetic samples are directly generated from these shared weak concept models at the central site. Both the shared weak concept models and the synthetic samples are then incorporated to learn a reliable and complete global concept model. A computational approach is developed to automatically achieve a good trade off between the privacy disclosure risk, the sharing benefit and the data utility. The third step deals with validating the combined classifier by distributing the global concept model to all these data sites in the collaboration network while at the same time limiting the potential privacy breaches. Our approach has been validated through extensive experiments carried out on four UCI machine learning data sets and two image data sets.

A trust degree based access control in grid environments

The purpose of grid computing is to enable coordinated resource sharing and support cooperative work between different domains in dynamic grid environments. In order to protect each participant’s privilege and security, a secure and efficient access control is essential. This paper presents a new approach of access mechanism based on trust relationships across domains. A new calculation method of trust in grid is proposed and the difference between intro-domain trust and inter-domain trust is analyzed. In addition, a novel access control framework combined with trust degree is given from this proposal. It is shown to be adaptive for both intro-domain and inter-domain conditions. Hence, a prototype system based on the proposed model is introduced; furthermore, it has been shown as a dynamic and fine-granularity access control method through performance analyses and has also been demonstrated as a suitable system for grid environments.