IrisNet: an architecture for a worldwide sensor Web

We discuss about the IrisNet (Internet-scale resource-intensive sensor network services) project at Intel Research, we design an architecture and build a system that enable easy deployment of such wide-area sensing services. We aim to provide the missing software components for realizing a worldwide sensor Web. Wide-area architectures for pervasive sensing enable a new generation of powerful distributed sensing services. A worldwide sensor Web, in which users can query, as a single unit, vast quantities of data from thousands or even millions of widely distributed, heterogeneous sensors. Internet-connected PCs that source sensor feeds and cooperate to answer users' queries will form the global sensor Web's backbone. Developers of wide-area sensing services (service authors) deploy the services on this distributed infrastructure.     

Graphical Web directory for Web search

This study addresses graphical Web directory, a new way to present hierarchical structure of Web directory. An analysis of the characteristics and problems of current Web directories is presented. The feature graphical Web directory for supporting information processing and decision making in Web directory browsing in Web search is proposed to improve users' performance and satisfaction. An experiment was conducted to test the effectiveness of the proposed feature. The results of the experiment indicated that (a) the enhancement of this feature improved users' initial and overall search performance by 32.6 and 43.4%, respectively, (b) the enhancement of this feature also improved users' satisfaction by 27.7%.     

Graphical Web directory for Web search

This study addresses graphical Web directory, a new way to present hierarchical structure of Web directory. An analysis of the characteristics and problems of current Web directories is presented. The feature graphical Web directory for supporting information processing and decision making in Web directory browsing in Web search is proposed to improve users' performance and satisfaction. An experiment was conducted to test the effectiveness of the proposed feature. The results of the experiment indicated that (a) the enhancement of this feature improved users' initial and overall search performance by 32.6 and 43.4%, respectively, (b) the enhancement of this feature also improved users' satisfaction by 27.7%.     

RaDaR: a scalable architecture for a global Web hosting service

As commercial interest in the Internet grows, more and more companies are offering the service of hosting and providing access to information that belongs to third-party information providers. In the future, successful hosting services may host millions of objects on thousands of servers deployed around the globe. To provide reasonable access performance to popular resources, these resources will have to be mirrored on multiple servers. In this paper, we identify some challenges due to the scale that a platform for such global services would face, and propose an architecture capable of handling this scale. The proposed architecture has no bottleneck points. A trace-driven simulation using an access trace from AT&T's hosting service shows very promising results for our approach.     

EMPOWER: a cluster architecture supporting network emulation

Network research generally requires a simulation or emulation environment to test protocol implementations, to evaluate the performance of a scheme or a system, and to study complicated and highly varying network operations. For large network simulation, simulators consume a large amount of time and memory; and its result is largely based on some modeling assumptions that may not hold in the real world. Emulators are difficult to scale for large network emulation because of the high cost of equipment if a one-to-one mapping scheme is employed. Otherwise, the target network has to be abstracted to a single router modeled with some performance metrics. We present a distributed IP network emulator cluster EMPOWER, which not only can be used to emulate a large network with a limited number of commodity computers, but also can generate user-defined arbitrary network conditions and traffic dynamics at packet level for specific test scenarios. EMPOWER is highly scalable in that each emulator node could be configured to emulate multiple network nodes, and the increment of the number of emulator nodes does not affect emulation validity. Some significant research issues such as network mapping and mobile wireless network emulation are discussed and addressed. Preliminary emulation results show that EMPOWER is capable of assisting the study of both wireline and wireless network protocols and applications.     

Topic-sensitive PageRank: a context-sensitive ranking algorithm for Web search

The original PageRank algorithm for improving the ranking of search-query results computes a single vector, using the link structure of the Web, to capture the relative "importance" of Web pages, independent of any particular search query. To yield more accurate search results, we propose computing a set of PageRank vectors, biased using a set of representative topics, to capture more accurately the notion of importance with respect to a particular topic. For ordinary keyword search queries, we compute the topic-sensitive PageRank scores for pages satisfying the query using the topic of the query keywords. For searches done in context (e.g., when the search query is performed by highlighting words in a Web page), we compute the topic-sensitive PageRank scores using the topic of the context in which the query appeared. By using linear combinations of these (precomputed) biased PageRank vectors to generate context-specific importance scores for pages at query time, we show that we can generate more accurate rankings than with a single, generic PageRank vector. We describe techniques for efficiently implementing a large-scale search system based on the topic-sensitive PageRank scheme.     

Building a multisite Web architecture

Any large organizations that first came online in the late 1990s are now facing the decision whether to upgrade their Web systems or to start anew. Given the speed with which new technologies are introduced in the Web environment, system deployment life cycles have shrunk significantly-but so have system life spans. After only a few years, an organization's Internet infrastructure is likely to need a major overhaul. In late 2001, the systems architecture team to which I belong took on these issues for an organization that wanted to rebuild its Web infrastructure. The existing infrastructure contained multiple single points of failure, could not scale to expected usage patterns, was built on proprietary systems, and had a high management overhead. The legacy infrastructure had grown organically over the previous five years as administrators added unplanned features and functionality, and usage had grown 100-fold since the specifications were initially developed. Because of the age and condition of the legacy systems, we decided to redesign the solution from scratch to overcome the inherent limitations. This case study describes the process our systems architecture team followed for designing and deploying the new architecture. I detail the component selection rationale, with implementation details where allowed. Ours is just one successful approach to deploying a. multisite, fully redundant Web-based system for a large organization; other reasonable and viable ways to build such a system also exist.     

The Web Beyond Google: Innovative Search Tools and Their Implications for Reference Services

This article explores Internet search tools that harness emerging technologies and user-centric features such as Web 2.0 sites, mobile applications, metasearching, real-time information, peer-to-peer searches, visual interfaces, and computational abilities. Reference librarians everywhere have incorporated Google into their searching repertoires, but what about other search engines that feature “bleeding edge” technologies and human-friendly designs? Our article is a concise guide to nine innovative search engines—Silobreaker, Zuula, Bing, ChaCha, CompletePlanet, DuckDuckGo, Spezify, Wolfram|Alpha, and Wowd—which give us a glimpse into the future of searching technology. We conclude our article by discussing implications for reference services.     

IoT-enabled Web warehouse architecture: a secure approach

Web warehouse (WWh) has overcome the geographical dependencies of data warehouse. With the rapid development of WWh, decision makers (humans) and intelligent devices are able to remotely retrieve the information for supporting the effective decision-making process. This paper presents a secure Web service-oriented architecture of the WWh. The proposed architecture provides the better scalability, availability of WWh, and secure analytical service for human and Internet of Things for effective decision making. In addition, the service-oriented architecture of WWh integrates intelligent devices for the process of decision making. The proposed architecture uses XML-based Web services to provide authentication, authorization, and data confidentiality and integrity. Experiments show that the proposed architecture is more reliable, scalable, and secure.     

The Continuous Media Web: a distributed multimedia information retrieval architecture extending the World Wide Web

The World Wide Web, with its paradigms of surfing and searching for information, has become the predominant system for computer-based information retrieval. Media resources, however information-rich, only play a minor role in providing information to Web users. While bandwidth (or the lack thereof) may be an excuse for this situation, the lack of surfing and searching capabilities on media resources are the real issue. We present an architecture that extends the Web to media, enabling existing Web infrastructures to provide seamless search and hyperlink capabilities for time-continuous Web resources, with only minor extensions. This makes the Web a true distributed information system for multimedia data. The article provides an overview of the specifications that have been developed and submitted to the IETF for standardization. It also presents experimental results with prototype applications.     

Flash环境Google Web API的实现

随着Internet技术的发展,网页搜索的作用越来越重要。基于Web Services技术的应用程序接口（API）技术的发展,使网页搜索应用软件的开发成为可能。文章介绍了Flash环境下Google Web API的实现,给出了用AS3.0语言实现的部分程序清单,并对程序作了注释。     

Reinforcement learning for neural architecture search: A review

Deep neural networks are efficient and flexible models that perform well for a variety of tasks such as image, speech recognition and natural language understanding. In particular, convolutional neural networks (CNN) generate a keen interest among researchers in computer vision and more specifically in classification tasks. CNN architecture and related hyperparameters are generally correlated to the nature of the processed task as the network extracts complex and relevant characteristics allowing the optimal convergence. Designing such architectures requires significant human expertise, substantial computation time and does not always lead to the optimal network. Reinforcement learning (RL) has been extensively used in automating CNN models design generating notable advances and interesting results in the field. This work aims at reviewing and discussing the recent progress of RL methods in Neural Architecture Search task and the current challenges that still require further consideration.     

Web search using a genetic algorithm

Webnaut is an intelligent agent system that uses a genetic algorithm to collect and recommend Web pages. A feedback mechanism adapts to user interests as they evolve. The authors first describe intelligent assistant systems in general and then present the Webnaut architecture, its learning agent, and the genetic algorithm. They conclude with results from two preliminary experiments that tested the accuracy and adaptability of the learning agent     

Exploiting location information for Web search

Most Web pages contain location information, which are usually neglected by traditional search engines. Queries combining location and textual terms are called as spatial textual Web queries. Based on the fact that traditional search engines pay little attention in the location information in Web pages, in this paper we study a framework to utilize location information for Web search. The proposed framework consists of an offline stage to extract focused locations for crawled Web pages, as well as an online ranking stage to perform location-aware ranking for search results. The focused locations of a Web page refer to the most appropriate locations associated with the Web page. In the offline stage, we extract the focused locations and keywords from Web pages and map each keyword with specific focused locations, which forms a set of <keyword, location> pairs. In the second online query processing stage, we extract keywords from the query, and computer the ranking scores based on location relevance and the location-constrained scores for each querying keyword. The experiments on various real datasets crawled from nj.gov, BBC and New York Time show that the performance of our algorithm on focused location extraction is superior to previous methods and the proposed ranking algorithm has the best performance w.r.t different spatial textual queries.     

基于本体相似的Web服务搜索

随着越来越多的Web服务被提交到UDDI注册中心注册,搜索一个合适的Web服务开始变得和在互联网上找到一个合适的页面同样困难。现有的技术主要是通过关键字匹配的技术来对UDDI注册中心Web服务的描述信息进行检索。但鉴于Web服务描述信息非常稀疏的特点,传统的信息检索技术并不能得到很好的效果,因此提出了基于本体的Web服务检索技术。在已有的对Web服务检索流程的改进的基础上,充分利用了从UDDI注册中心得到的Web服务描述信息,用本体来描述其内部的关系,并在此基础上应用本体相似技术来比较和匹配Web服务描述信息。     

神经网络结构搜索在脑数据分析领域的研究进展

神经网络结构搜索(neural architecture search,NAS)是自动化机器学习的重要组成部分,已被广泛应用于多个领域,包括计算机视觉、语音识别等,能够针对特定数据、场景、任务寻找最优的深层神经网络结构.将NAS引入至脑数据分析领域,能够在图像分割、特征提取、辅助诊断等多个应用领域大幅度提升性能,展现低能耗自动化机器学习的优势.基于NAS进行脑数据分析是当前的研究热点之一,同时也具有一定挑战.目前,在此领域,国内外可供参考的综述性文献较少.对近年来国内外相关文献进行了细致地调研分析,从算法模型、研究任务、实验数据等不同方面对NAS在脑数据分析领域的研究现状进行了综述.同时,也对能够支撑NAS训练的脑数据集进行了系统性总结,并对NAS在脑数据分析中存在的挑战和未来的研究方向进行了分析和展望.     

Semi-supervised transformable architecture search for feature distillation

Pattern Analysis and Applications - The designed method aims to perform image classification tasks efficiently and accurately. Different from the traditional CNN-based image classification methods,...     

Analyzing Google Earth application in a heterogeneous commodity cluster display wall

Nowadays, clusters of interconnected workstations have become a common solution for powering large composite displays, or “cluster display walls”, to visualize high resolution images. Our paper is focused on analyzing a specific cluster display wall developed by Google, named Liquid Galaxy, made up of heterogeneous commodity hardware with different degrees of heterogeneity, running master-slave (Google Earth) and client-server (Quake III Arena) multimedia applications. With this in mind, we define and test different scenarios, representing the behavior of many kinds of users. Our results show that the CPU, memory and network are good enough to execute the client-server application, while, depending on the user behavior, the external network constitutes the bottleneck of the system in Google Earth. So, the master-slave application has focused our attention. Likewise, in order to analyze the users’ point of view when interacting with Google Earth in the Liquid Galaxy, we define a new metric, named Visualization Rate (VR), which enables a relationship to be established between the user experience and the platform performance. In order to set the minimum acceptable value of the VR parameter according to users perception, we carried out different tests with real users. Then, this minimum threshold was compared with the VR value obtained from the automated benchmarking performed afterwards on clusters with different heterogeneity degrees. Finally, we analyzed the VR trend when the Liquid Galaxy is scaled from 3 up to 8 nodes in both the homogeneous and heterogeneous architectures to study the scalability of the system.