A Genre-Aware Approach to Focused Crawling

Focused crawlers have as their main goal to crawl Web pages that are relevant to a specific topic or user interest, playing an important role for a great variety of applications. In general, they work by trying to find and crawl all kinds of pages deemed as related to an implicitly declared topic. However, users are often not simply interested in any document about a topic, but instead they may want only documents of a given type or genre on that topic to be retrieved. In this article, we describe an approach to focused crawling that exploits not only content-related information but also genre information present in Web pages to guide the crawling process. This approach has been designed to address situations in which the specific topic of interest can be expressed by specifying two sets of terms, the first describing genre aspects of the desired pages and the second related to the subject or content of these pages, thus requiring no training or any kind of preprocessing. The effectiveness, efficiency and scalability of the proposed approach are demonstrated by a set of experiments involving the crawling of pages related to syllabi of computer science courses, job offers in the computer science field and sale offers of computer equipments. These experiments show that focused crawlers constructed according to our genre-aware approach achieve levels of F1 superior to 88%, requiring the analysis of no more than 65% of the visited pages in order to find 90% of the relevant pages. In addition, we experimentally analyze the impact of term selection on our approach and evaluate a proposed strategy for semi-automatic generation of such terms. This analysis shows that a small set of terms selected by an expert or a set of terms specified by a typical user familiar with the topic is usually enough to produce good results and that such a semi-automatic strategy is very effective in supporting the task of selecting the sets of terms required to guide a crawling process.     

Traffic analysis of peer-to-peer IPTV communities

The Internet is currently experiencing one of the most important challenges in terms of content distribution since its first uses as a medium for content delivery: users from passive downloaders and browsers are moving towards content producers and publishers. They often distribute and retrieve multimedia contents establishing network communities. This is the case of peer-to-peer IPTV communities.In this work we present a detailed study of P2P IPTV traffic, providing useful insights on both transport- and packet-level properties as well as on the behavior of the peers inside the network. In particular, we provide novel results on the (i) ports and protocols used; (ii) differences between signaling and video traffic; (iii) behavior of the traffic at different time scales; (iv) differences between TCP and UDP traffic; (v) traffic generated and received by peers; (vi) peers neighborhood and session duration. The knowledge gained thanks to this analysis is useful for several tasks, e.g. traffic identification, understanding the performance of different P2P IPTV technologies and the impact of such traffic on network nodes and links, and building more realistic models for simulations.¹     

Degree-Optimal Routing for P2P Systems

We define a family of Distributed Hash Table systems whose aim is to combine the routing efficiency of randomized networks—e.g. optimal average path length O(log ² n/δlog δ) with δ degree—with the programmability and startup efficiency of a uniform overlay—that is, a deterministic system in which the overlay network is transitive and greedy routing is optimal. It is known that Ω(log n) is a lower bound on the average path length for uniform overlays with O(log n) degree (Xu et al., IEEE J. Sel. Areas Commun. 22(1), 151–163, 2004). Our work is inspired by neighbor-of-neighbor (NoN) routing, a recently introduced variation of greedy routing that allows us to achieve optimal average path length in randomized networks. The advantage of our proposal is that of allowing the NoN technique to be implemented without adding any overhead to the corresponding deterministic network. We propose a family of networks parameterized with a positive integer c which measures the amount of randomness that is used. By varying the value c, the system goes from the deterministic case (c=1) to an “almost uniform” system. Increasing c to relatively low values allows for routing with asymptotically optimal average path length while retaining most of the advantages of a uniform system, such as easy programmability and quick bootstrap of the nodes entering the system. We also provide a matching lower bound for the average path length of the routing schemes for any c. This work was partially supported by the Italian FIRB project “WEB-MINDS” (Wide-scalE, Broadband MIddleware for Network Distributed Services), .     

月球探测(车)机器人技术的发展与展望

简述当前国内外探月研究进展及月球探测(车)机器人研究发展情况.基于月球的复杂环境特征,对比轮式运动与步行式运动月球探测(车)机器人的优势与缺陷,重点介绍了一种新型六轮腿式月球探测机器人方案.该方案具有高机动性,越障能力强,容错性好等特点,对该方案主要技术性能、本体结构、运动系统、轮腿切换机构工作原理等关键技术进行了探讨.     

Tracking social provenance in chains of retweets

Knowledge and Information Systems - In the era of massive sharing of information, the term social provenance is used to denote the ownership, source or origin of a piece of information which has...     

情景感知驱动的移动对象多模式轨迹预测技术综述

如何利用多源异构时空数据进行准确的轨迹预测并且反映移动对象的移动特性是轨迹预测领域的核心问题.现有的大多数轨迹预测方法是长序列轨迹模式预测模型,根据历史轨迹的特点进行预测,或将当前移动对象的轨迹位置放入时空语义场景根据历史移动对象轨迹预测位置.综述当前常用的轨迹预测模型和算法,涉及不同的研究领域.首先,阐述了多模式轨迹预测的主流工作,轨迹预测的基本模型类;其次,对不同类的预测模型进行总结,包括数学统计类、机器学习类、滤波算法,以及上述领域具有代表性的算法;再次,对情景感知技术进行了介绍,描述了不同领域的学者对情景感知的定义,阐述了情景感知技术所包含的关键技术点,诸如情景感知计算、情景获取和情景推理的不同类模型,分析了情景感知的不同分类、过滤、存储和融合以及它们的实现方法等.详细介绍了情景感知驱动的轨迹预测模型技术路线及各阶段任务的工作原理.给出了情景感知技术在真实场景中的应用,包括位置推荐,兴趣点推荐等,通过与传统算法对比,分析情景感知技术在此类应用中的优劣.详细介绍了情景感知结合LSTM (long short-term memory)技术应用于行人轨迹预测领域的新方法.最后,总结了...     

Generation and interpretation of parsimonious predictive models for load forecasting in smart heating networks

Applied Intelligence - Forecasting future heat load in smart district heating networks is a key problem for utility companies that need such predictions for optimizing their operational activities....     

Recommendation of RILEM Technical Committee 250-CSM: Test method for Textile Reinforced Mortar to substrate bond characterization

Textile Reinforced Mortar (TRM), also known as Fabric Reinforced Mortar or Fabric Reinforced Cementitious Matrix, composites are an emerging technology for the external repair and strengthening of existing structures. For most applications, the effectiveness of the TRM reinforcement relies on its bond performance. This recommendation identifies the best practice to characterize the bond behaviour of TRM. A shear bond test method is proposed to determine the peak axial stress (associated with the maximum load that can be transferred from the structural member to the externally bonded TRM reinforcement), the stress–slip relationship and the failure mode that controls the TRM-to-substrate load transfer capacity. Guidelines on specimen manufacturing, experimental setup, test execution, and determination of test results are provided.     

Undivided and Indistinguishable Histories in Branching-Time Logics

In the tree-like representation of Time, two histories are undivided at a moment t whenever they share a common moment in the future of t. In the present paper, it will first be proved that Ockhamist and Peircean branching-time logics are unable to express some important sentences in which the notion of undividedness is involved. Then, a new semantics for branching-time logic will be presented. The new semantics is based on trees endowed with an indistinguishability function, a generalization of the notion of undividedness. It will be shown that Ockhamist and Peircean semantics can be viewed as limit cases of the semantics developed in this paper.