11911666
Secure online examination architecture based on distributed firewall
Online (Web-based) examination is an effective solution for mass education evaluation. However, due to the incomplete of network security, students can communicate with each other, and we can't prevent the cheating. Therefore, keeping the security of a online examination has become an important issue. This paper focuses on how to implement a secure environment for online-examination in the general academic network environment without the need of special network topologies and hardware devices. It not only reduces the system administrator's load, but also enhances the system flexibility to fit every teacher's needs. We use (1) the distributed firewall techniques to control the network packets of all machines, and (2) the centralized security policy management to control the security policies for all machines. Beside the above mentioned, we also design some mechanisms to prevent the possible network attacks and cheating, which enhance the security of the online examination environment.
16162932
AntSearch: An Ant Search Algorithm in Unstructured Peer-to-Peer Networks
The most prevalent peer-to-peer (P2P) application till today is file sharing, and unstructured P2P networks can support inherent heterogeneity of peers, are highly resilient to peers’ failures, and incur low overhead at peer arrivals and departures. Dynamic querying (DQ) is a new flooding technique which could estimate a proper time-to-live (TTL) value for a query flooding by estimating the popularity of the searched files, and retrieve sufficient results under controlled flooding range for reducing network traffic. Recent researches show that a large amount of peers in the P2P file sharing system are the free-riders, and queries are seldom hit by those peers. The free-riders problem causes a large amount of redundant messages in the DQ-like search algorithm. In this paper, we proposed a new search algorithm, called "AntSearch", to solve the problem. In AntSearch, each peer maintains its hit rate of previous queries, and records a list of pheromone values of its immediate neighbors. Based on the pheromone values, a query is only flooded to those peers which are not likely to be the free-riders. Our simulation results show that, compared with DQ and its enhanced algorithm DQ+, the AntSearch algorithm averagely reduces 50% network traffic at almost the same search latency as DQ+, while retrieving sufficient results for a query with a given required number of results.
29113389
Approximate string matching in LDAP based on edit distance
As the E-Commerce rapidly grows up, searching data is almost necessary in every application. Approximate string matching problems play a very important role to search with errors. Against these problems "Edit distance" and "Soundex" are two common techniques, especially the latter one is a "sound-like" method and had been applied to the LDAP server. Nevertheless, it is not adequate for certain situations especially when we perform the symbol matching (as in DNA); it doesn't make sense to use the "sound-like" method. On the other hand, "Edit distance" has a clear definition and also is widely used in many fields of application. Since the design of LDAP server is optimized for reading, applying edit distance technique to LDAP server has the problem of lowering speed. In this paper we design efficient data structures and an algorithm to solve the speed problem, and furthermore we use three filter conditions [1] based on the n-gram technique to achieve a well filter performance. Finally we also demonstrate experimentally the benefits of applying our algorithm and its limitations.
6388420
Proof: A DHT-Based Peer-to-Peer Search Engine
In this paper we focus on building a large scale keyword search service over structured peer-to-peer (P2P) networks. Current state-of-the-art keyword search approaches for structured P2P systems are based on inverted list intersection. However, the biggest challenge in those approaches is that when the indices are distributed over peers, a simple query may cause a large amount of data to be transmitted over the network. We propose a new P2P keyword search scheme, called "Proof", to reduce network traffic for queries. The key idea is storing a content summary for each Web page in the inverted list, so that a query can be processed by only transmitting a small size of candidate results. Our simulation results showed that, compared with previous DHT-based P2P systems, Proof can dramatically reduce network traffic and computation time. It provides 100% precision and 90.09% recall of search results, at an acceptable cost of storage overhead, even when the number of peers and documents increases continually
6906500
Web Appearance Disambiguation of Personal Names Based on Network Motif
Searching for information about a particular person is a common activity on search engines. However, current search engines do not provide any special function for search a person. Previous research has solved the problem by using additional background knowledge, such as a friend list, to cluster the searched Web pages. However, it is still difficult to retrieve and choose suitable background knowledge. In this paper, we propose a Web appearance disambiguation (WAD) system to solve the problem by only using the hyperlink structures between Web pages. The key idea of the WAD system is to find out smaller node motifs as evidences of close relationship between pages for clustering searched Web pages. Our experimental results show that, under no background knowledge, the performance of the WAD system achieves 70% for the F-measure
21367461
A feasible method to find areas with constraints using hierarchical depth-first clustering
Addresses a reliable, feasible method to find geographical areas with constraints using hierarchical depth-first clustering. The method involves multi-level hierarchical clustering with a depth-first strategy, depending on whether the area of each cluster satisfies the given constraints. The attributes used in the hierarchical clustering are the coordinates of the grid data points. The constraints are an average value range and the minimum size of an area with a small proportion of missing data points. Convex-hull and point-in-polygon algorithms are involved in examining the constraint satisfaction. The method is implemented for an Earth science data set for vegetation studies - the Normalized Difference Vegetation Index (NVDI).
40685825
Value Range Queries on Earth Science Data via Histogram Clustering
Remote sensing data as well as ground-based and model output data about the Earth system can be very large in volume. On the other hand, in order to use the data efficiently, scientists need to search for data based on not only metadata but also actual data values. To answer value range queries by scanning very large volumes of data is obviously unrealistic. This article studies a clustering technique on histograms of data values on predefined cells to index the cells. Through this index system, the so-called statistical range queries can be answered quickly and approximately together with an accuracy assessment. Examples of using this technique for Earth science data sets are given in this article.
7799230
Passage feedback with IRIS
We compare a user-defined passage feedback (pf) system to a document feedback (df) system. Df employed the adaptive linear model for retrieval, while pf used weighted query expansion based on positive and negative feedback. Twenty-four searchers performed the same six tasks in varying search and system-order per TREC-8 guidelines. We hypothesized that pf, which featured interactive query expansion, would outperform df, which relied on automatic query expansion. Initial analysis appeared to reject this hypothesis, as df showed slightly higher overall performance than pf. However, analysis by system-order groups indicates only the first pf use had lower performance. These data suggest that pf was more difficult to learn than df, though the second pf use yielded competitive performance. If performance of pf is indeed affected by learning, an improved pf system with usability enhancements may prove to be an effective mechanism for interactive information retrieval.
61441845
Towards the classical communication complexity of entanglement distillation protocols with incomplete information
Entanglement is an essential resource for quantum communication and quantum computation, similar to shared random bits in the classical world. Entanglement distillation extracts nearly-perfect entanglement from imperfect entangled state. The classical communication complexity of these protocols is the minimal amount of classical information that needs to be exchanged for the conversion. In this paper, we focus on the communication complexity of protocols that operate with incomplete information, i.e., where the inputs are mixed states and/or prepared adversarially. We consider three models of imperfect entanglement, namely, the bounded measurement model, the depolarization model, and the fidelity model. We describe these models as well as the motivations for studying them. For the bounded measurement model and the depolarization model, we prove tight and almost-tight bounds on the output quality of non-interactive protocols. For the fidelity model, we prove a lower bound that matches the upper bound given by Ambainis et al., and thus completely characterizes communication complexity of entanglement distillation protocols for this model. Our result also suggests the optimality of the BB84 protocol in terms of communication complexity. We emphasize that although some of the results appear intuitively straightforward, their proofs are not. In fact, two novel techniques are developed for proving these results. We believe that these techniques are of independent interests, too.
2422439
The IOC algorithm: efficient many-class non-parametric classification for high-dimensional data
This paper is about a variant of k nearest neighbor classification on large many-class high dimensional datasets.K nearest neighbor remains a popular classification technique, especially in areas such as computer vision, drug activity prediction and astrophysics. Furthermore, many more modern classifiers, such as kernel-based Bayes classifiers or the prediction phase of SVMs, require computational regimes similar to k-NN. We believe that tractable k-NN algorithms therefore continue to be important.This paper relies on the insight that even with many classes, the task of finding the majority class among the k nearest neighbors of a query need not require us to explicitly find those k nearest neighbors. This insight was previously used in (Liu et al., 2003) in two algorithms called KNS2 and KNS3 which dealt with fast classification in the case of two classes. In this paper we show how a different approach, IOC (standing for the International Olympic Committee) can apply to the case of n classes where n > 2.IOC assumes a slightly different processing of the datapoints in the neighborhood of the query. This allows it to search a set of metric trees, one for each class. During the searches it is possible to quickly prune away classes that cannot possibly be the majority.We give experimental results on datasets of up to 5.8 x 105 records and 1.5 x 103 attributes, frequently showing an order of magnitude acceleration compared with each of (i) conventional linear scan, (ii) a well-known independent SR-tree implementation of conventional k-NN and (iii) a highly optimized conventional k-NN metric tree search.