31977888
Polynomial Time Productivity, Approximations, and Levelability
This paper studies polynomial-time approximations to intractable sets using the concept of p-productivity. It is shown that every (deterministic and nondeterministic) superpolynomial-time computable p-productive set is p-levelable. All $ \leq _m^p $-complete sets for any deterministic superpolynomial time class are shown to be p-productive. It is then shown that the complement of any honest k-creative set in NP is p-levelable. This settles an open problem in Homer [Theoret. Comput. Sci., 47 (1986), pp. 169–180].
15313247
Performance of a MAC protocol for WDM networks with on-line scheduling
This paper investigates the performance of media access protocols based on on-line scheduling for an optically connected star-coupled system with wavelength division multiple access channels. Hybrid access protocols combining reservation and pre-allocation of receiver channels have been previously proposed. Transmission consisted of a reservation phase followed by a data phase. Each node was allowed to place a reservation for one WDM channel. This protocol is extended in this paper by allowing reservations on multiple channels and using simple on-line scheduling algorithms. Existing scheduling algorithms (all are off-line) for similar reservation problems tend to have high implementation and computational complexity. We require that scheduling algorithms be real-time and amenable to hardware implementation. The performance of the extended protocol incorporating two simple on-line scheduling algorithms is presented in this paper. We show that the slight increased computational overhead with scheduling is justified by reduced packet latency and higher utilization, especially for client-server traffic.
7902129
Efficient Minimization of Numerical Summation Errors
Given a multiset X={x 1,... x n } of real numbers, the floating-point set summation (FPS) problem asks for S n = x 1+...+ x n , and the floating point prefix set summation problem (FPPS) asks for S k =x 1+...+x k for all k = 1, ... n. Let E k * denote the minimum worst-case error over all possible orderings of evaluating S k . We prove that if X has both positive and negative numbers, it is NP-hard to compute S n with the worst-case error equal to E n * . We then give the first known polynomial-time approximation algorithm for computing Sn that has a provably small error for arbitrary X. Our algorithm incurs a worstcase error at most 2([log(n−1)]+1) E n * . After X is sorted, it runs in O(n) time, yielding an O(n 2)-time approximation algorithm for computing S k for all k = 1, ..., n such that the worst-case error for each S k is less than 2(⌈ log(k−1)⌋+ 1)E k * .
206565969
Immunity of Complete Problems
Abstract Two necessary and sufficient conditions for all E -complete sets to contain dense P subsets are shown. We then prove that every ≤ p m -hard for E set and its complement contain dense E ∩UP subsets. As a corollary, every NE -complete set and its complement contain dense E ∩UP subsets.
38052408
Towards a practical, secure, and very large scale online election
We propose a practical and secure electronic voting protocol for large-scale online elections. Our protocol satisfies a large set of important criteria that has never been put together in a single protocol before. Among all electronic voting schemes in the literature, Sensus, a security-conscious electronic voting protocol proposed by Cranor and Cytron (1997), satisfies most of our criteria. Sensus has been implemented and used in mock elections. However, Sensus suffers from several major drawbacks. For instance, we show that even if all voters follow the Sensus protocol honestly, some voters' votes may still be replaced with different votes without being detected. Our protocol overcomes these drawbacks.
32837558
On average P vs. average NP
Structures of polynomial-time computable distributions and polynomial-time many-one reductions on randomized decision problems are investigated. The scope is widened from the most studied class DNP (distributional-NP) to the class ANP (average-NP), which consists of randomized decision problems accepted by nondeterministic Turing machines in average polynomial time. Results indicate that the structures of average-case complexity classes are very different from their counterparts in worst-case complexity due to the complex structures of probability distributions and distribution functions.<<ETX>>
30210949
Exact and approximate algorithms for unordered tree matching
We consider the problem of comparison between unordered trees, i.e., trees for which the order among siblings is unimportant. The criterion for comparison is the distance as measured by a weighted sum of the costs of deletion, insertion and relabel operations on tree nodes. Such comparisons may contribute to pattern recognition efforts in any field (e.g., genetics) where data can naturally be characterized by unordered trees. In companion work, we have shown this problem to be NP-complete. This paper presents an efficient enumerative algorithm and several heuristics leading to approximate solutions. The algorithms are based on probabilistic hill climbing and bipartite matching techniques. The paper evaluates the accuracy and time efficiency of the heuristics by applying them to a set of trees transformed from industrial parts based on a previously proposed morphological model. >
2380687
Packet classification using multidimensional cutting
This paper introduces a classification algorithm called phHyperCuts. Like the previously best known algorithm, HiCuts, HyperCuts is based on a decision tree structure. Unlike HiCuts, however, in which each node in the decision tree represents a hyperplane, each node in the HyperCuts decision tree represents a k--dimensional hypercube. Using this extra degree of freedom and a new set of heuristics to find optimal hypercubes for a given amount of storage, HyperCuts can provide an order of magnitude improvement over existing classification algorithms. HyperCuts uses 2 to 10 times less memory than HiCuts optimized for memory, while the worst case search time of HyperCuts is 50--500% better than that of HiCuts optimized for speed. Compared with another recent scheme, EGT-PC, HyperCuts uses 1.8--7 times less memory space while the worst case search time is up to 5 times smaller. More importantly, unlike EGT-PC, HyperCuts can be fully pipelined to provide one classification result every packet arrival time, and also allows fast updates.
14765514
Information Discovery, Extraction and Integration for the Hidden Web
In this paper, we report our initial investigations on the problems of automatically extracting data objects from a given hidden-web source (i.e., the web site with an HTML search form) and automatically assigning semantics to the extracted data. We also propose some future work to address the problem of information discovery and integration for hidden-web sources.
207558887
Self-adaptive neuro-fuzzy systems for autonomous underwater vehicle control
This paper presents a systematic approach for developing a concise self-adaptive neurofuzzy inference system (SANFIS) with a fast hybrid parameter learning algorithm for on-line learning of the control knowledge for autonomous underwater vehicle (AUV) control. The multi-layered network structure of SANFIS incorporates fuzzy basis functions for better function approximations. We investigate three SANFIS structures with three different types of fuzzy IF-THEN rule-based models and cast the rule formation problem as a clustering problem. A recursive least-squares algorithm and a modified Levenberg-Marquardt algorithm with limited memory are exploited to accelerate the parameter learning process. This hybrid parameter learning algorithm together with an on-line clustering technique and rule examination provide SANFIS with the capability of selforganizing and self-adapting its internal structure (i.e. the fuzzy rules and term sets) for learning the required control knowledge for an AUV to follow desired trajectories. Computer simulations for modeling a control system for an AUV have been conducted to validate the effectiveness of the proposed SANFIS.
5805118
New techniques for best-match retrieval
A scheme to answer best-match queries from a file containing a collection of objects is described. A best-match query is to find the objects in the file that are closest (according to some (dis)similarity measure) to a given target.
Previous work [5, 331] suggests that one can reduce the number of comparisons required to achieve the desired results using the triangle inequality, starting with a data structure for the file that reflects some precomputed intrafile distances. We generalize the technique to allow the optimum use of any given set of precomputed intrafile distances. Some empirical results are presented which illustrate the effectiveness of our scheme, and its performance relative to previous algorithms.