Tsan-sheng Hsu

A linear time algorithm for triconnectivity augmentation

The problem of finding the smallest set of edges whose addition triconnects an undirected graph is considered. This is a fundamental graph-theoretic problem that has applications in designing reliable networks and fault-tolerant computing. A linear time sequential algorithm is given for the problem. This is a substantial improvement over the best previous algorithm for this problem, which runs in O(n(n+m)/sup 2/) time on a graph with n vertices and m edges.<<ETX>>

Privacy-Preserving Collaborative Recommender Systems

Collaborative recommender systems use various types of information to help customers find products of personalized interest. To increase the usefulness of collaborative recommender systems in certain circumstances, it could be desirable to merge recommender system databases between companies, thus expanding the data pool. This can lead to privacy disclosure hazards during the merging process. This paper addresses how to avoid privacy disclosure in collaborative recommender systems by comparing with major cryptology approaches and constructing a more efficient privacy-preserving collaborative recommender system based on the scalar product protocol.

Finding a smallest augmentation to biconnect a graph

The problem of finding a minimum number of edges whose addition biconnects an undirected graph is considered. This problem has been studied by several other researchers, two of whom presented a linear-time algorithm for this problem in an earlier volume of this journal. However, that algorithm contains an error that is exposed in this paper. A corrected linear-time algorithm for this problem, as well as a new efficient parallel algorithm, are presented. The parallel algorithm runs in

Task allocation on a network of processors

O(\log ^2 n)

On four-connecting a triconnected graph

time with a linear number of processors on an EREW PRAM, where n is the number of vertices in the input graph.

Scheduling Problems in a Practical Allocation Model

This paper studies the scheduling of tasks on a pool of identical workstations in a network where message passing is used for data transfer and communication between processors and where the precedence relations among tasks form a send-receive graph. Bur parallel computation model differs from previous models by including all of the following practical considerations: 1) the sending and receiving of multiple messages from one processor to another is performed sequentially, 2) communication overhead is proportional to the message size, and 3) the starting and ending task must be performed on the same machine. These factors are crucial when performing parallel task execution using a pool of workstations whose communication primitives are provided by off-the-shelf packages, such as PVM, and whose message sizes are nontrivial. Although our model is new, using reduction from other well-known scheduling results shows that finding a scheduling with the optimal makespan is NP-hard. Our focus, therefore, is on developing and analyzing approximation algorithms for this problem. When the number of workstations in the network is abundant, a linear approximation algorithm is given with a proven performance bound of two times the optimal. When the number of available workstations is a fixed constant k that is greater than 2, we show an O(nlogn)-time approximation algorithm which always performs better than (3+k-2/k) times the optimal. Simulation results show that, on the average, both of our approximation algorithms perform much better than the worst case analysis and both generate schedulings whose makespans are very close to optimal.

Privacy Protection in Social Network Data Disclosure Based on Granular Computing

The author considers the problem of finding a smallest set of edges whose addition four-connects a triconnected graph. This is a fundamental graph-theoretic problem that has applications in designing reliable networks. He presents an O(n alpha (m,n)+m) time sequential algorithm for four-connecting an undirected graph G that is triconnected by adding the smallest number of edges, where n and m are the number of vertices and edges in G, respectively, and alpha (m, n) is the inverse Ackermann function. He presents a new lower bound for the number of edges needed to four-connect a triconnected graph. The form of this lower bound is different from the form of the lower bound known for biconnectivity augmentation and triconnectivity augmentation. The new lower bound applies for arbitrary k, and gives a tighter lower bound than the one known earlier for the number of edges needed to k-connect a (k-1)-connect graph. For k=4, he shows that this lower bound is tight by giving an efficient algorithm for finding a set edges with the required size whose addition four-connects a triconnected graph.<<ETX>>

Chinese Dark Chess

A parallel computational model is defined which addresses I/O contention,latency, and pipe-lined message passing between tasks allocated to differentprocessors. The model can be used for parallel task-allocation on either anetwork of workstations or on a multi-stage inter-connected parallel machine.To study performance bounds more closely, basic properties are developed forwhen the precedence constraints form a directed tree. It is shown that theproblem of optimally scheduling a directed one-level precedence tree on anunlimited number of identical processors in this model is NP-hard. Theproblem of scheduling a directed two-level precedence tree is also shown tobe NP-hard even when the system latency is zero. An approximation algorithm is then presented for scheduling directedone-level task trees on an unlimited number of processors with anapproximation ratio of 3. Simulation results show that this algorithm is, infact, much faster than its worst-case performance bound. Better simulationresults are obtained by improving our approximation algorithm usingheusistics. Restricting the problem to the case of equal task executiontimes, a linear-time algorithm is presented to find an optimal schedule.

A Logical Model for Privacy Protection

Social network analysis is an important methodology in sociological research. Though social network data is very useful to researchers and policy makers, releasing such data to the public may cause an invasion of privacy. We generalize the techniques for protecting personal privacy in tabulated data, and propose some metrics of anonymity for assessing the risk of breaching confidentiality by disclosing social network data. We assume a situation of data publication, where data is released to the general public. We adopt description logic as the underlying knowledge representation formalism, and consider the metrics of anonymity in open world and closed world contexts respectively.

On Four-Connecting a Triconnected Graph

Chinese dark chess is a popular and easy-to-learn game in Asia. The characteristic of possible revealing of unknown pieces makes it different from Chinese chess or Western chess. Players with luck may win a game by chance. Thus, there is a probabilistic behavior that a player has to consider. Computer Chinese dark chess problems can be divided into three phases: 1) the opening game, 2) the middle game, and 3) the endgame. Revealing pieces reasonably and effectively is the main issue in the opening game. In the middle game, the choice of revealing pieces or moving pieces becomes the critical issue. Designing a good evaluation function is also important both in the middle game and endgame. In an advantageous endgame, how to capture all opponent’s pieces to win the game is also a hard problem. Search-based methods, such as αβ pruning, are only suitable in the positions where revealing actions do not influence the results. However, according to our experiments, programs that reveal pieces reasonably and effectively have better playing strength. In this paper, we introduce the game Chinese dark chess and give some research topics about this game. We also discuss some strategies for considering the revealing actions in this paper.