10564243
Discovering the linear writing order of a two-dimensional ancient hieroglyphic script
This paper demonstrates how machine learning methods can be applied to deal with a real-world decipherment problem where very little background knowledge is available. The goal is to discover the linear order of a two-dimensional ancient script, Hieroglyphic Luwian. This paper records a complete decipherment process including encoding, modeling, parameter learning, optimization, and evaluation. The experiment shows that the proposed approach is general enough to recover the linear order of various manually generated two-dimensional scripts without needing to know in advance what language they represent and how the two-dimensional scripts were generated. Since the proposed method does not require domain specific knowledge, it can be applied not only to language problems but also order discovery tasks in other domains such as biology and chemistry.
45339234
Interesting Instance Discovery in Multi-Relational Data
The general area of machine discovery focuses on methods to use computers to perform or assist discovery tasks. Herbert Simon described it as “gradual problemsolving processes of searching large problem spaces for incompletely defined goal objects” [Simon, 1995, p.171]. Today machine discovery research falls into two major categories, scientific discovery and knowledge discovery and data mining (KDD). In this paper we propose a new research direction that lies somewhere in-between these two trends: we call it interesting instance discovery (IID) which aims at discovering interesting instances in large, multi-relational datasets. There are three important characteristics for IID research: (1) Unlike scientific discovery and KDD, it aims at the discovery of particular interesting instances as opposed to general laws or patterns. (2) It is dealing with multi-relational data instead of numeric data that is best described as a relational graph or a semantic net. In such a network nodes represent objects and links represent relationships between them – see Figure 1 for an example of such a network from a bibliography domain. (3) Similar to KDD, it also focuses on data that are too large and complex to be analyzed manually by humans.
3645474
Exploiting a search engine to develop more flexible Web agents
With the rapid growth of the World Wide Web, more and more people rely on the online services to acquire and integrate information. However, it is time consuming to find the online services that are perfectly suited for a given task. First, the users might not have enough information to fill in the required input fields for querying an online service. Second, the online service might generate only partial information. Third, the user might only find the inverse version of the desired service. We propose a framework to develop flexible Web agents that handle these imperfect situations. In this framework we exploit a search engine as a general information discovery tool to assist finding and pruning information. To demonstrate this framework, we implemented two Web agents: the Internet inverse geocoder and the address lookup module.
15193012
Unsupervised link discovery in multi-relational data via rarity analysis
A significant portion of knowledge discovery and data mining research focuses on finding patterns of interest in data. Once a pattern is found, it can be used to recognize satisfying instances. The new area of link discovery requires a complementary approach, since patterns of interest might not yet be known or might have too few examples to be learnable. We present an unsupervised link discovery method aimed at discovering unusual, interestingly linked entities in multi-relational datasets. Various notions of rarity are introduced to measure the "interestingness" of sets of paths and entities. These measurements have been implemented and applied to a real-world bibliographic dataset where they give very promising results.
746230
Designing the Market Game for a Trading Agent Competition
The authors discuss the design and operation of a trading agent competition, focusing on the game structure and some of the key technical issues in running and playing the game. They also describe the competition's genesis, its technical infrastructure, and its organization. The article by A. Greenwald and P. Stone (2001), describes the competition from a participant's perspective and describes the strategies of some of the top-placing agents. A visualization of the competition and a description of the preliminary and final rounds of the TAC are available in IC Online (http://computer.org/internet/tac.htm).
8641285
A data compression technique for sensor networks with dynamic bandwidth allocation
In this paper, we have presented a new data compression technique, designed for historical information compression in sensor networks. Our method employs the LVQ learning process to construct the codebook and the codebook's updates are compressed to save bandwidth for sensor data transmission. In addition, we have addressed the dynamic bandwidth allocation problem in sensor networks. Our DBA algorithm can dynamically adjust the communication bandwidth of different sensors in order to balance data compression qualities at different sensors.
1538276
Distributed spatio-temporal similarity search
In this paper we introduce the distributed spatio-temporal similarity search problem: given a query trajectory Q, we want to find the trajectories that follow a motion similar to Q, when each of the target trajectories is segmented across a number of distributed nodes. We propose two novel algorithms, UB-K and UBLB-K, which combine local computations of lower and upper bounds on the matching between the distributed subsequences and Q. Such an operation generates the desired result without pulling together all the distributed subsequences over the fundamentally expensive communication medium. Our solutions find applications in a wide array of domains, such as cellular networks, wild life monitoring and video surveillance. Our experimental evaluation using realistic data demonstrates that our framework is both efficient and robust to a variety of conditions.
31903182
The Fourier slice theorem for range data reconstruction
Abstract This paper proposes a new approach to resolve the ambiguity problem in multistriping laser triangulation systems [1] . The application of this new method to circularly symmetrical objects can achieve time and space complexity of order one. No optical shutter is required in this new method. The new solution is based on the Fourier slice theorem, which forms the basis of X-ray CT (computed tomography) reconstruction. This paper demonstrates how this theorem can also be used to resolve multistripe laser ambiguity effectively.
1266771
Applying LVQ techniques to compress historical information in sensor networks
Summary form only given. In the emerging area of wireless sensor networks, a typical challenge is to retrieve historical information from the sensor nodes. We propose a new technique, called adaptive learning vector quantization (ALVQ), to compress this historical information. Our technique is based on the following two observations: (1) in sensor networks, the historical information exhibits similar patterns over time; and (2) different measurements are intrinsically correlated. Our algorithm works as follows: first, the codebook is obtained through a LVQ (learning vector quantization), which adjusts the codebook to be nearer to the optimal codebook. Second, ALVQ compresses the codebook update data pieces and transfers the compressed information to the base station. Using 2-level piece-wise regression, ALVQ can compress the updates with high precision while saving more bandwidth for data transmission in order to increase the quality of the approximation. In our experiments we used weather data to compare the performance of the ALVQ algorithm with the recently proposed SBR (self based regression) technique. Our experimental results demonstrate that the LVQ learning process significantly improves the quality of the codebook, thus increasing the regression precision. In addition the use of two-level regression for transmitting the codebook updates further minimizes the required bandwidth. Overall the ALVQ technique can achieve the same precision with SBR while using 75% of the bandwidth.
22001959
A Pinwheel Scheduler for Three Distinct Numbers with a Tight Schedulability Bound
Abstract. Given a multiset of positive integers 
 $A=\{a_1,a_2,\ldots,a_n\}$ , the pinwheel problem is to find an infinite sequence over 
 $\{1,2,\ldots,n\}$ such that there is at least one symbol i within any subsequence of length ai. The density of A is defined as 
 $\rho(A)=\sum^n_{i=1} (1/a_i)$ . In this paper we limit ourselves to instances composed of three distinct integers. The best scheduler [5] published previously can schedule all instances with a density of less than 0.77. A new and fast scheduling scheme based on spectrum partitioning is presented in this paper which improves the 0.77 result to a new 
 $\frac{5}{6}\approx 0.83$ density threshold. This scheduler has achieved the tight schedulability bound of this problem.