2416912
Adaptive Generalized Estimation Equation with Bayes Classifier for the Job Assignment Problem
We propose combining advanced statistical approaches with data mining techniques to build classifiers to enhance decision-making models for the job assignment problem. Adaptive Generalized Estimation Equation (AGEE) approaches with Gibbs sampling under Bayesian framework and adaptive Bayes classifiers based on the estimations of AGEE models which uses modified Naive Bayes algorithm are proposed. The proposed classifiers have several important features. Firstly, it accounts for the correlation among the outputs and the indeterministic subjective noise into the estimation of parameters. Secondly, it reduces the number of attributes used to predict the class. Moreover, it drops the assumption of independence made by the Naive Bayes classifier. We apply our techniques to the problem of assigning jobs to Navy officers, with the goal of enhancing happiness for both the Navy and the officers. The classification results were compared with nearest neighbor, Multi-Layer Perceptron and Support Vector Machine approaches.
7539391
Compressed-domain video indexing techniques using DCT and motion vector information in MPEG video
Development of various multimedia applications hinges on the availability of fast and efficient storage, browsing, indexing, and retrieval techniques. Given that video is typically stored efficiently in a compressed format, if we can analyze the compressed representation directly, we can avoid the costly overhead of decompressing and operating at the pixel level. Compressed domain parsing of video has been presented in earlier work where a video clip is divided into shots, subshots, and scenes. In this paper, we describe key frame selection, feature extraction, and indexing and retrieval techniques that are directly applicable to MPEG compressed video. We develop a frame-type independent representation of the various types of frames present in an MPEG video in which al frames can be considered equivalent. Features are derived from the available DCT, macroblock, and motion vector information and mapped to a low-dimensional space where they can be accessed with standard database techniques. The spatial information is used as primary index while the temporal information is used to enhance the robustness of the system during the retrieval process. The techniques presented enable fast archiving, indexing, and retrieval of video. Our operational prototype typically takes a fraction of a second to retrieve similar video scenes from our database, with over 95% success.
17442336
Ordinal association rules for error identification in data sets
A new extension of the Boolean association rules, ordinal association rules, that incorporates ordinal relationships among data items, is introduced. One use for ordinal rules is to identify possible errors in data. A method that finds these rules and identifies potential errors in data is proposed.
46019766
A Study on Textual Entailment
In this paper we study a graph-based approach to the task of textual entailment between a text and hypothesis. The approach takes into account the full lexico-syntactic context of both the text and hypothesis and relies heavily on the concept of subsumption. It starts with mapping the text and hypothesis into graph structures where nodes represent concepts and edges represent lexico-syntactic relations among concepts. Based on a subsumption score between the text-graph and hypothesis-graph an entailment decision is then made. The impact of synonymy on entailment is quantified and discussed. An important advantage of our solution is the ability to customize it so that high-confidence results are obtained
18594508
An index structure for efficient reverse nearest neighbor queries
The Reverse Nearest Neighbor (RNN) problem is to find all points in a given data set whose nearest neighbor is a given query point. Just like the Nearest Neighbor (NN) queries, the RNN queries appear in many practical situations such as marketing and resource management. Thus, efficient methods for the RNN queries in databases are required. The paper introduces a new index structure, the Rdnn-tree, that answers both RNN and NN queries efficiently. A single index structure is employed for a dynamic database, in contrast to the use of multiple indexes in previous work. This leads to significant savings in dynamically maintaining the index structure. The Rdnn-tree outperforms existing methods in various aspects. Experiments on both synthetic and real world data show that our index structure outperforms previous methods by a significant margin (more than 90% in terms of number of leaf nodes accessed) in RNN queries. It also shows improvement in NN queries over standard techniques. Furthermore, performance in insertion and deletion is significantly enhanced by the ability to combine multiple queries (NN and RNN) in one traversal of the tree. These facts make our index structure extremely preferable in both static and dynamic cases.
18266651
A similarity-based soft clustering algorithm for documents
Document clustering is an important tool for applications such as Web search engines. Clustering documents enables the user to have a good overall view of the information contained in the documents that he has. However, existing algorithms suffer from various aspects, hard clustering algorithms (where each document belongs to exactly one cluster) cannot detect the multiple themes of a document, while soft clustering algorithms (where each document can belong to multiple clusters) are usually inefficient. We propose SISC (similarity-based soft clustering), an efficient soft clustering algorithm based on a given similarity measure. SISC requires only a similarity measure for clustering and uses randomization to help make the clustering efficient. Comparison with existing hard clustering algorithms like K-means and its variants shows that SISC is both effective and efficient.
42000677
Real-Time Database Benchmark Design for Avionics Systems
Traditionally, database systems have been developed for data processing applications. With the development of CAD/CAM applications, object-oriented databases have been shown to be more flexible and effective in providing the kind of performance more adequate for CAD users. Recently, as a result of the rapid growth in embedded and autonomous systems applications, many database applications are moving toward the real-time domain that is defined by high data access and/or transaction execution rates as well as stringent timing constraints. These real-time characteristics require databases to have special capabilities to handle time-critical transactions. Some examples are databases for safety-critical applications like air traffic control, avionics systems, process control, and medical service systems.
362708
Low-Cost RFID Identification Variation
Interests continue to grow in recent years for the adoption of Radio Frequency Identification (RFID) in many different areas including transportation and supply chain management. Those RFID-included objects can be targeted more efficiently by real-time tracking and instant management. However, because of the contact-less type of RFID remote retrieval, the transmission of data in the air is very vulnerable to eavesdropping or appropriation. A primary security concern surrounding RFID technology is the illicit tracking of consumer location and analyzing of their shopping habits or behavior. This paper proposes a more secure and lightweight RFID variation protection protocol which enhances the security of the transmission of information as well as the consumer privacy protection by using randomized control access and two-way identification. In addition, this protocol also improves the overall performance and lowers the cost of RFID tag without any complicated calculation.
27364789
Efficient on-line schedulability tests for priority driven real-time systems
Many computer systems, such as those for open system environments or multimedia services, need an efficient schedulability test for online admission control of new jobs. Although various polynomial-time schedulability tests have been proposed, they often fail to decide the schedulability of the system precisely when the system is heavily loaded. This paper presents efficient online schedulability tests which are are shown to be more precise and efficient than any existing polynominal-time schedulability tests. Moreover, our proposed tests can be used for the multiframe model, where a task may have different computation times in different periods. We show the performance of the proposed schedulability tests in several simulation experiments.
4707602
An initial strategy for comparing proteins at the domain architecture level
MOTIVATION
Ideally, only proteins that exhibit highly similar domain architectures should be compared with one another as homologues or be classified into a single family. By combining three different indices, the Jaccard index, the Goodman-Kruskal gamma function and the domain duplicate index, into a single similarity measure, we propose a method for comparing proteins based on their domain architectures.


RESULTS
Evaluation of the method using the eukaryotic orthologous groups of proteins (KOGs) database indicated that it allows the automatic and efficient comparison of multiple-domain proteins, which are usually refractory to classic approaches based on sequence similarity measures. As a case study, the PDZ and LRR_1 domains are used to demonstrate how proteins containing promiscuous domains can be clearly compared using our method. For the convenience of users, a web server was set up where three different query interfaces were implemented to compare different domain architectures or proteins with domain(s), and to identify the relationships among domain architectures within a given KOG from the Clusters of Orthologous Groups of Proteins database.


CONCLUSION
The approach we propose is suitable for estimating the similarity of domain architectures of proteins, especially those of multidomain proteins.


AVAILABILITY
http://cmb.bnu.edu.cn/pdart/.
41113698
Scheduling Jobs with Temporal Distance Constraints
The job scheduling problems for real-time jobs with temporal distance constraints (JSD) are presented. In JSD, the start times of two related jobs must be within a given distance. The general JSD problem is NP-hard. We define the multilevel unit-time JSD (MUJSD) problem for systems with $m$ chains of unit-time jobs in which neighboring jobs in each chain must be scheduled within $c$ time units. We present an $O(n^2)$-time algorithm, where $n$ is the total number of jobs in the system, and also an $O(m^2 c^2)$-time algorithm. Some other variations of the JSD problems are also investigated.