LINA: a laboratory inventory system for oligonucleotides, microbial strains, and cell lines

In this article, we present the Laboratory Inventory Network Application (LINA), a software system that assists research laboratories in keeping track of their collections of biologically relevant materials. This open source application uses relational Microsoft Access database technology as a back end and a Microsoft .NET application as a front end. Preconstructed table templates are provided that contain standardized and customizable data fields. As new samples are added to the inventory, each is provided with a unique laboratory identifier, which is assigned automatically and sequentially, allowing rapid retrieval when a given reagent is required. The LINA contains a number of useful search tools including a general search, which allows database searches using up to four user-defined criteria. The LINA represents an easily implemented and useful organizational tool for biological laboratories with large numbers of strains, clones, or other reagents.     

Measuring computer software reliability

Under certain conditions, traditional hypothesis-testing techniques may be used as a management tool by software developers or software purchasers who wish to insure that their packages have some specified reliability level. These conditions are: (1) the existence of independent collections of test data, (2) a way of determining the correctness of processing of these collections, and (3) a way of randomly selecting test data.Two basic approaches have been described. In a fixed sample size test, the user decides on the reliability desired. He can then determine the number of test cases which must be examined and the acceptance/rejection criteria. In a sequential test, the desired reliability level is again pre-determined, but samples are tested one at a time until an accept/reject decision can be made.Experiments with a large amount of error data derived from six separate systems indicate that reliability results derived from these models are consistent with actual reliability figures.Most current acceptance procedures are based on a naive assumption that a large program can be exhaustively tested and delivered in an error-free condition. Because these expectations cannot be fulfilled, the manager of a software development project or the purchaser of a software product is provided with no quantitative information on which to base an acceptance decision and is thus forced to make these decisions based mostly on intuition and his own experience in similar situations. These models allow one to replace these intuition-based decisions with quantitatively-based decisions and thus constitute an important contribution to the science of management of software development efforts.     

Relevance feedback based on n-tuplewise comparison and the ELECTRE methodology and an application in content-based image retrieval

In this article we propose a method for information retrieval based on relational Multi-Criteria Decision Making. We assume that a user cannot define precise search criteria so that these criteria must be found based on the user’s assessment of several sample alternatives (‘alternatives’ here are database records, e.g. images). This situation is common in Content-based Image Retrieval, where it is easier for a user to indicate relevant images than to describe a proper query, especially in formal language. The proposed algorithm for the elicitation of criteria is based on ELECTRE III—a method originally designed for ranking a set of alternatives according to defined criteria. In our algorithm, however, the direction of reasoning is reversed: we start with several sample alternatives that have been assigned a rank by the user and then we select criteria that are compatible (in the sense of ELECTRE methodology) with the user’s preferences expressed on a sample set. Then, having determined the user’s criteria, we apply classical ELECTRE III to retrieve the relevant solutions from the database. We implemented the method in Matlab and tested it on the Microsoft Cambridge Image Database.     

The spectral database SPECCHIO for improved long-term usability and data sharing

The organised storage of spectral data described by metadata is important for long-term use and data sharing with other scientists. Metadata describing the sampling environment, geometry and measurement process serves to evaluate the suitability of existing data sets for new applications. There is a need for spectral databases that serve as repositories for spectral field campaign and reference signatures, including appropriate metadata parameters. Such systems must be (a) highly automated in order to encourage users entering their spectral data collections and (b) provide flexible data retrieval mechanisms based on subspace projections in metadata spaces.The recently redesigned SPECCHIO system stores spectral and metadata in a relational database based on a non-redundant data model and offers efficient data import, automated metadata generation, editing and retrieval via a Java application.RSL is disseminating the database and software to the remote sensing community in order to foster the use and further development of spectral databases.     

Intraclass retrieval of nonrigid 3D objects: application to face recognition

As the size of the available collections of 3D objects grows, database transactions become essential for their management with the key operation being retrieval (query). Large collections are also precategorized into classes so that a single class contains objects of the same type (e.g., human faces, cars, four-legged animals). It is shown that general object retrieval methods are inadequate for intraclass retrieval tasks. We advocate that such intraclass problems require a specialized method that can exploit the basic class characteristics in order to achieve higher accuracy. A novel 3D object retrieval method is presented which uses a parameterized annotated model of the shape of the class objects, incorporating its main characteristics. The annotated subdivision-based model is fitted onto objects of the class using a deformable model framework, converted to a geometry image and transformed into the wavelet domain. Object retrieval takes place in the wavelet domain. The method does not require user interaction, achieves high accuracy, is efficient for use with large databases, and is suitable for nonrigid object classes. We apply our method to the face recognition domain, one of the most challenging intraclass retrieval tasks. We used the Face Recognition Grand Challenge v2 database, yielding an average verification rate of 95.2 percent at 10-3 false accept rate. The latest results of our work can be found at http://www.cbl.uh.edu/UR8D/.     

An efficient data mining approach for discovering interesting knowledge from customer transactions

Mining association rules and mining sequential patterns both are to discover customer purchasing behaviors from a transaction database, such that the quality of business decision can be improved. However, the size of the transaction database can be very large. It is very time consuming to find all the association rules and sequential patterns from a large database, and users may be only interested in some information.

Moreover, the criteria of the discovered association rules and sequential patterns for the user requirements may not be the same. Many uninteresting information for the user requirements can be generated when traditional mining methods are applied. Hence, a data mining language needs to be provided such that users can query only interesting knowledge to them from a large database of customer transactions. In this paper, a data mining language is presented. From the data mining language, users can specify the interested items and the criteria of the association rules or sequential patterns to be discovered. Also, the efficient data mining techniques are proposed to extract the association rules and the sequential patterns according to the user requirements.     

A command structured approach to structural object plotting and automated line dimensioning

The command structured DRAW1 system is a comprehensive plot program which allows the user to generate, position, display and dimension a complex object. Views or frames of the object in orthographic or perspective projections are automatically generated and displayed based upon commands denned by the engineer. The dimensioning of lines or points within a viewing frame is possible by using the standard criteria provided by the program, a user modification of that standard, or an original criteria selection defined by the user.Frame generation and base manipulation are effectively managed by a command interpreter and a flexible series of command forms. The command interpreter reads and interprets all information supplied to the system from a main line program. Real, integer, text and command form data are directed to a form interpreter which verifies the proper form and related element sequence. Free format command forms are provided to facilitate the manipulation of graphic data.Command and associated data are used to define, position and list all active coordinate and line information about a given object. Orthographic or perspective projections of an object are easily generated using pitch, yaw and roll commands to incrementally rotate the object into desired positions or orientations for frame plots.The automated dimensioning of a structured object may be accomplished by using the provided program standard criteria, which allows the user to select different types of arrow heads, dimensioning lines, architectural or engineering formats, stacking heights and dimensioning units. Any part of the standard criteria may be modified to better suit the user's needs; or, he may select his own special dimensioning criteria for his particular application. The user may define a standard dimensioning form to be used in dimensioning any line or line group.     

Interfacing similarity search software with the sequence retrieval system ACNUC

A method of interfacing sequence similarity search software with the fast sequence retrieval system ACNUC is described. The method is written in FORTRAN 77 and is straightforward to implement because no text-processing code is required--a minimum of 12 extra lines of FORTRAN provided the interface for most applications. The method is also efficient, since sequences are located by simple indexing techniques, with no linear searches of large database files necessary.     

Emission inventory database management system using microcomputers

An emissions inventory database management system called EMIDBMS has been developed for storage, retrieval, and processing of air pollution emission data for both urban and industrial sources. It is a generalized user-friendly database system developed on a microcomputer using the query language of dBASE III Plus. Moreover, it is a relational interactive database which has been prepared and installed on an IBM PC/AT. The menu-driven option of the software allows the user easy access to the information for retrieval by simply executing the command files in a question/answer session. This could also provide air quality enforcement authorities a system for on-site use.EMIDBMS is comprised of a number of command and database files. The database files are designed to store data on all aspects of emission inventories, while the command files are designed for storage, processing, and retrieval of the various types of information stored in the database files. The emissions inventory data retrieved by EMIDBMS can either be used as input for an air pollution dispersion model installed on a microcomputer or can be transferred to an IBM mainframe for further use.     

Rational behavior in peer-to-peer profile obfuscation for anonymous keyword search

User profiling in web search has the advantage of enabling personalized web search: the quality of the results offered by the search engine to the user is increased by taking the user’s interests into account when presenting those results. The negative side is that the interests and the query history of users may contain information considered as private; hence, technology should be provided for users to avoid profiling if they wish so. There are several anti-profiling approaches in web search, from basic level countermeasures to private information retrieval and including profile obfuscation. Except private information retrieval (PIR), which hides the retrieved item from the database, the rest of approaches focus on anonymizing the user’s identity and fall into the category of anonymous keyword search (also named sometimes user-private information retrieval). Most current PIR protocols are ill-suited to provide PIR from a search engine or large database, due to their complexity and their assumption that the database actively cooperates in the PIR protocol. Peer-to-peer profile obfuscation protocols appear as a competitive option provided that peers are rationally interested in helping each other. We present a game-theoretic analysis of P2P profile obfuscation protocols which shows under which conditions helping each other is in the peers’ rational interest.     

Fuzzy querying of incomplete, imprecise, and heterogeneously structured data in the relational model using ontologies and rules

In this paper, we present a new method, called multiview fuzzy querying, which permits to query incomplete, imprecise and heterogeneously structured data stored in a relational database. This method has been implemented in the MIEL software. MIEL is used to query the Sym'Previus database which gathers information about the behavior of pathogenic germs in food products. In this database, data are incomplete because information about all possible food products and all possible germs is not available; data are heterogeneous because they come from various sources (scientific bibliography, industrial data, etc); data may be imprecise because of the complexity of the underlying biological processes that are involved. To deal with heterogeneity, MIEL queries the database through several views simultaneously. To query incomplete data, MIEL proposes to use a fuzzy set, expressing the query preferences of the user. Fuzzy sets may be defined on a hierarchized domain of values, called an ontology (values of the domain are connected using the a kind of semantic link). MIEL also proposes two optional functionalities to help the user query the database: i) MIEL can use the ontology to enlarge the querying in order to retrieve the nearest data corresponding to the selection criteria; and ii) MIEL proposes fuzzy completion rules to help the user formulate his/her query. To query imprecise data stored in the database with fuzzy selection criteria, MIEL uses fuzzy pattern matching.     

An image retrieval system with automatic query modification

Most interactive "query-by-example" based image retrieval systems utilize relevance feedback from the user for bridging the gap between the user's implied concept and the low-level image representation in the database. However, traditional relevance feedback usage in the context of content-based image retrieval (CBIR) may not be very efficient due to a significant overhead in database search and image download time in client-server environments. In this paper, we propose a CBIR system that efficiently addresses the inherent subjectivity in user perception during a retrieval session by employing a novel idea of intra-query modification and learning. The proposed system generates an object-level view of the query image using a new color segmentation technique. Color, shape and spatial features of individual segments are used for image representation and retrieval. The proposed system automatically generates a set of modifications by manipulating the features of the query segment(s). An initial estimate of user perception is learned from the user feedback provided on the set of modified images. This largely improves the precision in the first database search itself and alleviates the overheads of database search and image download. Precision-to-recall ratio is improved in further iterations through a new relevance feedback technique that utilizes both positive as well as negative examples. Extensive experiments have been conducted to demonstrate the feasibility and advantages of the proposed system.     

Query-by-Gaming: Interactive spatio-temporal querying and retrieval using gaming controller

Spatio-temporal querying and retrieval is a challenging task due to the lack of simple user interfaces for building queries despite the availability of powerful indexing structures and querying languages. In this paper, we propose Query-by-Gaming scheme for spatio-temporal querying that can benefit from gaming controller for building queries. By using Query-by-Gaming, we introduce our spatio-temporal querying and retrieval system named as GStar to interactively build subsequent spatio-temporal queries to determine if a state is directly reachable from current state and eventual spatio-temporal queries to know whether a spatial state is reachable from a current state. Queries are built using features of gaming controller by displaying the original video frames rather than on a graphical interface using a mouse or a keyboard. GStar has three main components: building the query, searching and retrieval of clips, and displaying query results. The queries are applied to an indexing structure called semantic sequence state graph (S³G) and results of the query are displayed dynamically to provide timely feedback to the user. Experimental results and user interface are provided for a tennis video database. Users define desired game state (player and ball position) using an interactive interface at multiple points in time and GStar automatically retrieves all rallies that contain both states. Finally, the user interface evaluation comparing gamepad-based interface and mouse interface for spatio-temporal querying has been studied.     

Video-graphic query facility for database retrieval

The goal of this project was to develop a prototype to demonstrate the use of video and graphic techniques applied to the human-machine interface for data retrieval from a typical computerized database. Data is presented to a user via video and graphic means; queries are formulated in one of several graphic formats; control operations are handled through joystick, touch panel, or single-keystroke maneuvers. To accomplish this, we made use of videodisc, interactive computer graphics, and relational database technologies. Still pictures, video segments, and pictures of text are used as visual cues to a user who indicates interest in a data item in a pointing gesture by touching the panel through which the item may be seen. The user may find the actual data item s/he desires, then pose a query for additional information in one of four graphic query formats. A specially designed database was developed to handle the video and graphic data needed for this user facility.     

Multimedia information retrieval based on pairwise comparison and its application to visual search

Finding a way to elicit user preferences in the context of multimedia information retrieval is an important issue that remains to be solved. Users are not usually able to find a sought after image or provide an example of what they want. One of several possible methods that might be used to solve this problem involves reasoning about user queries through the assessment of several samples. In this article we propose a method by which user queries are retrieved based on the pairwise comparison of sample alternatives. Pairwise comparison was originally designed for the ranking of alternatives. In our method we rank criteria according to their importance for the user and then use this information to retrieve relevant records from the database. The method was implemented in Matlab and tested on the Microsoft Research Cambridge Image Database.     

Retrieval and Visualization of Human Motion Data via Stick Figures

We propose 2D stick figures as a unified medium for visualizing and searching for human motion data. The stick figures can express a wide range or human motion, and they are easy to be drawn by people without any professional training. In our interface, the user can browse overall motion by viewing the stick figure images generated from the database and retrieve them directly by using sketched stick figures as an input query. We started with a preliminary survey to observe how people draw stick figures. Based on the rules observed from the user study, we developed an algorithm converting motion data to a sequence of stick figures. The feature‐based comparison method between the stick figures provides an interactive and progressive search for the users. They assist the user's sketching by showing the current retrieval result at each stroke. We demonstrate the utility of the system with a user study, in which the participants retrieved example motion segments from the database with 102 motion files by using our interface.     

An adaptive learning automata-based ranking function discovery algorithm

Due to the massive amount of heterogeneous information on the web, insufficient and vague user queries, and use of the same query by different users for different aims, the information retrieval process deals with a huge amount of uncertainty and doubt. Under such circumstances, designing an efficient retrieval function and ranking algorithm by which the most relevant results are provided is of the greatest importance. In this paper, a learning automata-based ranking function discovery algorithm in which different sources of information are combined is proposed. In this method, the learning automaton is used to adjust the portion of the final ranking that is assigned to each source of evidence based on the user feedback. All sources of information are first given the same importance. The proportion of a given source increases, if the documents provided by this source are reviewed by the user and decreases otherwise. As the proposed algorithm proceeds, the probability of appearance of each source in the final ranking gets proportional to its relevance to the user queries. Several simulation experiments are conducted on well-known data collections and query types to show the performance of the proposed algorithm. The obtained results demonstrate that the proposed algorithm outperforms several existing methods in terms of precision at position n, mean average precision, and normalized discount cumulative gain.     

Large-scale information retrieval with latent semantic indexing

As the amount of electronic information increases, traditional lexical (or Boolean) information retrieval techniques will become less useful. Large, heterogeneous collections will be difficult to search since the sheer volume of unranked documents returned in response to a query will overwhelm the user. Vector-space approaches to information retrieval, on the other hand, allow the user to search for concepts rather than specific words, and rank the results of the search according to their relative similarity to the query. One vector-space approach, Latent Semantic Indexing (LSI), has achieved up to 30% better retrieval performance than lexical searching techniques by employing a reduced-rank model of the term-document space. However, the original implementation of LSI lacked the execution efficiency required to make LSI useful for large data sets. A new implementation of LSI, LSI++, seeks to make LSI efficient, extensible, portable, and maintainable. The LSI++ Application Programming Interface (API) allows applications to immediately use LSI without knowing the implementation details of the underlying system. LSI++ supports both serial and distributed searching of large data sets, providing the same programming interface regardless of the implementation actually executing. In addition, a World Wide Web interface was created to allow simple, intuitive searching of document collections using LSI++. Timing results indicate that the serial implementation of LSI++ searches up to six times faster than the original implementation of LSI, while the parallel implementation searches nearly 180 times faster on large document collections.     

PRONUC: a software package for the analysis of protein and nucleic acid sequences

PRONUC is a menu-driven software package from which a molecular biologist may gain access to a variety of tools for the analysis of protein and nucleic acid sequences. Features include various algorithms for sequence comparisons, secondary structure prediction, sequence manipulation (translation complementation etc.) and finding restriction enzyme cut-sites. The sequences under study can be retrieved from several databases of published sequences or a users sequence(s) can be entered by means of a sequence editor or retrieved from a database constructed by the user. PRONUC comes with a comprehensive manual and on-line help which reflects several years of user feedback and is available for Digital VAX computer systems running the VMS or micro-VMS operating system.