Browsing by Author "Cline, Ben E."

Now showing 1 - 8 of 8
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    Development of a Modern OPAC: From REVTOLC to MARIAN
    Fox, Edward A.; France, Robert K.; Sahle, Eskinder; Daoud, Amjad M.; Cline, Ben E. (Department of Computer Science, Virginia Polytechnic Institute & State University, 1993-02-01)
    In the Retrieval Experiment -- Virginia Tech OnLine Catalog (REVTOLC) study we carried out a large pilot test in 1987 and a larger, controlled investigation in 1990, with 216 users and roughly 500,000 MARC records. Results indicated that a forms-based interface coupled with vector and relevance feedback retrieval methods would be well received. Recent efforts developing the Multiple Access and Retrieval of Information with ANnotations (MARIAN) system have involved use of a specially developed object-oriented DBMS, construction of a client running under NeXTSTEP, programming of a distributed server with a thread assigned to each user session to increase concurrency on a small network of NeXTs, refinement of algorithms to use objects and stopping rules for greater efficiency, usability testing and iterative interface refinement.
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    A DOS-M Primer
    Cline, Ben E. (Department of Computer Science, Virginia Polytechnic Institute & State University, 1974)
    No abstract available.
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    Garbage Collection Scheduling for Utility Accrual Real-Time Systems
    Feizabadi, Shahrooz Shojania (Virginia Tech, 2006-12-07)
    Utility Accrual (UA) scheduling is a method of dynamic real-time scheduling that is designed to respond to overload conditions by producing a feasible schedule that heuristically maximizes a pre-defined metric of utility. Whereas utility accrual schedulers have traditionally focused on CPU overload, this dissertation explores memory overload conditions during which the aggregate memory demand exceeds a system's available memory bandwidth. Real-time systems are typically implemented in C or other languages that use explicit dynamic memory management. Taking advantage of modern type-safe languages, such as Java, necessitates the use of garbage collection (GC). The timeliness requirements of real-time systems, however, impose specific demands on the garbage collector. Garbage collection introduces a significant source of unpredictability in the execution timeline of a task because it unexpectedly interjects pauses of arbitrary length, at arbitrary points in time, with an arbitrary frequency. To construct a feasible schedule, a real-time scheduler must have the ability to predict the collector's activities and plan for them accordingly. We have devised CADUS (Collector-Aware Dynamic Utility Scheduler), a utility accrual algorithm that tightly links CPU scheduling with the memory requirements -and the corresponding garbage collection activities - of real-time tasks. By constructing and storing memory time allocation profiles, we address the problem of GC activation strategy. We estimate GC latency by using a real-time collector and modeling its behavior. We project GC frequency by planning, at schedule construction time, the memory bandwidth available to the collector. CADUS can point the collector's activities to any specific task in the system. The runtime system provides this ability by maintaining separate logical heaps for all tasks. We demonstrate the viability of CADUS through extensive simulation studies. We evaluated the behavior of CADUS under a wide range of CPU and memory load conditions and utility distributions. We compared its performance against an existing GC-unaware UA scheduler and found that CADUS consistently outperformed its GC-unaware counterpart. We investigated and identified the reasons for the superior performance of CADUS and quantified our results. Most significantly, we found that in an overloaded dynamic soft real-time system, a scheduler's preemption decisions have a highly significant impact on GC latency. A dynamic real-time scheduler therefore must predict the impact of its preemption decisions on GC latency in order to construct time-feasible schedules.
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    Implications of Natural Categories for Natural Language Generation
    Cline, Ben E.; Nutter, J. Terry (Department of Computer Science, Virginia Polytechnic Institute & State University, 1989)
    Psychological research has shown that natural taxonomies contain a distinguished or basic level. Adult speakers use the names of these categories most frequently and can list a large number of attributes for them. They typically can list many attributes for superordinate categories and list few additional attributes for subordinate categories. Because natural taxonomies are important to human language, their use in natural language processing systems appears well founded. In the past, however, most AI systems have been implemented around uniform taxonomies in which there is no distinguished level. It has recently been demonstrated that natural taxonomies enhance natural language processing systems by allowing selection of appropriate category names and by providing the means to handle implicit focus. We propose that additional benefits from the use of natural categories can be realized in multi-sentential connected text generation systems. After discussing the psychological research on natural taxonomies that relates to natural language processing systems, the use of natural categorizations in current natural language processing systems is presented. We then describe how natural categories can be used in multiple sentence generation systems to allow the selection of appropriate category names, to provide the mechanism to help determine salience to aid in the selection of discourse schema. to provide for the shallow modeling audience expertise, and to increase the efficiency of taxonomy inheritance.
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    Knowledge intensive natural language generation with revision
    Cline, Ben E. (Virginia Tech, 1994-05-31)
    Traditional natural language generation systems use a pipelined architecture. Two problems with this architecture are poor task decomposition and the lack of interaction between conceptual and stylistic decisions making. A revision architecture operating in a knowledge intensive environment is proposed as a means to deal with these two problems. In a revision system. text is produced and refined iteratively. A text production cycle consists of two steps. First, the text generators produce initial text. Second, this text is examined for defects by revisors. When defects are found the revisors make suggestions for the regeneration of the text. The text generator/revision cycle continues to polish the text iteratively until no more defects can be found. Although previous research has focused on stylistic revisions only. this paper describes techniques for both stylistic and conceptual revisions. Using revision to produce extended natural language text through a series of drafts provides three significant advantages over a traditional natural language generation system. First, it reduces complexity through task decomposition. Second, it promotes text polishing techniques that benefit from the ability to examine generated text in the context of the underlying knowledge from which it was generated. Third, it provides a mechanism for the integrated handling of conceptual and stylistic decisions. For revision to operate intelligently and efficiently, the revision component must have access to both the surface text and the underlying knowledge from which it was generated. A knowledge intensive architecture with a uniform knowledge base allows the revision software to quickly locate referents, choices made in producing the defective text, alternatives to the decisions made at both the conceptual and stylistic levels, and the intent of the text. The revisors use this knowledge, along with facts about the topic at hand and knowledge about how text is produced. to select alternatives for improving the text. The Kalos system was implemented to illustrate revision processing in a natural language generation system. It produces advanced draft quality text for a microprocessor users' guide from a knowledge base describing the microprocessor. It uses revision techniques in a knowledge intensive environment to iteratively polish its initial generation. The system performs both conceptual and stylistic revisions. Example output from the system, showing both types of revision, is presented and discussed. Techniques for dealing with the computational problems caused by the system's uniform knowledge base are described.
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    MARIAN Design
    France, Robert K.; Cline, Ben E.; Fox, Edward A. (1995-02-14)
    MARIAN (Multiple Access Retrieval of library Information with ANotations) is an online library catalog information system. Intended for library end-users rather than catalogers, it provides controlled search by author, subject entry, and imprint; keyword search by title, subject, and other MARC text fields; feedback, locating the closest books to a relevant book or books; and user annotations of books.
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    Natural Categories for More Natural Generation
    Cline, Ben E.; Nutter, J. Terry (Department of Computer Science, Virginia Polytechnic Institute & State University, 1990)
    Psychological research has shown that natural taxonomies contain a distinguished or basic level. Adult speakers use the names of these categories most frequently and can list a large number of attributes for them. They typically cannot list many attributes for superordinate categories and few list additional attributes for subordinate categories. Because natural taxonomies are important to human language, their use in natural language processing systems appears well founded. In the past, however, most AI systems have been implemented around uniform taxonomies in which there is no distinguished level. It has recently been demonstrated that natural taxonomies enhance language processing systems by allowing selection of appropriate category names and by providing the means to handle implicit focus. In previous research, we have argued that benefits from the use of natural categories can be realized in multi-sentential connected generation systems. We briefly summarize the psychological research on natural taxonomies that relates to natural language processing systems, the use of natural categorizations in current natural language processing systems, and the results of our previous research in which we show how natural categories can be used in multiple sentence generation systems to allow the selection of appropriate category names, to provide a mechanism to help determine salience, and to provide for the shallow modeling of audience expertise. We then describe additional benefits of natural categories in generation systems by demonstrating that natural categories provide a mechanism that aids selection of discourse schemes and increase the efficiency of inheritance.
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    Using Concept Maps as a Tool for Cross-Language Relevance Determination
    Richardson, W. Ryan (Virginia Tech, 2007-06-06)
    Concept maps, introduced by Novak, aid learners' understanding. I hypothesize that concept maps also can function as a summary of large documents, e.g., electronic theses and dissertations (ETDs). I have built a system that automatically generates concept maps from English-language ETDs in the computing field. The system also will provide Spanish translations of these concept maps for native Spanish speakers. Using machine translation techniques, my approach leads to concept maps that could allow researchers to discover pertinent dissertations in languages they cannot read, helping them to decide if they want a potentially relevant dissertation translated. I am using a state-of-the-art natural language processing system, called Relex, to extract noun phrases and noun-verb-noun relations from ETDs, and then produce concept maps automatically. I also have incorporated information from the table of contents of ETDs to create novel styles of concept maps. I have conducted five user studies, to evaluate user perceptions about these different map styles. I am using several methods to translate node and link text in concept maps from English to Spanish. Nodes labeled with single words from a given technical area can be translated using wordlists, but phrases in specific technical fields can be difficult to translate. Thus I have amassed a collection of about 580 Spanish-language ETDs from Scirus and two Mexican universities and I am using this corpus to mine phrase translations that I could not find otherwise. The usefulness of the automatically-generated and translated concept maps has been assessed in an experiment at Universidad de las Americas (UDLA) in Puebla, Mexico. This experiment demonstrated that concept maps can augment abstracts (translated using a standard machine translation package) in helping Spanish speaking users find ETDs of interest.