2044368
AGATHA: An Integrated Expert System to Test and Diagnose Complex Personal Computer Boards
PA-RISC is Hewlett-Packard's (HP) reduced instruction set computer (RISC) architecture that is used in its high-performance computer systems (Mahon et al. 1986). Implementations of this architecture have produced some of the most complex processor boards that HP makes (Robinson et al. 1987; Gassman et al. 1987): They can contain as many as 8 very large scale integrated (VLSI) chips—most of them custom, from central processing units to bus controllers to floating-point pro-cessors—several high-speed random-access memory arrays, one or more high-speed buses with over 100 lines, and many other components. In large part because of this complexity, the testing of PA-RISC processor boards became a bottleneck, resulting in an undesirable backlog of undiagnosed boards, growing at a rate of 10 percent each month.
6190398
Time Representation in Prolog Circuit Modelling
This paper is an examination of some issues in domain ontology and of various knowledge representation techniques for the temporal modelling of digital electronic circuits in Prolog. It should serve as an example of the advantages of such analysis, an area of relative neglect within the logic programming community.
6504247
Hardware Diagnosis as Program Debugging
Horn clauses provide a useful framework for writing executable structural representations for digital circuits. This paper discusses how these representations can be used to diagnose faulty circuits using algorithmic program debugging techniques developed by Shapiro. The sound theoretical basis of these techniques is one of the major advantages of this approach. This framework also provides a new perspective on some of the hardware diagnosis techniques suggested in the literature.
32641793
Document Summarization using Wikipedia
Although most of the developing world is likely to first access the Internet through mobile phones, mobile devices are constrained by screen space, bandwidth and limited attention span. Single document summarization techniques have the potential to simplify information consumption on mobile phones by presenting only the most relevant information contained in the document. In this paper we present a language independent single-document summarization method. We map document sentences to semantic concepts in Wikipedia and select sentences for the summary based on the frequency of the mapped-to concepts. Our evaluation on English documents using the ROUGE package indicates our summarization method is competitive with the state of the art in single document summarization.
37839097
Retaining personal expression for social search
Web is being extensively used for personal expression, which includes ratings, reviews, recommendations, blogs. This user created content, e.g. book review on Amazon.com, becomes the property of the website, and the user often does not have easy access to it. In some cases, user's feedback may get averaged with feedback from other users e.g. ratings of a video. We argue that the creator of such content needs to be able to retain (a link to) her created content. We introduce the concept of MEB which is a user controlled store of such retained links. A MEB allows a user to access/share all the reviews she has given on different websites. With this capability users can allow their friends to search through their feedback. Searching through one's social network allows harnessing the power of social networks where known relationships provide the context & trust necessary to interpret feedback.
496243
Mini Track: 'Ethical, Legal and Economic Issues in the Digital Economy: Intellectual Property Rights, Piracy, Trust, Security and Privacy'
The emergence of the Internet as the de facto medium for business has accentuated the role of factors such as intellectual property rights (IPR), consumer privacy, and trust. Newer technologies have it made it a challenging task to protect content for sellers and private information for consumers. This is the second time for this mini track at HICSS and it includes technical, analytical, empirical, and conceptual research spanning diverse research areas and methodologies including the social, behavioral, and economic issues. In his paper “Does drop in copying cost support copyright term extension?” Michael Y. Yuan examines copyright duration and analyzes the validity of the extending copyright duration. Through a simulation of an information product market, this model recommends that decrease in copying cost of information products reduces the optimal duration of copyright. Further, while longer life expectancy may support copyright term extension, this work finds that increase in demand for information products supports reduction in copyright duration.
17746198
Large—scale parallel numerical integration
We present and analyze strategies which can be used for the parallel computation of large numbers of integrals which may be of different levels of difficulty. Parallelization on the integral level, which is generally used for large numbers of integrals, is combined with parallelization on the subregion level, which enables handling local integration difficulties within individual problems. This results in a new, hierarchical algorithm which incorporates load balancing on the integral level and on the subregion level. We report test results of the software and show that the hierarchical approach leads to a scalable integration algorithm.
887786
Efficient Design of Boltzmann Machines
43889456
Integrating User Preferences and Real-Time Workload in Information Services
We propose priority pricing as an on-line adaptive resource scheduling mechanism to manage real-time databases within organizations. These databases provide timely information for delay sensitive users. The proposed approach allows diverse users to optimize their own objectives while collectively maximizing organizational benefits. We rely on economic principles to derive priority prices by modeling the fixed-capacity real-time database environment as an economic system. Each priority is associated with a price and a delay, and the price is the premium (congestion toll resulting from negative externalities) for accessing the database. At optimality, the prices are equal to the aggregate delay cost imposed on all other users of the database. These priority prices are used to control admission and to schedule user jobs in the database system. The database monitors the arrival processes and the state of the system, and incrementally adjusts the prices to regulate the flow. Because our model ignores the operational intricacies of the real-time databases (e.g., intermediate queues at the CPU and disks, memory size, etc.) to maintain analytical tractability, we evaluate the performance of our pricing approach through simulation. We evaluate the database performance using both the traditional real-time database performance metrics (e.g., the number of jobs serviced on time, average tardiness) and the economic benefits (e.g., benefits to the organization). The simulation results, under various database workload parameters, show that our priority pricing mechanism not only maximizes organizational benefits but also outperforms in all aspects of traditional performance measures compared to frequently used database scheduling techniques, such as first-come-first-served, earliest deadline first and least slack first.
2504115
Database-guided segmentation of anatomical structures with complex appearance
The segmentation of anatomical structures has been traditionally formulated as a perceptual grouping task, and solved through clustering and variational approaches. However, such strategies require the a priori knowledge to be explicitly defined in the optimization criterion, e.g., "high-gradient border", "smoothness"', or "similar intensity or texture". This approach is limited by the validity of underlying assumptions and cannot capture complex structure appearance. This paper introduces database-guided segmentation as a new data-driven paradigm that directly exploits expert annotation of interest structures in large medical databases. Segmentation is formulated as a two-step learning problem. The first step is structure detection where we learn how to discriminate between the object of interest and background. The resulting classifier based on a boosted cascade of simple features also provides a global rigid transformation of the structure. The second step is shape inference where we use a sample-based representation of the joint distribution of appearance and shape annotations. To learn the association between the complex appearance and shape we propose a feature selection mechanism and the corresponding metric. We show that the selected features are better than using directly the appearance and illustrate the performance of the proposed method on a large set of ultrasound heart images.