Situating natural language understanding within experience-based design

Building useful systems with an ability to understand "real" natural language input has long been an elusive goal for Artificial Intelligence. Well-known problems such as ambiguity, indirectness, and incompleteness of natural language inputs have thwarted efforts to build natural language interfaces to intelligent systems. In this article, we report on our work on a model of understanding natural language design specifications of physical devices such as simple electrical circuits. Our system, called KA, solves the classical problems of ambiguity, incompleteness and indirectness by exploiting the knowledge and problem-solving processes in the situation of designing simple physical devices. In addition, KA acquires its knowledge structures (apart from a basic ontology of devices) from the results of its problem-solving processes. Thus, KA can be bootstrapped to understand design specifications and user feedback about new devices using the knowledge structures it acquired from similar devices designed previously.In this paper, we report on three investigations in the KA project. Our first investigation demonstrates that KA can resolve ambiguities in design specifications as well as infer unarticulated requirements using the ontology, the knowledge structures, and the problem-solving processes provided by its design situation. The second investigation shows that KA's problem-solving capabilities help ascertain the relevance of indirect design specifications, and identify unspecified relations between detailed requirements. The third investigation demonstrates the extensibility of KA's theory of natural language understanding by showing that KA can interpret user feedback as well as design requirements. Our results demonstrate that situating language understanding in problem solving, such as device design in KA, provides effective solutions to unresolved problems in natural language processing.     

Reliability models for computer systems: An overview including dataflow graphs

The reliability of a system is the probability that the system will perform its intended mission under given conditions. This paper provides an overview of the approaches to reliability modelling and identifies their strengths and weaknesses. The models discussed include structure models, simple stochastic models and decomposable stochastic models. Ignoring time-dependence, structure models give reliability as a function of the topological structure of the system. Simple stochastic models make direct use of the properties of underlying stochastic processes, while decomposable models consider more complex systems and analyse them through subsystems. Petri nets and dataflow graphs facilitate the analysis of complex systems by providing a convenient framework for reliability analysis.     

Modeling Multithreaded Applications Using Petri Nets

Since most modern computing systems contain multiple processing elements, applications are relying on multithreaded programming techniques that allow a program to execute multiple tasks concurrently to take advantage of the processing capabilities. Multithreaded programs are more difficult to design and test because of the nondeterministic execution orders and synchronization among the threads. Different approaches can be used to test Multithreaded Applications. In our approach we use Petri nets to represent the key elements of interactions among threads to identify potential problems such as race conditions, lost signals, and deadlocks. A tool called C2Petri has been developed which converts C-Pthreads programs to the equivalent Petri net model. This tool helps verification of Pthread-based programs. At present the tool has limited capabilities and we hope to expand the capabilities of our tool in the near future.     

The pressure is on [computer systems research]

That computing and communication systems are becoming increasingly interdependent is evident in almost every aspect of society. Applications of these integrated systems are also spreading. As this trend continues, it will force the computing community not only to develop revolutionary systems but also to redefine “computer system” and the roles of traditional research disciplines, such as operating systems, architectures, compilers, languages, and networking. Systems research faces an unprecedented challenge. Systems developers are facing a major discontinuity in the scale and nature of both applications and execution environments. Applications are changing from transforming data to directly interacting with humans; they will use hardware and data that span wide area, even global, networks of resources and involve interactions among users as well. Even the architecture of individual processors is uncertain. The authors look at three challenges facing systems research, describe developing solutions, and review remaining obstacles. Using this information, they formulate three clear first steps to addressing the identified challenges: (a) define a new paradigm for systems research; (b) attack problems common to all system development; (c) build a research infrastructure     

Real-time air monitoring of occupational exposures to particulate matter among hairdressers in Maryland: A pilot study

Hairdressers are exposed to particulate matter (PM), a known air pollutant linked to adverse health effects. Still, studies on occupational PM exposures in hair salons are sparse. We characterized indoor air PM concentrations in three salons primarily serving an African/African American (AA) clientele, and three Dominican salons primarily serving a Latino clientele. We also assessed the performance of low-cost sensors (uRAD, Flow, AirVisual) by comparing them to high-end sensors (DustTrak) to conduct air monitoring in each salon over 3 days to quantify work shift concentrations of PM_2.5, respirable PM (RPM), and PM₁₀. We observed high spatial and temporal variability in 30-min time-weighted average (TWA) RPM concentrations (0.18–5518 μg/m³). Readings for the uRAD and AirVisual sensors were highly correlated with the DustTrak (R² = 0.90–0.99). RPM 8-hour TWAs ranged from 18 to 383 µg/m³ for AA salons, and 9–2115 µg/m³ for Dominican salons. Upper 95th percentiles of daily RPM exposures ranged from 439 to 2669 µg/m³. The overall range of 30-min TWA PM_2.5 and PM₁₀ concentrations was 0.13–5497 and 0.36-,541 μg/m³, respectively. Findings suggest that hairdressers could be overexposed to RPM during an 8-hour shift. Additional comprehensive monitoring studies are warranted to further characterize temporal and spatial variability of PM exposures in this understudied occupational population.     

Reliability Analysis of Computer Systems Using Dataflow Graph Models

We use dataflow graphs to represent the computational structure, analogous to Petri nets and Turing machines, and have developed a method for analyzing the reliability of computer systems modeled as dataflow graphs. Because of the hierarchical nature of dataflow graphs, systems can be analyzed at several levels of abstraction. Reliabilities of subgraphs can be calculated using either traditional techniques or dataflow approach presented here (recursively). The reliabilities of subgraphs can then be combined leading to decomposition-aggregation approach. The time needed for an actor to complete its operation is not included in our analysis of dataflow graphs. Incorporation of the time element compounds the problem and we have not studied it yet.     

Subspace tracking with adaptive threshold rank estimation

In frequency and direction of arrival (DOA) tracking problems, singular value decomposition (SVD) can be used to track the signal subspace. Typically, for a problem sizen, only a few, sayr dominant eigencomponents need to be tracked, wherern. In this paper we show how to modify the Jacobi-type SVD to track only ther-dimensional signal subspace by forcing the (n-r)-dimensional noise subspace to be spherical. Therby, the computational complexity is brought down fromO(n²) toO(nr) per update. In addition to tracking the subspace itself, we demonstrate how to exploit the structure of the Jacobi-type SVD to estimate the signal subspace dimension via a simple adptive threshold comparison technique. Most available computationally efficient subspace tracking algorithms rely on off-line estimation of the signal subspace dimension, which acts as a bottleneck in real-time parallel implementations. The noise averaged Jacobi-type SVD updating algorithm presented in this paper is capable of simultaneously tracking the signal subspace and its dimension, while preserving both the low computational cost ofO(nr) and the parallel structure of the method, as demonstrated in a systolic implementation. Furthermore, the algorithm tracks all signal singular values. Their squares are estimates of the powers in the orthogonal modes of the signal. Thus, applications of the algorithm are not limited to only DOA and frequency tracking where information about the powers of signal components is not exploited.     

Changing structure of income indoor air pollution relationship in India

Bio fuels are still a major source for cooking by many households in developing countries such as India causing significant disease burden due to indoor air pollution. While household income influences the choice of fuel the policies that affect accessibility and price of fuels also have an important role in determining the fuel choice. This study analyzes the pollution–income relationship for the period 1983–2000, separately across rural and urban households in India based on unit record data on fuel consumption obtained through National Sample Surveys. While a non-monotonic relationship is observed in rural India in both the decades, in urban India a similar relationship is observed only for the initial period indicating faster transition towards ‘cleaner’ fuels mainly enabled by policies that have been pro-urban. The study also finds that the impact of household size and composition on bio fuels is more negative than for clean fuels and is increasingly negative over time possibly due to greater awareness about the ill effects of such fuels.