42742272
Definition and Analysis of Election Processes
This paper shows that process definition and analysis technologies can be used to reason about the vulnerability of election processes with respect to incorrect or fraudulent behaviors by election officials. The Little-JIL language is used to model example election processes, and various election worker fraudulent behaviors. The FLAVERS finite-state verification system is then used to determine whether different combinations of election worker behaviors cause the process to produce incorrect election results or whether protective actions can be used to thwart these threats.
694380
Automatic Fault Tree Derivation from Little-JIL Process Definitions
Defects in safety critical processes can lead to accidents that result in harm to people or damage to property. Therefore, it is important to find ways to detect and remove defects from such processes. Earlier work has shown that Fault Tree Analysis (FTA) [3] can be effective in detecting safety critical process defects. Unfortunately, it is difficult to build a comprehensive set of Fault Trees for a complex process, especially if this process is not completely well-defined. The Little-JIL process definition language has been shown to be effective for defining complex processes clearly and precisely at whatever level of granularity is desired [1]. In this work, we present an algorithm for generating Fault Trees from Little-JIL process definitions. We demonstrate the value of this work by showing how FTA can identify safety defects in the process from which the Fault Trees were automatically derived.
2315502
SMARTPAPER: An Interactive and User Friendly Sketching System
This paper describes an interactive sketching system for 3D design/modeling that diverts from the conventional menu-and-button interfaces of CAD tools. The system, dubbed SMARTPAPER, offers a unified sketching environment that supports direct sketching as well as gestured sketching with more emphasis on the former to encourage natural sketching styles. SMARTPAPER also provides a unified 2D and 3D drawing domain by allowing the user to sketch directly on a 3D model in addition to the usual 2D sketching from scratch. A natural sketching experience is offered by supporting casual sketching consisting of wiggly, discontinuous, overlapping strokes. The system is empowered by an array of seamlessly integrated 2D and 3D features such as 2D sketch cleaning, 3D reconstruction from 2D sketch, 3D transformations, sketching on 3D, and conventional 3D CSG operations like cutting and joining. The key to the success of SMARTPAPER is efficient and robust 3D reconstruction from a single freehand 2D sketch with minimal hints. We have employed and improved Lipson’s optimization method, originally designed for offline reconstruction of engineering drawings, in our interactive system by leveraging additional clues obtained by interaction during sketching.
38323853
Content-based Language Models for Spoken Document Retrieval
Spoken document retrieval (SDR) has been extensively studied in recent years because of its potential use in navigating large multimedia collections in the near future. This paper presents a novel concept of applying content-based language models to spoken document retrieval. In an example task for retrieval of Mandarin Chinese broadcast news data, the content-based language models, either trained on automatic transcriptions of spoken documents or adapted from baseline language models using automatic transcriptions of spoken documents, were used to create more accurate recognition results and indexing terms from both spoken documents and speech queries. We report on some interesting findings obtained in this research.
2373278
A discriminative HMM/N-gram-based retrieval approach for mandarin spoken documents
In recent years, statistical modeling approaches have steadily gained in popularity in the field of information retrieval. This article presents an HMM/N-gram-based retrieval approach for Mandarin spoken documents. The underlying characteristics and the various structures of this approach were extensively investigated and analyzed. The retrieval capabilities were verified by tests with word- and syllable-level indexing features and comparisons to the conventional vector-space model approach. To further improve the discrimination capabilities of the HMMs, both the expectation-maximization (EM) and minimum classification error (MCE) training algorithms were introduced in training. Fusion of information via indexing word- and syllable-level features was also investigated. The spoken document retrieval experiments were performed on the Topic Detection and Tracking Corpora (TDT-2 and TDT-3). Very encouraging retrieval performance was obtained.
7426756
Content-based language models for spoken document retrieval
Spoken document retrieval (SDR) has been extensively studied in recent years because of its potential use in navigating large multimedia collections in the near future. This paper presents a novel concept of applying the content-based language models to spoken document retrieval. In an example task for retrieval of Mandarin broadcast news, the content-based language models either trained with the automatic transcriptions of the spoken documents or adapted from the baseline language models using the automatic transcriptions of the spoken documents were used to create the more accurate recognition results and indexing terms from both the spoken documents and the speech queries. We report on some interesting findings obtained in this research.
4028543
On-Line Scheduling of Two-Machine Open Shops Where Jobs Arrive Over Time
We investigate the problem of on-line scheduling two-machine open shops with the objective of minimizing the makespan.Jobs arrive independently over time, and the existence of a job is not known until its arrival. In the clairvoyant on-line model, the processing requirement of every job becomes fully known at the arrival of the job, while inthe non-clairvoyant on-line model, this processing requirement is notknown until the job is processed and completed.In both models, scheduling of a job is irrevocable.We study the two-machine open shop problem for both models in the preemptive and in the non-preemptive version. For each of the four variants, we provide an algorithm that is best possible with respect to the worst-case performance. In the clairvoyant on-line model, the best worst-case performance ratios are 5/4 (preemptive) and 3/2 (non-preemptive), and in the non-clairvoyant on-line model, they are 3/2 (preemptive and non-preemptive).