15709806
Biometrics and the threat to civil liberties
Biometrics refers to the automatic identification or verification of living persons using their enduring physical or behavioral characteristics. Biometric systems fall into two categories: authentication and identification, with authentication systems being far more common. We discuss about the Biometric system and its use in authentication and identification. We also discuss about the face recognition system and the threat caused on the civil liberties due to illegal modification of the databases in the biometric system.
40293864
The US Software Industry
The US software industry is a combination of survivors, successful start-ups, and companies barely getting by. What is fueling the industry's growth? First, the potential of the Internet and networked and distributed computing is immense-we have only begun to tap into the possibilities. Many of our most innovative new companies are poised to take advantage of these new technologies. Second, the business and operational components of the nation's corporations are recognizing the fundamental role that PT now plays in their success. IT organizations are not the service and support centers of the past; they are an equal partner in the overall enterprise. Finally, Americans love new technologies. The software industry provides a never-ending source of innovative toys and gadgets for home and business.
678679
Sympathetic interfaces: using a plush toy to direct synthetic characters
We introduce the concept of a sympathetic inter$ace forcontrolling an animated synthetic character in a 3D virtualenvironment. A plush doll embedded with wireless sensors is used tomanipulate the virtual character in an iconic and intentionalmanner. The interface extends from the novel physical input devicethrough interpretation of sensor data to the behavioral brain ofthe virtual character. We discuss the design of the interface andfocus on its latest instantiation in the Swamped! exhibit atSIGGRAPH 98. We also present what we learned from hundreds ofcasual users, who ranged from young children to adults.
13391708
Early Deletion of Fillers In Processing Conversational Speech
This paper evaluates the benefit of deleting fillers (e.g. you know, like) early in parsing conversational speech. Readability studies have shown that disfluencies (fillers and speech repairs) may be deleted from transcripts without compromising meaning (Jones et al., 2003), and deleting repairs prior to parsing has been shown to improve its accuracy (Charniak and Johnson, 2001). We explore whether this strategy of early deletion is also beneficial with regard to fillers. Reported experiments measure the effect of early deletion under in-domain and out-of-domain parser training conditions using a state-of-the-art parser (Charniak, 2000). While early deletion is found to yield only modest benefit for in-domain parsing, significant improvement is achieved for out-of-domain adaptation. This suggests a potentially broader role for disfluency modeling in adapting text-based tools for processing conversational speech.
104744
Sentence-Internal Prosody Does not Help Parsing the Way Punctuation Does
This paper investigates the usefulness of sentence-internal prosodic cues in syntactic parsing of transcribed speech. Intuitively, prosodic cues would seem to provide much the same information in speech as punctuation does in text, so we tried to incorporate them into our parser in much the same way as punctuation is. We compared the accuracy of a statistical parser on the LDC Switchboard treebank corpus of transcribed sentence-segmented speech using various combinations of punctuation and sentence-internal prosodic information (duration, pausing, and f0 cues). With no prosodic or punctuation information the parser’s accuracy (as measured by F-score) is 86.9%, and adding punctuation increases its F-score to 88.2%. However, all of the ways we have tried of adding prosodic information decrease the parser’s F-score to between 84.8% to 86.8%, depending on exactly which prosodic information is added. This suggests that for sentence-internal prosodic information to improve speech transcript parsing, either different prosodic cues will have to used or they will have be exploited in the parser in a way different to that used currently.
930298
On Compressing Encrypted Data without the Encryption Key
When it is desired to transmit redundant data over an insecure and bandwidth-constrained channel, it is customary to first compress the redundant data and then encrypt it for security reasons. In this paper, we investigate the novelty of reversing the order of these steps, i.e. first encrypting and then compressing. Although counter-intuitive, we show surprisingly that through the use of coding with side information principles, this reversal in order is indeed possible. In fact, for lossless compression, we show that the theoretical compression gain is unchanged by performing encryption before compression. We show that the cryptographic security of the reversed system is directly related to the strength of the key generator.
12779913
Recognizing disfluencies in conversational speech
We present a system for modeling disfluency in conversational speech: repairs, fillers, and self-interruption points (IPs). For each sentence, candidate repair analyses are generated by a stochastic tree adjoining grammar (TAG) noisy-channel model. A probabilistic syntactic language model scores the fluency of each analysis, and a maximum-entropy model selects the most likely analysis given the language model score and other features. Fillers are detected independently via a small set of deterministic rules, and IPs are detected by combining the output of repair and filler detection modules. In the recent Rich Transcription Fall 2004 (RT-04F) blind evaluation, systems competed to detect these three forms of disfluency under two input conditions: a best-case scenario of manually transcribed words and a fully automatic case of automatic speech recognition (ASR) output. For all three tasks and on both types of input, our system was the top performer in the evaluation