1,517 research outputs found

    Reinventing the Wheel: Explaining Question Duplication in Question Answering Communities

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    Duplicate questions are common occurrences in Question Answering Communities (QACs) and impede the development of efficacious problem-solving communities. Yet, there is a dearth of research that has sought to shed light on the mechanisms underlying question duplication. Building on the information adoption model, we advance a research model that posits information quality and source credibility as factors deterring users from asking redundant questions within QACs. Furthermore, considering the question-answer dichotomy intrinsic to QACs, we distinguish the quality and credibility of questions from those of answers as distinctive inhibitors of question duplication. We empirically validate our hypotheses on a leading QAC platform by harnessing a deep learning algorithm to detect duplications on over 9,380,000 question pairs. Results revealed that while the credibility of both questions and answers could alleviate question duplication, visual and actionable elements are more effective in preventing question duplication by boosting the quality of questions and answers respectively

    Functionality learning through specification instructions

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    Test suites assess natural language processing models' performance on specific functionalities: cases of interest involving model robustness, fairness, or particular linguistic capabilities. They enable fine-grained evaluations of model aspects that would otherwise go unnoticed in standard evaluation datasets, but they do not address the problem of how to fix the failure cases. Previous work has explored functionality learning by fine-tuning models on suite data. While this improves performance on seen functionalities, it often does not generalize to unseen ones and can harm general performance. This paper analyses a fine-tuning-free approach to functionality learning. For each functionality in a suite, we generate a specification instruction that encodes it. We combine the obtained specification instructions to create specification-augmented prompts, which we feed to language models pre-trained on natural instruction data to generate suite predictions. A core aspect of our analysis is to measure the effect that including a set of specifications has on a held-out set of unseen, qualitatively different specifications. Our experiments across four tasks and models ranging from 80M to 175B parameters show that smaller models struggle to follow specification instructions. However, larger models (> 3B params.) can benefit from specifications and even generalize desirable behaviors across functionalities.Comment: 33 pages, 8 figure

    Advancing duplicate question detection with deep learning

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    Transient Addressing for Related Processes: Improved Firewalling by Using IPV6 and Multiple Addresses per Host

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    Traditionally, hosts have tended to assign relatively few network addresses to an interface for extended periods. Encouraged by the new abundance of addressing possibilities provided by IPv6, we propose a new method, called Transient Addressing for Related Processes (TARP), whereby hosts temporarily employ and subsequently discard IPv6 addresses in servicing a client host's network requests. The method provides certain security advantages and neatly finesses some well-known firewall problems caused by dynamic port negotiation used in a variety of application protocols. A prototype implementation exists as a small set of kame/BSD kernel enhancements and allows socket programmers and applications nearly transparent access to TARP addressing's advantages

    Problems in Designing Huge Datawarehouses and Datamarts

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    Deception Detection: Study of Information Manipulation through Electronic Identity Theft-Email Forgery in the U.S. Military

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    This research describes the results of a field experiment which examines the effects of warnings on system trust and individual awareness in government computer systems through the use of email forgery. The experiment consisted of forging a trusted government email account and trying to get government computer users to reply to a forged email address. The results revealed that warning individuals about possible email forgery did not increase their awareness or reduce their level of system thrust in the email system nor did it increase their ability to detect email forgery. The results did determine that government computer users are extremely vulnerable to email forgery and that new security measures need to be adapted to protect these systems from this type of threat. The culmination of this effort was to support the use of email authentication through the use of the new common access card (i.e., smart card or CAC) by the military. Recommendations to implement effective email authentication and encryption capabilities
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