With the growth of model sizes and the scale of their deployment, their sheer size burdens the infrastructure requiring more network and more storage to accommodate these. While there is a vast model compression literature deleting parts of the model weights for faster inference, we investigate a more traditional type of compression - one that represents the model in a compact form and is coupled with a decompression algorithm that returns it to its original form and size - namely lossless compression. We present ZipNN, a lossless compression tailored to neural networks. Somewhat surprisingly, we show that specific lossless compression can gain significant network and storage reduction on popular models, often saving 33% and at times reducing over 50% of the model size. We investigate the source of model compressibility and introduce specialized compression variants tailored for models that further increase the effectiveness of compression. On popular models (e.g. Llama 3) ZipNN shows space savings that are over 17% better than vanilla compression while also improving compression and decompression speeds by 62%. Using multiple workers and threads, ZipNN can achieve decompression speeds of up to 80GB/s and compression speed of up to 13GB/s. We estimate that these methods could save over an ExaByte per year of network traffic downloaded from a large model hub like Hugging Face.

This paper targets the problem of speech act detection in conversations about bug repair. We conduct a ``Wizard of Oz'' experiment with 30 professional programmers, in which the programmers fix bugs for two hours, and use a simulated virtual assistant for help. Then, we use an open coding manual annotation procedure to identify the speech act types in the conversations. Finally, we train and evaluate a supervised learning algorithm to automatically detect the speech act types in the conversations. In 30 two-hour conversations, we made 2459 annotations and uncovered 26 speech act types. Our automated detection achieved 69% precision and 50% recall. The key application of this work is to advance the state of the art for virtual assistants in software engineering. Virtual assistant technology is growing rapidly, though applications in software engineering are behind those in other areas, largely due to a lack of relevant data and experiments. This paper targets this problem in the area of developer Q/A conversations about bug repair.

To satisfy the need for a wide range of enterprise modelling choices, it is necessary to have a rich set of process based and role based modelling concepts. It is also necessary to have a sound and expressive business event model that includes a flexible way of associating business events with business processes and roles. However, the ability to construct detailed and expressive enterprise models is not useful in a business context without some means of realising the model in technology. The paper introduces the basic modelling concepts from the DSTC's UML Profile for Enterprise Distributed Object Computing before describing in detail the profile's business event model. The paper then describes how this event model can be mapped in a straightforward manner to implementations using the OMG's CORBA Notification Service. This mapping can then be used as the basis for building automated tools that support generating implementations of systems from high level enterprise models.

Applications are often designed to take advantage of the potential for integration with each other via shared information. Current approaches for integration are limited, affecting both the programmer and end-user. In this paper, we present CyberDesk, a framework for self-integrating software in which integration is driven by user context. It relieves the burden on programmers by removing the necessity to predict how software should be integrated. It also relieves the burden from users by removing the need to understand how to make different software components work together. q 1998 Elsevier Science B.V. All rights reserved.

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No abstract available.

If signaling channels can only be driven by a trusted path, they cannot be exploited by trojan horses in untrusted software. To this end, the SMITE secure computer system provides a general-purpose trusted path, based on a screen editor, which would act as the users' normal interface to the system. The feasibility of the approach relies on the use of a sympathetic computer architecture, which supports a fine grain of protection. The authors describe the trusted path and the user interface of the SMITE system. They discuss the formal specification of the display functions. They examine the use of SMITE for high-assurance applications.>