A Correctness Proof of a Cache Coherence Protocol

SCI -- Scalable Coherent Interface -- is an IEEE standard for specifying communication between multiprocessors in a shared memory model. In this paper we model part of SCI by a program written in a UNITY-like programming language. This part of SCI is formally specified in Manna and Pnueli's Linear Time Temporal Logic (LTL). We prove that the program satisfies its specification. The proof is carried out within LTL and uses history variables. Structuring of the proof is achieved by careful formulation of lemmata and the use of auxiliary predicates as an abstraction mechanism. 1 Introduction In this paper we formalize and verify part of the SCI (Scalable Coherent Interface) protocol [19]. This protocol is an IEEE standard for specifying communication between shared memory multiprocessors. It is called scalable because the protocol is intended to be performed in a system which may consist of up to 64,000 processors. The correctness proof we present in the current paper is carried out for ..

Formalizing Inductive Proofs of Network Algorithms

. Theorem proving and model checking are combined to fully formalize a correctness proof of a broadcasting protocol. The protocol is executed in a network of processors which constitutes a binary tree of arbitrary size. We use the theorem prover Coq and the model checker Spin to verify the broadcasting protocol. Our goals in this work are twofold. The first one is to provide a strategy for carrying out formal, mechanical correctness proofs of distributed network algorithms. Even though logical specifications of programs implementing such algorithms are often defined precisely enough to allow a human verifier to prove the program's correctness, the definition of the network is often only informal or implicit. Our example illustrates how an underlying network can be formally defined by means of induction, and how to reason about network algorithms by structural induction. Our second goal is to integrate theorem proving and model checking to increase the class of algorithms for which me..

Protocol Verification in Nuprl

. This paper presents work directed toward making the Nuprl interactive theorem prover a more effective tool for protocol verification while retaining existing advantages of the system, and describes application of the prover to verifying the SCI cache coherence protocol. The verification is based, in part, on formal mathematics imported from another theorem-proving system, exploiting a connection we implemented between Nuprl and HOL. We have designed and implemented a type annotation scheme for Nuprl's logic that allows type information to be effectively applied by the system's automated reasoning facilities. This is significant because Nuprl's powerful constructive type theory buys much of its expressive power and flexibility at the cost of giving up the more manageable kinds of type system found in other logics. 1 Introduction Nuprl [2] is an interactive theorem-proving system in the lineage of LCF. One of its main distinguishing characteristics is its highly expressive formal logi..

PIGLOW application for animal welfare self-assessment by farmers

The PIGLOW smartphone app was designed for organic and free-range farmers to monitor the welfare of their own pigs. The app contains animal-based questions that relate to one of the 4 Welfare Quality principles: good housing, good feeding, good health and appropriate behaviour. The results include scores/average percentages for each welfare indicator, automated advice with risk factors for possible welfare problems, and an anonymous comparison with the scores of other app users (benchmarking). Once a farmer has completed multiple welfare scans, a graph will show how their scores have changed over time, providing a historical record of welfare on the farm. Using the PIGLOW app could help increase awareness of potential welfare problems and its risk factors, which could make it easier to prevent problems from occurring. Farmers are advised to discuss the results with their veterinarian or other advisors to, if relevant, come up with the best approach to improve animal welfare on their farm

Opinion of organic and free-range pig farmers on animal welfare and the PIGLOW app for animal welfare self-assessments

The PIGLOW app was designed for the PPILOW project, enabling organic and free-range pig farmers to monitor the welfare of their pigs. The app is based on the 4 principles of the Welfare Quality protocol: good housing, good feeding, good health and appropriate behaviour. The tool includes automated feedback and anonymous benchmarking. A longitudinal study on 20-30 pig farms has started in order to determine the long-term effect of the use of the app on animal welfare. A survey is being conducted to assess participants’ views on animal welfare and their expectations of the app (n=10). Answers are given on a scale of 1 (disagree completely/not important at all) to 7 (agree completely/very important). When asked how they would define good animal welfare, 7/10 farmers included the possibility to express natural behaviour. The farmers scored the importance of 16 welfare aspects addressed in the PIGLOW app. The lowest score was given for thermal comfort (x̄=5.3, sd=1.1) and the highest score for the availability of drinking water (x̄=7, sd=0). Thus, even the least important of the indicators were scored above the point of neutrality (score 4). When asked how they think their own farm performs on these same 16 aspects, the scores for all except one (feed structure) were lower than those they gave for the importance of the aspect. The mean difference between these two values was largest for absence of wounds/lesions (x̄1-2=1, sd=1.3) and absence of lameness (x̄1-2=1, sd=1.7). It therefore seems likely that these are the welfare aspects for which farmers think improvement on their farm is most desirable. Farmers expect a historical record of their data (x̄=5.9, sd=1.2) and benchmarking (x̄=5.7, sd=1.5) to be the most useful aspects of the PIGLOW app. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N°816172

Development of tools for farmers to self-assess the welfare of poultry and pigs in organic and outdoor systems

A goal of the PPILOW project is to develop and evaluate tools for animal welfare self-assessments by poultry and pig farmers of organic and outdoor systems. To accomplish this goal, two smartphone applications are being modified or developed in eight different languages. The EBENE® app, which was primarily developed for conventional poultry farms, was adapted to be more suitable for outdoor farming systems and the PIGLOW® app for outdoor pig farms was newly developed. For both apps, a selection of welfare indicators to be included in the assessments was made based on a suitability analysis of indicators from existing welfare monitoring protocols (e.g. Welfare Quality© , Dierenwelzijnscan). The opinions of National Practitioner Groups (NPGs), including representatives of feed producers, consumer associations, retailers, veterinarians, processors and farmers, were taken into account in this decision process. NPGs from six and three countries were surveyed for EBENE® and PIGLOW®, respectively. Preference was given to animal-based indicators, all of which fit into one of the four welfare principles of the Welfare Quality© protocols, namely good feeding, good housing, good health and appropriate behaviour. A trade-off was made between the time investment by the farmers and the level of detail of the assessment, resulting in assessments that take approximately one hour to complete. The results of the apps include scores for all welfare indicators and anonymous benchmarking. The farmers also receive automated feedback for each welfare indicator that includes an explanation of and risk factors for related potential welfare problems. Separate welfare assessments are available for broilers and laying hens, and for fattening pigs and sows at different production stages. Additionally, both apps have the possibility to assess the depopulation process. The user-friendliness and feasibility of the apps was tested during on-farm trials in Belgium and France, after which minor adjustments were made to wording and specific questions. The use of these tools for welfare self-assessments could sensitize farmers to the presence of potential welfare problems and the automated feedback could motivate and guide them to take corrective actions or seek additional advice of experts. A longitudinal study to assess the effect of the use of the apps on animal welfare and on the farmers’ opinions of the apps on commercial fattening pig and broiler farms is currently being conducted. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement N°816172

EBENE application for poultry welfare self-assessment by farmers

The EBENE® smartphone app was designed for farmers to monitor the welfare of their own poultry and rabbits in different production systems, including free-range and organic systems. The app contains animal-based questions that relate to one of the 4 Welfare Quality principles: good housing, good feeding, good health and appropriate behaviour. The results include scores for each welfare indicator and aggregated criteria and automated advice with risk factors for possible welfare problems. An anonymous comparison with the scores of other app users is also possible within a specific company (benchmarking). Using the EBENE® app could help increase awareness of potential welfare problems and risk factors, which could make it easier to prevent problems from occurring. Farmers are advised to discuss the results with their veterinarian or other advisors to, if relevant, come up with the best approach to improve animal welfare on their farm