Attribute Computations in the DPoPb Graph Transformation Engine

One of the challenges of attributed graph rewriting systems concerns the implementation of attribute computations. Most of the existing systems adopt the standard algebraic approach where graphs are attributed using sigma-algebras. However, for the sake of efficiency considerations and convenient uses, these systems do not generally implement the whole attribute computations but rely on programs written in a host language. In previous works we introduced the Double Pushout Pullback (DPoPb) framework which integrates attributed graph rewriting and computation on attributes in a unified categorical approach. This paper discusses the DPoPb’s theoretical and practical advantages when using inductive types and lambda-calculus. We also present an implementation of the DPoPb system in the Haskell language which thoroughly covers the semantics of this graph rewriting system

Adequate encodings of logical systems in UTT

In this paper, we present an existing and formalized type theory (UTT) as a logical framework. We compare the resulting framework with LF and give the representation of two significant type systems in the framework: the typed lambda calculus which is closely related to higher-order logic and a linear type system which is not possible to encode in LF.Postprint (published version

Sequence Processing with Quantum Tensor Networks

We introduce complex-valued tensor network models for sequence processing motivated by correspondence to probabilistic graphical models, interpretability and resource compression. Inductive bias is introduced to our models via network architecture, and is motivated by the correlation structure inherent in the data, as well as any relevant compositional structure, resulting in tree-like connectivity. Our models are specifically constructed using parameterised quantum circuits, widely used in quantum machine learning, effectively using Hilbert space as a feature space. Furthermore, they are efficiently trainable due to their tree-like structure. We demonstrate experimental results for the task of binary classification of sequences from real-world datasets relevant to natural language and bioinformatics, characterised by long-range correlations and often equipped with syntactic information. Since our models have a valid operational interpretation as quantum processes, we also demonstrate their implementation on Quantinuum's H2-1 trapped-ion quantum processor, demonstrating the possibility of efficient sequence processing on near-term quantum devices. This work constitutes the first scalable implementation of near-term quantum language processing, providing the tools for large-scale experimentation on the role of tensor structure and syntactic priors. Finally, this work lays the groundwork for generative sequence modelling in a hybrid pipeline where the training may be conducted efficiently in simulation, while sampling from learned probability distributions may be done with polynomial speed-up on quantum devices

Modeling, analysis and defense strategies against Internet attacks.

Third, we have analyzed the tradeoff between delay caused by filtering of worms at routers, and the delay due to worms' excessive amount of network traffic. We have used the optimal control problem, to determine the appropriate tradeoffs between these two delays for a given rate of a worm spreading. Using our technique we can minimize the overall network delay by finding the number of routers that should perform filtering and the time at which they should start the filtering process.Many early Internet protocols were designed without a fundamentally secure infrastructure and hence vulnerable to attacks such as denial of service (DoS) attacks and worms. DoS attacks attempt to consume the resources of a remote host or network, thereby denying or degrading service to legitimate users. Network forensics is an emerging area wherein the source or the cause of the attacker is determined using IDS tools. The problem of finding the source(s) of attack(s) is called the "trace back problem". Lately, Internet worms have become a major problem for the security of computer networks, causing considerable amount of resources and time to be spent recovering from the disruption of systems. In addition to breaking down victims, these worms create large amounts of unnecessary network data traffic that results in network congestion, thereby affecting the entire network.In this dissertation, first we solve the trace back problem more efficiently in terms of the number of routers needed to complete the track back. We provide an efficient algorithm to decompose a network into connected components and construct a terminal network. We show that for a terminal network with n routers, the trace back can be completed in O(log n) steps.Second, we apply two classical epidemic SIS and SIR models to study the spread of Internet Worm. The analytical models that we provide are useful in determining the rate of spread and time required to infect a majority of the nodes in the network. Our simulation results on large Internet like topologies show that in a fairly small amount of time, 80% of the network nodes is infected

Thing Theory: Connecting Humans to Smart Healthcare

Healthcare providers will enter location-aware smart environments with the expectation that their devices will integrate, their location will be incorporated, and the environment that they are within will specifically respond to their needs, as well as to the needs of their patients. Cooperation and coordination in complex environments requires people to have access to appropriate contextually sensitive information, some of which must be shared between them. To plan and design effective location-aware smart environments for healthcare, tools are required for integrating and responding to human needs and anticipating human intents and desires. A location-aware healthcare smart environment is another layer within this already highly heterogeneous system of communication. Each component in a location-aware smart environment network can generate data and send messages that must be processed, understood and responded to in some manner. In a healthcare environment, well placed software agents can help manage critical messages shared between sensors, low level software agents and the people who act on this information, improving care for patients and outcomes for providers. The authors’ propose a framework based on the agency of both humans and environmental components: Thing Theory, a logic-based agent framework that evolves discussion on how to connect humans to a healthcare environment designed to function for their benefit

NMF: A Modeling Framework for the .NET Platform

For its promises in terms of increased productivity, Modeldriven engineering (MDE) is getting applied increasingly often in both industry and academia. However, most tools currently available are based on the Eclipse Modeling Framework (EMF) and hence based on the Java platform whereas tool support for other platforms is limited. This leads to a language and tool adoption problem for developers of other platforms such as .NET. As a result, few projects on the .NET platform adopt MDE. Furthermore, the limited tool availability introduces a technical barrier in the interoperability between EMF and .NET applications. In this paper, we present the .NET Modeling Framework (NMF), a tool set for model repositories, model-based incrementalization, model transformation, model synchronization and code generation. The framework makes intensive use of the C# language as host language for model transformation and synchronization languages, whereas the model repository serialization is compatible with EMF. This solves the language adoption problem for C# programmers and creates a bridge to the EMF platform