On affine usages in signal-based communication

We describe a type system for a synchronous pi-calculus formalising the notion of affine usage in signal-based communication. In particular, we identify a limited number of usages that preserve affinity and that can be composed. As a main application of the resulting system, we show that typable programs are deterministic

DeepMarks: A Digital Fingerprinting Framework for Deep Neural Networks

This paper proposes DeepMarks, a novel end-to-end framework for systematic fingerprinting in the context of Deep Learning (DL). Remarkable progress has been made in the area of deep learning. Sharing the trained DL models has become a trend that is ubiquitous in various fields ranging from biomedical diagnosis to stock prediction. As the availability and popularity of pre-trained models are increasing, it is critical to protect the Intellectual Property (IP) of the model owner. DeepMarks introduces the first fingerprinting methodology that enables the model owner to embed unique fingerprints within the parameters (weights) of her model and later identify undesired usages of her distributed models. The proposed framework embeds the fingerprints in the Probability Density Function (pdf) of trainable weights by leveraging the extra capacity available in contemporary DL models. DeepMarks is robust against fingerprints collusion as well as network transformation attacks, including model compression and model fine-tuning. Extensive proof-of-concept evaluations on MNIST and CIFAR10 datasets, as well as a wide variety of deep neural networks architectures such as Wide Residual Networks (WRNs) and Convolutional Neural Networks (CNNs), corroborate the effectiveness and robustness of DeepMarks framework

CLASS: A Logical Foundation for Typeful Programming with Shared State

Software construction depends on imperative state sharing and concurrency, which are naturally present in several application domains and are also exploited to improve the structure and efficiency of computer programs. However, reasoning about concurrency and shared mutable state is hard, error-prone and the source of many programming bugs, such as memory leaks, data corruption, deadlocks and non-termination. In this thesis, we develop CLASS: a core session-based language with a lightweight substructural type system, that results from a principled extension of the propositions-astypes correspondence with second-order classical linear logic. More concretely, CLASS offers support for session-based communication, mutex-protected first-class reference cells, dynamic state sharing, generic polymorphic algorithms, data abstraction and primitive recursion. CLASS expresses and types significant realistic programs, that manipulate memoryefficient linked data structures (linked lists, binary search trees) with support for updates in-place, shareable concurrent ADTs (counters, stacks, functional and imperative queues), resource synchronisation methods (fork-joins, barriers, dining philosophers, generic corecursive protocols). All of these examples are guaranteed to be safe, a result that follows by the logical approach. The linear logical foundations guarantee that well-typed CLASS programs do not go wrong: they never deadlock on communication or reference cell acquisition, do not leak memory and always terminate, even if they share complex data structures protected by synchronisation primitives. Furthermore, since we follow a propositions-as-types approach, we can reason about the behaviour of concurrent stateful processes by algebraic program manipulation. The feasibility of our approach is witnessed by the implementation of a type checker and interpreter for CLASS, which validates and guides the development of many realistic programs. The implementation is available with an open-source license, together with several examples.A construção de software depende de estado partilhado imperativo e concorrência, que estão naturalmente presentes em vários domínios de aplicação e que também são explorados para melhorar o a estrutura e o desempenho dos programas. No entanto, raciocinar sobre concorrência e estado mutável partilhado é difícil e propenso à introdução de erros e muitos bugs de programação, tais como fugas de memória, corrupção de dados, programas bloqueados e programas que não terminam a sua execução. Nesta tese, desenvolvemos CLASS: uma linguagem baseada em sessões, com um sistema de tipos leve e subestrutural, que resulta de uma extensão metodológica da correspondência proposições-como-tipos com a lógica linear clássica de segunda ordem. Mais concretamente, a linguagem CLASS oferece suporte para comunicação baseada em sessões, células de memória protegidas com mutexes de primeira classe, partilha dinâmica de estado, algoritmos polimórficos genéricos, abstração de dados e recursão primitiva. A linguagem CLASS expressa e tipifica programas realistas significativos, que manipulam estruturas de dados ligadas eficientes (listas ligadas, árvores de pesquisa binária) suportando actualização imperativa local, TDAs partilhados e concorrentes (contadores, pilhas, filas funcionais e imperativas), métodos de sincronização e partilha de recursos (bifurcar-juntar, barreiras, jantar de filósofos, protocolos genéricos corecursivos). Todos estes exemplos são seguros, uma garantia que resulta da nossa abordagem lógica. Os fundamentos, baseados na lógica linear, garantem que programas em CLASS bem tipificados não incorrem em erros: nunca bloqueiam, quer na comunicação, quer na aquisição de células de memória, nunca causam fugas de memória e terminam sempre, mesmo que compartilhem estruturas de dados complexas protegidas por primitivas de sincronização. Além disso, uma vez que seguimos uma abordagem de proposições-comotipos, podemos raciocinar sobre o comportamento de processos concorrentes, que usam estado, através de manipulação algébrica. A viabilidade da nossa abordagem é evidenciada pela implementação de um verificador de tipos e interpretador para a linguagem CLASS, que valida e orienta o desenvolvimento de vários programs realistas. A implementação está disponível com uma licença de acesso livre, juntamente com inúmeros exemplos

uC: Ubiquitous Collaboration Platform for Multimodal Team Interaction Support

A human-centered computing platform that improves teamwork and transforms the “human- computer interaction experience” for distributed teams is presented. This Ubiquitous Collaboration, or uC (“you see”), platform\u27s objective is to transform distributed teamwork (i.e., work occurring when teams of workers and learners are geographically dispersed and often interacting at different times). It achieves this goal through a multimodal team interaction interface realized through a reconfigurable open architecture. The approach taken is to integrate: (1) an intuitive speech- and video-centric multi-modal interface to augment more conventional methods (e.g., mouse, stylus and touch), (2) an open and reconfigurable architecture supporting information gathering, and (3) a machine intelligent approach to analysis and management of heterogeneous live and stored sensor data to support collaboration. The system will transform how teams of people interact with computers by drawing on both the virtual and physical environment

Metric combinatorics of convex polyhedra: cut loci and nonoverlapping unfoldings

This paper is a study of the interaction between the combinatorics of boundaries of convex polytopes in arbitrary dimension and their metric geometry. Let S be the boundary of a convex polytope of dimension d+1, or more generally let S be a `convex polyhedral pseudomanifold'. We prove that S has a polyhedral nonoverlapping unfolding into R^d, so the metric space S is obtained from a closed (usually nonconvex) polyhedral ball in R^d by identifying pairs of boundary faces isometrically. Our existence proof exploits geodesic flow away from a source point v in S, which is the exponential map to S from the tangent space at v. We characterize the `cut locus' (the closure of the set of points in S with more than one shortest path to v) as a polyhedral complex in terms of Voronoi diagrams on facets. Analyzing infinitesimal expansion of the wavefront consisting of points at constant distance from v on S produces an algorithmic method for constructing Voronoi diagrams in each facet, and hence the unfolding of S. The algorithm, for which we provide pseudocode, solves the discrete geodesic problem. Its main construction generalizes the source unfolding for boundaries of 3-polytopes into R^2. We present conjectures concerning the number of shortest paths on the boundaries of convex polyhedra, and concerning continuous unfolding of convex polyhedra. We also comment on the intrinsic non-polynomial complexity of nonconvex polyhedral manifolds.Comment: 47 pages; 21 PostScript (.eps) figures, most in colo

Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review

Interest point detection is one of the most fundamental and critical problems in computer vision and image processing. In this paper, we carry out a comprehensive review on image feature information (IFI) extraction techniques for interest point detection. To systematically introduce how the existing interest point detection methods extract IFI from an input image, we propose a taxonomy of the IFI extraction techniques for interest point detection. According to this taxonomy, we discuss different types of IFI extraction techniques for interest point detection. Furthermore, we identify the main unresolved issues related to the existing IFI extraction techniques for interest point detection and any interest point detection methods that have not been discussed before. The existing popular datasets and evaluation standards are provided and the performances for eighteen state-of-the-art approaches are evaluated and discussed. Moreover, future research directions on IFI extraction techniques for interest point detection are elaborated

Towards Precise Localisation : Subsample Methods, Efficient Estimation and Merging of Maps

Over the last couple of years audio and radio sensors have become cheaper and more common in our everyday life. Such sensors can be used to form a network, from which one can obtain distance measures by correlating the different received signals. One example of such distance measures is time-difference of arrival measurements (TDoA), which can be used to estimate the positions of the senders and receivers. The result is a 3D map of the environment, similar to what you get from doing structure from motion (SfM) with images. If a new sensor appears, the map can in turn be used to determine the position of that sensor, i.e. for localisation. In this thesis we present three studies that take us towards precise localisation. Paper I involves finding exact — on a subsample level — TDoA measurements. These types of subsample refinements give a higher precision, but are sensitive to noise. We present an explicit expression for the variance of the TDoA estimate and study the impact that noise in the signals have. In Paper III TDoA measurements are used to estimate sender and receiver positions in an efficient way. We present a new initialisation approach followed by a scheme for performing local optimisation for TDoA data with constant offset, i.e. when the sound events are repetitive with some constant period. The sender and receiver positions together constitute a map of the environment and such maps are studied in Paper II. Assuming that we have a number of different map representations of the same environment — coming from either sound, radio or image data — we present an algorithm for how to merge these representations into one map, in an efficient way using only a small memory footprint representation. The final map has a higher precision and the method can also be used to detect changes that have occurred between the creation of the different map representations. Thus, altogether, we present a number of improvements of the localisation process. We perform analysis as well as experimental evaluation of each of these improvements