Optimal Dynamic Distributed MIS

Finding a maximal independent set (MIS) in a graph is a cornerstone task in distributed computing. The local nature of an MIS allows for fast solutions in a static distributed setting, which are logarithmic in the number of nodes or in their degrees. The result trivially applies for the dynamic distributed model, in which edges or nodes may be inserted or deleted. In this paper, we take a different approach which exploits locality to the extreme, and show how to update an MIS in a dynamic distributed setting, either \emph{synchronous} or \emph{asynchronous}, with only \emph{a single adjustment} and in a single round, in expectation. These strong guarantees hold for the \emph{complete fully dynamic} setting: Insertions and deletions, of edges as well as nodes, gracefully and abruptly. This strongly separates the static and dynamic distributed models, as super-constant lower bounds exist for computing an MIS in the former. Our results are obtained by a novel analysis of the surprisingly simple solution of carefully simulating the greedy \emph{sequential} MIS algorithm with a random ordering of the nodes. As such, our algorithm has a direct application as a $3$-approximation algorithm for correlation clustering. This adds to the important toolbox of distributed graph decompositions, which are widely used as crucial building blocks in distributed computing. Finally, our algorithm enjoys a useful \emph{history-independence} property, meaning the output is independent of the history of topology changes that constructed that graph. This means the output cannot be chosen, or even biased, by the adversary in case its goal is to prevent us from optimizing some objective function.Comment: 19 pages including appendix and reference

Algorithmes auto-stabilisants pour la construction de structures couvrantes réparties

This thesis deals with the self-stabilizing construction of spanning structures over a distributed system. Self-stabilization is a paradigm for fault-tolerance in distributed algorithms. It guarantees that the system eventually satisfies its specification after transient faults hit the system. Our model of distributed system assumes locally shared memories for communicating, unique identifiers for symmetry-breaking, and distributed daemon for execution scheduling, that is, the weakest proper daemon. More generally, we aim for the weakest possible assumptions, such as arbitrary topologies, in order to propose the most versatile constructions of distributed spanning structures. We present four original self-stabilizing algorithms achieving k-clustering, (f,g)-alliance construction, and ranking. For every of these problems, we prove the correctness of our solutions. Moreover, we analyze their time and space complexity using formal proofs and simulations. Finally, for the (f,g)-alliance problem, we consider the notion of safe convergence in addition to self-stabilization. It enforces the system to first quickly satisfy a specification that guarantees a minimum of conditions, and then to converge to a more stringent specification.Cette thèse s'intéresse à la construction auto-stabilisante de structures couvrantes dans un système réparti. L'auto-stabilisation est un paradigme pour la tolérance aux fautes dans les algorithmes répartis. Plus précisément, elle garantit que le système retrouve un comportement correct en temps fini après avoir été perturbé par des fautes transitoires. Notre modèle de système réparti se base sur des mémoires localement partagées pour la communication, des identifiants uniques pour briser les symétries et un ordonnanceur inéquitable, c'est-à-dire le plus faible des ordonnanceurs. Dans la mesure du possible, nous nous imposons d'utiliser les plus faibles hypothèses, afin d'obtenir les constructions les plus générales de structures couvrantes réparties. Nous présentons quatre algorithmes auto-stabilisants originaux pour le k-partitionnement, la construction d'une (f,g)-alliance et l'indexation. Pour chacun de ces problèmes, nous prouvons la correction de nos solutions. De plus, nous analysons leur complexité en temps et en espace à l'aide de preuves formelles et de simulations. Enfin, pour le problème de (f,g)-alliance, nous prenons en compte la notion de convergence sûre qui vient s'ajouter à celle d'auto-stabilisation. Elle garantit d'abord que le comportement du système assure rapidement un minimum de conditions, puis qu'il continue de converger jusqu'à se conformer à une spécification plus exigeante

Unsupervised Understanding of Location and Illumination Changes in Egocentric Videos

Wearable cameras stand out as one of the most promising devices for the upcoming years, and as a consequence, the demand of computer algorithms to automatically understand the videos recorded with them is increasing quickly. An automatic understanding of these videos is not an easy task, and its mobile nature implies important challenges to be faced, such as the changing light conditions and the unrestricted locations recorded. This paper proposes an unsupervised strategy based on global features and manifold learning to endow wearable cameras with contextual information regarding the light conditions and the location captured. Results show that non-linear manifold methods can capture contextual patterns from global features without compromising large computational resources. The proposed strategy is used, as an application case, as a switching mechanism to improve the hand-detection problem in egocentric videos.Comment: Submitted for publicatio

From Tap to Table: Consumer Values, Producer Attitudes, and Vermont Maple Syrup in a Dynamic Landscape

Harvesting the sap of maple trees [Acer saccharum] for use in the production of syrups and sugars has a storied history stretching back to the pre-Columbian practices of North America’s indigenous peoples. Since its adaptation by European settlers in the late seventeenth century and into the present day, the production of maple syrup has become especially integral to the livelihoods and cultural identities of farmers in Vermont. While oftentimes esteemed as a timeless agrarian tradition, market forces and environmental changes have led maple syrup producers (or sugarmakers) to adopt new production practices that scarcely resemble the taps, buckets, and draft animals which feature so prominently on promotional packaging material. Adapting to challenges posed by climate change, competition in commodity markets, and a shifting regulatory environment is necessary for maple producers. However, maple enterprises differ in fundamental ways that can shape their perceptions of risks and their willingness – or ability – to adapt. Regional stakeholders – especially maple consumers – are also aware of the pressures bringing change to the industry and concerned about what the consequences entail for producers, communities, and rural landscapes. This thesis uses data collected from surveys of Vermont residents and maple sugarmakers to explore consumers’ purchasing behavior and how producers prioritize different threats to their enterprise. I first examine how the ways consumers define local products and perceive threats to the regional maple industry affect their willingness to pay for “Made in Vermont” maple syrup. Then I show how concerns expressed by maple producers to different social-ecological threats relate to specific enterprise characteristics, production practices, and types of maple enterprises. Findings seek to better understand the concerns expressed by Vermont maple producers and consumers – and what implications these attitudes may have for the industry

Automated Deception Detection from Videos: Using End-to-End Learning Based High-Level Features and Classification Approaches

Deception detection is an interdisciplinary field attracting researchers from psychology, criminology, computer science, and economics. We propose a multimodal approach combining deep learning and discriminative models for automated deception detection. Using video modalities, we employ convolutional end-to-end learning to analyze gaze, head pose, and facial expressions, achieving promising results compared to state-of-the-art methods. Due to limited training data, we also utilize discriminative models for deception detection. Although sequence-to-class approaches are explored, discriminative models outperform them due to data scarcity. Our approach is evaluated on five datasets, including a new Rolling-Dice Experiment motivated by economic factors. Results indicate that facial expressions outperform gaze and head pose, and combining modalities with feature selection enhances detection performance. Differences in expressed features across datasets emphasize the importance of scenario-specific training data and the influence of context on deceptive behavior. Cross-dataset experiments reinforce these findings. Despite the challenges posed by low-stake datasets, including the Rolling-Dice Experiment, deception detection performance exceeds chance levels. Our proposed multimodal approach and comprehensive evaluation shed light on the potential of automating deception detection from video modalities, opening avenues for future research.Comment: 29 pages, 17 figures (19 if counting subfigures

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Synthesis Of Distributed Protocols From Scenarios And Specifications

Distributed protocols, typically expressed as stateful agents communicating asynchronously over buffered communication channels, are difficult to design correctly. This difficulty has spurred decades of research in the area of automated model-checking algorithms. In turn, practical implementations of model-checking algorithms have enabled protocol developers to prove the correctness of such distributed protocols. However, model-checking techniques are only marginally useful during the actual development of such protocols; typically as a debugging aid once a reasonably complete version of the protocol has already been developed. The actual development process itself is often tedious and requires the designer to reason about complex interactions arising out of concurrency and asynchrony inherent to such protocols. In this dissertation we describe program synthesis techniques which can be applied as an enabling technology to ease the task of developing such protocols. Specifically, the programmer provides a natural, but incomplete description of the protocol in an intuitive representation — such as scenarios or an incomplete protocol. This description specifies the behavior of the protocol in the common cases. The programmer also specifies a set of high-level formal requirements that a correct protocol is expected to satisfy. These requirements can include safety requirements as well as liveness requirements in the form of Linear Temporal Logic (LTL) formulas. We describe techniques to synthesize a correct protocol which is consistent with the common-case behavior specified by the programmer and also satisfies the high-level safety and liveness requirements set forth by the programmer. We also describe techniques for program synthesis in general, which serve to enable the solutions to distributed protocol synthesis that this dissertation explores