Premise Selection for Mathematics by Corpus Analysis and Kernel Methods

Smart premise selection is essential when using automated reasoning as a tool for large-theory formal proof development. A good method for premise selection in complex mathematical libraries is the application of machine learning to large corpora of proofs. This work develops learning-based premise selection in two ways. First, a newly available minimal dependency analysis of existing high-level formal mathematical proofs is used to build a large knowledge base of proof dependencies, providing precise data for ATP-based re-verification and for training premise selection algorithms. Second, a new machine learning algorithm for premise selection based on kernel methods is proposed and implemented. To evaluate the impact of both techniques, a benchmark consisting of 2078 large-theory mathematical problems is constructed,extending the older MPTP Challenge benchmark. The combined effect of the techniques results in a 50% improvement on the benchmark over the Vampire/SInE state-of-the-art system for automated reasoning in large theories.Comment: 26 page

Spatial aspects of the design and targeting of agricultural development strategies:

Two increasingly shared perspectives within the international development community are that (a) geography matters, and (b) many government interventions would be more successful if they were better targeted. This paper unites these two notions by exploring the opportunities for, and benefits of, bringing an explicitly spatial dimension to the tasks of formulating and evaluating agricultural development strategies. We first review the lingua franca of land fragility and find it lacking in its capacity to describe the dynamic interface between the biophysical and socioeconomic factors that help shape rural development options. Subsequently, we propose a two-phased approach. First, development strategy options are characterized to identify the desirable ranges of conditions that would most favor successful strategy implementation. Second, those conditions exhibiting important spatial dependency – such as agricultural potential, population density, and access to infrastructure and markets – are matched against a similarly characterized, spatially-referenced (GIS) database. This process generates both spatial (map) and tabular representations of strategy-specific development domains. An important benefit of a spatial (GIS) framework is that it provides a powerful means of organizing and integrating a very diverse range of disciplinary and data inputs. At a more conceptual level we propose that it is the characterization of location, not the narrowly-focused characterization of land, that is more properly the focus of attention from a development perspective. The paper includes appropriate examples of spatial analysis using data from East Africa and Burkina Faso, and concludes with an appendix describing and interpreting regional climate and soil data for Sub-Saharan Africa that was directly relevant to our original goal.Spatial analysis (Statistics), Agricultural development., Burkina Faso., Africa, Sub-Saharan.,

Interaction and Behaviour Evaluation for Smart Homes: Data Collection and Analytics in the ScaledHome Project

Nowadays more and more devices are becoming "smart", in fact they can take autonomous decision and interact proactively with the surrounding environment. Smart home is just one of the most popular terms related with this relevant change we are witnessing and its relevance in this project is mainly due to the fact that the residential sector account an important percentage in terms of energy consumption. New ways to share and save energy have to be taken into account in order to optimize the usage of the devices needed by houses to make the environment cozy and comfortable for their inhabitants. The work done with Professor Turgut's team has improved the knowledge in the smart home system area providing a scalable and reliable architecture, a new dataset and an example of application of these data useful to save energy while satisfying the demands of its inhabitants

On-device training of neural network models on embedded systems

En els últims anys, la creixent popularitat de l'aprenentatge automàtic i la intel·ligència artificial ha tingut un impacte en els dispositius IoT. Malgrat la seva capacitat informàtica limitada, hi ha un interès creixent en entrenar xarxes neuronals directament en aquests dispositius. El mètode tradicional d'entrenar models en màquines potents i desplegar-los en microcontroladors presenta limitacions en adaptabilitat i privacitat de dades. No obstant això, no hi ha moltes implementacions disponibles per a l'entrenament en dispositiu amb xarxes neuronals. Frameworks populars com Tensorflow i PyTorch ofereixen mètodes per desplegar models entrenats en microcontroladors, però aquest enfocament no permet que els models segueixin aprenent de noves dades no vistes. Aquest treball presenta el desenvolupament d'una biblioteca d'aprenentatge automàtic escrita en C++ sense dependre de llibreries externes. La biblioteca se centra en la flexibilitat i adaptabilitat tant per a l'ús independent com per al Federated Learning en dispositius petits. Per validar la nostra implementació, vam comparar el rendiment dels models creats amb la nostra biblioteca amb una implementació de referència en Tensorflow/Keras. A més, vam desplegar les xarxes neuronals desenvolupades en la placa de microcontrolador Arduino Portenta i vam obtenir resultats prometedors amb l'entrenament en dispositiu.The popularity of machine learning and artificial intelligence has impacted IoT devices. Despite limited computing capacity, there's growing interest in training neural networks directly on these devices. Traditional methods of training models on powerful machines and deploying them to microcontrollers face limitations in adaptability and data privacy. However, there are few available implementations for on-device training with deep neural networks. Popular frameworks like Tensorflow and PyTorch allow deploying trained models on microcontrollers, but they don't support ongoing learning from new data. This work introduces a C++ machine learning library that doesn't rely on third-party dependencies. The library prioritizes flexibility and adaptability for standalone usage and federated learning on edge devices. To validate the implementation, we compare the performance of models created with our library to a reference implementation in Tensorflow/Keras. Additionally, we successfully deploy the developed neural networks on the Arduino Portenta microcontroller board, achieving promising results with on-device training

Discovering Causal Relations and Equations from Data

Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries. Discoveries emerge from observing the world and, when possible, performing interventional studies in the system under study. With the advent of big data and the use of data-driven methods, causal and equation discovery fields have grown and made progress in computer science, physics, statistics, philosophy, and many applied fields. All these domains are intertwined and can be used to discover causal relations, physical laws, and equations from observational data. This paper reviews the concepts, methods, and relevant works on causal and equation discovery in the broad field of Physics and outlines the most important challenges and promising future lines of research. We also provide a taxonomy for observational causal and equation discovery, point out connections, and showcase a complete set of case studies in Earth and climate sciences, fluid dynamics and mechanics, and the neurosciences. This review demonstrates that discovering fundamental laws and causal relations by observing natural phenomena is being revolutionised with the efficient exploitation of observational data, modern machine learning algorithms and the interaction with domain knowledge. Exciting times are ahead with many challenges and opportunities to improve our understanding of complex systems.Comment: 137 page

Computing on the Edge of the Network

Um Systeme der fünften Generation zellularer Kommunikationsnetze (5G) zu ermöglichen, sind Energie effiziente Architekturen erforderlich, die eine zuverlässige Serviceplattform für die Bereitstellung von 5G-Diensten und darüber hinaus bieten können. Device Enhanced Edge Computing ist eine Ableitung des Multi-Access Edge Computing (MEC), das Rechen- und Speicherressourcen direkt auf den Endgeräten bereitstellt. Die Bedeutung dieses Konzepts wird durch die steigenden Anforderungen von rechenintensiven Anwendungen mit extrem niedriger Latenzzeit belegt, die den MEC-Server allein und den drahtlosen Kanal überfordern. Diese Dissertation stellt ein Berechnungs-Auslagerungsframework mit Berücksichtigung von Energie, Mobilität und Anreizen in einem gerätegestützten MEC-System mit mehreren Benutzern und mehreren Aufgaben vor, das die gegenseitige Abhängigkeit der Aufgaben sowie die Latenzanforderungen der Anwendungen berücksichtigt.To enable fifth generation cellular communication network (5G) systems, energy efficient architectures are required that can provide a reliable service platform for the delivery of 5G services and beyond. Device Enhanced Edge Computing is a derivative of Multi-Access Edge Computing (MEC), which provides computing and storage resources directly on the end devices. The importance of this concept is evidenced by the increasing demands of ultra-low latency computationally intensive applications that overwhelm the MEC server alone and the wireless channel. This dissertation presents a computational offloading framework considering energy, mobility and incentives in a multi-user, multi-task device-based MEC system that takes into account task interdependence and application latency requirements

Graph-based reasoning in collaborative knowledge management for industrial maintenance

Capitalization and sharing of lessons learned play an essential role in managing the activities of industrial systems. This is particularly the case for the maintenance management, especially for distributed systems often associated with collaborative decision-making systems. Our contribution focuses on the formalization of the expert knowledge required for maintenance actors that will easily engage support tools to accomplish their missions in collaborative frameworks. To do this, we use the conceptual graphs formalism with their reasoning operations for the comparison and integration of several conceptual graph rules corresponding to different viewpoint of experts. The proposed approach is applied to a case study focusing on the maintenance management of a rotary machinery system

Improving Android app security and privacy with developers

Existing research has uncovered many security vulnerabilities in Android applications (apps) caused by inexperienced, and unmotivated developers. Especially, the lack of tool support makes it hard for developers to avoid common security and privacy problems in Android apps. As a result, this leads to apps with security vulnerability that exposes end users to a multitude of attacks. This thesis presents a line of work that studies and supports Android developers in writing more secure code. We first studied to which extent tool support can help developers in creating more secure applications. To this end, we developed and evaluated an Android Studio extension that identifies common security problems of Android apps, and provides developers suggestions to more secure alternatives. Subsequently, we focused on the issue of outdated third-party libraries in apps which also is the root cause for a variety of security vulnerabilities. Therefore, we analyzed all popular 3rd party libraries in the Android ecosystem, and provided developers feedback and guidance in the form of tool support in their development environment to fix such security problems. In the second part of this thesis, we empirically studied and measured the impact of user reviews on app security and privacy evolution. Thus, we built a review classifier to identify security and privacy related reviews and performed regression analysis to measure their impact on the evolution of security and privacy in Android apps. Based on our results we proposed several suggestions to improve the security and privacy of Android apps by leveraging user feedbacks to create incentives for developers to improve their apps toward better versions.Die bisherige Forschung zeigt eine Vielzahl von Sicherheitslücken in Android-Applikationen auf, welche sich auf unerfahrene und unmotivierte Entwickler zurückführen lassen. Insbesondere ein Mangel an Unterstützung durch Tools erschwert es den Entwicklern, häufig auftretende Sicherheits- und Datenschutzprobleme in Android Apps zu vermeiden. Als Folge führt dies zu Apps mit Sicherheitsschwachstellen, die Benutzer einer Vielzahl von Angriffen aussetzen. Diese Dissertation präsentiert eine Reihe von Forschungsarbeiten, die Android-Entwickler bei der Entwicklung von sichereren Apps untersucht und unterstützt. In einem ersten Schritt untersuchten wir, inwieweit die Tool-Unterstützung Entwicklern beim Schreiben von sicherem Code helfen kann. Zu diesem Zweck entwickelten und evaluierten wir eine Android Studio-Erweiterung, die gängige Sicherheitsprobleme von Android-Apps identifiziert und Entwicklern Vorschläge für sicherere Alternativen bietet. Daran anknüpfend, konzentrierten wir uns auf das Problem veralteter Bibliotheken von Drittanbietern in Apps, die ebenfalls häufig die Ursache von Sicherheitslücken sein können. Hierzu analysierten wir alle gängigen 3rd-Party-Bibliotheken im Android-Ökosystem und gaben den Entwicklern Feedback und Anleitung in Form von Tool-Unterstützung in ihrer Entwicklungsumgebung, um solche Sicherheitsprobleme zu beheben. Im zweiten Teil dieser Dissertation untersuchten wir empirisch die Auswirkungen von Benutzer-Reviews im Android Appstore auf die Entwicklung der Sicherheit und des Datenschutzes von Apps. Zu diesem Zweck entwickelten wir einen Review-Klassifikator, welcher in der Lage ist sicherheits- und datenschutzbezogene Reviews zu identifizieren. Nachfolgend untersuchten wir den Einfluss solcher Reviews auf die Entwicklung der Sicherheit und des Datenschutzes in Android-Apps mithilfe einer Regressionsanalyse. Basierend auf unseren Ergebnissen präsentieren wir verschiedene Vorschläge zur Verbesserung der Sicherheit und des Datenschutzes von Android-Apps, welche die Reviews der Benutzer zur Schaffung von Anreizen für Entwickler nutzen