1,433 research outputs found
Evaluating Architectural Safeguards for Uncertain AI Black-Box Components
Although tremendous progress has been made in Artificial Intelligence (AI), it entails new challenges. The growing complexity of learning tasks requires more complex AI components, which increasingly exhibit unreliable behaviour. In this book, we present a model-driven approach to model architectural safeguards for AI components and analyse their effect on the overall system reliability
Adaptive Microarchitectural Optimizations to Improve Performance and Security of Multi-Core Architectures
With the current technological barriers, microarchitectural optimizations are increasingly important to ensure performance scalability of computing systems. The shift to multi-core architectures increases the demands on the memory system, and amplifies the role of microarchitectural optimizations in performance improvement. In a multi-core system, microarchitectural resources are usually shared, such as the cache, to maximize utilization but sharing can also lead to contention and lower performance. This can be mitigated through partitioning of shared caches.However, microarchitectural optimizations which were assumed to be fundamentally secure for a long time, can be used in side-channel attacks to exploit secrets, as cryptographic keys. Timing-based side-channels exploit predictable timing variations due to the interaction with microarchitectural optimizations during program execution. Going forward, there is a strong need to be able to leverage microarchitectural optimizations for performance without compromising security. This thesis contributes with three adaptive microarchitectural resource management optimizations to improve security and/or\ua0performance\ua0of multi-core architectures\ua0and a systematization-of-knowledge of timing-based side-channel attacks.\ua0We observe that to achieve high-performance cache partitioning in a multi-core system\ua0three requirements need to be met: i) fine-granularity of partitions, ii) locality-aware placement and iii) frequent changes. These requirements lead to\ua0high overheads for current centralized partitioning solutions, especially as the number of cores in the\ua0system increases. To address this problem, we present an adaptive and scalable cache partitioning solution (DELTA) using a distributed and asynchronous allocation algorithm. The\ua0allocations occur through core-to-core challenges, where applications with larger performance benefit will gain cache capacity. The\ua0solution is implementable in hardware, due to low computational complexity, and can scale to large core counts.According to our analysis, better performance can be achieved by coordination of multiple optimizations for different resources, e.g., off-chip bandwidth and cache, but is challenging due to the increased number of possible allocations which need to be evaluated.\ua0Based on these observations, we present a solution (CBP) for coordinated management of the optimizations: cache partitioning, bandwidth partitioning and prefetching.\ua0Efficient allocations, considering the inter-resource interactions and trade-offs, are achieved using local resource managers to limit the solution space.The continuously growing number of\ua0side-channel attacks leveraging\ua0microarchitectural optimizations prompts us to review attacks and defenses to understand the vulnerabilities of different microarchitectural optimizations. We identify the four root causes of timing-based side-channel attacks: determinism, sharing, access violation\ua0and information flow.\ua0Our key insight is that eliminating any of the exploited root causes, in any of the attack steps, is enough to provide protection.\ua0Based on our framework, we present a systematization of the attacks and defenses on a wide range of microarchitectural optimizations, which highlights their key similarities.\ua0Shared caches are an attractive attack surface for side-channel attacks, while defenses need to be efficient since the cache is crucial for performance.\ua0To address this issue, we present an adaptive and scalable cache partitioning solution (SCALE) for protection against cache side-channel attacks. The solution leverages randomness,\ua0and provides quantifiable and information theoretic security guarantees using differential privacy. The solution closes the performance gap to a state-of-the-art non-secure allocation policy for a mix of secure and non-secure applications
A robotic platform for precision agriculture and applications
Agricultural techniques have been improved over the centuries to match with the growing demand of an increase in global population. Farming applications are facing new challenges to satisfy global needs and the recent technology advancements in terms of robotic platforms can be exploited.
As the orchard management is one of the most challenging applications because of its tree structure and the required interaction with the environment, it was targeted also by the University of Bologna research group to provide a customized solution addressing new concept for agricultural vehicles.
The result of this research has blossomed into a new lightweight tracked vehicle capable of performing autonomous navigation both in the open-filed scenario and while travelling inside orchards for what has been called in-row navigation. The mechanical design concept, together with customized software implementation has been detailed to highlight the strengths of the platform and some further improvements envisioned to improve the overall performances.
Static stability testing has proved that the vehicle can withstand steep slopes scenarios. Some improvements have also been investigated to refine the estimation of the slippage that occurs during turning maneuvers and that is typical of skid-steering tracked vehicles.
The software architecture has been implemented using the Robot Operating System (ROS) framework, so to exploit community available packages related to common and basic functions, such as sensor interfaces, while allowing dedicated custom implementation of the navigation algorithm developed.
Real-world testing inside the university’s experimental orchards have proven the robustness and stability of the solution with more than 800 hours of fieldwork.
The vehicle has also enabled a wide range of autonomous tasks such as spraying, mowing, and on-the-field data collection capabilities. The latter can be exploited to automatically estimate relevant orchard properties such as fruit counting and sizing, canopy properties estimation, and autonomous fruit harvesting with post-harvesting estimations.Le tecniche agricole sono state migliorate nel corso dei secoli per soddisfare la crescente domanda di aumento della popolazione mondiale. I recenti progressi tecnologici in termini di piattaforme robotiche possono essere sfruttati in questo contesto.
Poiché la gestione del frutteto è una delle applicazioni più impegnative, a causa della sua struttura arborea e della necessaria interazione con l'ambiente, è stata oggetto di ricerca per fornire una soluzione personalizzata che sviluppi un nuovo concetto di veicolo agricolo.
Il risultato si è concretizzato in un veicolo cingolato leggero, capace di effettuare una navigazione autonoma sia nello scenario di pieno campo che all'interno dei frutteti (navigazione interfilare). La progettazione meccanica, insieme all'implementazione del software, sono stati dettagliati per evidenziarne i punti di forza, accanto ad alcuni ulteriori miglioramenti previsti per incrementarne le prestazioni complessive.
I test di stabilità statica hanno dimostrato che il veicolo può resistere a ripidi pendii. Sono stati inoltre studiati miglioramenti per affinare la stima dello slittamento che si verifica durante le manovre di svolta, tipico dei veicoli cingolati.
L'architettura software è stata implementata utilizzando il framework Robot Operating System (ROS), in modo da sfruttare i pacchetti disponibili relativi a componenti base, come le interfacce dei sensori, e consentendo al contempo un'implementazione personalizzata degli algoritmi di navigazione sviluppati.
I test in condizioni reali all'interno dei frutteti sperimentali dell'università hanno dimostrato la robustezza e la stabilità della soluzione con oltre 800 ore di lavoro sul campo.
Il veicolo ha permesso di attivare e svolgere un'ampia gamma di attività agricole in maniera autonoma, come l'irrorazione, la falciatura e la raccolta di dati sul campo. Questi ultimi possono essere sfruttati per stimare automaticamente le proprietà più rilevanti del frutteto, come il conteggio e la calibratura dei frutti, la stima delle proprietà della chioma e la raccolta autonoma dei frutti con stime post-raccolta
Urban Visual Intelligence: Studying Cities with AI and Street-level Imagery
The visual dimension of cities has been a fundamental subject in urban
studies, since the pioneering work of scholars such as Sitte, Lynch, Arnheim,
and Jacobs. Several decades later, big data and artificial intelligence (AI)
are revolutionizing how people move, sense, and interact with cities. This
paper reviews the literature on the appearance and function of cities to
illustrate how visual information has been used to understand them. A
conceptual framework, Urban Visual Intelligence, is introduced to
systematically elaborate on how new image data sources and AI techniques are
reshaping the way researchers perceive and measure cities, enabling the study
of the physical environment and its interactions with socioeconomic
environments at various scales. The paper argues that these new approaches
enable researchers to revisit the classic urban theories and themes, and
potentially help cities create environments that are more in line with human
behaviors and aspirations in the digital age
Leveraging Sentiment Analysis for Twitter Data to Uncover User Opinions and Emotions
Huge amounts of emotion are expressed on social media in the form of tweets, blogs, and updates to posts, statuses, etc. Twitter, one of the most well-known microblogging platforms, is used in this essay. Twitter is a social networking site that enables users to post status updates and other brief messages with a maximum character count of 280. Twitter sentiment analysis is the application of sentiment analysis to Twitter data (tweets) in order to derive user sentiments and opinions. Due to the extensive usage, we intend to reflect the mood of the general people by examining the thoughts conveyed in the tweets. Numerous applications require the analysis of public opinion, including businesses attempting to gauge the market response to their products, the prediction of political outcomes, and the analysis of socioeconomic phenomena like stock exchange. Sentiment classification attempts to estimate the sentiment polarity of user updates automatically. So, in order to categorize a tweet as good or negative, we need a model that can accurately discern sarcasm from the lexical meaning of the text. The main objective is to create a practical classifier that can accurately classify the sentiment of twitter streams relating to GST and Tax. Python is used to carry out the suggested algorithm
Food - Media - Senses: Interdisciplinary Approaches
Food is more than just nutrition. Its preparation, presentation and consumption is a multifold communicative practice which includes the meal's design and its whole field of experience. How is food represented in cookbooks, product packaging or in paintings? How is dining semantically charged? How is the sensuality of eating treated in different cultural contexts? In order to acknowledge the material and media-related aspects of eating as a cultural praxis, experts from media studies, art history, literary studies, philosophy, experimental psychology, anthropology, food studies, cultural studies and design studies share their specific approaches
Digital Twins and Blockchain for IoT Management
We live in a data-driven world powered by sensors getting data from anywhere at any time. This advancement is possible thanks to the Internet of Things (IoT). IoT embeds common physical objects with heterogeneous sensing, actuating, and communication capabilities to collect data from the environment and people. These objects are generally known as things and exchange data with other things, entities, computational processes, and systems over the internet. Consequently, a web of devices and computational processes emerges involving billions of entities collecting, processing, and sharing data. As a result, we now have an internet of entities/things that process and produce data, an ever-growing volume that can easily exceed petabytes. Therefore, there is a need for novel management approaches to handle the previously unheard number of IoT devices, processes, and data streams.
This dissertation focuses on solutions for IoT management using decentralized technologies. A massive number of IoT devices interact with software and hardware components and are owned by different people. Therefore, there is a need for decentralized management. Blockchain is a capable and promising distributed ledger technology with features to support decentralized systems with large numbers of devices. People should not have to interact with these devices or data streams directly. Therefore, there is a need to abstract access to these components. Digital twins are software artifacts that can abstract an object, a process, or a system to enable communication between the physical and digital worlds. Fog/edge computing is the alternative to the cloud to provide services with less latency. This research uses blockchain technology, digital twins, and fog/edge computing for IoT management. The systems developed in this dissertation enable configuration, self-management, zero-trust management, and data streaming view provisioning from a fog/edge layer. In this way, this massive number of things and the data they produce are managed through services distributed across nodes close to them, providing access and configuration security and privacy protection
- …