4,387 research outputs found

    Contract-Based Design of Dataflow Programs

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    Quality and correctness are becoming increasingly important aspects of software development, as our reliance on software systems in everyday life continues to increase. Highly complex software systems are today found in critical appliances such as medical equipment, cars, and telecommunication infrastructure. Failures in these kinds of systems may have disastrous consequences. At the same time, modern computer platforms are increasingly concurrent, as the computational capacity of modern CPUs is improved mainly by increasing the number of processor cores. Computer platforms are also becoming increasingly parallel, distributed and heterogeneous, often involving special processing units, such as graphics processing units (GPU) or digital signal processors (DSP) for performing specific tasks more efficiently than possible on general-purpose CPUs. These modern platforms allow implementing increasingly complex functionality in software. Cost efficient development of software that efficiently exploits the power of this type of platforms and at the same time ensures correctness is, however, a challenging task. Dataflow programming has become popular in development of safetycritical software in many domains in the embedded community. For instance, in the automotive domain, the dataflow language Simulink has become widely used in model-based design of control software. However, for more complex functionality, this model of computation may not be expressive enough. In the signal processing domain, more expressive, dynamic models of computation have attracted much attention. These models of computation have, however, not gained as significant uptake in safety-critical domains due to a great extent to that it is challenging to provide guarantees regarding e.g. timing or determinism under these more expressive models of computation. Contract-based design has become widespread to specify and verify correctness properties of software components. A contract consists of assumptions (preconditions) regarding the input data and guarantees (postconditions) regarding the output data. By verifying a component with respect to its contract, it is ensured that the output fulfils the guarantees, assuming that the input fulfils the assumptions. While contract-based verification of traditional object-oriented programs has been researched extensively, verification of asynchronous dataflow programs has not been researched to the same extent. In this thesis, a contract-based design framework tailored specifically to dataflow programs is proposed. The proposed framework supports both an extensive subset of the discrete-time Simulink synchronous language, as well as a more general, asynchronous and dynamic, dataflow language. The proposed contract-based verification techniques are automatic, only guided by user-provided invariants, and based on encoding dataflow programs in existing, mature verification tools for sequential programs, such as the Boogie guarded command language and its associated verifier. It is shown how dataflow programs, with components implemented in an expressive programming language with support for matrix computations, can be efficiently encoded in such a verifier. Furthermore, it is also shown that contract-based design can be used to improve runtime performance of dataflow programs by allowing more scheduling decisions to be made at compile-time. All the proposed techniques have been implemented in prototype tools and evaluated on a large number of different programs. Based on the evaluation, the methods were proven to work in practice and to scale to real-world programs.Kvalitet och korrekthet blir idag allt viktigare aspekter inom mjukvaruutveckling, dÄ vi i allt högre grad förlitar oss pÄ mjukvarusystem i vÄra vardagliga sysslor. Mycket komplicerade mjukvarusystem finns idag i kritiska tillÀmpningar sÄ som medicinsk utrustning, bilar och infrastruktur för telekommunikation. Fel som uppstÄr i de hÀr typerna av system kan ha katastrofala följder. Samtidigt utvecklas kapaciteten hos moderna datorplattformar idag frÀmst genom att öka antalet processorkÀrnor. DÀrtill blir datorplattformar allt mer parallella, distribuerade och heterogena, och innefattar ofta specialla processorer sÄ som grafikprocessorer (GPU) eller signalprocessorer (DSP) för att utföra specifika berÀkningar snabbare Àn vad som Àr möjligt pÄ vanliga processorer. Den hÀr typen av plattformar möjligör implementering av allt mer komplicerade berÀkningar i mjukvara. Kostnadseffektiv utveckling av mjukvara som effektivt utnyttjar kapaciteten i den hÀr typen av plattformar och samtidigt sÀkerstÀller korrekthet Àr emellertid en mycket utmanande uppgift. Dataflödesprogrammering har blivit ett populÀrt sÀtt att utveckla mjukvara inom flera omrÄden som innefattar sÀkerhetskritiska inbyggda datorsystem. Till exempel inom fordonsindustrin har dataflödessprÄket Simulink kommit att anvÀndas i bred utstrÀckning för modellbaserad design av kontrollsystem. För mer komplicerad funktionalitet kan dock den hÀr modellen för berÀkning vara för begrÀnsad betrÀffande vad som kan beksrivas. Inom signalbehandling har mera expressiva och dynamiska modeller för berÀkning attraherat stort intresse. De hÀr modellerna för berÀkning har ÀndÄ inte tagits i bruk i samma utstrÀckning inom sÀkerhetskritiska tillÀmpningar. Det hÀr beror till en stor del pÄ att det Àr betydligt svÄrare att garantera egenskaper gÀllande till exempel timing och determinism under sÄdana hÀr modeller för berÀkning. Kontraktbaserad design har blivit ett vanligt sÀtt att specifiera och verifiera korrekthetsegenskaper hos mjukvarukomponeneter. Ett kontrakt bestÄr av antaganden (förvillkor) gÀllande indata och garantier (eftervillkor) gÀllande utdata. Genom att verifiera en komponent gentemot sitt konktrakt kan man bevisa att utdatan uppfyller garantierna, givet att indatan uppfyller antagandena. Trots att kontraktbaserad verifiering i sig Àr ett mycket beforskat omrÄde, sÄ har inte verifiering av asynkrona dataflödesprogram beforskats i samma utstrÀckning. I den hÀr avhandlingen presenteras ett ramverk för kontraktbaserad design skrÀddarsytt för dataflödesprogram. Det föreslagna ramverket stödjer sÄ vÀl en stor del av det synkrona sprÄket. Simulink med diskret tid som ett mera generellt asynkront och dynamiskt dataflödessprÄk. De föreslagna kontraktbaserade verifieringsteknikerna Àr automatiska. Utöver kontraktets för- och eftervillkor ger anvÀndaren endast de invarianter som krÀvs för att möjliggöra verifieringen. Verifieringsteknikerna grundar sig pÄ att omkoda dataflödesprogram till input för existerande och beprövade verifieringsverktyg för sekventiella program sÄ som Boogie. Avhandlingen visar hur dataflödesprogram implementerade i ett expressivt programmeringssprÄk med inbyggt stöd för matrisoperationer effektivt kan omkodas till input för ett verifieringsverktyg som Boogie. Utöver detta visar avhandlingen ocksÄ att kontraktbaserad design ocksÄ kan förbÀttra prestandan hos dataflödesprogram i körningsskedet genom att möjliggöra flera schemalÀggningsbeslut redan i kompileringsskedet. Alla tekniker som presenteras i avhandlingen har implementerats i prototypverktyg och utvÀrderats pÄ en stor mÀngd olika program. UtvÀrderingen bevisar att teknikerna fungerar i praktiken och Àr tillrÀckligt skalbara för att ocksÄ fungera pÄ program av realistisk storlek

    Modern computing: Vision and challenges

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    Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

    Natural and Technological Hazards in Urban Areas

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    Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events

    A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks

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    Transformer is a deep neural network that employs a self-attention mechanism to comprehend the contextual relationships within sequential data. Unlike conventional neural networks or updated versions of Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM), transformer models excel in handling long dependencies between input sequence elements and enable parallel processing. As a result, transformer-based models have attracted substantial interest among researchers in the field of artificial intelligence. This can be attributed to their immense potential and remarkable achievements, not only in Natural Language Processing (NLP) tasks but also in a wide range of domains, including computer vision, audio and speech processing, healthcare, and the Internet of Things (IoT). Although several survey papers have been published highlighting the transformer's contributions in specific fields, architectural differences, or performance evaluations, there is still a significant absence of a comprehensive survey paper encompassing its major applications across various domains. Therefore, we undertook the task of filling this gap by conducting an extensive survey of proposed transformer models from 2017 to 2022. Our survey encompasses the identification of the top five application domains for transformer-based models, namely: NLP, Computer Vision, Multi-Modality, Audio and Speech Processing, and Signal Processing. We analyze the impact of highly influential transformer-based models in these domains and subsequently classify them based on their respective tasks using a proposed taxonomy. Our aim is to shed light on the existing potential and future possibilities of transformers for enthusiastic researchers, thus contributing to the broader understanding of this groundbreaking technology

    SCV-GNN: Sparse Compressed Vector-based Graph Neural Network Aggregation

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    Graph neural networks (GNNs) have emerged as a powerful tool to process graph-based data in fields like communication networks, molecular interactions, chemistry, social networks, and neuroscience. GNNs are characterized by the ultra-sparse nature of their adjacency matrix that necessitates the development of dedicated hardware beyond general-purpose sparse matrix multipliers. While there has been extensive research on designing dedicated hardware accelerators for GNNs, few have extensively explored the impact of the sparse storage format on the efficiency of the GNN accelerators. This paper proposes SCV-GNN with the novel sparse compressed vectors (SCV) format optimized for the aggregation operation. We use Z-Morton ordering to derive a data-locality-based computation ordering and partitioning scheme. The paper also presents how the proposed SCV-GNN is scalable on a vector processing system. Experimental results over various datasets show that the proposed method achieves a geometric mean speedup of 7.96×7.96\times and 7.04×7.04\times over CSC and CSR aggregation operations, respectively. The proposed method also reduces the memory traffic by a factor of 3.29×3.29\times and 4.37×4.37\times over compressed sparse column (CSC) and compressed sparse row (CSR), respectively. Thus, the proposed novel aggregation format reduces the latency and memory access for GNN inference

    La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.

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    Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (ForlĂŹ Campus) in collaboration with the Romagna Chamber of Commerce (ForlĂŹ-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices

    Adaptive Data-driven Optimization using Transfer Learning for Resilient, Energy-efficient, Resource-aware, and Secure Network Slicing in 5G-Advanced and 6G Wireless Systems

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    Title from PDF of title page, viewed January 31, 2023Dissertation advisor: Cory BeardVitaIncludes bibliographical references (pages 134-141)Dissertation (Ph.D)--Department of Computer Science and Electrical Engineering. University of Missouri--Kansas City, 20225G–Advanced is the next step in the evolution of the fifth–generation (5G) technology. It will introduce a new level of expanded capabilities beyond connections and enables a broader range of advanced applications and use cases. 5G–Advanced will support modern applications with greater mobility and high dependability. Artificial intelligence and Machine Learning will enhance network performance with spectral efficiency and energy savings enhancements. This research established a framework to optimally control and manage an appropriate selection of network slices for incoming requests from diverse applications and services in Beyond 5G networks. The developed DeepSlice model is used to optimize the network and individual slice load efficiency across isolated slices and manage slice lifecycle in case of failure. The DeepSlice framework can predict the unknown connections by utilizing the learning from a developed deep-learning neural network model. The research also addresses threats to the performance, availability, and robustness of B5G networks by proactively preventing and resolving threats. The study proposed a Secure5G framework for authentication, authorization, trust, and control for a network slicing architecture in 5G systems. The developed model prevents the 5G infrastructure from Distributed Denial of Service by analyzing incoming connections and learning from the developed model. The research demonstrates the preventive measure against volume attacks, flooding attacks, and masking (spoofing) attacks. This research builds the framework towards the zero trust objective (never trust, always verify, and verify continuously) that improves resilience. Another fundamental difficulty for wireless network systems is providing a desirable user experience in various network conditions, such as those with varying network loads and bandwidth fluctuations. Mobile Network Operators have long battled unforeseen network traffic events. This research proposed ADAPTIVE6G to tackle the network load estimation problem using knowledge-inspired Transfer Learning by utilizing radio network Key Performance Indicators from network slices to understand and learn network load estimation problems. These algorithms enable Mobile Network Operators to optimally coordinate their computational tasks in stochastic and time-varying network states. Energy efficiency is another significant KPI in tracking the sustainability of network slicing. Increasing traffic demands in 5G dramatically increase the energy consumption of mobile networks. This increase is unsustainable in terms of dollar cost and environmental impact. This research proposed an innovative ECO6G model to attain sustainability and energy efficiency. Research findings suggested that the developed model can reduce network energy costs without negatively impacting performance or end customer experience against the classical Machine Learning and Statistical driven models. The proposed model is validated against the industry-standardized energy efficiency definition, and operational expenditure savings are derived, showing significant cost savings to MNOs.Introduction -- A deep neural network framework towards a resilient, efficient, and secure network slicing in Beyond 5G Networks -- Adaptive resource management techniques for network slicing in Beyond 5G networks using transfer learning -- Energy and cost analysis for network slicing deployment in Beyond 5G networks -- Conclusion and future scop

    Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks

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    Despite the basic premise that next-generation wireless networks (e.g., 6G) will be artificial intelligence (AI)-native, to date, most existing efforts remain either qualitative or incremental extensions to existing ``AI for wireless'' paradigms. Indeed, creating AI-native wireless networks faces significant technical challenges due to the limitations of data-driven, training-intensive AI. These limitations include the black-box nature of the AI models, their curve-fitting nature, which can limit their ability to reason and adapt, their reliance on large amounts of training data, and the energy inefficiency of large neural networks. In response to these limitations, this article presents a comprehensive, forward-looking vision that addresses these shortcomings by introducing a novel framework for building AI-native wireless networks; grounded in the emerging field of causal reasoning. Causal reasoning, founded on causal discovery, causal representation learning, and causal inference, can help build explainable, reasoning-aware, and sustainable wireless networks. Towards fulfilling this vision, we first highlight several wireless networking challenges that can be addressed by causal discovery and representation, including ultra-reliable beamforming for terahertz (THz) systems, near-accurate physical twin modeling for digital twins, training data augmentation, and semantic communication. We showcase how incorporating causal discovery can assist in achieving dynamic adaptability, resilience, and cognition in addressing these challenges. Furthermore, we outline potential frameworks that leverage causal inference to achieve the overarching objectives of future-generation networks, including intent management, dynamic adaptability, human-level cognition, reasoning, and the critical element of time sensitivity
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