97 research outputs found

    Computer Aided Verification

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    This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book

    Scalable Hash Tables

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    The term scalability with regards to this dissertation has two meanings: It means taking the best possible advantage of the provided resources (both computational and memory resources) and it also means scaling data structures in the literal sense, i.e., growing the capacity, by “rescaling” the table. Scaling well to computational resources implies constructing the fastest best per- forming algorithms and data structures. On today’s many-core machines the best performance is immediately associated with parallelism. Since CPU frequencies have stopped growing about 10-15 years ago, parallelism is the only way to take ad- vantage of growing computational resources. But for data structures in general and hash tables in particular performance is not only linked to faster computations. The most execution time is actually spent waiting for memory. Thus optimizing data structures to reduce the amount of memory accesses or to take better advantage of the memory hierarchy especially through predictable access patterns and prefetch- ing is just as important. In terms of scaling the size of hash tables we have identified three domains where scaling hash-based data structures have been lacking previously, i.e., space effi- cient growing, concurrent hash tables, and Approximate Membership Query data structures (AMQ-filter). Throughout this dissertation, we describe the problems in these areas and develop efficient solutions. We highlight three different libraries that we have developed over the course of this dissertation, each containing mul- tiple implementations that have shown throughout our testing to be among the best implementations in their respective domains. In this composition they offer a comprehensive toolbox that can be used to solve many kinds of hashing related problems or to develop individual solutions for further ones. DySECT is a library for space efficient hash tables specifically growing space effi- cient hash tables that scale with their input size. It contains the namesake DySECT data structure in addition to a number of different probing and cuckoo based im- plementations. Growt is a library for highly efficient concurrent hash tables. It contains a very fast base table and a number of extensions to adapt this table to match any purpose. All extension can be combined to create a variety of different interfaces. In our extensive experimental evaluation, each adaptation has shown to be among the best hash tables for their specific purpose. Lpqfilter is a library for concurrent approximate membership query (AMQ) data structures. It contains some original data structures, like the linear probing quotient filter, as well as some novel approaches to dynamically sized quotient filters

    Tools and Algorithms for the Construction and Analysis of Systems

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    This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems

    DISRUPTION RECOVERY IN COMMERCIAL AVIATION

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    This thesis presents three major contributions for commercial aviation planning and disruption recovery in commercial aviation. The first contribution presented in this thesis consists of a flight planning model to calculate Block Time and Fuel (BTF) consumed for an aircraft model during the flight. The BTF model computes the ground distance between the origin and destination airports, derives the flight’s cruise altitude, and by integrating two institutional data sets calculates the duration and the fuel consumed for the whole of taxi-out, take-off, climb, cruise, descent, approach, landing, and taxi-in phases. The model renders very good results for block time and consumed fuel however, it does not consider aircraft weight loss neither the influence of the wind. The second contribution of this thesis consists of a recovery procedure for disrupted aircraft rotations, the Constructive Heuristic for the Aircraft Recovery Problem (CHARP). The CHARP recovers the infeasible rotation combining a meta-heuristic that performs a pincer movement over the search space and Constraint Programming (CP). Additionally, the CHARP uses Constraint Propagation to reduce the size of the search therefore reducing computing. The initial experiments demonstrated that if Constraint Propagation was not used computing time would double. The recovery strategy included flight creation delays and cancellations however it did not include aircraft swap. The third contribution of this thesis combines the BTF model and the CHARP. Since the BTF model returns lower block time flights than those used by the CHARP this thesis investigates six disruption scenarios with shorter block time

    Big Data and Artificial Intelligence in Digital Finance

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    This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

    Selected Papers from the 5th International Electronic Conference on Sensors and Applications

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    This Special Issue comprises selected papers from the proceedings of the 5th International Electronic Conference on Sensors and Applications, held on 15–30 November 2018, on sciforum.net, an online platform for hosting scholarly e-conferences and discussion groups. In this 5th edition of the electronic conference, contributors were invited to provide papers and presentations from the field of sensors and applications at large, resulting in a wide variety of excellent submissions and topic areas. Papers which attracted the most interest on the web or that provided a particularly innovative contribution were selected for publication in this collection. These peer-reviewed papers are published with the aim of rapid and wide dissemination of research results, developments, and applications. We hope this conference series will grow rapidly in the future and become recognized as a new way and venue by which to (electronically) present new developments related to the field of sensors and their applications

    Graduate Academic Catalog 2021-2022

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    Reinforcement Learning

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    Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

    Computer Aided Verification

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    This open access two-volume set LNCS 11561 and 11562 constitutes the refereed proceedings of the 31st International Conference on Computer Aided Verification, CAV 2019, held in New York City, USA, in July 2019. The 52 full papers presented together with 13 tool papers and 2 case studies, were carefully reviewed and selected from 258 submissions. The papers were organized in the following topical sections: Part I: automata and timed systems; security and hyperproperties; synthesis; model checking; cyber-physical systems and machine learning; probabilistic systems, runtime techniques; dynamical, hybrid, and reactive systems; Part II: logics, decision procedures; and solvers; numerical programs; verification; distributed systems and networks; verification and invariants; and concurrency

    Robust and Scalable Data Representation and Analysis Leveraging Isometric Transformations and Sparsity

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    The main focus of this doctoral thesis is to study the problem of robust and scalable data representation and analysis. The success of any machine learning and signal processing framework relies on how the data is represented and analyzed. Thus, in this work, we focus on three closely related problems: (i) supervised representation learning, (ii) unsupervised representation learning, and (iii) fault tolerant data analysis. For the first task, we put forward new theoretical results on why a certain family of neural networks can become extremely deep and how we can improve this scalability property in a mathematically sound manner. We further investigate how we can employ them to generate data representations that are robust to outliers and to retrieve representative subsets of huge datasets. For the second task, we will discuss two different methods, namely compressive sensing (CS) and nonnegative matrix factorization (NMF). We show that we can employ prior knowledge, such as slow variation in time, to introduce an unsupervised learning component to the traditional CS framework and to learn better compressed representations. Furthermore, we show that prior knowledge and sparsity constraint can be used in the context of NMF, not to find sparse hidden factors, but to enforce other structures, such as piece-wise continuity. Finally, for the third task, we investigate how a data analysis framework can become robust to faulty data and faulty data processors. We employ Bayesian inference and propose a scheme that can solve the CS recovery problem in an asynchronous parallel manner. Furthermore, we show how sparsity can be used to make an optimization problem robust to faulty data measurements. The methods investigated in this work have applications in different practical problems such as resource allocation in wireless networks, source localization, image/video classification, and search engines. A detailed discussion of these practical applications will be presented for each method
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