2,894 research outputs found

    Evaluation Methodologies in Software Protection Research

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    Man-at-the-end (MATE) attackers have full control over the system on which the attacked software runs, and try to break the confidentiality or integrity of assets embedded in the software. Both companies and malware authors want to prevent such attacks. This has driven an arms race between attackers and defenders, resulting in a plethora of different protection and analysis methods. However, it remains difficult to measure the strength of protections because MATE attackers can reach their goals in many different ways and a universally accepted evaluation methodology does not exist. This survey systematically reviews the evaluation methodologies of papers on obfuscation, a major class of protections against MATE attacks. For 572 papers, we collected 113 aspects of their evaluation methodologies, ranging from sample set types and sizes, over sample treatment, to performed measurements. We provide detailed insights into how the academic state of the art evaluates both the protections and analyses thereon. In summary, there is a clear need for better evaluation methodologies. We identify nine challenges for software protection evaluations, which represent threats to the validity, reproducibility, and interpretation of research results in the context of MATE attacks

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    Current issues of the management of socio-economic systems in terms of globalization challenges

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    The authors of the scientific monograph have come to the conclusion that the management of socio-economic systems in the terms of global challenges requires the use of mechanisms to ensure security, optimise the use of resource potential, increase competitiveness, and provide state support to economic entities. Basic research focuses on assessment of economic entities in the terms of global challenges, analysis of the financial system, migration flows, logistics and product exports, territorial development. The research results have been implemented in the different decision-making models in the context of global challenges, strategic planning, financial and food security, education management, information technology and innovation. The results of the study can be used in the developing of directions, programmes and strategies for sustainable development of economic entities and regions, increasing the competitiveness of products and services, decision-making at the level of ministries and agencies that regulate the processes of managing socio-economic systems. The results can also be used by students and young scientists in the educational process and conducting scientific research on the management of socio-economic systems in the terms of global challenges

    Modern meat: the next generation of meat from cells

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    Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community. The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World. The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia

    New perspectives on A.I. in sentencing. Human decision-making between risk assessment tools and protection of humans rights.

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    The aim of this thesis is to investigate a field that until a few years ago was foreign to and distant from the penal system. The purpose of this undertaking is to account for the role that technology could plays in the Italian Criminal Law system. More specifically, this thesis attempts to scrutinize a very intricate phase of adjudication. After deciding on the type of an individual's liability, a judge must decide on the severity of the penalty. This type of decision implies a prognostic assessment that looks to the future. It is precisely in this field and in prognostic assessments that, as has already been anticipated in the United, instruments and processes are inserted in the pre-trial but also in the decision-making phase. In this contribution, we attempt to describe the current state of this field, trying, as a matter of method, to select the most relevant or most used tools. Using comparative and qualitative methods, the uses of some of these instruments in the supranational legal system are analyzed. Focusing attention on the Italian system, an attempt was made to investigate the nature of the element of an individual's ‘social dangerousness’ (pericolosità sociale) and capacity to commit offences, types of assessments that are fundamental in our system because they are part of various types of decisions, including the choice of the best sanctioning treatment. It was decided to turn our attention to this latter field because it is believed that the judge does not always have the time, the means and the ability to assess all the elements of a subject and identify the best 'individualizing' treatment in order to fully realize the function of Article 27, paragraph 3 of the Constitution

    Distributed consensus in wireless network

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    Connected autonomous systems, which are powered by the synergistic integration of the Internet of Things (IoT), Artificial Intelligence (AI), and 5G technologies, predominantly rely on a central node for making mission-critical decisions. This reliance poses a significant challenge that the condition and capability of the central node largely determine the reliability and effectiveness of decision-making. Maintaining such a centralized system, especially in large-scale wireless networks, can be prohibitively expensive and encounters scalability challenges. In light of these limitations, there’s a compelling need for innovative methods to address the increasing demands of reliability and latency, especially in mission-critical networks where cooperative decision-making is paramount. One promising avenue lies in the distributed consensus protocol, a mechanism intrinsic to distributed computing systems. These protocols offer enhanced robustness, ensuring continued functionality and responsiveness in decision-making even in the face of potential node or communication failures. This thesis pivots on the idea of leveraging distributed consensus to bolster the reliability of mission-critical decision-making within wireless networks, which delves deep into the performance characteristics of wireless distributed consensus, analyzing and subsequently optimizing its attributes, specifically focusing on reliability and latency. The research begins with a fundamental model of consensus reliability in an crash fault tolerance protocol Raft. A novel metric termed ReliabilityGain is introduced to analyze the performance of distributed consensus in wireless network. This innovative concept elucidates the linear correlation between the reliability inherent to consensus-driven decision-making and the reliability of communication link transmission. An intriguing discovery made in my study is the inherent trade-off between the time latency of achieving consensus and its reliability. These two variables appear to be in contradiction, which brings further performance optimization issues. The performance of the Crash and Byzantine fault tolerance protocol is scrutinized and they are compared with original centralized consensus. This exploration becomes particularly pertinent when communication failures occur in wireless distributed consensus. The analytical results are juxtaposed with performance metrics derived from a centralized consensus mechanism. This comparative analysis illuminates the relative merits and demerits of these consensus strategies, evaluated from the dual perspectives of comprehensive consensus reliability and communication latency. In light of the insights gained from the detailed analysis of the Raft and Hotstuff BFT protocols, my thesis further ventures into the realm of optimization strategies for wireless distributed consensus. A central facet of this exploration is the introduction of a tailored communication resource allocation scheme. This scheme, rooted in maximizing the performance of consensus mechanisms, dynamically assesses the network conditions and allocates communication resources such as transmit power and bandwidth to ensure efficient and timely decision-making, which ensures that even in varied and unpredictable network conditions, consensus can be achieved with minimized latency and maximized reliability. The research introduces an adaptive protocol of distributed consensus in wireless network. This proposed adaptive protocol’s strength lies in its ability to autonomously construct consensus-enabled network even if node failures or communication disruptions occur, which ensures that the network’s decision-making process remains uninterrupted and efficient, irrespective of external challenges. The sharding mechanism, which is regarded as an effective solution to scalability issues in distributed system, does not only aid in managing vast networks more efficiently but also ensure that any disruption in one shard cannot compromise the functionality of the entire network. Therefore, this thesis shows the reliability and security analysis of sharding that implemented in wireless distributed system. In essence, these intertwined strategies, rooted in the intricate dance of communication resource allocation, adaptability, and sharding, together form the bedrock of my contributions to enhancing the performance of wireless distributed consensus

    Advanced analytical methods for fraud detection: a systematic literature review

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    The developments of the digital era demand new ways of producing goods and rendering services. This fast-paced evolution in the companies implies a new approach from the auditors, who must keep up with the constant transformation. With the dynamic dimensions of data, it is important to seize the opportunity to add value to the companies. The need to apply more robust methods to detect fraud is evident. In this thesis the use of advanced analytical methods for fraud detection will be investigated, through the analysis of the existent literature on this topic. Both a systematic review of the literature and a bibliometric approach will be applied to the most appropriate database to measure the scientific production and current trends. This study intends to contribute to the academic research that have been conducted, in order to centralize the existing information on this topic

    Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

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    Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications
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