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                <text>The rise of Artificial Intelligence applications is accelerating the pace and magnitude of the political, securitarian, and ethical challenges we are now struggling to manage in cyberspace and beyond. So far, the relationship between Artificial Intelligence and cyberspace has been investigated mostly in terms of the effects that AI could have on the digital domain, and thus on our societies. What has been explored less is the opposite relationship, namely, how the cyberspace geopolitics can affect AI. Yet, AI applications have so far suffered from growing unrest, disorder, and lack of normative solutions in cyberspace. As such, from algorithm biases, to surveillance and offensive applications, AI could accelerate multiple growing threats and challenges in and through cyberspace. This report by ISPI and The Brookings Institution is an effort to shed light on this less studied, but extremely relevant, relationship.</text>
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                <text>AI Knowledge Transfer from the University to Society: Applications in High-Impact Sectors brings together examples from the "Innovative Ecosystem with Artificial Intelligence for Andalusia 2025" project at the University of Seville, a series of sub-projects composed of research groups and different institutions or companies that explore the use of Artificial Intelligence in a variety of high-impact sectors to lead innovation and assist in decision-making. Key Features Includes chapters on health and social welfare, transportation, digital economy, energy efficiency and sustainability, agro-industry, and tourism Great diversity of authors, expert in varied sectors, belonging to powerful research groups from the University of Seville with proven experience in the transfer of knowledge to the productive sector and agents attached to the Andalucía TECH Campus</text>
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                <text>Air quality is personal. Its management is highly so. Asthmatic or air-pollutant-sensitive individuals depend on accurate air quality forecasts to help manage their daily activities. However, the adverse effects of poor air quality on public health and visibility extend far beyond the daily time horizon. Pneumonic and cardiac vascular responses of individuals in all age groups can be both acute, episodic and short-term, as well as chronic, accumulative and long-term. Urban haze resulting from stagnant poor air can linger for many days. In this Special Issue, seven papers cover a wide range of air pollution forecasting technology and emission control responses. It is paramount to verify and improve air quality forecast modeling systems constantly by as many quality-assured and cross-calibrated measurements as possible. Improvements from national centers such as the U. S. National Oceanic and Atmospheric Administration’s (NOAA) research arms must produce verification statistics satisfying operational center performance metrics over multiple seasons before implementation is possible. High quality, compact, and mobile monitors are a significant player in air quality and atmospheric composition continuous measurements and are poised to become even more important. Five papers in this issue provide insight on observation technological advances and data assimilation. Air quality monitoring and forecasting sciences necessarily advance in lock-step and improvements for one benefit the other.</text>
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                <text>This thesis is concerned with investigating elements of computational social choice in the light of real-world applications. We contribute to a better understanding of the areas of fair allocation and multiwinner voting. For both areas, inspired by real-world scenarios, we propose several new notions and extensions of existing models. Then, we analyze the complexity of answering the computational questions raised by the introduced concepts. To this end, we look through the lens of parameterized complexity. We identify different parameters which describe natural features specific to the computational problems we investigate. Exploiting the parameters, we successfully develop efficient algorithms for spe- cific cases of the studied problems. We complement our analysis by showing which parameters presumably cannot be utilized for seeking efficient algorithms. Thereby, we provide comprehensive pictures of the computational complexity of the studied problems. 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Motivated by such settings, we come up with a new model of graph envy-freeness by adapting the classical envy-freeness notion to account for social relations of agents modeled as social networks. We show that if the given social network of agents is simple (for example, if it is a directed acyclic graph), then indeed we can sometimes find fair allocations efficiently. However, we contrast tractability results with showing NP-hardness for several cases, including those in which the given social network has a constant degree. Fair allocations among few agents with bounded rationality. Bounded rationality is the idea that humans, due to cognitive limitations, tend to simplify problems that they face. One of its emanations is that human agents usually tend to report simple utilities over the resources that they want to allocate; for example, agents may categorize the available resources only into two groups of desirable and undesirable ones. Applying techniques for solving integer linear programs, we show that exploiting bounded rationality leads to efficient algorithms for finding envy-free and Pareto-efficient allocations, assuming a small number of agents. Further, we demonstrate that our result actually forms a framework that can be applied to a number of different fairness concepts like envy-freeness up to one good or envy-freeness up to any good. This way, we obtain efficient algorithms for a number of fair allocation problems (assuming few agents with bounded rationality). We also empirically show that our technique is applicable in practice. Further, we study multiwinner voting, where we are given a collection of voters and their preferences over a set of candidates. The outcome of a multiwinner voting rule is a group (or a set of groups in case of ties) of candidates that reflect the voters’ preferences best according to some objective. In this context, we investigate the following themes. The robustness of election outcomes. We study how robust outcomes of multiwinner elections are against possible mistakes made by voters. Assuming that each voter casts a ballot in a form of a ranking of candidates, we represent a mistake by a swap of adjacent candidates in a ballot. We find that for rules such as SNTV, k-Approval, and k-Borda, it is computationally easy to find the minimum number of swaps resulting in a change of an outcome. This task is, however, NP-hard for STV and the Chamberlin-Courant rule. We conclude our study of robustness with experimentally studying the average number of random swaps leading to a change of an outcome for several rules. Strategic voting in multiwinner elections. We ask whether a given group of cooperating voters can manipulate an election outcome in a favorable way. We focus on the k-Approval voting rule and we show that the computational complexity of answering the posed question has a rich structure. We spot several cases for which our problem is polynomial-time solvable. However, we also identify NP-hard cases. For several of them, we show how to circumvent the hardness by fixed-parameter tractability. We also present experimental studies indicating that our algorithms are applicable in practice.</text>
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                <text>This book provides a critical study of the power, trust, and legitimacy of algorithmic gatekeepers. The news and public information which citizens see and hear is no longer solely determined by journalists, but increasingly by algorithms. Van Dalen demonstrates the gatekeeping power of social media algorithms by showing how they affect exposure to diverse information and misinformation and shape the behaviour of professional communicators. Trust and legitimacy are foregrounded as two crucial antecedents for the acceptance of this algorithmic power. </text>
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                <text>Are algorithms ruling the world today? Is artificial intelligence making life-and-death decisions? Are social media companies able to manipulate elections? As we are confronted with public and academic anxieties about unprecedented changes, this book offers a different analytical prism to investigate these transformations as more mundane and fraught. Aradau and Blanke develop conceptual and methodological tools to understand how algorithmic operations shape the government of self and other. While disperse and messy, these operations are held together by an ascendant algorithmic reason. Through a global perspective on algorithmic operations, the book helps us understand how algorithmic reason redraws boundaries and reconfigures differences. The book explores the emergence of algorithmic reason through rationalities, materializations, and interventions. It traces how algorithmic rationalities of decomposition, recomposition, and partitioning are materialized in the construction of dangerous others, the power of platforms, and the production of economic value. The book shows how political interventions to make algorithms governable encounter friction, refusal, and resistance. The theoretical perspective on algorithmic reason is developed through qualitative and digital methods to investigate scenes and controversies that range from mass surveillance and the Cambridge Analytica scandal in the UK to predictive policing in the US, and from the use of facial recognition in China and drone targeting in Pakistan to the regulation of hate speech in Germany. Algorithmic Reason offers an alternative to dystopia and despair through a transdisciplinary approach made possible by the authors’ backgrounds, which span the humanities, social sciences, and computer sciences.</text>
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