Preference Elicitation in Constraint-Based Models: Models, Algorithms, and Applications

Constraint-based models offer powerful approaches for describing and resolving many combinatorial optimization problems in a centralized or distributed environment. In such models, the goal is to find a value assignment to a set of variables given a set of preferences expressed by means of cost functions such that the sum over all costs is optimized. The importance of constraint-based models is outlined by the impact of their applications in a wide range of agent-based systems. Many real-life combinatorial problems can be naturally formalized using constraint-based models. Examples of such applications are supply-chain management, roster scheduling, meeting scheduling, combinatorial auctions, bioinformatics, and smart home automation. The majority of these constraint-based models assume that all constraint costs are specified or known a priori. Unfortunately, such an assumption is impractical, especially in many human-in-the-loop applications, such as scheduling problems where constraints encode the preferences of human users. These constraints may not be fully specified because it is unrealistic to accurately know the preferences of users for all possible scenarios in an application. These constraint costs are only known after they are queried or elicited from domain experts or human users. This dissertation proposes solutions for further improving the applicability of constraint-based models. These solutions aim for better formalizing and solving optimization problems, requiring human interactions to address the above limitation. Our core contributions are listed as follows: We begin by introducing the uncertain constraint-based model that represents the uncertainty in users\u27 preferences (i.e., constraint costs) as Gaussian distributions. To solve such a constraint-based model with uncertainty, we propose probabilistic heuristics that select a subset of constraints to elicit and choose those that significantly impact the overall solution quality. The elicitation of these preferences occurs prior to the execution of the search algorithm for an optimal solution. Human users are likely bothered by repeated elicitations and will refuse to provide an unbounded number of preferences. Hence, as our next contribution, we propose the incomplete weighted constraint satisfaction problems with elicitation costs (IWCSPs+EC) that takes into consideration how much users are bothered by queries. To solve IWCSPs+EC, we offer three parameterized heuristics that allow users to trade off solution quality for fewer elicited preferences and faster computation times. Further, they provide theoretical quality guarantees for problems where elicitations are free. Finally, we extend IWCSPs to distributed problems and introduce incomplete distributed constraint optimization problems (I-DCOPs). To solve I-DCOPs, we propose an extended version of SyncBB -- a complete search algorithm -- with two parameterized heuristics. These heuristics interleave the elicitation process with the search for an optimal solution. Our proposed heuristics for SyncBB allow users to trade off solution quality for fewer elicited preferences and faster computation times. To improve the scalability of our proposed framework, we offer an extended version of ALS-MGM -- a local search algorithm -- which can solve much larger I-DCOPs. Local search algorithms are computationally much faster and provide sub-optimal or close to optimal solutions. The number of elicited preferences is significantly smaller than SyncBB with heuristics, and its solution quality is not far from optimal. We apply our proposed model to smart home device scheduling and distributed meeting scheduling applications with partial users\u27 preferences. The empirical results show the significance of our contributions against random methods and the previous state-of-the-art approaches. Our models and heuristics thus extend the state-of-the-art in constraint reasoning to better model and solve agent-based applications with user preferences

Textractor: Ferramenta OSINT baseada na extração e análise de dados áudio/vídeo

Dissertação de Mestrado em Engenharia InformáticaO ciber ataques têm aumentado tanto em número como em sofisticação. Detetar e prevenir a ocorrência desses ataques é um trabalho árduo que requer uma visão holística de segurança envolvendo pessoas, processos e tecnologias. Uma das iniciativas de prevenção e deteção de ciber ataques passa pela definição de um projeto de ciber inteligência. Estes projetos incluem um ciclo de ações e promovem o conhecimento sobre os atores maliciosos, modos de operação e vulnerabilidades em antecipação aos ciber ataques. No âmbito da ciber inteligência uma das referências basilares à construção de um programa é o OSINT (Open Source Intelligence). Trata-se d recolha de informação em fontes abertas que se junta à informação interna e promove um melhor conhecimento sobre as ciber ameaças e formas de mitigação...Cyber attacks have increased in both number and sophistication. Detecting and preventing the occurrence of such attacks is a hard work that requires a holistic view of security involving people, processes, and technologies. One of the initiatives to prevent and detect cyber attacks involves the creation and maintenance of a cyber intelligence program..

Electron-ion recombination rate coefficients of Be-like 40Ca16+

Electron–ion recombination rate coefficients for beryllium-like calcium ions in the center of mass energy from 0 to51.88 eV have been measured by means of the electron–ion merged-beam technique at the main cooler storage ringat the Institute of Modern Physics in Lanzhou, China. The measurement energy range covers the dielectronicrecombination (DR) resonances associated with the 2s2 1S0  -> 2s2p 3P0,1,2, 1P1 core excitations and the trielectronicrecombination (TR) resonances associated with the 2s2 1S0  -> 2p2 3P0,1,2, 1D2, 1S0 core excitations. In addition,the AUTOSTRUCTURE code was used to calculate the recombination rate coefficients for comparison with theexperimental results. Resonant recombination originating from parent ions in the long-lived metastable state 2s2p 3P0 ions has been identified in the recombination spectrum below 1.25 eV. A good agreement is achievedbetween the experimental recombination spectrum and the result of the AUTOSTRUCTURE calculations whenfractions of 95% ground-state ions and 5% metastable ions are assumed in the calculation. It is found thatthe calculated TR resonance positions agree with the experimental peaks, while the resonance strengths areunderestimated by the theoretical calculation. Temperature dependent plasma rate coefficients for DR and TR inthe temperature range of 103–108 K were derived from the measured electron–ion recombination rate coefficientsand compared with the available theoretical results from the literature. In the temperature range of photoionizedplasmas, the presently calculated rate coefficients and the recent results of Gu & Colgan et al. are up to 30% lowerthan the experimentally derived ones, and the older atomic data are even up to 50% lower than the presentexperimental result. This is because strong resonances situated below electron–ion collision energies of 50 meVwere underestimated by the theoretical calculation, which also has a severe influence on the rate coefficients inlow-temperature plasmas. In the temperature range of collisionally ionized plasmas, agreement within 25% wasfound between the experimental result and the present calculation as well as the calculation by Colgan et al. Thepresent result constitutes a set of benchmark data for use in astrophysical modeling

Diachronic Delta: A computational and dialectical method for analysing literary corpora

Much has been written on computational literary studies' (CLS) rigidity and reductiveness in comparison with literary criticism's more pragmatic and intuitive means of approaching its object of study. This thesis attempts to undermine this antinomy via dialectical materialist philosophy as proposed by George Wilhelm Friedrich Hegel and developed by Karl Marx. As a science and a method of social critique, dialectics not only has a long history of usage within literary criticism, but they also provide a means of mediating the distinctions between empirical evidence, logic and intuition. We do so by operationalising .John Burrows' 'Delta' method across time mther than as it is conventionally applied, across text (.T. Burrows, "'Delta': a Measure of Stylistic Difference and a Guide to Likely Authorship"). We thereby identify particular years as being associated with extensive amounts of 'novelty' (which years introduce the most amount of distance from proceeding years) and also possess extensive amounts of 'resonance' (are relatively proximate to their successor years). This is undertaken on the basis that agents within a dataset which score highly for both of these metrics are highly innovative, as they are significantly different from the years which come before, and relatively similar to the years which come after (Barron et al., "Individuals, institutions, and innovation in the debates of the French Revolution."; Barron et al., "Supplementary Information: Individuals, Institutions, and Innovation in the Debates of the French Revolution"). We refer to these years as 'breaks' and identify them in order to contrast their behaviour with the longue-dunie approach increasingly prevalent within CLS (Underwood, Distant Horizons: Digital Evidence and Literary Change 25). Though this theory of incremental change ultimately remains in overall terms robust, this thesis nevertheless presents significant results arising from this method and demonstrates the ways in which dialectical materialist philosophy may ground the use of empirical methods more coherently within contemporary literary critical practice

Harnessing the Masses: International Conflict and Chinese Public Opinion

My dissertation project (``Harnessing the Masses: International Conflict and Chinese Public Opinion ) examines the interaction between Chinese foreign policy and public opinion. It comprises three empirical papers. I generalize theories of international relations and political economy developed under democratic settings and explore the role of domestic audiences in Chinese foreign policymaking. My main findings are two-fold. On the one hand, the state holds considerable political resources and plays an influential role in setting the political agenda. It has the power to mobilize popular nationalism in support of hawkish policies under favorable circumstances (e.g., territorial disputes). On the other hand, blind patriotism or passive loyalty to the authoritarian regime does not fully explain the micro-level dynamics of public opinion. Chinese citizens are sophisticated and deliberate when processing information about international conflict, which generates bottom-up pressure and constrains the authoritarian state. My dissertation challenges the conventional wisdom that the domestic audience is solely driven by state-led nationalism and that the authoritarian government can garner public support at zero cost. In the first paper (``External Coercion and Public Support ), I explore the dynamics of public support in the US--China trade war using two waves of online surveys and large-scale social media data. In the survey experiments, I randomly assign respondents to different hypothetical bargaining outcomes based on the real-world interaction between China and the US. I uncover two main causal mechanisms that explain the variations in public approval of the government: the state’s reputation for resolve and the economic consequences. The relative explanatory power of the two mechanisms is contingent on individual preferences and situational changes. Additional topic analysis on a large corpus of social media data collected during the US--China trade war reaffirms the importance of the two mechanisms and discloses the temporal variation in popular topics, especially citizens’ increasing economic considerations. I also discover considerable differences between social media content and official messages, indicating the state’s imperfect control over the public discourse. In the second paper (``Does Nationalism Rally Political Support for Authoritarian States? ), I evaluate the logic of diversionary conflict under the Chinese context. I examine the change of general political attitudes based on two major conflicts: the 2012 Diaoyu Islands dispute and the 2018–19 US--China trade war. With survey data collected before and after the outbreak of the two conflicts, I test whether international conflict can boost domestic support for the authoritarian government. I separate the concept of nationalism into two dimensions: anti-foreign sentiment (negative) and in-group solidarity (positive). I show that while anti-foreign sentiment was moderately strengthened by international conflict, in-group solidarity remained largely stable, and the level of general political support was unchanged. I conclude that the domestic benefits of international conflict should not be exaggerated: The temporary spike in anti-foreign sentiment does not necessarily dampen citizens’ sensitivity to domestic problems or make citizens less critical of their government. In the third paper (``Weaponizing the Masses: Popular Nationalism and Chinese Economic Statecraft ), I explore the state’s influence on public opinion and its relationship with economic statecraft. Specifically, I estimate the effect of interstate conflict on economic exchanges mediated by state mobilization of popular nationalism. I argue that state-sponsored nationalism disrupts international economic exchanges and conveys a costly signal of resolve to the targeted state. One mechanism I highlight is that popular nationalism powerfully politicizes economic issues and pressures economic agents to follow the red flag. For the empirical analysis, I first examine two sets of cases from 2008 to 2019, including major conflicts between China and Japan, South Korea and the US respectively, and two most similar events between China and France and the UK that are expected to have the so-called ``Dalai Lama Effect. I show that the economic impact of political conflict is not homogeneous, and that stronger nationalist activism (as indicated by large-scale protests and consumer boycotts) is associated with a sharper decline in Chinese imports from other countries. To make a stronger causal claim, I examine regional variations in popular nationalism in the 2012 Diaoyu Islands dispute and discover a negative effect of nationalism on imports and direct investments from Japan using the diff-in-diff (DID) design. Taken together, my dissertation unveils a sophisticated picture of Chinese nationalism. On the one hand, the disruptive effect of popular nationalism on economic exchanges makes it a coercive tool for state leaders to impose sanctions on foreign actors. On the other hand, the state’s influence over public opinion should not be exaggerated as citizens still make sophisticated calculations of the conflict and their support for the government is not unconditional. Under certain circumstances, public support for hawkish policies may dwindle and state leaders are incentivized to back down

Event-based stream classification framework – a supervised clustering approach for social media applications

Reuter T. Event-based stream classification framework – a supervised clustering approach for social media applications. Bielefeld: Universitätsbibliothek; 2015.Events play a very prominent role in our lifes. Therefore many social media documents describe or are related to some event. However, it is difficult for a human to gather relevant information without any structure in these documents. The organization of social media documents with respect to events thus seems to be a promising approach to better manage and organize the ever-increasing amount of content that is shared using social media applications. It is a challenge to automatize this process so that incoming documents can be assigned to their corresponding event without any user intervention. In this dissertation we present an event-based stream classification framework that is able to classify a never-ending stream of social media data into a growing and evolving set of events. By doing this, we successfully perform the assignment of a social media item newly uploaded to some social media site to its corresponding event (if it already exists) or create a new event to which future data items can be assigned. We refer to this problem as the event detection problem and propose to use machine learning techniques to tackle it. We successfully address several key challenges that arise in this context: i) handling the data in a stream-based setting, i.e. addressing the challenges arising from the need to process a never-ending stream of data, ii) scaling to the data sizes and rates usually encountered in social media applications, and iii) tackling the new event detection problem, i.e. the problem of determining whether an incoming data item belongs to a new or to an already known event. We address these challenges through a classification approach allowing us to process the data in one single pass. Furthermore, we include a suitable candidate event retrieval step which retrieves a set of event candidates that the incoming data point is likely to belong to and we include a function trained using machine learning techniques that determines whether the incoming data point belongs to the top-scored candidate or rather to a new event. The performance of our system is maximized using different optimization strategies so that it outperforms many other state-of-the-art approaches. Further, we extend our framework so that it can be used in a multi-pass setting. Using this approach we show that we can improve the quality of the clustering significantly in comparison to the single-pass approach, while also lowering the computational time by one order of magnitude. We show that this extension can be used in a stream-based setting while reaching the quality of a computationally very expensive offline clustering algorithm. We prove that our highly efficient approach is capable of successfully clustering a real-world and non-toy dataset by introducing a new dataset consisting of user-contributed images together with associated metadata describing the events they depict. The dataset was already published earlier and is well known in the community. Our single-pass and multi-pass strategies reach an F-measure score of 88.6% and 93.9%, respectively. In conclusion, we show that our framework is not only capable of addressing the above mentioned challenging issues but also outperforms other state-of-the-art approaches in terms of quality and scalability

Optimal labor income taxation with perks

Incentive problems arise in many economic relationships, between workers and firms as well as between those agents and the fiscal authority. If it is well known, in fact, that labor contracts tying wages to performance may mitigate the efficiency costs from unobservable effort, it is also an empirical fact that real word contracts, and the incentives provided wherein, are more frequently based on some sort of non-monetary compensation – fringe benefits, or perks, as they are called. As a result, the fiscal authority is more often called to decide on the eligibility of those perquisite goods and, therefore, on their taxability. In spite of their diffusion, however, there is still little consensus among researchers on the reasons why perks should be provided, and what their effect on welfare might be. The standard explanation for perks usually relies on some form of agency problem. However, monitoring concerns are certainly less relevant when dealing with a fiscal authority, that, for the most part, may monitor the provision of perks better than the payment of cash. Motivated by the concerns about how fringe benefits may restrict the revenue collection problem and affect the redistribution of resources and incentives, this research aims at investigating the relationship between the provision of perks and i) the progressivity/regressivity of the optimal transfer-tax system, and ii) the top-income marginal tax rate. To the best of our knowledge, this thesis constitutes the first theoretical attempt to deal with the issue at hand. In fact, all existing papers that analytically study the moral hazard problem with perks, either focus on their effect on the effort responsiveness to monetary incentives, or they study the cash-perks substitutability in the optimal labor contract, absent any consideration for the insurance/incentive purposes of income taxation. To some extent, we also contribute to the literature on optimal taxation, by emphasizing the connection between the agents' responses to the fiscal system and the structure of the labor market. Methodologically, we investigate our research agenda by following two different approaches. In Chapter 2, we develop a static version of a standard stochastic Ramsey's problem with a representative agent and an utilitarian, resource-seeking government who takes the post of the principal and owner of the only firm in the economy. When designing the optimal rules for cash payment and perk provision, the government, who is constrained by his budget balance, takes into account the agent's unobservable reactions to the tax system, and set optimal taxes so as to provide either insurance and right incentives. In Chapter 3, however, we take an alternative perspective, and study a decentralized economy wherein an independent, self-interest firm retains full control of the provision of perks and wage payments, given the fiscal policy announced by the government. Our analysis aims at highlighting the existence of a trade-off between the opposite interests of the government and the employer, that will serve as a rationale for the characterization of the equilibrium marginal tax rate that we derive from a Nash non-cooperative interaction game between the fiscal authority and the agents in the labor market. Our analysis suggests that, whenever perks are efficiently provided, the government trades off progressivity for perk provision. Our main conclusion, in fact, is that the (centralized) second-best efficient taxation scheme with perks is more regressive compared to its perk-less equivalent. Notably, we also find that, whenever society's preferences for public expenditure and agents' risk aversion are sufficiently low, a regressive top-income marginal tax is also consistent with a positive provision of perks. In a numerical exercise, for example, we show that a marginal tax rate of 30% allows for either i) a positive provision of perks that accounts for 1.6% of the gross income (3.2% of the agent's wage), and ii) a per-capita public expenditure which is 7% of the top-brackets taxpayers' gross income (15% of their earnings). An equilibrium allocation without perks results, instead, for a 45% marginal tax rate. However, in spite of the ability of our theoretical framework to capture the relationship between risk aversion and efficient provision of perks, through their effect on the optimal trade-off between the insurance and incentive motives for taxation, our model has its weakness, as to regard its sensitiveness to the stochastic dominance properties of the probability distribution and the level of income in the economy. If, in the former case, deviations from the benchmark distribution are qualitatively important but quantitatively small, in the latter case, the quantitative implications of different income levels are quite important. A more precise calibration of the models and a better characterization of the results are left to future works