Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks

This book presents collective works published in the recent Special Issue (SI) entitled "Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks”. These works expose the readership to the latest solutions and techniques for MANETs and VANETs. They cover interesting topics such as power-aware optimization solutions for MANETs, data dissemination in VANETs, adaptive multi-hop broadcast schemes for VANETs, multi-metric routing protocols for VANETs, and incentive mechanisms to encourage the distribution of information in VANETs. The book demonstrates pioneering work in these fields, investigates novel solutions and methods, and discusses future trends in these field

Mass Customization of Cloud Services - Engineering, Negotiation and Optimization

Several challenges hinder the entry of mass customization principles into Cloud computing: Firstly, the service engineering on provider side needs to be automated. Secondly, there has to be a suitable negotiation mechanism helping provider and consumer on finding an agreement on Quality-of-Service and price. Thirdly, finding the optimal configuration requires adequate and efficient optimization techniques. The work at hand addresses these challenges through technical and economic contributions

Prediction of Alcoholics Anonymous affiliation from pretreatment factors : methods and theory to inform provider referrals

Introduction: Treatment providers commonly emphasize and teach Alcoholics Anonymous (AA) philosophies and beliefs in treating alcohol disorders. The high rate of post-treatment relapse into harmful drinking suggests a need for extended care beyond initial treatment, and practitioners commonly refer clients to AA for aftercare. Although An abundance of correlational research suggests that greater AA affiliation is moderately and consistently associated with improved drinking outcomes, the vast majority of those who try AA drop out or substantially curtail affiliation ( disaffiliation ) within a year.Despite this, AA generally is believed to represent an affordable and effective adjunct and extension of initial treatment. Objective: The purpose of this study is to predict AA affiliation using information available at treatment and to advance knowledge surrounding affiliative processes. Literature from previous research and theory is used to develop hypotheses and a model of affiliation involving domains of motivation, coping skills and cultural fit. Methods: Using the Project MATCH data, two classification methods (binary logistic regression and artificial neural networks) are used to test hypotheses and predict AA affiliation at one-year post-treatment. Affiliation was operationalized to include dimensions of (1) meeting attendance or exposure, and (2) level of involvement or participation in AA activities. The affiliation construct was measured using the Alcoholics Anonymous Involvement scale (AAI) administered at 12 months post-treatment (N = 1,506). The study attempts to optimize classification accuracy on a dichotomous response variable that includes disaffiliates or moderate to high affiliates class membership. Results: Significance tests of 58 predictor variables suggested that the pretreatment AAI, divorced and separated marital statuses, age.treatment assignment, treatment site (representing inpatient or outpatient subjects),guilt/worry surrounding drinking and religiosity are significant predictors of affiliation.Prior affiliation (the pretreatment AAI) is a good predictor of affiliation. Despiteconsiderable prior theory and empirical evidence, motivation, severity, self-efficacy and external help-seeking measures were not significant predictors. Results are explained using theories borrowed from the organizational culture and climate literature. Attempts To predict affiliation were moderately successful (kappa = .42, sensitivity = 74%,specificity = 68%)

Essays on Matching Markets

The thesis consists of three chapters in the theory of matching markets. In Chapter 2, I study a central authority's ability to commit to a publicly announced mechanism in a one-to-one agent-object matching model. The authority announces a strategy-proof mechanism and then privately selects a mechanism to initiate a matching. An agent's observation in form of the final matching has an innocent explanation (Akbarpour and Li, 2020), if given the agent's reported preferences, there is a combination with other agents' preferences leading to an identical observation under the announced mechanism. The authority can only commit up to safe deviations (Akbarpour and Li, 2020)---mechanisms that produce only observations with innocent explanations. For efficient or stable announcements, I show that no safe deviation exists if and only if the announced mechanism is dictatorial. I establish that the Deferred Acceptance (DA) Mechanism (Gale and Shapley, 1962) implies commitment to stability. Moreover, group strategy-proof and efficient announcements allow commitment to efficiency only if they are dictatorial. In Chapter 3, we study The Efficiency Adjusted Deferred Acceptance Matching Rule (EDA) which is a promising candidate mechanism for public school assignment. A potential drawback of EDA is that it could encourage students to game the system since it is not strategy-proof. However, to successfully strategize, students typically need information that is unlikely to be available to them in practice. We model school choice under incomplete information and show that EDA is regret-free truth-telling, which is a weaker incentive property than strategy-proofness and was introduced by (Fernandez, 2020). We also show that there is no efficient matching rule that weakly Pareto dominates a stable matching rule and is regret-free truth-telling. Note that without modifications the EDA as introduced by (Kesten, 2010) weakly Pareto dominates a stable matching rule, but it is not efficient. In Chapter 4, I study agents’ incentives in a one-to-one object allocation model where agents are envious. Among all agents whose assignment is tracked by some agent, the agent envies those who are matched to an object she prefers to her own assignment. Given a mechanism and agents’ actions, agent i’s envy towards agent j at object x under the induced matching is inevitable if i has no action where she does not envy j at x, given one keeps the actions of other agents unchanged. A matching mechanism is envyproof, if for each market and agent, any envy under truthful revelation of preferences is inevitable. Envy-proofness is a concept that is stronger than strategy-proofness. I show that the Top Trading Cycle (TTC) Mechanism is envy-proof. Moreover, the unique strategy-proof and stable mechanism, the Deferred Acceptance Mechanism, is not envy-proof

(Dis)Obedience in Digital Societies: Perspectives on the Power of Algorithms and Data

Algorithms are not to be regarded as a technical structure but as a social phenomenon - they embed themselves, currently still very subtle, into our political and social system. Algorithms shape human behavior on various levels: they influence not only the aesthetic reception of the world but also the well-being and social interaction of their users. They act and intervene in a political and social context. As algorithms influence individual behavior in these social and political situations, their power should be the subject of critical discourse - or even lead to active disobedience and to the need for appropriate tools and methods which can be used to break the algorithmic power

(Dis)Obedience in Digital Societies

Algorithms are not to be regarded as a technical structure but as a social phenomenon - they embed themselves, currently still very subtle, into our political and social system. Algorithms shape human behavior on various levels: they influence not only the aesthetic reception of the world but also the well-being and social interaction of their users. They act and intervene in a political and social context. As algorithms influence individual behavior in these social and political situations, their power should be the subject of critical discourse - or even lead to active disobedience and to the need for appropriate tools and methods which can be used to break the algorithmic power

Advancements in Safe Deep Reinforcement Learning for Real-Time Strategy Games and Industry Applications

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