A new hierarchical ranking aggregation method

International audienceThe purpose of ranking aggregation (or fusion) is to combine multiple rankings to a consensus one. In the ranking aggregation, some of the items’ preference orders are easy to distinguish, however, some others’ are not. To specifically compare the ambiguous items, i.e., the items whose aggregated preference orders are difficult to distinguish, is helpful for ranking aggregation. In this paper, a new hierarchical ranking aggregation method is proposed. The items whose preference orders are easy to distinguish are first divided into different ranking levels (i.e., the ordered items subsets), and the ambiguous items are put into the same ranking level. The items in high ranking levels are ranked higher than the items in low ranking levels in the aggregated ranking. Then the items in the same ranking level are further compared and divided into multiple ranking sub-levels. The aggregated ranking is generated hierarchically by dividing the same ranking levels’ (or sub-levels’) items into sub-levels until each sub-level only includes one item. Furthermore, we discuss the way of using the insertion sort method for merging the adjacent levels’ rankings to improve the quality of the aggregated ranking. The experiments and simulations show that our new hierarchical methods perform well in ranking aggregation

Stochastic spectral-spatial permutation ordering combination for nonlocal morphological processing

International audienceThe extension of mathematical morphology to mul-tivariate data has been an active research topic in recent years. In this paper we propose an approach that relies on the consensus combination of several stochastic permutation orderings. The latter are obtained by searching for a smooth shortest path on a graph representing an image. The construction of the graph can be based on both spatial and spectral information and naturally enables patch-based nonlocal processing

What We Know About Ranked-Choice Voting

As ranked-choice voting (RCV) gains momentum in American politics, a new body of research has emerged to examine the reform's effects on voters, candidates, campaigns, and policy. This report offers a systematic overview of the literature on RCV in the United States. Broadly, the research shows that RCV is an improvement over the more traditional single-vote plurality voting system, with clear benefits in some areas—especially campaign quality and descriptive representation—and more marginal or no apparent benefits in other areas. The research should also allay fears that RCV is too confusing or discriminatory: voters understand RCV, and learn to like it, too, particularly with experience.However, many promised benefits of RCV appear to be more modest than many had initially hoped and/or difficult to quantify based on limited usage thus far in the United States. It is possible that these benefits will take time to become apparent as candidates and voters learn and attitudes change. It is also possible that the adoption of RCV nationwide would be more transformative than city-by-city and even state-by-state adoption. But given the broader structural forces at play in our deteriorating national politics, stronger medicine may be needed

River stage prediction based on a distributed support vector regression

Author name used in this publication: K. W. Chau2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Representing Reliability-Based Peer Pressure

Abstract This thesis proposes and explores three strategies for aggregating individual preferences: the non-contentious, majority, and plurality methods. The proposed methods rely on the lexicographic rule but use a reliability ordering over sets of agents instead of only agents. The preservation of properties (reflexivity, transitivity, totality, antisymmetry, and unanimity) by each method are examined and compared. The thesis contributes to understanding preference aggregation strategies and their relevance in real-life decision-making contexts.Masteroppgave i informasjonsvitenskapINFO390MASV-INF

Probabilistic Models for Droughts: Applications in Trigger Identification, Predictor Selection and Index Development

The current practice of drought declaration (US Drought Monitor) provides a hard classification of droughts using various hydrologic variables. However, this method does not yield model uncertainty, and is very limited for forecasting upcoming droughts. The primary goal of this thesis is to develop and implement methods that incorporate uncertainty estimation into drought characterization, thereby enabling more informed and better decision making by water users and managers. Probabilistic models using hydrologic variables are developed, yielding new insights into drought characterization enabling fundamental applications in droughts

A Framework for Aggregation of Multiple Reinforcement Learning Algorithms

Aggregation of multiple Reinforcement Learning (RL) algorithms is a new and effective technique to improve the quality of Sequential Decision Making (SDM). The quality of a SDM depends on long-term rewards rather than the instant rewards. RL methods are often adopted to deal with SDM problems. Although many RL algorithms have been developed, none is consistently better than the others. In addition, the parameters of RL algorithms significantly influence learning performances. There is no universal rule to guide the choice of algorithms and the setting of parameters. To handle this difficulty, a new multiple RL system - Aggregated Multiple Reinforcement Learning System (AMRLS) is developed. In AMRLS, each RL algorithm (learner) learns individually in a learning module and provides its output to an intelligent aggregation module. The aggregation module dynamically aggregates these outputs and provides a final decision. Then, all learners take the action and update their policies individually. The two processes are performed alternatively. AMRLS can deal with dynamic learning problems without the need to search for the optimal learning algorithm or the optimal values of learning parameters. It is claimed that several complementary learning algorithms can be integrated in AMRLS to improve the learning performance in terms of success rate, robustness, confidence, redundance, and complementariness. There are two strategies for learning an optimal policy with RL methods. One is based on Value Function Learning (VFL), which learns an optimal policy expressed as a value function. The Temporal Difference RL (TDRL) methods are examples of this strategy. The other is based on Direct Policy Search (DPS), which directly searches for the optimal policy in the potential policy space. The Genetic Algorithms (GAs)-based RL (GARL) are instances of this strategy. A hybrid learning architecture of GARL and TDRL, HGATDRL, is proposed to combine them together to improve the learning ability. AMRLS and HGATDRL are tested on several SDM problems, including the maze world problem, pursuit domain problem, cart-pole balancing system, mountain car problem, and flight control system. Experimental results show that the proposed framework and method can enhance the learning ability and improve learning performance of a multiple RL system

Monitoring Snow Cover and Snowmelt Dynamics and Assessing their Influences on Inland Water Resources

Snow is one of the most vital cryospheric components owing to its wide coverage as well as its unique physical characteristics. It not only affects the balance of numerous natural systems but also influences various socio-economic activities of human beings. Notably, the importance of snowmelt water to global water resources is outstanding, as millions of populations rely on snowmelt water for daily consumption and agricultural use. Nevertheless, due to the unprecedented temperature rise resulting from the deterioration of climate change, global snow cover extent (SCE) has been shrinking significantly, which endangers the sustainability and availability of inland water resources. Therefore, in order to understand cryo-hydrosphere interactions under a warming climate, (1) monitoring SCE dynamics and snowmelt conditions, (2) tracking the dynamics of snowmelt-influenced waterbodies, and (3) assessing the causal effect of snowmelt conditions on inland water resources are indispensable. However, for each point, there exist many research questions that need to be answered. Consequently, in this thesis, five objectives are proposed accordingly. Objective 1: Reviewing the characteristics of SAR and its interactions with snow, and exploring the trends, difficulties, and opportunities of existing SAR-based SCE mapping studies; Objective 2: Proposing a novel total and wet SCE mapping strategy based on freely accessible SAR imagery with all land cover classes applicability and global transferability; Objective 3: Enhancing total SCE mapping accuracy by fusing SAR- and multi-spectral sensor-based information, and providing total SCE mapping reliability map information; Objective 4: Proposing a cloud-free and illumination-independent inland waterbody dynamics tracking strategy using freely accessible datasets and services; Objective 5: Assessing the influence of snowmelt conditions on inland water resources

Swarm-Based Drone-as-a-Service for Delivery

There has been a growing interest in the applications of drones as a cost-effective, efficient, and environmentally friendly alternative in various domains. Particularly in the context of delivery services, the demand for contactless and efficient delivery solutions has surged. Drone delivery offers faster and greener deliveries. However, existing methods focus primarily on point-to-point delivery, limiting their potential for optimisation. This thesis proposes a novel approach to servitise drone delivery by operating through a skyway network composed of building rooftops, enabling drones to traverse between source and destination while recharging at intermediate nodes. Although single drone delivery offers numerous advantages, it faces significant challenges in scenarios where multiple packages require simultaneous delivery. Flight regulations, which often limit the carrying capacity of individual drones, necessitate the exploration of alternative solutions. Therefore, this thesis presents a novel Swarm-Based Drone-as-a-Service (SDaaS) model and framework for multiple package delivery. The proposed framework prioritises the composition of services that optimise Quality of Service (QoS) factors, such as delivery time and energy consumption. This thesis identifies swarm-specific constraints and leverages the unique characteristics of drone swarms. It explores swarm formations, in-flight wireless charging between drones, and allocation problems to maximise drone utilisation for consumer deliveries. Furthermore, this research investigates the recommendation of services to consumers based on their preferences, aiming to increase their satisfaction. Moreover, the framework addresses the resilience of SDaaS by addressing issues related to drone soft failures and their impact on other swarm members. Ultimately, this work paves the way for the widespread adoption and optimisation of swarm-based drone services in the context of last-mile delivery