Data-driven optimization of bus schedules under uncertainties

Plusieurs sous-problèmes d’optimisation se posent lors de la planification des transports publics. Le problème d’itinéraires de véhicule (PIV) est l’un d’entre eux et consiste à minimiser les coûts opérationnels tout en assignant exactement un autobus par trajet planifié de sorte que le nombre d’autobus entreposé par dépôt ne dépasse pas la capacité maximale disponible. Bien que les transports publics soient sujets à plusieurs sources d’incertitude (à la fois endogènes et exogènes) pouvant engendrer des variations des temps de trajet et de la consommation d’énergie, le PIV et ses variantes sont la plupart du temps résolus de façon déterministe pour des raisons de résolubilité. Toutefois, cette hypothèse peut compromettre le respect de l’horaire établi lorsque les temps des trajets considérés sont fixes (c.-à-d. déterministes) et peut produire des solutions impliquant des politiques de gestion des batteries inadéquates lorsque la consommation d’énergie est aussi considérée comme fixe. Dans cette thèse, nous proposons une méthodologie pour mesurer la fiabilité (ou le respect de l’horaire établi) d’un service de transport public ainsi que des modèles mathématiques stochastiques et orientés données et des algorithmes de branch-and-price pour deux variantes de ce problème, à savoir le problème d’itinéraires de véhicule avec dépôts multiples (PIVDM) et le problème d’itinéraires de véhicule électrique (PIV-E). Afin d’évaluer la fiabilité, c.-à-d. la tolérance aux délais, de certains itinéraires de véhicule, nous prédisons d’abord la distribution des temps de trajet des autobus. Pour ce faire, nous comparons plusieurs modèles probabilistes selon leur capacité à prédire correctement la fonction de densité des temps de trajet des autobus sur le long terme. Ensuite, nous estimons à l'aide d'une simulation de Monte-Carlo la fiabilité des horaires d’autobus en générant des temps de trajet aléatoires à chaque itération. Nous intégrons alors le modèle probabiliste le plus approprié, celui qui est capable de prédire avec précision à la fois la véritable fonction de densité conditionnelle des temps de trajet et les retards secondaires espérés, dans nos modèles d'optimisation basés sur les données. Deuxièmement, nous introduisons un modèle pour PIVDM fiable avec des temps de trajet stochastiques. Ce problème d’optimisation bi-objectif vise à minimiser les coûts opérationnels et les pénalités associées aux retards. Un algorithme heuristique basé sur la génération de colonnes avec des sous-problèmes stochastiques est proposé pour résoudre ce problème. Cet algorithme calcule de manière dynamique les retards secondaires espérés à mesure que de nouvelles colonnes sont générées. Troisièmement, nous proposons un nouveau programme stochastique à deux étapes avec recours pour le PIVDM électrique avec des temps de trajet et des consommations d’énergie stochastiques. La politique de recours est conçue pour rétablir la faisabilité énergétique lorsque les itinéraires de véhicule produits a priori se révèlent non réalisables. Toutefois, cette flexibilité vient au prix de potentiels retards induits. Une adaptation d’un algorithme de branch-and-price est développé pour évaluer la pertinence de cette approche pour deux types d'autobus électriques à batterie disponibles sur le marché. Enfin, nous présentons un premier modèle stochastique pour le PIV-E avec dégradation de la batterie. Le modèle sous contrainte en probabilité proposé tient compte de l’incertitude de la consommation d’énergie, permettant ainsi un contrôle efficace de la dégradation de la batterie grâce au contrôle effectif de l’état de charge (EdC) moyen et l’écart de EdC. Ce modèle, combiné à l’algorithme de branch-and-price, sert d’outil pour balancer les coûts opérationnels et la dégradation de la batterie.The vehicle scheduling problem (VSP) is one of the sub-problems of public transport planning. It aims to minimize operational costs while assigning exactly one bus per timetabled trip and respecting the capacity of each depot. Even thought public transport planning is subject to various endogenous and exogenous causes of uncertainty, notably affecting travel time and energy consumption, the VSP and its variants are usually solved deterministically to address tractability issues. However, considering deterministic travel time in the VSP can compromise schedule adherence, whereas considering deterministic energy consumption in the electric VSP (E-VSP) may result in solutions with inadequate battery management. In this thesis, we propose a methodology for measuring the reliability (or schedule adherence) of public transport, along with stochastic and data-driven mathematical models and branch-and-price algorithms for two variations of this problem, namely the multi-depot vehicle scheduling problem (MDVSP) and the E-VSP. To assess the reliability of vehicle schedules in terms of their tolerance to delays, we first predict the distribution of bus travel times. We compare numerous probabilistic models for the long-term prediction of bus travel time density. Using a Monte Carlo simulation, we then estimate the reliability of bus schedules by generating random travel times at each iteration. Subsequently, we integrate the most suitable probabilistic model, capable of accurately predicting both the true conditional density function of the travel time and the expected secondary delays, into the data-driven optimization models. Second, we introduce a model for the reliable MDVSP with stochastic travel time minimizing both the operational costs and penalties associated with delays. To effectively tackle this problem, we propose a heuristic column generation-based algorithm, which incorporates stochastic pricing problems. This algorithm dynamically computes the expected secondary delays as new columns are generated. Third, we propose a new two-stage stochastic program with recourse for the electric MDVSP with stochastic travel time and energy consumption. The recourse policy aims to restore energy feasibility when a priori vehicle schedules are unfeasible, which may lead to delays. An adapted algorithm based on column generation is developed to assess the relevance of this approach for two types of commercially available battery electric buses. Finally, we present the first stochastic model for the E-VSP with battery degradation. The proposed chance-constraint model incorporates energy consumption uncertainty, allowing for effective control of battery degradation by regulating the average state-of-charge (SOC) and SoC deviation in each discharging and charging cycle. This model, in combination with a tailored branch-and-price algorithm, serves as a tool to strike a balance between operational costs and battery degradation

Scheduling, Characterization and Prediction of HPC Workloads for Distributed Computing Environments

As High Performance Computing (HPC) has grown considerably and is expected to grow even more, effective resource management for distributed computing sys- tems is motivated more than ever. As the computational workloads grow in quantity, it is becoming more crucial to apply efficient resource management and workload scheduling to use resources efficiently while keeping the computational performance reasonably good. The problem of efficiently scheduling workloads on resources while meeting performance standards is hard. Additionally, non-clairvoyance of job dimen- sions makes resource management even harder in real-world scenarios. Our research methodology investigates the scheduling problem compliant for HPC and researches the challenges for deploying the scheduling in real world-scenarios using state of the art machine learning and data science techniques.To this end, this Ph.D. dissertation makes the following core contributions: a) We perform a theoretical analysis of space-sharing, non-preemptive scheduling: we studied this scheduling problem and proposed scheduling algorithms with polyno- mial computation time. We also proved constant upper-bounds for the performance of these algorithms. b) We studied the sensitivity of scheduling algorithms to the accuracy of runtime and devised a meta-learning approach to estimate prediction accuracy for newly submitted jobs to the HPC system. c) We studied the runtime prediction problem for HPC applications. For this purpose, we studied the distri- bution of available public workloads and proposed two different solutions that can predict multi-modal distributions: switching state-space models and Mixture Density Networks. d) We studied the effectiveness of recent recurrent neural network models for CPU usage trace prediction for individual VM traces as well as aggregate CPU usage traces. In this dissertation, we explore solutions to improve the performance of scheduling workloads on distributed systems.We begin by looking at the problem from the theoretical perspective. Modeling the problem mathematically, we first propose a scheduling algorithm that finds a constant approximation of the optimal solution for the problem in polynomial time. We prove that the performance of the algorithm (average completion time is the constant approximation of the performance of the optimal scheduling. We next look at the problem in real-world scenarios. Considering High-Performance Computing (HPC) workload computing environments as the most similar real-world equivalent of our mathematical model, we explore the problem of predicting application runtime. We propose an algorithm to handle the existing uncertainties in the real world and show-case our algorithm with demonstrative effectiveness in terms of response time and resource utilization. After looking at the uncertainty problem, we focus on trying to improve the accuracy of existing prediction approaches for HPC application runtime. We propose two solutions, one based on Kalman filters and one based on deep density mixture networks. We showcase the effectiveness of our prediction approaches by comparing with previous prediction approaches in terms of prediction accuracy and impact on improving scheduling performance. In the end, we focus on predicting resource usage for individual applications during their execution. We explore the application of recurrent neural networks for predicting resource usage of applications deployed on individual virtual machines. To validate our proposed models and solutions, we performed extensive trace-driven simulation and measured the effectiveness of our approaches

Facilitating Reliable Autonomy with Human-Robot Interaction

Autonomous robots are increasingly deployed to complex environments in which we cannot predict all possible failure cases a priori. Robustness to failures can be provided by humans enacting the roles of: (1) developers who can iteratively incorporate robustness into the robot system, (2) collocated bystanders who can be approached for aid, and (3) remote teleoperators who can be contacted for guidance. However, assisting the robot in any of these roles can place demands on the time or effort of the human. This dissertation develops modules to reduce the frequency and duration of failure interventions in order to increase the reliability of autonomous robots, while also reducing the demand on humans. In pursuit of that goal, the dissertation makes the following contributions: (1) A development paradigm for autonomous robots that separates task specification from error recovery. The paradigm reduces burden on developers while making the robot robust to failures. (2) A model for gauging the interruptibility of collocated humans. A human-subjects study shows that using the model can reduce the time expended by the robot during failure recovery. (3) A human-subjects experiment on the effects of decision support provided to remote operators during failures. The results show that humans need both diagnosis and action recommendations as decision support during an intervention. (4) An evaluation of model features and unstructured Machine Learning (ML) techniques in pursuit of learning robust suggestions models from intervention data, in order to reduce developer effort. The results indicate that careful crafting of features can lead to improved performance, but that without such feature selection, current ML algorithms lack robustness in addressing a domain where the robot's observations are heavily influenced by the user's actions.Ph.D

Computation in Complex Networks

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin

Emotion-aware voice interfaces based on speech signal processing

Voice interfaces (VIs) will become increasingly widespread in current daily lives as AI techniques progress. VIs can be incorporated into smart devices like smartphones, as well as integrated into autos, home automation systems, computer operating systems, and home appliances, among other things. Current speech interfaces, however, are unaware of users’ emotional states and hence cannot support real communication. To overcome these limitations, it is necessary to implement emotional awareness in future VIs. This thesis focuses on how speech signal processing (SSP) and speech emotion recognition (SER) can enable VIs to gain emotional awareness. Following an explanation of what emotion is and how neural networks are implemented, this thesis presents the results of several user studies and surveys. Emotions are complicated, and they are typically characterized using category and dimensional models. They can be expressed verbally or nonverbally. Although existing voice interfaces are unaware of users’ emotional states and cannot support natural conversations, it is possible to perceive users’ emotions by speech based on SSP in future VIs. One section of this thesis, based on SSP, investigates mental restorative effects on humans and their measures from speech signals. SSP is less intrusive and more accessible than traditional measures such as attention scales or response tests, and it can provide a reliable assessment for attention and mental restoration. SSP can be implemented into future VIs and utilized in future HCI user research. The thesis then moves on to present a novel attention neural network based on sparse correlation features. The detection accuracy of emotions in the continuous speech was demonstrated in a user study utilizing recordings from a real classroom. In this section, a promising result will be shown. In SER research, it is unknown if existing emotion detection methods detect acted emotions or the genuine emotion of the speaker. Another section of this thesis is concerned with humans’ ability to act on their emotions. In a user study, participants were instructed to imitate five fundamental emotions. The results revealed that they struggled with this task; nevertheless, certain emotions were easier to replicate than others. A further study concern is how VIs should respond to users’ emotions if SER techniques are implemented in VIs and can recognize users’ emotions. The thesis includes research on ways for dealing with the emotions of users. In a user study, users were instructed to make sad, angry, and terrified VI avatars happy and were asked if they would like to be treated the same way if the situation were reversed. According to the results, the majority of participants tended to respond to these unpleasant emotions with neutral emotion, but there is a difference among genders in emotion selection. For a human-centered design approach, it is important to understand what the users’ preferences for future VIs are. In three distinct cultures, a questionnaire-based survey on users’ attitudes and preferences for emotion-aware VIs was conducted. It was discovered that there are almost no gender differences. Cluster analysis found that there are three fundamental user types that exist in all cultures: Enthusiasts, Pragmatists, and Sceptics. As a result, future VI development should consider diverse sorts of consumers. In conclusion, future VIs systems should be designed for various sorts of users as well as be able to detect the users’ disguised or actual emotions using SER and SSP technologies. Furthermore, many other applications, such as restorative effects assessments, can be included in the VIs system

Cyber Security

This open access book constitutes the refereed proceedings of the 16th International Annual Conference on Cyber Security, CNCERT 2020, held in Beijing, China, in August 2020. The 17 papers presented were carefully reviewed and selected from 58 submissions. The papers are organized according to the following topical sections: access control; cryptography; denial-of-service attacks; hardware security implementation; intrusion/anomaly detection and malware mitigation; social network security and privacy; systems security

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics