324 research outputs found
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
SCALING UP TASK EXECUTION ON RESOURCE-CONSTRAINED SYSTEMS
The ubiquity of executing machine learning tasks on embedded systems with constrained resources has made efficient execution of neural networks on these systems under the CPU, memory, and energy constraints increasingly important. Different from high-end computing systems where resources are abundant and reliable, resource-constrained systems only have limited computational capability, limited memory, and limited energy supply. This dissertation focuses on how to take full advantage of the limited resources of these systems in order to improve task execution efficiency from different aspects of the execution pipeline. While the existing literature primarily aims at solving the problem by shrinking the model size according to the resource constraints, this dissertation aims to improve the execution efficiency for a given set of tasks from the following two aspects. Firstly, we propose SmartON, which is the first batteryless active event detection system that considers both the event arrival pattern as well as the harvested energy to determine when the system should wake up and what the duty cycle should be. Secondly, we propose Antler, which exploits the affinity between all pairs of tasks in a multitask inference system to construct a compact graph representation of the task set for a given overall size budget. To achieve the aforementioned algorithmic proposals, we propose the following hardware solutions. One is a controllable capacitor array that can expand the system’s energy storage on-the-fly. The other is a FRAM array that can accommodate multiple neural networks running on one system.Doctor of Philosoph
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
Towards addressing training data scarcity challenge in emerging radio access networks: a survey and framework
The future of cellular networks is contingent on artificial intelligence (AI) based automation, particularly for radio access network (RAN) operation, optimization, and troubleshooting. To achieve such zero-touch automation, a myriad of AI-based solutions are being proposed in literature to leverage AI for modeling and optimizing network behavior to achieve the zero-touch automation goal. However, to work reliably, AI based automation, requires a deluge of training data. Consequently, the success of the proposed AI solutions is limited by a fundamental challenge faced by cellular network research community: scarcity of the training data. In this paper, we present an extensive review of classic and emerging techniques to address this challenge. We first identify the common data types in RAN and their known use-cases. We then present a taxonomized survey of techniques used in literature to address training data scarcity for various data types. This is followed by a framework to address the training data scarcity. The proposed framework builds on available information and combination of techniques including interpolation, domain-knowledge based, generative adversarial neural networks, transfer learning, autoencoders, fewshot learning, simulators and testbeds. Potential new techniques to enrich scarce data in cellular networks are also proposed, such as by matrix completion theory, and domain knowledge-based techniques leveraging different types of network geometries and network parameters. In addition, an overview of state-of-the art simulators and testbeds is also presented to make readers aware of current and emerging platforms to access real data in order to overcome the data scarcity challenge. The extensive survey of training data scarcity addressing techniques combined with proposed framework to select a suitable technique for given type of data, can assist researchers and network operators in choosing the appropriate methods to overcome the data scarcity challenge in leveraging AI to radio access network automation
On-premise containerized, light-weight software solutions for Biomedicine
Bioinformatics software systems are critical tools for analysing large-scale biological
data, but their design and implementation can be challenging due to the need for reliability, scalability, and performance. This thesis investigates the impact of several
software approaches on the design and implementation of bioinformatics software
systems. These approaches include software patterns, microservices, distributed
computing, containerisation and container orchestration. The research focuses on
understanding how these techniques affect bioinformatics software systems’ reliability, scalability, performance, and efficiency. Furthermore, this research highlights
the challenges and considerations involved in their implementation. This study also
examines potential solutions for implementing container orchestration in bioinformatics research teams with limited resources and the challenges of using container
orchestration. Additionally, the thesis considers microservices and distributed computing and how these can be optimised in the design and implementation process to
enhance the productivity and performance of bioinformatics software systems. The
research was conducted using a combination of software development, experimentation, and evaluation. The results show that implementing software patterns can
significantly improve the code accessibility and structure of bioinformatics software
systems. Specifically, microservices and containerisation also enhanced system reliability, scalability, and performance. Additionally, the study indicates that adopting
advanced software engineering practices, such as model-driven design and container
orchestration, can facilitate efficient and productive deployment and management of
bioinformatics software systems, even for researchers with limited resources. Overall, we develop a software system integrating all our findings. Our proposed system
demonstrated the ability to address challenges in bioinformatics. The thesis makes
several key contributions in addressing the research questions surrounding the design,
implementation, and optimisation of bioinformatics software systems using software
patterns, microservices, containerisation, and advanced software engineering principles and practices. Our findings suggest that incorporating these technologies can
significantly improve bioinformatics software systems’ reliability, scalability, performance, efficiency, and productivity.Bioinformatische Software-Systeme stellen bedeutende Werkzeuge für die Analyse
umfangreicher biologischer Daten dar. Ihre Entwicklung und Implementierung kann
jedoch aufgrund der erforderlichen Zuverlässigkeit, Skalierbarkeit und Leistungsfähigkeit eine Herausforderung darstellen. Das Ziel dieser Arbeit ist es, die Auswirkungen von Software-Mustern, Microservices, verteilten Systemen, Containerisierung
und Container-Orchestrierung auf die Architektur und Implementierung von bioinformatischen Software-Systemen zu untersuchen. Die Forschung konzentriert sich
darauf, zu verstehen, wie sich diese Techniken auf die Zuverlässigkeit, Skalierbarkeit,
Leistungsfähigkeit und Effizienz von bioinformatischen Software-Systemen auswirken
und welche Herausforderungen mit ihrer Konzeptualisierungen und Implementierung
verbunden sind. Diese Arbeit untersucht auch potenzielle Lösungen zur Implementierung von Container-Orchestrierung in bioinformatischen Forschungsteams mit begrenzten Ressourcen und die Einschränkungen bei deren Verwendung in diesem Kontext. Des Weiteren werden die Schlüsselfaktoren, die den Erfolg von bioinformatischen Software-Systemen mit Containerisierung, Microservices und verteiltem Computing beeinflussen, untersucht und wie diese im Design- und Implementierungsprozess optimiert werden können, um die Produktivität und Leistung bioinformatischer
Software-Systeme zu steigern. Die vorliegende Arbeit wurde mittels einer Kombination aus Software-Entwicklung, Experimenten und Evaluation durchgeführt. Die
erzielten Ergebnisse zeigen, dass die Implementierung von Software-Mustern, die Zuverlässigkeit und Skalierbarkeit von bioinformatischen Software-Systemen erheblich
verbessern kann. Der Einsatz von Microservices und Containerisierung trug ebenfalls zur Steigerung der Zuverlässigkeit, Skalierbarkeit und Leistungsfähigkeit des
Systems bei. Darüber hinaus legt die Arbeit dar, dass die Anwendung von SoftwareEngineering-Praktiken, wie modellgesteuertem Design und Container-Orchestrierung,
die effiziente und produktive Bereitstellung und Verwaltung von bioinformatischen
Software-Systemen erleichtern kann. Zudem löst die Implementierung dieses SoftwareSystems, Herausforderungen für Forschungsgruppen mit begrenzten Ressourcen. Insgesamt hat das System gezeigt, dass es in der Lage ist, Herausforderungen im Bereich
der Bioinformatik zu bewältigen und stellt somit ein wertvolles Werkzeug für Forscher in diesem Bereich dar. Die vorliegende Arbeit leistet mehrere wichtige Beiträge
zur Beantwortung von Forschungsfragen im Zusammenhang mit dem Entwurf, der
Implementierung und der Optimierung von Software-Systemen für die Bioinformatik unter Verwendung von Prinzipien und Praktiken der Softwaretechnik. Unsere
Ergebnisse deuten darauf hin, dass die Einbindung dieser Technologien die Zuverlässigkeit, Skalierbarkeit, Leistungsfähigkeit, Effizienz und Produktivität bioinformatischer Software-Systeme erheblich verbessern kann
Toward Data Efficient Online Sequential Learning
Can machines optimally take sequential decisions over time? Since decades, researchers have been seeking an answer to this question, with the ultimate goal of unlocking the potential of artificial general intelligence (AGI) for a better and sustainable society. Many are the sectors that would be boosted by machines being able to take efficient sequential decisions over time. Let think at real-world applications such as personalized systems in entertainment (content systems) but also in healthcare (personalized therapy), smart cities (traffic control, flooding prevention), robots (control and planning), etc.. However, letting machines taking proper decisions in real-life is a highly challenging task. This is caused by the uncertainty behind such decisions (uncertainty on the actual reward, on the context, on the environment, etc.). A viable solution is to learn by experience (i.e., by trial and error), letting the machines uncover the uncertainty while taking decisions, and refining its strategy accordingly. However, such refinement is usually highly data-hungry (data-inefficiency), requiring a large amount of application specified data, leading to very slow learning processes -- hence very slow convergence to optimal strategies (curse of dimensionality). Luckily, data is usually intrinsically structured. Identifying and exploiting such structure substantially improves the data-efficiency of sequential learning algorithms. This is the key hypothesis underpinning the research in this thesis, in which novel structural learning methodologies are proposed for decision-making strategies problems such as Recommendation System (RS), Multi-armed Bandit (MAB) and Reinforcement Learning (RL), with the ultimate goal of making the learning process more (data)-efficient. Specifically, we tackle such goal from the perspective of modelling the problem structure as graphs, embedding tools from graph signal processing into decision learning theory.
As the first step, we study the application of graph-clustering techniques for RS, in which the curse of dimensionality is addressed by grouping data into clusters via graph-clustering techniques. Next, we exploit spectral graph structure for MAB problems, representing online learning problems. A key challenge is to learn sequentially the unknown bandit vector. Exploiting the smoothness-prior (i.e., bandit vector smooth on a given underpinning graph), we study theoretically the Laplacian-regularized estimator and provide both empirical evidences and theoretical analysis on the benefits of exploiting the graph structure in MABs. Then, we focus on the theoretical understanding of the Laplacian-regularized estimator. To this end, we derive a theoretical error upper bound on the estimator, which illustrates the impact of the alignment between the data and the graph structure as well as the graph spectrum on the estimation accuracy.
We then move to RL problems, focusing on the specific problem of learning a proper representation of the state-action (representation learning problem). Motivated by the fact that a good representation should be informative of the value function, we seek a learning algorithm able to preserve continuity between the value function and the representation space. Showing that state values are intrinsically correlated to the state transition dynamic structure and the diffusion of the reward on the MDP graph, we build a new loss function based on the newly defined diffusion distance and we propose a novel method to learn state representation with such desirable property.
In summary, in this thesis we address both theoretically and empirically important online sequential learning problems leveraging on the intrinsic data structure, showing the gain of the proposed solutions toward more data-efficient sequential learning strategies
TPMCF: Temporal QoS Prediction using Multi-Source Collaborative Features
Recently, with the rapid deployment of service APIs, personalized service
recommendations have played a paramount role in the growth of the e-commerce
industry. Quality-of-Service (QoS) parameters determining the service
performance, often used for recommendation, fluctuate over time. Thus, the QoS
prediction is essential to identify a suitable service among functionally
equivalent services over time. The contemporary temporal QoS prediction methods
hardly achieved the desired accuracy due to various limitations, such as the
inability to handle data sparsity and outliers and capture higher-order
temporal relationships among user-service interactions. Even though some recent
recurrent neural-network-based architectures can model temporal relationships
among QoS data, prediction accuracy degrades due to the absence of other
features (e.g., collaborative features) to comprehend the relationship among
the user-service interactions. This paper addresses the above challenges and
proposes a scalable strategy for Temporal QoS Prediction using Multi-source
Collaborative-Features (TPMCF), achieving high prediction accuracy and faster
responsiveness. TPMCF combines the collaborative-features of users/services by
exploiting user-service relationship with the spatio-temporal auto-extracted
features by employing graph convolution and transformer encoder with multi-head
self-attention. We validated our proposed method on WS-DREAM-2 datasets.
Extensive experiments showed TPMCF outperformed major state-of-the-art
approaches regarding prediction accuracy while ensuring high scalability and
reasonably faster responsiveness.Comment: 10 Pages, 7 figure
Modelling, Dimensioning and Optimization of 5G Communication Networks, Resources and Services
This reprint aims to collect state-of-the-art research contributions that address challenges in the emerging 5G networks design, dimensioning and optimization. Designing, dimensioning and optimization of communication networks resources and services have been an inseparable part of telecom network development. The latter must convey a large volume of traffic, providing service to traffic streams with highly differentiated requirements in terms of bit-rate and service time, required quality of service and quality of experience parameters. Such a communication infrastructure presents many important challenges, such as the study of necessary multi-layer cooperation, new protocols, performance evaluation of different network parts, low layer network design, network management and security issues, and new technologies in general, which will be discussed in this book
General Course Catalog [2022/23 academic year]
General Course Catalog, 2022/23 academic yearhttps://repository.stcloudstate.edu/undergencat/1134/thumbnail.jp
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