Academic competitions

Academic challenges comprise effective means for (i) advancing the state of the art, (ii) putting in the spotlight of a scientific community specific topics and problems, as well as (iii) closing the gap for under represented communities in terms of accessing and participating in the shaping of research fields. Competitions can be traced back for centuries and their achievements have had great influence in our modern world. Recently, they (re)gained popularity, with the overwhelming amounts of data that is being generated in different domains, as well as the need of pushing the barriers of existing methods, and available tools to handle such data. This chapter provides a survey of academic challenges in the context of machine learning and related fields. We review the most influential competitions in the last few years and analyze challenges per area of knowledge. The aims of scientific challenges, their goals, major achievements and expectations for the next few years are reviewed

eTOP: Early Termination of Pipelines for Faster Training of AutoML Systems

Recent advancements in software and hardware technologies have enabled the use of AI/ML models in everyday applications has significantly improved the quality of service rendered. However, for a given application, finding the right AI/ML model is a complex and costly process, that involves the generation, training, and evaluation of multiple interlinked steps (called pipelines), such as data pre-processing, feature engineering, selection, and model tuning. These pipelines are complex (in structure) and costly (both in compute resource and time) to execute end-to-end, with a hyper-parameter associated with each step. AutoML systems automate the search of these hyper-parameters but are slow, as they rely on optimizing the pipeline's end output. We propose the eTOP Framework which works on top of any AutoML system and decides whether or not to execute the pipeline to the end or terminate at an intermediate step. Experimental evaluation on 26 benchmark datasets and integration of eTOPwith MLBox4 reduces the training time of the AutoML system upto 40x than baseline MLBox.Comment: N

OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System

Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to take humans out of the loop when building artificial intelligence (AI) applications, scant literature has focused on how AutoML works well in open-environment scenarios such as the process of training and updating large models, industrial supply chains or the industrial metaverse, where people often face open-loop problems during the search process: they must continuously collect data, update data and models, satisfy the requirements of the development and deployment environment, support massive devices, modify evaluation metrics, etc. Addressing the open-environment issue with pure data-driven approaches requires considerable data, computing resources, and effort from dedicated data engineers, making current AutoML systems and platforms inefficient and computationally intractable. Human-computer interaction is a practical and feasible way to tackle the problem of open-environment AI. In this paper, we introduce OmniForce, a human-centered AutoML (HAML) system that yields both human-assisted ML and ML-assisted human techniques, to put an AutoML system into practice and build adaptive AI in open-environment scenarios. Specifically, we present OmniForce in terms of ML version management; pipeline-driven development and deployment collaborations; a flexible search strategy framework; and widely provisioned and crowdsourced application algorithms, including large models. Furthermore, the (large) models constructed by OmniForce can be automatically turned into remote services in a few minutes; this process is dubbed model as a service (MaaS). Experimental results obtained in multiple search spaces and real-world use cases demonstrate the efficacy and efficiency of OmniForce

Hybrid Automated Machine Learning System for Big Data

A lot of machine learning (ML) models and algorithms exist and in designing classification systems, it is often a challenge looking for and selecting the best performing ML algorithm(s) to use for a dataset in a short period of time. Often, one must learn thor-oughly about the data set structure and content, decide whether to use a supervised, semi-supervised or an unsupervised learning strategy, and then investigate, select or design via trial and error a classification or clustering algorithm that would work most accurately for that specific dataset. This can be quite a time consuming and tedious process. Additionally, a classification algorithm may not perform very well with a dataset as compared to using a clustering algorithm. Meta-learning (learning to learn) and automatic ML (autoML) are data mining-based formalisms for modelling evolving conventional ML functions and toolkit systems. The concept of modelling a decision tree-based combination of both formalisms as a Hybrid-AutoML toolkit extends that of traditional complex autoML systems. In hybrid-autoML, single or multiple predictive models are built by combining a three-layered decision learning architecture for automatic learning mode and model selection, by engaging formal-isms for selecting from a variety of supervised or unsupervised ML algorithms and generic meta information obtained from varying multi-datasets. The work presented in this thesis aims to study, conceptualize, design and develop this hybrid-autoML toolkit. By extending in the simplest form, some existing methodologies for the model training aspect of autoML systems. The theoretical and experimental development focuses on the extension of autoWeka and use of existing meta-learning, algorithm selection and deci-sion tree concepts. It addresses the issue of efficient ML mode (supervised or unsupervised) and model selection for varying multi-datasets, learning methods representations of practical alternative use cases and structuring of layered decision ML un-folding, and algorithms for constructing the unfolding. The im-plementation aims to develop tools for hybrid-autoML based model visualization or evaluation, use case simulations and analysis on single or multi varying datasets. An open source tool called hybrid-autoML has been developed to support these functionali-ties. Hybrid-autoML provides a user-friendly graphical interface that facilitates single or multi varying datasets entry, sup-ports automatic learning mode or strategy selection, automatic model selection on single or multi-varying datasets, supports predictive testing, and allows the automatic visualization and use of a set of analytical tools for model evaluation. It is highly extensible and saves a lot of time

ALMA: ALgorithm Modeling Application

As of today, the most recent trend in information technology is the employment of large-scale data analytic methods powered by Artificial Intelligence (AI), influencing the priorities of businesses and research centers all over the world. However, due to both the lack of specialized talent and the need for greater compute, less established businesses struggle to adopt such endeavors, with major technological mega-corporations such as Microsoft, Facebook and Google taking the upper hand in this uneven playing field. Therefore, in an attempt to promote the democratization of AI and increase the efficiency of data scientists, this work proposes a novel no-code/low-code AI platform: the ALgorithm Modeling Application (ALMA). Moreover, as the state of the art of such platforms is still gradually maturing, current solutions often fail into encompassing security/safety aspects directly into their process. In that respect, the solution proposed in this thesis aims not only to achieve greater development and deployment efficiency while building machine learning applications but also to build upon others by addressing the inherent pitfalls of AI through a ”secure by design” philosophy.Atualmente, a tendência mais recente no domínio das tecnologias de informação e a utilização de métodos de análise de dados baseados em Inteligência Artificial (IA), influenciando as prioridades das empresas e centros de investigação de todo o mundo. No entanto, devido à falta de talento especializado no mercado e a necessidade de obter equipamentos com maior capacidade de computação, negócios menos estabelecidos têm maiores dificuldades em realizar esse tipo de investimentos quando comparados a grandes empresas tecnológicas como a Microsoft, o Facebook e a Google. Deste modo, na tentativa de promover a democratização da IA e aumentar a eficiência dos cientistas de dados, este trabalho propõe uma nova plataforma de no-code/low- code: “THe Algorithm Modeling Application” (ALMA). Por outro lado, e visto que a maioria das soluções atuais falham em abranger aspetos de segurança relativos ˜ a IA diretamente no seu processo, a solução proposta nesta tese visa não só alcançar maior eficiência na construção de soluções baseadas em IA, mas também abordar as questões de segurança implícitas ao seu uso

Performance Enhancement Using NOMA-MIMO for 5G Networks

The integration of MIMO and NOMA technologies addresses key challenges in 5G and beyond, such as connectivity, latency, and dependability. However, resolving these issues, especially in MIMO-enabled 5G networks, required additional research. This involved optimizing parameters like bit error rate, downlink spectrum efficiency, average capacity rate, and uplink transmission outage probability. The model employed Quadrature Phase Shift Keying modulation on selected frequency channels, accommodating diverse user characteristics. Evaluation showed that MIMO-NOMA significantly improved bit error rate and transmitting power for the best user in download transmission. For uplink transmission, there was an increase in the average capacity rate and a decrease in outage probability for the best user. Closed-form formulas for various parameters in both downlink and uplink NOMA, with and without MIMO, were derived. Overall, adopting MIMO-NOMA led to a remarkable performance improvement for all users, even in challenging conditions like interference or fading channels