PredictChain: Empowering Collaboration and Data Accessibility for AI in a Decentralized Blockchain-based Marketplace

Limited access to computing resources and training data poses significant challenges for individuals and groups aiming to train and utilize predictive machine learning models. Although numerous publicly available machine learning models exist, they are often unhosted, necessitating end-users to establish their computational infrastructure. Alternatively, these models may only be accessible through paid cloud-based mechanisms, which can prove costly for general public utilization. Moreover, model and data providers require a more streamlined approach to track resource usage and capitalize on subsequent usage by others, both financially and otherwise. An effective mechanism is also lacking to contribute high-quality data for improving model performance. We propose a blockchain-based marketplace called "PredictChain" for predictive machine-learning models to address these issues. This marketplace enables users to upload datasets for training predictive machine learning models, request model training on previously uploaded datasets, or submit queries to trained models. Nodes within the blockchain network, equipped with available computing resources, will operate these models, offering a range of archetype machine learning models with varying characteristics, such as cost, speed, simplicity, power, and cost-effectiveness. This decentralized approach empowers users to develop improved models accessible to the public, promotes data sharing, and reduces reliance on centralized cloud providers

Provable Robustness for Streaming Models with a Sliding Window

The literature on provable robustness in machine learning has primarily focused on static prediction problems, such as image classification, in which input samples are assumed to be independent and model performance is measured as an expectation over the input distribution. Robustness certificates are derived for individual input instances with the assumption that the model is evaluated on each instance separately. However, in many deep learning applications such as online content recommendation and stock market analysis, models use historical data to make predictions. Robustness certificates based on the assumption of independent input samples are not directly applicable in such scenarios. In this work, we focus on the provable robustness of machine learning models in the context of data streams, where inputs are presented as a sequence of potentially correlated items. We derive robustness certificates for models that use a fixed-size sliding window over the input stream. Our guarantees hold for the average model performance across the entire stream and are independent of stream size, making them suitable for large data streams. We perform experiments on speech detection and human activity recognition tasks and show that our certificates can produce meaningful performance guarantees against adversarial perturbations

Implementing decision tree-based algorithms in medical diagnostic decision support systems

As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems. Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks. We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models

Human fall detection on videos using convolutional neural networks with multiple channels

Orientador: Hélio PedriniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Baixas taxas de mortalidade infantil, avanços na medicina e mudanças culturais aumentaram a expectativa de vida nos países desenvolvidos para mais de 60 anos. Alguns países esperam que, até 2030, 20% da sua população tenham mais de 65 anos. A qualidade de vida nessa idade avançada é altamente determinada pela saúde do indivíduo, que ditará se o idoso pode se engajar em atividades importantes para o seu bem estar, independência e satisfação pessoal. O envelhecimento é acompanhado por problemas de saúde causados por limitações biológicas e fraqueza muscular. Esse enfraquecimento facilita a ocorrência de quedas, responsáveis pela morte de aproximadamente 646.000 pessoas em todo o mundo e, mesmo quando uma pequena queda ocorre, ela ainda pode fraturar ossos ou danificar tecidos moles, que não cicatrizam completamente. Lesões e danos dessa natureza, por sua vez, podem afetar a autoconfiança do indivíduo, diminuindo sua independência. Neste trabalho, propomos um método capaz de detectar quedas humanas em sequências de vídeo usando redes neurais convolucionais (CNNs) multicanais. Nós desenvolvemos dois métodos para detecção de quedas, o primeiro utilizando uma CNN 2D e o segundo utilizando uma CNN 3D. Nossos métodos utilizam características extraídas previamente de cada quadro do vídeo e as classificam. Após a etapa de classificação, uma máquina de vetores de suporte (SVM) é aplicada para ponderar os canais de entrada e indicar se houve ou não uma queda. Experimentamos quatro tipos de características, a saber: (i) fluxo óptico, (ii) ritmo visual, (iii) estimativa de pose e (iv) mapa de saliência. As bases de dados utilizadas (URFD e FDD) estão disponíveis publicamente e nossos resultados são comparados com os da literatura. As métricas selecionadas para avaliação são acurácia balanceada, acurácia, sensibilidade e especificidade. Nossos métodos apresentaram resultados competitivos com os obtidos pelo estado da arte na base de dados URFD e superam os obtidos na base de dados FDD. Ao conhecimento dos autores, nós somos os primeiros a realizar testes cruzados entre os conjuntos de dados em questão, e a reportar resultados de acurácia balanceada. Os métodos propostos são capazes de detectar quedas nas bases selecionadas. A detecção de quedas, bem como a classificação de atividades em vídeos, está fortemente relacionada à capacidade da rede de interpretar informações temporais e, como esperado, o fluxo óptico é a característica mais relevante para a detecção de quedasAbstract: Lower child mortality rates, advances in medicine, and cultural changes have increased life expectancy in developed countries over 60 years old. Some countries expect that, by 2030, 20% of their population will be over 65 years old. The quality of life at this advanced age is highly dictated by the individual's health, which will determine whether the elderly can engage in important activities to their well-being, independence, and personal satisfaction. Old age is accompanied by health problems caused by biological limitations and muscle weakness. This weakening facilitates the occurrence of falls, which are responsible for the deaths of approximately 646,000 people worldwide and, even when a minor fall occurs, it can still cause fractures, break bones or damage soft tissues, which will not heal completely. Injuries and damages of this nature, in turn, will consume the self-confidence of the individual, diminishing their independence. In this work, we propose a method capable of detecting human falls in video sequences using multichannel convolutional neural networks (CNN). We developed two methods for fall detection, the first using a 2D CNN and the second using a 3D CNN. Our method uses features previously extracted from each frame and classifies them with a CNN. After the classification step, a support vector machine (SVM) is applied to weight the input channels and indicate whether or not there was a fall. We experiment with four types of features, namely: (i) optical flow, (ii) visual rhythm, (iii) pose estimation, and (iv) saliency map. The benchmarks used (URFD and FDD) are publicly available and our results are compared to those in the literature. The metrics selected for evaluation are balanced accuracy, accuracy, sensitivity, and specificity. Our results are competitive with those obtained by the state of the art on the URFD data set and surpass those on the FDD data set. To the authors' knowledge, we are the first to perform cross-tests between the datasets in question and to report results for the balanced accuracy metric. The proposed method is able to detect falls in the selected benchmarks. Fall detection, as well as activity classification in videos, is strongly related to the network's ability to interpret temporal information and, as expected, optical flow is the most relevant feature for detecting fallsMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Policy Smoothing for Provably Robust Reinforcement Learning

The study of provable adversarial robustness for deep neural networks (DNNs) has mainly focused on static supervised learning tasks such as image classification. However, DNNs have been used extensively in real-world adaptive tasks such as reinforcement learning (RL), making such systems vulnerable to adversarial attacks as well. Prior works in provable robustness in RL seek to certify the behaviour of the victim policy at every time-step against a non-adaptive adversary using methods developed for the static setting. But in the real world, an RL adversary can infer the defense strategy used by the victim agent by observing the states, actions, etc. from previous time-steps and adapt itself to produce stronger attacks in future steps. We present an efficient procedure, designed specifically to defend against an adaptive RL adversary, that can directly certify the total reward without requiring the policy to be robust at each time-step. Our main theoretical contribution is to prove an adaptive version of the Neyman-Pearson Lemma -- a key lemma for smoothing-based certificates -- where the adversarial perturbation at a particular time can be a stochastic function of current and previous observations and states as well as previous actions. Building on this result, we propose policy smoothing where the agent adds a Gaussian noise to its observation at each time-step before passing it through the policy function. Our robustness certificates guarantee that the final total reward obtained by policy smoothing remains above a certain threshold, even though the actions at intermediate time-steps may change under the attack. Our experiments on various environments like Cartpole, Pong, Freeway and Mountain Car show that our method can yield meaningful robustness guarantees in practice

Innovations and Social Media Analytics in a Digital Society

Recent advances in digitization are transforming healthcare, education, tourism, information technology, and some other sectors. Social media analytics are tools that can be used to measure innovation and the relation of the companies with the citizens. This book comprises state-ofthe-art social media analytics, and advanced innovation policies in the digitization of society. The number of applications that can be used to create and analyze social media analytics generates large amounts of data called big data, including measures of the use of the technologies to develop or to use new services to improve the quality of life of the citizens. Digitization has applications in fields from remote monitoring to smart sensors and other devices. Integration generates data that need to be analyzed and visualized in an easy and clear way, that will be some of the proposals of the researchers present in this book. This volume offers valuable insights to researchers on how to design innovative digital analytics systems and how to improve information delivery remotely.info:eu-repo/semantics/publishedVersio

Innovations and Social Media Analytics in a Digital Society

info:eu-repo/semantics/publishedVersio