Control chart patterns recognition with constrained data

Recognition and classification of non-random patterns of manufacturing process data can provide clues to the possible causes that contributed to the product defects. Early detection of abnormal process patterns, particularly in highly precise and rapid automated manufacturing is necessary to avoid wastage and catastrophic failures. Towards this end, various control chart patterns recognition (CCPR) methods have been proposed by researchers. Most of the existing control chart patterns recognizers assumed that data is fully available and complete. However, in reality, process data streams may be constrained due to missing, imbalanced or inadequate data acquisition and measurement problems, erroneous entries and technical failure during data acquisition process. The aim of this study is to investigate and develop an effective recognition scheme capable of handling constrained control chart patterns. Various scenarios of data constraints involving missing rates, missing mechanisms, dataset size and imbalance rate were investigated. The proposed scheme comprises the following key components: (i) characterization of input data stream, (ii) imputation and feature extraction, and (iii) alternative recognition schemes. The proposed scheme was developed and tested to recognize the constrained patterns, namely, random, increasing/decreasing trend, upward/downward shift and cyclic patterns. The effect of design parameters on the recognition performance was examined. The Exponentially-Weighted Moving Average (EWMA) imputation, oversampling and Fuzzy Information Decomposition (FID) were investigated. This research revealed that some constraints in the dataset can eventually change the distribution and violate the normality assumption. The performance of alternative designs was compared by mean square error, percentage of correct recognition, confusion matrix, average run length (ARL), t-test, sensitivity, specificity and G-mean. The results demonstrated that the scheme with an ANNfuzzy recognizer trained using FID-treated constrained patterns significantly reduce false alarms and has better discriminative ability. The proposed scheme was verified and validated through comparative studies with published works. This research can be further extended by investigating an adaptive fuzzy router to assign incoming input data stream to an appropriate scheme that matches complexity in the constrained data streams, amongst others

Discrimination-aware data analysis for criminal intelligence

The growing use of Machine Learning (ML) algorithms in many application domains such as healthcare, business, education and criminal justice has evolved great promises as well challenges. ML pledges in proficiently analysing a large amount of data quickly and effectively by identifying patterns and providing insight into the data, which otherwise would have been impossible for a human to execute in this scale. However, the use of ML algorithms, in sensitive domains such as the Criminal Intelligence Analysis (CIA) system, demands extremely careful deployment. Data has an important impact in ML process. To understand the ethical and privacy issues related to data and ML, the VALCRI (Visual Analytics for sense-making in the CRiminal Intelligence analysis) system was used . VALCRI is a CIA system that integrated machine-learning techniques to improve the effectiveness of crime data analysis. At the most basic level, from our research, it was found that lack of harmonised interpretation of different privacy principles, trade-offs between competing ethical principles, and algorithmic opacity as concerning ethical and privacy issues among others. This research aims to alleviate these issues by investigating awareness of ethical and privacy issues related to data and ML. Document analysis and interviews were conducted to examine the way different privacy principles were understood in selected EU countries. The study takes a qualitative and quantitative research approach and is guided by various methods of analysis including interviews, observation, case study, experiment and legal document analysis. The findings of this research indicate that a lack of ethical awareness on data has an impact on ML outcome. Also, due to the opaque nature of the ML system, it is difficult to scrutinize and as a consequence, it leads to a lack of clarity in terms of how certain decisions were made. This thesis provides some novel solutions that can be used to tackle these issues

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

A Performance-Explainability-Fairness Framework For Benchmarking ML Models

Machine learning (ML) models have achieved remarkable success in various applications; however, ensuring their robustness and fairness remains a critical challenge. In this research, we present a comprehensive framework designed to evaluate and benchmark ML models through the lenses of performance, explainability, and fairness. This framework addresses the increasing need for a holistic assessment of ML models, considering not only their predictive power but also their interpretability and equitable deployment. The proposed framework leverages a multi-faceted evaluation approach, integrating performance metrics with explainability and fairness assessments. Performance evaluation incorporates standard measures such as accuracy, precision, and recall, but extends to overall balanced error rate, overall area under the receiver operating characteristic (ROC) curve (AUC), to capture model behavior across different performance aspects. Explainability assessment employs state-of-the-art techniques to quantify the interpretability of model decisions, ensuring that model behavior can be understood and trusted by stakeholders. The fairness evaluation examines model predictions in terms of demographic parity, equalized odds, thereby addressing concerns of bias and discrimination in the deployment of ML systems. To demonstrate the practical utility of the framework, we apply it to a diverse set of ML algorithms across various functional domains, including finance, criminology, education, and healthcare prediction. The results showcase the importance of a balanced evaluation approach, revealing trade-offs between performance, explainability, and fairness that can inform model selection and deployment decisions. Furthermore, we provide insights into the analysis of tradeoffs in selecting the appropriate model for use cases where performance, interpretability and fairness are important. In summary, the Performance-Explainability-Fairness Framework offers a unified methodology for evaluating and benchmarking ML models, enabling practitioners and researchers to make informed decisions about model suitability and ensuring responsible and equitable AI deployment. We believe that this framework represents a crucial step towards building trustworthy and accountable ML systems in an era where AI plays an increasingly prominent role in decision-making processes

Socio-Cognitive and Affective Computing

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli. Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science. Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm. This book integrates proposals from researchers who use signals from the brain and/or body to infer people's intentions and psychological state in smart computing systems. The design of this kind of systems combines knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing

Toward Accident Prevention Through Machine Learning Analysis of Accident Reports

Occupational safety remains of interest in the construction sector. The frequency of accidents has decreased in Sweden but only to a level that remains constant over the last ten years. Although Sweden shows to be performing better in comparison to other European countries, the construction industry continues to contribute to a fifth of fatal accidents in Europe. The latter situation pushes towards the need for reducing the frequency and fatalities of occupational accident occurrences in the construction sector. In the Swedish context, several initiatives have been established for prevention and accident frequency reduction. However, risk analysis models and causal links have been found to be rare in this context.The continuous reporting of accidents and near-misses creates large datasets with potentially useful information about accidents and their causes. In addition to that, there has been an increased research interest in analysing this data through machine learning (ML). The state-of-art research efforts include applying ML to analyse the textual data within the accumulated accident reports, identifying contributing factors, and extracting accident information. However, solutions that are created by ML models can lead to changes for a company and the industry. ML modelling includes a prototype development that is accompanied by the industry’s and domain experts’ requirements. The aim of this thesis is to investigate how ML based methods and techniques could be used to develop a research-based prototype for occupational accident prevention in a contracting company. The thesis focus is on the exploration of a development processes that bridges ML data analysis technical part with the context of safety in a contracting company. The thesis builds on accident causation models (ACMs) and ML methods, utilising the Cross Industry Standard Process Development Method (CRISP-DM) as a method. These were employed to interpret and understand the empirical material of accident reports and interviews within the health and safety (H&S) unit.The results of the thesis showed that analysing accident reports via ML can lead to the discovery of knowledge about accidents. However, there were several challenges that were found to hinder the extraction of knowledge and the application of ML. The identified challenges mainly related to the standardization of the development process and, the feasibility of implementation and evaluation. Moreover, the tendency of the ML-related literature to focus on predicting severity was found not compatible either with the function of ML analysis or the findings of accident causation literature which considers severity as a stochastic element. The analysis further concluded that ACMs seemed to have reached a mature stage, where a new approach is needed to understand the rules that govern the relationships between emergent new risks – rather than the systemization of risks themselves. The analysis of accident reports by ML needs further research in systemized methods for such analysis in the domain of construction and in the context of contracting companies – as only few research efforts have focused on this area regarding ML evaluation metrics and data pre-processing

Text Similarity Between Concepts Extracted from Source Code and Documentation

Context: Constant evolution in software systems often results in its documentation losing sync with the content of the source code. The traceability research field has often helped in the past with the aim to recover links between code and documentation, when the two fell out of sync. Objective: The aim of this paper is to compare the concepts contained within the source code of a system with those extracted from its documentation, in order to detect how similar these two sets are. If vastly different, the difference between the two sets might indicate a considerable ageing of the documentation, and a need to update it. Methods: In this paper we reduce the source code of 50 software systems to a set of key terms, each containing the concepts of one of the systems sampled. At the same time, we reduce the documentation of each system to another set of key terms. We then use four different approaches for set comparison to detect how the sets are similar. Results: Using the well known Jaccard index as the benchmark for the comparisons, we have discovered that the cosine distance has excellent comparative powers, and depending on the pre-training of the machine learning model. In particular, the SpaCy and the FastText embeddings offer up to 80% and 90% similarity scores. Conclusion: For most of the sampled systems, the source code and the documentation tend to contain very similar concepts. Given the accuracy for one pre-trained model (e.g., FastText), it becomes also evident that a few systems show a measurable drift between the concepts contained in the documentation and in the source code.</p

Antecipação na tomada de decisão com múltiplos critérios sob incerteza

Orientador: Fernando José Von ZubenTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A presença de incerteza em resultados futuros pode levar a indecisões em processos de escolha, especialmente ao elicitar as importâncias relativas de múltiplos critérios de decisão e de desempenhos de curto vs. longo prazo. Algumas decisões, no entanto, devem ser tomadas sob informação incompleta, o que pode resultar em ações precipitadas com consequências imprevisíveis. Quando uma solução deve ser selecionada sob vários pontos de vista conflitantes para operar em ambientes ruidosos e variantes no tempo, implementar alternativas provisórias flexíveis pode ser fundamental para contornar a falta de informação completa, mantendo opções futuras em aberto. A engenharia antecipatória pode então ser considerada como a estratégia de conceber soluções flexíveis as quais permitem aos tomadores de decisão responder de forma robusta a cenários imprevisíveis. Essa estratégia pode, assim, mitigar os riscos de, sem intenção, se comprometer fortemente a alternativas incertas, ao mesmo tempo em que aumenta a adaptabilidade às mudanças futuras. Nesta tese, os papéis da antecipação e da flexibilidade na automação de processos de tomada de decisão sequencial com múltiplos critérios sob incerteza é investigado. O dilema de atribuir importâncias relativas aos critérios de decisão e a recompensas imediatas sob informação incompleta é então tratado pela antecipação autônoma de decisões flexíveis capazes de preservar ao máximo a diversidade de escolhas futuras. Uma metodologia de aprendizagem antecipatória on-line é então proposta para melhorar a variedade e qualidade dos conjuntos futuros de soluções de trade-off. Esse objetivo é alcançado por meio da previsão de conjuntos de máximo hipervolume esperado, para a qual as capacidades de antecipação de metaheurísticas multi-objetivo são incrementadas com rastreamento bayesiano em ambos os espaços de busca e dos objetivos. A metodologia foi aplicada para a obtenção de decisões de investimento, as quais levaram a melhoras significativas do hipervolume futuro de conjuntos de carteiras financeiras de trade-off avaliadas com dados de ações fora da amostra de treino, quando comparada a uma estratégia míope. Além disso, a tomada de decisões flexíveis para o rebalanceamento de carteiras foi confirmada como uma estratégia significativamente melhor do que a de escolher aleatoriamente uma decisão de investimento a partir da fronteira estocástica eficiente evoluída, em todos os mercados artificiais e reais testados. Finalmente, os resultados sugerem que a antecipação de opções flexíveis levou a composições de carteiras que se mostraram significativamente correlacionadas com as melhorias observadas no hipervolume futuro esperado, avaliado com dados fora das amostras de treinoAbstract: The presence of uncertainty in future outcomes can lead to indecision in choice processes, especially when eliciting the relative importances of multiple decision criteria and of long-term vs. near-term performance. Some decisions, however, must be taken under incomplete information, what may result in precipitated actions with unforeseen consequences. When a solution must be selected under multiple conflicting views for operating in time-varying and noisy environments, implementing flexible provisional alternatives can be critical to circumvent the lack of complete information by keeping future options open. Anticipatory engineering can be then regarded as the strategy of designing flexible solutions that enable decision makers to respond robustly to unpredictable scenarios. This strategy can thus mitigate the risks of strong unintended commitments to uncertain alternatives, while increasing adaptability to future changes. In this thesis, the roles of anticipation and of flexibility on automating sequential multiple criteria decision-making processes under uncertainty are investigated. The dilemma of assigning relative importances to decision criteria and to immediate rewards under incomplete information is then handled by autonomously anticipating flexible decisions predicted to maximally preserve diversity of future choices. An online anticipatory learning methodology is then proposed for improving the range and quality of future trade-off solution sets. This goal is achieved by predicting maximal expected hypervolume sets, for which the anticipation capabilities of multi-objective metaheuristics are augmented with Bayesian tracking in both the objective and search spaces. The methodology has been applied for obtaining investment decisions that are shown to significantly improve the future hypervolume of trade-off financial portfolios for out-of-sample stock data, when compared to a myopic strategy. Moreover, implementing flexible portfolio rebalancing decisions was confirmed as a significantly better strategy than to randomly choosing an investment decision from the evolved stochastic efficient frontier in all tested artificial and real-world markets. Finally, the results suggest that anticipating flexible choices has lead to portfolio compositions that are significantly correlated with the observed improvements in out-of-sample future expected hypervolumeDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétric

Truck Activity Pattern Classification Using Anonymous Mobile Sensor Data

To construct, operate, and maintain a transportation system that supports the efficient movement of freight, transportation agencies must understand economic drivers of freight flow. This is a challenge since freight movement data available to transportation agencies is typically void of commodity and industry information, factors that tie freight movements to underlying economic conditions. With recent advances in the resolution and availability of big data from Global Positioning Systems (GPS), it may be possible to fill this critical freight data gap. However, there is a need for methodological approaches to enable usage of this data for freight planning and operations. To address this methodological need, we use advanced machine-learning techniques and spatial analyses to classify trucks by industry based on activity patterns derived from large streams of truck GPS data. The major components are: (1) derivation of truck activity patterns from anonymous GPS traces, (2) development of a classification model to distinguish trucks by industry, and (3) estimation of a spatio-temporal regression model to capture rerouting behavior of trucks. First, we developed a K-means unsupervised clustering algorithm to find unique and representative daily activity patterns from GPS data. For a statewide GPS data sample, we are able to reduce over 300,000 daily patterns to a representative six patterns, thus enabling easier calibration and validation of the travel forecasting models that rely on detailed activity patterns. Next, we developed a Random Forest supervised machine learning model to classify truck daily activity patterns by industry served. The model predicts five distinct industry classes, i.e., farm products, manufacturing, chemicals, mining, and miscellaneous mixed, with 90% accuracy, filling a critical gap in our ability to tie truck movements to industry served. This ultimately allows us to build travel demand forecasting models with behavioral sensitivity. Finally, we developed a spatio-temporal model to capture truck rerouting behaviors due to weather events. The ability to model re-routing behaviors allows transportation agencies to identify operational and planning solutions that mitigate the impacts of weather on truck traffic. For freight industries, the prediction of weather impacts on truck driver’s route choices can inform a more accurate estimation of billable miles