The Use of Transfer Learning for Activity Recognition in Instances of Heterogeneous Sensing

Transfer learning is a growing field that can address the variability of activity recognition problems by reusing the knowledge from previous experiences to recognise activities from different conditions, resulting in the leveraging of resources such as training and labelling efforts. Although integrating ubiquitous sensing technology and transfer learning seem promising, there are some research opportunities that, if addressed, could accelerate the development of activity recognition. This paper presents TL-FmRADLs; a framework that converges the feature fusion strategy with a teacher/learner approach over the active learning technique to automatise the self-training process of the learner models. Evaluation TL-FmRADLs is conducted over InSync; an open access dataset introduced for the first time in this paper. Results show promising effects towards mitigating the insufficiency of labelled data available by enabling the learner model to outperform the teacher’s performance

A Web-based Framework for the Evaluation of Predictive Process Monitoring Techniques

Äriprotsesside juhtimine keskendub ettevõtte siseste tegevuste optimeerimisele peamiste tulemuslikkuse näitajate suhtes. Protsesside jälgimine on üks äriprotsesside juhtimise osadest, mille eesmärgiks on teha kindlaks, et peamiste tulemuslikkuse näitajate nõuded oleksid täidetud. Ennustuslik protsesside jälgimine (EPJ) on uus protsesside jälgimise tüüp, mis tuleneb onlain protsesside jälgimise tehnikast. EPJ kasutab andmeid, mis kirjeldavad varasemalt toimunud äriprotsesse selleks, et konstrueerida masinõppe meetodite abil ennustatavat mudelit. Treenitud mudelit rakendatakse reaalajas toimuvate protsesside voole, selleks et ennustada protsesside käitumist.EPJ tüüpilisteks ülesanneteks on ennustada, kas antud äriprotsess lõpeb õigeks ajaks või mitte või milline on järgmine ülesanne, mida hakatakse protsessi raames täitma. Kuigi nende probleemide lahendamiseks on olemas vabavaralisi tarkvara lahendusi, tavaliselt nad keskenduvad ainult ühele eelmainitud probleemidest. Lisaks, sellised lahendused keskenduvad kogenud kasutajatele ja tarbivad palju riistvara ressursse simulatsioonide jooksutamiseks.Antud töö kirjeldab autori poolt loodud veebirakendust, mis lubab erineva kogemustasemega kasutajatel treenida, valideerida ja võrrelda treenitud mudeleid ning võimaldab ka mitme tulemuslikkuse näitaja ennustamist, kasutades erinevaid EPJ tehnikaid, millest räägitakse täpsemalt 'seotud töö' peatükis. Rakendus jooksutab kõiki simulatsioone serveris, seega ei pea kasutaja omama võimsat riistvara simulatsioonide jooksutamiseks.Lisaks, autor võrdleb oma poolt loodud rakendust juba olemasolevate rakendustega ning toob esile nendevahelisi erinevusi.Business process management (BPM) focuses on optimizations of various activities within the organization, with respect to key performance indicators (KPI). An important task among BPM-related activities is process monitoring which aims to make sure that business processes comply with KPIs.Process monitoring can be performed either offline, using historical data to analyze process execution in the past or online, i.e. analyzing event streams in real-time to identify the problems as soon as they arise. Predictive monitoring is an emerging type of online process monitoring that uses historical data to construct a predictive model using various machine learning methods and then applies this model to a live event stream in order to predict the future performance of ongoing process cases. Various techniques have been proposed to address typical predictive monitoring problems, such as whether this ongoing case will finish on time or what activity will be executed next in the case.Even though many of these techniques have publicly available software implementations, they typically target one specific predictive monitoring problem. Furthermore, due to variations in evaluation procedures (different data splits, different evaluation metrics reported, etc.), users do not have a readily available way to compare predictive accuracy across multiple techniques.Finally, such solutions are targeting experienced users and also consume a lot of users hardware resources to run the simulations. In this thesis, we have built a web application that allows users with various degrees of expertise in the subject to train, validate and compare models to predict multiple KPIs, using a wide range of predictive monitoring techniques proposed in related work.Moreover, the models can be exported for further use. This application runs all of the computations on the server side, thus eliminating the need for the powerful hardware to construct the models. We compare our solution with existing implementations and highlight clear distinctions and differences

An Eye into the Future: Leveraging A-Priori Knowledge in Predictive Business Process Monitoring

Ikka leidub juhtumeid, kus lisaks andmetele minevikust, eksisteerib täiendavaid teadmisi (apriori teadmised) selle kohta, kuidas protsessid teostuvad tulevikus. Neid teadmisi saab kasutada selleks, et parandada tuleviku ennustusi juhtumitele, mille kohta ei ole olevikus informatsiooni. Käesolevas töös tutvustame kahte meetodit - nad põhinevad rekurrentsetel tehisnärvivõrkudel, mis kasutavad pika lühiajalise mäluga (PLM) rakke. Need meetodid kasutavad informatsiooni protsessiteostusjuhtumite struktuuri kohta ja a priori teadmisi protsessi võimalike tulemite kohta, et ennustada järgmisi juhtumeid protsessi teostuse ahelas. Testides neid meetodeid kuue elulise sündmuste logiga näitavad meetodite täpsuse paranemist võrreldes tavaliste PLM-il põhinevate meetoditega.Predictive business process monitoring aims at leveraging past process execution data to predict how ongoing (uncompleted) process executions will unfold up to their completion. Nevertheless, cases exist in which, together with pastexecution data, some additional knowledge (a-prioriknowledge) about how a process execution will develop in the future is available. This knowledge about the future can be leveraged forimproving the quality of the predictions of events that are currently unknown. In this thesis, we present two techniques - based on Recurrent Neural Networks with Long Short-Term Memory (LSTM) cells - able to leverage knowledge about the structure of the process execution traces as well as a-priori knowledge about how they will unfold in the future for predicting the sequence of future activities of ongoing process executions. The results obtained by applying these techniques on six real-life logs show an improvement in terms of accuracy over a plain LSTM-based baseline

Machine Learning in Business Process Monitoring: A Comparison of Deep Learning and Classical Approaches Used for Outcome Prediction

Predictive process monitoring aims at forecasting the behavior, performance, and outcomes of business processes at runtime. It helps identify problems before they occur and re-allocate resources before they are wasted. Although deep learning (DL) has yielded breakthroughs, most existing approaches build on classical machine learning (ML) techniques, particularly when it comes to outcome-oriented predictive process monitoring. This circumstance reflects a lack of understanding about which event log properties facilitate the use of DL techniques. To address this gap, the authors compared the performance of DL (i.e., simple feedforward deep neural networks and long short term memory networks) and ML techniques (i.e., random forests and support vector machines) based on five publicly available event logs. It could be observed that DL generally outperforms classical ML techniques. Moreover, three specific propositions could be inferred from further observations: First, the outperformance of DL techniques is particularly strong for logs with a high variant-to-instance ratio (i.e., many non-standard cases). Second, DL techniques perform more stably in case of imbalanced target variables, especially for logs with a high event-to-activity ratio (i.e., many loops in the control flow). Third, logs with a high activity-to-instance payload ratio (i.e., input data is predominantly generated at runtime) call for the application of long short term memory networks. Due to the purposive sampling of event logs and techniques, these findings also hold for logs outside this study

Adversarial Infidelity Learning for Model Interpretation

Model interpretation is essential in data mining and knowledge discovery. It can help understand the intrinsic model working mechanism and check if the model has undesired characteristics. A popular way of performing model interpretation is Instance-wise Feature Selection (IFS), which provides an importance score of each feature representing the data samples to explain how the model generates the specific output. In this paper, we propose a Model-agnostic Effective Efficient Direct (MEED) IFS framework for model interpretation, mitigating concerns about sanity, combinatorial shortcuts, model identifiability, and information transmission. Also, we focus on the following setting: using selected features to directly predict the output of the given model, which serves as a primary evaluation metric for model-interpretation methods. Apart from the features, we involve the output of the given model as an additional input to learn an explainer based on more accurate information. To learn the explainer, besides fidelity, we propose an Adversarial Infidelity Learning (AIL) mechanism to boost the explanation learning by screening relatively unimportant features. Through theoretical and experimental analysis, we show that our AIL mechanism can help learn the desired conditional distribution between selected features and targets. Moreover, we extend our framework by integrating efficient interpretation methods as proper priors to provide a warm start. Comprehensive empirical evaluation results are provided by quantitative metrics and human evaluation to demonstrate the effectiveness and superiority of our proposed method. Our code is publicly available online at https://github.com/langlrsw/MEED.Comment: 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '20), August 23--27, 2020, Virtual Event, US

Estimação da Cobertura de Gelo Marinho nos Mares Antárticos de Weddell, Belingshausen e Amundsen com Redes Neurais Artificiais

O gelo marinho desempenha um papel fundamental na regulação térmica das regiões polares. Observações de satélites evidenciam que na Antártica o gelo apresentava, na série histórica, tendências positivas em cobertura e extensão. Em 2019 houve um padrão de inversões entre os valores da normal climatológica e dos dados de reanálise. Nesse contexto, este estudo teve como principal objetivo avaliar o potencial de previsibilidade de cobertura de gelo marinho com a aplicação de técnicas de RNAs em 3 mares que banham o continente Antártico, a saber: Weddell, Bellingshausen e Amundsen. Para tanto, foram utilizados como previsores a temperatura da superfície do mar, a temperatura do ar a 2 metros, a velocidade do vento a 10 metros, o albedo e os fluxos de calor latente e sensível, no período de 1979 a 2019. Os dados foram particionados em 70% para treinamento e 30% para testes. Modelos SARIMAX serviram como valores de referência para aferição da precisão das previsões com RNAs. Em todos os meses com anomalias absolutas superiores a 15% de concentração, o modelo de RNA CNN-LSTM superou os modelos MLP e SARIMAX

PREDICTIVE BUSINESS PROCESS MONITORINGWITH CONTEXT INFORMATION FROM DOCUMENTS

Predictive business process monitoring deals with predicting a process’s future behavior or the value of process-related performance indicators based on process event data. A variety of prototypical predictive business process monitoring techniques has been proposed by researchers in order to help process participants performing business processes better. In practical settings, these techniques have a low predictive quality that is often close to random, so that predictive business process monitoring applications are rare in practice. The inclusion of process-context data has been discussed as a way to improve the predictive quality. Existing approaches have considered only structured data as context. In this paper, we argue that process-related unstructured documents are also a promising source for extracting process-context data. Accordingly, this research-in-progress paper outlines a design-science research process for creating a predictive business process monitoring technique that utilizes context data from process-related documents to predict a process instance’s next activity more accurately