Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N

A Visual Programming Paradigm for Abstract Deep Learning Model Development

Deep learning is one of the fastest growing technologies in computer science with a plethora of applications. But this unprecedented growth has so far been limited to the consumption of deep learning experts. The primary challenge being a steep learning curve for learning the programming libraries and the lack of intuitive systems enabling non-experts to consume deep learning. Towards this goal, we study the effectiveness of a no-code paradigm for designing deep learning models. Particularly, a visual drag-and-drop interface is found more efficient when compared with the traditional programming and alternative visual programming paradigms. We conduct user studies of different expertise levels to measure the entry level barrier and the developer load across different programming paradigms. We obtain a System Usability Scale (SUS) of 90 and a NASA Task Load index (TLX) score of 21 for the proposed visual programming compared to 68 and 52, respectively, for the traditional programming methods

Data-Driven Anomaly Detection in Industrial Networks

Since the conception of the first Programmable Logic Controllers (PLCs) in the 1960s, Industrial Control Systems (ICSs) have evolved vastly. From the primitive isolated setups, ICSs have become increasingly interconnected, slowly forming the complex networked environments, collectively known as Industrial Networks (INs), that we know today. Since ICSs are responsible for a wide range of physical processes, including those belonging to Critical Infrastructures (CIs), securing INs is vital for the well-being of modern societies. Out of the many research advances on the field, Anomaly Detection Systems (ADSs) play a prominent role. These systems monitor IN and/or ICS behavior to detect abnormal events, known or unknown. However, as the complexity of INs has increased, monitoring them in the search of anomalous trends has effectively become a Big Data problem. In other words, IN data has become too complex to process it by traditional means, due to its large scale, diversity and generation speeds. Nevertheless, ADSs designed for INs have not evolved at the same pace, and recent proposals are not designed to handle this data complexity, as they do not scale well or do not leverage the majority of the data types created in INs. This thesis aims to fill that gap, by presenting two main contributions: (i) a visual flow monitoring system and (ii) a multivariate ADS that is able to tackle data heterogeneity and to scale efficiently. For the flow monitor, we propose a system that, based on current flow data, builds security visualizations depicting network behavior while highlighting anomalies. For the multivariate ADS, we analyze the performance of Multivariate Statistical Process Control (MSPC) for detecting and diagnosing anomalies, and later we present a Big Data, MSPCinspired ADS that monitors field and network data to detect anomalies. The approaches are experimentally validated by building INs in test environments and analyzing the data created by them. Based on this necessity for conducting IN security research in a rigorous and reproducible environment, we also propose the design of a testbed that serves this purpose

Visual diagnosis of tree boosting methods

Tree boosting, which combines weak learners (typically decision trees) to generate a strong learner, is a highly effective and widely used machine learning method. However, the development of a high performance tree boosting model is a time-consuming process that requires numerous trial-and-error experiments. To tackle this issue, we have developed a visual diagnosis tool, BOOSTVis, to help experts quickly analyze and diagnose the training process of tree boosting. In particular, we have designed a temporal confusion matrix visualization, and combined it with a t-SNE projection and a tree visualization. These visualization components work together to provide a comprehensive overview of a tree boosting model, and enable an effective diagnosis of an unsatisfactory training process. Two case studies that were conducted on the Otto Group Product Classification Challenge dataset demonstrate that BOOSTVis can provide informative feedback and guidance to improve understanding and diagnosis of tree boosting algorithms

Proposal for a software-prototype to assist patients with peripherally inserted central catheter (PICC)

Objective: To propose a software prototype as a strategy to improve the communication process and records pertaining to the nursing care of patients undergoing Peripherally Inserted Central Catheter (PICC), and describe the steps for designing a software prototype. Methods: Intervention Research was used in the Intervention Process modality and as a method of operation the Prototyping associated to computer tools. Results: A simple graphics interface computerized model was created and designed to be used in open or private virtual networks. Conclusion: Development of the software prototype to run on web network will make the practical implementation process. It adds that validation with health staff will be carried out before use

Unrestricted faecal calprotectin testing performs poorly in the diagnosis of inflammatory bowel disease in patients in primary care

BACKGROUND: Faecal calprotectin (FC) measurement distinguishes patients with inflammatory bowel disease (IBD) from those with irritable bowel syndrome but evidence of its performance in primary care is limited. AIMS: To assess the yield of IBD from FC testing in primary care. METHODS: Retrospective review of hospital records to assess the outcome following FC testing in primary care. Investigations for all patients undergoing FC testing in a single laboratory for 6 months from 1 October 2013 to 28 February 2014 were reviewed. RESULTS: 410 patients (162 male; median age 42; range 16-91) were included. FC>50 µg/g was considered positive (FC+). 148/410 (36.1%; median age 44 (17-91)) were FC+ (median FC 116.5 µg/g (51-1770)). 122/148 FC-positive patients (82.4%) underwent further investigation. 97 (65.5%) underwent lower gastrointestinal endoscopy (LGIE), of which 7 (7.2%) had IBD. 49/262 (18.7%) FC-negative (FC-) patients (FC ≤50 µg/g) (median age 47 (19-76)) also underwent LGIE, of whom 3 (6.1%) had IBD.IBD was diagnosed in 11/410 (2.7%; 4 ulcerative colitis, 3 Crohn's disease, 4 microscopic colitis). 8/11 were FC+ (range 67-1170) and 3 FC-. At a 50 µg/g threshold, sensitivity for detecting IBD was 72.7%, specificity 64.9%, positive predictive value (PPV) 5.41% and negative predictive value 98.9%. Increasing the threshold to 100 µg/g reduced the sensitivity of the test for detecting IBD to 54.6%. CONCLUSIONS: FC testing in primary care has low sensitivity and specificity with poor PPV for diagnosing IBD. Its use needs to be directed to those with a higher pretest probability of disease. Local services and laboratories should advise general practitioners accordingly

Koneoppimiskehys petrokemianteollisuuden sovelluksille

Machine learning has many potentially useful applications in process industry, for example in process monitoring and control. Continuously accumulating process data and the recent development in software and hardware that enable more advanced machine learning, are fulfilling the prerequisites of developing and deploying process automation integrated machine learning applications which improve existing functionalities or even implement artificial intelligence. In this master's thesis, a framework is designed and implemented on a proof-of-concept level, to enable easy acquisition of process data to be used with modern machine learning libraries, and to also enable scalable online deployment of the trained models. The literature part of the thesis concentrates on studying the current state and approaches for digital advisory systems for process operators, as a potential application to be developed on the machine learning framework. The literature study shows that the approaches for process operators' decision support tools have shifted from rule-based and knowledge-based methods to machine learning. However, no standard methods can be concluded, and most of the use cases are quite application-specific. In the developed machine learning framework, both commercial software and open source components with permissive licenses are used. Data is acquired over OPC UA and then processed in Python, which is currently almost the de facto standard language in data analytics. Microservice architecture with containerization is used in the online deployment, and in a qualitative evaluation, it proved to be a versatile and functional solution.Koneoppimisella voidaan osoittaa olevan useita hyödyllisiä käyttökohteita prosessiteollisuudessa, esimerkiksi prosessinohjaukseen liittyvissä sovelluksissa. Jatkuvasti kerääntyvä prosessidata ja toisaalta koneoppimiseen soveltuvien ohjelmistojen sekä myös laitteistojen viimeaikainen kehitys johtavat tilanteeseen, jossa prosessiautomaatioon liitettyjen koneoppimissovellusten avulla on mahdollista parantaa nykyisiä toiminnallisuuksia tai jopa toteuttaa tekoälysovelluksia. Tässä diplomityössä suunniteltiin ja toteutettiin prototyypin tasolla koneoppimiskehys, jonka avulla on helppo käyttää prosessidataa yhdessä nykyaikaisten koneoppimiskirjastojen kanssa. Kehys mahdollistaa myös koneopittujen mallien skaalautuvan käyttöönoton. Diplomityön kirjallisuusosa keskittyy prosessioperaattoreille tarkoitettujen digitaalisten avustajajärjestelmien nykytilaan ja toteutustapoihin, avustajajärjestelmän tai sen päätöstukijärjestelmän ollessa yksi mahdollinen koneoppimiskehyksen päälle rakennettava ohjelma. Kirjallisuustutkimuksen mukaan prosessioperaattorin päätöstukijärjestelmien taustalla olevat menetelmät ovat yhä useammin koneoppimiseen perustuvia, aiempien sääntö- ja tietämyskantoihin perustuvien menetelmien sijasta. Selkeitä yhdenmukaisia lähestymistapoja ei kuitenkaan ole helposti pääteltävissä kirjallisuuden perusteella. Lisäksi useimmat tapausesimerkit ovat sovellettavissa vain kyseisissä erikoistapauksissa. Kehitetyssä koneoppimiskehyksessä on käytetty sekä kaupallisia että avoimen lähdekoodin komponentteja. Prosessidata haetaan OPC UA -protokollan avulla, ja sitä on mahdollista käsitellä Python-kielellä, josta on muodostunut lähes de facto -standardi data-analytiikassa. Kehyksen käyttöönottokomponentit perustuvat mikropalveluarkkitehtuuriin ja konttiteknologiaan, jotka osoittautuivat laadullisessa testauksessa monipuoliseksi ja toimivaksi toteutustavaksi