LODE: Linking Digital Humanities Content to the Web of Data

Numerous digital humanities projects maintain their data collections in the form of text, images, and metadata. While data may be stored in many formats, from plain text to XML to relational databases, the use of the resource description framework (RDF) as a standardized representation has gained considerable traction during the last five years. Almost every digital humanities meeting has at least one session concerned with the topic of digital humanities, RDF, and linked data. While most existing work in linked data has focused on improving algorithms for entity matching, the aim of the LinkedHumanities project is to build digital humanities tools that work "out of the box," enabling their use by humanities scholars, computer scientists, librarians, and information scientists alike. With this paper, we report on the Linked Open Data Enhancer (LODE) framework developed as part of the LinkedHumanities project. With LODE we support non-technical users to enrich a local RDF repository with high-quality data from the Linked Open Data cloud. LODE links and enhances the local RDF repository without compromising the quality of the data. In particular, LODE supports the user in the enhancement and linking process by providing intuitive user-interfaces and by suggesting high-quality linking candidates using tailored matching algorithms. We hope that the LODE framework will be useful to digital humanities scholars complementing other digital humanities tools

Explaining Neural Matrix Factorization with Gradient Rollback

Explaining the predictions of neural black-box models is an important problem, especially when such models are used in applications where user trust is crucial. Estimating the influence of training examples on a learned neural model's behavior allows us to identify training examples most responsible for a given prediction and, therefore, to faithfully explain the output of a black-box model. The most generally applicable existing method is based on influence functions, which scale poorly for larger sample sizes and models. We propose gradient rollback, a general approach for influence estimation, applicable to neural models where each parameter update step during gradient descent touches a smaller number of parameters, even if the overall number of parameters is large. Neural matrix factorization models trained with gradient descent are part of this model class. These models are popular and have found a wide range of applications in industry. Especially knowledge graph embedding methods, which belong to this class, are used extensively. We show that gradient rollback is highly efficient at both training and test time. Moreover, we show theoretically that the difference between gradient rollback's influence approximation and the true influence on a model's behavior is smaller than known bounds on the stability of stochastic gradient descent. This establishes that gradient rollback is robustly estimating example influence. We also conduct experiments which show that gradient rollback provides faithful explanations for knowledge base completion and recommender datasets.Comment: 35th AAAI Conference on Artificial Intelligence, 2021. Includes Appendi

ProcK: Machine Learning for Knowledge-Intensive Processes

We present a novel methodology to build powerful predictive process models. Our method, denoted ProcK (Process & Knowledge), relies not only on sequential input data in the form of event logs, but can learn to use a knowledge graph to incorporate information about the attribute values of the events and their mutual relationships. The idea is realized by mapping event attributes to nodes of a knowledge graph and training a sequence model alongside a graph neural network in an end-to-end fashion. This hybrid approach substantially enhances the flexibility and applicability of predictive process monitoring, as both the static and dynamic information residing in the databases of organizations can be directly taken as input data. We demonstrate the potential of ProcK by applying it to a number of predictive process monitoring tasks, including tasks with knowledge graphs available as well as an existing process monitoring benchmark where no such graph is given. The experiments provide evidence that our methodology achieves state-of-the-art performance and improves predictive power when a knowledge graph is available

Sensor-based human activity recognition: Overcoming issues in a real world setting

The rapid growing of the population age in industrialized societies calls for advanced tools to continuous monitor the activities of people. The goals of those tools are usually to support active and healthy ageing, and to early detect possible health issues to enable a long and independent life. Recent advancements in sensor miniaturization and wireless communications have paved the way to unobtrusive activity recognition systems. Hence, many pervasive health care systems have been proposed which monitor activities through unobtrusive sensors and by machine learning or artificial intelligence methods. Unfortunately, while those systems are effective in controlled environments, their actual effectiveness out of the lab is still limited due to different shortcomings of existing approaches. In this work, we explore such systems and aim to overcome existing limitations and shortcomings. Focusing on physical movements and crucial activities, our goal is to develop robust activity recognition methods based on external and wearable sensors that generate high quality results in a real world setting. Under laboratory conditions, existing research already showed that wearable sensors are suitable to recognize physical activities while external sensors are promising for activities that are more complex. Consequently, we investigate problems that emerge when coming out of the lab. This includes the position handling of wearable devices, the need of large expensive labeled datasets, the requirement to recognize activities in almost real-time, the necessity to adapt deployed systems online to changes in behavior of the user, the variability of executing an activity, and to use data and models across people. As a result, we present feasible solutions for these problems and provide useful insights for implementing corresponding techniques. Further, we introduce approaches and novel methods for both external and wearable sensors where we also clarify limitations and capabilities of the respective sensor types. Thus, we investigate both types separately to clarify their contribution and application use in respect of recognizing different types of activities in a real world scenario. Overall, our comprehensive experiments and discussions show on the one hand the feasibility of physical activity recognition but also recognizing complex activities in a real world scenario. Comparing our techniques and results with existing works and state-of-the-art techniques also provides evidence concerning the reliability and quality of the proposed techniques. On the other hand, we also identify promising research directions and highlight that combining external and wearable sensors seem to be the next step to go beyond activity recognition. In other words, our results and discussions also show that combining external and wearable sensors would compensate weaknesses of the individual sensors in respect of certain activity types and scenarios. Therefore, by addressing the outlined problems, we pave the way for a hybrid approach. Along with our presented solutions, we conclude our work with a high-level multi-tier activity recognition architecture showing that aspects like physical activity, (emotional) condition, used objects, and environmental features are critical for reliable recognizing complex activities

An Incremental Approach to Entity Resolution

We present a query-time entity resolution process that works in a highly parallel fashion. We use the application MobEx to showcase our process, which consists of a mobile client and a server, where the server takes the role of a mediator and carries out the resolution. Results are propagated to the client as early as possible. Resolution results that are produced later in the process are send as updates to the client and thus improve earlier results

Exploring semi-supervised methods for labeling support in multimodal datasets

Working with multimodal datasets is a challenging task as it requires annotations which often are time consuming and difficult to acquire. This includes in particular video recordings which often need to be watched as a whole before they can be labeled. Additionally, other modalities like acceleration data are often recorded alongside a video. For that purpose, we created an annotation tool that enables to annotate datasets of video and inertial sensor data. In contrast to most existing approaches, we focus on semi-supervised labeling support to infer labels for the whole dataset. This means, after labeling a small set of instances our system is able to provide labeling recommendations. We aim to rely on the acceleration data of a wrist-worn sensor to support the labeling of a video recording. For that purpose, we apply template matching to identify time intervals of certain activities. We test our approach on three datasets, one containing warehouse picking activities, one consisting of activities of daily living and one about meal preparations. Our results show that the presented method is able to give hints to annotators about possible label candidates

An approach for incremental entity resolution at the example of social media data

When querying data providers on the web, one has no guarantee that they will reply within a given time. Some providers may even not answer at all. This makes it infeasible to wait for a complete result before beginning with the entity resolution. In order to solve this problem, we present a query-time entity resolution approach that takes the asynchronous nature of the replies from data providers into account by starting the entity resolution as soon as first results are returned. Resolved entities are propagated from the entity resolution engine to the mobile client as early as possible. Resolution results that are produced later are send as updates to the client and thus improve earlier results

Challenges in Annotation of useR Data for UbiquitOUs Systems: Results from the 1st ARDUOUS Workshop

Labelling user data is a central part of the design and evaluation of pervasive systems that aim to support the user through situation-aware reasoning. It is essential both in designing and training the system to recognise and reason about the situation, either through the definition of a suitable situation model in knowledge-driven applications, or through the preparation of training data for learning tasks in data-driven models. Hence, the quality of annotations can have a significant impact on the performance of the derived systems. Labelling is also vital for validating and quantifying the performance of applications. In particular, comparative evaluations require the production of benchmark datasets based on high-quality and consistent annotations. With pervasive systems relying increasingly on large datasets for designing and testing models of users' activities, the process of data labelling is becoming a major concern for the community. In this work we present a qualitative and quantitative analysis of the challenges associated with annotation of user data and possible strategies towards addressing these challenges. The analysis was based on the data gathered during the 1st International Workshop on Annotation of useR Data for UbiquitOUs Systems (ARDUOUS) and consisted of brainstorming as well as annotation and questionnaire data gathered during the talks, poster session, live annotation session, and discussion session

Aquaporin–graphene interface: relevance to point-of-care device for renal cell carcinoma and desalination

© 2018 The Author(s) Published by the Royal Society. All rights reserved. The aquaporin superfamily of hydrophobic integral membrane proteins constitutes water channels essential to the movement of water across the cell membrane, maintaining homeostatic equilibrium. During the passage of water between the extracellular and intracellular sides of the cell, aquaporins act as ultra-sensitive filters. Owing to their hydrophobic nature, aquaporins self-assemble in phospholipids. If a proper choice of lipids is made then the aquaporin biomimetic membrane can be used in the design of an artificial kidney. In combination with graphene, the aquaporin biomimetic membrane finds practical application in desalination and water recycling using mostly Escherichia coli AqpZ. Recently, human aquaporin 1 has emerged as an important biomarker in renal cell carcinoma. At present, the ultra-sensitive sensing of renal cell carcinoma is cumbersome. Hence, we discuss the use of epitopes from monoclonal antibodies as a probe for a point-of-care device for sensing renal cell carcinoma. This device works by immobilizing the antibody on the surface of a single-layer graphene, that is, as a microfluidic device for sensing renal cell carcinoma