000 Informatik, Informationswissenschaft, allgemeine Werke
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Modern data analysis tasks often involve control flow statements, such as the iterations in PageRank and K-means. To achieve scalability, developers usually implement these tasks in distributed dataflow systems, such as Spark and Flink. Designers of such systems have to choose between providing imperative or functional control flow constructs to users. Imperative constructs are easier to use, but functional constructs are easier to compile to an efficient dataflow job. We propose Mitos, a system where control flow is both easy to use and efficient. Mitos relies on an intermediate representation based on the static single assignment form. This allows us to abstract away from specific control flow constructs and treat any imperative control flow uniformly both when building the dataflow job and when coordinating the distributed execution.
Stem cells are capable of sensing and processing environmental inputs, converting this information to output a specific cell lineage through signaling cascades. Despite the combinatorial nature of mechanical, thermal, and biochemical signals, these stimuli have typically been decoupled and applied independently, requiring continuous regulation by controlling units. We employ a programmable polymer actuator sheet to autonomously synchronize thermal and mechanical signals applied to mesenchymal stem cells (MSC5). Using a grid on its underside, the shape change of polymer sheet, as well as cell morphology, calcium (Ca2+) influx, and focal adhesion assembly, could be visualized and quantified. This paper gives compelling evidence that the temperature sensing and mechanosensing of MSC5 are interconnected via intracellular Ca2+. Up-regulated Ca2+ levels lead to a remarkable alteration of histone H3K9 acetylation and activation of osteogenic related genes. The interplay of physical, thermal, and biochemical signaling was utilized to accelerate the cell differentiation toward osteogenic lineage. The approach of programmable bioinstructivity provides a fundamental principle for functional biomaterials exhibiting multifaceted stimuli on differentiation programs. Technological impact is expected in the tissue engineering of periosteum for treating bone defects.
Stem cells are capable of sensing and processing environmental inputs, converting this information to output a specific cell lineage through signaling cascades. Despite the combinatorial nature of mechanical, thermal, and biochemical signals, these stimuli have typically been decoupled and applied independently, requiring continuous regulation by controlling units. We employ a programmable polymer actuator sheet to autonomously synchronize thermal and mechanical signals applied to mesenchymal stem cells (MSC5). Using a grid on its underside, the shape change of polymer sheet, as well as cell morphology, calcium (Ca2+) influx, and focal adhesion assembly, could be visualized and quantified. This paper gives compelling evidence that the temperature sensing and mechanosensing of MSC5 are interconnected via intracellular Ca2+. Up-regulated Ca2+ levels lead to a remarkable alteration of histone H3K9 acetylation and activation of osteogenic related genes. The interplay of physical, thermal, and biochemical signaling was utilized to accelerate the cell differentiation toward osteogenic lineage. The approach of programmable bioinstructivity provides a fundamental principle for functional biomaterials exhibiting multifaceted stimuli on differentiation programs. Technological impact is expected in the tissue engineering of periosteum for treating bone defects.
During a crisis event, social media enables two-way communication and many-to-many information broadcasting, browsing others’ posts, publishing own content, and public commenting. These records can deliver valuable insights to approach problematic situations effectively. Our study explores how social media communication can be analyzed to understand the responses to health crises better. Results based on nearly 800 K tweets indicate that the coping and regulation foci framework holds good explanatory power, with four clusters salient in public reactions: 1) “Understanding” (problem-promotion); 2) “Action planning” (problem-prevention); 3) “Hope” (emotion-promotion) and 4) “Reassurance” (emotion-prevention). Second, the inter-temporal analysis shows high volatility of topic proportions and a shift from self-centered to community-centered topics during the course of the event. The insights are beneficial for research on crisis management and practicians who are interested in large-scale monitoring of their audience for well-informed decision-making.
Eskalation des Commitments in Wirtschaftsinformatik Projekten: eine kognitiv-affektive Perspektive
(2024)
Projekte im Bereich der Wirtschaftsinformatik (IS-Projekte) sind von zentraler Bedeutung für die Steuerung von Unternehmensstrategien und die Aufrechterhaltung von Wettbewerbsvorteilen, überschreiten jedoch häufig das Budget, sprengen den Zeitrahmen und weisen eine hohe Misserfolgsquote auf. Diese Dissertation befasst sich mit den psychologischen Grundlagen menschlichen Verhaltens - insbesondere Kognition und Emotion - im Zusammenhang mit einem weit verbreiteten Problem im IS-Projektmanagement: der Tendenz, an fehlgehenden Handlungssträngen festzuhalten, auch Eskalation des Commitments (Englisch: “escalation of commitment” - EoC) genannt.
Mit einem kombinierten Forschungsansatz (dem Mix von qualitativen und quantitativen Methoden) untersuche ich in meiner Dissertation die emotionalen und kognitiven Grundlagen der Entscheidungsfindung hinter eskalierendem Commitment zu scheiternden IS-Projekten und deren Entwicklung über die Zeit. Die Ergebnisse eines psychophysiologischen Laborexperiments liefern Belege auf die Vorhersagen bezüglich der Rolle von negativen und komplexen situativen Emotionen der kognitiven Dissonanz Theorie gegenüber der Coping-Theorie und trägt zu einem besseren Verständnis dafür bei, wie sich Eskalationstendenzen während sequenzieller Entscheidungsfindung aufgrund kognitiver Lerneffekte verändern. Mit Hilfe psychophysiologischer Messungen, einschließlich der Daten-Triangulation zwischen elektrodermaler und kardiovaskulärer Aktivität sowie künstliche Intelligenz-basierter Analyse von Gesichtsmikroexpressionen, enthüllt diese Forschung physiologische Marker für eskalierendes Commitment. Ergänzend zu dem Experiment zeigt eine qualitative Analyse text-basierter Reflexionen während der Eskalationssituationen, dass Entscheidungsträger verschiedene kognitive Begründungsmuster verwenden, um eskalierende Verhaltensweisen zu rechtfertigen, die auf eine Sequenz von vier unterschiedlichen kognitiven Phasen schließen lassen.
Durch die Integration von qualitativen und quantitativen Erkenntnissen entwickelt diese Dissertation ein umfassendes theoretisches Model dafür, wie Kognition und Emotion eskalierendes Commitment über die Zeit beeinflussen. Ich schlage vor, dass eskalierendes Commitment eine zyklische Anpassung von Denkmodellen ist, die sich durch Veränderungen in kognitiven Begründungsmustern, Variationen im zeitlichen Kognitionsmodus und Interaktionen mit situativen Emotionen und deren Erwartung auszeichnet. Der Hauptbeitrag dieser Arbeit liegt in der Entflechtung der emotionalen und kognitiven Mechanismen, die eskalierendes Commitment im Kontext von IS-Projekten antreiben. Die Erkenntnisse tragen dazu bei, die Qualität von Entscheidungen unter Unsicherheit zu verbessern und liefern die Grundlage für die Entwicklung von Deeskalationsstrategien. Beteiligte an „in Schieflage geratenden“ IS-Projekten sollten sich der Tendenz auf fehlgeschlagenen Aktionen zu beharren und der Bedeutung der zugrundeliegenden emotionalen und kognitiven Dynamiken bewusst sein.
Deep metric learning employs deep neural networks to embed instances into a metric space such that distances between instances of the same class are small and distances between instances from different classes are large. In most existing deep metric learning techniques, the embedding of an instance is given by a feature vector produced by a deep neural network and Euclidean distance or cosine similarity defines distances between these vectors. This paper studies deep distributional embeddings of sequences, where the embedding of a sequence is given by the distribution of learned deep features across the sequence. The motivation for this is to better capture statistical information about the distribution of patterns within the sequence in the embedding. When embeddings are distributions rather than vectors, measuring distances between embeddings involves comparing their respective distributions. The paper therefore proposes a distance metric based on Wasserstein distances between the distributions and a corresponding loss function for metric learning, which leads to a novel end-to-end trainable embedding model. We empirically observe that distributional embeddings outperform standard vector embeddings and that training with the proposed Wasserstein metric outperforms training with other distance functions.
Classification, prediction and evaluation of graph neural networks on online social media platforms
(2024)
The vast amount of data generated on social media platforms have made them a valuable source of information for businesses, governments and researchers. Social media data can provide insights into user behavior, preferences, and opinions. In this work, we address two important challenges in social media analytics. Predicting user engagement with online content has become a critical task for content creators to increase user engagement and reach larger audiences. Traditional user engagement prediction approaches rely solely on features derived from the user and content. However, a new class of deep learning methods based on graphs captures not only the content features but also the graph structure of social media networks.
This thesis proposes a novel Graph Neural Network (GNN) approach to predict user interaction with tweets. The proposed approach combines the features of users, tweets and their engagement graphs. The tweet text features are extracted using pre-trained embeddings from language models, and a GNN layer is used to embed the user in a vector space. The GNN model then combines the features and graph structure to predict user engagement. The proposed approach achieves an accuracy value of 94.22% in classifying user interactions, including likes, retweets, replies, and quotes.
Another major challenge in social media analysis is detecting and classifying social bot accounts. Social bots are automated accounts used to manipulate public opinion by spreading misinformation or generating fake interactions. Detecting social bots is critical to prevent their negative impact on public opinion and trust in social media. In this thesis, we classify social bots on Twitter by applying Graph Neural Networks. The proposed approach uses a combination of both the features of a node and an aggregation of the features of a node’s neighborhood to classify social bot accounts. Our final results indicate a 6% improvement in the area under the curve score in the final predictions through the utilization of GNN.
Overall, our work highlights the importance of social media data and the potential of new methods such as GNNs to predict user engagement and detect social bots. These methods have important implications for improving the quality and reliability of information on social media platforms and mitigating the negative impact of social bots on public opinion and discourse.
Efficiently managing large state is a key challenge for data management systems. Traditionally, state is split into fast but volatile state in memory for processing and persistent but slow state on secondary storage for durability. Persistent memory (PMem), as a new technology in the storage hierarchy, blurs the lines between these states by offering both byte-addressability and low latency like DRAM as well persistence like secondary storage. These characteristics have the potential to cause a major performance shift in database systems.
Driven by the potential impact that PMem has on data management systems, in this thesis we explore their use of PMem. We first evaluate the performance of real PMem hardware in the form of Intel Optane in a wide range of setups. To this end, we propose PerMA-Bench, a configurable benchmark framework that allows users to evaluate the performance of customizable database-related PMem access. Based on experimental results obtained with PerMA-Bench, we discuss findings and identify general and implementation-specific aspects that influence PMem performance and should be considered in future work to improve PMem-aware designs. We then propose Viper, a hybrid PMem-DRAM key-value store. Based on PMem-aware access patterns, we show how to leverage PMem and DRAM efficiently to design a key database component. Our evaluation shows that Viper outperforms existing key-value stores by 4–18x for inserts while offering full data persistence and achieving similar or better lookup performance. Next, we show which changes must be made to integrate PMem components into larger systems. By the example of stream processing engines, we highlight limitations of current designs and propose a prototype engine that overcomes these limitations. This allows our prototype to fully leverage PMem's performance for its internal state management. Finally, in light of Optane's discontinuation, we discuss how insights from PMem research can be transferred to future multi-tier memory setups by the example of Compute Express Link (CXL).
Overall, we show that PMem offers high performance for state management, bridging the gap between fast but volatile DRAM and persistent but slow secondary storage. Although Optane was discontinued, new memory technologies are continuously emerging in various forms and we outline how novel designs for them can build on insights from existing PMem research.
The landscape of software self-adaptation is shaped in accordance with the need to cost-effectively achieve and maintain (software) quality at runtime and in the face of dynamic operation conditions. Optimization-based solutions perform an exhaustive search in the adaptation space, thus they may provide quality guarantees. However, these solutions render the attainment of optimal adaptation plans time-intensive, thereby hindering scalability. Conversely, deterministic rule-based solutions yield only sub-optimal adaptation decisions, as they are typically bound by design-time assumptions, yet they offer efficient processing and implementation, readability, expressivity of individual rules supporting early verification. Addressing the quality-cost trade-of requires solutions that simultaneously exhibit the scalability and cost-efficiency of rulebased policy formalism and the optimality of optimization-based policy formalism as explicit artifacts for adaptation. Utility functions, i.e., high-level specifications that capture system objectives, support the explicit treatment of quality-cost trade-off. Nevertheless, non-linearities, complex dynamic architectures, black-box models, and runtime uncertainty that makes the prior knowledge obsolete are a few of the sources of uncertainty and subjectivity that render the elicitation of utility non-trivial.
This thesis proposes a twofold solution for incremental self-adaptation of dynamic architectures. First, we introduce Venus, a solution that combines in its design a ruleand an optimization-based formalism enabling optimal and scalable adaptation of dynamic architectures. Venus incorporates rule-like constructs and relies on utility theory for decision-making. Using a graph-based representation of the architecture, Venus captures rules as graph patterns that represent architectural fragments, thus enabling runtime extensibility and, in turn, support for dynamic architectures; the architecture is evaluated by assigning utility values to fragments; pattern-based definition of rules and utility enables incremental computation of changes on the utility that result from rule executions, rather than evaluating the complete architecture, which supports scalability. Second, we introduce HypeZon, a hybrid solution for runtime coordination of multiple off-the-shelf adaptation policies, which typically offer only partial satisfaction of the quality and cost requirements. Realized based on meta-self-aware architectures, HypeZon complements Venus by re-using existing policies at runtime for balancing the quality-cost trade-off.
The twofold solution of this thesis is integrated in an adaptation engine that leverages state- and event-based principles for incremental execution, therefore, is scalable for large and dynamic software architectures with growing size and complexity. The utility elicitation challenge is resolved by defining a methodology to train utility-change prediction models. The thesis addresses the quality-cost trade-off in adaptation of dynamic software architectures via design-time combination (Venus) and runtime coordination (HypeZon) of rule- and optimization-based policy formalisms, while offering supporting mechanisms for optimal, cost-effective, scalable, and robust adaptation. The solutions are evaluated according to a methodology that is obtained based on our systematic literature review of evaluation in self-healing systems; the applicability and effectiveness of the contributions are demonstrated to go beyond the state-of-the-art in coverage of a wide spectrum of the problem space for software self-adaptation.
Open edX is an incredible platform to deliver MOOCs and SPOCs, designed to be robust and support hundreds of thousands of students at the same time. Nevertheless, it lacks a lot of the fine-grained functionality needed to handle students individually in an on-campus course. This short session will present the ongoing project undertaken by the 6 public universities of the Region of Madrid plus the Universitat Politècnica de València, in the framework of a national initiative called UniDigital, funded by the Ministry of Universities of Spain within the Plan de Recuperación, Transformación y Resiliencia of the European Union. This project, led by three of these Spanish universities (UC3M, UPV, UAM), is investing more than half a million euros with the purpose of bringing the Open edX platform closer to the functionalities required for an LMS to support on-campus teaching. The aim of the project is to coordinate what is going to be developed with the Open edX development community, so these developments are incorporated into the core of the Open edX platform in its next releases. Features like a complete redesign of platform analytics to make them real-time, the creation of dashboards based on these analytics, the integration of a system for customized automatic feedback, improvement of exams and tasks and the extension of grading capabilities, improvements in the graphical interfaces for both students and teachers, the extension of the emailing capabilities, redesign of the file management system, integration of H5P content, the integration of a tool to create mind maps, the creation of a system to detect students at risk, or the integration of an advanced voice assistant and a gamification mobile app, among others, are part of the functionalities to be developed. The idea is to transform a first-class MOOC platform into the next on-campus LMS.