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Emergency Detection in Private Households Utilizing Existing Data Sources for Human Activity Event Recognition
No full textIn an aging society, the need for efficient emergency detection systems in smart homes is becoming increasingly important. Over 30% of those aged 65 and older experience at least one fall per year, often resulting in the inability to rise without assistance, leading to ‘long lies’ lasting hours or even days. Systems for detecting such emergency events usually rely on wearable sensors or specific installations of ambient sensors, which can be intrusive and complex, hindering acceptance. This thesis proposes a novel approach that utilizes existing digital data sources within the residential infrastructure to detect human activities and identify potential emergencies.
A survey identifies 44 potential data sources in private households for recognizing human activity. However, extracting activity information often requires complex preprocessing. In this thesis, methodologies are developed for three of these data sources to highlight practical applications: Smart Power Meters, Smart Water Meters, and Home Weather Stations. It is shown that detecting human activities using these sources is feasible in a practical environment, although accuracy and reliability vary. Notably, Smart Water Meters demonstrate high reliability, with a precision of 0.86 and a recall of 1.00, making them particularly suitable for emergency detection.
Existing emergency detection methods are not designed to handle uncertain activity data. This thesis introduces a novel approach based on probabilistic activity information, employing an Inactivity Score that provides a probabilistic weighting of inactivity periods based on the reliability of sensor measurements. By analyzing historical Inactivity Scores, anomalies that potentially represent an emergency can be identified. Evaluations across seven datasets show this approach outperforms existing methods, achieving a mean time to detect emergencies of approximately 05:23:28 hours and producing 0.09 false positives per day under noise-free conditions. Moreover, unlike related approaches, the proposed method remains effective with noisy data.
This thesis demonstrates that emergencies in private households can be detected using existing data sources from the home infrastructure, offering a cost-effective and non-intrusive solution to enhance the safety and autonomy of the elderly at home
Bridging the gap: Applying machine learning techniques in digital forensics
No full textWith the increasing adoption of virtualization technologies across various industries, virtual machines (VMs) offer cost-effective solutions for obtaining computing power without the burden of initial investment or ongoing maintenance. However, the widespread use of VMs also increases the risk of malicious actors attempting to gain unauthorized access due to the possibility of accessing the VMs via standard internet protocols. Virtual Machine Introspection (VMI) and Forensic Memory Analysis (FMA) are two key cybersecurity methods for addressing these threats. While FMA leverages digital forensic techniques to extract and analyse information from system memory to explain security incidents, VMI typically works with live systems, analysing running processes to detect real-time threats. Both approaches face a significant challenge known as the ”semantic gap,” which arises from the need to infer high-level system information from low-level data such as physical memory and CPU registers.
This dissertation explores using machine learning to bridge the semantic gap in FMA and VMI applications. The research uses OpenSSH process heap dumps as a use-case to extract high-level structures, such as OpenSSH encryption keys, from raw process memory dumps. The study employs various techniques to isolate relevant memory sections, from basic memory chunking and entropy analysis to more advanced methods utilizing pointers and malloc headers. During this research study, we also identified the need for a foundation model in memory forensics. Foundation models are general purpose models trained on large amounts of data and users can later use these models to perform different tasks by finetuning the model. This research also addresses the challenge of detecting malware by analysing system-level API calls and employing custom feature engineering techniques. Given that the threat landscape is constantly evolving, we also investigate concept drift — a phenomenon where input data distribution changes affect predictive models’ performance. To mitigate the degradation in performance due to concept drift, we introduce a concept drift detection algorithm complemented by a custom sampling method that optimizes training data selection. This approach reduces the training dataset size by one-third, enhancing the efficiency of model training while maintaining high performance
Efficiency of poll-based multi-period forecasting systems for German state elections
No full textElection polls are frequently employed to reflect voter sentiment with respect to a particular election (or fixed-event). Despite their widespread use as forecasts and inputs for predictive algorithms, there is substantial uncertainty regarding their efficiency. This uncertainty is amplified by judgment in the form of pollsters applying unpublished weighting schemes to ensure the representativeness of the sampled voters for the underlying population. Efficient forecasting systems incorporate past information instantly, which renders a given fixed-event unpredictable based on past information. This results in all sequential adjustments of the fixed-event forecasts across adjacent time periods (or forecast revisions) being martingale differences. This paper illustrates the theoretical conditions related to weak efficiency of fixed-event forecasting systems based on traditional least squares loss and asymmetrically weighted least absolute deviations (or quantile) loss. Weak efficiency of poll-based multi-period forecasting systems for all German federal state elections since the year 2000 is investigated. The inefficiency of almost all considered forecasting systems is documented and alternative explanations for the findings are discussed
Seed catalogue of woody species from coastal Kenya
No full textKenyan coastal forests harbor a high diversity of woody plants, comprising 88 families and 937 species. These species-rich forests are highly threatened by human disturbances, leading to forest degradation and habitat loss. To preserve this biodiversity, effective conservation strategies are essential. Conservation efforts may involve both site-based and species-based measures, including botanical surveys and seed collection for propagation in tree nurseries. The aim of this study was to develop a seed catalogue of woody species from coastal Kenya to support seed identification in the field and at various tree nurseries. So far, the catalogue characterizes seeds (and if available also fruits) of 69 woody species across 31 plant families. Each entry includes a brief description, size and weight measurements, and photographs to facilitate identification
Supplementary Material for the Work "Exposing the Gaps: The State of Supply Chain Coverage in Current Security Standards"
No full textThis document contains the supplementary material for our work "Exposing the Gaps: The State of Supply Chain Coverage in Current Security Standards", which should be consulted for further information and details. While this supplement has not been peer-reviewed, the above article has. The reviewers also had access to all the data presented in this document during the review phase.
The article is available from 26 July 2025 as part of the "IFIP Advances in Information and Communication Technology", volume 742: 10.1007/978-3-031-94924-1_1
Maßnahmenkatalog Green IT an bayerischen Hochschulen
No full text"Der vorliegende Maßnahmenkatalog bietet abgestimmte Empfehlungen, um Nachhaltigkeit für bayerische Hochschulen im Bereich Green IT zu fördern. Ziel ist es, energieeffizientes Verhalten zu unterstützen und die IT-Landschaft nachhaltiger zu gestalten. Die Maßnahmen zielen nicht nur auf die Förderung eines verantwortungsvollen Handelns ab, sondern bieten auch das Potenzial, langfristig Kosteneinsparungen durch einen energieeffizienten Betrieb zu erzielen. Gleichzeitig wird nachhaltiges Handeln durch gesetzliche Vorgaben wie dem Energieeffizienzgesetz zunehmend verbindlicher, da auch Hochschulen diesen Anforderungen unterliegen. Der Maßnahmenkatalog unterstützt dabei, diese regulatorischen Anforderungen zu erfüllen und gleichzeitig einen wesentlichen Beitrag zur Ressourcenschonung sowie zur Reduzierung von Treibhausgasemissionen zu leisten."
(Zielsetzung, Seite III
Static Analyses of Interlanguage Interoperations
No full textSoftware Developers are moving towards a multilingual development where they combine two languages in a single application to harness the strengths of each language. For example, performance-critical components of a Java application can be implemented in C language. It provides flexibility, at the same time, it becomes difficult to statically analyze these applications. The amalgamation of two languages in a single application may introduce bugs ranging from type-mismatch to security vulnerabilities. Therefore, it is necessary to develop static analysis techniques to aid developers in multilingual development. In this thesis, we develop techniques to study and analyze these applications.
In the first part of the thesis, we study the prevalence of security and privacy vulnerabilities in hybrid apps. Hybrid apps are Android apps that combine both Java and Javascript components, where the Android part is secured (on the device), while the JavaScript part is exposed to web. Additionally, some of the Java functions are available to JavaScript component through an interface called as bridge interface. In the pursuit of the goal, we adopt a static backtracking of data dependencies to determine the flow of information from the android component to the web component. Our study revealed the potential sources of unsoundness in the existing static analyses. Static backtracing also induces imprecision in the analysis, i.e., there might be some flows that are not possible during runtime albeit are reported by the analysis. These were mitigated through a manual verification. This work reveals that the android-web hybridization can lead to (potential) vulnerabilities that might impact the confidentiality as well as the integrity properties of these apps.
From the communication patterns occurring in Android WebView, we noticed that its is feasible for an attacker to jeopardize the integrity of apps by corrupting some value, say an input on the web through bridge interfaces. Motivated by this, we define a information flow analysis of the bridge interfaces and the associated data flows in hybrid apps. In the first step, we propose a novel threat model where we model the attacker as someone who wants to influence the behavior of android app as an integrity violation. Based on this threat model, we then propose a demand-driven analysis technique to detect confidentiality and integrity violations. Our analysis leverages, a demand-driven technique, where it only analyzes the relevant part of app for the information flow analysis with the help of function summaries --- escaping the need of a whole-program analysis.
In the second part of the thesis, we generalize the approach to static analysis of multilingual applications. To this end, we investigate into the question of combining existing single language analyses to analyze multilingual programs. To provide an affirmative answer, we define an analysis to leverage single language analyses for call-graph and pointer analysis of multilingual programs. Our analysis takes two existing unilingual analyses and analyzes the complete multilingual program. It uses a novel summary specialization technique that resolves the information flows at the bridge interfaces by utilizing independent pre-analyses (modulo foreign function interfaces) of each language component. We apply this technique to analyze Android-NDK and GraalVM Java-Python multilingual applications for generating call-graphs.
In summary, we have developed novel techniques for information flow and call-graph analysis for multilingual programs. With this, we motivate the need of static analyses for multilingual applications and its applications which includes, vulnerability detection, program understanding, amongst others
Search Engines and Foreign Influence: How Google and Yandex Mediate Russia’s Propaganda Abroad
No full textThis cumulative dissertation examined how the US-based Google and Russia-based Yandex mediate the propaganda efforts of Russia’s ruling elites abroad on a case study of Belarus. The findings presented here contribute to a nuanced understanding of how algorithmic gatekeepers, particularly search engines, can be strategically appropriated or manipulated to reinforce and export state narratives beyond national borders. The main conclusion of this dissertation is that both Yandex and Google, albeit through different mechanisms and to varying extents, can serve as mediators of Russia’s propaganda efforts abroad
Advancing Pattern Detection, Theory Development and Decision Making with Explainable AI
No full textThe application of explainable artificial intelligence (XAI) methods in data-driven decision-making and computationally intensive theory development (CTD) is a subject of ongoing debate, particularly concerning how and whether these methods can be effectively employed, and how the reliability of their explanations can be ensured. This dissertation addresses these issues by systematically analyzing the usability of XAI for pattern detection, CTD, and decision-making, drawing on various real-world and synthetic datasets and employing different empirical methods and perspectives. The dissertation consists of four studies, each addressing distinct issues in the field of XAI application
Liability for Negligent Healthcare Clinical Risk Management in the United States and Germany
No full textSystems-focused error prevention efforts are internationally recognized in the healthcare industry, and industry efforts to identify and correct organizational defects through the process of CRM are well established in the U.S. and Germany. However, in both countries, there is no clear corresponding liability for healthcare organizations who fail to engage in systems-based learning through the process of clinical risk management (CRM). Although both jurisdictions do recognize organization-based theories of liability, liability for negligent CRM has not been explicitly recognized by courts in either jurisdiction to date. German legal scholars, recognizing this gap in liability for healthcare organizations, have written in support of finding liability for negligent CRM under existing tort law; however, there is no corresponding discussion in the American legal literature. This dissertation fills that gap with a comparative analysis of medical negligence law in the U.S. and Germany through the international lens of modern medical error prevention science and policy to articulate a legal basis and sketch the evidentiary framework for tort liability based on negligent CRM