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Florian Matthes

Publications and source records attributed to Florian Matthes.

At least 91 records · Page 5Linked to original sources
arXiv2023

A Taxonomy of Decentralized Identifier Methods for Practitioners

A core part of the new identity management paradigm of Self-Sovereign Identity (SSI) is the W3C Decentralized Identifiers (DIDs) standard. The diversity of interoperable implementations encouraged by the paradigm is key for a less centralized future, and it is made possible by the concept of DIDs. However, this leads to a kind of dilemma of choices, where practitioners are faced with the difficult decision of which methods to choose and support in their applications. Due to the decentralized development of DID method specifications and the overwhelming number of different choices, it is hard to get an overview. In this paper, we propose a taxonomy of DID methods with the goal to empower practitioners to make informed decisions when selecting DID methods. To that end, our taxonomy is designed to provide an overview of the current landscape while providing adoption-relevant characteristics. For this purpose, we rely on the Nickerson et al. methodology for taxonomy creation, utilizing both conceptual-to-empirical and empirical-to-conceptual approaches. During the iterative process, we collect and survey an extensive and potentially exhaustive list of around 160 DID methods from various sources. The taxonomy we arrive at uses a total of 7 dimensions and 22 characteristics to span the contemporary design space of DID methods from the perspective of a practitioner. In addition to elaborating on these characteristics, we also discuss how a practitioner can use the taxonomy to select suitable DID methods for a specific use case.

cs.SE↗
arXiv2023

From Data to Dialogue: Leveraging the Structure of Knowledge Graphs for Conversational Exploratory Search

Exploratory search is an open-ended information retrieval process that aims at discovering knowledge about a topic or domain rather than searching for a specific answer or piece of information. Conversational interfaces are particularly suitable for supporting exploratory search, allowing users to refine queries and examine search results through interactive dialogues. In addition to conversational search interfaces, knowledge graphs are also useful in supporting information exploration due to their rich semantic representation of data items. In this study, we demonstrate the synergistic effects of combining knowledge graphs and conversational interfaces for exploratory search, bridging the gap between structured and unstructured information retrieval. To this end, we propose a knowledge-driven dialogue system for exploring news articles by asking natural language questions and using the graph structure to navigate between related topics. Based on a user study with 54 participants, we empirically evaluate the effectiveness of the graph-based exploratory search and discuss design implications for developing such systems.

cs.CL↗
arXiv2023

Exploring the Landscape of Natural Language Processing Research

As an efficient approach to understand, generate, and process natural language texts, research in natural language processing (NLP) has exhibited a rapid spread and wide adoption in recent years. Given the increasing research work in this area, several NLP-related approaches have been surveyed in the research community. However, a comprehensive study that categorizes established topics, identifies trends, and outlines areas for future research remains absent. Contributing to closing this gap, we have systematically classified and analyzed research papers in the ACL Anthology. As a result, we present a structured overview of the research landscape, provide a taxonomy of fields of study in NLP, analyze recent developments in NLP, summarize our findings, and highlight directions for future work.

cs.CL↗
arXiv2023

Efficient Domain Adaptation of Sentence Embeddings Using Adapters

Sentence embeddings enable us to capture the semantic similarity of short texts. Most sentence embedding models are trained for general semantic textual similarity tasks. Therefore, to use sentence embeddings in a particular domain, the model must be adapted to it in order to achieve good results. Usually, this is done by fine-tuning the entire sentence embedding model for the domain of interest. While this approach yields state-of-the-art results, all of the model's weights are updated during fine-tuning, making this method resource-intensive. Therefore, instead of fine-tuning entire sentence embedding models for each target domain individually, we propose to train lightweight adapters. These domain-specific adapters do not require fine-tuning all underlying sentence embedding model parameters. Instead, we only train a small number of additional parameters while keeping the weights of the underlying sentence embedding model fixed. Training domain-specific adapters allows always using the same base model and only exchanging the domain-specific adapters to adapt sentence embeddings to a specific domain. We show that using adapters for parameter-efficient domain adaptation of sentence embeddings yields competitive performance within 1% of a domain-adapted, entirely fine-tuned sentence embedding model while only training approximately 3.6% of the parameters.

cs.CL↗
arXiv2023

AspectCSE: Sentence Embeddings for Aspect-based Semantic Textual Similarity Using Contrastive Learning and Structured Knowledge

Generic sentence embeddings provide a coarse-grained approximation of semantic textual similarity but ignore specific aspects that make texts similar. Conversely, aspect-based sentence embeddings provide similarities between texts based on certain predefined aspects. Thus, similarity predictions of texts are more targeted to specific requirements and more easily explainable. In this paper, we present AspectCSE, an approach for aspect-based contrastive learning of sentence embeddings. Results indicate that AspectCSE achieves an average improvement of 3.97% on information retrieval tasks across multiple aspects compared to the previous best results. We also propose using Wikidata knowledge graph properties to train models of multi-aspect sentence embeddings in which multiple specific aspects are simultaneously considered during similarity predictions. We demonstrate that multi-aspect embeddings outperform single-aspect embeddings on aspect-specific information retrieval tasks. Finally, we examine the aspect-based sentence embedding space and demonstrate that embeddings of semantically similar aspect labels are often close, even without explicit similarity training between different aspect labels.

cs.CL↗
arXiv2023

SoK: Assessing the State of Applied Federated Machine Learning

Machine Learning (ML) has shown significant potential in various applications; however, its adoption in privacy-critical domains has been limited due to concerns about data privacy. A promising solution to this issue is Federated Machine Learning (FedML), a model-to-data approach that prioritizes data privacy. By enabling ML algorithms to be applied directly to distributed data sources without sharing raw data, FedML offers enhanced privacy protections, making it suitable for privacy-critical environments. Despite its theoretical benefits, FedML has not seen widespread practical implementation. This study aims to explore the current state of applied FedML and identify the challenges hindering its practical adoption. Through a comprehensive systematic literature review, we assess 74 relevant papers to analyze the real-world applicability of FedML. Our analysis focuses on the characteristics and emerging trends of FedML implementations, as well as the motivational drivers and application domains. We also discuss the encountered challenges in integrating FedML into real-life settings. By shedding light on the existing landscape and potential obstacles, this research contributes to the further development and implementation of FedML in privacy-critical scenarios.

cs.LG↗
arXiv2023

Time Moves Faster When There is Nothing You Anticipate: The Role of Time in MEV Rewards

This study explores the intricacies of waiting games, a novel dynamic that emerged with Ethereum's transition to a Proof-of-Stake (PoS)-based block proposer selection protocol. Within this PoS framework, validators acquire a distinct monopoly position during their assigned slots, given that block proposal rights are set deterministically, contrasting with Proof-of-Work (PoW) protocols. Consequently, validators have the power to delay block proposals, stepping outside the honest validator specs, optimizing potential returns through MEV payments. Nonetheless, this strategic behaviour introduces the risk of orphaning if attestors fail to observe and vote on the block timely. Our quantitative analysis of this waiting phenomenon and its associated risks reveals an opportunity for enhanced MEV extraction, exceeding standard protocol rewards, and providing sufficient incentives for validators to play the game. Notably, our findings indicate that delayed proposals do not always result in orphaning and orphaned blocks are not consistently proposed later than non-orphaned ones. To further examine consensus stability under varying network conditions, we adopt an agent-based simulation model tailored for PoS-Ethereum, illustrating that consensus disruption will not be observed unless significant delay strategies are adopted. Ultimately, this research offers valuable insights into the advent of waiting games on Ethereum, providing a comprehensive understanding of trade-offs and potential profits for validators within the blockchain ecosystem.

cs.CR↗
arXiv2023

Challenges in Domain-Specific Abstractive Summarization and How to Overcome them

Large Language Models work quite well with general-purpose data and many tasks in Natural Language Processing. However, they show several limitations when used for a task such as domain-specific abstractive text summarization. This paper identifies three of those limitations as research problems in the context of abstractive text summarization: 1) Quadratic complexity of transformer-based models with respect to the input text length; 2) Model Hallucination, which is a model's ability to generate factually incorrect text; and 3) Domain Shift, which happens when the distribution of the model's training and test corpus is not the same. Along with a discussion of the open research questions, this paper also provides an assessment of existing state-of-the-art techniques relevant to domain-specific text summarization to address the research gaps.

cs.CL↗
arXiv2023

Identifying Practical Challenges in the Implementation of Technical Measures for Data Privacy Compliance

Modern privacy regulations provide a strict mandate for data processing entities to implement appropriate technical measures to demonstrate compliance. In practice, determining what measures are indeed "appropriate" is not trivial, particularly in light of vague guidelines provided by privacy regulations. To exacerbate the issue, challenges arise not only in the implementation of the technical measures themselves, but also in a variety of factors involving the roles, processes, decisions, and culture surrounding the pursuit of privacy compliance. In this paper, we present 33 challenges faced in the implementation of technical measures for privacy compliance, derived from a qualitative analysis of 16 interviews with privacy professionals. In addition, we evaluate the interview findings in a survey study, which gives way to a discussion of the identified challenges and their implications.

cs.CR↗
arXiv2023

Scientific Fact-Checking: A Survey of Resources and Approaches

The task of fact-checking deals with assessing the veracity of factual claims based on credible evidence and background knowledge. In particular, scientific fact-checking is the variation of the task concerned with verifying claims rooted in scientific knowledge. This task has received significant attention due to the growing importance of scientific and health discussions on online platforms. Automated scientific fact-checking methods based on NLP can help combat the spread of misinformation, assist researchers in knowledge discovery, and help individuals understand new scientific breakthroughs. In this paper, we present a comprehensive survey of existing research in this emerging field and its related tasks. We provide a task description, discuss the construction process of existing datasets, and analyze proposed models and approaches. Based on our findings, we identify intriguing challenges and outline potential future directions to advance the field.

cs.CL↗
arXiv2023

Sebis at SemEval-2023 Task 7: A Joint System for Natural Language Inference and Evidence Retrieval from Clinical Trial Reports

With the increasing number of clinical trial reports generated every day, it is becoming hard to keep up with novel discoveries that inform evidence-based healthcare recommendations. To help automate this process and assist medical experts, NLP solutions are being developed. This motivated the SemEval-2023 Task 7, where the goal was to develop an NLP system for two tasks: evidence retrieval and natural language inference from clinical trial data. In this paper, we describe our two developed systems. The first one is a pipeline system that models the two tasks separately, while the second one is a joint system that learns the two tasks simultaneously with a shared representation and a multi-task learning approach. The final system combines their outputs in an ensemble system. We formalize the models, present their characteristics and challenges, and provide an analysis of achieved results. Our system ranked 3rd out of 40 participants with a final submission.

cs.CL↗
arXiv2023

Unlocking the Potential of Collaborative AI -- On the Socio-technical Challenges of Federated Machine Learning

The disruptive potential of AI systems roots in the emergence of big data. Yet, a significant portion is scattered and locked in data silos, leaving its potential untapped. Federated Machine Learning is a novel AI paradigm enabling the creation of AI models from decentralized, potentially siloed data. Hence, Federated Machine Learning could technically open data silos and therefore unlock economic potential. However, this requires collaboration between multiple parties owning data silos. Setting up collaborative business models is complex and often a reason for failure. Current literature lacks guidelines on which aspects must be considered to successfully realize collaborative AI projects. This research investigates the challenges of prevailing collaborative business models and distinct aspects of Federated Machine Learning. Through a systematic literature review, focus group, and expert interviews, we provide a systemized collection of socio-technical challenges and an extended Business Model Canvas for the initial viability assessment of collaborative AI projects.

cs.AI↗
arXiv2023

Voice-Based Conversational Agents and Knowledge Graphs for Improving News Search in Assisted Living

As the healthcare sector is facing major challenges, such as aging populations, staff shortages, and common chronic diseases, delivering high-quality care to individuals has become very difficult. Conversational agents have shown to be a promising technology to alleviate some of these issues. In the form of digital health assistants, they have the potential to improve the everyday life of the elderly and chronically ill people. This includes, for example, medication reminders, routine checks, or social chit-chat. In addition, conversational agents can satisfy the fundamental need of having access to information about daily news or local events, which enables individuals to stay informed and connected with the world around them. However, finding relevant news sources and navigating the plethora of news articles available online can be overwhelming, particularly for those who may have limited technological literacy or health-related impairments. To address this challenge, we propose an innovative solution that combines knowledge graphs and conversational agents for news search in assisted living. By leveraging graph databases to semantically structure news data and implementing an intuitive voice-based interface, our system can help care-dependent people to easily discover relevant news articles and give personalized recommendations. We explain our design choices, provide a system architecture, share insights of an initial user test, and give an outlook on planned future work.

cs.CL↗
arXiv2023

Investigating Conversational Search Behavior For Domain Exploration

Conversational search has evolved as a new information retrieval paradigm, marking a shift from traditional search systems towards interactive dialogues with intelligent search agents. This change especially affects exploratory information-seeking contexts, where conversational search systems can guide the discovery of unfamiliar domains. In these scenarios, users find it often difficult to express their information goals due to insufficient background knowledge. Conversational interfaces can provide assistance by eliciting information needs and narrowing down the search space. However, due to the complexity of information-seeking behavior, the design of conversational interfaces for retrieving information remains a great challenge. Although prior work has employed user studies to empirically ground the system design, most existing studies are limited to well-defined search tasks or known domains, thus being less exploratory in nature. Therefore, we conducted a laboratory study to investigate open-ended search behavior for navigation through unknown information landscapes. The study comprised of 26 participants who were restricted in their search to a text chat interface. Based on the collected dialogue transcripts, we applied statistical analyses and process mining techniques to uncover general information-seeking patterns across five different domains. We not only identify core dialogue acts and their interrelations that enable users to discover domain knowledge, but also derive design suggestions for conversational search systems.

cs.CL↗
arXiv2023

Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches

Text classification of unseen classes is a challenging Natural Language Processing task and is mainly attempted using two different types of approaches. Similarity-based approaches attempt to classify instances based on similarities between text document representations and class description representations. Zero-shot text classification approaches aim to generalize knowledge gained from a training task by assigning appropriate labels of unknown classes to text documents. Although existing studies have already investigated individual approaches to these categories, the experiments in literature do not provide a consistent comparison. This paper addresses this gap by conducting a systematic evaluation of different similarity-based and zero-shot approaches for text classification of unseen classes. Different state-of-the-art approaches are benchmarked on four text classification datasets, including a new dataset from the medical domain. Additionally, novel SimCSE and SBERT-based baselines are proposed, as other baselines used in existing work yield weak classification results and are easily outperformed. Finally, the novel similarity-based Lbl2TransformerVec approach is presented, which outperforms previous state-of-the-art approaches in unsupervised text classification. Our experiments show that similarity-based approaches significantly outperform zero-shot approaches in most cases. Additionally, using SimCSE or SBERT embeddings instead of simpler text representations increases similarity-based classification results even further.

cs.CL↗
arXiv2022

Semantic Similarity-Based Clustering of Findings From Security Testing Tools

Over the last years, software development in domains with high security demands transitioned from traditional methodologies to uniting modern approaches from software development and operations (DevOps). Key principles of DevOps gained more importance and are now applied to security aspects of software development, resulting in the automation of security-enhancing activities. In particular, it is common practice to use automated security testing tools that generate reports after inspecting a software artifact from multiple perspectives. However, this raises the challenge of generating duplicate security findings. To identify these duplicate findings manually, a security expert has to invest resources like time, effort, and knowledge. A partial automation of this process could reduce the analysis effort, encourage DevOps principles, and diminish the chance of human error. In this study, we investigated the potential of applying Natural Language Processing for clustering semantically similar security findings to support the identification of problem-specific duplicate findings. Towards this goal, we developed a web application for annotating and assessing security testing tool reports and published a human-annotated corpus of clustered security findings. In addition, we performed a comparison of different semantic similarity techniques for automatically grouping security findings. Finally, we assess the resulting clusters using both quantitative and qualitative evaluation methods.

cs.CL↗
arXiv2022

Lessons Learned: Surveying the Practicality of Differential Privacy in the Industry

Since its introduction in 2006, differential privacy has emerged as a predominant statistical tool for quantifying data privacy in academic works. Yet despite the plethora of research and open-source utilities that have accompanied its rise, with limited exceptions, differential privacy has failed to achieve widespread adoption in the enterprise domain. Our study aims to shed light on the fundamental causes underlying this academic-industrial utilization gap through detailed interviews of 24 privacy practitioners across 9 major companies. We analyze the results of our survey to provide key findings and suggestions for companies striving to improve privacy protection in their data workflows and highlight the necessary and missing requirements of existing differential privacy tools, with the goal of guiding researchers working towards the broader adoption of differential privacy. Our findings indicate that analysts suffer from lengthy bureaucratic processes for requesting access to sensitive data, yet once granted, only scarcely-enforced privacy policies stand between rogue practitioners and misuse of private information. We thus argue that differential privacy can significantly improve the processes of requesting and conducting data exploration across silos, and conclude that with a few of the improvements suggested herein, the practical use of differential privacy across the enterprise is within striking distance.

cs.CR↗
arXiv2022

Lbl2Vec: An Embedding-Based Approach for Unsupervised Document Retrieval on Predefined Topics

In this paper, we consider the task of retrieving documents with predefined topics from an unlabeled document dataset using an unsupervised approach. The proposed unsupervised approach requires only a small number of keywords describing the respective topics and no labeled document. Existing approaches either heavily relied on a large amount of additionally encoded world knowledge or on term-document frequencies. Contrariwise, we introduce a method that learns jointly embedded document and word vectors solely from the unlabeled document dataset in order to find documents that are semantically similar to the topics described by the keywords. The proposed method requires almost no text preprocessing but is simultaneously effective at retrieving relevant documents with high probability. When successively retrieving documents on different predefined topics from publicly available and commonly used datasets, we achieved an average area under the receiver operating characteristic curve value of 0.95 on one dataset and 0.92 on another. Further, our method can be used for multiclass document classification, without the need to assign labels to the dataset in advance. Compared with an unsupervised classification baseline, we increased F1 scores from 76.6 to 82.7 and from 61.0 to 75.1 on the respective datasets. For easy replication of our approach, we make the developed Lbl2Vec code publicly available as a ready-to-use tool under the 3-Clause BSD license.

cs.CL↗