Contributions published at Dynamic and Distributed Information Systems (Abraham Bernstein)

While artificial intelligence (AI) is increasingly applied for decision- making processes, ethical decisions pose challenges for AI applica- tions. Given that humans cannot always agree on the right thing to do, how would ethical decision-making by AI systems be perceived and how would responsibility be ascribed in human-AI collabora- tion? In this study, we investigate how the expert type (human vs. AI) and level of expert autonomy (adviser vs. decider) influence trust, perceived responsibility, and reliance. We find that partici- pants consider humans to be more morally trustworthy but less capable than their AI equivalent. This shows in participants’ re- liance on AI: AI recommendations and decisions are accepted more often than the human expert’s. However, AI team experts are per- ceived to be less responsible than humans, while programmers and sellers of AI systems are deemed partially responsible instead.

In recent years, top-K recommender systems with implicit feed- back data gained interest in many real-world business scenarios. In particular, neural networks have shown promising results on these tasks. However, while traditional recommender systems are built on datasets with frequent user interactions, insurance recommenders often have access to a very limited amount of user interactions, as people only buy a few insurance products. In this paper, we shed new light on the problem of top-K recommendations for interaction-sparse recommender problems. In particular, we analyze six different recommender algorithms, namely a popularity-based baseline and compare it against two matrix factorization methods (SVD++, ALS), one neural network approach (JCA) and two combinations of neural network and factorization machine approaches (DeepFM, NeuFM). We evaluate these algorithms on six different interaction-sparse datasets and one dataset with a less sparse interaction pattern to elucidate the unique behavior of interaction-sparse datasets. In our experimental evaluation based on real-world insurance data, we demonstrate that DeepFM shows the best performance followed by JCA and SVD++, which indicates that neural network approaches are the dominant technologies. However, for the remaining five datasets we observe a different pattern. Overall, the matrix factorization method SVD++ is the winner. Surprisingly, the simple popularity-based approach comes out second followed by the neural network approach JCA. In summary, our experimental evaluation for interaction-sparse datasets demonstrates that in general matrix factorization methods outperform neural network approaches. As a consequence, traditional well- established methods should be part of the portfolio of algorithms to solve real-world interaction-sparse recommender problems.

Research in the area of human information interaction (HII) typically represents viewpoints on debated topics in a binary fashion, as either against or in favor of a given topic (e.g., the feminist movement). This simple taxonomy, however, greatly reduces the latent richness of viewpoints and thereby limits the potential of research and practical applications in this field. Work in the communication sciences has already demonstrated that viewpoints can be represented in much more comprehensive ways, which could enable a deeper understanding of users’ interactions with debated topics online. For instance, a viewpoint's stance usually has a degree of strength (e.g., mild or strong), and, even if two viewpoints support or oppose something to the same degree, they may use different logics of evaluation (i.e., underlying reasons). In this paper, we draw from communication science practice to propose a novel, two-dimensional way of representing viewpoints that incorporates a viewpoint's stance degree as well as its logic of evaluation. We show in a case study of tweets on debated topics how our proposed viewpoint label can be obtained via crowdsourcing with acceptable reliability. By analyzing the resulting data set and conducting a user study, we further show that the two-dimensional viewpoint representation we propose allows for more meaningful analyses and diversification interventions compared to current approaches. Finally, we discuss what this novel viewpoint label implies for HII research and how obtaining it may be made cheaper in the future.

In this paper, we present BAM, a unified Benchmark for Argument Mining (AM). We propose a method to homogenize both the evaluation process and the data to provide a common view in order to ultimately produce comparable results. Built as a four stage and end-to-end pipeline, the benchmark allows for the direct inclusion of additional argument miners to be evaluated. First, our system pre-processes a ground truth set used both for training and testing. Then, the benchmark calculates a total of four measures to assess different aspects of the mining process. To showcase an initial implementation of our approach, we apply our procedure and evaluate a set of systems on a corpus of scientific publications. With the obtained comparable results we can homogeneously assess the current state of AM in this domain.

The Video Browser Showdown addresses difficult video search challenges through an annual interactive evaluation campaign attracting research teams focusing on interactive video retrieval. The campaign aims to provide insights into the performance of participating interactive video retrieval systems, tested by selected search tasks on large video collections. For the first time in its ten year history, the Video Browser Showdown 2021 was organized in a fully remote setting and hosted a record number of sixteen scoring systems. In this paper, we describe the competition setting, tasks and results and give an overview of state-of-the-art methods used by the competing systems. By looking at query result logs provided by ten systems, we analyze differences in retrieval model performances and browsing times before a correct submission. Through advances in data gathering methodology and tools, we provide a comprehensive analysis of ad-hoc video search tasks, discuss results, task design and methodological challenges. We highlight that almost all top performing systems utilize some sort of joint embedding for text-image retrieval and enable specification of temporal context in queries for known-item search. Whereas a combination of these techniques drive the currently top performing systems, we identify several future challenges for interactive video search engines and the Video Browser Showdown competition itself.

In an experimental setting, data anonymization is vital to get valid results. For studies dealing with news articles, white-labelling their source is a non-trivial task, since news outlets might possess traceable writing styles. In this thesis, modern neural network architectures for natural language processing are utilized to transfer texts to a uniform style. The method does not rely on parallel corpora, which is usually the bottleneck for many systems. Instead, a pseudo-parallel corpus is created using monolingual data and masked-language modeling. Additionally, a new scraper architecture is designed and implemented to easily obtain article from news websites and store them in a homogeneous format.

In the last decade, user-centric video search competitions have facilitated the evolution of interactive video search systems. So far, these competitions focused on a small number of search task categories, with few attempts to change task category configurations. Based on our extensive experience with interactive video search contests, we have analyzed the spectrum of possible task categories and propose a list of individual axes that define a large space of possible task categories. Using this concept of category space, new user-centric video search competitions can be designed to benchmark video search systems from different perspectives. We further analyse the three task categories considered so far at the Video Browser Showdown and discuss possible (but sometimes challenging) shifts within the task category space.

Scene-text has been shown to be an effective query target for video retrieval applications in a known-item search context. While much progress has been made in scene-text extraction from individual pictures, the special case of video has so far received less attention. This paper introduces HyText, a scene-text extraction method for video with a focus on retrieval applications. HyText uses intermittent scene-text detection in combination with bi-directional tracking in order to increase throughput without reducing detection accuracy.

This paper presents the version of vitrivr participating at the Video Browser Showdown (VBS) 2022. vitrivr already supports a wide range of query modalities, such as color and semantic sketches, OCR, ASR and text embedding. In this paper, we briefly introduce the system, then describe our new approach to queries specifying temporal context, ideas for color-based sketches in a competitive retrieval setting and a novel approach to pose-based queries.

In multimedia search, appropriate user interfaces (UIs) are essential to enable effective specification of the user’s information needs and the user-friendly presentation of search results. vitrivr-VR addresses these challenges and provides a novel Virtual Reality-based UI on top of the multimedia retrieval system vitrivr. In this paper we present the version of vitrivr-VR participating in the Video Browser Showdown (VBS) 2022. We describe our visual-text co-embedding feature and new query interfaces, namely text entry, pose queries and temporal queries.

News recommender systems provide a technological architecture that helps shaping public discourse. Following a normative approach to news recommender system design, we test utility and external effects of a diversity-aware news recommender algorithm. In an experimental study using a custom-built news app, we show that diversity-optimized recommendations (1) perform similar to methods optimizing for user preferences regarding user utility, (2) that diverse news recommendations are related to a higher tolerance for opposing views, especially for politically conservative users, and (3) that diverse news recommender systems may nudge users towards preferring news with differing or even opposing views. We conclude that diverse news recommendations can have a depolarizing capacity for democratic societies.

Knowledge graph (KG) embedding methods are at the basis of many KG-based data mining tasks, such as link prediction and node clustering. However, graphs may contain confidential information about people or organizations, which may be leaked via embeddings. Research recently studied how to apply differential privacy to a number of graphs (and KG) analyses, but embedding methods have not been considered so far. This study moves a step towards filling such a gap, by proposing the Differential Private Knowledge Graph Embedding (DPKGE) framework. DPKGE extends existing KG embedding methods (e.g., TransE, TransM, RESCAL, and DistMult) and processes KGs containing both confidential and unrestricted statements. The resulting embeddings protect the presence of any of the former statements in the embedding space using differential privacy. Our experiments identify the cases where DPKGE produces useful embeddings, by analyzing the training process and tasks executed on top of the resulting embeddings.

Knowledge graphs (KGs) have drawn a wide research attention in recent years, since they enable semi-structured information to be stored in an unified, connected and organized way. The inherent features of this data structure are leveraged in many tasks, such as information retrieval, recommendation systems, etc. Meanwhile, there are challenges in understanding and reasoning on a subset of a KG. One scenario would be question answering over KGs. Natural language questions can be flexible in expressions, which means that it is difficult for machines to retrieve an answer from a KG given a question posed by human. [Qiu et al., 2020] proposed a reinforcement learning-based (RL-based) approach, which finds answer entities for multi-hop questions via stepwise reasoning over KGs. Inspired by its work, this thesis adopts the model’s main body as a baseline architecture and investigates three research questions. The premise of KG reasoning is the accurate selection of topic entities. This work adapts a passive entity linker to link question mentions to KG nodes. In reasoning processes, an attention mechanism is implemented to associate history of actions with semantic information from questions, such that an agent can learn on which part of questions to focus. Conventional RL-based reasoning returns terminal rewards after complete reasoning episodes, resulting in a lack of guidance in sequential decision process. To address this problem, we use potential-based shaping rewards instead. The empirical results show that the reward shaping function improves the hits@1 performances on two benchmarks.

In this thesis, a recommender system is built for portfolio management based on social media. With the emergence of social media and so-called influencers, people hold on to recommendations from famous  financial investors. However, to what extent the social media posts and other mediums are able to explain changes in the composition of the financial actors remains unknown. This thesis is aimed at answering this question through a pipeline which consists of news scraping, content analysis, and a recommender system. The first two parts are used to create the data model inspired by a knowledge graph, consisting of various information about the financial influencer or the entity. Whereas the third part, the recommender system, proposes user-based or item-based recommendations, with the addition that various parameters can be set to create different investing strategies. Moreover, it should be included that the system allows user-specific recommendations for a certain period of time, which sets a basis for future research questions.

Modern neural network based classifications system often require large training sets and struggle with degrading classification performance when confronted with unseen objects categories. This thesis investigates practical and effective ways to implement a large-scale active learning pipeline for concept detection in videos, which is capable to constantly learn new object categories from annotated images provided by human supervisors. The proposed pipeline uses an active learning loop with a simple uncertainty-based heuristic to select the most informative images for annotation to achieve this goal. The evaluation of four different convolutional neural networks for image feature embedding showed that the InceptionResNetV2 architecture delivers the best performance over all studied classification scenarios. Furthermore, there is no single classification methods which works best in all classification scenarios. It is advantageous to let the system chose the ‘best’ classifier for each classification task. Moreover, the classification performance can be further improved for very small training sets if extracted box images are added as training instances.

The research on argumentation in natural text is evolving, but a perfect way to model, annotate and mine argumentative structures is yet to be found. High-quality annotation corpora are created in complex and time consuming manual work, to represent annotations for the training, testing and improvement of automated Argument Mining tools. The value such corpora have for a machine is out of question. But the fact, that the referenced argumentative structures in the annotation file of the corpus are completely separated from their actual context, within their original text, makes it difficult for a human reader to benefit on a similar level from the data they incorporate. In this thesis, we address that problem and implement a tool to generate visually annotated PDF documents from corpus data. The produced documents support human readers to understand and comprehend the visible annotations and the presented relationships they have to other annotations within the text. Attaching and embedding the original text and annotation files as well as the annotation structure, created during the creation process to our documents, makes these PDF documents to an all in one file solution. As proof of our concept, we processed an example corpus with our tool.

The Lifelog Search Challenge (LSC) is an annual benchmarking challenge for comparing approaches to interactive retrieval from multi-modal lifelogs. LSC'21, the fourth challenge, attracted sixteen participants, each of which had developed interactive retrieval systems for large multimodal lifelogs. These interactive retrieval systems participated in a comparative evaluation in front of an online live-audience at the LSC workshop at ACM ICMR'21. This overview presents the motivation for LSC'21, the lifelog dataset used in the competition, and the participating systems.

The multi-modal and interrelated nature of lifelog data makes it well suited for graph-based representations. In this paper, we present the second iteration of LifeGraph, a Knowledge Graph for Lifelog Data, initially introduced during the 3rd Lifelog Search Challenge in 2020. This second iteration incorporates several lessons learned from the previous version. While the actual graph has undergone only small changes, the mechanisms by which it is traversed during querying as well as the underlying storage system which performs the traversal have been changed. The means for query formulation have also been slightly extended in capability and made more efficient and intuitive. All these changes have the aim of improving result quality and reducing query time.