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

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In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed.

Knowledge Graphs (KGs) have become increasingly popular in the recent years. However, as knowledge constantly grows and changes, it is inevitable to extend existing KGs with entities that emerged or became relevant to the scope of the KG after its creation. Research on updating KGs typically relies on extracting named entities and relations from text. However, these approaches cannot infer entities or relations that were not explicitly stated. Alternatively, embedding models exploit implicit structural regularities to predict missing relations, but cannot predict missing entities. In this article, we introduce a novel method to enrich a KG with new entities given their textual description. Our method leverages joint embedding models, hence does not require entities or relations to be named explicitly. We show that our approach can identify new concepts in a document corpus and transfer them into the KG, and we find that the performance of our method improves substantially when extended with techniques from association rule mining, text mining, and active learning.

Gendered voice based on pitch is a prevalent design element in many contemporary Voice Assistants(VAs) but has shown to strengthen harmful stereotypes. Interestingly, there is a dearth of research that systematically analyses user perceptions of different voice genders in VAs. This study investigates gender-stereotyping across two different tasks by analyzing the influence of pitch (low, high) and gender (women, men) on stereotypical trait ascription and trust formation in an exploratory online experiment with 234 participants. Additionally, we deploy a gender-ambiguous voice to compare against gendered voices. Our findings indicate that implicit stereotyping occurs for VAs. Moreover, we can show that there are no significant differences in trust formed towards a gender-ambiguous voice versus gendered voices, which highlights their potential for commercial usage.

Most existing personalization systems promote items that match a user’s previous choices or those that are popular among similar users. This results in recommendations that are highly similar to the ones users are already exposed to, resulting in their isolation inside familiar but insulated information silos. In this context, we develop a novel recommendation framework with a goal of improving information diversity using a modified random walk exploration of the user-item graph. We focus on the problem of political content recommendation, while addressing a general problem applicable to personalization tasks in other social and information networks. For recommending political content on social networks, we first propose a new model to estimate the ideological positions for both users and the content they share, which is able to recover ideological positions with high accuracy. Based on these estimated positions, we generate diversified personalized recommendations using our new random-walk based recommendation algorithm. With experimental evaluations on large datasets of Twitter discussions, we show that our method based on random walks with erasure is able to generate more ideologically diverse recommenda- tions. Our approach does not depend on the availability of labels regarding the bias of users or content producers. With experiments on open benchmark datasets from other social and information networks, we also demonstrate the effectiveness of our method in recommending diverse long-tail items.

In radio astronomy, antennas monitor portions of the sky to collect radio signals. The antennas produce data streams that are of high volume and velocity (2.5 GB/s) and the inverse Fourier transform is used to convert the collected signals into sky images that astrophysicists use to conduct their research. Applying the inverse Fourier transform in a streaming setting, however, is not ideal since its computational complexity is quadratic in the size of the image. In this article, we propose the Single Point Incremental Fourier Transform (SPIFT), a novel incremental algorithm to produce sequences of sky images. SPIFT computes the Fourier transform for a new signal in a linear number of complex multiplications by exploiting twiddle factors, multiplicative constant coefficients. We prove that twiddle factors are periodic and show how circular shifts can be exploited to reuse multiplication results. The cost of the additive operations can be curbed by exploiting the embarrassingly parallel nature of the additions, which modern big data streaming frameworks can leverage to compute slices of the image in parallel. Our experiments suggest that SPIFT can efficiently generate sequences of sky images: it computes the complex multiplications 4 to 12x faster than the Discrete Fourier Transform, and its parallelisation of the additive operations shows linear speedup.

Early risk detection in food safety aims to identify emerging risks and trends before they can impact the health of consumers. In Switzerland, the FSVO established a network-based process to find microbiological, chemical, and nutritional issues in food, food stuffs, and commodities that might impact the Swiss population in the future. This case study investigates the feasibility of improving the current early detection process by implementing crowdsourcing methods. A series of interviews with people who are involved in the process determined the state-of-the-art and main challenges but left questions about the assessment process of potentially relevant information open. These questions were addressed by an online study that concluded that a crowdsourcing-based information filtering process might be feasible. A literature survey that presents different crowdsourcing implementations in the food domain completes the case study. Following up on the results from the case study, the proposal presents ideas on how the next steps towards an improved early detection process at the FSVO could look like.

For research results to be comparable, it is important to have common datasets for experimentation and evaluation. The size of such datasets, however, can be an obstacle to their use. The Vimeo Creative Commons Collection (V3C) is a video dataset designed to be representative of video content found on the web, containing roughly 3800 hours of video in total, split into three shards. In this paper, we present insights on the second of these shards (V3C2) and discuss their implications for research areas, such as video retrieval, for which the dataset might be particularly useful. We also provide all the extracted data in order to simplify the use of the dataset.

News diversity in the media has for a long time been a foundational and uncontested basis for ensuring that the communicative needs of individuals and society at large are met. Today, people increasingly rely on online content and recommender systems to consume information challenging the traditional concept of news diversity. In addition, the very concept of diversity, which differs between disciplines, will need to be re-evaluated requiring an interdisciplinary investigation, which requires a new level of mutual cooperation between computer scientists, social scientists, and legal scholars. Based on the outcome of a interdisciplinary workshop, we have the following recommendations, directed at researchers, funders, legislators, regulators, and the media industry: - Conduct interdisciplinary research on news recommenders and diversity. - Create a safe harbor for academic research with industry data. - Strengthen the role of public values in news recommenders. - Create a meaningful governance framework for news recommenders. - Fund a joint lab to spearhead the needed interdisciplinary research, boost practical innovation, develop reference solutions, and transfer insights into practice.

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Speech is a salient information channel in recorded media, usually containing relevant semantic information complementing the visual signal. In a video retrieval setting, the speech signal can be transcribed automatically to enable spoken document retrieval by text query. Even though not the only factor, automatic transcription performance is the most important for the quality of such a retrieval system. In this work, we first assess the transcription quality of current state-of-the-art ASR systems and quantify the errors such systems make on a realistic dataset. We then examine if audio-visual speech enhancement methods can be used to improve the transcription quality. Based on these two preliminary studies' findings, we build three spoken document retrieval pipelines to index videos by what was said. We evaluate these systems on a set of manually captioned YouTube videos and find that speech enhancement slightly increases retrieval performance.

The goal of person re-identification (re-id) is to recognize all instances of a particular person from an image in a gallery of images or videos. So far, research was mostly focused on the re-id of pedestrians in surveillance cameras. Person re-id is not only useful in surveillance scenarios, but also for video analysis and multimedia retrieval applications, wherein all types of videos are relevant. In order to recognize people in videos, a person detection step must be carried out before the re-id step. However, these two tasks pursue opposing goals, which is why one-step methods that combine these tasks are particularly suitable for person search. We analyze two such one-step methods of person search, Online Instance Matching (OIM) and Norm-Aware Embedding (NAE), and test how well they perform on a movie-based dataset. Multi-Object Tracking (MOT) is another task suitable for identifying and tracking several people within a video. Here, FairMOT and JDE are very effective and fast, we test both methods to find out which one gives us better re-identification results.

Epidemic spreading is a widely studied process due to its importance and possibly grave consequences for society. While the classical context of epidemic spreading refers to pathogens transmitted among humans or animals, it is straightforward to apply similar ideas to the spread of information (e.g., a rumor) or the spread of computer viruses. This paper addresses the question of how to optimally select nodes for monitoring in a network of timestamped contact events between individuals. We consider three optimization objectives: the detection likelihood, the time until detection, and the population that is affected by an outbreak. The optimization approach we use is based on a simple greedy approach and has been proposed in a seminal paper focusing on information spreading and water contamination. We extend this work to the setting of disease spreading and present its application with two example networks: a timestamped network of sexual contacts and a network of animal transports between farms. We apply the optimization procedure to a large set of outbreak scenarios that we generate with a susceptible-infectious-recovered model. We find that simple heuristic methods that select nodes with high degree or many contacts compare well in terms of outbreak detection performance with the (greedily) optimal set of nodes. Furthermore, we observe that nodes optimized on past periods may not be optimal for outbreak detection in future periods. However, seasonal effects may help in determining which past period generalizes well to some future period. Finally, we demonstrate that the detection performance depends on the simulation settings. In general, if we force the simulator to generate larger outbreaks, the detection performance will improve, as larger outbreaks tend to occur in the more connected part of the network where the top monitoring nodes are typically located. A natural progression of this work is to analyze how a representative set of outbreak scenarios can be generated, possibly taking into account more realistic propagation models.

Despite the fact that automatic content analysis has made remarkable progress over the last decade - mainly due to significant advances in machine learning - interactive video retrieval is still a very challenging problem, with an increasing relevance in practical applications. The Video Browser Showdown (VBS) is an annual evaluation competition that pushes the limits of interactive video retrieval with state-of-the-art tools, tasks, data, and evaluation metrics. In this paper, we analyse the results and outcome of the 8th iteration of the VBS in detail. We first give an overview of the novel and considerably larger V3C1 dataset and the tasks that were performed during VBS 2019. We then go on to describe the search systems of the six international teams in terms of features and performance. And finally, we perform an in-depth analysis of the per-team success ratio and relate this to the search strategies that were applied, the most popular features, and problems that were experienced. A large part of this analysis was conducted based on logs that were collected during the competition itself. This analysis gives further insights into the typical search behavior and differences between expert and novice users. Our evaluation shows that textual search and content browsing are the most important aspects in terms of logged user interactions. Furthermore, we observe a trend towards deep learning based features, especially in the form of labels generated by artificial neural networks. But nevertheless, for some tasks, very specific content-based search features are still being used. We expect these findings to contribute to future improvements of interactive video search systems.

Huge volumes of human knowledge are available in many different data sources, many of which contain thoughtful and well-reasoned arguments in the form of natural language text. Along with recent advances in machine learning (ML) techniques, researchers have increasingly started to investigate possibilities to automatically extract argumentative components and the relations between them – a process which is called Argument Mining (AM). As an emerging research area, one of the major challenges in AM research is the lack of annotated datasets. These datasets are needed as training data as well as for benchmark experiments. With a focus on scientific publications, we implemented a system that could be used to create gold standard datasets for AM with the help of hundreds of thousands of ordinary workers (i.e. the crowd). Our system includes two types of tasks, one for the annotation of argument components and one for the annotation of argumentative relations that hold between those components. To evaluate and improve our system, we conducted experiments for both of these task types with 70 participants on the crowdsourcing platform Amazon Mechanical Turk. Detecting argumentative components and relations is a very complex task, especially for untrained, non-expert crowdworkers. We found that by introducing a quality assurance filter mechanism, it is possible to detect high-performing workers and also to detain workers who are expected to perform poorly from participating. In this way, it is possible, to some extent, to steer the quality of the crowd-annotated dataset, in exchange for money and time – money, because workers need to complete the task that will determine whether they will be filtered out or not, and time, because filtering out workers results in a smaller workforce, meaning it could take longer for all annotation tasks to be completed by the crowd. Our work denotes another step towards an effective interaction between researchers and the crowd in the field of AM and, thereby, decisively contributes to an emerging research area.

Increasingly complex and autonomous systems require machine ethics to maximize the benefits and minimize the risks to society arising from the new technology. It is challenging to decide which type of ethical theory to employ and how to implement it effectively. This survey provides a threefold contribution. First, it introduces a trimorphic taxonomy to analyze machine ethics implementations with respect to their object (ethical theories), as well as their nontechnical and technical aspects. Second, an exhaustive selection and description of relevant works is presented. Third, applying the new taxonomy to the selected works, dominant research patterns, and lessons for the field are identified, and future directions for research are suggested.