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Social media has become an integral part of daily life for many individuals, serving as a platform for communication, information sharing, and entertainment. However, the extensive use of social media has led to issues such as information overload and social media fatigue, where users feel overwhelmed and emotionally exhausted by constant interaction and content. This paper investigates cognitive social media literacy skills—appraisal, comprehension, curation, and interaction—and their ability to mitigate perceived overload. Based on a quantitative study of 335 respondents, the results confirm that higher social media literacy reduces perceived information overload, but only the skill “appraisal” significantly lowers communication overload. The study highlights the critical role of social media literacy in reducing negative social media effects, offering practical insights for policymakers, educators, and platform developers to address social media fatigue.
In the realm of process engineering, the pursuit of sustainability is paramount. Traditional approaches can be time-consuming and often struggle to address modern environmental challenges effectively. This article explores the integration of generative AI, as a powerful tool to generate solution ideas and solve problems in process engineering using a Solution-Driven Approach (SDA). SDA applies nature-inspired principles to tackle intricate engineering challenges. In this study, generative AI is trained to understand and use the SDA patterns to suggest solutions to complex engineering challenges.
In recent years, diffusion models (DMs) have drawn significant attention for their success in approximating data distributions, yielding state-of-the-art generative results. Nevertheless, the versatility of these models extends beyond their generative capabilities to encompass various vision applications, such as image inpainting, segmentation, adversarial robustness, among others. This study is dedicated to the investigation of adversarial attacks through the lens of diffusion models. However, our objective does not involve enhancing the adversarial robustness of image classifiers. Instead, our focus lies in utilizing the diffusion model to detect and analyze the anomalies introduced by these attacks on images. To that end, we systematically examine the alignment of the distributions of adversarial examples when subjected to the process of transformation using diffusion models. The efficacy of this approach is assessed across CIFAR-10 and ImageNet datasets, including varying image sizes in the latter. The results demonstrate a notable capacity to discriminate effectively between benign and attacked images, providing compelling evidence that adversarial instances do not align with the learned manifold of the DMs.
Datasets labelled by human annotators are widely used in the training and testing of machine learning models. In recent years, researchers are increasingly paying attention to label quality and correctness. However, it is not always possible to objectively determine, whether an assigned label is correct or not. The present work investigates this ambiguity in the annotation of autonomous driving datasets as an important dimension of data quality. Our experiments show that excluding highly ambiguous data from the training improves model performance of a state-of-the-art pedestrian detector in terms of LAMR, precision and F1-score, thereby saving training time and annotation costs. Furthermore, we demonstrates that, in order to safely remove ambiguous instances and ensure the retained representativeness of the training data, an understanding of the properties of the dataset and class under investigation is crucial.
The robust generalization of models to rare, in-distribution (ID) samples drawn from the long tail of the training distribution and to out-of-training-distribution (OOD) samples is one of the major challenges of current deep learning methods. For image classification, this man-ifests in the existence of adversarial attacks, the performance drops on distorted images, and a lack of generalization to concepts such as sketches. The current under-standing of generalization in neural networks is very lim-ited, but some biases that differentiate models from human vision have been identified and might be causing these lim-itations. Consequently, several attempts with varying success have been made to reduce these biases during training to improve generalization. We take a step back and sanity-check these attempts. Fixing the architecture to the well-established ResNet-50, we perform a large-scale study on 48 ImageNet models obtained via different training methods to understand how and if these biases - including shape bias, spectral biases, and critical bands - interact with generalization. Our extensive study results reveal that contrary to previous findings, these biases are insufficient to accu-rately predict the generalization of a model holistically.
Vision language models (VLMs) have drastically changed the computer vision model landscape in only a few years, opening an exciting array of new applications from zero-shot image classification, over to image captioning, and visual question answering. Unlike pure vision models, they offer an intuitive way to access visual content through language prompting. The wide applicability of such models encourages us to ask whether they also align with human vision - specifically, how far they adopt human-induced visual biases through multimodal fusion, or whether they simply inherit biases from pure vision models. One important visual bias is the texture vs. shape bias, or the dominance of local over global information. In this paper, we study this bias in a wide range of popular VLMs. Interestingly, we find that VLMs are often more shape-biased than their vision encoders, indicating that visual biases are modulated to some extent through text in multimodal models. If text does indeed influence visual biases, this suggests that we may be able to steer visual biases not just through visual input but also through language: a hypothesis that we confirm through extensive experiments. For instance, we are able to steer shape bias from as low as 49% to as high as 72% through prompting alone. For now, the strong human bias towards shape (96%) remains out of reach for all tested VLMs.
Digital technologies have the potential to change well-established practices in business-to-business (B2B) sales. This paper gives an overview to understand how digital trends (e.g., emerging technologies) will impact the future of solution selling. For this purpose, solution business is seen from a process-oriented point of view. This article presents insights into digital use case scenarios changing the solution selling process.
With the increasing popularity of voice user interfaces (VUIs), there is a growing interest in the evaluation of not only their usability, but also the quality of the user experience (UX). Previous research has shown that UX evaluation in human-machine interaction is significantly influenced by emotions. As a consequence, the measurement of emotions through the user’s speech signal may enable a better measure of the voice user experience and thus allow for the optimization of human-computer interaction through VUIs.
With our study, we want to contribute to the research on speech emotion recognition in the context of voice user experience. We recorded 45 German participants while they were interacting with a voice assistant in a Wizard-of-Oz scenario. The interactions contained some typical user annoyances that might occur in voice-based human-computer interaction. Three analysis modules provided insight into the voice user experience of our participants: (1) a UX-questionnaire; (2) the UEQ+ scales for voice assistants; (3) speech emotion recognition with OpenVokaturi.
Digital Transformation in Banking–How Do Customers Assess the Quality of Digital Banking Services?
(2024)
The digital transformation is presenting financial institutions with a number of challenges, not only in terms of technology, but also in terms of delivering new, user-optimized services to customers. In order to provide the best possible touchpoints and access to services, banking institutions need to identify the wishes and needs of their customers. This paper deals with the results of an iterative research with German banks based on the five SERVQUAL model dimensions: tangibles, reliability, responsiveness, assurance and empathy. It presents the results of two quantitative surveys of bank customers in Southern Germany and provides recommendations for financial institutions for their future digital activities.
Artificial intelligence (AI) and Machine Learning (ML) are rapidly turning from trending topics to requirement for competitiveness for enterprises. For marketing departments, AI and ML offer potential for improvement of their processes such as optimizing user experience and personalizing campaigns for selected audiences. Nevertheless, the integration of new technologies such as AI and ML into the existing marketing mix portfolio means a great challenge for marketing managers as their implementation requires new skills and knowledge which is not always already developed. The objective of the paper is to demonstrate how an industry-university cooperation (IUC) can enable the adaptation to new business contexts. Thus, this paper proposes a framework on IUC involving different project phases. It describes the process for placing AI-generated individual content, recommendations and references for specific interests.