Multiband Dual Fermion Approach to Quantum Criticality in the Hubbard Honeycomb Lattice

We study the Hubbard model on the honeycomb lattice in the vicinity of the quantum critical point by means of a multiband formulation of the Dual Fermion approach. Beyond the strong local correlations of the dynamical mean field, critical fluctuations on all length scales are included by means of a ladder diagram summation. Analysis of the susceptibility yields an estimate of the critical interaction strength of the quantum phase transition from a paramagnetic semimetal to an antiferromagnetic insulator, in good agreement to other numerical methods. We further estimate the crossover temperature to the renormalized classical regime. Our data imply that, at large interaction strengths, the Hubbard model on the honeycomb lattice behaves like a quantum nonlinear sigma model, while being in a non-Fermi liquid state.Comment: - changed the last sentence in the abstract - rewrote Sec. II.A - added references 11,18 and 29 - changed styles in Fig.4, Fig.5 and Fig.6 to improve coherence - revised Fig.7 promoted insets to individual panels and added polynomial extrapolation of the self-energy data - Rewrote Sec. III.B. - Rewrote parts of the appendix - added Refs. 19-2

A Conceptual Framework for Enhancing Product Search with Product Information from Reviews

Product search today is limited, as users can only search and filter for a restricted set of product features, e.g. 15” and 1TB hard disk when searching for a laptop. The often decision- critical aspects of a product are however hidden in user reviews (“noisy fan” or “bright display”) and are not available until a product has been found. This paper proposes a conceptual framework for the integration of product aspects, that have been mined and derived from consumer reviews, into the product search. The framework structures the challenges that arise in four major fields and gives an overview of existing research for each one of them: Data challenges, user experience challenges, purchase process challenges and business challenges. It may inform researchers from various disciplines to perform target-oriented research as well as practitioners what to consider when building up such an enriched product search

Managing Temporal Dynamics of Filter Bubbles

Filter bubbles have attracted much attention in recent years in terms of their impact on society. Whereas it is commonly agreed that filter bubbles should be managed, the question is still how. We draw a picture of filter bubbles as dynamic, slowly changing constructs that underlie temporal dynamics and that are constantly influenced by both machine and human. Anchored in a research setting with a major public broadcaster, we follow a design science approach on how to design the temporal dynamics in filter bubbles and how to design users' influence over time. We qualitatively evaluate our approach with a smartphone app for personalized radio and found that the adjustability of filter bubbles leads to a better co-creation of information flows between information broadcaster and listener

CISO-BERT: Matching Information Security Requirements by Fine-Tuning the BERT Language Model

In today\u27s digital age, information security is of utmost importance. Many organizations are adopting information security management systems and pursuing certifications like ISO 27001. However, the process of creating and maintaining these policies is often manual and time-consuming. Organizations must merge requirements from different frameworks and stay updated with evolving regulations. To address these challenges, we propose a novel approach that leverages large language models, specifically fine-tuning a pre-trained BERT model. Our research focuses on automatically identifying and matching cybersecurity requirements, particularly those outlined in ISO 27001. This approach aims to support the merging of requirements from various frameworks into a unified policy and ensure the consistency of company-specific policies with updated frameworks over time. By utilizing advanced natural language processing techniques and the power of BERT, we aim to streamline the process of policy creation and maintenance, reducing manual effort and enabling organizations to stay compliant with changing regulations

Consumers’ Need for Negative Product Information Before Reading Reviews

Negative product-related information is crucial to consumers in purchase decisions. Consumers perceive negative information stronger than positive, and next to a stronger perception, consumers also have a high demand for negative product aspects, as these show the problem areas of a product and can help avoid losses. But negative product-related information is not available in the product search process until the customer reads reviews at a very late phase of the decision process. Even though we know about a bias in perception of negative information, little is known about the exact need for negative product-related information during the search process. We examine the need for negative product-related information throughout the purchase-decision process for different product types. Insights about the need for negative product-related information can inform ecommerce platform providers how to design a better product search on their site

AUTOMATED KEYWORD GENERATION IN THE PUBLIC RADIO SECTOR USING WORD EMBEDDINGS

Public broadcasters find themselves in a difficult situation when it comes to digital offers. In more and more use cases, metadata is needed, e.g. to allow radio editors to search for content pieces, to set up content-based recommendation services, to allow users to browse by categories or tags, or to optimize content for search engines. They are in need of proper metadata to manage digital products and to offer new and timely services. Public broadcasters often have their own taxonomy of keywords at hand. The manual distilling of metadata in particular in form of keywords may however become a bottleneck in operation, whereas automatic keyword generation does not always provide the desired accuracy and also requires continuous human effort for training classifiers and controlling the accuracy. Building upon more recent techniques of word embedding we present a novel approach to assign keywords from a taxonomy to documents on the basis of distributed representation of words and documents that does not require annotation by human experts and evaluate it with a large dataset of a German nation-wide broadcaster. Preliminary results are promising that keywords can be automatically generated in an unsupervised way in the public radio sector

Digital Transformation of Radio Broadcasting: An Exploratory Analysis of Challenges and Solutions for New Digital Radio Services

Like other media industries before, radio broadcasting is increasingly facing competition from new media platforms and changing consumer expectations. Many broadcasters are experimenting with possible solutions and are changing their production processes. While this is necessary, research is needed to capture the whole phenomenon of digital transformation of radio broadcasting. We conducted exploratory qualitative content analysis on talks of radio practitioner to identify current challenges, possible solutions, and specific aesthetics that shape current and future radio experience. We conceptualize the case of digital transformation of radio from the perspective of service-dominant logic and digital service innovation and discuss relevant areas of service innovation. We thus offer orientation for practitioners and contribute to a rather new, yet fruitful area of research for the information systems discipline

Making Filter Bubbles Understandable

Recommender systems tend to create filter bubbles and, as a consequence, lower diversity exposure, often with the user not being aware of it. The biased preselection of content by recommender systems has called for approaches to deal with exposure diversity, such as giving users control over their filter bubble. We analyze how to make filter bubbles understandable and controllable by using interactive word clouds, following the idea of building trust in the system. On the basis of several prototypes, we performed explorative research on how to design word clouds for the controllability of filter bubbles. Our findings can inform designers of interactive filter bubbles in personalized offers of broadcasters, publishers, and media houses