Information Systems Frontiers最新文献

Effective management of colorectal cancer (CRC) necessitates precise prognostication and informed decision-making, yet existing literature often lacks emphasis on parsimonious variable selection and conveying complex interdependencies among factors to medical practitioners. To address this gap, we propose a decision support system integrating Elastic Net (EN) and Simulated Annealing (SA) algorithms for variable selection, followed by Tree Augmented Naive Bayes (TAN) modeling to elucidate conditional relationships. Through k-fold cross-validation, we identify optimal TAN models with varying variable sets and explore interdependency structures. Our approach acknowledges the challenge of conveying intricate relationships among numerous variables to medical practitioners and aims to enhance patient-physician communication. The stage of cancer emerges as a robust predictor, with its significance amplified by the number of metastatic lymph nodes. Moreover, the impact of metastatic lymph nodes on survival prediction varies with the age of diagnosis, with diminished relevance observed in older patients. Age itself emerges as a crucial determinant of survival, yet its effect is modulated by marital status. Leveraging these insights, we develop a web-based tool to facilitate physician–patient communication, mitigate clinical inertia, and enhance decision-making in CRC treatment. This research contributes to a parsimonious model with superior predictive capabilities while uncovering hidden conditional relationships, fostering more meaningful discussions between physicians and patients without compromising patient satisfaction with healthcare provision.

The COVID-19 pandemic has highlighted the critical need for advanced technology in healthcare. Clinical Decision Support Systems (CDSS) utilizing Artificial Intelligence (AI) have emerged as one of the most promising technologies for improving patient outcomes. This study’s focus on developing a deep state-space model (DSSM) is of utmost importance, as it addresses the current limitations of AI predictive models in handling high-dimensional and longitudinal electronic health records (EHRs). The DSSM’s ability to capture time-varying information from unstructured medical notes, combined with label-dependent attention for interpretability, will allow for more accurate risk prediction for patients. As we move into a post-COVID-19 era, the importance of CDSS in precision medicine cannot be ignored. This study’s contribution to the development of DSSM for unstructured medical notes has the potential to greatly improve patient care and outcomes in the future.

Recommendation systems are becoming an invaluable assistant not only for users, who may be disoriented in the presence of a huge number of different alternatives, but also for service providers or sellers, who would like to be able to guide the choice of customers toward particular items with specific characteristics. This influence capability can be particularly useful in the tourism domain, where the need to manage the industry in a more sustainable way and the ability to predict and control the level of crowding of PoIs (Points of Interest) have become more pressing in recent years. In this paper, we study the role of contextual information in determining both PoI occupations and user preferences, and we explore how machine learning and deep learning techniques can help produce good recommendations for users by enriching historical information with its contextual counterpart. As a result, we propose the architecture of ARTEMIS, a context-Aware Recommender sysTEM wIth crowding forecaSting, able to learn and forecast user preferences and occupation levels based on historical contextual features. Throughout the paper, we refer to a real-world application scenario regarding the tourist visits performed in Verona, a municipality in Northern Italy, between 2014 and 2019.

Recently, 5G/B5G/6G networks (5G for short) have been gloriously developed to give us colorful lives and make our daily activities more convenient than before. On the other hand, online meetings, like video conferences and online educations, have been popularly held everywhere in the world. Nevertheless, in such a meeting on 5G networks, a packet P transmitted from a User Plane Function (UPF), e.g., UPF₁ of a 5G, e.g., 5G₁, to P’s destinations, e.g., the set {UPF₂, UPF₃, …UPF_n}, respectively, in {5G₂, 5G₃, …5G_n}, is not secure, even not encrypted, particularly when P goes through the Internet. Hackers may duplicate P, i.e., data leakage on the connections among these UPFs. The situation needs to be avoided when data of the meetings ought not to be leaked, e.g., for an important military conference. Therefore, in this study, we propose a security architecture, named Group Key using IKA (GKIKA), which encrypts/decrypt packets before sending them, e.g., EC₁ in 5G₁ encrypts P and then sends P’s ciphertext which will be decrypted by edge computers {EC₂, EC₃, …EC_n} where EC_j is in 5G_j, (2le jle n). Our security scenarios include data transmission among n-parties, n (ge) 2. When n (=) 2, symmetric or asymmetric cryptography is adopted depending on the security level and time constraints required. As n (>) 2, the Initial Key Agreement (IKA) is utilized to establish a secret key for all participating ECs. Other security mechanisms, like message authentication code and time stamp, are also utilized to enhance the security level of data transmission. Our analyses show that the GKIKA can effectively avoid some types of attacks.

There are high expectations towards extended reality (XR), namely the “metaverse”. However, human performance in the metaverse has been called into question when undertaking everyday activities (e.g., working, shopping, and learning etc.), as complex human-technology interaction required may hinder cognitive abilities such as processing of information. Therefore, this study attempts to address whether and how XR impacts abilities to recall and recognize information in daily-life settings. We investigated the effects of VR and AR in a 2 (VR: yes vs. no) × 2 (AR: yes vs. no) between-subjects design experiment related to a shopping task (N = 153) on textual (product names) and pictorial (product pictures) recognition and recall. The results show that textual information recall and pictorial information recognition did not significantly suffer in XR compared to shopping in a brick-and-mortar store. While regarding textual information recognition performance, the results show that fully physical environments offered the highest performance compared to the different XR technologies being used. Overall, the study provides important findings and guidance for the use of extended reality technologies in consumer-facing businesses, as well as the use of XR in everyday life in general.

In response to climate change, nations have been tasked with reducing energy consumption and lessening their carbon footprint through targeted actions. While digital technologies can support this goal, our understanding of energy practices in a private household context remains nascent. This challenge is amplified by the ‘invisible’ nature of users’ interaction with energy systems and the impact of unconscious habits. Our objective is to explore how touchpoints embedded in digital sustainability platforms shape energy-efficiency behaviours among users. Building on data from semi-structured interviews and a two-hour co-creation workshop with 25 energy experts in the ECO2 project, we first identify three user representations of relevance to such platforms: energy-unaware, living in denial, and energy-aware and active. Our findings suggest that ‘static’ user representations (based on user demographics and average consumption) are giving way to socio-cognitive representations that follow users’ journeys in energy efficiency. We then develop a set of design principles to promote sustainable energy behaviours through digital sustainability platforms across user-owned, social/external, brand-owned, and partner-owned touchpoints. An analysis of user feedback from the ECO2 project shows support for our design principles across users’ journeys. Of 62 respondents covering all three representations, 76% of them intended to “implement changes in terms of energy consumption and energy efficiency”.

Recent advancements in digital technologies, including artificial intelligence (AI), Internet of Things (IoT), and information and communication technologies (ICT), are transforming homes into interconnected ecosystem of services. Yet, discourse on home technologies remains fragmented due to inconsistent terminologies. This paper addresses the lack of a framework, studying distinctions between smart and non-smart homes and forecasting connectivity and automation growth. Experts (21) participated in online surveys and interviews in 2021, exploring language, structure, and technical/social aspects of basic and smarter homes. Quantitative survey data and qualitative interview analyses yield insights on defining smarter homes, barriers to adoption, and framework improvements to establish universal definitions. This study underscores the urgency of harmonizing language and concepts in the domain of smart homes, revealing user understanding gaps and usability issues as barriers. This bridges gaps for consumer engagement and tech adoption.

As part of the P2P sharing economy, cryptocurrencies offer both creative and criminal opportunities. To deal with offenders, solutions such as legislation and regulation are proposed. However, these are foreign to the P2P spirit of trusted interactions and transactions. This paper aims to identify solutions that align with P2P technologies and relationships to combat the criminal use of cryptocurrencies. In line with our research question, we adopt the method of grounded theory. Based on 45 interviews on 1,500 h of podcasts, blogs, and TV shows, we observed how experts in finance, technology, and cryptocurrency analyzed the hazards, as well as, the solutions for cryptocurrencies schemes. The results indicate that this new technology has also engendered new types of criminal schemes; thus, we can categorize malicious behaviors into conventional and P2P hazards. In addition, experts also point to conventional and P2P solutions to crypto-crimes at the individual, organizational, communal, and national levels. As such, they underscore the discrepancies between those who push for solutions favoring conventional regulatory forces versus those advocating for normative legitimacy, hence pulling the industry to preserve the P2P identity. Following institutional theory and the need for legitimacy in this new and disruptive industry, we discuss the tension between agendas and suggest unorthodox solutions for an innovative yet troubled technology.

We introduce an advanced method for validating data quality, which is crucial for ensuring reliable analytics insights. Traditional data quality validation relies on data unit tests, which use global metrics to determine if data quality falls within expected ranges. Unfortunately, these existing approaches suffer from two limitations. Firstly, they offer only coarse-grained assessments, missing fine-grained errors. Secondly, they fail to pinpoint the specific data causing test failures. To address these issues, we propose a novel approach using conditional metrics, enabling more detailed analysis than global metrics. Our method involves two stages: unit test discovery and monitoring/error identification. In the discovery phase, we derive conditional metric-based unit tests from historical data, focusing on stability to select appropriate metrics. The monitoring phase involves using these tests for new data batches, with conditional metrics helping us identify potential errors. We validate the effectiveness of this approach using two datasets and seven synthetic error scenarios, showing significant improvements over global metrics and promising results in fine-grained error detection for data ingestion validation.

Artificial intelligence (AI) technologies have become the key driver of innovation in society. However, numerous vulnerabilities of AI systems can lead to negative consequences for society, such as biases encoded in the training data and algorithms and lack of transparency. This calls for AI systems to be audited to ensure that the impact on society is understood and mitigated. To enable AI audits, auditability measures need to be implemented. This study provides a systematic review of academic work and regulatory work on AI audits and AI auditability. Results reveal the current understanding of the AI audit scope, audit challenges, and auditability measures. We identify and categorize AI auditability measures for each audit area and specific process to be audited and the party responsible for each process to be audited. Our findings will guide existing efforts to make AI systems auditable across the lifecycle of AI systems.