Design of a Novel Information System for Semi-automated Management of Cybersecurity in Industrial Control Systems

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Ameri, Kimia; Hempel, Michael; Sharif, Hamid; Lopez, Juan; Perumalla, Kalyan

Abstract: There is an urgent need in many critical infrastructure sectors, including the e ... Expand

Abstract: There is an urgent need in many critical infrastructure sectors, including the energy sector, for attaining detailed insights into cybersecurity features and compliance with cybersecurity requirements related to their Operational Technology (OT) deployments. Frequent feature changes of OT devices interfere with this need, posing a great risk to customers. One effective way to address this challenge is via a semi-automated cyber-physical security assurance approach, which enables verification and validation of the OT device cybersecurity claims against actual capabilities, both pre- and post-deployment. To realize this approach, this article presents new methodology and algorithms to automatically identify cybersecurity-related claims expressed in natural language form in ICS device documents. We developed an identification process that employs natural language processing (NLP) techniques with the goal of semi-automated vetting of detected claims against their device implementation. We also present our novel NLP components for verifying feature claims against relevant cybersecurity requirements. The verification pipeline includes components such as automated vendor identification, device document curation, feature claim identification utilizing sentiment analysis for conflict resolution, and reporting of features that are claimed to be supported or indicated as unsupported. Our novel matching engine represents the first automated information system available in the cybersecurity domain that directly aids the generation of ICS compliance reports. Collapse

Topics: IT security database system website Python electronic authentication

Methods: computational algorithm sentiment analysis natural language processing BERT language model

The Core Industry Manufacturing Process of Electronics Assembly Based on Smart Manufacturing

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Chen, Rongli; Chen, Xiaozhong; Wang, Lei; Li, Jianxin

Abstract: This research takes a case study approach to show the development of a diverse a ... Expand

Abstract: This research takes a case study approach to show the development of a diverse adoption and product strategy distinct from the core manufacturing industry process. It explains the development status in all aspects of smart manufacturing, via the example of ceramic circuit board manufacturing and electronic assembly, and outlines future smart manufacturing plans and processes. The research proposed two experiments using artificial intelligence and deep learning to demonstrate the problems and solutions regarding methods in manufacturing and factory facilities, respectively. In the first experiment, a Bayesian network inference is used to find the cause of the problem of metal residues between electronic circuits through key process and quality correlations. In the second experiment, a convolutional neural network is used to identify false defects that were overinspected during automatic optical inspection. This improves the manufacturing process by enhancing the yield rate and reducing cost. The contributions of the study built in circuit board production. Smart manufacturing, with the application of a Bayesian network to an Internet of Things setup, has addressed the problem of residue and redundant conductors on the edge of the ceramic circuit board pattern, and has improved and prevented leakage and high-frequency interference. The convolutional neural network and deep learning were used to improve the accuracy of the automatic optical inspection system, reduce the current manual review ratio, save labor costs, and provide defect classification as a reference for preprocess improvement. Collapse

Topics: artificial intelligence smart manufacturing production management Python internet of things

Methods: experiment deep learning case study convolutional neural network Bayesian network

Introduction to the Special Issue on Smart Systems for Industry 4.0 and IoT

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Chen, Mu-Yen; Thuraisingham, Bhavani; Egrioglu, Erol; Rubio, Jose De Jesus

Abstract: INTRODUCTIONThe development of big data applications is driving the dramatic gr ... Expand

Abstract: INTRODUCTIONThe development of big data applications is driving the dramatic growth of hybrid data, often in the form of complex sets of cross-media content including text, images, videos, audios, and time series. Tremendous volumes of these heterogeneous data are derived from multiple IoT sources and present new challenges for the design, development, and implementation of effective information systems and decision support frameworks to meet heterogeneous computing requirements. Emerging technologies allow for the near real-time extraction and analysis of heterogeneous data to find meaningful information.Machine-learning algorithms allow computers to learn automatically, analyzing existing data to establish rules to predict outcomes of unknown data. However, traditional machine learning approaches do not meet the needs for Internet of Things (IoT) applications, calling for new technologies. Deep learning is a good example of emerging technologies that tackle the limitations of traditional machine learning through feature engineering, providing superior performance in highly complex applications. However, these technologies also raise new security and privacy concerns. Technology adoption and trust issues are of timely importance as well.Industrial operations are in the midst of rapid transformations, sometimes referred to as Industry 4.0, Industrial Internet of Things (IIoT), or smart manufacturing. These transformations are bringing fundamental changes to factories and workplaces, making them safer and more efficient, flexible, and environmentally friendly. Machines are evolving to have increased autonomy, and new human-machine interfaces such as smart tools, augmented reality, and touchless interfaces are making interaction more natural. Machines are also becoming increasingly interconnected within individual factories as well as to the outside world through cloud computing, enabling many opportunities for operational efficiency and flexibility in manufacturing and maintenance.An increasing number of countries have put forth national advanced manufacturing development strategies, such as Germany's Industry 4.0, the United States' Industrial Internet and manufacturing system based on CPS (Cyber-Physical Systems), and China's Internet Plus Manufacturing and Made in China 2025 initiatives. Smart Manufacturing aims to maximize transparency and access of all manufacturing process information across entire manufacturing supply chains and product lifecycles, with the Internet of Things (IoT) as a centerpiece to increase productivity and output value. This manufacturing revolution depends on technology connectivity and the contextualization of data, thus putting intelligent systems support and data science at the center of these developments. ACM Reference format: Collapse

Topics: production management computer data storage smart manufacturing differential privacy privacy

Methods: computational algorithm convolutional neural network machine learning

Evaluating Effects of the Payment Ecosystem on Central Bank Digital Currency Adoption and Design

2023 | HICSS | Citations: 0

Authors: Digiammaria, Chiara; Omarini, Anna; Kauffman, Robert; Kim, Kwansoo

Abstract: CBDC has become a discussion topic with worldwide economic and societal relevanc ... Expand

Abstract: CBDC has become a discussion topic with worldwide economic and societal relevance. Payment system efficiency is a top driver for digital currency adoption. We examine literature gaps for payment ecosystem impact on CBDC adoption and design, and conduct test for 65 World Bank countries. We measure payment alternatives and preferences impacts and estimate least squares and limited dependent variables regressions. CBDC adoption is driven by payment ecosystem, alternative characteristics, e-money usage, perceived safety, and infrastructure. This signals that user familiarity with digital payment methods, trust in authorities, and structural capabilities support CBDC adoption. Adoption is inversely related to alternatives that dampen the need for CBDC. Transaction volume and trust in authorities are linked to choosing a central database, not a digital ledger technology (DLT) infrastructure. Deviations occur from ambiguous effects of cash and DLT usage on CBDC adoption and industry concentration, architecture, and linkages. Collapse

Topics: analytical information system digital innovation

Methods: robustness check deep learning propensity score matching convolutional neural network causality analysis

AI-Assisted Diagnosis of Bone Tuberculosis: A Design Science Research Approach

2023 | International Conference on Information Systems | Citations: 0

Authors: Ding, Wenwen; Hoehle, Hartmut

Abstract: Bone Tuberculosis (TB) is a significant public health challenge requiring early ... Expand

Abstract: Bone Tuberculosis (TB) is a significant public health challenge requiring early and precise diagnosis for effective treatment. Traditional methods like radiography and biopsy are invasive and costly. Our study introduces a holistic AI-assisted orthopedic clinical diagnosis system developed through an Action Design Research approach. Unlike previous efforts focused solely on algorithmic design, our system is iteratively validated with real-world clinical data, ensuring both theoretical rigor and practical applicability. By fine-tuning AI algorithms to meet actual clinical needs, we bridge the gap between technological innovation and healthcare relevance. Our research offers innovative insights into the design and evaluation of AI-assisted systems, emphasizing the role of empirical data and diverse evaluation metrics. The study is expected to have broader implications for the adoption of AI in clinical settings, offering a more comprehensive and reliable solution for bone TB diagnosis. Collapse

Topics: artificial intelligence healthcare data innovation management digital innovation

Methods: computational algorithm action design research machine learning magnetic resonance imaging longitudinal research

Exploring Personality-based Heterogeneity in Metaknowledge and Human-AI Collaboration

2023 | International Conference on Information Systems | Citations: 0

Authors: Gass, Daniel F

Abstract: While artificial intelligence (AI) has great potential to transform how we work ... Expand

Abstract: While artificial intelligence (AI) has great potential to transform how we work, it's clear that in many contexts, humans and AI will work side by side. According to recent research, humans' ability to capture complementarities while working collaboratively with AI is critically constrained by our limited ability to assess what we know, i.e., metaknowledge. However, extant research has largely overlooked a critical difference between human and AI: Human behavior is shaped by personality. I explore how differences arising from personality traits impact metaknowledge and delegation behavior towards AI. Using an image classification experiment, I find that higher levels of conscientiousness improve, while higher levels extraversion harm humans' ability to work collaboratively with AI. Differences in metaknowledge help to explain this relationship. These findings motivate further research on personality in human-AI collaboration and the design of collaborative systems which foster metaknowledge and improved performance. Collapse

Topics: artificial intelligence personality computer supported cooperative work database system human computer interaction

Methods: experimental group experiment survey linear regression analysis descriptive statistic

Theories: big five model

Explanation matters: An experimental study on explainable AI

2023 | Electronic Markets | Citations: 0

Authors: Hamm, Pascal; Klesel, Michael; Coberger, Patricia; Wittmann, H. Felix

Abstract: Explainable artificial intelligence (XAI) is an important advance in the field o ... Expand

Abstract: Explainable artificial intelligence (XAI) is an important advance in the field of machine learning to shed light on black box algorithms and thus a promising approach to improving artificial intelligence (AI) adoption. While previous literature has already addressed the technological benefits of XAI, there has been little research on XAI from the user’s perspective. Building upon the theory of trust, we propose a model that hypothesizes that post hoc explainability (using Shapley Additive Explanations) has a significant impact on use-related variables in this context. To test our model, we designed an experiment using a randomized controlled trial design where participants compare signatures and detect forged signatures. Surprisingly, our study shows that XAI only has a small but significant impact on perceived explainability. Nevertheless, we demonstrate that a high level of perceived explainability has a strong impact on important constructs including trust and perceived usefulness. A post hoc analysis shows that hedonic factors are significantly related to perceived explainability and require more attention in future research. We conclude with important directions for academia and for organizations. Collapse

Topics: explainable artificial intelligence artificial intelligence perceived ease of use perceived usefulness digital dashboard

Methods: experimental group experiment parametric test experimental task ANOVA

Theories: information systems theory trust theory

What Do They Meme? Exploring the Role of Memes as Cultural Symbols of Online Communities

2023 | International Conference on Information Systems | Citations: 0

Authors: Henn, Theresa; Posegga, Oliver

Abstract: Analyzing symbols shared within online communities (OCs) is essential to better ... Expand

Abstract: Analyzing symbols shared within online communities (OCs) is essential to better understand communities' expressed cultures. To evaluate how OCs differ in their expressed culture and analyze the effects of community rules (CR) and moderation policies (MP), we examined meme sharing of subreddit and interaction communities on Reddit. To detect memes shared within subreddits automatically, we trained a convolutional neural network and applied a feature-matching algorithm to create meme networks with components consisting of visually similar memes. Based on each community's component composition, we created community-specific meme languages that we compared across subreddit and interaction communities. Our results show that memes can be aggregated to characteristic meme languages linked to individual OCs; yet MP and CR do not impact the homogeneity of shared memes. Based on these findings, we plan to analyze dynamically the relationship between memes and OCs, examining memes' textual content and diving deeper into users' individual meme languages. Collapse

Topics: Reddit internet technology social media

Methods: computational algorithm nonparametric test Mann Whitney U test convolutional neural network Keras

Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability

2023 | International Journal of Information Management | Citations: 0

Authors: Herm, Lukas-Valentin; Heinrich, Kai; Wanner, Jonas; Janiesch, Christian

Abstract: Machine learning algorithms enable advanced decision making in contemporary inte ... Expand

Abstract: Machine learning algorithms enable advanced decision making in contemporary intelligent systems. Research indicates that there is a tradeoff between their model performance and explainability. Machine learning models with higher performance are often based on more complex algorithms and therefore lack explainability and vice versa. However, there is little to no empirical evidence of this tradeoff from an end user perspective. We aim to provide empirical evidence by conducting two user experiments. Using two distinct datasets, we first measure the tradeoff for five common classes of machine learning algorithms. Second, we address the problem of end user perceptions of explainable artificial intelligence augmentations aimed at increasing the understanding of the decision logic of high-performing complex models. Our results diverge from the widespread assumption of a tradeoff curve and indicate that the tradeoff between model performance and explainability is much less gradual in the end user’s perception. This is a stark contrast to assumed inherent model interpretability. Further, we found the tradeoff to be situational for example due to data complexity. Results of our second experiment show that while explainable artificial intelligence augmentations can be used to increase explainability, the type of explanation plays an essential role in end user perception. Collapse

Topics: explainable artificial intelligence artificial intelligence task productivity data quality decision making

Methods: computational algorithm deep learning survey experiment random forest classification

IMPACT OF EXPLAINABLE AI ON COGNITIVE LOAD: INSIGHTS FROM AN EMPIRICAL STUDY

2023 | European Conference On Information Systems | Citations: 0

Authors: Herm, Lukas-Valentin

Abstract: While the emerging research field of explainable artificial intelligence (XAI) ... Expand

Abstract: While the emerging research field of explainable artificial intelligence (XAI) claims to address the lack of explainability in high-performance machine learning models, in practice, XAI targets developers rather than actual end-users. Unsurprisingly, end-users are often unwilling to use XAI-based decision support systems. Similarly, there is limited interdisciplinary research on end-users' behavior during XAI explanations usage, rendering it unknown how explanations may impact cognitive load and further affect end-user performance. Therefore, we conducted an empirical study with 271 prospective physicians, measuring their cognitive load, task performance, and task time for distinct implementationindependent XAI explanation types using a COVID-19 use case. We found that these explanation types strongly influence end-users' cognitive load, task performance, and task time. Further, we contextualized a mental efficiency metric, ranking local XAI explanation types best, to provide recommendations for future applications and implications for sociotechnical XAI research. Collapse

Topics: explainable artificial intelligence task productivity artificial intelligence decision support system shapley additive explanation

Methods: descriptive statistic statistical hypothesis test literature study machine learning survey

Theories: theory of abduction