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This paper proposes a novel framework – “Transparent Reasoning in Artificial intelligence Cause Explanation” (TRACE) – that combines root cause analysis, explainable artificial intelligence, and machine learning in an understandable way for the worker. The goal is to enhance transparency, interpretability, and explainability in AI-driven decision-making processes as well as to increase the acceptance of AI within an industrial manufacturing area. The paper outlines the need of such a framework, describes the design process, and shows a preliminary mockup, a possible underlying software architecture as well as an evaluation and integration plan in an industrial environment.
The maintenance of special tools is an expensive business. Either manual inspection by an expert costs valuable resources, or the loss of a tool due to irreparable wear is associated with high replacement costs, while reconditioning requires only a fraction. In order to avoid higher costs and drive forward the automation process in production, a German gear manufacturer wants to create an automatic evaluation of skiving gears. As a sub-step of this automated condition detection, it is necessary for wheels to be automatically aligned within a vision-based inspection cell. In extension to a study conducted last year, further image preprocessing steps are implemented in this publication and a new alignment algorithm from the autoencoder family is evaluated. By using an additional synthetic dataset, previous limitations could be clarified. The results show that thorough data preparation is beneficial for all solution approaches and that neural networks can even beat a brute force algorithm.
This paper introduces an artificial intelligence (AI) interactive system featuring a self-growing memory network designed to enhance self-efficacy, reduce loneliness, and maintain social interaction among the elderly. The system dynamically analyzes and processes user-written diaries, generating empathic and personalized responses tailored to each individual. The system architecture includes an experience extraction model, a self-growing memory network that provides a contextual understanding of the user’s daily life, a chat agent, and a feedback loop that adaptively learns the user’s behavioral patterns and emotional states. By drawing on both successful and challenging experiences, the system crafts responses that reinforce the self-efficacy of the user, fostering a sense of accomplishment and engagement. This approach improves the psychological well-being of elderly users and promotes their mental health and overall quality of life through consistent interaction. To validate our proposed method, we developed a diary application to facilitate user interaction and collect diary entries. Over time, the system’s capacity to learn and adapt further refines the user experience, suggesting that AI-driven solutions hold significant potential for mitigating the effects of declining self-efficacy on mental health and social interactions. With the proposed system, we achieve an average system usability scale score of 77.3 (SD = 5.4) and a general self-efficacy scale score of 34.2 (SD = 3.5).
Following calls by international business scholars to pay attention to how multinational enterprises contribute to the Sustainable Development Goals, we make implementable recommendations for international B2B partnerships in Africa. We postulate that international businesses can alleviate extreme poverty and eradicate child labor by selecting ethical African suppliers. Furthermore, partners must exercise transparency in how they adopt clean and environmentally sound technologies. We also highlight that partners should demonstrate flexibility and open-mindedness to African indigenous know-how and sustainable practices to facilitate knowledge-sharing. International B2B partners may consider our recommendations to guide the building of ethical and responsible businesses in Africa.
This study explores the potential role of virtual influencers in future B2B marketing. Combining expert interviews and a literature review, the findings reveal the growing popularity of virtual influencers due to Web 3.0 and Metaverse advancements. The study suggests that virtual influencers may revolutionize brand-customer interactions in the digital space, with implications for B2B marketing strategies.
Downtimes (outages) are unfavorable and costly events in production. Although approaches exist, they have to be implemented mainly manually and with a huge effort. Language models could be useful to support the production root cause failure analysis and help to get production up and running again more quickly. However, sparse research focused on this point so far. Therefore, it is still unclear how the usage of language models for human assistance in production failure root case analysis should be implemented. A qualitative expert study was conducted to reveal the potentials of such an approach and to find suitable use cases for language models. Based on the insights triggering factors, use cases as well as benefits and risks were identified and summarized within a model.
Recent research has suggested that there is no general similarity measure, which can be applied on arbitrary databases without any parameterization. Hence, the optimal combination of similarity measures and parameters must be identified for each new image repository. This optimization loop is time consuming and depends on the experience of the designer as well as the knowledge of the medical expert. It would be useful if results that have been obtained for one data set can be transferred to another without extensive re-design. This transfer is vital if content-based image retrieval is integrated into complex environments such as picture archiving and communication systems. The image retrieval in medical applications (IRMA) project defines a framework that strictly separates data administration and application logic. This permits an efficient transfer of the data abstraction of one database on another without re-designing the software. In the ImageCLEF competition, the query performance was evaluated on the CasImage data set without optimization of the feature combination successfully applied to the IRMA corpus. IRMA only makes use of basic features obtained from grey-value representations of the images without additional textual annotations. The results indicate that transfer of parameterization is possible without time consuming parameter adaption and significant loss of retrieval quality.
This article analyzes the Korean innovation system and presents suggestions for its further improvement on the basis of three other country models, that of the U.S., Japan and Germany. We suggest that innovation systems are embedded in their respective managerial, economic, socio-political, and ultimately cultural context and that those contextual factors exert a significant influence on a national innovation system. Furthermore, we propose that the influence of those contextual factors result in innovation systems that are either more transformational or incremental in nature. We conclude that Korea has (like Japan) due to its societal context a particular strength in incremental innovations but will increasingly need to build up its ability to generate also transformational innovations (a particular strength of the US model). Due to distinct differences in the societal context, it might be difficult, however, for Korea to follow the U.S. model. We suggest, therefore, that Korea might find some valuable lessons in the German innovation system, particularly regarding small and medium enterprises.
The ImageCLEF 2006 medical automatic annotation task encompasses 11,000 images from 116 categories, compared to 57 categories for 10,000 images of the similar task in 2005. As a baseline for comparison, a run using the same classifiers with the identical parameterization as in 2005 is submitted. In addition, the parameterization of the classifier was optimized according to the 9,000/1,000 split of the 2006 training data. In particular, texture-based classifiers are combined in parallel with classifiers, which use spatial intensity information to model common variabilities among medical images. However, all individual classifiers are based on global features, i.e. one feature vector describes the entire image. The parameterization from 2005 yields an error rate of 21.7%, which ranks 13th among the 28 submissions. The optimized classifier yields 21.4% error rate (rank 12), which is insignificantly better.
Zwischen dem deutschen und dem chinesischen Managementstil und den jeweiligen beruflichen Umgangsformen gibt es eine Reihe teilweise gravierender Unterschiede. Da der chinesische Wirtschaftsraum für deutsche Unternehmen von großer und stetig wachsender Bedeutung ist, verwundert die hohe Nachfrage nach interkulturellen Trainings zu China nicht weiter.
