The TriRhenaTech alliance presents the accepted papers of the 'Upper-Rhine Artificial Intelligence Symposium' held on October 27th 2021 in Kaiserslautern, Germany. Topics of the conference are applications of Artificial Intellgence in life sciences, intelligent systems, industry 4.0, mobility and others. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, Offenburg and Trier, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.
When 5G began its commercialisation journey around 2020, the discussion on the vision of 6G also surfaced. Researchers expect 6G to have higher bandwidth, coverage, reliability, energy efficiency, lower latency, and, more importantly, an integrated "human-centric" network system powered by artificial intelligence (AI). Such a 6G network will lead to an excessive number of automated decisions made every second. These decisions can range widely, from network resource allocation to collision avoidance for self-driving cars. However, the risk of losing control over decision-making may increase due to high-speed data-intensive AI decision-making beyond designers and users' comprehension. The promising explainable AI (XAI) methods can mitigate such risks by enhancing the transparency of the black box AI decision-making process. This survey paper highlights the need for XAI towards the upcoming 6G age in every aspect, including 6G technologies (e.g., intelligent radio, zero-touch network management) and 6G use cases (e.g., industry 5.0). Moreover, we summarised the lessons learned from the recent attempts and outlined important research challenges in applying XAI for building 6G systems. This research aligns with goals 9, 11, 16, and 17 of the United Nations Sustainable Development Goals (UN-SDG), promoting innovation and building infrastructure, sustainable and inclusive human settlement, advancing justice and strong institutions, and fostering partnership at the global level.
Bommasani, Rishi, Hudson, Drew A., Adeli, Ehsan, Altman, Russ, Arora, Simran, von Arx, Sydney, Bernstein, Michael S., Bohg, Jeannette, Bosselut, Antoine, Brunskill, Emma, Brynjolfsson, Erik, Buch, Shyamal, Card, Dallas, Castellon, Rodrigo, Chatterji, Niladri, Chen, Annie, Creel, Kathleen, Davis, Jared Quincy, Demszky, Dora, Donahue, Chris, Doumbouya, Moussa, Durmus, Esin, Ermon, Stefano, Etchemendy, John, Ethayarajh, Kawin, Fei-Fei, Li, Finn, Chelsea, Gale, Trevor, Gillespie, Lauren, Goel, Karan, Goodman, Noah, Grossman, Shelby, Guha, Neel, Hashimoto, Tatsunori, Henderson, Peter, Hewitt, John, Ho, Daniel E., Hong, Jenny, Hsu, Kyle, Huang, Jing, Icard, Thomas, Jain, Saahil, Jurafsky, Dan, Kalluri, Pratyusha, Karamcheti, Siddharth, Keeling, Geoff, Khani, Fereshte, Khattab, Omar, Kohd, Pang Wei, Krass, Mark, Krishna, Ranjay, Kuditipudi, Rohith, Kumar, Ananya, Ladhak, Faisal, Lee, Mina, Lee, Tony, Leskovec, Jure, Levent, Isabelle, Li, Xiang Lisa, Li, Xuechen, Ma, Tengyu, Malik, Ali, Manning, Christopher D., Mirchandani, Suvir, Mitchell, Eric, Munyikwa, Zanele, Nair, Suraj, Narayan, Avanika, Narayanan, Deepak, Newman, Ben, Nie, Allen, Niebles, Juan Carlos, Nilforoshan, Hamed, Nyarko, Julian, Ogut, Giray, Orr, Laurel, Papadimitriou, Isabel, Park, Joon Sung, Piech, Chris, Portelance, Eva, Potts, Christopher, Raghunathan, Aditi, Reich, Rob, Ren, Hongyu, Rong, Frieda, Roohani, Yusuf, Ruiz, Camilo, Ryan, Jack, Ré, Christopher, Sadigh, Dorsa, Sagawa, Shiori, Santhanam, Keshav, Shih, Andy, Srinivasan, Krishnan, Tamkin, Alex, Taori, Rohan, Thomas, Armin W., Tramèr, Florian, Wang, Rose E., Wang, William, Wu, Bohan, Wu, Jiajun, Wu, Yuhuai, Xie, Sang Michael, Yasunaga, Michihiro, You, Jiaxuan, Zaharia, Matei, Zhang, Michael, Zhang, Tianyi, Zhang, Xikun, Zhang, Yuhui, Zheng, Lucia, Zhou, Kaitlyn, Liang, Percy
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.
Content delivery networks (CDNs) provide efficient content distribution over the Internet. CDNs improve the connectivity and efficiency of global communications, but their caching mechanisms may be breached by cyber-attackers. Among the security mechanisms, effective anomaly detection forms an important part of CDN security enhancement. In this work, we propose a multi-perspective unsupervised learning framework for anomaly detection in CDNs. In the proposed framework, a multi-perspective feature engineering approach, an optimized unsupervised anomaly detection model that utilizes an isolation forest and a Gaussian mixture model, and a multi-perspective validation method, are developed to detect abnormal behaviors in CDNs mainly from the client Internet Protocol (IP) and node perspectives, therefore to identify the denial of service (DoS) and cache pollution attack (CPA) patterns. Experimental results are presented based on the analytics of eight days of real-world CDN log data provided by a major CDN operator. Through experiments, the abnormal contents, compromised nodes, malicious IPs, as well as their corresponding attack types, are identified effectively by the proposed framework and validated by multiple cybersecurity experts. This shows the effectiveness of the proposed method when applied to real-world CDN data.