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Towards new realities: Implications of personalized online layers in our daily lives

i-com 2024年第2期23卷 221-229页

作者： Herder, Eelco Stojko, Laura Strecker, Jannis Neumayr, Thomas Yigitbas, Enes Augstein, Mirjam Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Department of Computer Science University of the Bundeswehr Munich Neubiberg Germany Institute of Computer Science University of St. Gallen St. Gallen Switzerland Research Group Personalized Environments and Collaborative Systems University of Applied Sciences Upper Austria Hagenberg Austria Faculty of Electrical Engineering Computer Science and Mathematics Paderborn University Paderborn Germany

We are currently in a period of upheaval, as many new technologies are emerging that open up new possibilities to shape our everyday lives. Particularly, within the field of Personalized Human-Computer Interaction we observe high potential, but also challenges. In this article, we explore how an increasing amount of online services and tools not only further facilitates our lives, but also shapes our lives and how we perceive our environments. For this purpose, we adopt the metaphor of personalized 'online layers' and show how these layers are and will be interwoven with the lives that we live in the 'human layer' of the real world. © 2024 the author(s), published by De Gruyter, Berlin/Boston.

关键词： Human computer interaction

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Trustworthy deep learning for encrypted traffic classification

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Soft computing 2025年第2期29卷 645-662页

作者： Li, Zheng Liu, Yanbei Zhang, Changqing Shan, Wanjin Zhang, Haifeng Zhu, Xiaoming School of Electronic and Information Engineering Tiangong University Tianjin300387 China School of Life Sciences Tiangong University Tianjin300387 China Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems Tianjin300387 China College of Intelligence and Computing Tianjin University Tianjin300072 China Science and Technology on Communication Networks Laboratory Shijiazhuang050081 China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang050081 China

Network traffic classification refers to the identification of collected network traffic data of various applications, which is widely used in research fields such as network resource allocation, traffic scheduling and intrusion detection systems. With the widespread application of encryption technology in the network, encrypted traffic classification has become a hot research topic. At present, most existing methods only focus on the accuracy of network traffic classification. Yet, few work studies the reliability of the classification model, which plays an important role in network regulation and network security. In this paper, we propose a novel traffic classification method based on trustworthy deep learning model, which can effectively improve the reliability of encrypted traffic classification models by correcting the confidence of model output. Specifically, we firstly perform data preprocessing on the original network traffic, and then adopt a ConvNet for feature learning and a ClassifyNet for traffic classification in the initial stage. At the same time, we utilize a trustworthy confidence criterion to design a ConfidNet trained according to the probability of the true class. The ConfidNet can provide a reliable confidence measure for the prediction of the classification model. Finally, we demonstrate the effectiveness of our framework through comprehensive experiments on two benchmark datasets ISCX VPN-nonVPN and USTC-TFC2016, and show that our method can improve the reliability of the classification model and has a good ability to identify misclassified samples compared with state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Network intrusion

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Automatic diagnosis of COVID-19 using a tailored transformer-like network 4

Automatic diagnosis of COVID-19 using a tailored transformer...

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2021 4th International Conference on Computer information Science and Application Technology, CISAT 2021

作者： Liu, Chengeng Yin, Qingshan Department of Software and Computing Systems The University of Melbourne MelbourneVIC Australia Shandong New Generation Information Industry Technology Research Institute Inspur Group Shandong Jinan China

The emergence of the novel coronavirus(COVID-19) has left disastrous effect on global health and individuals. Even though in most areas, the RT-PCR test used as the dominant approach for diagnosis of COVID-19 has shown good accuracy, the test requires equipment, personnel and it is time-consuming. researches have shown the effectiveness of X-ray images for predicting COVID-19. In this study, we applied a transformer-like deep-learning model on this problem with transfer learning technique, to diagnose X-ray images as COVID-19 or normal. The model outperformed the CNN SOTA. The model achieved a classification accuracy of 99.7% on the targeting dataset. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

关键词： COVID-19

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Architecture and Key Technologies of parallel Dispatching System for Railway Technical Operation Stations

Architecture and Key Technologies of Parallel Dispatching Sy...

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2023 International Annual Conference on Complex systems and Intelligent Science, CSIS-IAC 2023

作者： Xiong, Gang Yang, Donghu Xu, Wei Li, Runmei Chen, Shichao Song, Bing Dong, Xisong Zhu, Fenghua Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China Institute of Automation Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Signal and Communication Research Institute China Academy of Railway Science Corporation Limited Beijing100081 China The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100081 China Beijing Huairou Academy of Parallel Sensing Beijing101400 China Chinese Academy of Sciences Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Dongguan523808 China

ISBN: (纸本)9798350309003

A railroad technical operation station is an extensive and complex operation system and a variety of random disturbing factors further increase the complexity of its command and dispatching. To enhance the railroad technical operation stations' intelligence, and integration level, and enhance its dispatching management ability of complex operation plans, based on the ACP approach, this paper designs the parallel dispatching system (PDS) architecture, and describes its key technologies including the artificial dispatching system (ADS), computing experiments, parallel execution, etc. On the basis of the artificial dispatching system, computational experiments can optimize various types of technical operations and the overall operational process, to achieve parallel execution between the actual dispatching system and ADS. PDS can provide more efficient and intelligent command and dispatching solutions for railroad technical work stations, and promote the development of the railroad transportation industry in the direction of higher quality and efficiency. © 2023 IEEE.

关键词： Railroads

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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CL4KGE: A Curriculum Learning Method for Knowledge Graph Embedding

arXiv

引用

arXiv 2024年

作者： Liu, Yang Zhou, Chuan Zhang, Peng Cao, Yanan Liu, Yongchao Li, Zhao Chen, Hongyang Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Institute of Information Engineering Chinese Academy of Sciences Beijing China Ant Group Hangzhou China Research Center for Graph Computing Zhejiang Lab Hangzhou China

Knowledge graph embedding (KGE) constitutes a foundational task, directed towards learning representations for entities and relations within knowledge graphs (KGs), with the objective of crafting representations comprehensive enough to approximate the logical and symbolic interconnections among entities. In this paper, we define a metric Z-counts to measure the difficulty of training each triple () in KGs with theoretical analysis. Based on this metric, we propose CL4KGE, an efficient Curriculum Learning based training strategy for KGE. This method includes a difficulty measurer and a training scheduler that aids in the training of KGE models. Our approach possesses the flexibility to act as a plugin within a wide range of KGE models, with the added advantage of adaptability to the majority of KGs in existence. The proposed method has been evaluated on popular KGE models, and the results demonstrate that it enhances the state-of-the-art methods. The use of Z-counts as a metric has enabled the identification of challenging triples in KGs, which helps in devising effective training strategies. © 2024, CC BY.

关键词： Knowledge graph

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CDCONV: A Benchmark for Contradiction Detection in Chinese Conversations

CDCONV: A Benchmark for Contradiction Detection in Chinese C...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Zheng, Chujie Zhou, Jinfeng Zheng, Yinhe Peng, Libiao Guo, Zhen Wu, Wenquan Niu, Zheng-Yu Wu, Hua Huang, Minlie The CoAI Group Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems China Beijing National Research Center for Information Science and Technology DCST Tsinghua University Beijing100084 China College of Intelligence and Computing Tianjin University Tianjin China Lingxin AI Beijing100084 China Baidu Inc. China

Dialogue contradiction is a critical issue in open-domain dialogue systems. The contextualization nature of conversations makes dialogue contradiction detection rather challenging. In this work, we propose a benchmark for Contradiction Detection in Chinese Conversations, namely CDCONV. It contains 12K multi-turn conversations annotated with three typical contradiction categories: Intra-sentence Contradiction, Role Confusion, and History Contradiction. To efficiently construct the CDCONV conversations, we devise a series of methods for automatic conversation generation, which simulate common user behaviors that trigger chatbots to make contradictions. We conduct careful manual quality screening of the constructed conversations and show that state-of-the-art Chinese chatbots can be easily goaded into making contradictions. Experiments on CDCONV show that properly modeling contextual information is critical for dialogue contradiction detection, but there are still unresolved challenges that require future research. © 2022 Association for Computational Linguistics.

关键词： Behavioral research

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PreMatch: A Prediction-based Scheduler for Dataflow Asynchronous Processing on the computing Continuum 25

PreMatch: A Prediction-based Scheduler for Dataflow Asynchro...

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Proceedings of the 40th ACM/SIGAPP Symposium on Applied computing

作者： Narges Mehran Zahra Najafabadi Samani Samira Afzal Radu Prodan Frank Pallas Peter Dorfinger Intelligent Connectivity Group Salzburg Research Forschungsgesellschaft mbH Salzburg Salzburg Austria Faculty of Digital and Analytical Sciences Paris Lodron University of Salzburg Salzburg Salzburg Austria Distributed and Parallel Systems Group University of Innsbruck Innsbruck Tyrol Austria Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Overijssel Netherlands Institute of Information Technology Alpen-Adria-University of Klagenfurt Klagenfurt Carinthia Austria

ISBN: (纸本)9798400706295

The popularity of asynchronous data exchange patterns has recently increased, as evidenced by 23% of the communication between microservices in an Alibaba trace analysis. Such workloads necessitate methods for reducing dataflow processing and completion time by forecasting the future requirements of their microservices and (re-)scheduling them. Therefore, we investigate a prediction-based scheduling method of asynchronous dataflow processing applications by considering the stochastic changes due to dynamic user requirements. We present a microservice scaling and scheduling method named PreMatch combining a machine learning (ML) prediction strategy based on gradient boosting with ranking and game theory matching scheduling principles. Firstly, PreMatch predicts the number of microservice replicas, and then, the ranking method orders the microservice replicas and devices based on microservice and transmission times. Thereafter, the PreMatch schedules microservice replicas requiring dataflow processing on computing devices. Experimental analysis of the PreMatch method shows lower completion times on average 13% compared to a related prediction-based scheduling method.

关键词： microservice

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TAROT: Spatio-Temporal Function Placement for Serverless Smart City Applications

TAROT: Spatio-Temporal Function Placement for Serverless Sma...

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IEEE International Conference on Utility and Cloud computing (UCC)

作者： Vincenzo De Maio David Bermbach Ivona Brandic Institute of Information Systems Engineering Vienna University of Technology Vienna Austria TU Berlin & Einstein Center Digital Future Mobile Cloud Computing Research Group Berlin Germany

Emerging smart city applications (i.e., traffic management, smart tourism) have to (i) process data coming from different IoT devices and (ii) deliver results of data processing to various user devices (e.g., smart vehicles or smartphone) while considering applications’ latency constraints. Serverless edge computing has proven to be very effective for latency-aware processing of IoT data, since it allows application developers to define data processing logic in terms of functions which react to data events. However, data processing functions should be dynamically placed and migrated while considering IoT data sources’ location and user devices’ mobility to minimize end-to-end latency. Unfortunately, current serverless computing solutions do not support mobility-aware placement of functions. In this paper, we propose dynamic function placement based on user devices’ mobility to address latency requirements of smart city applications. We consider serverless smart city applications, since this computational model allows to model application as a function execution in response to specific events, which makes it suitable for event-driven applications typical of smart city and IoT. First, we identify the parameters affecting end-to-end latency of serverless smart cities’ applications. Then, based on our findings, we design TAROT, a latency-aware function placement method based on data-driven mobility predictions. Results show improvements up to 46% for average end-to-end latency in comparison to state-of-the-art solutions.

关键词： Smart cities Computational modeling Serverless computing Data processing Internet of Things Vehicle dynamics Edge computing

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Facilitating Multi-turn Emotional Support Conversation with Positive Emotion Elicitation: A Reinforcement Learning Approach

arXiv

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arXiv 2023年

作者： Zhou, Jinfeng Chen, Zhuang Wang, Bo Huang, Minlie The CoAI Group DCST Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China College of Intelligence and Computing Tianjin University Tianjin China

Emotional support conversation (ESC) aims to provide emotional support (ES) to improve one’s mental state. Existing works stay at fitting grounded responses and responding strategies (e.g., question), which ignore the effect on ES and lack explicit goals to guide emotional positive transition. To this end, we introduce a new paradigm to formalize multi-turn ESC as a process of positive emotion elicitation. Addressing this task requires finely adjusting the elicitation intensity in ES as the conversation progresses while maintaining conversational goals like coherence. In this paper, we propose SUPPORTER, a mixture-of-expert-based reinforcement learning model, and well design ES and dialogue coherence rewards to guide policy’s learning for responding. Experiments verify the superiority of SUPPORTER in achieving positive emotion elicitation during responding while maintaining conversational goals including coherence. © 2023, CC BY.

关键词： Reinforcement learning

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