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arXiv 2024年

作者： Wang, Yan Obie, Humphrey O. Li, Zhuying Salim, Flora D. Grundy, John Mueller, Florian Floyd Exertion Game Lab Department of Human-Centred Computing Monash University MelbourneVIC Australia Department of Computing Technologies Swinburne University of Technology HumaniSE Lab Department of Software Systems and Cybersecurity Monash University MelbourneVIC Australia School of Computer Science and Engineering Southeast University Jiangsu Nanjing China Sydney SydneyNSW Australia HumaniSE Lab Department of Software Systems and Cybersecurity Monash University MelbourneVIC Australia Exertion Games Lab Department of Human-Centred Computing Monash University MelbourneVIC Australia

The pleasure that often comes with eating can be further enhanced with intelligent technology, as the field of human-food interaction suggests. However, knowledge on how to design such pleasure-supporting eating systems is limited. To begin filling this knowledge gap, we designed "GustosonicSense", a novel gustosonic eating system that utilizes wireless earbuds for sensing different eating and drinking actions with a machine learning algorithm and trigger playful sounds as a way to facilitate pleasurable eating experiences. We present the findings from our design and a study that revealed how we can support the "stimulation", "hedonism", and "reflexivity" for playful human-food interactions. Ultimately, with our work, we aim to support interaction designers in facilitating playful experiences with food. © 2024, CC BY.

关键词： Learning algorithms

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Quantum Reinforcement Learning with Quantum World Model

Quantum Reinforcement Learning with Quantum World Model

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IEEE Conference on Global Communications (GLOBECOM)

作者： Peigen Zeng Ying He F. Richard Yu Victor C.M. Leung Shenzhen Key Lab of Digital & Intelligent Technologies & Systems College of Computer Science and Software Engineering Shenzhen University China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China

Quantum reinforcement learning (QRL) can outperform classical reinforcement learning (RL) by utilizing quantum parallel theory and quantum phenomena such as superposition and entanglement. Although some excellent work has been done on QRL, most existing works either fail to show the exponential advantage of quantum computation over classical computation in terms of performance or are too demanding on quantum devices. In this paper, we provide a novel perspective on combining quantum computing and RL with faster convergence speed and relatively relaxed demands on quantum devices. Specifically, we propose a method to construct a world model with quantum circuit that allows it to interact in a quantum way. In addition, we use Grover's algorithm to efficiently extract high-value information from the quantum world model. Extensive simulation results show that the proposed method can have superior performance compared to classical RL algorithms.

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TPTO: A Transformer-PPO based Task Offloading Solution for Edge Computing Environments

TPTO: A Transformer-PPO based Task Offloading Solution for E...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Niloofar Gholipour Marcos Dias de Assuncao Pranav Agarwal Julien Gascon-Samson Rajkumar Buyya Dept. of Software Engineering and IT École de Technologie Supérieure Univ. of Quebec Montreal Canada CLOUDS Lab School of Computing and Information Systems The Univ. of Melbourne Australia

Emerging applications in healthcare, autonomous vehicles, and wearable assistance require interactive and low-latency data analysis services. Unfortunately, cloud-centric architectures cannot fulfill the low-latency demands of these applications, as user devices are often distant from cloud data centers. Edge computing aims to reduce the latency by enabling processing tasks to be offloaded to resources located at the network’s edge. However, determining which tasks must be offloaded to edge servers to reduce the latency of application requests is not trivial, especially if the tasks present dependencies. This paper proposes a Deep Reinforcement Learning (DRL) approach called TPTO, which leverages Transformer Networks and Proximal Policy Optimization (PPO) to offload dependent tasks of IoT applications in edge computing. We consider users with various preferences, where devices can offload computation to an edge server via wireless channels. Performance evaluation results demonstrate that under fat application graphs, TPTO is more effective than state-of-the-art methods, such as Greedy, HEFT, and MRLCO, by reducing latency by 30.24%, 29.61%, and 12.41%, respectively. In addition, TPTO presents a training time approximately 2.5 times faster than an existing DRL approach.

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Task Offloading and Resource Allocation for SLAM Back-End Optimization: A Rewardless Active Inference Approach

Task Offloading and Resource Allocation for SLAM Back-End Op...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jingcheng Fang Ying He F. Richard Yu Jianqiang Li Victor C. Leung Shenzhen Key Lab of Digital & Intelligent Technologies & Systems College of Computer Science and Software Engineering Shenzhen University China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China

With the increasingly sophisticated algorithms of simultaneous localization and mapping (SLAM), it is difficult for mobile terminals with limited resources to exploit the performance of SLAM algorithms fully. Traditional deep reinforcement learning (DRL)-based approaches have offloaded SLAM tasks to servers. However, existing solutions suffer from low data efficiency and poor generalization problems. This paper proposes a novel approach based on recent advances in rewardless active inference for SLAM back-end optimization. Specifically, the reward function is replaced with simple rewardless guidance in active inference. In addition, instead of simply considering the SLAM task as a whole, we delve into the sub-tasks of back-end optimization of SLAM for offloading and resource allocation. Simulation results show the superior performance of the proposed scheme.

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TPTO: A Transformer-PPO based Task Offloading Solution for Edge Computing Environments

arXiv

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arXiv 2023年

作者： Gholipour, Niloofar de Assuncao, Marcos Dias Agarwal, Pranav Gascon-Samson, Julien Buyya, Rajkumar Dept. of Software Engineering and IT École de Technologie Supérieure Univ. of Quebec Montreal Canada CLOUDS Lab School of Computing and Information Systems The Univ. of Melbourne Australia

关键词： Reinforcement learning

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Large Language Models (LLMs) Inference Offloading and Resource Allocation in Cloud-Edge Networks: An Active Inference Approach

Large Language Models (LLMs) Inference Offloading and Resour...

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IEEE Conference on Vehicular Technology (VTC)

As the research and applications of large language model (LLM) become increasingly sophisticated, it is difficult for resource-limited mobile terminals to run large-model inference tasks efficiently. Traditional deep reinforcement learning (DRL) based approaches have been used to offload LLM inference tasks to servers. However, existing solutions suffer from data inefficiency, insensitivity to latency requirements, and non-adaptability to task load variations. In this paper, we propose an active inference with rewardless guidance algorithm using expected future free energy for offloading decisions and allocating resources for the LLM inference task offloading and resource allocation problem of cloud-edge networks systems. Experimental results show that our proposed method has superior performance over mainstream DRLs, improves in data utilization efficiency, and is more adaptable to changing task load scenarios.

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Resource Allocation for Cognitive Radio Inspired Non-Orthogonal Multiple Access Networks: A Quantum Soft Actor-Critic Method

Resource Allocation for Cognitive Radio Inspired Non-Orthogo...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Chao Huang Ying He Fei Yu Peigen Zeng Shenzhen Key Lab of Digital & Intelligent Technologies & Systems College of Computer Science and Software Engineering Shenzhen University China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China

With the growth of the communication industry, the demand for spectrum resources has been increasing steadily. However, the spectrum resources are limited and the utilization rate is low. In this case, Cognitive Radio (CR) technologies and Non-Orthogonal Multiple Access (NOMA) technology are proposed to improve the number of user access and spectrum resource utilization. In this paper, we consider a CR-inspired NOMA network and propose a power allocation problem based on a time-varying system. Due to the dynamic nature of CR, traditional methods are often faced with problems such as low training efficiency and high network volatility. To address the problem, we propose a novel Quantum Reinforcement Learning (QRL) algorithm with quantum world model, interacting with the environment in the quantum way. Specifically, we construct a quantum soft actor-critic algorithm using variable quantum circuit (VQC), and the experience data is encoded into a quantum sum tree and transformed into quantum world model. Simulation results show the superior performance of the proposed framework.

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Zero-shot stance detection based on cross-domain feature enhancement by contrastive learning

arXiv

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arXiv 2022年

作者： Zhao, Xuechen Zou, Jiaying Zhang, Zhong Xie, Feng Zhou, Bin Tian, Lei School of Computer National University of Defense Technology Changsha China Key Lab. of Software Engineering for Complex Systems Changsha China

Zero-shot stance detection is challenging because it requires detecting the stance of previously unseen targets in the inference phase. The ability to learn transferable target-invariant features is critical for zero-shot stance detection. In this work, we propose a stance detection approach that can efficiently adapt to unseen targets, the core of which is to capture target-invariant syntactic expression patterns as transferable knowledge. Specifically, we first augment the data by masking the topic words of sentences, and then feed the augmented data to an unsupervised contrastive learning module to capture transferable features. Then, to fit a specific target, we encode the raw texts as target-specific features. Finally, we adopt an attention mechanism, which combines syntactic expression patterns with target-specific features to obtain enhanced features for predicting previously unseen targets. Experiments demonstrate that our model outperforms competitive baselines on four benchmark datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Zero-shot learning

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N-Gram-Based Machine Learning Approach for Bot or Human Detection from Text Messages 22

N-Gram-Based Machine Learning Approach for Bot or Human Dete...

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6th International Conference on Intelligent systems, Metaheuristics and Swarm Intelligence, ISMSI 2022

作者： Kavadi, Durga Prasad Sanaboina, Chandra Sekhar Patan, Rizwan Gandomi, Amir Department Of Information Technology B V Raju Institute Of Technology Narsapur Telangana India Department Of Computer Science And Engineering University College Of Engineering Kakinada Jawaharlal Nehru Technological University Kakinada India Decentralized Science Lab College Of Computing And Software Engineering Kennesaw State University MariettaGA30060 United States Faculty Of Engineering And Information Systems University Of Technology Sydney UltimoNSW Australia

ISBN: (纸本)9781450396288

Social bots are computer programs created for automating general human activities like the generation of messages. The rise of bots in social network platforms has led to malicious activities such as content pollution like spammers or malware dissemination of misinformation. Most of the researchers focused on detecting bot accounts in social media platforms to avoid the damages done to the opinions of users. In this work, n-gram based approach is proposed for a bot or human detection. The content-based features of character n-grams and word n-grams are used. The character and word n-grams are successfully proved in various authorship analysis tasks to improve accuracy. A huge number of n-grams is identified after applying different pre-processing techniques. The high dimensionality of features is reduced by using a feature selection technique of the Relevant Discrimination Criterion. The text is represented as vectors by using a reduced set of features. Different term weight measures are used in the experiment to compute the weight of n-grams features in the document vector representation. Two classification algorithms, Support Vector Machine, and Random Forest are used to train the model using document vectors. The proposed approach was applied to the dataset provided in PAN 2019 competition bot detection task. The Random Forest classifier obtained the best accuracy of 0.9456 for bot/human detection. © 2022 ACM.

关键词： Feature Selection

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Maturity Evaluation of Domain-Specific Language Ecosystems for Cyber-Physical Production systems

Maturity Evaluation of Domain-Specific Language Ecosystems f...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Sandra Greiner Bianca Wiesmayr Kevin Feichtinger Kristof Meixner Marco Konersmann Jérôme Pfeiffer Michael Oberlehner David Schmalzing Andreas Wortmann Bernhard Rumpe Rick Rabiser Alois Zoitl Software Engineering Group University of Bern Switzerland LIT CPS Lab Johannes Kepler University Linz Austria CDL SQI Institute of Information Systems Engineering Technische Universität Wien Austria Software Engineering RWTH Aachen University Germany Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Germany CDL VaSiCS LIT CPS Lab Johannes Kepler University Linz Austria

engineering Cyber-Physical Production systems (CPPSs) heavily relies on Domain-Specific Languages (DSLs), which are tailored to a specific class of problems inherent to CPPSs. DSLs enable non-programming experts to solve problems in their domain, such as modeling production processes, implementing control software, or managing the variability of production systems. A DSL ecosystem encompasses the entire infrastructure (e.g., libraries and tools) built around its language and contributes to the successful and easy adoption thereof by domain experts. We present a maturity evaluation model for DSL ecosystems serving two aims: to developers, it reveals missing but essential aspects of the ecosystem, and users (e.g., industrial companies) can evaluate the maturity of a DSL ecosystem. We propose criteria to evaluate the maturity of DSL ecosystems and apply them to existing, publicly available DSLs which have been adopted in CPPSs. The results demonstrate that all components of the model are covered and they allow for deriving hypotheses about DSL ecosystems used in CPPSs.

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