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5th IEEE International Conference on Cybernetics, CYBCONF 2021

作者： Ohsaki, Miho Sasaki, Hayato Kishimoto, Naoya Katagiri, Shigeru Ohnishi, Kei Sebastian, Yakub Then, Patrick Doshisha University Graduate School of Science and Engineering Kyotanabe Japan Doshisha University Department of Information Systems Design Kyotanabe Japan Charles Darwin University College of Engineering Information Technology and Environment Casuarina Australia Swinburne University of Technology Faculty of Engineering Computing and Science Sarawak Malaysia

ISBN: (纸本)9781665403207

It is desired in a variety of fields to identify which variables are dependent, and variable dependence measures have been studied. The majority of such measures detect a linear or a certain range of nonlinear dependence between paired variables. To go beyond them, a method based on Neural Network Regression, Group Lasso, and Information Aggregation has been proposed in our past study. It can detect a wide range of nonlinear dependences among multi variables and discover the sets and representatives of the detected dependences. Its fundamental effectiveness has already been examined using synthesized artificial datasets containing a single dependence. For further evaluation in the present study, we conducted an experiment using those containing multi dependences. The proposed method succeeded in discovering the sets and representatives, and its performance was robust to data size and noise rate. The experimental results suggested that the proposed method works well for difficult tasks to handle multi dependences. © 2021 IEEE.

关键词： Neural networks

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Green Federated Learning over Cloud-RAN with Limited Fronthaul and Quantized Neural Networks

Green Federated Learning over Cloud-RAN with Limited Frontha...

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Communications and Networking (MeditCom), IEEE International Mediterranean Conference on

作者： Jiali Wang Yijie Mao Ting Wang Yuanming Shi MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China School of Information Science and Technology (SIST) ShanghaiTech University China

In this paper, we investigate a green federated learning (FL) framework over cloud radio access network (Cloud-RAN) system that comprises a server, multiple devices and remote radio heads (RRHs). Each device utilizes quantized neural networks (QNNs) and sends quantized model parameters to the server to save energy consumption via RRHs. The server aggregates all the signals to update the global model parameters and broadcasts the updated parameters to the selected devices. In this context, we propose an energy consumption model for the QNN training and communication model over Cloud-RAN. We develop an energy minimization problem based on the proposed energy model. We jointly design fronthaul rate allocation, device transmit power, and precision level of QNNs while ensuring target accuracy, transmit power budget and limited fronthaul capacity. Guided by the convergence analysis, we adopt alternative optimization method to solve the energy minimization problem. The simulation outcomes demonstrate that the FL framework suggested can considerably diminish energy usage in comparison to other traditional methods. This has immense potential in realizing a sustainable and eco-friendly FL over Cloud-RAN.

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FinXABSA: Explainable Finance through Aspect-Based Sentiment Analysis

FinXABSA: Explainable Finance through Aspect-Based Sentiment...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Keane Ong Wihan van der Heever Ranjan Satapathy Erik Cambria Gianmarco Mengaldo Asian Institute of Digital Finance National University of Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore College of Design & Engineering National University of Singapore Singapore

This paper presents a novel approach for explain-ability in financial analysis by deriving financially-explainable statistical relationships through aspect-based sentiment analysis, Pearson correlation, Granger causality & uncertainty coefficient. The proposed methodology involves constructing an aspect list from financial literature and applying aspect-based sentiment analysis on social media text to compute sentiment scores for each aspect. Pearson correlation is then applied to uncover financially explainable relationships between aspect sentiment scores and stock prices. Findings for derived relationships are made robust by applying Granger causality to determine the forecasting ability of each aspect sentiment score for stock prices. Finally, an added layer of interpretability is added by evaluating uncertainty coefficient scores between aspect sentiment scores and stock prices. This allows us to determine the aspects whose sentiment scores are most statistically significant for stock prices. Relative to other methods, our approach provides a more informative and accurate understanding of the relationship between sentiment analysis and stock prices. Specifically, this methodology enables an interpretation of the statistical relationship between aspect-based sentiment scores and stock prices, which offers explainability to AI-driven financial decision-making.

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Study on Soft Robotic Pinniped Locomotion

Study on Soft Robotic Pinniped Locomotion

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Dimuthu D. K. Arachchige Tanmay Varshney Umer Huzaifa Iyad Kanj Thrishantha Nanayakkara Yue Chen Hunter B. Gilbert Isuru S. Godage School of Computing Jarvis College of Computing and Digital Media DePaul University Chicago IL USA College of Engineering Ohio State University Columbus OH USA Dyson School of Design Engineering Faculty of Engineering Imperial College London UK Department of Biomedical Engineering Georgia Institute of Technology Atlanta GA USA Department of Mechanical and Industrial Engineering Louisiana State University Baton Rouge LA USA Departments of Engineering Technology & Industrial Distribution J. Mike Walker '66 Mechanical Engineering (affiliated) and Multidisciplinary Engineering (affiliated) Texas A&M University College Station TX USA

Legged locomotion is a highly promising but under–researched subfield within the field of soft robotics. The compliant limbs of soft-limbed robots offer numerous benefits, including the ability to regulate impacts, tolerate falls, and navigate through tight spaces. These robots have the potential to be used for various applications, such as search and rescue, inspection, surveillance, and more. The state-of-the-art still faces many challenges, including limited degrees of freedom, a lack of diversity in gait trajectories, insufficient limb dexterity, and limited payload capabilities. To address these challenges, we develop a modular soft-limbed robot that can mimic the locomotion of pinnipeds. By using a modular design approach, we aim to create a robot that has improved degrees of freedom, gait trajectory diversity, limb dexterity, and payload capabilities. We derive a complete floating-base kinematic model of the proposed robot and use it to generate and experimentally validate a variety of locomotion gaits. Results show that the proposed robot is capable of replicating these gaits effectively. We compare the locomotion trajectories under different gait parameters against our modeling results to demonstrate the validity of our proposed gait models.

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Effective Chloramine Management Without "Burn" in Biofilm Affected Nitrifying Tanks Using a Low Dose of Copper

SSRN

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SSRN 2023年

作者： Sarker, Dipok Chandra Krishna, Bal K.C. Ginige, Maneesha P. Sathasivan, Arumugam Department of Civil and Construction Engineering Curtin University GPO Box U1987 PerthWA6845 Australia School of Engineering Design and Built Environment Western Sydney University Locked Bag 1797 PenrithNSW2751 Australia CSIRO Environment 147 Underwood Avenue FloreatWA6014 Australia School of Computing Engineering and Mathematics Australia

This paper highlights the potential to effectively inhibit nitrification and restore chloramine levels using a low copper concentration in a biofilm-affected (surface-to-volume ratio 16 m-1) continuous-flow laboratory-scale chloraminated system. High nitrite and low chloramine containing tanks are always recovered with chlorine "burn" by water utilities. The "burn" is not only costly and operationally complex, but also compromises the water quality, public health, and customer relations. A laboratory system comprising five reactors connected in series was operated. Each reactor simulated conditions typically encountered in full-scale systems. Low amount of copper (0.1-0.2 mg-Cu L-1) was dosed once per day into nitrified reactors. At any given time, only one reactor was dosed with copper. Not only inhibition of nitrification, chloramine decay associated with bulk water, biofilm and sediments also improved. However, the improvement was quicker and more significant when the influent to the reactor contained a high chloramine and a low nitrite concentration. Ammonia oxidising microbes exhibited resilience when exposed to low copper and chloramine concentrations for an extended period. Chloramine decay due to planktonic microbes and chemical reactions in bulk water decreased more rapidly than decay attributed to biofilm and sediments. The concept "biostable residual chlorine" explained how copper and chloramine can inhibit nitrification. Once nitrification was inhibited, the chloramine supplied from upstream effectively continued to suppress downstream nitrification, and this effect lasted more than 50 days even at 22 oC. The findings could be used to develop short-term copper dosing strategies and prevent negative impacts of nitrification and breakpoint chlorination. © 2023, The Authors. All rights reserved.

关键词： Potable water

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SFedXL: Semi-Synchronous Federated Learning With Cross-Sharpness and Layer-Freezing

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IEEE Internet of Things Journal 2025年第12期12卷 18692-18707页

作者： Zhao, Mingxiong Zhao, Shihao Feng, Chenyuan Yang, Howard H. Niyato, Dusit Quek, Tony Q. S. Yunnan University National Pilot School of Software Kunming650500 China Ministry of Education Engineering Research Center of Integration and Application of Digital Learning Technology Beijing100039 China Ministry of Education Engineering Research Center of Cyberspace Kunming650504 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650221 China EURECOM Sophia Antipolis06410 France Zhejiang University University of Illinois Urbana-Champaign Institute Zhejiang University Haining314400 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore

Federated learning (FL) emerges as a potential solution for enabling multiple terminal devices to collaboratively accomplish computational tasks within an Unmanned Aerial Vehicle (UAV) swarm. However, traditional FL approaches, predicated on synchronous data aggregation, are not feasible for a UAV swarm owing to the inherently variable and dynamic nature of their communication networks compared with terrestrial systems. Furthermore, the data procured by UAVs is often highly heterogeneous, attributable to disparities in deployment environments and device attributes. Considering the distinct flight paths and unique operational conditions encountered by different UAVs, a considerable amount of data remains unlabeled. To tackle the challenges associated with asynchronous operations and the prevalence of unlabeled data, we introduce a novel framework termed Semi-synchronous FL with Cross-Sharpness and Layer-Freezing (SFedXL), tailored for a UAV swarm. In particular, we devise a cross-sharpness model training strategy aimed at optimizing the utilization of both labeled and unlabeled datasets. Additionally, we propose an innovative semi-synchronous model aggregation protocol, complemented by client-specific layer-freezing and client cluster scheduling, designed to expedite the training process. Our simulation results indicate that the proposed algorithm surpasses current FL methods in terms of object recognition accuracy and communication efficiency, albeit with a trade-off of increased local computation latency. © 2014 IEEE.

关键词： Federated learning

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Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks

arXiv

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arXiv 2024年

作者： He, Jiayi Luo, Xiaofeng Kang, Jiawen Du, Hongyang Xiong, Zehui Chen, Ci Niyato, Dusit Shen, Xuemin School of Automation Guangdong University of Technology Guangzhou510006 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Semantic Communication (SemCom) plays a pivotal role in 6G networks, offering a viable solution for future efficient communication. Deep Learning (DL)-based semantic codecs further enhance this efficiency. However, the vulnerability of DL models to security threats, such as adversarial attacks, poses significant challenges for practical applications of SemCom systems. These vulnerabilities enable attackers to tamper with messages and eavesdrop on private information, especially in wireless communication scenarios. Although existing defenses attempt to address specific threats, they often fail to simultaneously handle multiple heterogeneous attacks. To overcome this limitation, we introduce a novel Mixture-of-Experts (MoE)based SemCom system. This system comprises a gating network and multiple experts, each specializing in different security challenges. The gating network adaptively selects suitable experts to counter heterogeneous attacks based on user-defined security requirements. Multiple experts collaborate to accomplish semantic communication tasks while meeting the security requirements of users. A case study in vehicular networks demonstrates the efficacy of the MoE-based SemCom system. Simulation results show that the proposed MoE-based SemCom system effectively mitigates concurrent heterogeneous attacks, with minimal impact on downstream task accuracy. © 2024, CC BY.

关键词： Latent semantic analysis

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Wavefront computing in Solids: The design Parameters and the Ideal Lens

Wavefront Computing in Solids: The Design Parameters and the...

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2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021

作者： Yang, Zaifeng Ng, Eldwin J. Chai, Kevin Tshun-Chuan Phuong, Bui Viet Oo, Zaw Zaw Png, Ching Eng Lal, Amit Institute of High Performance Computing ASTAR Department of Electronics and Photonics Singapore Singapore Institute of Microelectronics ASTAR Department of IC Design Singapore Singapore School of Electrical and Computer Engineering Cornell University IthacaNY United States

ISBN: (纸本)9781728146706

This paper presents a theoretical derivation for the design parameters of wavefront computing (WFC) system in solids including the aperture/transducer sizes, focal length, and the ideal lens. To achieve Fourier transform at the expected focal plane based on the wavefront computing system with ideal lens, the design parameters are derived for various transducers arrays from 4 by 4 to 32 by 32. Based on the theory in geometrical optics, the equations of ideal thick plano-concave lens are derived in solids for a given focal length and material properties. To validate the derived design parameters and the ideal lens, time domain simulation is used to show the propagation of the elastic waves in fused silica block. It can be observed that the waves excited by four piezoelectric transducers will get focused at the expected focal plane after the ideal lens. © 2021 IEEE.

关键词： Wavefronts

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Multi-granularity Information Flow Enhanced Skeleton-based Infant Seizure Detection

Multi-granularity Information Flow Enhanced Skeleton-based I...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Huiyuan Chen Weiting Chen Yiqing Zhang Na Li MOE Research Center of Software/Hardware Co-Design Engineering Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Children’s Hospital Shanghai Jiao Tong University School of Medicine Shanghai China

Automatic video seizure detection can assist in the diagnosis of seizures. Existing works usually rely on the information of single granularity for learning seizure patterns and the performance of these works is often constrained by the limited annotated data. To overcome these problems, we introduce a multi-granularity information flow method to enhance skeleton-based infant seizure detection from different perspectives. A forward-backward consistency constraint is proposed to use the reverse temporal sequences as augmented data, helping make better predictions. Besides, an importance-aware module is proposed to adaptively pay more attention to important frames. Experimental results indicate that our method can improve the performance of seizure detection and achieve high performance with a relatively low amount of parameters.

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Distributed Denial of Service Attack Detection by Machine Learning Techniques

Distributed Denial of Service Attack Detection by Machine Le...

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Learning and Technology Conference (L&T)

作者： Varkha K. Jewani Prafulla E. Ajmire Mohd. Atique Bhisham Sharma Imed Ben Dhaou Suhashini Awadhesh Chaurasia Sant Gadge Baba Amravati University Amravati India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science Hekma School of Engineering Computing and Design Dar Al-Hekma University Jeddah Saudi Arabia Department of Computer Science RTM Nagpur University Nagpur

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

The exponential expansion of computer networks and the internet makes it clear that there is a chance of being attacked and harmed. In the meantime, one of the most crucial defensive measures against the more complex and frequent network attacks is the intrusion detection and prevention systems (IDSs and IPSs). Due to insufficient datasets, anomaly-based research in intrusion detection systems is plagued by imprecise deployment, analysis, and evaluation. The researchers analyzed a variety of datasets, including DARPA98, KDD99, ISC2012, and ADFA13, to assess how well their suggested intrusion detection and intrusion prevention techniques performed. There are several issues: lack of adequate attack coverage, unrepresentative payloads, and anonymised packet information, insufficient traffic volume and diversity, or a deficiency in feature set and metadata. This study is focused on CICIDS-2017 which is the recently updated IDS dataset that meets real-world requirements and is accessible publicly. In addition to benign network flows and distributed denial of service (DDoS) attacks, it contains seven common attack network flows as well. To provide the optimal combination of features for the identification of the attack category, it also assesses the efficacy of several machine learning (ML) algorithms and network traffic feature sets. Even though various ML techniques like Linear Regression, Random Forests, and Decision Tree can be used, and performed very well, Hybrid Algorithm is designed to get better analysis on dataset.

关键词： Accuracy Machine learning algorithms Prevention and mitigation Linear regression Intrusion detection Training data Predictive models Decision trees Computer crime Random forests

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