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2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021

作者： Hong, Qian-Bei Wu, Chung-Hsien Nguyen, Thanh Binh Wang, Hsin-Min National Cheng Kung University and Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan Taiwan National Cheng Kung University Department of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9789881476890

Multi-channel speech separation has been successfully applied in a complex real-world environment such as the far-field condition. The common solution to deal with the far-field condition is using a multi-channel signal captured by a structured microphone array and leveraging the inner difference between channels to enhance the speech separation performance. The spatial feature has been widely used in recent speech separation research. This feature appears to be insufficient when the location information becomes ambiguous. This is known as the spatial ambiguity problem. In order to deal with the spatial ambiguity problem, this study proposes an attention mechanism for the Temporal-Spatial Neural Filter (TSNF), in which the channel attention on merged features and the feature map of 1D convolution block in the temporal convolution network is proposed. The proposed method is evaluated on the multi-channel reverberant dataset which is built based on the WSJ0-2mix dataset. The dataset is simulated in the real-environment room by using the Room Impulse Response generator. In the experimental results, the proposed methods produced the SI -SNR improvement of about 1.2dB in close speakers' case, while a small decrease of 0.1dB in other cases. © 2021 APSIPA.

关键词： Convolution

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A Model-Driven Approach for Interoperability Among SaaS and DaaS/DBaaS: The MIDAS Case

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SN Computer Science 2022年第4期3卷 301页

作者： Mane, Babacar Mascarenhas, Ana Patrícia F. Magalhães Quinteiro, Gustavo Maciel, Rita Suzana Pitangueira Claro, Daniela Barreiro Formalisms and Semantic Applications Research Group (FORMAS) Institute of Computing Federal University of Bahia Av. Milton Santos Ondina Bahia Salvador 40170-110 Brazil Models for Software Engineering (MAtisse) Department of Exact Sciences and Earth State University of Bahia 2555 Silveira Martins St. Cabula Bahia Salvador Brazil Post Graduate Program in Computing and Systems Salvador University 251 Dr. Jose Peroba St. Bahia Salvador Brazil Models for Software Engineering (MAtisse) Institute of Computing Federal University of Bahia Av. Milton Santos Ondina Bahia Salvador 40170-110 Brazil

Cloud Platforms are heterogeneous, and users may face interoperability issues migrating applications or exchanging data among distinct clouds due, for instance, to the lack of standards solutions. Several solutions have been proposed to overcome lock-in situations, and middleware platforms are one of them. A semantic interoperability solution named middleware for Data as a Service (DaaS)/Database as a Service (DBaaS) and Software as a Service (SaaS)—MIDAS has been developed to overcome this lock-in issue. It is an intermediate communication layer to retrieve data from DaaS or DBaaS through a Structured Query Language (SQL) or a Not Only SQL (NoSQL) created at the SaaS level. MIDAS is a platform for software execution, but software development needs support for its entire life cycle. Therefore, we propose the Model drIven Approach for MIDAS (MIAMI), which enables the specification of platform-independent middleware models and their use to generate code on different cloud platforms. MIAMI comprises a Domain-Specific Modeling Language (DSML) that enables middleware models and a transformation specification, which defines how these models can be converted to code. MIAMI offers a strategy for MIDAS specification and code generation phases to help middleware developers’ activities. MIAMI was applied to code generation specifications in Cloud Foundry, Amazon Web Services, OpenShift, and Heroku providers to evaluate our approach. This specification shows MIAMI’s feasibility and points out that MDD is a promising approach to improving cloud interoperability solutions. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing Interoperability Metamodel Model-driven development

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H∞ gain-scheduled dynamic output feedback control with transient performance applied to electrical microgrid

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Journal of the Franklin Institute 2024年第7期361卷

作者： Fuentes, R.M. Lacerda, M.J. Morais, C.F. Oliveira, R.C.L.F. Palma, J.M. Engineering Systems Doctoral Program Faculty of Engineering Universidad de Talca Curicó3340000 Chile Centre for Communications Technology School of Computing and Digital Media London Metropolitan University LondonN7 8DB United Kingdom Pontifical Catholic University of Campinas – PUC-Campinas Polythecnic School Postgraduate Program in Telecommunication Networks Management SP Campinas13086-900 Brazil SP Campinas13083-852 Brazil Department of Electrical Engineering Faculty of Engineering Universidad de Talca Curicó334000 Chile

This paper introduces an approach for designing gain-scheduled dynamic output feedback controllers for continuous-time Linear Parameter-Varying (LPV) systems. The aim is to improve transient response by incorporating both D-stability and H∞ criteria into the synthesis conditions. We achieve this by employing changes of variables and congruence transformations, which lead to new synthesis conditions expressed as linear matrix inequalities. Additional fine-tuned scalar parameters can be used to enhance further the controller performance in terms of less conservative H∞ guaranteed costs. To assess the effectiveness of our proposed synthesis procedure, we conduct computational experiments in the context of a microinverter-based distributed power generation system. These experiments take into account real-world operational characteristics, providing a comprehensive evaluation of our approach. The results demonstrate that the designed controller effectively ensures the desired closed-loop system behavior, even when dealing with state noise and discretization errors originating from digital implementation. © 2024 The Franklin Institute

关键词： Transient analysis

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HAEE: Question Classification Using Hierarchical Intra-Attention Enhancement Encoder

HAEE: Question Classification Using Hierarchical Intra-Atten...

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26th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2021

作者： Wang, Jen-Wei Chen, Kai-Hsiang Huang, Jen-Wei National Cheng Kung University Department of Electrical Engineering Tainan Taiwan National Cheng Kung University and Academia Sinica Graduate Program for Multimedia Systems and Intelligent Computing Taiwan

ISBN: (纸本)9781665408257

With the development of E-commerce, an Automated Question-Answering system takes a crucial part in customer service. Question classification, which assigns labels to questions according to the answer types, is one of the tasks in question answering. Previous methods usually used handcraft features like named entity recognition, but it needs the predefined dictionary or tools. The machine learning approaches are recently applied to this task and achieve high accuracy. In this paper, we proposed HAEE, a Hierarchical intra-Attention Enhancement Encoder which composed of bidirectional GRUs and intra-attentions. In addition, we adopt the character input to address the issue of the OOV (Out-Of-Vocabulary) problem and create multiple intra-attentions to simulate the certain relationships between characters (Chinese) or words (English) to enhance the influence of tokens on the sentence. We evaluate the HAEE model in an actual corporate setting and several datasets. As shown in the experimental results, our HAEE model outperforms the existing state-of-the-art models on question classification tasks, especially for the Chinese corpus. © 2021 IEEE.

关键词： Signal encoding

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ZeroCAN: Anomaly-Based Zero-Day Attack Detection in Vehicular CAN Bus Networks

ZeroCAN: Anomaly-Based Zero-Day Attack Detection in Vehicula...

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Jonathan Rendel William Balte Harrison Kurunathan Hazem Ismail Ali Alexandre Dos Santos Roque Wagner Ourique De Morais Mahdi Fazeli Department for Computing and Electronics for Real-time and Embedded Systems (CERES) School of Information Technology (ITE) Halmstad University Halmstad Sweden CISTER/ISEP Polytechnic Institute of Porto Portugal Graduate Program of Electrical Engineering Federal University of Rio Grande do Sul - UFRGS Porto Alegre Brazil

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Zero-day attacks present a significant security threat to vehicular networks, exploiting vulnerabilities at both software and hardware levels within such systems that remain undiscovered. Mitigating these threats is essential to ensuring the safety and security of vehicular systems. Support Vector Machine (SVM) is a good candidate for anomaly detection of zero-day attacks within vehicular networks because it can handle highdimensional data and effectively distinguish between normal and abnormal patterns in complex and dynamic environments. A trained SVM on the normal operation data of in-vehicular network can identify flag deviations, thus making it effective in the detection of any previously unknown attack patterns, which is a common behaviour of zero-day attacks. In this paper, we introduce an anomaly detection method called “ZeroCAN” which models the behaviour of every single electronic control unit on the network with a separate SVM and a set of high-level features that capture the timing and data payload aspects of CANbus traffic. This approach achieves an anomaly detection rate of over $\mathbf{9 9 \%}$ and a false positive rate below $\mathbf{0. 0 1 \%}$ during normal operation in most cases.

关键词： Support vector machines Telecommunication traffic Feature extraction Data models Timing Security Vehicle dynamics Anomaly detection Standards Payloads

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Classifying Ore Deposits Based on La-Icp-Ms Geochemistry of Natural Gold

SSRN

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SSRN 2024年

作者： Verbel, Angel Augusto Schutesky, Maria Emilia Gregory, Daniel D. Verbel, Arturo University of Brasilia Institute of Geosciences Graduate Program in Geology DF Brasília70910-900 Brazil University of Toronto Department of Earth Sciences TorontoONM5S 3B1 Canada Universidad de los Andes Systems and Computing Engineering Department School of Engineering Bogotá111711 Colombia

Systematic sampling of gold grains and their characterization in abundance and morphology can be used as a vectoring tool for mineral deposits. However, the processes that lead to the formation of an ore deposit are multiscale and nonlinear. As a result, the elemental compositions recorded in gold grains during the mineralization process seem mostly irregular and unpredictable. Here, we took advantage of the capabilities of neural networks to approximate, a natural nonlinear function that better explains the chemical variability observed in gold allowing us to make predictions. this was achieved by using published data of trace elements in gold of four types of gold deposits (i.e., Orogenic, VMS, porphyry and epithermal) from 47 different localities, obtained by laser ablation-inductively coupled plasma-mass spectrometry (LA- ICP-MS). The model was optimized by training five different architectures on the most influential elements, determined by principal component analysis (PCA). As a result, it was found that two hidden layers with 10 neurons each, is the best architecture for using trace elements as predictors of the type of deposit that formed a natural gold grain. In order to encourage the use of the findings made in this paper, we introduce OreGenes, an app developed in Matlab2023b based on the best-obtained model. It allows any user who possesses compatible data, the ability to import and process it to obtain a prediction with an average accuracy level of 88.9% confidence to determine the mineral deposit type that an unknown gold grain came from which grants the user the ability to have a powerful tool for exploration or research. © 2024, The Authors. All rights reserved.

关键词： Deposits

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Adaptive Contextual Caching for Mobile Edge Large Language Model Service

arXiv

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arXiv 2025年

作者： Liu, Guangyuan Liu, Yinqiu Wang, Jiacheng Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

Mobile edge Large Language Model (LLM) deployments face inherent constraints, such as limited computational resources and network bandwidth. Although Retrieval-Augmented Generation (RAG) mitigates some challenges by integrating external knowledge bases, inefficient cache management can still result in high retrieval latency and frequent cache updates. To address these issues, we propose an Adaptive Contextual Caching (ACC) framework that anticipates user needs by proactively caching semantically relevant data for mobile-edge LLMs. ACC utilizes a deep reinforcement learning (DRL) module to refine cache replacement policies, balancing user context, document similarity, and the overhead associated with cache misses. Experimental results demonstrate that ACC increases cache hit rates to over 80% after only 11 training episodes, outperforming FIFO, LRU, and semantic-only caching while reducing retrieval latency by up to 40%. In particular, ACC also reduces local caching overhead (i.e., the cost of updating the cache when a miss occurs) by as much as 55%, enabling scalable, low-latency LLM services in resource-constrained edge environments. © 2025, CC BY.

关键词： Deep reinforcement learning

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IoT-based Wireless Sensor Networks for Monitoring Drinking Water Treatment Plants

IoT-based Wireless Sensor Networks for Monitoring Drinking W...

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International Workshop on Secure Internet of Things (SIoT)

作者： Luiz Octávio Martini Lima Lucas Carvalho Goncalves Gustavo Campos Menezes Lucas Silva de Oliveira Gleb Wataghin Institute of Physics UNICAMP Campinas SP Brazil Master Degree Program in Automation and Systems CEFET-MG Leopoldina MG Brazil Department of Electronics and Computing CEFET-MG Contagem MG Brazil Department of Mechatronics Engineering CEFET-MG Divinópolis MG Brazil

ISBN: (数字)9798331507466

ISBN: (纸本)9798331507473

The increasing need for efficient monitoring of parameters in challenging industrial environments, such as basic sanitation, has led to the search for integrated and user-friendly solutions that can address specific challenges of this setting. This project aims to develop a solution to meet monitoring requirements in water treatment plants. The proposed solution involves the development of both hardware and software for collecting data from the water treatment process using sensors and transmitting this information to a server structure, database, and communication interface with the cloud-based user. The effectiveness of the proposed device is being evaluated in six water treatment units located in the states of Alagoas, Mato Grosso, Paraná, Rio de Janeiro, and São Paulo. Preliminary results indicate that the proposed solution is capable of monitoring the variables involved in the water flocculation stage and generating a comprehensive set of information about the process over time.

关键词： Cloud computing Chemical sensors Wireless sensor networks User interfaces Hardware Software Sensors Social Internet of Things Servers Monitoring

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TEAM: Task-Clustering and Enhanced Adaptability for Meta-Reinforcement Learning on Robotics Through Multi-Task Diffusion and Optimization

TEAM: Task-Clustering and Enhanced Adaptability for Meta-Rei...

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IEEE International Conference on Robotic computing (IRC)

作者： Joshua Ho Chien-Min Wang Chung-Ta King Chi-Wei Feng Shih-Yun Sunkuan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

The ability of robots to imitate human learning strategies-rapidly adapting to new tasks without large datasets-has garnered significant attention in meta-learning. Meta-reinforcement learning seeks to enhance robotic agent flexibility across diverse tasks and contexts, offering promise where single-task learning often fails. Despite advancements like multi-task diffusion models and task-weighted optimization mechanisms, effectively training tasks with varying complexities simultaneously remains a major challenge. This paper introduces a novel meta-reinforcement learning method that addresses this issue by clustering the training tasks of robotic arms based on semantic and trajectory similarities, while leveraging adaptive learning rates and task-specific weights proposed by the multitask optimization techniques. Our approach, TEAM, emphasizes performance-driven semantic clustering, optimizing based on robotic task similarity, complexity, and convergence objectives. We also integrate fast adaptive and multi-task optimization of the diffusion model to enhance computational efficiency and adaptability. More specifically, we introduce a cluster-specific optimization technique, using specialized parameters for each group to allow more refined task handling. The experimental validation demonstrates the effectiveness of this scalable method in improving performance, adaptability, and efficiency in real-world, heterogeneous robotic tasks, further advancing robotic computing in meta-reinforcement learning.

关键词： Training Metalearning program processors Semantics Multitasking Diffusion models Complexity theory Trajectory Robots Optimization

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Extraction of c- F os Protein Regions from Mouse Brain Slice Images using Multi-channelization, 1 x 1 Convolution and U -Net

Extraction of c- F os Protein Regions from Mouse Brain Slice...

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International Symposium on Soft computing and Intelligent systems (SCIS)

作者： Ziyue Wang Hibiki Okamura Shijie Luo Yu Hayashi Hotaka Takizawa Master's Program in Computer Science University of Tsukuba Tsukuba Japan Ph.D. Program in Humanics International Institute for Integrative Sleep Medicine University of Tsukuba Tsukuba Japan International Institute for Integrative Sleep Medicine University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering Academic Computing & Communications Center University of Tsukuba Tsukuba Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

This study proposed a novel extraction method of c-Fos protein regions in DAB(3,3‘-diaminobenzidine)-stained mouse brain slice images using the U-Net model combined with the multi-channelization and $1\times 1$ convolution techniques. Our U-Net m odel, called $1\times 1$ conv U-Net, was applied to three DAB- strainedmouse slice images whose resolutions were $1000\mathrm{x}1000$ pixels. The experimental results demonstrated that the $1\times 1$ conv U-Net outperformed the conventional U-Net.

关键词： Proteins Visualization Image resolution Image analysis Convolution Feature extraction Brain modeling Biology Biomedical imaging

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