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Persian FrameNet: A Novel Approach to build FrameNet in the Persian Language Applicable to Islamic Context 8

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Persian FrameNet: A Novel Approach to build FrameNet in the ...

8th Iranian Joint Congress on Fuzzy and Intelligent Systems, CFIS 2020

作者： Baghini, Mohammad Hossein Shojaei Janfada, Behrooz Bidgoli, Behrooz Minaei University of Science and Technology Data Mining Lab Faculty of Computer Engineering Tehran Iran

ISBN: (纸本)9781728173016

FrameNet is a lexical research project that produces a glossary containing very detailed information about syntax (semantic relationships of specific English words such as verbs, nouns, and adjectives). Both human and computer users can use this glossary, so it has become an essential lexical knowledge base for use in fields such as natural language processing and vocabulary semantics. Due to the importance of FrameNet in natural language processing, there is also a need to create FrameNet in languages other than English. There have been few attempts to produce a FrameNet in Persian so far. On the other hand, the existence of an efficient Persian FrameNet can help with the processing of natural language in Islamic texts. In this paper, we present a novel approach to building our first version of the Persian FrameNet. We will then show that although this version is in its infancy, it can be comparable to the FrameNet of some other important languages in terms of frame coverage. © 2020 IEEE.

关键词： Semantics

A Joint Communication and Computation Framework for Digital Twin over Wireless Networks

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arXiv 2024年

作者： Yang, Zhaohui Chen, Mingzhe Liu, Yuchen Zhang, Zhaoyang The College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China Zhejiang Hangzhou310007 China Zhejiang Lab Zhejiang Hangzhou311100 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States Department of Computer Science North Carolina State University RaleighNC27606 United States

In this paper, the problem of low-latency communication and computation resource allocation for digital twin (DT) over wireless networks is investigated. In the considered model, multiple physical devices in the physical network (PN) needs to frequently offload the computation task related data to the digital network twin (DNT), which is generated and controlled by the central server. Due to limited energy budget of the physical devices, both computation accuracy and wireless transmission power must be considered during the DT procedure. This joint communication and computation problem is formulated as an optimization problem whose goal is to minimize the overall transmission delay of the system under total PN energy and DNT model accuracy constraints. To solve this problem, an alternating algorithm with iteratively solving device scheduling, power control, and data offloading subproblems. For the device scheduling subproblem, the optimal solution is obtained in closed form through the dual method. For the special case with one physical device, the optimal number of transmission times is reveled. Based on the theoretical findings, the original problem is transformed into a simplified problem and the optimal device scheduling can be found. Numerical results verify that the proposed algorithm can reduce the transmission delay of the system by up to 51.2% compared to the conventional schemes. © 2024, CC BY-SA.

关键词： Budget control

Vision-Based Multimodal Interfaces: A Survey and Taxonomy for Enhanced Context-Aware System Design

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arXiv 2025年

作者： Hu, Yongquan Tang, Jingyu Gong, Xinya Zhou, Zhongyi Zhang, Shuning Elvitigala, Don Samitha Mueller, Florian Hu, Wen Quigley, Aaron J. School of Computer Science and Engineering University of New South Wales Sydney Australia The University of Tokyo Tokyo Japan Exertion Games Lab Department of Human-Centred Computing Monash University Melbourne Australia School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Tsinghua University Beijing China Department of Computer Science and Engineering South University of Science and Technology Shenzhen China CSIRO’s Data61 University of New South Wales Sydney Australia

The recent surge in artificial intelligence, particularly in multimodal processing technology, has advanced human-computer interaction, by altering how intelligent systems perceive, understand, and respond to contextual information (i.e., context awareness). Despite such advancements, there is a significant gap in comprehensive reviews examining these advances, especially from a multimodal data perspective, which is crucial for refining system design. This paper addresses a key aspect of this gap by conducting a systematic survey of data modality-driven Vision-based Multimodal Interfaces (VMIs). VMIs are essential for integrating multimodal data, enabling more precise interpretation of user intentions and complex interactions across physical and digital environments. Unlike previous task- or scenario-driven surveys, this study highlights the critical role of the visual modality in processing contextual information and facilitating multimodal interaction. Adopting a design framework moving from the whole to the details and back, it classifies VMIs across dimensions, providing insights for developing effective, context-aware *** Codes 68U35, 68T07 Copyright © 2025, The Authors. All rights reserved.

关键词： Taxonomies

A Physics-based and data-driven Approach for Localized Statistical Channel Modeling

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arXiv 2023年

作者： Zhang, Shutao Ning, Xinzhi Zheng, Xi Shi, Qingjiang Chang, Tsung-Hui Luo, Zhi-Quan School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China School of Software Engineering Tongji University Shanghai China Pengcheng Lab Shenzhen China Networking and User Experience Lab Huawei Technologies Shenzhen China

Localized channel modeling is crucial for offline performance optimization of 5G cellular networks, but the existing channel models are for general scenarios and do not capture local geographical structures. In this paper, we propose a novel physics-based and data-driven localized statistical channel modeling (LSCM), which is capable of sensing the physical geographical structures of the targeted cellular environment. The proposed channel modeling solely relies on the reference signal receiving power (RSRP) of the user equipment, unlike the traditional methods which use full channel impulse response matrices. The key is to build the relationship between the RSRP and the channel’s angular power spectrum. Based on it, we formulate the task of channel modeling as a sparse recovery problem where the non-zero entries of the sparse vector indicate the channel paths’ powers and angles of departure. A computationally efficient weighted non-negative orthogonal matching pursuit (WNOMP) algorithm is devised for solving the formulated problem. Finally, experiments based on synthetic and real RSRP measurements are presented to examine the performance of the proposed method. © 2023, CC BY.

关键词： Statistical Physics

Learning Beams Adaptive to the Environment: An RSRP-based Codebook Design

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Learning Beams Adaptive to the Environment: An RSRP-based Co...

IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Xinzhi Ning Shutao Zhang Ye Xue Xi Zheng Qingjiang Shi Tsung-Hui Chang School of Software Engineering Tongji University Shanghai China Pengcheng Lab Shenzhen China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China Networking and User Experience Lab Huawei Technologies Shenzhen China

The design of an effective codebook is crucial for the fifth generation (5G) systems, particularly in the context of massive multiple-input-multiple-output (MIMO) and millimeter wave communication. However, existing codebook design methods that rely on the instantaneous channel impulse response (CIR) face challenges in adapting to dynamic channels that undergo small-scale fading changes. To address this issue, this paper proposes a novel two-stage codebook design framework based solely on the reference signal receiving power (RSRP), which provides large-scale statistical information about environment attenuation. The proposed approach involves two stages: first, the angular power spectrum (APS) is estimated through sparse Bayesian learning (SBL), which uses the statistical relationship between APS and RSRP. Second, an iterative clustering and optimization algorithm is used to determine the optimal codebook based on the estimated APS. Experimental results demonstrate that the proposed codebook design framework with a smaller number of beams can adapt to localized environment well and achieves better performance than the classical DFT codebook.

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Intelligent pneumonia image recognition and classification method based on improved convolutional neural network 4

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Intelligent pneumonia image recognition and classification m...

4th IEEE International Conference on Automation, Electronics and Electrical engineering, AUTEEE 2021

作者： Liu, Jiahang Zhao, Wenkang Li, Zongyi Song, Zeyang Toe, Teoh Teik Artificial Intelligence of Automation University of Science and Techonology Beijing Beijing China Data Science and Big Data Technology Harbin Institute of Technology Harbin China Beijing University of Posts and Telecommunications Telecommunications Engineering With Management Beijing China Nanjing Tech University Mechanical Engineering Nanjing China Nanyang Technological University NTU Business AI Lab Singapore Singapore

ISBN: (纸本)9781665428392

This paper is an attempt to apply neural networks to classify pneumonia images. The classifying model is built based on thousands of the actual X-ray images, which relatively belong to both pneumonia patients and normal people. The classification neural network, a popular way of machine learning, will serve as a supplementary means to help diagnose pneumonia and perform pre-screening. The paper analyzes and contains details of the algorithms we used. By analyzing the structure of the network, we come up with several useful methods to improve the model, so as to achieve a better model structure and application to classification. The new structure we propose is demonstrated in this paper. The final training accuracy is over 95% and the test accuracy is approximately 92%. © 2021 IEEE.

关键词： Python

Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning

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arXiv 2023年

作者： Zhang, Hangtao Yao, Zeming Zhang, Leo Yu Hu, Shengshan Chen, Chao Liew, Alan Li, Zhetao School of Cyber Science and Engineering Huazhong University of Science and Technology China Swinburne University of Technology Australia Griffith University Australia National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab. Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China RMIT University Australia Xiangtan University China

Federated learning (FL) is vulnerable to poisoning attacks, where adversaries corrupt the global aggregation results and cause denial-of-service (DoS). Unlike recent model poisoning attacks that optimize the amplitude of malicious perturbations along certain prescribed directions to cause DoS, we propose a Flexible Model Poisoning Attack (FMPA) that can achieve versatile attack goals. We consider a practical threat scenario where no extra knowledge about the FL system (e.g., aggregation rules or updates on benign devices) is available to adversaries. FMPA exploits the global historical information to construct an estimator that predicts the next round of the global model as a benign reference. It then fine-tunes the reference model to obtain the desired poisoned model with low accuracy and small perturbations. Besides the goal of causing DoS, FMPA can be naturally extended to launch a fine-grained controllable attack, making it possible to precisely reduce the global accuracy. Armed with precise control, malicious FL service providers can gain advantages over their competitors without getting noticed, hence opening a new attack surface in FL other than DoS. Even for the purpose of DoS, experiments show that FMPA significantly decreases the global accuracy, outperforming six state-of-the-art attacks. © 2023, CC BY.

关键词： Federated learning

WHO IS CHATGPT? BENCHMARKING LLMS’ PSYCHOLOGICAL PORTRAYAL USING PSYCHOBENCH

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arXiv 2023年

作者： Huang, Jen-Tse Wang, Wenxuan Li, Eric John Lam, Man Ho Ren, Shujie Yuan, Youliang Jiao, Wenxiang Tu, Zhaopeng Lyu, Michael R. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Institute of Psychology Tianjin Medical University China Tencent AI Lab. China School of Data Science The Chinese University of Hong Kong Shenzhen China

Large Language Models (LLMs) have recently showcased their remarkable capacities, not only in natural language processing tasks but also across diverse domains such as clinical medicine, legal consultation, and education. LLMs become more than mere applications, evolving into assistants capable of addressing diverse user requests. This narrows the distinction between human beings and artificial intelligence agents, raising intriguing questions regarding the potential manifestation of personalities, temperaments, and emotions within LLMs. In this paper, we propose a framework, PsychoBench, for evaluating diverse psychological aspects of LLMs. Comprising thirteen scales commonly used in clinical psychology, PsychoBench further classifies these scales into four distinct categories: personality traits, interpersonal relationships, motivational tests, and emotional abilities. Our study examines five popular models, namely text-davinci-003, ChatGPT, GPT-4, LLaMA-2-7b, and LLaMA-2-13b. Additionally, we employ a jailbreak approach to bypass the safety alignment protocols and test the intrinsic natures of LLMs. We have made PsychoBench openly accessible via https://***/CUHK-ARISE/PsychoBench. Copyright © 2023, The Authors. All rights reserved.

关键词： Medicine

Multi-Agent Enhanced Complex Decision-Making Support Framework: An Urban Emergency Case Study

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Multi-Agent Enhanced Complex Decision-Making Support Framewo...

Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Lyulong He Hongyuan Zhang Kunxiao Liu Xi Wu Joint Operations College National Defense University Beijing China Software Engineering. Northwestern Polytechnical University Suzhou Key Lab of Aerospace Industry Software and Data Science Yangtze River Delta Research Institute of NPU Taicang China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

With the rapid development of large language model (LLM) technology, its advantages in text generation and experimental design are gradually emerging, such as powerful semantic understanding and generation capabilities, as well as high flexibility and adaptability. Complex decision-making problem experiment design is a field that requires high flexibility and complex design, where traditional methods often struggle to cope with this complexity, resulting in low accuracy, poor consistency, and inefficiency. This paper presents a MA-DMS framework, a multi-agent enhanced design framework for the formulation of experiments on complex decision-making problems based on large language models. Through multi-round collaboration among multiple intelligent roles, this framework transforms broad and complex strategic challenges into experiment-ready problems with clear conditions and well-defined objectives. The experimental results show that the MA-DMS system performs excellently in the transformation process of complex decision-making problem, significantly enhancing the efficiency and accuracy of the problem formulation phase.

关键词： Analytical models Accuracy Large language models Computational modeling Decision making Semantics Collaboration Transforms Risk management Resource management