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作者： Bao, Yongtang Liu, Xiang Qi, Yue Liu, Ruijun Li, Haojie School of Computer Science and Engineering Shandong University of Science and Technology Qingdao China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Virtual Reality Research Institute Beihang University Qingdao Research Institute Qingdao China School of Software Beihang University Beijing China

Personality recognition is of great significance in deepening the understanding of social relations. While personality recognition methods have made significant strides in recent years, the challenge of heterogeneity between modalities during feature fusion still needs to be solved. This paper introduces an adaptive multi-modal information fusion network (AMIF-Net) capable of concurrently processing video, audio, and text data. First, utilizing the AMIF-Net encoder, we process the extracted audio and video features separately, effectively capturing long-term data relationships. Then, adding adaptive elements in the fusion network can alleviate the problem of heterogeneity between modes. Lastly, we concatenate audio-video and text features into a regression network to obtain Big Five personality trait scores. Furthermore, we introduce a novel loss function to address the problem of training inaccuracies, taking advantage of its unique property of exhibiting a peak at the critical mean. Our tests on the ChaLearn First Impressions V2 multi-modal dataset show partial performance surpassing state-of-the-art networks. © 2024 John Wiley & Sons Ltd.

关键词： Information fusion

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VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models

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Procedia Computer Science 2025年 257卷 85-92页

作者： Leila Ismail Nada Shahin Henok Tesfaye Alain Hennebelle Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Independent Researcher

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively.

关键词： Artificial intelligence Computer vision Deep learning Fine-tuning Machine translation Neural machine translation Sign language Sign language recognition Transfer learning YOLO

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Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet

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Computer systems Science & engineering 2023年第9期46卷 2741-2754页

作者： Sana Zahir Rafi Ullah Khan Mohib Ullah Muhammad Ishaq Naqqash Dilshad Amin Ullah Mi Young Lee Institute of Computer Sciences and Information Technology(ICS/IT) The University of AgriculturePeshawar25130Khyber PakhtunkhwaPakistan Department of Convergence Engineering for Intelligent Drone Sejong UniversitySeoul05006Korea Collaborative Robotics and Intelligent Systems(CoRIS)Institute Oregon State UniversityORUSA Department of Software Sejong UniversitySeoul05006Korea

The analysis of overcrowded areas is essential for flow monitoring,assembly control,and *** counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary *** crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test *** this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd *** proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training *** decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive *** benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.

关键词： Artificial intelligence deep learning crowd counting scene understanding

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Development of Automatic Cartesian Robot Cannabis Seeder using LabVIEW 10

Development of Automatic Cartesian Robot Cannabis Seeder usi...

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10th International Conference on Digital Arts, Media and technology and 8th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Kosarat, Rujipan Sangwai, Keattipoom Wongjan, Anan Rajamangala University of Technology Lanna Faculty of Engineering Department of Software Engineering Chiangmai Thailand Rajamangala University of Technology Lanna Faculty of Engineering Department of Electronic Engineering and Automatic Control Systems Chiangmai Thailand

ISBN: (纸本)9798331543273

This article describes the invention of autonomous cannabis seeding equipment to reduce contamination and planting time. The automated cannabis seeder employs an NI myRIO control board to operate a y-axis stepper motor attached to a ball screw to move along the breadth of each row of 14 seed trays. Soil drilling and burial Drill all holes at the same depth. This promotes seed germination and lowers human-seed germ contamination. These 7 soil drill heads and 7 seeders use 3D printers to design and shape the workpiece. An 800-watt air pump smokes the cannabis seeds. Linear electric motors function in two rows in the z-axis. The buffer reverses the drowning process from row 14 to the first row, starting from the 3rd row and continuing through the final series of steps until 14 rows in the y-axis are complete. The hardware controls all program activities in LabVIEW. We tested the automated cannabis seeding machine 100 times on average, sowing cannabis seeds into 98 seed trays and dropping 93 seeds at 94.80% efficiency in 10.38 minutes. A 16 percent faster machine than human work is available. © 2025 IEEE.

关键词： Stepping motors

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On deployment of Eclipse Kuksa as a framework for an intelligent moving test platform for research of autonomous vehicles 2

On deployment of Eclipse Kuksa as a framework for an intelli...

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2nd Eclipse Research International Conference on Security, Artificial Intelligence, Architecture and Modelling for Next Generation Mobility, SAAM Mobility 2021

作者： Hirvonsalo, Harri Seppänen, Pertti Faculty of Information Technology and Electrical Engineering Empirical Software Engineering in Software Systems and Services M3S Group University of Oulu Oulu Finland

After an era of huge propagation within the field of mobile communications, digitalization has been spreading during the latest years at an accelerating speed to automotive technology and business. Following the developments experienced earlier in the mobile communications branch, open solutions and platforms are emerging in the automotive industries, challenging the traditional proprietary systems. Like in mobile communications, open platforms enable development of a wide variety of novel applications and systems that speed up the digitalization of the automotive and traffic ecosystems and offer the developers new attractive business opportunities. One of such open approaches is the Eclipse Kuksa framework developed in a consortium of European research institutions and automotive industries. In this study, we explored how the Kuksa framework could be used as a technology basis for an automotive data system combining in-vehicle functionality to cloud-based services. The study was carried out as a part of the SMAD research project of the University of Oulu aiming at an intelligent moving test platform for research of autonomous vehicles built on top of a Toyota Rav4 hybrid car. Our study covered subsystems of the Kuksa framework, the in-vehicle subsystem, cloud subsystems and the data communications between them. Our results indicate that the Kuksa framework is a feasible basis for the development of open, intelligent automotive data systems, though with a considerable learning needs. The results and experiences of our study provide besides additional knowledge for our continuing research on automotive software, also new, valuable contribution to the Eclipse Kuksa community and the practitioners planning to deploy open approaches in their automotive software development. Copyright © 2021 for this paper by its authors.

关键词： Open systems

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Verify All Traffic: Towards Zero-Trust In-Network Intrusion Detection against Multipath Routing

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2155-2171页

作者： Zhao, Ziming Li, Zhaoxuan Xie, Xiaofei Liu, Zhipeng Li, Tingting Yu, Jiongchi Zhang, Fan Chen, Binbin School of Software Technology Zhejiang University Ningbo315100 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China ZJU-Hangzhou Global Scientific and Technological Innovation Center 311200 China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province 310027 China Jiaxing Research Institute Zhejiang University 314000 China Zhengzhou Xinda Institute of Advanced Technology Zhengzhou450001 China Singapore State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security UCAS Beijing100049 China School of Computing and Information Systems Singapore Management University Singapore188065 Singapore Advanced Digital Sciences Center Singapore138632 Singapore Singapore University of Technology and Design SingaporeB96049 Singapore

With the popularity of encryption protocols, machine learning (ML)-based traffic analysis technologies have attracted widespread attention. To adapt to modern high-speed bandwidth, recent research is dedicated to advancing zero-trust intrusion detection by offloading feature extraction and model inference into the network dataplane. Especially, with the rise of programmable switches, achieving line-speed ML inference becomes promising. However, existing research only considers a single switch node as a relay to conduct evaluation. This is far from real-world deployments involving multiple switches (given that zero-trust security assumes that threats can originate from anywhere, including within the network), particularly the multi-path routing phenomenon that exists in practice. In this paper, we reveal practical challenges in the context of enabling line-speed model inference in the network dataplane. Furthermore, we propose FCPlane, the forwarding and computing integrated dataplane for zero-trust intrusion detection that aims to enable efficient load balancing while providing reliable traffic analysis results, even against multipath routing. The core idea is to reconcile forwarding and computation to the flowlet level, for which a tailor-made Markov chain model is designed. Based on two public traffic datasets, we evaluate seven state-of-the-art in-network traffic analysis models deployed in four types of topologies (three with multipath routing and one without) to explore performance impact and demonstrate the effectiveness of our proposal. © 1983-2012 IEEE.

关键词： Network intrusion

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Detecting Duplicate Contributions in Pull-Based Model CombiningTextual and Change Similarities

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Journal of Computer Science & technology 2021年第1期36卷 191-206页

作者： Zhi-Xing Li Yue Yu Tao Wang Gang Yin Xin-Jun Mao Huai-Min Wang Key Laboratory of Parallel and Distributed Computing College of ComputerNational University of Defense Technology Changsha 410073China Laboratory of Software Engineering for Complex Systems College of ComputerNational University of Defense TechnologyChangsha 410073China

Communication and coordination between OSS developers who do not work physically in the same location have always been the challenging *** pull-based development model,as the state-of-art collaborative development mechanism,provides high openness and transparency to improve the visibility of contributors'***,duplicate contributions may still be submitted by more than one contributors to solve the same problem due to the parallel and uncoordinated nature of this *** not detected in time,duplicate pull-requests can cause contributors and reviewers to waste time and energy on redundant *** this paper,we propose an approach combining textual and change similarities to automatically detect duplicate contributions in pull-based model at submission *** a new-arriving contribution,we first compute textual similarity and change similarity between it and other existing *** then our method returns a list of candidate duplicate contributions that are most similar with the new contribution in terms of the combined textual and change *** evaluation shows that 83.4%of the duplicates can be found in average when we use the combined textual and change similarity compared to 54.8%using only textual similarity and 78.2%using only change similarity.

关键词： Pull-request Duplicate detection textual similarity change similarity

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Controller Hardware-in-the-Loop Evaluation of a Microgrid Controller for a Microgrid System with Multiple Grid-Forming Inverters

Controller Hardware-in-the-Loop Evaluation of a Microgrid Co...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Xie, Fuhong Wang, Jing Ganguly, Subhankar Singh, Shashank Wang, Wenzong Baum, Jacqueline Jha, Rahul R National Renewable Energy Laboratory Power Systems Engineering Center GoldenCO United States Siemens Technology and Services Private Limited Grid Software Bangalore India Epri Distributed Energy Resources Integration Knoxville United States Commonwealth Edison Smart Grid - Emerging Technology Chicago United States

ISBN: (纸本)9798350376067

This paper presents the laboratory evaluation of a commercial Microgrid Management System (MGMS) implemented in the real-world Bronzeville Microgrid which features a futuristic scenario with high renewable energy integration and the use of multiple Grid-Forming (GFM) inverters. The primary objective of the performance evaluation for the MGMS is to assess the MGMS's capability to dispatch GFM units, including a GFM PV unit and two GFM battery units, to maintain the system stability and ensure economic operation, thus guaranteeing the microgrid's resilience during prolonged outages and dynamic events. The laboratory controller hardware-in-the-loop provides realistic testing environment through detailed electromagnetic transient modeling of the microgrid system, hardware MGMS, and standard communication protocols (DNP3). The CHIL evaluation shows how the MGMS effectively manages the GFM inverters, highlighting its performance in maintaining stability, reliability, and survivability in a microgrid environment with a high penetration of renewable energy sources. © 2024 IEEE.

关键词： Renewable energy

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MePof: A Modular and End-to-End Profile-Guided Optimization Framework for Android Kernels

MePof: A Modular and End-to-End Profile-Guided Optimization ...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Keyuan Zong Baojian Hua Yang Wang Shuang Hu Zhizhong Pan School of Software Engineering University of Science and Technology of China Suzhou Institute for Advanced Research University of Science and Technology of China

Profile-Guided Optimization (PGO) is a novel compiler optimization leveraging runtime feedback and has been applied successfully to optimize Android kernels gaining significant performance improvements. However, current studies as well as implementations of PGO-based Android kernel optimizations still suffer from three problems: 1) optimization inflexibility due to restricted algorithms for generating profiles and for simulating real-world usage scenarios; 2) considerable optimization efforts due to the extensive manual interventions needed; and 3) optimization failures due to kernel version *** paper presents MePof, the first modular and end-to-end PGO framework for Android kernels. The MePof framework consists of three key components: 1) a tool orchestration, that integrates two novel algorithms for generating profiles, and three methods for simulating real-world scenarios that can be flexibly switched according to the usage scenario; 2) a domain-specific language (DSL) that can specify PGO-based optimization strategies and a corresponding compiler translating the DSL programs into configuration files necessary for optimization; and 3) an adapter that automatically triggers and completes the optimization when the Android kernel version *** have implemented a prototype for MePof and have conducted extensive experiments to evaluate its effectiveness, performance, and usability. Experimental results demonstrated that: 1) MePof is effective, with performance improvement 9.39% on average; 2) MePof is efficient by saving up to 39.07% of time than manual optimizations; and 3) MePof is highly usable by requiring only one manual intervention instead of more than 30 manual interventions as in the existing optimization framework.

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MMCo-Clus - An Evolutionary Co-clustering Algorithm for Gene Selection (Extended abstract) 39

MMCo-Clus - An Evolutionary Co-clustering Algorithm for Gene...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Cui, Laizhong Acharya, Sudipta Mishra, Sumit Pan, Yi Huang, Joshua Zhexue Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Queen's University School of Computing ON Canada Indian Institute of Information Technology Department of Cse Assam Guwahati India Chinese Academy of Sciences Shenzhen Institute of Advanced Technology China

ISBN: (纸本)9798350322279

Dimensionality reduction through feature selection becomes inevitable to overcome the problem of the Curse of dimensionality. In this article, we propose a feature (gene) selection method for high dimensional gene expression (GE) data through a Multi-objective optimization-based Multi-view Co-Clustering algorithm (named MMCo-Clus). A thorough comparative analysis with existing feature selection algorithms using external/internal evaluation metrics supports our proposed method's potency. © 2023 IEEE.

关键词： Clustering algorithms

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