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International Symposium on Networks, Computers and Communications (ISNCC)

作者： Nada Shahin Leila Ismail Department of CS and Software Engineering Intelligent Distributed Computing and Systems (INDUCE) Lab College of IT National Water and Energy Center UAE University Al-Ain UAE CLOUDS lab School of Computing and Information Systems The University of Melbourne Melbourne Australia

ChatGPT is a language model based on Generative AI. Existing research work on ChatGPT focused on its use in various domains. However, its potential for Sign Language Translation (SLT) is yet to be explored. This paper addresses this void. Therefore, we present GPT's evolution aiming a retrospective analysis of the improvements to its architecture for SLT. We explore ChatGPT's capabilities in translating different sign languages in paving the way to better accessibility for deaf and hard-of-hearing community. Our experimental results indicate that ChatGPT can accurately translate from English to American (ASL), Australian (AUSLAN), and British (BSL) sign languages and from Arabic Sign Language (ArSL) to English with only one prompt iteration. However, the model failed to translate from Arabic to ArSL and ASL, AUSLAN, and BSL to Arabic. Consequently, we present challenges and derive insights for future research directions.

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Temperature Control Method for Batch Reactors Based on Adaptive Delay Compensation

Temperature Control Method for Batch Reactors Based on Adapt...

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Chinese Automation Congress (CAC)

作者： Dehui Mao Peng Li Mingxi Ai Jiande Wu Chuangyan Yang Hancheng Wang School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

To address the instability issue caused by time-delay and nonlinear characteristics of intermittent reactions in batch reactors, a temperature control method for batch reactors based on adaptive delay compensation was proposed. By integrating the chemical reaction mechanism inside the reactor with graphical analysis and actual production data, a time-varying second-order delayed transfer function model for the reactor is established. The adaptive rate for controlling the time-varying delayed system is designed based on the control error between the actual controlled object and the nominal model. Furthermore, standard Smith predictor is employed to compensate for the delay in the error term. When applied to temperature control in batch reactors, experimental results demonstrate that this method effectively reduces the temperature control errors caused by time-varying delays, significantly accelerates the convergence speed and diminishes the oscillation amplitude of the temperature curve.

关键词： Adaptation models Adaptive systems Transfer functions Production Temperature control Delays Inductors Time-varying systems Standards Convergence

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Graph Neural Network with Heterogeneous Attributes for Node Classification

Graph Neural Network with Heterogeneous Attributes for Node ...

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Neural Networks, information and Communication Engineering (NNICE), International Conference on

作者： Bohou Jiang Jiahui Wang Kun Yue School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

Node classification on heterogeneous graphs with multiple node types and relationships has been widely used in commodity classification, friend recommendation, and so on. Most of the existing models need to define the meta paths manually with incomplete attribute information, leading to the low accuracy of classification. To improve the accuracy of node classification task, we propose a novel graph neural network with heterogeneous attributes (GNNHA) by introducing the heterogeneous attributes of nodes and edges without defining meta paths. First, we design a spatial mapping for heterogeneous edges by mapping and fusing the source and target nodes to a hidden layer space. Then, we make a unique linear projection for each node to obtain the embeddings containing the attributes of nodes and edges by aggregating the information of neighbor nodes. Finally, we provide an improved loss function by leveraging the information loss of mapping and node class imbalance. Our model can capture more complete heterogeneous graph attributes and obtain more objective and accurate classification results. Experimental results show that our proposed model outperforms state-of-the-art ones.

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Comparative Analysis of Hate Speech Detection: Traditional vs. Deep Learning Approaches

Comparative Analysis of Hate Speech Detection: Traditional v...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Haibo Pen Nicole Teo Zhaoxia Wang Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin China School of Computing and Information Systems Singapore Management University Singapore

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

Detecting hate speech on social media poses a significant challenge, especially in distinguishing it from offensive language, as learning-based models often struggle due to nuanced differences between them, which leads to frequent misclassifications of hate speech instances, with most research focusing on refining hate speech detection methods. Thus, this paper seeks to know if traditional learning-based methods should still be used, considering the perceived advantages of deep learning in this domain. This is done by investigating advancements in hate speech detection. It involves the utilization of deep learning-based models for detailed hate speech detection tasks and compares the results with those obtained from traditional learning-based baseline models through multidimensional aspect analysis. By considering various aspects to gain a comprehensive understanding, we can discern the strengths and weaknesses in current state-of-the-art techniques. Our research findings reveal the performance of traditional learning-based hate speech detection outperforms that of deep learning-based methods. While acknowledging the potential demonstrated by deep learning methodologies, this study emphasizes the significance of traditional machine learning approaches in effectively addressing hate speech detection tasks. It advocates for a balanced perspective, highlighting that dismissing the capabilities of traditional methods in favor of emerging deep learning-based techniques may not consistently yield the most effective results.

关键词： Deep learning Learning systems Measurement Support vector machines Analytical models Logistic regression Social networking (online)

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BTC-Shadow: an analysis and visualization system for exposing implicit behaviors in Bitcoin transaction graphs

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Frontiers of Computer Science 2023年第6期17卷 167-169页

作者： Ding BAO Wei REN Yuexin XIANG Weimao LIU Tianqing ZHU Yi REN Kim-Kwang Raymond CHOO School of Computer Science China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Geo-Information Processing Wuhan 430074China NSFOCUS Technologies Group Co. Ltd.Beijing 100089China School of Computing Sciences University of East AngliaNorwich NR47TJUK Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX 78249-0631USA

1 Introduction A Bitcoin ledger comprises a sizable number of transaction records,which can be utilized to make it easier to track and analyze the traits and patterns of cryptocurrency-related *** facilitate the visual analysis of Bitcoin,numerous tools with various aims have been *** example,MiningVis[1]and SuPoolVisor[2]are the analytics systems for Bitcoin mining pools,as well as[3-5]focus on the Bitcoin transaction graphs ***,due to our previous research requirements for Bitcoin transaction graphs,none of the available tools can provide exploring the features of connection related to the address and observe significant visual ***,using these tools is challenging to navigate to abnormal node clusters effortlessly from large node groups and then analyze the local interlink characteristics between transaction nodes with interactive analytics.

关键词： analytic implicit utilized

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Trusted Non-intrusive Data Exchange based on Ontology in Logistics Industry 23

Trusted Non-intrusive Data Exchange based on Ontology in Log...

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Proceedings of the 2023 3rd International Conference on Big Data, Artificial Intelligence and Risk Management

作者： Lianzheng Xu Deqian Fu Jianlong Qiu School of Information Science and Engineering Linyi University China Key Laboratory of Complex Systems and Intelligent Computing in Universities of Shandong Linyi University China

ISBN: (纸本)9798400716478

The logistics industry is becoming increasingly important in our daily lives, leading to a growing demand for digitalization within the sector. However, due to concerns about data privacy, logistics entities have formed natural information silos, which have made logistics data difficult to exchange and share. To address this issue, this paper proposes an ontology-based logistics data exchange model that utilizes blockchain technology to establish user trust among information silos. The model in this paper fuses non-intrusive data access and Ontology-Based Data Access (OBDA) to achieve both precise control over the data access process and interconnection among heterogeneous systems. The proposed model creates a new trustworthy and controllable solution for the circulation of logistics data within the logistics industry, without the need for the secondary development of logistics information systems.

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TivNe-SLAM: Dynamic Mapping and Tracking via Time-Varying Neural Radiance Fields

TivNe-SLAM: Dynamic Mapping and Tracking via Time-Varying Ne...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Chengyao Duan Zhiliu Yang School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Previous attempts to integrate Neural Radiance Fields (NeRF) into the Simultaneous Localization and Mapping (SLAM) framework either rely on the assumption of static scenes or require the ground truth camera poses, which impedes their application in real-world scenarios. This paper proposes a time-varying representation to track and reconstruct the dynamic scenes. Firstly, two processes, a tracking process and a mapping process, are maintained simultaneously in our framework. In the tracking process, all input images are uniformly sampled and then progressively trained in a self-supervised paradigm. In the mapping process, we leverage motion masks to distinguish dynamic objects from the static background, and sample more pixels from dynamic areas. Secondly, the parameter optimization for both processes is comprised of two stages: the first stage associates time with 3D positions to convert the deformation field to the canonical field. The second stage associates time with the embeddings of the canonical field to obtain colors and a Signed Distance Function (SDF). Lastly, we propose a novel keyframe selection strategy based on the overlapping rate. Our approach is evaluated on two synthetic datasets and one real-world dataset, and the experiments validate that our method achieves competitive results in both tracking and mapping when compared to existing state-of-the-art NeRF-based dynamic SLAM systems.

关键词： Simultaneous localization and mapping Three-dimensional displays Tracking Dynamics Robot vision systems Neural radiance field Cameras Time-varying systems Image reconstruction Synthetic data

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Long‐time target tracking algorithm based on re‐detection multi‐feature fusion

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IET Cyber-systems and Robotics 2022年第1期4卷 38-50页

作者： Junsuo Qu Chenxue Tang Yuan Zhang Kai Zhou Abolfazl Razi School of Automation Xi'an Key Laboratory of Advanced Control and Intelligent ProcessXi'an University of Posts and TelecommunicationsXi'anShaanxiChina School of Communication and Information Engineering Xi'an University of Posts and TelecommunicationsXi'anShaanxiChina School of Informatics Computing and Cyber SystemsNorthern Arizona UniversityFlagstaffAZUSA

This paper considers the problem of long-term target tracking in complex scenes when tracking failures are unavoidable due to illumination change,target deformation,scale change,motion blur,and other *** specifically,a target tracking algorithm,called re-detection multi-feature fusion,is proposed based on the fusion of scale-adaptive kernel correlation filtering and *** target tracking algorithm trains three kernel correlation filters based on the histogram of oriented gradients,colour name,and local binary pattern features and then obtains the fusion weight of response graphs corresponding to different features based on average peak correlation energy criterion and uses weighted average to complete the position estimation of the tracked *** order to deal with the problem that the target is occluded and disappears in the tracking process,a random fern classifier is trained to perform re-detection when the target is *** comparing the OTB-50 target tracking dataset,the experimental results show that the proposed tracker can track the target well in the occlusion attribute video sequence in the OTB-100 test dataset and has a certain improvement in tracking accuracy and success rate compared with the traditional correlation filter tracker.

关键词： machine learning pedestrian identification robustness visual surveillance visual tracking

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Quantum Direct Steganography Scheme Based on Modified Generator Projection Directions of Steane Code over a Single-Type Pauli Channel

arXiv

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arXiv 2025年

作者： Hao, Chaolong Ma, Quangong Qu, Dan Shi, Dawei School of Information Systems Engineering Information Engineering University 62 Science Avenue Zhengzhou450001 China Laboratory for Advanced Computing and Intelligence Engineering Jiangsu Wuxi214000 China

In quantum mechanics, measurements of a quantum state in various directions yield distinct outcomes, a principle that forms the foundation of quantum communication theory. This paper expands upon this concept by introducing a method to modify generator projection directions (MGPD) within quantum stabilizer codes. Employing the Steane code ((7, 1, 3) code), as a fundamental carrier, we develop a novel scheme for direct quantum steganography across a single-type Pauli channel. The infeasibility of eavesdropping decoding under MGPD is proven. We detail the steganographic encoding and decoding schemes, corresponding quantum circuits, and eavesdropping detection principles. We also use a’Sudoku’-style strategy to balance steganographic error probabilities and provide the complete steganography protocol. Relative to existing studies, the MGPD method achieves embedding rates approaching and attaining the upper limit of the information capacity for the (n, k, d) = (7, 1, 3) code within a noise probability range of approximately 1/(n + 1) = 12.5%. It also reduces the consumption of auxiliary keys from O(log (N)) to O(1), while enabling eavesdropping detection and steganography of arbitrary quantum states. We investigate its potential applications in quantum communication and assess its benefits in the context of secret information transmission and eavesdropping detection in noisy channels. Although the MGPD method incorporates certain idealized assumptions and limitations, it provides novel perspectives on the concealment of quantum information. Copyright © 2025, The Authors. All rights reserved.

关键词： Decoding

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Bridging the Gap using Contrastive Learning and Semantic Consistency for Unsupervised Neural Machine Translation

Bridging the Gap using Contrastive Learning and Semantic Con...

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Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： Chuancai Zhang Dan Qu Liming Du Kaiyuan Yang School of Information Systems Engineering University of Information Engineering Zhengzhou China Laboratory for Advanced Computing and Intelligence Engineering Zhengzhou China School of Cyberspace Security Zhengzhou University Zhengzhou China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

In Unsupervised Neural Machine Translation (UNMT) tasks, the lack of extensive parallel corpora makes it challenging for the model to directly optimize the correspondence between the source and target languages. UNMT models primarily rely on learning from monolingual data, making it difficult to fully capture cross-linguistic semantic alignment and consistency during training. Therefore, effectively enhancing semantic alignment between the source and target languages under unsupervised conditions has become a key challenge in the field of UNMT. To address this issue, this study proposes a semantic alignment and consistency enhancement mechanism based on contrastive learning, aiming to improve the model's ability to capture cross-lingual semantic differences by constructing an optimization objective focused on semantic consistency. The method utilizes a pretrained cross-lingual model to generate semantic embeddings for both the source and target languages, then designs a contrastive learning-based loss function that minimizes the semantic distance between correct translations and source sentences while increasing the semantic distance between incorrect translations and source sentences, thereby enhancing the model's performance in semantic consistency. The experiments validate the effectiveness and broad applicability of the semantic consistency optimization method based on contrastive learning, demonstrating great potential and advantages, especially in multilingual tasks of UNMT. The model achieved significant improvements in BLEU scores across various language pairs.

关键词： Training Translation Computational modeling Semantics Optimization methods Neural machine translation Contrastive learning Data models Multilingual Artificial intelligence

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