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International Conference on computer Supported Cooperative Work in Design

作者： Xiaoyang Li Zhenyu Yang Yiwen Li Zhijun Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Emotion recognition in conversation (ERC) is a prominent research topic in natural language processing, widely applicable in various scenarios. However, accessing external commonsense knowledge and related dialogue topics, along with the complexity of fusing utterances, presents significant challenges. In this paper, we introduce a Topic-based Multilayer Knowledge Filtering (TMKF) model to enhance dialog emotion recognition accuracy. TMKF acquires commonsense knowledge for each utterance and employs two Variational Autoencoder (VAE) modules to extract global and speaker-level dialogue topics. We utilize Global Knowledge Filtering (GKF) and Local Knowledge Filtering (LKF) to obtain topic-specific knowledge representations after filtering commonsense information through global and local topic hierarchies. Subsequently, we leverage relational graphs to convolve commonsense knowledge with utterances, resulting in knowledge-constrained utterance representations for classification. Our proposed model is evaluated through extensive experiments on four widely used benchmark datasets for conversational emotion recognition. The results underscore the effectiveness of TMKF, which significantly outperforms other methods in standard metric evaluations.

关键词： Measurement Emotion recognition Filtering Convolution Oral communication Benchmark testing Nonhomogeneous media

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WHAT-IF SCENARIO ANALYSIS OF REGIONAL AIR MOBILITY OPERATIONS IN SOUTH KOREA 34th

WHAT-IF SCENARIO ANALYSIS OF REGIONAL AIR MOBILITY OPERATION...

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34th Congress of the International Council of the Aeronautical sciences, ICAS 2024

作者： Lee, Juchan Kim, Seulki Ahn, Sumin Kim, Junghyun School of Applied Artificial Intelligence Handong Global University Korea Republic of School of Aerospace Engineering Georgia Institute of Technology United States School of Computer Science and Electrical Engineering Handong Global University Korea Republic of School of Aerospace Engineering Sejong University Korea Republic of

With the global emergence of Regional Air Mobility (RAM) as a new transportation concept, the Korean government has recently recognized the growing interest in employing RAM services to enhance regional connectivity within South Korea. This research starts by formulating the following research question: "What are market dynamics associated with the adoption of RAM services in South Korea?" To answer the question, this research proposes a methodology that leverages a data-driven approach incorporating four modules: 1) potential vertiport location identification, 2) trip data preparation, 3) RAM fare and travel time estimations, and 4) market share estimation. Utilizing the modules, this research conducts what-if scenario analysis on five transportation scenarios across potential origin-destination pairs and estimates market shares for different transportation modes to assess feasibility and competitiveness related to the RAM services as a new transportation system. The main contribution of this research is to establish a basis for uncovering market dynamics of the upcoming RAM operations in South Korea and to provide insights for stakeholders, policymakers, and researchers prior to the actual implementation of the RAM services in South Korea. © 2024, International Council of the Aeronautical sciences. All rights reserved.

关键词： Market Research

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Decoding the Granular Puzzle of Macromolecules: Efficient 3D Protein Structure Alignment in the Age of Big Data with Apache Spark

Decoding the Granular Puzzle of Macromolecules: Efficient 3D...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Malysiak-Mrozek, Bozena Pawlowicz, Paulina Sunderam, Vaidy Hung, Che-Lun Kwiecien, Andrzej Mrozek, Dariusz Silesian University of Technology Department of Distributed Systems and Informatic Devices Gliwice Poland Silesian University of Technology Department of Applied Informatics Gliwice Poland Emory University Department of Computer Science AtlantaGA United States National Yang Ming Chiao Tung University Institute of Biomedical Informatics Taipei City Taiwan

ISBN: (纸本)9798350362480

Proteins are complex biological information granules that play a crucial role in various cellular processes within living organisms. Processing 3D protein structures, which are the most informative from the biological point of view, is both intricate and time-consuming. In particular, performing 3D protein structure searches against large protein datasets involves identifying similarities and conducting structural alignments across numerous molecules (granules). This task demands advanced methods for matching identical and similar regions within protein structures and substantial computational resources to handle large collections of macromolecular data efficiently. In this paper, we present our parallel implementation of scalable 3D structural alignment on the Apache Spark big data platform. We describe a customized approach that leverages Spark data transformations within the data processing pipeline for the alignment process. Our experimental results demonstrate that this solution, tightly integrated with the Spark processing model, is both efficient and scalable, even with the increasing volume of protein structure data. © 2024 IEEE.

关键词： Information granules

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An Efficient Identity Based Multi- Proxy Multi-Signcryption Scheme for Electronic Commerce using Bilinear Pairing

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Procedia computer science 2025年 259卷 1592-1601页

作者： Tej Singh Rashid Ali Vikas Tyagi Department of Applied Sciences ABES Engineering College Ghaziabad UP India Department of Applied Sciences KIET Group of Institutions Ghaziabad UP India School of Computer Science Engineering & Technology Bennett University Greater Noida UP India

With the rapid growth of electronic commerce and the increasing need for secure communication in online transactions, there is a pressing demand for efficient and secure cryptographic schemes. The researcher is using various approaches to secure communication in online transactions. However, ensuring secure communication and protecting sensitive information in electronic commerce (E-commerce) systems remains a significant concern. Traditional cryptographic schemes often suffer from limitations in terms of efficiency, scalability, and flexibility. Thus, the proposed work presents an innovative Identity Based Multi-Proxy Multi-Signcryption (IB-MPMS) scheme for electronic commerce applications. It involves the collaboration of two distinct groups of signcrypters namely original signcryption and group of proxy signcrypters. To facilitate this process in the domain of secure and efficient communication in electronic commerce, we design a multi-proxy multi-signcryption scheme with the help of the bilinear pairings. This authorization enables the generation of a multi-proxy multi-signcryption in the proposed IB-MPMS scheme. Moreover, the collaborative effort of all signcrypters in the proxy group is considered towards the implementation of these multi-proxy multi-signcryption approaches. Due to the utilization of identity-based cryptography, the proposed scheme IB-MPMS effectively addresses the key management problem, resulting in simplified processes. However, in the proposed IB-MPMS scheme both the original and proxy signcrypter ensures fair protection during E-commerce transactions. Moreover, in comparison to previous schemes, our proposed scheme exhibits more efficiency. From a computational perspective, our scheme only necessitates 4 pairing computations, whereas the state of art requires 8 pairing computations.

关键词： Identity-based cryptography bilinear pairings multi-signature ID-based signcryption proxy-signature

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Multi-objective VNF Placement Optimization with NSGA-III 1st

Multi-objective VNF Placement Optimization with NSGA-III

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1st International Conference on Advances in Computing Research, ACR’23

作者： Bekhit, Mahmoud Fathalla, Ahmed Eldesouky, Esraa Salah, Ahmad Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Kent Institute Australia Melbourne Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia College of Computers and Informatics Zagazig University Zagazig Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman

ISBN: (纸本)9783031337420

With the rising acceptance of virtual network functions (VNFs) as a replacement for traditional network functions, the optimal placement of VNFs has become a crucial task for ensuring constant performance within constrained resources. This research investigates the challenge of mapping and scheduling VNFs as a multi-objective optimization problem. The state-of-the-art methods have focused on optimizing the mapping or scheduling of VNFs while considering one or two objectives continuously. In this study, we aim to optimize two objectives: minimizing the link capacity cost and maximizing the average resource utilization of each virtual machine. To address this problem, an adaptation of the NSGA-III optimization algorithm is proposed and thoroughly evaluated through a set of experiments. In terms of objective values, the results outlined that the proposed method outperformed the other methods of comparison by a significant margin. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mapping

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Design and Development of Offline Navigation System (ONS) Using the Smart Phone Sensors

Design and Development of Offline Navigation System (ONS) Us...

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Network, Multimedia and Information technology (NMITCON) International Conference on

作者： Deepak Varadam Yash Agrawal Sumit Singh Rathore Sugali Yashwanth Naik Yash Verma Vishal Pareta Department of Computer Science and Engineering Faculty of Engineering and Technology M S Ramaiah University of Applied Sciences Bengaluru India Computer Science and Engineering M S Ramaiah University of Applied Sciences Bengaluru India

An Offline Navigation system to navigate the user without any connectivity is implemented by using smartphone sensors. The implementation of the system is carried out by extracting the relevant data from the smartphone sensors and discovering some meaningful insights from it. The offline navigation system is completely independent of GPS and any network connectivity such as Wi-Fi, Cellular Network, Bluetooth, etc. The system uses the Accelerometer sensor, Gyroscope sensor, and Magnetic sensor which are already present in the smartphone. The sensor fusion concept is used to combine the data from all the sensors and fuse it to give the device's orientation. The accelerometer sensor provides the acceleration of the device in all three directions. Similarly, the gyroscope sensor provides the angular velocity of the device, and the magnetometer sensor provides the magnetic fields. The data from all three sensors are combined to calculate the orientation angle such as pitch, roll, yaw, and azimuth of the device. The filtering algorithm used is known as Kalman Filter. It fuses the data from different sensors which results in less error on the combined data. The angles then use the position estimation algorithm and step detection algorithm, to find the next step and the coordinates of the device to track the user. The algorithm makes use of the previously determined coordinates of the device.

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CAFI: News Recommendation with Candidate Perception of Fine-Grained Interaction Information

CAFI: News Recommendation with Candidate Perception of Fine-...

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International Conference on computer Supported Cooperative Work in Design

作者： Yiwen Li Zhenyu Yang Xiaoyang Li Zhijun Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The most critical task in personalized news recommendation is to perform exact matching between candidate news and users’ interests. Existing news recommendation methods usually model users’ interests from historical clicked news items without considering candidate news, which makes it difficult to precisely match candidate news with users’ interests. Moreover, it is challenging to distinguish similar news when forming a vector representation. Simply adding candidate news to the user interest modeling process without considering word-level information does not provide sufficient discrimination for recommending appropriate news. In this paper, we propose a news recommendation model with candidate-aware fine-grained interaction information (CAFI). In our approach, we propose a fine-grained interaction module that matches candidate news items and text fragments of each historical news item at each semantic granularity to achieve interaction between the two at the word level and help similar historical news form more specific and accurate representations. In addition, our proposed gated self-attention mechanism utilizes the candidate news features as channel moderation gates to implement the process of filtering out information that is irrelevant to the candidate news for learning user interest representations, thereby better matching the candidate news to specific user interests. Experiments conducted on the large real-world Microsoft News Dataset (MIND) show that our model significantly outperforms the previously developed models in terms of all metrics. Our code is published at the following URL: https://***/liyiweneven/CAFI.

关键词： Uniform resource locators Measurement Codes Filtering Federated learning Semantics Focusing

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Unseen Named Entity Recognition for Thai Language

Unseen Named Entity Recognition for Thai Language

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International SoC Design Conference, ISOCC

作者： Watcharet Kuntichod Wilaiporn Lee Akara Prayote Department of Computer and Information Science Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

Most common challenges in Named-entity recognition (NER) require suitable and annotated datasets, tailored to the specific domain of the documents under analysis, for example, legal, medical, and financial documents each require distinct datasets. Reliable identification of named entities within the training set is crucial. Annotations is a vital role in building an effective mechanism. However, creating training sets demands significant human effort, contributing to the high costs associated with NER. High quality training materials are indispensable for evolving machine-learning algorithms, underscoring the importance of dependable training data. Thus, the primary challenge lies in acquiring and managing data rather than in the algorithms themselves. To overcome most common situations of finding new unseen names, this paper proposes a mechanism to detect a new undiscovered name in Thai language based on sentence contexts. The method uses BI-LSTM model to learn sentence structures and predict a new unseen name from its context. Our framework comprises two main tasks. The first one is word entity extraction. The second task is using the model to identify unrecognized named entities. Evaluation of the proposed method is described with a successful rate of accuracy in discovering newly identified entities. This contributes to the expansion of entity recognition capabilities in Thai language processing

关键词： Training Accuracy Text analysis Training data Named entity recognition Machine learning Predictive models Prediction algorithms Reliability Stress

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Effective Density-Based Concept Drift Detection for Evolving Data Streams 24th

Effective Density-Based Concept Drift Detection for Evolving...

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24th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2023

作者： Cui, Zelin Tian, Hui Shen, Hong Institute of Information Security Engineering Chinese Academy of Sciences Beijing China School of Information and Communication Technology Griffith University Brisbane Australia Faculty of Applied Sciences Macao Polytechnic University China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

ISBN: (纸本)9789819982103

Concept drift is a common phenomenon appearing in evolving data streams of a wide range of applications including credit card fraud protection, weather forecast, network monitoring, etc. For online data streams it is difficult to determine a proper size of the sliding window for detection of concept drift, making the existing dataset-distance based algorithms not effective in application. In this paper, we propose a novel framework of Density-based Concept Drift Detection (DCDD) for detecting concept drifts in data streams using density-based clustering on a variable-size sliding window through dynamically adjusting the size of the sliding window. Our DCDD uses XGBoost (eXtreme Gradient Boosting) to predict the amount of data in the same concept and adjusts the size of the sliding window dynamically based on the collected information about concept drifting. To detect concept drift between two datasets, DCDD calculates the distance between the datasets using a new detection formula that considers the attribute of time as the weight for old data and calculates the distance between the data in the current sliding window and all data in the current concept rather than between two adjacent windows as used in the exiting work DCDA [2]. This yields an observable improvement on the detection accuracy and a significant improvement on the detection efficiency. Experimental results have shown that our framework detects the concept drift more accurately and efficiently than the existing work. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Enhancing Cooperative Spectrum Sensing Performance with Fusion Rule in Cognitive Radio: Hybrid Techniques 2

Enhancing Cooperative Spectrum Sensing Performance with Fusi...

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2nd International Conference Computational and Characterization Techniques in Engineering and sciences, IC3TES 2024

作者： Deepthi, K. Sreevani, N. Raj, Vijilius Helena Sangeetha, A. Sobti, Rajeev Salman, Z. Avanthi Insistute of Engineering and Technology Department of Mechanical Engineering India Hyderabad India New Horizon College of Engineering Department of Applied Sciences Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lovely Professional University Phagwara India Hilla University College Babylon Iraq

ISBN: (纸本)9798350364699

The issue of limited wireless spectrum may be addressed with the use of Cognitive Radio (CR). Without affecting the primary users (PU), or licenced users, it allows other users, known as secondary users or unlicensed users, to exploit spectrum gaps, improving spectrum efficiency. Energy detection, a matching filter, and cyclostationary detection make up the CR, which determines whether the spectrum is idle or not. Cognitive Radio Networks (CRNS) are hindered in their ability to improve performance by noise. No signal-to-noise ratio (SNR) may be efficiently achieved with a cooperative spectrum employing a set sensing time in terms of throughput performance. There are two ways that this research suggests they might improve the effectiveness of energy detectors in the wireless spectrum. At its core, the first method is an energy detector-based hybrid of the Additive Wavelet Transform as well as the Homomorphic (AWTH). Mixing the Haar Discrete Wavelet Transform with the Homomorphic technique (HDWTH) is the basis of the second approach. The Two Quality Metrics Used: False Alarm Probability (Pf) and Probability Detection (Pd). The efficiency of the energy detector is enhanced by implementing the two methods that have been suggested. Metrics for quality demonstrate that the first method improves the wireless spectrum's Pd point perspective. But from a throughput perspective, the second strategy succeeds in maximising. © 2024 IEEE.

关键词： Signal to noise ratio

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