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A Novel Neural Network-Based Recommender System for Drug Recommendation 1

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24th international conference on Engineering Applications of Neural Networks (EANN)

作者： Al Mubasher, Hadi Doughan, Ziad Sliman, Layth Haidar, Ali Beirut Arab Univ Fac Engn Debbieh Lebanon Ecole Ingn Gen Numer Paris France

ISBN: (数字)9783031342042

ISBN: (纸本)9783031342035;9783031342042

With the advancement of Machine Learning, recommender systems have emerged with the aim of improving the user experience in a world where data and available alternatives are tremendously growing. Employing Natural Language Processing with such systems can provide them with a sense of empowerment, given that most of the users' opinions are reflected through reviews. Artificial Neural Networks, the core of Deep Learning, have sparked a lot of interest in many research fields, owing to the appealing property of learning feature representations out of nowhere. To that end, this paper presents a novel hybrid recommender system that is based on Natural Language Processing and Artificial Neural Networks. the proposed model is evaluated and compared with a similar model, where the advantages of the proposed model are clearly presented. the paper is concluded by highlighting research opportunities that can be done in the future.

关键词： Machine Learning Recommender systems Natural Language Processing Deep Learning Artificial Neural Networks Drug Recommendation

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Towards Robust Token Embeddings for Extractive Question Answering 24th

Towards Robust Token Embeddings for Extractive Question Ans...

引用

24th international conference on Web Information systems Engineering, WISE 2023

作者： Yao, Xun Ma, Junlong Hu, Xinrong Yang, Jie Guo, Yi Liu, Junping School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan China School of Computing and Information Technology University of Wollongong Wollongong Australia School of Computer Data and Mathematical Sciences Western Sydney University Penrith Australia

ISBN: (纸本)9789819972531

Extractive Question Answering (EQA) tasks have gained intensive attention in recent years, while Pre-trained Language Models (PLMs) have been widely adopted for encoding purposes. Yet, PLMs typically take as initial input token embeddings and rely on attention mechanisms to extract contextual representations. In this paper, a simple yet comprehensive framework, termed perturbation for alignment (PFA), is proposed to investigate variations towards token embeddings. A robust encoder is further formed being tolerant against the embedding variation and hence beneficial to subsequent EQA tasks. Specifically, PFA consists of two general modules, including the embedding perturbation (a transformation to produce embedding variations) and the semantic alignment (to ensure the representation similarity from original and perturbed embeddings). Furthermore, the framework is flexible to allow several alignment strategies with different interpretations. Our framework is evaluated on four highly-competitive EQA benchmarks, and PFA consistently improves state-of-the-art models. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Embeddings

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Modeling and Simulation Verification of Operating Mode Switching of Train control System Based on Train-to-Train Communication 24th

Modeling and Simulation Verification of Operating Mode Swi...

引用

24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Qiang Liao, Ian Wen, Sheng Xiang, Yang Lanzhou Jiaotong University Lanzhou China Swinburne University of Technology Melbourne Australia Top-Rail Service Pittsburg United States

ISBN: (纸本)9789819615506

the train control system needs to accommodate various operational scenarios through the design of distinct working modes, each triggered by changing input conditions. these modes correspond to specific speed limits and safety requirements, ensuring the safety, efficiency, and continuity of train operations. the future of next-generation urban rail transit lies in train control systems based on train-to-train communication. Due to structural changes, traditional CBTC (Communication-Based Train control) model designs are no longer applicable, and there is currently no comprehensive model design available. this paper examines the mode-switching function and processes appropriate for train-to-train communication control systems. A new mode definition and switching scheme is proposed. In the event of train-to-train communication failure, the train switches to a backup operation mode using train-to-ground communication to acquire position information from ground equipment. To describe this transition, we use the theory of Colored Petri Nets to establish a mode-switching model based on Hierarchical Timed Colored Petri Nets (HTCPN). the model effectively transitions trains from train-to-train mode (T2T) to train-to-ground mode (T2G) under failure conditions. We studied the impact of different operational intervals in the backup mode on system performance through simulations, which show that trains can effectively switch to train-to-ground mode during communication failures. Analysis of successful mode switches and switching times under different backup mode intervals reveals that the success rate increases with longer intervals. An interval of 240 s minimizes train operation delays to within 3 min. the HTCPN model established serves as a reference for verifying and analyzing other multi-mode train control systems. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Petri nets

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Defense Against Textual Backdoors via Elastic Weighted Consolidation-Based Machine Unlearning 24th

Defense Against Textual Backdoors via Elastic Weighted Cons...

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24th international conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xuan, Haojun Wang, Yajie Wu, Huishu Liu, Tao Zhang, Chuan Zhu, Liehuang Beijing Institute of Technology Beijing China China University of Political Science and Law Beijing China China Academy of Information and Communications Technology Beijing China

ISBN: (纸本)9789819615506

Backdoor attacks pose significant threats to Natural Language Processing (NLP) models. Various backdoor defense methods for NLP models primarily function by identifying and subsequently manipulating backdoor triggers within provided samples. However, such methods predominantly operate at the level of data filtering, essentially failing to cleanse the affected model. To solve this problem, we present ELUDE—a groundbreaking method designed to excise the backdoor triggers embedded within the corrupted model. ELUDE’s architecture comprises two core components: the backdoor trigger identifier and the backdoor trigger remover, operating synergistically in a pipeline procedure. While the former employs a perplexity-based approach to locate the backdoor trigger, the latter eradicates the inserted backdoor’s influence on the tainted model using machine unlearning. To counteract the issue of catastrophic forgetting engendered by machine unlearning, we incorporate Elastic Weight Consolidation (EWC) within the backdoor trigger remover. Our experiments on SST-2, OLID, and AG News text classification datasets exemplify the efficacy of ELUDE, as comparative results indicate that ELUDE effectively reduces the success rate of three cutting-edge backdoor attack methods by an average of 60%—simultaneously maintaining comparable performance on the original task. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Natural language processing systems

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Bilateral Insider threat Detection: Harnessing Standalone and Sequential Activities with Recurrent Neural Networks 24th

Bilateral Insider Threat Detection: Harnessing Standalone an...

引用

24th international conference on Web Information systems Engineering, WISE 2023

作者： Manoharan, Phavithra Hong, Wei Yin, Jiao Zhang, Yanchun Ye, Wenjie Ma, Jiangang Institute for Sustainable Industries and Liveable Cities Victoria University MelbourneVIC3011 Australia Institute of Innovation Science and Sustainability Federation University BerwickVIC Australia

ISBN: (纸本)9789819972531

Insider threats involving authorised individuals exploiting their access privileges within an organisation can yield substantial damage compared to external threats. Conventional detection approaches analyse user behaviours from logs, using binary classifiers to distinguish between malicious and non-malicious users. However, existing methods focus solely on standalone or sequential activities. To enhance the detection of malicious insiders, we propose a novel approach: bilateral insider threat detection combining RNNs to incorporate standalone and sequential activities. Initially, we extract behavioural traits from log files representing standalone activities. Subsequently, RNN models capture features of sequential activities. Concatenating these features, we employ binary classification to detect insider threats effectively. Experiments on the CERT 4.2 dataset showcase the approach’s superiority, significantly enhancing insider threat detection using features from both standalone and sequential activities. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recurrent neural networks

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Glide Ratio Optimization for Wind Turbine Airfoils Based on Genetic Algorithms 24th

Glide Ratio Optimization for Wind Turbine Airfoils Based on ...

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24th international conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023

作者： Radi, Jinane Djebli, Abdelouahed Sierra-Garcia, Jesús Enrique Santos, Matilde Energetic Laboratory Department of Physics Faculty of Sciences University of Abdelmalek Saadi Tetouan Tétouan Morocco Automation and Systems Engineering University of Burgos Burgos09006 Spain Institute of Knowledge Technology University Complutense of Madrid Madrid28040 Spain

ISBN: (纸本)9783031482311

the main objective in the design of wind turbine blades is the use of suitable airfoils to increase the aerodynamic performance and decrease the cost of energy. the objective of this research is to employ numerical optimization techniques to develop airfoils for wind turbines. To achieve this objective, a mathematical model is formulated by combining genetic algorithms with the XFOIL flow solver. three different airfoils with different characteristics are created using the genetic algorithm. throughout the optimization procedure, XFOIL software is used to determine the lift and drag coefficients. this study confirms the feasibility and effectiveness of the innovative design approach. Furthermore, it provides a valuable design concept that can be effectively applied to the airfoils of medium-thickness wind turbines. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Airfoils

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Matching Using Sufficient Dimension Reduction for Heterogeneity Causal Effect Estimation 24

Matching Using Sufficient Dimension Reduction for Heterogene...

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24th IEEE international conference on High Performance Computing and Communications, 8th IEEE international conference on Data science and systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhao, Haoran Zhang, Yinghao Cheng, Debo Li, Chen Feng, Zaiwen College of Science Huazhong Agricultural University Wuhan430070 China College of Computer Science and Engineering Guangxi Normal University Guilin541000 China University of South Australia Stem Australia Kyushu Institute of Technology Department of Computer Science and Systems Engineering Iizuka820-8502 Japan College of Informatics Huazhong Agricultural University Wuhan430070 China Huazhong Agricultural University Hubei Key Laboratory of Agricultural Bioinformatics Wuhan430070 China Huazhong Agricultural University Hubei Hongshan Laboratory Wuhan430070 China Huazhong Agricultural University National Key Laboratory of Crop Genetic Improvement Wuhan430070 China Wuhan University State Key Laboratory of Hybrid Rice 299 Bayi Rd Wuhan430070 China Macro Agricultural Research Institute Huazhong Agricultural University Wuhan430070 China

ISBN: (纸本)9798350319934

Causal inference plays an important role in understanding the underlying mechanisation of the data generation process across various domains. It is challenging to estimate the average causal effect and individual causal effects from observational data with high-dimensional covariates due to the curse of dimension and the problem of data sufficiency. the existing matching methods can not effectively estimate individual causal effect or solve the problem of dimension curse in causal inference. To address this challenge, in this work, we prove that the reduced set by sufficient dimension reduction (SDR) is a balance score for confounding adjustment. Under the theorem, we propose to use an SDR method to obtain a reduced representation set of the original covariates and then the reduced set is used for the matching method. In detail, a non-parametric model is used to learn such a reduced set and to avoid model specification errors. the experimental results on real-world datasets show that the proposed method outperforms the compared matching methods. Moreover, we conduct an experiment analysis and the results demonstrate that the reduced representation is enough to balance the imbalance between the treatment group and control group individuals. © 2022 IEEE.

关键词： Causal Inference Individual Causal Effect Matching Sufficient Dimension Reduction

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Spiral Capacitor Models that Consider the Capacitance and Inductance of Each Turn 24

Spiral Capacitor Models That Consider the Capacitance and In...

引用

24th IEEE international conference of Young Professionals in Electron Devices and Materials, EDM 2023

作者： Nikolayev, Ilya I. Komnatnov, Maxim E. Drozdova, Anastasiya A. Tomsk State University of Control Systems and Radioelectronics Department of Television and Control Tomsk Russia

ISBN: (纸本)9798350336870

Mathematical models that allow calculating the capacitance and inductance of each turn of spiral capacitor electrodes are presented. Comparison of values obtained using the proposed mathematical models and quasi-static calculation by increasing the number of turns of the spiral capacitor. Comparison demonstrated the suitability of the developed models for calculating capacity and inductance with an acceptable error rate not exceeding 0.92%. the capacitance of a real capacitor was calculated with given geometrical and electrical parameters, and an error rate was no more than 3.4%. A circuit model algorithm for spiral capacitors that takes into account the capacitance and inductance of each turn was developed. the transfer coefficients of three circuits were compared when the capacitor was connected in series between the source and the load. Comparison of frequency dependencies showed that the difference in values did not exceed 0.16%. the developed spiral capacitor models allow taking into account the compression or stretching of each turn of the spiral capacitor, caused by the changes in temperature and in the geometric dimensions of the capacitor coating. © 2023 IEEE.

关键词： Inductance

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Knowledge-Grounded Dialogue Generation with Contrastive Knowledge Selection 24th

Knowledge-Grounded Dialogue Generation with Contrastive Kno...

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24th international conference on Web Information systems Engineering, WISE 2023

作者： Wang, Bin Xu, Fuyong Zhu, Zhenfang Liu, Peiyu School of Information Science and Engineering Shandong Normal University Shandong Jinan250014 China School of Information Science and Electrical Engineering Shandong Jiaotong University Shandong Jinan250357 China

ISBN: (纸本)9789819972531

Knowledge selection is the key component in knowledge-ground dialogues, which aims to choice correct knowledge based on external knowledge for dialogue generation. the quality of knowledge selection depend on knowledge representation methods. However, the knowledge representation exploration is still challenging. We propose a knowledge-grounded dialogue model, which incorporates a knowledge-grounded module and a dialogue generation module, aiming to choose the most appropriate knowledge and fuse it into response generation. In addition, supervised contrastive knowledge representation signal is designed to obtain knowledge representation dynamically. Experiments on FoCus dataset show that our model outperforms the baseline models. the ablation study further demonstrates the effectiveness of each sub-modules. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Knowledge representation

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Counterfactual Explanations for Sequential Recommendation with Temporal Dependencies 24th

Counterfactual Explanations for Sequential Recommendation w...

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24th international conference on Web Information systems Engineering, WISE 2023

作者： He, Ming An, Boyang Wang, Jiwen Wen, Hao Beijing University of Technology Beijing China

ISBN: (纸本)9789819972531

Explanations can substantially enhance users’ trust and satisfaction with recommender systems. Counterfactual explanations have demonstrated remarkable effectiveness in enhancing the performance of explainable sequential recommendation. However, existing counterfactual explanation models for sequential recommendation ignore temporal dependencies in a user’s historical behavior sequence. Moreover, counterfactual histories must be as close as possible to the real history;otherwise, they will violate the user’s real behavioral preferences. In this paper, we propose Counterfactual Explanations with Temporal Dependencies (CETD), a counterfactual explanation model based on a Variational Autoencoder (VAE) for sequential recommendation that handles temporal dependencies. When generating counterfactual histories, CETD uses a Recurrent Neural Network (RNN) to capture both long-term preferences and short-term behavior in the user’s real behavioral history, which can enhance explainability. Meanwhile, CETD fits the distribution of reconstructed data in a latent space, and then uses the variance obtained from learning to make counterfactual sequences closer to the original sequence, which will reduce the proximity of counterfactual histories. Extensive experiments on two real-world datasets show that the proposed CETD consistently outperforms state-of-the-art methods. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recommender systems

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