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2022 Findings of the Association for Computational Linguistics: NAACL 2022

作者： Sheng, Xin Xu, Linli Xu, Yinlong Jiang, Deqiang Ren, Bo School of Computer Science and Technology University of Science and Technology of China. China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China IFLYTEK Co. Ltd. China Tencent Youtu Lab China

ISBN: (纸本)9781955917766

We propose a novel siamese generative adversarial net for abstractive text summarization (SSPGAN), which can preserve the main semantics of the source text. Different from previous generative adversarial net based methods, SSPGAN is equipped with a siamese semanticpreserving discriminator, which can not only be trained to discriminate the machine-generated summaries from the human-summarized ones, but also ensure the semantic consistency between the source text and target summary. As a consequence of the min-max game between the generator and the siamese semantic-preserving discriminator, the generator can generate a summary that conveys the key content of the source text more accurately. Extensive experiments on several text summarization benchmarks in different languages demonstrate the effectiveness of the proposed method. © Findings of the Association for Computational Linguistics: NAACL 2022 - Findings.

关键词： Semantics

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A survey on causal inference for recommendation

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The Innovation 2024年第2期5卷 130-144页

作者： Huishi Luo Fuzhen Zhuang Ruobing Xie Hengshu Zhu Deqing Wang Zhulin An Yongjun Xu Institute of Artificial Intelligence Beihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100094China WeChat Search Application Department TencentBeijing 100080China The Career Science Laboratory BOSS ZhipinBeijing 100028China SKLSDE School of Computer ScienceBeihang UniversityBeijing 100191China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Causal inference has recently garnered significant interest among recommender system(RS)researchers due to its ability to dissect cause-and-effect relationships and its broad applicability across multiple *** offers a framework to model the causality in RSs such as confounding effects and deal with counterfactual problems such as offline policy evaluation and data *** there are already some valuable surveys on causal recommendations,they typically classify approaches based on the practical issues faced in RS,a classification that may disperse and fragment the uni-fied causal *** RS researchers’unfamiliarity with causality,it is necessary yet challenging to comprehensively review relevant studies from a coherent causal theoretical perspective,thereby facilitating a deeper integration of causal inference in *** survey provides a systematic review of up-to-date papers in this area from a causal theory standpoint and traces the evolutionary development of RS methods within the same causal ***,we introduce the fundamental concepts of causal inference as the basis of the following ***,we propose a novel theory-driven taxonomy,categorizing existing methods based on the causal theory employed,namely those based on the potential outcome framework,the structural causal model,and general *** review then delves into the technical details of how existing methods apply causal inference to address particular recommender ***,we highlight some promising directions for future research in this *** papers and open-source resources will be progressively available at https://***/Chrissie-Law/Causal-Inference-forRecommendation.

关键词： survey details causal

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A Unified Representation Model and View Mapping Algorithm for Path-Mapping-Based Redirection Gains in Redirected Walking

A Unified Representation Model and View Mapping Algorithm fo...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Bao, Xiyu Bian, Yulong Xin, Shiqing Qi, Meng Yang, Chenglei Shandong University Shandong Jinan China Shandong University Weihai China Shandong University Jinan China School of Information Science and Engineering Shandong Normal University Jinan China School of Computer Science & Technology Shandong University Jinan China Shandong Key Laboratory of Intelligent Electronic Packaging Testing and Application Weihai China

ISBN: (纸本)9798400713958

Among various redirected walking techniques, redirection gains guide users’ visual perception by introducing deviations between virtual and physical walking trajectories. This allows users wearing virtual reality headsets to explore a larger virtual space within a limited physical area. Current redirection gain manipulation methods primarily establish mapping relationships between virtual and physical paths, typically represented by straight lines or arcs, to align users’ physical and virtual perspectives. In this study, we propose a unified representation model and viewpoint mapping algorithm for path-mapping-based redirection gains. By inputting a few parameters, the model generates corresponding path mappings for various redirection gains, significantly simplifying the design and application of redirection gains. Furthermore, our viewpoint mapping algorithm effectively addresses mapping errors caused by dynamic gains in existing algorithms, which often lead to discontinuous trajectories and teleportation artifacts. This algorithm improves the continuity of the mapped trajectories and enhances the overall user experience during virtual exploration. © 2025 Copyright held by the owner/author(s).

关键词： Photomapping

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A Deep Learning Framework for Classifying Autism Spectrum Disorder from fMRI Images

A Deep Learning Framework for Classifying Autism Spectrum Di...

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Cybernation and Computation (CYBERCOM), International Conference on

作者： Rajat Kishor Varshney Alok Katiyar Prashant Johri School of Computing Science & Engineering Galgotias University Greater Noida India School of Computer Application & Technology Galgotias University Greater Noida India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

The objective of this work is to propose a new machine learning based approach for ASD classification from fMRI data employing deep learning architecture and novel preprocessing method. Main datasets included in ABIDE I, ABIDE II, Human Connectome Project (HCP), and Social Functions and Autism Research Interface (SFARI). Many supplements include graph neural networks for brain segmentation, 3D-BRA for accurate mapping of the brain than just using conventional registration technique, EN-CC for feature selection. The approach incorporated graph neural network for segmentation of the brain, 3DBRA mapping that translates and rotates the brain precisely unlike the general technique of registration, EN-CC for selecting the features. The deep learning models under consideration were ResNet-50, VGG-16, Inception-v3, and Efficient Net which were improved through ABSC and MSTC. Classification accuracy of the proposed models was higher than 95% on all the datasets with Efficient Net scoring 99% on the SFARI dataset. This means that only the Stratified Group Cross-Validation (SGCV) was employed to make sure the results would be more reliable. The results present a considerable improvement in both accuracy and the interpretability of the derived connectivity profiles of the ASD-related brain.

关键词： Deep learning Autism Accuracy Three-dimensional displays Functional magnetic resonance imaging Brain modeling Feature extraction Graph neural networks Reliability Testing

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DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented Generation

arXiv

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arXiv 2025年

作者： Guo, Hanghui Zhu, Jia Di, Shimin Shi, Weijie Chen, Zhangze Xu, Jiajie School of Computer Science and Technology Zhejiang Normal University China The Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University China School of Computer Science and Engineering Southeast University China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong School of Computer Science and Technology Soochow University China

Dynamic Retrieval-augmented Generation (RAG) has shown great success in mitigating hallucinations in large language models (LLMs) during generation. However, existing dynamic RAG methods face significant limitations in two key aspects: 1) Lack of an effective mechanism to control retrieval triggers, and 2) Lack of effective scrutiny of retrieval content. To address these limitations, we propose an innovative dynamic RAG method, DioR (Adaptive Cognitive Detection and Contextual Retrieval Optimization), which consists of two main components: adaptive cognitive detection and contextual retrieval optimization, specifically designed to determine when retrieval is needed and what to retrieve for LLMs is useful. Experimental results demonstrate that DioR achieves superior performance on all tasks, demonstrating the effectiveness of our work. Copyright © 2025, The Authors. All rights reserved.

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Email Spam Detection Ensemble Methods

Email Spam Detection Ensemble Methods

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Disruptive Technologies (ICDT), International Conference on

作者： Sudhir Prakash Gond Sudeept Singh Yadav Md Arfat Alam Satyajit Sahoo School of Computer Application and Technology (SCAT) Galgotias University Greater Noida Uttar Pradesh India School of Computer Science and Engineering (SCSE) Galgotias University Greater Noida Uttar Pradesh India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Finding on-demand messages has become a key task in today's generation of virtual verbal exchanges as emails and unsolicited messages flood inboxes. Spammers maximize email messaging system provisioning, especially for the scope of unwanted content that can dampen net overall performance, staff. Loan the records can catch up, and degrade the user experience. This paper provides a thorough look at using the device for unwanted mail detection methods to identify models. Several styles were observed including Naive Bayes, Support Vector Machine (SVM), Decision Trees, and Random Forest, Voting Classifier, and other ensemble methods. The data set used consisted of 1,00,000 text messages, equally cut into spam and ham (unsolicited mail) between. Our experimental results show that ensemble methods, especially random forest and alternative classification trees, provide the highest accuracy and precision in junk mail detection, achieving an accuracy of 0.97975. The experiment demonstrates the tool's knowledge-enhancing capabilities; it will likely enhance the security of e-mail messages with powerful junk mail detection.

关键词： Support vector machines Accuracy Filtering Unsolicited e-mail Clustering algorithms Feature extraction Classification algorithms Ensemble learning Random forests Postal services

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An Effective Prediction of Rainfall Using Machine Learning Technique 5

An Effective Prediction of Rainfall Using Machine Learning T...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Rana, Nisha Guru Prasad, M. Sharma, Ashutosh Pratap, M.S. Anand Kumar, M. Aditya Pai, H. Dept. of Computer Science and Engineering Rishikesh India Graphic Era Deemed University Dept. Computer Science and Engineering Dehradun India Rishikesh India School of Computing and Information Technology Reva University Bengaluru India Dept. of Computer Application Dehradun India Dept. of Computer Science and Engg. Dehradun India

ISBN: (纸本)9781665493604

Climatic information such as temperature, humidity, and precipitation are useful for agriculturists, businesses, researchers, and the government. Estimating precipitation is a major factor that affects the environment and is one of the most important aspects of meteorological science. In this study, factual approaches and machine learning methods are used to estimate and forecast meteorological parameters for the government. Estimating precipitation is a major factor that affects the environment and is one of the most important aspects of meteorological science. Approaches are used to forecast and estimate precipitation. Daily observations were used as part of the experiment. Validation of results using real-world data is used to check the accuracy of forecasting model experimentation. The experimental results show that distribution, correlation, line-plots, and neural networks perform well for forecasting meteorological characteristics and have the best classification accuracy when compared to other types of machine learning methods for precipitation forecasting. © 2023 IEEE.

关键词： Machine learning

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Unsharp Mask Sharpening Detection via Global Analysis 35

Unsharp Mask Sharpening Detection via Global Analysis

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35th Irish Systems and Signals Conference, ISSC 2024

作者： Lu, Rong-Hui Chen, Tzong-Jer Pan, Junhong Wuyi University Information Technology and Laboratory Management Center Fujian Wuyishan354300 China School of Mathematics and Computer Science Wuyi University Fujian Wuyishan354300 China Wuyi University Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry China

ISBN: (纸本)9798350352986

Image sharpening prediction plays a significant role in image restoration, forensics, and computer vision. This work proposes a new method for detecting sharpening and indexing in digital images. Images initially undergo manipulation using Gaussian filters and Unsharp Masking (UM) with various degrees of intensity. This is followed by transformation into the frequency domain using Fourier transform. The resulting amplitude values are calculated, and their absolute values are summed along the frequency axis to produce a profile curve. These data curves are subsequently smoothed. The UM curves exhibit concavity and symmetry relative to the curve of the original image. The values of these curves at the concave positions, corresponding to various UM intensities, can be utilized as a sharpness index by comparing them to the curve values of the original image at the same positions. These sharpness indices are dependent on the UM amplification factors. This methodology finds utility in medical image processing and computer vision systems for sharpness detection and indexing. The features identified in this study, based on Fourier power spectrum analysis, can be input into a machine learning classifier for classification. © 2024 IEEE.

关键词： Fourier transforms

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Delay-Beat Differential Phase Demodulation for Laser Phase Noise Immunity in Phase-Sensitive OTDR

Delay-Beat Differential Phase Demodulation for Laser Phase N...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Qian, Heng Li, Chuan Li, Chengli Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Faculty of Information Engineering and Automation Kunming China School of Physics and Electronic Information Yunnan Normal University Yunnan Key Laboratory of Opo-electronic Information Technology Kunming China

ISBN: (纸本)9798350312614

A phase demodulation method immune to laser phase noise based on time-delay beat-frequency coherent Φ-OTDR is proposed. In this method, the two Rayleigh backscattered light beams with time delay are beat with local light beams respectively, and the phase difference of the two beat signals is used instead of the traditional distance-difference phase to obtain the local disturbance information. In this way, the impact of laser phase noise introduce by the distance-difference phase is avoid, and the constraint of laser linewidth on phase demodulation performance is weakened. In the experiment, a demodulation accuracy of -63.5dB rad2/Hz is achieved under the condition of a laser with a linewidth of 100kHz, which is improve by 11dB compared to traditional phase demodulation method. © 2023 IEEE.

关键词： distributed optical fiber sensing laser phase noise phase demodulation phase-sensitive optical time-domain reflectometry

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Enhanced Route Optimization for Wireless Networks Using Meta-Heuristic Engineering

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computer Systems Science & Engineering 2022年第10期43卷 17-26页

作者： S.Navaneetha Krishnan P.Sundara Vadivel D.Yuvaraj T.Satyanarayana Murthy Sree Jagadeesh Malla S.Nachiyappan S.Shanmuga Priya Department of Electronics and Communication Engineering SACS MAVMM Engineering CollegeMaduraiIndia Department of Computer Science and Engineering SACS MAVMM Engineering CollegeMaduraiIndia Department of Computer Science Cihan University-DuhokKurdistan RegionIraq Department of Computer Science and Engineering Bapatla Engineering College BapatlaAndhra PradeshIndia Department of Computer Application National Institute of TechnologyThiruchirapalliIndia School of Computer Science and Engineering Vellore Institute of TechnologyChennaiIndia Department of Computer Science and Engineering M.I.E.T Engineering CollegeTrichyIndia

Wireless Sensor Networks(WSN)are commonly used to observe and monitor precise *** consist of a large number of inexpensive sensor nodes that have been separated and distributed in different *** base station received the amount of data collected by the numerous *** current developments designate that the attentFgion in applications of WSNs has been increased and extended to a very large *** Trust-Based Adaptive Acknowledgement(TRAACK)Intrusion-Detection System for Wireless Sensor Networks(WSN)is described based on the number of active positive deliveries and The Kalman filter used in Modified Particle Swarm Optimization(MPSO)has been proposed to predict knot *** were run for non-malicious networks(0%malicious)and different percentages of malicious nodes were *** findings suggest that the proposed method TRAACK Modified Particle Swarm Optimization(MPSO)packet delivery rate outperforms TRAACKPSO by 3.3%with 0%malicious ***,the packet delivery rate of TRAACKMPSO is 30%malicious,3.5%better than TRAACKPSO in WSN.

关键词： Trust-based adaptive acknowledgment(TRAACK) modified particle swarm optimization(MPSO) intrusion detection system(IDS) kalman filter

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