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Fine-Tuning BERT for Aspect Extraction in Multi-domain ABSA

Informatica (Slovenia) 2023年第9期47卷 123-131页

作者： Akram, Arwa Sabir, Aliea Faculty of Computer Science and Information Technology Computer Information System Dept. University of Basrah Basrah Iraq

Aspect extraction plays a crucial role in understanding the fine-grained nuances of text data, allowing businesses and researchers to gain deeper insights into customer opinions, sentiment distributions, and preferences. This study presents a BERT-based framework for aspect extraction in ABSA and evaluates its performance. Our research focuses on the comprehensive analysis of aspect extraction as we test our method using the SEMEVAL dataset of various consumer evaluations across diverse domains, including laptops, restaurants, and Twitter. By fine-tuning BERT on a large dataset, we aim to overcome the limitations of traditional approaches and improve the accuracy and efficiency of aspect extraction in ABSA. The experimental findings provide evidence of the efficacy of our methodology with a noteworthy aspect extraction accuracy of 0.98, highlighting its capacity to properly and consistently extract features. The article also explores the applicability of our approach to new domains and its possible applications in real-world scenarios. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Sentiment analysis

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Specificity-preserving RGB-D saliency detection

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Computational Visual Media 2023年第2期9卷 297-317页

作者： Tao Zhou Deng-Ping Fan Geng Chen Yi Zhou Huazhu Fu School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Key Laboratory of System Control and Information Processing Ministry of EducationShanghaiChina Computer Vision Lab ETH Z¨urichZ¨urichSwitzerland School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’anChina School of Computer Science and Engineering Southeast UniversityNanjingChina Inception Institute of Artificial Intelligence Abu DhabiUnited Arab Emirates

Salient object detection(SOD)in RGB and depth images has attracted increasing research *** RGB-D SOD models usually adopt fusion strategies to learn a shared representation from RGB and depth modalities,while few methods explicitly consider how to preserve modality-specific *** this study,we propose a novel framework,the specificity-preserving network(SPNet),which improves SOD performance by exploring both the shared information and modality-specific ***,we use two modality-specific networks and a shared learning network to generate individual and shared saliency prediction *** effectively fuse cross-modal features in the shared learning network,we propose a cross-enhanced integration module(CIM)and propagate the fused feature to the next layer to integrate cross-level ***,to capture rich complementary multi-modal information to boost SOD performance,we use a multi-modal feature aggregation(MFA)module to integrate the modalityspecific features from each individual decoder into the shared *** using skip connections between encoder and decoder layers,hierarchical features can be fully *** experiments demonstrate that our SPNet outperforms cutting-edge approaches on six popular RGB-D SOD and three camouflaged object detection *** project is publicly available at https://***/taozh2017/SPNet.

关键词： salient object detection(SOD) RGB-D cross-enhanced integration module(CIM) multi-modal feature aggregation(MFA)

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Spearphishing Attack Detection Model Using Machine Learning Approach Based On Network Traffic Analysis 10

Spearphishing Attack Detection Model Using Machine Learning ...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Atmojo, Yohanes Priyo Hostiadi, Dandy Pramana Susila, I Made Darma Pradipta, Gede Angga Liandana, Made Ayu, Putu Desiana Wulaning Institut Teknologi dan Bisnis STIKOM Bali Faculty of Informatics and Computer Department of Information Technology Bali Indonesia Institut Teknologi dan Bisnis STIKOM Bali Department of Magister Information Systems Bali Indonesia Institut Teknologi dan Bisnis STIKOM Bali Faculty of Informatics and Computer Department of Computer System Bali Indonesia

ISBN: (纸本)9798350363104

In the digital era, people use e-mail as the primary means of communication, as well as credentials and private communication in their work. Sending fake e-mails is usually illegal and aims to steal data or implant malware. Fake e-mails must be prevented and detected as early as possible. This paper proposes a new model for detecting fake e-mail-sending activities using a machine learning classifier approach: NaÏve Bayes, k-NN and Decision Tree. The aim is to see the performance of several classification methods and analyze the best detection performance from using classification methods. This paper contributed to introducing a new extracting features technique and modelling it as a detection model using a machine learning approach. The experiment shows that the decision tree model can detect optimally with an accuracy value of 98.11%, precision of 94.04%, and recall of 99.93%. This model can help system administrators handle appropriate procedures. © 2024 IEEE.

关键词： Phishing

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

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10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

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AI Bot to Increase the Accuracy and Efficiency of Hiring Process of Business Organizations

AI Bot to Increase the Accuracy and Efficiency of Hiring Pro...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Dilusha, N.H.A.S. Weerasinghe, E.J.K.D. Chamuditha, S.M.A. Wijekoon, K.W.M. Thelijjagoda, Samantha Giguruwa, Nishantha Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer System Engineering Malabe Sri Lanka College of Sustainability and Tourism Ritsumeikan Asia Pacific University Oita Prefecture Beppu Japan

ISBN: (纸本)9798331543617

Recruitment in most organizations is normally a rather inefficient process where it takes quite a long time to match the right candidate to a given job. In this research, AI-based hiring system is recommended to improve the efficiency and accuracy of the hiring process with the help of NLP and machine learning algorithms. Employment enabling processes such as CV filtering, skill evaluation and personality profiling as well as job matching are achieved by the system. An NLP-based tool is used for short listing of CVs, assessment quizzes are used to evaluate different skills and qualifications of the candidates and chatbot enables communication between candidates and HRs, while an AI bot is used to complete different document verifications' processes. Recruitment operation is made easier and efficient, and matching of candidates to the available jobs is done better since it is less time-consuming and energy-demanding, giving a solution that may be implemented on a large scale. Further improvements are to be made in the fine tuning of algorithms, enlargement of comprehensiveness of systems and the implementation of actual sample cases before the introduction into the marketplace. © 2024 IEEE.

关键词： Bot (Internet)

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A sketch recognition method based on bi-modal model using cooperative learning paradigm

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Neural Computing and Applications 2024年第23期36卷 14275-14290页

作者： Zhang, Shihui Wang, Lei Cui, Zhiguo Wang, Shi School of Information Science and Engineering Yanshan University Hebei Street West Section Hebei Province Qinhuangdao066000 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Hebei Street West Section Hebei Province Qinhuangdao066000 China School of Information Technology Hebei University of Business and Economics Xuefu Road Hebei Province Shijiazhuang050061 China School of Mathematics and Information Science and Technology Hebei Normal University of Science and Technology Hebei Street West Section Hebei Province Qinhuangdao066000 China

Static image is an important form of displaying a sketch, representing the appearance information of the sketch. And a stroke sequence composed of several points can also express the shape and contour information of the sketch. Therefore, it is very reasonable to treat a sketch as point-modal data and image-modal data simultaneously. In this paper, a method based on bi-modal model using cooperative learning paradigm is proposed for the sketch recognition task. Specifically, in the point-modal branch, a structural point convolution block is developed by properly dividing local regions to preserve the structural information. In the image-modal branch, the hierarchical residual structure is used to fully extract image-modal features. To reduce the negative impact of noisy samples on the recognition performance, a cooperative learning paradigm is designed based on different perceptual abilities of two modal branches on noisy samples, that is, when training the two branches, the noisy samples can be filtered out through information exchanges and mutual learning. Extensive experiments on the sketch datasets TU-Berlin and QuickDraw show that the proposed method outperforms most baseline methods and has many advantages such as no dependence on additional data and stroke information. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolution

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Design Thinking and Innovative Tool Use in Business Model

Design Thinking and Innovative Tool Use in Business Model

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2024 International Conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Singhi, Nitika Verma, Ankush Agrawal, Atul Khan, Irfan Computer Science Engineering Shri Vaishnav Vidhyapeeth Vishwavidyalava Indore India Data Analytics and Information System Prestige Institute of Management and Research Indore India Computer Science Engineering Sushila Devi Bansal College of Technology Indore India Computer Science Engineering Bansal Institute of Science and Technology Bhopal India

ISBN: (纸本)9798350375237

In past few year designs thinking has worked on several new effective problem and has generated innovative solution for new business model. For the scenario of today's demand, to better understand how design thinking can work on different perspectives in business models and how it can bring new innovative solutions for any problem is essential However, studies of existing literature explore the application, potential, and challenges of Design Thinking towards the comprehensive aim of this paper, which regards business model innovation as a fundamental subject of Design Thinking. The paper provides practical tools for exploring Design Thinking along with various critical aspects for the researchers to explore how Design Thinking can be applied to innovative business models. The main focus of this paper is to explain the basics of Design Thinking, how to frame the mind through different techniques used, the Design Techniques used for a business model, the methodology of a design thinker, and different tools like BPM, Blue Work Live, Mind Mapping, Brainstorming, and RICPS. © 2024 IEEE.

关键词： Mapping

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AI-Based Child Care Parental Control system 4

AI-Based Child Care Parental Control System

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Jayasekara, Udara Maniyangama, Hansindu Vithana, Kalhan Weerasinghe, Tharana Wijekoon, Janaka Panchendrarajan, Rrubaa Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka Department of Computer System Engineering Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (纸本)9798350398090

Due to the prevalence of the COVID-19 epidemic around the globe, children were compelled to engage in remote learning through online platforms, hence mobile phone has become one of their predominant devices. Mobile device with Internet access offers a major outlet for education, entertainment, and social connection, but this combination can lead to several significant bad sequences such as online exploitation, harmful addictions, and other negative impacts of online social networking. To address harmful effects, parental controls are becoming more crucial, yet Sri Lankan parents are less aware of this. Consequently, this study proposes a parental control system to monitor their child's activities. Android, Microsoft Azure, Java, Python, OpenCV, MySQL, and FastAPI are among the most prominent technologies utilized in the proposed application's development. The suggested approach focuses primarily on the Sri Lankan context and aims to enhance parental digital literacy while safeguarding children from cyber threats. Yielded results showed the proposed mobile application for the identification of toxic words, drugs & alcohol content, game character images, and Instagram Sinhala comments severity as 94%, 95%, 97%, and 55% respectively in controlled experiments. © 2022 IEEE.

关键词： Control systems

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Position Measurement Based Slave Torque Feedback Control for Teleoperation systems With Time-Varying Communication Delays

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 388-402页

作者： Xian Yang Jing Yan Changchun Hua Xinping Guan the Institute of Information Science and Engineering Yanshan University the Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004China IEEE the Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004China the Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Bilateral teleoperation system is referred to as a promising technology to extend human actions and intelligence to manipulating objects *** the tracking control of teleoperation systems,velocity measurements are necessary to provide feedback ***,due to hardware technology and cost constraints,the velocity measurements are not always *** addition,the time-varying communication delay makes it challenging to achieve tracking *** paper provides a solution to the issue of real-time tracking for teleoperation systems,subjected to unavailable velocity signals and time-varying communication *** order to estimate the velocity information,immersion and invariance(I&I)technique is employed to develop an exponential stability velocity *** the proposed velocity observer,a linear relationship between position and observation state is constructed,through which the need of solving partial differential and certain integral equations can be ***,the mean value theorem is exploited to separate the observation error terms,and hence,all functions in our observer can be analytically *** the estimated velocity information,a slave-torque feedback control law is presented.A novel Lyapunov-Krasovskii functional is constructed to establish asymptotic tracking *** particular,the relationship between the controller design parameters and the allowable maximum delay values is ***,simulation and experimental results reveal that the proposed velocity observer and controller can guarantee that the observation errors and tracking error converge to zero.

关键词： Asymptotic stability bilateral teleoperation time-varying delay velocity observer

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Modeling and Verification of Aircraft Takeoff Through Novel Quantum Nets

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computers, Materials & Continua 2022年第8期72卷 3331-3348页

作者： Maryam Jamal Nazir Ahmad Zafar Atta-ur-Rahman Dhiaa Musleh Mohammed A.Gollapalli Sghaier Chabani School of Systems and Technology University of Management&Technology(UMT)C-II Block C 2 Phase 1 Johar TownLahore54770PunjabPakistan Department of Computer Science COMSATS University Islamabad–Sahiwal CampusSahiwal57000PunjabPakistan Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)Dammam31441Saudi Arabia Department of Computer Information System(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)Dammam31441Saudi Arabia

The formal modeling and verification of aircraft takeoff is a challenge because it is a complex safety-critical *** task of aircraft takeoff is distributed amongst various computer-based controllers,however,with the growing malicious threats a secure communication between aircraft and controllers becomes highly *** research serves as a starting point for integration of BB84 quantum protocol with petri nets for secure modeling and verification of takeoff *** integrated model combines the BB84 quantum cryptographic protocol with powerful verification tool support offered by petri *** model certain important properties of BB84,a new variant of petri nets coined as Quantum Nets are proposed by defining their mathematical foundations and overall system dynamics,furthermore,some important system properties are also abstractly *** proposed QuantumNets are then applied for modeling of aircraft takeoff process by defining three quantum nets:namely aircraft,runway controller and gate *** authentication between quantum nets,the use of external places and transitions is demonstrated to describe the encryptiondecryption process of qubits ***,the developed takeoff quantum network is verified through simulation offered by colored petri-net(CPN)***,reachability tree(RT)analysis is also performed to have greater confidence in feasibility and correctness of the proposed aircraft takeoff model through the Quantum Nets.

关键词： Aircraft takeoff BB84 quantum cryptography petri-nets quantum nets formal modeling and verification

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