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1st International Workshop on Nakba Narratives as Language Resources, Nakba-NLP 2025

作者： Hamed, Osama Zaidkilani, Nadeem Department of Computer Systems Engineering Palestine Technical University-Kadoorie Palestine Department of Computer Engineering and Security Mathematics University Rovira i Virgili Spain Department of Engineering and Technology Al-Zaytona University of Science and Technology Palestine

ISBN: (纸本)9798891762169

In this paper, we enrich Arabic Natural Language Processing (NLP) resources by introducing the "Nakba Topic Classification Corpus (NTCC)," a novel annotated Arabic corpus derived from narratives about the Nakba. The NTCC comprises approximately 470 sentences extracted from eight short stories and captures the thematic depth of the Nakba narratives, providing insights into both historical and personal dimensions. The corpus was annotated in a two-step process. One third of the dataset was manually annotated, achieving an IAA of 87% (later resolved to 100%), while the rest was annotated using a rule-based system based on thematic patterns. This approach ensures consistency and reproducibility, enhancing the corpus’s reliability for NLP research. The NTCC contributes to the preservation of the Palestinian cultural heritage while addressing key challenges in Arabic NLP, such as data scarcity and linguistic complexity. By like topic modeling and classification tasks, the NTCC offers a valuable resource for advancing Arabic NLP research and fostering a deeper understanding of the Nakba narratives. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Deep Learning Approach to Offline Signature Forgery Prevention 9

Deep Learning Approach to Offline Signature Forgery Preventi...

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9th International Conference on Advanced Computing and Communication systems, ICACCS 2023

作者： Blessy, Prasanna Kathiresan, K. Yuvaraj, N. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397376

Image analytics solutions are very crucial for not only security, but also for authentication purposes. One such intriguing new Deep Learning application is automatic signature verification. Signed papers are the foundation of all transactions and governance in banks and government agencies, to mention a few examples. Verifying signatures on these documents is an important aspect of the back-office operations. Manual signature verification is not only time intensive, but also prone to mistakes and possible fraud. These flaws may be removed using AI-based automated signature analysis, resulting in considerable efficiency benefits. Based on the Siamese Neural Network, this research proposes an offline solution for signature identification and verification. To construct a feature vector, a collection of global features is utilised. © 2023 IEEE.

关键词： Deep learning

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Harmonizing Healthcare Perspectives and In-Depth Strategy for Unifying Diverse Medical Big Data Sources 10

Harmonizing Healthcare Perspectives and In-Depth Strategy fo...

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Manikandan, A. Thirumal, S. Kumar, N. Vels Institute of Science Technology &advanced Studies Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

The healthcare sector has generated vast amounts of data, posing a significant challenge in terms of integration. Recently, deep learning frameworks have gained extensive utilization for forecasting relationships within healthcare systems. Specifically, the success of the Heterogeneous Multi-Layer Deep Learning Network (HMLDN) in seamlessly amalgamating diverse medical data for enhanced diagnostics and storage is explored in this paper. The initial discussion delves into capsule optimized networks and attention maps, laying the groundwork for their application in the formation of HMLDN to achieve efficient data integration. © 2024 IEEE.

关键词： Electronic health record

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Wide and fault diameter in Kneser graphs for enhanced fault tolerance in parallel computing

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International Journal of Networking and Virtual Organisations 2024年第3期31卷 169-190页

作者： Sundara Rajan, R. Kirithiga Nandini, G. Lin, Yuqing Reji, Remi Mariam Department of Mathematics Hindustan Institute of Technology and Science Chennai603 103 India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai603 103 India School of Electrical Engineering and Computing The University of Newcastle CallaghanNSW2308 Australia Department of Mathematics Christian College Kerala Chengannur689 122 India

A system’s fault tolerance is its capacity to function even if one or more of its components fail. Implementing a fault-tolerant network becomes an important criterion for reliable computing. Reliability measures play a significant part in recognising the role of faulty and non-faulty processors in a parallel computing system. Parallel computing is used primarily for saving time, solving big problems, and doing multiple tasks at once at the same time. Various reliability measures have been introduced to evaluate a network’s fault-tolerance capability. We have measured the wide diameter and fault diameter of the Kneser graphs in this study. Also, we have verified the fault diameter obtained using an experimental study. Further, we have described some applications of wide diameter and fault diameter in parallel and distributed computing. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Fault tolerant computer systems

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Video Recommendation System Using Machine-Learning Techniques

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Journal of Harbin institute of technology(New Series) 2024年第4期31卷 24-33页

作者： Meesala Sravani Ch Vidyadhari S Anjali Devi Department of Computer Science and Engineering GMR Institute of TechnologyRajam 532127Andhra PradeshIndia Department of Information Technology Gokaraju Rangaraju Institute of Engineering and TechnologyKukatpallyHydarabad 500090TelanganaIndia Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522302Andhra PradeshIndia

In the realm of contemporary artificial intelligence,machine learning enables automation,allowing systems to naturally acquire and enhance their capabilities through *** this cycle,Video recommendation is finished by utilizing machine learning strategies.A suggestion framework is an interaction of data sifting framework,which is utilized to foresee the“rating”or“inclination”given by the different *** expectation depends on past evaluations,history,interest,IMDB rating,and so *** can be carried out by utilizing collective and substance-based separating approaches which utilize the data given by the different clients,examine them,and afterward suggest the video that suits the client at that specific *** required datasets for the video are taken from *** recommender framework is executed by utilizing Python Programming *** building this video recommender framework,two calculations are utilized,for example,K-implies Clustering and KNN grouping.K-implies is one of the unaided AI calculations and the fundamental goal is to bunch comparable sort of information focuses together and discover the *** that K-implies searches for a steady‘k'of bunches in a dataset.A group is an assortment of information focuses collected due to specific similitudes.K-Nearest Neighbor is an administered learning calculation utilized for characterization,with the given information;KNN can group new information by examination of the‘k'number of the closest information *** last qualities acquired are through bunching qualities and root mean squared mistake,by using this algorithm we can recommend videos more appropriately based on user previous records and ratings.

关键词： video recommendation system KNN algorithms collaborative filtering content⁃based filtering classification algorithms artificial intelligence

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Efficient IoT-based heart disease prediction framework with Weight Updated Trans-Bidirectional Long Short Term Memory-Gated Recurrent Unit

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Web Intelligence 2025年第Pre-press期Pre-press卷 1-28页

作者： Sasirekha, K. Asha, D. Sivaganga, P. Harini, R. Computer Science and Business Systems R.M.D Engineering College India Information technology Loyola Institute of Technology India Artificial Intelligence and Data Science Vel Tech High Tech Dr. R. Rangarajan Dr. Sakunthala Engineering College India AI&DS VelTech Multi Tech Dr.Rangarajan Dr. Sakunthala Engineering College India

The integrated system has generated numerous features for the users, like as identifying heart disease by its symptoms, forwarding the information to the doctors regarding the phase of the probability of disease as well as aiding to fix it. When an emergency situation exists, the system forwards the emergency alert to the respective doctor. Moreover, the automatic system is needed to diagnose heart disease but, the larger data is not sufficient to train the model. Thus, the Internet of Things (IoT) is employed to manage the huge amount of data. Therefore, a novel prediction of heart diseases is implemented with the aid of IoT-based deep learning approaches. Here, the collected data is collected from the three standard databases and then perform preprocessed over the gathered data. Here, the IoT assisted deep learning model is performed to predict heart related diseases accurately. Further, the acquired features of heart diseases are selected using the developed Hybrid Chameleon Electric Fish Swarm Optimization (HCEFSO) via Chameleon Swarm Algorithm (CSA) and Electric Fish Optimization (EFO). Then, the optimally selected features are fed to the training process, where the Trans-Bi-directional Long Short-Term Memory with Gated Recurrent Unit (Trans-Bi-LSTM-GRU) is adopted for predicting heart diseases. Here, the weights are updated with the developed HCEFSO while validating the training phase. The trained Trans-Bi-LSTM-GRU network is used in the testing phase for predicting heart diseases. © 2024 – IOS Press. All rights reserved.

关键词： Deep learning

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computer-controlled ultra high voltage amplifier for dielectric elastomer actuators

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Biomimetic Intelligence & Robotics 2024年第1期4卷 14-23页

作者： Ardi Wiranata Zebing Mao Yu Kuwajima Yuya Yamaguchi Muhammad Akhsin Muflikhun Hiroki Shigemune Naoki Hosoya Shingo Maeda Department of Mechanical and Industrial Engineering University of Gadjah MadaYogyakarta 55281Indonesia Department of Mechanical Engineering Tokyo Institute of TechnologyTokyo 152-8550Japan Department of Engineering and Science Shibaura Institute of TechnologyTokyo 135-8548Japan Department of Electrical Engineering Shibaura Institute of TechnologyTokyo 135-8548Japan Department of Engineering Science and Mechanics Shibaura Institute of TechnologyTokyo 135-8548Japan Living Systems Materialogy(LiSM)Research Group International Research Frontiers Initiative(IRFI)Tokyo Institute of TechnologyYokohama 226-8501Japan

Soft robotics is a breakthrough technology to support human-robot *** soft structure of a soft robot can increase safety during human and robot *** of the promising soft actuators for soft robotics is dielectric elastomer actuators(DEAs).DEAs can operate silently and have an excellent energy *** simple structure of DEAs leads to the easy fabrication of soft *** simplicity combined with silent operation and high energy density make DEAs interesting for soft robotics *** actuation follows the Maxwell-pressure *** pressure produced in the DEAs actuation depends much on the voltage *** DEAs requires high voltage to gain an *** the power consumption of DEAs is in the milli-Watt range,the current needed to operate the DEAs can be *** commercially available DC-DC converters can convert the volt range to the kV *** order to get a voltage in the 2-3 kV range,the reliable DC-DC converter can be pricy for each *** problem hinders the education of soft actuators,especially for a newcomer laboratory that works in soft electric *** paper introduces an entirely do-it-yourself(DIY)Ultrahigh voltage amplifier(UHV-Amp)for education in soft ***-Amp can amplify 12 V to at a maximum of 4 kV *** a demonstration,we used this UHV-Amp to test a single layer of powdered-based *** strategy to build this educational type UHV-Amp was utilizing a Cockcroft-Walton circuit structure to amplify the voltage range to the kV *** its current state,the UHV-Amp has the potential to achieve approximately 4 *** created a simple platform to control the UHV-Amp from a personal *** near future,we expect this easy control of the UHV-Amp can contribute to the education of soft electric actuators.

关键词： Dielectric elastomer actuators Electric amplifier Soft actuators Soft robotics Soft actuator education

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GENTEEL-NEGOTIATOR: LLM-Enhanced Mixture-of-Expert-Based Reinforcement Learning Approach for Polite Negotiation Dialogue 39

GENTEEL-NEGOTIATOR: LLM-Enhanced Mixture-of-Expert-Based Rei...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Priya, Priyanshu Chigrupaatii, Rishikant Firdaus, Mauajama Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India Dhanbad India School of Artificial Intelligence and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)157735897X

Developing intelligent negotiation dialogue systems that resolve conflicts and promote equitable, inclusive, and sustainable outcomes is at the forefront of advancing automated negotiation technology for social good. Negotiation involves balancing cooperation and competition to maximize value without causing offense. Using polite language fosters mutual understanding and creates a respectful and collaborative environment essential for successful negotiations in various domains. Considering this, in this paper, we propose a polite negotiation dialogue system, GENTEEL-NEGOTIATOR for social good applications to boost the overall quality of negotiation outcomes. We focus on developing a negotiation dialogue system for two key application areas, namely tourism and e-commerce. We begin by curating a unique negotiation dialogue dataset, NEGOCHAT for tourism. We further enrich the NEGOCHAT and Integrative Negotiation Dataset (IND) for e-commerce with various negotiation strategies. These datasets are then used to develop the GENTEEL-NEGOTIATOR, leveraging the Large Language Model (LLM) and mixture-of-expert (MoE)-based reinforcement learning approach. The proposed MoE-based method employs heuristic experts dedicated to negotiation, politeness, and dialogue coherence to facilitate the learning of diverse semantics by analyzing the dialogue context. A novel reward function with negotiation strategy congruence, politeness, dialogue coherence, and engagingness rewards is designed to guide the policy's learning for generating responses. Automatic and human evaluations on NEGOCHAT and IND datasets validate the effectiveness of GENTEEL-NEGOTIATOR in generating polite responses during negotiation while maintaining conversation goals, including coherence and engagingness. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Reinforcement learning

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Comparative Study of Different Optical Character Recognition Models on Handwritten and Printed Medical Reports

Comparative Study of Different Optical Character Recognition...

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2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Kumar, Anand Singh, Pardeep Lata, Kusum National Institute of Technology Hamirpur Department of Computer Science and Engineering Hamirpur India Sharda University Department of Computer Science and Engineering School of Engineering and Technology India

ISBN: (纸本)9798350397208

Text may be extracted from an image using a method called optical character recognition (OCR). An OCR is mostly used to translate existing paper documents or image data into editable documents. To create an OCR, a large variety of algorithms are needed, and it essentially functions in two stages, such as character and word detection. To retain a document's structure while using a more advanced technique, an OCR additionally focuses. The advancement in OCR has been very promising lately. Different tools have arrived in the market ranging from open-source tools PyTesseract to big established companies releasing their own OCR tools like Amazon's AWS Textract and Google's Google Vision. The accuracy of modern OCR techniques like AWS Textract, Google Vision, and updated versions of Pytesseract haven't been analyzed for different datasets. For complex datasets like a CBC report, there are various parameters and fields in a tabular and unstructured fashion. Every algorithm has a different approach to extracting the text from the image, so it becomes difficult to determine how accurate these models are on such datasets. Based on 2 different datasets, one handwritten and the other printed CBC images, this study performed analysis using the above 3 models by building an end-to-end pipeline to retrieve the necessary fields, thus making it feasible to access the accuracy of these models. Upon testing these models' accuracy, the study concluded that the AWS Textract model was the highest-performing model for both datasets, achieving an accuracy of 95% for the printed dataset and 89.42% for the handwritten IAM dataset after preprocessing. © 2023 IEEE.

关键词： Blood

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Multi-layered Clustering for Context-aware Monitoring of District Heating Network

Multi-layered Clustering for Context-aware Monitoring of Dis...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Boeva, Veselka Abghari, Shahrooz Devagiri, Vishnu Manasa Brage, Jens Blekinge Institute of Technology Department of Computer Science Karlskrona Sweden Noda Intelligent Systems Ab Karlshamn Sweden

ISBN: (纸本)9798350362480

In this study, we propose to explore multi-layered clustering to provide a context-aware data analytics tool for monitoring the network behavior of subsystems, such as a district heating (DH) network. Multi-layer clustering, in contrast to multi-view clustering, does not assume conditional independence of layers. The main idea of our approach is based on the integration of clustering models produced by considering different perspectives that capture information about the monitored subsystems' operational behavior or performance as well as their contextual environment. The initial clustering layer can reflect a static context, which is important for the subsystems' performance. It will be used as a base on which clustering models produced with respect to other analyzed operational characteristics and contexts will be layered. This will facilitate analysis and comparison of the subsystems' behavior in two comparable time periods and, eventually, identification of deviations that need attention. The proposed approach is evaluated and validated in a use case from the DH domain. The multi-layered clustering is applied and demonstrated to be robust for continuous context-aware analysis of the performance of a network of DH substations. © 2024 IEEE.

关键词： context-aware analysis district heating hypergraph visualization multi-layer clustering shared nearest neighbor similarity

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