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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Hongliang Chen, Shuo Luo, Lei Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China School of Intelligence Science and Technology Nanjing University China

ISBN: (纸本)157735897X

Spherical Sliced-Wasserstein (SSW) has recently been proposed to measure the discrepancy between spherical data distributions in various fields, such as geology, medical domains, computer vision, and deep representation learning. However, in the original SSW, all projection directions are treated equally, which is too idealistic and cannot accurately reflect the importance of different projection directions for various data distributions. To address this issue, we propose a novel data-adaptive Discriminative Spherical Sliced-Wasserstein (DSSW) distance, which utilizes a projected energy function to determine the discriminative projection direction for SSW. In our new DSSW, we introduce two types of projected energy functions to generate the weights for projection directions with complete theoretical guarantees. The first type employs a non-parametric deterministic function that transforms the projected Wasserstein distance into its corresponding weight in each projection direction. This improves the performance of the original SSW distance with negligible additional computational overhead. The second type utilizes a neural network-induced function that learns the projection direction weight through a parameterized neural network based on data projections. This further enhances the performance of the original SSW distance with less extra computational overhead. Finally, we evaluate the performance of our proposed DSSW by comparing it with several state-of-the-art methods across a variety of machine learning tasks, including gradient flows, density estimation on real earth data, and self-supervised learning. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Self-supervised learning

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Leveraging Smart Video-Based Learning to Enhance Student Engagement and Collaboration 6

Leveraging Smart Video-Based Learning to Enhance Student Eng...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Serasinghe, Chanukya Perera, Nashali Samarasinghe, Devindu Muthusinghe, Madusha Wijesundara, Malitha Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Humanities and Sciences Malabe Sri Lanka

ISBN: (纸本)9798331517878

The COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. This research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. The system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. The methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. The results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. The efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. This innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience. © 2024 IEEE.

关键词： Federated learning

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A Frog based Nature Inspired Algorithm for Solving Optimization Problem 1

A Frog based Nature Inspired Algorithm for Solving Optimizat...

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1st International Conference on Informatics, ICI 2022

作者： Sharma, Archana Rajpoot, Dharmveer Singh Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Noida India

ISBN: (纸本)9781665437219

Humans have always been inspired by nature, and they have continued to adapt and absorb various environmental systems. Several Nature Inspired Algorithms have been created and used to a wide range of challenging engineering optimization problems. The self-defense strategy used by prey animals prompted us to write this paper. In nature, we see that one species is the food of another, implying that one animal's survival is dependent on the survival of another. As a result, individuals are exposed to a variety of dangers in their daily lives. As a result, many species develop defense mechanisms to avoid becoming prey to another predator animal. The suggested Improved Anti-predatory Nature Inspired algorithm (IAPNIA) is inspired by frogs' self-defense strategies for survival. We have improved the directional flee functionality of the algorithm by observing that the frog can only observe the nearest peer frog and thus makes a jump towards it. The suggested method's findings were compared to the existing Anti-predatory Nature Inspired Algorithm (APNIA). We tested the algorithm on 10 benchmark functions and compared it with the existing APNIA, in which we performed better in 5 benchmarks and similar in 4 benchmarks. We also compared the convergence plots on 6 benchmark functions on which our algorithm converges faster than the existing APNIA. This demonstrates the value of the proposed algorithm in terms of convergence and quality of the solution achieved. © 2022 IEEE.

关键词： Optimization

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Fingerprint generation and authentication though Adaptive convolution generative adversarial network (ADCGAN) 7

Fingerprint generation and authentication though Adaptive co...

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7th International Multi-Topic ICT Conference: IMTIC 2023

作者： Mustafa, Syed Muhammad Nabeel Zehra, Syeda Sundus Baber, Alina Siddiqui, Maria Andleeb Ned University of Engineering and Technology Dept. of Computer Science & Information Technology Karachi Pakistan Sir Syed University of Engineering and Technology Dept of Software Engineering Karachi Pakistan

ISBN: (纸本)9798350338461

Fingerprints are crucial in identification of humans. The uniqueness of finger prints makes it an interesting subject. Fingerprints are termed as a technique used to define, assess, and quantify a person's physical and behavioral property. Deep learning has made its application in all the major fields such as natural language processing, computer vision and speech processing. Deep learning has also found its application in the important subject of fingerprint synthesis and biometric. The ever-growing complexity of fingerprint authentication issues, from cellphone authentication to airport security systems, seems to be best handled by these models. In recent years, deep learning-based models have been used more and more to raise the accuracy of various fingerprint recognition systems. The persuasive capacity of Generative Adversarial Networks (GANs) to generate believable instances can be credibly taken from an existing distribution of samples. GAN exhibits exceptional performance on data generation-based tasks and also encourages study in privacy and security. In this work, using Adaptive Deep Convolution Generative Adversarial Networks (ADCGAN), we develop a model that generates and authenticate the fingerprints. A Socofing dataset was trained on ADGAN model. The model gave 92% accuracy. The conduct of fingerprint research has been made possible due to ADGAN, without restrictions related to the confidential nature of biometric data. © 2023 IEEE.

关键词： Generative adversarial networks

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Digital Dissemination of information Based on Knowledge Graph Recommendation Algorithm

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第19期48卷 31-44页

作者： Wang, Lu Zhang, Yunzhen Department of Investigation Henan Police College Zhengzhou450046 China College of Computer Science and Information Engineering Anyang Institute of Technology Anyang455000 China School of Energy and Intelligence Engineering Henan University of Animal Husbandry and Economy Zhengzhou450046 China

The rapid advancement of technology has made recommendation systems a research focus in modern information processing. To enhance the accuracy of recommendation systems and effectively capture and recommend user interests in complex data environments, this study starts with the combination of knowledge graphs and graph neural networks. Firstly, the water wave network algorithm is introduced to diffuse user interest information and fully utilize the global information of the knowledge graph. Secondly, the processed knowledge graph is input into the graph neural network for deep feature learning, and finally, a recommendation algorithm combining the ripple network and graph neural network is proposed. The test results showed that when the final recommendation results of the model were 5, 10, 15, 20, 25, and 30, the matching degrees of the recommendation results were 99.2%, 98.3%, 97.4%, 96.5%, 95.8%, and 95.2%, respectively. On the 100% dataset, the improved model had a 7.52% higher hit rate than the original knowledge graph convolutional network, and the mean reciprocal ranking value increased by 14.29. The normalized discounted cumulative gain on different datasets has increased by an average of 7.0%. Experiments have shown that by combining knowledge graphs and graph neural networks, the dynamic adaptability and recommendation quality of recommendation systems have been effectively improved, providing an efficient and accurate solution for information recommendation. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Knowledge graph

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FedBully: A Cross-Device Federated Approach for Privacy Enabled Cyber Bullying Detection using Sentence Encoders

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Journal of Cyber Security and Mobility 2023年第4期12卷 465-496页

作者： Shetty, Nisha P. Muniyal, Balachandra Priyanshu, Aman Das, Vedant Rishi Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India

Cyberbullying has become one of the most pressing concerns for online platforms, putting individuals at risk and raising severe public concerns. Recent studies have shown a significant correlation between declining mental health and cyberbullying. Automated detection offers a great solution to this problem;however, the sensitivity of client-data becomes a concern during data collection, and as such, access may be restricted. This paper demonstrates FedBully, a federated approach for cyberbullying detection using sentence encoders for feature extraction. This paper introduces concepts of secure aggregation to ensure client privacy in a cross-device learning system. Optimal hyper-parameters were studied through comprehensive experiments, and a computationally and communicationally inexpensive network is proposed. Experiments reveal promising results with up to 93% classification AUC (Area Under the Curve) using only dense networks to fine-tune sentence embeddings on IID datasets and 91% AUC on non-IID datasets, where IID refers to Independent and Identically Distributed data. The analysis also shows that data independence profoundly impacts network performance, with AUC decreasing by a mean of 5.1% between Non-IID and IID. A rich and extensive study has also been performed on client network size and secure aggregation protocols, which prove the robustness and practicality of the proposed model. The novel approach presented offers an efficient and practical solution to training a cross-device cyberbullying detector while ensuring client-privacy. © 2023 River Publishers.

关键词： Learning systems

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Pindel-TD:A Tandem Duplication Detector Based on A Pattern Growth Approach

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Genomics, Proteomics & Bioinformatics 2024年第1期22卷 99-104页

作者： Xiaofei Yang Gaoyang Zheng Peng Jia Songbo Wang Kai Ye School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Center for Mathematical Medical the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China Genome Institute the First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Life Science and Technology Xi’an Jiaotong UniversityXi’an 710049China Faculty of Science Leiden UniversityLeiden 2311 EZNetherland

Tandem duplication(TD)is a major type of structural variations(SVs)that plays an important role in novel gene formation and human ***,TDs are often missed or incorrectly classified as insertions by most modern SV detection methods due to the lack of specialized operation on TD-related mutational ***,we developed a TD detection module for the Pindel tool,referred to as Pindel-TD,based on a TD-specific pattern growth ***-TD is capable of detecting TDs with a wide size range at single nucleotide *** simulated and real read data from HG002,we demonstrated that Pindel-TD outperforms other leading methods in terms of precision,recall,F1-score,and ***,by applying Pindel-TD to data generated from the K562 cancer cell line,we identified a TD located at the seventh exon of SAGE1,providing an explanation for its high ***-TD is available for non-commercial use at https://***/xjtu-omics/pindel.

关键词： Tandem duplication Pattern growth Short-read sequencing Structural variation SAGE1

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Blockchain-Based Anonymous Authentication Scheme with Traceable Pseudonym Management in ITS 6th

Blockchain-Based Anonymous Authentication Scheme with Trace...

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6th International Conference on Machine Learning for Cyber Security, ML4CS 2024

作者： Wang, Mingliang Tan, Haowen Zheng, Wenying School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China

ISBN: (纸本)9789819645657

With the continuous development of intelligent transportation systems (ITS), the Vehicular Ad-hoc Networks, as a core component of smart transportation, has become a crucial technology for improving traffic safety and transportation efficiency. However, the open communication environment and highly dynamic nature of VANETs make them vulnerable to many security threats. The existing schemes are difficult to get a balance between anonymity and traceability. Therefore, this paper proposes a blockchain-based anonymous authentication scheme with pseudonym management protocol. Using the decentralized nature of blockchain, the scheme incorporates a pseudonym management mechanism based on smart contracts, ensuring that a user’s real identity can only be revealed upon consensus from a certain number of RSU, thereby enhancing identity privacy protection. Additionally, using the immutability of smart contracts, a vehicle authentication protocol is designed to improve system decentralization, data security, and identity traceability. Through security analysis, the proposed scheme demonstrates superior security performance against common threats. The analysis of performance shows that the performance of our proposed scheme is improved. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication Protocol

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Machine Speech Chain with Emotion Recognition 11

Machine Speech Chain with Emotion Recognition

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11th International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2024

作者： Naufal, Akeyla Pradia Lestari, Dessi Puji Purwarianti, Ayu Azizah, Kurniawati Tanaya, Dipta Sakti, Sakriani School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia University of Indonesia Faculty of Computer Science Depok Indonesia Graduate School of Information Science Nara Institute of Science and Technology Nara Japan

ISBN: (纸本)9798331520311

Developing natural speech recognition and speech synthesis systems requires speech data that authentically represents real emotions. However, this type of data is often challenging to obtain. Machine speech chain offers a solution to this challenge by using unpaired data to continue training models initially trained with paired data. Given the relative abundance of unpaired data compared to paired data, machine speech chain can be instrumental in recognizing emotions in speech where training data is limited. This study investigates the application of machine speech chain in speech emotion recognition and speech recognition of emotional speech. Our findings indicate that a model trained with 50% paired neutral emotion speech data and 22% paired non-neutral emotional speech data shows a reduction in Character Error Rate (CER) from 37.55% to 34.52% when further trained with unpaired neutral emotion speech data. The CER further decreases to 33.75% when additionally trained with combined unpaired speech data. The accuracy of recognizing non-neutral emotions ranged from 2.18% to 53.51 %, though the F1 score fluctuated, increasing by up to 20.6% and decreasing by up to 23.4%. These results suggest that the model demonstrates a bias towards the majority class, as reflected by the values of the two metrics. © 2024 IEEE.

关键词： Chains

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An Enhanced Approach to Intelligent computer-Assisted Localization of Liver Tumor on Computed Tomography Images

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SN computer science 2024年第7期5卷 809页

作者： Arakeri, Megha Lakshmana Reddy, Raghavendra Ravishankar, H. Deepa, K.R. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India Seamovations Labs Pvt Ltd Karnataka Bengaluru India School of Computer Science and Engineering REVA University Karnataka Bengaluru India School of Computer Science and Information Technology REVA University Karnataka Bengaluru India School of Electrical and Electronics Engineering REVA University Karnataka Bengaluru India

Computed Tomography (CT) Imaging is frequently used to find liver cancer. CT imaging of the liver generates cross-sectional images of the abdominal region. The task of segmenting a liver tumor on CT images is tedious due to the anatomic complexity of the liver. Therefore, liver tumor detection has been performed manually or semi-automatically which is an exhaustive process. Further, a tumor may be present on only some slices of the CT scan and hence there is a need to automatically identify images that contain tumors from acquired CT images and then locate a region of the tumor on those images for further diagnosis. Automatic detection helps the radiologist to obtain fast, accurate, and effective results. Accordingly, in this paper, an enhanced approach is proposed based on deep learning, efficient fuzzy c-means (EFCM) clustering, and a region-based algorithm for the detection of a liver tumor. A deep learning model built on a convolutional neural network is used to identify whether the CT image contains a tumor. If it contains the tumor, then the proposed EFCM and region-based algorithms are applied to locate the tumor on the CT image. The EFCM algorithm is combined with a region-based technique to automatically determine the initial pixel and threshold values required to segment the liver tumor. The proposed method is applied to several CT abdominal images. The results of the tumor segmentation are validated by comparing them with the tumor regions detected by radiologists. The estimated results of the experiment show that the proposed technique successfully detects the liver tumor on the CT image with less than 2% relative error. © The Author(s) 2024.

关键词： Computed tomography computer-aided detection Healthcare Liver tumor Medical image Segmentation

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