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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Kumar, R. Thanga Sunori, S.K. Yuvaraj, K. Gupta, Mridula Ashok, Bhandari Mahesh Naveena, S. Sathiya School of Management - UG Management Karnataka Bangalore India Graphic Era Hill University Uttarakhand Bhimtal248002 India Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Vishwakarma Institute of Technology Pune India Prince Shri Venkateshwara Padmavathy Engineering College Department of Business Administration Chennai127 India

ISBN: (纸本)9798350388916

For many therapeutic uses, including image-guided therapies, picture registration is crucial in medicine. Though challenging, this field of medical picture registration has lately seen significant algorithmic performance improvements because to the rise of machine learning. Some medical applications are made possible by deep neural networks, such as faster and more accurate picture registration, which is crucial in the fight against malignancies during surgery. Medical image registration procedures sometimes include complex distortion issues. Despite the several suggested sophisticated registration models, deformable registration continues to be a tough task, particularly when dealing with large volumetric deformations, in terms of accuracy and efficiency. Our unsupervised pyramid attention networks (PAN) and network are designed for non-rigid and flexible registration, respectively, with this goal in mind. In contrast to networks that depend on high-weight attentions or transformers, our network is a clean convolutional pyramid, making full use of the pyramid structure's inherent benefits. Specifically, our network guarantees the deformation field's rationality by predicting it from coarse to fine using a step-by-step recursion method that incorporates high-level semantics. Our network is able to achieve deformable registration without the need for separate affine pre-alignment because of the recursive pyramid technique. In terms of Dice scores, average symmetric surface separation, Hausdorff distance, and Jacobian, our network constantly outperforms state-of-the-art, according to experimental data. Our network continues to perform well in adjusting for the huge deformation even when the affine pre-alignment is not included in the data. © 2024 IEEE.

关键词： Deep neural networks

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Domain Adaptation for Large-Vocabulary Object Detectors

arXiv

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arXiv 2024年

作者： Jiang, Kai Huang, Jiaxing Xie, Weiying Lei, Jie Li, Yunsong Shao, Ling Lu, Shijian State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China S-lab School of Computer Science and Engineering Nanyang Technological University Singapore UCAS-Terminus AI Lab University of Chinese Academy of Sciences Beijing China School of Electrical and Data Engineering The University of Technology Sydney Australia

Large-vocabulary object detectors (LVDs) aim to detect objects of many categories, which learn super objectness features and can locate objects accurately while applied to various downstream data. However, LVDs often struggle in recognizing the located objects due to domain discrepancy in data distribution and object vocabulary. At the other end, recent vision-language foundation models such as CLIP demonstrate superior open-vocabulary recognition capability. This paper presents KGD, a Knowledge Graph Distillation technique that exploits the implicit knowledge graphs (KG) in CLIP for effectively adapting LVDs to various downstream domains. KGD consists of two consecutive stages: 1) KG extraction that employs CLIP to encode downstream domain data as nodes and their feature distances as edges, constructing KG that inherits the rich semantic relations in CLIP explicitly;and 2) KG encapsulation that transfers the extracted KG into LVDs to enable accurate cross-domain object classification. In addition, KGD can extract both visual and textual KG independently, providing complementary vision and language knowledge for object localization and object classification in detection tasks over various downstream domains. Experiments over multiple widely adopted detection benchmarks show that KGD outperforms the state-of-the-art consistently by large margins. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Assessing the Validity of Automated Data Analysis Methods Based on Artificial Intelligence

Assessing the Validity of Automated Data Analysis Methods Ba...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： K. Yuvaraj Balakumar P Prema S. Kadam Prakash Mishra Jambi Ratna Raja Kumar Anupama Verma Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Management Mangalayatan University Jabalpur India Department of Computer Engineering Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra India Faculty of Business Mangagement & Commerce Usha Martin University Ranchi India

Automated records analysis strategies primarily based on artificial intelligence (AI) have become increasingly famous in recent years because of their capability to quickly and effectively method enormous quantities of facts. However, concerns about the validity of those methods and their potential for introducing bias or mistakes have been raised. This study aims to assess the validity of automatic facts evaluation methods based on AI. It entails evaluating those methods' accuracy, reliability, and consistency compared to conventional guide evaluation methods. An expansion of study methods may be used, which includes experimental research, case research, and surveys. Facts will be gathered from computerized AI techniques and manual strategies, after which they will be compared and evaluated to decide the extent of agreement between them. One capability mission in this research is the dearth of a gold standard for statistics analysis. Unlike in medical fields wherein there may be a clean popular for prognosis, information analysis methods can also vary depending on the unique dataset and research question. Expanding a hard and fast of criteria or metrics for evaluating the validity of different AI techniques is essential. Another challenge is the rapidly evolving nature of the AI era. As new techniques and algorithms constantly evolve, keeping up with modern-day improvements and their potential impact on records analysis could be challenging.

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Aluminum Scandium Nitride Lamb Wave Acoustic Delay Lines with Over 6% Fractional Bandwidth

Aluminum Scandium Nitride Lamb Wave Acoustic Delay Lines wit...

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International Conference on Solid State Sensors and Actuators (TRANSDUCERS)

作者： Zhifang Luo Shuai Shao Chengkuo Lee Tao Wu School of Information Science and Technology ShanghaiTech University China Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Department of Electrical & Computer Engineering National University of Singapore Singapore Center for Intelligent Sensors and MEMS National University of Singapore Singapore Shanghai Engineering Research Center of Energy Efficient and Custom AI IC China

We report the low loss and broad fractional bandwidth (FBW) Acoustic Delay Lines (ADLs) based on 16% Sc-doped Aluminum Scandium Nitride (Al 0.84 Sc 0.16 N) Thin film. The fabricated ADLs show 3-dB FBW of 6.3% and minimum insertion loss (IL) of 5.6 dB at approximately 808 MHz. The ADLs with delay ranges from 20 ns to 41 ns have been demonstrated. A high group velocity of 9116 m/s is extracted utilizing fundamental symmetric (S0) mode Lamb wave. The simultaneous high-speed and broadband illustrate the great potential of AlScN ADLs for signal processing in the future.

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FEW-NNN: A Fuzzy Entropy Weighted Natural Nearest Neighbor Method for Flow-Based Network Traffic Attack Detection

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China Communications 2020年第5期17卷 151-167页

作者： Liangchen Chen Shu Gao Baoxu Liu Zhigang Lu Zhengwei Jiang School of Computer Science and Technology Wuhan University of TechnologyWuhan 430063China Institute of Information Engineering Chinese Academy of SciencesBeijing 100093China School of Applied Technology China University of Labor RelationsBeijing 100048China School of Cyber Security University of Chinese Academy of SciencesBeijing 100049China

Attacks such as APT usually hide communication data in massive legitimate network traffic, and mining structurally complex and latent relationships among flow-based network traffic to detect attacks has become the focus of many initiatives. Effectively analyzing massive network security data with high dimensions for suspicious flow diagnosis is a huge challenge. In addition, the uneven distribution of network traffic does not fully reflect the differences of class sample features, resulting in the low accuracy of attack detection. To solve these problems, a novel approach called the fuzzy entropy weighted natural nearest neighbor(FEW-NNN) method is proposed to enhance the accuracy and efficiency of flowbased network traffic attack detection. First, the FEW-NNN method uses the Fisher score and deep graph feature learning algorithm to remove unimportant features and reduce the data dimension. Then, according to the proposed natural nearest neighbor searching algorithm(NNN_Searching), the density of data points, each class center and the smallest enclosing sphere radius are determined correspondingly. Finally, a fuzzy entropy weighted KNN classification method based on affinity is proposed, which mainly includes the following three steps: 1、 the feature weights of samples are calculated based on fuzzy entropy values, 2、 the fuzzy memberships of samples are determined based on affinity among samples, and 3、 K-neighbors are selected according to the class-conditional weighted Euclidean distance, the fuzzy membership value of the testing sample is calculated based on the membership of k-neighbors, and then all testing samples are classified according to the fuzzy membership value of the samples belonging to each class;that is, the attack type is determined. The method has been applied to the problem of attack detection and validated based on the famous KDD99 and CICIDS-2017 datasets. From the experimental results shown in this paper, it is observed that the FEW-NNN method

关键词： fuzzy entropy weighted KNN network attack detection fuzzy membership natural nearest neighbor network security intrusion detection system

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CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction

arXiv

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arXiv 2022年

作者： Wang, Yequan Li, Xiang Sun, Aixin Meng, Xuying Liao, Huaming Guo, Jiafeng Beijing Academy of Artificial Intelligence Beijing China Alibaba Group China School of Computer Science and Engineering Nanyang Technological University Singapore Institute of Computing Technology Chinese Academy of Sciences Beijing China

Quotation extraction aims to extract quotations from written text. There are three components in a quotation: source refers to the holder of the quotation, cue is the trigger word(s), and content is the main body. Existing solutions for quotation extraction mainly utilize rule-based approaches and sequence labeling models. While rule-based approaches often lead to low recalls, sequence labeling models cannot well handle quotations with complicated structures. In this paper, we propose the Context and Former-Label Enhanced Net (CofeNet) for quotation extraction. CofeNet is able to extract complicated quotations with components of variable lengths and complicated structures. On two public datasets (i.e., PolNeAR and Riqua) and one proprietary dataset (i.e., PoliticsZH), we show that our CofeNet achieves state-of-the-art performance on complicated quotation extraction. © 2022, CC BY.

关键词： Extraction

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Multimodal Hypothetical Summary for Retrieval-based Multi-image Question Answering

arXiv

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arXiv 2024年

作者： Li, Peize Si, Qingyi Fu, Peng Lin, Zheng Wang, Yan School of Artificial Intelligence Jilin University Changchun China Institute of Information Engineering Chinese Academy of Sciences Beijing China Huawei Technologies Co. Ltd. Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Jilin University Changchun China

Retrieval-based multi-image question answering (QA) task involves retrieving multiple question-related images and synthesizing these images to generate an answer. Conventional "retrieve-then-answer" pipelines often suffer from cascading errors because the training objective of QA fails to optimize the retrieval stage. To address this issue, we propose a novel method to effectively introduce and reference retrieved information into the QA. Given the image set to be retrieved, we employ a multimodal large language model (visual perspective) and a large language model (textual perspective) to obtain multimodal hypothetical summary in question-form and description-form. By combining visual and textual perspectives, MHyS captures image content more specifically and replaces real images in retrieval, which eliminates the modality gap by transforming into text-to-text retrieval and helps improve retrieval. To more advantageously introduce retrieval with QA, we employ contrastive learning to align queries (questions) with MHyS. Moreover, we propose a coarse-to-fine strategy for calculating both sentence-level and word-level similarity scores, to further enhance retrieval and filter out irrelevant details. Our approach achieves a 3.7% absolute improvement over state-of-the-art methods on RETVQA and a 14.5% improvement over CLIP. Comprehensive experiments and detailed ablation studies demonstrate the superiority of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Question answering

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An Analysis of Fuzzy Logic Techniques for Improving Ad-Hoc Wireless Network Security

An Analysis of Fuzzy Logic Techniques for Improving Ad-Hoc W...

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International Conference on Computing and Networking technology (ICCNT)

作者： Harsimrat Kandhari Saritha Srinivasmurthy Raghotham Sonali Prashant Bhoite N. Thangarasu Mithhil Arora S. Sathya Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement – UG Management JAIN (Deemed to be University) Bangalore Karnataka India IT Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper examines fuzzy good judgment strategies as a probable strategy for enhancing the safety of Wi-Fi networks. It analyzes the contemporary protection methods, inclusive of encryption algorithms, authentication techniques, and distributed get right of entry to manage frameworks, and contrasts them with fuzzy logic techniques. It then explains the important thing characteristics of the two strategies and opinions on the primary software areas wherein fuzzy good judgment strategies may be used to increase ad-hoc Wi-Fi safety. Subsequently, the paper concludes by summarizing the potential advantages and boundaries of fuzzy common sense technologies for enhancing ad-hoc wireless network protection.

关键词： Fuzzy logic Wireless networks Software algorithms Authentication Routing Ad hoc networks Software Security Protection Wireless fidelity

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MIKIDQD: Machine Intelligence and Knowledge Infused Duplicate Question Detection

MIKIDQD: Machine Intelligence and Knowledge Infused Duplicat...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Vaibhava Lakshmi R Gerard Deepak Santhanavijayan A Radha S Department of Computer Technology Madras Institute of Technology Chennai India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering National Institute of Technology Tiruchirapalli India School of Advanced Sciences Vellore Institute of Technology Chennai India

ISBN: (纸本)9781665489638

Quora is a flourishing online question answer forum where millions of users post questions and answer questions in the subjects they have knowledge. A plethora of questions are posted by users and some of them can be similarly worded. This would make it tedious for the users as answering similarly worded questions can be time consuming. Hence, there is a need to detect similarly worded questions or duplicate questions. This research work proposes a MIKIDQD framework for detecting duplicate questions in the Quora forum by using the Quora dataset. The knowledge-centric, ontologybased and semantically-driven strategy incorporated in the proposed model surpasses the other baseline models in terms of the performance measures. The proposed model yields the highest accuracy value of 98.07% and the least False Negative Rate of 0.01 indicating the best performance.

关键词： Cloud computing Computational modeling Question answering (information retrieval) Problem-solving Machine intelligence

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Pyscribe - Learning to Describe Python Code

SSRN

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SSRN 2022年

作者： Guo, Juncai Liu, Jin Wan, Yao Zhao, Yanjie Li, Li Liu, Kui Klein, Jacques Bissyandé, Tegawendé F. School of Computer Science Wuhan University Wuhan China Key Laboratory of Network Assessment Technology Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Faculty of Information Technology Monash University VIC Australia Huawei Software Engineering Application Technology Lab Hangzhou China SnT Centre University of Luxembourg 1855 Luxembourg

Code comment generation, which aims to describe the functionality of source code in natural language, plays an important role in automatic software development. Currently, several structural neural networks have been exploited to preserve the syntax structure of source code based on Abstract Syntax Trees (ASTs). However, they can not well capture both the long-distance and local relations between nodes while retaining the overall structural information of AST. To mitigate this problem, we present to the community a prototype tool called PYSCRIBE, which expands the Transformer model to a new encoder-decoder architecture to incorporate triplet positions into the node-level and edge-level features of AST towards automatically generating comments for Python code. To the best of our knowledge, this paper makes the first attempt to consider the edges of AST as an explicit component for better code representation, as to generate higher qualified comments. By specifying triplet positions for each node and edge, the overall structural information can be well preserved in the learning process. In the decoder, we perform a two-stage decoding process over the extracted node and edge features to generate comments. To evaluate the effectiveness of PYSCRIBE, we resort to a large dataset of code-comment pairs by mining Jupyter notebooks from GitHub, for which we have made it publicly available to support further studies. The evaluation results show that PYSCRIBE is indeed effective, outperforming the state-of-the-art by achieving an average BLEU score (i.e., av-BLEU) of ≈0.28. © 2022, The Authors. All rights reserved.

关键词： Large dataset

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