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6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Porna, Shweta Bhattacharjee Kabir, Md. Firoz Rana, Md. Imran Chowdhury Sajol, Md. Saiful Islam Roy, Tonmoy Khan, Mohammad Aman Ullah Adnan, Md. Al Bhavani, Girigula Durga Ahsanullah University of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh Master's in Information Technology University of the Cumberlands United States Bachelor of Business Administration International American university Los Angeles CA United States Louisiana State University Department of Computer Science and Engineering LA United States Data Analytics & Information Systems Utah State University Logan United States Brac University School of Data and Sciences Department of Computer Science and Engineering Dhaka1212 Bangladesh Noakhali Science And Technology University Bsc in software engineering Bangladesh Andhra Pradesh Amaravati522237 India

ISBN: (纸本)9798331505790

The classification of mango leaf diseases is critical for effective disease management and ensuring high-quality yields in mango cultivation. This paper presents a comprehensive study on using deep learning techniques to classify various mango leaf diseases, leveraging convolutional neural networks (CNNs) and hybrid models. A total of 7,524 images were used in our study. These included 4,000 training samples and 3,524 testing samples. The images were split into eight groups, which were powdery mildew, cutting weevil, anthracnose, bacterial canker, sooty mold, gall midge, healthy, and die back. The suggested method starts with feature extraction using VGG19 and MobileNetB1, then classification using both standalone models (ResNet50V2 + EfficientNetB1 and VGG16 + MobileNetB1). We employed data augmentation techniques like random brightness adjustment, rotation, and flipping to enhance the robustness of the model. We conducted hyperparameter tuning using hyperband and Bayesian optimization to optimize the model's performance. Experimental results demonstrate that the hybrid models achieved superior performance, with ResNet50V2 and EfficientNetB1 attaining a perfect accuracy of 100 % on the test set. These findings highlight the potential of deep learning techniques to improve the accuracy and reliability of mango leaf disease diagnosis, contributing significantly to the advancement of precision agriculture. © 2024 IEEE.

关键词： Fruits

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Akane: Perplexity-Guided Time Series Data Cleaning

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Proceedings of the ACM on Management of Data 2024年第3期2卷 1-26页

作者： Xiaoyu Han Haoran Xiong Zhenying He Peng Wang Chen Wang X. Sean Wang School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China National Engineering Research Center for Big Data Software EIRI Tsinghua University Beijing China

Dirty data are prevalent in time series, such as energy consumption or stock data. Existing data cleaning algorithms present shortcomings in dirty data identification and unsatisfactory cleaning decisions. To handle these drawbacks, we leverage inherent recurrent patterns in time series, analogize them as fixed combinations in textual data, and incorporate the concept of perplexity. The cleaning problem is thus transformed to minimize the perplexity of the time series under a given cleaning cost, and we design a four-phase algorithmic framework to tackle this problem. To ensure the framework's feasibility, we also conduct a brief analysis of the impact of dirty data and devise an automatic budget selection strategy. Moreover, to make it more generic, we additionally introduce advanced solutions, including an ameliorative probability calculation method grounded in the homomorphic pattern aggregation and a greedy-based heuristic algorithm for resource savings. Experiments on 12 real-world datasets demonstrate the superiority of our methods.

关键词： data cleaning perplexity recurrent patterns time series

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Dina: Deformable Interaction Analogy

SSRN

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SSRN 2024年

作者： Huang, Zeyu Dai, Sisi Xu, Kai Zhang, Hao Huang, Hui Hu, Ruizhen College of Computer Science and Software Engineering Shenzhen University China School of Computer Science National University of Defense Technology China School of Computing Science Simon Fraser University Canada

We introduce Deformable Interaction Analogy (DINA) to create similar interactions between 3D objects. Our goal is to generate multiple analogous 3D interactions between an anchor object and different target objects based on a single demo interaction. To achieve this, we optimize the anchor object's pose or shape to mimic the demo interaction with new target objects. We propose using an Interaction Interface (ITF) as a descriptive and robust representation of interactions. For interaction analogy, we transform the ITF to guide feature matching during the anchor object's repositioning and deformation. Qualitative and quantitative experiments demonstrate the effectiveness of our ITF-guided deformable interaction analogy, even when compared to state-of-the-art methods that utilize more complex interaction representations and feature learning from large datasets. © 2024, The Authors. All rights reserved.

关键词： Large datasets

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A Multiple-Path Learning Neural Network Model for Code Completion

A Multiple-Path Learning Neural Network Model for Code Compl...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Liu, Yi Liu, Jianxun Zhang, Xiangping Hu, Haize Hunan University of Science and Technology Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Xiangtan411201 China Hunan University of Humanities Science and Technology School of Informationn Loudi417000 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350304855

Code completion, which can accelerate the software development process and improve the quality of software products, is an essential part of today's integrated development environments. It has become an important research topic in the field of software engineering. Recent studies have shown that the method of code completion based on the Abstract Syntax Tree (AST) learns syntactic information about the code, which helps to improve the accuracy of code completion. However, when modeling neural networks for ASTs, the sequencing operation of nodes leads to the loss of their hierarchical structure information. Meanwhile, traditional neural networks cannot predict many Out-of-Vocabulary (OoV) words in the terminal node values of AST. To alleviate the above problem, in this paper, we propose a Multiple-Path Learning neural network model for code completion (MPL) based on an AST by learning from a large-scale corpus. In this model, multiple paths such as context path, root path, and terminal node path are established to understand different code features required for node prediction and improve code representation ability. Based on the principle of program local repeatability, it also adopts a replication mechanism to copy the appropriate OoV words from the local terminal node path as the prediction result, further improving the prediction accuracy. The experimental results show that the MPL model has better performance than existing methods on the code completion task. © 2023 IEEE.

关键词： Neural network models

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Interpretability Research of Variational Autoencoder Generation Process Based on Feature Disentanglement 5

Interpretability Research of Variational Autoencoder Generat...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Xi, Sicheng Peng, Yanhui College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Computational Intelligence Chongqing China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Computational Intelligence Chongqing China

ISBN: (纸本)9798350385557

Variational Autoencoder (VAE), as one of the main generative models, has a powerful representation learning capability. However, the hidden space representation learned by VAE is a high-dimensional and complex vector space, which makes it difficult to explain how the model gradually learns and composes the final generated results on different semantic features. To address this problem, firstly, this paper increases the degree of decoupling between different semantic features by increasing the independence between the hidden variables of the modal hermitian space, and explains the learning process of the model on different hermitian spaces by visualization based on the feature decoupling model. In addition, this paper also proposes a hidden variable contribution index to measure the influence of different dimensional hidden variables on the generation results, so as to explain the learning process of the model. © 2024 IEEE.

关键词： Learning systems

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GATBind: Accurate protein-RNA binding sites prediction via graph attention networks with pre-trained language model

GATBind: Accurate protein-RNA binding sites prediction via g...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Siyuan Shen Shangqian Wu Jie Chen Zhijian Huang Lei Deng School of Computer Science and Engineering Central South University Changsha China School of Software Xinjiang University Urumqi China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Protein-RNA interactions play a pivotal role in various biological processes, making them essential for discovering novel therapeutic targets. Understanding these interactions is crucial for identifying potential drug targets and designing effective therapeutics. However, traditional experimental approaches are costly and time-consuming, highlighting the need for accurate and efficient computational methods to predict RNA-binding sites. In this study, we propose a novel method called GATBind, designed to predict RNA-binding sites on proteins. GATBind introduces a local environment-aware module that captures the local structural information surrounding target residues. Additionally, we incorporate sequence and structural features, including PSSM, HMM, DSSP, and sequence embeddings generated by the pre-trained protein language model ESMFold-2. These features are integrated and processed through a Graph Attention Network (GAT) module to predict RNA-binding sites accurately. Experimental results demonstrate that GATBind outperforms existing state-of-the-art methods, and case studies further validate its effectiveness in predicting RNA-binding sites.

关键词： Proteins Drugs Protein engineering Accuracy Three-dimensional displays Hidden Markov models Predictive models Feature extraction Prediction algorithms Graph neural networks

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Bias Unveiled: Investigating Social Bias in LLM-Generated Code

arXiv

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arXiv 2024年

作者： Ling, Lin Rabbi, Fazle Wang, Song Yang, Jinqiu Computer Science and Software Engineering Concordia University Montreal Canada Lassonde School of Engineering York University Toronto Canada

Large language models (LLMs) have significantly advanced the field of automated code generation. However, a notable research gap exists in evaluating social biases that may be present in the code produced by LLMs. To solve this issue, we propose a novel fairness framework, i.e., Solar, to assess and mitigate the social biases of LLM-generated code. Specifically, Solar can automatically generate test cases for quantitatively uncovering social biases of the auto-generated code by LLMs. To quantify the severity of social biases in generated code, we develop a dataset that covers a diverse set of social problems. We applied Solar and the crafted dataset to four state-of-the-art LLMs for code generation. Our evaluation reveals severe bias in the LLM-generated code from all the subject LLMs. Furthermore, we explore several prompting strategies for mitigating bias, including Chain-of-Thought (CoT) prompting, combining positive role-playing with CoT prompting and dialogue with Solar. Our experiments show that dialogue with Solar can effectively reduce social bias in LLM-generated code by up to 90%. Last, we make the code and data publicly available is highly extensible to evaluate new social problems. Copyright © 2024, The Authors. All rights reserved.

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Spike-Visnet: A Novel Framework for Visual Recognition with Focuslayer-Stdp Learning

SSRN

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SSRN 2024年

作者： Liu, Ying Luo, Xiaoling Zhang, Ya Zhang, Yun Zhang, Wei Qu, Hong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Computer Science and Engineering Sichuan University of Science and Engineering Yibin644000 China School of Computer Science and Software Engineering Southwest Petroleum University Chengdu610500 China School of Electronic and Information Engineering Hebei University of Technology Tianjin300401 China

Current vision-inspired spiking neural networks (SNNs) face key challenges due to their model structures typically focusing on single mechanisms and neglecting the integration of multiple biological features. These limitations, coupled with limited synaptic plasticity, hinder their ability to implement biologically realistic visual processing. To address these issues, we propose Spike-VisNet, a novel retina-inspired framework designed to enhance visual recognition capabilities. This framework simulates both the functional and layered structure of the retina. To further enhance this architecture, we integrate the FocusLayer-STDP learning rule, allowing Spike-VisNet to dynamically adjust synaptic weights in response to varying visual stimuli. This rule combines channel attention, inhibition mechanisms, and competitive mechanisms with spike-timing-dependent plasticity (STDP), significantly improving synaptic adaptability and visual recognition performance. Comprehensive evaluations on benchmark datasets demonstrate that Spike-VisNet outperforms other STDP-based SNNs, achieving precision scores of 98.6% on MNIST, 93.29% on ETH-80, and 86.27% on CIFAR-10. These results highlight its effectiveness and robustness, showcasing Spike-VisNet's potential to simulate human visual processing and its applicability to complex real-world visual challenges. © 2024, The Authors. All rights reserved.

关键词： Neural networks

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A Highly Secured Image Encryption Scheme using Quantum Walk and Chaos

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computers, Materials & Continua 2022年第10期73卷 657-672页

作者： Muhammad Islam Kamran Muazzam A.Khan Suliman A.Alsuhibany Yazeed Yasin Ghadi Arshad Jameel Arif Jawad Ahmad Department of Computer Science Quaid-i-Azam UniversityIslamabad45320Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science and Software Engineering Al Ain UniversityAbu Dhabi15551UAE Institute for Energy and Environment University of StrathclydeGlasgowG11XQUK School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

The use of multimedia data sharing has drastically increased in the past few decades due to the revolutionary improvements in communication technologies such as the 4th generation(4G)and 5th generation(5G)*** have proposed many image encryption algorithms based on the classical random walk and chaos theory for sharing an image in a secure *** of the classical random walk,this paper proposes the quantum walk to achieve high image *** random walk exhibits randomness due to the stochastic transitions between states,on the other hand,the quantum walk is more random and achieve randomness due to the superposition,and the interference of the wave *** proposed image encryption scheme is evaluated using extensive security metrics such as correlation coefficient,entropy,histogram,time complexity,number of pixels change rate and unified average intensity *** experimental results validate the proposed scheme,and it is concluded that the proposed scheme is highly secured,lightweight and computationally *** the proposed scheme,the values of the correlation coefficient,entropy,mean square error(MSE),number of pixels change rate(NPCR),unified average change intensity(UACI)and contrast are 0.0069,7.9970,40.39,99.60%,33.47 and 10.4542 respectively.

关键词： Cryptography chaotic maps logistic map quantum walk security

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Deepfake Detection: Emerging Techniques and Evolving Challenges

Deepfake Detection: Emerging Techniques and Evolving Challen...

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Annual Information Technology, Electromechanical engineering and Microelectronics Conference (IEMECON)

作者： Krishnaraj Natarajan Abeer Mathur Kshitiz Bhargava Manvendra Singh Moulik Tejpal Department of Database Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India Department of Software Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798350387315

ISBN: (纸本)9798350387322

Several newly developed techniques and tools for manipulating images, audio, and videos have been introduced as an outcome of the recent and rapid breakthroughs in AI, machine learning, and deep learning. While most applications for these techniques or tools are in the fields of entertainment and education, some individuals with unlawful intent have also been benefited from them. These individuals use such techniques for various purposes, including the spread of misleading information and unnecessary propaganda, the incitement of political instability, hate and unrest, as well as for purposes of torture and blackmail. These high-quality and convincing manipulated images, audio, or videos are commonly referred to as ‘Deepfakes’.Since then, various solutions to the problems raised by Deepfakes have been proposed in academic studies. This literature review contains relevant publications that offered a variety of approaches to give an updated summary of the research activities in different types of Deepfake attacks, their detection, and countermeasures. It also assesses the effectiveness of the detection capabilities of different techniques with various datasets and algorithms applied in Deepfake detection, while also outlining the various benefits and drawbacks of various methodologies.

关键词： Deep learning Deepfakes Smart contracts Microwave communication Media Forgery Blockchains Internet of Things Systematic literature review Microwave imaging

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