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Intelligent Technologies (ACOIT), Asian Conference on

作者： Ujan Pradhan M.N. Aditya R. Raj Kumar Department of Computing Technologies SRM Institute of Science & Technology Kattankulathur Chennai India Department of Electronics & Communication Engineering SRM Institute of Science & Technology Kattankulathur Chennai India Department of Data Science & Business Systems SRM Institute of Science & Technology Kattankulathur Chennai India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Socio-economic impacts of coastal erosion are serious, more so in Asia. Indeed, there is a greater need for advanced predictive models to be able to manage and reduce risks from increased coastal hazards due to climate change. Most studies provide predictions for shoreline changes using historical data or simple statistical models, which are less accurate and interpretable for proper decision-making. Results show that combining machine learning with explainable AI techniques gives an improvement in predictive accuracy and transparent insight into coastal erosion for informed management decisions. Other methods, such as ensemble techniques, like Random Forest, have been used to improve accuracy and avoid overfitting. In our paper, “Analyzing Land Submergence Impact on Temperature Using Random Forest Regression and Explainable AI Techniques,” we have applied Random Forest Regression for the prediction of the effect of land submergence on temperature change by using the strength of the model. Explainable AI techniques like LIME and SHAP enhance insight into the decision process of the model, but very few studies apply these to coastal erosion. This research fills this gap by using ML models that have explainable AI for more accurate and more interpretable forecasts of temperature variations affecting coastal erosion. A number of explainable AI models are presented in this paper for improving interpretability. Traditionally, black-box ML models are very good at prediction, though often lack transparency, making it hard for stakeholders to understand and build trust in. Explainable models present how each feature contributes to the prediction This transparency enhances trust among coastal managers and policymakers. It aids better decision-making by explaining the drivers of erosion to users. Interpretable models thus help the stakeholders to focus mitigation efforts with directed attention on their part, which helps in ensuring sustainable coastal management.

关键词： Accuracy Temperature Explainable AI Prevention and mitigation Sea measurements Predictive models Land surface temperature Stakeholders Random forests Erosion

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Forest-based Evolutionary Algorithm for Reconstructing Boolean Gene Regulatory Networks

Forest-based Evolutionary Algorithm for Reconstructing Boole...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Nicolò Stranieri Francesca M. Buffa Andrea Tangherloni Department of Computing Sciences Bocconi University Milan Italy Department of Oncology University of Oxford Oxford UK Department of Computing Sciences Bocconi Institute for Data Science and Analytics Bocconi University Milan Italy

ISBN: (数字)9798350356632

ISBN: (纸本)9798350356649

Gene Regulatory Networks (GRNs) play a fundamental role in orchestrating the expression of our genes through complex interactions between DNA, RNA, proteins, and other molecules. Accurately reconstructing such networks from gene expression data is a critical yet challenging task in Systems Biology due to their intricate nature and limited data availability. In this work, we introduce a novel Forest-based Evolutionary Algorithm (FP) designed for reconstructing Boolean GRNs from time series data of gene expressions. Unlike traditional methods that struggle with scalability and accurate representation of regulatory interactions, FP utilizes a forest structure where each tree represents the logical relationships between genes, enhancing the model’s capacity to depict complex networks efficiently. Our comprehensive testing indicates that FP rapidly converges towards potential solutions within a limited number of generations, although a higher fitness score does not always equate to a more accurate GRN representation. Implementing mini-batching techniques, inspired by their effectiveness in gradient descent optimization, shows promise in improving computational efficiency without sacrificing performance. A comparative analysis against the main state-of-the-art approaches reveals FP’s tendency towards conservative predictions, emphasizing precision over recall, making it particularly suitable for contexts where the cost of false positives is high. These initial results suggest that FP is a robust and efficient tool for GRN inference.

关键词： Measurement Accuracy Scalability Time series analysis Forestry Evolutionary computation Computational efficiency Gene expression Optimization Testing

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Multi-layer network embedding on scc-based network with motif

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Digital Communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

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Generative Adversarial Network with Soft-Dynamic Time Warping and Parallel Reconstruction for Energy Time Series Anomaly Detection

arXiv

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arXiv 2024年

作者： Prabhu, Hardik Valadi, Jayaraman Arjunan, Pandarasamy Robert Bosch Centre for Cyber Physical Systems Indian Institute of Science Bengaluru India School of Computing and Data Sciences FLAME University Pune India

In this paper, we employ a 1D deep convolutional generative adversarial network (DCGAN) for sequential anomaly detection in energy time series data. Anomaly detection involves gradient descent to reconstruct energy sub-sequences, identifying the noise vector that closely generates them through the generator network. Soft-DTW is used as a differentiable alternative for the reconstruction loss and is found to be superior to Euclidean distance. Combining reconstruction loss and the latent space’s prior probability distribution serves as the anomaly score. Our novel method accelerates detection by parallel computation of reconstruction of multiple points and shows promise in identifying anomalous energy consumption in buildings, as evidenced by performing experiments on hourly energy time series from 15 buildings. © 2024, CC BY.

关键词： Anomaly detection

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A Collaborative Filtering Algorithm with Improved User Similarity Calculation for Enhanced Recommendation Accuracy and Personalization 12

A Collaborative Filtering Algorithm with Improved User Simil...

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12th International Conference of Information and Communication Technology, ICTech 2023

作者： Ding, Shili Fan, Yue Liu, Jing Zhang, Yonghong Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China School of Information Science and Technology Taishan University Shandong Taian China Shandong University Laboratory of Big Data and Artificial Intelligence Technology Jinan China

ISBN: (纸本)9798350332919

The conventional method for calculating user similarity in the collaborative filtering recommendation algorithm based on users fails to address the challenge of variations in the number, value, and popularity of items that are collaboratively rated by multiple users. In order to solve the above problems, a new user similarity calculation method is proposed. By fusing cosine similarity and Jackard similarity, the problem of difference in the number of common scoring items is alleviated, so as to reduce the error of scoring prediction. Finally, experiments were performed on the MovieLens dataset, and the findings demonstrated that the enhanced algorithm outperforms the established benchmark similarity algorithm regarding mean absolute error (MAE). In addition, the improved algorithm has good robustness in predicting the accuracy of user ratings and recommendation quality. © 2023 IEEE.

关键词： Collaborative filtering

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STRONG BASELINE AND BAG OF TRICKS FOR COVID-19 DETECTION OF CT SCANS

arXiv

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arXiv 2023年

作者： Hsu, Chih-Chung Jian, Chih-Yu Lee, Chia-Ming Tsai, Chi-Han Dai, Sheng-Chieh Institute of Data Science Department of Statistics Center of Data Science Miin Wu School of Computing National Cheng Kung University No.1 University Rd Tainan City Taiwan

This paper investigates the application of deep learning models for lung Computed Tomography (CT) image analysis. Traditional deep learning frameworks encounter compatibility issues due to variations in slice numbers and resolutions in CT images, which stem from the use of different machines. Commonly, individual slices are predicted and subsequently merged to obtain the final result;however, this approach lacks slice-wise feature learning and consequently results in decreased performance. We propose a novel slice selection method for each CT dataset to address this limitation, effectively filtering out uncertain slices and enhancing the model’s performance. Furthermore, we introduce a spatial-slice feature learning (SSFL) technique[1] that employs a conventional and efficient backbone model for slice feature training, followed by extracting one-dimensional data from the trained model for COVID and non-COVID classification using a dedicated classification model. Leveraging these experimental steps, we integrate one-dimensional features with multiple slices for channel merging and employ a 2D convolutional neural network (CNN) model for classification. In addition to the aforementioned methods, we explore various high-performance classification models, ultimately achieving promising results. © 2023, CC BY.

关键词： COVID-19

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Black-box Backdoor Defense via Zero-shot Image Purification

arXiv

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arXiv 2023年

作者： Shi, Yucheng Du, Mengnan Wu, Xuansheng Guan, Zihan Sun, Jin Liu, Ninghao School of Computing University of Georgia United States Department of Data Science New Jersey Institute of Technology United States

Backdoor attacks inject poisoned samples into the training data, resulting in the misclassification of the poisoned input during a model’s deployment. Defending against such attacks is challenging, especially for real-world black-box models where only query access is permitted. In this paper, we propose a novel defense framework against backdoor attacks through Zero-shot Image Purification (ZIP). Our framework can be applied to poisoned models without requiring internal information about the model or any prior knowledge of the clean/poisoned samples. Our defense framework involves two steps. First, we apply a linear transformation (e.g., blurring) on the poisoned image to destroy the backdoor pattern. Then, we use a pre-trained diffusion model to recover the missing semantic information removed by the transformation. In particular, we design a new reverse process by using the transformed image to guide the generation of high-fidelity purified images, which works in zero-shot settings. We evaluate our ZIP framework on multiple datasets with different types of attacks. Experimental results demonstrate the superiority of our ZIP framework compared to state-of-the-art backdoor defense baselines. We believe that our results will provide valuable insights for future defense methods for black-box models. Our code is available at https://***/sycny/ZIP. © 2023, CC BY-NC-ND.

关键词： Purification

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Distributed and Localized Model-Predictive Control-Part I: Synthesis and Implementation

Distributed and Localized Model-Predictive Control-Part I: S...

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作者： Alonso, Carmen Amo Li, Jing Shuang Anderson, James Matni, Nikolai California Institute of Technology Department of Computing and Mathematical Sciences PasadenaCA91106 United States Columbia University Department of Electrical Engineering and the Data Science Institute New YorkNY10027 United States University of Pennsylvania Department of Electrical and Systems Engineering PhiladelphiaPA19104 United States

The increasing presence of large-scale distributed systems highlights the need for scalable control strategies where only local communication is required. Moreover, in safety-critical systems, it is imperative that such control strategies handle constraints in the presence of disturbances. In response to this need, in this article, we present the distributed and localized model-predictive control (DLMPC) algorithm for large-scale linear systems. DLMPC is a distributed closed-loop model-predictive control (MPC) scheme, wherein only local state and model information needs to be exchanged between subsystems for the computation and implementation of control actions. We use the system-level synthesis framework to reformulate the centralized MPC problem and show that this allows us to naturally impose localized communication constraints between subcontrollers. The structure of the resulting problem can be exploited to develop an alternating-direction-method-of-multipliers-based algorithm that allows for a distributed and localized computation of closed-loop control policies. We demonstrate that the computational complexity of the subproblems solved by each subsystem in DLMPC is independent of the size of the global system. DLMPC is the first MPC algorithm that allows for the scalable computation and implementation of distributed closed-loop control policies and deals with additive disturbances. In our companion paper, we show that this approach enjoys recursive feasibility and asymptotic stability. © 2014 IEEE.

关键词： Model predictive control

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S2CCT: Self-Supervised Collaborative CNN-Transformer for Few-shot Medical Image Segmentation

S2CCT: Self-Supervised Collaborative CNN-Transformer for Few...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhou, Rongzhou Shu, Ziqi Xie, Weixing Yao, Junfeng Hong, Qingqi Xiamen University National Institute for Data Science in Health and Medicine China Xiamen University Center for Digital Media Computing School of Film School of Informatics Xiamen361005 China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage Fujian and Taiwan Ministry of Culture and Tourism China Xiamen University Institute of Artificial Intelligence Xiamen361005 China

ISBN: (纸本)9798350386226

Self-supervised pre-training followed by fine-tuning is a potent paradigm for few-shot learning, leveraging extensive unlabeled data with remarkable efficacy. Current self-supervised methods often lean towards Vision Transformers (ViTs) rather than CNN-Transformer hybrid architectures, which generally demonstrate superior performance. However, this reliance on ViTs can lead to poor perception of local features by the model. The challenge lies in designing a suitable proxy task for hybrid architectures like CNN-Transformers, which have significant structural differences. Additionally, the current organization of CNN-Transformer hybrid backbones is often sequential, hindering collaboration during pre-training and the acquisition of robust representations. To address these issues, we propose Self-Supervised Collaborative CNN-Transformer (S2CCT) for few-shot medical image segmentation. This framework introduces three innovative designs: (1) a composite proxy task based on image masking and image super-resolution tailored for CNN-Transformer hybrid architectures, enabling the backbone to acquire robust representations during pre-training that can be transferred to downstream tasks;(2) a parallel CNN-Transformer architecture that better attends to multi-scale features in images, making it more suitable for dense prediction tasks like image segmentation;(3) a sparse and dense feature fusion module to enhance collaboration between the two encoders. Experiments demonstrate that S2CCT outperforms previous state-of-the-art methods on two public medical image segmentation benchmarks, i.e., ACDC and KiTs19. The code and pretrained models will be released soon. © 2024 IEEE.

关键词： Image segmentation

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Compressing VAE-Based Out-of-Distribution Detectors for Embedded Deployment

Compressing VAE-Based Out-of-Distribution Detectors for Embe...

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IEEE International Conference on Embedded and Real-Time computing Systems and Applications (RTCSA)

作者： Aditya Bansal Michael Yuhas Arvind Easwaran College of Computing and Data Science Nanyang Technological University Singapore Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore

ISBN: (数字)9798350387957

ISBN: (纸本)9798350387964

Out-of-distribution (OOD) detectors can act as safety monitors in embedded cyber-physical systems by identifying samples outside a machine learning model’s training distribution to prevent potentially unsafe actions. However, OOD detectors are often implemented using deep neural networks, which makes it difficult to meet real-time deadlines on embedded systems with memory and power constraints. We consider the class of variational autoencoder (VAE) based OOD detectors where OOD detection is performed in latent space, and apply quantization, pruning, and knowledge distillation. These techniques have been explored for other deep models, but no work has considered their combined effect on latent space OOD detection. While these techniques increase the VAE’s test loss, this does not correspond to a proportional decrease in OOD detection performance and we leverage this to develop lean OOD detectors capable of real-time inference on embedded CPUs and GPUs. We propose a design methodology that combines all three compression techniques and yields a significant decrease in memory and execution time while maintaining AUROC for a given OOD detector. We demonstrate this methodology with two existing OOD detectors on a Jetson Nano and reduce GPU and CPU inference time by 20% and 28% respectively while keeping AUROC within 5% of the baseline.

关键词： Training Quantization (signal) Embedded systems Memory management Graphics processing units Detectors Machine learning Real-time systems Safety Monitoring

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