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SO(3) Equivariant Framework for Spatial Networks

SO(3) Equivariant Framework for Spatial Networks

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Xia, Tian Aykent, Sarp Microsoft CambridgeMA United States Auburn University Computer Science and Software Engineering Department AuburnAL United States

ISBN: (纸本)9798350362480

Representation learning on spatial networks is emerging as a distinct area in machine learning and is attracting much attention in diverse domains. Some applications include molecular graphs for drug discovery and screening, brain networks for neuroscience, social networks for recommender systems, etc. However, most proposed approaches either use only spatial or network data that cannot distinguish certain types of graphs and limit their network expressivity or are tied to specific input data domains and network architectures. To address this gap, we introduce an equivariant message-passing network architecture that simultaneously leverages the spatial and network properties. In addition, we propose to take advantage of the geometric representations and extend the classical scalar features with 3D vectors. To exploit the spatial and network features, we present a Spatial Vector Neuron, which can be easily incorporated into the existing graph neural network architectures and allow the model to scale up by stacking more layers for larger receptive fields. A comprehensive set of experiments on both synthetic and real-world datasets demonstrate the strength of our proposed method and the potential of geometric representation learning for Spatial Networks. © 2024 IEEE.

关键词： Adversarial machine learning

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Neural processor microarchitecture design for CNN processing acceleration 11

Neural processor microarchitecture design for CNN processing...

引用

Optoelectronic Imaging and Multimedia Technology XI 2024

作者： Tabunshchik, Sergei Bykovskii, Sergei Kustarev, Pavel Faculty of Software Engineering and Computer Systems ITMO University 49 Kronverksky Pr. St. Petersburg197101 Russia

ISBN: (纸本)9781510682061

Mixed reality systems that integrate the real and digital worlds have recently gained popularity. The development of mixed reality devices in the form of wearable devices has been a significant trend in recent years. Key challenges in designing wearable mixed reality devices include reducing power consumption, enhancing productivity, and minimizing the size of the device. To address these challenges, neural processors are being incorporated into mixed reality systems. These are specialized hardware accelerators for convolutional neural network (CNN) processing. The use of neural processors is due to the fact that most environmental analysis and visualizations in mixed reality rely on CNNs. The use of these energy-efficient, high-performance processing devices enables increased comfort in using the wearable device through the high-performance processing of environmental data on a compact device. The paper is devoted to the design of the microarchitecture of a neural processor for hardware acceleration of CNN processing based on the author's architecture of processor. The paper presents various microarchitectural solutions that can be used to accelerate CNN processing. We explore methods to optimize hardware resources and reduce time required for CNN processing. To achieve high throughput in pipelined computation, different algorithms for convolution calculations in a systolic array are examined. Based on the results of this research, we provide estimates of the characteristics of the neural processor with the proposed microarchitecture. © 2024 SPIE.

关键词： Convolutional neural networks

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Leveraging Geographic Information Systems and Remote Sensing for Enhanced Water Resource Management in Kazakhstan 4

Leveraging Geographic Information Systems and Remote Sensing...

引用

4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Hamada, Mohamed Ahmed Tolkyn, Orden Hamada, Gehad M. International It University Information Systems Department Almaty Kazakhstan International It University Computer Systems and Software Engineering Department Almaty Kazakhstan

ISBN: (纸本)9798350387490

Kazakhstan's deputy member is vast, and is one of sparse population, scattering and creating an overwhelming challenge to map and assess the water resources. Most of the waters are in remote and poorly equipped areas, making their monitoring by conventional means arduous. This includes costly and inefficient monitoring that results in a blurred condition of the water resources. Such a predicament stands in the way of effective management and decision-making regarding water resources. Therefore, we used Sentinel-3 satellite images to assess the spatiotemporal dynamics of water resources in Kazakhstan and obtained the results through GIS analysis. The main case of the region for case studies reflecting problems in the management of water resources in the country is the northern part of the Aral Sea. It presents a study characterized by in-depth data analysis and mapping that provided insight into the spatial trends of water resources, their variability, and potential areas of concern. The present research found that GIS and remote sensing technologies have emerged as a strong water resource observation platform. They show promise in the resource sector decision support. The development thus justifies the integration of these technology tools within the water resource management practices to ensure sustainable water use in Kazakhstan. The publication has equally availed a model for similarly water-stressed regions regarding technology-driven solutions in matters of environmental sustainability and resource management. © 2024 IEEE.

关键词： Information management

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DDN: Dual-domain Dynamic Normalization for Non-stationary Time Series Forecasting 38

DDN: Dual-domain Dynamic Normalization for Non-stationary Ti...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Dai, Tao Wu, Beiliang Liu, Peiyuan Li, Naiqi Yuerong, Xue Xia, Shu-Tao Zhu, Zexuan College of Computer Science and Software Engineering Shenzhen University China Tsinghua Shenzhen International Graduate School Tsinghua University China

Deep neural networks (DNNs) have recently achieved remarkable advancements in time series forecasting (TSF) due to their powerful ability of sequence dependence modeling. To date, existing DNN-based TSF methods still suffer from unreliable predictions for real-world data due to its non-stationarity characteristics, i.e., data distribution varies quickly over time. To mitigate this issue, several normalization methods (e.g., SAN) have recently been specifically designed by normalization in a fixed period/window in the time domain. However, these methods still struggle to capture distribution variations, due to the complex time patterns of time series in the time domain. Based on the fact that wavelet transform can decompose time series into a linear combination of different frequencies, which exhibits distribution variations with time-varying periods, we propose a novel Dual-domain Dynamic Normalization (DDN) to dynamically capture distribution variations in both time and frequency domains. Specifically, our DDN tries to eliminate the non-stationarity of time series via both frequency and time domain normalization in a sliding window way. Besides, our DDN can serve as a plug-in-play module, and thus can be easily incorporated into other forecasting models. Extensive experiments on public benchmark datasets under different forecasting models demonstrate the superiority of our DDN over other normalization methods. Code is available at https://***/Hank0626/DDN. © 2024 Neural information processing systems foundation. All rights reserved.

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QUEST: Quadruple Multimodal Contrastive Learning with Constraints and Self-Penalization 38

QUEST: Quadruple Multimodal Contrastive Learning with Constr...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Song, Qi Gong, Tianxiang Gao, Shiqi Zhou, Haoyi Li, Jianxin School of Software Beihang University China School of Computer Science and Engineering Beihang University China Zhongguancun Laboratory Beijing China

Multimodal contrastive learning (MCL) has recently demonstrated significant success across various tasks. However, the existing MCL treats all negative samples equally and ignores the potential semantic association with positive samples, which limits the model's ability to achieve fine-grained alignment. In multi-view scenarios, MCL tends to prioritize shared information while neglecting modality-specific unique information across different views, leading to feature suppression and suboptimal performance in downstream tasks. To address these limitations, we propose a novel contrastive framework named QUEST: Quadruple Multimodal Contrastive Learning with Constraints and Self-Penalization. In the QUEST framework, we propose quaternion contrastive objectives and orthogonal constraints to extract sufficient unique information. Meanwhile, a shared information-guided penalization is introduced to ensure that shared information does not excessively influence the optimization of unique information. Our method leverages quaternion vector spaces to simultaneously optimize shared and unique information. Experiments on multiple datasets show that our method achieves superior performance in multimodal contrastive learning benchmarks. On public benchmark, our approach achieves state-of-the-art performance, and on synthetic shortcut datasets, we outperform existing baseline methods by an average of 97.95% on the CLIP model. © 2024 Neural information processing systems foundation. All rights reserved.

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Evaluating Generative Language Models with Prompt engineering for Categorizing User Stories to its Sector Domains 9

Evaluating Generative Language Models with Prompt Engineerin...

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Alawaji, Batool Hakami, Mona Alshemaimri, Bader Qassim University Department of Computer Qassim Saudi Arabia King Saud University Department of Software Engineering Riyadh Saudi Arabia

ISBN: (纸本)9798350394474

In Agile software development, user stories capture requirements through a concise, user-centric approach. Manual categorization of these stories is both labor-intensive and error-prone. This study addresses the gap in existing research by exploring the application of generative language models, specifically GPT-style models, with prompt engineering for user story *** research introduces experiments utilizing two generative language models, gpt-3.5-turbo and Llama-2-chat-hf, emphasizing innovative prompt engineering. The evaluation of gpt-3.5-turbo and Llama-2-chat-hf demonstrates commendable performance in zero-shot experiments. Notably, gpt-3.5-turbo outperforms in few-shot prompting with a higher F-score of 67.93%. The gpt-3.5-turbo showcased superior compliance with instructions compared to Llama-2-chat-hf. The study underscores the promising capability of generative language models, with prompt engineering, to automate user story categorization *** work contributes to the potential of automating user story categorization in low-resource settings, showcasing how leveraging LLMs can enhance accuracy and streamline the categorization process in software development, ultimately leading to more efficient outcomes. © 2024 IEEE.

关键词： Classification (of information)

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Unified Approach Integrating Machine Learning Algorithms for Detection of e-Mail Phishing Attacks 1

Unified Approach Integrating Machine Learning Algorithms for...

引用

1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Mathur, Abeer Dubey, Abhinav Sudhakar Mahara, Pranav Singh Gandhi, Usha Devi School of Computer Science & Engineering Vellore Institute of Technology Department of Software Systems Vellore India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Department of Information Technology Vellore India

ISBN: (纸本)9798350353648

With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by novel incorporation of three techniques: Support Vector Machine (SVM), Natural Language Processing (NLP) and Random Forest (RF). Utilizing NLP for deep analysis of text in phishing e-mails, SVM for strong categorization and Random Forest's decision making by group helps in improving detection. This combination of language and pattern analysis along with group learning builds a fortified system. This approach differs from prior work on a variety of factors such as reliability, security, integrity, scalability, interpretability, transparency, and robustness. Thorough evaluation on a reliable dataset aimed to demonstrate the method's effectiveness, which resulted in 98.7 % of accuracy in detection. This majorly aims at showcasing the potential of ensemble techniques to combat against cyber-attacks like phishing for laying a strong foundation for a secure digital environment. © 2024 IEEE.

关键词： Phishing

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AI-Driven Contextual Story Creation with Integrated Text and Visual Generation

AI-Driven Contextual Story Creation with Integrated Text and...

引用

2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024

作者： Gulsoy, Muhammed Kokach, Vael Kocaçınar, Büşra Akbulut, Fatma Patlar Department of Computer Engineering Istanbul Kültür University Istanbul Turkey Department of Software Engineering Istanbul Kültür University Istanbul Turkey

ISBN: (纸本)9798350379433

This study proposes an AI-driven framework for generating coherent narratives and corresponding visuals tailored for children's stories. We produce textual material by using Long Short-Term Memory (LSTM) models, and visually appealing images by using Generative Adversarial Networks (GANs) and fine-tuned Stable Diffusion Models. Furthermore, we investigate various prefix-based story starting points to increase the variety and interest of the story. Our method includes a significant innovation: the Stable Diffusion model is fine-tuned for image generation, enabling the production of high-quality, contextually relevant images from particular story segments. We show by experiments that the Stable Diffusion model can be fine-tuned to greatly improve the quality and relevance of the produced images, and that the layered LSTM model with extended training produces the best text generation performance. Using these advanced models together improved the storytelling experience by fusing imaginative AI with rich, narrative-driven imagery. © 2024 IEEE.

关键词： Generative adversarial networks

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EDAW: Enhanced Knowledge Distillation and Adaptive Pseudo Label Weights for Continual Named Entity Recognition

EDAW: Enhanced Knowledge Distillation and Adaptive Pseudo La...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Sheng, Yubin Zhang, Zuping Tang, Panrui Huang, Bo Xiao, Yao School of Computer Science and Engineering Central South University Chang-sha China School of Software Xinjiang University Urumqi China

ISBN: (纸本)9781665410205

Continual Learning for Named Entity Recognition (CL-NER) is designed to train models capable of adapting to evolving data by continuously introducing new entity types. This approach is crucial in dynamic environments where data evolves, such as social media, healthcare, and legal documents, necessitating the model to retain the memory of previously learned entity types while learning to identify new ones. However, due to the neural network's tendency to acquire new knowledge and forget old knowledge in continual learning and the unique non-entity type annotations in NER tasks, CL-NER faces severe catastrophic forgetting and semantic drift issues. In this paper, we propose Enhanced Knowledge Distillation and Entropy-based Adaptive Pseudo Label Weights (EDAW) to address the catastrophic forgetting and semantic drift issues in CL-NER. Specifically, we develop an enhanced knowledge distillation method that combines Kullback-Leibler divergence and feature cosine discrepancy. This method effectively minimizes the variance in output probability distributions and aligns the internal feature spaces between new and old models, thus reducing catastrophic forgetting. Additionally, we propose an entropy-based adaptive pseudo label weight method that allows the model to assign different weights to pseudo labels with varying certainties during training, effectively alleviating semantic drift and error accumulation caused by erroneous relabeling of pseudo labels. Notably, this study pioneers the in-clusion of a Chinese dataset in CL-NER, enhancing the model's robustness and demonstrating its efficacy in a multilingual context. Experiments on fourteen CL-NER settings across four public NER datasets show that EDAW improves average Micro-F1 and Macro-F1 scores by 3.44% and 3.72%, respectively, over existing state-of-the-art(SOTA) methods. We make our code available at: https://***/livosr/EDAW/tree/master © 2024 IEEE.

关键词： Semantics

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A new fully convolutional neural network architecture with text kernel for offline handwritten chinese text recognition 3

A new fully convolutional neural network architecture with t...

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3rd International Conference on Electronic Information engineering and Data Processing, EIEDP 2024

作者： Li, Yuanyuan Yang, Xinguang Ren, Zhiwei School of Software Dalian Jiaotong University Dalian China School of Computer and Communication Engineering Dalian Jiaotong University Dalian China

ISBN: (纸本)9781510680531

It’s challenging to optimize offline handwritten Chinese recognition as a topic of pattern recognition as the consequence of the complex structures of Chinese characters. To solve this problem, many data pre-processing methods and neural network architectures have been optimized to improve the recognition accuracy. To this end, we propose a fully convolutional neural network architecture with text kernel for offline handwritten Chinese text recognition in this paper. To make the features of the image clearer, the text kernel which can align the characters of handwritten Chinese text is implemented. This novel fully convolutional neural network captures the feature maps in two branch and promote the efficiency of neural networks. Experimenting on the CASIA-HWDB 2.0-2.2 datasets, our proposed method achieves a competitive performance and the accuracy achieves 97.87% with language model. © 2024 SPIE.

关键词： Convolutional neural networks

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