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arXiv 2022年

作者： Chen, Chuan Zhang, Lei Li, Yihao Liao, Tianchi Zhao, Siran Zheng, Zibin Huang, Huawei Wu, Jiajing School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510275 China School of Software Engineering Sun Yat-Sen University Zhuhai519000 China

With the continuous development of web technology, Web3.0 has attracted a considerable amount of attention due to its unique decentralized characteristics. The digital economy is an important driver of high-quality economic development and is currently in a rapid development stage. In the digital economy scenario, the centralized nature of the Internet and other characteristics usually bring about security issues such as infringement and privacy leakage. Therefore, it is necessary to investigate how to use Web3.0 technologies to solve the pain points encountered in the development of the digital economy by fully exploring the critical technologies of digital economy and Web3.0. In this paper, we discuss the aspects of Web3.0 that should be integrated with the digital economy to better find the entry point to solve the problems by examining the latest advances of Web3.0 in machine learning, finance, and data management. We hope this research will inspire those who are involved in both academia and industry, and finally help to build a favourable ecology for the digital economy. Copyright © 2022, The Authors. All rights reserved.

关键词： Blockchain

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Mining High Utility Itemset with Multiple Minimum Utility Thresholds Based on Utility Deviation

Mining High Utility Itemset with Multiple Minimum Utility Th...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Naji Alhusaini Jing Li Philippe Fournier-Viger Ammar Hawbani Guilin Chen School of Computer and Information Engineering. Chuzhou University Chuzhou China School of Computer Since and Technology University of Since and Technology of China Hefei China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

High Utility Itemset Mining (HUIM) is the task of extracting actionable patterns considering the utility of items such as profits and quantities. An important issue with traditional HUIM methods is that they evaluate all items using a single threshold, which is inconsistent with reality due to differences in the nature and importance of items. Recently, algorithms were proposed to address this problem by assigning a minimum item utility threshold to each item. However, since the minimum item utility (MIU) is expressed as a percentage of the external utility, these methods still face two problems, called “itemset missing” and “itemset explosion”. To solve these problems, this paper introduces a novel notion of Utility Deviation (UD), which is calculated based on the standard deviation. The U D and actual utility are jointly used to calculate the MIU of items. By doing so, the problems of “itemset missing” and “itemset explosion” are alleviated. To implement and evaluate the U D notion, a novel algorithm is proposed, called HUI-MMU-UD. Experimental results demonstrate the effectiveness of the proposed notion for solving the problems of “itemset missing” and “itemset explosion”. Results also show that the proposed algorithm outperforms the previous HUI-MMU algorithm in many cases, in terms of runtime and memory usage.

关键词： Runtime Itemsets Conferences Explosions Data mining Task analysis Standards

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Reasoning3D - Grounding and Reasoning in 3D: Fine-Grained Zero-Shot Open-Vocabulary 3D Reasoning Part Segmentation via Large Vision-Language Models

arXiv

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arXiv 2024年

作者： Chen, Tianrun Yu, Chunan Li, Jing Zhang, Jianqi Zhu, Lanyun Ji, Deyi Zhang, Yong Zang, Ying Li, Zejian Sun, Lingyun Tech. Co. Ltd China College of Computer Science and Technology Zhejiang University China School of Information Engineering Huzhou University China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore School of Software Technology Zhejiang University China School of Information Science and Technology University of Science and Technology of China China

In this paper, we introduce a new task: Zero-Shot 3D Reasoning Segmentation for parts searching and localization for objects, which is a new paradigm to 3D segmentation that transcends limitations for previous category-specific 3D semantic segmentation, 3D instance segmentation, and open-vocabulary 3D segmentation. We design a simple baseline method, Reasoning3D, with the capability to understand and execute complex commands for (fine-grained) segmenting specific parts for 3D meshes with contextual awareness and reasoned answers for interactive segmentation. Specifically, Reasoning3D leverages an off-the-shelf pre-trained 2D segmentation network, powered by Large Language Models (LLMs), to interpret user input queries in a zero-shot manner. Previous research have shown that extensive pre-training endows foundation models with prior world knowledge, enabling them to comprehend complex commands, a capability we can harness to "segment anything" in 3D with limited 3D datasets (source efficient). Experimentation reveals that our approach is generalizable and can effectively localize and highlight parts of 3D objects (in 3D mesh) based on implicit textual queries, including these articulated 3d objects and real-world scanned data. Our method can also generate natural language explanations corresponding to these 3D models and the decomposition. Moreover, our training-free approach allows rapid deployment and serves as a viable universal baseline for future research of part-level 3d (semantic) object understanding in various fields including robotics, object manipulation, part assembly, autonomous driving applications, augment reality and virtual reality (AR/VR), and medical applications. The code, the model weight, the deployment guide, and the evaluation protocol are: http://***/Reason3D/. © 2024, CC BY-NC-ND.

关键词： Semantics

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LALISA: Adaptive Bitrate Ladder Optimization in HTTP-based Adaptive Live Streaming

LALISA: Adaptive Bitrate Ladder Optimization in HTTP-based A...

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IEEE Symposium on Network Operations and Management

作者： Farzad Tashtarian Abdelhak Bentaleb Hadi Amirpour Babak Taraghi Christian Timmerer Hermann Hellwagner Roger Zimmermann Christian Doppler Laboratory ATHENA Alpen-Adria-Universitat Klagenfurt Austria Department of Computer Science and Software Engineering Concordia University Canada School of Computing National University of Singapore Singapore

Video content in Live HTTP Adaptive Streaming (HAS) is typically encoded using a pre-defined, fixed set of bitrate-resolution pairs (termed Bitrate Ladder), allowing play-back devices to adapt to changing network conditions using an adaptive bitrate (ABR) algorithm. However, using a fixed one-size-fits-all solution when faced with various content complexities, heterogeneous network conditions, viewer device resolutions and locations, does not result in an overall maximal viewer quality of experience (QoE). Here, we consider these factors and design LALISA, an efficient framework for dynamic bitrate ladder optimization in live HAS. LALISA dynamically changes a live video session’s bitrate ladder, allowing improvements in viewer QoE and savings in encoding, storage, and bandwidth costs. LALISA is independent of ABR algorithms and codecs, and is deployed along the path between viewers and the origin server. In particular, it leverages the latest developments in video analytics to collect statistics from video players, content delivery networks and video encoders, to perform bitrate ladder tuning. We evaluate the performance of LALISA against existing solutions in various video streaming scenarios using a trace-driven testbed. Evaluation results demonstrate significant improvements in encoding computation (24.4%) and bandwidth (18.2%) costs with an acceptable QoE.

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Lithological Classification Using Densely Connected Convolution Network on Landsat-9 and Aster Datasets in a Semi-Arid Environment

Lithological Classification Using Densely Connected Convolut...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Michael Appiah-Twum Xu Wenbo Cobbinah B. Mawuli Peter Atandoh School of Resources and Environment University of Electronic Science and Technology of China Chengdu Sichuan China Yangtze Delta Region Institute (Huzhou) University of Electronic Science and Technology of China Huzhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu Sichuan China

Remote sensing-based geological interpretation provides insight from numerous sensors. That notwithstanding, high-dimensional feature learning requires full-scale diagnosis of intrinsic geological features in remotely sensed datasets. This study proposes a multi-sensor fusion approach. Integrating Landsat-9 and ASTER spectral features with DenseNet for lithological classification, displaces RF and KNN by 2.12 % and surpasses ResNet50 and Xception with a classification accuracy of 83.89%. Furthermore, this study underscores the effectiveness of merging new and old generation sensors for geological interpretation.

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A Novel Attention-based Explainable Deep Learning Framework Towards Medical Image Classification

A Novel Attention-based Explainable Deep Learning Framework ...

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Gladys W. Muoka Ding Yi Chiagoziem Chima Ukwuoma Moindi Duke Martin Ahmet Arif Aydin Mugahed A. Al-Antari School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu China Department of Computer Science Unicaf University Lilongwe Lilongwe Malawi Department of Computer Engineering Faculty of Engineering İnönü University Turkey Department of Artificial Intelligence College of Software & Convergence Technology Daeyang AI Center Sejong University Seoul Korea

Deep learning applications for medical image classification have shown remarkable promise, particularly incorporating attention-based neural networks. This is particularly relevant in medical imaging, where the integration of Artificial Intelligence assists with various imaging tasks, including classification, segmentation, and detection. Deep learning is revolutionizing medical research and playing a significant role in advancing personalized clinical treatment. However, the lack of interpretability in these models presents a significant obstacle to their adoption in clinical practice. Therefore, there is a growing need for a comprehensive understanding of artificial intelligence systems and their internal mechanisms, capabilities, and limitations, which is the focus of the field of explainable AI. This study proposes a novel attention-based explainable deep learning framework for medical image classification tasks, including Covid-19, breast cancer (BreakHis), lung cancer (LC2500), and Retinal optical coherence tomography (OCT). The proposed framework recorded overall accuracies of 98% (Covid-19 Radiography), 95% (BreakHis), 99.8% (LC2500), and 95%(OCT). For visual analysis of the outcomes, we employ and use the LIME, SHAP, and ELI-5 to analyze the achieved results. The study’s primary goal is to bridge the gap between the high performance achieved by attention-based models and the necessity for transparency and interpretability in medical image diagnostics.

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Dual Branch CNN and Transformer for Cardiac Atherosclerotic Plaque Classification

Dual Branch CNN and Transformer for Cardiac Atherosclerotic ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Haijun Lei Jia Zhao Guanjie Tong Longjiang Zhang Huaqiang Su Baiying Lei College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Radiology Jinling Hospital Medical School of Nanjing University Shenzhen University Nanjing China School of Biomedical Engineering& Shenzhen University Medical School Shenzhen University Shenzhen China

Accurate classification of coronary artery plaques can provide effective assistance for the diagnosis of coronary artery disease(CAD). The task of coronary artery plaque classification remains extremely challenging due to the complex anatomical structure and background of coronary arteries. 3D convolution still has limitations in feature modeling, so this study builds a dual branch bridge network based on convolution neural network (CNN) and Transformer framework, and fused the local feature extraction ability of convolution and the global modeling ability of Transformer through the bridge communication module. By using a shift attention (SA) module at the intersection of dual branch information to utilizes minimal computational complexity to fuse feature maps from both branches. The ghost plus (GP) module was aimed at balancing the enormous computational power issues of building rich semantic information and training difficulties in 3D Transformers. The proposed method have demonstrated the effectiveness through a large number of comparative and ablation experiment.

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Edge-Guided Multilevel Feature Fusion Network for Lightweight Camouflaged Object Detection

Edge-Guided Multilevel Feature Fusion Network for Lightweigh...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xingpeng Zhang Meilin Gao Guohai Gao Xin Wang Qiuli Wang School of Computer Science and Software Engineering Southwest Petroleum University Chengdu China Engineering Research Center for Intelligent Oil & Gas Exploration and Development of Sichuan Province Chengdu China Department of Radiology The First Affiliated Hospital of Army Medical University Chongging China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Camouflaged object detection (COD) aims to accurately recognize targets in intricate environments that blend into the background. Although numerous camouflage object identification techniques have demonstrated effectiveness, they often possess a substantial number of parameters. Therefore, we propose a new lightweight edge-guided multilevel feature fusion camouflaged object detection network, codenamed as LEMFNet. Initially, we adopt a lightweight CNN network model for feature extraction to reduce model complexity. Subsequently, we introduced a neighborhood feature association module (NFAM) to integrate feature information from different stages to obtain complementary feature representations and enhance the overall model performance. Furthermore, to obtain a more complete object structure, we introduce a boundary aggregation module (BAM) to delve into the edge semantics associated with the target and integrate the edge features into the proposed edge-guided aggregation module (EGAM). Experiments on three challenging benchmarks demonstrate our approach, with fewer parameters, achieves comparable or superior performance, effectively balancing resource utilization and accuracy.

关键词： Accuracy Target recognition Image edge detection Semantics Neural networks Object detection Benchmark testing

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DNMDA: Deep Non-negative Matrix Factorization with Multi-level Integration for MiRNA-Drug Interaction Prediction

DNMDA: Deep Non-negative Matrix Factorization with Multi-lev...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yujie Qi Jiaxin Yang Yu-An Huang Zhu-Hong You Zimai Zhang Lun Hu Xi Zhou Pengwei Hu Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Xinjiang University The School of Software Urumqi China School of Engineering and Applied Science Columbia University NY USA School of Computer Science Northwestern Polytechnical University Xian China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of Sciences Urumqi China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Numerous studies have demonstrated that the interaction between miRNAs and drugs plays a pivotal role in regulating gene expression and cellular function. Therefore, predicting these interactions is crucial for the development of novel drugs and personalized therapies. Existing methods for predicting miRNA-drug interactions often fail to leverage the full spectrum of molecular and biological features and overlook complex high-dimensional patterns. Deep non-negative matrix factorization (DNMF) addresses these limitations by extracting higher-level representations, thereby enhancing prediction accuracy and robustness. Building on this, this paper proposes a model called DNMDA. In this model, we integrate multiple similarity networks for both miRNAs and drugs and then extract their features through three key modules. Moreover, autoencoders are used to combine various feature sets, allowing for the capture of complementary information and enhancing the model’s capacity for making precise and reliable predictions. The resulting features are consolidated into a unified feature vector for each miRNA-drug pair. Ultimately, these feature vectors and their associated labels are provided to the classifier for training. To verify the predictions, a five-fold cross-validation was conducted. The five-fold cross-validation demonstrated a clear advantage in DNMDA’s metrics, underscoring its reliability in predicting potential miRNA-drug interactions. This claim is further supported by the predictive results section in the paper, offering concrete evidence of DNMDA’s efficacy in this field.

关键词： Drugs Training Accuracy Biological system modeling Computational modeling Scalability Predictive models Feature extraction Vectors Data models

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On-Chain Analysis-Based Detection of Abnormal Transaction Amount on Cryptocurrency Exchanges

SSRN

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SSRN 2022年

作者： Gu, Zhuoming Lin, Dan Wu, Jiajing School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China School of Software Engineering Sun Yat-sen University Zhuhai519000 China

Cryptocurrency exchanges play an indispensable role in the cryptocurrency market. However, some exchanges are suspected to be involved in various abnormal or malicious behaviors while providing services to users, such as money laundering, wash trading and even running away. Besides, these behaviors are reported to be often accompanied by an anomalous increase in the transaction amount. Therefore, it is a topic worthy of study to detect whether the abnormal transaction amount occurs in the exchange and when it occurs. This paper uses web crawler tools to collect a relatively complete dataset of exchanges and then conducts a correlation analysis to obtain the most important factors that influence the transaction amount of different exchanges. Then, the prediction model of the influence of various factors on the transaction amount is obtained based on deep learning. The deviation between the predicting transaction amount and the actual transaction amount is calculated to provide a basis for abnormal transaction amount detection. Finally, through a case study on the detection results, some abnormal transaction amounts are related to policy changes and industry events, while the others are suspected to be related to illegal behaviors. © 2022, The Authors. All rights reserved.

关键词： Blockchain

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