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IEEE International Conference on Systems, Man and Cybernetics

作者： KaKit Chio Wenhao Zhu Lihua He Dian Zhang Xu Yang Wuman Luo Faculty of Applied Sciences Macao Polytechnic University Macao S.A.R China School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Province China Department of Computer Science and Software Engineering Shenzhen University Shenzhen China

Personalized patient representation learning (PPRL) is a critical element in clinical risk prediction. It aims to obtain a complete portrait of each patient based on Electronic Health Records (EHR). Although existing works have achieved remarkable progress in healthcare prediction, there are still three major issues. First, feature correlation is crucial for risk prediction, but it has not yet been fully exploited by existing works. Second, variation pattern of dynamic feature contains useful information about patient's physical status, but adaptive pattern recognition is still a challenge. Third, existing works usually adopt a two-stage embedding process to process each dimension of the EHR data. However, some useful low-level information for PPRL will be lost. To address these issues, in this paper, we propose a fine-grained PPRL architecture named FG RL- N et for clinical risk prediction based on EHR. Specifically, we propose a Medical Feature Correlation Detection Module (FCM) to effectively learn the feature correlations for each patient and a Temporal Variation Pattern Recognition Module (TVM) to effectively detect the variation patterns of each dynamic feature. Moreover, we design a Fine-Grained Representation Mechanism (FGRM) to preserve the low-level information (from both feature and visit dimensions) useful for risk prediction. In addition, in the stage of data preprocessing, We utilize generic medical classification knowledge to classify numerical dynamic data. We conduct the in-hospital mortality experiment and the decompensation experiment on a real-world dataset. The experiment results show that the FGRL-Net outperforms state-of-the-art approaches. The source code is provided in github https://***/JackyChio/FGRL-Net.

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Solubility Measurement, Correlation and Thermodynamics Properties of Oxibendazole in 14 Pure Solvents and 2 Binary Solvents from 283.15 K to 328.15 K

SSRN

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SSRN 2023年

作者： Yu, Chao Zhang, Hao Tian, Ailing Zhou, Yiyu Li, Haohuan Xu, Funeng Fu, Hualin Wang, Xianxiang Shu, Gang Lin, Juchun Tang, Huaqiao Zhang, Wei Department of Pharmacy College of Veterinary Medicine Sichuan Agricultural University Sichuan Chengdu611130 China Intelligent Science and Technology School of Computer and Software Engineering Sichuan Yibin644005 China College of Science Sichuan Agricultural University Sichuan Chengdu611130 China

Oxibendazole (OXIB) is an oral anthelmintic heterocyclic compound used in humans and veterinary, whose solubility is closely related to its formulation. In this study, the molar fraction solubility of OXIB in 14 pure solvents and two binary mixed solvents were determined at standard atmospheric pressure and in the temperature range of 283.15K to 328.15K, and the results showed that the solubility of OXIB in all solvents increased with increasing temperature. OXIB has the highest molar fraction solubility in N,N-Dimethylacetamide (DMA) among single solvents. In binary solvents, the molar fractional solubility of OXIB increases with the increase of the ratio of N,N-Dimethylformamide (DMF) and DMA. The Apelblat equation and Van’t Hoff model were employed to correlate solubility data in 14 single solvents. The modified Apelblat equation, Van’t Hoff model, GSM model, and Jouyban-Acree model were employed to correlate solubility data in binary solvents. At the same time, the Hansen solubility parameters (HSPs) were used to analyze the solubility order of OXIB in 14 pure solvents and 2 binary solvent systems. Meanwhile, ideal solubility and activity coefficients indicate solute-solvent molecular interactions. Furthermore, from the results of thermodynamic parameters such as enthalpy, entropy, and apparent standard Gibbs free energy, the dissolution of OXIB is an endothermic and entropy-friendly process. © 2023, The Authors. All rights reserved.

关键词： Solubility

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Network Intrusion Detection by an Approximate Logic Neural Model

Network Intrusion Detection by an Approximate Logic Neural M...

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IEEE International Conference on software Reliability Engineering Workshops (ISSRE Wksp)

作者： Jiajun Zhao Qiuzhen Lin Junkai Ji College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science and Technology Dongguan University of Technology Dongguan China

ISBN: (纸本)9781665426046

With a growing threat of cyber-attacks, network intrusion detection remains challenging in the domain of cyberspace security. To defend against cyber-attacks on computer systems, various machine learning approaches have been applied for intrusion detection over the past few decades, such as random forest, support vector machine and long short-term memory. Although most of these approaches can provide satisfactory detection performances in terms of accuracy, recall and area under the receiver operating characteristic curve (AUC), their performances rely heavily on the training sample amount of attacks. When the type of attacks is unknown and the training sample amount is insufficient, the performances of these approaches may degenerate more or less. Therefore, based on a recently emerging approximate logic neural model (ALNM), a novel intrusion detection approach termed ALNM-IDA is proposed to overcome the issue in this paper. In the ALNM-IDA, the k-means clustering is first applied to discretize continuous features, and the maximum relevance minimum redundancy is adopted to select essential features. Then, the training dataset of normal and attack inputs is fed to the ALNM. In addition, adaptive moment estimation (Adam) is used as the training algorithm to improve the detection performance and accelerate the training phase. To validate the effectiveness of the ALNM-IDA, three benchmark intrusion detection datasets are employed in our experiments. Comparative results demonstrate that the ALNM-IDA can provide superior detection performance than other widely-used machine learning approaches in the case of insufficient training information.

关键词： Training Support vector machines Machine learning algorithms Redundancy Network intrusion detection Clustering algorithms Receivers

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A Logical Evaluation of Predicting Immune System Strength by using Enhanced Deep Learning Strategy

A Logical Evaluation of Predicting Immune System Strength by...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： K. Malathi M. Ramkumar E D Kanmani Ruby G.B. Santhi Vivek Korde Hamzeh J Aljawawdeh Department of CSE Saveetha School of Engineering Saveetha Institute of Science and Technology (SIMATS) Saveetha University Chennai Department of ECE Veltech Rangarajan Dr Sagunthala R & D Institute of Science & Technology Chennai Department of Computer Science and Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Department of software engineering Faculty of information technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The immune system plays a crucial role in defending the body against diseases, making it vital to develop models that can accurately predict immune system strength. This study presents an Enhanced Deep Learning Strategy (EDLS) designed to predict immune system strength based on immune-related biomarkers such as White Blood Cell (WBC) count and Immunoglobulins. A dataset consisting of 23,654 records with 12 features was collected from clinical sources. The data underwent comprehensive preprocessing, including noise reduction using a Median Filter, feature selection with Random Forest, and data fusion using Kalman Filters. The EDLS model integrates AutoEncoder for dimensionality reduction and Cascaded Long Short-Term Memory (LSTM) for capturing temporal dependencies. The proposed model outperforms nine traditional and advanced machine learning models, including Logistic Regression, Support Vector Machine (SVM), and Convolutional Neural Network (CNN), with an accuracy of 94.72%. Evaluation metrics such as Precision (92.01%), Recall (93.77 % ), and F1-Score (92.88 % ) highlight the model's effectiveness in predicting immune system strength. Feature importance analysis reveals that WBC count and Immunoglobulins are the most influential predictors. This study demonstrates the potential of EDLS for enhancing immune strength prediction, which can have significant implications for personalized healthcare and disease prevention.

关键词： Deep learning Support vector machines Biological system modeling Autoencoders Predictive models Biomarkers Convolutional neural networks Long short term memory Immune system Diseases

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Enhancing Question Answering for Enterprise Knowledge Bases using Large Language Models

arXiv

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arXiv 2024年

作者： Jiang, Feihu Qin, Chuan Yao, Kaichun Fang, Chuyu Zhuang, Fuzhen Zhu, Hengshu Xiong, Hui University of Science and Technology of China China Career Science Lab BOSS Zhipin Beijing China PBC School of Finance Tsinghua University Beijing China Institute of Software Chinese Academy of Sciences China Institute of Artificial Intelligence Beihang University China Zhongguancun Laboratory China The Thrust of Artificial Intelligence The Hong Kong University of Science and Technology Guangzhou China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Guangzhou HKUST Fok Ying Tung Research Institute China

Efficient knowledge management plays a pivotal role in augmenting both the operational efficiency and the innovative capacity of businesses and organizations. By indexing knowledge through vectorization, a variety of knowledge retrieval methods have emerged, significantly enhancing the efficacy of knowledge management systems. Recently, the rapid advancements in generative natural language processing technologies paved the way for generating precise and coherent answers after retrieving relevant documents tailored to user queries. However, for enterprise knowledge bases, assembling extensive training data from scratch for knowledge retrieval and generation is a formidable challenge due to the privacy and security policies of private data, frequently entailing substantial costs. To address the challenge above, in this paper, we propose EKRG, a novel Retrieval-Generation framework based on large language models (LLMs), expertly designed to enable question-answering for Enterprise Knowledge bases with limited annotation costs . Specifically, for the retrieval process, we first introduce an instruction-tuning method using an LLM to generate sufficient document-question pairs for training a knowledge retriever. This method, through carefully designed instructions, efficiently generates diverse questions for enterprise knowledge bases, encompassing both fact-oriented and solution-oriented knowledge. Additionally, we develop a relevance-aware teacher-student learning strategy to further enhance the efficiency of the training process. For the generation process, we propose a novel chain of thought (CoT) based fine-tuning method to empower the LLM-based generator to adeptly respond to user questions using retrieved documents. Finally, extensive experiments on real-world datasets have demonstrated the effectiveness of our proposed framework. © 2024, CC BY.

关键词： Knowledge management

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Deep Feature Surgery: Towards Accurate and Efficient Multi-Exit Networks

arXiv

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arXiv 2024年

作者： Gong, Cheng Chen, Yao Luo, Qiuyang Lu, Ye Li, Tao Zhang, Yuzhi Sun, Yufei Zhang, Le College of Software Nankai University China College of Computer Science Nankai University China National University of Singapore Singapore School of Information and Communication Engineering University of Electronic Science and Technology of China China HAIHE Lab of ITAI China Tianjin Key Laboratory of Network and Data Security Technology China Key Laboratory of Data and Intelligent System Security Ministry of Education

Multi-exit network is a promising architecture for efficient model inference by sharing backbone networks and weights among multiple exits. However, the gradient conflict of the shared weights results in sub-optimal accuracy. This paper introduces Deep Feature Surgery (DFS), which consists of feature partitioning and feature referencing approaches to resolve gradient conflict issues during the training of multi-exit networks. The feature partitioning separates shared features along the depth axis among all exits to alleviate gradient conflict while simultaneously promoting joint optimization for each exit. Subsequently, feature referencing enhances multi-scale features for distinct exits across varying depths to improve the model accuracy. Furthermore, DFS reduces the training operations with the reduced complexity of backpropagation. Experimental results on Cifar100 and ImageNet datasets exhibit that DFS provides up to a 50.00% reduction in training time and attains up to a 6.94% enhancement in accuracy when contrasted with baseline methods across diverse models and tasks. Budgeted batch classification evaluation on MSDNet demonstrates that DFS uses about 2× fewer average FLOPs per image to achieve the same classification accuracy as baseline methods on Cifar100. The code is available at https: //***/GongCheng1919/dfs. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

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A survey on exploring the challenges and applications of wireless body area networks (WBANs)

Cyber Security and Applications

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Cyber Security and Applications 2024年 2卷

作者： Rajasekaran, Arun Sekar Sowmiya, L. Maria, Azees Kannadasan, R. Department of ECE SR University Telangana Warangal 506371 India Department of ECE KPR Institute of Engineering and Technology Coimbatore India School of Computer Science and Engineering Inavolu Beside AP Secretariat VIT-AP University Andhra Pradesh 522237 India Department of Software systems School of Computer Science and Engineering VIT University-Vellore Tamilnadu 632014 India

Networks play an important role in the day-to-day life of every individual. Networks are involved in the transmission of necessary information between the sender and receiver in the channel. Wireless Body Area Network (WBAN) is a major advancement in the field of network communication. Due to the arrival of the Micro Electromechanical System (MEMS) and several intelligent sensors, collaboration with WBAN makes accurate predictions of parameters in the human body. WBAN has numerous applications in medical and non-medical fields. WBANs have demonstrated remarkable capabilities in real-time health monitoring, facilitating the collection of vital physiological data from individuals in diverse environments. Firstly, their low-power and energy-efficient design ensures prolonged device operation, making them suitable for continuous monitoring over extended periods. Additionally, the miniaturization of sensors and the integration of wireless communication technologies enable seamless data transmission to centralized healthcare systems. Furthermore, the integration of artificial intelligence and machine learning algorithms in WBAN systems has enabled personalized health analytics, allowing for more precise and context-aware health monitoring. This paper gives a survey of the WBAN standard, security in WBAN, several authentication approaches, routing, and MAC protocols. In addition, this paper describes the challenges faced by WBAN, such as network partitioning, changes in postures, lifetime issues, and quality of service (QoS). At last, the advancement in WBAN is for future improvement in the area of body area networks. © 2024

关键词： Quality of service Routing protocol Security Sensors Wireless body area network

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Large Scale Foundation Models for Intelligent Manufacturing Applications: A Survey

arXiv

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arXiv 2023年

作者： Zhang, Haotian Dereck, Semujju Stuart Wang, Zhicheng Lv, Xianwei Xu, Kang Wu, Liang Jia, Ye Wu, Jing Long, Zhuo Liang, Wensheng Ma, X.G. Zhuang, Ruiyan The College of Information Science and Engineering Northeastern University Shenyang110819 China The Foshan Graduate School Northeastern University Foshan528311 China The School of mechanical engineering and automation South China University of Technology Shenyang110819 China The School of Software Engineering South China University of Technology Guangzhou510006 China The Departement of Computer and Information Science Qinghai University of Science and Technology Xining China The Enterprise AI Midea Group Foshan528311 China

'Although the applications of artificial intelligence especially deep learning had greatly improved various aspects of intelligent manufacturing, they still face challenges for wide employment due to the poor generalization ability, difficulties to establish high-quality training datasets, and unsatisfactory performance of deep learning methods. The emergence of large scale foundational models(LSFMs) had triggered a wave in the field of artificial intelligence, shifting deep learning models from single-task, single-modal, limited data patterns to a paradigm encompassing diverse tasks, multimodal, and pre-training on massive datasets. Although LSFMs had demonstrated powerful generalization capabilities, automatic high-quality training dataset generation and superior performance across various domains, applications of LSFMs on intelligent manufacturing were still in their nascent stage. A systematic overview of this topic was lacking, especially regarding which challenges of deep learning can be addressed by LSFMs and how these challenges can be systematically tackled. To fill this gap, this paper systematically expounded current statue of LSFMs and their advantages in the context of intelligent manufacturing. and compared comprehensively with the challenges faced by current deep learning models in various intelligent manufacturing applications. We also outlined the roadmaps for utilizing LSFMs to address these challenges. Finally, case studies of applications of LSFMs in real-world intelligent manufacturing scenarios were presented to illustrate how LSFMs could help industries, improve their efficiency. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Dual-Source CBCT for Larger Longitudinal Coverage: System Design and Image Reconstruction

Dual-Source CBCT for Larger Longitudinal Coverage: System De...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Xusheng Zhang Tianling Lyu Yijie Shi Xinyun Zhong Zhan Wu Yan Xi Shijie Wang Yang Chen Wentao Zhu School of Software Southeast University Nanjing China Zhejiang Lab Hangzhou China Laboratory of Image Science and Technology the School of Computer Science and Engineering Southeast University Nanjing China First-Imaging Tech. Shanghai China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The application of CBCT systems in intraoperative environments has become increasingly common, but concurrent CBCT systems are unsuitable for situations that require a large longitudinal imaging FoV, such as orthopedics. To increase longitudinal coverage, we developed a dual-source CBCT (DS-CBCT) system in which two ray sources are symmetrically placed along the central plane. After further analyzing its geometric characteristics, we propose an analytical reconstruction algorithm termed DT-FDK specialized for DS-CBCT, which combines cone-beam rebinning and two rays with smaller cone angles among the four conjugate rays to further suppress cone beam artifacts. The system design and reconstruction algorithm are tested on both simulated and real-scanned data. Results show that the DS-CBCT can expand the effective imaging volume by 37.1% compared to single-source CBCT systems. The reconstructed images by DT-FDK also show improved image quality compared to traditional reconstruction algorithms.

关键词： Image quality Orthopedic surgery Imaging Reconstruction algorithms Engineering in medicine and biology Image reconstruction System analysis and design

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Understanding Self-Supervised Pretraining with Part-Aware Representation Learning

arXiv

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arXiv 2023年

作者： Zhu, Jie Qi, Jiyang Ding, Mingyu Chen, Xiaokang Luo, Ping Wang, Xinggang Liu, Wenyu Wang, Leye Wang, Jingdong Key Lab of High Confidence Software Technologies [Peking University Ministry of Education China School of Computer Science Peking University China School of AI Peking University China UC Berkeley United States School of EIC Huazhong University of Science & Technology China University of Hong Kong Hong Kong Baidu China

In this paper, we are interested in understanding self-supervised pretraining through studying the capability that self-supervised methods learn part-aware representations. The study is mainly motivated by that random views, used in contrastive learning, and random masked (visible) patches, used in masked image modeling, are often about object parts. We explain that contrastive learning is a part-to-whole task: the projection layer hallucinates the whole object representation from the object part representation learned from the encoder, and that masked image modeling is a part-to-part task: the masked patches of the object are hallucinated from the visible patches. The explanation suggests that the self-supervised pretrained encoder leans toward understanding the object part. We empirically compare the off-the-shelf encoders pretrained with several representative methods on object-level recognition and part-level recognition. The results show that the fully-supervised model outperforms self-supervised models for object-level recognition, and most self-supervised contrastive learning and masked image modeling methods outperform the fully-supervised method for part-level recognition. It is observed that the combination of contrastive learning and masked image modeling further improves the performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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