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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Yang, Hanyu Zhao, Xiaoyong Wang, Lei Zhang, Jingle Li, Jianhua Beijing Information Science & Technology University School of Information & Management Beijing China Beijing Information Science & Technology University Information Systems Institute Beijing Advanced Innovation Center for Materials Genome Engineering Beijing China City Brain Zhongguancun Science City City Brain Co. Ltd. Beijing China

ISBN: (纸本)9798350302455

Big data streams have massive and complex characteristics. Due to the lack of global static information, the normalization of large data streams is susceptible to outliers and conceptual drift, which leads to incorrect modeling results. In addition, in online anomaly detection applications, it is significant to ensure real-time data processing to prevent severe consequences from missing critical points due to data flow backlogs. In this paper, we propose a method for interval adaptive normalization based on ChatGPT knowledge augmentation. The approach combines domain knowledge obtained by ChatGPT knowledge augmentation with interval adaptive sliding windows. Experimental results on three datasets in the cybersecurity domain show that the anomaly detection algorithm incorporating our proposed normalization algorithm achieves the maximum improvement of 32.46% and 32.68% in AUCROC and AUCPR compared to the traditional normalization method. © 2023 IEEE.

关键词： Anomaly detection

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Comparative Analysis of Word Embedding and Machine Learning Techniques for Classification of Software Developer Communications on Gitter 18

Comparative Analysis of Word Embedding and Machine Learning ...

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18th Conference on Computer science and Intelligence systems, FedCSIS 2023

作者： Akshar, Tumu Kumar, Lov Yogita Murthy, Lalita Bhanu Department of Computer Science & Information Systems BITS Pilani Hyderabad Campus India National Institute of Technology Department of Computer Engineering Kurukshetra India

ISBN: (纸本)9788396744784

In recent times, software developers widely use instant messaging and collaboration platforms, as these plat-forms aid them in exploring new technologies, raising different development-related issues, and seeking solutions from their peers virtually. Gitter is one such platform that has a heavy userbase. It generates a tremendous volume of data, analysis of which is helpful to gain insights about trends in open-source software development and the developers' inclination toward various technologies. Analyzing these trends helps these platforms better cater to the needs of the developers, in turn increasing the usage of these platforms and promoting collaborations between more developers. The classification techniques can be deployed for this purpose. The selection of an apt word embedding for a given dataset of text messages plays a vital role in determining the performance of classification techniques. In the present work, the comparative analysis of nine-word embeddings in combination with seventeen classification techniques with onevsone and onevs-rest has been performed on the GitterCom dataset for categorizing text messages into one of the pre-determined classes based on their purpose. Further, two feature selection methods have been applied. The SMOTE technique has been used for handling data imbalance. It resulted in a total of 1836 classification pipelines for analysis. The objective is to analyze their performances to recommend efficient pipelines for the classification task at hand. The experimental results show that word2vect, GLOVE with 300 vector size, and GLOVE with 100 vector size are three top-performing word embeddings having performance values taken across different classification techniques. The models trained using ANOVA features performed similarly to those models trained using all features. Finally, using the SMOTE technique helps models to get a better prpediction ability. © 2023 Polish information Processing Society.

关键词： Classification (of information)

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Methods, Models and Hardware-Software Complex of Distributed Monitoring Based on Iot and Blockchain technology

Methods, Models and Hardware-Software Complex of Distributed...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Goncharov, Dmitry V. Fedorov, Vyacheslav I. Ivashchuk, Oleg O. Institute of Engineering Technology and Natural Sciences Belgorod State National Research University Dept of Information and Robotic Systems Belgorod Russia Caspian University of Technology and Engineering Named after Sh. Yessenov Dept of Computer Science Aktau Kazakhstan

ISBN: (纸本)9798350395044

This research work explores the possibility of simultaneous use of Industry 4.0 technologies, namely the Internet of Things (IoT) and distributed systems (Blockchain) for collecting, processing, storing and confirming the adequacy of environmental data characterizing the state of sources of anthropogenic impact significantly distributed throughout the study area. It also describes the dynamics of the quality of natural environments during the dispersion and accumulation of pollutants. This approach is especially important for the implementation of market relations in the field of carbon quotas giving the possibility of redistributing the area of responsibility of production enterprises. The authors have developed a subsystem of an automated environmental safety management system, including a hardware and software complex that, based on Internet of Things technology, allows for the collection and integration of heterogeneous environmental information received from various sensors with its further storage and processing in a cloud system. The proposed subsystem, based on distributed registry technology, ensures openness and accessibility of the environmental data. It suggests decentralization of subsequent management decisions, so that it includes a specialized digital platform developed by the authors. The corresponding models and algorithms are presented in the research work, and their software implementation is developed. © 2024 IEEE.

关键词： Internet of things

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Self-Learning Based Dependable Offloading Optimization in Semi-Trusted Vehicular Edge Computing and Networks

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IEEE Transactions on Vehicular technology 2025年第5期74卷 8141-8157页

作者： Li, Xuehan Jing, Tao Li, Ruinian Wang, Xiaoxuan Yan, Yu Fan, Xin Huo, Yan Yu, Fei Richard Beijing Jiaotong University School of Electronic and Information Engineering National Engineering Research Center of Advanced Network Technologies Beijing100044 China Bowling Green State University Department of CS Bowling GreenOH43403 United States Beijing Forestry University School of Information Science and Technology Beijing100083 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

As vehicular edge computing and networks (VECONs) emerge, Internet of vehicles (IoVs) is gaining significant advantages. To provide users with dependable services, VECONs need to offer efficient and secure task offloading services. Unfortunately, little effort has been put into addressing security issues such as the risk of privacy exposure when offloading data. Modeling and optimizing offloading dependability are affected by these issues. To address these concerns, a dependable three-layer offloading framework involving blockchain technology is presented in this paper, with fully homomorphic encryption (FHE) algorithm incorporated in order to mitigate privacy exposure risks. The framework divides tasks reasonably and introduces a novel measure of offloading dependability called cost from energy consumption, delay, and privacy exposure risk (cEDP) for constructing dependable offloading optimization. Additionally, a self-learning based dependable privacy preservation offloading (SDPPO) algorithm is presented that is capable of solving the optimal offloading problem in a decentralized manner. Simulation results show that the SDPPO algorithm outperforms its counterparts in terms of faster convergence speed, better cEDP convergence results, and improved scalability and tunability characteristics. © 1967-2012 IEEE.

关键词： Differential privacy

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Multimodal Fusion for Enhanced Parkinson's Disease Screening: Integrating Brain MRI, Hand Drawing, Facial Expressions, and Voice Analysis 5

Multimodal Fusion for Enhanced Parkinson's Disease Screening...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Warnakulasuriya, Naveen De Silva, Shalinika Madushika, Janani Gamage, Sadali Jayawardena, Sanath Karunasena, Anuradha Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Parkinson's disease (PD) profoundly impacts millions in Sri Lanka, emphasizing the importance of early detection for better patient outcomes. We introduce 'NeuraTrace PD,' an innovative application for early PD screening, combining brain MRI, hand-drawn image analysis, facial expression assessment, and voice analysis. For brain MRI analysis, we employed a Graph Convolutional Network (GCN), achieving an impressive accuracy of 91% and identifying distinct graph connectivity patterns using graph theory. We also use a deep Convolutional Neural Network (CNN) to reach 90% accuracy in analyzing hand-drawn images, while optimizing image resolution for efficiency and information retention. Facial expression analysis is a vital component, leveraging CNN and Constrained Local Models (CLM) to accurately extract Facial Action Units (FAUs), which contribute to a remarkable 95% accuracy using the XGBoost machine learning model. We introduce a Machine Learning (ML) algorithm and advanced signal processing to enhance voice feature analysis, offering a thorough understanding and classification of PD patients. It reaches 99% accuracy. In summary, our approach holds significant promise for advancing early PD diagnosis, ultimately improving the quality of life for those affected by this challenging condition. © 2023 IEEE.

关键词： Image resolution

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A Deep Learning Approach for Multimodal Deception Detection 19th

A Deep Learning Approach for Multimodal Deception Detection

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19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018

作者： Krishnamurthy, Gangeshwar Majumder, Navonil Poria, Soujanya Cambria, Erik Singapore Singapore Information Systems Technology and Design Singapore University of Technology and Design Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9783031237928

Automatic deception detection is an important task that has gained momentum in computational linguistics due to its potential applications. In this paper, we propose a simple yet tough to beat multimodal neural model for deception detection. By combining features from different modalities such as video, audio, and text along with Micro-Expression features, we show that detecting deception in real life videos can be more accurate. Experimental results on a dataset of real-life deception videos show that our model outperforms existing techniques for deception detection with an accuracy of 96.14% and ROC-AUC of 0.9799. © 2023, Springer Nature Switzerland AG.

关键词： Deep learning

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Integrated sensor based on acoustics-electricity-mechanics coupling effect for wireless passive gas detection

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Nano Research 2023年第2期16卷 3130-3141页

作者： Licheng Zhou Bohui Zhai Zhixiang Hu Mingqi Zhang Long Li Xiangxin Wang Guangzu Zhang Jingting Luo Honglang Li Bingbing Chen Shenglin Jiang Hua-Yao Li Huan Liu School of Optical and Electronic Information Wuhan National Laboratory for OptoelectronicsOptics Valley LaboratoryHuazhong University of Science and TechnologyWuhan 430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen Virtual University ParkShenzhen 518000China Key Laboratory of Optoelectronic Devices and Systems College of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China CAS Center for Excellence in Nanoscience National Center for Nanoscience and TechnologyBeijing 100190China School of Energy Science and Engineering Nanjing Tech UniversityNanjing 211816China

Integrated sensor combines multiple sensor functions into a single unit,which has the advantages of miniaturization and better application ***,limited by the sensing platforms of the sensor and the selectivity of the sensitive film,there are still challenges to realize multi-component gas detection in one ***,a principle integration method is proposed to achieve the multi-component gas detection based on the acoustics-electricity-mechanics coupling *** electrical and mechanical properties of the Bi_(2)S_(3)nanobelts materials in different atmospheres indicate the possibility of realizing the principle *** the same time,the surface acoustic wave(SAW)sensor as a multivariable physical transducer can sense both electrical and mechanical *** exposure to 10 ppm NO_(2),NH_(3),and their mixtures,the integrated SAW gas sensor shows a 4.5 kHz positive frequency shift(acoustoelectric effect),an 11 kHz negative frequency shift(mechanics effects),and a reduced 4 kHz negative frequency shift(acoustics-electricity-mechanics coupling effect),***,we realize wireless passive detection of NO_(2)and NH_(3)based on the SAW *** work provides valuable insights that can serve as a guide to the design and fabrication of single sensors offering multi-component gas detection via different gas sensing mechanisms.

关键词： integrated gas sensor bismuth sulfide surface acoustic wave coupling effect wireless passive

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A Framework for Securing Saudi Arabian Hospital Industry: Vision-2030 Perspective

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Intelligent Automation & Soft Computing 2023年第6期36卷 2773-2786页

作者： Hosam Alhakami Abdullah Baz Mohammad Al-shareef Rajeev Kumar Alka Agrawal Raees Ahmad Khan Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955SaudiArabia Department of Computer Engineering College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Security Forces Hospital Makkah21955Saudi Arabia Department of Computer Science and Engineering Babu Banarasi Das UniversityLucknow226028Uttar PradeshIndia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025Uttar PradeshIndia

Recent transformation of Saudi Arabian healthcare sector into a reven-ue producing one has signaled several advancements in healthcare in the *** healthcare management into Smart hospital systems is one of *** hospital management systems which are breach-proof only can be termed as effective smart hospital *** the perspective of Saudi Vision-2030,many practitioners are trying to achieve a cost-effective hospital management sys-tem by using smart *** this row,the proposed framework posits the main objectives for creating smart hospital management systems that can only be acknowledged by managing the security of healthcare data and medical ***,the proposed framework will also be helpful in gaining satisfactory rev-enue from the healthcare sector by reducing the cost and time involved in mana-ging the smart hospital *** framework is based on a hybrid approach of three key methods which include:employing the Internet of Medical Things(IoMT)and blockchain methodologies for maintaining the security and privacy of healthcare data and medical practices,and using big data analytics methodol-ogy for raising the funds and revenue by managing the bulk volume of healthcare ***,the framework will also be helpful for both the patients and the doctors,thus enabling the Kingdom of Saudi Arabia(KSA)to meet its goals of Vision-2030 by ensuring low cost,yet credible,healthcare services.

关键词： Smart healthcare healthcare industry data security

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Opportunistic Division and Allocation of Machine Learning Task for WSN 14

Opportunistic Division and Allocation of Machine Learning Ta...

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14th International Conference on Ubiquitous and Future Networks, ICUFN 2023

作者： Hosonuma, Eri Tanaka, Nobuyuki Yamazaki, Takuma Taya, Akihito Nishiyama, Yuuki Sezaki, Kaoru Miyoshi, Takumi Yamazaki, Taku The University of Tokyo Institute of Industrial Science Tokyo Japan Collage of Systems Engineering and Science Shibaura Institute of Technology Saitama Japan Graduate School of Engineering and Science Shibaura Institute of Technology Saitama Japan The University of Tokyo Center for Spatial Information Science Chiba Japan

ISBN: (纸本)9798350335385

Machine learning (ML)-applied sensing systems are widely deployed in real environments in the research and development of wireless sensor networks (WSNs). However, in these systems, the central server must deal with the large amounts of sensing data and high processing costs to execute ML tasks. To deal with these issues, in-network processing methods of ML tasks have been proposed for WSNs. However, their main focus is to decide the division points and allocation strategy of ML tasks, and therefore they do not consider the routing algorithm in distributed environments. This paper proposes an opportunistic division and allocation method of ML tasks in distributed WSN environments that does not rely on a specific path. In the proposed method, each node autonomously makes a forwarding decision based on the remaining computational resource and hop count to distribute the computational load while considering the number of relays. A simulation was performed, and it revealed that the proposed method can appropriately allocate the computational processes of ML tasks and distribute them to WSN nodes. © 2023 IEEE.

关键词： Machine learning

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Scavenger: Better Space-Time Trade-Offs for Key-Value Separated LSM-trees 40

Scavenger: Better Space-Time Trade-Offs for Key-Value Separa...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Zhang, Jianshun Wang, Fang Qiu, Sheng Wang, Yi Ou, Jiaxin Huang, Junxun Li, Baoquan Fang, Peng Feng, Dan School of Computer Science and Technology Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China China Shenzhen Huazhong University of Science and Technology Research Institute China Bytefrance Inc.

ISBN: (纸本)9798350317152

Key-Value Stores (KVS) implemented with log-structured merge-tree (LSM-tree) have gained widespread ac-ceptance in storage systems. Nonetheless, a significant challenge arises in the form of high write amplification due to the compaction process. While KV-separated LSM-trees successfully tackle this issue, they also bring about substantial space am-plification problems, a concern that cannot be overlooked in cost-sensitive scenarios. Garbage collection (GC) holds significant promise for space amplification reduction, yet existing GC strategies often fall short in optimization performance, lacking thorough consideration of workload characteristics. Additionally, current KV-separated LSM-trees also ignore the adverse effect of the space amplification in the index LSM-tree. In this paper, we systematically analyze the sources of space amplification of KV-separated LSM-trees and introduce Scavenger, which achieves a better trade-off between performance and space amplification. Scavenger initially proposes an I/O-efficient garbage collection scheme to reduce I/O overhead and incorporates a space-aware compaction strategy based on compensated size to minimize the space amplification of index LSM-trees. Extensive experiments show that Scavenger significantly improves write performance and achieves lower space amplification than other KV-separated LSM-trees (including BlobDB, Titan, and TerarkDB). © 2024 IEEE.

关键词： Economic and social effects

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