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A Survey on Physical Event Verification in User-centric Smart Home Systems

A Survey on Physical Event Verification in User-centric Smar...

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IEEE international conference on Software Reliability Engineering Workshops (ISSRE Wksp)

作者： Bing Huang Kwok-Yan Lam School of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350367041

ISBN: (纸本)9798350367058

Recent advances in IoT technologies have led to the rise of home automation (HA) systems, greatly improving users’ daily life quality. With the growing use of smart devices in homes, event sensors play a key role by reporting physical events like doors opening or lights turning on, automatically or manually. However, these sensor reports can be unreliable due to faults or active attackers, leading to false events (event spoofing). This is critical, as smart homes can trigger event chains without user involvement. Therefore, verifying the authenticity of physical events is important in securing smart home automation systems. Despite increasing attention from academia and industry, no survey has yet summarized HA systems’ security research on physical event verification. This paper provides a comprehensive review of physical event verification and future research directions.

关键词： Surveys Systematics Reviews Smart homes Turning Software reliability Security Object recognition Smart devices Intelligent sensors

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SMMGCL: a novel multi-level graph contrastive learning framework for integrating spatial multi-omics data

SMMGCL: a novel multi-level graph contrastive learning frame...

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Bo Wang Wei Liu Jiawei Luo Xiangtao Chen Chee Keong Kwoh College of Computer Science and Electronic Engineering Hunan University China College of Computing & Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Recent advances in spatial omics technologies have allowed various omics data to be obtained from a single tissue section. To fully explore the relationships among these different types of omics data, it is urgent to develop more effective methods for spatial multi-omics data integration. In this work, we propose a novel Multi-level Graph Contrastive Learning framework, named SMMGCL, to simultaneously mine complementary information at both spot and graph levels for integrating Spatial Multi-omics data. Specifically, to adaptively fuse multi-omics modalities, we first design a multi-modality autoencoder that integrates spatial locations with spot omic expressions to extract modality-specific embeddings. These embeddings are then fused into a consensus representation using an attention mechanism to capture spot-level cross-omics representations. Next, to explore the complex inter-omic structural information, we connect corresponding spots across different omics adjacency graphs into a heterogeneous graph. We then employ a graph convolutional network (GCN) to extract spatial correlations across the omics, learning a graph-level cross-omics global representation. Finally, SMMGCL aligns feature similarity matrixes between spot-level and graph-level representations with their pseudo-label similarity matrix, ensuring multi-level clustering consistency and leading to more accurate spatial multi-omics integration. Experimental results on simulated and real datasets from across tissues show that SMMGCL consistently outperforms other state-of-the-art methods in spatial multi-omics integration performance. The code for SMMGCL is available for download from the GitHub repository at https://***/cs-wangbo/SMMGCL.

关键词： Correlation Codes Graph convolutional networks Fuses data integration Contrastive learning Feature extraction Spatial databases Bioinformatics Software development management

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Sustainable Supply Chain Analytics for Anomalously Potential Fraudulent Logistics

Sustainable Supply Chain Analytics for Anomalously Potential...

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Trends in Quantum computing and Emerging Business technologies (TQCEBT), international conference on

作者： Benny J Godwin N Preethi Fr Jossy P George School of Business and Management CHRIST University Lavasa Pune India Department of Data Science CHRIST University Lavasa Pune India

ISBN: (纸本)9781665453622

The primary focus of this research is to detect potential anomalous and fraudulent cotton ginning transactions. The analysis of monitoring systems utilizing substantial analytics is often time-consuming and requires painstaking analysis afterward. In addition, the paper discusses how Third-Party Logistics affects the warehousing process and its antidromic role in distribution channels. data for this study came from an established cotton gin operation in Tanganyika/Tanzania - East Africa. Ultimately, the results should allow cotton ginning to be improved by understanding anomalous activities. Cotton ginning fraud will be explained for the first time in scholarly journals using supply chain analytics.

关键词： Quantum computing Supply chains Warehousing Africa Market research Fraud Cotton

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Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review

Privacy Threats and Countermeasures in Federated Learning fo...

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IEEE/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Adel ElZemity Budi Arief School of Computing University of Kent Canterbury United Kingdom

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

Federated Learning (FL) in the Internet of Things (IoT) environments can enhance machine learning by utilising decentralised data, but at the same time, it might introduce significant privacy and security concerns due to the constrained nature of IoT devices. This represents a research challenge that we aim to address in this paper. We systematically analysed recent literature to identify privacy threats in FL within IoT environments, and evaluate the defensive measures that can be employed to mitigate these threats. Using a Systematic Literature Review (SLR) approach, we searched five publication databases (Scopus, IEEE Xplore, Wiley, ACM, and science Direct), collating relevant papers published between 2017 and April 2024, a period which spans from the introduction of FL until now. Guided by the PRISMA protocol, we selected 49 papers to focus our systematic review on. We analysed these papers, paying special attention to the privacy threats and defensive measures – specifically within the context of IoT – using inclusion and exclusion criteria tailored to highlight recent advances and critical insights. We identified various privacy threats, including inference attacks, poisoning attacks, and eavesdropping, along with defensive measures such as Differential Privacy and Secure Multi-Party Computation. These defences were evaluated for their effectiveness in protecting privacy without compromising the functional integrity of FL in IoT settings. Our review underscores the necessity for robust and efficient privacy-preserving strategies tailored for IoT environments. Notably, there is a need for strategies against replay, evasion, and model stealing attacks. Exploring lightweight defensive measures and emerging technologies such as blockchain may help improve the privacy of FL in IoT, leading to the creation of FL models that can operate under variable network conditions.

关键词： Privacy Differential privacy Social computing Computational modeling Multi-party computation Robustness Blockchains Internet of Things Time factors Security

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Improving Obfuscated Malware Classification: A Machine Learning Approach with Feature Selection and data Balancing

Improving Obfuscated Malware Classification: A Machine Learn...

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international conference on Advanced Computer Control, ICACC

作者： Gundu Bhavana Mokshagna Bhuvan Chowdary Pudota S Saravanan K Deepak Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350379136

ISBN: (纸本)9798350379143

Obfuscated malware refers to malicious software that has been deliberately modified to hide its malicious purpose and avoid detection by conventional security mechanisms. Classifying obfuscated malware poses a significant challenge due to its deceptive nature and the dynamic techniques used by attackers to obfuscate code. Existing approaches in malware analysis often struggle to accurately classify obfuscated malware, leading to potential security vulnerabilities and data breaches. In this research, we investigate the application of machine learning algorithms for malware analysis, specifically focusing on multi-class classification of obfuscated malware using the CIC-MalMem-2022, a latest dataset. We apply data preprocessing techniques, including Synthetic Minority Over-sampling Technique (SMOTE) and normalization, to enhance the dataset's balance and consistency. Subsequently, we train and evaluate various machine learning algorithms, including Random Forest (RF), XGBoost, K-Nearest Neighbors (KNN), LightGBM, and Decision Trees (DT), on different datasets comprising SMOTE, normalized, and SMOTE+normalized instances. Our study also explores the effect of feature selection process on the performance of the model. We identify the top 35 features for optimal accuracy, addressing the dimensionality challenge inherent in large-scale datasets. Our findings reveal that the Random Forest (RF) model achieves the highest accuracy of 96.23% on the dataset that is balanced by SMOTE with top 35 features.

关键词： Radio frequency Accuracy Machine learning algorithms Scalability Nearest neighbor methods Feature extraction Solids Malware Random forests Testing

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Enabling Software Defined Networking for Industry 4.0 Using OpenStack 17th

Enabling Software Defined Networking for Industry 4.0 Using ...

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17th Annual conference of the international-Association-of-Online-Engineering (IAOE) and the Global-Online-Laboratory-Consortium (GOLC) on Remote Engineering and Virtual Instrumentation (REV) - Cross Reality and data science in Engineering

作者： Gowripeddi, Venkata Vivek Ganesan, Nithya Shailendra, Samar Thangaraju, B. Bapat, Jyotsna Int Inst Informat Technol Bangalore Karnataka India

ISBN: (纸本)9783030525750;9783030525743

Industry 4.0 refers to the fourth generation of advancements in Manufacturing that enables data driven processes, Predictive maintenance, Lights-Out Manufacturing among others. Industry 4.0 comprises of elements from Cyber Physical Systems, Industrial IoT, Big data, Cloud computing, AI and ML. However, Networking at the which is at the heart of these technologies has not had a major improvement, we wish to address this in this paper. The goal of this paper is to highlight the challenges in the current networking infrastructure for Industry 4.0 and propose a new standard for networking in Industry 4.0. Software Defined Networking is a emerging technology in the field of networking which provides for a centralised Control Plane and enables Network Function Virtualisation for Industry 4.0. OpenFlow Stack is used for building the solution. The outcomes of the paper include: Provide an SDN Architecture for the above system. Different zones have to be integrated to provide the ability for Centralized Control plane without compromising confidentiality. The system should be scalable for widespread deployment across multiple plants and manufacturers. This project deals with design and development of a Software Defined Networking (SDN) architecture for Industry 4.0, which involves integration of various components of OpenStack and openFlow. In this project, we identify the limitations and gaps in the current networking architecture for Industry 4.0 and propose a solution for the same. The openstack implementation of the project is done, Different Results of the implementation and its advantages are discussed along with scope for future implementation. This addresses a key challenge in Networking for Industry 4.0.

关键词： Industry 4.0 Networking data science SDN OpenStack

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English-Chinese Translation Online Teaching System Based on data Mining Algorithm

English-Chinese Translation Online Teaching System Based on ...

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： Chenxi Li Department of Public Foreign Language Teaching and Research Jilin University of Finance and Economics ChangchunJilin China International School of Chinese Studies Northeast Normal University ChangchunJilin China

ISBN: (纸本)9781665493970

With the rapid development of modern communication technology, mobile phones, as an indispensable communication tool for people, record the trajectory of people's activities, not only in the real space, but also in the network space. The analysis and mining of all kinds of information data has been very. data mining, which plays a crucial role in many scenarios, can help us discover a lot of valuable knowledge and anomaly patterns. The cultivation of translation ability is the core goal of current translation teaching, and the research on the composition of translation ability and the methods to improve translation ability is increasingly becoming the focus of translation teaching research. The development of translation ability is a dynamic interactive process, not a linear process guided by gradual input, but a dyna mic systematic behavior full of peaks, valleys, advances, reversals, stagnation, and even leaps forward, since it is composed of multiple sub-abilities and the multi-sub-abilities do not develop synchronously. The characteristics of dynamic assessment coincide with the dynamic change and complexity of the development of translation ability. In this paper, data mining algorithms are used to study the existing English-Chinese translation online systems.

关键词： Microwave integrated circuits Systematics Heuristic algorithms Education Mobile handsets Regression analysis Windows

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Supporting Green Neuromorphic computing: Machine Learning Guided Microfabrication for Resistive Random Access Memory

Supporting Green Neuromorphic Computing: Machine Learning Gu...

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IEEE/ACM international Symposium on Big data computing (BDC)

作者： Abdi Yamil Vicenciodelmoral Md Mehedi Hasan Tanim Feng Zhao Xinghui Zhao School of Engineering and Computer Science Washington State University Vancouver Vancouver WA

The growing popularity of big data and machine learning applications call for a more powerful and energy-efficient way to execute deep learning workflows. Neuromorphic chips provide a potential solution, as they attempt to mimic the neuronal architectures in human brain and show great potentials in reducing energy consumption in the order of magnitude and also improve the computational performance. However, the fabrication process for neuromophic chips is costly and currently based on trial-and-error, which adds complexity to the design process. In this paper, we address this challenge by designing and developing machine learning guided microfabrication process for Resistive Random Access Memory (RRAM), which is a key device in neuromorphic chips. Experimental results show that our approach is effective in terms of predicting the performance of RRAM devices fabricated under various process conditions.

关键词： Performance evaluation Deep learning Energy consumption Neuromorphic engineering Microfabrication Resistive RAM Big data

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Propelling Agritech Forward: Federated Learning and CNN for Sweet Potato Disease Diagnosis

Propelling Agritech Forward: Federated Learning and CNN for ...

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international conference on computing and Networking Technology (ICCNT)

作者： Shiva Mehta Vinay Kukreja Satvik Vats Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India

In the current study, a unique method is suggested to address the problem of identifying illnesses in sweet potato leaves by combining federated learning with Convolutional Neural Networks (CNNs). This work advances the field by showcasing how federated learning, applied via local and global model training, may result in highly accurate and effective disease detection in sweet potato leaves. The approach was rigorously tested using four customers' local data sets, each including six different classes of sweet potato leaf diseases. The local models consistently achieved precision, recall, F1-Score, and accuracy scores in the mid-to-high 90s, successfully classifying all illness classes. For instance, the model for Client C earned a commendable F1-score of 97.02 for Class F, whereas the model for Client A displayed an equally good F1-score of 94.50 for Class. For instance, the global model assessment of the data from Client C produced an F1-Score of 95.55, showing the model's resilience in the precise identification of illness classifications. Macro, weighted, and micro averages were used to assess the global model's overall performance in the final step of the investigation. The model produced an F1-Score for the macro-average of 95.98 and an even more excellent F1-Score for the weighted and micro-averages of 96.02. In conclusion, the study effectively illustrates the potential of federated learning and CNNs in improving the detection and treatment of illnesses affecting sweet potato leaves. This strategy offers a very efficient, doable answer to farmers all over the globe, greatly enhancing the production and health of the world's agriculture.

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Teaching and Training System Based on WEB3.0 Technology

Teaching and Training System Based on WEB3.0 Technology

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： Ting Zhang Jiangxi University of Applied Science Jiangxi China

ISBN: (纸本)9781665493970

The research on Application-oriented Undergraduate Teaching Reform Based on data mining technology is a research to investigate the research problems and solutions in the field of data mining. The purpose of this paper is to summarize the existing research in this field and discuss some new directions for future research. This paper will be divided into five parts: the first part briefly introduces data mining, the second part discusses the current research activities related to teaching reform, the third part describes the main findings of previous research and puts forward new directions for future research, and the fourth part discusses the limitations and conclusions. The purpose of this study is to study the effect of data mining technology on applied undergraduate teaching. The application of data mining technology can provide a lot of information about students, teachers and other people involved in education. This information can be used in various ways to improve the quality of teaching.

关键词： Training Education data mining

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