2023 IEEE International Conference on Smart Computing (SMARTCOMP)最新文献_第5页

Addressing Domain Shift in Pedestrian Detection from Thermal Cameras without Fine-Tuning or Transfer Learning 无微调或迁移学习的热像仪行人检测中的域移位问题

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00078

M. Fanfani, Matteo Marulli, P. Nesi

引用次数: 0

Synchronized Sub-Second Arbitrary Changes to Decoupled Components for Ultimate Resilience in Cross-Platform Geo-Distributed Smart Factories 跨平台地理分布式智能工厂中解耦组件的同步亚秒任意更改的最终弹性

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00056

M. Soderi, J. Breslin

引用次数: 0

Using Innovations in Data Analytics and Smart Technologies to Fight Opioid Overdose Crisis 使用创新的数据分析和智能技术来对抗阿片类药物过量危机

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00052

Nasibeh Zohrabi, Jacqueline B. Britz, A. Krist, Mostafa Zaman, S. Abdelwahed

引用次数: 0

Detection of False Data Injection in Smart Water Metering Infrastructure 智能水表基础设施中虚假数据注入检测

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00070

Ayanfeoluwa Oluyomi, Shameek Bhattacharjee, Sajal Kumar Das

{"title":"Detection of False Data Injection in Smart Water Metering Infrastructure","authors":"Ayanfeoluwa Oluyomi, Shameek Bhattacharjee, Sajal Kumar Das","doi":"10.1109/SMARTCOMP58114.2023.00070","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00070","url":null,"abstract":"Smart water metering (SWM) infrastructure collects real-time water usage data that is useful for automated billing, leak detection, and forecasting of peak periods. Cyber/physical attacks can lead to data falsification on water usage data. This paper proposes a learning approach that converts smart water meter data into a Pythagorean mean-based invariant that is highly stable under normal conditions but deviates under attacks. We show how adversaries can launch deductive or camouflage attacks in the SWM infrastructure to gain benefits and impact the water distribution utility. Then, we apply a two-tier approach of stateless and stateful detection, reducing false alarms without significantly sacrificing the attack detection rate. We validate our approach using real-world water usage data of 92 households in Alicante, Spain for varying attack scales and strengths and prove that our method limits the impact of undetected attacks and expected time between consecutive false alarms. Our results show that even for low-strength, low-scale deductive attacks, the model limits the impact of an undetected attack to only C0.2199375 and for high-strength, low-scale camouflage attack, the impact of an undetected attack was limited to C1.434375","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125422047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detecting Potholes from Dashboard Camera Images Using Ensemble of Classification Mechanisms 基于集成分类机制的仪表盘相机图像凹坑检测

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00031

Hiroo Bekku, Miku Minami, Takafumi Kawasaki, J. Nakazawa

{"title":"Detecting Potholes from Dashboard Camera Images Using Ensemble of Classification Mechanisms","authors":"Hiroo Bekku, Miku Minami, Takafumi Kawasaki, J. Nakazawa","doi":"10.1109/SMARTCOMP58114.2023.00031","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00031","url":null,"abstract":"Road damage such as potholes may occur on roads due to aging, which may affect traffic. Periodic inspections of road damages are difficult due to the high cost of road surveys. The development of a system that automatically detects potholes and other road damages from dash cam images can allow inexpensive road inspections and can overall improve the problem of the long-term overlook of road damages. Last year, we conducted a demonstration experiment in Edogawa City, Tokyo, using an existing image-based road damage detection method. From that experiment, we found that the detection of potholes on actual roads often causes false positives in detecting shadows and manholes. In this study, we propose a method to reduce false positives in pothole detection, which was considered to be a problem through the demonstration experiment. Since the evaluation based on a pothole-only dataset is not practical, we constructed a dataset for evaluation by adding shadow and manhole images. Our method consists of two main components: data augmentation and an ensemble of classification mechanisms for object detection models. The result of the test on the reconstructed pothole dataset showed that the Average Precision (AP), which is a measure to evaluate the performance of object detection, and F1, which is the harmonic mean of precision and recall, were improved compared to the existing method. Our new method is expected to be an effective pipeline for tasks and situations where false positives are likely to occur and where false positives are more considered as an issue than false negatives, given that they are not dependent on the domain of potholes.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121626636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Designing a Hybrid Push-Pull Architecture for Mobile Crowdsensing using the Web of Things 为使用物联网的移动众传感设计一个混合推拉架构

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00081

L. Sciullo, Federico Montori, Ivan D. Zyrianoff, Lorenzo Gigli, Davide Tinti, M. D. Felice

引用次数: 0

HeteroSys: Heterogeneous and Collaborative Sensing in the Wild 异源:野外的异质和协同感知

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00073

Indrajeet Ghosh, Adam Goldstein, Avijoy Chakma, Jade Freeman, T. Gregory, Niranjan Suri, S. R. Ramamurthy, Nirmalya Roy

{"title":"HeteroSys: Heterogeneous and Collaborative Sensing in the Wild","authors":"Indrajeet Ghosh, Adam Goldstein, Avijoy Chakma, Jade Freeman, T. Gregory, Niranjan Suri, S. R. Ramamurthy, Nirmalya Roy","doi":"10.1109/SMARTCOMP58114.2023.00073","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00073","url":null,"abstract":"Advances in Internet-of-Things, artificial intelligence, and ubiquitous computing technologies have contributed to building the next generation of context-aware heterogeneous systems with robust interoperability to control and monitor the environmental variables of smart environments. Motivated by this, we propose HeteroSys, an end-to-end multi-functional smart IoT-based system prototype for heterogeneous and collaborative sensing in a smart IoT-based environment. A unique characteristic of HeteroSys is that it relies on Home Assistant (HA) to collate heterogeneous sensors (e.g., passive infrared sensors (PIR), reed (door) switches, object tags, wearable wrist-mounted, water leak sensors, and internet protocol cameras), and uses a variety of networking protocols such as Zigbee open standard for mesh networking, WiFi, and Bluetooth Low Energy (BLE) for communication. The reliance on HA (and its broad community support) makes HeteroSys ideal for various applications such as object detection, human activity recognition and behavior patterns. We articulated the development phase, integration, testing challenges and evaluation of the HeteroSys. We conducted an extensive 24-hour longitudinal data collection from 5 participants performing 6 activities by deploying in an indoor home environment. Our assessment of the acquired dataset reveals that the representations learned using deep learning architecture aid in improving the detection of activities to 83.1% accuracy.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"56 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122288188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Reinforcement Learning Performance through a Behavioral Psychology-Inspired Variable Reward Scheme 通过行为心理学启发的可变奖励方案提高强化学习性能

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00050

Heena Rathore, Henry Griffith

引用次数: 2

GNN-RL: Dynamic Reward Mechanism for Connected Vehicle Security using Graph Neural Networks and Reinforcement Learning 基于图神经网络和强化学习的网联汽车安全动态奖励机制

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00047

Heena Rathore, Henry Griffith

{"title":"GNN-RL: Dynamic Reward Mechanism for Connected Vehicle Security using Graph Neural Networks and Reinforcement Learning","authors":"Heena Rathore, Henry Griffith","doi":"10.1109/SMARTCOMP58114.2023.00047","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00047","url":null,"abstract":"This paper introduces a new approach to incentivise the vehicles in connected vehicle (CV) networks based on the reputation measures along with a combination of graph neural network and reinforcement learning (GNN-RL). The proposed method enables vehicles to create reputation estimates of their nearby vehicles by analyzing broadcasted kinematic data and onboard sensor estimates, as well as the network connectivity topology. This data is then utilized to create a graphical representation of reputation distribution. A centralized RL agent is used for providing reward signals to each vehicle based on a Laplacian matrix, which encourages the vehicles to make more accurate reputation estimates. The proposed algorithm is based on a GNN-RL algorithm previously used for coordinated navigation, which has been adapted to the cybersecurity domain in this paper. The simulation results show that the model was effective in giving dynamic rewards to vehicles based on their reputation scores. Further, Laplace matrices helped in analyzing the connectivity and behavior of CVs in the network.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133978057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

HIJACK: Learning-based Strategies for Sound Classification Robustness to Adversarial Noise HIJACK:基于学习的声音分类策略对对抗噪声的鲁棒性

2023 IEEE International Conference on Smart Computing (SMARTCOMP) Pub Date : 2023-06-01 DOI: 10.1109/SMARTCOMP58114.2023.00082

Derek Sweet, Emanuele Zangrando, Francesca Meneghello

{"title":"HIJACK: Learning-based Strategies for Sound Classification Robustness to Adversarial Noise","authors":"Derek Sweet, Emanuele Zangrando, Francesca Meneghello","doi":"10.1109/SMARTCOMP58114.2023.00082","DOIUrl":"https://doi.org/10.1109/SMARTCOMP58114.2023.00082","url":null,"abstract":"The effective deployment of smart service systems within homes, workspaces and cities, requires gaining context and situational awareness to take action when changes are detected. To this end, sound classification systems are widely adopted and integrated into several smart devices to continuously monitor the environment. However, sound classification algorithms are prone to adversarial attacks that pose a considerable security threat to smart service systems where they are integrated. In this paper, we devise HIJACK, a novel machine learning framework entailing five neural network strategies to enforce the robustness of sound classification systems to adversarial noise injection. The HIJACK methodologies can be applied to any neural network-based sound classifier and consist of tailored transformations of the input audio during training along with specific additional layers added to the neural network architecture. To assess the noise robustness provided by the HIJACK strategies, we design a measure based on a L2-adversarial attack to sound classification – identified as the normalized fast gradient method (NFGM) – that constructs the adversarial noise by maximizing the sound mis-classification probability. We assessed the robustness of HIJACK to the proposed NFGM attack on a publicly available dataset. The results show that the combination of the five HIJACK strategies allows reaching robustness to adversarial noise 58 times larger than state-of-the-art neural networks for sound classification, guaranteeing a classification accuracy above 83%.","PeriodicalId":163556,"journal":{"name":"2023 IEEE International Conference on Smart Computing (SMARTCOMP)","volume":"246 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129858282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0