Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers最新文献

Smartphone location identification and transport mode recognition using an ensemble of generative adversarial networks 智能手机位置识别和运输模式识别使用生成对抗网络的集合

Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers Pub Date : 2020-09-10 DOI: 10.1145/3410530.3414353

Lukas Günthermann, Ivor Simpson, D. Roggen

引用次数: 8

Summary of the sussex-huawei locomotion-transportation recognition challenge 2020

Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers Pub Date : 2020-09-10 DOI: 10.1145/3410530.3414341

Lin Wang, H. Gjoreski, Mathias Ciliberto, P. Lago, Kazuya Murao, Tsuyoshi Okita, D. Roggen

{"title":"Summary of the sussex-huawei locomotion-transportation recognition challenge 2020","authors":"Lin Wang, H. Gjoreski, Mathias Ciliberto, P. Lago, Kazuya Murao, Tsuyoshi Okita, D. Roggen","doi":"10.1145/3410530.3414341","DOIUrl":"https://doi.org/10.1145/3410530.3414341","url":null,"abstract":"In this paper we summarize the contributions of participants to the third Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenge organized at the HASCA Workshop of UbiComp/ISWC 2020. The goal of this machine learning/data science challenge is to recognize eight locomotion and transportation activities (Still, Walk, Run, Bike, Bus, Car, Train, Subway) from the inertial sensor data of a smartphone in a user-independent manner with an unknown target phone position. The training data of a \"train\" user is available from smartphones placed at four body positions (Hand, Torso, Bag and Hips). The testing data originates from \"test\" users with a smartphone placed at one, but unknown, body position. We introduce the dataset used in the challenge and the protocol of the competition. We present a meta-analysis of the contributions from 15 submissions, their approaches, the software tools used, computational cost and the achieved results. Overall, one submission achieved F1 scores above 80%, three with F1 scores between 70% and 80%, seven between 50% and 70%, and four below 50%, with a latency of maximum of 5 seconds.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79443735","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: 46

Malware detection using artificial bee colony algorithm 利用人工蜂群算法进行恶意软件检测

Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers Pub Date : 2020-09-10 DOI: 10.1145/3410530.3414598

F. Mohammadi, Farzan Shenavarmasouleh, M. Amini, H. Arabnia

引用次数: 3

How particle detector can aid visual inspection for defect detection of TFT-LCD manufacturing 粒子探测器如何辅助TFT-LCD制造缺陷的目视检测

Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers Pub Date : 2020-09-10 DOI: 10.1145/3410530.3414596

M. Khakifirooz, M. Fathi

引用次数: 0

Nurse care activity recognition challenge: a comparative verification of multiple preprocessing approaches 护理活动识别挑战:多种预处理方法的比较验证

Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers Pub Date : 2020-09-10 DOI: 10.1145/3410530.3414333

Hitoshi Matsuyama, Takuto Yoshida, Nozomi Hayashida, Yuto Fukushima, Takuro Yonezawa, Nobuo Kawaguchi

{"title":"Nurse care activity recognition challenge: a comparative verification of multiple preprocessing approaches","authors":"Hitoshi Matsuyama, Takuto Yoshida, Nozomi Hayashida, Yuto Fukushima, Takuro Yonezawa, Nobuo Kawaguchi","doi":"10.1145/3410530.3414333","DOIUrl":"https://doi.org/10.1145/3410530.3414333","url":null,"abstract":"Although activity recognition has been studied considerably for the last two decades, it is still not so easy to handle complicated activity classes in a specific domain. The 2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data aims to explore a part of those complicated activities by focusing on the nurse caring. Our team, \"UCLab\", found that the main problem in the challenge is the imbalance and unevenness of the dataset, each of which often happens in real-field data. Considering the problem, we approached the challenge using a Random Forest-based method with multiple preprocessing to classify 12 activity modes. Our approach consists of the following steps: We first preprocessed the acceleration data to obtain uniformly sampled signals. Then we extracted acceleration data with respect to each row of the given label data and extracted feature values. We adopted Random Forest for classification and performed post-processing to the predicted data obtained from the classifier. As a result, we obtained 51.5% accuracy with the trial-based evaluation.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"83 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85540240","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: 2

WatchOver WatchOver

Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers Pub Date : 2020-09-10 DOI: 10.1145/3410530.3414373

Sahiti Kunchay, Saeed Abdullah

引用次数: 1

DOOM: a novel adversarial-DRL-based op-code level metamorphic malware obfuscator for the enhancement of IDS DOOM:一种新型的基于对抗性drl的操作码级变形恶意软件混淆器，用于增强IDS

Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers Pub Date : 2020-09-10 DOI: 10.1145/3410530.3414411

Mohit Sewak, S. Sahay, Hemant Rathore

{"title":"DOOM: a novel adversarial-DRL-based op-code level metamorphic malware obfuscator for the enhancement of IDS","authors":"Mohit Sewak, S. Sahay, Hemant Rathore","doi":"10.1145/3410530.3414411","DOIUrl":"https://doi.org/10.1145/3410530.3414411","url":null,"abstract":"We designed and developed DOOM (Adversarial-DRL based Opcode level Obfuscator to generate Metamorphic malware), a novel system that uses adversarial deep reinforcement learning to obfuscate malware at the op-code level for the enhancement of IDS. The ultimate goal of DOOM is not to give a potent weapon in the hands of cyber-attackers, but to create defensive-mechanisms against advanced zero-day attacks. Experimental results indicate that the obfuscated malware created by DOOM could effectively mimic multiple-simultaneous zero-day attacks. To the best of our knowledge, DOOM is the first system that could generate obfuscated malware detailed to individual op-code level. DOOM is also the first-ever system to use efficient continuous action control based deep reinforcement learning in the area of malware generation and defense. Experimental results indicate that over 67% of the metamorphic malware generated by DOOM could easily evade detection from even the most potent IDS. This achievement gains significance, as with this, even IDS augment with advanced routing sub-system can be easily evaded by the malware generated by DOOM.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82155333","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: 19

Nurse care activity recognition based on machine learning techniques using accelerometer data 基于加速计数据的机器学习技术的护士护理活动识别

Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers Pub Date : 2020-09-10 DOI: 10.1145/3410530.3414339

Mohammad Sabik Irbaz, Abir Azad, Tanjila Alam Sathi, Lutfun Nahar Lota

{"title":"Nurse care activity recognition based on machine learning techniques using accelerometer data","authors":"Mohammad Sabik Irbaz, Abir Azad, Tanjila Alam Sathi, Lutfun Nahar Lota","doi":"10.1145/3410530.3414339","DOIUrl":"https://doi.org/10.1145/3410530.3414339","url":null,"abstract":"Sensor-based human activity recognition has become one of the challenging and emerging research areas. Several machine learning algorithm with appropriate feature extraction has been used to solve human activity recognition task. However, recent research mainly focused on various deep learning algorithms, our focus of this study is measuring the performance of traditional machine learning algorithms with the incorporation of frequency-domain features. Because deep learning methods require a high computational cost. In this paper, we used Naive Bayes, K-Nearest Neighbour, SVM, Random Forest and Multilayer Perceptron with necessary feature extraction for our experimentation. We achieved best performance for K-Nearest Neighbour. Our experiment was a part of \"The 2nd Nurse Care Activity Recognition Challenge Using Lab and Field Data\" followed by the team MoonShot_BD. We concluded that with proper feature extraction, machine learning techniques may be useful to solve activity recognition with a low computational cost.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76558786","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: 3

Fine-grained activities recognition with coarse-grained labeled multi-modal data 使用粗粒度标记的多模态数据进行细粒度活动识别

Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers Pub Date : 2020-09-10 DOI: 10.1145/3410530.3414320

Zhizhang Hu, Tong Yu, Yue Zhang, Shijia Pan

{"title":"Fine-grained activities recognition with coarse-grained labeled multi-modal data","authors":"Zhizhang Hu, Tong Yu, Yue Zhang, Shijia Pan","doi":"10.1145/3410530.3414320","DOIUrl":"https://doi.org/10.1145/3410530.3414320","url":null,"abstract":"Fine-grained human activities recognition focuses on recognizing event- or action-level activities, which enables a new set of Internet-of-Things (IoT) applications such as behavior analysis. Prior work on fine-grained human activities recognition relies on supervised sensing, which makes the fine-grained labeling labor-intensive and difficult to scale up. On the other hand, it is much more practical to collect coarse-grained label at the level of activity of daily living (e.g., cooking, working), especially for real-world IoT systems. In this paper, we present a framework that learns fine-grained human activities recognition with coarse-grained labeled and a small amount of fine-grained labeled multi-modal data. Our system leverages the implicit physical knowledge on the hierarchy of the coarse- and fine-grained labels and conducts data-driven hierarchical learning that take into account the coarse-grained supervised prediction for fine-grained semi-supervised learning. We evaluated our framework and CFR-TSVM algorithm on the data gathered from real-world experiments. Results show that our CFR-TSVM achieved an 81% recognition accuracy over 10 fine-grained activities, which reduces the prediction error of the semi-supervised learning baseline TSVM by half.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"124 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87993941","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: 11

Using iOS for inconspicuous data collection: a real-world assessment 使用iOS进行不显眼的数据收集:现实世界的评估

Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers Pub Date : 2020-09-10 DOI: 10.1145/3410530.3414369

Yuuki Nishiyama, Denzil Ferreira, Wataru Sasaki, T. Okoshi, J. Nakazawa, A. Dey, K. Sezaki

引用次数: 2