Wiem Fekih Hassen, L. Brunie, Asma Nechba, H. Kosch
{"title":"Continuous indoor/outdoor pathway display algorithm for pedestrian navigation service","authors":"Wiem Fekih Hassen, L. Brunie, Asma Nechba, H. Kosch","doi":"10.1145/3365921.3365944","DOIUrl":"https://doi.org/10.1145/3365921.3365944","url":null,"abstract":"Over the last decade, pedestrian navigation services are gaining a high interest in guiding and tracking the user at any place. Outdoor, GPS persists as the navigation standard. Nevertheless, indoor navigation services, mainly developed to meet the need of a single floor building, are still under development stage. This paper introduces a novel algorithm that computes and displays the pathway, in a continuous manner, between two different points (i.e. source point and destination point) located inside a multi-floor building or outdoor. The calculated pathway is composed of two parts; the first part is displayed in the source floor and the second part is viewed in the destination floor. In cases where the source point and the destination point are in different floors and after displaying the first part of the pathway, the proposed algorithm automatically detects if the user is close to the destination floor based on the difference of altitudes. Therefore, the first part of the pathway is dropped and the second part is displayed. We conducted several real experiments, inside the campus of the University of Passau in Germany, to evaluate the performance of the proposed algorithm. They yielded promising results in terms of continuity of navigation service and we achieved an accuracy in estimating the floor equal to 0.093 m.","PeriodicalId":162326,"journal":{"name":"Proceedings of the 17th International Conference on Advances in Mobile Computing & Multimedia","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129259088","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}
Daniel Hintze, Matthias Füller, Sebastian Scholz, R. Findling, Muhammad Muaaz, Philip G. Kapfer, Wilhelm Nüßer, R. Mayrhofer
{"title":"CORMORANT: On Implementing Risk-Aware Multi-Modal Biometric Cross-Device Authentication For Android","authors":"Daniel Hintze, Matthias Füller, Sebastian Scholz, R. Findling, Muhammad Muaaz, Philip G. Kapfer, Wilhelm Nüßer, R. Mayrhofer","doi":"10.1145/3365921.3365923","DOIUrl":"https://doi.org/10.1145/3365921.3365923","url":null,"abstract":"This paper presents the design and open source implementation of Cormorant, an Android authentication framework able to increase usability and security of mobile authentication. It uses transparent behavioral and physiological biometrics like gait, face, voice, and keystrokes dynamics to continuously evaluate the user's identity without explicit interaction. Using signals like location, time of day, and nearby devices to assess the risk of unauthorized access, the required level of confidence in the user's identity is dynamically adjusted. Authentication results are shared securely, end-to-end encrypted using the Signal messaging protocol, with trusted devices to facilitate cross-device authentication for co-located devices, detected using Bluetooth low energy beacons. Cormorant is able to reduce the authentication overhead by up to 97% compared to conventional knowledge-based authentication whilst increasing security at the same time. We share our perspective on some of the successes and shortcomings we encountered implementing and evaluating Cormorant to hope to inform others working on similar projects.","PeriodicalId":162326,"journal":{"name":"Proceedings of the 17th International Conference on Advances in Mobile Computing & Multimedia","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115101169","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}
{"title":"Automatic Vehicle Identification Through Visual Features","authors":"Imran Ahmad, B. Boufama","doi":"10.1145/3365921.3365938","DOIUrl":"https://doi.org/10.1145/3365921.3365938","url":null,"abstract":"Detection and recognition of a vehicle license plate is a fundamental requirement of any intelligent transport system, primarily to support activities like finding a stolen vehicle, vehicle surveillance/tracking, parking-toll collection, traffic flow planning and management, etc. However, a license plate can easily be stolen and/or changed by those with criminal intent to conceal their identity. This paper proposes a new vehicle identification system to obtain high degree of accuracy and success rate by not only considering the license plate but also shape of the vehicle. The proposed system is based on four steps: license plate detection, license plate recognition, license plate jurisdiction (province) detection and the vehicle shape detection. In the proposed system, the features are converted into local binary pattern (LBP) and Histogram of Oriented Gradients (HOG) as training dataset. To obtain high degree of accuracy in real-time application, a novel method based on cascaded classifiers is used to update the system. The proposed system allows us to store features of vehicles and related information in the database, thus, allowing us to automatically detect any discrepancy between a license plate and vehicle associated with it.","PeriodicalId":162326,"journal":{"name":"Proceedings of the 17th International Conference on Advances in Mobile Computing & Multimedia","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130919672","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}
S. Ghafari, Aditya Joshi, A. Beheshti, Cécile Paris, S. Yakhchi, M. Orgun
{"title":"DCAT","authors":"S. Ghafari, Aditya Joshi, A. Beheshti, Cécile Paris, S. Yakhchi, M. Orgun","doi":"10.1145/3365921.3365940","DOIUrl":"https://doi.org/10.1145/3365921.3365940","url":null,"abstract":"Customer reviews are now increasingly available on Online Social Networks (OSNs) for a wide range of products and services. Trust in the review's author is a crucial basis for believing in the reliability of reviews generated on such networks. In this context, the main challenge is to predict the unknown trust relationship between two users. Existing trust prediction approaches fail to incorporate textual footprint of users. To address this challenge, we present a deep learning-based graph analytics model to predict trust relations in OSNs. We leverage and extend GraphSAGE, a method for computing node representations in an inductive manner, to develop a deep classifier. We present our experiment with datasets from review websites to train classifiers that predict trust relations between pairs of users, and highlight how our approach significantly improves the quality of predicted trust relations compared to the state-of-the-art approaches.","PeriodicalId":162326,"journal":{"name":"Proceedings of the 17th International Conference on Advances in Mobile Computing & Multimedia","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128327822","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}
{"title":"An Adaptive Recursive Control Method for Peak Shaving in Zero Energy Community","authors":"Hong-soon Nam, Youn-Kwae Jeong","doi":"10.1145/3365921.3365946","DOIUrl":"https://doi.org/10.1145/3365921.3365946","url":null,"abstract":"This paper proposes an adaptive recursive control method for peak shaving in zero energy community (ZEC). A ZEC uses renewable energy resources for power generation, however, which may increase the energy gap between demand and supply. Therefore, if no proper measure is applied, electricity costs are not adequately saved. The proposed method is to control energy storage systems (ESSs) to reduce both energy charge and demand charge simultaneously. It firstly calculates ESS control power depending on time-sensitive energy pricing for energy charge reduction and then recalculates the control power to mitigate peak powers for demand charge reduction using an adaptive recursive control method. Simulation results show that the method can effectively mitigate peak powers resulting in cost reduction.","PeriodicalId":162326,"journal":{"name":"Proceedings of the 17th International Conference on Advances in Mobile Computing & Multimedia","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123652946","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}
{"title":"Tennis Stroke Classification: Comparing Wrist and Racket as IMU Sensor Position","authors":"Christopher Ebner, R. Findling","doi":"10.1145/3365921.3365929","DOIUrl":"https://doi.org/10.1145/3365921.3365929","url":null,"abstract":"Automatic tennis stroke recognition can help tennis players improve their training experience. Previous work has used sensors positions on both wrist and tennis racket, of which different physiological aspects bring different sensing capabilities. However, no comparison of the performance of both positions has been done yet. In this paper we comparatively assess wrist and racket sensor positions for tennis stroke detection and classification. We investigate detection and classification rates with 8 well-known stroke types and visualize their differences in 3D acceleration and angular velocity. Our stroke detection utilizes a peak detection with thresholding and windowing on the derivative of sensed acceleration, while for our stroke recognition we evaluate different feature sets and classification models. Despite the different physiological aspects of wrist and racket as sensor position, for a controlled environment results indicate similar performance in both stroke detection (98.5%-99.5%) and user-dependent and independent classification (89%-99%).","PeriodicalId":162326,"journal":{"name":"Proceedings of the 17th International Conference on Advances in Mobile Computing & Multimedia","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127965959","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}
{"title":"Towards Context-Aware Social Behavioral Analytics","authors":"A. Beheshti, V. Hashemi, S. Yakhchi","doi":"10.1145/3365921.3365942","DOIUrl":"https://doi.org/10.1145/3365921.3365942","url":null,"abstract":"The confluence of technological and societal advances, and more specifically, engagement with Web, social media, and smart devices has the potential to affect the mental behavior of the individuals. Examples include extremist and criminal behaviors such as radicalization and cyber-bullying, which are causing serious issues for humanity. Major barriers to the effective understanding of behavioral disorders on social networks includes the ability to understand the content and context of social documents, as well as the activity of social users. Understanding the patterns of behavioral disorders (e.g., criminal and extremist activities) on social networks, is challenging and requires techniques to contextualize the content of social documents based on the time-aware analysis of personality, behaviour and past activities of social users. In this context, semantic information extraction and enrichment from social documents has the potential to become a vital asset to explore the sign of behavioral disorders and prevent serious issues such as cyber-bullying, suicidal related behavior and radicalization. To address this challenge, in this paper, we present a novel social document analysis pipeline to enable analysts engage with social documents (e.g., a Tweet in Twitter or a post on Facebook) to explore cognitive aspects of behavioral disorders. We implement the pipeline as an extensible and scalable architecture and present the evaluation results.","PeriodicalId":162326,"journal":{"name":"Proceedings of the 17th International Conference on Advances in Mobile Computing & Multimedia","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121867418","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}
{"title":"A Reinforcement Learning and Synthetic Data Approach to Mobile Notification Management","authors":"Rowan Sutton, Kieran Fraser, Owen Conlan","doi":"10.1145/3365921.3365932","DOIUrl":"https://doi.org/10.1145/3365921.3365932","url":null,"abstract":"Mobile push-notifications are the primary mechanism for communicating new information to smartphone users, however they can also have a negative impact on user emotions, reduce work effectiveness and decrease current task performance. Through analysing state-of-the-art research on mobile Notification Management Systems, it was identified that few open-source notification data sets and, corresponding benchmarks, have been created and the majority of NMSs apply supervised learning methods. This paper investigates the use of a, freely shareable, synthetic mobile notification data set for developing and evaluating NMS performance using Reinforcement Learning. A Q-learning and Deep Q-learning agent were trained using synthetic data and an OpenAI Gym environment was created for evaluation. Final results illustrated that the Q-learning and Deep Q-learning agents could predict a users action toward notifications with ≈80% success when trained and evaluated upon real or synthetic data and ≈65% success when trained on synthetic and evaluated upon real notification data.","PeriodicalId":162326,"journal":{"name":"Proceedings of the 17th International Conference on Advances in Mobile Computing & Multimedia","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116811141","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}
{"title":"Security solution methods in the Vehicular Ad-Hoc Networks","authors":"Krzysztof Stepien, A. Poniszewska-Marańda","doi":"10.1145/3365921.3365926","DOIUrl":"https://doi.org/10.1145/3365921.3365926","url":null,"abstract":"The vehicles that are currently manufactured have become increasingly related to the Internet, supporting a range of recent features that are valuable towards both automakers and drivers. Vehicular Ad-Hoc Networks (VANET) have recently emerged collectively of the solutions that would facilitate connecting many cars to the vast network. Not solely it may preserve the protection, but also the efficiency of the traffic. However, VANETs themselves are prone to attacks. Those attacks could directly cause the corruption of networks and so presumably provoke a substantial loss of driver's time, money, and even their lives. The paper focuses on the Timing and Bogus attacks and presents the attainable solutions that would solve the safety leaks in the vehicle networks.","PeriodicalId":162326,"journal":{"name":"Proceedings of the 17th International Conference on Advances in Mobile Computing & Multimedia","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122838671","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}
{"title":"An Interaction Design Model for Information Visualization in Immersive Augmented Reality platform","authors":"Shafaq Irshad, D. R. A. Rambli, S. Sulaiman","doi":"10.1145/3365921.3365939","DOIUrl":"https://doi.org/10.1145/3365921.3365939","url":null,"abstract":"Recent advances in immersive technologies such as Augmented Reality (AR) provide new opportunities to explore, analyze and present data through Immersive Analytics (IA). This study presents a thorough overview of current research in immersive AR and further proposes a preliminary model for design of immersive AR applications. The goal is to present data in AR settings where users can rely on familiar perceptions to draw in-depth conclusions on the data. This will be achieved by developing a tangible AR system for information visualization in future. The results obtained from this study will help researchers and designers in the field of Information Visualization, immersive Analytics, Virtual Reality (VR), Augmented Reality (AR), HCI, and Natural User Interfaces (NUI).","PeriodicalId":162326,"journal":{"name":"Proceedings of the 17th International Conference on Advances in Mobile Computing & Multimedia","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123678454","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}