Anonymity on Byzantine-Resilient Decentralized Computing

In recent years, decentralized computing has gained popularity in various domains such as decentralized learning, financial services and the Industrial Internet of Things. As identity privacy becomes increasingly important in the era of big data, safeguarding user identity privacy while ensuring the security of decentralized computing systems has become a critical challenge. To address this issue, we propose ADC (Anonymous Decentralized Computing) to achieve anonymity in decentralized computing. In ADC, the entire network of users can vote to trace and revoke malicious nodes. Furthermore, ADC possesses excellent Sybil-resistance and Byzantine fault tolerance, enhancing the security of the system and increasing user trust in the decentralized computing system. To decentralize the system, we propose a practical blockchain-based decentralized group signature scheme called Group Contract. We construct the entire decentralized system based on Group Contract, which does not require the participation of a trusted authority to guarantee the above functions. Finally, we conduct rigorous privacy and security analysis and performance evaluation to demonstrate the security and practicality of ADC for decentralized computing with only a minor additional time overhead

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001

a fall detection algorithm based on pattern recognition and human posture analysis

Detecting fall is a particular important task in security monitoring and healthcare applications of sensor networks. However traditional approaches suffer from either a high false positive rate or high false negative rate, especially when the collected sensor data are unbalanced. Therefore, there is a lack of tradeoff between false alarms and misses for many traditional data mining methods to be applied. To solve this problem a novel fall detection algorithm based on pattern recognition and human posture analysis is presented in this paper. It firstly extracts thirty temporal features from the original data traces for different length adaptation of samples, and then exploits Hidden Markov Model (HMM) to filter the noisy character data and reduce the dimension of feature vectors. After that, it performs a closer classification with one-class Support Vector Machine (OCSVM) to filter the high false positive samples, and finally applies posture analysis to counteract the effects of high false negative samples until a satisfying accuracy is achieved. Simulation with real data demonstrates that the proposed algorithm outperforms other existing approaches.Detecting fall is a particular important task in security monitoring and healthcare applications of sensor networks. However traditional approaches suffer from either a high false positive rate or high false negative rate, especially when the collected sensor data are unbalanced. Therefore, there is a lack of tradeoff between false alarms and misses for many traditional data mining methods to be applied. To solve this problem a novel fall detection algorithm based on pattern recognition and human posture analysis is presented in this paper. It firstly extracts thirty temporal features from the original data traces for different length adaptation of samples, and then exploits Hidden Markov Model (HMM) to filter the noisy character data and reduce the dimension of feature vectors. After that, it performs a closer classification with one-class Support Vector Machine (OCSVM) to filter the high false positive samples, and finally applies posture analysis to counteract the effects of high false negative samples until a satisfying accuracy is achieved. Simulation with real data demonstrates that the proposed algorithm outperforms other existing approaches

Spring Test for Automatic Measuring of Signal Complexity

Abstract In order to automatically measure the complexity of time series, we invented 3S plot, and the embedded spring is a one-dimensional manifold. Three automatic statistical indexes are defined by self-similarity measure, extreme value search acceleration and neighborhood rotation phase, respectively. The above three new metrics and another two comparison standards, Lyapunov exponent and spectral entropy complexity, can pass the benchmark system test of unified equation. Under the guidance of visualization of bifurcation diagram, three probability measuring methods with relatively weak independent recognition abilities and one phase measuring method with relatively sensitive detection ability can easily identify the order and chaos states following Chua's equation. Finally, five main measuring tools are used to successfully characterize the new 555 timer-based chaotic signals.</jats:p