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Truly Scalable Real-time Coincidence Processor based on Serial and parallel Hybrid Architecture

Truly Scalable Real-time Coincidence Processor based on Seri...

引用

IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Mingyue Cui Zhixiang Zhao Huifan Qu Qiu Huang School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Many PET systems use coincidence processors based on a serial architecture for coincidence detection, but this single-threaded coincidence detection has high latency and may even lose events in the case of a high event rate. The parallel coincidence processor has high event processing efficiency. However, the increase in detectors leads to a rapid expansion in the usage of hardware resources. In this study, we investigate a coincidence processor based on a serial and parallel hybrid architecture. The proposed processor first groups and merges data streams to reduce the number of them then uses serial processing to detect coincidence events within the merged data streams. After that, the processor uses a homogeneously distributed coincidence detection network to achieve parallel coincidence detection between different streams. The hybrid architecture of the coincidence processor saves hardware resource usage when the number of input data streams is high. When the coincidence processor has 10 input channels in the second stage of coincidence detection, it can achieve a maximum data transfer rate of 400M events/s and has a coincidence detection efficiency larger than 99.97%. The hybrid architecture of the coincidence processor presented in this paper enables real-time coincidence detection for systems with high event rates such as the long-axis PET while saving lots of hardware resources.

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Intrusion Detection for Marine Meteorological Sensor network

Intrusion Detection for Marine Meteorological Sensor Network

引用

IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Xin Su Guifu Zhang Minxue Zhang Baoliu Ye Hongyan Xing College of IOT Engineering Hohai University Changzhou Jiangsu China Information Department Hohai University Nanjing Jiangsu China Collaborative Innovation Center for Meteorological Disaster Prediction and Evaluation Nanjing University of Information Science and Technology Nanjing China Jiangsu Key Laboratory of Meteorological Detection and Information Processing Nanjing University of Information Science and Technology Nanjing China

The rapid development of intelligent devices and wireless communication has greatly promoted the intelligence of the maritime Internet of Things (MIoT) in recent years, and the marine meteorological sensor network (MMSN) is an essential part of MIoT, providing accurate meteorological data for MIoT services. However, the frequent information interaction between the device and the external world also causes information leakage risks for MMSN. Intrusion detection technology based on deep learning is an effective defensive mechanism. Thus, we propose a lightweight one-dimensional convolutional neural network (LCNN) intrusion detection model to adapt to the MMSN environment characteristics and device heterogeneity. In addition, an improved supervised adversarial autoencoder (ISAAE) is proposed for synthesizing samples to address the problem that minority class attack samples are difficult to collect and detect. Finally, the simulation experimental results on the NSL-KDD dataset verify the effectiveness of the proposed method.

关键词： Training Wireless communication Adaptation models Convolution Computational modeling Simulation Intrusion detection

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Learning Dynamic Behavior Patterns for Fraud Detection

Learning Dynamic Behavior Patterns for Fraud Detection

引用

IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Zijun Huang Junhang Wu Lingfei Ren Ruimin Hu Dengshi Li National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China School of Cyber Engineering Xidian University Xi'an China School of Artificial Intelligence Jianghan University Wuhan China

Telecom fraud detection is considered a needle in a haystack task due to the fact that fraudsters are usually hidden among a large number of normal users. In fact, a fraudster will usually act in a continuous manner over a period of time to achieve fraudulent purposes. At the same time, fraudsters are constantly looking for new fraudulent targets. Therefore, we consider a behavioral pattern-based fraudster identification system called HADyn-LSTM for telecommunication network fraud detection. Motivated by the breakthroughs in deep learning (DL), we make an effort to judge whether a user is a fraudster is based on the user's behavior sequence in the social network, which means that we study the difference between normal users and fraudsters through dynamic behavior patterns rather than static statistical attributes. Particularly, based on sequential neural networks and attention mechanisms, we design an end-to-end system that learns and extracts the dynamic patterns of user behavior sequences. Furthermore, We tested our system on three datasets of different sizes. The test results show that the HADyn-LSTM system performs better than the static attribute-based method.

关键词： Social networking (online) Neural networks Telephone sets Behavioral sciences Fraud Telecommunications Safety

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StreamChain: Building a Low-Latency Permissioned Blockchain For Enterprise Use-Cases 4

StreamChain: Building a Low-Latency Permissioned Blockchain ...

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4th IEEE International conference on Blockchain (Blockchain)

作者： Kuhring, Lucas Istvan, Zsolt Sorniotti, Alessandro Vukolic, Marko Otto GmbH & Co KG Hamburg Germany Tech Univ Darmstadt Darmstadt Germany IBM Res Zurich Switzerland Protocol Labs Zurich Switzerland IMDEA Software Inst Madrid Spain

ISBN: (纸本)9781665417600

Permissioned blockchains are a promising technology for secure decentralized data management in business-tobusiness use-cases. In contrast to Bitcoin and similar public blockchains, which rely on Proof-of-Work for consensus and are deployed on thousands of geo-distributed nodes, business-tobusiness use-cases (such as supply chain management and banking) require significantly fewer nodes, cheaper consensus, and are often deployed in controlled environments with fast networking and low latency. However, permissioned blockchains often follow the architectural thinking behind their WAN-oriented public relatives, which results in end-to-end latency several orders of magnitude higher than necessary. In this work, we propose a fundamental shift in permissioned blockchain design, eliminating blocks in favor of processing transactions in a streaming fashion. This results in a drastically lower latency without reducing throughput or forfeiting reliability and security guarantees. To demonstrate the wide applicability of our design, we prototype StreamChain based on the Hyperledger Fabric, and show that it delivers latency two orders of magnitude lower than Fabric, while sustaining similar throughput. This performance makes StreamChain a potential alternative to traditional databases and, thanks to its streaming paradigm, enables further research, for instance, around reducing latency through relying on modern programmable hardware accelerators.

关键词： Blockchain FPGA parallel processing Software Pipelining

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Modeling Ledger Dynamics in IOTA Blockchain

Modeling Ledger Dynamics in IOTA Blockchain

引用

GLOBECOM 2022 - 2022 IEEE Global Communications conference

作者： Fengyang Guo Xun Xiao Artur Hecker Schahram Dustdar Munich Research Center Huawei Technologies Munich Germany Distributed Systems Group TU Wien Vienna Austria

ISBN: (纸本)9781665435413

IOTA blockchain is a new type of distributed ledger systems that is lightweight without mining and feeless-of-using. Rather than using a chain structure as in traditional blockchains, IOTA organizes ledger records with a directed acyclic graph (DAG), called Tangle. When message entries are committed into the ledger, the ledger tangle grows in a special way where multiple messages could be attached by different processing nodes in parallel. Such a unique evolution process motivates us to study the ledger tangle dynamics, which is unexplored so far. In this paper, we present the first generative modeling for IOTA tangle based on stochastic analysis. A key finding is that IOTA tangle renders a double Pareto Lognormal (dPLN) distribution, rather not typical network models (e.g., Power-Law and Exponential distributions). Quantitative comparisons show that the fitting quality of our model outperforms existing popular models on official real world datasets published by IOTA Foundation. Estimated model parameters are provided, which is immediately instrumental for a more realistic IOTA network generator design. The proposed generative model also provides a deeper understanding of the internal mechanics of IOTA network.

关键词： Analytical models network topology Instruments Fitting Stochastic processes Predictive models Generators

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Nighttime Pedestrian Detection based on Feature Attention and Transformation 25

Nighttime Pedestrian Detection Based on Feature Attention an...

引用

25th International conference on Pattern Recognition (ICPR)

作者： Li, Gang Zhang, Shanshan Yang, Jian Nanjing Univ Sci & Technol Key Lab Intelligent Percept & Syst High Dimens In Jiangsu Key Lab Image & Video Understanding Socia PCA LabSch Comp Sci & EngnMinist Educ Nanjing Peoples R China

ISBN: (纸本)9781728188089

Pedestrian detection at nighttime is an important yet challenging task, which is fundamental for many practical applications, e.g. autonomous driving, video surveillance. To address this problem, in this work we start with some analysis, from which we find that the nighttime features have much more noise than that of daytime, resulting in low discrimination ability. Besides, we also observe some pedestrian examples are under adverse illumination conditions, and they can hardly provide sufficient information for accurate detection. based on these findings, we propose the Feature Attention Module (FAM) and Feature Transformation Module (FTM) to enhance nighttime features. In FAM, guided by progressive segmentation supervision, hierarchical feature attention is produced to enhance multi-level features. On the other hand, FTM is introduced to enforce features from adverse illumination to approach that from better illumination. based on feature attention and transformation (FAT) mechanism, a two-stage detector called FATNet is constructed for nighttime pedestrian detection. We conduct extensive experiments on nighttime datasets of EuroCity Persons (Night) and NightOwls to demonstrate the effectiveness of our method. On both datasets, our method achieves significant improvements to the baseline and also outperforms state-of-the-art detectors.

关键词： Security systems

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Resource Allocation Strategy for Virtualized Wireless Sensor networks based on Actor-Critic

Resource Allocation Strategy for Virtualized Wireless Sensor...

引用

IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Changjin Li Guangxu Zhou Yue Guo Junfeng Hou Hongjun Zhang Song Li Xuchang Branch of Henan Provincial Tobacco Company Xuchang Henan China

Aiming at the problem of effective utilization of virtualized WSN resources, a reinforcement learning-based resource allocation strategy for virtualized WSNs is proposed from the perspective of service requests of virtualized WSNs. A virtual sensor network request deployment utility maximization model is established based on the quality requirements of service sensing information and resource capacity constraints. Considering the randomness of service requests and the dynamic changes in the network environment, the resource optimization problem is transformed into MDP and solved using Actor-Critic reinforcement learning methods, and the best resource allocation strategy is obtained. Finally, a large number of simulation experiments are carried out to verify the effectiveness of the proposed method.

关键词： Wireless sensor networks Soft sensors Computational modeling Reinforcement learning Ubiquitous computing Dynamic scheduling Resource management

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An Inception-based Architecture for Haemodialysis Time Series Classification 17th

An Inception-Based Architecture for Haemodialysis Time Seri...

引用

17th IFIP WG 12.5 International conference on Artificial Intelligence Applications and Innovations, AIAI 2021, 6th Workshop on 5G-Putting Intelligence to the network Edge, 5G-PINE 2021, Artificial Intelligence in Biomedical Engineering and Informatics Workshop, AI-BIO 2021, Workshop on Defense Applications of AI, DAAI 2021, distributed AI for Resource-Constrained Platforms Workshop, DARE 2021, Energy Efficiency and Artificial Intelligence Workshop, EEAI 2021, and 10th Mining Humanistic Data Workshop, MHDW 2021

作者： Leonardi, Giorgio Montani, Stefania Striani, Manuel DISIT Computer Science Institute Università del Piemonte Orientale Alessandria Italy

ISBN: (纸本)9783030791568

Classifying haemodialysis sessions, on the basis of the evolution of specific clinical variables over time, allows the physician to identify patients that are being treated inefficiently, and that may need additional monitoring or corrective interventions. In this paper, we propose a deep learning approach to clinical time series classification, in the haemodialysis domain. Specifically, grounding on our previous experience in adopting convolutional neural networks on haemodialysis time series, we have defined an inception-based architecture, able to exploit kernels of different sizes in parallel. The proposed architecture has outperformed the results obtained by resorting both to a more standard convolutional neural network, and to the state of the art approach ROCKET, since we reached higher accuracy values, coupled with a good Matthews Correlation Coefficient. © 2021, IFIP International Federation for Information processing.

关键词： Convolution

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Multi-Antenna based Fast Routing Recovery Protocol for Dynamic UAV networks

Multi-Antenna Based Fast Routing Recovery Protocol for Dynam...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Chuanxin Zhou Chao Dong Xiaojun Zhu Lei Zhang The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China

Unmanned Aerial Vehicle (UAV) network is a wireless network composed of many dynamic nodes. Due to the characteristics of UAV network, in the battlefield, it is easy for UAV nodes to fail and disconnect from other nodes. At this time, it is necessary to quickly delete the failed node from the routing table of all nodes in order to restore the normal routing. The time spent during this period is called the routing recovery time. It is found that for OLSR (Optimized Link State Routing) protocol, when the number of nodes is small, the routing recovery time is mainly related to the expiration time of the neighbor table. When the number of nodes increases, the routing recovery time is mainly related to the propagation speed of the control message. Therefore, this paper proposes a fast routing recovery method based on multi-Antenna to improve the performance of OLSR protocol. Adaptive algorithm is used to dynamically adjust the transmission interval of control messages and the expiration time of neighbor table in OLSR protocol. Simulation results show that the routing recovery time is reduced by using these two schemes.

关键词： Adaptation models Wireless networks Simulation Routing Autonomous aerial vehicles Routing protocols Vehicle dynamics

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Construction of Political Science Research Application Platform based on Big Data Analysis Technology

Construction of Political Science Research Application Platf...

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Education, Knowledge and Information Management (ICEKIM), International conference on

作者： Qiao Li Dawei Zhang Hulunbuir University Hulunbuir Inner Mongolia China Hulunbuir Economic and Social Research Institute Hulunbuir Inner Mongolia China

ISBN: (纸本)9781665416672

based on big data analysis technology, Hadoop and Spark big data parallel computing framework and HDFS distributed file storage database, we can complete the construction of political science research application platform. The political science research application platform pays attention to data capture, data cleaning, analysis and mining, visual display and other operations on the public comments and related responses of the masses under the government open platform and the opinions and contents published by netizens under the media social platform, which are involved in public opinion surveys under the current network environment, so as to complete the description and analysis of political phenomena with the help of the unique advantages of big data technology, and realize the innovation of political science research methods and the expansion of research fields. At the same time, it also makes an innovative attempt for the scientific and standardized research of political science.

关键词： Visualization Technological innovation Statistical analysis Memory Big Data parallel processing Media

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