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1
Allen, M. O., and S. L. Benedict. "SNA Management Services architecture for APPN networks." IBM Systems Journal 31, no. 2 (1992): 336–52. http://dx.doi.org/10.1147/sj.312.0336.
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Ziemke, Tom. "Radar Image Segmentation Using Self-Adapting Recurrent Networks." International Journal of Neural Systems 08, no. 01 (February 1997): 47–54. http://dx.doi.org/10.1142/s0129065797000070.
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This paper presents a novel approach to the segmentation and integration of (radar) images using a second-order recurrent artificial neural network architecture consisting of two sub-networks: a function network that classifies radar measurements into four different categories of objects in sea environments (water, oil spills, land and boats), and a context network that dynamically computes the function network's input weights. It is shown that in experiments (using simulated radar images) this mechanism outperforms conventional artificial neural networks since it allows the network to learn to solve the task through a dynamic adaptation of its classification function based on its internal state closely reflecting the current context.
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Hsu, Ming-Fu, Te-Min Chang, and Sin-Jin Lin. "NEWS-BASED SOFT INFORMATION AS A CORPORATE COMPETITIVE ADVANTAGE." Technological and Economic Development of Economy 26, no. 1 (November 21, 2019): 48–70. http://dx.doi.org/10.3846/tede.2019.11328.
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This study establishes a decision-making conceptual architecture that evaluates decision making units (DMUs) from numerous aspects. The architecture combines financial indicators together with a variety of data envelopment analysis (DEA) specifications to encapsulate more information to give a complete picture of a corporate’s operation. To make outcomes more accessible to non-specialists, multidimensional scaling (MDS) was performed to visualize the data. Most previous studies on forecasting model construction have relied heavily on hard information, with quite a few works taking into consideration soft information, which contains much denser and more diverse messages than hard information. To overcome this challenge, we consider two different types of soft information: supply chain influential indicator (SCI) and sentimental indicator (STI). SCI is computed by joint utilization of text mining (TM) and social network analysis (SNA), with TM identifying the corporate’s SC relationships from news articles and SNA to determining their impact on the network. STI is extracted from an accounting narrative so as to comprehensively illustrate the relationships between pervious and future performances. The analyzed outcomes are then fed into an artificial intelligence (AI)-based technique to construct the forecasting model. The introduced model, examined by real cases, is a promising alternative for performance forecasting.
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Speranza, Nicholas A., Christopher J. Rave, and Yong Pei. "Energy-Efficient On-Platform Target Classification for Electric Air Transportation Systems." Electricity 2, no. 2 (April 6, 2021): 110–23. http://dx.doi.org/10.3390/electricity2020007.
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Due to the predicted rise of Unmanned Aircraft Systems (UAS) in commercial, civil, and military operations, there is a desire to make UASs more energy efficient so they can proliferate with ease of deployment and maximal life per charge. To address current limitations, a three-tiered approach is investigated to mitigate Unmanned Aerial Vehicle (UAV) hover time, reduce network datalink transmission to a ground station, and provide a real-time framework for Sense-and-Avoidance (SAA) target classification. An energy-efficient UAS architecture framework is presented, and a corresponding SAA prototype is developed using commercial hardware to validate the proposed architecture using an experimental methodology. The proposed architecture utilizes classical computer vision methods within the Detection Subsystem coupled with deeply learned Convolutional Neural Networks (CNN) within the Classification Subsystem. Real-time operations of three frames per second are realized enabling UAV hover time and associated energy consumption during SAA processing to be effectively eliminated. Additional energy improvements are not addressed in the scope of this work. Inference accuracy is improved by 19% over baseline COTS models and current non-adaptive, single-stage SAA architectures. Overall, by pushing SAA processing to the edge of the sensors, network offload transmissions and reductions in processing time and energy consumption are feasible and realistic in future battery-powered electric air transportation systems.
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Køien, Geir M. "On Threats to the 5G Service Based Architecture." Wireless Personal Communications 119, no. 1 (February 19, 2021): 97–116. http://dx.doi.org/10.1007/s11277-021-08200-0.
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AbstractThe 3GPP-based 5G System marks a clear departure form the previous generations. There is a new radio system and a complete overhaul of the core network design. The core network is redesigned both on the control plane parts and the transport plane. The control plane signalling within the core network is now largely based on the service based architecture (SBA) design, featuring Web-based technologies and the associated security solutions. In this paper we conduct a preliminary generic survey of threats to the SBA.
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Kaliva, Eleni, Dimitrios Katsioulas, Efthimios Tambouris, and Konstantinos Tarabanis. "Understanding Researchers Collaboration in eParticipation using Social Network Analysis." International Journal of Electronic Government Research 11, no. 4 (October 2015): 38–68. http://dx.doi.org/10.4018/ijegr.2015100103.
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Over the past years electronic participation (eParticipation) became a political priority worldwide. Consequently, research on the field has dramatically grown. However, eParticipation is still an unconsolidated research area that lacks generally agreed upon definitions, research disciplines, methods and boundaries. The aim of this paper is to contribute to the establishment of the eParticipation identity by investigating the scientific collaborations in the domain. The study of the nature of academic collaboration reveals the structure and the intellectual roots of the research community and the most influential authors. The approach followed in this paper includes the construction of the co-authorship network and the calculation of the social network analysis (SNA) metrics that describe the nature of the collaboration. The results revealed that eParticipation is a rather active academic field in the last decade including a high degree of collaboration and a core network of very influential researchers.
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Allen, Michael O., Sandra L. Benedict, and Marcia L. Peters. "Meeting the challenge of a peer-to-peer network: An SNA management services infrastructure for APPN." Journal of Network and Systems Management 1, no. 2 (June 1993): 189–212. http://dx.doi.org/10.1007/bf01035887.
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Sood, Sandeep K. "SNA based QoS and reliability in fog and cloud framework." World Wide Web 21, no. 6 (January 27, 2018): 1601–16. http://dx.doi.org/10.1007/s11280-018-0525-x.
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Gupta, Ashish. "Optimized Parallel Counting Sort Algorithm for Distinct Numeric Values on Biswapped Hyper Hexa-Cell Optoelectronic Network." International Journal of Computer Network and Information Security 14, no. 1 (February 8, 2021): 69–80. http://dx.doi.org/10.5815/ijcnis.2022.01.06.
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The optoelectronic Biswapped-Hyper Hexa-Cell is a recently reported recursive and a symmetrical architecture of Biswapped Family. This symmetrical network has claimed and proved to be advantageous in terms of network diameter, bisection width, minimum node-degree and network cost compared to its counterpart architecture of OTIS family named ‘OTIS Hyper Hexa-Cell’ and traditional grid-based architecture of Biswapped family named ‘Biswapped-Mesh’. In this paper, we present a novel and efficient parallel algorithm for counting sort for sorting distinct numeric values on dh-dimensional Biswapped-Hyper Hexa-Cell optoelectronic network. The parallel algorithm demands 10d_h+12+ log_2〖S_A 〗 electronic and 10 optical moves, where SA is the size of count array: Acip[SA], and SA equals to maximal minus minimal numeric value plus one. On the basis of analysis, it is concluded that proposed algorithm delivers better performance since speedup and efficiency improved for worst case scenario (difference between maximal and minimal data values becomes larger) with the increase of only few communication moves required for sorting.
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Mei, Jing, Huahu Xu, Yang Li, Minjie Bian, and Yuzhe Huang. "MFCNet: Mining Features Context Network for RGB–IR Person Re-Identification." Future Internet 13, no. 11 (November 18, 2021): 290. http://dx.doi.org/10.3390/fi13110290.
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RGB–IR cross modality person re-identification (RGB–IR Re-ID) is an important task for video surveillance in poorly illuminated or dark environments. In addition to the common challenge of Re-ID, the large cross-modality variations between RGB and IR images must be considered. The existing RGB–IR Re-ID methods use different network structures to learn the global shared features associated with multi-modalities. However, most global shared feature learning methods are sensitive to background clutter, and contextual feature relationships are not considered among the mined features. To solve these problems, this paper proposes a dual-path attention network architecture MFCNet. SGA (Spatial-Global Attention) module embedded in MFCNet includes spatial attention and global attention branches to mine discriminative features. First, the SGA module proposed in this paper focuses on the key parts of the input image to obtain robust features. Next, the module mines the contextual relationships among features to obtain discriminative features and improve network performance. Finally, extensive experiments demonstrate that the performance of the network architecture proposed in this paper is better than that of state-of-the-art methods under various settings. In the all-search mode of the SYSU and RegDB data sets, the rank-1 accuracy reaches 51.64% and 69.76%, respectively.
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Zhao, Zixiao, Qinghe Du, Dawei Wang, Xiao Tang, and Houbing Song. "Overview of Prospects for Service-Aware Radio Access towards 6G Networks." Electronics 11, no. 8 (April 16, 2022): 1262. http://dx.doi.org/10.3390/electronics11081262.
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The integration of space–air–ground–sea networking in 6G, which is expected to not only achieve seamless coverage but also offer service-aware access and transmission, has introduced many new challenges for current mobile communications systems. Service awareness requires the 6G network to be aware of the demands of a diverse range of services as well as the occupation, utilization, and variation of network resources, which will enable the capability of deriving more intelligent and effective solutions for complicated heterogeneous resource configuration. Following this trend, this article investigates potential techniques that may improve service-aware radio access using the heterogeneous 6G network. We start with a discussion on the evolution of cloud-based RAN architectures from 5G to 6G, and then we present an intelligent radio access network (RAN) architecture for the integrated 6G network, which targets balancing the computation loads and fronthaul burden and achieving service-awareness for heterogeneous and distributed requests from users. In order for the service-aware access and transmissions to be equipped for future heterogeneous 6G networks, we analyze the challenges and potential solutions for the heterogeneous resource configuration, including a tightly coupled cross-layer design, resource service-aware sensing and allocation, transmission over multiple radio access technologies (RAT), and user socialization for cloud extension. Finally, we briefly explore some promising and crucial research topics on service-aware radio access for 6G networks.
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Kanchibhotla, Chaitanya, Somayajulu D. V. L. N., and Radha Krishna P. "A Parallel Particle Swarm Optimization for Community Detection in Large Attributed Graphs." International Journal of Applied Metaheuristic Computing 13, no. 1 (January 1, 2022): 1–23. http://dx.doi.org/10.4018/ijamc.306913.
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Social network analysis (SNA) is an active research domain that mainly deals with large social graphs and their properties. Community detection (CD) is one of the active research topics belonging to this domain. Social graphs in real-time are huge, complex, and require more computational resources to process. In this paper, the authors present a CPU-based hybrid parallelization architecture that combines both master-slave and island models. They use particle swarm optimization (PSO)-based clustering approach, which models community detection as an optimization problem and finds communities based on concepts of PSO. The proposed model is scalable, suitable for large datasets, and is tested on real-time social networking datasets with node attributes belonging to all three sizes (small, medium, and large). The model is tested on standard benchmark functions and evaluated on well-known evaluation strategies related to both community clusters and parallel systems to show its efficiency.
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Das, Sajal K., Dirk H. Hohndel, Maximilian Ibel, and Sabine R. Öhring. "Efficient Communication in Folded Petersen Networks." International Journal of Foundations of Computer Science 08, no. 02 (June 1997): 163–85. http://dx.doi.org/10.1142/s0129054197000136.
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Fast and efficient communication is one of the most important requirements in today's multicomputers. When reaching a larger scale of processors, the probability of faults in the network increases, hence communication must be robust and fault tolerant. The recently introduced family of folded Petersen networks, constructed by iteratively applying the cartesian product operation on the well-known Petersen graph, provides a regular, node– and edge-symmetric architecture with optimal connectivity (hence maximal fault-tolerance), and logarithmic diameter. Compared to the closest sized hypercube, the folded petersen network has a smaller diameter, lower node degree and higher packing density. In this paper, we study fundamental communication primitives like single routing, permutation routing, one-to-all broadcasting, multinode-broadcasting (gossiping), personalized communications like scattering, and total exchange on the folded Petersen networks, considering two communication models, namely single link availability (SLA) and multiple link availability (MLA). We derive lower bounds for these problems and design optimal algorithms in terms of both time and the number of message transmissions. The results are based on the construction of minimal height spanning trees in the fault-free folded Petersen network. We further analyze these communication primitives in faulty networks, where processing nodes and transmission links cease working. This analysis is based on multiple arc-disjoint spanning trees, a construct also useful for analyzing other families of multicomputer networks.
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Cheng, Haosu, Jianwei Liu, Jian Mao, Mengmeng Wang, Jie Chen, and Jingdong Bian. "A Compatible OpenFlow Platform for Enabling Security Enhancement in SDN." Security and Communication Networks 2018 (November 15, 2018): 1–20. http://dx.doi.org/10.1155/2018/8392080.
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Software-defined networking (SDN) is a representative next generation network architecture, which allows network administrators to programmatically initialize, control, change, and manage network behavior dynamically via open interfaces. SDN is widely adopted in systems like 5G mobile networks and cyber-physical systems (CPS). However, SDN brings new security problems, e.g., controller hijacking, black-hole, and unauthorized data modification. Traditional firewall or IDS based solutions cannot fix these challenges. It is also undesirable to develop security mechanisms in such an ad hoc manner, which may cause security conflict during the deployment procedure. In this paper, we propose OSCO (Open Security-enhanced Compatible OpenFlow) platform, a unified, lightweight platform to enhance the security property and facilitate the security configuration and evaluation. The proposed platform supports highly configurable cryptographic algorithm modules, security protocols, flexible hardware extensions, and virtualized SDN networks. We prototyped our platform based on the Raspberry Pi Single Board Computer (SBC) hardware and presented a case study for switch port security enhancement. We systematically evaluated critical security modules, which include 4 hash functions, 8 stream/block ciphers, 4 public-key cryptosystems, and key exchange protocols. The experiment results show that our platform performs those security modules and SDN network functions with relatively low computational (extra 2.5% system overhead when performing AES-256 and SHA-256 functions) and networking performance overheads (73.7 Mb/s TCP and 81.2Mb/s UDP transmission speeds in 100Mb/s network settings).
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Zhu, Meng-Yuan, Ke-Fan Chen, Zhuo Chen, and Na Lv. "All-Domain Fusion-Based Time Synchronization Protocol in SD-ATSN." Mobile Information Systems 2022 (March 11, 2022): 1–16. http://dx.doi.org/10.1155/2022/7433556.
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As combat scales up and weapons become more intelligent, airborne networks (AN) must facilitate high-precision communications. This means that the airborne network should have communication capability with minimum delay, low jitter, and high reliability, and this new type of AN is called an airborne time-sensitive network (ATSN). A prerequisite to guarantee the above communication capability is to have a high precision time synchronization protocol. To plug this gap, we have designed a software-defined airborne time-sensitive network (SD-ATSN) architecture based on the benefits of software-defined centralization and flexibility. It supports our proposed all-domain fusion-based time synchronization protocol (AF-TSP) to support precise time synchronization between ATSN platforms. In AF-TSP, we innovatively propose an all-domain (land, sea, air, and space) master clock hot standby mechanism to cope with the existing instability and poor robustness in AN. In the key problem of the master clock election, we first completed a rough election utilizing improved K-Means++. Subsequently, on top of the rough election, we inserted a mixed mutation operator to improve the convergence of the multiobjective optimization algorithm No Dominant Sorting Genetic Algorithm-II (NSGA-II). The control delay, clock accuracy, and path reliability coefficient are the optimization objectives for selecting the appropriate master clock. Simulation results demonstrate that our protocol has advantages in terms of synchronization precision, communication delay, and network robustness.
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Fu, Xianglin, Changsheng Xie, Ruifang Liu, and Shenggang Pan. "The architecture and performance evaluation of iSCSI-based United Storage Network merging NAS and SAN." International Journal of Computer Applications in Technology 19, no. 2 (2004): 84. http://dx.doi.org/10.1504/ijcat.2004.003637.
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Danty, Paul, Amandine Magnaudeix, Emeline Renaudie, Fanny Leborgne, Vincent Pateloup, Valery Valle, Eric Champion, and Patricia Pascuad-Mathieu. "Processing by Laser Stereolithography and <i>In Vitro</i> Biological Evaluation of Hydroxyapatite Scaffolds Mimicking Human Trabecular Bone Architecture." Solid State Phenomena 339 (December 19, 2022): 103–8. http://dx.doi.org/10.4028/p-zn71xt.
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Hydroxyapatite (HA) ceramic scaffolds are commonly used as bone graft substitutes. Design of such scaffolds is a challenge to improve biological properties and extend the applications of HA ceramics in the field of bone tissue engineering. In this work, we investigated the processing and the in vitro properties of HA ceramic scaffolds mimicking human trabecular bone architecture. Samples of human tibial trabecular bone were collected (University Hospital Center of Limoges) and scanned by X-Ray μ-computed tomography (μ-CT) to generate 3D model database. From this computer-aided design, HA ceramic scaffolds were shaped layer-by-layer by additive manufacturing using laser stereolithography (SLA). Then, green parts were sintered to obtain dense ceramic scaffolds. The shaped parts were compared to the model (wall thickness, size, and geometry of the porous network) using image analysis. A good agreement was found. Only small differences were detected due to a light overpolymerization or to some unprinted very small details that were not linked to a polymerized area of the previous layer. Due to part shrinkage during sintering a magnifying factor has to be applied to the scanned CAO model to match the real dimensions of the trabecular bone sample. Human mesenchymal stem cell (hMSC) cultures were performed to investigate the biological properties of these scaffolds (cell attachment and proliferation of hMSC). These preliminary biological evaluations show the good biocompatibility and cell adhesion of the HA substitute. This work evidences the efficiency of SLA to produce ceramic scaffold architectures mimicking that of the natural trabecular bone with promising biological behavior.
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Belani, Jainam, Yugma Thakore, Nirali Kotak, Komal Borisagar, and Bhavin Sedani. "Performance Analysis of Various 5G Mobile Architectures." International Journal of Interactive Mobile Technologies (iJIM) 16, no. 08 (April 26, 2022): 94–106. http://dx.doi.org/10.3991/ijim.v16i08.29003.
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The field of Telecommunication has progressed and emerged to be a successful field of technology. From communicating via pigeons and cannon balls to video conferencing for a meeting, the communication has improved surpassing every user’s expectation. With the introduction of wireless communication, the field of communication took a different turn. Just when we thought we reached the zenith of wireless communication, an esoteric concept of StandAlone (S.A.) and Non-StandAlone (N.S.A.) opened the gates to a more efficient communication, expanding the horizons of technology. The 5G network has started developing over some regions in the world but the real question remains; which is, whether an efficient communication is worth the economy spent on developing it or certain advantages of this networks should be given up to protect the country’s economy.The 3rd Generation Partnership Project (3GPP) presents two essential architecture for 5G deployment from LTE: Non-Standalone (NSA) and Standalone (SA). NSA empowers quick 5G benefit arrangement with minimum investment by leveraging the existing LTE infrastructures. SA comprises of a single Radio Access Technology (RAT), meaning it is conceivable to supply full 5G upgrades designed to work only in the 5G New Radio (NR) This analytical paper elaborates the performance of these two architectures for the implementation of 5G cellular technology, where in the first one would be highly technology centric approach with less cost efficiency and the other one would be the economy savvy aspect with little bit negotiation with technology upgrades
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Belani, Jainam, Yugma Thakore, Nirali Kotak, Komal Borisagar, and Bhavin Sedani. "Performance Analysis of Various 5G Mobile Architectures." International Journal of Interactive Mobile Technologies (iJIM) 16, no. 08 (April 26, 2022): 94–106. http://dx.doi.org/10.3991/ijim.v16i08.29003.
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The field of Telecommunication has progressed and emerged to be a successful field of technology. From communicating via pigeons and cannon balls to video conferencing for a meeting, the communication has improved surpassing every user’s expectation. With the introduction of wireless communication, the field of communication took a different turn. Just when we thought we reached the zenith of wireless communication, an esoteric concept of StandAlone (S.A.) and Non-StandAlone (N.S.A.) opened the gates to a more efficient communication, expanding the horizons of technology. The 5G network has started developing over some regions in the world but the real question remains; which is, whether an efficient communication is worth the economy spent on developing it or certain advantages of this networks should be given up to protect the country’s economy.The 3rd Generation Partnership Project (3GPP) presents two essential architecture for 5G deployment from LTE: Non-Standalone (NSA) and Standalone (SA). NSA empowers quick 5G benefit arrangement with minimum investment by leveraging the existing LTE infrastructures. SA comprises of a single Radio Access Technology (RAT), meaning it is conceivable to supply full 5G upgrades designed to work only in the 5G New Radio (NR) This analytical paper elaborates the performance of these two architectures for the implementation of 5G cellular technology, where in the first one would be highly technology centric approach with less cost efficiency and the other one would be the economy savvy aspect with little bit negotiation with technology upgrades
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GAO, Lei, and Guang-Zhou ZENG. "SAN-EBON: A Novel P2P Location Network Based on Structured Overlay Network for P2P Workflow System." Chinese Journal of Computers 33, no. 12 (May 23, 2011): 2353–63. http://dx.doi.org/10.3724/sp.j.1016.2010.02353.
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Shahzad, Suleman, Muhammad Ilyas, M. Ikram Ullah Lali, Hafiz Tayyab Rauf, Seifedine Kadry, and Emad Abouel Nasr. "Sperm Abnormality Detection Using Sequential Deep Neural Network." Mathematics 11, no. 3 (January 18, 2023): 515. http://dx.doi.org/10.3390/math11030515.
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Sperm morphological analysis (SMA) is an essential step in diagnosing male infertility. Using images of human sperm cells, this research proposes a unique sequential deep-learning method to detect abnormalities in semen samples. The proposed technique identifies and examines several components of human sperm. In order to conduct this study, we used the online Modified Human Sperm Morphology Analysis (MHSMA) dataset containing 1540 sperm images collected from 235 infertile individuals. For research purposes, this dataset is freely available online. To identify morphological abnormalities in different parts of human sperm, such as the head, vacuole, and acrosome, we proposed sequential deep neural network (SDNN) architecture. This technique is also particularly effective with low-resolution, unstained images. Sequential deep neural networks (SDNNs) demonstrate high accuracy in diagnosing morphological abnormalities based on the given dataset in our tests on the benchmark. Our proposed algorithm successfully detected abnormalities in the acrosome, head, and vacuole with an accuracy of 89%, 90%, and 92%, respectively. It is noteworthy that our system detects abnormalities of the acrosome and head with greater accuracy than current state-of-the-art approaches on the suggested benchmark. On a low-specification computer/laptop, our algorithm also requires less execution time. Additionally, it can classify photos in real time. Based on the results of our study, an embryologist can quickly decide whether to use the given sperm.
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Lv, Wenjing, Rui Wang, Jun Wu, Zhijun Fang, and Songlin Cheng. "Joint Design of Beamforming and Edge Caching in Fog Radio Access Networks." Security and Communication Networks 2021 (September 18, 2021): 1–8. http://dx.doi.org/10.1155/2021/1935453.
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In this paper, we study a novel transmission framework based on statistical channel state information (SCSI) by incorporating edge caching and beamforming in a fog radio access network (F-RAN) architecture. By optimizing the statistical beamforming and edge caching, we formulate a comprehensive nonconvex optimization problem to minimize the backhaul cost subject to the BS transmission power, limited caching capacity, and quality-of-service (QoS) constraints. By approximating the problem using the l 0 -norm, Taylor series expansion, and other processing techniques, we provide a tailored second-order cone programming (SOCP) algorithm for the unicast transmission scenario and a successive linear approximation (SLA) algorithm for the joint unicast and multicast transmission scenario. This is the first attempt at the joint design of statistical beamforming and edge caching based on SCSI under the F-RAN architecture.
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Aslam, Muhammad Muzamil, Liping Du, Xiaoyan Zhang, Yueyun Chen, Zahoor Ahmed, and Bushra Qureshi. "Sixth Generation (6G) Cognitive Radio Network (CRN) Application, Requirements, Security Issues, and Key Challenges." Wireless Communications and Mobile Computing 2021 (October 13, 2021): 1–18. http://dx.doi.org/10.1155/2021/1331428.
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Recently, 5G installation has been started globally. Different capabilities are in the consistent procedure, like ultrareliability, mass connectivity, and specific low latency. Though, 5G is insufficient to meet all the necessities of the future technology in 2030 and so on. Next generation information and communication technology is playing an important role in attraction of researchers, industries, and technical people. With respect to 5G networks, sixth-generation (6G) CR networks are anticipated to familiarize innovative use cases and performance metrics, such as to offer worldwide coverage, cost efficiency, enhanced spectral, energy improved intelligence, and safety. To reach such requirements, upcoming 6G CRNs will trust novel empowering technologies. Innovative network architecture and transmission technologies and air interface are of excessive position, like multiple accesses, waveform design, multiantenna technologies, and channel coding schemes. (1) To content, the condition should be of worldwide coverage, there will be no limit on 6G to global CR communication networks that may require to be completed with broadcast networks, like satellite communication networks, therefore, attaining a sea integrated communication network. (2) The spectrums overall will be entirely travelled to the supplementary rise connection density data rates in optical frequency bands, millimeter wave (mmWave), sub-6 GHz, and terahertz (THz). (3) To see big datasets created because of tremendously varied CR communication networks, antenna rush, diverse communication scenarios, new provision necessities, wide bandwidth, and 6G CRNs will allow an innovative variety of intelligent applications with the assistance of big data and AI technologies. (4) Need to improve network security when deploying 6G technology in CR networks. 6G is decentralized, intended, intelligent innovative, and distributed network. In this article, we studied a survey of current developments and upcoming trends. We studied the predicted applications, possible technologies, and security issues for 6G CR network communication. We also discussed predicted future key challenges in 6G.
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Lo, Hong K. "Organizing for Intelligent Transportation Systems: Case Study of Emergency Operations in San Francisco Bay Area." Transportation Research Record: Journal of the Transportation Research Board 1603, no. 1 (January 1997): 34–40. http://dx.doi.org/10.3141/1603-05.
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Computer-integrated transportation (CIT) is envisioned as an integrated network of public and private transportation organizations, each with unique responsibilities but working toward a common mission of facilitating travel across all modes of transportation. Research on CIT is extended to emergency operations (EOs) and presented. EOs in California are examined and their role in gathering and using traffic incident information is identified. The basis of coordination between EOs and transportation management centers is established. Intelligent Transportation System (ITS) services and technologies that may be beneficial to EOs are identified, and the similarities and differences between California EOs and the emerging ITS national architecture are compared.
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Dechesne, Clément, Sébastien Lefèvre, Rodolphe Vadaine, Guillaume Hajduch, and Ronan Fablet. "Ship Identification and Characterization in Sentinel-1 SAR Images with Multi-Task Deep Learning." Remote Sensing 11, no. 24 (December 13, 2019): 2997. http://dx.doi.org/10.3390/rs11242997.
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The monitoring and surveillance of maritime activities are critical issues in both military and civilian fields, including among others fisheries’ monitoring, maritime traffic surveillance, coastal and at-sea safety operations, and tactical situations. In operational contexts, ship detection and identification is traditionally performed by a human observer who identifies all kinds of ships from a visual analysis of remotely sensed images. Such a task is very time consuming and cannot be conducted at a very large scale, while Sentinel-1 SAR data now provide a regular and worldwide coverage. Meanwhile, with the emergence of GPUs, deep learning methods are now established as state-of-the-art solutions for computer vision, replacing human intervention in many contexts. They have been shown to be adapted for ship detection, most often with very high resolution SAR or optical imagery. In this paper, we go one step further and investigate a deep neural network for the joint classification and characterization of ships from SAR Sentinel-1 data. We benefit from the synergies between AIS (Automatic Identification System) and Sentinel-1 data to build significant training datasets. We design a multi-task neural network architecture composed of one joint convolutional network connected to three task specific networks, namely for ship detection, classification, and length estimation. The experimental assessment shows that our network provides promising results, with accurate classification and length performance (classification overall accuracy: 97.25%, mean length error: 4.65 m ± 8.55 m).
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Stojce Ilcev, Dimov. "Architecture of African satellite augmentation system (ASAS) for Africa and middle east." International Journal of Engineering & Technology 8, no. 4 (November 17, 2019): 567. http://dx.doi.org/10.14419/ijet.v8i4.30005.
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This paper introduces architecture of African Satellite Augmentation System (ASAS) project designed by African for Africa, with coverage of entire African Continent and Middle East for maritime, land (road and rail) and aeronautical applications. The ASAS network is de facto Regional Satellite Augmentation System (RSAS) as integration component of the Global Satellite Augmentation System (GSAS) employing current and new Satellite Communication, Navigation and Surveillance (CNS) for improved traffic control and management at sea, on land and in the air. This Network also enhances safety and emergency systems, transport security and control of transportation freight, logistics and the security of the crew and passengers onboard transport systems. The current infrastructures of the first generation of Global Navigation Satellite System (GNSS-1) applications are represented by old fundamental solutions for Position, Velocity and Time (PVT) of the satellite navigation and determination systems such as the US GPS and Russian (former-USSR) GLONASS military requirements, respectively. The establishment of Local Satellite Augmentation System (LSAS) and mobile movement guidance and control are also discussed as special infrastructures in seaports, land and airports environments.
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Liu, Yun Lin, Yan Kai Chen, Wei Xiong Li, and Yang Zhang. "Model design and parameter optimization of CNN for side-channel cryptanalysis." PeerJ Computer Science 7 (January 5, 2022): e829. http://dx.doi.org/10.7717/peerj-cs.829.
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Background The side-channel cryptanalysis method based on convolutional neural network (CNNSCA) can effectively carry out cryptographic attacks. The CNNSCA network models that achieve cryptanalysis mainly include CNNSCA based on the VGG variant (VGG-CNNSCA) and CNNSCA based on the Alexnet variant (Alex-CNNSCA). The learning ability and cryptanalysis performance of these CNNSCA models are not optimal, and the trained model has low accuracy, too long training time, and takes up more computing resources. In order to improve the overall performance of CNNSCA, the paper will improve CNNSCA model design and hyperparameter optimization. Methods The paper first studied the CNN architecture composition in the SCA application scenario, and derives the calculation process of the CNN core algorithm for side-channel leakage of one-dimensional data. Secondly, a new basic model of CNNSCA was designed by comprehensively using the advantages of VGG-CNNSCA model classification and fitting efficiency and Alex-CNNSCA model occupying less computing resources, in order to better reduce the gradient dispersion problem of error back propagation in deep networks, the SE (Squeeze-and-Excitation) module is newly embedded in this basic model, this module is used for the first time in the CNNSCA model, which forms a new idea for the design of the CNNSCA model. Then apply this basic model to a known first-order masked dataset from the side-channel leak public database (ASCAD). In this application scenario, according to the model design rules and actual experimental results, exclude non-essential experimental parameters. Optimize the various hyperparameters of the basic model in the most objective experimental parameter interval to improve its cryptanalysis performance, which results in a hyper-parameter optimization scheme and a final benchmark for the determination of hyper-parameters. Results Finally, a new CNNSCA model optimized architecture for attacking unprotected encryption devices is obtained—CNNSCAnew. Through comparative experiments, CNNSCAnew’s guessing entropy evaluation results converged to 61. From model training to successful recovery of the key, the total time spent was shortened to about 30 min, and we obtained better performance than other CNNSCA models.
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Takatoi, Go, Takeshi Sugawara, Kazuo Sakiyama, Yuko Hara-Azumi, and Yang Li. "The Limits of SEMA on Distinguishing Similar Activation Functions of Embedded Deep Neural Networks." Applied Sciences 12, no. 9 (April 20, 2022): 4135. http://dx.doi.org/10.3390/app12094135.
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Artificial intelligence (AI) is progressing rapidly, and in this trend, edge AI has been researched intensively. However, much less work has been performed around the security of edge AI. Machine learning models are a mass of intellectual property, and an optimized network is very valuable. Trained machine learning models need to be black boxes as well because they may give away information about the training data to the outside world. As selecting the appropriate activation functions to enable fast training of accurate deep neural networks is an active area of research, it is important to conceal the information of the activation functions used in a neural network architecture as well. There has been research on the use of physical attacks such as the side-channel attack (SCA) in areas other than cryptography. The SCA is highly effective against edge artificial intelligence due to its property of the device computing close to the user. We studied a previously proposed method to retrieve the activation functions of a black box neural network implemented on an edge device by using simple electromagnetic analysis (SEMA) and improved the signal processing procedure for further noisy measurements. The SEMA attack identifies activation functions by directly observing distinctive electromagnetic (EM) traces that correspond to the operations in the activation function. This method requires few executions and inputs and also has little implementation dependency on the activation functions. We distinguished eight similar activation functions with EM measurements and examined the versatility and limits of this attack. In this work, the machine learning architecture is a multilayer perceptron, evaluated on an Arduino Uno.
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Yu, Dingguo, Nan Chen, and Xu Ran. "Computational modeling of Weibo user influence based on information interactive network." Online Information Review 40, no. 7 (November 14, 2016): 867–81. http://dx.doi.org/10.1108/oir-12-2015-0391.
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Purpose With the development and application of mobile internet access, social media represented by Weibo, WeChat, etc. has become the main channel for information release and sharing. High-impact users in social networks are key factors stimulating the large-scale propagation of information within social networks. User influence is usually related to the user’s attention rate, activity level, and message content. The paper aims to discuss these issues. Design/methodology/approach In this paper, the authors focused on Sina Weibo users, centered on users’ behavior and interactive information, and formulated a weighted interactive information network model, then present a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc., the model incorporated the time dimension, through the calculation of users’ attribute influence and interactive influence, to comprehensively measure the user influence of Sina Weibo users. Findings Compared with other models, the model reflected the dynamics and timeliness of the user influence in a more accurate way. Extensive experiments are conducted on the real-world data set, and the results validate the performance of the approach, and demonstrate the effectiveness of the dynamics and timeliness. Due to the similarity in platform architecture and user behavior between Sina Weibo and Twitter, the calculation model is also applicable to Twitter. Originality/value This paper presents a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc.
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Korona, Mateusz, Paweł Szumełda, Mariusz Rawski, and Artur Janicki. "Comparison of Hash Functions for Network Traffic Acquisition Using a Hardware-Accelerated Probe." Electronics 11, no. 11 (May 25, 2022): 1688. http://dx.doi.org/10.3390/electronics11111688.
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In this article we address the problem of efficient and secure monitoring of computer network traffic. We proposed, implemented, and tested a hardware-accelerated implementation of a network probe, using the DE5-Net FPGA development platform. We showed that even when using a cryptographic SHA-3 hash function, the probe uses less than 17% of the available FPGA resources, offering a throughput of over 20 Gbit/s. We have also researched the problem of choosing an optimal hash function to be used in a network probe for addressing network flows in a flow cache. In our work we compared five 32-bit hash functions, including two cryptographic ones: SHA-1 and SHA-3. We ran a series of experiments with various hash functions, using traffic replayed from the CICIDS 2017 dataset. We showed that SHA-1 and SHA-3 provide flow distributions as uniform as the ones offered by the modified Vermont hash function proposed in 2008 (i.e., with low means and standard deviations of the bucket occupation), yet assuring higher security against potential attacks on a network probe.
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Li, Jingwei, and Bo Sun. "A Network Attack Detection Method Using SDA and Deep Neural Network Based on Internet of Things." International Journal of Wireless Information Networks 27, no. 2 (October 5, 2019): 209–14. http://dx.doi.org/10.1007/s10776-019-00462-7.
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Xu, Yichun, Chen Wang, Zhiping Dan, Shuifa Sun, and Fangmin Dong. "Deep Recurrent Neural Network and Data Filtering for Rumor Detection on Sina Weibo." Symmetry 11, no. 11 (November 15, 2019): 1408. http://dx.doi.org/10.3390/sym11111408.
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Social media makes it easy for individuals to publish and consume news, but it also facilitates the spread of rumors. This paper proposes a novel deep recurrent neural model with a symmetrical network architecture for automatic rumor detection in social media such as Sina Weibo, which shows better performance than the existing methods. In the data preparing phase, we filter the posts according to the followers of the user. We then use sequential encoding for the posts and multiple embedding layers to get better feature representation, and multiple recurrent neural network layers to capture the dynamic temporal signals characteristic. The experimental results on the Sina Weibo dataset show that: 1. the sequential encoding performs better than the term frequency-inverse document frequency (TF-IDF) or the doc2vec encoding scheme; 2. the model is more accurate when trained on the posts from the users with more followers; and 3. the model achieves superior improvements over the existing works on the accuracy of detection, including the early detection.
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Khoshkholghi, Mohammad Ali, Michel Gokan Khan, Kyoomars Alizadeh Noghani, Javid Taheri, Deval Bhamare, Andreas Kassler, Zhengzhe Xiang, Shuiguang Deng, and Xiaoxian Yang. "Service Function Chain Placement for Joint Cost and Latency Optimization." Mobile Networks and Applications 25, no. 6 (November 21, 2020): 2191–205. http://dx.doi.org/10.1007/s11036-020-01661-w.
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AbstractNetwork Function Virtualization (NFV) is an emerging technology to consolidate network functions onto high volume storages, servers and switches located anywhere in the network. Virtual Network Functions (VNFs) are chained together to provide a specific network service, called Service Function Chains (SFCs). Regarding to Quality of Service (QoS) requirements and network features and states, SFCs are served through performing two tasks: VNF placement and link embedding on the substrate networks. Reducing deployment cost is a desired objective for all service providers in cloud/edge environments to increase their profit form demanded services. However, increasing resource utilization in order to decrease deployment cost may lead to increase the service latency and consequently increase SLA violation and decrease user satisfaction. To this end, we formulate a multi-objective optimization model to joint VNF placement and link embedding in order to reduce deployment cost and service latency with respect to a variety of constraints. We, then solve the optimization problem using two heuristic-based algorithms that perform close to optimum for large scale cloud/edge environments. Since the optimization model involves conflicting objectives, we also investigate pareto optimal solution so that it optimizes multiple objectives as much as possible. The efficiency of proposed algorithms is evaluated using both simulation and emulation. The evaluation results show that the proposed optimization approach succeed in minimizing both cost and latency while the results are as accurate as optimal solution obtained by Gurobi (5%).
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Qin, Rixia, Xiaohong Zhao, Wenbo Zhu, Qianqian Yang, Bo He, Guangliang Li, and Tianhong Yan. "Multiple Receptive Field Network (MRF-Net) for Autonomous Underwater Vehicle Fishing Net Detection Using Forward-Looking Sonar Images." Sensors 21, no. 6 (March 10, 2021): 1933. http://dx.doi.org/10.3390/s21061933.
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Underwater fishing nets represent a danger faced by autonomous underwater vehicles (AUVs). To avoid irreparable damage to the AUV caused by fishing nets, the AUV needs to be able to identify and locate them autonomously and avoid them in advance. Whether the AUV can avoid fishing nets successfully depends on the accuracy and efficiency of detection. In this paper, we propose an object detection multiple receptive field network (MRF-Net), which is used to recognize and locate fishing nets using forward-looking sonar (FLS) images. The proposed architecture is a center-point-based detector, which uses a novel encoder-decoder structure to extract features and predict the center points and bounding box size. In addition, to reduce the interference of reverberation and speckle noises in the FLS image, we used a series of preprocessing operations to reduce the noises. We trained and tested the network with data collected in the sea using a Gemini 720i multi-beam forward-looking sonar and compared it with state-of-the-art networks for object detection. In order to further prove that our detector can be applied to the actual detection task, we also carried out the experiment of detecting and avoiding fishing nets in real-time in the sea with the embedded single board computer (SBC) module and the NVIDIA Jetson AGX Xavier embedded system of the AUV platform in our lab. The experimental results show that in terms of computational complexity, inference time, and prediction accuracy, MRF-Net is better than state-of-the-art networks. In addition, our fishing net avoidance experiment results indicate that the detection results of MRF-Net can support the accurate operation of the later obstacle avoidance algorithm.
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Buongiorno Nardelli, Bruno, Davide Cavaliere, Elodie Charles, and Daniele Ciani. "Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms." Remote Sensing 14, no. 5 (February 26, 2022): 1159. http://dx.doi.org/10.3390/rs14051159.
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Surface ocean dynamics play a key role in the Earth system, contributing to regulate its climate and affecting the marine ecosystem functioning. Dynamical processes occur and interact in the upper ocean at multiple scales, down to, or even less than, few kilometres. These scales are not adequately resolved by present observing systems, and, in the last decades, global monitoring of surface currents has been based on the application of geostrophic balance to absolute dynamic topography maps obtained through the statistical interpolation of along-track satellite altimeter data. Due to the cross-track distance and repetitiveness of satellite acquisitions, the effective resolution of interpolated data is limited to several tens of kilometres. At the kilometre scale, sea surface temperature pattern evolution is dominated by advection, providing indirect information on upper ocean currents. Computer vision techniques are perfect candidates to infer this dynamical information from the combination of altimeter data, surface temperature images and observing-system geometry. Here, we exploit one class of image processing techniques, super-resolution, to develop an original neural-network architecture specifically designed to improve absolute dynamic topography reconstruction. Our model is first trained on synthetic observations built from a numerical general-circulation model and then tested on real satellite products. Provided concurrent clear-sky thermal observations are available, it proves able to compensate for altimeter sampling/interpolation limitations by learning from primitive equation data. The algorithm can be adapted to learn directly from future surface topography, and eventual surface currents, high-resolution satellite observations.
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Ampririt, Phudit, Ermioni Qafzezi, Kevin Bylykbashi, Makoto Ikeda, Keita Matsuo, and Leonard Barolli. "FSSLA: A Fuzzy-based scheme for slice service level agreement in 5G wireless networks and its performance evaluation." Journal of High Speed Networks 28, no. 1 (February 11, 2022): 47–64. http://dx.doi.org/10.3233/jhs-220678.
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The Software-Defined Network (SDN) with Network Slicing will be a good approach for admission control in the Fifth Generation (5G) wireless network, which is planned to be adaptable to meet user requirements. The system’s resources are limited, and the number of devices is growing much faster than it can handle. So, the overloading problem will be a very critical problem. To deal with these problems, this paper presents a Fuzzy-based Scheme for Service Level Agreement (SLA) evaluation (FSSLA). We compare two models: FSSLA1 and FSSLA2. The FSSLA1 considers three input parameters: Reliability (Re), Availability (Av), Latency (La) and the output parameter is SLA. In FSSLA2, we consider Traffic Load (Tl) as a new parameter. From simulation results, we conclude that the considered parameters have different effects on the SLA. When Re and Av are increasing, the SLA parameter is increased but when La and Tl are increasing, the SLA parameter is decreased. When the Tl value is changed from 20% to 80%, the SLA is decreased by 16.62% when Re 10%, Av 90% and the La value is 1 ms. When the Re is increased from 10% to 90% and 50% to 90% and the Tl value is 50%, the Av is 50% and the La is 1 ms, the SLA is increased by 22.76% and 11.38%, respectively.
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Fujii, S., J. Tahara, F. Zhang, M. Koike, Y. Ohta, and Y. Watanabe. "Motion control of deep sea vehicle ‘OTOHIME’: modeling with neural network." Advanced Robotics 35, no. 24 (October 13, 2021): 1500–1512. http://dx.doi.org/10.1080/01691864.2021.1985606.
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Rahman, Mohammad Saidur, Ibrahim Khalil, and Mohammed Atiquzzaman. "Blockchain-Enabled SLA Compliance for Crowdsourced Edge-Based Network Function Virtualization." IEEE Network 35, no. 5 (September 2021): 58–65. http://dx.doi.org/10.1109/mnet.011.2100086.
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Fan, Yuqi, Chen Wang, Bei Zhang, Shuyang Gu, Weili Wu, and Dingzhu Du. "Data placement in distributed data centers for improved SLA and network cost." Journal of Parallel and Distributed Computing 146 (December 2020): 189–200. http://dx.doi.org/10.1016/j.jpdc.2020.07.006.
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Hoang, Nam-Thang, Hai-Nam Nguyen, Hai-Anh Tran, and Sami Souihi. "A Novel Adaptive East–West Interface for a Heterogeneous and Distributed SDN Network." Electronics 11, no. 7 (March 22, 2022): 975. http://dx.doi.org/10.3390/electronics11070975.
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In the years since its initiation, the software-defined network paradigm has evolved into a distinguished networking technology by causing a revolution in separating the control logic from physical devices and centralizing software-based controllers. Despite its indisputable benefits compared with the traditional network, the SDN raises the challenge of scalability with its physically centralized control. The only potential solution is to transform it into physically distributed SDN control. However, this solution requires the interoperability between SDN controllers, and the consistency of network state being distributed across these controllers. Although some east–west interfaces that help SDN controllers exchange network information have been released, they reveal several drawbacks. First, they cannot support a heterogeneous SDN system where SDN controllers are developed by different providers. Secondly, their consistency solution is simple in disregarding the tradeoff between the consistency level and the performance of SDN networks. This paper proposes an east–west interface, called SINA, to provide the interoperability of a heterogeneous and distributed SDN network. In addition, a novel reinforcement-learning-based consistency algorithm is introduced for an adaptive, quorum-based replication mechanism. The experimental results showed that SINA successfully connects heterogeneous and distributed SDN domains and balances the consistency and network performance.
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Tseng, Shih-Huan, and Wei-Hao Sun. "Sea–Land Segmentation Using HED-UNET for Monitoring Kaohsiung Port." Mathematics 10, no. 22 (November 10, 2022): 4202. http://dx.doi.org/10.3390/math10224202.
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In recent years, it has become a trend to analyze shoreline changes through satellite images in coastal engineering research. The results of sea–land segmentation are very important for shoreline detection. CoastSat is a time-series shoreline detection system that uses an artificial neural network (ANN) on sea–land segmentation. However, the method of CoastSat only uses the spectral features of a single pixel and ignores the local relationships of adjacent pixels. This impedes optimal category prediction, particularly considering interference by climate features such as clouds, shadows, and waves. It is easy to cause the classifier to be disturbed in the classification results, resulting in classification errors. To solve the problem of misclassification of sea–land segmentation caused by climate interference, this paper applies HED-UNet to the image dataset obtained from CoastSat and learns the relationship between adjacent pixels by training the deep network architecture, thereby improving the results of erroneous sea–land segmentation due to climate disturbances. By using different optimizers and loss functions in the HED-Unet model, the experiment verifies that Adam + Focal loss has the best performance. The results also show that the deep learning model, HED-Unet, can effectively improve the accuracy of the sea–land segmentation to 97% in a situation with interference from atmospheric factors such as clouds and waves.
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Pan, Jeng-Shyang, Fang Fan, Shu-Chuan Chu, Hui-Qi Zhao, and Gao-Yuan Liu. "A Lightweight Intelligent Intrusion Detection Model for Wireless Sensor Networks." Security and Communication Networks 2021 (April 30, 2021): 1–15. http://dx.doi.org/10.1155/2021/5540895.
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The wide application of wireless sensor networks (WSN) brings challenges to the maintenance of their security, integrity, and confidentiality. As an important active defense technology, intrusion detection plays an effective defense line for WSN. In view of the uniqueness of WSN, it is necessary to balance the tradeoff between reliable data transmission and limited sensor energy, as well as the conflict between the detection effect and the lack of network resources. This paper proposes a lightweight Intelligent Intrusion Detection Model for WSN. Combining k-nearest neighbor algorithm (kNN) and sine cosine algorithm (SCA) can significantly improve the classification accuracy and greatly reduce the false alarm rate, thereby intelligently detecting a variety of attacks including unknown attacks. In order to control the complexity of the model, the compact mechanism is applied to SCA (CSCA) to save the calculation time and space, and the polymorphic mutation (PM) strategy is used to compensate for the loss of optimization accuracy. The proposed PM-CSCA algorithm performs well in the benchmark functions test. In the simulation test based on NSL-KDD and UNSW-NB15 data sets, the designed intrusion detection algorithm achieved satisfactory results. In addition, the model can be deployed in an architecture based on cloud computing and fog computing to further improve the real-time, energy-saving, and efficiency of intrusion detection.
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Rana, Arun, Sharad Sharma, Kashif Nisar, Ag Asri Ag Ibrahim, Sachin Dhawan, Bhawani Chowdhry, Samreen Hussain, and Nitin Goyal. "The Rise of Blockchain Internet of Things (BIoT): Secured, Device-to-Device Architecture and Simulation Scenarios." Applied Sciences 12, no. 15 (July 30, 2022): 7694. http://dx.doi.org/10.3390/app12157694.
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Most Internet of Things (IoT) resources are exposed to security risks due to their essential functionality. IoT devices, such as smartphones and tablets, have a limited network, computation, and storage capacity, making them more vulnerable to attacks. In addition, the huge volume of data generated by IoT devices remains an open challenge for existing platforms to process, analyze, and discover underlying trends to create a convenient environment. As a result, to deliver acceptable services, a new solution is necessary to secure data accountability, increase data privacy and accessibility, and extract hidden patterns and usable knowledge. Moving the Internet of Things to a distributed ledger system might be the most effective way to solve these issues. One of the most well-known and extensively utilized distributed ledger systems is the blockchain. Due to its unique properties, such as privacy, accountability, immutability, and anonymity, blockchain technology has recently attracted a lot of interest. Using IoT in conjunction with blockchain technology can bring several benefits. This paper reviews the current state of the art different BIoT architectures, with a focus on current technologies, applications, challenges, and opportunities. The test findings prove that the decentralized authentication platform-based blockchain-based IoT (BIoT) device-to-device architecture has a significantly higher throughput than the gateway-based architecture. To encrypt the elliptical curve cryptographic (ECC) and to generate keys, the Chinese remainder theorem (CRT)-based scheme is proposed and compared with the secure hash algorithm (SHA-256). Finally, ECC-CRT is used to access system performance in terms of latency, throughput, and resource consumption, simulated through the Contiki Cooja (CC) simulator, and alter orderer and peer nodes for performance study in BIoT. A comprehensive analysis and simulation results show that the proposed scheme is secure against a variety of known attacks, including the man-in-the-middle (MiM) attack, and outperforms the SHA-256 cryptographic algorithm. Moreover, the significance of blockchain and IoT, as well as their analysis of proposed architecture, is discussed. This paper will help readers and researchers understand the IoT and its applicability to the real world.
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Gouru, Navya, and NagaLakshmi Vadlamani. "CoPS - Cooperative Provenance System with ZKP using Ethereum Blockchain Smart Contracts." International Journal of Distributed Systems and Technologies 9, no. 4 (October 2018): 40–53. http://dx.doi.org/10.4018/ijdst.2018100103.
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The redesign of cloud storage with the amalgamation of cooperative cloud and an immutable and unhackable distributed database blockchain thrives towards a strong CIA triad and secured data provenance. The conspiracy ideology associated with the traditional cloud has economized with cooperative cloud storage like Storj and Sia, decentralized storage, which allows renting the unused hard drive space and getting monetary compensation in an exchange with cryptocurrency. In this article, the authors explain how confidentiality, integrity and availability can be progressed with cooperative cloud storage along with tamper-proof data provenance management with ethereum smart contracts using zero-knowledge proof (ZKP). A contemporary architecture is proposed with regards to storing data on the cooperative cloud and collecting and verifying the provenance data from the cloud and publishing the provenance data into blockchain network as transactions.
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Kumaraguru, Shanthi, and M. R. Ebenezar Jebarani. "Trust aware routing using sunflower sine cosine-based stacked autoencoder approach for EEG signal classification in WSN." Journal of High Speed Networks 27, no. 2 (July 7, 2021): 101–19. http://dx.doi.org/10.3233/jhs-210654.
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Trust-aware routing is the significant direction for designing the secure routing protocol in Wireless Sensor Network (WSN). However, the trust-aware routing mechanism is implemented to evaluate the trustworthiness of the neighboring nodes based on the set of trust factors. Various trust-aware routing protocols are developed to route the data with minimum delay, but detecting the route with good quality poses a challenging issue in the research community. Therefore, an effective method named Sunflower Sine Cosine (SFSC)-based stacked autoencoder is designed to perform Electroencephalogram (EEG) signal classification using trust-aware routing in WSN. Moreover, the proposed SFSC algorithm incorporates Sunflower Optimization (SFO) and Sine Cosine Algorithm (SCA) that reveals an optimal solution, which is the optimal route used to transmit the EEG signal. Initially, the trust factors are computed from the nodes simulated in the network environment, and thereby, the trust-based routing is performed to achieve EEG signal classification. The proposed SFSC-based stacked autoencoder attained better performance by selecting the optimal path based on the fitness parameters, like energy, trust, and distance. The performance of the proposed approach is analyzed using the metrics, such as sensitivity, accuracy, and specificity. The proposed approach acquires 94.708%, 94.431%, and 95.780% sensitivity, accuracy, and specificity, respectively, with 150 nodes.
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Ravi, Chandrasekar. "Fuzzy Crow Search Algorithm-Based Deep LSTM for Bitcoin Prediction." International Journal of Distributed Systems and Technologies 11, no. 4 (October 2020): 53–71. http://dx.doi.org/10.4018/ijdst.2020100104.
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Prediction of stock market trends is considered as an important task and is of great attention as predicting stock prices successfully may lead to attractive profits by making proper decisions. Stock market prediction is a major challenge owing to non-stationary, blaring, and chaotic data and thus, the prediction becomes challenging among the investors to invest the money for making profits. Initially, the blockchain network is fed to the blockchain network bridge from which the bitcoin data is acquired that is followed with the bitcoin prediction. Bitcoin prediction is performed using the proposed FuzzyCSA-based Deep Long short-term memory (LSTM). At first, the flow strength indicators are extracted based on Double exponential moving average (DEMA), Rate of Change (ROCR), Average True Range (ATR), Simple Moving Average (SMA), and Moving Average Convergence Divergence (MACD) from the blockchain data. Based on the extracted features, the prediction is done using FuzzyCSA-based Deep LSTM, which is the combination of FuzzyCSA with Deep LSTM. Then, the CSA is modified using the fuzzy operator for determining the optimal weights in Deep LSTM. The experimentation of the proposed method is performed from the openly available dataset. The analysis of the method in terms of Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) reveals that the proposed FuzzyCSA-based Deep LSTM acquired a minimal MAE of 0.4811, and the minimal RMSE of 0.3905, respectively.
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Li, Ji, Huagang Xiong, Qiao Li, Feng Xiong, and Jiaying Feng. "Run-Time Reconfiguration Strategy and Implementation of Time-Triggered Networks." Electronics 11, no. 9 (May 5, 2022): 1477. http://dx.doi.org/10.3390/electronics11091477.
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Time-triggered networks are deployed in avionics and astronautics because they provide deterministic and low-latency communications. Remapping of partitions and the applications that reside in them that are executing on the failed core and the resulting re-routing and re-scheduling are conducted when a permanent end-system core failure occurs and local resources are insufficient. We present a network-wide reconfiguration strategy as well as an implementation scheme, and propose an Integer Linear Programming based joint mapping, routing, and scheduling reconfiguration method (JILP) for global reconfiguration. Based on scheduling compatibility, a novel heuristic algorithm (SCA) for mapping and routing is proposed to reduce the reconfiguration time. Experimentally, JILP achieved a higher success rate compared to mapping-then-routing-and-scheduling algorithms. In addition, relative to JILP, SCA/ILP was 50-fold faster and with a minimal impact on reconfiguration success rate. SCA achieved a higher reconfiguration success rate compared to shortest path routing and load-balanced routing. In addition, scheduling compatibility plays a guiding role in ILP-based optimization objectives and ‘reconfigurable depth’, which is a metric proposed in this paper for the determination of the reconfiguration potential of a TT network.
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Lisetskii, Fedor, Stanislav Ilyashenko, and Zhanna Buryak. "Development of the information retrieval system to analyze the dynamics of the formation of settlements network." InterCarto. InterGIS 27, no. 4 (2021): 202–17. http://dx.doi.org/10.35595/2414-9179-2021-4-27-202-217.
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The use of geographic information systems provides an effective solution to the goal of mapping the results of the population distribution and redistribution of the population in the region of primary development, as well as to study the dynamics of settlement by key time slices. For effective spatial analysis of the results of the distribution and redistribution of the population, it is advisable to adapt the functionality of the information retrieval system using a map server, as well as a web application that provides data processing and interaction with a database that includes geodata and attributive information. The article presents the experience of developing an information retrieval system in the form of a cartographic web application and its application to establish the territorial logic of the regional settlement system for five historical and geographical periods (from the middle of the 18th century to date). The information retrieval system provides an option of graphic selection and display of settlements on the map, taking into account the formation of the settlement system according to the historical and geographical stages, as well as an option of searching by both modern and historical names of settlements. The performance efficiency of the information retrieval system was demonstrated using the case study of one of the Northern Black Sea regions, where the interests of Poland, Turkey and Russia clashed from the middle of the 18th century; and the spatial and temporal dynamics of the settlement system reflected the change in state borders. A series of electronic maps on five historical and geographical stages of the formation of a network of settlements was obtained thanks to its import into the geodatabase of an information retrieval system and the automatic distribution of settlements by chronological intervals (up to 1794, 1795–1860, 1861–1905, 1906–1918, 1919–2020). The final stage of stabilizing the growth of the number of settlements, which determined the settlement network density of 3.7 units per 100 km2, falls during the period of 1906–1941.
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Liu, Yanyang, Jing Ran, Hefei Hu, and Bihua Tang. "Energy-Efficient Virtual Network Function Reconfiguration Strategy Based on Short-Term Resources Requirement Prediction." Electronics 10, no. 18 (September 17, 2021): 2287. http://dx.doi.org/10.3390/electronics10182287.
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In Network Function Virtualization, the resource demand of the network service evolves with the change of network traffic. VNF dynamic migration has become an effective method to improve network performance. However, for the time-varying resource demand, how to minimize the long-term energy consumption of the network while guaranteeing the Service Level Agreement (SLA) is the key issue that lacks previous research. To tackle this dilemma, this paper proposes an energy-efficient reconfiguration algorithm for VNF based on short-term resource requirement prediction (RP-EDM). Our algorithm uses LSTM to predict VNF resource requirements in advance to eliminate the lag of dynamic migration and determines the timing of migration. RP-EDM eliminates SLA violations by performing VNF separation on potentially overloaded servers and consolidates low-load servers timely to save energy. Meanwhile, we consider the power consumption of servers when booting up, which is existing objectively, to avoid switching on/off the server frequently. The simulation results suggest that RP-EDM has a good performance and stability under machine learning models with different accuracy. Moreover, our algorithm increases the total service traffic by about 15% while ensuring a low SLA interruption rate. The total energy cost is reduced by more than 20% compared with the existing algorithms.
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Su, Yu-Sheng, Chien-Linag Lin, Shih-Yeh Chen, and Chin-Feng Lai. "Bibliometric study of social network analysis literature." Library Hi Tech 38, no. 2 (December 18, 2019): 420–33. http://dx.doi.org/10.1108/lht-01-2019-0028.
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Purpose The purpose of this paper is to use bibliometric analysis to identify the current state of the academic literature regarding social network analysis (SNA) and analyze its knowledge base such as research authors, research countries, document type, keyword analysis and subject areas. Design/methodology/approach Bibliometric analysis is used and furthermore, Lotka’s and Bradford’s law is applied to perform author productivity analyses in this field during 1999 and 2018, respectively, in turn, discovering historical vein and research tendency in the future. Findings It appears that the research on SNA has been very popular and still in the highly mature period. So far, the USA takes the lead among the published paper. The top 2 subject areas are “Computer Science” and “Business Economics.” The primary journal that SNA articles were published is Computers in Human Behavior. SNA has been related to many research areas, such as “Social network analysis,” “Computer-mediated communication,” “Online learning,” “Social Network” and “Community of inquiry.” Finally, Kolmogorov–Smirnov (K-S) test proved that the frequency indexes of author productivity distribution certainly followed Lotka’s law. Research limitations/implications First, the productivity distribution may inform researchers and scholars of current issues and development of SNA. Second, the study proposed a theoretical model, based on Lotka’s law, for author productivity analysis of SNA, which can serve as reference for different areas of study in the evaluation of author productivity models. Also, in order to allow researchers to gain in-depth insights, this study aimed to report the most published institutions and keep track of the growth and trend of author productivity, by which scholars in related fields are provided with more opportunities for academic communication and technological cooperation. Originality/value This research on the productivity distribution of SNA may inform researchers and scholars of current issues and development of SNA. The findings report the major publication outlets and related discussion issues about SNA. Such information would be valuable for related authors, who are writing the manuscript on SNA, and also for practitioners, who may be interested in applying the theory or ideas of SNA.