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检索条件"任意字段=2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023"

共 711 条记录，以下是51-60 订阅

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The Cluster-based cognitive Radio Sensor Networks that are Wireless Aware of PUEA and SSDF Attacks 1

The Cluster-based Cognitive Radio Sensor Networks that are W...

引用

1st IEEE international conference on Applied Intelligence and Sustainable computing, ICAISC 2023

作者： Thakkar, Hina Goswami, Sachin ATLAS Skill Tech University ISME - School of Management & Entrepreneurship Mumbai India IIMT University School of Media Film & Television Studies Meerut India

ISBN: (纸本)9798350323795

This paper presents a secure cognitive RadioWireless Sensor Network (CR-WSN) architecture. CR-WSN is a cluster based network in which the sensors are hierarchically structured. Each cluster is chosen with single cluster head that operates on ISM band frequencies. Sensor data from cluster members are aggregated by cluster head. Cluster heads forwards the collected data to sink based on selection of a shortest path using Dijkstra algorithm. Due to the problem of spectrum scarcity, cognitive Radios are subjected to two main attacks for utilizing the unlicensed spectrum. We resolve Spectrum Sensing data Falsification attack and Primary User Emulation attack. Dynamic Spectrum Sensing Technique is involved for performing spectrum sensing. We propose a regression based spectrum sensing data falsification attack detection technique and mathematical SHA with Digital Signature is applied for primary user emulation attack detection. Extensive implementation is handled in Network Simulator 3 and comparative results are illustrated for betterment of throughput and end-to-end delay efficiencies. © 2023 IEEE.

关键词： cognitive radio

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Defense Against Spectrum Sensing data Falsification Attacks Using Cluster Network Channeling with Distance-Based Attacker Detection in cognitive Radio Ad-Hoc Networks 14

Defense Against Spectrum Sensing Data Falsification Attacks ...

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14th international conference on Mobile computing and Ubiquitous Network, ICMU 2023

作者： Taggu, Amar Samuel Kharsohnoh, Bryan NERIST Department of Computer Science and Engineering Nirjuli India

ISBN: (纸本)9784907626525

The growing demand for wireless and smart technologies has led to an unprecedented increase in the utilization of the radio spectrum. However, as the number of users and devices continues to surge, the spectrum is becoming over-crowded, impeding access and efficient utilization. To address this challenge, cognitive Radio (CR) technology is increasingly becoming the forerunner. cognitive Radio Ad-Hoc Networks (CRAHNs) consists of cognitive intelligent nodes where the CR nodes operating in an infrastructure-less mode, can sense and hop across different frequencies dynamically. CR nodes adapt broadcast constraints to minimize interference among primary and secondary users. By continuously sensing the radio frequency spectrum, cognitive users identify available licensed channels for communication, selecting optimal channels. CRAHNs are subjected to various security attacks which are unique to such networks only. Spectrum Sensing data Falsification (SSDF) attack is one such attack where the malicious CR nodes falsify sensing data in an attempt to influence the final fused data. In this work, we simulate a CRAHN consisting of clusters and cluster heads (CH). Apart from implementing routing across the clusters via the CHs, the current work also proposes a distance-based SSDF attacker detection method which operates primarily in the CHs. Comparison with an existing work [1] demonstrates improved detection. © 2023 IPSJ.

关键词： cognitive radio

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cognitive Load Assessment in Learning Construction Sensor data Analytics within an End User Programming Environment

Cognitive Load Assessment in Learning Construction Sensor Da...

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ASCE international conference on computing in Civil Engineering (I3CE)

作者： Khalid, Mohammad Akanmu, Abiola A. Yusuf, Anthony O. Murzi, Homero Awolusi, Ibukun Gonsalves, Nihar Virginia Tech Myers Lawson Sch Construct Blacksburg VA 24061 USA Virginia Tech Dept Engn Educ Blacksburg VA USA Univ Texas San Antonio Sch Civil & Environm Engn & Construct Management San Antonio TX USA

ISBN: (数字)9780784485224

ISBN: (纸本)9780784485224

Computational thinking-supported educational training can equip the construction workforce with the necessary skills and knowledge to implement sensing technologies and perform sensor data analytics aimed at enhancing construction operations. However, construction students struggle to understand the computational concepts and workflows required to translate low-level sensor data into knowledge for supporting decisions. This study compared the cognitive load of construction students performing similar data analytics tasks using an end-user programming environment (EUP) to conventional MS Excel. Comparative analysis using descriptive and inferential statistics demonstrated that participants using EUP perceived lower levels of cognitive loads and more positive experiences than those who used the conventional techniques. The study's findings will advance the understanding of the potential cognitive effects of adopting EUPs for construction sensor data analytics. This study contributes to the cognitive theory of multimedia learning by illustrating how multimodal programming environments can influence learners' cognitive demands.

关键词： data Analytics

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Evaluation of Trusted Channel cognitive Radio Network using WPM Method 1

Evaluation of Trusted Channel Cognitive Radio Network using ...

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1st international conference on cognitive computing and Engineering Education, ICCCEE 2023

作者： Shrivastava, Monto Sharma, Kamal GLA University Department of Mechanical Engineering Mathura India

ISBN: (纸本)9798350332803

Sensitivity to devices in distant then frequently unnoticed health, corporate complexes, and home is crucial in critical situations. Though, depending on devices data, it presents a significant challenge in informing decision-making, as standardized and trusted methods must be used. Previous efforts have focused on standardized platform modules, but these lack desirable features such as Intel SGX with consistent execution similar to TEE as Global Platform. In this paper, we propose TEE for devices to achieve reliable sensitivity including TPMs, and differentiating its use in remote devices providing common conviction assurances. We also include secure communication issues in TEE between small-sized devices with TEEs, for which we provide a secure and reliable channel protocol that enables mutual remote certification in the flow. The analysis takes into account bandwidth, duty cycle, economic cost, channel decision, cognitive radio, and data transmission as evaluation parameters. The results show that the Channel decision ranks first, while the Bandwidth and Decision Based on Economic Cost have the lowest rank. © 2023 IEEE.

关键词： Bandwidth

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Multi-Request data Cache Optimization Strategy in Edge computing 26

Multi-Request Data Cache Optimization Strategy in Edge Compu...

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26th ACIS international Winter conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed computing, SNPD-Winter 2023

作者： Wang, Futian Wu, Zhimin Tang, Jin Li, Chenglong Zhang, Cheng School of Computer Science and Technology Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Hefei China School of Computer Science and Technology Anhui University Hefei China Institute of Artificial Intelligence Anhui University Hefei China

ISBN: (纸本)9798350345865

With the rapid growth in the number of mobile devices, more and more data is created and requested by users. By caching the data to the edge server, users can obtain the content they request in a closer place, which reduces the network load and improves the user experience. However, existing articles only focus on a single request of users, while users often request multiple content at one time, which leads to more complex caching scenarios. In this paper, we will study an edge data cache scenario with multiple requests, and design a cost function to optimize the cache results. In this paper, we formulate the data cache problem as a mixed integer programming problem and minimize our cost function. Extensive experiments are conducted on a real-world dataset that contains the locations of edge servers and mobile users, and the results reveal that our approach significantly outperform the baseline approaches. © 2023 IEEE.

关键词： Cost functions

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AttentioNet: Monitoring Student Attention Type in Learning with EEG-Based Measurement System 11

AttentioNet: Monitoring Student Attention Type in Learning w...

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11th international conference on Affective computing and Intelligent Interaction (ACIIW)

作者： Verma, Dhruv Bhalla, Sejal Santosh, S. V. Sai Yadav, Saumya Parnami, Aman Shukla, Jainendra Univ Toronto Toronto ON Canada NVIDIA Corp Austin MN USA IIIT Delhi Human Machine Interact Lab Delhi India IIIT Delhi Weave Lab Delhi India IIIT Delhi Delhi India

ISBN: (纸本)9798350327434

Student attention is an indispensable input for uncovering their goals, intentions, and interests, which prove to be invaluable for a multitude of research areas, ranging from psychology to interactive systems. However, most existing methods to classify attention fail to model its complex nature. To bridge this gap, we propose AttentioNet, a novel Convolutional Neural Network-based approach that utilizes Electroencephalography (EEG) data to classify attention into five states: Selective, Sustained, Divided, Alternating, and relaxed state. We collected a dataset of 20 subjects through standard neuropsychological tasks to elicit different attentional states. The average across-student accuracy of our proposed model at this configuration is 92.3% (SD=3.04), which is well-suited for end-user applications. Our transfer learning-based approach for personalizing the model to individual subjects effectively addresses the issue of individual variability in EEG signals, resulting in improved performance and adaptability of the model for real-world applications. This represents a significant advancement in the field of EEG-based classification. Experimental results demonstrate that AttentioNet outperforms a popular EEGnet baseline (p-value < 0.05) in both subject-independent and subject-dependent settings, confirming the effectiveness of our proposed approach despite the limitations of our dataset. These results highlight the promising potential of AttentioNet for attention classification using EEG data.

关键词： EEG Attention Affective computing cognitive Engagement Assessment

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Wearables for In-Situ Monitoring of cognitive States: Challenges and Opportunities

Wearables for In-Situ Monitoring of Cognitive States: Challe...

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2023 IEEE international conference on Pervasive computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

作者： Radhakrishnan, Meera Kandappu, Thivya Gulati, Manoj Misra, Archan Institute of High Performance Computing Agency for Science Technology and Research Singapore Management University Singapore School of Computing and Information Systems Singapore Management University Singapore

ISBN: (纸本)9781665453813

We propose using wrist and ear-based sensing, via multiple novel and complementary modalities, to unobtrusively infer activity-aware, complex cognitive and affective states (such as confusion, boredom, and recall failure) of individuals. While state-of-the-art wearable devices are predominantly used (a) independently, with limited coordination among multiple devices, and (b) to capture macro-level physical activity and physiological state, we seek to expand the ambit of unobtrusive wearable sensing to capture the cognitive states while performing commonplace physical activities. Such states typically manifest via fine-grained, almost unobservable, microscopic head, face, and eye movements. We identify some of these fine-grained physical markers that serve as proxies for cognitive/affective states and show that earable-mounted pressure, EMG, and ultrasonic sensing hold promise for canturing such markers. © 2023 IEEE.

关键词： Eye movements

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A Space-Saving Based MLCS Algorithm

A Space-Saving Based MLCS Algorithm

引用

2023 IEEE international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on Cloud and Big data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Jiang, Botian Wang, Chunyang Fu, Yuanyuan Liu, Hai-Lin Guo, Ping Wang, Yuping School of Computer Science and Technology Xidian University Xi'an China Airborne Avionics Aviation Industry Corporation of China Shanghai China School of Mathematics and Statistics Guangdong University of Technology Guangzhou China International Academic Center of Complex Systems Beijing Normal University Beijing China

ISBN: (纸本)9798350304602

In this paper, we propose a branch and bound space saving method for finding all multiple longest common subsequences (MLCS). First, a more reasonable lower bound estimation strategy is designed. To construct a local directed acyclic graph (DAG graph) for the sequences, the size and distribution of the matching point coordinates are both considered when retaining nodes in each layer, so that the real common subsequence obtained is as long as possible. Then, a simpler upper bound estimation strategy is devised. A new concept 'boundary point' is defined, and a new upper bound calculation formula is derived by using the properties of the boundary point. So the upper bound value of the length of the branch of the current node can be quickly estimated, and the node is added to the graph only when this upper bound value is not less than the lower bound value. In addition, a storage-saving graph model is designed, which only retains the nodes with in-degree 0 at any time, and each node stores the string set corresponding to all paths from the source point to the node itself. The comparison experiments with 3 state-of-the-art algorithms on the standard biological data set indicate that the proposed algorithm is more effective and efficient. © 2023 IEEE.

关键词： data mining

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Proceedings - 2023 international conference on data, Information and computing Science, CDICS 2023

Proceedings - 2023 International Conference on Data, Informa...

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2023 international conference on data, Information and computing Science, CDICS 2023

ISBN: (纸本)9798350382778

The proceedings contain 12 papers. The topics discussed include: state-of-the-art advances of deep-learning linguistic steganalysis research;comparison of entropy coders for lossless grayscale image compression;comparison of entropy coders for lossless grayscale image compression;the development & evaluation of a machine learning-assisted recommendation system for generic competencies development;a modified TOPSIS method considering interference effect with quantum cognitive theory;order structure analysis of node importance by super-adjacency correlation feature temporal evolution of dynamic multi-layer network;and effects of digital transformation on energy conservation and emission reduction in the manufacturing industry: theory and empirical evidence from China.

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Semantic Modelling of Multivariate Time-Series data in cognitive IoT 9

Semantic Modelling of Multivariate Time-Series Data in Cogni...

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9th international conference on Advanced computing and Communication Systems, ICACCS 2023

作者： Jha, Vidyapati Tripathi, Priyanka NIT Raipur Department of Computer Application India

ISBN: (纸本)9798350397376

With the use of sensors and intelligent devices to assist interactions with the physical environment, we observe a significant rise in the amount of sensory input, which is continual, volatile, sometimes actual, and diverse in structure. The Internet of Things (IoT) encapsulates this phenomenon. In addition, when IoT integrates cognition into system design, it is called cognitive IoT (CIoT). CIoT requires techniques to autonomously extract semantic patterns from plain sensory data for efficient service delivery. Only a little scientific attention has been paid to automatically identifying semantic patterns from a massive time-series data set. This research presents a scaffold for real-time automatic semantic pattern extraction in the cognitive Internet of Things. It utilizes the Symbolic Aggregate Approximation (SAX) technique for sensory data with a multivariate time series. Further, the output of SAX is used to create a symbol table with their frequency. This frequency table is transformed into its corresponding probabilistic value. Finally, this value is used to generate the semantic pattern, which can be further processed using a rule-based method to create a machine-interpretable pattern. Experimental evaluation of this research study reveals that the proposed method can extract semantic patterns from heterogeneous multivariate sensory data. © 2023 IEEE.

关键词： Internet of things

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