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Cloud Technology Impact on Human Behavior Based on cognitive Theory 4

Cloud Technology Impact on Human Behavior Based on Cognitive...

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4th IEEE international conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Wiryawan, Drajad Rattanto, Basil Shaharudin, Muhammad Shabir Suhartono, Joni Mailangkay, Adele Bernadet Lingkan Rumangkit, Stefanus School of Information Systems Bina Nusantara University Jakarta Indonesia School of Management Universiti Sains Malaysia Penang Malaysia School of Management Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350395914

The relationship between cognitive Theory and Cloud Technology is a learning stage for humans, especially technology users, in various innovations used by users in modeling reasoning to develop human direction toward a better future. It includes learning on a large scale, such as objectively coordinated thinking, which provides for decision-making, social interaction, and access to various technological devices used. The above also focuses on human familiarity with various distributed computing services in cloud technology, including PaaS, SaaS, and IaaS. This research used a systems literature review model for over 20 years (2000 - 2023). Meanwhile, the applications used are Vos Viewer and Publish or Perish. Due to the author's limited access to IEEE, ACM, and WoS, the database used and the focus of the literature is the Scopus database. In five years (2019-2023), the writing purpose accounted for only 55 articles, with 39 explicitly addressing cognitive theory and human behavior in the context of cloud technology. In this research, five factors influence human behavior with cloud technology: social interaction, access and connectivity, problem-solving, decision-making, and data confidentiality security. However, of the five impacts entered and obtained from the papers and journals studied, the most critical findings from this research are social interaction, data privacy and security, and decision-making. People will use cloud technology for social interaction, to protect and maintain the confidentiality of their data, and to make various decisions related to their activities. This research also shows that most references come from journals, not conference proceedings articles. © 2024 IEEE.

关键词： Cloud platforms

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Maximizing cognitive Readiness of Individuals in Project Management 12

Maximizing Cognitive Readiness of Individuals in Project Man...

引用

12th IEEE international conference on Intelligent data Acquisition and Advanced computing Systems: Technology and Applications, IDAACS 2023

作者： Mgbere, Chinwi Winston, Rebecca Di Fillipo, Ivano Office of Nazarbayev University Medicine Aoe Nazarbayev University Astana Kazakhstan Winston Strategic Management Consulting Italy

ISBN: (纸本)9798350358056

This study explores how aligned factors such as goal/desire, identity, values, and beliefs maximize cognitive readiness, which is a critical factor in the ability of project managers to process situational information or project signals in an increasingly turbulent post-COVID environment and improve decision-making. We show that cognitive readiness is highest when project goals dovetail with the personal goals and identity of the team. This could create intrinsic motivation in team members and enhance the clarity of choices that supports attaining the desired individual and project goals. The findings indicate that if individuals in project management set goals congruent and aligned with their highest value, their sensory awareness (11Sensory awareness - the direct focus on specific sensory aspects of the body or outer or inner environment. R. T. Hurlburt Christopher L. Heavey and A. Bensaheb, Sensory Awareness, Article in Journal of Consciousness Studies, January 2009, Accessed June 2023, [Online]. Available: https://***/publication/233693913 selective bias) filters out noise, they are present, mindful and aware, they are in flow, focused and diligent, they see opportunities, they take advantage of the synchronicities,22Synchronicity is a concept first introduced by analytical psychologist Carl G. Jung 'to describe circumstances that appear meaningfully related yet lack a causal connection.' https://***/wiki/Synchronicity make decisions quick, and take inspired actions. © 2023 IEEE.

关键词： awareness behavior beliefs capabilities cognitive readiness environment flow identity mindfulness personal goals project goals values

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Big data Enabled Intrusion Detection with Honeypot Intelligence System on Apache Flink (BDE-IDHIS) 14

Big Data Enabled Intrusion Detection with Honeypot Intellige...

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14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Mudgal, Akshay Bhatia, Shaveta Manav Rachna International Institute of Research and Studies Faculty of Computer Applications Faridabad India

ISBN: (纸本)9798350335095

The importance of data security and data analysis strategies for Big data has recently altered as a result of an enormous amount of data and its continual expansion. An intrusion detection system (IDS) is a device that keeps track of and examines data in order to find intrusions in networks and other systems. The data analysis process is exceedingly complex and requires a huge volume, variety, and speed of data to detect assaults using traditional methods. In order to achieve the goal of effective system protection, intrusion redirection technology has been used to identify potentially harmful access for traceless forwarding and forward it to a honeypot (HP) scheme. From a unique standpoint, HP method is a form of active defensive system that uses enticement to lure invaders so that more data may be collected. In the Apache Flink environment, this article describes a novel intrusion detection method called Big data Enabled Intrusion Detection with Honeypot Intelligence System (BDE-IDHIS). The BDE-IDHIS technique seeks to first detect the presence of intrusions thereafter diverting traffic to the HP system for effective security of the actual system. Moreover, the MQTT Honeypot with Decision Engine and Redirection Engine is used in the BDE-IDHIS method. In addition, Apache Flink is utilized in this study to manage huge data settings. A number of experiments are used to test the experimental result analysis of the BDE-IDHIS technique, and the results are then examined from a variety of angles. The experimental outcomes stated the better performance of the BDE-IDHIS technique over other existing techniques. © 2023 IEEE.

关键词： Intrusion detection

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cognitive Fire Alert Framework for Realizing Fire in Unfriendly Environment using Wireless Sensor Network 7

Cognitive Fire Alert Framework for Realizing Fire in Unfrien...

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7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Sathiya, A. Velmurugan, A.K. Santhuja, P. Indra, G. Department of Artificial Intelligence and Data Science Sri Sairam Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India Department of Computer Science Engineering B V Raju Institute of Technology Telangana Hyderabad India Department of Computer Science Engineering R.M.K College of Engineering and Technology Tamil Nadu Chennai India

ISBN: (纸本)9781665464086

Nowadays, Wireless Sensor Networks (WSNs) are extensively distributed in environmental monitoring, operational health monitoring, and industrial monitoring. It is an inexpensive application. It contains small, less energy-consuming, lower-cost devices combined with restricted computation, observation, and radio transmission capacities. People nowadays use fire alarms with sensors to detect fires. Fire sensors are unreliable. With the fast growth of WSNs, people can now remotely watch their surroundings to ascertain the current status. A standard fire alarm system is only able to detect one of the environmental parameters at a time. It is possible that there is a fire if the detection value is greater than the threshold;this would indicate that there is a fire. Copper, insulated, or cables are used to make the connections between detectors and alarms in a traditional fire alarm system. Due to the presence of a large number of elements at the site of the fire, the smoke alarm system will be unable to make an accurate determination as to the location of the flames. This article proposes a cognitive fire alert framework (CFAF) for realizing fire in unfriendly environments using WSN. It has a phase for wireless transmission, a phase for cognitive fire recognition, and a phase for the Random Forest Method (RFM). This approach utilizes the RFM to classify the fire by data classification. The results of the experiments show that the suggested technique minimizes the number of false alarms by evaluating the delay value by utilizing data samples and comparing the temperature, humidity, pressure, and dust level. © 2023 IEEE.

关键词： Wireless sensor networks

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cognitive Cameras on the Edge for Crowd Physical Distancing Monitoring in the Covid-19 Era 14

Cognitive Cameras on the Edge for Crowd Physical Distancing ...

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14th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th international conference on Emerging data and Industry 4.0, EDI40 2023

作者： Raimondo, Francesco De Rango, Floriano Spezzano, Giandomenico Dimes University of Calabria Via P. Bucci 42c Rende87036 Italy National Research Council of Italy Via P. Bucci 8-9c Rende87036 Italy

With the rise of the Internet of Things (IoT) architectures and protocols, new video analytics systems and surveillance applications have been developed. In conventional systems, all the streams produced by cameras are sent to a centralized node where they can be seen by human operators whose task is to identify uncommon on abnormal situations. However, this way, much bandwidth is necessary for the system to work, and the number of necessary resources is proportional to the number of cameras and streams involved. In this paper, we propose an interesting approach to this problem: transforming any IP camera into a cognitive object. A cognitive camera (CC) can be considered a classic connected camera with onboard computational power for intelligent video processing. A CC can understand and interact with the surroundings, intelligently analyze complex scenes, and interact with the users. The IoT Edge computing approach decreases latency in the decision-making process and consumes a tiny portion of bandwidth concerning the stream of a video, even in low resolution. CCs can help to address COVID-19. As a preventive measure, proper crowd monitoring and management systems must be installed in public places to limit sudden outbreaks and improve healthcare. The number of new infections can be significantly reduced by adopting physical distancing measures earlier. Motivated by this notion, a real-time crowd monitoring and management system for physical distance classification using CCs is proposed in this research paper. The experiment on Movidius board, an AI accelerator device, provides promising results of our proposed method in which the accuracies can achieve more than 85% from different datasets. © 2023 Elsevier B.V.. All rights reserved.

关键词： Internet of things

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Proceedings of the 2023 9th international conference on computing and data Engineering, iccdE 2023

Proceedings of the 2023 9th International Conference on Comp...

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9th international conference on computing and data Engineering, iccdE 2023

ISBN: (纸本)9781450398022

The proceedings contain 14 papers. The topics discussed include: an on-demand cloud-native containerized storage design and its practice of HDFS-on-Kubernetes;profiling cultural tourists by using user generated big data from online travel agencies;classification and prediction on cardiovascular disease datasets;new fitness evaluation for a single machine scheduling problem with an overtime option;efficient face generation and clustering using generative adversarial networks;a closed-loop detection algorithm for online updating of bag-of-words model;research on the prediction method of rural industry integration based on improved RBF neural network model;multi-view subspace clustering with complex noise modeling;breast microcalcification detection in digital mammograms using deep transfer learning approaches;an anchor free car damage detection method;and computer-aided design system for anti-corrosion of coal preparation equipment based on improved technology of high-salt coal washing wastewater.

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Few-Shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge Distillation 4

Few-Shot Class-Incremental Semantic Segmentation via Pseudo-...

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4th international conference on Information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Jiang, Chengjia Wang, Tao Li, Sien Wang, Jinyang Wang, Shirui Antoniou, Antonios Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Fujian Education Institutions Wuyi University The Key Laboratory of Cognitive Computing and Intelligent Information Processing Wuyishan China College of Computer and Data Science Fuzhou University Fuzhou China European University Cyprus Department of Computer Science and Engineering Nicosia Cyprus

ISBN: (纸本)9798350337181

We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data to capture the underlying class distribution. Secondly, it is challenging to retain knowledge for existing classes and to avoid catastrophic forgetting. For learning from limited data, we propose a pseudo-labeling strategy to augment the few-shot training annotations in order to learn novel classes more effectively. Given only one or a few images labeled with the novel classes and a much larger set of unlabeled images, we transfer the knowledge from labeled images to unlabeled images with a coarse-to-fine pseudo-labeling approach in two steps. Specifically, we first match each labeled image to its nearest neighbors in the unlabeled image set at the scene level, in order to obtain images with a similar scene layout. This is followed by obtaining pseudo-labels within this neighborhood by applying classifiers learned on the few-shot annotations. In addition, we use knowledge distillation on both labeled and unlabeled data to retain knowledge on existing classes. We integrate the above steps into a single convolutional neural network with a unified learning objective. Extensive experiments on the Cityscapes and KITTI datasets validate the efficacy of the proposed approach in the self-driving domain. Code is available from https://***/ChasonJiang/FSCILSS. © 2023 IEEE.

关键词： Distillation

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Ontology-Based Generation of data Platform Assets

Ontology-Based Generation of Data Platform Assets

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2023 IEEE international conference on Big data, Bigdata 2023

作者： De Leo, Vincenzo Fenu, Gianni Greco, David Bidotti, Nicolo Platter, Paolo Motta, Enrico Nuzzolese, Andrea Giovanni Osborne, Francesco Recupero, Diego Reforgiato University of Cagliari Department of Math and Computer Science Cagliari Italy Big Data Laboratory AgileLab S.r.l Rome Italy Knowledge Media Institute Open University Milton Keynes United Kingdom Institute of Cognitive Sciences and Technologies National Council of Research Bologna Italy

ISBN: (纸本)9798350324457

The design and management of modern big data platforms are extremely complex. It requires carefully integrating multiple storage and computational platforms as well as implementing approaches to protect and audit data access. Therefore, onboarding new data and implementing new data transformation processes is typically time-consuming and expensive. In many cases, enterprises construct their data platforms without a clear distinction between logical and technical concerns. Consequently, these platforms lack sufficient abstraction and are closely tied to particular technologies, making the adaptation to technological evolution very costly. This paper illustrates a novel approach to designing data platform models based on a formal ontology that structures various domain components into an accessible knowledge graph. We also describe the preliminary version of AGILE-DM, a novel ontology that we built for this purpose. Our solution is flexible, technologically agnostic, and more adaptable to changes and technical advancements. © 2023 IEEE.

关键词： data platform Knowledge Graphs Multi-level architecture Ontology

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DETECTING cognitive FATIGUE IN SUBJECTS WITH TRAUMATIC BRAIN INJURY FROM FMRI SCANS USING SELF-SUPERVISED LEARNING 23

DETECTING COGNITIVE FATIGUE IN SUBJECTS WITH TRAUMATIC BRAIN...

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16th ACM international conference on Pervasive Technologies Related to Assistive Environments (PETRA)

作者： Jaiswal, Ashish Babu, Ashwin Ramesh Zadeh, Mohammad Zaki Wylie, Glenn Makedon, Fillia Univ Texas Arlington Arlington TX 76019 USA Kessler Fdn E Hanover NJ 07936 USA

ISBN: (纸本)9798400700699

Understanding cognitive states from fMRI data have yet to be investigated to its full extent due to its complex nature. In this work, the problem of understanding cognitive fatigue among TBI patients has been formulated as a multi-class classification problem. We built a Spatio-temporal encoder model using convolutions and LSTMs as the building blocks to extract spatial features and to model the 4D nature of fMRI scans. To learn a better representation of the data and the condition, we used a self-supervised learning technique called "Contrastive Learning" to pretrain our encoder with a public dataset BOLD5000 and further fine-tuned our labeled dataset to predict cognitive fatigue. Furthermore, we present an fMRI dataset that contains scans from a mix of Traumatic Brain Injury (TBI) patients and healthy controls (HCs) while performing a series of standardized N-back cognitive tasks. This method establishes a state-of-the-art technique to analyze cognitive fatigue from fMRI data and beats previous approaches to solve this problem with different modalities. Besides, the ability of our models to take in raw fMRI scans (noisy images with artifacts output directly from the scanner) eliminates the need to implement a manual signal processing pipeline that varies based on the scanner used. Finally, we study the impact of different brain regions contributing to CF. The proposed technique outperforms the state-of-the-art method by over 13 percent on this dataset.

关键词： cognitive fatigue machine learning fMRI brain imaging self-supervised learning

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Physical Layer Security in MIMO Relay-Based cognitive Radio Network 1

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10th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2022

作者： Upadhyay, V. Bharti, M.R. National Institute of Technology HP Hamirpur177005 India

ISBN: (数字)9789811975240

ISBN: (纸本)9789811975233

cognitive radio networks are one of the predominant technologies to solve the spectrum scarcity problem. In this paper we tackle the problem of secure and safe data transmission between secondary user transmitter and secondary user receiver through relays in the presence of eavesdroppers in cognitive radio-based MIMO system. MIMO stands for Multiple-Input Multiple-Output. MIMO is a wireless technology that increases the data capacity with the help of multiple transmitting and receiving antennas. In this work, the best amplify and forward relay to maximize the secrecy capacity in a MIMO system is selected. The simulation and analytical results show the improved performance through secrecy capacity and secrecy outage probability. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： cognitive radio

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