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Social media discontinuation: A systematic literature review on drivers and inhibitors

Telematics and Informatics 2023年 77卷

作者： Farooq, Ali Dahabiyeh, Laila Maier, Christian Department of Computing Faculty of Technology University of Turku Finland Department of Management Information Systems School of Business The University of Jordan Amman Jordan Department of Information Systems and Services University of Bamberg Germany

Recent practical reports reveal that users leave social media. This behavior is named social media discontinuation (SMD) and is attributed to stress, fatigue, and further negative effects caused while using social media. While current literature points to some causes of SMD, a systematic literature review is needed to identify current research trends, carve out future research directions, and guide practitioners in encouraging users not to close their social media accounts. We thus examine existing research on SMD and identify its drivers and inhibitors. We conducted a systematic literature review by screening 1,270 studies, published in journals and conference proceedings since 2005. We focused on 32 studies that matched our screening guidelines. Our results come up with three SMD drivers and inhibitors categories: individual, relational and platform-specific. Individual drivers and inhibitors can further be divided into cognitive, behavioral, and emotional drivers, whereas platform-specific drivers are related to content and other social media characteristics. Most drivers are from the individual or the relational category. The findings contribute to SMD research by providing a comprehensive state-of-the-art perspective on what drives and inhibits SMD. This lets us develop valuable future research directions, such as the need for future research on SMD inhibitors, which have received little attention. The study further offers valuable practical implications to social media service providers. © 2022 The Author(s)

关键词： Social networking (online)

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Bidirectional Adversarial Autoencoders for the design of Plasmonic Metasurfaces

arXiv

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arXiv 2024年

作者： Liu, Yuansan Panisilvam, Jeygopi Dower, Peter Kim, Sejeong Bailey, James Department of Computing and Information Systems Faculty of Engineering and Information Technology University of Melbourne Australia Department of Electrical and Electronic Engineering Faculty of Engineering and Information Technology University of Melbourne Australia

Deep Learning has been a critical part of designing inverse design methods that are computationally efficient and accurate. An example of this is the design of photonic metasurfaces by using their photoluminescent spectrum as the input data to predict their topology. One fundamental challenge of these systems is their ability to represent nonlinear relationships between sets of data that have different dimensionalities. Existing design methods often implement a conditional Generative Adversarial Network in order to solve this problem, but in many cases the solution is unable to generate structures that provide multiple peaks when validated. It is demonstrated that in response to the target spectrum, the Bidirectional Adversarial Autoencoder is able to generate structures that provide multiple peaks on several occasions. As a result the proposed model represents an important advance towards the generation of nonlinear photonic metasurfaces that can be used in advanced metasurface design. © 2024, CC BY.

关键词： Design

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A Framework for Blockchain and Fogging-based Efficient Authentication in Internet of Things 2

A Framework for Blockchain and Fogging-based Efficient Authe...

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2nd International Conference on computing and information technology, ICCIT 2022

作者： Alsaeed, Norah Nadeem, Farrukh King Khalid University Department of Computer Science Abha Saudi Arabia King Abdulaziz University Faculty of Computing and Information Technology Department of Information Systems Jeddah Saudi Arabia

ISBN: (纸本)9781665436052

IoT paradigm has laid the foundation to interconnect billions of objects for gathering, storing, and analyzing data. In an IoT complex ecosystem, maintaining the security and privacy of objects as well as data becomes a critical issue. Efficient authentication of IoT objects to each other is restricted by resource-constrained IoT objects. A recent approach to authentication is to integrate blockchain technology, fog computing to develop a lightweight, robust authentication framework for IoT systems. Several works have adopted this integration to improve their authentication solutions for IoT systems. This paper aims to present a review of the authentication frameworks that integrate blockchain and fog computing technologies. In light of the review conclusions, the paper proposes a fogging-based authentication framework using blockchain to enhance IoT system's security, and scalability. The proposed framework employs fog nodes to support IoT devices with close computation and storage resources. Moreover, it adopts multi-level blockchain platforms to support cross-domain authentication among IoT systems. Finally, the paper discusses the trade-offs of adopting blockchain and fogging-based authentication frameworks in IoT and shows how the proposed framework could balance those trade-offs. © 2022 IEEE.

关键词： Fog computing

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QH2O: Energy Efficient Task Scheduling Using Quasi Reflected Harris Hawks Algorithm in Fog Environment 9th

QH2O: Energy Efficient Task Scheduling Using Quasi Reflected...

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9th International Conference on Frontiers in Intelligent computing: Theory and Applications, FICTA 2021

作者： Barik, Lalbihari Patra, Sudhansu Shekhar Mukherjee, Pratyusa Mohanty, Jnyana Ranjan Barik, Rabindra Kumar Department of Information Systems Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Rabigh Saudi Arabia School of Computer Applications KIIT Deemed to be University Bhubaneswar India

ISBN: (纸本)9789811666155

The volume of the data is increasing exponentially due to the enourmous growth of Industrial Internet of Things (IIoT), digitization with advancement of wireless technologies. Cloud computing was evolved as a technological solution for handling and processing voluminous data. With cloud computing, the next technological advancement known as fog computing is an emerging technology that helps manage a massive volume of IoTs data by delivering their services as well as applications toward nearer to the network edge. There is a rise concern for improving the Quality of Services (QoS) provided to the users all through cloud computing infrastructure. Transferring and communicating all the desired data to the cloud layer and moving back from the cloud layer affect the high latency as well as necessitate high amount of network bandwidth. To consider this situation, we considered the fog computing strategies which are assisting the cloud infrastructure for IIoT. In specific IIoT applications/services, there are sufficient amount of energy needed in the fog computing layer, which is one of the urging area for handling energy minimization scheme by tmany cloud computing service providers. Apart from energy minimization schme, Task scheduling is also crucial factor that adds toward the energy consumptions in fog computing servers. The present research paper proposed an energy-saving task scheduling algorithm based on a meta-heuristic named quasi reflected Harris Hawks optimization technique to achieve energy conservation by attaining the QoS. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fog computing

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DualOpt: A Dual Divide-and-Optimize Algorithm for the Large-scale Traveling Salesman Problem

arXiv

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arXiv 2025年

作者： Zhou, Shipei Ding, Yuandong Zhang, Chi Cao, Zhiguang Jin, Yan School of Computer Science Huazhong University of Science and Technology China School of Computing and Information Systems Singapore Management University Singapore

This paper proposes a dual divide-and-optimize algorithm (DualOpt) for solving the large-scale traveling salesman problem (TSP). DualOpt combines two complementary strategies to improve both solution quality and computational efficiency. The first strategy is a grid-based divide-and-conquer procedure that partitions the TSP into smaller sub-problems, solving them in parallel and iteratively refining the solution by merging nodes and partial routes. The process continues until only one grid remains, yielding a high-quality initial solution. The second strategy involves a path-based divide-and-optimize procedure that further optimizes the solution by dividing it into sub-paths, optimizing each using a neural solver, and merging them back to progressively improve the overall solution. Extensive experiments conducted on two groups of TSP benchmark instances, including randomly generated instances with up to 100,000 nodes and real-world datasets from TSPLIB, demonstrate the effectiveness of DualOpt. The proposed DualOpt achieves highly competitive results compared to 10 state-of-the-art algorithms in the literature. In particular, DualOpt achieves an improvement gap up to 1.40% for the largest instance TSP100K with a remarkable 104x speed-up over the leading heuristic solver LKH3. Additionally, DualOpt demonstrates strong generalization on TSPLIB benchmarks, confirming its capability to tackle diverse real-world TSP applications. © 2025, CC0.

关键词： Traveling salesman problem

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Sea Cense - A Commercially Valued Sea Cucumber Analyzer 5

Sea Cense - A Commercially Valued Sea Cucumber Analyzer

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5th International Conference on Advancements in computing, ICAC 2023

作者： Hettiarachchi, Thavishi Herath, Gagani Jayasekara, Kasuni Thushamini, Malmi Attanayaka, Buddhima Gamage, Narmada Nishanthan, G. Dissanayake, D.C.T. SLIIT Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka SLIIT Faculty of Computing Department of Information Technology Malabe Sri Lanka South Easetern University Faculty of Technology Department of BST Oluvil Sri Lanka University of Sri Jayawardanapura Department of Zoology Nugegoda Sri Lanka

ISBN: (纸本)9798350358131

In Sri Lanka, sea cucumbers are a highly sought-after marine product that is traded abroad. It has gained significant attention in recent years due to its high demand in the seafood market and its medicinal properties. Yet, the Sri Lankan Sea cucumber industry has not been able to fully realize its potential due to barriers like inaccurate methods for identifying and predicting the quality of sea cucumber species and the inability to provide a proper market value. This study offers an innovative solution that automates the evaluation and classification of sea cucumbers utilizing advanced computer vision and machine learning approaches. The proposed analyzer incorporates a multi-step approach, including image preprocessing, feature extraction, and classification. A set of preprocessed images are used to extract textural and distinguished color-based characteristics. These characteristics are then used to accurately classify sea cucumber species and grade them according to quality criteria using a machine learning algorithm. This project introduces a novel approach to analyzing sea cucumber length to calculate the price using a mobile application, making the task more efficient and accessible. The proposed solution 'Sea Cense' comprises 4 models that are developed using the CNN algorithm. These models' experimental findings demonstrated accuracy levels exceeding 85%. © 2023 IEEE.

关键词： Forecasting

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Motive Metrics: A Jira Plug-In for Personality, Motivation and Performance Tracking

Motive Metrics: A Jira Plug-In for Personality, Motivation a...

引用

IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： An Cao Jie Xiang Fan Akash Saggar Kunj Dave Sharan Sharabinth Jiten Verma Dulaji Hidellaarachchi John Grundy Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Melbourne Australia

Requirements Engineering (RE) is an essential part of software Engineering (SE). As RE activities rely heavily on people, the success of RE is influenced by the human aspects of the team involved. In this research, we develop a prototype application, called Motive Metrics, to improve RE activities by allowing managers to track developers’ personality and motivation and monitor their impact on developer performance and satisfaction. The tool takes the form of an extension to Jira and was developed through rapid prototyping. The effectiveness and usability of the tool were evaluated by student teams split into managers and team members. When evaluating Motive Metrics, 45.5% of participants rated 4 out of 5 for the application’s effectiveness in capturing personalities, but only 9.1% of participants rated 4 out of 5 for capturing motivations. Motive Metrics is likely ineffective in monitoring satisfaction and performance, as 45% of participants rated 1 out of 5 in comfort in sharing their responses. Our evaluation results also show that Motive Metrics might not be beneficial in tracking the influence of motivation on the outcome but slightly more beneficial in tracking the influence of personality on RE task performance.

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Literature Review: Cyber Security Monitoring in Maritime

arXiv

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arXiv 2025年

作者： Vaarandi, Risto Tsiopoulos, Leonidas Visky, Gabor Rehman, Muaan Ur Bahşi, Hayretdin Department of Software Science Tallinn University of Technology Tallinn Estonia School of Informatics Computing and Cyber Systems Northern Arizona University United States

In recent years, many cyber incidents have occurred in the maritime sector, targeting the information technology (IT) and operational technology (OT) infrastructure. Although several literature review papers have been published in the maritime field, none of the previous studies has focused on cyber security monitoring, which aims at timely detection of cyber attacks with automated methods. The current article addresses this research gap and surveys the methods, algorithms, tools and architectures used for cyber security monitoring in the maritime sector. For the survey, a systematic literature review of cyber security monitoring studies is conducted. The first contribution of this article is the bibliometric analysis of related literature and the identification of the main research themes in previous works. For that purpose, our article presents a taxonomy for existing studies which highlights the main properties of maritime cyber security monitoring research. The second contribution of this article is an in-depth analysis of previous works and the identification of research gaps and limitations in existing literature. Based on our findings, we outline future research directions for cyber security monitoring in the maritime field. © 2025, CC BY.

关键词： Network intrusion

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Practical fixed-time adaptive fuzzy control of uncertain nonlinear systems with time-varying asymmetric constraints: a unified barrier function based approach

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Frontiers of information technology & Electronic Engineering 2024年第9期25卷 1282-1294页

作者： Zixuan HUANG Huanqing WANG Ben NIU Xudong ZHAO Adil M.AHMAD College of Engineering Bohai UniversityJinzhou 121013China College of Mathematical Science Bohai UniversityJinzhou 121013China School of Information Science and Engineering Shandong Normal UniversityJinan 250014China Faculty of Electronic Information and Engineering Dalian University of TechnologyDalian 116024China Communication Systems and Networks Research Group Department of Information TechnologyFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia

A practical fixed-time adaptive fuzzy control strategy is investigated for uncertain nonlinear systems with time-varying asymmetric constraints and input quantization. To overcome the difficulties of designing controllers under the state constraints, a unified barrier function approach is employed to construct a coordinate transformation that maps the original constrained system to an equivalent unconstrained one, thus relaxing the time-varying asymmetric constraints upon system states and avoiding the feasibility check condition typically required in the traditional barrier Lyapunov function based control approach. Meanwhile, the “explosion of complexity” problem in the traditional backstepping approach arising from repeatedly derivatives of virtual controllers is solved by using the command filter method. It is verified via the fixed-time Lyapunov stability criterion that the system output can track a desired signal within a small error range in a predetermined time, and that all system states remain in the constraint range. Finally, two simulation examples are offered to demonstrate the effectiveness of the proposed strategy.

关键词： Unified barrier function Time-varying asymmetric state constraints Fuzzy logic systems Fixed-time control Command filter

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Effectiveness of Using Deep Learning for Blister Blight Identification in Sri Lankan Tea

Effectiveness of Using Deep Learning for Blister Blight Iden...

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2023 International Research Conference on Smart computing and systems Engineering, SCSE 2023

作者： Hewawitharana, G.H.A.U. Nawarathne, U.M.L.A. Hassan, A.S.F. Wijerathna, Lochana M. Sinniah, G.D. Vidhanaarachchi, Samitha P. Wickramarathne, Jagath Wijekoon, Janaka L. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Tea Research Institute of Sri Lanka Plant Pathology Division Talawakelle Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Military Technological College Systems Engineering Department Muscat Oman

ISBN: (纸本)9798350341454

Ceylon tea industry faces a major challenge in the form of pathogen-induced crop loss, with Blister Blight (BB) caused by Exobasidium vexans posing the greatest threat, leading to harvest losses of over 30%. This fungus attacks the tender tea shoots, resulting in a direct negative impact on the tea harvest. This paper presents a system to identify the suspicious tea leaves and BB disease at its early stages along with an assessment of severity, offering a potential solution to this critical issue. By utilizing real-time object detection, the system filters out non-tea leaves from the captured initial image of a segment of a tea plant. The identified tea leaves are then subjected to BB identification and severity assessment based on differing visual symptoms of the BB stages. This approach enables the system to accurately identify BB in the initial stage and severity stage, allowing for timely and targeted intervention to minimize crop losses. The YOLOv8 model has been able to correctly identify 98% of the objects it has detected as relevant (precision), and it has been able to correctly identify 96% of all the relevant objects present in the scene (recall). The Residual Network 50 (Resnet50) convolutional neural network (CNN) model was selected as the final model, achieving an accuracy of 89.90% during the training phase and an accuracy of 88.26% during the testing phase. © 2023 IEEE.

关键词： Convolutional neural networks

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