| 1. |
- Elahi, Haroon, et al.
(författare)
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A qualitative study of app acquisition and management
- 2023
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Ingår i: IEEE Transactions on Computational Social Systems. - : IEEE. - 2329-924X. ; , s. 1-19
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Tidskriftsartikel (refereegranskat)abstract
- Smartphone users rely on Apps for their daily lives but simultaneously struggle to protect their privacy and device security from potentially harmful and malicious Apps. However, scientific literature lacks in-depth studies mapping user struggles, factors undermining their efforts, and implications. We cover this gap by engaging 24 smartphone users in 44 interview sessions. We observe them performing different App acquisition and management tasks, seek explanations, and analyze collected data to make the following contributions. First, we develop a theoretical App acquisition and management model describing different phenomena involved in App acquisition and management in Android smartphones. Causal conditions of these phenomena and contexts, and intervening conditions influencing user strategies are discovered grounded in the data acquired through the interview sessions. It shows the challenges they face, the strategies they develop and use to deal with the faced challenges, and their consequences. Second, we systematically discover and relate different App acquisition and management concepts in 34 subcategories related to user struggles. None of the existing studies discovers, explains, and relates actual user behaviors involving this many factors in one place. Third, this research discovers six problems unaddressed by the literature: the usage of untrusted App repositories, mandatory and forced installations, the installation process changes, the Settings App complexities, the void contracts problem, and the psychological consequences of failure to protect privacy in Android phones. Finally, we provide general guidelines for users, App stores, developers, and regulators to assist them in enhancing privacy and security protection in the Android ecosystem.
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| 2. |
- Groen, D., et al.
(författare)
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Large-Scale Parallelization of Human Migration Simulation
- 2024
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Ingår i: IEEE Transactions on Computational Social Systems. - 2329-924X. ; 11:2, s. 2135-2146
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Tidskriftsartikel (refereegranskat)abstract
- Forced displacement of people worldwide, for example, due to violent conflicts, is common in the modern world, and today more than 82 million people are forcibly displaced. This puts the problem of migration at the forefront of the most important problems of humanity. The Flee simulation code is an agent-based modeling tool that can forecast population displacements in civil war settings, but performing accurate simulations requires nonnegligible computational capacity. In this article, we present our approach to Flee parallelization for fast execution on multicore platforms, as well as discuss the computational complexity of the algorithm and its implementation. We benchmark parallelized code using supercomputers equipped with AMD EPYC Rome 7742 and Intel Xeon Platinum 8268 processors and investigate its performance across a range of alternative rule sets, different refinements in the spatial representation, and various numbers of agents representing displaced persons. We find that Flee scales excellently to up to 8192 cores for large cases, although very detailed location graphs can impose a large initialization time overhead.
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| 3. |
- Huang, Haiping, et al.
(författare)
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An Efficient Signature Scheme Based on Mobile Edge Computing in the NDN-IoT Environment
- 2021
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Ingår i: IEEE Transactions on Computational Social Systems. - : IEEE. - 2329-924X. ; 8:5, s. 1108-1120
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Tidskriftsartikel (refereegranskat)abstract
- Named data networking (NDN) is an emerging information-centric networking paradigm, in which the Internet of Things (IoT) achieves excellent scalability. Recent literature proposes the concept of NDN-IoT, which maximizes the expansion of IoT applications by deploying NDN in the IoT. In the NDN, the security is built into the network by embedding a public signature in each data package to verify the authenticity and integrity of the content. However, signature schemes in the NDN-IoT environment are facing several challenges, such as signing security challenge for resource-constrained IoT end devices (EDs) and verification efficiency challenge for NDN routers. This article mainly studies the data package authentication scheme in the package-level security mechanism. Based on mobile edge computing (MEC), an efficient certificateless group signature scheme featured with anonymity, unforgeability, traceability, and key escrow resilience is proposed. The regional and edge architecture is utilized to solve the device management problem of IoT, reducing the risks of content pollution attacks from the data source. By offloading signature pressure to MEC servers, the contradiction between heavy overhead and shortage of ED resources is avoided. Moreover, the verification efficiency in NDN router is much improved via batch verification in the proposed scheme. Both security analysis and experimental simulations show that the proposed MEC-based certificateless group signature scheme is provably secure and practical.
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| 4. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Behavioral Modeling and Prediction in Social Perception and Computing : A Survey
- 2023
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Ingår i: IEEE Transactions on Computational Social Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2329-924X. ; 10:4, s. 2008-2021
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Tidskriftsartikel (refereegranskat)abstract
- More data are generated through interaction between cyber space, physical space, and social space thanks to mobile network technology, giving birth to the so-called cyber–physical social intelligent ecosystem (C&P-SIE). This survey studies the development of physical social intelligence. First, it classifies and discusses the behavior modeling, learning, and adaptation applications of C&P-SIE from intelligent transportation, healthcare, public service, economy, and social networking. Then, it prospects the application of behavior modeling in the C&P-SIE from the perspectives of information security, data-driven techniques, and modeling learning under cooperative artificial intelligence technologies. The research provides a theoretical basis and new opportunities for the digital and intelligent development of smart cities and social systems. IEEE
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| 5. |
- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Cognitive Computing for Brain-Computer Interface-Based Computational Social Digital Twins Systems
- 2022
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Ingår i: IEEE Transactions on Computational Social Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2329-924X. ; 9:6, s. 1635-1643
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Tidskriftsartikel (refereegranskat)abstract
- To accurately and effectively analyze electroencephalogram (EEG) with high complexity, large amount of data, and strong uncertainty, brain-computer interface (BCI) cognitive computing and its signal analysis algorithms are studied based on the digital twins (DTs) cognitive computing platform. To avoid the influence of noise on EEG analysis results, it is necessary to use filtering and defalsification methods to process EEG. Four methods, including Butterworth filter, finite impulse response (FIR) filter, elliptic filter, and wavelet decomposition, are summarized. Based on the Riemann manifold theory, a feature extraction algorithm under transfer learning based on tangent space selection (TL-TSS) is proposed. In the process of decoding EEG, an EEG decoding method combining entropy measure and singular spectrum analysis (SSA) is proposed. An algorithm performance is tested on the motor imagery dataset of the two International BCI Competitions. It is found that when the training sample size accounts for 5%, the TL-TSS algorithm proposed in this work is superior to other algorithms in classification accuracy. In particular, compared with common spatial pattern (CSP) algorithm, it has great advantages. The classification accuracy of A2, A4, A8, and A9 users is the best, and especially for A8 users, the classification accuracy reaches 97.88%. In summary, in the EEG interface technology of DT cognitive computing platform, the combination of cognitive computing and deep learning can improve the recognition and analysis effect of EEG, which is of great value for further optimization of DT cognitive computing system.
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| 6. |
- Mercuur, Rijk, et al.
(författare)
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Integrating Social Practice Theory in Agent-Based Models : A Review of Theories and Agents
- 2020
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Ingår i: IEEE Transactions on Computational Social Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2329-924X. ; 7:5, s. 1131-1145
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Forskningsöversikt (refereegranskat)abstract
- Evidence-driven agent-based modeling plays a useful part in understanding social phenomena. By integrating social-cognitive theories in our agent models, we bear evidence from social and psychological studies on our models for human decision-making. Social practice theory (SPT) provides a socio-cognitive theory that emphasizes three empirically and theoretically grounded aspects of behavior: habituality, sociality, and interconnectivity. Previous work has emphasized the importance of SPT for agents, has made abstract models of SPT, or used SPT to study energy systems. This article provides a set of requirements for integrating SPT in agent models and an evaluation of 11 current agent models with respect to these requirements. We find that current agent models do not fully capture habituality, sociality, or interconnectivity, nor is there a model that aims to integrate all three aspects. For example, current models do not support context-dependent habits, use a comprehensive set of collective concepts, and support hierarchies of activities. Our evaluation allows researchers to pick one of the current agent models depending on their needs regarding habituality, sociality, and interconnectivity. Furthermore, this article shows the usefulness of an agent model that integrates SPT and provides requirements that help modelers to achieve this model.
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| 7. |
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| 8. |
- Pandya, Sharnil, Researcher, 1984-, et al.
(författare)
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InfusedHeart : A Novel Knowledge-Infused Learning Framework for Diagnosis of Cardiovascular Events
- 2022
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Ingår i: IEEE Transactions on Computational Social Systems. - : IEEE. - 2329-924X .- 2373-7476. ; , s. 1-10
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Tidskriftsartikel (refereegranskat)abstract
- In the undertaken study, we have used a customized dataset termed "Cardiac-200'' and the benchmark dataset ``PhysioNet.'' which contains 1500 heartbeat acoustic event samples (without augmentation) and 1950 samples (with augmentation) heartbeat acoustic events such as normal, murmur, extrasystole, artifact, and other unlabeled heartbeat acoustic events. The primary reason for designing a customized dataset, "cardiac-200,'' is to balance the total number of samples into categories such as normal and abnormal heartbeat acoustic events. The average duration of the recorded heartbeat acoustic events is 10-12 s. In the undertaken study, we have analyzed and evaluated various heartbeat acoustic events using audio processing libraries such as Chromagram, Chroma-cq, Chroma-short-time Fourier transform (STFT), Chroma-cqt, and Chroma-cens to extract more information from the recorded heartbeat sound signals. The noise removal process has been carried out using local binary pattern (LBP) methodology. The noise-robust heartbeat acoustic images are classified using long short-term memory (LSTM)-convolutional neural network (CNN), recurrent neural network (RNN), LSTM, Bi-LSTM, CNN, K-means Clustering, and support vector machine (SVM) methods. The obtained results have shown that the proposed InfusedHeart Framework had outclassed all the other customized machine learning and deep learning approaches such as RNN, LSTM, Bi-LSTM, CNN, K-means Clustering, and SVM-based classification methodologies. The proposed Knowledge-infused Learning Framework has achieved an accuracy of 89.36% (without augmentation), 93.38% (with augmentation), and a standard deviation of 10.64 (without augmentation), and 6.62 (with augmentation). Furthermore, the proposed framework has been tested for various signal-to-noise ratio conditions such as SignaltoNoiseRatio0, SignaltoNoiseRatio3, SignaltoNoiseRatio6, SignaltoNoiseRatio9, SignaltoNoiseRatio12, SignaltoNoiseRatio15, and SignaltoNoiseRatio18. In the end, we have shown a detailed comparison of texture and without texture approaches and have discussed future enhancements and prospective ways for future directions.
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| 9. |
- Rout, Jitendra Kumar, et al.
(författare)
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Understanding large-scale network effects in detecting review spammers
- 2023
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Ingår i: IEEE Transactions on Computational Social Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2329-924X.
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Tidskriftsartikel (refereegranskat)abstract
- Opinion spam detection is a challenge for online review systems and social forum operators. Opinion spamming costs businesses and people money since it deceives customers as well as automated opinion mining and sentiment analysis systems by bestowing undeserved positive opinions on target firms and/or bestowing fake negative opinions on others. One popular detection approach is to model a review system as a network of users, products, and reviews, for example using review graph models. In this article, we study the effects of network scale on network-based review spammer detection models, specifically on the trust model and the SpammerRank model. We then evaluate both network models using two large publicly available review datasets, namely: the Amazon dataset (containing 6 million reviews by more than 2 million reviewers) and the UCSD dataset (containing over 82 million reviews by 21 million reviewers). It has been observed thatSpammerRank model provides a better scaling time for applications requiring reviewer indicators and in case of trust model distributions are flattening out indicating variance of reviews with respect to spamming. Detailed observations on the scaling effects of these models are reported in the result section.
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| 10. |
- Wang, J., et al.
(författare)
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Fake News in Virtual Community, Virtual Society, and Metaverse : A Survey
- 2023
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Ingår i: IEEE Transactions on Computational Social Systems. - : Institute of Electrical and Electronics Engineers Inc.. - 2329-924X. ; , s. 1-15
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Tidskriftsartikel (refereegranskat)abstract
- In the trend of the accelerated progression of communication network technology, the emergence of virtual communities (VCs), virtual societies (VSs), metaverse, and other technologies not only makes data access and sharing easier but also leads to the proliferation of fake news (FN). To effectively monitor and identify FN in VC, VS, and metaverse, and to create a safer virtual space, this work takes FN in VC, VS, and metaverse as objects. First, the content and display methods of FN are reviewed and explained, and it is understood that FN is mainly displayed by single-modal and multimodal representations. Second, the application scenarios in many important fields such as transportation are reviewed and analyzed, so as to further understand the impact and detection effect of FN in different scenarios. Finally, an intelligent outlook and summary analysis are carried out on the detection and information security of FN, which provides theoretical reference and new opportunities for the detection and identification of FN in the virtual cyberspace. IEEE
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