Optimal estimation of low-rank factors via feature level data fusion of multiplex signal systems

Li, Hui-Jia; Wang, Zhen; Pei, Jian; Cao, Jie; Shi, Yong

doi:10.1109/tkde.2020.3015914

Cited by 53 publications

(53 citation statements)

References 38 publications

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“…The hybrid analysis approach involves the extraction of integrated features by combining any of the other malware analysis approaches to detect and classify malware. The combinations of static and dynamic features improve detection performance [24] [47]. [19] combined static features such as function length-frequency vectors and printable string information vectors and dynamic API features as a single vector.…”

Section: Hybrid Analysismentioning

confidence: 99%

Intelligent Vision-Based Malware Detection and Classification Using Deep Random Forest Paradigm

et al. 2020

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Malware is a rapidly increasing menace to modern computing. Malware authors continually incorporate various sophisticated features like code obfuscations to create malware variants and elude detection by existing malware detection systems. The classification of unseen malware variants with similar characteristics into their respective families is a significant challenge, even if the classifier is trained with known variants belonging to the same family. The identification and extraction of distinct features for each malware is another issue for generalizing the malware detection system. Features that contribute to the generalization capability of the classifier are difficult to be engineered with modifications in each malware. Conventional malware detection systems employ static signature-based methods and dynamic behavior-based methods, which are inefficient in analyzing and detecting advanced and zero-day malware. To address these issues, this work employs a visualization approach where malware is represented as 2D images and proposes a robust machine learning-based anti-malware solution. The proposed system is based on a layered ensemble approach that mimics the key characteristics of deep learning techniques but performs better than the latter. The proposed system does not require hyperparameter tuning or backpropagation and works with reduced model complexity. The proposed model outperformed other state-of-the-art techniques with a detection rate of 98.65%, 97.2%, and 97.43% for Malimg, BIG 2015, and MaleVis malware datasets, respectively. The results demonstrate that the proposed solution is effective in identifying new and advanced malware due to its diverse features.

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Section: Hybrid Analysismentioning

confidence: 99%

Intelligent Vision-Based Malware Detection and Classification Using Deep Random Forest Paradigm

et al. 2020

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“…How to establish a mutually beneficial mechanism between the manufacturer and the retailer on the allocation of the profit from the online channel is a future research direction. Furthermore, we can study the impact of competition between online and offline sales on inventory in dual-channel supply chain from the perspective of the game theory [21][22][23].…”

Section: Resultsmentioning

confidence: 99%

Investigating Inventory Strategy Under Demand Distribution Fluctuation in Dual-Channel Supply Chain

Cheng

Cai

2021

Front. Phys.

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When manufacturers construct a dual-channel distribution system, which includes online and offline sales channels, they need to solve the inventory management problem to ensure supply and reduce inventory costs of the supply chain system. The dual-channel supply chain is the research object, and the inventory decision model is designed to achieve optimal profit when market demand is divided into online and offline demands. The results of the numerical analysis and simulations, conducted using MATLAB, indicate that both the manufacturer and the retailer increase their inventories and that their profits decrease when demand uncertainty increases. Besides, the increase in the online demand ratio causes the increase in the manufacturer’s inventory and reduces the profits of the retailer and the entire supply chain.

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“…First, our approach has evolved through experience in the Recovery Research Team, and is not based on empirical evaluation. Recent advances in data science provide novel approaches to evaluating frameworks for optimising system performance and leadership through game theory [ 44 ], for example using fusion engines [ 45 ]. Future research might model the impact of varying key parameters for our approach on performance, e.g., number of papers, proportion of papers in higher impact factor journals, mean citation percentile for papers assessed using InCites reports.…”

Section: Discussionmentioning

confidence: 99%

Management of a High-Performing Mental Health Recovery Research Group

Slade

2021

IJERPH

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A personal perspective is given on the processes involved in managing and sustaining a high-performing mental health recovery research group. The broader context of scholarship in the United Kingdom is outlined, in which academic productivity is commodified specifically in relation to peer-reviewed journal papers. Four leadership choices in developing a high-performing research group are discussed: optimal group size; sharing the workload; maintaining a programmatic focus; and performance expectations. Approaches to maximising innovation are identified, including emotional and intellectual engagement of team members, working with diverse stakeholders and convening communities of practice. We use a highly managed approach to publications from inception to acceptance, which is described in detail. The use of these approaches is illustrated in relation to the Recovery Research Team which was formed in 2009. Specific recovery-related issues covered include demonstrating the ability to develop a significant recovery research portfolio (our four current large [>UK£2 m] studies relate to recovery narratives, global mental health peer support work, digital interventions and Recovery Colleges); the positive implications of actively recruiting researchers with mental health lived experience; how performance issues are managed; our approach to involving lived experience co-authors in papers; and our decision to conduct mixed-methods rather than solely qualitative studies.

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Optimal estimation of low-rank factors via feature level data fusion of multiplex signal systems

Cited by 53 publications

References 38 publications

Intelligent Vision-Based Malware Detection and Classification Using Deep Random Forest Paradigm

Intelligent Vision-Based Malware Detection and Classification Using Deep Random Forest Paradigm

Investigating Inventory Strategy Under Demand Distribution Fluctuation in Dual-Channel Supply Chain

Management of a High-Performing Mental Health Recovery Research Group

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