@article {Iraqi2022,
	title = {Communizer: A collaborative cloud-based self-protecting software communities framework - Focus on the alert coordination system},
	journal = {Computers and Security},
	volume = {117},
	year = {2022},
	note = {cited By 0},
	abstract = {Popular software has always been appealing to adversaries, as related vulnerabilities are synonymous with millions of exposed businesses. Collaborative intrusion detection, as well as software self-protection, try to alleviate this situation. However, they lack either autonomy and adaptation, or Internet-scale oversight and mitigation. In this work, we present Communizer: a collaborative cloud-based framework that creates communities of self-protecting software across organizations. It allows community members to turn their common weaknesses into collaborative and proactive self-protection, empowering them to detect intrusions, exchange alerts, and anticipate attacks. We start by integrating multiple autonomic MAPE-K loops through cloud-based coordination, and a novel hierarchical, regional coordination pattern (HRCP), optimizing scalability, resiliency, accuracy and privacy. Then, we design a trust-based multi-level alert coordination system (TMACS), as well as a lightweight alert coordination message exchange format (ACMEF). At its core, TMACS aggregates, validates, and shares security alerts among community members while fostering agreement and managing trust. It also addresses insider attacks by detecting and blacklisting rogue members. Moreover, TMACS identifies and neutralizes selfish members through a specifically designed probabilistic model. The analysis, optimization, and evaluation of TMACS show a good trade-off between the precision and recall of untrustworthy and selfish members detection. More importantly, we demonstrate a drastic reduction of monitoring loads on community members while ensuring a high collaborative attack detection and anticipation rate, even for small-scope attacks. {\textcopyright} 2022 Elsevier Ltd},
	keywords = {Autonomic Computing, collaboration, Coordination systems, Economic and social effects, Intrusion detection, Mape, MAPE-K, Self protecting, Self-protecting software, Selfishness, Software community, Trust, Trusted computing},
	doi = {10.1016/j.cose.2022.102692},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127747467\&doi=10.1016\%2fj.cose.2022.102692\&partnerID=40\&md5=246bfc07692396ec6709a73d31126fb6},
	author = {Iraqi, O. and Bakkali, H.E.}
}
@article {Wadghiri2022,
	title = {Ensemble blood glucose prediction in diabetes mellitus: A review},
	journal = {Computers in Biology and Medicine},
	volume = {147},
	year = {2022},
	note = {cited By 0},
	abstract = {Considering the complexity of blood glucose dynamics, the adoption of a single model to predict blood glucose level does not always capture the inter- and intra-patients{\textquoteright} context changes. Ensembles are a set of machine learning techniques combining multiple single learners to find a better variance/bias trade-off and hence improve the prediction accuracy. The present paper aims to review the state of the art in predicting blood glucose using ensemble methods with regard to 8 criteria: publication year and sources, datasets used to train/evaluate the models, types of ensembles used, single learners involved to construct ensembles, combination schemes used to aggregate the base learners, metrics and validation methods adopted to assess the performance of ensembles, reported overall performance of the predictors and accuracy comparison of ensemble techniques with single models. A systematic literature review has been conducted in order to analyze and synthetize primary studies published between 2000 and 2020 in six digital libraries. A total of 32 primary papers were selected and reviewed with regard to eight review questions. The results show that ensembles have gained wider interest during the last years and improved in general the performance compared with other single models. However, multiple gaps have been identified concerning the ensembles construction process and the performance metrics used. Several recommendations have been made in this regard to design accurate ensembles for blood glucose level prediction. {\textcopyright} 2022 Elsevier Ltd},
	keywords = {algorithm, Algorithms, Blood, Blood glucose, Blood glucose level, Data mining, Diabetes mellitus, Digital libraries, Economic and social effects, Ensemble methods, Forecasting, Glucose, glucose blood level, Glucose dynamics, human, Humans, Machine learning, Machine learning techniques, Machine-learning, Performance, Single models, Trade off},
	doi = {10.1016/j.compbiomed.2022.105674},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132341147\&doi=10.1016\%2fj.compbiomed.2022.105674\&partnerID=40\&md5=05bd59c3726654494a4be346e4e6a682},
	author = {Wadghiri, M.Z. and Idri, A. and El Idrissi, T. and Hakkoum, H.}
}
@article {Yatribi2020,
	title = {Gradient-descent decoding of one-step majority-logic decodable codes},
	journal = {Physical Communication},
	volume = {39},
	year = {2020},
	note = {cited By 1},
	abstract = {In this paper, a new low-complexity gradient-descent based iterative majority-logic decoder (GD-MLGD) is proposed for decoding One-Step Majority-Logic Decodable (OSMLD) codes. We give a formulation of the decoding problem of binary OSMLD codes, as a maximization problem of a derivable objective function. The optimization problem is solved using a pseudo gradient-descent algorithm, which performs iteratively an update towards the optimal estimated codeword been transmitted, based on the first-order partial derivatives of each variable calculated in the previous iteration. The proposed decoding scheme achieves a fast convergence to an optimum codeword compared to other decoding techniques reviewed in this paper, at the cost of lower computational complexity. The quantized version (QGD-MLGD) is also proposed in order to further reduce the computational complexity. Simulation results show that the proposed decoding algorithms outperform all the existing majority-logic decoding schemes, and also various gradient-descent based bit-flipping algorithms, and performs nearly close to the belief propagation sum{\textendash}product (BP-SP) decoding algorithm of LDPC codes, especially for high code lengths, providing an efficient trade-off between performance and decoding complexity. Moreover, the proposed quantized algorithm has shown to perform better than all the existing decoding techniques. The proposed decoding algorithms have shown to be suitable for ultra reliable, low latency and energy-constrained communication systems where both high performances and low-complexity are required. {\textcopyright} 2020 Elsevier B.V.},
	keywords = {AWGN channel, Backpropagation, Computational complexity, Computer circuits, Decoding complexity, Difference sets, Economic and social effects, Electronic trading, Gradient descent, Gradient methods, Iterative decoding, LDPC codes, Majority logic, Majority logic decoding, Maximum likelihood, Maximum likelihood decoding, Optimization, OSMLD codes},
	doi = {10.1016/j.phycom.2019.100999},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077749482\&doi=10.1016\%2fj.phycom.2019.100999\&partnerID=40\&md5=830156cfb12a8ec3dbff2e002b3ade63},
	author = {Yatribi, A. and Belkasmi, M. and Ayoub, F.}
}
