MEMPERKUAT NALAR TEOLOGI ISLAM MODERAT DALAM MENYIKAPI PANDEMI COVID-19 DI PIMPINAN RANTING PEMUDA MUHAMMADIYAH BANDAR PULAU PEKAN

Religion for some people in the world is a guideline for determining the direction to be directed in their life. In life in this world, the most important thing is a peace of mind and body in society. In keeping up with the current changes, a good reasoning process is urgently needed as a solution to addressing this to break the chain of the spread of the Covid-19 outbreak. Moderate Islam is a teaching that is able to keep up with the times and does not abandon the teachings afterwards. So that Moderate Islam is a religion that is able to balance the movement of changes in life in society. The understanding of moderate Islamic values is currently being eroded by the changing times, so that many young people tend to be pragmatic, exclusive and intolerant in responding to the situation and conditions of the Covid-19 pandemic which is spreading so rapidly today

Leveraging Decentralized Artificial Intelligence to Enhance Resilience of Energy Networks

This paper reintroduces the notion of resilience in the context of recent issues originated from climate change triggered events including severe hurricanes and wildfires. A recent example is PG&E's forced power outage to contain wildfire risk which led to widespread power disruption. This paper focuses on answering two questions: who is responsible for resilience? and how to quantify the monetary value of resilience? To this end, we first provide preliminary definitions of resilience for power systems. We then investigate the role of natural hazards, especially wildfire, on power system resilience. Finally, we will propose a decentralized strategy for a resilient management system using distributed storage and demand response resources. Our proposed high fidelity model provides utilities, operators, and policymakers with a clearer picture for strategic decision making and preventive decisions

Sparsity-Based Error Detection in DC Power Flow State Estimation

This paper presents a new approach for identifying the measurement error in the DC power flow state estimation problem. The proposed algorithm exploits the singularity of the impedance matrix and the sparsity of the error vector by posing the DC power flow problem as a sparse vector recovery problem that leverages the structure of the power system and uses $l_1$-norm minimization for state estimation. This approach can provably compute the measurement errors exactly, and its performance is robust to the arbitrary magnitudes of the measurement errors. Hence, the proposed approach can detect the noisy elements if the measurements are contaminated with additive white Gaussian noise plus sparse noise with large magnitude. The effectiveness of the proposed sparsity-based decomposition-DC power flow approach is demonstrated on the IEEE 118-bus and 300-bus test systems

Privacy Risks Analysis and Mitigation in Federated Learning for Medical Images

Federated learning (FL) is gaining increasing popularity in the medical domain for analyzing medical images, which is considered an effective technique to safeguard sensitive patient data and comply with privacy regulations. However, several recent studies have revealed that the default settings of FL may leak private training data under privacy attacks. Thus, it is still unclear whether and to what extent such privacy risks of FL exist in the medical domain, and if so, "how to mitigate such risks?". In this paper, first, we propose a holistic framework for Medical data Privacy risk analysis and mitigation in Federated Learning (MedPFL) to analyze privacy risks and develop effective mitigation strategies in FL for protecting private medical data. Second, we demonstrate the substantial privacy risks of using FL to process medical images, where adversaries can easily perform privacy attacks to reconstruct private medical images accurately. Third, we show that the defense approach of adding random noises may not always work effectively to protect medical images against privacy attacks in FL, which poses unique and pressing challenges associated with medical data for privacy protection.Comment: V