A document by Luis Hernandez Corporales . Click on the document to view its contents.

Sustainable resource management within a cloud computing environment is a highly critical and prominently studied research topic. In this context, this paper presented a comprehensive survey of potential sustainable resource management (Sus-RM) strategies that have addressed the energy optimization challenges during cloud workload scheduling and resource management. The perspective of sustainable cloud resource management followed by a discussion of intended motivation, challenges, objectives, and approaches is manifested. The designed research methodology with the proposed method-centric classification and taxonomy of Sus-RM approaches is conferred. Based on the common features of managing sustainability dealing with common operations including task scheduling, virtual machine (VM) placement, and VM rescheduling or migration, the Sus-RM strategies are further grouped into a common class or category. The concept behind each methodbased approach and respective state-of-the-art strategies belonging to each category are concisely discussed with their pandect comparative summary. Besides, conceptual and theoretical analysis, the critical takeaways and lessons learned outlining each method are presented. Further, the trade-off among the leading strategies is capsuled and discussed respectively to their critical features to put forward imperative concluding remarks about the holistic study of cloud Sus-RM. Finally, the scientific survey study is concluded with insightful and concrete future research directions.

Human Abnormal Behavior Detection is needed in several important sectors, especially in the field of public safety. Video Surveillance Systems are a trigger in detecting strange human behavior. However, this abnormal behavior data is rare and requires more costs to obtain, so the unsupervised learning method is used to study normal human behavior patterns only. There have been several previous studies that have provided significant results using this technique. There have been several previous survey studies related to human abnormal behavior detection, but they did not focus on unsupervised learning methods and the scope was too broad. In this paper, I present a survey regarding human abnormal behavior detection in more depth conceptually and classify it into two large parts, such as reconstruction-based detection and generative-based detection. This paper also offers a more extensive comparison with several popular datasets that are often used in previous research. Finally, I also shared several challenges and open research issues that emerged from our survey regarding future directions for future development.

Task-oriented semantic communication is an emerging technology that transmits only the relevant semantics of a message instead of the whole message to achieve a specific task. It reduces latency, compresses the data, and is more robust in low SNR scenarios. This work presents a multitask-oriented semantic communication framework for connected and autonomous vehicles (CAVs). We propose a convolutional autoencoder (CAE) that performs the semantic encoding of the road traffic signs. These encoded images are then transmitted from one CAV to another CAV through satellite in challenging weather conditions where visibility is impaired. In addition, we propose task-oriented semantic decoders for image reconstruction and classification tasks. Simulation results show that the proposed framework outperforms the conventional schemes, such as QAM-16, regarding the reconstructed image's similarity and the classification's accuracy. In addition, it can save up to 89% of the bandwidth by sending fewer bits.

Machine learning models are increasingly vital for clinical decision making. However, the integration of these models into health information systems (HISs) poses significant challenges, mainly due to the disparate formats of electronic health records (EHR) in various healthcare facilities. In response to this challenge, we proposed Model Cabinet Architecture (MoCab), a framework designed to leverage fast healthcare interoperability resources (FHIR) as the standard for data storage and retrieval when deploying machine learning models across various HISs. MoCab simplifies the deployment process by importing and configuring saved prediction models, facilitating patient data retrieval from FHIR servers, and transmitting it to the prediction model for analysis. The framework further incorporates Clinical Decision Support (CDS) Hooks for issuing clinical alerts and uses Substitutable Medical Apps Reusable Technologies (SMART) on FHIR to develop Apps for displaying laboratory results. Finally, MoCab offers the ability to continuously fine-tune and enhance the performance of deployed models over time. We demonstrate MoCab's efficacy through the successful integration of three model types, highlighting its potential in streamlining decision making amidst heterogeneous EHRs. Our proposed MoCab framework not only promotes the reusability of machine learning models across multiple EHRs but also contributes to improving clinical decision making, offering a promising solution to the challenges of integrating machine learning models into healthcare settings.

Bengali is one of the world's most widely spoken languages. Due to its 49 alphabets, this language poses a number of challenges in Optical Character Recognition (OCR). Identifying handwritten graphemes is one of them. Now, the grapheme root, vowel diacritic, and consonant diacritic are the three components of the Bengali grapheme. Therefore, we address this topic by treating it as a multi-label classification problem. In this study, we used the MobileNet architecture. Also, the model we designed performs really well.

Deep learning (DL) has gained prominence as an effective approach for enhancing the efficiency of various applications including smart grids (SG). Although these models excel significantly in the classification tasks of power quality disturbances, their vulnerability to trojan attacks introduces potential complications. In this paper, we introduce two novel algorithms for executing trojan attacks on DL models handling time series data in SG, tailored for both white-box and black-box. For white-box, our algorithm titled 'Sneaky Spectral Strike (S 3)' utilizes the frequency domain and trigger optimization to perform trojan attacks, which demonstrates a remarkable average fooling rate of 99.9% across various DL models. The algorithm also balances the signal-to-noise ratio, trojan model accuracy on clean data, and fooling rate to be highly effective in fooling DL model and imperceptible to human observers in the power control center (PCC). For black-box, we propose a novel algorithm, 'Lite Datanet Sneaky Spectral Strike', that integrates a simple DL model with a small sample dataset to create trojan triggers that are highly effective, stealthy, and transferable to the DL model deployed in PCC. This approach achieves a 99.86% average fooling rate for different advanced DL models, highlighting the effectiveness of resource-efficient strategies in DL-based SG. Both algorithms underscore the potential vulnerabilities in DL models used in SG , and mark a significant advancement in adversarial machine learning.

In this study, we propose a novel cooling scheme that utilizes copper inverse opals (CIOs) for surface enhancement in a single-phase impingement jet cooling system. We perform computational fluid dynamics (CFD) simulations to evaluate the cooling performance of the CIO jet coolers. Our modeling results indicate that the proposed CIO-coated cooler can significantly reduce the average temperature and improve the temperature uniformity across the entire chip surface. The average Nusselt number of the CIO-coated cooler can reach up to 2.8 times that of the flat surface jet cooler. However, the porous structure of the CIO-coated cooler increases the total pressure drop. To determine designs with high cooling performance and low energy consumption, we investigate two crucial design factors, namely the inlet velocity and the nozzle-to-CIO distance. Our analysis reveals that increasing the inlet velocity further enhances the heat transfer, but at the expense of high pressure drop. On the other hand, a larger nozzle-to-CIO distance results in a lower pressure drop but also reduces the heat transfer coefficient. The effects of nozzle-to-CIO distance are further understood by studying the flow resistance network. Furthermore, we present a reduced-order model that accurately captures the thermofluidic characteristics of the proposed design.

With the rise of social networking sites (SNS), a growing concern has emerged regarding the unintentional disclosure of sensitive information, also known as information leakage. This study investigates the causes and consequences of information leakage on SNS through a simulation-based approach. A virtual environment replicating user interactions and data sharing patterns on popular platforms was developed, simulating various scenarios involving unintentional data disclosure, malicious actor activity, and data breaches. The results revealed that oversharing and weak privacy settings were the most common causes of information leakage, while social engineering attacks and data breaches also posed significant risks. The study highlights the importance of user education, improved platform security, and robust privacy controls to mitigate the risks associated with information leakage on SNS.

The Just Noticeable Difference (JND) refers to the smallest distortion in an image or video that can be perceived by Human Visual System (HVS), and is widely used in optimizing image/video compression. However, accurate JND modeling is very challenging due to its content dependence, and the complex nature of the HVS. Recent solutions train deep learning based JND prediction models, mainly based on a Quantization Parameter (QP) value, representing a single JND level, and train separate models to predict each JND level. We point out that a single QPdistance is insufficient to properly train a network with millions of parameters, for a complex content-dependent task. Inspired by recent advances in learned compression and multitask learning, we propose to address this problem by (1) learning to reconstruct the JND-quality frames, jointly with the QP prediction, and (2) jointly learning several JND levels to augment the learning performance. We propose a novel solution where first, an effective feature backbone is trained by learning to reconstruct JNDquality frames from the raw frames. Second, JND prediction models are trained based on features extracted from latent space (i.e., compressed domain), or reconstructed JND-quality frames. Third, a multi-JND model is designed, which jointly learns three JND levels, further reducing the prediction error. Extensive experimental results demonstrate that our multi-JND method outperforms the state-of-the-art and achieves an average JND1 prediction error of only 1.57 in QP, and 0.72 dB in PSNR. Moreover, the multitask learning approach, and compressed domain prediction facilitate lightweight inference by significantly reducing the complexity and the number of parameters.

As various deep learning applications have been deployed in diverse areas, the explainability of neural networks is becoming increasingly important in the research field. Besides being desirable on its own account, explainability also often helps further improve performance of deep learning models. In this work, we introduce float neurons and fixed neurons to describe the neuron-level stability in a network based on the activation pattern of neurons on given input. With the proposed concept, we quantify the expressive ability and robustness of a neural network with a neuron entropy metric and illustrate their relationship by decomposing the computational graph of a neural network. We find theoretically that networks with better generalization have more diverse activation patterns across the input space, which results in a higher neuron entropy globally. On the other hand, the prediction of neural networks is prone to be affected by perturbation when there are locally more float neurons, which respond with additional impulses to local stimuli. Empirically, we show that the proposed analytical framework can be applied to downstream applications, including network pruning and randomized smoothing of network prediction.

In this note, it is shown that a three-machine power system with 2 PV and 1 slack busses can be modelled by using two Single Machine Infinite Bus models. It was found that a relation between the active powers of the two models at final resulted equations maintains interaction between two SMIB models. It provides a theoretical background to understand dynamic behavior of such power networks.

Light-emitting diodes (LEDs) are widely used in visible light communication (VLC) systems, but their bandwidth is generally incomparable to the laser diodes (LDs). Though LDs are exploited in free space optical systems to provide high data rate, such systems generally fail for mobile users due to the challenges in acquisition, tracking and pointing. Recently, various centimeter-level visible light positioning (VLP) systems have been realized, making it possible to realize VLP-assisted communication systems. In this work, we design a novel VLP-assisted indoor laser communication system, where an LED provides the positioning signal for the mobile user and a narrow-beam laser carries high-speed data stream. The estimated position is updated regularly to continuously orient the laser boresight to the user. The error of the VLP system and the outage probability of the VLP-assisted laser communication system are both derived in closed forms. According to the analytical and simulation results, the outage probability is related to the changes of the system parameters, such as the transmission power of the laser and the user position. Moreover, the outage probability can be minimized by optimizing the laser's beam divergence angle. An experimental platform is built to verify the feasibility of our proposed system.

The AI-based technologies used in healthcare systems have witnessed significant growth and innovation, as this growth is attributed to innovations in AI and rise in data collection in the healthcare sector. This survey paper provides a comprehensive overview of the diverse technological advancements reshaping the healthcare landscape. The reviewed topics include Medical Image Interpretation using Deep Learning, Generative AI-based Large Language Models (LLMs), Natural Language Processing for Healthcare Records to give a sense of what AI based systems look like in healthcare. For each of these topics, we've delved into their technical aspects and their applications. Through an overview of these cutting-edge technologies, this research aims to shed light on their current state, challenges, and potential implications for the future of healthcare. From enhancing diagnostics to improving patient care and accessibility, AI is poised to play pivotal roles in shaping the healthcare industry for years to come. Furthermore, this survey also delves into the ethical considerations surrounding these technologies.

This paper investigates the Diophantine equation p r + (p + 1) s = z 2 Where p > 3, s ≥ 3 , z is an even integer. The focus of the study is to establish rigorous results concerning the existence of solutions within this specific parameter space. The main result presented in this paper demonstrates the absence of solutions under the stated conditions. The proof employs mathematical techniques to systematically address the case when the prime p exceeds 3, and the exponent s is equal to or greater than 2, while requiring the solution to conform to the constraint of an even z. This work contributes to the understanding of the solvability of the given Diophantine equation and provides valuable insights into the interplay between prime powers and the resulting solutions.

This paper explores the realm of intelligent systems through an analogy inspired by RLC circuits, delving into the interconnected dynamics of reasoning, learning, and control. Leveraging the simplicity and clarity of the analogy, we navigate the conceptual landscape by drawing parallels between electrical components and the cognitive functions of modern AI. The presented analogical framework is the conclusion of the personal experiences of the author in developing intelligent systems, sparked by conversations with fellow researchers and students and presentations of research outcomes. It is worth recognizing the limitations of this analogy, as its reductionist nature may simplify the complexities inherent in intelligent systems. Yet, this exploration provides a fresh perspective on the foundational components of intelligent systems through the lens of the well-established RLC circuit theory.

The implementation of a big data mart using NoSQL technologies, in particular the key-value store, is investigated in this report. It examines the relationship between modern database management systems' benefits and traditional data warehousing methods. Based on actual experience using the Oracle NoSQL Database, the process of going from conceptual schema to logical models is detailed, and its benefits and drawbacks are noted.

A key component of cybersecurity defense is effective information sharing, which promotes teamwork and quick reaction to emerging threats. However, putting strong information-sharing principles into practice might be difficult for practitioners. This summary paper explores cybersecurity specialists’ perceptions on information sharing during real-world international cybersecurity exercises (CDX) and looks at the barriers to the development of better information-sharing skills. The study intends to shed light on the difficulties faced by cybersecurity professionals and pinpoint areas where information-sharing procedures could be improved.