Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Multiphysics Simulation Workloads on Cloud Infrastructure

This work presents a performance analysis of multiphysics simulation workloads using the LS-DYNA application on Amazon Web Services cloud infrastructure. The study aims to evaluate the impact of various factors on simulation performance and provide insights into optimizing the cloud environment for such workloads. By comparing different instance types, including general-purpose, compute-optimized, and HPC-optimized instances based on both Intel and AMD processors, the research identifies the most suitable options for achieving efficient and cost-effective execution of multiphysics simulations. The results highlight the superior performance of HPC-optimized instances, both Intel and AMD, indicating their suitability for demanding simulations. Furthermore, the analysis showcases the HPC-optimized AMD instance as a cost-effective alternative, striking a balance between performance and price. These findings contribute to the understanding of cloud-based multiphysics simulations and offer practical guidance for optimizing cloud infrastructure for LS-DYNA workloads, emphasizing the importance of thoughtful resource selection to enhance computational performance in the cloud.
Generation of Controlled Face Images using Curated Datasets and StyleGAN2

GANs have been researched and developed a lot in the past few years to advance the field of Image Generation and StyleGAN especially has been worked upon extensively to produce 3 successive versions. Despite this, due to its algorithmic design of combining or mixing of “styles”, there has so far been no way of definitively controlling the output of the images/faces produced by a trained StyleGAN model. In this paper, the proposed approach is, to control the sizes of facial features in the images generated by StyleGAN2 by feeding curated image data (produced via the discussed data pre-processing method) to the GAN model. The said data preprocessing approach comprises facial-feature size-ratio calculation, data discretization through binning and a unique querying approach. Testing has been performed to check whether the approach improves the quality of output images produced either in terms of metrics like FID/KID or in terms of time-efficiency by comparing the curated based training output to its non-curated counterpart. The generated output is also subjected to a verification process to estimate the levels of control obtained over the sizes of the facial features of generated images. It was found that the proposed data preprocessing methodology helps manipulate facial feature sizes in at most 70% of the generated images (based on sample testing). It is to be noted that these values were achieved using relatively primitive methods of image discretization and also with the GAN system being trained on dataset sizes which were smaller than recommended.
Communication Optimization Algorithms for Distributed Deep Learning Systems: A Survey

Deep learning's widespread adoption in various fields has made distributed training across multiple computing nodes essential. However, frequent communication between nodes can significantly slow down training speed, creating a bottleneck in distributed training. To address this issue, researchers are focusing on communication optimization algorithms for distributed deep learning systems. In this paper, we propose a standard that systematically classifies all communication optimization algorithms based on mathematical modeling, which is not achieved by existing surveys in the field. We categorize existing works into four categories based on the optimization strategies of communication: communication masking, communication compression, communication frequency reduction, and hybrid optimization. Finally, we discuss potential future challenges and research directions in the field of communication optimization algorithms for distributed deep learning systems.
Deep Learning-Based Recommendation System: Systematic Review and Classification

In recent years, recommendation systems have become essential for businesses to enhance customer satisfaction and generate revenue in various domains, such as e-commerce and entertainment. Deep learning techniques have significantly improved the accuracy and efficiency of these systems. However, there is a lack of literature on classification in systematic review papers that summarize the latest deep-learning techniques used in recommendation systems. Moreover, certain existing review papers have either overlooked state-of-the-art techniques or restricted their coverage to a narrow spectrum of domains. To address these research gaps, we present a systematic review paper that comprehensively analyzes the literature on deep learning techniques in recommendation systems, specifically using term classification.We analyzed relevant studies published between 2018 and February 2023, examining the techniques, datasets, domains, and measurement metrics used in these studies, utilizing a thorough SLR strategy. Our review reveals that deep learning techniques, such as graph neural networks, convolutional neural networks, and recurrent neural networks, have been widely used in recommendation systems. Furthermore, our study highlights the emerging area of research in domain classification, which has shown promising results in applying deep learning techniques to domains such as social networks, e-commerce, and e-learning. Our review paper offers insights into the deep learning techniques used across different recommendation systems and provides suggestions for future research. Our review fills a critical research gap and offers a valuable resource for researchers and practitioners interested in deep learning techniques for recommendation systems.
Mathematical Expression Recommendation Model Based on Genetic Algorithm

In scientific research, education, engineering and many other fields, mathematical expressions as a kind of complex, semantic strong important information. People need to recognize the two-dimensional structure of expressions and the semantic content of expressions in its retrieval. However, because the mathematical expression contains many symbols such as numbers, operators and letters, and the structure is complex and diverse, it is difficult to realize the index and retrieval of the full text. Based on this, this paper adopts genetic algorithm to improve and design the mathematical expression recommendation model. Firstly, the location selection mode of mathematical expression expression is added to the input end of mathematical expression query, so that the search results are initially close to user preferences. Then, the genetic algorithm is added to the process of similarity calculation, and a collaborative filtering recommendation algorithm based on genetic algorithm to improve the similarity is obtained. At the same time, similar user characteristics based on genetic algorithm are introduced to improve the accuracy of recommendation. Experimental results show that in K-nearest neighbor IBCF, with the increase of k value, the effect of GA recommendation presents a gradual optimization process. Moreover, in terms of the recall rate and accuracy recommended by the number of 1500 mathematical expressions, the recall rate and accuracy of GA improved similarity arithmetic are 89% and 97%. High recall rate and accuracy were obtained.
Development of User Friendly Based Home Health Monitoring System for The Prediction of Hypertension Using Machine Learning Algorithm

Hypertension, or high blood pressure, is a serious and potentially deadly disorder that afflicts one in three adults. It is a major risk factor for heart attack, stroke, and other cardiovascular diseases, and is an important predictor of premature death. Machine learning algorithms can be used to predict the occurrence of hypertension in individuals, based on a range of factors such as age, gender, genetic predisposition, and lifestyle. Machine learning algorithms rely on data science techniques, such as clustering and classification techniques, to analyze vast amounts of data, such as medical records, to identify patterns and make predictions about the disease. Additionally, machine learning algorithms can be used to identify potential risk factors for hypertension, or factors that can be altered to reduce a person's risk of developing the disease. By combining the insights gained from machine learning algorithms with the knowledge of healthcare professionals, healthcare organizations can improve diagnosis, treatment, and prevention of this life threatening condition.
Enhancing Design and Performance Analysis of Satellite Entanglement-Based CV-QKD/FSO Systems

Satellite QKD/FSO systems, which facilitate quantum key distribution (QKD) over free-space optical (FSO) links between satellites and ground stations, present a promising pathway toward achieving global security in upcoming sixth-generation (6G) wireless communications. Our study focuses on a superior type of these systems, the satellite EB/CV-QKD/FSO, which utilizes the continuous-variable (CV) method for quantum state representation and the entanglement-based (EB) scheme for QKD implementation. We propose the use of optical phase-shift keying (QPSK) signaling and dual-threshold/heterodyne detection (DT/HD) receivers to bolster the reliability and feasibility of satellite EB/CV-QKD/FSO systems. Closed-form expressions for key system performance metrics are derived using improved channel modeling. Numerical results are presented to showcase the effects of channel impairments on the system performance. We also provide recommendations for optimal system setup parameters, aiming to enhance performance.
Dynamic Service Composition Method Based on Zero-Sum Game Integrated Inverse Reinforcement Learning

Automatically generating service composition solutions that meet user application requirements is one of the hot research topics in the field of service composition in the context of Web service big data. To address the challenges of accurately obtaining reward function values and significant increase in time complexity when dealing with large-scale data in the context of reinforcement learning-based service composition, this paper proposes a novel approach that combines zero-sum game and inverse reinforcement learning. The proposed method models the service composition problem as a Markov Decision Process (MDP) and dynamically adjusts the service composition solution by solving for the optimal policy. By leveraging runtime records of service composition operations, we generate an expert experience dataset and develop a novel inverse reinforcement learning algorithm based on the integration of zero-sum game principles. Experimental results demonstrate that the proposed method effectively reduces the dependence of the inverse reinforcement learning algorithm on the quality of expert experience data and reduces the time cost of service composition.
Perceptual Low-Rank Learning and Geometry-Preserving Feature Selection for Categorizing High-Resolution Aerial Photos

Recognizing the multiple categories of an high-resolution (HR) aerial photos is an indispensable technique in geoscience and remote sensing. In this work, a perceptual low-rank algorithm combined with a geometry-preserving feature selection (FS) is proposed for categorizing HR aerial photos. In practice, the theory of human visual perception indicates that for each scenery, the background non-salient regions are highly correlated, whereas the foreground visually/semantically salient regions are almost uncorrelated. Motivated by this, we design a novel low-rank algorithm that seeks a sparse set of foreground visually/semantically salient image patches. These patches are sequentially linked into a so-called GSP (path reflecting gaze movement) to mimick human vision system. Afterward, a geometry-preserving FS algorithm is proposed to select highly discriminative features from the aforementioned gaze features, wherein a classifier can be trained simultaneously. Comprehensive experimental validation on our Internet-scale image set have shown its superiority.
Research and Implementation of Learning Resource Recommendation Method for Online Education

The role of learning resource recommendation in online education is very important, but there is a problem that the recommendation is unreasonable. Traditional resource integration methods cannot solve the problems of resource arrangement and resource classification in massive data, and the recommendation effect is not satisfactory. Therefore, this paper proposes an online recommendation algorithm. Firstly, the resource planning theory is used to divide the resources and realize the preprocessing of resource recommendation. Then, use online division to form a collection of resource recommendations. MATLAB simulation shows that the recommendation accuracy and readability of online recommendation algorithms are better than traditional resource integration methods under certain requirements of online education.
Distributed Asynchronous Protocol for Service Provisioning in the Edge-Cloud Continuum

In the edge-cloud continuum, datacenters provide microservices (MSs) to mobile users, with each MS having specific latency constraints and computational requirements. Deploying such a variety of MSs matching their requirements with the available computing resources is challenging. In addition, time-critical MSs may have to be migrated as the users move, to keep meeting their latency constraints. Unlike previous work relying on a central orchestrator with an always-updated global view of the available resources and of the users’ locations, this work envisions a distributed solution to the above issues. In particular, we propose a distributed asynchronous protocol for MS deployment in the cloud-edge continuum that (i) dramatically reduces the system overhead compared to a centralized approach, and (ii) increases the system stability by avoiding having a single point of failure as in the case of a central orchestrator. Our solution ensures cost-efficient feasible placement of MSs, while using negligible bandwidth.
Demonstration of QKD Integration into 5G Campus Network

The paper deals with a proposal and implementation of the quantum key distribution in the 5G campus network. The authors share here their practical experience with the implementation of quantum cryptography in 5G which has been achieved within projects the NATO QUANTUM5 and the Czech Ministry of Interior NeSPoQ, both project contributing to the security of 5G mobile networks. Finally, the results of the experimental research demonstrate how the network performance is affected by the use of QKD.
Development of a New Dynamic Approach for Facial Recognition and Emotion Detection

This article is a research concerning different approaches for face recognition and emotion detection. Despite there are many existent technologies developed by different big tech companies which gave us more than we need, we aim to develop a new dynamic approach that may be a newborn Application Programming Interface (API) using deep learning algorithms such as Yolo, CNN, LeNet5 as well as fine tunning the model through hyperparameters optimization. This approach helps the user to follow up the fluctuation of the metrics while changing the hyper-parameters.
DAACN: Dense Atrous Asymmetric Convolutional Network for HIFU Treatment Target Region Extraction

In the context of HIFU treatment monitoring for ultrasonic image segmentation, there is a trade-off between the segmentation accuracy and the complexity of semantic segmentation methods. High-accuracy methods tend to be computationally complex, while low-complexity methods are prone to missegmentation issues related to the shape and boundary of the treatment target region. To address this challenge., this paper proposes a novel approach called Dense Atrous Asymmetric Convolutional Network (DAACN). DAACN is a lightweight network with an encoder-decoder architecture that incorporates non-symmetric convolutions and dense atrous convolutions. It achieves efficient extraction of image features while maintaining a low computational complexity. Additionally, this study proposes the use of multi-cross-entropy loss to supervise the network and preserve more details and contextual information during the training process, thereby further improving the accuracy of the segmentation. The experimental results demonstrate that the DAACN trained with the multi-cross-entropy loss function achieved a parameter count of 3.22M. Compared to other networks, it exhibits excellent segmentation accuracy, with MIoU and DSC values of 95.07% and 97.47% respectively.
A Real-Time System to Assist Blind People Through Face Recognition and Emotion Detection

As per the recent report by the world health organization (WHO), there are 2.2 billion people with various kinds of vision impairment. Around 50% of people with visual impairment are entirely blind and require the support of technology to make them more accessible in their life. In this paper, we propose an assistive system to help blind people. In general, blind people will understand their friends and relatives through their voice, smell, or rhythm of their footsteps. But the blind may not be able to judge emotions too quickly since facial expressions demonstrate the emotions of individuals. This work proposes a system that will use a combination of face recognition and an emotion detection approach to recognize the persons and identify the emotions for assisting the blind through personalized audio outputs. The system will identify the person and their emotion in those who are approaching the blind, and it will give an audio description about them well in advance to the blind so that he/she can fine-tune the interactions in a better way. Certainly, in some situations, the blind may want to interact with strangers, and in that situation, our system will help the blind by giving an audio description that includes approximate age, gender, other physical parameters, etc. A detailed experimental study is carried out with randomly selected video samples and the results show that the system works well in real-time.
Data Analytics over Encrypted Data from Fully Homomorphic Encryption

Recently, in machine learning research over private data becomes a need. Over the cloud, open data can be easily stolen and misused. However, dealing with data in every aspect of security and privacy of data as well as secure computation, analysis and prediction with the machine learning and deep learning models is a bit challenging due to several security-threatening issues. This paper helps to incorporate secure computation and analysis using a fully homomorphic encryption model without exposing data. Further, by deploying the Logistic regression, the ML model is used to predict the values over encrypted datasets. The logistic regression helps to predict whether a person is suffering from diabetes or not, securely over an encrypted dataset using the proposed FHE model while maintain the privacy of data without exposing data to any third party. Additionally, the proposed FHE-based ML model is compared with the standard ML model based on different performance measures. The results show that the ML model performs comparably and shows promising results while keeping data private and secure throughout computation and processing of dataset.
On Belief Propagation Decoding of Quantum Codes with Quaternary Reliability Statistics

In this paper, we investigate the use of quaternary reliability statistics for ordered statistics decoding (OSD) of quantum codes. OSD can be used to improve the performance of belief propagation (BP) decoding when it fails to correct the error syndrome. We propose an approach that leverages quaternary reliability information and the hard-decision history output by BP to perform reliability sorting for OSD. This approach improves upon previous methods that separately treat X and Z errors, by preserving the X/Z correlations during the sorting step. Our simulations show that the refined BP with scalar messages and the proposed OSD outperforms previous BP-OSD combinations. We achieve thresholds of roughly 17.5%–17.7% for toric, surface, and XZZX codes, and 15.42% for hexagonal planar color codes.
An Innovative Arabic Text Sign Language Translator

In recent years, several software and hardware solutions have been proposed for object detection, motion tracking, and gesture identification. However, these solutions failed to efficiently identify appropriate gestures and track their motion in a stipulated time range. To overcome the above-mentioned challenges, we propose a novel sign language translator application, which uses the MediaPipe Holistic model along with Long Short-Term Memory (LSTM), integrated with Neural Network (NN) model to build the proposed translator model. This model will pick up the gestures from different angles quickly and accurately to translate them into appropriate text. This paper has been divided into two parts, the first part involves research and data collection, and the second part involves training and testing the collected data for real-time implementation. This model has been trained and tested with real-time data collected from the Leap motion controller and cameras. The model provides appreciable results.
Minimum Gaussian Noise Variance of Federated Learning in the Presence of Mutual Information Based Differential Privacy

Federated learning (FL), which not only protects data security and privacy, but also training models on distributed devices, receives much attention in the literature. Traditionally, stochastic gradient descent (SGD) is used in FL since its excellent empirical performance, but user’s private information can still be leaked by analyzing weight updates during FL iterations. Differential privacy (DP) is an effective way to solve this privacy leakage problem, which adds noise to the user’s data gradient and this artificial noise helps to prevent information leakage. However, note that the SGD based FL with DP is not yet investigated with a comprehensive theoretical analysis considering privacy and data utility jointly, especially from the information-theoretic aspect. In this paper, we investigate the FL in the presence of mutual information based DP (MI-DP). Specifically, first, Gaussian DP mechanism is applied to either clients or central server of the FL model, and privacy and utility of the FL model are characterized by conditional mutual information and distortion, respectively. For a given privacy budget, we establish lower bounds on the variance of the Gaussian noise added to clients or central server of the FL model, and show that the utility of the global model remains the same for both cases. Next, we study the privacy-utility trade-off problem by considering a more general case, where both the model parameter and the privacy requirement of the clients are flexible. A privacy-preserving scheme is proposed, which maximizes the utility of the global model while different privacy requirements of all clients are preserved. Finally, the results of this paper are further explained by experimental results.