Reviewerly/exHarmony huggingface dataset:
Gap and Glitch Simulation for Taiji Data Challenge

Simulating the detection process of a space-based gravitational wave (GW) measurement system is crucial for the task demonstration, design, operation and data processing. Gaps and glitches, which are common types of abnormal events in the detection process, are an important part of data simulation. This paper explores two gap models and three glitch models, provides a comprehensive overview of these models. The implementation of these models in Taiji Data, the simulation data of the Taiji program, is designed. Dedicated software modules were developed to incorporate the gap and glitch models into the simulation process. The impact of adding gap and glitch to the measurement is illustrated and analyzed. Finally, the time-series simulation detection data of space GWs that integrates gap and glitch models has been obtained, providing support for promoting the improvement of the detection data processing capabilities.
A Systematic Literature Review of Latest Cloud Forensic Frameworks, Tools and Challenges

Background: Cloud computing is widely used in this era of IoTs. Cloud users utilize cloud computing to access various cloud services. The defects in cloud services are exploited by suspicious actors. On the other hand, cloud forensics help against such suspicious actors. It has been observed on many occasions that cloud services were not well designed. Objective: Cloud computing with its services exposed to cyber threats, due to the bad implementation of cloud services, helps suspicious actors to exploit vulnerabilities in the cloud environment. The main objective is to identify different cloud forensic frameworks. Method: We have used the method of systematic literature review to find and analyze different research studies, which mainly are published between 2010-2022. Results: In this paper, we have discovered 36 different cloud forensic frameworks, with some forensic tools. In this SLR we also mentioned some challenges of cloud forensics with recommendations. Conclusion: This systematic literature review unveils the latest developments in cloud forensic frameworks, tools, and challenges. The insights provided in this paper will serve as an invaluable resource for future researchers in this domain, facilitating the enhancement of cloud security practices.
OLR-WA: Online Weighted Average Linear Regression in Multivariate Data Streams

Online learning updates models incrementally with new data, avoiding large storage requirements and costly model recalculations. In this paper, we introduce “OLR-WA; OnLine Regression with Weighted Average”, a novel and versatile multivariate online linear regression model. We also investigate scenarios involving drift, where the underlying patterns in the data evolve over time, conduct convergence analysis, and compare our approach with existing online regression models. The results of OLR-WA demonstrate its ability to achieve performance comparable to the batch regression, while also showcasing comparable or superior performance when compared with other state-of-the-art online models, thus establishing its effectiveness. Moreover, OLR-WA exhibits exceptional performance in terms of rapid convergence, surpassing other online models with consistently achieving high r <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values as a performance measure from the first iteration to the last iteration, even when initialized with minimal amount of data points, as little as 1% to 10% of the total data points. In addition to its ability to handle time-based (temporal drift) scenarios, remarkably, OLR-WA stands out as the only model capable of effectively managing confidence-based challenging scenarios. It achieves this by adopting a conservative approach in its updates, giving priority to older data points with higher confidence levels. In summary, OLR-WA’s performance further solidifies its versatility and utility across different contexts, making it a valuable solution for online linear regression tasks.
An Adaptive Hierarchical Method for Anytime Set-wise Clustering of Variable and High-Speed Data Streams

Set-wise Clustering is a clustering technique for data streams that groups sets of objects based on distribution patterns, applicable in contexts like retail chain clustering, text-based community clustering, restaurant categorization, etc. The existing set-wise clustering method cannot handle variable and high-speed streams with reasonable accuracy. This paper presents an Anytime Set-wise Clustering method for data streams known as ANYSETCLUS. The method handles the variable inter-arrival rates of stream objects using a proposed indexing structure called AnySetClusTree, which stores a hierarchy of micro-clusters of multi-set entities at varying granularity. ANYSETCLUS is highly adaptive as it supports incremental model updates, segregates outliers, enables outlier-to-concept transition, and captures concept drift. The method also enables anytime offline clustering wherein it can generate multiple clusterings of varying granularity and purity depending upon the available time allowance for final clustering. The experimental results affirm the superior efficacy of the proposed method in handling variable and high-speed streams compared to the state-of-the-art method. The experimental results also showcase its effectiveness in achieving significantly higher micro-cluster purity for low and high-speed streams. This contrasts with the state-of-the-art method, which is unable to generate valid clustering results for high-speed streams. The experiments further validate the proposed method’s capability for anytime offline clustering.
A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on Social Media Using Synthetic Data

Social media cyberbullying has a detrimental effect on human life. As online social networking grows daily, the amount of hate speech also increases. Such terrible content can cause depression and actions related to suicide. This paper proposes a trustable LSTM-Autoencoder Network for cyberbullying detection on social media using synthetic data. We have demonstrated a cutting-edge method to address data availability difficulties by producing machine-translated data. However, several languages such as Hindi and Bangla still lack adequate investigations due to a lack of datasets. We carried out experimental identification of aggressive comments on Hindi, Bangla, and English datasets using the proposed model and traditional models, including Long Short-Term Memory (LSTM), Bidirectional Long ShortTerm Memory (BiLSTM), LSTM-Autoencoder, Word2vec, Bidirectional Encoder Representations from Transformers (BERT), and Generative Pre-trained Transformer 2 (GPT-2) models. We employed evaluation metrics such as f1-score, accuracy, precision, and recall to assess the models’ performance. Our proposed model outperformed all the models on all datasets, achieving the highest accuracy of 95%. Our model achieves state-of-the-art results among all the previous works on the dataset we used in this paper.
Model Parallelism Optimization for Distributed DNN Inference on Edge Devices

Deep neural networks (DNNs) have recently gained widespread application in various domains. However, the computational and memory requirements of DNN models pose challenges for deploying them on resource-constrained edge devices. With the widespread use of the Internet of Things (IoT), heterogeneous edge devices with diverse computational capabilities and network conditions are increasingly employed for DNN inference. This paper proposes a distributed DNN model deployment scheme for edge device clusters. The DNN model partitioning is performed using two algorithms: Edge Layer Partitioning (EdgeLP), which partitions a single neural network layer, and Edge Model Partitioning (EdgeMP), which performs complete model partitioning. These algorithms consider both the computational capabilities and network conditions of the edge devices. To address the transmission overhead between collaborative edge devices, layer fusion and data quantization are applied to reduce the amount of transmitted data. Experimental results show that our method dramatically improves the performance of distributed DNN inference in heterogeneous scenarios. Specifically, on a cluster of three edge devices, the proposed scheme achieves DNN inference time acceleration speedup of 1.38–1.72× without accuracy loss compared to a state-of-the-art scheme.
Construction and Design of a Fengtu Architecture Analysis System Based on Improved GA Algorithm

With the progress of social economy, urban construction has regarded the resources of local customs as one of the indispensable and important factors. Terrestrial resources have enormous development potential and value, therefore reliable data and information support has become particularly important in building planning and design, construction, operation and maintenance, and post management. This article aims to study and analyze the design technology of Fengtu architecture based on improved genetic algorithm (GA), and introduce some brief overview of system construction and implementation. At the same time, by elaborating on the parameter setting methods of traditional universal standard controllers and the optimization principles of related functions, a comprehensive model of soil resource management system will be established. Afterwards, this article tested the performance of the system model, and the test results showed that the memory usage rate of the geotechnical building analysis system was within 2%. It shows that the system has a high efficiency in resource utilization.
A 3D Framework for Brain Tumor Segmentation from Multi-modal MR Images

Segmentation of brain tumors from multi-modal magnetic resonance (MR) images is a crucial task in medical imaging, with significant clinical implications for the diagnosis and treatment planning of relevant brain disorders. In this paper, we present a 3D framework, that utilizes multi-modal MR images, including T1-weighted, T1ce-weighted, T2-weighted, and FLAIR images, to capture comprehensive information about the tumor for precise and automated brain tumor segmentation. The proposed framework leverages a multi-modal MRI fusion task to facilitate the optimization and training of brain tumor segmentation. Comparative experiments have indicated the effectiveness and superiority of the proposed framework for brain tumor segmentation tasks.
Improving Knowledge Distillation for Federated Learning on Non-IID Data

Federated learning (FL) leverages knowledge from decentralized clients to train a global model in a privacy-preserving manner. One of the critical challenges in FL is the heterogeneity of client local data (i.e. non-IID data), which can lead to significant performance degradation. Federated Distillation (FD) can mitigate this issue by distilling the client predictions on the unlabeled public data into a global student model. However, a simple averaging of the client predictions without considering model confidences may lead to erroneous predictions. Moreover, it is challenging to rectify the predicted pseudo labels as no human labels are available in the public data. To address these issues, we propose a novel FD framework named FedUSL, aiming to distill a powerful global model on non-IID data. Specifically, we estimate the uncertainty of each client’s prediction on the unlabeled data and adopt a weighted ensemble of their predictions in consideration of uncertainties. We further rectify the global model predictions by a self-label reassigning method, without the requirement of manual labels. Extensive experiments on image and text tasks show that our proposal can achieve superior performance than state-of-the-art methods, and incurs no extra computing burden on the client side. The code is available at https://anonymous.4open.science/r/FedUSL-6338/.
Detection of Encrypted and Malicious Network Traffic using Deep Learning

Network traffic has increased by a factor of ten due to the meteoric rise of the Internet. The prevalence of encryption methods makes it difficult to identify malicious traffic. Reason being, without being able to decipher encrypted traffic, conventional detection methods are useless. Recent work on detecting harmful encrypted traffic has concentrated on feature extraction and the selection of deep learning techniques rather than on breaking the encryption itself. For the most part, today's edge node devices are responsible for processing massive amounts of data, extracting critical elements of network traffic, and then sending that information on to a cloud server. However, how to more rapidly and precisely identify network traffic remains a hard topic, as mobile terminal tools performance in discovering and categorizing encrypted &malicious traffic lags after. We develop a model of a convolutional-neural-network (CNN) that integrates normalized and attention processes; this model is called I-D-CNN with hexadecimal data (HexCNN-ID). Global-Attention-Block (GAB) and Category-Attention-Block (CAB) are modules of the attention mechanism that help identify and categorize network traffic. Our algorithm is able to recognize most types of network-traffic, plus encrypted &malicious traffic data, by mining efficient load information from hexadecimal network traffic. An average accuracy of 98.8% was found during experimental testing. Our methodology has the potential to significantly enhance the reliability of traffic data recognition in networks.
FedSym: Unleashing the Power of Entropy for Benchmarking the Algorithms for Federated Learning

Federated learning (FL) is a decentralized machine learning approach where independent learners process data privately. Its goal is to create a robust and accurate model by aggregating and retraining local models over multiple rounds. However, FL faces challenges regarding data heterogeneity and model aggregation effectiveness. In order to simulate real-world data, researchers use methods for data partitioning that transform a dataset designated for centralized learning into a group of sub-datasets suitable for distributed machine learning with different data heterogeneity. In this paper, we study the currently popular data partitioning techniques and visualize their main disadvantages: the lack of precision in the data diversity, which leads to unreliable heterogeneity indexes, and the inability to incrementally challenge the FL algorithms. To resolve this problem, we propose a method that leverages entropy and symmetry to construct ’the most challenging’ and controllable data distributions with gradual difficulty. We introduce a metric to measure data heterogeneity among the learning agents and a transformation technique that divides any dataset into splits with precise data diversity. Through a comparative study, we demonstrate the superiority of our method over existing FL data partitioning approaches, showcasing its potential to challenge model aggregation algorithms. Experimental results indicate that our approach gradually challenges the FL strategies, and the models trained on FedSym distributions are more distinct.
Improving the performance of DeepFace CNN using Canonical Correlation Analysis

Face recognition is a type of pattern recognition problem which allows humans to be identified thanks to their face images. One of the most important steps in recognition is the extraction of features which is usually carried out using algorithms and models such as Convolutional Neural Networks. However, the latter do not take into consideration all the variations (such as illumination and aging) that face images incur. In order to deal with the mentioned problem, Canonical Correlation Analysis was used to fuse the feature vectors of the DeepFace Convolutional Neural Network and a modified version of the VGG-Face Convolutional Neural Network from a face verification experiment. For this scenario, the FG-NET database was used as it focuses particularly on aging faces of same persons. Using Canonical Correlation Analysis, a significant improvement (for negative pairs) in accuracy regarding misclassified results was noted. Features extracted using DeepFace produced a high misclassification rate of 80.0% as compared to 33.3% (lower is better) when the features from both Convolutional Neural Networks were combined using Canonical Correlation Analysis. The latter thus shows the potential of fusing feature vectors in facial verification, particularly when aging is a concern.
Ancient Mural Image Region Regeneration and Filling Using Patch Matching Based on NonExistent Elements Priority

Ancient Mural Images (AMI) are one of the most important treasures that signify the culture and heritage of any region. AMI also reflects the information regarding the artistic values, art techniques, and materials along with evidence of rich history and culture. Every small or big nation had felt the thirst to conserve their ancestor's wealth carried from decades of sustaining natural and artificial attacks. As a part of reconstructing and repair, the work presented in this paper presents a Patch-Matching Algorithm (PMA) based on Non-Existent Elements Priority (NEEP) to regenerate the missing regions and fill the background using the known information in the mural image. Two statistical approaches for regeneration and filling are used respectively to make the construction resemble the degraded image. The NEEP mechanism considers the patch to be constructed or filled first and relies on several elements to recover. The color texture-structure conserving PMA reproduces the degraded regions in the mural images with a high peak signal-to-noise ratio (PSNR). The inpainting, region filling, and reconstruction quality of the proposed PMA-NEEP scheme showed superior performance in terms of perception when tested over low-resolution texture-structure images at the cost of computational complexity.
Revocable Attribute-Base Scheme with Enhanced Security and Privacy for Healthcare Data Sharing

Cloud-assisted healthcare system is playing an increasingly important role in smart healthcare delivery. Before outsourcing patients information to the cloud, the sensitive information contained therein is usually encrypted to protect privacy. However, traditional encryption methods have limitations in addressing dynamic control of data access. To address this issue, we propose an attribute-based secure sharing scheme that supports attribute revocation for flexible data sharing in cloud-assisted healthcare systems. We tag the user’s identity using a collection of attributes, leading to dynamic change in the user’s identity translating into the control over the collection of attributes. We design a dual revocation mechanism combining direct and indirect revocation methods to cope with user revocation as well as attribute revocation. To accomplish user revocation, we use a proxy server to provide an identity proof based on Merkel tree, resulting that any revoked user cannot be authenticated through the cloud. To improve the efficiency of attribute revocation, we build the attribute tree for each attribute to record the users who hold this attribute, and use the KUNodes algorithm to achieve efficient attribute revocation. We conduct a series of experiments on our scheme to evaluate its effectiveness. The experimental results show that our scheme can achieve the revocation goal.
Hierarchical Federated Learning-Based Method for privacy-preserving in the Healthcare Environment

The COVID-19 pandemic has led to increase the demand for medical equipment with internet of things (IoMT) capabilities forcing doctors to diagnose and treat patients remotely. This has increased awareness of health, leading to a surge in purchasing of IoMT-capable medical equipment. However, medical data is sensitive and valuable on the dark web, making it vulnerable to cybercriminals. Due to limited storage and processing capabilities of network devices, system administrators struggle to enhance security measures. This work aims to investigate rapidly spreading attacks before they compromise wellness systems. Hierarchical federated learning is (HFL) proposed using a model built on Dew-cloud, which offers improved data privacy due to the increased availability of IoMT critical applications. Dew servers with distributed architecture and a cloud computing-backed backend implement hierarchical long-term memory (HLSTM) method. The implemented HFL-HLSTM method’s performance is evaluated using NSL-KDD dataset. Implemented method achieved better performance than the existing intrusion detection methods including MiTed, LS2DNN, and MetaCID and the metrics are 98.62% of f-measure, 99.53% of accuracy, 99.21% of precision, and 99.10% of recall.
Identifying Duress Through Voice During Speaker Authentication

Biometric authentication improved accessibility to sensitive information as it provided a faster and more effortless approach to accessing sensitive data. The accuracy of many biometric authentication approaches was enhanced to the extent that it has become implemented as primary authentication. Regrettably, a user can still be authenticated under duress. This research suggests an approach for detecting duress when a person authenticates themselves using their voice. To detect possible duress within the RAVDESS dataset, several combinations of feature extraction techniques (MFCC, Chroma, Mel, Contrast, and Tonnetz) were employed in conjunction with the Random Forest classifier. As a result, a modified confusion matrix was used to more accurately measure performance and provide context for the results of emotion detection.
Exploration on Accounting Data Encryption Processing System Based on DES Algorithm

The confidentiality of accounting data is crucial for the security and trade secrets of enterprises. The aim of this study is to explore an accounting data encryption processing system that improves the DES algorithm, enhancing the efficiency and security of data encryption processing. This article optimized the system architecture and algorithm design by introducing safer AES (Advanced Encryption Standard) algorithms, and combined hardware to accelerate the technological process. The experimental results showed that under the same amount of data, the AES algorithm reduced the encryption time by an average of 40% compared to the DES algorithm. This indicated the need to further strengthen key management. It needs to establish a data backup and recovery mechanism, and strengthen security training and awareness enhancement to improve the security performance of the accounting data encryption processing system of DES algorithm. These improvements would provide enterprises with more reliable and effective accounting data protection solutions, ensuring the confidentiality and integrity of accounting data.
Effective and Efficient Big OLAP Data Cube Compression in Mobile Cloud Environments: The IQTS Algorithm

This paper introduces the Indexed Quad-Tree Summary (IQTS) algorithm, along with its concepts, models, and “philosophy”. IQTS allows us to compress multidimensional OLAP views derived from big OLAP data cubes that populate Mobile Clouds. In addition to this, IQTS effectively and efficiently supports approximate query answering over compressed multidimensional OLAP views. These functionalities turn to be enabling functionalities for big data applications in a wide collection of modern scenarios, such as healthcare analytics. In light of these considerations, this paper provides motivations, anatomy and procedures of IQTS, enriched by several case studies that contribute to pinpoint the benefits coming from our proposed algorithm.