Reviewerly/exHarmony huggingface dataset:
Standards for Building the Cyber Pro Operating System that Protects and Scans the National Network

With the increasing reliance on computer networks for national security, it is critical to have an operating system that can protect and scan the national network for vulnerabilities and software defects. This paper presents standards for building the Cyber Pro operating system that can address these challenges. The standards include security, compatibility, performance, and usability. The security standard ensures that Cyber Pro is resilient to cyber-attacks and can protect sensitive information. The compatibility standard ensures that Cyber Pro is compatible with different hardware and software configurations. The performance standard ensures that Cyber Pro can operate efficiently and effectively in a high-volume network environment. The usability standard ensures that Cyber Pro is user-friendly and easy to operate.
Towards effective feature selection in estimating software effort using machine learning

Abstract Software effort estimation is a vital process in the software industry for successfully administering 5Ds of the software development life cycle (SDLC). The 5Ds stand for demand, development, direction, deployment, and designated cost of the software. Software development effort estimation (SDEE) is an effort prediction mechanism to calculate the effort for the development of the software product in order to minimize the challenges in the software field. Academics and practitioners are striving to identify which machine learning estimation technique yields more accurate results based on evaluation metrics, datasets, and other pertinent aspects. The feature selection techniques impact accuracy by selecting the main and relevant features in the dataset and eliminating the redundant and irrelevant features in the dataset. To achieve accurate estimations, the paper utilizes feature selection algorithms, along with various machine learning techniques, which predict the desired effort and the performance of the model has been measured in terms of prediction accuracy, value, relative error, and mean absolute error. The datasets China and Maxwell are trained with the relevant features by applying feature selection algorithms, and estimation techniques are applied to predict the effort. The performance is compared with the regression models and feature selection techniques utilized by many authors previously. The result of the proposed methodology significantly gives the best performance with the combination of feature selection and estimation models than all regression models when applied alone, to both datasets. From the results, it is perceptible that random forest is performing well with the feature selection techniques and obtains the highest prediction accuracy of 99.33% with the China and 89.47% with the Maxwell datasets.
Reconstructing 3D human pose and shape from a single image and sparse IMUs

Model-based 3D pose estimation has been widely used in many 3D human motion analysis applications, in which vision-based and inertial-based are two distinct lines. Multi-view images in a vision-based markerless capture system provide essential data for motion analysis, but erroneous estimates still occur due to ambiguities, occlusion, or noise in images. Besides, the multi-view setting is hard for the application in the wild. Although inertial measurement units (IMUs) can obtain accurate direction without occlusion, they are usually susceptible to magnetic field interference and drifts. Hybrid motion capture has drawn the attention of researchers in recent years. Existing 3D pose estimation methods jointly optimize the parameters of the 3D pose by minimizing the discrepancy between the image and IMU data. However, these hybrid methods still suffer from the issues such as complex peripheral devices, sensitivity to initialization, and slow convergence.This article presents an approach to improve 3D human pose estimation by fusing a single image with sparse inertial measurement units (IMUs). Based on a dual-stream feature extract network, we design a model-attention network with a residual module to closely couple the dual-modal feature from a static image and sparse inertial measurement units. The final 3D pose and shape parameters are directly obtained by a regression strategy.Extensive experiments are conducted on two benchmark datasets for 3D human pose estimation. Compared to state-of-the-art methods, the per vertex error (PVE) of human mesh reduces by 9.4 mm on Total Capture dataset and the mean per joint position error (MPJPE) reduces by 7.8 mm on the Human3.6M dataset. The quantitative comparison demonstrates that the proposed method could effectively fuse sparse IMU data and images and improve pose accuracy.
Educational Innovation with Alternative Credentials as a Driver of the Digital Transformation of the University: A Case Study in Latin America

The dynamics of change in the work environment are becoming more dizzying, given that adopting new technologies generates new knowledge and jobs. This research analyzed a case study of a Mexican university implementing alternative credentials. The method was instrumental case study research, with exploratory and descriptive categories, applying three instruments: documentary analysis of alternative credential programs, a questionnaire, and interviews with the experts involved in designing and delivering alternative credentials. In this case, the implementation of alternative credentials coincided with the reference frameworks of the European Union and the province of Ontario, Canada. Their frameworks mention the vision and institutional mission of alternative credentialing for the value offered, its definition, operation, award processes, accreditation, and quality. The case provides data for interested higher education institutions, such as why to do it, the strategy to follow, the added value offered, the elements that define it and its design, the assessment process and assignment, the timing of accreditation, and where it is recognized. This research contributes recommendations for defining and managing alternative credentials to serve as a reference for other universities interested in incorporating technology-supported educational innovations.
Postures anomaly tracking and prediction learning model over crowd data analytics

Innovative technology and improvements in intelligent machinery, transportation facilities, emergency systems, and educational services define the modern era. It is difficult to comprehend the scenario, do crowd analysis, and observe persons. For e-learning-based multiobject tracking and predication framework for crowd data via multilayer perceptron, this article recommends an organized method that takes e-learning crowd-based type data as input, based on usual and abnormal actions and activities. After that, super pixel and fuzzy c mean, for features extraction, we used fused dense optical flow and gradient patches, and for multiobject tracking, we applied a compressive tracking algorithm and Taylor series predictive tracking approach. The next step is to find the mean, variance, speed, and frame occupancy utilized for trajectory extraction. To reduce data complexity and optimization, we applied T-distributed stochastic neighbor embedding (t-SNE). For predicting normal and abnormal action in e-learning-based crowd data, we used multilayer perceptron (MLP) to classify numerous classes. We used the three-crowd activity University of California San Diego, Department of Pediatrics (USCD-Ped), Shanghai tech, and Indian Institute of Technology Bombay (IITB) corridor datasets for experimental estimation based on human and nonhuman-based videos. We achieve a mean accuracy of 87.00%, USCD-Ped, Shanghai tech for 85.75%, and IITB corridor of 88.00% datasets.
Removing Rain Streaks from Visual Image Using a Combination of Bilateral Filter and Generative Adversarial Network

Images acquired using vision sensors are easily affected by environmental limitations, especially rain streaks. These streaks will seriously reduce image quality, which, in turn, reduces the accuracy of the algorithms that use the resulting images in vision sensor systems. In this paper, we proposed a method that combined the bilateral filter with the generative adversarial network to eliminate the interference of rain streaks. Unlike other methods that use all the information in an image as the input to the generative adversarial network, we used a bilateral filter to preprocess and separate the high frequency part of the original image. The generator for the high-frequency layer of the image was designed to generate an image with no rain streaks. The high-frequency information of the image was used in a high-frequency global discriminator designed to measure the authenticity of the generated image from multiple perspectives. We also designed a loss function based on the structural similarity index to further improve the effect of removal of the rain streaks. An ablation experiment proved the validity of the method. We also compared images in synthetic and real-world datasets. Our method could retain more image information, and the generated image was clearer.
Understanding decision making in security operations centres: building the case for cyber deception technology

A Security Operations Centre (SOC) is a command centre where analysts monitor network activity, analyse alerts, investigate potential threats, and respond to incidents. By analysing data activities around the clock, SOC teams are crucial in ensuring the prompt detection and response to security incidents. SOC analysts work under considerable pressure to triage and respond to alerts in very short time frames. Cyber deception technology offers the promise of buying SOC analysts more time to respond by wasting the resources and time of attackers, yet such technology remains underutilised.We carried out a series of interviews with experts to uncover the barriers which prevent the effective implementation of cyber deception in SOCs.By using thematic analysis on the data, it was clear that while cyber deception technology is promising it is hindered by a lack of use cases, limited empirical research that demonstrates the efficacy of the technology, hesitancy to embrace a more active form of cyber defence, issues surrounding the over promising of results by off-the-shelf vendors, and an aversion to interrupting the decision-making processes of SOC analysts.Taking this last point about the decision-making processes of SOC analysts we make the case that naturalistic decision making (NDM) would help us better understand how SOC analysts make decisions and how cyber deception technology could be used to best effect.
The Impact Mechanism of Blockchain Technology on Financial Risk

The three stages of the development of block technology are blockchain 1.0 – the application of digital currency; Blockchain 2.0 – Smart contracts, smart assets; Blockchain 3.0 – Decentralized trust mechanisms. Nowadays, both foreign and domestic scholars have studied many fields on the application of blockchain technology. General Secretary Xi Jinping gave a specific implementation path at the 18th Group study of the Political Bureau of the CPC Central Committee in October 2019, stressing that China’s financial industry should take blockchain technology as an important tool for global financial competition. And the blockchain technology is extended to digital asset transaction, digital economy, digital finance, supply chain finance, inclusive finance, Internet of Things and intelligent manufacturing and other fields. At present, most of the applications of blockchain are carried out around the financial industry. It is particularly important to realize a safe and controllable transition and ensure the stability of the overall financial system during the large-scale application of blockchain technology. In order to solve the problems of how to achieve a safe and controllable transition and how to ensure the stability of the overall financial system in the process of large-scale application of blockchain technology mentioned above, this project will analyze the influence mechanism of blockchain technology on financial risks in detail from the perspectives of public perception, application scenarios and virtual currency bubble.
Assessing the Design of Interactive Radial Data Visualizations for Mobile Devices

The growing use of mobile devices in daily life has led to an increased demand for the display of large amounts of data. In response, radial visualizations have emerged as a popular type of visualization in mobile applications due to their visual appeal. However, previous research has highlighted issues with these visualizations, namely misinterpretation due to their column length and angles. This study aims to provide guidelines for designing interactive visualizations on mobile devices and new evaluation methods based on the results of an empirical study. The perception of four types of circular visualizations on mobile devices was assessed through user interaction. All four types of circular visualizations were found to be suitable for use within mobile activity tracking applications, with no statistically significant difference in responses by type of visualization or interaction. However, distinguishing characteristics of each visualization type were revealed depending on the category that is in focus (memorability, readability, understanding, enjoyment, and engagement). The research outcomes provide guidelines for designing interactive radial visualizations on mobile devices, enhance the user experience, and introduce new evaluation methods. The study's results have significant implications for the design of visualizations on mobile devices, particularly in activity tracking applications.
Scalable image compression algorithms with small and fixed-size memory

The SPIHT image compression algorithm is characterized by low computational complexity, good performance, and the production of a quality scalable bitstream that can be decoded at several bit-rates with image quality enhancement as more bits are received. However, it suffers from the enormous computer memory consumption due to utilizing linked lists of size of about 2–3 times the image size. In addition, it does not exploit the multi-resolution feature of the wavelet transform to produce a resolution scalable bitstream by which the image can be decoded at numerous resolutions (sizes). The Single List SPIHT (SLS) algorithm resolved the high memory problem of SPIHT by using only one list of fixed size equals to just 1/4 the image size, and state marker bits with an average of 2.25 bits/pixel. This paper introduces two new algorithms that are based on SLS. Like SLS, the first algorithm also produces a quality scalable bitstream. However, it has lower time complexity and better performance than SLS. The second algorithm, which is the major contribution of the work, upgrades the first algorithm to produce a bitstream that is both quality and resolution scalable. As such, the algorithm is very suitable for the modern heterogeneous nature of the internet users to satisfy their different capabilities and desires in terms of image quality and resolution.
Image compression based on vector quantization and optimized code-book design using Genetic Mating Influenced Slime Mould (GMISM) algorithm

Large amounts of storage are required to store the recent massive influx of fresh photographs that are uploaded to the internet. Many analysts created expert image compression techniques during the preceding decades to increase compression rates and visual quality. In this research work, a unique image compression technique is established for Vector Quantization (VQ) with the K-means Linde–Buzo–Gary (KLBG) model. As a contribution, the codebooks are optimized with the aid of hybrid optimization algorithm. The projected KLBG model included three major phases: an encoder for image compression, a channel for transitions of the compressed image, and a decoder for image reconstruction. In the encoder section, the image vector creation, optimal codebook generation, and indexing mechanism are carried out. The input image enters the encoder stage, wherein it’s split into immediate and non-overlapping blocks. The proposed GMISM model hybridizes the concepts of the Genetic Algorithm (GA) and Slime Mould Optimization (SMO), respectively. Once, the optimal codebook is generated successfully, the indexing of the every vector with index number from index table takes place. These index numbers are sent through the channel to the receiver. The index table, optimal codebook and reconstructed picture are all included in the decoder portion. The received index table decodes the received indexed numbers. The optimally produced codebook at the receiver is identical to the codebook at the transmitter. The matching code words are allocated to the received index numbers, and the code words are organized so that the reconstructed picture is the same size as the input image. Eventually, a comparative assessment is performed to evaluate the proposed model. Especially, the computation time of the proposed model is 69.11%, 27.64%, 62.07%, 87.67%, 35.73%, 62.35%, and 14.11% better than the extant CSA, BFU-ROA, PSO, ROA, LA, SMO, and GA algorithms, respectively.
An improved generative network model for tackling mode collapse in medical leaf image generation

In this study, a unique generative adversarial network (GAN) architectural variation was suggested, which engages in adversarial game serve by preserving an appropriate distance in the latent dimension of the network. This method overcomes the mode collapse problem with a small dataset. Extensive experiments are conducted using the segmented medical leaf dataset with various classes and the generator network is able to produce all the artificial image classes. This is accomplished by combining a unique training technique with a reasonably simple model design.
Research on dual-channel user portrait construction method based on DPCNN-BiGRU and attention mechanism

To address the problem that single-channel neural networks cannot fully extract text semantic features in traditional user portrait construction methods, this paper proposes a dual-channel user portrait model based on DPCNN-BIGRU and attention mechanism. The model first uses Bidirectional Encoder Representation from Transformers(Bert) and CK-means+ to obtain the fusion vector of semantic features and topic features, and then feeds the vector into Deep Pyramid Convolutional Neural Networks (DPCNN) and Bidirectional Gated Recurrent Unit (BiGRU). Deep features and global features of the text are obtained simultaneously, and then weights are assigned by the attention mechanism. Finally, the output features of the dual channels are fused and classified. It is tested on the Sogou user portrait datasets, and the experimental results prove that the dual-channel model outperforms the baseline model.
Design and implementation of a fast integration method for multi-source data in high-speed network

The data collected by the distributed high-speed network has multiple sources. Therefore, in order to realize the rapid integration of multi-source data, this paper designs a rapid data integration method based on the characteristics of the distributed high-speed network. First, we use linear regression analysis to build a distributed perceptual data model, so that network nodes can only transmit the parameter information of the regression model, so as to simplify the data collection. Then, a dead band amplitude limiting nonlinear link is added at the high frequency channel side to filter and assimilate the data. Finally, the data feature vectors are extracted as the training samples of the neural network to obtain the mapping relationship between different feature vectors, and then the decision level data integration is achieved by training the neural network. The experimental results show that this method can accurately collect high-speed network data, and the data collection deviation is always less than 5 μrad; This method has good filtering effect on data and can eliminate the interference of burr signal; The convergence speed of this method is fast, and the data assimilation can be completed within 0.4 s, which is conducive to improving the speed of data integration; With the increase of network size, the average traffic load of this method increases less.
Lightweight Encryption Assisted Man-in-The-Middle Attack-Resilient Steganography Model for Secure Satellite Imagery Services: LEMARS

The role of communication technologies has become increasingly vital in various fields such as industrial communication, surveillance and monitoring, healthcare, and data communication, leading to a surge in demand for these technologies in recent years. In this research paper, the LEMARS model is proposed as a novel and robust lightweight encryption assisted Man-in-The-Middle (MITM) attack-resilient steganography model for secure satellite imagery services. The proposed solution combines lightweight encryption and attack-resilient embedding to achieve higher attack-resilience with low computational cost and optimal reconstruction quality. The use of Feistel architecture-driven substitution and permutation-based block-cipher encryption and heuristic-driven pixel adjustment method for MITM-Adaptive B-Channel Embedding results in higher imperceptibility and superior attack-resilience under uncertain channels. The strategic amalgamation of FSPN-BCE, L2D-IWT, and HD-OMABE enabled attack-resilient steganography for secure satellite communication. The performance of LEMARS has been evaluated using Peak Signal-to-Noise Ratio (PSNR), entropy, Non-Parametric Change Rate (NPCR), Unified Average Change Intensity (UACI), and regular and singular block pattern changes. LEMARS achieved a PSNR value of 58.02 dB, an entropy value of 6.15 dB, an NPCR value of 99.84%, a UACI value of 33.70, and exhibited minimal block pattern changes. These results indicate that the proposed model achieved higher attack-resilience with low computational cost and optimal reconstruction quality. The research aimed to improve encryption and embedding methods to achieve an MITM-attack resilient embedding solution for secure satellite imagery services. The proposed LEMARS model exhibited superiority over other state-of-the-art methods, affirming the robustness of the proposed LEMARS model for real-time multimedia data security purposes, including remote sensing, satellite imagery services, telemedicine, and EHR. In conclusion, the LEMARS model offers an optimal solution for multimedia data security, combining lightweight encryption and attack-resilient embedding to achieve higher attack-resilience with low computational cost and optimal reconstruction quality.
Quantization-Based Watermarking Methods with Reduced Distortion

The article discusses new quantization-based watermarking algorithms characterized by a reduced level of distortions introduced when embedding a watermark relative to other algorithms of this family. These algorithms are studied on model data with a given distribution of values, and also applied to real data: grayscale images, as well as JPEG DCT coefficients. The conducted experiments confirmed the advantages of the proposed algorithms in comparison with analogues.