Reviewerly/exHarmony huggingface dataset:
A Method of Image Semantic Segmentation Based on PSPNet

Image semantic segmentation is a visual scene understanding task. The goal is to predict the category label of each pixel in the input image, so as to achieve object segmentation at the pixel level. Semantic segmentation is widely used in automatic driving, robotics, medical image analysis, video surveillance, and other fields. Therefore, improving the effect and accuracy of image semantic segmentation has important theoretical research significance and practical application value. This paper mainly introduces the pyramid scene parsing network PSPNet based on pyramid pooling and proposes a parameter optimization method based on PSPNet model using GPU distributed computing method. Finally, it is compared with other models in the field of semantic segmentation. The experimental results show that the accuracy of the improved PSPNet model in this paper has been significantly improved on Pascal VOC 2012 + 2017 data set.
When to choose ranked area integrals versus integrated gradient for explainable artificial intelligence – a comparison of algorithms

Purpose Explainable artificial intelligence (XAI) has importance in several industrial applications. The study aims to provide a comparison of two important methods used for explainable AI algorithms. Design/methodology/approach In this study multiple criteria has been used to compare between explainable Ranked Area Integrals (xRAI) and integrated gradient (IG) methods for the explainability of AI algorithms, based on a multimethod phase-wise analysis research design. Findings The theoretical part includes the comparison of frameworks of two methods. In contrast, the methods have been compared across five dimensions like functional, operational, usability, safety and validation, from a practical point of view. Research limitations/implications A comparison has been made by combining criteria from theoretical and practical points of view, which demonstrates tradeoffs in terms of choices for the user. Originality/value Our results show that the xRAI method performs better from a theoretical point of view. However, the IG method shows a good result with both model accuracy and prediction quality.
Effect of Ambient Lights on the Accuracy of a 3-Dimensional Optical Scanner for Face Scans: An In Vitro Study

Most 3D scanners use optical technology that is impacted by lighting conditions, especially in triangulation with structured-light or laser techniques. However, the effect of ambient lights on the accuracy of the face scans remains unclear. The purpose of this study is to investigate the effect of ambient lights on the accuracy of the face scans obtained from the face scanner (EinScan Pro 2X Plus, Shining 3D Tech. Co., LTD., Hangzhou, China). A head model was designed in Rhinoceros 5 software (Rhino, Robert McNeel and Associates for Windows, Washington DC, USA) and printed with 200 micron resolution of polylactic acid and was dented with 2.0 mm of carbide bur to aid in superimposition in software. The head model was measured by a coordinate-measuring machine (CMM) to generate a reference stereolithography (STL) file as a control. The face model was scanned four times under nine light conditions: cool white (CW), warm white (WW), daylight (DL), natural light (NL), and illuminant (9w, 18w, 22w). Scan data were exported into an STL file. The scan STL files obtained were compared with the reference STL file by 3D inspection software (Geomagic Control X version 17, Geomagic, Morrisville, NC, USA). The deviations and root mean square errors (RMSEs) between the reference model (trueness) and within the group (precision) were selected for the statistical analysis. The statistical analysis was done using SPSS 20.0 (IBM Company, Chicago, USA). The trueness and precision were evaluated with the one-way ANOVA with multiple comparisons using the Tukey method. For trueness, the scanner showed the lowest RMSE under the NL group (77.18 ± 3.22) and the highest RMSE under the 18w-DL group (95.33 ± 6.89). There was a statistically significant difference between the NL group and the 18w-DL group (p < 0.05) for trueness. Similarly, for precision, the scanner showed the lowest RMSE under the NL group (56.92 ± 4.56) and the highest RMSE under the 9w-CW group (78.52 ± 10.61). There was statistically significant difference between NL, 18w-WW, 18w-CW, 18w-DL, 22w-WW, 22w-DL, 9w-CW, 9w-WW, and 9w-DL (p < 0.05) for the precision. Ambient lights affected the face scans. Under the natural light condition, the face scanner had the best accuracy in terms of both trueness and precision. The 18w-DL and 9w-WW conditions showed the least trueness whereasthe 9w-CW and 9w-DL conditions showed the least precision.
Scattering-medium-based optical image encryption by chaos and digital optical phase conjugation

Scattering-medium-based optical image encryption provides an intrinsically tamper-resistant physical key. In this paper, we utilize a scattering medium with ideal optical properties as the physical key. The plaintext is transformed into four noise-like holograms with the help of the four-step phase-shifting digital holography. Low-frequency components of the four holograms are extracted and combined into a new image by the lifting wavelet transform (LWT). Then the combined image is scrambled by the nonlinear 3D chaos-based cyclic shift. In decryption, the digital optical phase conjugation (DOPC) technique is utilized to extract plaintext. Numerical simulations prove the feasibility of the proposed method, and the security analysis shows that our method can resist different attacks.
An animal dynamic migration optimization method for directional association rule mining

In the area of association rule mining, many optimization algorithms have been proposed to improve the computational efficiency of rule mining or the quality and diversity of association rules. However, in real applications, since the user may have prior knowledge and research trends for some key items, the association rules containing key items are more valuable and meaningful for these users. This contributes to a new issue that association rules related to key items should be mined in a targeted manner. To solve this issue, this paper proposes a novel animal dynamic migration optimization (ADMO) method to realize directional rule mining as well as maintain high mining efficiency and high rule quality. Taking the support and confidence of frequent itemsets as input, the method first identifies valuable rules and then initializes and updates the animal population to search for the best animal. The support and confidence of the best animal are defined as threshold values to delete unnecessary rules and discover more key rules. During the optimization, the population size value is dynamically generated. The effectiveness and applicability of ADMO are validated on 11 open-source datasets and a real-world elevator case. The results reveal that the ADMO method has a faster mining speed and obtains more key rules than the ARM-PSO, ARM-AMO, ARM-MOPSO, ARM-WOA, and ARM-DE methods. In the elevator case, the association rule generated by ADMO can provide a higher success rate and accuracy for requirement transformation.
High-Resolution Histopathological Image Classification Model Based on Fused Heterogeneous Networks with Self-Supervised Feature Representation

Applying machine learning technology to automatic image analysis and auxiliary diagnosis of whole slide image (WSI) may help to improve the efficiency, objectivity, and consistency of pathological diagnosis. Due to its extremely high resolution, it is still a great challenge to directly process WSI through deep neural networks. In this paper, we propose a novel model for the task of classification of WSIs. The model is composed of two parts. The first part is a self-supervised encoding network with a UNet-like architecture. Each patch from a WSI is encoded as a compressed latent representation. These features are placed according to their corresponding patch's original location in WSI, forming a feature cube. The second part is a classification network fused by 4 famous network blocks with heterogeneous architectures, with feature cube as input. Our model effectively expresses the feature and preserves location information of each patch. The fused network integrates heterogeneous features generated by different networks which yields robust classification results. The model is evaluated on two public datasets with comparison to baseline models. The evaluation results show the effectiveness of the proposed model.
A Proactive Approach to assess web application security through the integration of security tools in a Security Orchestration Platform

The increasing number of attacks leads to a growing research and development interest in cybersecurity systems. As a response to the increasingly distributed nature of attacks, many organizations have demonstrated willingness to exchange information concerning threats, incidents, and mitigation strategies with security detection tools and techniques. Various security detection techniques such as signature recognition, anomaly detection, etc fail to completely detect complicated attacks. The current situation can be dealt with as a significant tool that helps auditors and administrators to manage and identify distributed threats. In this paper, a novel social spider communicating behavior-based swarm intelligent open-source Orchestrated Continuous Vulnerability Assessment (OCVA) scanning tool is proposed. The proposed OCVA tool addresses the requirement of orchestration of continuous vulnerability assessment of all automated cybersecurity detection processes. It scans, monitors, visualizes, analyzes, mitigates, and remediates the vulnerabilities of the network, assets, and web applications. It helps the developers and security auditors overcome challenges by providing the desired visualizations and analytics of the vulnerable assets. Two case studies are conducted on the basis of the algorithmic comparative analysis with BRICK, Fuzzing, ACO, PSO and GA based vulnerability scanners along with the tool based comparative evaluation with W3af, ZAP, Wapiti, and Arachni in terms of vulnerability detection rate, accuracy, false positive rate, true positive rate and consistency. The results indicate that the proposed OCVA tool outperforms in terms of accuracy, vulnerability remediation rate, and consistency in both.
Relationship of ethical leadership, organizational culture, corporate social responsibility and organizational performance: a test of two mediation models

Purpose The sound leadership style can be indicative of organizational success and explanatory of quality performance. Besides this, there are various factors that can impact organizational performance. To this end, this study aims to investigate the effect of ethical leadership on organizational performance, with the mediating role of corporate social responsibility (CSR) and organizational culture. Design/methodology/approach The service sector in Jordan was targeted by this research, and data were collected from 371 middle-level and top-level managers working in service companies. These responses were analyzed by using analysis of a moment structure. Findings The result conveyed that ethical leadership does not only influence organizational performance, but it also, and positively so, affects the organizational culture and CSR. In addition, CSR and organizational culture significantly mediate the relationship between ethical leadership and organizational performance. Practical implications The findings of this study are a guide for managers and owners of service companies who are aiming to enhance organizational performance. If they follow the ethical leadership approach and emphasize CSR initiatives and organizational culture, they can attain, and naturally so, the maximum level of organizational performance. Originality/value To the best of the authors’ knowledge, this research paper is the first to analyze ethical leadership in the context of the service sector in Jordan and highlight its influence on organizational culture, CSR and ultimately organizational performance. Moreover, it examined the mediating effects of organizational culture and CSR between ethical leadership and organizational performance.
Entity Linking Method for Chinese Short Text Based on Siamese-Like Network

Entity linking plays a fundamental role in knowledge engineering and data mining and is the basis of various downstream applications such as content analysis, relationship extraction, question and answer. Most existing entity linking models rely on sufficient context for disambiguation but do not work well for concise and sparse short texts. In addition, most of the methods use pre-training models to directly calculate the similarity between the entity text to be disambiguated and the candidate entity text, and do not dig deeper into the relationship between them. This article proposes an entity linking method for Chinese short texts based on Siamese-like networks to address the above shortcomings. In the entity disambiguation task, the features of the Siamese-like network are used to deeply parse the semantic relationships in the text and make full use of the feature information of the entity text to be disambiguated, capturing the interdependent features within the sentences through an attention mechanism, aiming to find out the most critical elements in the entity text description. The experimental demonstration on the CCKS2019 dataset shows that the F1 value of the method reaches 87.29%, increase of 11.02% compared to the F1 value(that) of the baseline method, fully validating the superiority of the model.
Mobile Traffic Forecasting for Network Slices: A Federated-Learning Approach

Network slicing is one of the cornerstones for next-generation mobile communication systems. Specifically, it enables Mobile Virtual Network Operators (MVNOs) to offer various types of services over the same physical infrastructure owned by an Infrastructure Provider (InP). To satisfy the dynamic user requirements and ensure resource efficiency, MVNOs need to estimate the future traffic demand in advance, to pre-allocate/reconfigure the resources at the base stations. However, this per-slice traffic forecasting exploits information that is clearly sensitive for the MVNOs from a business point of view, and which might even disclose private data regarding some users. Hence, it is vital for MVNOs to ensure data privacy while conducting traffic forecasting. Bearing this in mind, we propose the Federated Proximal Long Short-Term Memory (FPLSTM) framework, which allows MVNOs to train their local models with their private dataset at each base station without compromising data privacy. Simultaneously, an InP global model is updated through the aggregation of local models weights. Prediction results obtained by training the models on a real-world dataset indicate that the forecasting performance of FPLSTM is as accurate as state-of-the-art solutions, while ensuring data privacy, computation and communication cost efficiency.
Generative focused feedback residual networks for image steganalysis and hidden information reconstruction

Image steganalysis is the process of detecting the presence of hidden information in an image. Existing image steganalysis methods cannot fully extract hidden image information even if the presence of hidden information is successfully detected, resulting in either poor detection performance or inability to reconstruct hidden information. This paper proposes a generative image steganalysis algorithm based on focused feedback residual convolutional neural network for simultaneous detection and extraction of hidden information. First, a possible stego image was processed by a preprocessing network consisting of several convolutional layers and two fresh focus modules, and several enhanced feature maps were output. Then, the resulting enhanced feature maps were fetched into both a classification network and a reconstruction network. The classification network identified whether the feature maps came from a stego image or a simple cover image. The reconstruction network, consisting of some layers of convolutional units, pixel shufflers and feedback residual modules, completed the reconstruction of hidden information. Experimental results show that the proposed image steganalysis algorithm can obtain state-of-the-art results in terms of detection rate and hidden information reconstruction compared with classical rich models and several recent deep learning-based methods. • A generative focus feedback residual network for image steganalysis. • Steganalyzer consisting of preprocessing, classification and reconstruction. • Simultaneous hidden information detection and reconstruction. • U-shaped multi-feedback, focus module, pixel shuffle and ResNet contribute.
Block chain enabled auditing with optimal multi‐key homomorphic encryption technique for public cloud computing environment

Summary Cloud storage is a significant cloud computing (CC) service which enables the client to save and retrieve the data at anytime and anywhere. Due to the increased demand and familiarity of the CC environment, different kinds of security threats and susceptibilities are raised. Data integrity and privacy are the major problems in CC environment where the data can be saved in distinct geographic regions. So, privacy preservation and data integrity become important factors of the user concern related to the CC environment. Several auditing protocols are majorly dependent upon the conventional public key infrastructure, which led to high computational complexity and it is unsuitable for the setting of multiple users. To resolve these issues, this study develops a new block chain enabled auditing with optimal multi‐key homomorphic encryption (BEA‐OMKHE) technique for public cloud environment. The proposed BEA‐OMKHE technique aims to assure data integrity, security, and auditing in public cloud storage. Besides, an OMKHE technique is derived to accomplish data integrating into the cloud environment by the design of end to end encryption system. A secure generation of keys and encryption processes are carried out by the use of MKHE technique; thereby the data becomes highly secure. In addition, the choice of keys is performed by the improved beetle antenna search optimization (IBAS) algorithm. Therefore, the proposed BEA‐OMKHE technique offers an efficient way of enhancing the data integrity in CC method. The performance validation of the BEA‐OMKHE technique takes place and the results are inspected under various aspects. The comparative result analysis ensured the betterment of the BEA‐OMKHE technique interms of different measures such as communication cost, encryption time, decryption time, computation cost, privacy preserving rate, and authentication accuracy.
Bayesian neuroevolution using distributed swarm optimization and tempered MCMC

The major challenge of Bayesian neural networks has been in developing effective sampling methods that address deep neural networks and big data-related problems. As an alternative to gradient-based training methods, neuro-evolution features evolutionary algorithms that provide a black-box approach to learning in neural networks. Neuroevolution employs evolutionary and swarm optimization methods to provide an alternative where the training algorithm is not constrained to the architecture of the network and has the potential to reduce local-minima and vanishing gradient problems. Bayesian neural networks use variational inference and Markov chain Monte Carlo (MCMC) sampling methods. Tempered MCMC is a powerful MCMC method that can take advantage of a parallel computing environment and efficient proposal distributions. In this paper, we present a synergy of neuroevolution and Bayesian neural networks where operators in particle swarm optimization (PSO) are used for forming efficient proposals in tempered MCMC sampling. The results show that the proposed method provides better prediction accuracy when compared to random-walk proposal distribution in MCMC for both time-series and pattern classification problems. The results also show substantial computational time reduction compared to gradient-based proposals while generating comparable accuracy performance. The Bayesian neuroevolution framework can be further introduced to models that do not have gradient information. • Effective sampling methods are crucial to the scalability of Bayesian neural networks • Evolutionary operators are introduced into MCMC methods to form effective proposals • Improve time efficiency of tempered MCMC sampling through parallel computing • Swarm proposals show improved sampling efficiency over gradient proposals in MCMC • The proposed framework can be extended to models that lack gradient information
The Influence of Ethical Beliefs and Attitudes, Norms, and Prior Outcomes on Cybersecurity Investment Decisions

Recent data breaches underscore the importance of organizational cybersecurity. However, the high costs of such security can force chief financial officers (CFOs) to make difficult financial and ethical trade-offs that have both business and societal implications. We employ a 2 × 2 randomized experiment that varies both an observed scenario CFO’s investment decision (invest/not invest in security) and organizational outcomes (positive/negative) to investigate these trade-offs. Participant managers assess the observed CFO’s investment behavior and indicate their own intentions to invest. Results indicate that when the observed scenario CFO invests in security, managers primarily follow their peers when making investment decisions. However, when the observed CFO does not invest in security, managers make their own decisions by engaging in more in-depth reasoning that includes assessment of the seriousness of consequences, as well as the ethical and societal considerations. Moderated mediation findings further deconstruct and corroborate these relationships.
A generalized line segmentation method for multi-script handwritten text documents

Handwritten document image segmentation into text-lines is a crucial stage towards unconstrained handwritten document recognition. In the context of Indian subcontinent various scripts are used for communication where a system for multi-script handwritten text line segmentation is very much essential. This paper presents a multi-script text line segmentation algorithm based on newly developed light projection, start point detection, and boundary tracking methods. The proposed approach is capable of overcoming most of the hindrance faced by state-of-the-art methods. The experiment is performed on our proposed Bangla handwritten document image dataset WBSUBNdb_text and also on a variety of well-known public handwritten datasets namely: CMATERdb, PhDIndic_11, KHATT, HIT-MW, ISI Bengali Writer Identification/Verification dataset, ICDAR 2013 segmentation contest dataset, ICDAR 2013 writer identification contest benchmark dataset, and obtained promising results.