Reviewerly/exHarmony huggingface dataset:
The Interactive Role of Social Capital in the Relationship Between Dynamic Capabilities and Strategic Renewal / an Analytical Research of the Views of a Sample of Managers in the Southern Cement Company in the Province of Najaf

Purpose:The current research sought to measure the interactive impact Social capital in the relationship between of dynamic capabilities and strategic renewal. Theoretical framework:as it builds within its theoretical framework of dynamic capabilities an independent variable and strategic renewal is a dependent variable, and social capital is an interactive variable Design/methodology/approach :In order to process this data and information, the researcher used the statistical methods, which are: (interaction analysis method).through the use of the intentional sample distributed (120) forms A questionnaire on the administrative leaders in the Iraqi General Company for Cement - Kufa Cement Factory. Findings : The effect of the interaction variable (social capital) on the relationship between dynamic capabilities and strategic renewal was negative (inversely), which means that the elements of social capital currently in force in the company do not work to strengthen the relationship between the independent and dependent variables. Research,Practical &amp; Social implications:The results of the current research showed an interactive effect of social capital in the relationship between dynamic capabilities and strategic renewal. Originality/value: Increasing interest in the social capital mechanisms currently adopted and working on developing them in order to strengthen the relationship between dynamic capabilities and strategic renewal.
Outcome Logic: A Unifying Foundation for Correctness and Incorrectness Reasoning

Program logics for bug-finding (such as the recently introduced Incorrectness Logic) have framed correctness and incorrectness as dual concepts requiring different logical foundations. In this paper, we argue that a single unified theory can be used for both correctness and incorrectness reasoning. We present Outcome Logic (OL), a novel generalization of Hoare Logic that is both monadic (to capture computational effects) and monoidal (to reason about outcomes and reachability). OL expresses true positive bugs, while retaining correctness reasoning abilities as well. To formalize the applicability of OL to both correctness and incorrectness, we prove that any false OL specification can be disproven in OL itself. We also use our framework to reason about new types of incorrectness in nondeterministic and probabilistic programs. Given these advances, we advocate for OL as a new foundational theory of correctness and incorrectness.
Copyright protection of multiple CT images using Octonion Krawtchouk moments and grey Wolf optimizer

This paper proposes a novel Octonion Krawtchouk Moments (OKMs) transform to deal with a set of images in a compact manner, and based on this transform, a local zero-watermarking scheme is proposed to protect the copyright of CT medical images. The scheme first annotates regions of interest (ROIs) on seven medical images and then uses the OKMs of these ROIs to construct a single feature image called zero-watermark. This scheme adopts the gray Wolf Optimizer (GWO) algorithm to have a blind nature and to improve robustness against common image processing manipulations and attacks (zero-watermarking requirements). In addition, our scheme uses the trained U-net (R231) model to reduce the search space for the GWO algorithm and prevent this algorithm from getting stuck in a local optimal solution. The experimental results show that the proposed method is very robust against common image processing manupilations and attacks and has superiority compared with superb other zero-watermarking methods.
1 - Enhancing the Digital Transformation of African Universities: Covid-19 as Accelerator

The COVID-19 pandemic has had negative effects on virtually every sector of the global economy and educational institutions have not been spared. This has inevitably led to the need for African think tanks to consider ways through which the continent’s unprecedented youth bulge could be the cog around which growth and competitiveness of the African economy could be achieved. The first section introduces the paper and highlights how Higher Education Institutions (HEIs) have a key role to play towards ensuring the youth are adequately equipped with skills that are relevant to the twenty-first century’s digital transformation. The second section discusses the theories behind the mega trends of the twenty-first century by international agencies, consultancy firms, governments, academics, and pundits while the third section examines the digitalization of HEIs and cites various publications on higher education and its evolution in the digital era. The fourth section builds on the authors’ findings from previous sections to detail a twelve- point Digital Transformation Agenda for Africa. The fifth section highlights the technological infrastructure that is required to support Africa’s Digital Transformation Agenda and twenty-first century research and innovation before the authors draw conclusions.
Awareness model for minimizing the effects of social engineering attacks in web applications

Social Engineering (SE) Attacks against information systems continue to pose a potentially devastating impact. Security information systems are becoming increasingly significant as the number of SE incidents rapidly increased and became more aggressive than before. The World Wide Web (WWW) has evolved for information exchange and knowledge-sharing. It enables the sharing of information in a timely, effective, and transparent manner. Identity theft and identity misuse are two sides of cybercrime in which hackers and fraudulent users collect sensitive information from current legal users in order to perform fraud or deceit for financial gain. Malicious links are used as phishing methods, in which malicious links are planted beneath legitimate-looking links. As the number of web pages grows, the number of malicious web pages and the attacks of such become more complex. In this paper, we provide a method for identifying malicious web pages using a crawling and classification approach that helps to support the automatic discovery of the malicious links. The proposed approach can successfully complete the crawling session even if the page requires partial page refreshment and authentication credentials. The evaluation of the proposed approach shows a higher accuracy compared to an existing approach with an overall accuracy of 72% in three custom applications. Moreover, the proposed approach will calculate the significance and the impact severances of each link on the website and it better differentiates malicious web pages and normal links. The results of the proposed approach will also help in providing a set of recommendations which can increase the awareness level of the end-users, website administrators on how to better deal with these types of SE attacks.
Effects of JPEG Compression on Vision Transformer Image Classification for Encryption-then-Compression Images

This paper evaluates the effects of JPEG compression on image classification using the Vision Transformer (ViT). In recent years, many studies have been carried out to classify images in the encrypted domain for privacy preservation. Previously, the authors proposed an image classification method that encrypts both a trained ViT model and test images. Here, an encryption-then-compression system was employed to encrypt the test images, and the ViT model was preliminarily trained by plain images. The classification accuracy in the previous method was exactly equal to that without any encryption for the trained ViT model and test images. However, even though the encrypted test images can be compressible, the practical effects of JPEG, which is a typical lossy compression method, have not been investigated so far. In this paper, we extend our previous method by compressing the encrypted test images with JPEG and verify the classification accuracy for the compressed encrypted-images. Through our experiments, we confirm that the amount of data in the encrypted images can be significantly reduced by JPEG compression, while the classification accuracy of the compressed encrypted-images is highly preserved. For example, when the quality factor is set to 85, this paper shows that the classification accuracy can be maintained at over 98% with a more than 90% reduction in the amount of image data. Additionally, the effectiveness of JPEG compression is demonstrated through comparison with linear quantization. To the best of our knowledge, this is the first study to classify JPEG-compressed encrypted images without sacrificing high accuracy. Through our study, we have come to the conclusion that we can classify compressed encrypted-images without degradation to accuracy.
BacterialWmark: telemedicine watermarking technique using bacterial foraging for smart healthcare system

A transform domain digital image watermarking technique is proposed using bacterial foraging optimization (BFO) for telemedicine applications. To make a trade-off between robustness, imperceptibility, and authenticity is a significant focus of our work. The field of telemedicine is one of the critical areas for an intruder to make unwanted changes. The size of medical data is exponentially increasing since the pandemic. So, the security of medical data is also a big concern. One of the most important techniques to safeguard medical data is image watermarking. The digital imaging and communications in medical images of liver ultrasound are taken as input host images. Peak signal-to-noise ratio, Kullback–Leibler distance, and root-mean-square difference are used for BFO. The proposed BacterialWmark technique shows a significant improvement in robustness and imperceptibility compared with other existing techniques. The computational complexity of the proposed work is also less. The authentication of the watermarked image is successfully executed as the features are perfectly matched with the aid of the MinEigen algorithm. So, the BacterialWmark algorithm can be used in the field of telemedicine efficiently.
Error statistics and scalability of quantum error mitigation formulas

Abstract Quantum computing promises advantages over classical computing in many problems. Nevertheless, noise in quantum devices prevents most quantum algorithms from achieving the quantum advantage. Quantum error mitigation provides a variety of protocols to handle such noise using minimal qubit resources. While some of those protocols have been implemented in experiments for a few qubits, it remains unclear whether error mitigation will be effective in quantum circuits with tens to hundreds of qubits. In this paper, we apply statistics principles to quantum error mitigation and analyse the scaling behaviour of its intrinsic error. We find that the error increases linearly O ( ϵ N ) with the gate number N before mitigation and sublinearly $$O({\epsilon }^{{\prime} }{N}^{\gamma })$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:msup> <mml:mrow> <mml:mi>ϵ</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>′</mml:mo> </mml:mrow> </mml:msup> <mml:msup> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>γ</mml:mi> </mml:mrow> </mml:msup> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> after mitigation, where γ ≈ 0.5, ϵ is the error rate of a quantum gate, and $${\epsilon }^{{\prime} }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>ϵ</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>′</mml:mo> </mml:mrow> </mml:msup> </mml:math> is a protocol-dependent factor. The $$\sqrt{N}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msqrt> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> </mml:msqrt> </mml:math> scaling is a consequence of the law of large numbers, and it indicates that error mitigation can suppress the error by a larger factor in larger circuits. We propose the importance Clifford sampling as a key technique for error mitigation in large circuits to obtain this result.
A blockchain-based security and trust mechanism for AI-enabled IIoT systems

The complexity and heterogeneity of artificial intelligence (AI) enabled industrial Internet of things (IIoT) lead to lacks of mutual trust between devices and directly affects the data security for the network. Therefore, it is necessary to provide trust mechanisms in AI-enabled IIoT. However, the existing trust mechanisms are not suitable in distributed networks. Thus, this paper presents a blockchain-based cloud-edge-end framework and designs a trust mechanism based on blockchain consensus. It utilizes the BLS-based proof of replication (PoRep) as consensus mechanisms and realizes device mutual trust. The device data is stored in servers to generate proofs and reach consensuses after broadcasting to other nodes. This ensures the data security of networks and provides mutual trust mechanisms between various devices. Moreover, the secret sharing based verifiable delay functions (VDF) is utilized to prevent the servers from calculating data replicas dynamically in PoRep and improve the efficiency of reaching consensus among nodes.
Gray-Level Co-occurrence Matrix Analysis of Nuclear Textural Patterns in Laryngeal Squamous Cell Carcinoma: Focus on Artificial Intelligence Methods

Abstract Gray-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) analyses are two contemporary computational methods that can identify discrete changes in cell and tissue textural features. Previous research has indicated that these methods may be applicable in the pathology for identification and classification of various types of cancers. In this study, we present findings that squamous epithelial cells in laryngeal carcinoma, which appear morphologically intact during conventional pathohistological evaluation, have distinct nuclear GLCM and DWT features. The average values of nuclear GLCM indicators of these cells, such as angular second moment, inverse difference moment, and textural contrast, substantially differ when compared to those in noncancerous tissue. In this work, we also propose machine learning models based on random forests and support vector machine that can be successfully trained to separate the cells using GLCM and DWT quantifiers as input data. We show that, based on a limited cell sample, these models have relatively good classification accuracy and discriminatory power, which makes them suitable candidates for future development of AI-based sensors potentially applicable in laryngeal carcinoma diagnostic protocols.
A novel time series prediction method based on pooling compressed sensing echo state network and its application in stock market

In the prediction of time series, the echo state network (ESN) exhibits exclusive strengths and a unique training structure. Based on ESN model, a pooling activation algorithm consisting noise value and adjusted pooling algorithm is proposed to enrich the update strategy of the reservoir layer in ESN. The algorithm optimizes the distribution of reservoir layer nodes. And the nodes set will be more matched to the characteristics of the data. In addition, we introduce a more efficient and accurate compressed sensing technique based on the existing research. The novel compressed sensing technique reduces the amount of spatial computation of methods. The ESN model based on the above two techniques overcomes the limitations in traditional prediction. In the experimental part, the model is validated with different chaotic time series as well as multiple stocks, and the method shows its efficiency and accuracy in prediction.
Explainable Crowd Decision Making methodology guided by expert natural language opinions based on Sentiment Analysis with Attention-based Deep Learning and Subgroup Discovery

There exist a high demand to provide explainability to artificial intelligence systems, where decision making models are included. This paper focuses on crowd decision making using natural language evaluations from social media with the aim to provide explainability. We present the Explainable Crowd Decision Making based on Subgroup Discovery and Attention Mechanisms (ECDM-SDAM) methodology as an a posteriori explainable process that captures the wisdom of crowds that is naturally provided in social media opinions. It extracts the opinions from social media texts using a deep learning based sentiment analysis approach called Attention based Sentiment Analysis Method. The methodology includes a backward process that provides explanations to justify its sense-making procedure by applying mainly the attention mechanism on texts and subgroup discovery on opinions. We evaluate the methodology in the real case study of the TripR-2020Large dataset for restaurant choice. The results show that the ECDM-SDAM methodology provides easy understandable explanations that elucidates the key reasons that support the output of the decision process.
Unsupervised Anomaly Detection and Localization of Machine Audio: A Gan-Based Approach

Automatic detection of machine anomaly remains challenging for machine learning. We believe the capability of generative adversarial network (GAN) suits the need of machine audio anomaly detection, yet rarely has this been investigated by previous work. In this paper, we propose AEGAN-AD, a totally unsupervised approach in which the generator (also an autoencoder) is trained to reconstruct input spectrograms. It is pointed out that the denoising nature of reconstruction deprecates its capacity. Thus, the discriminator is redesigned to aid the generator during both training stage and detection stage. The performance of AEGAN-AD on the dataset of DCASE 2022 Challenge TASK 2 demonstrates the state-of-the-art result on five machine types. A novel anomaly localization method is also investigated. Source code available at: www.github.com/jianganbai/AEGAN-AD
A new combination method based on Pearson coefficient and information entropy for multi-sensor data fusion

When confronted with greatly contradictory evidence, the Dempster-Shafer evidence theory may exhibit certain constraints that lead to fused results which are inconsistent with common understanding. Within the existing Internet of Things landscape, there are occasions when a small number of sensors may malfunction and contradict each other. This study addresses contradictory information by processing the bodies of evidence beforehand. Additionally, an enhanced fusion technique for conflicting evidence is introduced, which employs Pearson correlation coefficient and information entropy. We propose a novel combination approach for multi-sensor data fusion based on evidence theory. Firstly, the credibility for each piece of evidence is computed through amalgamating correlation measurements with evidence distance between two pieces of evidence. Next, based on the information volume, the credibility is adjusted, resulting in the final weighting factor for the evidence. The reasonable weighted average evidence is then created using the weighting factor of each piece of evidence. Finally, the combined result is obtained by applying Dempster's combination rule, which combines the weighted average evidence N−1 times. Upon comparing the fusion results, it has been observed that the performance of the proposed method surpasses that of other methods. Our method can effectively minimize the ramifications of profoundly conflicting evidence in the fusion process, resulting in more logical fusion results than other methods. The outcomes of numerical examples expose that the technique put forward in this manuscript can manage highly conflicting evidence, thereby yielding fusion results that are more precise and conducive to making sound decisions.
Aplikasi E-Learning Siswa Smk Berbasis Web

The purpose of writing this thesis is to analyze the system and design a web-based E-Learning application for vocational students to be able to assist in the process of teaching and learning activities. With this application, it is expected to be able to help problems faced by teachers and students outside of class hours, such as lack of communication time between teacher and students, search for information about the material being taught, and of course to facilitate teachers in delivering material to students. The results achieved are the availability of applications supporting teaching and learning activities that can be obtained without being bound by time and place. The conclusion with the existence of web-based E-Learning is to facilitate teacher and student communication, provision of material, ease of assignment and assignment of assignments, as well as facilitate the informational value.
Error-distribution-free kernel extreme learning machine for traffic flow forecasting

Traffic flow modeling plays a crucial role in intelligent transportation systems, which is of vital significance for mitigating traffic congestion and reducing carbon emissions. Owing to the uncertainties and nonlinear characteristics of traffic flow, it confronts a considerable challenge to establish a model to predict traffic flow efficiently and robustly. Kernel-based extreme learning machine (KELM), a natural extension of extreme learning machine (ELM) that incorporates kernel learning, has demonstrated excellent performance in traffic flow prediction. However, the performance of KELM may significantly decrease when the noise is non-Gaussian, as it was developed under the minimum mean square error (MMSE) criterion assuming Gaussian noise. To address this issue, we propose an error-distribution-free kernel extreme learning machine, termed ɛDFKELM, by embedding a more robust optimization criterion to guide the training. In addition, we further develop an online version of the ɛDFKELM model for continual forecasting, called ɛDFKELMv2. We perform extensive experiments on two widely-used public benchmark traffic flow datasets, which illustrate that the ɛDFKELM model outperforms the state-of-the-art approaches in terms of forecasting performance. ɛDFKELM model achieves RMESE values of 251.49 vehs/h, 196.27 vehs/h, 216.97 vehs/h, and 160.92 vehs/h on the A1, A2, A4, and A8 highways of Amsterdam dataset, respectively.
Performing <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>SU</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>d</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:mrow></mml:math> Operations and Rudimentary Algorithms in a Superconducting Transmon Qudit for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>d</mml:mi><mml:mo>=</mml:mo><mml:mn>3</mml:mn></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/…

Quantum computation architecture based on $d$-level systems, or qudits, has attracted considerable attention recently due to their enlarged Hilbert space. Extensive theoretical and experimental studies have addressed aspects of algorithms and benchmarking techniques for qudit-based quantum computation and quantum information processing. Here, we report a physical realization of qudit with upto 4 embedded levels in a superconducting transmon, demonstrating high-fidelity initialization, manipulation, and simultaneous multi-level readout. In addition to constructing SU($d$) operations and benchmarking protocols for quantum state tomography, quantum process tomography, and randomized benchmarking etc, we experimentally carry out these operations for $d=3$ and $d=4$. Moreover, we perform prototypical quantum algorithms and observe outcomes consistent with expectations. Our work will hopefully stimulate further research interest in developing manipulation protocols and efficient applications for quantum processors with qudits.