Reviewerly/exHarmony huggingface dataset:
MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors

Many individuals worldwide pass away as a result of inadequate procedures for prompt illness identification and subsequent treatment. A valuable life can be saved or at least extended with the early identification of serious illnesses, such as various cancers and other life-threatening conditions. The development of the Internet of Medical Things (IoMT) has made it possible for healthcare technology to offer the general public efficient medical services and make a significant contribution to patients' recoveries. By using IoMT to diagnose and examine BreakHis v1 400× breast cancer histology (BCH) scans, disorders may be quickly identified and appropriate treatment can be given to a patient. Imaging equipment having the capability of auto-analyzing acquired pictures can be used to achieve this. However, the majority of deep learning (DL)-based image classification approaches are of a large number of parameters and unsuitable for application in IoMT-centered imaging sensors. The goal of this study is to create a lightweight deep transfer learning (DTL) model suited for BCH scan examination and has a good level of accuracy. In this study, a lightweight DTL-based model "MobileNet-SVM", which is the hybridization of MobileNet and Support Vector Machine (SVM), for auto-classifying BreakHis v1 400× BCH images is presented. When tested against a real dataset of BreakHis v1 400× BCH images, the suggested technique achieved a training accuracy of 100% on the training dataset. It also obtained an accuracy of 91% and an F1-score of 91.35 on the test dataset. Considering how complicated BCH scans are, the findings are encouraging. The MobileNet-SVM model is ideal for IoMT imaging equipment in addition to having a high degree of precision. According to the simulation findings, the suggested model requires a small computation speed and time.
Artificial intelligence in service industries: customers’ assessment of service production and resilient service operations

Artificial intelligence (AI) is increasingly embedded into service firms’ operations. However, production systems and operations management scholars have not yet examined if AI-empowered service operations are positively judged by service customers. To bridge this gap, this study draws on the three-factor theory of customer satisfaction applied to online review data, to capture the effect of AI-empowered service operations on overall customer satisfaction, operationalised by means of online review ratings. Based on text analytics techniques applied to a sample of more than 50,000 TripAdvisor ORs covering 35 international hotels in Asia and America, we develop a penalty–reward contrast analysis. The findings reveal that the effects of customer interaction with mechanical AI on customer satisfaction with service operations are asymmetric: positive customer interaction with mechanical AI positively and significantly influences overall customer satisfaction with AI-empowered service operations, whereas negative customer interaction with mechanical AI does not significantly alter customer satisfaction. Taken together, these findings suggest that mechanical AI constitutes a key element of resilient AI-empowered service operations.
Validated digital literacy measures for populations with low levels of internet experiences

A growing body of evidence suggests that digital literacy is an important barrier constraining adoption and use of Internet and digital technologies in the developing world. By enabling people to effectively find valuable information online, digital literacy can play a crucial role in expanding economic opportunities, thereby leading to human development and poverty reduction. Unfortunately, there is a dearth of validated survey measures for capturing digital literacy of populations who have limited prior exposure to technology. We present a novel approach for measuring digital literacy of low literacy and new Internet users, an important segment of users in developing countries. Using a sample of 143 social media users in Pakistan, which includes a significant fraction of low literacy individuals, we measure digital literacy by observing the effectiveness of participants in completing a series of tasks and by recording a set of self-reported survey responses. We then use machine learning methods (e.g., Random Forest) to identify a parsimonious set of survey questions that are most predictive of ground truth digital literacy established through participant observation. Our approach is easily scalable in low-resource settings and can aid in tracking digital literacy as well as designing interventions and policies tailored to users with different levels of digital literacy.
Financing practices of Micro and Small Entrepreneurs using WSM MCDM Method

A small or micro enterprise is usually a one-person show. A partnership is held by a firm or corporation Even in small units, operations are mainly carried out by shareholders or Carried out by one of the directors. In practice, others are sleeping partners or directors, essentially helping out financing. A company is classified If the paid up capital is less than or equal to 20,000 Birr As micro. Similarly, a company has its paid-up capital when less than or equal to Birr 500,000 considered small. However, it is the size of jobs or number of employees in MSE Does not provides information about Key Differences between Small Business and Small Business are scale and size. A small business is a type of small a business employing less than 10 persons, small Businesses with up to 500 employees including Haksever has fewer than 500 employees Define and characterizes a small business with shows following characteristics Management is independent; usually the manager is also the owner. Working Stress Design Method Reinforced A concrete design method is used Concrete is elastic, whereas steel and concrete are elastic in nature, in which the relationship between loads and stresses is linear. Working pressure method (WSM) This traditional design is perfect.
Forensic Symmetry for DeepFakes

In this paper, we propose a new DeepFakes forensics approach called forensic symmetry, which determines whether two symmetrical face patches contain the same or different natural features. To do this, we propose a multi-stream learning structure composed of two feature extractors. The first feature extractor obtains symmetry feature from the front face images. The second feature extractor obtains similarity feature from the side face images. Symmetry feature and similarity feature are collectively called natural feature. Forensic symmetry system maps the pair of symmetrical face patches into the angular hyperspace to quantify the difference of their natural features. The greater the difference of natural features, the higher the tamper probability of face images. The heuristic prediction algorithm is designed to compute the tamper probability of DeepFakes at video level. A series of experiments are carried out to evaluate the effectiveness of our proposed forensic symmetry system. Experimental results show that our approach is effective for DeepFakes detection under the scenarios of homologous detection, heterogeneous detection, and re- compression detection.
Cost-sensitive learning with modified Stein loss function

Cost-sensitive learning (CSL), which has gained widespread attention in class imbalance learning (CIL), can be implemented either by tuning penalty parameters or by designing new loss functions. In this paper, we propose a cost-sensitive learning method with a modified Stein loss function (CSMS) and a robust CSMS (RCSMS). Specifically, CSMS is flexible, as it realizes CSL from above two aspects simultaneously. In contrast, RCSMS merely achieves CSL by tuning penalty parameters, but the adopted loss function makes it insensitive to noise. To our best knowledge, it is the first time for Stein loss function derived from statistics to be applied in machine learning, which not only offers two alternative class imbalance solutions but also provides a novel idea for the design of loss functions in CIL. The mini-batch stochastic sub-gradient descent (MBGD) approach is employed to optimize CSMS and RCSMS. Meanwhile, the Rademacher complexity is used to analyze their generalization error bounds. Extensive experiments profoundly confirm the superiority of both models over benchmarks.
A Robust Deep Learning Enabled Semantic Communication System for Text

With the advent of the 6G era, the concept of semantic communication has attracted increasing attention. Compared with conventional communication systems, semantic communication systems are not only affected by physical noise existing in the wireless communication environment, e.g., additional white Gaussian noise, but also by semantic noise due to the source and the nature of deep learning-based systems. In this paper, we elaborate on the mechanism of semantic noise. In particular, we categorize semantic noise into two categories: literal semantic noise and adversarial semantic noise. The former is caused by written errors or expression ambiguity, while the latter is caused by perturbations or attacks added to the embedding layer via the semantic channel. To prevent semantic noise from influencing semantic communication systems, we present a robust deep learning enabled semantic communication system (R-DeepSC) that leverages a calibrated self-attention mechanism and adversarial training to tackle semantic noise. Compared with baseline models that only consider physical noise for text transmission, the proposed R-DeepSC achieves remarkable performance in dealing with semantic noise under different signal-to-noise ratios.
Improved Reptile Search Optimization Algorithm: Application on Regression and Classification Problems

The reptile search algorithm is a newly developed optimization technique that can efficiently solve various optimization problems. However, while solving high-dimensional nonconvex optimization problems, the reptile search algorithm retains some drawbacks, such as slow convergence speed, high computational complexity, and local minima trapping. Therefore, an improved reptile search algorithm (IRSA) based on a sine cosine algorithm and Levy flight is proposed in this work. The modified sine cosine algorithm with enhanced global search capabilities avoids local minima trapping by conducting a full-scale search of the solution space, and the Levy flight operator with a jump size control factor increases the exploitation capabilities of the search agents. The enhanced algorithm was applied to a set of 23 well-known test functions. Additionally, statistical analysis was performed by considering 30 runs for various performance measures like best, worse, average values, and standard deviation. The statistical results showed that the improved reptile search algorithm gives a fast convergence speed, low time complexity, and efficient global search. For further verification, improved reptile search algorithm results were compared with the RSA and various state-of-the-art metaheuristic techniques. In the second phase of the paper, we used the IRSA to train hyperparameters such as weight and biases for a multi-layer perceptron neural network and a smoothing parameter (σ) for a radial basis function neural network. To validate the effectiveness of training, the improved reptile search algorithm trained multi-layer perceptron neural network classifier was tested on various challenging, real-world classification problems. Furthermore, as a second application, the IRSA-trained RBFNN regression model was used for day-ahead wind and solar power forecasting. Experimental results clearly demonstrated the superior classification and prediction capabilities of the proposed hybrid model. Qualitative, quantitative, comparative, statistical, and complexity analysis revealed improved global exploration, high efficiency, high convergence speed, high prediction accuracy, and low time complexity in the proposed technique.
Experimental Evaluation of Quantum Machine Learning Algorithms

Machine learning and quantum computing are both areas with considerable progress in recent years. The combination of these disciplines holds great promise for both research and practical applications. Recently there have also been many theoretical contributions of quantum machine learning algorithms with experiments performed on quantum simulators. However, most questions concerning the potential of machine learning on quantum computers are still unanswered such as How well do current quantum machine learning algorithms work in practice? How do they compare with classical approaches? Moreover, most experiments use different datasets and hence it is currently not possible to systematically compare different approaches. In this paper we analyze how quantum machine learning can be used for solving small, yet practical problems. In particular, we perform an experimental analysis of kernel-based quantum support vector machines and quantum neural networks. We evaluate these algorithm on 5 different datasets using different combinations of quantum feature maps. Our experimental results show that quantum support vector machines outperform their classical counterparts on average by 3 to 4% in accuracy both on a quantum simulator as well as on a real quantum computer. Moreover, quantum neural networks executed on a quantum computer further outperform quantum support vector machines on average by up to 5% and classical neural networks by 7%.
Smart contract diffusion in the pharmaceutical blockchain: the battle of counterfeit drugs

Counterfeit drugs have long been a major concern. In search of a solution, this study conducted a systematic literature review. Using an automated content analysis of pharmaceutical blockchains, this study identifies the (1) unique characteristics of smart contracts on blockchain platforms in the pharmaceutical supply chain, (2) role of smart contracts to combat counterfeit drugs, and (3) avenues for future studies. Theoretical and managerial implications are discussed at the end of this paper.
A Caching-Based Dual <i>K</i>-Anonymous Location Privacy-Preserving Scheme for Edge Computing

Location-based services have become prevalent and the risk of location privacy leakage increases. Most existing schemes use third-party-based or third-party-free system architectures; the former suffers from a single point of failure (SPOF) and the latter experiences a heavy load on user terminal equipment and higher communication costs. Ensuring location privacy while lowering system overhead becomes a challenge. Many existing schemes fail to leverage the responses from an LBS server; such responses can be cached to answer subsequent queries. As a result, providing users with a relatively comprehensive level of location privacy protection is troublesome. In this article, we propose a caching-based dual <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> -anonymous (CDKA) location privacy-preserving scheme in edge computing environments. Our scheme employs an edge server to intercedes between a user and LBS the server. We reduce the load on the user device by applying multilevel caching and to protect location privacy through dual anonymity. To ensure the location privacy in our construction, we set mobile clients and edge servers as anonymous. We use caching to lower the communication overhead and enforce the location privacy. The security analysis of our scheme supports its robustness against the edge server and the LBS server privacy offenses. We rely on the computation time, the communication cost, and the cache hit ratio to evaluate our work against existing constructions. The results are twofold: our work possesses a better response rate down to 15–32.6 ms and exhibits lower communication cost requirements down to 6.2–38.9 kB compared to existing works. Our scheme witnesses a higher cache hit ratio of up to 13.6% and 39.1% compared to the literature.
Memory-augmented appearance-motion network for video anomaly detection

Video anomaly detection is a promising yet challenging task, where only normal events are observed in the training phase. Without any explicit classification boundary between normal and abnormal events, anomaly detection can be turned into an outlier detection problem by regarding any event that does not conform to the normal patterns as an anomaly. Most of the existing works mainly focus on improving the representation of normal events, while ignore the relationship between normal and abnormal events. Besides, the lack of restrictions on classification boundaries also leads to performance degradation. To address the above problems, we design a novel autoencoder-based Memory-Augmented Appearance-Motion Network (MAAM-Net), which consists of a novel end-to-end network to learn appearance and motion feature of a given input frame, a fused memory module to build a bridge for normal and abnormal events, a well-designed margin-based latent loss to relieve the computation costs, and a pointed Patch-based Stride Convolutional Detection (PSCD) algorithm to eliminate the degradation phenomenon. Specifically, the memory module is embedded between the encoder and decoder, which serves as a sparse dictionary of normal patterns, therefore it can be further employed to reintegrate abnormal events during inference. To further distort the reintegration quality of abnormal events, the margin-based latent loss is leveraged to enforce the memory module to select a sparse set of critical memory items. Last but not least, the simple yet effective detection method focuses on patches rather than the overall frame responses, which can benefit from the distortion of abnormal events. Extensive experiments and ablation studies on three anomaly detection benchmarks, i.e., UCSD Ped2, CUHK Avenue, and ShanghaiTech, demonstrate the effectiveness and efficiency of our proposed MAAM-Net. Notably, we achieve superior AUC performances on UCSD Ped2 (0.977), CHUK Avenue (0.909), and ShanghaiTech (0.713). The code is publicly available at https://github.com/Owen-Tian/MAAM-Net.
Domain Adaptation: Challenges, Methods, Datasets, and Applications

Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well on another set of data (target domain), which is different but has similar properties as the source domain. Domain Adaptation (DA) strives to alleviate this problem and has great potential in its application in practical settings, real-world scenarios, industrial applications and many data domains. Various DA methods aimed at individual data domains have been reported in the last few years; however, there is no comprehensive survey that encompasses all these data domains, focuses on the datasets available, the methods relevant to each domain, and importantly the applications and challenges. To that end, this survey paper discusses how DA can help DNNs work efficiently in these settings by reviewing DA methods and techniques. We have considered five data domains: computer vision, natural language processing, speech, time-series, and multi-modal data. We present a comprehensive taxonomy, including the methods, datasets, challenges, and applications corresponding to each domain. Our goal is to discuss industrial use cases and DA implementation for those. Our final aim is to provide future research directions based on evolving methods and results, the datasets used, and industrial applications.
Malicious Node Detection Using Machine Learning and Distributed Data Storage Using Blockchain in WSNs

In the proposed work, blockchain is implemented on the Base Stations (BSs) and Cluster Heads (CHs) to register the nodes using their credentials and also to tackle various security issues. Moreover, a Machine Learning (ML) classifier, termed as Histogram Gradient Boost (HGB), is employed on the BSs to classify the nodes as malicious or legitimate. In case, the node is found to be malicious, its registration is revoked from the network. Whereas, if a node is found to be legitimate, then its data is stored in an Interplanetary File System (IPFS). IPFS stores the data in the form of chunks and generates hash for the data, which is then stored in blockchain. In addition, Verifiable Byzantine Fault Tolerance (VBFT) is used instead of Proof of Work (PoW) to perform consensus and validate transactions. Also, extensive simulations are performed using the Wireless Sensor Network (WSN) dataset, referred as WSN-DS. The proposed model is evaluated both on the original dataset and the balanced dataset. Furthermore, HGB is compared with other existing classifiers, Adaptive Boost (AdaBoost), Gradient Boost (GB), Linear Discriminant Analysis (LDA), Extreme Gradient Boost (XGB) and ridge, using different performance metrics like accuracy, precision, recall, micro-F1 score and macro-F1 score. The performance evaluation of HGB shows that it outperforms GB, AdaBoost, LDA, XGB and Ridge by 2-4%, 8-10%, 12-14%, 3-5% and 14-16%, respectively. Moreover, the results with balanced dataset are better than those with original dataset. Also, VBFT performs 20-30% better than PoW. Overall, the proposed model performs efficiently in terms of malicious node detection and secure data storage.
Understanding VR-Based Construction Safety Training Effectiveness: The Role of Telepresence, Risk Perception, and Training Satisfaction

The use of virtual reality as a safety training technology is gaining attention in the construction industry. While current studies focus mainly on the development of VR-based safety training programs, studies focusing on improving its effectiveness is still lacking. Thus, this study aims to understand the psychological process of training transfer and determine the factors that affect VR safety training effectiveness. The study analysed survey data from 248 construction workers who finished construction safety training using VR using PLS-SEM. The results show that the telepresence experienced through the VR and the risk perception of the trainees regarding occupational accidents significantly affect their satisfaction with VR safety training, which affected its effectiveness. Considering that the use of VR in the construction safety training context is still in its early stages, the results of our study, which comprehensively analyses both the technological and psychological aspects of VR safety training, could provide meaningful implications to VR training content developers. Furthermore, the theoretical approach of our study could be implemented in future studies focusing on the topic of training effectiveness.
Topic Classification of Online News Articles Using Optimized Machine Learning Models

Much news is available online, and not all is categorized. A few researchers have carried out work on news classification in the past, and most of the work focused on fake news identification. Most of the work performed on news categorization is carried out on a benchmark dataset. The problem with the benchmark dataset is that model trained with it is not applicable in the real world as the data are pre-organized. This study used machine learning (ML) techniques to categorize online news articles as these techniques are cheaper in terms of computational needs and are less complex. This study proposed the hyperparameter-optimized support vector machines (SVM) to categorize news articles according to their respective category. Additionally, five other ML techniques, Stochastic Gradient Descent (SGD), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbor (KNN), and Naïve Bayes (NB), were optimized for comparison for the news categorization task. The results showed that the optimized SVM model performed better than other models, while without optimization, its performance was worse than other ML models.