Reviewerly/exHarmony huggingface dataset:
Addressing service profit chain with artificial and emotional intelligence

In view of the prevalence of artificial intelligence (AI) – powered applications in service organizations, this paper draws on service profit chain theory and proposes these applications as a service product for employees (referred to as internal customers) and customers. The outcome of which is to examine the influence of AI service quality on internal and external customer loyalty. Customer satisfaction and engagement are modeled as mediators, whereas emotional intelligence (EI) is a moderator in AI – customer loyalty relationships. Two studies were undertaken with employees and customers in Australian-based hotels to examine these relationships. The results show that AI service quality is significantly related to internal and external customer satisfaction and engagement. Both internal and external customer engagement have significant effects on customer loyalty and play a significant mediation role in the service quality – customer loyalty relationship. EI has only a significant moderation effect on the relationship between AI service quality and customer engagement for internal and external customers. Discussion and implications of the research findings are provided for researchers and practitioners.
Feature Extraction based Online Job Portal

Job search portals and proper portal log-ins are the main goals of this research project. Placements are becoming increasingly important, and many people’s lives are dependent on them. Both job seekers and recruiters can use a job portal to find the ideal company for their needs. In the event of activity seekers, their educational qualifications and their preferences are taken into consideration. For those looking for work, activity portals display a list of companies, while for recruiters, a database of thousands of potential employees provides the ideal applicants. When it comes to recruiting human resources, the utilisation of an electronic platform for job postings and attracting qualified applications has evolved throughout time.
Explaining predictions and attacks in federated learning via random forests

Abstract Artificial intelligence (AI) is used for various purposes that are critical to human life. However, most state-of-the-art AI algorithms are black-box models, which means that humans cannot understand how such models make decisions. To forestall an algorithm-based authoritarian society, decisions based on machine learning ought to inspire trust by being explainable . For AI explainability to be practical, it must be feasible to obtain explanations systematically and automatically. A usual methodology to explain predictions made by a (black-box) deep learning model is to build a surrogate model based on a less difficult, more understandable decision algorithm. In this work, we focus on explaining by means of model surrogates the (mis)behavior of black-box models trained via federated learning. Federated learning is a decentralized machine learning technique that aggregates partial models trained by a set of peers on their own private data to obtain a global model. Due to its decentralized nature, federated learning offers some privacy protection to the participating peers. Nonetheless, it remains vulnerable to a variety of security attacks and even to sophisticated privacy attacks. To mitigate the effects of such attacks, we turn to the causes underlying misclassification by the federated model, which may indicate manipulations of the model. Our approach is to use random forests containing decision trees of restricted depth as surrogates of the federated black-box model. Then, we leverage decision trees in the forest to compute the importance of the features involved in the wrong predictions. We have applied our method to detect security and privacy attacks that malicious peers or the model manager may orchestrate in federated learning scenarios. Empirical results show that our method can detect attacks with high accuracy and, unlike other attack detection mechanisms, it can also explain the operation of such attacks at the peers’ side.
Deterministic Sampling Classifier with weighted Bagging for drifted imbalanced data stream classification

One of the most critical data analysis tasks is the streaming data classification, where we may also observe the concept drift phenomenon, i.e., changing the decision model’s probabilistic characteristics. From a practical point of view, we may face this type of banking, medicine, or cybersecurity task to enumerate only a few. A vital characteristic of these problems is that the classes we are interested in (e.g., fraudulent transactions, treats, or serious diseases) are usually infrequent, which hinders the classification system design. The paper presents a novel algorithm DSCB (Deterministic Sampling Classifier with weighted Bagging) employs data preprocessing methods and weighted bagging technique to classify non-stationary imbalanced data stream. It builds models based on an incoming data chunk, but it also takes previously arrived instances into account. The proposed approach has been evaluated based on a wide range of computer experiments carried out on real and artificially generated data streams with various imbalance ratios, label noise levels, and concept drift types. The results confirmed that the weighted bagging ensemble coupled with data preprocessing could outperform state-of-the-art methods.
Robust Optimization Based Extreme Learning Machine for Sentiment Analysis in Big Data

Increasing use of social media has increased consumer interest in reading product evaluations and ratings before making a purchase. There is now a mechanism to examine natural language processing, sentiment analysis, and domain adaptation separately. A classifier trained on one set of datasets may underperform when applied to another collection of data. Therefore, it&#x0027;s critical to retain an open mind while experimenting with new classifiers. Reviewing datasets in big data is currently taking place. When applied to large datasets, the sentiment analysis algorithm designed for single machines or small datasets will not perform well. Robust Optimization-based Extreme Learning Machine (ROELM) is a classifier proposed in this work for sentiment analysis in massive data. ROELM is using natural wolf-like behavior to analyze an enormous review database. The single-layer hidden layer of ELM improves classification performance by one factor. This classifier&#x0027;s accuracy and f-measure performance have been assessed. According to the results, the suggested classifier achieves a higher level of classification accuracy than current classifiers.
Revisiting Gradient Boosting-Based Approaches for Learning Imbalanced Data: A Case of Anomaly Detection on Power Grids

Gradient boosting ensembles have been used in the cyber-security area for many years; nonetheless, their efficacy and accuracy for intrusion detection systems (IDSs) remain questionable, particularly when dealing with problems involving imbalanced data. This article fills the void in the existing body of knowledge by evaluating the performance of gradient boosting-based ensembles, including gradient boosting machine (GBM), extreme gradient boosting (XGBoost), LightGBM, and CatBoost. This paper assesses the performance of various imbalanced data sets using the Matthew correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC), and F1 metrics. The article discusses an example of anomaly detection in an industrial control network and, more specifically, threat detection in a cyber-physical smart power grid. The tests’ results indicate that CatBoost surpassed its competitors, regardless of the imbalance ratio of the data sets. Moreover, LightGBM showed a much lower performance value and had more variability across the data sets.
Malaria Diagnosis Using a Lightweight Deep Convolutional Neural Network

The applications of AI in the healthcare sector are increasing day by day. The application of convolutional neural network (CNN) and mask-region-based CNN (Mask-RCCN) to the medical domain has really revolutionized medical image analysis. CNNs have been prominently used for identification, classification, and feature extraction tasks, and they have delivered a great performance at these tasks. In our study, we propose a lightweight CNN, which requires less time to train, for identifying malaria parasitic red blood cells and distinguishing them from healthy red blood cells. To compare the accuracy of our model, we used transfer learning on two models, namely, the VGG-19 and the Inception v3. We train our model in three different configurations depending on the proportion of data being fed to the model for training. For all three configurations, our proposed model is able to achieve an accuracy of around 96%, which is higher than both the other models that we trained for the same three configurations. It shows that our model is able to perform better along with low computational requirements. Therefore, it can be used more efficiently and can be easily deployed for detecting malaria cells.
Estimating cluster masses from SDSS multiband images with transfer learning

The total masses of galaxy clusters characterize many aspects of astrophysics and the underlying cosmology. It is crucial to obtain reliable and accurate mass estimates for numerous galaxy clusters over a wide range of redshifts and mass scales. We present a transfer-learning approach to estimate cluster masses using the ugriz-band images in the SDSS Data Release 12. The target masses are derived from X-ray or SZ measurements that are only available for a small subset of the clusters. We designed a semi-supervised deep learning model consisting of two convolutional neural networks. In the first network, a feature extractor is trained to classify the SDSS photometric bands. The second network takes the previously trained features as inputs to estimate their total masses. The training and testing processes in this work depend purely on real observational data. Our algorithm reaches a mean absolute error (MAE) of 0.232 dex on average and 0.214 dex for the best fold. The performance is comparable to that given by redMaPPer, 0.192 dex. We have further applied a joint integrated gradient and class activation mapping method to interpret such a two-step neural network. The performance of our algorithm is likely to improve as the size of training dataset increases. This proof-of-concept experiment demonstrates the potential of deep learning in maximizing the scientific return of the current and future large cluster surveys.
A Lightweight Certificateless Group Key Agreement Method without Pairing Based on Blockchain for Smart Grid

In smart grids, the access verification of a large number of intelligent gateways and terminal devices has become one of the main concerns to ensure system security. This means that smart grids need a new key management method that is safe and efficient and has a low computational cost. Although a large number of scholars have conducted relevant research, most of these schemes cannot balance the computational overhead and security. Therefore, we propose a lightweight and secure key management method, having a low computational overhead, based on blockchain for smart grids. Firstly, we redesigned the architecture of the smart grid based on blockchain and completed the division of various entities. Furthermore, we designed a pairing-free certification authenticated group key agreement method based on blockchain under the architecture. Finally, we achieved higher security attributes, and lower authentication delay and computational overhead, compared to the traditional schemes, as shown in performance analysis and comparison.
Video Transcript Summarizer

This project proposes a video summarizing system based on natural language processing (NLP) and Machine Learning to summarize the YouTube video transcripts without losing the key elements. The quantity of videos available on web platforms is steadily expanding. The content is made available globally, primarily for educational purposes. Additionally, educational content is available on YouTube, Facebook, Google, and Instagram. A significant issue of extracting information from videos is that unlike an image, where data can be collected from a single frame, a viewer must watch the entire video to grasp the context. This study aims to shorten the length of the transcript text of the given video. The suggested method involves retrieving transcripts from the video link provided by the user and then summarizing the text by using Hugging Face Transformers and Pipelining. The built model accepts video links and the required summary duration as input from the user and generates a summarized transcript as output. According to the results, the final translated text was obtained in less time when compared with other proposed techniques. Furthermore, the video’s central concept is accurately present in the final text without any deviations.
RGBT tracking based on cooperative low-rank graph model

The existing graph-based RGBT tracking methods mainly focus on assigning a weight to each local image patch to suppress background influence in target bounding box, but the influences of background clutter might limit the improvement of tracking performance. To solve this problem, we propose a new algorithm, called cooperative low-rank graph model, to suppress background clutter. Specifically, the proposed feature decomposition module decomposes input dual-modal features into low-rank components and sparse noisy components, which could be used collaboratively by regularizing graph learning by combining modal weights. Besides, to avoid SVD (Singular Value Decomposition) operations we have designed an efficient solver based on ADMM (Alternating Direction Methods of Multipliers), which could factorize the low-rank matrix into two low-dimensional submatrices. Extensive experiments on four RGBT tracking benchmark data sets show that our method performs favorably against other state-of-the-art tracking algorithms, and achieves more robust tracking performance.
A canonical Hamiltonian for open quantum systems

If an open quantum system is initially uncorrelated from its environment, then its dynamics can be written in terms of a Lindblad-form master equation. The master equation is divided into a unitary piece, represented by an effective Hamiltonian, and a dissipative piece, represented by a hermiticity-preserving superoperator; however, the division of open system dynamics into unitary and dissipative pieces is non-unique. For finite-dimensional quantum systems, we resolve this non-uniqueness by specifying a norm on the space of dissipative superoperators and defining the canonical Hamiltonian to be the one whose dissipator is minimal. We show that the canonical Hamiltonian thus defined is equivalent to the Hamiltonian initially defined by Lindblad, and that it is uniquely specified by requiring the dissipator's jump operators to be traceless, extending a uniqueness result known previously in the special case of Markovian master equations. For a system weakly coupled to its environment, we give a recursive formula for computing the canonical effective Hamiltonian to arbitrary orders in perturbation theory, which we can think of as a perturbative scheme for renormalizing the system's bare Hamiltonian.
Valid prediction intervals for regression problems

Over the last few decades, various methods have been proposed for estimating prediction intervals in regression settings, including Bayesian methods, ensemble methods, direct interval estimation methods and conformal prediction methods. An important issue is the validity and calibration of these methods: the generated prediction intervals should have a predefined coverage level, without being overly conservative. So far, no study has analysed this issue whilst simultaneously considering these four classes of methods. In this independent comparative study, we review the above four classes of methods from a conceptual and experimental point of view in the i.i.d. setting. Results on benchmark data sets from various domains highlight large fluctuations in performance from one data set to another. These observations can be attributed to the violation of certain assumptions that are inherent to some classes of methods. We illustrate how conformal prediction can be used as a general calibration procedure for methods that deliver poor results without a calibration step.
Kernel density estimation for circular data: a Fourier series-based plug-in approach for bandwidth selection

In this paper, we derive asymptotic expressions for the mean integrated squared error of a class of delta sequence density estimators for circular data. This class includes the class of kernel density estimators usually considered in the literature, as well as a new class that is closer in spirit to the class of Parzen–Rosenblatt estimators for linear data. For these two classes of kernel density estimators, a Fourier series-based direct plug-in approach for bandwidth selection is presented. The proposed bandwidth selector has a n−1/2 relative convergence rate whenever the underlying density is smooth enough and the simulation results testify that it presents a very good finite sample performance against other bandwidth selectors in the literature.
Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation

Energy efficiency is a critical issue in data centre management, which is the foundation for cloud computing. The VM placement has a considerable impact on a data centre's energy efficiency and resource utilisation. The assignment of VMs to PMs is an NP-hard problem without an easy way to find an optimal solution, particularly in large-scale data centres. In this study, the VM placement problem is formulated as a constrained optimisation problem. The Genetic Algorithm (GA) is a suitable method for solving this problem in terms of the quality of the solution. However, GA is time-consuming to obtain an optimal solution in the large scale optimisation problem. Therefore, this paper focuses on accelerated GA for energy-efficient VM placement. As the most time-consuming element of the GA is the calculation of its fitness function, this paper simplifies this calculation through a new fitness function in GA. Simulation results of small-, medium-, and large-scale data centres demonstrate that our accelerated GA is faster than the standard GA and gives better quality of solution than the First Fit Decreasing (FFD) algorithm, respectively. The findings of our GA with the new fitness function reveal an 8% energy saving for our GA compared to FFD and a 66% reduction in our GA execution time compared to the standard GA with standard energy formula as a fitness function. The number of generations in our GA is reduced by about 50% in comparison with the standard GA. Moreover, we started with 3000 PMs in the large-scale dataset, and only 1086 PMs were actually used after running our GA. Therefore, we may switch off far more PMs for energy savings from our GA results than those from the standard GA.
Multitasking Learning Model Based on Hierarchical Attention Network for Arabic Sentiment Analysis Classification

Limited approaches have been applied to Arabic sentiment analysis for a five-point classification problem. These approaches are based on single task learning with a handcrafted feature, which does not provide robust sentence representation. Recently, hierarchical attention networks have performed outstandingly well. However, when training such models as single-task learning, these models do not exhibit superior performance and robust latent feature representation in the case of a small amount of data, specifically on the Arabic language, which is considered a low-resource language. Moreover, these models are based on single task learning and do not consider the related tasks, such as ternary and binary tasks (cross-task transfer). Centered on these shortcomings, we regard five ternary tasks as relative. We propose a multitask learning model based on hierarchical attention network (MTLHAN) to learn the best sentence representation and model generalization, with shared word encoder and attention network across both tasks, by training three-polarity and five-polarity Arabic sentiment analysis tasks alternately and jointly. Experimental results showed outstanding performance of the proposed model, with high accuracy of 83.98%, 87.68%, and 84.59 on LABR, HARD, and BRAD datasets, respectively, and a minimum macro mean absolute error of 0.632% on the Arabic tweets dataset for five-point Arabic sentiment classification problem.
Language matters: humanizing service robots through the use of language during the COVID-19 pandemic

Service robots are emerging quickly in the marketplace (e.g., in hotels, restaurants, and healthcare), especially as COVID-19-related health concerns and social distancing guidelines have affected people's desire and ability to interact with other humans. However, while robots can increase efficiency and enable service offerings with reduced human contact, prior research shows a systematic consumer aversion toward service robots relative to human service providers. This potential dilemma raises the managerial question of how firms can overcome consumer aversion and better employ service robots. Drawing on prior research that supports the use of language for building interpersonal relationships, this research examines whether the type of language (social-oriented vs. task-oriented language) a service robot uses can improve consumer responses to and evaluations of the focal service robot, particularly in light of consumers' COVID-19-related stress. The results show that consumers respond more favorably to a service robot that uses a social-oriented (vs. task-oriented) language style, particularly when these consumers experience relatively higher levels of COVID-19-related stress. These findings contribute to initial empirical evidence in marketing for the efficacy of leveraging robots' language style to improve customer evaluations of service robots, especially under stressful circumstances. Overall, the results from two experimental studies not only point to actionable managerial implications but also to a new avenue of research on service robots that examines customer-robot interactions through the lens of language and in contexts that can be stressful for consumers (e.g., healthcare or some financial service settings).The online version contains supplementary material available at 10.1007/s11002-022-09630-x.
An efficient dual semantic preserving hashing for cross-modal retrieval

Hashing methods have recently received widespread attention due to their flexibility and effectiveness for cross-modal retrieval tasks. However, most existing cross-modal hashing methods have some challenging problems, in particular, effective exploitation of semantic information and learning discriminative hash codes. To address these challenges, we propose an efficient Dual Semantic Preserving Hashing (DSPH) method, which first leverages matrix factorization to obtain low-level latent semantic representations of different modalities and remove redundant information. To enhance the discriminative capability of hash codes, we preserve the high-level pairwise semantics and the learned low-level latent semantics into the unified hash codes. Finally, DSPH adopts discrete optimization strategy to learn the hash codes directly. Experimental results on three benchmark datasets demonstrate that the proposed DSPH method outperforms many state-of-the-art cross-modal hashing methods in terms of retrieval accuracy, especially when dealing with short hash code.