Reviewerly/exHarmony huggingface dataset:
Factors influencing the adoption of robotic technologies in academic libraries: A systematic literature review (SLR)

The study aimed to identify the factors influencing the adoption of robotic technologies in academic libraries, investigate the impact of robots on library services, and reveal challenges to adopt robotic applications in academic libraries. A systematic literature review was applied to address the study’s objectives. The 16 most relevant seminal studies published in peer-reviewed journals were selected to conduct the study. Findings displayed that the factors of innovation, advancements in artificial intelligence, library security, and personalized services influenced positively the adoption of robotic technologies in academic libraries. The study also illustrated that robots had a significant positive impact on academic libraries by performing a variety of roles, solving space issues, and providing entertainment to library patrons. Results also revealed that loss of critical thinking, technical issues, lack of organizational planning, financial hurdles, fear of job loss, shortage of skilled manpower, and ethical considerations caused barriers to the successful implementation of robotic applications in academic libraries. The study has added valuable literature to the existing body of knowledge. It is the first systematic literature review on the factors influencing the adoption of robotic technologies in academic libraries. It has provided practical solutions in light of evidence-based data for the effective adoption of robotic technologies in academic libraries.
Athena – The NSF AI Institute for Edge Computing

Abstract The National Science Foundation (NSF) Artificial Intelligence (AI) Institute for Edge Computing Leveraging Next Generation Networks (Athena) seeks to foment a transformation in modern edge computing by advancing AI foundations, computing paradigms, networked computing systems, and edge services and applications from a completely new computing perspective. Led by Duke University, Athena leverages revolutionary developments in computer systems, machine learning, networked computing systems, cyber‐physical systems, and sensing. Members of Athena form a multidisciplinary team from eight universities. Athena organizes its research activities under four interrelated thrusts supporting edge computing: Foundational AI, Computer Systems, Networked Computing Systems, and Services and Applications, which constitute an ambitious and comprehensive research agenda. The research tasks of Athena will focus on developing AI‐driven next‐generation technologies for edge computing and new algorithmic and practical foundations of AI and evaluating the research outcomes through a combination of analytical, experimental, and empirical instruments, especially with target use‐inspired research. The researchers of Athena demonstrate a cohesive effort by synergistically integrating the research outcomes from the four thrusts into three pillars: Edge Computing AI Systems, Collaborative Extended Reality (XR), and Situational Awareness and Autonomy. Athena is committed to a robust and comprehensive suite of educational and workforce development endeavors alongside its domestic and international collaboration and knowledge transfer efforts with external stakeholders that include both industry and community partnerships.
A secure and efficient electronic medical record data sharing scheme based on blockchain and proxy re-encryption

Abstract With the rapid development of the Internet of Medical Things (IoMT) and the increasing concern for personal health, sharing Electronic Medical Record (EMR) data is widely recognized as a crucial method for enhancing the quality of care and reducing healthcare expenses. EMRs are often shared to ensure accurate diagnosis, predict prognosis, and provide health advice. However, the process of sharing EMRs always raises significant concerns about potential security issues and breaches of privacy. Previous research has demonstrated that centralized cloud-based EMR systems are at high risk, e.g., single points of failure, denial of service (DoS) attacks, and insider attacks. With this motivation, we propose an EMR sharing scheme based on a consortium blockchain that is designed to prioritize both security and privacy. The interplanetary file system (IPFS) is used to store the encrypted EMR while the returned hash addresses are recorded on the blockchain. Then, the user can authorize other users to decrypt the EMR ciphertext via the proxy re-encryption algorithm, ensuring that only authorized personnel may access the files. Moreover, the scheme attains personalized access control and guarantees privacy protection by employing attribute-based access control. The safety analysis shows that the designed scheme meets the expected design goals. Security analysis and performance evaluation show that the scheme outperforms the comparison schemes in terms of computation and communication costs.
From Strings to Sensors: Movement Representation in AI Theatre

The assimilation of AI technology into theatre practices has inaugurated an expansive frontier of possibilities for both thespians and spectators. In terms of movement, this involves the use of avatars, which inhabit a customary screen milieu (encompassing three-dimensional in-world scenography) that necessitates simultaneous consideration of a tridimensional theatrical space and coexisting performers, within a moment of real-time inception and interconnectedness. This complex confluence raises questions pertaining to the ‘avatarisation’ of corporeal embodiments on the theatrical stage and the consequent emergence of novel performative methodologies. Within AI-enabled performances, the use of motion capture technology, commonly known as ‘mocap’, entails the recording of skeletal data from physical actors, referred to as ‘mocaptors’, who wear a geo-spatial system for motion capture. This is then translated into digital data that can subsequently be used to animate digital characters or avatars.
Classification of Rice Growth Phases Using the K-Nearest Neighbor Algorithm in the Irrigation area of Seulimeum Sub District, Aceh, Indonesia

Abstract Rice ( Oryza sativa L.) is the main food commodity for most of Indonesia’s population. The existence of irrigation areas dramatically affects the water demand in each phase of rice growth. Due to a lack of remote sensing knowledge, classifying the rice growth phase is done manually. This study aims to classify rice growth phases using the K-Nearest Neighbor Algorithm in the irrigation area of the Seulimuem Sub-District. This research uses Google Earth satellite imagery in November 2021 through three stages: multi-resolution segmentation, guided classification, and accuracy testing. The results showed that the paddy and non-paddy fields are 1,425.56 ha and 6,581.70 ha, respectively. The classification results of rice plant growth phases consist of 4 phases, namely the inundation phase, the vegetative phase, the generative phase, and the fallow phase. Based on the accuracy assessment results in November 2021, the overall accuracy ranged from 90.65% to 92.92%, with a kappa index value of 0.85 to 0.87, categorizing it as “almost perfect.”
Utilizing modern computer architectures to solve mathematical optimization problems: A survey

Numerical algorithms to solve mathematical optimization problems efficiently are essential to applications in many areas of engineering and computational science. To solve optimization problems of ever-increasing scale and complexity, we need methods that exploit emerging hardware systems. However, the complexities of specific architectures and their impact on performance can be challenging. This article provides an overview of emerging hardware architectures and how they are used to solve mathematical optimization problems. We focus on parallel high-performance computing architectures, which are well-established yet challenging to employ for optimization, as well as digital quantum computing, which has recently gained attention due to its potential for transformative computational performance. Furthermore, we highlight several other emerging hardware architectures that may become relevant for mathematical optimization. We intend for this review to encourage the optimization and process engineering communities to increasingly consider both hardware and software developments in the pursuit of superior computational performance.
Fast random opposition-based learning aquila optimization algorithm

Meta-heuristic algorithms are usually employed to address a variety of challenging optimization problems. In recent years, there has been a continuous effort to develop new and efficient meta-heuristic algorithms. The Aquila Optimization (AO) algorithm is a newly established swarm-based method that mimics the hunting strategy of Aquila birds in nature. However, in complex optimization problems, the AO has shown a sluggish convergence rate and gets stuck in the local optimal region throughout the optimization process. To overcome this problem, in this study, a new mechanism named Fast Random Opposition-Based Learning (FROBL) is combined with the AO algorithm to improve the optimization process. The proposed approach is called the FROBLAO algorithm. To validate the performance of the FROBLAO algorithm, the CEC 2005, CEC 2019, and CEC 2020 test functions, along with six real-life engineering optimization problems, are tested. Moreover, statistical analyses such as the Wilcoxon rank-sum test, the t-test, and the Friedman test are performed to analyze the significant difference between the proposed algorithm FROBLAO and other algorithms. The results demonstrate that FROBLAO achieved outstanding performance and effectiveness in solving an extensive variety of optimization problems.
Secure Medical Data on Cloud storage via DNA Homomorphic Encryption technique

Cloud computing offers considerable flexibility and financial savings to data owners for outsourcing their complicated data management systems from local sites to the commercial public cloud. However, an effective encryption scheme is needed to protect the private medical data of the user in the cloud without leaking it. Therefore, in this paper an efficient Deoxyribonucleic Acid Homomorphic Encryption algorithm (DNA-HE) has been proposed to encrypt and store the medical images in the cloud securely. Initially, the data is mapped to DNA sequences and then encrypted using homomorphic encryption. The key for the homomorphic algorithm is generated using Rider Optimization Technique which ensures security. It acts as a double encryption technique. The same procedure tends to decrypt the encrypted data. The efficiency of the proposed technique is assessed employing several parameters such as execution time, encryption time, and decryption time. The experimental results shows that the proposed DNA-HE system reduces encryption time of 10%, 20%, and 35% than OHE, HE and ECC algorithms.
Radiation-training system with a custom survey-meter mock-up in a browser-based mixed reality environment

Training for radiation protection and control requires a visual understanding of radiation, which cannot be perceived by the human senses. Trainees must also master the effective use of measuring instruments. Traditionally, such training has exposed trainees to radiation sources. Here, we present a novel e-training strategy that enables safe, exposure-free handling of a radiation measuring tool called a survey meter. Our mixed reality radiation-training system merges the physical world with a digital one. Collaborating with a mixed reality headset (HoloLens 2), this system constructs a mock-up of a survey meter in real-world space. The HoloLens 2 employs a browser-based application to visualize radiation and to simulate/share the use of the survey meter, including its physical movements. To provide a dynamic learning experience, the system adjusts the survey-meter mock-up readings according to the operator's movements, distance from the radiation source, the response time of survey meter, and shielding levels. Through this approach, we expect that trainees will acquire practical skills in interpreting survey-meter readings and gain a visual understanding of radiation in real-world situations.
Robust sequential fusion Kalman estimators with asymptotic equivalence and stability for networked uncertain sensor systems

For networked uncertain sensor systems (NUSSs) with an improved unified sensor model with “hard” and “soft” sensors including five uncertainties plus two-step random measurement delays and colored measurement noises, applying the block matrix inversion formula, a new sequential inverse covariance fusion (SICF) approach is presented. It can sequentially compute the global inverse covariance according to the time order of the received data, but need not wait until all data are received. The corresponding minimax robust time-varying and steady-state sequential fusion (SF) Kalman estimators (predictor, filter, and smoother) are presented in the sense that their actual error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. They have lower computation complexities and higher accuracies equivalent to ones of the robust optimal batch-fusion (BF) Kalman estimators, and are suitable for real-time applications. Their asymptotic equivalence, absolute asymptotic stability, and accuracy relations are proved. Their strict complexity analysis is given. They constitute a novel universal robust SF estimation, convergence and stability theory for NUSSs. One simulation example applied to the uncertain ARMA signal processing verifies the effectiveness of the proposed approach and theory.
Simplicity in joy and detail in anger: Intertwining effect of cognitive and affective review disposition on review helpfulness

Review length and readability, are cognitive dispositions of reviews supposed to reflect diagnostic content and lead to favorable evaluation of review helpfulness. However, underlying these two cognitive dispositions of reviews, are discrepancies that make it difficult for a helpful review to simultaneously satisfy both of them. To resolve the discrepancies, this present study, drawn on cognitive tuning theory, proposes and validates the intertwining effect of cognitive dispositions (length and readability) and affective dispositions (joy and anger expression) in judging review helpfulness. Based on analysis of heterogeneous hotel review data, our findings indicate joy expression in reviews, decreased the positive impact of review length on review helpfulness, while enhancing the positive impact of review readability on helpfulness. Anger expression in reviews, shows reverse effects, enhancing the positive impact of review length on helpfulness. Our findings contribute to an in-depth understanding of review characteristics and review helpfulness judgment.
Cross-scale Vision Transformer for crowd localization

Crowd localization can provide the positions of individuals and the total number of people, which has great application value for security monitoring and public management, meanwhile it meets the challenges of lighting, occlusion and perspective effect. In recent times, Transformer has been applied in crowd localization to overcome these challenges. Yet such kind of methods only consider to integrate the multi-scale information once, which results in incomplete multi-scale information fusion. In this paper, we propose a novel Transformer network named Cross-scale Vision Transformer (CsViT) for crowd localization, which simultaneously fuses multi-scale information during both the encoder and decoder stages and meanwhile building the long-range context dependencies on the combined feature maps. To this end, we design the multi-scale encoder to fuse the feature maps of multiple scales at corresponding positions so as to obtain the combined feature maps, and meanwhile design the multi-scale decoder to integrate the tokens at multiple scales when modeling the long-range context dependencies. Furthermore, we propose Multi-scale SSIM (MsSSIM) loss to adaptively compute head regions and optimize the similarity at multiple scales. Specifically, we set the adaptive windows with different scales for each head and compute the loss values within these windows so as to enhance the accuracy of the predicted distance transform map. We perform comprehensive experiments on five public datasets, and the results obtained validate the effectiveness of our method.
A Needs Assessment for Promoting Transformational Change Towards Accessibility in Education and Competence Mobilization for eLearning

During the COVID-19 pandemic, higher education institutions worldwide encountered issues related to student dissatisfaction with online education. Several nations are poised to migrate to distance learning for technological and economic reasons. It becomes clear that current online learning should be applied under certain circumstances. The deployment of e-learning resources (ELR) has emerged as a strategy for addressing problems related to instructional design, technological packages, and easy access to education. In this context, Rwanda has used open and distance learning (ODL) to thrive in a continuously evolving educational environment. However, learners still have limited information about the use of e-learning platforms. To keep up with the changing nature of education, today's educators and students require ELR tools and information and communication technology (ICT) competencies. This research aimed to evaluate the needs, preparedness, and willingness of ODL professionals and learners at the University of Rwanda (UR) to enhance the use of ELR in the education and learning environment. In this research, a systematic questionnaire was considered for data collection purposes. The analyses were descriptive and consisted of either determining the proportion of answers to items in different segments of the questionnaire or determining the percentage of answers using various Likert scales and calculating the differences between scales. The results highlighted that several participants admitted to the University of Rwanda to having moderate knowledge of e-learning resources, and most of them regularly used text-based documents on e-learning platforms. Moreover, the participants demonstrated a need to avail internet connectivity and updated IT infrastructure, organize workshops and training, and conduct an awareness campaign on ELR and learning management system (LMS), It was also demonstrated that the ICT basic skills are needed to effectively implement e-learning at the University of Rwanda.
Data Analytics for Imbalanced Dataset

The primary issue in real-time big data classification is imbalanced datasets. Even though we have many balancing techniques to reduce imbalance ratio which is not suitable for big data that has scalability issues. This study is envisioned to explore different balancing techniques with experimental study. We tried comparing the effectiveness of various balancing strategies, including cutting-edge approaches for severely unbalanced data from online repositories. Here we apply SMOTE, SMOTE ENN and SMOTE Tomek balancing algorithms for dermatology, wine quality and diabetes datasets. After balancing the dataset, the balanced dataset is classified with AdaBoost and random forest algorithms. On three datasets, the outcomes show that the classification algorithm with the balancing technique improves the classification performance for imbalanced datasets. Experiment results showed that the SMOTE ENN technique produces higher classification with accuracy than the SMOTE and SMOTE Tomek techniques. The findings are analyzed with other factors like execution time and scalability. Though SMOTE Tomek produces 1.0 for a few datasets, its execution time is longer than SMOTE ENN. Therefore, SMOTE ENN with random forest classification produces 1.0 accuracy for all three datasets with less execution time. This experimental study analyses to create a novel ensemble technique for balancing highly imbalanced data.
Deepfake Detection using Deep Feature Stacking and Meta-learning

<h2>Abstract</h2> Deepfake is a type of face manipulation technique using deep learning that allows for the replacement of faces in videos in a very realistic way. While this technology has many practical uses, if used maliciously, it can have a significant number of bad impacts on society, such as spreading fake news or cyberbullying. Therefore, the ability to detect deepfake has become a pressing need. This paper aims to address the problem of deepfake detection by identifying deepfake forgeries in video sequences. In this paper, a solution to the said problem is presented, which at first uses a stacking based ensemble approach, where features obtained from two popular deep learning models, namely Xception and EfficientNet-B7, are combined. Then by selecting a near-optimal subset of features using a ranking based approach, the final classification is performed to classify real and fake videos using a meta-learner, called multi-layer perceptron. In our experimentation, we have achieved an accuracy of 96.33% on Celeb-DF (V2) dataset and 98.00% on the FaceForensics++ dataset using the meta-learning model both of which are higher than the individual base models. Various types of experiments have been conducted to validate the robustness of the current method.
Fuzzy Logic Controller for UAV With Gains Optimized Via Genetic Algorithm

A gains optimizer of a fuzzy controller system for an Unmanned Aerial Vehicle (UAV) based on a metaheuristic algorithm is developed in the present investigation. The contribution of the work is the adjustment by the Genetic Algorithm (GA) to tune the gains at the input of a fuzzy controller. First, a typical fuzzy controller was modeled, designed, and implemented in a mathematical model obtained by Newton-Euler methodology. Subsequently, the control gains were optimized using a metaheuristic algorithm. The control objective is that the UAV consumes the least amount of energy. With this basis, the Genetic Algorithm finds the necessary gains to meet the design parameters. The tests were performed using the Matlab-Simulink environment. The results indicate an improvement, reducing the error in tracking trajectories from 30% in some tasks and following trajectories that could not be completed without a tuned controller in other tasks.
A Fake Profile Detection Model Using Multistage Stacked Ensemble Classification

Fake profile identification on social media platforms is essential for preserving a reliable online community. Previous studies have primarily used conventional classifiers for fake account identification on social networking sites, neglecting feature selection and class balancing to enhance performance. This study introduces a novel multistage stacked ensemble classification model to enhance fake profile detection accuracy, especially in imbalanced datasets. The model comprises three phases: feature selection, base learning, and meta-learning for classification. The novelty of the work lies in utilizing chi-squared feature-class association-based feature selection, combining stacked ensemble and cost-sensitive learning. The research findings indicate that the proposed model significantly enhances fake profile detection efficiency. Employing cost-sensitive learning enhances accuracy on the Facebook, Instagram, and Twitter spam datasets with 95%, 98.20%, and 81% precision, outperforming conventional and advanced classifiers. It is demonstrated that the proposed model has the potential to enhance the security and reliability of online social networks, compared with existing models.