Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence in the Context of Digital Marketing

This chapter examines the drivers for the adoption of artificial intelligence (AI) in digital marketing and the challenges that organizations face while implementing AI-based marketing strategies. The chapter begins with an overview of the potential applications connected with using AI in digital marketing, including automation. It then explores the key drivers for the adoption of AI in digital marketing. This chapter also provides a framework for greater adoption of AI in digital marketing. The framework has many well-known components relative to other known AI frameworks but is focused upon digital marketing. The chapter discusses the challenges that organizations face when implementing AI-based marketing strategies. The chapter also explores the potential risks associated with using AI in digital marketing, including algorithmic bias and the risk of over-reliance on AI. Overall, this chapter provides insights into the drivers and challenges of using AI in digital marketing and offers recommendations for organizations looking to implement AI-based marketing strategies.
Multi-scale graph neural network for global stereo matching

Currently, deep learning-based stereo matching is solely based on local convolution networks, which lack enough global information for accurate disparity estimation. Motivated by the excellent global representation of the graph, a novel Multi-scale Graph Neural Network (MGNN) is proposed to essentially improve stereo matching from the global aspect. Firstly, we construct the multi-scale graph structure, where the multi-scale nodes with projected multi-scale image features can be directly linked by the inner-scale and cross-scale edges, instead of solely relying on local convolutions for deep learning-based stereo matching. To enhance the spatial position information at non-Euclidean multi-scale graph space, we further propose a multi-scale position embedding to embed the potential position features of Euclidean space into projected multi-scale image features. Secondly, we propose the multi-scale graph feature inference to extract global context information on multi-scale graph structure. Thus, the features not only be globally inferred on each scale, but also can be interactively inferred across different scales to comprehensively consider global context information with multi-scale receptive fields. Finally, MGNN is deployed into dense stereo matching and experiments demonstrate that our method achieves state-of-the-art performance on Scene Flow, KITTI 2012/2015, and Middlebury Stereo Evaluation v.3/2021.
The Relationship Between Supportive Behaviors of School Administrators and Psychological Empowerment According to Teacher Perceptions

This study aims to determine the relationship between school administrators' supportive behaviours and psychological empowerment according to teacher perceptions, relational survey model as a quantitative research method was used. The population of the study consisted of 400 teachers working at primary schools in central Bolu, Turkey in the 2020-2021 academic year. As data collection instruments, Administrator Support Scale (PSS) and Psychological Empowerment Scale (PES) were used. Since the data showed normal distribution, parametric tests were employed in the analysis of the data. According to the results of the study, the fact that the teachers' perceptions of the supportive behaviors of the school administrators in terms of emotional and educational support are at the level of slightly agree shows that school administrators are inadequate at supporting teachers emotionally and educationally. Since the school administrators who should be the closest source of information in the professional development of the teachers have supervision and guidance duties, it should be taken into consideration that they are incapable of supporting the teachers in educational sense. That the teachers’ perceptions of psychological empowerment in the dimension of self-determination are at the moderate level is also a striking result. There is a positive and highly significant relationship between the supportive behaviours of school administrators and teachers' perceptions of psychological empowerment. Supportive behaviors of school administrators are a significant predictor of teachers' perceptions of psychological empowerment
K‐Plus anticlustering: An improved <i>k</i>‐means criterion for maximizing between‐group similarity

Anticlustering refers to the process of partitioning elements into disjoint groups with the goal of obtaining high between-group similarity and high within-group heterogeneity. Anticlustering thereby reverses the logic of its better known twin-cluster analysis-and is usually approached by maximizing instead of minimizing a clustering objective function. This paper presents k-plus, an extension of the classical k-means objective of maximizing between-group similarity in anticlustering applications. K-plus represents between-group similarity as discrepancy in distribution moments (means, variance, and higher-order moments), whereas the k-means criterion only reflects group differences with regard to means. While constituting a new criterion for anticlustering, it is shown that k-plus anticlustering can be implemented by optimizing the original k-means criterion after the input data have been augmented with additional variables. A computer simulation and practical examples show that k-plus anticlustering achieves high between-group similarity with regard to multiple objectives. In particular, optimizing between-group similarity with regard to variances usually does not compromise similarity with regard to means; the k-plus extension is therefore generally preferred over classical k-means anticlustering. Examples are given on how k-plus anticlustering can be applied to real norming data using the open source R package anticlust, which is freely available via CRAN.
Adaptive neural network ensemble using prediction frequency

Abstract Neural network (NN) ensembles can reduce large prediction variance of NN and improve prediction accuracy. For highly non-linear problems with insufficient data set, the prediction accuracy of NN models becomes unstable, resulting in a decrease in the accuracy of ensembles. Therefore, this study proposes a prediction frequency-based ensemble that identifies core prediction values, which are core prediction members to be used in the ensemble and are expected to be concentrated near the true response. The prediction frequency-based ensemble classifies core prediction values ​​supported by multiple NN models ​​by conducting statistical analysis with a frequency distribution, which is a collection of prediction values ​​obtained from various NN models for a given prediction point. The prediction frequency-based ensemble searches for a range of prediction values that contains prediction values above a certain frequency, and thus the predictive performance can be improved by excluding prediction values with low accuracy ​​and coping with the uncertainty of the most frequent value. An adaptive sampling strategy that sequentially adds samples based on the core prediction variance calculated as the variance of the core prediction values is proposed to improve the predictive performance of the prediction frequency-based ensemble efficiently. Results of various case studies show that the prediction accuracy of the prediction frequency-based ensemble is higher than that of Kriging and other existing ensemble methods. In addition, the proposed adaptive sampling strategy effectively improves the predictive performance of the prediction frequency-based ensemble compared with the previously developed space-filling and prediction variance-based strategies.
A rotation-invariant corner detector based on the median of subpixelized triangle

Corners are stable local image features that have played a crucial role in various computer vision and image processing tasks. Existing corner detectors generally assume that the distance between every two adjacent pixels is a constant during curvature estimation. However, this assumption is actually invalid, and thus those pixel-based measures of discrete curvature developed and exploited in the existing corner detectors may suffer instability under rotation transformations. To address this fundamental problem, a novel curvature measure is proposed in this paper, which exploits the median of a subpixelized triangle located at the current point to estimate its discrete curvature. It is shown that our proposed curvature measure is invariant under rotation transformations. Based on this novel curvature measure, a new corner detector is further developed. Extensive experimental results show that our proposed corner detector can deliver superior performance over the existing state-of-the-art methods.
Applying Blockchain consensus mechanisms to Network Service Federation: Analysis and performance evaluation

Cutting edge (vertical) applications may swiftly saturate service providers’ virtualized infrastructure. In 5G and beyond, service providers seek out for innovative solutions such as Network Service Federation (NSF) which allows orchestration of external domain services/resources to provide a zero-downtime end-to-end vertical service. Distributed ledger technologies, such as Blockchain, are used to enhance the federation process. In this article, we propose and evaluate the application of Blockchain technology for NSF. We evaluate four different consensus mechanisms: Proof-of-Work (PoW), Proof-of-Authority (PoA), Practical Byzantine-Fault Tolerant (PBFT), and Proof-of-Stake (PoS). The experimental evaluation is executed using Ethereum, Tendermint and Cosmos platforms. Results show that the evaluated consensus mechanisms enable the use of NSF for both latency-sensitive and security stringent vertical applications.
Prostate segmentation accuracy using synthetic MRI for high-dose-rate prostate brachytherapy treatment planning

Objective. Both computed tomography (CT) and magnetic resonance imaging (MRI) images are acquired for high-dose-rate (HDR) prostate brachytherapy patients at our institution. CT is used to identify catheters and MRI is used to segment the prostate. To address scenarios of limited MRI access, we developed a novel generative adversarial network (GAN) to generate synthetic MRI (sMRI) from CT with sufficient soft-tissue contrast to provide accurate prostate segmentation without MRI (rMRI).Approach. Our hybrid GAN, PxCGAN, was trained utilizing 58 paired CT-MRI datasets from our HDR prostate patients. Using 20 independent CT-MRI datasets, the image quality of sMRI was tested using mean absolute error (MAE), mean squared error (MSE), peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). These metrics were compared with the metrics of sMRI generated using Pix2Pix and CycleGAN. The accuracy of prostate segmentation on sMRI was evaluated using the Dice similarity coefficient (DSC), Hausdorff distance (HD) and mean surface distance (MSD) on the prostate delineated by three radiation oncologists (ROs) on sMRI versus rMRI. To estimate inter-observer variability (IOV), these metrics between prostate contours delineated by each RO on rMRI and the prostate delineated by treating RO on rMRI (gold standard) were calculated.Main results. Qualitatively, sMRI images show enhanced soft-tissue contrast at the prostate boundary compared with CT scans. For MAE and MSE, PxCGAN and CycleGAN have similar results, while the MAE of PxCGAN is smaller than that of Pix2Pix. PSNR and SSIM of PxCGAN are significantly higher than Pix2Pix and CycleGAN (p < 0.01). The DSC for sMRI versus rMRI is within the range of the IOV, while the HD for sMRI versus rMRI is smaller than the HD for the IOV for all ROs (p ≤ 0.03).Significance. PxCGAN generates sMRI images from treatment-planning CT scans that depict enhanced soft-tissue contrast at the prostate boundary. The accuracy of prostate segmentation on sMRI compared to rMRI is within the segmentation variation on rMRI between different ROs.
Vision-guided Unified Task and Motion Planning for Multiple Robots in Cluttered Environments

Efficient coordination of multiple mobile and fixed base robot arms to perform independent or cooperative tasks in a shared workspace while avoiding collisions is a challenging research problem that has been studied across various robotic fields. Although robotic systems are commonly used in industrial applications such as automotive production, they typically operate independently with limited collaboration between two or more units. In this study, we propose a unified methodology for motion and task planning for multiple robot arm systems with high degrees of freedom that are simultaneously performing different tasks in a shared workspace. Our methodology introduces an integration module between the high-level task planning module and low-level motion planning module to achieve reliable and collision-free execution of tasks. We also introduce the concept of a dynamic shared space graph (D-SSG) to assess the cooperation between arm pairs that share certain parts of the workspace, which is essential for selecting arm sequences and scheduling each arm to perform a task or sub-task.
Enhancing Performance of CUDA Quicksort Through Pivot Selection and Branching Avoidance Methods

This paper presents a fine-tuned implementation of the quicksort algorithm for highly parallel multicore NVIDIA graphics processors. The described approach focuses on algorith-mic and implementation-level improvements to achieve enhanced performance. Several fine-tuning techniques are explored to identify the best combination of improvements for the quicksort algorithm on GPUs. The results show that this approach leads to a significant reduction in execution time and an improvement in algorithmic operations, such as the number of iterations of the algorithm and the number of operations performed compared to its predecessors. The experiments are conducted on an NVIDIA graphics card, taking into account several distributions of input data. The findings suggest that this fine-tuning approach can enable efficient and fast sorting on GPUs for a wide range of applications.
KF-RRT: Obstacles Tracking and Safe Dynamic Motion Planning for Robotic Manipulators

This paper presents KF-RRT algorithm: a novel approach to path planning for robotic manipulators in dynamic environments. It is based on a modified RRT algorithm combined with Kalman filtering technique. RRT modification implies two aspects. The first one is related to continuous update of struc-ture/ordering within the tree to accommodate for online execution of the algorithm. The second one relies on forest-based replanning by combining connected components. On the other hand, Kalman filter is used to track/predict the motion of obstacles. Virtually augmented obstacles influence the growth of trees, which yields the improved safety margin of the resulting motion. KF-RRT is validated within a simulation study, where it is compared to comneting algorithms,
Using Genetic Algorithms for Load Balancing in Cloud Computing

The cloud has become an essential part of modern computing, and its popularity continues to rise with each passing day. Currently, cloud computing is faced with certain challenges that are, due to the increasing demands, becoming urgent to address. One such challenge is the problem of load balancing, which involves the proper distribution of user requests within the cloud. This paper proposes a genetic algorithm for load balancing of the received requests across cloud resources. The algorithm is based on the processing of individual requests instantly upon arrival. The conducted test simulations showed that the proposed approach has better response and processing time compared to round robin, ESCE and throttled load balancing algorithms. The algorithm outperformed an existing genetic based load balancing algorithm, DTGA, as well.
Generic Motion Primitives-based Safe Motion Planner under Uncertainty for Autonomous Navigation in Cluttered Environments

The safe and efficient operation of autonomous vehicles in cluttered environments with uncertainties remains an ongoing research challenge. Current planning algorithms often use simplified models that do not fully exploit the system's dynamic potential and can lead to collisions, or violate the constraints. To address this issue, we propose a novel moving horizon planning and control approach using the concept of motion primitives. Our approach formulates the planning problem as an optimization over generic motion primitives sets and incorporates ideas from robust predictive control for collision avoidance and handling uncertainties. The proposed approach reduces computational complexity while improving the feasibility of the generated path. We reformulate the problem as a mixed in-teger linear optimization program and present simulation results demonstrating that our approach maximizes vehicle functionality while avoiding obstacles. The proposed framework is tested on simulation scenarios. The results demonstrate its effectiveness and robustness in generating safe, efficient, and reliable trajecto-ries for autonomous systems operating in uncertain environments, highlighting its potential for real-world implementation.
Perspective Projection Approach to a Faster UAV Navigation and Obstacle Avoidance Learning

Interest in research of the navigation problem for Unmanned Aerial Vehicles (UAVs) is on the rise. The aim of such a task is reaching a goal position while avoiding obstacles on the way. In this paper, we propose a different approach to Deep Reinforcement Learning (DRL) of navigation decision making process by introducing the reward function based of Artificial Potential Fields (APF). The validation of the proposed approach is performed by the comparison to the state-of-the-art approach. In terms of training performance, success rate, memory usage and the inference time, our approach, though sparser in terms of perceived information about the environment, yield better results.
The Digital Transformation of Instructional Materials. Views and Practices of Teachers, Families and Editors

Abstract The purpose of this article is to present the design and main findings of a three-year study analyzing the transition from printed to digital educational materials for primary education in Spain. Special attention has been given to the metamorphosis of didactic materials in the context of the digital society. The research focused on two issues: (a) the opinion of educational agents regarding digital educational material, and (b) the use made of digital educational materials in schools and classrooms. Two different but complementary studies were designed and carried out. The first explored the perspectives or subjectivities of educational agents—teachers, material creators and families. The second was a multi-case study involving several schools and analyzing the educational use of materials by teachers. Our findings indicate that the dissemination of digital educational materials is carried out through both paid commercial platforms and public access platforms promoted by educational administrations and that an expository teaching model underlies most online teaching materials. Educational agents have high expectations and a positive predisposition towards digital resources. Likewise, it has been found that teachers make functional and hybrid didactic adaptations of digital resources. Finally, it is suggested that the study’s approach and methodology have potential for extrapolation to other national contexts.
Unpaired sonar image denoising with simultaneous contrastive learning

Implementing underwater sonar image denoising algorithms through unpaired clean and noisy images is feasible and meaningful, since compliant paired real underwater sonar data is nearly impractical. Nevertheless, acquiring high-performance denoising algorithm is challenging without pairs of data as learning objects. In addition, the wide domain gap between noisy and clean samples as well as the noise contained in underwater sonar images is significantly more complex than that involved in normal images. Therefore, directly utilizing current unpaired optimization algorithms (e.g. generative adversarial learning and circular consistency learning) in underwater sonar image denoising tasks is not sufficient to explore the essential connection from noisy inputs to clean outputs. In this paper, we propose a novel framework for unpaired sonar image denoising which probes the same features of unpaired samples through a dual contrast optimization approach in latent space, named as NCD-GAN. The designed algorithm is composed of two basic parts: Simultaneous Transition Structure (STS) and Separable Contrast Unit (SCU). Separately, STS adopts a cyclic structure of multi-domain transformation to generate valuable surface information, and mines potential feature distributions between different domains via the addition of bidirectional mappings. Meanwhile, SCU introduces additional constraints in layer domains to encourage closer background layers between adjacent domains, while increasing the distance between noise layers so as to better promote the removal of noise and assist in image restoration. Sufficient experiments confirm that our algorithm provides better performance than existing unpaired denoising algorithms on multiple publicly available sonar image datasets and exhibits excellent robustness in the processing of multiple noises. Compared to the latest denoising algorithms, there is at most a 26.43% improvement in the evaluation metrics. Furthermore, it gains a 22.75% mAP boost on the subsequent advanced visual tasks.
Exploring digitalisation and its impact on Higher Educational Institutes in a contemporary setting: A Study of the Higher Educational Institutes in Sri Lanka

Globally, higher education institutions are increasingly digitising their operations. A bustling and expanding ecosystem of digital platforms in higher education includes online teaching and research, decision-making using learning and business analytics, and building "smart" campuses. Universities do not digitalise on their own but depend on proprietary digital platforms. This study focuses on how digitalisation impacts higher education institutes in Sri Lanka. This study used a methodology based on a qualitative survey, and the research used personal interviews to collect the required data. The research findings disclosed that although all stakeholders within the higher education institutes had to adapt to forced digitalisation, most were not ready to accept it as the main element defining current-day higher education. Higher education institutes, students, and teachers are the mainly affected parties in the forced digitalisation because of Covid-19. The main factors influencing these parties were inadequate digital literacy, poor financial capabilities, and lack of basic requirements for a digitalised learning environment. However, even with minimum resources, learning is underway in almost all higher education institutes, but it resulted in a digital divide, primarily impacting students and their learning abilities.