Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence in Business Communication: The Changing Landscape of Research and Teaching

The rapid, widespread implementation of artificial intelligence technologies in workplaces has implications for business communication. In this article, the authors describe current capabilities, challenges, and concepts related to the adoption and use of artificial intelligence (AI) technologies in business communication. Understanding the abilities and inabilities of AI technologies is critical to using these technologies ethically. The authors offer a proposed research agenda for researchers in business communication concerning topics of implementation, lexicography and grammar, collaboration, design, trust, bias, managerial concerns, tool assessment, and demographics. The authors conclude with some ideas regarding how to teach about AI in the business communication classroom.
Multi-Exposure Image Fusion Techniques: A Comprehensive Review

Multi-exposure image fusion (MEF) is emerging as a research hotspot in the fields of image processing and computer vision, which can integrate images with multiple exposure levels into a full exposure image of high quality. It is an economical and effective way to improve the dynamic range of the imaging system and has broad application prospects. In recent years, with the further development of image representation theories such as multi-scale analysis and deep learning, significant progress has been achieved in this field. This paper comprehensively investigates the current research status of MEF methods. The relevant theories and key technologies for constructing MEF models are analyzed and categorized. The representative MEF methods in each category are introduced and summarized. Then, based on the multi-exposure image sequences in static and dynamic scenes, we present a comparative study for 18 representative MEF approaches using nine commonly used objective fusion metrics. Finally, the key issues of current MEF research are discussed, and a development trend for future research is put forward.
A Survey on Multimedia Services QoE Assessment and Machine Learning-Based Prediction

The groundbreaking evolution in mobile and wireless communication networks design in recent years, in combination with the advancement of mobile terminal equipment capabilities, has led in an exponential growth of mobile internet technologies, and arose an ever-growing demand for innovative multimedia services. The highly demanding in terms of network resources over-the-top media services, as well as the emergence of new and complex mobile multimedia services such as video gaming, ultra-high-definition video, and extended reality, requires the enhancement of end-users’ perceived quality of experience (QoE). QoE has garnered much research interest in recent years, and has emerged as a key component in the evaluation of network services and operations. As a result, a QoE-aware network planning approach is getting increasingly favored, and novel design challenges, such as how to quantify and measure QoE, have arisen. In this regard, a paradigm shift in network implementations is being envisioned, in which the focus will be on machine learning (ML) methodologies for developing QoE prediction models, directly related to end-user’s personalized experience. In this survey, an analysis on application-oriented, ML-based QoE prediction models for the goal of QoE management for multimedia services is presented. In addition, an examination of the state-of-the-art ML-based QoE predictive models and some of the innovative techniques and challenges related to multimedia services quality assessment with focus on extended reality and video gaming applications are outlined.
Global Optimization of UAV Area Coverage Path Planning Based on Good Point Set and Genetic Algorithm

When performing area coverage tasks in some special scenarios, fixed-wing aircraft conventionally adopt the scan-type of path planning, where the distance between two adjacent tracks is usually less than the minimum turning radius of the aircraft. This results in increased energy consumption during turning between adjacent tracks, which means a reduced task execution efficiency. To address this problem, the current paper proposes an area coverage path planning method for a fixed-wing unmanned aerial vehicle (UAV) based on an improved genetic algorithm. The algorithm improves the primary population generation of the traditional genetic algorithm, with the help of better crossover operator and mutation operator for the genetic operation. More specifically, the good point set algorithm (GPSA) is first used to generate a primary population that has a more uniform distribution than that of the random algorithm. Then, the heuristic crossover operator and the random interval inverse mutation operator are employed to reduce the risk of local optimization. The proposed algorithm is verified in tasks with different numbers of paths. A comparison with the conventional genetic algorithm (GA) shows that our algorithm can converge to a better solution.
Mineral prospectivity mapping based on wavelet neural network and Monte Carlo simulations in the Nanling W-Sn metallogenic province

The Nanling Range in South China is endowed with abundant W-Sn and other important rare metal resources associated with granitic intrusions, but the rate of new major mineral discoveries has been drastically diminished over the last decades. Data-driven mineral prospectivity mapping (MPM) using machine learning is emerging as a powerful tool in support of mineral exploration targeting at regional or camp scale. However, MPM based on supervised learning faces some general problems, notably including subtle information obscured in exploration data and imbalanced training samples. In this paper, we proposed a novel ensemble scheme for MPM using wavelet neural network (WNN) and Monte Carlo simulation (MCs) to address the forementioned issues. Specifically, the purpose of using WNN based on multiscale wavelet analysis is to capture weak or hidden (geophysical and geochemical) information caused by concealed ore deposits, while an undersampling MCs (UMCs) algorithm is employed to assess uncertainties caused by data imbalance and lessen its impact on MPM. Subtle (weak and mixing) information related to concealed mineralization, including geophysical and geochemical anomalies, were extracted using wavelet analysis at first, and then a dozen of predictive evidences was integrated based on WNN using UMCs for producing a W-Sn perspectivity map. The results suggest that WNN has a higher prediction accuracy (∼90%) in comparison to the artificial neural network (ANN) (∼85%). Moreover, the improved AUC value (∼0.9) of WNN based on UMCs compared to the classic ANN (∼0.82) and WNN (∼0.85) suggests that the proposed ensemble algorithm improves the geological generalization of machine learning. Revisiting WNN predictions by uncertainties produced more sharply focused exploration targets of W-Sn deposits (covering ∼5% of the study area). The resulting predictive map provides important clues of W-Sn deposit occurrences which could guide and stimulate future mineral exploration in the Nanling Range.
A survey: Distributed Machine Learning for 5G and beyond

5G is the fifth generation of cellular networks. It enables billions of connected devices to gather and share information in real time; a key facilitator in Industrial Internet of Things (IoT) applications. It has more capabilities in terms of bandwidth, latency/delay, processing powers and flexibility to utilize either edge or cloud resources. Furthermore, 6G is expected to be equipped with the new capability to converge ubiquitous communication, computation, sensing and controlling for a variety of sectors, which heightens the complexity in a more heterogeneous environment This increased complexity, combined with energy efficiency and Service Level Agreement (SLA) requirements makes application of Machine Learning (ML) and distributed ML necessary. A decentralized approach stemming from distributed learning is a very attractive option compared with a centralized architecture for model learning and inference. Distributed ML exploits recent Artificial Intelligence (AI) technology advancements to allow collaborated ML, whilst safeguarding private data, minimizing both communication and computation overhead along with addressing ultra-low latency requirements. In this paper, we review a number of distributed ML architectures and designs, that focus on optimizing communication, computation and resource distribution. Privacy, information security and compute frameworks, are also analyzed and compared with respect to different distributed ML approaches. We summarize the major contributions and trends in this area and highlight the potential of distributed ML to help researchers and practitioners make informed decisions on selecting the right ML approach for 5G and Beyond related AI applications. To enable distributed ML for 5G and Beyond, communication, security, and computing platform often counter balance each other, thus, consideration and optimization of these aspects at an overall system level is crucial to realize the full potential of AI for 5G and Beyond. These different aspects do not only pertain to 5G, but will also enable careful design of distributed machine learning architectures to circumvent the same hurdles that will inevitably burden 5G and Beyond network generations. This is the first survey paper that brings together all these aspects for distributed ML.
HeteroSAg: Secure Aggregation With Heterogeneous Quantization in Federated Learning

Secure model aggregation across many users is a key component of federated learning systems. The state-of-the-art protocols for secure model aggregation, which are based on additive masking, require all users to quantize their model updates to the same level of quantization. This severely degrades their performance due to lack of adaptation to available communication resources, e.g., bandwidth, at different users. As the main contribution of our paper, we propose <i>HeteroSAg</i>, a scheme that allows secure model aggregation while using heterogeneous quantization. HeteroSAg enables the edge users to adjust their quantization proportional to their available communication resources, which can provide a substantially better trade-off between the accuracy of training and the communication time. Our proposed scheme is based on a grouping strategy by partitioning the network into groups, and partitioning the local model updates of users into segments. Instead of applying aggregation protocol to the entire local model update vector, it is applied on segments with specific coordination between users. We further demonstrate how HeteroSAg can enable Byzantine robustness while achieving secure aggregation simultaneously. Finally, we prove the convergence guarantees of HeteroSAg under heterogeneous quantization in the non-Byzantine scenario.
Usability Testing of Virtual Reality Applications—The Pilot Study

The need for objective data-driven usability testing of VR applications is becoming more tangible with the rapid development of numerous VR applications and their increased accessibility. Traditional methods of testing are too time and resource consuming and might provide results that are highly subjective. Thus, the aim of this article is to explore the possibility of automation of usability testing of VR applications by using objective features such as HMD built-in head and hands tracking, EEG sensor, video recording, and other measurable parameters in addition to automated analysis of subjective data provided in questionnaires. For this purpose, a simple VR application was created which comprised relatively easy tasks that did not generate stress for the users. Fourteen volunteers took part in the study and their signals were monitored to acquire objective automated data. At the same time the observer was taking notes of subjects' behaviour, and their subjective opinions about the experience were recorded in a post-experiment questionnaire. The results acquired from signal monitoring and questionnaires were juxtaposed with observation and post-interview results to confirm the validity and efficacy of automated usability testing. The results were very promising, proving that automated usability testing of VR applications is potentially achievable.
Meta-seg: A survey of meta-learning for image segmentation

A well-performed deep learning model in image segmentation relies on a large number of labeled data. However, it is hard to obtain sufficient high-quality raw data in industrial applications. Meta-learning, one of the most promising research areas, is recognized as a powerful tool for approaching image segmentation. To this end, this paper reviews the state-of-the-art image segmentation methods based on meta-learning. We firstly introduce the background of the image segmentation, including the methods and metrics of image segmentation. Second, we review the timeline of meta-learning and give a more comprehensive definition of meta-learning. The differences between meta-learning and other similar methods are compared comprehensively. Then, we categorize the existing meta-learning methods into model-based, optimization-based, and metric-based. For each categorization, the popular used meta-learning models are discussed in image segmentation. Next, we conduct comprehensive computational experiments to compare these models on two pubic datasets: ISIC-2018 and Covid-19. Finally, the future trends of meta-learning in image segmentation are highlighted.
Hierarchical Reinforcement Learning: A Survey and Open Research Challenges

Reinforcement learning (RL) allows an agent to solve sequential decision-making problems by interacting with an environment in a trial-and-error fashion. When these environments are very complex, pure random exploration of possible solutions often fails, or is very sample inefficient, requiring an unreasonable amount of interaction with the environment. Hierarchical reinforcement learning (HRL) utilizes forms of temporal- and state-abstractions in order to tackle these challenges, while simultaneously paving the road for behavior reuse and increased interpretability of RL systems. In this survey paper we first introduce a selection of problem-specific approaches, which provided insight in how to utilize often handcrafted abstractions in specific task settings. We then introduce the Options framework, which provides a more generic approach, allowing abstractions to be discovered and learned semi-automatically. Afterwards we introduce the goal-conditional approach, which allows sub-behaviors to be embedded in a continuous space. In order to further advance the development of HRL agents, capable of simultaneously learning abstractions and how to use them, solely from interaction with complex high dimensional environments, we also identify a set of promising research directions.
Synthesizing Personalized Construction Safety Training Scenarios for VR Training

Construction industry has the largest number of preventable fatal injuries, providing effective safety training practices can play a significant role in reducing the number of fatalities. Building on recent advancements in virtual reality-based training, we devised a novel approach to synthesize construction safety training scenarios to train users on how to proficiently inspect the potential hazards on construction sites in virtual reality. Given the training specifications such as individual training preferences and target training time, we synthesize personalized VR training scenarios through an optimization approach. We validated our approach by conducting user studies where users went through our personalized guidance VR training, free exploration VR training, or slides training. Results suggest that personalized guidance VR training approach can more effectively improve users' construction hazard inspection skills.
Complete Face Recovery GAN: Unsupervised Joint Face Rotation and De-Occlusion from a Single-View Image

Although various face-related tasks have significantly advanced in recent years, occlusion and extreme pose still impede the achievement of higher performance. Existing face rotation or de-occlusion methods only have emphasized the aspect of each problem. In addition, the lack of high-quality paired data remains an obstacle for both methods. In this work, we present a self-supervision strategy called Swap-R&R to overcome the lack of ground-truth in a fully unsupervised manner for joint face rotation and de-occlusion. To generate an input pair for self-supervision, we transfer the occlusion from a face in an image to an estimated 3D face and create a damaged face image, as if rotated from a different pose by rotating twice with the roughly de-occluded face. Furthermore, we propose Complete Face Recovery GAN (CFR-GAN) to restore the collapsed textures and disappeared occlusion areas by leveraging the structural and textural differences between two rendered images. Unlike previous works, which have selected occlusion-free images to obtain ground-truths, our approach does not require human intervention and paired data. We show that our proposed method can generate a de-occluded frontal face image from an occluded profile face image. Moreover, extensive experiments demonstrate that our approach can boost the performance of facial recognition and facial expression recognition. The code is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
COVID-19 CT image denoising algorithm based on adaptive threshold and optimized weighted median filter

CT image of COVID-19 is disturbed by impulse noise during transmission and acquisition. Aiming at the problem that the early lesions of COVID-19 are not obvious and the density is low, which is easy to confuse with noise. A median filtering algorithm based on adaptive two-stage threshold is proposed to improve the accuracy for noise detection. In the advanced stage of ground-glass lesion, the density is uneven and the boundary is unclear. It has similar gray value to the CT images of suspected COVID-19 cases such as adenovirus pneumonia and mycoplasma pneumonia (reticular shadow and strip shadow). Aiming at the problem that the traditional weighted median filter has low contrast and fuzzy boundary, an adaptive weighted median filter image denoising method based on hybrid genetic algorithm is proposed. The weighted denoising parameters can adaptively change according to the detailed information of lung lobes and ground-glass lesions, and it can adaptively match the cross and mutation probability of genetic combined with the steady-state regional population density, so as to obtain a more accurate COVID-19 denoised image with relatively few iterations. The simulation results show that the improved algorithm under different density of impulse noise is significantly better than other algorithms in peak signal-to-noise ratio (PSNR), image enhancement factor (IEF) and mean absolute error (MSE). While protecting the details of lesions, it enhances the ability of image denoising.
A Short Review on Minimum Description Length: An Application to Dimension Reduction in PCA

The minimun description length (MDL) is a powerful criterion for model selection that is gaining increasing interest from both theorists and practicioners. It allows for automatic selection of the best model for representing data without having a priori information about them. It simply uses both data and model complexity, selecting the model that provides the least coding length among a predefined set of models. In this paper, we briefly review the basic ideas underlying the MDL criterion and its applications in different fields, with particular reference to the dimension reduction problem. As an example, the role of MDL in the selection of the best principal components in the well known PCA is investigated.
A hierarchical recommendation system for E-commerce using online user reviews

Recommendation systems are considered as one of the important components of e-commerce platforms due to their direct impact on profitability. In this study, we propose a hierarchical recommendation system to increase the performance of the e-commerce recommendation system. Our DeepIDRS approach has a two-level hierarchical structure: (1) The first level uses bidirectional encoder representations to represent textual information of an item (title, description, and a subset of item reviews), efficiently and accurately; (2) The second level is an attention-based sequential recommendation model that uses item embeddings derived from the first level of the hierarchical structure. Furthermore, we compare our approach DeepIDRS with various approaches from different perspectives. Our results in the real-world dataset show that DeepIDRS provides at least 10% better [email protected] and [email protected] performance than other review-based models. With this study, for e-commerce, we clearly show that a hierarchical, explainable recommendation system that accurately represents the item title, description, and a subset of item reviews, improves performance.
Taming energy and electronic waste generation in bitcoin mining: Insights from Facebook prophet and deep neural network

The Bitcoin mining hosted in the blockchain network consumes enormous amounts of energy and generates electronic waste at an alarming rate. The paper aims to model and predict the future values of these two hazardous variables linked to conventional Bitcoin mining. We develop two predictive models using Facebook's Prophet algorithm and deep neural networks to identify and explain energy consumption and electronic waste generation patterns. The models rely on several explanatory features linked to the blockchain microstructure and the Bitcoin marketplace. We assess the predictive performance of the two models based on daily data of energy consumption and electronic waste generation and eleven key input features. We use local interpretable model-agnostic explanation (LIME) and Shapley additive explanation (SHAP) for explaining how these inputs can predict and control energy consumption and electronic waste generation. The findings assist in accurately estimating the future figures of energy discharge and electronic waste accumulation in the present Bitcoin mining setup. The study also reveals the block size to be the major driver.