Reviewerly/exHarmony huggingface dataset:
Image Quality Assessment Using Contrastive Learning

We consider the problem of obtaining image quality representations in a self-supervised manner. We use prediction of distortion type and degree as an auxiliary task to learn features from an unlabeled image dataset containing a mixture of synthetic and realistic distortions. We then train a deep Convolutional Neural Network (CNN) using a contrastive pairwise objective to solve the auxiliary problem. We refer to the proposed training framework and resulting deep IQA model as the CONTRastive Image QUality Evaluator (CONTRIQUE). During evaluation, the CNN weights are frozen and a linear regressor maps the learned representations to quality scores in a No-Reference (NR) setting. We show through extensive experiments that CONTRIQUE achieves competitive performance when compared to state-of-the-art NR image quality models, even without any additional fine-tuning of the CNN backbone. The learned representations are highly robust and generalize well across images afflicted by either synthetic or authentic distortions. Our results suggest that powerful quality representations with perceptual relevance can be obtained without requiring large labeled subjective image quality datasets. The implementations used in this paper are available at https://github.com/pavancm/CONTRIQUE.
Neuro-Symbolic Speech Understanding in Aircraft Maintenance Metaverse

In the emerging world of metaverses, it is essential for speech communication systems to be aware of context to interact with virtual assets in the 3D world. This paper proposes the metaverse for aircraft maintenance training and education of Boeing-737, supplied with legacy manuals, 3D models, 3D simulators, and aircraft maintenance knowledge. Furthermore, to navigate and control operational flow in the metaverse, which is strictly followed by maintenance manuals, the context-aware speech understanding module Neuro-Symbolic Speech Executor (NSSE) is presented. Unlike conventional speech recognition methods, NSSE applies Neuro-Symbolic AI, which combines neural networks and traditional symbolic reasoning, to understand users’ requests and reply based on context and aircraft-specific knowledge. NSSE is developed with an industrially flexible approach by applying only synthetic data for training. Nevertheless, the evaluation process performed with various automatic speech recognition metrics on real users’ data showed sustainable results with an average accuracy of 94.7%, Word Error Rate (WER) of 7.5%, and the generalization ability to handle speech requests of users with the non-native pronunciation. The proposed Aircraft Maintenance Metaverse is a cheap and scalable solution for aviation colleges since it replaces expensive physical aircraft with virtual one that can be easily modified and updated. Moreover, the Neuro-Symbolic Speech Executor, playing the role of field expert, provides technical guidance and all the resources to facilitate effective training and education of aircraft maintenance.
Facial Appearance Dissatisfaction Explains Differences in Zoom Fatigue

Viewing self-video during videoconferences potentially causes negative self-focused attention that contributes to virtual meeting (VM) or "Zoom" fatigue. The present research examines this proposition, focusing on facial dissatisfaction-feeling unhappy about one's own facial appearance-as a potential psychological mechanism of VM fatigue. A study of survey responses from a panel of 613 adults found that VM fatigue was 14.9 percent higher for women than for men, and 11.1 percent higher for Asian than for White participants. These gender and race/ethnicity differences were found to be mediated by facial dissatisfaction. This study replicates earlier VM fatigue research, extends the theoretical understanding of facial dissatisfaction as a psychological mechanism of VM fatigue, and suggests that practical approaches to mitigating VM fatigue could include implementing technological features that reduce self-focused attention during VMs (e.g., employing avatars).
A Weighted Chimp Optimization Algorithm

These days, a sizable number of meta-heuristic algorithms are utilized to address many problems with numerous variables and huge complexity. One of the most popular swarm intelligence-based meta-heuristic methods is the chimp optimization algorithm inspired by chimps’ individual intelligence and sexual motivation in their group hunting. This paper proposes a weighted chimp optimization algorithm to tackle two main issues in large-scale numerical optimization problems, such as low convergence speed and local optima trapping to solve high-dimensional problems. The main difference between the weighted and standard chimp optimization algorithms is that a position-weighted equation is offered to enhance convergence speed and avoid local optima. Moreover, the balance between exploration and exploitation is carried out in the proposed method that is crucial in the swarm intelligence-based algorithms. The presented weighted chimp optimization algorithm method is evaluated in different conditions to prove that it is the best. For this purpose, a classical set of 30 unimodal, multimodal, and fixed-dimension multimodal benchmark functions is applied to investigate the pros and cons of characteristics of the weighted chimp optimization algorithm. Besides, the proposed algorithm is tested on the IEEE congress of evolutionary computation benchmark test functions. In order to shed more light on probing the performance of the weighted chimp optimization algorithm in large-scale numerical optimization and real-world problems, it is examined by 13 high-dimensional and ten real-world optimization problems. The results show that the suggested algorithm outperforms in terms of convergence speed, the probability of getting stuck in local minimums, exploration, and exploitation compared to state-of-the-art methods in the literature. Source codes are publicly available at https://se.mathworks.com/matlabcentral/fileexchange/99344-a-weighted-chimp-optimization-algorithm.
A commentary of GPT-3 in MIT Technology Review 2021

Through the development of large-scale natural language models with writing and dialogue capabilities, artificial intelligence (AI) has taken a significant stride towards better natural language understanding (NLU) and human-computer interaction (HCI). As of today, the GPT-3 model, developed by OpenAI, is the language model with the most parameters, the largest scale, and the strongest capabilities. Using a large amount of Internet text data and thousands of books for model training, GPT-3 can imitate the natural language patterns of humans nearly perfectly. This language model is extremely realistic and is considered the most impressive model as of today. Despite its powerful modeling and description capabilities, there are significant issues and limitations. First and foremost, the GPT-3 model does not understand writing (natural language generation) well and sometimes generates uncontrollable content. Secondly, training the GPT-3 model requires a large amount of computing power, data, and capital investment, and releases significant carbon dioxide emissions. Developing similar models is only possible in laboratories with adequate resources. Furthermore, as the GPT-3 model is trained with Internet text data rife with error messages and prejudices, it often produces chapters and paragraphs with biased content similar to the training data.①
Voice assistants in hospitality: using artificial intelligence for customer service

Purpose Voice assistants (VAs) empower human–computer interactions by recognising human speech and implementing commands pronounced by users. This paper aims to investigate VA-enabled interactions between hotels and guests in the hospitality context. The research positions VAs within the artificial intelligence (AI)-enabled Internet of Things (IoT) context, disrupting old practices and processes. Smart hospitality uses VAs to support effortless value cocreation for guests cost-effectively. The research examines consumer perceptions and expectations of hospitality VAs and explores VA capabilities through expert technology providers. Design/methodology/approach This empirical paper investigates the current use and future implications of VAs for hotel environments. It uses qualitative, semi-structured in-depth interviews with 7 expert hospitality VA technology providers and 21 hotel guests who have VA experience. The research adopts a demand and supply approach, addressing the VAs in hospitality holistically. Findings The findings illustrate the requirements from both end-users’ sides, hotels and guests, exploring VA advantages and challenges. The analysis demonstrates that VAs increasingly become digital assistants. VA technology helps hotels to improve customer service, expand operational capability and reduce costs. Although in its infancy, VA technology has made progress towards optimising hotel operations and upgrading customer service. The study proposes a speech-enabled interactions model. Research limitations/implications This research stimulates the transformation of hospitality services by using VAs and the development of smart hospitality and tourism ecosystems. The study can benefit from further research with hotel managers, to reflect hoteliers’ points of view and investigate their perception of VAs. Further research can also explore different aspects of consumer–VA interaction in different contexts. Practical implications The paper makes a significant contribution to hospitality management and human–computer interaction best practices. It supports technology providers to reconsider how to develop suitable technology solutions towards improving their strategic competitiveness. It also explains how to use VAs cost-effectively and profitably while adding value to travellers’ experience. Originality/value VA studies are often focussed on the technology in private households, rather than in commercial or hotel spaces. This paper contributes to the emerging literature on AI and IoT in smart hospitality and explores the acceptance and operationalisation of VAs. The research contributes to the conceptualisation of VA-enabled hotel services and explores positive and negative features, as well as future prospects.
Application of cloud computing in banking and e-commerce and related security threats

Cloud computing is one of the most powerful inventions that has grabbed the curiosity of technologists all around the world. Cloud computing has many advantages, but it also has a slew of security risks that no organization can afford to ignore. For a successful Cloud Computing adoption in a corporation, proper planning and awareness of emerging risks, threats, vulnerabilities, and potential solutions are necessary. As a result, determining the most effective solution instructions to increase cloud security has become important for all cloud operations. In this research, we are investigating and assessing the most noteworthy network security and data security risks on cloud systems based on a literature review. Because many businesses have promoted and marketed virtual environments as the solution to current security concerns, a deeper look finds that virtualization adds extra software to the network system, which may have a negative influence on security if built and deployed poorly. Furthermore, data center hubs link their servers through software, which means that if something goes wrong, the final effect might be damaging to security. Consumers must rely on trust mechanisms since they have no control over the cloud's resources. This article examines several cloud computing applications in banking and e-commerce, as well as the security issues associated with them.
Land Requirements for Utility-Scale PV: An Empirical Update on Power and Energy Density

The rapid deployment of large numbers of utility-scale photovoltaic (PV) plants in the United States, combined with heightened expectations of future deployment, has raised concerns about land requirements and associated land-use impacts. Yet our understanding of the land requirements of utility-scale PV plants is outdated and depends in large part on a study published nearly a decade ago, while the utility-scale sector was still young. We provide updated estimates of utility-scale PVs power and energy densities based on empirical analysis of more than 90% of all utility-scale PV plants built in the United States through 2019. We use ArcGIS to draw polygons around satellite imagery of each plant within our sample and to calculate the area occupied by each polygon. When combined with plant metadata, these polygon areas allow us to calculate power (MW/acre) and energy (MWh/acre) density for each plant in the sample, and to analyze density trends over time, by fixed-tilt versus tracking plants, and by plant latitude and site irradiance. We find that the median power density increased by 52% for fixed-tilt plants and 43% for tracking plants from 2011 to 2019, while the median energy density increased by 33% for fixed-tilt and 25% for tracking plants over the same period. Those relying on the earlier benchmarks published nearly a decade ago are, thus, significantly overstating the land requirements of utility-scale PV.
AUCTION: Automated and Quality-Aware Client Selection Framework for Efficient Federated Learning

The emergency of federated learning (FL) enables distributed data owners to collaboratively build a global model without sharing their raw data, which creates a new business chance for building data market. However, in practical FL scenarios, the hardware conditions and data resources of the participant clients can vary significantly, leading to different positive/negative effects on the FL performance, where the client selection problem becomes crucial. To this end, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AUCTION</i> , an <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> utomated and q <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">U</u> ality-aware <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> lient selec <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TION</u> framework for efficient FL, which can evaluate the learning quality of clients and select them automatically with quality-awareness for a given FL task within a limited budget. To design <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AUCTION</i> , multiple factors such as data size, data quality, and learning budget that can affect the learning performance should be properly balanced. It is nontrivial since their impacts on the FL model are intricate and unquantifiable. Therefore, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AUCTION</i> is designed to encode the client selection policy into a neural network and employ reinforcement learning to automatically learn client selection policies based on the observed client status and feedback rewards quantified by the federated learning performance. In particular, the policy network is built upon an encoder-decoder deep neural network with an attention mechanism, which can adapt to dynamic changes of the number of candidate clients and make sequential client selection actions to reduce the learning space significantly. Extensive experiments are carried out based on real-world datasets and well-known learning models to demonstrate the efficiency, robustness, and scalability of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AUCTION</i> .
Real-Time Hand Gesture Recognition Using Fine-Tuned Convolutional Neural Network

Hand gesture recognition is one of the most effective modes of interaction between humans and computers due to being highly flexible and user-friendly. A real-time hand gesture recognition system should aim to develop a user-independent interface with high recognition performance. Nowadays, convolutional neural networks (CNNs) show high recognition rates in image classification problems. Due to the unavailability of large labeled image samples in static hand gesture images, it is a challenging task to train deep CNN networks such as AlexNet, VGG-16 and ResNet from scratch. Therefore, inspired by CNN performance, an end-to-end fine-tuning method of a pre-trained CNN model with score-level fusion technique is proposed here to recognize hand gestures in a dataset with a low number of gesture images. The effectiveness of the proposed technique is evaluated using leave-one-subject-out cross-validation (LOO CV) and regular CV tests on two benchmark datasets. A real-time American sign language (ASL) recognition system is developed and tested using the proposed technique.
Learning Student Networks via Feature Embedding

Deep convolutional neural networks have been widely used in numerous applications, but their demanding storage and computational resource requirements prevent their applications on mobile devices. Knowledge distillation aims to optimize a portable student network by taking the knowledge from a well-trained heavy teacher network. Traditional teacher-student-based methods used to rely on additional fully connected layers to bridge intermediate layers of teacher and student networks, which brings in a large number of auxiliary parameters. In contrast, this article aims to propagate information from teacher to student without introducing new variables that need to be optimized. We regard the teacher-student paradigm from a new perspective of feature embedding. By introducing the locality preserving loss, the student network is encouraged to generate the low-dimensional features that could inherit intrinsic properties of their corresponding high-dimensional features from the teacher network. The resulting portable network, thus, can naturally maintain the performance as that of the teacher network. Theoretical analysis is provided to justify the lower computation complexity of the proposed method. Experiments on benchmark data sets and well-trained networks suggest that the proposed algorithm is superior to state-of-the-art teacher-student learning methods in terms of computational and storage complexity.
Applications of Federated Learning; Taxonomy, Challenges, and Research Trends

The federated learning technique (FL) supports the collaborative training of machine learning and deep learning models for edge network optimization. Although a complex edge network with heterogeneous devices having different constraints can affect its performance, this leads to a problem in this area. Therefore, some research can be seen to design new frameworks and approaches to improve federated learning processes. The purpose of this study is to provide an overview of the FL technique and its applicability in different domains. The key focus of the paper is to produce a systematic literature review of recent research studies that clearly describes the adoption of FL in edge networks. The search procedure was performed from April 2020 to May 2021 with a total initial number of papers being 7546 published in the duration of 2016 to 2020. The systematic literature synthesizes and compares the algorithms, models, and frameworks of federated learning. Additionally, we have presented the scope of FL applications in different industries and domains. It has been revealed after careful investigation of studies that 25% of the studies used FL in IoT and edge-based applications and 30% of studies implement the FL concept in the health industry, 10% for NLP, 10% for autonomous vehicles, 10% for mobile services, 10% for recommender systems, and 5% for FinTech. A taxonomy is also proposed on implementing FL for edge networks in different domains. Moreover, another novelty of this paper is that datasets used for the implementation of FL are discussed in detail to provide the researchers an overview of the distributed datasets, which can be used for employing FL techniques. Lastly, this study discusses the current challenges of implementing the FL technique. We have found that the areas of medical AI, IoT, edge systems, and the autonomous industry can adapt the FL in many of its sub-domains; however, the challenges these domains can encounter are statistical heterogeneity, system heterogeneity, data imbalance, resource allocation, and privacy.
End-to-End Dense Video Captioning with Parallel Decoding

Dense video captioning aims to generate multiple associated captions with their temporal locations from the video. Previous methods follow a sophisticated "localizethen-describe" scheme, which heavily relies on numerous hand-crafted components. In this paper, we proposed a simple yet effective framework for end-to-end dense video captioning with parallel decoding (PDVC), by formulating the dense caption generation as a set prediction task. In practice, through stacking a newly proposed event counter on the top of a transformer decoder, the PDVC precisely segments the video into a number of event pieces under the holistic understanding of the video content, which effectively increases the coherence and readability of predicted captions. Compared with prior arts, the PDVC has several appealing advantages: (1) Without relying on heuristic non-maximum suppression or a recurrent event sequence selection network to remove redundancy, PDVC directly produces an event set with an appropriate size; (2) In contrast to adopting the two-stage scheme, we feed the enhanced representations of event queries into the localization head and caption head in parallel, making these two sub-tasks deeply interrelated and mutually promoted through the optimization; (3) Without bells and whistles, extensive experiments on ActivityNet Captions and YouCook2 show that PDVC is capable of producing high-quality captioning results, surpassing the state-of-the-art two-stage methods when its localization accuracy is on par with them. Code is available at https://github.com/ttengwang/PDVC.