Reviewerly/exHarmony huggingface dataset:
Blockchain’s Role in Enhancing Quality and Safety and Promoting Sustainability in the Food and Beverage Industry

The objective of this paper is to assess the potential roles of blockchain technology in enhancing quality, safety, and sustainability throughout the production and distribution of food and beverage products. To achieve this, a multiple case study approach has been selected as the primary research methodology. This article underscores the transformative impact of blockchain implementation on inter-organizational transactions, reducing uncertainty among supply chain participants and fostering more equitable interdependence among partners in the value chain. These developments have the potential to bolster quality, safety, and sustainability within the food and beverage industry. The article also explores strategies for enhancing blockchain’s influence on interfirm governance structures within the food and beverage sector. It delves into the possibilities of broadening participation by increasing the number and variety of participants in blockchain networks. It investigates how the synergy between blockchain technology and other emerging technologies can further optimize their impact on reducing interfirm governance structures. Also addressed in the paper is the potential for blockchain-based solutions to enhance distributive fairness within the food and beverage industry, offering marginalized groups, such as small-holder farmers, greater opportunities for integration into the global economy. Special emphasis is placed on blockchain’s capacity to enhance interfirm governance in this industry by reducing uncertainty among supply chain participants and creating more symmetrical dependencies among them. The article also posits that by fostering entrepreneurial prospects for marginalized communities and promoting distributive fairness, blockchain technology can contribute to socially responsible actions. Overall, this study extends theories and concepts from information and communications technologies’ (ICTs) effects on agency, boundaries, and uncertainty in the context of organizational and inter-organizational dynamics.
Walking into a Modern Painting: The Impacts of Using Virtual Reality on Student Learning Performance and Experiences in Art Appreciation

This study reconstructed a modern painting into a fully-immersive Virtual Reality (VR) painting allowing users to walk into the painting's 3D world and move around, and employed constructivism with a model of art appreciation to investigate the effects of applying VR techniques in appreciating the modern painting on student learning outcomes. Participants in the study included 60 undergraduates from a university in the midwestern United States. Quasi-experimental study designs and quantitative analysis methods were adopted to analyze students' responses to the activities of modern painting appreciation through 2D and VR paintings. Results indicated that using the VR painting had significant negative impacts on students' performance of object recognition in the painting and interpretations towards the painting, but no effects on the painting analysis and judgements. Students using the VR painting showed significantly higher interest and enjoyment than those using the 2D painting. Compared to those viewing the VR painting prior to the 2D painting appreciation, students who only viewed the 2D painting had significantly better learning performance. This study contributes to the lack of empirical VR research in art appreciation, offering insights into the application of using VR in appreciating modern paintings, and providing recommendations for future studies.
Feature-Domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution

Convolutional neural network-based single image super-resolution (SISR) involves numerous parameters and high computational expenses to ensure improved performance, limiting its applicability in resource-constrained devices such as mobile phones. Knowledge distillation (KD), which transfers useful knowledge from a teacher network to a student network, has been investigated as a method to make networks more efficient in terms of performance. To this end, feature distillation (FD) has been utilized in KD to minimize the Euclidean distance-based loss of feature maps between teacher and student networks. However, this technique does not adequately consider the effective and meaningful delivery of knowledge from the teacher to the student network to improve the latter’s performance under given network capacity constraints. In this study, we propose a feature-domain adaptive contrastive distillation (FACD) method to train lightweight student SISR networks efficiently. We highlight the limitations of existing FD methods in terms of Euclidean distance-based loss, and propose a feature-domain contrastive loss, which causes student networks to learn richer information from the teacher’s representation in the feature domain. We also implement adaptive distillation that performs distillation selectively depending on the conditions of the training patches. Experimental results showed that the proposed FACD scheme improves student enhanced deep residual networks and residual channel attention networks not only in terms of the peak signal-to-noise ratio on all benchmark datasets and scales but also in terms of subjective image quality, compared to the conventional FD approaches. Code will be release at https://github.com/hcmoon0613/FACD.
DMT-Net: Deep Multiple Networks for Low-Light Image Enhancement Based on Retinex Model

Images captured under low-light conditions are typically prone to undesirable visual phenomena, particularly color distortion and additive noise, that impede the aesthetics and performance of high-level vision tasks. Therefore, developing an enhancement method is necessary to obtain visually pleasing images. This paper proposes deep multiple networks for low-light image enhancement based on the Retinex model (defined as DMT-Net), wherein each network is configured to perform its own role in the YCbCr color space. More specifically, Decoupled-Net is presented to accurately decouple the luminance channel into reflectance and illumination components using the Retinex model. Denoising-Net is connected to Decoupled-Net to eradicate additive noise in the reflectance component. Boosting-Net enhances the illumination component and reduces halo artifacts. In addition, we propose Chrominance- Net to mitigate the distortion of the Cb and Cr channels. Our study also focuses on having a suitable norm order for the loss function based on kurtosis characteristics, such that each network is properly trained to minimize the residual in the training process. Finally, the experimental results prove that our proposed method outperforms numerous other cutting-edge methods in both qualitative and quantitative terms.
Video Summarization Generation Based on Graph Structure Reconstruction

Video summarization aims to identify important segments in a video and merge them into a concise representation, enabling users to comprehend the essential information without watching the entire video. Graph structure-based video summarization approaches ignore the issue of redundant adjacency matrix. To address this issue, this paper proposes a video summary generation model based on graph structure reconstruction (VOGNet), in which the model first adopts a variational graph auto-encoders (VGAE) to reconstruct the graph structure to remove redundant information in the graph structure; followed by using the reconstructed graph structure in a graph attention network (GAT), allocating different weights to different shot features in the neighborhood; and lastly, in order to avoid the loss of information during the training of the model, a feature fusion approach is proposed to combine the training obtained shot features with the original shot features as the shot features for generating the summary. We perform extensive experiments on two standard datasets, SumMe and TVSum, and the experimental results demonstrate the effectiveness and robustness of the proposed model.
A Modified and Effective Blockchain Model for E-Healthcare Systems

The development of e-healthcare systems requires the application of advanced technologies, such as blockchain technology. The main challenge of applying blockchain technology to e-healthcare is to handle the impact of the delay that results from blockchain procedures during the communication and voting phases. The impacts of latency in blockchains negatively influence systems’ efficiency, performance, real-time processing, and quality of service. Therefore, this work proposes a modified model of a blockchain that allows delays to be avoided in critical situations in healthcare. Firstly, this work analyzes the specifications of healthcare data and processes to study and classify healthcare transactions according to their nature and sensitivity. Secondly, it introduces the concept of a fair-proof-of-stake consensus protocol for block creation and correctness procedures rather than famous ones such as proof-of-work or proof-of-stake. Thirdly, the work presents a simplified procedure for block verification, where it classifies transactions into three categories according to the time period limit and trustworthiness level. Consequently, there are three kinds of blocks, since every category is stored in a specific kind of block. The ideas of time period limits and trustworthiness fit with critical healthcare situations and the authority levels in healthcare systems. Therefore, we reduce the validation process of the trusted blocks and transactions. All proposed modifications help to reduce computational costs, speed up processing times, and enhance security and privacy. The experimental results show that the total execution time using a modified blockchain is reduced by about 49% compared to traditional blockchain models. Additionally, the number of messages using modified blockchain is reduced by about 53% compared to the traditional blockchain model.
An ensemble of CNNs with self-attention mechanism for DeepFake video detection

Abstract The availability of large-scale facial datasets with the rapid progress of deep learning techniques, such as Generative Adversarial Networks, has enabled anyone to create realistic fake videos. These fake videos can potentially become harmful when used for fake news, hoaxes, and identity fraud. We propose a deep learning bagging ensemble classifier to detect manipulated faces in videos. The proposed bagging classifier uses the convolution and self-attention network (CoAtNet) model as a base learner. CoAtNet model is vertically stacking depthwise convolution layers and self-attention layers in such a way that generalization, capacity, and efficiency are improved. Depthwise convolution captures local features from faces extracted from video then pass these features to the attention layers to extract global information and efficiently capture long-range dependencies of spatial details. Each learner is trained on a different subset randomly taken of training data with a replacement then models’ predictions are combined to classify the video either as real or fake. We also use CutMix data augmentation on the extracted faces to enhance the generalization and localization performance of the base learner model. Our experimental results show that our proposed method achieves higher efficiency compared to state-of-the-art methods with AUC values of 99.70%, 97.49%, 98.90%, and 87.62% on the different manipulation techniques of the FaceForensics++ dataset (DeepFakes (DF), Face2Face (F2F), FaceSwap (FS), and NeuralTextures (NT)), respectively, and 99.74% on the Celeb-DF dataset.
The continuous stochastic gradient method: part I–convergence theory

Abstract In this contribution, we present a full overview of the continuous stochastic gradient (CSG) method, including convergence results, step size rules and algorithmic insights. We consider optimization problems in which the objective function requires some form of integration, e.g., expected values. Since approximating the integration by a fixed quadrature rule can introduce artificial local solutions into the problem while simultaneously raising the computational effort, stochastic optimization schemes have become increasingly popular in such contexts. However, known stochastic gradient type methods are typically limited to expected risk functions and inherently require many iterations. The latter is particularly problematic, if the evaluation of the cost function involves solving multiple state equations, given, e.g., in form of partial differential equations. To overcome these drawbacks, a recent article introduced the CSG method, which reuses old gradient sample information via the calculation of design dependent integration weights to obtain a better approximation to the full gradient. While in the original CSG paper convergence of a subsequence was established for a diminishing step size, here, we provide a complete convergence analysis of CSG for constant step sizes and an Armijo-type line search. Moreover, new methods to obtain the integration weights are presented, extending the application range of CSG to problems involving higher dimensional integrals and distributed data.
The metaverse digital environments: a scoping review of the challenges, privacy and security issues

The concept of the "metaverse" has garnered significant attention recently, positioned as the "next frontier" of the internet. This emerging digital realm carries substantial economic and financial implications for both IT and non-IT industries. However, the integration and evolution of these virtual universes bring forth a multitude of intricate issues and quandaries that demand resolution. Within this research endeavor, our objective was to delve into and appraise the array of challenges, privacy concerns, and security issues that have come to light during the development of metaverse virtual environments in the wake of the COVID-19 pandemic. Through a meticulous review and analysis of literature spanning from January 2020 to December 2022, we have meticulously identified and scrutinized 29 distinct challenges, along with 12 policy, privacy, and security matters intertwined with the metaverse. Among the challenges we unearthed, the foremost were concerns pertaining to the costs associated with hardware and software, implementation complexities, digital disparities, and the ethical and moral quandaries surrounding socio-control, collectively cited by 43%, 40%, and 33% of the surveyed articles, respectively. Turning our focus to policy, privacy, and security issues, the top three concerns that emerged from our investigation encompassed the formulation of metaverse rules and principles, the encroachment of privacy threats within the metaverse, and the looming challenges concerning data management, all mentioned in 43%, 40%, and 33% of the examined literature. In summation, the development of virtual environments within the metaverse is a multifaceted and dynamically evolving domain, offering both opportunities and hurdles for researchers and practitioners alike. It is our aspiration that the insights, challenges, and recommendations articulated in this report will catalyze extensive dialogues among industry stakeholders, governmental bodies, and other interested parties concerning the metaverse's destiny and the world they aim to construct or bequeath to future generations.
Envisioning the future: a multi-disciplinary approach to human-centered intelligent environments

Abstract Humane or humanity-centered intelligent environments (IE) prioritize human users, communities, and societal needs in the system design, service, and operations. However, designing for a genuinely humanity-centric vision poses potential barriers related to the technical frameworks and methods of IEs. This paper introduces a multi-disciplinary innovation research approach grounded in a participatory ForSTI (i.e., Foresight in Science, Technology, and Innovation) methodology. We apply a Horizon scanning exercise in combination with expert interviews and a lead user workshop to develop a future humanity-centric roadmap for IEs that aligns with a coherent understanding of human and societal needs. Multiple technical visions are explored to foresee how ethics, human control, and agency can be preserved in developing future human-centric IEs. Our findings indicate that the “feasible” future vision is propelled forward by technical enchanted determinism , with weak resistance from the public, citizens, and society. The “possible” vision augments humans and the environment through technical advancement. In contrast, the most “desirable” vision is inclusive of all humanity, also the most vulnerable, and can bring forth meaningful human involvement and influence in the technical configurations of IEs. By carefully considering the potential drivers and barriers ahead, we can re-think how to design for the most desirable future vision in developing IEs.
Generating Natural Language Queries for More Effective Systematic Review Screening Prioritisation

Screening prioritisation in medical systematic reviews aims to rank the set of documents retrieved by complex Boolean queries. Prioritising the most important documents ensures that subsequent review steps can be carried out more efficiently and effectively. The current state of the art uses the final title of the review as a query to rank the documents using BERT-based neural rankers. However, the final title is only formulated at the end of the review process, which makes this approach impractical as it relies on ex post facto information. At the time of screening, only a rough working title is available, with which the BERT-based ranker performs significantly worse than with the final title. In this paper, we explore alternative sources of queries for prioritising screening, such as the Boolean query used to retrieve the documents to be screened and queries generated by instruction-based generative large-scale language models such as ChatGPT and Alpaca. Our best approach is not only viable based on the information available at the time of screening, but also has similar effectiveness to the final title.
The continuous stochastic gradient method: part II–application and numerics

Abstract In this contribution, we present a numerical analysis of the continuous stochastic gradient (CSG) method, including applications from topology optimization and convergence rates. In contrast to standard stochastic gradient optimization schemes, CSG does not discard old gradient samples from previous iterations. Instead, design dependent integration weights are calculated to form a convex combination as an approximation to the true gradient at the current design. As the approximation error vanishes in the course of the iterations, CSG represents a hybrid approach, starting off like a purely stochastic method and behaving like a full gradient scheme in the limit. In this work, the efficiency of CSG is demonstrated for practically relevant applications from topology optimization. These settings are characterized by both, a large number of optimization variables and an objective function, whose evaluation requires the numerical computation of multiple integrals concatenated in a nonlinear fashion. Such problems could not be solved by any existing optimization method before. Lastly, with regards to convergence rates, first estimates are provided and confirmed with the help of numerical experiments.
A Privacy Preserving System for Movie Recommendations Using Federated Learning

Recommender systems have become ubiquitous in the past years. They solve the tyranny of choice problem faced by many users, and are utilized by many online businesses to drive engagement and sales. Besides other criticisms, like creating filter bubbles within social networks, recommender systems are often reproved for collecting considerable amounts of personal data. However, to personalize recommendations, personal information is fundamentally required. A recent distributed learning scheme called federated learning has made it possible to learn from personal user data without its central collection. Consequently, we present a recommender system for movie recommendations, which provides privacy and thus trustworthiness on multiple levels: First and foremost, it is trained using federated learning and thus, by its very nature, privacy-preserving, while still enabling users to benefit from global insights. Furthermore, a novel federated learning scheme, called FedQ, is employed, which not only addresses the problem of non-i.i.d.-ness and small local datasets, but also prevents input data reconstruction attacks by aggregating client updates early. Finally, to reduce the communication overhead, compression is applied, which significantly compresses the exchanged neural network parametrizations to a fraction of their original size. We conjecture that this may also improve data privacy through its lossy quantization stage.
A Novel Scheduling Algorithm for Improved Performance of Multi-Objective Safety-Critical Wireless Sensor Networks Using Long Short-Term Memory

The multiple objective optimisation (MOO) challenges encountered in the context of wireless sensor networks (WSNs) present a formidable NP-hard problem. These issues primarily arise from the constraints imposed by critical factors such as connectivity, coverage, and, most notably, energy consumption. Simultaneously fulfilling these three requirements is no longer considered the standard approach for enhancing system dependability. To illustrate, a prospective solution may optimise one or two of these requirements while bolstering overall network energy efficiency. Nonetheless, prior endeavours documented in the extant literature reveal unexplored avenues for enhancement. Hence, this paper introduces a new methodology aimed at alleviating MOO concerns and thereby enhancing the quality of service (QoS) in WSNs. A long short-term memory (LSTM) model is proposed as an analytical tool to deliver an energy-efficient scheduling solution that aligns and optimises WSN parameters, striving to attain the most favourable system performance. The LSTM algorithm’s effectiveness is assessed through the iterative application of periods, confirming the desired QoS levels. The unique feature of LSTM lies in its capability to observe specific event sequences and subsequently establish them as the system’s default configuration for its entire operational lifespan. Once these favourable parameters are identified, LSTM automatically ensures consistent service availability and reliability throughout the network’s lifespan. The results obtained demonstrate the superiority of the proposed LSTM-based scheduling algorithm in comparison to the self-organising map (SOFM)-based node scheduling algorithm. The LSTM-based approach outperforms the SOFM-based alternative by a remarkable 75% in terms of coverage and exhibits a 20% enhancement in network lifetime, all while maintaining equivalent levels of connectivity (i.e., 99%) in both algorithms.