Reviewerly/exHarmony huggingface dataset:
Risk-Management Framework and Information-Security Systems for Small and Medium Enterprises (SMEs): A Meta-Analysis Approach

Information-technology (IT) security standards are regularly updated in a rapidly changing technological world to maintain pace with advanced technologies. This study was motivated by the realization that established IT risk-management frameworks might provide an adequate defence for small- and medium-sized enterprises (SMEs), especially those actively adopting new technologies. We reviewed that a dynamic IT risk-management framework, updated to reflect emerging technological changes, would offer improved security and privacy for SMEs. To evaluate this, we conducted a systematic literature review spanning 2016 to 2021, focusing on IT risk-management research in various application areas. This study revealed that, while established frameworks like NIST have their benefits, they need to be better suited to the unique needs of SMEs due to their high degree of abstractness, vague guidelines, and lack of adaptability to technological advancements. The findings suggest a pressing need to evolve IT risk-management frameworks, particularly by incorporating advanced methods such as system dynamics, machine learning, and technoeconomic and sociotechnological models. These innovative approaches provide a more dynamic, responsive, and holistic approach to risk management, thereby significantly improving the IT security of SMEs. The study’s implications underscore the urgency of developing flexible, dynamic, and technology-informed IT risk-management strategies, offering novel insights into a more practical approach to IT risk management.
Evaluating the intelligence capability of smart homes: A conceptual modeling approach

With the rapid development of Internet of Things technology, smart homes have gradually become an integral part of people’s lives, and the market share of smart homes has experienced a significant surge in recent years. As a result, there is a growing need for both producers and end-users to evaluate the intelligence of smart homes. While existing studies focus on simulating smart home environments, they do not provide an approach for automatically evaluating the intelligence of smart homes. In this study, we systematically establish a conceptual model of smart homes based on a wide range of smart home definitions, focusing on examining the factors that contribute to users feeling satisfied with their smart homes. Additionally, we proposed a framework for evaluating the intelligence capability of smart homes. To validate the effectiveness of our framework, we conducted an empirical study using an online user survey and collected 300 questionnaires about user ratings of three smart home suites. Our empirical results demonstrate that our framework is consistent with users’ perceptions of the intelligence level of smart homes. In order to further explore why users feel satisfied with their smart homes, we held a workshop with five participants. The results of our discussion showed a correlation between why users feel satisfied with their smart homes and the user needs that smart homes can fulfill.
Studies on the corrosion behavior of aluminum alloy in solid/molten salt hydrate phase change material based on fractal theory

The corrosion behavior of Al7075 in the solid and molten states of phase change material were investigated based on fractal theory in conjunction with electrochemical impedance spectroscopy. Results indicated that the surface texture parameters were properly used to describe the corrosion degree as well as identify the different pitting stages. Pitting conformed with Weibull distribution. The statistical parameters Skewness and Kurtosis had a good linear relation with fractal dimension (Ds). Additionally, there was linear relationship between Ds and DEIS based on EIS spectra, due to the fractal geometry of Al7075 electrodes that induced time-constant dispersion and resulted in CPE behaviors.
An Advanced Explainable and Interpretable ML-Based Framework for Educational Data Mining

During the last two decades, the adoption of machine learning techniques for addressing various challenging issues in the educational domain has gained much popularity. Nevertheless, there is still a lack of research on developing AI systems that focus on the interpretability and explainability of the associated models and algorithms, thus being able to present the data analysis results in a human understandable way. In this work, we propose a new explainable framework for predicting students’ performance, which provides accurate, reliable and interpretable results. Our framework builds on the recently proposed NGBoost algorithm for the development of an efficient prediction model, as well as on the LIME and SHAP methods for providing local and global explanations, respectively. The use cases presented in this paper demonstrate the applicability of our framework and give insights about the recommendations that can be provided to educators and students.
Blockchain and Smart Contracts for Telecommunications: Requirements vs. Cost Analysis

Blockchain technology offers solutions to numerous network problems by leveraging distributed record-keeping and collaborative decision-making features.However, deployment considerations such as blockchain infrastructure cost, performance requirements, and scalability are often overlooked.This paper provides an in-depth perspective on deploying blockchain-based solutions for telecommunications networks, estimating costs, comparing infrastructure options (on-premises, IaaS, BaaS), and choosing a suitable blockchain platform.To that end, we identify the performance limitations of the proposed solution under various deployment infrastructures by studying two prominent use cases: one proposing a distributed marketplace solution for 5G slice brokering and another one on the decentralization of federated learning (FL) through blockchain.For the slice brokering use case, our experiments showed that sub-second latency could be achieved for a maximum transaction throughput in the range of 10 to 200 transactions per second (TPS), whereas use cases requiring a higher throughput (300 to 400 TPS) would need more computational resources.Meanwhile, the FL use case provided insights into the achievable accuracy of distributed learning under various blockchain settings (public, consortium, and private), which led to the understanding that private and consortium blockchains can achieve acceptable accuracy in significantly lower training times compared to public blockchains.
Study on Synthetic Video Generation of Embryo Development

Biomedical imaging is commonly used to obtain a correct diagnosis of the patient condition, but in certain treatments, namely in vitro fertilization for infertile couples, it is particularly important to monitor the development over time of the primary object of the study, such as the embryo. Currently, many computer vision architectures and models focus on the analysis, classification, or semantic segmentation of static images, neglecting the essential developing nature of certain cases. However, image generation architectures based on Generative Adversarial Networks and Autoencoders have evolved to adapt to video generation, which can be adjusted to the analysis of the image-to-image development of a particular medical study. This paper analyzes different synthetic image generation models, including the Temporal-GAN-v2 and a Video Diffusion Model, applied to time-lapse pictures of embryo fertilization and culture. The aim of this study is to verify the performance of each model using objective quantitative measures such as Fréchet Inception Distance (FID) and Fréchet Video Distance (FVD), and to evaluate the generation of image sequences for specific embryo stages.
Categorizing UX Aspects for Voice User Interfaces Using the Kano Model

Although voice user interfaces (VUIs) are widely available, they currently face challenges such as low adoption rates and user concerns. Users assess products through user experience (UX) aspects. Thus, knowing UX aspects for VUIs and their prioritization will improve UX and reduce challenges. In this study, we use a user-centered mixed-methods approach to identify and prioritize UX aspects of VUIs. Thereby, we identified 32 VUI UX aspects from the perspective of intensive users. We then applied the Kano model to categorize these UX aspects for which we analyzed $$N=195$$ VUI users. One thing we found was that 21 VUI UX aspects are distinctively prioritized, such as privacy, data security, and ad-free as must-be, and simplicity, comprehension, and error-free as one-dimensional. These findings can help VUI developers to prioritize specific UX aspects according to their target group's needs, enabling them to create VUIs that benefit and excite their users.
Integrated diffractive optical neural network with space-time interleaving

Integrated diffractive optical neural networks (DONNs) have significant potential for complex machine learning tasks with high speed and ultralow energy consumption. However, the on-chip implementation of a high-performance optical neural network is limited by input dimensions. In contrast to existing photonic neural networks, a space-time interleaving technology based on arrayed waveguides is designed to realize an on-chip DONN with high-speed, high-dimensional, and all-optical input signal modulation. To demonstrate the performance of the on-chip DONN with high-speed space-time interleaving modulation, an on-chip DONN with a designed footprint of 0.0945 mm2 is proposed to resolve the vowel recognition task, reaching a computation speed of about 1.4×1013 operations per second and yielding an accuracy of 98.3% in numerical calculation. In addition, the function of the specially designed arrayed waveguides for realizing parallel signal inputs using space-time conversion has been verified experimentally. This method can realize the on-chip DONN with higher input dimension and lower energy consumption.
Cross-domain privacy-preserving broad network for fault diagnosis of rotating machinery

The broad learning system-based data processing method has been widely used in the field of industrial intelligent operation and maintenance, and has achieved impressive results. However, data island created by privacy concerns in real industrial scenarios makes centralized deep training extremely difficult. In addition, the fluctuating operating conditions of machines including speed and load significantly affect the distribution of monitoring data representing the operating state of the device. These two problems make it difficult to apply broad learning system to practical application scenarios. Therefore, in view of the above problems, a novel cross-domain privacy-preserving broad network (CDPPBN) for cross-domain fault classification of rotating machinery is presented. Specifically, a cross-domain interactive data encryption protocol feature extractor is designed firstly to ensure the privacy and security of both source and target domain data during feature extraction process. Then, a domain adaptation target classifier is constructed to achieve the fault classification task in the target domain without labels. In this classifier, a maximum mean discrepancy regularization term is constructed to match the mean projections of the source and target domains to adapt the source classifier to the target domain, and a semi-supervised local graph embedding regularization term is constructed to mine the structural features of the target domain data. CDPPBN not only has high performance in terms of data privacy protection, but also makes full use of both labeled source data and unlabeled target data to generate a domain adaptation target classifier. The effectiveness of the presented method is verified through bearing monitoring signal. Experimental analysis shows that the proposed CDPPBN has better data privacy protection capability and data cross-domain diagnosis ability compared with other methods.
Hyperspectral Image Compression Using Implicit Neural Representations

Hyperspectral images, which record the electro-magnetic spectrum for a pixel in the image of a scene, often store hundreds of channels per pixel and contain an order of magnitude more information than a similarly-sized RBG color image. Consequently, concomitant with the decreasing cost of capturing these images, there is a need to develop efficient techniques for storing, transmitting, and analyzing hyperspectral images. This paper develops a method for hyperspectral image compression using implicit neural representations where a multi-layer perceptron network f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Θ</inf> with sinusoidal activation functions “learns” to map pixel locations to pixel intensities for a given hyperspectral image I. f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Θ</inf> thus acts as a compressed encoding of this image, and the original image is reconstructed by evaluating f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ϑ</inf> at each pixel location. We have evaluated our method on four benchmarks-Indian Pines, Jasper Ridge, Pavia University, and Cuprite-and we show that the proposed method achieves better compression than JPEG, JPEG2000, and PCA-DCT at low bitrates.
AnycostFL: Efficient On-Demand Federated Learning over Heterogeneous Edge Devices

In this work, we investigate the challenging problem of on-demand federated learning (FL) over heterogeneous edge devices with diverse resource constraints. We propose a cost-adjustable FL framework, named AnycostFL, that enables diverse edge devices to efficiently perform local updates under a wide range of efficiency constraints. To this end, we design the model shrinking to support local model training with elastic computation cost, and the gradient compression to allow parameter transmission with dynamic communication overhead. An enhanced parameter aggregation is conducted in an element-wise manner to improve the model performance. Focusing on AnycostFL, we further propose an optimization design to minimize the global training loss with personalized latency and energy constraints. By revealing the theoretical insights of the convergence analysis, personalized training strategies are deduced for different devices to match their locally available resources. Experiment results indicate that, when compared to the state-of-the-art efficient FL algorithms, our learning framework can reduce up to 1.9 times of the training latency and energy consumption for realizing a reasonable global testing accuracy. Moreover, the results also demonstrate that, our approach significantly improves the converged global accuracy.
Collaborative Streaming and Super Resolution Adaptation for Mobile Immersive Videos

Tile-based streaming and super resolution are two representative technologies adopted to improve bandwidth efficiency of immersive video steaming. The former allows selective download of contents in the user viewport by splitting the video into multiple independently decodable tiles. The latter leverages client-side computation to reconstruct the received video into higher quality using advanced neural network models. In this work, we propose CASE, a collaborated adaptive streaming and enhancement framework for mobile immersive videos, which integrates super resolution with tile-based streaming to optimize user experience with dynamic bandwidth and limited computing capability. To coordinate the video transmission and reconstruction in CASE, we identify and address several key design issues including unified video quality assessment, computation complexity model for super resolution, and buffer analysis considering the interplay between transmission and reconstruction. We further formulate the quality-of-experience (QoE) maximization problem for mobile immersive video streaming and propose a rate adaptation algorithm to make the best decisions for download and for reconstruction based on the Lyapunov optimization theory. Extensive evaluation results validate the superiority of our proposed approach, which presents stable performance with considerable QoE improvement, while enabling trade-off between playback smoothness and video quality.
Addressing inter-device variations in optical coherence tomography angiography: will image-to-image translation systems help?

Optical coherence tomography angiography (OCTA) is an innovative technology providing visual and quantitative data on retinal microvasculature in a non-invasive manner.Due to variations in the technical specifications of different OCTA devices, there are significant inter-device differences in OCTA data, which can limit their comparability and generalizability. These variations can also result in a domain shift problem that may interfere with applicability of machine learning models on data obtained from different OCTA machines. One possible approach to address this issue may be unsupervised deep image-to-image translation leveraging systems such as Cycle-Consistent Generative Adversarial Networks (Cycle-GANs) and Denoising Diffusion Probabilistic Models (DDPMs). Through training on unpaired images from different device domains, Cycle-GANs and DDPMs may enable cross-domain translation of images. They have been successfully applied in various medical imaging tasks, including segmentation, denoising, and cross-modality image-to-image translation. In this commentary, we briefly describe how Cycle-GANs and DDPMs operate, and review the recent experiments with these models on medical and ocular imaging data. We then discuss the benefits of applying such techniques for inter-device translation of OCTA data and the potential challenges ahead.Retinal imaging technologies and deep learning-based domain adaptation techniques are rapidly evolving. We suggest exploring the potential of image-to-image translation methods in improving the comparability of OCTA data from different centers or devices. This may facilitate more efficient analysis of heterogeneous data and broader applicability of machine learning models trained on limited datasets in this field.
DIGITAL CURRENCIES, SWOT ANALYSIS

Abstract. The paper highlights the increased international trade and discussion in recent years of digital currencies, also known as virtual currencies or cryptocurrencies, including the bilateral trade coin bitcoin (BTC). Scholars have emphasised the need to consider different preconditions, focusing primarily on domestic perspectives of payment intermediation within individual countries. The inclusion of digital currencies in global trade is motivated by the recognition that time is playing an increasingly important role in trade dynamics. Scholars have presented arguments on the potential impact of CBDCs (central bank digital currencies), highlighting their ability to facilitate secure and seamless payment transactions in line with the primary objective of central banks. Conversely, sceptics argue that the mere issuance of CBDCs can potentially undermine the stability of the financial system. High interest rates tend to reduce the amount of cash in circulation. Therefore, higher interest rates do not stimulate digital currencies as investors seek to keep their funds tied up rather than floating. The purpose of this research is to consider the concept of digital currencies in global trade, reflecting the increasing importance of time in trade. Older generations have been reluctant to embrace this new way of doing business, and many questions have arisen. For example, digital currencies compete with traditional currencies, making government control difficult. There are also issues of trust, credibility, volatility, use across countries, use across time zones, trading time, uncertainty, disruption to current centralised economic systems, lender of last resort in the form of a central bank, as well as distance and culture in international trade. The methodology used is the well-known and classic SWOT analysis, which provides tools for analysing the pros and cons of digital currencies. It sheds light on the advantages and disadvantages of digital currencies, including strengths, weaknesses, opportunities and threats. The results vary depending on the weight of the four measures provided. The practical implications are that it is important to be aware of the measured factors when trading: Strengths, Weaknesses, Opportunities and Threats. Finally, it is believed that the value/originality of the research sheds light on issues that people need to be aware of when considering entering into a transaction using some form of digital currency.
An Integrated Framework for Understanding Multimodal Embodied Experiences in Interactive Virtual Reality

Virtual Reality (VR) technology enables "embodied interactions" in realistic environments where users can freely move and interact, with deep physical and emotional states. However, a comprehensive understanding of the embodied user experience is currently limited by the extent to which one can make relevant observations, and the accuracy at which observations can be interpreted.
The Mediating Effect of Dynamic Marketing Capabilities on the Relationship Between Customer Knowledge Management and Organizational Innovation

Today’s competitive business environment has made organizational innovation (OI) a central strategy for most firms. Not only does OI lead to obtaining new customers and market segments but it also enables firms to develop a reputation for being innovative. To ensure a sustained level of OI, firms need to enhance their customer knowledge management (CKM) as well as their dynamic marketing capabilities (DMCs). This study focuses on investigating the mediating effect of DMCs on the relationship between CKM and OI. To this end, a set of hypotheses were developed to analyse these relationships employing structural equation modelling. The data were collected from a total of 242 top management of firms in the pharmaceutical and healthcare industries in Jordan. The findings showed that knowledge for customer and knowledge about customer had strong and positive effects on the OI levels of the firms considered in this study. It was also revealed that knowledge from customer has no significant effect on OI. Moreover, the results indicated that DMCs have a mediating effect on the relationship between CKM and OI.
Adaptive Adjacent Layer Feature Fusion for Object Detection in Remote Sensing Images

Object detection in remote sensing images faces the challenges of a complex background, large object size variations, and high inter-class similarity. To address these problems, we propose an adaptive adjacent layer feature fusion (AALFF) method, which is developed on the basis of RTMDet. Specifically, the AALFF method incorporates an adjacent layer feature fusion enhancement (ALFFE) module, designed to capture high-level semantic information and accurately locate object spatial positions. ALFFE also effectively preserves small objects by fusing adjacent layer features and employs involution to aggregate contextual information in a wide spatial range for object essential features extraction in complex backgrounds. Additionally, the adaptive spatial feature fusion (ASFF) module is introduced to guide the network to select and fuse the crucial features to improve the adaptability to objects with different sizes. The proposed method achieves mean average precision (mAP) values of 77.1%, 88.9%, and 95.7% on the DIOR, HRRSD, and NWPU VHR-10 datasets, respectively. Notably, our approach achieves mAP75 values of 60.8% and 79.0% on the DIOR and HRRSD datasets, respectively, surpassing the state-of-the-art performance on the DIOR dataset.