Reviewerly/exHarmony huggingface dataset:
Learned Gradient Compression for Distributed Deep Learning

Training deep neural networks on large datasets containing high-dimensional data requires a large amount of computation. A solution to this problem is data-parallel distributed training, where a model is replicated into several computational nodes that have access to different chunks of the data. This approach, however, entails high communication rates and latency because of the computed gradients that need to be shared among nodes at every iteration. The problem becomes more pronounced in the case that there is wireless communication between the nodes (i.e., due to the limited network bandwidth). To address this problem, various compression methods have been proposed, including sparsification, quantization, and entropy encoding of the gradients. Existing methods leverage the intra-node information redundancy, that is, they compress gradients at each node independently. In contrast, we advocate that the gradients across the nodes are correlated and propose methods to leverage this inter-node redundancy to improve compression efficiency. Depending on the node communication protocol (parameter server or ring-allreduce), we propose two instances for the gradient compression that we coin Learned Gradient Compression (LGC). Our methods exploit an autoencoder (i.e., trained during the first stages of the distributed training) to capture the common information that exists in the gradients of the distributed nodes. To constrain the nodes’ computational complexity, the autoencoder is realized with a lightweight neural network. We have tested our LGC methods on the image classification and semantic segmentation tasks using different convolutional neural networks (CNNs) [ResNet50, ResNet101, and pyramid scene parsing network (PSPNet)] and multiple datasets (ImageNet, Cifar10, and CamVid). The ResNet101 model trained for image classification on Cifar10 achieved significant compression rate reductions with the accuracy of 93.57%, which is lower than the baseline distributed training with uncompressed gradients only by 0.18%. The rate of the model is reduced by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8095\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> compared with the baseline and the state-of-the-art deep gradient compression (DGC) method, respectively.
The Impact of Digital Transformation on Performance: Evidence from Vietnamese Commercial Banks

The role of digital transformation in creating value for commercial banks has been interesting to researchers for a long time. While many commercial banks have significantly investigated digital transformation, researchers and managers have still met many difficulties examining the distribution of digital transformation to business performance. This paper aims to evaluate the impact of digital transformation on Vietnamese commercial banks’ performance by different sizes, from there proposing policy implications of digital transformation to improve the banking performance. To achieve this goal, we used a quantitative research method. Specifically, we applied the GMM system (SGMM) of Blundell and Bond for the data of 13 joint-stock commercial banks in Vietnam in the period from 2011 to 2019. Then Bayesian analysis is performed to test the robustness of the models estimated by the SGMM method. The result shows that the digital transformation has a positive impact on the performance of Vietnamese commercial banks. Besides, we also find that the larger the banks, the greater the positive impact of digital transformation on bank performance. Therefore, the efficiency of digital transformation depends on a bank scale.
Investigating flood resilience perceptions and supporting collective decision-making through fuzzy cognitive mapping

Improving flood resilience of communities requires a holistic understanding of risks and resilience options as well as the preferences and priorities of different stakeholders. Innovations in risk and resilience assessment have helped communities to identify gaps in their flood risk management strategy but selecting and implementing resilience solutions remains a big challenge for many decision-makers. In addition to traditional appraisals and cost-benefit assessments this also calls for a participatory process in which various stakeholders are encouraged to adopt a system-level approach in identifying interventions that can maximise a range of benefits and co-benefits. In this study, we investigate how a combination of modelling and measurement methods can help decision-makers with their flood resilience strategies. We apply a participatory system thinking approach combining Fuzzy Cognitive Mapping (FCM) with a flood resilience measurement framework called Flood Resilience Measurement for Communities (FRMC). We first investigate stakeholders' biases on flood resilience interventions, and then lead them through a system thinking exercise using FCM and FRMC to elicit mental models representing important aspects of flood resilience and their interrelation. These are then aggregated, representing the collective perceptions and knowledge of stakeholders, and used to identify the most beneficial resilience actions in terms of direct and indirect impacts on flood resilience. We apply this approach to the case of Lowestoft, a coastal town in England exposed to significant flood risk. Developed in close collaboration with the local authorities, the ambition is to support decision-making on flood resilience interventions. We find that this combination of methods enables system-level thinking and inclusive decision-making about flood resilience which can ultimately encourage transformative decisions on prioritization of actions and investments.
D<sup>2</sup>-Net: Dual Disentanglement Network for Brain Tumor Segmentation With Missing Modalities

Multi-modal Magnetic Resonance Imaging (MRI) can provide complementary information for automatic brain tumor segmentation, which is crucial for diagnosis and prognosis. While missing modality data is common in clinical practice and it can result in the collapse of most previous methods relying on complete modality data. Current state-of-the-art approaches cope with the situations of missing modalities by fusing multi-modal images and features to learn shared representations of tumor regions, which often ignore explicitly capturing the correlations among modalities and tumor regions. Inspired by the fact that modality information plays distinct roles to segment different tumor regions, we aim to explicitly exploit the correlations among various modality-specific information and tumor-specific knowledge for segmentation. To this end, we propose a Dual Disentanglement Network (D2-Net) for brain tumor segmentation with missing modalities, which consists of a modality disentanglement stage (MD-Stage) and a tumor-region disentanglement stage (TD-Stage). In the MD-Stage, a spatial-frequency joint modality contrastive learning scheme is designed to directly decouple the modality-specific information from MRI data. To decompose tumor-specific representations and extract discriminative holistic features, we propose an affinity-guided dense tumor-region knowledge distillation mechanism in the TD-Stage through aligning the features of a disentangled binary teacher network with a holistic student network. By explicitly discovering relations among modalities and tumor regions, our model can learn sufficient information for segmentation even if some modalities are missing. Extensive experiments on the public BraTS-2018 database demonstrate the superiority of our framework over state-of-the-art methods in missing modalities situations. Codes are available at https://github.com/CityU-AIM-Group/D2Net.
Anthropomorphism and social presence in Human–Virtual service assistant interactions: The role of dialog length and attitudes

In this study, we delve into the perceived quality of recommendations provided by AI-based virtual service assistants (VSAs). Specifically, the role of the social presence of VSAs in influencing recommendation perceptions is investigated. We also explore how the social presence of a VSA is formed and how perceived anthropomorphism plays a vital role in shaping social presence and eventually instilling trust in VSAs among consumers. These relationships are examined in the context of online government services. The results indicate that consumer interaction with VSAs - manifesting via perceived anthropomorphism, social presence, dialog length, and attitudes - improves recommendation quality perceptions, which further instills trust in VSA-based recommendations. Perceived anthropomorphism was found to strongly influence the formation of social presence, whereas trust and recommendation quality - the outcomes of social presence - were found to be partially conditional on the dialog length and the degree of positive attitudes toward VSAs. The findings additionally suggest that a VSA can be considered a social actor that possesses the capability to bring a “human touch” to online services, therefore improving the overall online service experience.
Human Activity Recognition Based on Embedded Sensor Data Fusion for the Internet of Healthcare Things

Nowadays, the emerging information technologies in smart handheld devices are motivating the research community to make use of embedded sensors in such devices for healthcare purposes. In particular, inertial measurement sensors such as accelerometers and gyroscopes embedded in smartphones and smartwatches can provide sensory data fusion for human activities and gestures. Thus, the concepts of the Internet of Healthcare Things (IoHT) paradigm can be applied to handle such sensory data and maximize the benefits of collecting and analyzing them. The application areas contain but are not restricted to the rehabilitation of elderly people, fall detection, smoking control, sportive exercises, and monitoring of daily life activities. In this work, a public dataset collected using two smartphones (in pocket and wrist positions) is considered for IoHT applications. Three-dimensional inertia signals of thirteen timestamped human activities such as Walking, Walking Upstairs, Walking Downstairs, Writing, Smoking, and others are registered. Here, an efficient human activity recognition (HAR) model is presented based on efficient handcrafted features and Random Forest as a classifier. Simulation results ensure the superiority of the applied model over others introduced in the literature for the same dataset. Moreover, different approaches to evaluating such models are considered, as well as implementation issues. The accuracy of the current model reaches 98.7% on average. The current model performance is also verified using the WISDM v1 dataset.
A Structured and Methodological Review on Vision-Based Hand Gesture Recognition System

Researchers have recently focused their attention on vision-based hand gesture recognition. However, due to several constraints, achieving an effective vision-driven hand gesture recognition system in real time has remained a challenge. This paper aims to uncover the limitations faced in image acquisition through the use of cameras, image segmentation and tracking, feature extraction, and gesture classification stages of vision-driven hand gesture recognition in various camera orientations. This paper looked at research on vision-based hand gesture recognition systems from 2012 to 2022. Its goal is to find areas that are getting better and those that need more work. We used specific keywords to find 108 articles in well-known online databases. In this article, we put together a collection of the most notable research works related to gesture recognition. We suggest different categories for gesture recognition-related research with subcategories to create a valuable resource in this domain. We summarize and analyze the methodologies in tabular form. After comparing similar types of methodologies in the gesture recognition field, we have drawn conclusions based on our findings. Our research also looked at how well the vision-based system recognized hand gestures in terms of recognition accuracy. There is a wide variation in identification accuracy, from 68% to 97%, with the average being 86.6 percent. The limitations considered comprise multiple text and interpretations of gestures and complex non-rigid hand characteristics. In comparison to current research, this paper is unique in that it discusses all types of gesture recognition techniques.
Emergence as a Feature of Practice-based Design Research

Practice-based design research is often emergent. Methods, tactics, goals and even topics can unfold and change as researchers adapt and learn in the course of their projects. This adaptability is one of the strengths of design as an approach to research, but it seems to contradict assumptions about research as a systematic, inquiry-led investigation. This leads to a tension in practicing and reporting research that we unpack here, before making a series of suggestions for practice-based researchers and reviewers about how to better navigate emergence when pursuing, reporting and evaluating practice-based design research.
A survey of blockchain-based IoT eHealthcare: Applications, research issues, and challenges

Blockchain (BC) technology has recently emerged as an essential component for different applications, including healthcare and IoT, because of its decentralized ledger, source provenance, and tamper-proof nature. The Internet of Things (IoT) and BC have enabled health systems to expand their scalability and maintain consistency on a decentralized platform. As a result, many researchers have developed BC-enabled IoT eHealth systems and explored the application of BC technology in diverse fields of eHealthcare. This paper conducts a comprehensive survey on the emerging applications of BC technology in healthcare. We summarize applications, research issues, security threats, research challenges, opportunities, and the future scope of BC technologies in the IoT-enabled healthcare system when BC is adopted to handle the privacy and storage of current and future medical records. Furthermore, we analyze the state-of-the-art BC works in the medical area, assessing their benefits-drawbacks, and guiding future researchers to overcome the limitations of the existing articles.
Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality

Transitioning from fossil fuels to renewable sources and developing sustainable energy materials for energy production and storage are critical factors in achieving climate neutrality. These can be realized through innovative strategies to provide viable, economically competitive, and scalable technologies ranging across various sectors. Quantum computing (QC) has the potential to revolutionize various domains of science and engineering, including macro-energy systems and sustainable energy materials design. Conventional approaches for renewable and sustainable energy systems solely rely on classical computing techniques that may not scale well with the increasing size and complexity of applications. Owing to the advancements in quantum hardware and algorithms, QC and quantum artificial intelligence make promising tools to handle renewable and sustainable energy systems even at larger scales. In this review, we discuss the prospects of QC for various areas of applications in energy sustainability to help address climate change. In addition to providing a brief background on the operations of quantum computers, the constituent segments of widely adopted QC-based techniques that improve the computational efficiency of quantum chemistry calculations for sustainable energy materials along with quantum artificial intelligence methods that can address complex optimization and machine learning problems arising in renewable energy systems are also introduced in this paper. We screen the presented quantum algorithms based on their performance on current quantum devices despite their promising potential. Furthermore, sustainable energy applications that may draw advantages from QC-based strategies are identified in this work while simultaneously setting realistic expectations over the potential improvements offered over classical techniques.
Integration of Artificial Intelligence and Blockchain Technology in Healthcare and Agriculture

Over the last decade, the healthcare sector has accelerated its digitization and electronic health records (EHRs). As information technology progresses, the notion of intelligent health also gathers popularity. By combining technologies such as the internet of things (IoT) and artificial intelligence (AI), innovative healthcare modifies and enhances traditional medical systems in terms of efficiency, service, and personalization. On the other side, intelligent healthcare systems are incredibly vulnerable to data breaches and other malicious assaults. Recently, blockchain technology has emerged as a potentially transformative option for enhancing data management, access control, and integrity inside healthcare systems. Integrating these advanced approaches in agriculture is critical for managing food supply chains, drug supply chains, quality maintenance, and intelligent prediction. This study reviews the literature, formulates a research topic, and analyzes the applicability of blockchain to the agriculture/food industry and healthcare, with a particular emphasis on AI and IoT. This article summarizes research on the newest blockchain solutions paired with AI technologies for strengthening and inventing new technological standards for the healthcare ecosystems and food industry.
An innovative digitization evaluation scheme for Spatio-temporal coordination relationship between multiple knowledge driven rural economic development and agricultural ecological environment—Coupling coordination model analysis based on Guangxi

The agricultural ecological environment is the premise and foundation of rural economic development and the rural economy is an important manifestation of the vitality of the agricultural ecological environment. Taking 14 prefecture-level cities in Guangxi as the research object, this paper collects and sorts out the relevant data indicators of rural economy and agricultural ecological environment of them from 2007 to 2019, constructs the evaluation index system, calculates the development index by using the comprehensive index function, and finally uses an innovative digitization evaluation scheme to further evaluate the spatiotemporal coupling and coordination relationship between the rural economy and agricultural ecological environment. The following conclusions are ultimately drawn. First, the comprehensive level index of the rural economy shows linear growth with unbalanced regional development (values from 0.306 to 0.598). Second, the comprehensive index of the agricultural ecological environment shows fluctuating growth with obvious regional differences (values from 0.264 to 0.483). Third, the development of rural economy and agricultural ecological environment is not synchronized, and there is mutual influence. Fourth, the coordination level of the rural economy and agricultural ecological environment is low (coupling values from 0.369 to 0.513). Then, policy inspirations are proposed. First, the investment in agricultural science and technology innovation should be increased and the modern agricultural technology system. Second, agricultural industrial structure should be optimized and the agricultural industrial chain. Third, the construction of agricultural infrastructure should be improved, and the high-quality development of agriculture should be promoted. Fourth, the education of ecological civilization should be strengthened, and awareness of ecological and environmental protection should be established. Fifth, they should strengthen education and training and cultivate new professional farmers. Sixth, they should improve the utilization rate of land and enhance the arable land production capacity.
Optimal Machine Learning Based Privacy Preserving Blockchain Assisted Internet of Things with Smart Cities Environment

Currently, the amount of Internet of Things (IoT) applications is enhanced for processing, analyzing, and managing the created big data from the smart city. Certain other applications of smart cities were location-based services, transportation management, and urban design, amongst others. There are several challenges under these applications containing privacy, data security, mining, and visualization. The blockchain-assisted IoT application (BIoT) is offering new urban computing to secure smart cities. The blockchain is a secure and transparent data-sharing decentralized platform, so BIoT is suggested as the optimum solution to the aforementioned challenges. In this view, this study develops an Optimal Machine Learning-based Intrusion Detection System for Privacy Preserving BIoT with Smart Cities Environment, called OMLIDS-PBIoT technique. The presented OMLIDS-PBIoT technique exploits BC and ML techniques to accomplish security in the smart city environment. For attaining this, the presented OMLIDS-PBIoT technique employs data pre-processing in the initial stage to transform the data into a compatible format. Moreover, a golden eagle optimization (GEO)-based feature selection (FS) model is designed to derive useful feature subsets. In addition, a heap-based optimizer (HBO) with random vector functional link network (RVFL) model was utilized for intrusion classification. Additionally, blockchain technology is exploited for secure data transmission in the IoT-enabled smart city environment. The performance validation of the OMLIDS-PBIoT technique is carried out using benchmark datasets, and the outcomes are inspected under numerous factors. The experimental results demonstrate the superiority of the OMLIDS-PBIoT technique over recent approaches.
Design and Optimization of Solar using MPPT Algorithm in Electric Vehicle

Photovoltaic systems create no pollution or noise, and they do not utilize any fossil fuels to generate electricity. Despite this, they need little upkeep and, perhaps most significantly, have no end. Online approaches such as P&O and RCC may be used to calculate the maximum power output of the solar array based on the real current and voltage values. In this way, the system's performance may be improved. A series connection of two modules in a solar array, which only impacts one module, was explored in this study. Tracking in this condition (the penumbra) will be more difficult due to the presence of a local maximum point. P&O and RCC are two of the most often used methods for creating MPPT. The MATLAB software is used to implement the two previously outlined approaches in the simulation section of this paper. At the end of the investigation, the findings of the MSX60 model simulation of a proposed solar car are presented. P&O and RCC algorithms are combined in the proposed method to create MPPT. Both the RCC and P&O algorithms are used in the MPPT process, which is divided into two stages.