Reviewerly/exHarmony huggingface dataset:
Prediction of Band Gap Energy of Doped Graphitic Carbon Nitride Using Genetic Algorithm-Based Support Vector Regression and Extreme Learning Machine

Graphitic carbon nitride is a stable and distinct two dimensional carbon-based polymeric semiconductor with remarkable potentials in organic pollutants degradation, chemical sensors, the reduction of CO2, water splitting and other photocatalytic applications. Efficient utilization of this material is hampered by the nature of its band gap and the rapid recombination of electron-hole pairs. Heteroatom incorporation due to doping alters the symmetry of the semiconductor and has been among the adopted strategies to tailor the band gap for enhancing the visible-light harvesting capacity of the material. Electron modulation and enhancement of reaction active sites due to doping as evident from the change in specific surface area of doped graphitic carbon nitride is employed in this work for modeling the associated band gap using hybrid genetic algorithm-based support vector regression (GSVR) and extreme learning machine (ELM). The developed GSVR performs better than ELM-SINE (with sine activation function), ELM-TRANBAS (with triangular basis activation function) and ELM-SIG (with sigmoid activation function) model with performance enhancement of 69.92%, 73.59% and 73.67%, respectively, on the basis of root mean square error as a measure of performance. The four developed models are also compared using correlation coefficient and mean absolute error while the developed GSVR demonstrates a high degree of precision and robustness. The excellent generalization and predictive strength of the developed models would ultimately facilitate quick determination of the band gap of doped graphitic carbon nitride and enhance its visible-light harvesting capacity for various photocatalytic applications.
Sustained rubber hand illusion after the end of visuotactile stimulation with a similar time course for the reduction of subjective ownership and proprioceptive drift

Abstract The rubber hand illusion is a perceptual illusion in which participants experience an inanimate rubber hand as their own when they observe this model hand being stroked in synchrony with strokes applied to the person’s real hand, which is hidden. Earlier studies have focused on the factors that determine the elicitation of this illusion, the relative contribution of vision, touch and other sensory modalities involved and the best ways to quantify this perceptual phenomenon. Questionnaires serve to assess the subjective feeling of ownership, whereas proprioceptive drift is a measure of the recalibration of hand position sense towards the rubber hand when the illusion is induced. Proprioceptive drift has been widely used and thought of as an objective measure of the illusion, although the relationship between this measure and the subjective illusion is not fully understood. Here, we examined how long the illusion is maintained after the synchronous visuotactile stimulation stops with the specific aim of clarifying the temporal relationship in the reduction of both subjective ownership and proprioceptive drift. Our results show that both the feeling of ownership and proprioceptive drift are sustained for tens of seconds after visuotactile stroking has ceased. Furthermore, our results indicate that the reduction of proprioceptive drift and the feeling of ownership follow similar time courses in their reduction, suggesting that the two phenomena are temporally correlated. Collectively, these findings help us better understand the relationships of multisensory stimulation, subjective ownership, and proprioceptive drift in the rubber hand illusion.
Semantic Segmentation With Attention Mechanism for Remote Sensing Images

Semantic segmentation for high-resolution remote sensing images is one of the most significant tasks in the field of remote sensing applications. Remote sensing images contain substantial detailed information of ground objects, such as shape, location, and texture. Therefore, these objects make the images exhibit large intraclass variance and small interclass variance, which makes it very difficult to be recognized. In this study, an end-to-end attention-based semantic segmentation network (SSAtNet) is proposed. A pyramid attention pooling module is proposed to introduce the attention mechanism into the multiscale module for adaptive features refinement. To correct the detailed information, the pooling index correction module integrates pooling index maps from the encoder with high-level feature maps, which can help recover the fine-grained features. In the encoder phase, a more effective ResNet-101 backbone is designed to capture detailed features. What is more, a series of data augmentation methods are proposed to enhance the model&#x2019;s robustness. The proposed model is compared with several previous advanced networks and achieves the state of the art on the ISPRS Vaihingen dataset. The experiment results prove the effectiveness of the SSAtNet.
A survey of guidance, navigation, and control systems for autonomous multi-rotor small unmanned aerial systems

This survey paper presents a holistic perspective on the state-of-the-art in the design of guidance, navigation, and control systems for autonomous multi-rotor small unmanned aerial systems (sUAS). By citing more than 300 publications, this work recalls fundamental results that enabled the design of these systems, describes some of the latest advances, and compares the performance of several techniques. This paper also lists some techniques that, although already employed by different classes of mobile robots, have not been employed yet on sUAS, but may lead to satisfactory results. Furthermore, this publication highlights some limitations in the theoretical and technological solutions underlying existing guidance, navigation, and control systems for sUAS and places special emphasis on some of the most relevant gaps that hinder the integration of these three systems. In light of the surveyed results, this paper provides recommendations for macro-research areas that would improve the overall quality of autopilots for autonomous sUAS and would facilitate the transition of existing results from sUAS to larger autonomous aircraft for payload delivery and commercial transportation.
A novel blockchain-enabled heart disease prediction mechanism using machine learning

Heart disease is one of the leading causes for death in men and women across the globe. Several characteristics that can be monitored to predict the heart disease in the earlier stage are blood pressure, cholesterol level, blood sugar level and body weight. The technology is revolutionizing the existing healthcare infrastructure. With the inclusion of Internet of Things (IoT), now we can monitor patients remotely, store their data, and process it for further analysis. However, the need is to propose new and advanced secured algorithms for fast processing and efficient detection of events. In this article, a machine learning based Sine Cosine Weighted K-Nearest Neighbour (SCA_WKNN) algorithm is proposed for the heart disease prediction that learns from the data being stored in blockchain. Since the data stored in the blockchain are tamper resistant, it acts as an authentic source for learning data and also as a secure storage environment for patient information. The performance of proposed SCA_WKNN is assessed in comparison with other algorithms in terms of accuracy, precision, recall, F-score, and root mean square error. Our analysis indicates that SCA_WKNN achieves 4.59% and 15.61% maximum accuracy than W K-NN and K-NN, respectively. Also, blockchain-based storage is compared with peer-to-peer storage in terms of latency and throughput. The blockchain-based decentralized storage achieves 25.03% maximum throughput than peer-to-peer storage.
Benchmarking physics-informed machine learning-based short term PV-power forecasting tools

Uncertainty is one of the core challenges posed by renewable energy integration in power systems, especially for solar photovoltaic (PV), given its dependence on meteorological phenomena. This has motivated the development of numerous forecasting tools, recently focused on physics informed machine learning (ML). Virtually, every paper claims to provide better accuracy than the previous, yet the replicability of these studies is very low, motivating unfair, or erroneous comparisons. This paper reviews and compares the most relevant ML-methods identified in the literature (Random Forest, Support Vector Regression, Convolutional Neural Networks, Long–Short Term Memory and a Hybrid of the last two) with two statistical methods: persistence and an Semi-Parametric Auto-Regressive model. Furthermore, we propose a methodology to integrate a PV-performance model in ML models to forecast power several hours ahead with 5-min resolution. A basic dataset including power production and meteorological measurements is expanded with physics-informed features that capture the relationship between weather and PV operational state, while keeping strong correlation towards the intrinsic feature. This allows the models to learn about the physical interdependence of different features, potentially yielding a higher accuracy than conventional methods. Then, we also propose a physics-informed feature selection to tighten the search-space of the best performer. A case study of a PV array in Denmark is used for validation using both the original and expanded datasets. Results show how the best ML models consistently used physics-informed features in all cases.
Real-world model for bitcoin price prediction

Cryptocurrency is a new sort of digital asset that has evolved as a result of advances in financial technology, and it has provided a significant research opportunity. There are many algorithms for price prediction for crypto currencies like LSTM and ARIMA. However, the downside is that LSTM-based RNNs are difficult to comprehend, and gaining intuition into their behavior is tough. In order to produce decent outcomes, rigorous hyperparameter adjustment is also essential. Furthermore, crypto currencies do not precisely adhere to past data, and patterns change fast, reducing the accuracy of predictions. Cryptocurrency price forecasting is difficult due to price volatility and dynamism. Because the data is dynamic and heavily influenced by various seasons, the ARIMA model is unable to handle seasonal data. In order to provide better price predictions for crypto traders, a new model is required. The objective of the study is to apply Fbprophet model as the key model because it is superior in functionality as compared to LSTM and ARIMA additionally removing the pitfalls generated in LSTM and ARIMA model while analyzing the cryptocurrency data. This study provides a methodology for predicting the future price of bitcoin that does not rely solely on past data due to seasonality in historical data. So, after fitting the seasonality and smoothing, the model is constructed that can be useful for real-world use cases. In case of crypto currencies where less historical data is available and it is hard to find pattern, proposed method can easily deal this type of problems. Overall difference between predicted and actual values is low as compared to other model even after seasonal data was available.
IMPACT OF PROCESS VISIBILITY AND WORK STRESS TO IMPROVE SERVICE QUALITY: EMPIRICAL EVIDENCE FROM DUBAI RETAIL INDUSTRYIMPACT OF PROCESS VISIBILITY AND WORK STRESS TO IMPROVE SERVICE QUALITY: EMPIRICAL EVIDENCE FROM DUBAI RETAIL INDUSTRY

Service quality improvement is the biggest challenge faced by many organizations in the era of competition. Due to the advancement and customers’ knowledge about product and services organizations are facing many challenges to satisfy them. In this research study we have discussed how service quality effected by process visibility and work stress. Qualitative study was conducted and over the phone interview approached helped us in collecting primary data. The results show that retail industry has become very modernized due to best utilization of technology and easy to access customers, but this brought challenges also.
Do national development factors affect cryptocurrency adoption?

The adoption of cryptocurrencies is uneven across businesses, industries, and countries. Different forces drive cryptocurrency adoption (CA) dependent on the national level of development. We empirically assess the relationship between certain macro-national developmental indicators and cryptocurrency deployment across 137 countries. Linear regressions determine specific associations with cryptocurrency adoption. We report that CA correlates positively and in decreasing order with Education, the Human Development Index, the Network Readiness Index, the Gini index, Democracy, Regulatory Quality, and Gross Domestic Product, and negatively and in decreasing order with Control of Corruption, the Corruption Perception Index, and the Economic Freedom Index. We draw on our findings to point to policy implications tied to the usage of cryptocurrencies and blockchain technologies more widely and identify further research possibilities.
Identification of Face Mask and Social Distancing using YOLO Algorithm based on Machine Learning Approach

An outbreak of the coronavirus disease due to occur in 2020 has already had a significant impact on the human race. Wearing a "Face Mask" and maintaining "Social Distancing" are the only means to protect ourselves from this pandemic. Several service providers, such as airlines, hotels, hospitals, and train stations, demand their customers to access the service only if the Mask is worn correctly and social distance is maintained. Manually checking to see if the rule of mask wearing and social distance is being observed is impossible due to the significant human resource consumption. As a one-stage detector, the COVID-19 Face Mask and Social Distancing Detector System uses an artificial neural network to combine high-level semantic information with various feature maps and a machine learning module to identify face masks and social distances at the same time. It will also be able to detect persons without masks and the violence of social separation by using existing IP cameras, CCTV cameras, and computer vision. This technology eliminates the need for a manual surveillance system by providing instruments for safety and security. The technology can be used in any type of infrastructure, including hospitals, government offices, schools, and construction sites. Therefore, the face mask and social distance detector system developed, could aid to secure the protection and security of ourselves and our loved ones.
The effect of required warmth on consumer acceptance of artificial intelligence in service: The moderating role of AI-human collaboration

Recent technological advances allow artificial intelligence (AI) to perform tasks that require high warmth, such as caring, understanding others' feelings, and being friendly.However, current consumers may be reluctant to accept AI for such tasks.This research investigates the impact of required warmth to conduct a task on consumer acceptance of AI service and the moderating role of AI-human collaboration.A series of choice-based conjoint experiments and one survey yield two main findings.First, consumers tend to refuse AI for tasks that require high warmth due to the low perceived fit between AI and the task at hand.Second, an AI-human collaboration of AI supporting a human employee increases consumer acceptance of AI service for tasks that require high warmth.This is not the case for AI-human collaboration in which AI performs a task that is supervised by a human employee.Theoretically, this study increases our understanding of how consumer acceptance of AI service varies across tasks and how AI-human collaboration can advance AI acceptance.These findings provide insightful suggestions for managers regarding designing AI service and framing AI-human collaboration.
Chatbots and service failure: When does it lead to customer aggression

Artificial intelligence technology is changing the way services are delivered and introducing opportunities for new sources of service failure. The purpose of this paper is to examine how customers might respond (emotion- or problem-focused coping) to service failure of a chatbot when there is an option to interact with a human employee. Using data from 145 participants, we found that in a chatbot service failure context, telling a customer late in the service interaction that a human employee is available to help leads customers to engage in emotion-focused coping, resulting in customer aggression. The positive relationship between late disclosure and emotion-focused coping occurs with those who perceive low customer participation whereby they do not believe they are overly involved in co-producing and co-delivering the service. This research demonstrates how chatbot service failure in a service encounter can produce different effects on customers’ intention to engage in aggression.
A Comprehensive Survey of Digital Twins and Federated Learning for Industrial Internet of Things (IIoT), Internet of Vehicles (IoV) and Internet of Drones (IoD)

As a result of the advancement in the fourth industrial revolution and communication technology, the use of digital twins (DT) and federated learning (FL) in the industrial Internet of Things (IIoT), the Internet of Vehicles (IoV), and the Internet of Drones (IoD) is increasing. However, the deployment of DT and FL for IoV is challenging. In this survey, we focus on DT and FL for IIoT, IoV, and IoD. Initially, we analyzed the existing surveys. In this paper, we present the applications of DT and FL in IIoT, IoV, and IoD. We also present the open research issues and future directions.
Adaptive open set domain generalization network: Learning to diagnose unknown faults under unknown working conditions

Recently, domain generalization techniques have been introduced to enhance the generalization capacity of fault diagnostic models under unknown working conditions. Most existing studies assume consistent machine health states between the training and testing data. However, fault modes in the testing phase are unpredictable, and unknown fault modes usually occur, hindering the wide applications of domain generalization-based fault diagnosis methods in industries. To address such problems, this paper proposes an adaptive open set domain generalization network to diagnose unknown faults under unknown working conditions. A local class cluster module is implemented to explore domain-invariant representation space and obtain discriminative representation structures by minimizing triplet loss. An outlier detection module learns optimal decision boundaries for individual class representation spaces to classify known fault modes and recognize unknown fault modes. Extensive experimental results on two test rigs demonstrated the effectiveness and superiority of the proposed method.
Video Pivoting Unsupervised Multi-Modal Machine Translation

The main challenge in the field of unsupervised machine translation (UMT) is to associate source-target sentences in the latent space. As people who speak different languages share biologically similar visual systems, various unsupervised multi-modal machine translation (UMMT) models have been proposed to improve the performances of UMT by employing visual contents in natural images to facilitate alignment. Commonly, relation information is the important semantic in a sentence. Compared with images, videos can better present the interactions between objects and the ways in which an object transforms over time. However, current state-of-the-art methods only explore scene-level or object-level information from images without explicitly modeling objects relation; thus, they are sensitive to spurious correlations, which poses a new challenge for UMMT models. In this paper, we employ a spatial-temporal graph obtained from videos to exploit object interactions in space and time for disambiguation purposes and to promote latent space alignment in UMMT. Our model employs multi-modal back-translation and features pseudo-visual pivoting, in which we learn a shared multilingual visual-semantic embedding space and incorporate visually pivoted captioning as additional weak supervision. Experimental results on the VATEX Translation 2020 and HowToWorld datasets validate the translation capabilities of our model on both sentence-level and word-level and generalizes well when videos are not available during the testing phase.
Graph autoencoder-based unsupervised outlier detection

Outlier detection technologies play an important role in various application domains. Most existing outlier detection algorithms have difficulty detecting outliers that are mixed within normal object regions or around dense clusters. To address this problem, we propose a novel graph neural network structure called the graph autoencoder (GAE), which is capable of handling the task of outlier detection in Euclidean structured data. The GAE can perform feature value propagation in the form of a neural network that changes the distribution pattern of the original dataset, which can accurately detect outliers with low deviation. This method first converts the Euclidean structured dataset into a graph using the graph generation module, then inputs the dataset together with its corresponding graph into the GAE for training, and finally determines the top-n objects that are difficult to reconstruct in the output layer of the GAE as outliers. The results of comparing eight state-of-the-art algorithms on eight real-world datasets showed that GAE achieved the highest area under the receiver operating characteristic curve (ROC AUC) on six datasets. By comparing GAE with the autoencoder-based outlier detection algorithm, it was discovered that the proposed method improved the AUC by 16.9% on average for eight datasets.
Graph Trend Filtering Networks for Recommendation

Recommender systems aim to provide personalized services to users and are playing an increasingly important role in our daily lives. The key of recommender systems is to predict how likely users will interact with items based on their historical online behaviors, e.g., clicks, add-to-cart, purchases, etc. To exploit these user-item interactions, there are increasing efforts on considering the user-item interactions as a user-item bipartite graph and then performing information propagation in the graph via Graph Neural Networks (GNNs). Given the power of GNNs in graph representation learning, these GNNs-based recommendation methods have remarkably boosted the recommendation performance. Despite their success, most existing GNNs-based recommender systems overlook the existence of interactions caused by unreliable behaviors (e.g., random/bait clicks) and uniformly treat all the interactions, which can lead to sub-optimal and unstable performance. In this paper, we investigate the drawbacks (e.g., non-adaptive propagation and non-robustness) of existing GNN-based recommendation methods. To address these drawbacks, we introduce a principled graph trend collaborative filtering method and propose the Graph Trend Filtering Networks for recommendations (GTN) that can capture the adaptive reliability of the interactions. Comprehensive experiments and ablation studies are presented to verify and understand the effectiveness of the proposed framework. Our implementation based on PyTorch is available: https://github.com/wenqifan03/GTN-SIGIR2022.