Reviewerly/exHarmony huggingface dataset:
A <scp><i>k</i>‐</scp>nearest neighbor‐based averaging model for probabilistic <scp>PV</scp> generation forecasting

Abstract Probabilistic forecasting of PV generation is crucial in uncertainty management to reinforce PV‐integrated power systems for long‐term planning. In this context, developing a reliable probabilistic forecast model is challenging due to weather conditions' stochastic nature and varying daily PV production patterns at multiple time instants. Due to varying probability distribution patterns, a nonparametric approach, such as quantile regression, is challenging to approximate the forecast error distribution. A forecast combination concept that yields impressive probabilistic forecasting results has rarely been utilized for PV generation forecasting. The use of complementary and sensible point forecasters characterizing different aspects of intricate PV production patterns in a k ‐nearest neighbor quantile regression averaging framework is the proposed probabilistic forecasting model. The proposed forecasting model's accuracy is assessed with real‐world PV generation data from the USA for multi‐time instants. The probabilistic forecast performance of the proposed quantile k ‐nearest neighbors regression averaging model is evaluated against the traditional quantile regression averaging, quantile regression forests, quantile k ‐nearest neighbors, basic quantile regression, and regression bootstrapping in terms of the forecast (quantiles and prediction intervals) accuracy, using relevant error measures and scores. The proposed model has superior performance compared to the other models, as indicated by the lowest forecast errors and scores.
Quantum Information Science

Quantum computing is implicated as a next-generation solution to supplement traditional von Neumann architectures in an era of post-Moore's law computing. As classical computational infrastructure becomes more limited, quantum platforms offer expandability in terms of scale, energy consumption, and native 3-D problem modeling. Quantum information science is a multidisciplinary field drawing from physics, mathematics, computer science, and photonics. Quantum systems are expressed with the properties of superposition and entanglement, evolved indirectly with operators (ladder operators, master equations, neural operators, and quantum walks), and transmitted (via quantum teleportation) with entanglement generation, operator size manipulation, and error correction protocols. This article discusses emerging applications in quantum cryptography, quantum machine learning, quantum finance, quantum neuroscience, quantum networks, and quantum error correction.
Class-Incremental Learning for Semantic Segmentation in Aerial Imagery via Distillation in All Aspects

Incremental learning using neural networks achieves great success in semantic segmentation but still suffers from catastrophic forgetting. In this article, we propose an effective class-incremental segmentation method without storing old data. To alleviate the issue of forgetting, we present two important modules, i.e., the deep feature distillation (DFD) module and the label mixed (LM) module. The DFD module is established to learn a good feature representation of old classes by distilling a new compact feature representation from different layers of networks. The proposed LM module first identifies the examples (pixels) of old classes with high confidences utilizing the output of old models, and then, they are combined with examples of new classes to supervise the training of new models, which can achieve a good balance between learning new classing and avoiding forgetting old ones. Our ablation studies show that the DFD module and the LM module can make the learning network obtain 6.2% and 15% performance gains [mean Intersection over Union (mIOU)], respectively. Furthermore, by introducing the supervision of output distillation loss, we compare our method with several state-of-the-art methods in the extensive experiments, and the experimental results all show that our method is significantly superior to them on the dataset of aerial images.
Multilinear Modelling of Faces and Expressions

In this work, we present a new versatile 3D multilinear statistical face model, based on a tensor factorisation of 3D face scans, that decomposes the shapes into person and expression subspaces. Investigation of the expression subspace reveals an inherent low-dimensional substructure, and further, a star-shaped structure. This is due to two novel findings. (1) Increasing the strength of one emotion approximately forms a linear trajectory in the subspace. (2) All these trajectories intersect at a single point - not at the neutral expression as assumed by almost all prior works-but at an apathetic expression. We utilise these structural findings by reparameterising the expression subspace by the fourth-order moment tensor centred at the point of apathy. We propose a 3D face reconstruction method from single or multiple 2D projections by assuming an uncalibrated projective camera model. The non-linearity caused by the perspective projection can be neatly included into the model. The proposed algorithm separates person and expression subspaces convincingly, and enables flexible, natural modelling of expressions for a wide variety of human faces. Applying the method on independent faces showed that morphing between different persons and expressions can be performed without strong deformations.
Automatic Pruning for Quantized Neural Networks

Neural network quantization and pruning are two techniques commonly used to reduce the computational complexity and memory footprint of these models for deployment. However, most existing pruning strategies operate on full-precision and cannot be directly applied to discrete parameter distributions after quantization. In contrast, we study a combination of these two techniques to achieve further network compression. In particular, we propose an effective pruning strategy for selecting redundant low-precision filters. Furthermore, we leverage Bayesian optimization to efficiently determine the pruning ratio for each layer. We conduct extensive experiments on CIFAR-10 and ImageNet with various architectures and precisions. In particular, for ResNet-18 on ImageNet, we prune 26.12% of the model size with Binarized Neural Network quantization, achieving a top-1 classification accuracy of 47.32 % in a model of 2.47 MB and 59.30% with a 2-bit DoReFa-Net in 4.36 MB.
Stereo Matching by Self-supervision of Multiscopic Vision

Self-supervised learning for depth estimation possesses several advantages over supervised learning. The benefits of no need for ground-truth depth, online fine-tuning, and better generalization with unlimited data attract researchers to seek self-supervised solutions. In this work, we propose a new self-supervised framework for stereo matching utilizing multiple images captured at aligned camera positions. A cross photometric loss, an uncertainty-aware mutual-supervision loss, and a new smoothness loss are introduced to optimize the network in learning disparity maps end-to-end without ground-truth depth information. To train this framework, we build a new multiscopic dataset consisting of synthetic images rendered by 3D engines and real images captured by real cameras. After being trained with only the synthetic images, our network can perform well in unseen outdoor scenes. Our experiment shows that our model obtains better disparity maps than previous unsupervised methods on the KITTI dataset and is comparable to supervised methods when generalized to unseen data. Our source code and dataset are available at https://sites.google.com/view/multiscopic.
Deep Convolutional Networks With Tunable Speed–Accuracy Tradeoff for Human Activity Recognition Using Wearables

Activity recognition plays a critical role in various applications, such as medical monitoring and rehabilitation. Deep learning has recently made great development in the wearable-based human activity recognition (HAR) area. However, real HAR applications should be adaptive and flexible to the available computational budget. So far, this problem has rarely been explored. In contrast to existing deep HAR studies focusing on static networks, this article aims to investigate adaptive networks, which can adjust their structure conditioned on available computing resources to trade off between accuracy and speed. We, for the first time, present an adaptive convolutional neural network by dynamically modifying network width. Specifically, first, instead of a normal convolution, the network is stacked by lower triangular convolutional layers in order to remove the impact of activation statistics caused by varying widths. Second, instead of fixed sampling, we perform random sampling over width, which can provide smooth control for the tradeoff between accuracy and speed. As a consequence, the networks with different widths are simultaneously trained as subnetworks by accumulating their losses during each iteration. On multiple HAR datasets, such as UCI-HAR, PAMAP2, and OPPORTUNITY, extensive experiments verify that the proposed approach can consistently provide further improved efficiency on top of state-of-the-art CNNs for HAR. Finally, evaluations are conducted on a Raspberry Pi platform to demonstrate its usefulness and practicality.
A Novel Self-Knowledge Distillation Approach with Siamese Representation Learning for Action Recognition

Knowledge distillation is an effective transfer of knowledge from a heavy network (teacher) to a small network (student) to boost students' performance. Self-knowledge dis-tillation, the special case of knowledge distillation, has been proposed to remove the large teacher network training process while preserving the student's performance. This paper intro-duces a novel Self-knowledge distillation approach via Siamese representation learning, which minimizes the difference between two representation vectors of the two different views from a given sample. Our proposed method, SKD-SRL, utilizes both soft label distillation and the similarity of representation vectors. Therefore, SKD-SRL can generate more consistent predictions and representations in various views of the same data point. Our benchmark has been evaluated on various standard datasets. The experimental results have shown that SKD-SRL significantly improves the accuracy compared to existing supervised learning and knowledge distillation methods regardless of the networks.
MAFI: GNN-Based Multiple Aggregators and Feature Interactions Network for Fraud Detection Over Heterogeneous Graph

Recently, Graph Neural Networks (GNNs) have been widely used for fraud detection. GNNs first generate node embedding by aggregating neighboring information under different relations, and then use the final node embedding to detect the node’s suspiciousness. However, traditional GNNs employing only a single type of aggregator fail to capture neighbor information from multiple perspectives and treating different relations equally inevitably weakens the semantic information of heterogeneous graphs. Meanwhile, expressive ability of GNNs is limited by using conventional concatenating or averaging operations to update the center node. Also, camouflaged entities could damage GNN-based models. To handle these problems, a novel heterogeneous GNN model called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multiple Aggregators and Feature Interactions Network</i> (MAFI) is proposed in this paper to conduct fraud detection tasks. Concretely, multiple types of aggregators are applied on different relations to aggregate neighbor information and aggregator-level attention is utilized to learn the importance of different aggregators. Also, relation-level attention is leveraged to learn the importance of each relation. Besides, conventional update operations are replaced with vector-wise implicit and explicit feature interactions. Moreover, a trainable neighbor sampler is employed to filter camouflaged fraudsters. Comprehensive experiments on two real-world fraud datasets indicate that the proposed MAFI outperforms existing GNN-based fraud detectors.
A Comprehensive Study of Distinctive Methods of Waste Segregation and Management

Fallacious waste management is an enormous enemy towards a healthy environment. Currently, the world's waste is categorized into municipal solid waste, industrial waste, agricultural waste, and hazardous waste. In earlier days instead of using different methodologies for waste segregation and management, people used similar methods for assorted kinds of waste produced in varying categories. But in recent years there has been a huge leap in techniques that involve segregation of waste and then proper disposal process. The two major technologies that have been developed for eradicating the complications in the field of waste management are IoT (Internet of Things) and deep learning. Apart from segregation and managing the garbage, the collection of garbage is also an important task. When the garbage isn't collected at regular intervals of time, it affects the health of citizens residing in that surrounding area. Both the technologies have done great work in eradicating this communication gap and made sure that proper segregation of waste takes place leaving no stone unturned. This survey paper discusses various strategies of waste segregation and management.
Virtual Reality Zoo Therapy for Alzheimer’s Disease Using Real-Time Gesture Recognition

Alzheimer's disease affects almost ten million people every year. Negative emotions such as frustration and anxiety can have impact on brain capability in terms of memory functions. Alzheimer's patients experience more negative emotions than healthy older adults. Non-pharmacological treatment such as animal therapy could help Alzheimer patient but has restrictions and requirements. We propose a Virtual Reality Zoo Therapy system in which the patients are immersed in a virtual environment and can interact with animals using their hands. With the immersive experience of virtual reality (VR), patients feel that they are in a real therapy room and can freely interact with animals. This system is controlled by an intelligent agent which tracks the patients' emotions using electroencephalography and commands the animals according to their hand gesture and emotions. Experiments have been done and preliminary results show that it is possible to predict patients' hand gesture and interpret them in order to interact with virtual animals and the Zoo Therapy system can reduce the negative emotions.
Occupancy Grid Generation With Dynamic Obstacle Segmentation in Stereo Images

The detection of dynamic and static obstacles is a key task for the navigation of autonomous ground vehicles. The article presents a new algorithm for generating an occupancy map of the surrounding space from noisy point clouds obtained from one or several stereo cameras. The camera images are segmented by the proposed deep neural network FCN-ResNet-M-OC, which combines the speed of the FCN-ResNet method and improves the quality of the model using the concept of object context representation. The paper investigates supervised approaches to network training on unbalanced samples with road scenes such as the weighted cross entropy and the Focal Loss. The occupancy map is built from point clouds with semantic labels, in which static environment and potentially dynamic obstacles are highlighted. Our solution is operational in real time and applicable on platforms with limited computing resources. The approach was tested on autonomous vehicle datasets: Semantic KITTI, KITTI-360, Mapillary Vistas and custom OpenTaganrog. The usage of semantically labeled point clouds increased the precision of obstacle detection by an average of 17%. The performance of the entire approach on various computing platforms with Jetson Xavier, RTX3070, GPUs NVidia Tesla V100 is respectively from 10 to 15 FPS for input image resolution <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1920\times 1080$ </tex-math></inline-formula> pixels.
Development of an Autonomous Driving Smart Wheelchair for the Physically Weak

People who have difficulty moving owing to problems in walking spend their lives assisted by wheelchairs. In the past, research has been conducted regarding the application of various technologies to electric wheelchairs for user convenience. In this study, we evaluated a method of applying an autonomous driving function and developed an autonomous driving function using ROS. An electric wheelchair with a control unit designed to enable autonomous driving was used to test the basic performance of autonomous driving. The effectiveness of the technology was confirmed by comparing the results of autonomous driving with those of manual driving on the same route. It is expected that the evaluation and improvement of the usability and ride quality as well as additional studies will help improve the mobility convenience of physically disabled persons.
HSAN-capsule: A novel text classification model

Most text classification methods fail to fully consider the important role of the hierarchical structure of the text in determining the text category, and cannot well extract enough text sequence information. To address this problem, a novel model based on hierarchical self-attention mechanism capsule network was proposed for text classification, which was composed of two components, i.e., the hierarchical self-attention network and capsule network. First input the text data processed by word embedding into the hierarchical self-attention network for feature extraction, model the text from the two levels of words and sentences to extract the hierarchical features of the text; then the results are passed into the capsule network in order to refine the relationship between the part of the text and the whole, and further extract richer text semantic information. The experimental results on 5 text classification datasets show that compared with other baseline models, the model in this paper has achieved better classification results.
Scalability Challenges and Solutions in Blockchain Technology

Bitcoin has come out as a huge cryptocurrency success, thus changing the digital transaction (Nakamoto in Bitcoin: A Peer-to-Peer Electronic Cash System. Decentralized Business Review, p. 21260, 2008). In the earlier days, even though proof-of-work (PoW) consensus had performance-related issues, it was not considered as a major issue. The transaction processing mechanism is 3.3 to seven transactions per second with smallest 200–250 byte transactions. While this was sufficient at the beginning, the system has been overloaded over the years resulting in low transaction speed and outrageous transaction cost. Massive growth in cryptocurrency results in scalability issues in the blockchain. With more companies trying to shift their existing system to blockchain, it would be difficult to tackle with the existing PoW consensus caused by its scalability issues (Xie et al. in IEEE Network 33:166–173, 2019). The purpose of this research is to fix the scalability issues and increases the transaction speed by applying techniques such as lightning network, plasma cash, and hard/soft forks.
Image Inpainting and Classification Agent Training Based on Reinforcement Learning and Generative Models with Attention Mechanism

What distinguishes the field of artificial intelligence (AI) from others is to develop fully independent agents that learn optimal behavior, change, and evolve solely through the communication of trial and error with the surrounding environment. Reinforcement learning (RL) can be seen in multiple aspects of Machine Learning (ML), provided the environment, reward, actions, the state will be defined. Agent training in previous years is seen to only relate to robotics, games, and self-driving cars. While trying to divert the focus of researchers from the view of self-driving cars, games, robots, etc. Here, we investigated using reinforcement learning in the aspect of task completion. We deployed our architecture in an inpainting task where the agent generates the distorted or missing image content into an eminent fidelity completed the image by using reinforcement learning to influence the generative model utilized. The Generative Adversary Network (GAN) problem of not being steady and challenging to train was overwhelmed by utilizing latent space representation. The dimension is reduced compared to the distorted or corrupted image in training the GAN. Then reinforcement learning was deployed to pick the correct GAN input to get the image’s latent space representation that is most suitable for the current input of the missing or distorted image region. In this paper, we also learned that the trained agent enhances the accuracy in a classification task of images with missing data. We successfully examined the classification enhancement on images missing 30%, 50%, and 70%.
Incentive Mechanism for Differentially Private Federated Learning in Industrial Internet of Things

Federated learning (FL) is a newly emerging distributed machine learning paradigm, whereby a server can coordinate multiple clients to jointly train a learning model by using their private datasets. Many researches focus on designing incentive mechanisms in FL, but most of them cannot allow that clients flexibly determine privacy budgets by themselves. In this article, we propose a privacy-preserving incentive mechanism (NICE) based on differential privacy (DP) and Stackelberg game for FL systems in industrial Internet of Things. First, we design a flexible privacy-preserving mechanism for NICE, in which clients can add a Laplace noise into the loss function according to a customized privacy budget. Under this mechanism, we design two incentive utility functions for the server and clients. Next, we model the utility optimization problems as a two-stage Stackelberg game by seeing the server as a leader and the clients as followers. Finally, we derive an optimal Stackelberg equilibrium solution for both the stages of the whole game. Based on this solution, NICE can make the server and all clients achieve their maximum utilities simultaneously. In addition, we conduct extensive simulations on real-world datasets to demonstrate the significant performance of the proposed mechanism.
Escaping Loneliness Through Tourist-Chatbot Interactions

Abstract Social robots such as chatbots are regarded as a practical approach to alleviate loneliness. Few studies in the tourism field have focused on loneliness and its impact on the acceptance of chatbots used by the tourism industry. This paper explores the factors influencing tourists’ willingness to use chatbots from the perspective of loneliness by combining theories related to anthropomorphism and the uncanny valley effect. This paper adopts a qualitative research method by taking a semi-structured interview with 15 tourists who have used travel chatbots before. The results show that in addition to perceived ease of use and perceived usefulness, there are three factors (tourist loneliness, perceived anthropomorphism, and user anxiety) that directly influence tourists’ acceptance of travel chatbots. Moreover, tourist loneliness positively influences user anxiety through perceived anthropomorphism. User anxiety has a negative effect on perceived ease of use and perceived usefulness. This research then proposed an extended TAM model from the perspective of tourist loneliness. This paper enriches the research on loneliness as well as chatbots in the tourism field. The results provide suggestions for the practical application of travel chatbots.