Reviewerly/exHarmony huggingface dataset:
SST-Sal: A spherical spatio-temporal approach for saliency prediction in 360<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e563" altimg="si1.svg"><mml:msup><mml:mrow /><mml:mrow><mml:mo>∘</mml:mo></mml:mrow></mml:msup></mml:math> videos

Virtual reality (VR) has the potential to change the way people consume content, and has been predicted to become the next big computing paradigm. However, much remains unknown about the grammar and visual language of this new medium, and understanding and predicting how humans behave in virtual environments remains an open problem. In this work, we propose a novel saliency prediction model which exploits the joint potential of spherical convolutions and recurrent neural networks to extract and model the inherent spatio-temporal features from 360° videos. We employ Convolutional Long Short-Term Memory cells (ConvLSTMs) to account for temporal information at the time of feature extraction rather than to post-process spatial features as in previous works. To facilitate spatio-temporal learning, we provide the network with an estimation of the optical flow between 360° frames, since motion is known to be a highly salient feature in dynamic content. Our model is trained with a novel spherical Kullback–Leibler Divergence (KLDiv) loss function specifically tailored for saliency prediction in 360° content. Our approach outperforms previous state-of-the-art works, being able to mimic human visual attention when exploring dynamic 360° videos.
Deep Learning-Based Frameworks for Semantic Segmentation of Road Scenes

Semantic segmentation using machine learning and computer vision techniques is one of the most popular topics in autonomous driving-related research. With the revolution of deep learning, the need for more efficient and accurate segmentation systems has increased. This paper presents a detailed review of deep learning-based frameworks used for semantic segmentation of road scenes, highlighting their architectures and tasks. It also discusses well-known standard datasets that evaluate semantic segmentation systems in addition to new datasets in the field. To overcome a lack of enough data required for the training process, data augmentation techniques and their experimental results are reviewed. Moreover, domain adaptation methods that have been deployed to transfer knowledge between different domains in order to reduce the domain gap are presented. Finally, this paper provides quantitative analysis and performance evaluation and discusses the results of different frameworks on the reviewed datasets and highlights future research directions in the field of semantic segmentation using deep learning.
Deep stochastic configuration networks with different random sampling strategies

Stochastic configuration networks (SCNs) are a class of randomized learner models that ensure the universal approximation property, whereby random weights and biases are drawn from the uniform distribution and selected by a supervisory mechanism. This paper looks into the impact of the distribution of random weights on the performance of SCNs. In the light of a fundamental principle in machine learning, that is, a model with smaller parameters holds improved generalization, we recommend using symmetric zero-centered distributions in constructing SCNs to improve the generalization performance. Further, we introduce a scalar in the distributions to make the SCN model adaptively feasible to different datasets. Simulation results are reported for both regression and classification tasks over twenty-one benchmark datasets using SCN. Results are also presented on ten regression datasets using a deep implementation of SCN, known as deep stochastic configuration networks (DeepSCN).
A Framework of Blockchain Technology in Intelligent Water Management

At present, water resource information management in China is mainly a centralized model, and there exist some problems such as high cost, low efficiency, and data storage insecurity. Blockchain technology provides a good solution which can create an efficient trust mechanism among the links in the process of water resource utilization. It guarantees the security of the data, avoiding the sudden collapse of the central institutions caused by some normal operations of the entire system. Based on a decentralization blockchain, we propose a decentralized water resource information management system for the whole process of “supply-use-consumption-discharge,” which improves the traditional water data storage. Specifically, the monitoring and business data are encrypted by the blockchain and are transmitted using a peer-to-peer network. Moreover, the centralized management mode is changed and part of the management work is dispersed to each node. Thus, decisions and measures can be made and implemented quickly after discovering problems to improve the efficiency of information transmission and management. In addition, two typical blockchain-based application scenarios for water resource management are designed. A blockchain-based approach makes issuing and monitoring water abstraction permits more convenient and obtaining license information more secure and verifiable. A reliable mechanism for tracing water quality ensures the accuracy and reliability of water quality information, enables the detection of locations with inadequate water quality, and clarifies people’s responsibility, thus guaranteeing the water safety of the residents.
Towards the applicability of solar nowcasting: A practice on predictive PV power ramp-rate control

Solar forecasting has been widely adopted in modern power system operations to facilitate a reliable and continuous photovoltaic (PV) integration. Solar nowcasting, also known as intra-minute solar forecasting, is a new subdomain of solar forecasting. Nevertheless, despite the significant progress achieved in solar nowcasting over the last decade, one important aspect, that is, applicability—the value and operability of nowcasts in practical grid operations—is generally left out. To that end, this paper brings forth the applicability of solar nowcasting for the first time. Three time parameters involved in operational solar nowcasting are first identified, namely, forecast horizon, forecast resolution, and forecast model updating rate. Then paired with the state-of-the-art PV power ramp-rate control algorithm, i.e., predictive active power curtailment (PAPC), the effect of different time parameters is investigated, thus revealing the nowcasting applicability at large. Through four case studies and eight standardized deterministic and probabilistic solar nowcasting models, the applicability of solar nowcasting on PAPC is shown to be most characterized by the forecast horizon (up to a deviation of ramp smoothing rate around 12%, with smart persistence (SP) being the reference model), and least characterized by the forecast model updating rate (with a deviation of ramp smoothing rate less than 1% for SP). Moreover, the negatively-biased deterministic nowcasts and wider probabilistic nowcasts are found more applicable to PAPC. To promote solar nowcasting applicability on PAPC further, an outlook for future research is provided, from both a solar forecaster's and a system operator's viewpoints.
Cross-modal prototype learning for zero-shot handwritten character recognition

Traditional methods of handwritten character recognition rely on extensive labeled data. However, humans can generalize to unseen handwritten characters by watching a few printed examples in textbooks. To simulate this ability, we propose a cross-modal prototype learning method (CMPL) to realize zero-shot recognition. For each character class, a prototype is generated by mapping the printed character into a deep neural network feature space. For unseen character class, its prototype can be directly produced from a printed character sample, therefore, not requiring any handwritten samples to realize class-incremental learning. Specifically, CMPL considers different modalities simultaneously - online handwritten trajectories, offline handwritten images, and auxiliary printed character images. The joint learning of the above modalities is achieved through sharing printed prototypes between online and offline data. In zero-shot inference, we feed CMPL the printed samples to obtain corresponding class prototypes, and then the unseen handwritten character can be recognized by the nearest prototype. Our experimental results demonstrate that CMPL outperforms the state-of-the-art methods in both online and offline zero-shot handwritten Chinese character recognition. Moreover, we also show the cross-domain generalization of CMPL from two perspectives: cross-language and modern-to-ancient handwritten character recognition, focusing on the transferability between different languages and different styles (i.e., modern and historical handwritings).
A Blockchain-Enabled Trusted Identifier Co-Governance Architecture for the Industrial Internet of Things

Recently, the Industrial Internet of Things plays a vital role in the new round of technology innovation and industry competition, where the identity resolution system is its key component. However, there are some problems in the existing Handle-based identity resolution architecture. Therefore, a trusted identifier co-governance architecture is proposed, and a prototype system is designed and implemented in this article. Specifically, we design a blockchain-based decentralized framework for identifier service, identifier life cycle management based on smart contract, and a data storage mechanism for a trusted identifier. The whole architecture could solve the problems of single point of failure, data tampering, and governance deviation, and reduce the trust cost in the process of data circulation. The simulation results reveal that the system has achieved good results in terms of delay and throughput.
The Impact of Tourist–Robot Interaction on Tourist Engagement in the Hospitality Industry: A Mixed-Method Study

Service robots have become a topic of interest for tourism and hospitality researchers and practitioners. The success of service robot adoption lies in the effectiveness of tourist–robot interaction. There has been less interest in the performance of tourist–robot interaction, with research related to tourist engagement being particularly scarce. Drawing on the theoretical perspective of relationships, this article examines the impact of tourist–robot interaction on tourist engagement in the hospitality context. A mixed-methods approach is adopted, utilizing grounded theory and structural equation modeling. The findings reveal two types of tourist–robot interaction, emotional (fun and playfulness) and instrumental (convenience and ease of use). Tourist–robot emotional interactions influence tourist engagement by enhancing tourists’ needs satisfaction, tourist emotion, and social bonds with robots. It was found that tourist–robot instrumental interaction positively affects tourist engagement through needs satisfaction and tourist emotion but not social bonds. The findings extend our understanding of human–robot interaction and customer engagement.
Discovering unknowns: Context-enhanced anomaly detection for curiosity-driven autonomous underwater exploration

• Anomaly detection for unknowns towards to autonomous underwater exploration. • Autoencoder and autoregressive network to identify anomalies in unstructured dynamic underwater moving views. • Novel context-enhanced autoregressive network to learn feature dependence. • Patch learning paradigm to build an accurate latent feature space. • Validation on two benchmarks, simulation, and real data, outperforms state-of-arts. Discovering unknown objects from visual information as curiosity is highly demanded for autonomous exploration in underwater environment. In this research, we propose an end-to-end deep neural network for anomaly detection in the highly dynamic unstructured underwater background faced by a moving robot. A novel patch-level autoencoder combined with a context-enhanced autoregressive network is introduced to differentiate abnormal patterns (unknowns) from normal ones (knowns) in fine-scale regions. The autoencoder and autoregressive network share the same encoder to extract latent features. The autoregressive branch learns semantic dependence based on conditional probability to identify anomaly in a latent feature space. The overall anomaly score is weighted by both image reconstruction loss and feature similarity loss. The model outperforms state-of-the-art anomaly detection, demonstrated on the benchmark dataset CIFAR-10. Average discrimination performance AUROC improved 2.18%, and inception distance between normal and anomalous classes improved 9.33% in Z -score. The network has been tested using three underwater datasets from underwater simulation, a real-world undersea video and public SUIM data. The AUROC accuracy improved 6.36%, 32.45% and 40.17% respectively by using the proposed patch learning paradigm. It is the first report on unknown detection as navigation clues for curiosity-driven autonomous underwater exploration.
RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks

Temporal/spatial receptive fields of models play an important role in sequential/spatial tasks. Large receptive fields facilitate long-term relations, while small receptive fields help to capture the local details. Existing methods construct models with hand-designed receptive fields in layers. Can we effectively search for receptive field combinations to replace hand-designed patterns? To answer this question, we propose to find better receptive field combinations through a global-to-local search scheme. Our search scheme exploits both global search to find the coarse combinations and local search to get the refined receptive field combinations further. The global search finds possible coarse combinations other than human-designed patterns. On top of the global search, we propose an expectation-guided iterative local search scheme to refine combinations effectively. Our RF-Next models, plugging receptive field search to various models, boost the performance on many tasks, e.g., temporal action segmentation, object detection, instance segmentation, and speech synthesis. The source code is publicly available on http://mmcheng.net/rfnext.
Understanding the application of handwritten text recognition technology in heritage contexts: a systematic review of Transkribus in published research

Handwritten Text Recognition (HTR) technology is now a mature machine learning tool, becoming integrated in the digitisation processes of libraries and archives, speeding up the transcription of primary sources and facilitating full text searching and analysis of historic texts at scale. However, research into how HTR is changing our information environment is scant. This paper presents a systematic literature review regarding how researchers are using one particular HTR platform, Transkribus, to indicate the domains where HTR is applied, the approach taken, and how the technology is understood. 381 papers from 2015 to 2020 were gathered from Google Scholar, Scopus, and Web of Science, then grouped and coded into categories using quantitative and qualitative approaches. Published research that mentions Transkribus is international and rapidly growing. Transkribus features primarily in archival and library science publications, while a long tail of broad and eclectic disciplines, including history, computer science, citizen science, law and education, demonstrate the wider applicability of the tool. The most common paper categories were humanities applications (67%), technological (25%), users (5%) and tutorials (3%). This paper presents the first overarching review of HTR as featured in published research, while also elucidating how HTR is affecting the information environment.
EL-NAHL: Exploring labels autoencoding in augmented hidden layers of feedforward neural networks for cybersecurity in smart grids

Reliability and security of power distribution and data traffic in smart grid (SG) are very important for industrial control systems (ICS). Indeed, SG cyber-physical connectivity is subject to several vulnerabilities that can damage or disrupt its process immunity via cyberthreats. Today's ICSs are experiencing highly complex data change and dynamism, increasing the complexity of detecting and mitigating cyberattacks. Subsequently, and since Machine Learning (ML) is widely studied in cybersecurity, the objectives of this paper are twofold. First, for algorithmic simplicity, a small-scale ML algorithm that attempts to reduce computational costs is proposed. The algorithm adopts a neural network with an augmented hidden layer (NAHL) to easily and efficiently accomplish the learning procedures. Second, to solve the data complexity problem regarding rapid change and dynamism, a label autoencoding approach is introduced for Embedding Labels in the NAHL (EL-NAHL) architecture to take advantage of labels propagation when separating data scatters. Furthermore, to provide a more realistic analysis by addressing real-world threat scenarios, a dataset of an electric traction substation used in the high-speed rail industry is adopted in this work. Compared to some existing algorithms and other previous works, the achieved results show that the proposed EL-NAHL architecture is effective even under massive dynamically changed and imbalanced data.
An economic analysis of information security decisions with mandatory security standards in resource sharing environments

While mandatory security standards are salient in the management of information security, the related theoretic studies are scarce, especially when strategic hackers are considered. Using a game-theoretic model, this paper examines the strategic interaction in a resource sharing environment between two firms who invest in information security subject to the mandatory standard and one hacker who exerts attack efforts against the firms. It shows that the strict mandatory standard doesn’t always benefit each firm even though its information systems can be better protected. As the firms share more resource, each firm lacks strong motivation to invest enough in information security, and as a result stricter security standard should be formulated from the socially optimal standpoint. Moreover, we find that although compensation mechanism can urge each firm to invest more, this mechanism may harm each firm.
Anti-Occlusion UAV Tracking Algorithm with a Low-Altitude Complex Background by Integrating Attention Mechanism

In recent years, the increasing number of unmanned aerial vehicles (UAVs) in the low-altitude airspace have not only brought convenience to people’s work and life, but also great threats and challenges. In the process of UAV detection and tracking, there are common problems such as target deformation, target occlusion, and targets being submerged by complex background clutter. This paper proposes an anti-occlusion UAV tracking algorithm for low-altitude complex backgrounds by integrating an attention mechanism that mainly solves the problems of complex backgrounds and occlusion when tracking UAVs. First, extracted features are enhanced by using the SeNet attention mechanism. Second, the occlusion-sensing module is used to judge whether the target is occluded. If the target is not occluded, tracking continues. Otherwise, the LSTM trajectory prediction network is used to predict the UAV position of subsequent frames by using the UAV flight trajectory before occlusion. This study was verified on the OTB-100, GOT-10k and integrated UAV datasets. The accuracy and success rate of integrated UAV datasets were 79% and 50.5% respectively, which were 10.6% and 4.9% higher than those of the SiamCAM algorithm. Experimental results show that the algorithm could robustly track a small UAV in a low-altitude complex background.
A binary dandelion algorithm using seeding and chaos population strategies for feature selection

Feature selection (FS) is an important pre-processing step in data mining and pattern recognition. It can effectively compress the dimensionality of the feature space to reduce computation time and improve classification performance. The meta-heuristic algorithm-based feature selection method by finding the optimal set of features in the solution space has been widely used. However, this method is prone to trap into local optimality in a sufficiently large solution space. In this paper, we first propose a binary dandelion algorithm (BDA) to improve classification accuracy. In addition, to improve the performance of the algorithm, a binary dandelion algorithm using an improved seeding strategy and chaotic populations (SBDA) is proposed in this paper. Firstly, the strategy of optimizing the seeding radius by using the vibrational function and the historical optimal population increases the complexity of the search process and improves the search performance of the algorithm in the solution space. Secondly, when generating seeds, chaotic populations are generated using chaotic operators, which improves the ability of the algorithm to jump out of the local optimum and improves the stability of the algorithm. In this paper, 15 well-established datasets collected from the UCI machine learning database were adopted to compare four variants of BDA using only chaotic population improvement and in the next experiments, both mechanisms are verified to be effective in improving the performance of the algorithm. In addition, this paper compares the proposed BDA algorithm and SBDA algorithm with eight other classical algorithms. The experimental results show that SBDA can obtain fewer features with higher classification accuracy in most cases.
Combined empirical and machine learning modeling method for estimation of daily global solar radiation for general meteorological observation stations

Daily global solar radiation (H) is practically significant for human production and life, especially for solar power generation. Due to the high construction and maintenance cost of solar radiation observation equipment, solar radiation measurement cannot be easily obtained at many sites. In addition, the H estimation fitting model cannot be directly trained in the absence of site-specific historical data of H. Therefore, in this study, the H estimation modeling method is proposed specifically for the sites that cannot afford to install solar radiation measurement equipment. The method includes a novel coding method based on information gain, Pearson correlation coefficient, and principal component analysis (PCA), which analyzes the nonlinear and high-dimensional correlation between meteorological factors and solar radiation to decide the correlation of adjacent sites. Based on the results of the coding method, a hybrid H estimation model combining empirical and machine learning is proposed, which takes the empirical model as the base model, and the machine learning model adaptively assigns the corresponding weights to estimate H. The case studies show the proposed hybrid model outperforms the benchmark models and indicate that the H estimation modeling method can be applied to different regions without solar radiation measurement.