Reviewerly/exHarmony huggingface dataset:
A Fast-Converging Particle Swarm Optimization through Targeted, Position-Mutated, Elitism (PSO-TPME)

Abstract We improve convergence speed by two orders of magnitude and the global exploration capabilities of particle swarm optimization (PSO) through targeted position-mutated elitism (TPME). The proposed fast-converging TPME operator requires a fitness-based classification technique to categorize the particles. The introduced classification is motivated by its simplicity, low memory requirements, and automated termination criteria based on convergence. The three key innovations address particle classification, elitism, and mutation in the cognitive and social model. PSO-TPME is benchmarked against five popular PSO variants for multi-dimensional functions, which are extensively adopted in the optimization field, In particular, the convergence accuracy, convergence speed, and the capability to find global minima are investigated. The statistical error is assessed by numerous repetitions. The simulations confirmed that in ten of the thirteen investigated functions, the proposed PSO variant outperforms other variants in terms of convergence rate and accuracy by at least two orders of magnitude. On the other hand, the simulations demonstrated the early exploration capabilities of PSO-TPME in all tested functions. In the first ten iterations, PSO-TPME outperformed all the investigated PSO variants by at least two orders of magnitude.
Minimum spanning tree dynamic programming stereo‐matching method based on superpixels

Abstract The minimum spanning tree (MST) stereo‐matching method is an information‐infiltration process. The difference in edge attributes of an MST can cause the edge‐expansion phenomenon, which affects the matching accuracy. To accurately recover image‐depth information, a dynamic‐programming stereo‐matching method based on the MST was proposed. First, the colour Birchfield Tomasi cost‐calculation method based on image adaptive colour information was proposed to obtain stable initial cost values. Second, the image was segmented into superpixel regions using the simple linear iterative clustering algorithm. The pixel‐ and region‐level MSTs were then constructed. Next, combined with the idea of dynamic programming, the MST cost‐aggregation process was re‐deduced. On this basis, the aggregate cost values of the two MSTs were obtained. Finally, the aggregate cost values were combined adaptively to acquire the high‐precision smooth disparity map. The Middlebury 2014 dataset was used for the experiments. The experimental results indicate that the proposed method can effectively improve the accuracy of stereo‐matching.
An Enhanced GWO Algorithm with Improved Explorative Search Capability for Global Optimization and Data Clustering

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What is mineral informatics?

Minerals are information-rich materials that offer researchers a glimpse into the evolution of planetary bodies. Thus, it is important to extract, analyze, and interpret this abundance of information to improve our understanding of the planetary bodies in our solar system and the role our planet's geosphere played in the origin and evolution of life. Over the past several decades, data-driven efforts in mineralogy have seen a gradual increase. The development and application of data science and analytics methods to mineralogy, while extremely promising, has also been somewhat ad hoc in nature. To systematize and synthesize the direction of these efforts, we introduce the concept of "Mineral Informatics," which is the next frontier for researchers working with mineral data. In this paper, we present our vision for Mineral Informatics and the X-Informatics underpinnings that led to its conception, as well as the needs, challenges, opportunities, and future directions of the field. The intention of this paper is not to create a new specific field or a sub-field as a separate silo, but to document the needs of researchers studying minerals in various contexts and fields of study, to demonstrate how the systemization and enhanced access to mineralogical data will increase cross- and interdisciplinary studies, and how data science and informatics methods are a key next step in integrative mineralogical studies.
Secure digital documents sharing using blockchain and attribute-based cryptosystem

Education is developing very fast with the advancement of technology and the process of the smart era. One can store all educational certificates and credentials in the form of an electronic wallet or a folder. By using this electronic transformation of certificates, users can transfer the certificates from one place to another very easily. The “data island” phenomenon, central data storing, confidentiality, reduced security, and integrity are common problems of electronic data transfer. This study presents a safe sharing of digital documents which uses blockchain technology and an attributed-based cryptosystem to offer a creative solution to the abovementioned issues. The proposed scheme uses Ethereum smart contracts and achieves fine-grain access control by using attribute-based encryption. Finally, we verified our model using the test network and compared the performance with some existing state-of-arts. The results of proposed scheme generated by simulations are more feasible and effective in challenging environments.
Big Macs and the Beano: Is it time for the comic to drop the junk food brands?

The Beano website describes itself as “100% safe for children”—but is its junk food related content doing more harm than good? <b>Claire Mulrenan</b>, <b>Mark Petticrew</b>, and <b>Harry Wallop</b> investigate
Multi-Stream Progressive Restoration for Low-Light Light Field Enhancement and Denoising

Light Fields (LFs) are easily degraded by noise and low light. Low light LF enhancement and denoising are more challenging than single image tasks because the epipolar information among views should be taken into consideration. In this work, we propose a multiple stream progressive restoration network to restore the whole LF in just one forward pass. To make full use of the multiple views supplementary information and preserve the epipolar information, we design three types of input composed of view stacking. Each type of input corresponds to an restoration stream and provides specific complementary information. In addition, the weights are shared for each type of input in order to better maintain the epipolar information among views. To fully utilize the supplementary information, we then design a multi-stream interaction module to aggregate features from different restoration streams. Finally, the multiple stages restoration is introduced to reconstruct the LF progressively. We carry out extensive experiments to demonstrate that our model outperforms the state-of-the-art techniques on real world low light LF dataset and synthetic noisy LF dataset.
Adaptive Resource Allocation for Blockchain-Based Federated Learning in Internet of Things

The fast development of mobile communication and artificial intelligence (AI) technologies greatly promotes the prosperity of the Internet of Things (IoT), where various types of IoT devices can perform more intelligent tasks. Considering the privacy leakage and limited communication resources, federated learning (FL) has emerged to enable devices to collaboratively train AI models based on their local data without raw data exchanges. Nevertheless, it is still challenging for guaranteeing any FL models to be effective due to the sluggish willingness of IoT devices and the model poisoning attacks in the FL. To address these issues, in this article, we introduce blockchain technology and propose a blockchain-based FL framework for supporting a trustworthy and reliable FL paradigm in IoT. In the proposed framework, we design a committee-based participant selection mechanism that selects the aggregate node and local model updates dynamically to construct the global model. Moreover, considering the tradeoff between the energy consumption and the convergence rate of the FL model, we perform the channel allocation, block size adjustment, and block producer selection jointly. Since the remaining resources, handling transactions, and channel conditions are dynamically varying (i.e., stochastic environment), we formulate the problem as a Markov decision process (MDP) and adopt a deep reinforcement learning (DRL)-based algorithm to solve it. The simulation results demonstrate the effectiveness of the proposed framework and show the superior performance of the DRL-based resource allocation algorithm compared with other baseline methods in terms of energy consumption.
A Parallel Fusion Graph Convolutional Network for Aspect-Level Sentiment Analysis

Sentiment analysis has always been an important basic task in the NLP field. Recently, graph convolutional networks (GCNs) have been widely used in aspect-level sentiment analysis. Because GCNs have good aggregation effects, every node can contain neighboring node information. However, in previous studies, most models used only a single GCN to learn contextual information. The GCN relies on the construction method of the graph, and a single GCN will cause the model to focus on a certain relationship of nodes that depends on the construction method and ignore other information. In addition, when the GCN aggregates node information, it cannot determine whether the aggregated information is useful, so it will inevitably introduce noise. We propose a model that fuses two parallel GCNs to learn different relational features between sentences at the same time, and we add a gate mechanism to the GCN to filter the noise introduced by the GCN when aggregating information. Finally, we validate our model on public datasets, and the experiments show that compared to state-of-the-art models, our model performs better.
Visual Analysis of International Environmental Security Management Research (1997–2021) Based on VOSviewer and CiteSpace

To investigate the international development status and hot trends in the field of environmental security management in recent years, the published the environmental security management literature from 1997 to 2021, which was retrieved from Web of Science, with VOSviewer as the main and CiteSpace as the auxiliary, through the cooperation network of authors, scientific research institutions, and countries. The keywords were visualized by clustering, time zone analysis, and burst analysis. A total of 7596 articles were retrieved, forming six main clustering labels, including 28,144 authors. The research hotspots are from the fields of personal health, society, agriculture, ecological environment, energy, and sustainable development, as well as the development of internet environmental safety management, such as big data, Bayesian networks, and conceptual frameworks. Through cluster analysis, the cooperation of major research teams and scientific research institutions and the cooperation and development between countries were analyzed. The cooperation between scientific research institutions in various countries is relatively close. The United States currently occupies a dominant and authoritative position in this field. China has cooperated more closely with the United States, Britain, Australia, and India.
Mitigating Popularity Bias in Recommendation with Unbalanced Interactions: A Gradient Perspective

Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-tailed distribution. Such long-tailed data lead to popularity bias to recommend popular but not personalized items to users. We present a gradient perspective to understand two negative impacts of popularity bias in recommendation model optimization: (i) the gradient direction of popular item embeddings is closer to that of positive interactions, and (ii) the magnitude of positive gradient for popular items are much greater than that of unpopular items. To address these issues, we propose a simple yet efficient framework to mitigate popularity bias from a gradient perspective. Specifically, we first normalize each user embedding and record accumulated gradients of users and items via popularity bias measures in model training. To address the popularity bias issues, we develop a gradient-based embedding adjustment approach used in model testing. This strategy is generic, model-agnostic, and can be seamlessly integrated into most existing recommender systems. Our extensive experiments on two classic recommendation models and four real-world datasets demonstrate the effectiveness of our method over state-of-the-art debiasing baselines.
DRE-Net: A Dynamic Radius-Encoding Neural Network with an Incremental Training Strategy for Interactive Segmentation of Remote Sensing Images

Semantic segmentation of remote sensing (RS) images, which is a fundamental research topic, classifies each pixel in an image. It plays an essential role in many downstream RS areas, such as land-cover mapping, road extraction, traffic monitoring, and so on. Recently, although deep-learning-based methods have shown their dominance in automatic semantic segmentation of RS imagery, the performance of these existing methods has relied heavily on large amounts of high-quality training data, which are usually hard to obtain in practice. Moreover, human-in-the-loop semantic segmentation of RS imagery cannot be completely replaced by automatic segmentation models, since automatic models are prone to error in some complex scenarios. To address these issues, in this paper, we propose an improved, smart, and interactive segmentation model, DRE-Net, for RS images. The proposed model facilitates humans’ performance of segmentation by simply clicking a mouse. Firstly, a dynamic radius-encoding (DRE) algorithm is designed to distinguish the purpose of each click, such as a click for the selection of a segmentation outline or for fine-tuning. Secondly, we propose an incremental training strategy to cause the proposed model not only to converge quickly, but also to obtain refined segmentation results. Finally, we conducted comprehensive experiments on the Potsdam and Vaihingen datasets and achieved 9.75% and 7.03% improvements in NoC95 compared to the state-of-the-art results, respectively. In addition, our DRE-Net can improve the convergence and generalization of a network with a fast inference speed.
Avatar-mediated service encounters: impacts and research agenda

Technology has been expanding the service encounter concept. Avatars, including virtual and robotic avatars, have been gaining popularity as an emerging technology to generate more human-like and even enhanced remote interactions in technology-mediated service encounters. However, service researchers have paid lesser attention to human-controlled avatar technologies compared to service robots as autonomous avatars. In response to the emerging business and research interests, the technology-mediated service encounter model needs to be updated by integrating avatar technologies. To address this gap, this study develops a conceptual framework of avatar-mediated service encounters. This concept amalgamates features of traditional technology-mediated service encounters and service robots from the aspects of service flexibility and interaction modality. The applications of avatar technologies are categorized based on two axes – user type and avatar embodiment type – and the impacts and research agenda are outlined for each category. The proposed framework contributes to improving remote service experiences and realizing resilient service workplaces.
A Multi-Task Graph Neural Network with Variational Graph Auto-Encoders for Session-Based Travel Packages Recommendation

Session-based travel packages recommendation aims to predict users’ next click based on their current and historical sessions recorded by Online Travel Agencies (OTAs). Recently, an increasing number of studies attempted to apply Graph Neural Networks (GNNs) to the session-based recommendation and obtained promising results. However, most of them do not take full advantage of the explicit latent structure from attributes of items, making learned representations of items less effective and difficult to interpret. Moreover, they only combine historical sessions (long-term preferences) with a current session (short-term preference) to learn a unified representation of users, ignoring the effects of historical sessions for the current session. To this end, this article proposes a novel session-based model named STR-VGAE, which fills subtasks of the travel packages recommendation and variational graph auto-encoders simultaneously. STR-VGAE mainly consists of three components: travel packages encoder , users behaviors encoder , and interaction modeling . Specifically, the travel packages encoder module is used to learn a unified travel package representation from co-occurrence attribute graphs by using multi-view variational graph auto-encoders and a multi-view attention network. The users behaviors encoder module is used to encode user’ historical and current sessions with a personalized GNN, which considers the effects of historical sessions on the current session, and coalesce these two kinds of session representations to learn the high-quality users’ representations by exploiting a gated fusion approach. The interaction modeling module is used to calculate recommendation scores over all candidate travel packages. Extensive experiments on a real-life tourism e-commerce dataset from China show that STR-VGAE yields significant performance advantages over several competitive methods, meanwhile provides an interpretation for the generated recommendation list.
Making Reconstruction-based Method Great Again for Video Anomaly Detection

Anomaly detection in videos is a significant yet challenging problem. Previous approaches based on deep neural networks employ either reconstruction-based or prediction-based approaches. Nevertheless, existing reconstruction-based methods 1) rely on old-fashioned convolutional autoencoders and are poor at modeling temporal dependency; 2) are prone to overfit the training samples, leading to indistinguishable reconstruction errors of normal and abnormal frames during the inference phase. To address such issues, firstly, we get inspiration from transformer and propose Spatio-Temporal Auto-Trans-Encoder, dubbed as STATE, as a new autoencoder model for enhanced consecutive frame reconstruction. Our STATE is equipped with a specifically designed learnable convolutional attention module for efficient temporal learning and reasoning. Secondly, we put forward a novel reconstruction-based input perturbation technique during testing to further differentiate anomalous frames. With the same perturbation magnitude, the testing reconstruction error of the normal frames lowers more than that of the abnormal frames, which contributes to mitigating the overfitting problem of reconstruction. Owing to the high relevance of the frame abnormality and the objects in the frame, we conduct object-level reconstruction using both the raw frame and the corresponding optical flow patches. Finally, the anomaly score is designed based on the combination of the raw and motion reconstruction errors using perturbed inputs. Extensive experiments on benchmark video anomaly detection datasets demonstrate that our approach outperforms previous reconstruction-based methods by a notable margin, and achieves state-of-the-art anomaly detection performance consistently. The code is available at https://github.com/wyzjack/MRMGA4VAD.
Blockchain enabled smart community for bilateral energy transaction

The blockchain enabled near-real-time, peer-to-peer energy trading market is one of the promising ways to enhance energy system flexibility, achieve peer privacy preserving and transaction trackability. However, providing blockchain-based solutions compatible with energy trading is not an easy task, where real-world feasibility for business model, blockchain configuration, and physic limitations is rather to be investigated. In this work, a permissioned Hyperledger Fabric (HF) blockchain is leveraged to implement real-world applications for energy trading in a smart community. A near-real-time bilateral trading scheme, directly negotiated on a peer-to-peer basis without any intermediary, is designed for achieving supply–demand balance within power network limits. Thenceforward, the specific process of the HF blockchain establishment and the smart contract creation for system initialization, registration, negotiation, trading, balancing and settlement, are detailed demonstrated. In addition, the benchmark is presented to test the performance metrics of execution time, latency, throughput for feasibility and scalability analyses in real-world application. The results illustrate the proposed HF-assisted energy trading platform corroborates the feasibility for the future scalable P2P trading.
Integrated deep learning framework for unstable event identification and disruption prediction of tokamak plasmas

Abstract The ability to identify underlying disruption precursors is key to disruption avoidance. In this paper, we present an integrated deep learning (DL) based model that combines disruption prediction with the identification of several disruption precursors like rotating modes, locked modes, H-to-L back transitions and radiative collapses. The first part of our study demonstrates that the DL-based unstable event identifier trained on 160 manually labeled DIII-D shots can achieve, on average, 84% event identification rate of various frequent unstable events (like H-L back transition, locked mode, radiative collapse, rotating MHD mode, large sawtooth crash), and the trained identifier can be adapted to label unseen discharges, thus expanding the original manually labeled database. Based on these results, the integrated DL-based framework is developed using a combined database of manually labeled and automatically labeled DIII-D data, and it shows state-of-the-art (AUC = 0.940) disruption prediction and event identification abilities on DIII-D. Through cross-machine numerical disruption prediction studies using this new integrated model and leveraging the C-Mod, DIII-D, and EAST disruption warning databases, we demonstrate the improved cross-machine disruption prediction ability and extended warning time of the new model compared with a baseline predictor. In addition, the trained integrated model shows qualitatively good cross-machine event identification ability. Given a labeled dataset, the strategy presented in this paper, i.e. one that combines a disruption predictor with an event identifier module, can be applied to upgrade any neural network based disruption predictor. The results presented here inform possible development strategies of machine learning based disruption avoidance algorithms for future tokamaks and highlight the importance of building comprehensive databases with unstable event information on current machines.