Reviewerly/exHarmony huggingface dataset:
Personalized News Recommendation: Methods and Challenges

Personalized news recommendation is important for users to find interesting news information and alleviate information overload. Although it has been extensively studied over decades and has achieved notable success in improving user experience, there are still many problems and challenges that need to be further studied. To help researchers master the advances in personalized news recommendation, in this article, we present a comprehensive overview of personalized news recommendation. Instead of following the conventional taxonomy of news recommendation methods, in this article, we propose a novel perspective to understand personalized news recommendation based on its core problems and the associated techniques and challenges. We first review the techniques for tackling each core problem in a personalized news recommender system and the challenges they face. Next, we introduce the public datasets and evaluation methods for personalized news recommendation. We then discuss the key points on improving the responsibility of personalized news recommender systems. Finally, we raise several research directions that are worth investigating in the future. This article can provide up-to-date and comprehensive views on personalized news recommendation. We hope this article can facilitate research on personalized news recommendation as well as related fields in natural language processing and data mining.
A systematic literature review of IoT time series anomaly detection solutions

The rapid spread of the Internet of Things (IoT) devices has prompted many people and companies to adopt the IoT paradigm, as this paradigm allows the automation of several processes related to data collection and monitoring. In this context, the sensors (or other devices) generate huge amounts of data while monitoring physical spaces and objects. Therefore, the problem of managing and analyzing these huge amounts of data has stimulated researchers and practitioners to adopt anomaly detection techniques, which are automated solutions to enable the recognition of abnormal behaviors occurring in complex systems. In particular, in IoT environments, anomaly detection very often involves the analysis of time series data and this analysis should be accomplished under specific time or resource constraints. In this systematic literature review, we focus on the IoT time series anomaly detection problem by analyzing 62 articles written from 2014 to 2021. Specifically, we explore the methods and techniques adopted by researchers to deal with the issues related to dimensionality reduction, anomaly localization, and real-time monitoring, also discussing the datasets used, and the real-case scenarios tested. For each of these topics, we highlight potential limitations and open issues that need to be addressed in future work.
Big Data Seismology

Abstract The discipline of seismology is based on observations of ground motion that are inherently undersampled in space and time. Our basic understanding of earthquake processes and our ability to resolve 4D Earth structure are fundamentally limited by data volume. Today, Big Data Seismology is an emergent revolution involving the use of large, data‐dense inquiries that is providing new opportunities to make fundamental advances in these areas. This article reviews recent scientific advances enabled by Big Data Seismology through the context of three major drivers: the development of new data‐dense sensor systems, improvements in computing, and the development of new types of techniques and algorithms. Each driver is explored in the context of both global and exploration seismology, alongside collaborative opportunities that combine the features of long‐duration data collections (common to global seismology) with dense networks of sensors (common to exploration seismology). The review explores some of the unique challenges and opportunities that Big Data Seismology presents, drawing on parallels from other fields facing similar issues. Finally, recent scientific findings enabled by dense seismic data sets are discussed, and we assess the opportunities for significant advances made possible with Big Data Seismology. This review is designed to be a primer for seismologists who are interested in getting up‐to‐speed with how the Big Data revolution is advancing the field of seismology.
A ranking-system-based switching particle swarm optimizer with dynamic learning strategies

In this paper, a novel ranking-system-based switching particle swarm optimizer (RSPSO) is proposed. In particular, according to a ranking system, the swarm is divided into elite and normal group, then each particle has been assigned a fitness-based (for normal group member) or a distance-based neighborhood (for elite group member). It is remarkable that neighborhood of a particle is time-varying so that communication among swarm during whole searching process is greatly enhanced. In addition, searching process is divided into four stages by the switching framework, where learning strategies and parameter settings are changed in an adaptive way. Moreover, a newly proposed dimensional learning strategy has been hybridized in RSPSO so as to preserve useful information in the swarm and differential evolution algorithm is employed for a further exploration and also diversifying the swarm. Proposed RSPSO is comprehensively evaluated on series of benchmarks including uni-modal, multi-modal and rotated multi-modal functions. Furthermore, ablation study and sensitivity analysis are performed, where influences of the ranking system, time-varying neighborhood as well as other key parameters have been discussed in detail. Experimental results demonstrate the superiority of proposed algorithm which outperforms other five PSO variants on several indicators regarding to both solution accuracy and convergence performance on most benchmarks in a statistic sense.
Incentive Mechanisms for Smart Grid: State of the Art, Challenges, Open Issues, Future Directions

Smart grids (SG) are electricity grids that communicate with each other, provide reliable information, and enable administrators to operate energy supplies across the country, ensuring optimized reliability and efficiency. The smart grid contains sensors that measure and transmit data to adjust the flow of electricity automatically based on supply/demand, and thus, responding to problems becomes quicker and easier. This also plays a crucial role in controlling carbon emissions, by avoiding energy losses during peak load hours and ensuring optimal energy management. The scope of big data analytics in smart grids is huge, as they collect information from raw data and derive intelligent information from the same. However, these benefits of the smart grid are dependent on the active and voluntary participation of the consumers in real-time. Consumers need to be motivated and conscious to avail themselves of the achievable benefits. Incentivizing the appropriate actor is an absolute necessity to encourage prosumers to generate renewable energy sources (RES) and motivate industries to establish plants that support sustainable and green-energy-based processes or products. The current study emphasizes similar aspects and presents a comprehensive survey of the start-of-the-art contributions pertinent to incentive mechanisms in smart grids, which can be used in smart grids to optimize the power distribution during peak times and also reduce carbon emissions. The various technologies, such as game theory, blockchain, and artificial intelligence, used in implementing incentive mechanisms in smart grids are discussed, followed by different incentive projects being implemented across the globe. The lessons learnt, challenges faced in such implementations, and open issues such as data quality, privacy, security, and pricing related to incentive mechanisms in SG are identified to guide the future scope of research in this sector.
Can we trust a chatbot like a physician? A qualitative study on understanding the emergence of trust toward diagnostic chatbots

Technological advancements in the virtual assistants' domain pave the way to implement complex autonomous agents like diagnostic chatbots. Drawing on the assumption that chatbots are perceived as both technological tools and social actors, we aim to create a deep understanding of trust-building processes towards diagnostic chatbots compared to trust in medical professionals. We conducted a laboratory experiment in which participants interacted either with a diagnostic chatbot only or with an additional telemedicine professional before we interviewed them primarily on trust-building factors. We identified numerous software-related, user-related, and environment-related factors and derived a model of the initial trust-building process. The results support our assumption that it is equally essential to consider dimensions of physician and technology trust. One significant finding is that trust in a chatbot arises cognitively, while trusting a human agent is affect-based. We argue that the lack of affect-based trust inhibits the willingness to rely on diagnostic chatbots and facilitates the user's desire to keep control. Considering dimensions from doctor-patient trust, we found evidence that a chatbot's communication competencies are more important than empathic reactions as the latter may evoke incredibility feelings. To verify our findings, we applied the derived code system in a larger online survey.
Assessing the potential and utilization of solar energy at the building-scale in Shanghai

The assessment of potential and utilization of solar energy for each building has become an essential precondition of urban sustainable development. Currently, there are two challenges, namely high computation cost for an assessment within a large city and missing rooftop information for each building. In this study, by taking the downtown area in Shanghai as sample, a grid scheme with buffer was proposed to alleviate the computational burden and successfully estimate solar radiation with an accuracy of 0.91. Also, a quadrant-based segmentation method was developed to classify rooftop types with an overall accuracy of 83.63%, which helps better calculate solar energy utilization cost. Finally, we assumed PhotoVoltaic conversion (PV) systems or Domestic Solar Water Heater (DSWH) systems are installed on all suitable rooftops and analyzed their utilization cost, as well as economic and ecological benefits. The results show that 165,529 and 52,131 buildings are suitable for PV and DSWH systems, respectively. PV system's average payback time is about five years with reducing standard coal consumption of 1.85 × 1011kg. The DSWH system requires three more years to balance the cost and benefits. The criteria of further solar energy planning are also concluded from three perspectives: building height and density, and rooftop structure.
A proposal of virtual museum metaverse content for the MZ generation

This paper presents a marketing study of the metaverse's potential audience, particularly focusing on museums' use of cyberspace and how some of their physical functions can be replicated online. Among digital transformations, the metaverse has become one of the most significant alternatives to offline events, with museum exhibitions highly representative of it. However, existing virtual museums fail to attract the MZ generation despite the opportunities arising from their contactless lifestyle. Therefore, this study aims to create new content suggestions that the MZ generation positively reacts to and becomes involved in. This research was conducted through five methods: a literature review, case studies, survey, persona creation, and in-depth interviews. According to the market segmentation and strategy (MS&S) technique, two types of responses were used to create and target two types of personas of potential users—'explorative persona' and 'entertaining persona'. This study thus suggests three future directions and content for metaverse virtual museums to better target the MZ generation: technical development for users of both personas, user creation content to enhance user participation for the users of explorative persona, and more active marketing through various community for the users of entertaining persona.
High-Quality Model Aggregation for Blockchain-Based Federated Learning via Reputation-Motivated Task Participation

Federated learning is an emerging paradigm to conduct the machine learning collaboratively but avoid the leakage of original data. Then, how to motivate the data owners to participate federated learning and contribute high-quality data is the crucial issue. In this article, a blockchain-based federated learning (BFL) with a reputation mechanism for high-quality model aggregation is proposed. Specifically, the blockchain transforms the federated learning into a decentralized and trustworthy manner. Over the blockchain, federated learning tasks, undertaken by smart contracts, can be conducted transparently and fairly. Besides, a reputation-constrained data contribution and reward allocation mechanism is designed to encourage data owners to participate in BFL and contribute high-quality data. The noncooperative game is adopted to analyze the behavior strategies of data owners. The existence of the unique equilibrium is proved and the equilibrium point indicates that the data owners can acquire highest reward with the contribution of the highest quality data. Thus, the model quality of BFL is guaranteed. Finally, simulations on the public data sets (MNIST and CIFAR10) demonstrate that BFL with a reputation mechanism can well promote the high-quality model aggregation of federated learning as well as can prevent malicious nodes from corrupting the training task.
Wearable multi-sensor data fusion approach for human activity recognition using machine learning algorithms

Wearable sensor based human activity recognition (HAR) has a broad range of applications in healthcare, fitness, smart home, and surveillance. In spite of the substantial amount of computational research on HAR, the open challenges in multi-sensor based activity recognition including complex temporal data, identifying discriminative feature vectors from multi-modal data and dimensionality reduction require considerable research attention. Hence, in this paper, using multi-resolution time-frequency analysis of received signal strength (RSS) between the wearable sensors, we present a supervised machine learning (ML) based activity recognition framework. The multi-sensor activity data acquired using the wireless sensor network (WSN) nodes and inertial-sensor embedded in a smartphone are decomposed using the discrete wavelet transform (DWT) and the empirical mode decomposition (EMD) techniques for extracting the prominent feature vector. Using the discriminative statistical features from DWT along with the entropy features from EMD, the four ML classifier models such as support vector machine (SVM), K-nearest neighbour (KNN), ensemble classifier (EC), and decision tree (DT) are trained to classify various human activities. The efficacy of the proposed design is assessed on a publicly available dataset from UCI. The experimental results assessed using the confusion matrix and parallel coordinate plot (PCP) substantiate that the proposed ML based HAR framework can achieve a maximum classification accuracy of 99.63% and is superior to several state-of-the-art ML techniques.
Service robots with (perceived) theory of mind: An examination of humans’ reactions

In the near future, it is expected that we humans will receive an increasing part of various services from robots. Many observers, and several existing studies, indicate that we react more positively if our robots have humanlike attributes and capabilities, and the present study examines one such capability: theory of mind. It has to do with the ability to impute mental states to others, which is essential for human-to-human interaction (including interactions in service settings). More specifically, the present study examines the effects of perceptions of service robots' theory of mind capabilities in human-to-robot interactions when the main downstream variable is perceived service quality. Several mediators are also examined. To this end, two empirical studies comprising human-to-robot interactions in a domestic setting were conducted. Both studies indicate that a service robot with more as opposed to less perceived theory of mind enhances perceived service quality, and that this effect is mediated by perceived humanness and perceived usefulness in relation to the robot. It may be argued that a robot's theory of mind capability can also be seen as creepy, which may reduce perceived service quality, because a competent mind reader can create serious harm to others. In the present study, however, robotic theory of mind capabilities did not influence creepiness perceptions.
TAB-SAPP: A Trust-Aware Blockchain-Based Seamless Authentication for Massive IoT-Enabled Industrial Applications

The advancement of sensory technologies proliferates the development of low-cost electronics systems to operate the environmental features of smart cities. Global urbanization integrates networking systems to offer computing-based practical solutions for improving the quality of application-oriented services. Few existing studies have primarily focused on a single-point vulnerability for decentralized IoT applications. However, very few mechanisms address the issues concerning privacy-preserving and trust-aware authentication for IoT-enabled industrial applications. Moreover, the existing schemes are in fact not applicable to real-time scenarios, such as decentralized networks and long-term evolution advanced networks. Thus, this article presents a trust-aware blockchain-based seamless authentication with privacy-preserving (TAB-SAPP) to resolve the critical things, such as privacy, security, and packet delivery ratio. In the proposed TAB-SAPP, a novel data traffic pattern is utilized using identity management to show that the proposed mechanism can be more functional in expanding users’ connectivity to improve the communication metrics, such as packet delivery ratio and mobility speed.
An Effective Semi-Supervised Approach for Liver CT Image Segmentation

Despite the substantial progress made by deep networks in the field of medical image segmentation, they generally require sufficient pixel-level annotated data for training. The scale of training data remains to be the main bottleneck to obtain a better deep segmentation model. Semi-supervised learning is an effective approach that alleviates the dependence on labeled data. However, most existing semi-supervised image segmentation methods usually do not generate high-quality pseudo labels to expand training dataset. In this paper, we propose a deep semi-supervised approach for liver CT image segmentation by expanding pseudo-labeling algorithm under the very low annotated-data paradigm. Specifically, the output features of labeled images from the pretrained network combine with corresponding pixel-level annotations to produce class representations according to the mean operation. Then pseudo labels of unlabeled images are generated by calculating the distances between unlabeled feature vectors and each class representation. To further improve the quality of pseudo labels, we adopt a series of operations to optimize pseudo labels. A more accurate segmentation network is obtained by expanding the training dataset and adjusting the contributions between supervised and unsupervised loss. Besides, the novel random patch based on prior locations is introduced for unlabeled images in the training procedure. Extensive experiments show our method has achieved more competitive results compared with other semi-supervised methods when fewer labeled slices of LiTS dataset are available.
Task-Oriented Image Transmission for Scene Classification in Unmanned Aerial Systems

The vigorous developments of the Internet of Things make it possible to extend its computing and storage capabilities to computing tasks in the aerial system with the collaboration of cloud and edge, especially for artificial intelligence (AI) tasks based on deep learning (DL). Collecting a large amount of image/video data, unmanned aerial vehicles (UAVs) can only hand over intelligent analysis tasks to the back-end mobile edge computing (MEC) server due to their limited storage and computing capabilities. How to efficiently transmit the most correlated information for the AI model is a challenging topic. Inspired by task-oriented communication in recent years, we propose a new aerial image transmission paradigm for the scene classification task. A lightweight model is developed on the front-end UAV for semantic block transmission with the perception of images and channel states. To achieve the tradeoff between transmission latency and classification accuracy, deep reinforcement learning (DRL) is applied to explore the semantic blocks which have the greatest contribution to the back-end classifier under various channel states. Experimental results show that the proposed method can significantly improve classification accuracy by more than 4% under the same conditions, compared to other semantic saliency learning methods.
Investigating the potential effectiveness of earthquake early warning across Europe

Here we assess the potential implementation of earthquake early warning (EEW) across Europe, where there is a clear need for measures that mitigate seismic risk. EEW systems consist of seismic networks and mathematical models/algorithms capable of real-time data telemetry that alert stakeholders (e.g., civil-protection authorities, the public) to an earthquake's nucleation seconds before shaking occurs at target sites. During this time, actions can be taken that might decrease detrimental impacts. We investigate distributions of EEW lead times available across various parts of the Euro-Mediterranean region, based on seismicity models and seismic network density. We then determine the potential usefulness of these times for EEW purposes by defining their spatial relationship with population exposure, seismic hazard, and an alert accuracy proxy, using well-established earthquake-engineering tools for measuring the impacts of earthquakes. Our mapped feasibility results show that, under certain conditions, EEW could be effective for some parts of Europe.
Rethinking Low-Light Enhancement via Transformer-GAN

Images and videos shot in low light are often accompanied by severe image degradation, such as color noise, chromatic aberrations and loss of details. Most existing convolutional neural network (CNN)-based low-light enhancement methods focus on decomposing the image into illumination and reflection parts via the Retinex model, but these methods often fail to adequately consider controlling noise during enhancement and perform poorly in the face of complex lighting environments. In this letter, we propose a powerful Vision Transformer-based Generative Adversarial Network (Transformer-GAN) for enhancing low-light images. Transformer-GAN consists of two subnets as follows: (1) the feature extraction is achieved by an iterative multi-branch network in the feature extraction subnet, and (2) the enhancement is completed in the image reconstruction subnet. The innovative core works are multi-head multi-covariance self-attention (MHMCA) and Light feature-forward module structures (LFFM) in Transformer-GAN. Experiments demonstrate that our method outperforms state-of-the-art low-light enhancement methods on popular low-light datasets.