Reviewerly/exHarmony huggingface dataset:
Graph Signal Processing for Directed Graphs Based on the Hermitian Laplacian

Graph signal processing is a useful tool for representing, analyzing, and processing the signal lying on a graph, and has attracted attention in several fields including data mining and machine learning. A key to construct the graph signal processing is the graph Fourier transform, which is defined by using eigenvectors of the graph Laplacian of an undirected graph. The orthonormality of eigenvectors gives the graph Fourier transform algebraically desirable properties, and thus the graph signal processing for undirected graphs has been well developed. However, since eigenvectors of the graph Laplacian of a directed graph are generally not orthonormal, it is difficult to simply extend the graph signal processing to directed graphs. In this paper, we present a general framework for extending the graph signal processing to directed graphs. To this end, we introduce the Hermitian Laplacian which is a complex matrix obtained from an extension of the graph Laplacian. The Hermitian Laplacian is defined so as to preserve the edge directionality and Hermitian property and enables the graph signal processing to be straightforwardly extended to directed graphs. Furthermore, the Hermitian Laplacian guarantees some desirable properties, such as non-negative real eigenvalues and the unitarity of the Fourier transform. Finally, experimental results for representation learning and signal denoising of/on directed graphs show the effectiveness of our framework.
TIDES: A Trust-Aware IoT Data Economic System With Blockchain-Enabled Multi-Access Edge Computing

To make good use of valuable Internet of Things (IoT) data assets, this paper proposes a trust-aware IoT data economic system (TIDES) with complete IoT data pricing, trading and protection functions. To ensure reliable and automatic data trading, the entire trading process is automatically performed by smart contracts on a hierarchical blockchain. Moreover, we develop several sophisticated methods to ensure the efficiency and service quality of TIDES. First, a complete evaluation model that takes the data trading profile and reputation into consideration is proposed for both suppliers and demanders to assess the trustworthiness of their trading partners. Second, a client-centric data value evaluation model and a game-theory-based pricing model are used to promote win-win transactions in which the demanders obtain higher quality data at an acceptable price and the suppliers receive higher profits. Third, a dispute arbitration model is invoked to detect suspicious trading and refund these payments automatically. TIDES further utilizes a multi-access edge computing (MEC) architecture to alleviate the huge burdens of IoT devices from blockchain operations, reduce the trading latency, and help mobile devices to trade IoT data. The simulation results have shown the advantages of TIDES in terms of trading time, storage overhead, data trading profit, quality data trading, pricing efficiency, and reliability on data asset management and trading.
Evolutionary Multi-Objective Workflow Scheduling for Volatile Resources in the Cloud

The cloud has been widely used as a distributed computing platform for running scientific workflow applications. Most of the cloud providers encourage the use of their underutilized resources as spot instances for much cheaper prices compared with common resources as on-demand instances, however, the promise of lower costs for resources results in the volatility such that spot instances can be interrupted at any time by cloud providers. Many workflow scheduling algorithms have been proposed to deal with volatile resources. In this article, we consider the two most important features of the volatile resources namely fulfillment and interruption rates to fully model the instability of the cloud infrastructure. Subsequently, we propose a novel evolutionary multi-objective workflow scheduling approach to generate a set of trade-off solutions that outperform state-of-the-art algorithms in both makespan and economic costs. In addition, we explore the fluctuation of makespan and costs for our obtained schedules under different levels of fulfillment and interruption rates. Experimental results with the five well-known real-world workflows demonstrate that our evolutionary multi-objective workflow scheduling algorithm is competitive in terms of makespan and cost compared with state-of-the-art on-demand scheduling techniques.
Rapidly-Exploring Adaptive Sampling Tree*: A Sample-Based Path-Planning Algorithm for Unmanned Marine Vehicles Information Gathering in Variable Ocean Environments

This research presents a novel sample-based path planning algorithm for adaptive sampling. The goal is to find a near-optimal path for unmanned marine vehicles (UMVs) that maximizes information gathering over a scientific interest area, while satisfying constraints on collision avoidance and pre-specified mission time. The proposed rapidly-exploring adaptive sampling tree star (RAST*) algorithm combines inspirations from rapidly-exploring random tree star (RRT*) with a tournament selection method and informative heuristics to achieve efficient searching of informative data in continuous space. Results of numerical experiments and proof-of-concept field experiments demonstrate the effectiveness and superiority of the proposed RAST* over rapidly-exploring random sampling tree star (RRST*), rapidly-exploring adaptive sampling tree (RAST), and particle swarm optimization (PSO).
Models of good teaching practices for mobile learning in higher education

Abstract Mobile learning is an emerging teaching methodology in the university system. Every year, the International Horizon Reports highlight the trend of implementing mobile devices in the classroom. Therefore, the Spanish university system presents the current challenge of adapting these resources to improve student learning, in line with the knowledge society in which we are immersed. This requires examples of good teaching practice. The purposes of this paper were to evaluate the mobile learning (m-learning) practices implemented by university teachers and to compile experiences on good teaching practices of m-learning developed in the classroom. A mixed method was used in which the responses of 1125 professors from 59 different universities located throughout Spain were analyzed. The APMU scale developed by the authors was applied for the detection of good teaching practices of m-learning and the structured interview for the collection of concrete experiences of good teaching practices. The results showed that the largest proportion of good teaching practices were concentrated at the University of La Laguna, University of Almeria, University of La Rioja, Camilo José Cela University and University of Seville. Furthermore, three experiences carried out by teachers who were agents of good practice were collected. Based on this, three models of good teaching practices were generated and exemplified through the concept mapping technique. Finally, the main findings and implications of the study are discussed.
Collection and Analysis of Human Upper Limbs Motion Features for Collaborative Robotic Applications

(1) Background: The technologies of Industry 4.0 are increasingly promoting an operation of human motion prediction for improvement of the collaboration between workers and robots. The purposes of this study were to fuse the spatial and inertial data of human upper limbs for typical industrial pick and place movements and to analyze the collected features from the future perspective of collaborative robotic applications and human motion prediction algorithms. (2) Methods: Inertial Measurement Units and a stereophotogrammetric system were adopted to track the upper body motion of 10 healthy young subjects performing pick and place operations at three different heights. From the obtained database, 10 features were selected and used to distinguish among pick and place gestures at different heights. Classification performances were evaluated by estimating confusion matrices and F1-scores. (3) Results: Values on matrices diagonals were definitely greater than those in other positions. Furthermore, F1-scores were very high in most cases. (4) Conclusions: Upper arm longitudinal acceleration and markers coordinates of wrists and elbows could be considered representative features of pick and place gestures at different heights, and they are consequently suitable for the definition of a human motion prediction algorithm to be adopted in effective collaborative robotics industrial applications.
0EISUA: Zero Effort Indoor Secure User Authentication

Two-factor authentication (2FA) systems implement by verifying at least two factors. A factor can be one or more of something a user knows (password, or phrase), something a user possesses (smart card, or smartphone), something a user is (fingerprint, or iris), something a user does (keystroke), or somewhere a user is (location). In a conventional 2FA system, a user is required to interacts (e.g., typing a passcode) in order to implement the second layer of authentication, which is not very user-friendly. Nowadays, smart devices (phones, laptops, tablets, etc.) can receive signals from different radio frequency technologies within range. As these devices move among networks (Wi-Fi access points, cell phone towers, etc.), they receive broadcast messages, some of which can be used to collect information. This information can be utilized in a variety of ways, such as establishing a connection, sharing information, locating devices, and, most appropriately, identifying users in range. The principal benefit of broadcast messages is that the devices can read and process the embedded information without being connected to the broadcaster. Moreover, the broadcast messages can be received only within a range of the wireless access point sending the broadcast, thus inherently limiting access to those devices in close physical proximity and facilitating many applications dependent on that proximity. In this paper, 0EISUA is proposed, a zero-effort two-factor authentication scheme based on something that is in the user's environment (ambient access points). In our research, data from the broadcast messages are utilized to implement the second authentication factor by determining whether two devices are proximate or not to ensure that they belong to the same user, in a way that requires zero interaction from the user. The new proposed system introduced in this paper is experimentally tested.
Protecting IP of Deep Neural Networks with Watermarking: A New Label Helps

Deep neural network (DNN) models have shown great success in almost every artificial area. It is a non-trivial task to build a good DNN model. Nowadays, various MLaaS providers have launched their cloud services, which trains DNN models for users. Once they are released, driven by potential monetary profit, the models may be duplicated, resold, or redistributed by adversaries, including greedy service providers themselves. To mitigate this threat, in this paper, we propose an innovative framework to protect the intellectual property of deep learning models, that is, watermarking the model by adding a new label to crafted key samples during training. The intuition comes from the fact that, compared with existing DNN watermarking methods, adding a new label will not twist the original decision boundary but can help the model learn the features of key samples better. We implement a prototype of our framework and evaluate the performance under three different benchmark datasets, and investigate the relationship between model accuracy, perturbation strength, and key samples’ length. Extensive experimental results show that, compared with the existing schemes, the proposed method performs better under small perturbation strength or short key samples’ length in terms of classification accuracy and ownership verification efficiency.
Trust2Privacy: A Novel Fuzzy Trust-to-Privacy Mechanism for Mobile Social Networks

Mobile social applications have been widely used by Internet users. Users can efficiently acquire many kinds of information and share their statuses by various social platforms. However, when a user intends to share information through the user's social applications, the user can set the access permission only before the information is posted. Once the information is posted, it is completely beyond the user's control. Specifically, during the recommendation process (for friend or information) in social applications, a user cannot control who can get the recommendation and access his/her information. If one user is accessed by another malicious user, his/her privacy information can be disclosed, and even be further misused for malicious attacks. In this article, we propose Trust2Privacy, a trust-based access control mechanism to protect the personalized privacy of users after posting their information, which can effectively realize the transformation from trust to privacy. First, to represent the relations accurately among users, we define the direction of trust among users according to the user-follow status. Then we combine the similarity, correlation, and interaction among users to calculate the trust values. Considering the fuzzy relationship between the multi-dimensional features and trust levels, we propose a fuzzy comprehensive evaluation algorithm to compute the fuzzy trust. Moreover, for the sake of the high mobility of mobile social networks, we exploit so-called online-to-offline trust evidence to derive the trust value, including taking the location information (e.g., distance, semantics) into consideration. To meet the personalized privacy requirement, we design a filtering algorithm based on the trust relationships and the privacy policy of the target users or posted information, according to which the access requesters can get the list of accessible users or information. This enables achieving the goal of personalized privacy protection. The theoretical analysis and simulation experiment demonstrate that Trust2Privacy is able to achieve personalized privacy protection without bringing negative impact on the availability and usability of mobile social applications.
Very Short-Term Surface Solar Irradiance Forecasting Based On FengYun-4 Geostationary Satellite

An algorithm to forecast very short-term (30-180 min) surface solar irradiance using visible and near infrared channels (AGRI) onboard the FengYun-4A (FY-4A) geostationary satellite was constructed and evaluated in this study. The forecasting products include global horizontal irradiance (GHI) and direct normal irradiance (DNI). The forecast results were validated using data from Chengde Meteorological Observatory for four typical months (October 2018, and January, April, and July 2019), representing the four seasons. Particle Image Velocimetry (PIV) was employed to calculate the cloud motion vector (CMV) field from the satellite images. The forecast results were compared with the smart persistence (SP) model. A seasonal study showed that July and April forecasting is more difficult than during October and January. For GHI forecasting, the algorithm outperformed the SP model for all forecasting horizons and all seasons, with the best result being produced in October; the skill score was greater than 20%. For DNI, the algorithm outperformed the SP model in July and October, with skill scores of about 12% and 11%, respectively. Annual performances were evaluated; the results show that the normalized root mean square error (nRMSE) value of GHI for 30-180 min horizon ranged from 26.78% to 36.84%, the skill score reached a maximum of 20.44% at the 30-min horizon, and the skill scores were all above 0 for all time horizons. For DNI, the maximum skill score was 6.62% at the 180-min horizon. Overall, compared with the SP model, the proposed algorithm is more accurate and reliable for GHI forecasting and slightly better for DNI forecasting.
Applications of Raman Spectroscopy in Mineralogy and Geochemistry

The application of Raman spectroscopy for the identification and characterization of minerals and related materials has increased appreciably during recent years. Raman spectroscopy has proven to be a most valuable and versatile analytical tool. Successful applications cover virtually all the mineralogical sub-disciplines, and have become more numerous in geochemistry. We present a general summary of present applications, illustrated by selected examples. In addition, we briefly point out several aspects of spectral acquisition, data reduction, and interpretation of Raman results that are important for the application of Raman spectroscopy as a reliable analytical tool.
Exploring evolution trends in cryptocurrency study: From underlying technology to economic applications

This paper examines the research hotspots and evolution trends of cryptocurrency-related papers published between 2009 and 2019, and identify the important journals, scholars and research findings by bibliometric analysis. The empirical results show that research priorities of cryptocurrency shift from underlying technology to economic applications currently. In addition, research institutions of cryptocurrency are mainly distributed in Europe and China with complementary research fields. In the future, advanced methods of computer science and econometrics may be applied to the research of cryptocurrency, and some specific application schemes of cryptocurrency will become new research focus.
Annotating Argument Schemes

Abstract Argument schemes are abstractions substantiating the inferential connection between premise(s) and conclusion in argumentative communication. Identifying such conventional patterns of reasoning is essential to the interpretation and evaluation of argumentation. Whether studying argumentation from a theory-driven or data-driven perspective, insight into the actual use of argumentation in communicative practice is essential. Large and reliably annotated corpora of argumentative discourse to quantitatively provide such insight are few and far between. This is all the more true for argument scheme corpora, which tend to suffer from a combination of limited size, poor validation, and the use of ad hoc restricted typologies. In the current paper, we describe the annotation of schemes on the basis of two distinct classifications: Walton’s taxonomy of argument schemes, and Wagemans’ Periodic Table of Arguments. We describe the annotation procedure for each, and the quantitative characteristics of the resulting annotated text corpora. In doing so, we extend the annotation of the preexisting US2016 corpus of televised election debates, resulting in, to the best of our knowledge, the two largest consistently annotated corpora of schemes in argumentative dialogue publicly available. Based on evaluation in terms of inter-annotator agreement, we propose further improvements to the guidelines for annotating schemes: the argument scheme key, and the Argument Type Identification Procedure.
Surveying Software Practitioners on Technical Debt Payment Practices and Reasons for not Paying off Debt Items

Background: Little is known about the practices used for technical debt (TD) payment. The study of payment practices, as well as the reasons for not applying them, can help practitioners to control and manage TD items. Aims: To investigate, from the point of view of software practitioners, if TD items have been paid off in software projects, the practices that have been used to pay off TD and the reasons that hamper the implementation of these practices. Method: We analyzed - both quantitatively and qualitatively - a corpus of responses from a survey of 432 practitioners, from four countries, about the possibility of TD payment. Results: We found that, for most of the cases, TD items have not been eliminated from software projects. The main reasons for not paying off TD are lack of organizational interest, low priority on the debt, focus on short-term goals, cost, and lack of time. On the other hand, we identified that code refactoring, design refactoring, and update system documentation are the most used practices for TD payment. Practitioners also cited practices related to the prevention, prioritization, and creation of a favorable setting as part of TD payment initiatives. Conclusion: This paper summarizes the identified practices and reasons for not paying off debt items in a map. Our map reveals that the majority of payment practices are of a technical nature while the majority of reasons for not paying off debts are associated with non-technical issues.
A new international initiative for facilitating data-driven Earth science transformation

Abstract Data-driven techniques including machine-learning (ML) algorithms with big data are re-activating and re-empowering research in traditional disciplines for solving new problems. For geoscientists, however, what matters is what we do with the data rather than the amount of it. While recent monitoring data will help risk and resource assessment, the long-earth record is fundamental for understanding processes. Thus, how big data technologies can facilitate geoscience research is a fundamental question for most organizations and geoscientists. A quick answer is that big data technology may fundamentally change the direction of geoscience research. In view of the challenges faced by governments and professional organizations in contributing to the transformation of Earth science in the big data era, the International Union of Geological Sciences has established a new initiative: the IUGS-recognized Big Science Program. This paper elaborates on the main opportunities and benefits of utilizing data-driven approaches in geosciences and the challenges in facilitating data-driven earth science transformation. The main benefits may include transformation from human learning alone to integration of human learning and AI, including ML, as well as from known questions seeking answers to formulating as-yet unknown questions with unknown answers. The key challenges may be associated with intelligent acquisition of massive, heterogeneous data and automated comprehensive data discovery for complex Earth problem solving.
AMNN: Attention-Based Multimodal Neural Network Model for Hashtag Recommendation

In the real-world social networks, hashtags are widely applied for understanding the content of an individual microblog. However, users do not always take the initiative in attaching hashtags when posting a microblog so that much effort has been invested for automatically hashtag recommendation. As a new trend, users no longer only post texts but prefer to share with multimodal data, such as images. To deal with these situations, we propose an attention-based multimodal neural network model (AMNN) to learn the representations of multimodal microblogs and recommend relevant hashtags. In this article, we convert the hashtag recommendation task into a sequence generation problem. Then, we propose a hybrid neural network approach to extract the features of both texts and images and incorporate them into the sequence-to-sequence model for hashtag recommendation. Experimental results on the data set collected on Instagram and two public data sets demonstrate that the proposed method outperforms state-of-the-art methods. Our model achieves the best performance in three different metrics: precision, recall, and accuracy. The source code of this article can be obtained from “https://github.com/w5688414/AMNN.”