Reviewerly/exHarmony huggingface dataset:
Estimating the spatial distribution of soil heavy metals in oil mining area using air quality data

Air quality is a vital environment variable which determines spatial accumulation of soil heavy metals. It is very important to estimate the contribution of air quality for soil heavy metals in oil mining area. For the end, we collected 116 samples from surface soil of oil mining in the Yellow River Delta (YRD) of China, and analyzed the content of As, Cr, Ni, Pb, and Zn. Furthermore, 40 monitoring stations data of air quality were collected in study area, including CO, NO2, SO2, O3, PM2.5, and PM10. Spatial estimation and mapping of heavy metals in soil were carried out by hybrid geostatistical models, including multiple linear regression-ordinary kriging (MLROK), support vector machine-ordinary kriging (SVMOK) and random forest-ordinary kriging (RFOK). RFOK exhibited the highest estimation accuracy (R2) for As (65.76%), Cr (77.85%), Ni (61.47%), Pb (74.64%), and Zn (71.35%) in comparison with other models. And relative R2 of RFOK improved 30%, while MLROK and SVMOK increased over 100% for Zn (RIo = 121.90% and RIo = 121.64%) based on their original R2 of machine learning models. In addition, mapping results by RFOK showed the high concentrations of heavy metals were focused in the central and northeastern (As), northern (Cr), northeastern and northwestern (Ni), central and eastern (Pb), and northern (Zn). Especially, compared with vegetation index and topographic factors, PM2.5 is the highest driving variable for As (18.34%) and Zn (12.91%), and CO is the most important variable for Cr (18.22%), Ni (14.28%). The above results indicated that there is a mechanism of sources-receptor relationship between air quality and soil heavy metals, that is, oil well and factory in study area discharge heavy metal particles into the atmosphere, and then enter the soil through atmospheric deposition and precipitation. Enlightened by this study, variable selection should be focused on important sources for the accumulation of heavy metals in study area, who must take decisions to prevent and to early warn heavy metals pollution in mine soil.
Theorizing the 2017 blockchain ICO bubble as a network scam

In the popular imagination and in academic literature, scams are usually seen as dyadic, involving a con artist and a mark. This article retheorizes scams as networked, collective activity. Scams, like all commerce, are shaped by and in turn shape communication channels. The “network scam” is therefore offered as a lens for understanding scams in the digital economy more broadly. As a case study, this article documents the 2017 Initial Coin Offering (ICO) Bubble. ICOs were supposed to be a new, radically disruptive way of crowdfunding to finance the development of a new, radically disruptive blockchain technological ecosystem. All told, ICOs raised an estimated $5 billion in 2017 alone. But by all analyses—both from observers and participants, both during the bubble and after—the vast majority of ICO turned out to be scams. This article uses these scams to theorize the “network scam” as a collaborative effort to bring about a shared future, but one that is fundamentally characterized by arbitrage on uneven belief among participants in that future ever coming to pass.
Pedestrian Re-identification Algorithm Based on Visual Attention-positive Sample Generation Network Deep Learning Model

In view of the strong generalizability and self-learning capabilities of deep learning models, many scholars have studies how to apply deep learning theory in the pedestrian re-identification field. However, a number of problems persist in practically applying deep learning in this field, including determining how to make full use of the features of the sequence information in the salient region of an image and addressing the data gap between data-driven deep learning models and pedestrian re-identification tasks. In view of these problems, in this paper, a re-identification method is proposed based on a visual common attention mechanism. Initially, the method focuses on the local area of the image at the location specified by the given coordinates. Next, under the constraint of pedestrian image pairing tags, it focuses on the sequence of salient regions of image pairs based on deep learning techniques. Then, the global features and the local attention features are cascaded into joint features for use in pedestrian re-identification. To address the data gap between deep learning models and pedestrian re-identification, a new strategy for generating difficult positive samples is proposed primarily through a positive sample that mainly involves a positive sample generation network, a difficult positive sample conversion network, and a dual-stream twin network. We using this network, a large number of positive samples can be obtained to train the data-driven neural network and solve the re-identification task. The above ideas are combined to propose a pedestrian re-identification algorithm based on a visual attention-positive sample generation network deep learning model. The experimental results show that the method proposed in this paper not only achieves better recognition results than other deep learning methods, but also adapts well to a variety of databases. In addition, the method proposed in this paper is more robust to occluded pedestrian images than other deep learning methods. In addition, the method proposed in this paper is more robust than other deep learning methods for occluding pedestrian images.
Effect of writing style on social support in online health communities: A theoretical linguistic analysis framework

In online health communities (OHCs), patients can exchange social support through text-based communication. However, research on how various linguistic characteristics of patients' communication in these communities affect their social support outcomes remains limited. This study performs linguistic profiling on OHC participants based on a large dataset and empirically evaluates how lexical, syntactic, semantic, and pragmatic features affect users' communication and social support outcomes. The results show that lexical richness in health-related vocabulary negatively correlates with receiving informational support. The readability and brevity of written texts have positive relationships with incoming social support. Writing longer sentences positively correlates with receiving informational support but negatively correlates with receiving emotional support. Expressing negative sentiment leads to higher chances of receiving both types of social support. The use of terms related to perception and body parts increases the chances of receiving emotional support. The use of terms related to perception words additionally correlates to higher chances of receiving informational support. To receive social supports, being logical in expressions is also critical. Furthermore, the relationships between shared health language and social support are determined by the word category and social support type.
Alteration and mineralization patterns in orogenic gold deposits: Constraints from deposit observation and thermodynamic modeling

Hydrothermal alteration and mineralization types of orogenic gold deposits display many variations depending on formation depths, types of host rock, compositions of ore fluids, spatial positions, fluid/rock ratios, and repetitive modifications during multiple fluid infiltrations in the complicated processes of fluid-rock interaction. Therefore, establishing holistic reference patterns of hydrothermal alteration with associated mineralization for the orogenic gold system is necessary. Based on the observation of typical orogenic gold deposits and thermodynamic simulation of fluid-rock reaction, the general features of proposed patterns include: (1) Orogenic ore fluids are capable of transporting sufficient gold from deep (5 kbar and 600°C) to shallow (1 kbar and 200°C) crustal levels, resulting in alteration and mineralization occurred in a much-varied depth. Only considering the effects of fluid-rock reaction and sulfidation, the formation of pyrrhotite and pyrite at relatively high and low P-T conditions, respectively, is the most important mechanism driving gold precipitation. In addition, the changes of fluid f O 2 and pH can also trigger a decrease in the gold solubility in fluids. (2) Multiple waves of fluid infiltration are capable of dissolving the early-formedulfides and gold, and reprecipitate them in the distal alteration zones, which can explain the irregular gold concentration distribution patterns in wide alteration zones. The fluids under higher P-T conditions (5 kbar and 600°C and 3 kbar and 400°C) are more capable of dissolving early-formed sulfides and gold. Additionally, the later infiltrated fluids can transform early-formed pyrrhotite into pyrite. (3) The model of interactions between ore fluids and aluminosilicate rocks at varied P-T conditions indicates that the alteration mineral assemblages in hypozonal deposits (>12 km) mainly include biotite, amphibole, anorthite, K-feldspar, quartz, and pyrrhotite assemblages; that those in mesozonal deposits (6 to 12 km) are characterized by chlorite, calcite, quartz, muscovite, pyrite or pyrrhotite. The epizonal deposits (<6 km) commonly develop more abundant Fe-Mg bearing carbonates (e.g., dolomite, ankerite, and siderite), quartz, muscovite, and pyrite. The compositional variations of ore fluids and host rocks have important controls on the development of iron-oxides and carbonaceous material. (4) The alteration halo is relatively restricted under hypozonal to lower mesozonal conditions. However, broad carbonation develops under upper mesozonal to epizonal conditions, with Fe-Mg bearing carbonates and calcite distributed in proximal and distal zones, respectively.
Sign and Human Action Detection Using Deep Learning

Human beings usually rely on communication to express their feeling and ideas and to solve disputes among themselves. A major component required for effective communication is language. Language can occur in different forms, including written symbols, gestures, and vocalizations. It is usually essential for all of the communicating parties to be fully conversant with a common language. However, to date this has not been the case between speech-impaired people who use sign language and people who use spoken languages. A number of different studies have pointed out a significant gaps between these two groups which can limit the ease of communication. Therefore, this study aims to develop an efficient deep learning model that can be used to predict British sign language in an attempt to narrow this communication gap between speech-impaired and non-speech-impaired people in the community. Two models were developed in this research, CNN and LSTM, and their performance was evaluated using a multi-class confusion matrix. The CNN model emerged with the highest performance, attaining training and testing accuracies of 98.8% and 97.4%, respectively. In addition, the model achieved average weighted precession and recall of 97% and 96%, respectively. On the other hand, the LSTM model's performance was quite poor, with the maximum training and testing performance accuracies achieved being 49.4% and 48.7%, respectively. Our research concluded that the CNN model was the best for recognizing and determining British sign language.
How to Deal with the Results of PLS-SEM?

Despite the diversity of studies related to PLS-SEM, there is no study showing how to deal with the results of PLS-SEM in empirical studies. Therefore, this study aims to explain how to deal with the results of PLS-SEM briefly in order to facilitate the procedure for researchers. This study presented a hypothetical example in order to clarify the tests systematically. The example consists of two variables are human resource management practices as the independent variable and organizational performance as the dependent variable. The results of the analysis using the software of SmartPLS include three main steps namely initial description of the data, measurement model assessment, and structural model assessment. SmartPLS provides values for each predefined indicator including missing, mean, median, minimum, maximum, standard deviation, excess kurtosis, and skewness. The measurement model assessment includes three main aspects namely internal consistency reliability, convergent validity, and discriminant validity based on HTMT criterion. The structural model assessment includes four main aspects namely coefficient of determination, cross-validated redundancy, effect sizes, and path coefficients.
Dew-Cloud-Based Hierarchical Federated Learning for Intrusion Detection in IoMT

The coronavirus pandemic has overburdened medical institutions, forcing physicians to diagnose and treat their patients remotely. Moreover, COVID-19 has made humans more conscious about their health, resulting in the extensive purchase of IoT-enabled medical devices. The rapid boom in the market worth of the internet of medical things (IoMT) captured cyber attackers' attention. Like health, medical data is also sensitive and worth a lot on the dark web. Despite the fact that the patient's health details have not been protected appropriately, letting the trespassers exploit them. The system administrator is unable to fortify security measures due to the limited storage capacity and computation power of the resource-constrained network devices'. Although various supervised and unsupervised machine learning algorithms have been developed to identify anomalies, the primary undertaking is to explore the swift progressing malicious attacks before they deteriorate the wellness system's integrity. In this paper, a Dew-Cloud based model is designed to enable hierarchical federated learning (HFL). The proposed Dew-Cloud model provides a higher level of data privacy with greater availability of IoMT critical application(s). The hierarchical long-term memory (HLSTM) model is deployed at distributed Dew servers with a backend supported by cloud computing. Data pre-processing feature helps the proposed model achieve high training accuracy (99.31%) with minimum training loss (0.034). The experiment results demonstrate that the proposed HFL-HLSTM model is superior to existing schemes in terms of performance metrics such as accuracy, precision, recall, and f-score.
Modeling of Cellular Automata Markov Chain for Predicting the Carrying Capacity of Ambon City

Ambon is a city with the highest economic and population growth in Maluku Province, which makes built-up land have high and rapid growth so that it can reduce the carrying capacity of the city's land. This can lead to an imbalance between population and available housing and may result in higher population pressure on available areas. Prediction of spatial modeling is needed as a preventive measure to prevent excessive land cover changes in the future. This study aims to analyze the carrying capacity of residential areas and spatial modeling of land cover changes in Ambon City in 2010, 2015, and 2020 using Cellular Automata Markov Chain (CAMC) and identify settlement patterns based on population density in 2031 in Ambon city. The results of the Cellular Automata Markov Chain (CAMC) analysis show that the residential area in 2031 has increased to 7,910.03 ha and the development of residential areas in 2031 in Ambon City is centered on Sirimau District.&#x0D;
Learning Tailored Adaptive Bitrate Algorithms to Heterogeneous Network Conditions: A Domain-Specific Priors and Meta-Reinforcement Learning Approach

Internet adaptive video streaming is a typical form of video delivery that leverages adaptive bitrate (ABR) algorithms to provide video services with high quality of experience (QoE) for various users in diverse and unique network conditions. Such heterogeneous network environments, which can be viewed as exogenous input processes, often lead to the unstable performance of ABR algorithms. Unfortunately, learning-based ABR algorithm which generated by state-of-the-art reinforcement learning (RL) technologies achieves <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">good average performance</i> but fails to perform well in all kinds of network conditions. In this work, considering the video playback process as the Input-driven Markov Decision Process (IMDP), we propose <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{A}^{2}$ </tex-math></inline-formula> BR (Adaptation of ABR), a novel meta-RL ABR approach. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{A}^{2}$ </tex-math></inline-formula> BR is mainly composed of an online stage and an offline stage. It leverages meta-RL to learn an initial meta-policy with various network conditions at the offline stage and makes decisions in personalized network conditions at the online stage. At the same time, we continually optimize the meta-policy to the tailor-made ABR policy for varying the current network environment within few shots. Moreover, in order to improve the learning efficiency, we fully utilize domain knowledge for implementing a virtual player to replay the previously experienced network. Using trace-driven experiments on various scenarios including different vehicles, users, network types, and heterogeneous user-preferences, we show that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{A}^{2}$ </tex-math></inline-formula> BR outperforming recent ABR approaches with rapidly adapting to the personalized QoE metrics and specific network conditions. Testbed experimental results also illustrate the superiority of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{A}^{2}$ </tex-math></inline-formula> BR in adapting to the unseen environments.
Additive cyclic complementary dual codes over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub></mml:math>

An additive cyclic code of length n over F4 can be defined equivalently as an F2[x]/〈xn+1〉-submodule of F4[x]/〈xn+1〉. In this paper we study additive cyclic and complementary dual codes of odd length over F4 with respect to the trace Hermitian inner product and the trace Euclidean inner product. We characterize subfield subcodes and trace codes of these codes by their generators as binary cyclic codes.
Bitcoin price evolution versus energy consumption; trend analysis

Digital technology developments shape the behaviour, performances, standards of society, organizations and individuals imposing new ways of payments and new forms of money. In this environment in 2008 was developed a new type of currency, namely Bitcoin. Cryptocurrency, as this new form of money has been generically called, puts pressure on the traditional concept of money. Today, the economic value of cryptocurrencies is attested by their circulation and acceptance by user communities for trade. However, establishing this value raises debates in the literature. The research from this paper investigates and analyses if there is a strong enough connectedness between Bitcoin price evolution and energy consumption tendency (for mining), to influence Bitcoin value. Public data from January 2014 to July 2021 is used. An Artificial Neural Network (ANN) was used to study and predict the tendency of Bitcoin price and energy consumption. A comparison between the forecasting trend and the real trend (the evolution of energy consumption and Bitcoin price) was made. The conducted research starts with a quantitative one and ends with a qualitative one (trends). The obtained results show that qualitatively, there is a good correlation between monthly average values of BTC prices and electricity consumption for mining.
Websites Visits and Financial Performance for GCC Banks: The Moderating Role of Environmental, Social and Governance Performance

The volume of services provided through websites increases over time, particularly in the banking sector. It is challenging to develop banking services to achieve optimal financial performance. As a result, this article aims to investigate the impact of bank website visits on the financial performance of the Gulf Cooperation Council (GCC) banking sector, taking into account the moderating role of the environmental, social and governance (ESG) performance. The article is based on an empirical analysis through a content analysis approach based on a sample of 64 GCC banks from 2014 to 2020. The findings showed that the frequency of website visits significantly affects the financial performance measures of banks, such as return on assets (ROA), return on equity (ROE), market value (Tobin’s Q), net interest margin (NIM) and return on investment (ROI). In addition, the ESG performance of banks attracts more website visitors and enhances them with the idea of dealing with banks that enhances financial performance. The findings indicate that banking websites as a tool for public relations attract visitors and thus turn them into customers by changing the behavioural intention of individuals, which affects their decisions to engage in banking transactions and thus raises the level of financial performance of banks. To the best of our knowledge, this is the first study that examines the relationship between website visits and financial performance. Therefore, it bridges the research gap in this field and thus provides a roadmap for more potential research ideas in the future.
A three-way clustering approach using image enhancement operations

Three-way clustering receives its motivation from three-way decisions. It uses the core set and support set to describe a cluster. The two sets divide clustering results into three parts or regions called inside, outside, and partial. The division helps identify the central core and outer sparse regions of a cluster, which is useful when the clusters have dense regions but also have vague boundaries. One of the main challenges in three-way clustering is the meaningful construction of the two sets and three regions. In this article, we introduce a blurring and sharpening inspired three-way clustering algorithm or BS3 for short. We first explain the use of blurring and sharpening operations to create a three-way representation for a typical object in an image in the form of central primary (the clear part of the object), blurry (the unclear part of the object), and the non-object part. Next, by realizing similarities between the object and a cluster, we define cluster blur and cluster sharp operations to create a three-way representation for clusters. Experimental results on real-world and synthetic datasets suggest that BS3 is comparable to best performing approaches and in many cases has superior results.