Reviewerly/exHarmony huggingface dataset:
A New Strategy to Fuse Remote Sensing Data and Geochemical Data with Different Machine Learning Methods

Geochemical data can reflect geological features, making it one of the basic types of geodata that have been widely used in mineral exploration, environmental assessment, resource potential analysis and other research. However, final decisions regarding activities are often limited by the spatial accuracy of geochemical data. Geochemical sampling is sometimes difficult to conduct because of harsh natural and geographic conditions (e.g., mountainous areas with high altitude and complex terrain), meaning that only medium/low-precision survey data could be obtained, which may not be adequate for regional geochemical mapping and exploration. Modern techniques such as remote sensing could be used to address this issue. In recent decades, the development of remote sensing technology has provided a huge amount of earth observation data with high spatial, temporal and spectral resolutions. The advantage of rapid acquisition of spatial and spectral information of large areas has promoted the broad use of remote sensing data in geoscientific research. Remote sensing data can help to differentiate various ground features by recording the electromagnetic response of the surface to solar radiation. Many problems that occur during the process of fusing remote sensing and geochemical data have been reported, such as the feasibility of existing fusion methods and low fusion accuracies that are less useful in practice. In this paper, a new strategy for integrating geochemical data and remote sensing data (referred to as ASTER data) is proposed; this strategy is achieved through linear regression as well as random forest and support vector regression algorithms. The results show that support vector regression can obtain better results for the available data sets and prove that the strategy currently proposed can effectively support the fusion of high-spatial-resolution remote sensing data (15 m) and low-spatial-resolution geochemical data (2000 m) in wide-range accurate geochemical applications (e.g., lithological identification and geochemical exploration).
A Supervised Model to Detect Suspicious Activities in the Bitcoin Network

Shortly after its official launch in 2009, Bitcoin has gained rapid popularity worldwide, which in return attracted a variety of people especially malicious attackers, who get the advantage of its pseudo-anonymity to institute un-traceable threats, scams, and criminal activities. Recently, some Bitcoin thefts have been reported costing millions of dollars, causing serious harm and losses to innocent users or companies that lead them to declare bankruptcy. One of the main characteristics of Bitcoin is its anonymity, which makes Bitcoin the preferred choice for criminals to perform illicit activities that pose difficulties for law enforcement and financial authorities to identify suspicious behavior, making the existing fraud detection systems ineffective. In this paper, we propose a model for detecting suspicious activities in the Bitcoin network. We first construct a labeled dataset by collecting a set of illicit transactions from public online Bitcoin forums, as well as datasets from prior research. Next, a verification and filtration process has been performed to verify the gathered illicit transactions with the original dataset and manually marked them as either legal or illegal. Additionally, a new set of features that are based on time-slice was extracted, the skewed dataset was balanced, and three supervised classifiers (LR, NB, and ANN) were used for evaluating the proposed model. Finally, our findings found that the ANN classifier achieved the best performer among others, which attained Precision, Recall, F1 scores, and AUC of 95.2%, 88.7%, 89.8%, and 91.2% respectively. The performance of the supervised classifiers has significantly improved after balancing the training set.
Awareness of Phishing Attacks in the Public Sector: Review Types and Technical Approaches

Today, phishing attempts are a major problem and a significant cyber risk. Phishing is one of the most common ways attackers try to steal money on the Internet. Phishing attacks can cause victims to lose a lot, like sensitive information, their identities, businesses, and government secrets. Attackers use various creative and unique methods to launch increasingly sophisticated phishing attacks. Therefore, a detailed analysis of old and new phishing methods is required. This paper reviews the techniques used in phishing assaults. This essay intends to stimulate professional discussion on phishing assaults, public awareness of phishing tactics, and education about these attacks.
A Novel Mine-Specific Eco-Environment Index (MSEEI) for Mine Ecological Environment Monitoring Using Landsat Imagery

The excessive exploitation of mineral resources will lead to environmental pollution, resource depletion, environmental disaster, and other problems. The contradiction between the environment and development, and the management of the ecological environment in mining areas are urgent p-problems to be solved. An ecological environment assessment is an important part of the ecological environment in a mining area. The accurate evaluation of the ecological environment is the premise behind environmental governance in a mining area. However, current ecological assessment indicators were not developed specifically for mine environment monitoring and, thus, cannot provide an effective and comprehensive assessment of the mineral environment. To this end, in order to improve the environmental monitoring performance in mining areas, a novel Mine-Specific Eco-Environment Index (MSEEI) was proposed, integrating factors from five main aspects associated with minerals, including temperature, vegetation, soil moisture, atmospheric environment, and mining scale. Meanwhile, a widely concerned mine—Luanchuan mine—was used as the case area to test the performance of our MSEEI. The results showed a significant correlation between RSEI and MSEEI (p &lt; 0.01). The mean correlation achieved between RSEI and MSEEI was 0.91, which was much higher than the correlations between RSEI and enhanced vegetation index (EVI), soil moisture monitoring index (SMMI), normalized difference built-up and soil index (NDBSI), PM2.5 concentration (DI), and heat (LST). In addition, based on our long-term MSEEI results of Luanchuan mine from 1997 to 2021, the ecological status of Luanchuan mine showed a trend of first declining and then rising. Specifically, the MSEEI first declined from 0.85 to 0.77 between 1997 and 2012, and then rebounded to about 0.8 in recent years. The MSEEI exhibited a good applicability in the ecological assessment of mining areas. Our MSEEI can provide useful guidance for mine environment monitoring. MSEEI can directly reflect the ecological damage after mining, provide scientific guidance for the exploitation and utilization of mineral resources, and promote the protection and sustainable development of Earth’s resources and mine ecological environments.
A smart secured framework for detecting and averting online recruitment fraud using ensemble machine learning techniques

With the rise of the Internet and social media, information has become available at our fingertips. However, on the dark side, these advancements have opened doors for fraudsters. Online recruitment fraud (ORF) is one of the problems created by these modern technologies, as hundreds of thousands of applicants are victimized every year globally. Fraudsters advertise bogus jobs on online platforms and target job hunters with fake offerings such as huge salaries and desirable geographical locations. The objective of these fraudsters is to collect personal information to be misused in the future, leading to the loss of applicants' privacy. To prevent such situations, there is a need for an automatic detecting system that can distinguish between real and fake job advertisements and preserve the applicants' privacy. This study attempts to build a smart secured framework for detecting and preventing ORF using ensemble machine learning (ML) techniques. In this regard, four ensemble methods-AdaBoost (AB), Xtreme Gradient Boost (XGB), Voting, and Random Forest (RF)-are used to build a detection framework. The dataset used was pre-processed using several methods for cleaning and denoising in order to achieve better outcomes. The performance evaluation measures of the applied methods were accuracy, precision, sensitivity, F-measure, and ROC curves. According to these measures, AB performed best, followed by XGB, voting, and RF. In the proposed framework, AB achieved a high accuracy of 98.374%, showing its reliability for detecting and preventing ORF. The results of AB were compared to existing methods in the literature validating the reliability of the model to be significantly used for detecting ORF.
Microscopic Morphology and Indicative Significance of Nanoscale Au Particles in Soils and Fault Muds: A Case Study of Jiaojia, Shandong Province

In recent decades, the human demand for mineral resources has increased dramatically, and the mining of shallow deposits has basically been completed. The prospecting direction has turned to concealed deposits. For this reason, various penetrating geochemical methods have been developed to find concealed minerals and a series of geochemical exploration techniques have been proposed. At the same time, the use of the geochemical gene as a new technique presented in recent years is feasible in discussing component classification and provenance tracing. In this study, we applied these methods for mineral exploration in the Jiaojia gold deposit in Shandong Province, China. The results show that a large number of ore-forming element particles can be observed in ores, fault muds, and soils; compared with Chinese soil, Au has higher enrichment coefficients; according to the LG03 lithogene, the ores belong to a neutral composition, and the fault muds and soils belong to an acidic composition. Based on the above results, it can be found that the ore-forming elements in the Jiaojia gold deposit have migrated. Although this migration cannot change the original lithology, it can provide theoretical support for the fine-grained soil-prospecting method.
Object Detection-Based System for Traffic Signs on Drone-Captured Images

The construction industry is on the path to digital transformation. One of the main challenges in this process is inspecting, assessing, and maintaining civil infrastructures and construction elements. However, Artificial Intelligence (AI) and Unmanned Aerial Vehicles (UAVs) can support the tedious and time-consuming work inspection processes. This article presents an innovative object detection-based system which enables the detection and geo-referencing of different traffic signs from RGB images captured by a drone’s onboard camera, thus improving the realization of road element inventories in civil infrastructures. The computer vision component follows the typical methodology for a deep-learning-based SW: dataset creation, election and training of the most accurate object detection model, and testing. The result is the creation of a new dataset with a wider variety of traffic signs and an object detection-based system using Faster R-CNN to enable the detection and geo-location of traffic signs from drone-captured images. Despite some significant challenges, such as the lack of drone-captured images with labeled traffic signs and the imbalance in the number of images for traffic signal detection, the computer vision component allows for the accurate detection of traffic signs from UAV images.
Interaction with Virtual Humans and Effect of Emotional Expressions: Anger Matters!

Today we are experiencing a hybrid real-virtual society in which the interaction with virtual humans is normal and "quasi-social". Understanding the way we react to the interaction with virtual agents and the impact of emotions on social dynamics in the virtual world is fundamental. Therefore, in this study we investigated the implicit effect of emotional information by adopting a perceptual discrimination task. Specifically, we devised a task that explicitly required perceptual discrimination of a target while involving distance regulation in the presence of happy, neutral, or angry virtual agents. In two Immersive Virtual Reality experiments, participants were instructed to discriminate a target on the virtual agents' t-shirts, and they had to provide the response by stopping the virtual agents (or themselves) at the distance where they could identify the target. Thus, facial expressions were completely irrelevant to the perceptual task. The results showed that the perceptual discrimination implied a longer response time when t-shirts were worn by angry rather than happy or neutral virtual agents. This suggests that angry faces interfered with the explicit perceptual task people had to perform. From a theoretical standpoint, this anger-superiority effect could reflect an ancestral fear/avoidance mechanism that prompts automatic defensive reactions and bypasses other cognitive processes.
FedGR: Federated Graph Neural Network for Recommendation Systems

Social recommendation systems based on the graph neural network (GNN) have received a lot of research-related attention recently because they can use social information to improve recommendation accuracy and because of the benefits derived from the excellent performance of the graph neural network in graphic data modeling. A large number of excellent studies in this area have been proposed one after another, but they all share a common requirement that the data should be centrally stored. In recent years, there have been growing concerns about data privacy. At the same time, the introduction of numerous stringent data protection regulations, represented by general data protection regulations (GDPR), has challenged the recommendation models with conventional centralized data storage. For the above reasons, we have designed a flexible model of recommendation algorithms for social scenarios based on federated learning. We call it the federated graph neural network for recommendation systems (FedGR). Previous related work in this area has only considered GNN, social networks, and federated learning separately. Our work is the first to consider all three together, and we have carried out a detailed design for each part. In FedGR, we used the graph attention network to assist in modeling the implicit vector representation learned by users from social relationship graphs and historical item graphs. In order to protect data privacy, we used FedGR flexible data privacy protection by incorporating traditional cryptography encryption techniques with the proposed “noise injection” strategy, which enables FedGR to ensure data privacy while minimizing the loss of recommended performance. We also demonstrate a different learning paradigm for the recommendation model under federation. Our proposed work has been validated on two publicly available popular datasets. According to the experimental results, FedGR has decreased MAE and RMSE compared with previous work, which proves its rationality and effectiveness.
DDS: Deepfake Detection System through Collective Intelligence and Deep-Learning Model in Blockchain Environment

With the spread of mobile devices and the improvement of the mobile service environment, the use of various Internet content providers (ICPs), including content services such as YouTube and video hosting services, has increased significantly. Video content shared in ICP is used for information delivery and issue checking based on accessibility. However, if the content registered and shared in ICP is manipulated through deepfakes and maliciously distributed to cause political attacks or social problems, it can cause a very large negative effect. This study aims to propose a deepfake detection system that detects manipulated video content distributed in video hosting services while ensuring the transparency and objectivity of the detection subject. The detection method of the proposed system is configured through a blockchain and is not dependent on a single ICP, establishing a cooperative system among multiple ICPs and achieving consensus for the common purpose of deepfake detection. In the proposed system, the deep-learning model for detecting deepfakes is independently driven by each ICP, and the results are ensembled through integrated voting. Furthermore, this study proposes a method to supplement the objectivity of integrated voting and the neutrality of the deep-learning model by ensembling collective intelligence-based voting through the participation of ICP users in the integrated voting process and ensuring high accuracy at the same time. Through the proposed system, the accuracy of the deep-learning model is supplemented by utilizing collective intelligence in the blockchain environment, and the creation of a consortium contract environment for common goals between companies with conflicting interests is illuminated.
An improved poor and rich optimization algorithm

The poor and rich optimization algorithm (PRO) is a new bio-inspired meta-heuristic algorithm based on the behavior of the poor and the rich. PRO suffers from low convergence speed and premature convergence, and easily traps in the local optimum, when solving very complex function optimization problems. To overcome these limitations, this study proposes an improved poor and rich optimization (IPRO) algorithm. First, to meet the requirements of convergence speed and swarm diversity requirements across different evolutionary stages of the algorithm, the population is dynamically divided into the poor and rich sub-population. Second, for the rich sub-population, this study designs a novel individual updating mechanism that learns from the evolution information of the global optimum individual and that of the poor sub-population simultaneously, to further accelerate convergence speed and minimize swarm diversity loss. Third, for the poor sub-population, this study designs a novel individual updating mechanism that improves some evolution information by learning alternately from the rich and Gauss distribution, gradually improves evolutionary genes, and maintains swarm diversity. The IPRO is then compared with four state-of-the-art swarm evolutionary algorithms with various characteristics on the CEC 2013 test suite. Experimental results demonstrate the competitive advantages of IPRO in convergence precision and speed when solving function optimization problems.
A color image contrast enhancement method based on improved PSO

Image contrast enhancement uses the object intensity transformation function to maximize the amount of information to enhance an image. In this paper, the image enhancement problem is regarded as an optimization problem, and the particle swarm algorithm is used to obtain the optimal solution. First, an improved particle swarm optimization algorithm is proposed. In this algorithm, individual optimization, local optimization, and global optimization are used to adjust the particle's flight direction. In local optimization, the topology is used to induce comparison and communication between particles. The sparse penalty term in speed update formula is added to adjust the sparsity of the algorithm and the size of the solution space. Second, the three channels of the color images R, G, and B are represented by a quaternion matrix, and an improved particle swarm algorithm is used to optimize the transformation parameters. Finally, contrast and brightness elements are added to the fitness function. The fitness function is used to guide the particle swarm optimization algorithm to optimize the parameters in the transformation function. This paper verifies via two experiments. First, improved particle swarm algorithm is simulated and tested. By comparing the average values of the four algorithms under the three types of 6 test functions, the average value is increased by at least 15 times in the single-peak 2 test functions: in the multi-peak and multi-peak fixed-dimension 4 test functions, this paper can always search for the global optimal solution, and the average value is either the same or at least 1.3 times higher. Second, the proposed algorithm is compared with other evolutionary algorithms to optimize contrast enhancement, select images in two different data sets, and calculate various evaluation indicators of different algorithms under different images. The optimal value is the algorithm in this paper, and the performance indicators are at least a 5% increase and a minimum 15% increase in algorithm running time. Final results show that the effects the proposed algorithm have obvious advantages in both subjective and qualitative aspects.
An open problem of k-Galois hulls and its application

The k-Galois hull of a linear code C over the finite field Fq is defined as Hullk(C)=C∩C⊥k, where q=pe, p is prime and C⊥k is the k-Galois dual code of C with 0≤k<e. In this paper, by considering an equation of the matrix rank over finite fields, one of the two problems proposed by Cao about k-Galois hull of a linear code in (arXiv:2002.12892v2, 2020) is settled. Then we construct nine new classes of MDS EAQECCs with flexible parameters. In addition, our obtained MDS EAQECCs have parameters better than the ones available in the literature.
Assessing the usefulness of a visual programming IDE for large-scale automation software

Industrial control applications are usually designed by domain experts instead of software engineers. These experts frequently use visual programming languages based on standards such as IEC 61131-3 and IEC 61499. The standards apply model-based engineering concepts to abstract from hardware and low-level communication. Developing industrial control software is challenging due to the fact that control systems are usually unique and need to be maintained for many years. The arising challenges, together with the growing complexity of control software, require very usable model-based development environments for visual programming languages. However, so far only little empirical research exists on the practical usefulness of such environments, i.e., their usability and utility. In this paper, we discuss common control software maintenance tasks and tool capabilities based on existing research and show the realization of these capabilities in the 4diac IDE. We performed a walkthrough of the demonstrated capabilities using the cognitive dimensions of notations framework from the field of human–computer interaction. We then improved the tool and conducted a user study involving ten industrial automation engineers, who used the 4diac IDE in a realistic control software maintenance scenario. Based on lessons learnt from this study, we adapted the 4diac IDE to better handle large graphical models. We evaluated these changes in a reassessment study with automation engineers from seven industrial enterprises. We derive general implications with respect to large-scale applications for developers of IDEs that we deem applicable in the context of (visual) model-based engineering tools.