Reviewerly/exHarmony huggingface dataset:
Investigation of the Hamamayağı/Ladik (Samsun, Turkey) Geothermal Field and It’s Surroundings by Optical Remote Sensing with GIS Methods

In this study, the geothermal potential was investigated by remote sensing methods in the Ladik Hamamayağı region of Samsun province at the Central Black Sea region. The satellite images were evaluated in the GIS environment. Various parameters differ in remote sensing studies and post-exploration geothermal field remote sensing for exploration studies, and these parameters are explained in detail in the study., All alteration types that should be in a geothermal field were mapped with the ASTER data, lineaments were extracted, thermal anomalies were captured and interpreted with alterations in the GIS environment, since there are no alteration information in the Hamamayağı geothermal field that previous studies have uncovered. As a result of these processes and interpretations, remote sensing and GIS methods were used together for the Hamamayağı geothermal field to produce guiding data and maps for the discovery of new potential areas. Within the framework of these produced maps, technical suggestions were made to the authorities who have a say on the subject.
Attention-Based Background/Foreground Monocular Depth Prediction Model Using Image Segmentation

While many monocular depth estimation methods have been proposed, determining depth variations in outdoor scenes remains challenging. Accordingly, this paper proposes an image segmentation-based monocular depth estimation model with attention mechanisms that can address outdoor scene variations. The segmentation model segments images into foreground and background regions and individually predicts depth maps. Moreover, attention mechanisms are also adopted to extract meaningful features from complex scenes to improve foreground and background depth map prediction via a multi-scale decoding scheme. From our experimental results, we observed that our proposed model outperformed previous methods by 27.5% on the KITTI dataset.
Time-Series FY4A Datasets for Super-Resolution Benchmarking of Meteorological Satellite Images

Meteorological satellites are usually operated at high temporal resolutions, but the spatial resolutions are too poor to identify ground content. Super-resolution is an economic way to enhance spatial details, but the feasibility is not validated for meteorological images due to the absence of benchmarking data. In this work, we propose the FY4ASRgray and FY4ASRcolor datasets to assess super-resolution algorithms on meteorological applications. The features of cloud sensitivity and temporal continuity are linked to the proposed datasets. To test the usability of the new datasets, five state-of-the-art super-resolution algorithms are gathered for contest. Shift learning is used to shorten the training time and improve the parameters. Methods are modified to deal with the 16-bit challenge. The reconstruction results are demonstrated and evaluated regarding the radiometric, structural, and spectral loss, which gives the baseline performance for detail enhancement of the FY4A satellite images. Additional experiments are made on FY4ASRcolor for sequence super-resolution, spatiotemporal fusion, and generalization test for further performance test.
Neural Assemblies as Precursors for Brain Function

This concept paper gives a narrative about intelligence from insects to the human brain, showing where evolution may have been influenced by the structures in these simpler organisms. The ideas also come from the author's own cognitive model, where a number of algorithms have been developed over time and the precursor structures should be codable to some level. Through developing and trying to implement the design, ideas like separating the data from the function have become architecturally appropriate and there have been several opportunities to make the system more orthogonal. Similarly for the human brain, neural structures may work in-sync with the neural functions, or may be slightly separate from them. Each section discusses one of the neural assemblies with a potential functional result, that cover ideas such as timing or scheduling, structural intelligence and neural binding. Another aspect of self-representation or expression is interesting and may help the brain to realise higher-level functionality based on these lower-level processes.
Efficient Authenticated Key Agreement Protocol for Cloud-Based Internet of Things

The key agreement with an authenticated key protocol is a cryptographic primitive that, in theory, combines the operations of digital signature and public-key encryption in one step, resulting in a reduced computational cost than the usual signature-then-encryption technique. Authentication is another method for achieving simultaneity of secrecy and validation throughout the Internet of Things (IoT). We introduce a new authenticated key agreement protocol approach in this paper, which is based on the intractability of a group-based polynomial decomposition issue and can be employed in CIoT-based systems for secure data transfer.
Research on multi-dimensional optimisation design of user interface under Rhino/GH platform

Abstract With the rapid development of computer science and technology, there is an increasing diversity observed in the use of electronic computers. Users browse interactive content such as text, images, audio, video, etc. The increase of the interactive interface results in a slow interface response and affects the user experience. Therefore, this paper mainly studies the user interface under the multi-dimensional optimisation of the Rhino/GH platform, and introduces the long short-term memory and gated recurrent unit algorithms in the visualisation part for optimisation; the study results suggest that the overall response time is 50% but lower than the traditional interface, and the time fluctuation is within 23.7%, which is 23.6% but lower than the traditional 47.3%. When interacting with multiple interfaces, the interaction interface optimised by the Rhino/GH platform maintains a fluctuation range within 29.2%, and the time increases by 13 ms, showing excellent stability and efficiency.
Office Supplies Requisition-Disbursement System Processing on Cloud: Algorithms and Database Design Techniques

Digital business organization is an organization that has applied information technology to its operations and benefits from saving costs, saving resources, and making it quick to act and operate the business.However, many organizations lack systems to complement effectively internal management such as in-house office supplies requisitiondisbursement system.Past research papers have shown that a wide range of office supplies requisition-disbursement systems have been developed, but almost all of them are developed for one specific agency.Therefore, it brings to the development of this research, which aims 1.To develop a cloudprocessed office supplies requisition-disbursement system, 2. To design algorithmic systems and office supplies requisition-disbursement system databases that comply with cloud computing and 3. To evaluate the efficiency of cloud-processed office supplies requisition-disbursement system.As a methodology framework, the authors adopted the System Development Life Cycle (SDLC) development model, which involves problem identification, feasibility research, analysis, design, implementation, and maintenance.As a focus group for this study, the financial and managerial staff provided the required information to the author.The system was created using the PHP programming language and MySQL was utilized to store the database.The results of this research have led to an Office Supplies Requisition-Disbursement processing system on cloud (OSRD) which provide free services for organizations who want to use the online office supplies requisition-disbursement system through cloud computing system and registration via Gmail account with Google API service.Moreover, the researchers have designed algorithmic systems and databases to store data for future applications.In addition, it has tested the performance of the system on real users.Overall, the average of the evaluations was at its highest level in the operation accuracy test with an average of 5.000 and the lowest average was the functional requirement test and usability test with an average of 4.833.However, the OSRD is very beneficial and helps an organization that lacks IT staff to develop its system.This is to increase the potential of the organization sustainably.
Aspect-Bas1ed Sentiment Analysis on Twitter Using Logistic Regression with FastText Feature Expansion

Social media has recently been widely used by users, especially Indonesians, as a place to express themselves in sentences, pictures, sounds, or videos. Twitter is one of the social media favored by people of diverse ages. Twitter is a social media that provides features like social media in general. However, Twitter has a unique feature where users can send or read text messages limited to only a few characters. Therefore, user tweets with topics related to a particular product can be utilized by companies to become input in the development of these products. This research was conducted using tweet data on the topic of Telkomsel, which is divided into two aspects, namely signal and service. Aspect-based sentiment analysis of Telkomsel was carried out using Logistic Regression with FastText feature expansion to reduce vocabulary mismatch in tweets so that the classification stage can be performed optimally. In addition, the Synthetic Minority Oversampling Technique (SMOTE) sampling method was applied to overcome data imbalance. The test results prove that feature expansion can improve F1-Score values for signal and service aspects. For the signal aspect, F1-Score increased by 3.33% from the baseline with a value of 96.48%. While for the service aspect, F1-Score increased by 12.91% from the baseline with a value of 95.57%.&#x0D;
THE ROLE OF KNOWLEDGE MANAGEMENT ON THE RELATIONSHIP OF LEARNING ORGANISATIONS AND EMPLOYEES’ BEHAVIOR IN THAILAND’S HOTEL INDUSTRIES CRISIS

The purpose of this study is to find solutions to the epidemic crisis by using knowledge management (KM) and learning organization (LO) to assess their work engagement and organizational commitment in employees. The sample group in this study is 392 management personnel in the hotel industry in Bangkok, Thailand. The study uses the quantitative research methodology and the structural equation model analysis. The results of the study revealed that KM has a direct influence on the intermediate variable, namely, LO, and an indirect effect on LO with a correlation to work engagement and organisational commitment (p &lt; 0.05). The study represented almost variables has impact to each other. Human resource development could take into account the context and adapt the organization to be flexible and enthusiastic about learning amid changing circumstances, as well as understanding the condition of personnel in the organsation.
Security of private cloud using machine learning and cryptography

There are increased security challenges that target cloud systems. One of the most important requirements of users in cloud storage is protecting their cloud from attacks and keeping data secure. Modern technologies of machine learning are providing the ability to analyze and classify data perfectly. This paper proposes a model placed between users and the cloud, which is based on two phases. The first of which is protecting the cloud from different types of network attacks and detecting normal and abnormal flow. The second one is categorizing the users' data and then encrypting it based on its importance using different encryption algorithms. The accuracy results of random forest (RF) and decision tree (DT) are 100% of attack detection for each one. For the second phase of classifying data, the algorithms used are the logistic regression (LR) and stochastic gradient descent (SGD) learning which resulted in 98% accuracy for both. Besides, the encryption algorithms that have been adopted are rivest cipher (RC4), triple data encryption (3DES), and advanced encryption standard (AES) for encryption of the classified data according to the importance which will be then stored in the cloud in its secure form.
2D face recognition using PCA and triplet similarity embedding

The aim of this study is to propose a new robust face recognition algorithm by combining principal component analysis (PCA), Triplet Similarity Embedding based technique and Projection as a similarity metric at the different stages of the recognition processes. The main idea is to use PCA for feature extraction and dimensionality reduction, then train the triplet similarity embedding to accommodate changes in the facial poses, and finally use orthogonal projection as a similarity metric for classification. We use the open source ORL dataset to conduct the experiments to find the recognition rates of the proposed algorithm and compare them to the performance of one of the very well-known machine learning algorithms k-Nearest Neighbor classifier. Our experimental results show that the proposed model outperforms the kNN. Moreover, when the training set is smaller than the test set, the performance contribution of triplet similarity embedding during the learning phase becomes more visible compared to without it
Classifying thai news headlines using an artificial neural network

This research aimed to measure the effectiveness of Thai news headlines classification using an artificial neural network (ANN). The headlines consisted of i) political news, ii) sports news, iii) economic news, and iv) crime news, 1,200 headlines in total. The distribution of headlines was measured by using chi-square, information gain, and term frequency inverse class frequency (TFICF). Threshold default value was set in relation to terms of headlines before cross-validation was employed to categorize the data to examine the efficiency of the model using a neural network algorithm in classifying the headlines. The investigation of the news headline classification efficiency revealed that the 15-fold data division using TFICF was the most accurate in classifying headlines, with the accuracy rate of 99.60% and F-measure rate of 99.05%. Moreover, it was found that when more news headlines were provided as the learning data, the news headline classification became more accurate. Likewise, appropriate threshold value determination facilitated the selection of appropriate features in the headlines and resulted in more effective and accurate classification. Hence, it can be concluded that headline classification will be more accurate if the appropriate amount of learning data exists, and appropriate threshold value was set.
Trends in User Identity and Continuous Authentication

The challenges of continuous authentication have resulted in a surge of recent studies. This article surveys the state of the art and rising trends in continuous and adaptive context-aware authentication. It employs a statistical approach to collect metrics on recent works exploring machine learning techniques for continuous learning.
MATH APPS UTILIZATION: ITS PERCEIVED EFFECTS TO THE ACADEMIC PERFORMANCE OF MATHEMATICS MAJOR STUDENTS

This study aimed to determine the perceived effects of apps utilization to study habits and academic performance. Further, it also sought to determine the views of the participants and respondents on their engagement in math apps and their relationship with study habits and academic performance. This study made use of explanatory-sequential design. The purposive sampling design was used to determine the respondents in quantitative data while the quota sampling design was also used to determine the participants in qualitative data. The study revealed that the mathematical application that is mostly used by the students is the calculator app as it garnered the highest percentage value of 80.85.%. The participants agreed that math applications serve as an aid for math problems, aid for lack of teaching by the teacher, promote learning of specific lessons and increase, and help in improving their academic performance. Respondents’ perception of their utilization of math apps is due to technical problems. The respondents also agreed that math application also affected their study habits in which they become dependent on it which led to laziness and tiredness. In addition, this study also found that there is a significant relationship between respondents’ utilization of math applications and their study habits. Lastly, there is also a significant relationship between the study habits and academic performance of selected math students of Notre Dame of Midsayap College.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0555/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
A Spark-Based Artificial Bee Colony Algorithm for Unbalanced Large Data Classification

With the rapid development of internet technology, the amount of collected or generated data has increased exponentially. The sheer volume, complexity, and unbalanced nature of this data pose a challenge to the scientific community to extract meaningful information from this data within a reasonable time. In this paper, we implemented a scalable design of an artificial bee colony for big data classification using Apache Spark. In addition, a new fitness function is proposed to handle unbalanced data. Two experiments were performed using the real unbalanced datasets to assess the performance and scalability of our proposed algorithm. The performance results reveal that our proposed fitness function can efficiently deal with unbalanced datasets and statistically outperforms the existing fitness function in terms of G-mean and F1-Score. In additon, the scalability results demonstrate that our proposed Spark-based design obtained outstanding speedup and scaleup results that are very close to optimal. In addition, our Spark-based design scales efficiently with increasing data size.
Multitopic Coherence Extraction for Global Entity Linking

Entity linking is a process of linking mentions in a document with entities in a knowledge base. Collective entity disambiguation refers to mapping of multiple mentions in a document with their corresponding entities in a knowledge base. Most previous research has been based on the assumption that all mentions in the same document represent the same topic. However, mentions usually correspond to different topics. In this article, we proposes a new global model to explore the extraction of multitopic coherence in the same document. Herein, we present mention association graphs and candidate entity association graphs to obtain multitopic coherence features of the same document using graph neural networks (GNNs). In particular, we propose a variant GNN for our model and a particular graph readout function. We conducted extensive experiments on several datasets to demonstrate the effectiveness to the proposed model.
Dendrogram-based Artificial Neural Network modulation classification for dual-hop cooperative relaying communications

This paper contributes to the growing field of Artificial Neural Networks (ANNs) strategies of Automatic Modulation Identification (AMI) for Cognitive Radio (CR). Traditional AMI-based ANN methods suffer from many drawbacks such as overfitting due to ANN architectures with complex layers, low performance caused by falling into local minima, and the increased training time that keeps them from being used in real-time applications. We propose generated Dendrogram to decompose the AMI task into sub-components using the ANN architectures for improved classification accuracy and less training time. In addition, one of the most significant challenges in AMI is identifying the modulation types and orders in cooperative systems due to the combination of several received signals propagating through different unknown channels. We propose dual-hop based on Amplify and Forward protocol (DH-AF) relaying system to generate the dataset used to train and test the proposed model. In addition, DH-AF relaying systems provide better coverage and signal reliability for existing wireless communication systems. We consider that the source communicates with the destination over a direct link ( S − D ) and indirect link via an intermediate relay ( S − R − D ) using Distributed Space-Time Block Code (DSTBC). Then we used High Order Cumulants (HOC) and the High Order Moments (HOM) originating from the DSTBC-decoded signal as features, followed by the Dendrogram-based ANN (DANN) classifier. The simulation results confirm that the proposed method outperforms an ordinary ANN and other counterparts while taking less training time.
Modified Uncertainty Error Aware Estimation Model for Tracking the Path of Unmanned Aerial Vehicles

Recently, with the advancement of technology, unmanned aerial vehicles (UAVs) have had a significant impact on our daily lives. UAVs have gained critical importance due to their potential threat. In this study, the problem of UAV tracks were investigated. The first study deals with a particle filter (PF) and a diffusion map with a Kalman filter (DMK). From the experimental analysis, it is found that both PF and DMK are very suitable for drone tracking because the trajectories of drones are highly uncertain in highly dynamic and noisy environments. To address this problem, we introduce a Kalman filter (KFUEA) for drone tracking based on uncertainty and error. The KFUEA uses regularized least squares (RLS) to minimize measurement errors and provides an appropriate balance between confidence in previous estimates and future measurements. The experiment was conducted to evaluate the performance of KFUEA compared to PF and DMK, taking into account the high uncertainty and noisy UAV tracking environment. The KFUEA algorithm achieved an excellent result in the root mean square error (RMSE) compared to the non-parametric filtering algorithms PF and DMK.
Local Similarity Pattern and Cost Self-Reassembling for Deep Stereo Matching Networks

Although convolution neural network based stereo matching architectures have made impressive achievements, there are still some limitations: 1) Convolutional Feature (CF) tends to capture appearance information, which is inadequate for accurate matching. 2) Due to the static filters, current convolution based disparity refinement modules often produce over-smooth results. In this paper, we present two schemes to address these issues, where some traditional wisdoms are integrated. Firstly, we introduce a pairwise feature for deep stereo matching networks, named LSP (Local Similarity Pattern). Through explicitly revealing the neighbor relationships, LSP contains rich structural information, which can be leveraged to aid CF for more discriminative feature description. Secondly, we design a dynamic self-reassembling refinement strategy and apply it to the cost distribution and the disparity map respectively. The former could be equipped with the unimodal distribution constraint to alleviate the over-smoothing problem, and the latter is more practical. The effectiveness of the proposed methods is demonstrated via incorporating them into two well-known basic architectures, GwcNet and GANet-deep. Experimental results on the SceneFlow and KITTI benchmarks show that our modules significantly improve the performance of the model.