Reviewerly/exHarmony huggingface dataset:
Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus Mountains

Soil spectroscopy is a promising alternative to evaluate and monitor soil and water quality, particularly in mountainous agricultural lands characterized by intense degradation and limited soil tests reports; a few studies have evaluated the feasibility of VIS-NIR spectroscopy to predict Mehlich 3 (M3) extractable nutrients. This study aimed to (i) examine the potential of VIS-NIR spectroscopy in combination with partial least squares regression to predict M3-extractable elements (Ca, K, Mg, P, Fe, Cd, Cu, Mn, Pb, and Zn) and basic soil properties (clay, silt, sand, CaCO3, pH, and soil organic carbon-SOC), (ii) find optimal pre-processing techniques, and (iii) determine primary prediction mechanisms for spectrally featureless soil properties. Topsoil samples were collected from a representative area (114 samples from 525 ha) located in the mountainous region of NW Azerbaijan. A series of pre-processing steps and transformations were applied to the spectral data, and the models were calibrated and evaluated based on the coefficient of determination (R2), root mean square error (RMSE), and the residual prediction deviation (RPD). The leave-one-out cross-validated predictions showed that the first derivative spectra produce higher prediction accuracies (R2 = 0.51–0.91; RPD = 1.20–2.29) for most soil properties. The evaluation of the model performance with optimal pre-processing techniques revealed that both calibration and validation models produce considerable differences in RPD values associated with sample size and the random partition of the calibration or validation subsets. The prediction models were excellent or very good (RPD &gt; 2.0) for CaCO3, SOC, sand, silt, Ca, and Pb, good or fair (1.4 &lt; RPD &lt; 2.0) for clay, K, Cd, pH, Fe, Mn, and Cu, and poor (1.0 &lt; RPD &lt; 1.4) for Mg, P, and Zn. Principal component and correlation, stepwise regression analysis, and variable importance in projection procedures allowed to elucidate the underlying prediction mechanisms. Unlike the previous studies, the spectral estimations of pH, Ca, Mg, P, Fe, Pb, and Cd concentrations were linked to their correlation with CaCO3 rather than soil organic matter, whereas Mg and P concentrations were also connected to Fe-oxides. Soil particle sizes contributed to predicting K concentration but confounded the prediction of P and Zn concentration. The weaker correlations of Mn, Cu or Zn with CaCO3, particle sizes, SOC, Fe, and spectral data yielded to their lower prediction accuracy. The major prediction mechanisms for M3-extractable elements relied on their relations with CaCO3, pH, clay content and mineralogy, and exchangeable cations in the context of their association with land use. The results can be used in mountain lands to evaluate and control the effect of management on soil quality indices and land degradation neutrality. Further studies are needed to develop most advantageous sampling schemes and modeling.
Blockchain-based application at a governmental level: Disruption or illusion? The case of Estonia

Blockchain technology enables new kinds of decentralized systems. Thus, it has often been advocated as a 'disruptive' technology that could have the potentiality of reshaping political, economic and social relations, "solving" problems like corruption, power centralization and distrust towards political institutions. Blockchain has been gradually gaining attention beyond finance and is thus applied by a range of different actors. This includes local, regional, and national governments interested in the potentiality of experimenting with blockchain-supported governance. This article contributes to identifying blockchain as a contested socio-political object prone to contradictory political imaginaries regarding its potentialities, particularly when applied to policy. The article explores some of the most praised of blockchain's affordances (e.g. decentralization and transparency) in the context of Estonia, one of the most cited examples of blockchain governmental applications. Estonia has received international attention as the alleged first national infrastructure integrating blockchain. However, so far, few have asked: what kind of blockchain-based tools have been built by the Estonian government in practice and why? And to what extent do blockchain-based governmental applications reflect the original promises of disruption of the crypto-community? This article draws on a qualitative approach to explore several blockchain-based socio-technical objects to identify the narratives that have emerged in Estonia. The research shows clear contrasting views between stakeholders and technical experts from inside and outside the institutional sphere. The conflict revolves around two different social imaginaries associated with permissioned vs public blockchains. The paper concludes with an analysis of the profound political implications of each vision.
Artificial Neural Network Performance Analysis for Solar Radiation Prediction, Case Study at Baron Techno Park

Abstract As stated in Government Regulation No. 79 of 2014 on National Energy Policy (KEN), the New and Renewable Energy (NRE) mix target is at least 23% by 2025. Now the utilization of solar energy in Indonesia has only reached about 0.05% or 100 MW. Government compiles a roadmap for the use of solar energy that targets the installed PV mini-grid capacity until 2025 is 6.5 GW. Technically, before installing a solar power plant, solar potential data is needed for a certain period of time. This is absolutely necessary considering the potential for solar is intermittent. The solar data are then processed to create a model forecasting so that it can optimize the resulting energy output. Forecasting using artificial intelligence with artificial neural network algorithms is a good solution because it has higher accuracy. To see the comparison of the performance of the ANN of this research with previous research. The finding shows that Baron 1-7-1 performed better than 2P 1-2-1 and Baron 1-2-1, regardless that the RMSE have a slight difference but still Baron 1-7-1 outperformed the others, with the best value of RMSE 0.15185 and R2 of 0.88996.
Human Activity Recognition

The topic of Human activity recognition (HAR) is a prominent research area topic in the field of computer vision and image processing area. It has empowered state-of-art application in multiple sectors, surveillance, digital entertainment and medical healthcare. It is interesting to observe and intriguing to predict such kind of movements. Several sensor-based approaches have also been introduced to study and predict human activities such accelerometer, gyroscope, etc., it has its own advantages and disadvantages.[10] In this paper, an intelligent human activity recognition system is developed. Convolutional neural network (CNN) with spatiotemporal three dimensional (3D) kernels are trained using Kinetics data set which has 400 classes that depicts activities of humans in their everyday life and work and consist of 400 and more videos for each class. The 3D CNN model used in this model is RESNET-34. The videos were temporally cut down and last around tenth of a second. The trained model show satisfactory performance in all stages of training, testing. Finally the results show promising activity recognition of over 400 human actions.
Action Recognition, Tracking, and Optimization Analysis of Training Process Based on the Support Vector Regression Model

In order to study the action recognition, tracking, and optimization of the training process based on the support vector regression model, a method of human action recognition based on support vector machine optimization is proposed. This method uses the improved strategy of support vector machine to realize the action recognition through the human action recognition based on the optimization of the vector machine. During the recognition, the DAG SVM strategy is improved according to the recognition accuracy of the classifier, and when outputting the result, output the recognition result and the corresponding confidence level, and use the confidence level to process the recognition result. Finally, through the experimental results, it is realized that the recognition rate based on support vector optimization is 98.7%, indicating that this method is effective and can improve the accuracy and efficiency of human body action recognition.
Representation of structured data of the text genre as a technique for automatic text processing

The present article was developed in the field of Natural Language Processing and Language Studies based on a corpus compiled by computational tools. This study is based on the assumption that it is helpful to trace a close relationship between corpus generation/annotation and the assessment of the constitutive elements of the text genre source. It aims to demonstrate, through specific studies of structured data from the text genre ‘scientific article’, alternatives to automatic text processing techniques. In order to reach the intended goal, the authors created a computational model for the compilation of a linguistic, specialized Corpus, representative of the genre Scientific Article - CorpACE. The object of study includes the constitutive elements of scientific articles, marked in XML, extracted and collected from the SciELO-Scientific Electronic Library On-line database. The final product was a database obtained with information extracted and structured in XML format, which designates and identifies the markups of the genre being analyzed and is available for many tools and applications. The results demonstrate how the representation of constitutive elements of the genre can condense available information with hierarchical and dynamic processes built during the compilation. At the end of the study, it is believed that more research will be required for bringing Language Science and Computer Science closer with emphasis on NLP in the attempt to represent and manipulate linguistic knowledge in its many levels – morphological, syntactic, semantic and discursive – in order to improve implementation and manipulation of automatic text processing.
Behavior Control and Navigation of Two-Wheeled Robot Using ROS-Gazebo

Nowadays, robots serve a wide range of purposes, such as rescue and service. Due to the complexity of the environment, it is critical that the robot interacts with the environment and performs its task without losing context. This article uses a two-wheeled robot whose rotational speed is independently controlled by each wheel. This causes the robot to rotate in a horizontal plane without any steering. Our navigation algorithm, based on layered architecture of robot navigation, consists of two behaviors; Go to Goal and Avoid Obstacle. A hard-switch PID controller is then developed to track reference signals based on two behaviors. Using ROS-Gazebo and Python, all simulations and algorithms are developed and programmed for robot. Finally, the navigation algorithm is validated in two different environments based on their complexity.
To Extend or not to Extend? Complementary Documents

An agent in pursuit of a task may work with a corpus of documents with linked subjective content descriptions. Performing the task of document retrieval for a user or aiming to extend its own corpus, an agent so far relies on similarity measures to identify related documents. However, similarity may not be appropriate if looking for new information or different aspects of the same content. Therefore, this paper focuses on complementarity, specifically, contributing (i) a formal definition of complementarity using the available subjective content de-scriptions in the form of relational tuples as well as a taxonomy interrelating the concepts referenced in the tuples, (ii) a problem definition and solution approach for classifying complementary documents, and (iii) a case study assessing classification performance for complementary documents.
TFChain:Blockchain-based Trusted Forensics Scheme for Mobile Phone Data Whole Process

Nowadays, the usage of mobile phones to commit crimes is becoming more and more common. Mobile electronic evidence can be used to convict suspects. However, the credibility of electronic evidence is a key issue in the research of mobile phone forensics. Therefore, credible mobile phone forensics is crucial in police investigations. In previous studies, some scholars have used the computer live forensics of memory analysis to obtain the physical memory mapping files of mobile phones. This model can naturally distinguish the various stages of online forensics and meet the requirements of physical evidence technology. The rapid development of blockchain technology has brought research opportunities to the academic community. The decentralized, tamper-resistant, and traceable characteristics of blockchain technology have given new opportunities and connotations to the electronic evidence collection system. In this paper, we combine the advantages of blockchain and the effectiveness of memory analysis to propose a trusted mobile phone forensics program based on blockchain and memory analysis. Blockchain runs through the whole system process to ensure the credibility, integrity and auditability of evidence. Then, we use access control to realize the access authority of police investigators and forensic experts to the evidence. Next, forensic experts analyze the evidence and generate a forensic evaluation report. In the forensic phase, we use credible mobile phone memory acquisition tool to obtain evidence. In the identification phase, we use memory analysis to check the integrity and credibility of the evidence. Moreover, the online forensics based on memory analysis that we use is repeatable and verifiable. In addition, we choose the consortium chain to record the transaction behavior of entities. Finally, we tested our system on Hyperledger Fabric and analyzed the security and privacy of TFChain.
INTERACTIVE TOOLS FOR CREATING EDUCATIONAL CONTENT FOR PRIMARY SCHOOL STUDENTS

S. Skvortsova 1, T. Britskan 2, T. Symonenko 3, Y. Haievets 4 1South Ukrainian National Pedagogical University named after K.D. Ushynsky (UKRAINE) 2Izmail State University of Humanities (UKRAINE) 3Bohdan Khmelnytsky National University at Cherkasy (UKRAINE) 4South Ukrainian National Pedagogical University named after K. Ushynsky (UKRAINE)
Collusion Resistant Watermarking for Deep Learning Models Protection

Deep learning has been used in many fields, such as image classification and data analysis. Training a high-performance model is expensive; thus, its property value is high. Watermarking is a representative technology providing intellectual property protection for models. In this study, we proposed white box watermarking using a modified Barni’s method (our previous study) for image watermarking. Our method is applicable to pre-trained models because the watermark is embedded in the parameters of the network without training. Additionally, the proposed method embeds multiple watermarking into neural networks using different keys. We evaluated the method using two different networks: 5-layer convolutional neural networks trained on MNIST and ResNet-50 trained on CIFAR-10 datasets. The experimental results show that our proposed approach can embed 10 watermarks with less than 0.1% loss of accuracy, and it detects them completely even after 90% of the parameters are pruned.
Image Classification of White Blood Cells with Canonical Correlation

White blood cells are blood cells that are produced in the bone marrow and present in the blood and lymphatic tissue. They are a part of the immunological system of the body. They aid in the body&#x0027;s battle against infection and disease. WBC classification and identification can aid in the diagnosis of a variety of blood disorders.In the health-care industry, deep learning has numerous applications. One of the most promising areas of research is the classification of white blood cells. A tiny amount of blood is obtained and analyzed under the microscope to manually classify the WBC. This procedure takes a long time and has other drawbacks, including a lack of accuracy. Furthermore, there is the possibility that cells would overlap, which will impair accuracy. In this paper, we proposed a new method for identifying different types of White Blood Cells, such as monocytes, lymphocytes, eosinophils, and neutrophils. Convolutional neural networks are used to extract WBC features, and the CCA is used to remove nuclei from overlaying cells and learn from them. The proposed CNN with Canonical Correlation Analysis determines more accuracy than the previous approaches, according to experimental data.
“Women Conquerers” – The Faces of Burning Justice

Women are an important part of the world, but stereotypes about them stem from patriarchal ideologies in which women are expected to smile demurely and quietly acquiesce to declarations of love. Some men become enraged by such "unrequited love," but they never consider that women are also human beings with their own rights to live as they wish. Acid assault is a horrible crime that is usually done against women. Acid attacks on women are on the rise, primarily on young women between the ages of 15 and 30, with the purpose of disfiguring or killing them. However, it is still a highly gendered type of violence, with perpetrators nearly always being men and victims almost usually being women. The research revealed that the reported acid attack cases are for refusal to marry, rejection of love, or for any personal reasons. The women who were confronted with a vial of acid that forever transformed their lives have become a source of inspiration for all. Their lives have taken an exemplary path, and they have gone on to achieve remarkable heights in professional and personal arenas.
BinPointer: towards precise, sound, and scalable binary-level pointer analysis

Binary-level pointer analysis is critical to binary-level applications such as reverse engineering and binary debloating. In this paper, we propose BinPointer, a new binary-level interprocedural pointer analysis that relies on an offset-sensitive value-tracking analysis to achieve high precision. We also propose a soundness and precision evaluation methodology based on runtime memory accesses triggered by reference input data. Our experimental results demonstrate that BinPointer has higher precision over prior work, while maintaining acceptable scalability. The soundness of BinPointer is also validated through runtime data.
A REVIEW OF CHATBOTS IN HIGHER EDUCATION: CURRENT TRENDS, APPLICATIONS, CHALLENGES AND FUTURE IMPLICATIONS

The study explores the trends of chatbots in higher education by conducting a meta-analysis literature review. The analysis will discuss the various roles the chatbots in education, administration, teaching, and learning. The literature review research articles will be from 2015-2021. The literature will analyze papers published in Google Scholar, EBSCOhost, Science Direct, Social Science Citation Index (SSCI), and Web of Science. The research study will explain the current status of the adoption of chatbots in higher education and the definition in general and in higher education. In addition, an analysis of the current roles of chatbots in higher education. Further, the literature review will review the highest and lowest trend of chatbot applications. Challenges and opportunities on chatbots adoption and use and best practices for teachers in higher education included. The findings of this study present the factors and challenges that influence educators using chatbots in learning and teaching in higher education. In addition, the literature review highlights the instructors' experiences and attitudes towards the use of chatbots and their suggested recommendations. Implications for the use of chatbots in higher education will be highlighted. Finally, future applications will discuss how to use chatbots in higher education.
Internet Of Secure Elements Concepts And Applications. : Invited Paper

This paper introduces internet of secure elements paradigm and some applications. The main idea is to integrate TLS1.3 server in secure element (TLS-SE). These servers are identified by their server name (SN) attributes. On the client side authentication procedures are optionally hosted in dedicated identity module (TLS-IM). In internet of things context, TLS-SE stacks enhance object security. For example TLS-SE provides cryptographic resources (keystore), or interacts with sensors and actuators. We present grids of secure elements, optionally associated to secure element processors (SEP), called internet of secure elements servers (IOSE). Such server supports on-demand TLS-SE deployment; it also connect TLS-SE devices to internet. Finally we detail a procedure for transferring exclusive property of TLS-SE server to its user.
A novel approach for anomaly detection in automatic meter intelligence system using machine learning and pattern recognition

Anomaly detection for sensor systems is one of the most researched topics for the Internet of Thing systems. Researchers have been attracted to machine learning classification problems that are considered the most effective techniques. The novel model is proposed by combining anomaly pattern Symbolic Aggregate Approximation (SAX), processing imbalance data and machine learning techniques for sensor anomaly detection. The advantage of anomaly patterns and machine learning leads to the the proposed model to have better performance. The proposed model consists of three phases: finding anomaly pattern features, processing imbalanced data, exploring data by machine learning model. In this paper, the main contributions with respect to previous works can be listed as follows: (i) Successful modeling the new method of SAX for time series data for finding complex and dynamic anomaly patterns. (ii) Archiving applied anomaly pattern feature into machine learning model Random Forest and hyperparameters optimisation of these model. (iii) Fitfully proposed a model combining SAX, imbalance technique, and random forest to anomaly detection. (iv) Achieving applied proposal model in automatic meter intelligence system in Vietnam. The experiential results of the proposed model have described the robustness and better performance for detecting anomalies of power meter sensors.
Aircraft trajectory filtering method based on Gaussian‐sum and maximum correntropy square‐root cubature Kalman filter

Aiming at meetiing the need to filtering flight trajectory data for aircraft testing, a novel adaptive cubature Kalman filter (CKF) is proposed based on the maximum correntropy and Gaussian-sum in this paper. Firstly, based on the traditional CKF algorithm, we introduced a Gaussian-sum method to approximate non-Gaussian noise to get more accurate filtering results in view of the problem of reduced filtering accuracy caused by the inherent non-Gaussian nature of the noise and the system non-linearity. Secondly, the maximum correntropy criterion is introduced to solve further the problem of improving the filtering accuracy of the system in the case of non-linearity. Simulation results and actual data verification showed that the Square-root cubature Kalman filter algorithm based on the maximum correntropy and Gaussian-sum has higher accuracy than traditional filtering algorithms, which verified the algorithm's effectiveness in the application.