Reviewerly/exHarmony huggingface dataset:
An AK-iDNS framework to solve the condensation-driven aggregation with realistic kernels

In this study, a concise and fast framework based on the average kernel and iterative direct numerical simulation is proposed to solve the generalized Smoluchowski coagulation equation with a physically realistic kernel. Taking advantage of the symmetry of the homogeneous collision kernel, the realistic kernel function can be represented as a simple power function through Laplace transformation under the average kernel method, and the corresponding moment model and self-preserving model can then be constructed and solved analytically. Compared with the classical Taylor-series expansion method of moments, the present moment model has the same asymptotic growth rate, but the form is more concise and the particle number density is decoupled from the other moments. To obtain a better agreement with the experimental data, the iterative direct numerical simulation can be employed to correct the similarity solution using the analytical similarity solution as the initial condition. The corrected similarity solution overcomes the inherent contradiction between the analytical solution and experimental data discussed in the literature. The results reveal that the shape of self-preserving distribution is independent of the initial distribution and that it does depend on the mechanism of coagulation. The results also show the universality, reliability, and strong robustness of the iterative direct numerical simulation algorithm.
INFORMATION SECURITY RISKS MONITORING AND MANAGEMENT SYSTEM

In this research, the information phenomenon is examined from a wide perspective, the concepts of data, information and inventory are explained, the impact and dimensions of information technologies on information are revealed, the difficulty of obtaining information security arising from the need to protect information is investigated, and the main elements of information security are highlighted. This work summarizes the security processes needed to create effective information security in an environment where information security attacks are increasing in both number and variety. As a result, the issues explained, investigated and summarized were widely evaluated, and an important step was taken to create a new technology. Keywords: inventory, security, information security, information
The Internet is a scary place: How does evidence source and examinee race or ethnicity influence determinations of threat?

Violent rhetoric online is becoming increasingly relevant to the practice of forensic mental health assessment as examinee's virtual lives may transform into real-world acts of violence. With the rise of a diverse subculture of violent online communities, the aim of the present study was to inform how concerns with online sources of collateral data and racial/ethnic biases may influence determinations of violence potential. Using an experimental design, jury-eligible participants (N = 278) and forensic mental health experts (N = 78) were presented with mock Twitter (now referred to as X) posts that varied by data source (i.e., how information was accessed) and the examinee's race/ethnicity. Results showed no differences in participants' ratings of data credibility, how much weight they would place on the posts in a threat assessment, or how likely the examinee was to act violently against his intended target. Implications regarding the interpretation of social media evidence, relevant limitations, and future research are discussed.
INTELLIGENT CONTROL SYSTEM OF MULTI-MOTION MOBILE ROBOT

Technical devices with irregular motion are described by complex non-linear differential equations, as they have an uncertain environment. Due to interactions and simplifications in these types of control objects, their mathematical models may have certain errors. For this reason, by known methods, for example, the linear matrix inequality method, Lyapunov's quadratic function, etc. The synthesized automatic control systems (ACS) for dynamic objects written by nonlinear mathematical models based on the quadratic matrix determined by The synthesis of regulators for the management of dynamic objects described in nonlinear and uncertain conditions is based on fuzzy logic theory. Since the controllers synthesized by fuzzy logic are based on knowledge, their application to other objects (robots) is limited. In such systems, it is difficult or not at all to determine the dependencies between ACS quality indicators, stability and object parameters. Taking into account the above, a method of analytically parametric synthesis of regulators that ensures the degree of stability and accuracy in ACS’s designed for fuzzy TS-type dynamic objects is proposed. In the article, the solution to the problem of the synthesis of the control system of the mobile robot was considered, and it was successfully applied in the multi-motion mobile robot system. Modeling and experimental results confirm that the operation and stability of the obtained system fully meet the technical requirements. The proposed method has led to an increase in both tracking and control accuracy during high-speed movements. Keywords: multi-motion mobile robot, mathematical model, mechatronic modules, fuzzy TS model, robust controller, fuzzy logic.
A virtual reality data visualization tool for dimensionality reduction methods

Abstract In this paper, we present a virtual reality interactive tool for generating and manipulating visualizations for high-dimensional data in a natural and intuitive stereoscopic way. Our tool offers support for a diverse range of dimensionality reduction (DR) algorithms, enabling the transformation of complex data into insightful 2D or 3D representations within an immersive VR environment. The tool also allows users to include annotations with a virtual pen using hand tracking, to assign class labels to the data observations, and to perform simultaneous visualization with other users within the 3D environment to facilitate collaboration.
Servant leadership and knowledge employee performance: the mediating role of employee innovative behavior in knowledge-based firms

Purpose The purpose of this paper is to examine the relationship between servant leadership, employee innovative behavior and knowledge employee performance in knowledge-based firms. Design/methodology/approach A quantitative approach was used to conduct the present study. The respondents were sampled from knowledge-based firms in Iran. Overall, 726 knowledge employees in 121 firms were selected using convenience sampling. A confirmatory factor analysis was conducted to ascertain the validity and reliability of the observed items, and a structural equation model was employed for testing the hypotheses. Findings In the studied firms, servant leadership has a significant effect on employee innovative behavior. Moreover, the findings of this study show that firms that enhance their employees’ innovative behavior have higher knowledge employee performance. Research limitations/implications The study was conducted in knowledge-based firms in Iran. Therefore, our conclusions may not be applicable to other countries. Future studies should be carried out with samples from other contexts. Practical implications We found that servant leadership is conducive to employee innovative behaviors, and this effect leads to high knowledge employee performance. Accordingly, knowledge-based firms’ leaders should encourage employees’ innovative behavior through stimulating employee thriving at work, supporting employees’ development and empowering them with decision-making discretion. Originality/value This study contributes to advance research on servant leadership literature by linking servant leadership to knowledge employee performance in knowledge-based firms through employee innovative behavior as a mediator.
Banff scoring of kidney allograft biopsies: “Manual” application vs software-assisted sign-out

Abstract Objectives Pathologists interpreting kidney allograft biopsies using the Banff system usually start by recording component scores (eg, i, t, cg) using histopathologic criteria committed to memory. Component scores are then melded into diagnoses using the same manual/mental processes. This approach to complex Banff rules during routine sign-out produces a lack of fidelity and needs improvement. Methods We constructed a web-based “smart template” (software-assisted sign-out) system that uniquely starts with upstream Banff-defined additional diagnostic parameters (eg, infection) and histopathologic criteria (eg, percent interstitial inflammation) collectively referred to as feeder data that is then translated into component scores and integrated into final diagnoses using software-encoded decision trees. Results Software-assisted sign-out enables pathologists to (1) accurately and uniformly apply Banff rules, thereby eliminating human inconsistencies (present in 25% of the cohort); (2) document areas of improvement; (3) show improved correlation with function; (4) examine t-Distributed Stochastic Neighbor Embedding clustering for diagnosis stratification; and (5) ready upstream incorporation of artificial intelligence–assisted scoring of biopsies. Conclusions Compared with the legacy approach, software-assisted sign-out improves Banff accuracy and fidelity, more closely correlates with kidney function, is practical for routine clinical work and translational research studies, facilitates downstream integration with nonpathology data, and readies biopsy scoring for artificial intelligence algorithms.
Prediction of Agricultural Commodity Prices using Big Data Framework

The agriculture sector plays a crucial role in the economy of Pakistan, contributing significantly to the Gross Domestic Product (GDP) and the employment rate. However, this sector faces challenges such as climate change, water scarcity, and low productivity, which have a direct impact on agricultural commodity prices. Accurate forecasting of commodity prices is essential for farmers, traders, and policymakers to make informed decisions and improve economic outcomes. This paper explores the use of a big data framework for agricultural commodity price forecasting in Pakistan, using a historical dataset on commodity prices in various Pakistani cities from 2007 to 2022 and Apache Spark to preprocess and clean the data. Based on historical spinach prices in Vehari City, the machine learning models Auto-Regressive Moving Average (ARIMA), Random Forest, and Long-Short-Term Memory (LSTM) were applied to price trends, and their performance was compared using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and squared correlation coefficient (R2). LSTM outperformed ARIMA and Random Forest with a higher R2 value of 0.8 and the lowest MAE of 125.29. Such predictions can help farmers to effectively plan crop cultivation and traders to make well-informed decisions.
A Deep Learning Model to Inspect Image Forgery on SURF Keypoints of SLIC Segmented Regions

Copy-Move Forgery (CMF) is a common form of image manipulation attack that involves copying and pasting a part of an image to another position within the same image. This study proposes a Deep Learning (DL) model for detecting CMF, particularly in the presence of various malicious attacks. The proposed approach involves several steps, including converting the input image to grayscale, preprocessing the image using the Simple Linear Iterative Clustering (SLIC) algorithm to generate superpixel partitions, and then extracting keypoint features using the Speeded Up Robust Features (SURF) detector. Finally, a Generative Adversarial Network (GAN) is employed for feature description and matching. To assess the effectiveness of the approach, the types of features used for copy-move forgery were addressed. The proposed approach was examined under rotation, blurring, jpg compression, and scaling attacks. Furthermore, experimental results showed that the proposed approach can detect multiple CMFs with high accuracy. Finally, the proposed method was compared with recent state-of-the-art methods.
RIOD:Reinforced Image-based Object Detection for Unruly Weather Conditions

Deep Neural Network (DNN) object detectors have proved their efficiency in the detection and classification of objects in normal weather. However, these models suffer a lot during bad weather conditions (foggy, rain, haze, night, etc.). This study presents a new scheme to reduce the aforementioned issue by attenuating the noise in the input image before feeding it to any kind of neural network-based object detector. In this study, the image optimization function transforms subpar-quality images due to bad weather into pictures with the optimal possible quality by estimating the proper illumination and transmission function. These optimized images showed improved object detection rates in the YOLOv4 and YOLOv5 models. This improvement in object detection was also noticed in the case of video input. This scheme was tested with images/videos from various weather conditions, and the results showed an encouraging improvement in detection rates.
Word Sense Disambiguation applied to Assamese-Hindi Bilingual Statistical Machine Translation

Word Sense Disambiguation (WSD) is concerned with automatically assigning the appropriate sense to an ambiguous word. WSD is an important task and plays a crucial role in many Natural Language Processing (NLP) applications. A Statistical Machine Translation (SMT) system translates a source into a target language based on phrase-based statistical translation. MT plays a crucial role in a WSD system, as a source language word may be associated with multiple translations in the target language. This study aims to apply WSD to the input of the MT system to enhance the disambiguation output. Hindi WordNet was used by selecting the most frequent synonym to obtain the most accurate translation. This study also compared Naïve Bayes (NB) and Decision Tree (DT) to test and build a WSD model. NB was more appropriate for the WSD task than DT when evaluated in the Weka machine learning toolkit. To the best of our knowledge, no such work has been carried out yet for the Assamese Indo-Aryan language. The applied WSD achieved better results than the baseline MT system without embedding the WSD module. The results were analyzed by linguist scholars. Furthermore, the Assamese-Hindi transliteration system was merged with the baseline MT system for the translation of proper nouns. This study marks a remarkable contribution to Assamese NLP, which is a low computationally aware Indian language.
The Innovative Role of Process Mining in building Face Re-identification Trajectory

Face recognition and tracking technology have witnessed significant advancements during the recent decades. These advancements include improved accuracy and speed in identifying and tracking individuals, as well as the ability to recognize faces in various scale and lighting conditions. Leveraging the potential of face recognition and tracking, this article explores the integration of process mining techniques to discover and visualize face’s appearing trajectories in crowd scenarios, aiming to enhance crowd security, surveillance, and personal identification. Notably, existing face recognition tools typically focus on bounding box localization, neglecting the utilization of face coordinates to construct trajectory models upon face re-identification. In this paper, full system architecture for building a trajectory model of re-identified faces in a crowd is proposed. This approach significantly helped in building a large database of visitor faces, and the proposed trajectory model resulted in a high rate of true positive face re-identification.
Making Different Topographic Maps with the Surfer Software Package

The main objective of this study is to describe the preparation of topographic maps using the Surfer software. A total of 159 regularly distributed Ground Control Points (GCPs) were collected with the use of the Differential Global Positioning System (DGPS). Seven methods (Contour Map, Post Map, 3D Surface Map, 3D Wireframe Maps, Grid Vector-1 Map, Color Relief Map, and Shaded Relief Maps) at the Surfer environment were used to prepare the topographic maps at the Mukhtar Village near the Al-Fallujah City. Contour lines with other features were superimposed on the DEM layer, which refers to the topography of the terrain inside this study area. The accuracy of the database's results was estimated, essential maps were given, and the results were efficient and effective. The most appropriate method to represent topographic maps was proposed, each of these techniques has been enough to provide us with a general understanding of the subject area.
Presenting the Secure Collapsible Makerspace with Biometric Authentication

Traditional product design learning is inefficient, costly, and limited by safety concerns in physical and smaller-scale school workshops. The Secure Collapsible Makerspace is a mobile makerspace with smart sensors and an expandable work area facility. This study aims to develop a smart mobile makerspace platform using edge computing architecture and the biometric security model to compare real-time learners’ engagement time and design performance. The developed platform consists of four blocks that provide service as a makerspace for the learners and monitor their utilization in a secure environment. The edge computing architecture allows real-time analysis of the users’ makerspace utilization. A total of 750 learners were accessed for their product design outcome which presented a positive correlation of 0.72 between the engagement time and the design assessment with a corresponding p-value less than 0.001. This study concludes that makerspaces can be a platform that improves students’ performance in hands-on activity-related learning.
Weber's Law-based Regularization for Blind Image Deblurring

Blind image deblurring aims to recover an output latent image and a blur kernel from a given blurred image. Kernel estimation is a significant step in blind image deblurring and requires a regularization technique to minimize the cost function and the edges of objects to generate a sharp image in a better way. This study proposes a new image regularization technique called Weber's Law Regularization (WLR) based on the Weber law phenomenon. The Weber ratio was used to preserve the edges of small salient objects and to minimize the cost function to obtain a sharp image while minimizing the ringing effect. To validate the WLR, experiments were conducted on benchmark synthetic and real word images and compared with existing state-of-the-art methods. The experimental results showed that WLR can effectively and efficiently deblur images even in the absence of prior knowledge.
A T-S Fuzzy Approach with Extended LMI Conditions for Inverted Pendulum on a Cart

The Inverted Pendulum On a Cart (IPOC) system poses a challenge in control engineering due to its inherent instability, nonlinearity, and underactuation. This addresses the fundamental issues arising from its underactuated nature and introduces an approach that combines Takagi-Sugeno (T-S) fuzzy control with an awareness of real-world constraints to create a control system ensuring both stability and practicality. By aligning theoretical insights with extended considerations, the Linear Matrix Inequality (LMI)-based control design is demonstrated in a comprehensive framework. Theorems are introduced and validated, leading to the derivation of LMI conditions. The simulation results are assessed with accompanying comments to demonstrate the effectiveness of the theorems. Through this integration of T-S fuzzy control with additional considerations, the paper aims to bridge the gap between theory and practical applications, advancing the field of control engineering.
An Improved Machine Learning Method by applying Cloud Forensic Meta-Model to Enhance the Data Collection Process in Cloud Environments

Cloud computing has revolutionized the way businesses operate by offering accuracy in Normalized Mutual Information (NMI). However, with the growing adoption of cloud services, ensuring the accuracy and validation of common processes through machine learning and clustering of these common concepts as well as of the processes generated by cloud forensics experts’ data in cloud environments has become a paramount concern. The current paper proposes an innovative approach to enhance the data collection procedure in cloud environments by applying a Cloud Forensic Meta-Model (CFMM) and integrating it with machine learning techniques to improve the cloud forensic data. Through this approach, consistency and compatibility across different cloud environments in terms of accuracy are ensured. This research contributes to the ongoing efforts to validate the clustering process for data collection in cloud computing environments and advance the field of cloud forensics for standardizing the representation of cloud forensic data, certifying NMI and accuracy across different cloud environments.
Advancing Cloud Image Security via AES Algorithm Enhancement Techniques

Communication system and internet dominance in our society, has made image security a matter of paramount concern. Cryptography involves encrypting data to protect information exchange between senders and receivers, establishing a foundation for secure communication. The Advanced Encryption Standard (AES) is an exceptional algorithm that plays a pivotal role in this area because of its ability to consistently transform plain data into cipher data using the same encryption key. This algorithm engages intricate encryption techniques, harnessing a variety of algorithms and transformations to ensure robust data security. This study introduces an image encryption technique to comprehensively address security requirements. The proposed method uses the AES algorithm to provide high reliability and unparalleled safety, effectively protecting precious images against any form of malicious attack. The sender's image is intelligently fragmented into multiple pieces to maximize data confidentiality, employing a sophisticated algorithm. Upon decryption by the intended recipient, these fragments seamlessly merge to reconstruct the original image. The suggested technique allows users to securely store their data in the cloud, mitigating concerns related to potential data leakage, damage, or corruption. Thus, by integrating a cloud-based storage system, and utilizing digital signatures through OpenSSL, this method guarantees airtight security and reliability for sensitive data.