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

Authors:
raw_author_name: Mingliang Xie
raw_affiliation_string: State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology 3 , Wuhan 430074, China

Abstract: 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

Authors:
raw_author_name: Rahib Agababayev Rahib Agababayev

Abstract: 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
A virtual reality data visualization tool for dimensionality reduction methods

Authors:
raw_author_name: Laura Raya
raw_affiliation_string: U-Tad: University Center for Technology and Digital Art, C/ Playa de Liencres 2 bis, Las Rozas, 28290, Madrid, Spain
raw_author_name: Manuel Rubio-Sánchez
raw_affiliation_string: Department of Computer Science & Statistics, Universidad Rey Juan Carlos, C/ Tulipan s/n, Móstoles, 28933, Madrid, Spain
raw_author_name: Alberto Sanchez
raw_affiliation_string: Department of Computer Science & Statistics, Universidad Rey Juan Carlos, C/ Tulipan s/n, Móstoles, 28933, Madrid, Spain

Abstract: 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.
Banff scoring of kidney allograft biopsies: “Manual” application vs software-assisted sign-out

Authors:
raw_author_name: Anthony J Demetris
raw_affiliation_string: Department of Pathology, Division of Transplantation, University of Pittsburgh , Pittsburgh, PA , US

Abstract: 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

Authors:
raw_author_name: Muhammad Umer Farooq
raw_affiliation_string: Department of Computer Science & Information Technology, NED University of Engineering and Technology, Pakistan
raw_author_name: Mirza Adnan Baig
raw_affiliation_string: Department of Computer Science, IQRA University, Pakistan
raw_author_name: Muhammad Ali Akhtar
raw_affiliation_string: Department of Computer and Information Systems Engineering, NED University of Engineering and Technology, Pakistan

Abstract: 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.
RIOD:Reinforced Image-based Object Detection for Unruly Weather Conditions

Authors:
raw_author_name: K. Bhanu Rekha
raw_affiliation_string: Department of Electronics &Communication, Presidency University, India
raw_author_name: S. Safinaz
raw_affiliation_string: Department of Electronics &Communication, Presidency University, India

Abstract: 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

Authors:
raw_author_name: Anup Kumar Barman
raw_affiliation_string: Department of Computer Science & Engineering, Central Institute of Technology Kokrajhar, India
raw_author_name: Jumi Sarmah
raw_affiliation_string: Sarala Birla Gyan Jyoti Guwahati, India
raw_author_name: Amitava Nag
raw_affiliation_string: Department of Computer Science & Engineering, Central Institute of Technology Kokrajhar, India

Abstract: 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.
Weber's Law-based Regularization for Blind Image Deblurring

Authors:
raw_author_name: Malik Najmus Saqib
raw_affiliation_string: College of Computer Science and Engineering, University of Jeddah, Saudi Arabia
raw_author_name: Hussain Dawood
raw_affiliation_string: Department of Information Engineering Technology, National Skills University Islamabad, Pakistan | School of Computing, Skyline University College, University City of Sharjah, United Arab Emirates
raw_author_name: Ahmed Alghamdi
raw_affiliation_string: College of Computer Science and Engineering, University of Jeddah, Saudi Arabia

Abstract: 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

Authors:
raw_author_name: Thi Van Anh Nguyen
raw_affiliation_string: School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Vietnam
raw_author_name: Ngoc Hiep Tran
raw_affiliation_string: School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Vietnam

Abstract: 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

Authors:
raw_author_name: Siti Hajar Othman
raw_affiliation_string: Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia, Malaysia
raw_author_name: Arafat Al-Dhaqm
raw_affiliation_string: Computer & Information Sciences Department, Universiti Teknologi PETRONAS, Malaysia

Abstract: 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

Authors:
raw_author_name: Hadeel Qasem Gheni
raw_affiliation_string: Department of Computer Science, College of Science for Women, University of Babylon, Iraq
raw_author_name: Zahraa Jabbar Hussein
raw_affiliation_string: Department of Computer Science, College of Science for Women, University of Babylon, Iraq

Abstract: 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.
Using genetic algorithm for drawing path planning in a robotic arm pencil sketching system

Authors:
raw_author_name: Jin-Siang Shaw
raw_affiliation_string: Institute of Mechatronic Engineering, National Taipei University of Technology, Taipei City, Taiwan

Abstract: The purpose of this study is to implement a robotic arm pencil sketching system through a novel drawing path planning method. Firstly, to enhance the craftsmanship and uniqueness of the artwork, the original image is transformed into an animated style using AnimeGANv2. After image processing, the image is divided into boundary and background layers for drawing path planning. In the realm of path planning, traditional methods often involve manual path design by artists, and only recently have automated path planning methods been proposed. However, these recent approaches still have some limitations. For instance, a pursuit of efficiency can lead to overly uniform strokes, making the artwork resemble a print rather than a genuine piece of art. Therefore, this study introduces a new path-planning approach based on the traveling salesman problem (TSP), utilising various algorithms for path-planning experiments. Different stroke effects generated by distinct algorithms are applied to appropriate areas. The aim is to let the generated artworks exhibit the technological appeal of algorithms while retaining the distinctive characteristics of pencil sketches. For robotic arm control, a force sensor is integrated onto the arm, and a proportional-integral-derivative (PID) controller is employed to monitor and precisely control the drawing force. Experimental results demonstrate that artworks created using genetic algorithms to generate drawing paths exhibit distinct colour-layering effects, emphasising the characteristics of pencil strokes. In comparison with previous methods, this approach effectively addresses the issue of overly uniform and rigid strokes in past techniques.
The added value of temporal data and the best way to handle it: A use-case for atrial fibrillation using general practitioner data

Authors:
raw_author_name: Frank C. Bennis
raw_affiliation_string: Nivel, Netherlands Institute for Health Services Research, Utrecht, the Netherlands; Quantitative Data Analytics Group, Department of Computer Science, VU Amsterdam, Amsterdam, the Netherlands
raw_author_name: Claire Aussems
raw_affiliation_string: Nivel, Netherlands Institute for Health Services Research, Utrecht, the Netherlands
raw_author_name: Joke C. Korevaar
raw_affiliation_string: Nivel, Netherlands Institute for Health Services Research, Utrecht, the Netherlands
raw_author_name: Mark Hoogendoorn
raw_affiliation_string: Quantitative Data Analytics Group, Department of Computer Science, VU Amsterdam, Amsterdam, the Netherlands

Abstract: Temporal data has numerous challenges for deep learning such as irregularity of sampling. New algorithms are being developed that can handle these temporal challenges better. However, it is unclear how the performance ranges from classical non-temporal models to newly developed algorithms. Therefore, this study compares different non-temporal and temporal algorithms for a relevant use case, the prediction of atrial fibrillation (AF) using general practitioner (GP) data. Three datasets with a 365-day observation window and prediction windows of 14, 180 and 360 days were used. Data consisted of medication, lab, symptom, and chronic diseases codings registered by the GP. The benchmark discarded temporality and used logistic regression, XGBoost models and neural networks on the presence of codings over the whole year. Pattern data extracted common patterns of GP codings and tested using the same algorithms. LSTM and CKConv models were trained as models incorporating temporality. Algorithms which incorporated temporality (LSTM and CKConv, (max AUC 0.734 at 360 days prediction window) outperformed both benchmark and pattern algorithms (max AUC 0.723, with a significant improvement using the 360 days prediction window (p = 0.04). The difference between the benchmark and the LSTM or CKConv algorithm decreased with smaller prediction windows, indicating temporal importance for longer prediction windows. The CKConv and LSTM algorithm performed similarly, possibly due to limited sequence length. Temporal models outperformed non-temporal models for the prediction of AF. For temporal models, CKConv is a promising algorithm to handle temporal data using GP data as it can handle irregular data.
Syntactic and typological properties of constrained language: A study based on dependency treebanks of translated English and non-native English

Authors:
raw_author_name: Ruimin Ma
raw_affiliation_string: School of Foreign Studies, Xi'an Jiaotong University, Xi'an, China
raw_author_name: Yue Jiang
raw_affiliation_string: School of Foreign Studies, Xi'an Jiaotong University, Xi'an, China

Abstract: Abstract To explore if translation-intrinsic features are apparent in other types of bilingualism-influenced constrained language use such as non-native production, this study approaches syntactic and typological properties of constrained English translated from Chinese and written by native Chinese speakers via two cognitively-motivated dependency metrics, viz. mean dependency distance (MDD) and dependency direction (DDir). Results of this study show that translated English (both L1 and L2) and non-native English differ from the non-constrained native English in a similar way yet to a slightly different extent, but not from each other in both indicators. Syntactically, bilingually-constrained varieties exhibit reduced syntactic complexity with shorter MDDs, suggesting a simplification tendency. Typologically, cross-linguistic influences are detected in constrained varieties for being more head-final in word-order primed by the source or native language Chinese. Surprisingly, it seems that language directionality affects, albeit marginally, the affinity between constrained varieties, with non-native English being more syntactically and typologically similar to translated English from L1 than from L2.