Reviewerly/exHarmony huggingface dataset:
Powering Design of Portable Dynamic Positioning Thruster for 150 FT Cable Laying Barge: A Case Study of Untung Jawa-Tanjung Pasir Submarine Power Cable Laying

Abstract Indonesia, an archipelagic nation with most of its electrical power produced by steam power plant. The distribution of electrical power is done through the main island to the smaller island, by means of according to Ministry of Energy and Mineral Resource is done through a submarine cable. In the study case area, Untung Jawa-Tanjung Pasir sea, the most appropriate cable laying platform is a barge. A barge is not equipped with the propulsion system and considering the cable laying operation requirement, barge given condition, environmental disturbances, the barge must be equipped with portable dynamic positioning thruster. An azimuth thruster is the proposed way to overcome this problem. The research done shall determine the barge’s necessary engine and propeller to fulfil its need. Using ship manoeuvring equation, the required thrust of the barge for surge, sway, and yaw are 274.36 kN, 289.21 kN, 3292.67 kNm respectively. To produce the required forces and moment, the researcher utilizes Cummins KTA38 (634 kW) diesel engine, Masson Marine gearbox (5.542:1), and Ka4-70 Nozzle 24 with the diameter of 1.6 m and P/D value of 1.06. In the free running condition, the barge will run with the speed of 3.52 m/s or 6.85 knots.
Recovering Image Information from Speckle Noise by Image Processing

As a kind of noise, speckle seriously affects the imaging quality of optical imaging system. However, the speckle image carries a large amount of information related to the physical characteristics of the object surface, which can be used as the basis to identify and judge hidden objects. In this paper, speckle noise removal in optical imaging is studied. The average is derived for the squared moduli of spectra of short-exposure speckle images to recover the amplitude information. At the same time, cross-spectrum function is used to recover the phase information. We use this method to process the images. Then, the simulation experiment analysis is carried out by varying two aspects: the stacking numbers and the different objects. The results show that this method can recover the feature information from the speckle image, thus verifying the feasibility of the method.
Kavach: Lightweight masking techniques for polynomial arithmetic in lattice-based cryptography

Lattice-based cryptography has laid the foundation of various modern-day cryptosystems that cater to several applications, including post-quantum cryptography. For structured lattice-based schemes, polynomial arithmetic is a fundamental part. In several instances, the performance optimizations come from implementing compact multipliers due to the small range of the secret polynomial coefficients. However, this optimization does not easily translate to side-channel protected implementations since masking requires secret polynomial coefficients to be distributed over a large range. In this work, we address this problem and propose two novel generalized techniques, one for the number theoretic transform (NTT) based and another for the non-NTT-based polynomial arithmetic. Both these proposals enable masked polynomial multiplication while utilizing and retaining the small secret property.For demonstration, we used the proposed technique and instantiated masked multipliers for schoolbook as well as NTT-based polynomial multiplication. Both of these can utilize the compact multipliers used in the unmasked implementations. The schoolbook multiplication requires an extra polynomial accumulation along with the two polynomial multiplications for a first-order protected implementation. However, this cost is nothing compared to the area saved by utilizing the existing cheap multiplication units. We also extensively test the side-channel resistance of the proposed design through TVLA to guarantee its first-order security.
Vision-Based Measurement for the Transverse–Longitudinal–Rotational Displacement of Hoisting Rope by Modified Lucas–Kanade Algorithm

In the mine hoisting system, the hoisting rope generates transverse–longitudinal–rotational (TLR) motion due to external excitation and the structure of the hoisting rope. The motion parameters of the hoisting rope are the basis for the fault diagnosis and vibration control of the hoisting system. In this study, a modified Lucas–Kanade (LK) method is proposed to identify the TLR displacement of the hoisting rope, called TLR-LK algorithm. First, the longitudinal–rotational warp function for wire rope is constructed and the iterative calculation formula is derived. Then, a method of wire rope axis positioning and parameter identification is proposed based on projection minimization to obtain the transverse displacement and radial dimension of the rope. Finally, the performance and feasibility of the proposed TLR-LK algorithm are demonstrated by comparing the results with existing representative methods in the motion platform and a field experiment in colliery. Experimental results reveal that the proposed algorithm can obtain the transverse, longitudinal, and rotational parameters of the hoisting rope simultaneously with higher accuracy and efficiency.
Head and body cues guide eye movements and facilitate target search in real-world videos

Static gaze cues presented in central vision result in observer shifts of covert attention and eye movements, and benefits in perceptual performance in the detection of simple targets. Less is known about how dynamic gazer behaviors with head and body motion influence search eye movements and performance in perceptual tasks in real-world scenes. Participants searched for a target person (yes/no task, 50% presence), whereas watching videos of one to three gazers looking at a designated person (50% valid gaze cue, looking at the target). To assess the contributions of different body parts, we digitally erase parts of the gazers in the videos to create three different body parts/whole conditions for gazers: floating heads (only head movements), headless bodies (only lower body movements), and the baseline condition with intact head and body. We show that valid dynamic gaze cues guided participants' eye movements (up to 3 fixations) closer to the target, speeded the time to foveate the target, reduced fixations to the gazers, and improved target detection. The effect of gaze cues in guiding eye movements to the search target was the smallest when the gazer's head was removed from the videos. To assess the inherent information about gaze goal location for each body parts/whole condition, we collected perceptual judgments estimating gaze goals by a separate group of observers with unlimited time. Observers' perceptual judgments showed larger estimate errors when the gazer's head was removed. This suggests that the reduced eye movement guidance from lower body cueing is related to observers' difficulty extracting gaze information without the presence of the head. Together, the study extends previous work by evaluating the impact of dynamic gazer behaviors on search with videos of real-world cluttered scenes.
Multivariate Multiscale Entropy: An Approach to Estimating Vigilance of Driver

Various driver’s vigilance estimation techniques currently exist in the literature. But none of them estimates the driver’s vigilance in the complexity domain. In this research, we propose the recently introduced multivariate multiscale entropy method to fill the above mentioned research gap. We apply this technique to differential entropy features of electroencephalogram and electrooculogram signals to detect driver’s vigilance. Also, we employ it to the percentage of eye closure values to analyse the driver’s cognitive states (awake, tired and drowsy) in the complexity domain. The contribution of this research is to efficiently classify the driver’s cognitive states using a new feature based on multivariate multiscale entropy. The experimental complexity profile curves show the statistically significant differences (p &lt; 0.01) among brain electroencephalogram, forehead electroencephalogram and electrooculogram signals. Moreover, the difference in the multivariate sample entropy across all scales in awake (1.0828 ± 0.4664), tired (0.7841 ± 0.3183) and drowsy (0.2938 ± 0.1664) states are statistically significant (p &lt;0.01). Also, the support vector machine, a machine learning technique, discriminates the driver’s cognitive states with a promising classification accuracy of 76.2%. Therefore, the complexity profile of driver’s cognitive states could be an indicator for vigilance estimation.
Machine-checked proofs of privacy against malicious boards for Selene &amp; Co1

Privacy is a notoriously difficult property to achieve in complicated systems and especially in electronic voting schemes. Moreover, electronic voting schemes is a class of systems that require very high assurance. The literature contains a number of ballot privacy definitions along with security proofs for common systems. Some machine-checked security proofs have also appeared. We define a new ballot privacy notion that captures a larger class of voting schemes. This notion improves on the state of the art by taking into account that verification in many schemes will happen or must happen after the tally has been published, not before as in previous definitions. As a case study we give a machine-checked proof of privacy for Selene, which is a remote electronic voting scheme which offers an attractive mix of security properties and usability. Prior to our work, the computational privacy of Selene has never been formally verified. Finally, we also prove that MiniVoting and Belenios satisfies our definition.
Comparative Analysis of Handwritten Digit Recognition Investigation Using Deep Learning Model

Owing to Machine Learning and visual action recognition, Handwritten Digital Recognition (HDR) has become critical to most other research groups and intends to reach society. Because Arabic digits are more complicated than English models, quick creations are made. Convolutional Neural Networks (CNN) has the advantage of extracting and using feature data. The activity of the convolution process entails several layers. Specific activities would be executed in each layer, and the performance would be transferred to the next layer due to the function. Image processing techniques were used as convolutional layers at the first layer to create different Convolutional Neural Networks. Except for input and output layers, which operate minor matrix equations, all neurons in one layer generally produce routine arithmetic computations, and that’s how a layer initially comes. The training and classification of Machine Learning methods were chosen from the MNIST database, and the findings were enhanced using an improving method.
NFT Trading Platform : A Decentralized Application using the Ethereum Blockchain

Blockchain technology is poised to bring about a paradigm shift in the business environment, offering substantial benefits in the near future. Non-fungible Tokens are digital representations of material possessions like artwork, recordings, assets within the game, and films. These tokens are traded via the internet with multiple cryptocurrencies. NFTs have their own distinct characteristics from fungible tokens, which may be traded without restriction on numerous centralized or decentralized exchanges.Each NFT carries a unique digital signature that precludes its exchange for another NFT, and its value is determined by factors such as metadata, creator, and features.
Identification of Saline Soils Using Soil Geochemical Data: A Case Study in Soda-Salinization Areas, NE China

Identifying saline soils is of great importance for protecting land resources and for the sustainable development of agriculture. Total soil salinity (TSS) is the most commonly used indicator for determining soil salinization, but the application of soil geochemical data is rarely reported. In general, there is a significant relationship between TSS and the content of soil-soluble Na, which can be estimated by the difference between the bulk-soil Na2O content and its background value. In this study, the partial least squares regression (PLSR) method was employed to calculate the Na2O background value via a regression model between Na2O and SiO2, Al2O3, TFe2O3, Cr, Nb, and P in a 1:250,000 scale regional geochemical data set of soils in Jilin Province, NE China. We defined δNa as the difference between the bulk-soil Na2O value and the regression background value, which can be used as a geochemical indicator to identify saline soils. One hundred and five samples with known TSS contents in the study area were selected to test the capability of the indicator δNa. The result shows that the identification accuracy can be up to 75%, indicating that the indicator can provide a new means for saline soil identification.
Pandemic techno-politics in the Global South

Brazil has been standing out as one of the worst places on Earth to be during a global health crisis, especially for those whose struggle for basic humanitarian rights is already routine. How do the political environment and historical inequalities in countries like Brazil affect the ways in which public policy and technologies are framed as responses for the pandemic crisis? In this paper we aim to present the sequence of actions and omissions in the fight against sars-cov2 in Brazil, concentrating on measures based on the use of digital technologies and the sociotechnical arrangements unfolding in materialities that give shape to such measures. We will also discuss possible repercussions of the widespread adoption of surveillance technologies as a quick fix to the effects of the pandemic. Our focus is to explain how the materiality of the virus and its political as well as territorial effects are combined with digital technologies as responses (or lack of them) in the fields of healthcare, education, communication and labour in the context of the Global South.
Artificial Rabbits Optimizer With Machine Learning Based Emergency Department Monitoring and Medical Data Classification at KSA Hospitals

The Emergency Departments (EDs) in health centres located in the main areas of Saudi Arabia have heavy patient inflow because of the pandemic, viral infections, and even on some special occasions like Umrah or Hajj, where pilgrims who travel from one place to another with serious disorders. Other than the EDs, it was important to observe the patient’s activities from ED to other wards in the region or the hospital to track the spread of viral diseases. In this case, deep learning (DL) and machine learning (ML) methods have been used to track the target audience and classify the data into many classes. With this motivation, this study develops an artificial rabbit optimization with a machine learning-based healthcare data classification (AROML-HDC) technique for EDs. The AROML-HDC technique monitors and tracks the patient visit data, treatment given, and length of stay (LOS). In addition, the AROML-HDC technique designs an effective ARO algorithm for the optimal selection of feature subsets. Next, the class-specific cost regulation extreme learning machine (CSCR-ELM) classifier is applied for effective medical data classification. Finally, the grasshopper optimization algorithm (GOA) was used to adjust the parameters related to the CSCR-ELM classifier. The experimental outcome of the AROML-HDC approach is tested on the benchmark Cleveland dataset and the Statlog dataset comprising 297 and 270 samples, respectively. The simulation results signify the improved performance of the AROML-HDC technique over other recent methods with maximum accuracy of 93.22% and 94.05% Cleveland dataset and the Statlog dataset, respectively.
Understanding and Expressing The Factors Causing Difficulties of Children In Learning, Especially In The Field Of Reading

The discussion that we present aims to discuss the factors that cause learning difficulties, especially in the field of reading in SD 122/X Sungai Beras students, Mendahara Ulu District, East Tanjung Jabung Regency. From a combination of grades IV, V, and VI, there are 100 students. Of these, there were 7 students who experienced learning difficulties, especially in the field of reading. The data was obtained through interviews with teachers and direct tests on students who claimed to have difficulty reading. This difficulty is caused by factors (internal), namely the talents and interests of students in certain learning situations that are still lacking and there is no compulsion to learn in students which is called lazy, As well as (external) factors, namely the family is a supporter of the learning situation of students but is still lacking, such as; direction from parents who are still lacking, as well as parents who are preoccupied with work which reduces parental attention to their children. Some people think that one of the factors causing difficulties is a condition or situation that creates obstacles in the learning process of students. The discussion used is descriptive method which includes internal and external factors. This means that the reader has a picture of difficulties in learning. Some people think that one of the factors causing difficulties is a condition or situation that creates obstacles in the learning process of students. The discussion used is descriptive method which includes internal and external factors. This means that the reader has a picture of difficulties in learning. Some people think that one of the factors causing difficulties is a condition or situation that creates obstacles in the learning process of students. The discussion used is descriptive method which includes internal and external factors. This means that the reader has a picture of difficulties in learning.
Data-driven predictive maintenance framework for railway systems

The emergence of the Industry 4.0 trend brings automation and data exchange to industrial manufacturing. Using computational systems and IoT devices allows businesses to collect and deal with vast volumes of sensorial and business process data. The growing and proliferation of big data and machine learning technologies enable strategic decisions based on the analyzed data. This study suggests a data-driven predictive maintenance framework for the air production unit (APU) system of a train of Metro do Porto. The proposed method assists in detecting failures and errors in machinery before they reach critical stages. We present an anomaly detection model following an unsupervised approach, combining the Half-Space-trees method with One Class K Nearest Neighbor, adapted to deal with data streams. We evaluate and compare our approach with the Half-Space-Trees method applied without the One Class K Nearest Neighbor combination. Our model produced few type-I errors, significantly increasing the value of precision when compared to the Half-Space-Trees model. Our proposal achieved high anomaly detection performance, predicting most of the catastrophic failures of the APU train system.
LDAViewer: An Automatic Language-Agnostic System for Discovering State-of-the-Art Topics in Research Using Topic Modeling, Bidirectional Encoder Representations From Transformers, and Entity Linking

Advancements in knowledge are pivotal to academic progress, necessitating efficient methods for discovering the state-of-the-art in various fields. Existing approaches, however, are language-specific and lack automation, limiting their efficacy. This study aims to develop a language-agnostic software that streamlines the process of identifying state-of-the-art research across diverse academic topics. The software automatically retrieves articles from multiple databases and preprocesses the content through tokenization, case folding, token cleansing, stopword removal, and lemmatization. Subsequently, a numeric document-phrase matrix is created and analyzed using latent Dirichlet allocation (LDA) and bidirectional encoder representations from transformers (BERT) to discover and label topics automatically. The study introduces a novel topic-filtering method based on entity linking and filtering model outputs using a knowledge database to ensure topic relevance. The visual representation employs nested bubble and line charts, effectively illustrating current topics, gaps, and research evolution trends. A user survey spread to 52 student researchers assessing the interface, topic relevance, and research output of the developed software, revealed that the interface is user-friendly, easy to navigate, and the presented information is comprehensible. Survey results also indicated that the generated topics are consistent with the processed article content and relevant to the investigated topic. The visualization effectively aids in understanding the state-of-the-art and research map. This study demonstrates that integrating LDA, BERT, and the proposed topic filtering and labeling method yields a robust tool for preliminary research analysis with high precision and relevance.
Improved design of load balancing for multipath routing protocol

In this paper, an improved routing protocol for multipath network load balancing is proposed for defects in the traditional AOMDV (Ad hoc On-demand Multipath Distance Vector) protocol. This research work analyzes problems in traditional routing protocols and estimates the available path load according to network transmission in Wireless Mesh Networks (WMN). Moreover, we design a load distribution scheme according to a given load and improve multi-path load balancing by using the MCMR method. We also control path discovery and the number of paths while also establishing routing paths and probability balancing. Lastly, improvements are made to the AOMDV protocol and efficient data transmission is acheived. The performance results of the modified routing protocol show that the designed protocol can improve successful delivery rate and prolong network survival time.
Enhancing Conventional Geometry-Based Visual Odometry Pipeline Through Integration of Deep Descriptors

Geometry-based Visual Odometry (VO) techniques are renowned in the fields of computer vision and robotics. They use methods from multi-view geometry to estimate camera motion from visual data obtained from one or more cameras. Tracking the camera motion precisely between different views is dependent on the correct estimation of correspondences between salient points of the views. In practice, geometry-based methods are found to be quite effective but do not perform well in challenging cases due to tracking failures caused by abrupt motion, occlusions, textureless and low-light scenes, etc. On the contrary, end-to-end learning from visual data using deep neural networks is an emerging area of research and deals with challenging cases successfully. Despite being computationally expensive, these methods do not outperform their counterparts in conditions favorable to geometry-based methods. Considering these facts in this work, our goal is to integrate deep descriptors to improve the correspondence between image points for tracking in a traditional geometry-based VO pipeline. We propose a simple stereo VO pipeline inspired by popular techniques found in the literature. Two conventional and four deep descriptors have been used in our experiments conducted on various image sequences of the challenging KITTI benchmark dataset. We have determined empirically that deep descriptors can effectively minimize drift in the VO estimates and produce better camera trajectories. The experimental results on the KITTI dataset demonstrate that our VO method performs at par with the state-of-the-art works reported in the literature.