Reviewerly/exHarmony huggingface dataset:
A Dynamic Locality Sensitive Hashing Algorithm for Efficient Security Applications

The domain of information retrieval focuses on retrieving unstructured data, particularly text documents. Document search plays a pivotal role in modern information retrieval systems. Locality Sensitive Hashing (LSH) stands as one of the widely employed techniques for document search in high-dimensional spaces. LSH offers the advantage of theoretical assurance for query accuracy in multidimensional spaces. However, further enhancements can be made to LSH by incorporating additional steps. This paper introduces a new algorithm called Dynamic Locality Sensitive Hashing (DLSH) as an improved version of LSH. DLSH utilizes a hierarchical selection of LSH parameters (such as the number of bands, number of shingles, and number of permutation lists) based on achieved similarity to optimize search accuracy and increase its score. The proposed technique is evaluated using various tampered file structures, and its performance is assessed. In some scenarios, the matching accuracy of DLSH surpasses 95% when optimal parameter values are selected for the number of bands, number of shingles, and number of permutations lists in the DLSH algorithm. These results make the DLSH algorithm suitable for implementation in critical applications that rely on accurate searching, such as forensics technology.
Book review: A Dictionary of Geology and Earth Sciences (5th Edition), by Michael Allaby, 2020. Oxford University Press Academic UK, 720 pages, eBook price £14.99, ISBN: 9780198839033

Book review: A Dictionary of Geology and Earth Sciences (5th Edition), , by Michael Allaby, 2020. Oxford University Press Academic UK, 720 pages, eBook price £14.99, ISBN: 9780198839033.
Radar specific emitter identification via the Attention-GRU model

In radar specific emitter identification (SEI), various types of unintentional modulation on pulse (UMOP) are selected as the features for discriminating between different radars. Unintentional Phase Modulation on Pulse (UPMOP), a typical type of UMOP, can provide crucial information for identifying radars. In most radar SEI algorithms, sacrificing time efficiency for higher accuracy is a common trade-off. This paper proposes a method to solve this problem by combining denoised UPMOP sequences with an Attention-based Gated Recurrent Units (Attention-GRU) model, which showed an excellent performance. Firstly, the cause of UPMOP is analyzed and the phase observation model of radar emitter signals and mathematical model of UPMOP are given. Then, the least-squares method is used to eliminate the linear trend of the phase observation model and obtain a noised estimation of the UPMOP sequences. Thirdly, the uniform B-spline (UBS) curves are then used to fit the noised estimation, resulting in a denoised and refined UPMOP sequence. Finally, the Attention-GRU model is employed to extract features from the denoised UPMOP sequences to identify radar emitters automatically. Results from simulation and measured data experiments show that the overall recognition rate of the algorithm reaches over 93% and the algorithm has excellent performance, with high identification accuracy and relatively low time consumption, even in low signal-to-noise ratio (SNR) conditions.
Design and development of a Synergistic Educational Tool with Hiligaynon-English Recurrent Neural Network Model

Abstract This project aimed to develop a mobile app tool to teach the Hiligaynon language to elementary pupils, designed with Machine Learning features using the Hiligaynon-Filipino-English Translation RNN Model. The app included games as a pedagogical tool, background music, and incentives to engage learners. The RNN model was developed using Data Science Methodology, while Rapid Agile Development Methodology was used to develop the mobile app for Android operating systems. To assess the app’s effectiveness, elementary teachers and IT professionals evaluated its compliance with standards for acceptability. The app was found to be very acceptable, recommended for use in a gamified classroom setting and by parents. Overall, this project promotes technology in language learning to make it more accessible and enjoyable for elementary pupils.
Digital twin empowered lightweight and efficient blockchain for dynamic internet of vehicles

The Internet of Vehicles (IoV) has great potential for Intelligent Transportation Systems (ITS), enabling interactive vehicle applications, such as advanced driving and infotainment. It is crucial to ensure the reliability during the vehicle-to-vehicle interaction process. Although the emerging blockchainhas superiority in handling security-related issues, existing blockchain-based schemes show weakness in highly dynamic IoV. Both the transaction broadcast and consensus process require multiple rounds of communication throughout the whole network, while the high relative speed between vehicles and dynamic topology resulting in the intermittent connections will degrade the efficiency of blockchain. In this paper, we propose a Digital Twin (DT)-enabled blockchain framework for dynamic IoV, which aims to reduce both the communication cost and the operational latency of blockchain. To address the dynamic context, we propose a DT construction strategy that jointly considers the DT migration and blockchain computing consumption. Moreover, a communication-efficient Local Perceptual Multi-Agent Deep Deterministic Policy Gradient (LPMA-DDPG) algorithm is designed to execute the DT construction strategy among edge servers in a decentralized manner. The simulation results show that the proposed framework can greatly reduce the communication cost, while achieving good security performance. The dynamic DT construction strategy shows superiority in operation latency compared with benchmark strategies. The decentralized LPMA-DDPG algorithm is helpful for implementing the optimal DT construction strategy in practical ITS.
Penggunaan Framework Codeigniter Dalam Pembuatan Web Profil Program Studi Teknik Elektro Universitas Pembangunan Panca Budi Medan

The need for a website is very important in an institution in the current digital era, including the Electrical Engineering Study Program at the University of Panca Budi Medan. Through the Merdeka Learning Campus Merdeka (MBKM) program, students can apply their ideas and creativity in developing a website. With the help of the Codeigniter PHP programming framework, creating dynamic websites is easier to use because the CodeIgniter framework implements the Model, View and Controller methods. In developing a website application, a design method is also needed and in this study the author uses the Waterfall method in designing the system.
Hyperbolic Tangent - Based Adaptive Inertia Weight Particle Swarm Optimization

This paper presents a study on using adaptive inertia weight (AIW) in particle swarm optimization (PSO) for solving optimization problems. An AIW function based on the hyperbolic tangent function was proposed, with the function parameters adaptively tuned based on the particle best and global best values. The performance of the proposed AIW-PSO was compared with standard PSO and other PSO variations using seven benchmark functions. The results showed that the proposed AIW-PSO outperformed the other variations in terms of minimum cost and mean cost while reducing the standard deviation of cost. The performance of the different PSO variations was also analysed by plotting the best cost against iteration, with the proposed AIW-PSO showing a faster convergence rate. Overall, the study demonstrates the effectiveness of using an adaptive inertia weight function in PSO for optimizing problems.
Innovative Personal Assistance: Speech Recognition and NLP-Driven Robot Prototype

This paper presents the development and evaluation of a personal assistant robot prototype with advanced speech recognition and natural language processing (NLP) capabilities. Powered by a Raspberry Pi microprocessor, it is the core component of the robot's hardware. It is designed to receive commands and promptly respond by performing the requested actions, utilizing integrated speech recognition and NLP technologies. The prototype aims to enhance meeting efficiency and productivity through audio-to-text conversion and high-quality image capture. Results show excellent performance, with accuracy rates of 100% in Indonesian and 99% in English. The efficient processing speed, averaging 9.07 seconds per minute in Indonesian and 15.3 seconds per minute in English, further enhances the robot's functionality. Additionally, integrating a high-resolution webcam enables high-quality image capture at 1280 x 720 pixels. Real-time integration with Google Drive ensures secure storage and seamless data management. The findings highlight the prototype's effectiveness in facilitating smooth interactions and effective communication, leveraging NLP for intelligent language understanding. Integrating NLP-based speech recognition, visual documentation, and data transfer provides a comprehensive platform for managing audio, text, and image data. The personal assistant robot prototype presented in this research represents a significant advancement in human-robot interaction, particularly in meeting and collaborative work settings. Further refinements in NLP can enhance efficiency and foster seamless human-robot interaction experiences.
Enabling Guardian Angels: Designing and Constructing a Wireless Nurse CallSystem with IMU-Based Fall Detection for Enhanced Patient Safety

Falling poses a significant health concern across all age groups, with particular severityamong the elderly. Hospitalized patients, in particular, are vulnerable to injuries andevendeath due to falls. While patient supervision is essential for fall prevention, constant proximitybetween patients and healthcare staff is not always feasible. To tackle this challenge, thisstudy aimed to develop a solution that enables immediate assistance for patients who aredistant from the nurse call button when a fall occurs.The study employed the IMU sensor,which combines an accelerometer and a gyroscope. This sensor served as a transmitter todetect gravity acceleration and magnitude when afall event takes place. Thedata obtainedfrom the IMU sensor were further processed using an Arduino Uno microcontroller. Thesensor was integrated into a belt worn around the waist of the participants, who performedvarious movements such as falling facing down, falling up, falling to the right, falling to theleft, standing then sitting, and sitting then standing.The experimental tests yielded compellingresults, with all trials achieving an accuracy rate of 81.7%. The accuracy was determined byanalyzing the confusion matrix, which enabled accurate calculations.The utilization of thisinnovative tool significantly reduces the risk of patients experiencing detrimental outcomesfollowing falls by promptly notifying medical personnel, even when they aredistant from thenurse call button. Moreover, the implementation of this tool enhances overall safety forhospitalized patients, especially those at a high risk of falling. Future research can explore theintegration of additional sensors or the development of more sophisticated algorithms tofurther enhancethe accuracy and efficacy of this tool.
Performance Enhancement of Elephant Herding Optimization Algorithm Using Modified Update Operators

This research paper presents a modified version of the Elephant Herding Optimization (EHO) algorithm, referred to as the Modified Elephant Herding Optimization (MEHO) algorithm, to enhance its global performance. The focus of this study lies in improving the balance between exploration and exploitation within the algorithm through the modification of two key operators: the matriarch updating operator and the separation updating operator. By reframing the equations governing these operators, the proposed modifications aim to enhance the algorithm’s ability to discover optimal global solutions. The MEHO algorithm is implemented in the MATLAB environment, utilizing MATLAB R2019a. To assess its efficacy, the algorithm is subjected to rigorous testing on various standard benchmark functions. Comparative evaluations are conducted against the original EHO algorithm, as well as other established optimization algorithms, namely the Improved Elephant Herding Optimization (IEHO) algorithm, Particle Swarm Optimization (PSO) algorithm, and Biogeography-Based Optimization (BBO) algorithm. The evaluation metrics primarily focus on the algorithms’ capacity to produce the best global solution for the tested functions. The proposed MEHO algorithm outperformed the other algorithms on 75% of the tested functions, and 62.5% under two specific test scenarios. The findings highlight the effectiveness of the proposed modification in enhancing the global performance of the Elephant Herding Optimization algorithm. Overall, this work contributes to the field of optimization algorithms by presenting a refined version of the EHO algorithm that exhibits improved global search capabilities.
A Consensus Protocol for Incentivizing Contribution from Decentralized Community for Machine Learning-based Scamming Website Detection

The increasing proliferation of phishing and scamming websites has become a significant threat to the safety and security of internet users. Accurately detecting such websites is crucial in mitigating their negative impact. While various techniques for detecting phishing and scamming websites exist, machine learning-based approaches have gained significant attention in recent years due to their promising results. However, ensuring the authenticity of the large datasets used to train the models is challenging. This paper proposes a blockchain and ML-based solution with a consensus algorithm named The Proof of Anti-Scam (PoSS) that encourages and verifies data contributions from community members through a transparent incentivizing mechanism. Our experiments indicate that VGG16 is highly compatible with ML algorithms for accurately classifying phishing websites, and the PoAS consensus mechanism can work well in a large-scale network. This paper also highlights the potential benefits of using blockchain technology to incentivize data contributions to detect phishing and scamming websites more effectively.
Efficient Video Retrieval Method Based on Transition Detection and Video Metadata Information

In this paper, we propose an event retrieval support system that quickly finds videos in a large database based on user-entered content. The system addresses the challenges of providing fast and relevant results for a dataset of over 400 hours of videos and developing user-friendly tools. To achieve fast retrieval, we convert the videos into compact semantic features. This involves two steps: (1) Identifying keyframes that represent different content and (2) Extracting semantic features from these frames. We first use the TransNet model to find transition frames, which split the video into scenes with different content. Then we will extract the keyframes which are evenly distributed in these scenes. Finally, the CLIP model is used to extract features from these keyframes and connect them with text. This forms a compact and semantic feature database. When users search with text, we convert it into features and measure similarity with the database using cosine distance, then the most similar video is retrieved. In cases where CLIP model fails, we recommend leveraging news headlines and audio by applying Optical Character Recognition (OCR) and Automatic Speech Recognition (ASR) on videos to form a text database and comparing the entered text with this text database. Experimental results on a Vietnamese media news dataset demonstrate the effectiveness and accuracy of our method.
CPA-Unet: A Solution for Left Ventricle Segmentation from Magnetic Resonance Images

Medical image segmentation is a crucial first step in the development of healthcare and rehabilitation systems, particularly for the identification and planning of cardiovascular issues. In recent years, convolutional neural networks (CNNs) have produced outstanding results on a number of medical image segmentation tasks. Particularly the U-shaped architecture, also known as U-Net, has been extremely successful and set the de facto standard. U-Net often demonstrates difficulties in clearly expressing long-range dependency, nevertheless, as a result of the innate locality of convolution operations. In this study, we propose a new neural network architecture, namely CPA-Unet for problems involving cardiac image segmentation. The CPA-Unet model, which employs cutting-edge Deep Learning methods, more successfully provides better extraction of features for the segmentation of desired segmented objects, whereas earlier models did not contribute much because they ignored the details of the channel, spatial, and contextualization on big datasets. Our experiments upon this Sunnybrook Cardiac dataset and the ACDC dataset show that CPA-Unet outperforms other modern models in terms of the Dice coefficient and IoU metric, highlighting it’s own applicability for biomedical image segmentation solutions.
Drone-Based Inspection of the Appearance Defects for a Large Object

In general, the defect inspection of a large object, such as an aircraft, a bridge, or a building, etc., must need some tools or climbing high to achieve the inspection because the object is vast and high. However, climbing high is dangerous, and relying on other tools takes time and effort. Therefore, this paper aims to establish a drone system for detecting defects in the surface of a large object. In the system, the drone can fly along the object’s exterior with the shortest path and adjust the angle of its gimbal such that the drone’s camera can inspect the defects in the object’s appearance. The shortest path is obtained from solving the Travelling Salesman Problem of the navigation points. The navigation points are built based on the normal vectors of the object’s point cloud, which is established using OpenSfMThe shortest path is obtained from solving the Travelling Salesman Problem of the navigation points. The navigation points are built based on the normal vectors of the object’s point cloud. The point cloud is created using OpenSfM (Structure from Motion). Adopting Visual Simultaneous Localization and Mapping (V-SLAM) as the drone’s position control such that it can fly stably following the shortest path composed of navigation points. After the drone collects the whole image of the object’s appearance, the network YOLOv4-P6 is used to recognizes the defects. This study finally proposed an experiment to inspect car defects and found three types of defects: paint loss, corrosion, and dent, successfully and efficiently.
Intelligent Prediction of Cloud Movement Path

Due to the proportion of solar power generation in the power grid gradual increasing, weather forecasting plays an important role in the operation of power grid. In the case of a solar eclipse, the solar power generation will be influenced severely. In order to maintain grid stability, grid operations can precisely calculate the solar shading situation by the location and time of the solar eclipse, and then prepare in advance to reduce the impact of grid ramping event. The purpose of this paper is to predict the movement path of cloud blocks and estimate the solar shading by cloud blocks, and to estimate the variation of solar irradiance through Long Short-Term Memory (LSTM) network in order to provide dispatchers or Energy Management System (EMS) to effectively respond to the impact of cloud blocks on the power grid in advance.
A CNN-Transformer-based Approach for Medical Image Segmentation

Advances in deep convolutional neural networks (CNNs) have shown excellent performances on image processing applications including segmentation for medical images. Nevertheless, CNN-based approaches like the Fully Convolutional Neural Networks (FCNs), Unet and variants for image segmentation often meet difficulties when expressing long-range dependency because of the locality properties of convolutional operations. In an alternative, the network models based on transformers have global context of the image and features, thus better expressing long-range dependency. Though having advantages, the transformer-based approach often lacks local information context, thus limiting certain applications like medical images. In the current study, we propose a new model that can inherit advantages of both global and local contexts of the two above approaches by using CNN and Transformer branches, and introduced the Convmixer and Progressive Atrous Spatial Pyramidal Pooling modules in the bottlenecks of each branches. The proposed model has been validated on various medical image databases including the Data science bowls 2018, and GlaS datasets. High evaluation scores including Dice score and Intersection Over Union metric have shown performance of the proposed segmentation model while compared with recent neural network models.
PaaS: Planning as a Service for reactive driving in CARLA Leaderboard

End-to-end deep learning approaches have been proven to be efficient in autonomous driving and robotics. By using deep learning techniques for decision-making, those systems are often referred to as a black box, and the result is driven by data. In this paper, we propose PaaS (Planning as a Service), a vanilla module to generate local trajectory planning for autonomous driving in CARLA simulation. Our method is submitted in International CARLA Autonomous Driving Leaderboard (CADL), which is a platform to evaluate the driving proficiency of autonomous agents in realistic traffic scenarios. Our approach focuses on reactive planning in Frenet frame under complex urban street’s constraints and driver’s comfort. The planner generates a collection of feasible trajectories, leveraging heuristic cost functions with controllable driving style factor to choose the optimal-control path that satisfies safe traveling criteria. PaaS can provide sufficient solutions to handle well under challenging traffic situations in CADL. As the strict evaluation in CADL Map Track, our approach ranked 3rd out of 9 submissions regarding the measure of driving score. However, with the focus on minimizing the risk of maneuver and ensuring passenger safety, our figures corresponding to infraction penalties dominate the two leading submissions by 20 percent.
Multi-view Sparse Laplacian Eigenmaps for nonlinear Spectral Feature Selection

The complexity of high-dimensional datasets presents significant challenges for machine learning models, including overfitting, computational complexity, and difficulties in interpreting results. To address these challenges, it is essential to identify an informative subset of features that captures the essential structure of the data. In this study, the authors propose Multi-view Sparse Laplacian Eigenmaps (MSLE) for feature selection, which effectively combines multiple views of the data, enforces sparsity constraints, and employs a scalable optimization algorithm to identify a subset of features that capture the fundamental data structure. MSLE is a graph-based approach that leverages multiple views of the data to construct a more robust and informative representation of high-dimensional data. The method applies sparse eigendecomposition to reduce the dimensionality of the data, yielding a reduced feature set. The optimization problem is solved using an iterative algorithm alternating between updating the sparse coefficients and the Laplacian graph matrix. The sparse coefficients are updated using a soft-thresholding operator, while the graph Laplacian matrix is updated using the normalized graph Laplacian. To evaluate the performance of the MSLE technique, the authors conducted experiments on the UCI-HAR dataset, which comprises 561 features, and reduced the feature space by 10-90%. Our results demonstrate that even after reducing the feature space by 90%, the Support Vector Machine (SVM) maintains an error rate of 2.72%. Moreover, the authors observe that the SVM exhibits an accuracy of 96.69% with an 80% reduction in the overall feature space.