Reviewerly/exHarmony huggingface dataset:
Research on the planning of an ant colony algorithm in the planning of unmanned vehicle delivery path

The traditional ant colony algorithm has a slow convergence speed when the path is planned, and it is prone to local optimization. In the complex environment, the problem of significant decline in algorithms has dropped significantly, and an optimized ant colony algorithm is proposed. In each iteration process of path search, the pheromone will be re -distributed according to the rules of improvement. The improvement of the algorithm through dynamically adjusts the state transfer probability to prevent the algorithm from stagnation and avoid the problem of local optimization. In terms of solving the problem of ants' death lock, it can effectively speed up the convergence of algorithms and improve the robustness of the path planning algorithm. It can be seen from the simulation results that in the same environment, the optimal path length of the improving algorithm search in this article is reduced by 8.53% compared with the basic ant colony algorithm, and the search efficiency comparison of the basic ant colony algorithm is increased by 65.38%.
Restricted Area Sign Detector Using YOLO v5

This paper presents the implementation of a YOLO v5 model in a mobile robot for the detection of restricted area signs and the delivery of medicines. The objective is to enhance privacy by preventing the robot from entering restricted areas and also improve the time that a delivery round takes by avoiding unneeded paths. To achieve this, artificial vision and transfer learning techniques are used to train the YOLO v5 model, allowing the robot to detect and recognize a type of standard visual signal that indicates restricted areas. The implementation is performed on a Raspberry Pi 4, chosen for its processing power, 8GB of RAM, and easy installation on a mobile robot. Experimental results show that the implementation of this architecture in the mobile robot achieves a detection with maximum reliability of 93% in distances of 0.6 meters between the robot camera and the signal to be detected, thus allowing safe navigation of the robot and reducing the time to complete the delivery round.
Research on Evaluation Test Methods for Network Automation and Maintenance

In the current landscape of network automation operations and maintenance assessment methods, several issues have been identified, including poor noise resistance, low testing accuracy, instability, and extended testing durations, all of which impact the overall effectiveness of the testing process. To overcome these challenges, we introduce an innovative evaluation and testing approach. This method leverages the VRNN algorithm for detecting anomalies in network server data and incorporates a quantifiable security assessment model for AI-driven operations and maintenance testing. Our experimental results validate the effectiveness of this approach by demonstrating robust noise resistance and achieving a testing accuracy of over 92%. The testing process is characterized by its stability and is efficiently completed within a short timeframe. Remarkably, it can conduct 3000 data tests in as little as 60 seconds, thus underscoring the efficacy of our proposed methodology.
A Fine-Tuned ResNet50 Model for Multiclass Cancer Prediction

Cancer has maintained its position as a leading contributor to mortality across the world. To enhance treatment outcomes and elevate cancer survival rates, which exert significant economic and social repercussions, the development of a precise and early cancer prediction model is required. Equally important is a comprehensive grasp of the biological pathways underpinning cancer development. Over the past two decades, the categorization of diverse cancer types has undergone exploration through both machine learning and deep learning techniques. At the forefront of these efforts, molecular-level diagnostics utilizing microarray technology have the potential to provide accurate, unbiased, and methodical cancer classification. Given that the expression patterns across the entire genome are shaped by thousands of genes, the selective identification of pertinent genes becomes crucial for precise cancer diagnostics, given that not all genes are implicated in cancer processes. In this work, a deep learning-based pre-trained ResNet50 model has been utilized for the prediction of multiple types of cancer. The model has been fine-tuned by varying the number of epochs, optimizers and learning rates. This model has achieved an accuracy of 99% at epoch 50 which is higher in comparison to the existing models.
Correlation Between Operation of Single and Dual Axis Solar Tracker System

A handsome amount of money is being invested in and for development of renewable energy sources for solar and wind power plants. The elevation of the sun fluctuates as change in weather is considered, travelling from north to south in addition to its daily movement from east to west. The production from solar energy majorly depends on illumination, rainfalls, water cycles, and weather patterns. This paper compares solar panels with 1-axis and 2-axis tracking systems using an experimental design. The readings were taken for every half an hour. Along with the architectural and operational aspects of the tracking systems, the key characteristics of both 1-axis and 2-axis tracking systems are also covered. The purpose of this work is to use an experimental methodology to compare the efficiency of solar panels with 1-axis and 2-axis tracking systems. In order to assess the effect of various tracking systems on solar energy production, hourly data were acquired. The research also compares and contrasts 1-axis and 2-axis tracking systems, focusing on their distinctive architectural and operational features. Both single- and dual-axis tracking systems for solar panels were found to increase energy output compared to stationary solar panels. Differences between the two tracking methods were clearly seen in the data. Single-axis trackers, which primarily monitor the sun's east-west motion, have been shown to operate well under cloudless skies. However, dual-axis trackers, which follow the sun's east-west and north-south motions, performed better in a wider variety of climates.
A Comparative Study on Performance Measures of Smart Monitoring System

The Intelligent techniques are required to track patients' physiological data remotely and effectively. Individuals who are at high risk after surgery require a high degree of care. The primary goal is to assess a research model that employs a machine learning technique and deep learning algorithm to investigate clinical data using a Convolutional Neural Network Model. It is constructed in three sections. The goal of the ssytem is to provide a full monitoring and analysis platform powered by the MATLAB IDE that can be used to build patient observing systems all in one place. The current technique of checking patients in hospitals binds them to their beds and may be uncomfortable for patients to wear. The designed framework incorporates integrated sensors such as a skin temperature sensor.
A Comprehensive Analysis of Cloud Migration Strategies: Efficiency Comparison of Trickle, Big Bang, Refactoring, Lift and Shift, Replatforming Approaches

To resolve the poor efficiency issue encountered during database migration and ensure that the complete database is effectively transferred with less amount of time using Trickle Approach over Lift and Shift approach, Big Bang Approach, Refactoring technique, Replatforming technique. For thorough experimentation, we employ the trickle migration approach and the four traditional techniques. This experiment has a sample size of 10. The IBM Employee Dataset is used as a sample dataset, and it contains 1,059 rows of data. In this performed experimental analysis, the trickle migration approach is more efficient than Lift and Shift approach, Big Bang Approach, Refactoring technique, Replatforming technique in all aspects such as cost, time and quality of the data. The experiment shows the performance of Trickle migration approach with efficiency of (85.76%) when compared to the Big Bang Migration approach (77.49%), Refactoring technique (78.71%), Lift and Shift (76.72%), Replatforming Technique (76.96%). A significance test was performed on the five groups, resulting in a p-value of two-tailed is 0.008 for Big Bang, 0.016 for Refactoring, 0.004 for Lift and shift, 0.001 for Replatform, which is lesser than 0.05 (p<0.05) it is evident that there is an existence of statistically significant differences between these groups. The Trickle Migration approach is found to be more effective than the four traditional techniques in this research based on the data obtained.
Accurate Detection of Forgery in Signature using Deep Learning Algorithm in Comparison with Random Forest Model

This research is about the detection of forgery in signature images from the parameters taken from the data set using the Novel Random Forest Model (RFM) deep learning algorithm to compare the accuracy with Convolutional Neural Network-xg (CNN-xg). In total, 44 samples are taken for this study, with those samples being split evenly between two groups of 22, making the total number of samples gathered 44. Group 1 makes use of something called the Random Forest Model (RF), whereas Group 2 makes use of something called CNN-xg. In order to facilitate the creation of the code and its subsequent deployment, the machine learning capabilities of the Colab software were leveraged. For the purpose of determining the appropriate value for the sample size, the sample size is determined with the assistance of an online statistical analysis tool, a power value of 80%, and an alpha value of 0.05. This is done in order to arrive at the correct value for the sample size. The data that were obtained from the earlier studies are used. From simulation results, the Convolutional Neural network-xg Algorithm obtained a prediction accuracy of 97.43% and Random Forest Model (RF) a predicted accuracy of 72.291% with significance values of 0.040 (p<0.05). For the given dataset Convolutional Neural Network-xg performs significantly better than the RF in finding the accuracy for detecting forgery in signature images.
Animal Activity Recognition Using Convolutional Neural Network

It will be beneficial to observe and track animal behavior in order to avoid human-wildlife conflicts HWC. The introduction of wild animals produces multiple losses in a variety of biome zones, with agricultural loss accounting for a large portion of the losses. This conflict, in addition to crop loss, destroys human habitat and claims lives and property. Because loss occurs on both sides, this research may provide a solution to stop the loss of human and animal lives. In the proposed work, we use a mechanism to detect the animal correctly and sensitively. When an animal is discovered, the warning system emits a loud beep, alerting forest officers and those nearby to take appropriate action. The animal was recognized in this scenario utilizing a convolutional neural network filled with GLCM features and the deep learning method. The obtained image of the animal goes through several steps before being trained and checked against the database we collected. Finally, 98% of the desired production is obtained.
THEORETICAL AND LEGAL ANALYSIS OF E-JUSTICE IN THE REPUBLIC OF UZBEKISTAN

In the context of the subject of our article, we find it necessary to clarify the meaning of certain terms related to the use of ICT in the sphere of jurisprudence. Thus, it is essential to differentiate between the terms “electronic judicial process,” “digital court proceedings,” “judicial digitalization,” and “digital justice.” The term “judicial digitalization” refers to the provision of courts with computer resources and technologies that enable information processing. However, it does not fully elucidate the essence and procedure of conducting procedural actions in electronic form.
E-learning Software Development and Teaching Application Based on Simulation Technology: A Case Study

This study addresses the challenge of acquiring digital materials for E-learning software development. By employing finite element modeling and simulation techniques, a substantial collection of visual resources, including stress nephograms, was obtained for the three-point bending experiment. These materials formed the foundation for the development of an E-learning software program. The software was successfully implemented in classroom instruction, demonstrating its effectiveness in enhancing student understanding and engagement. Through a mixed methods research design, this study provides empirical evidence supporting the potential of simulation technology as a method for acquiring digital materials in E-learning. The systematic and scientific approach employed in this study offers insights for future development and implementation efforts in educational technology.
VARIOUS APPROACHES TO THE DEFFENITION OF TRANSLATION AND CLASSIFICATION OF INTERPRETATION

This research paper is devoted to the analysis of various approaches in translation and the classification of its types in the context of modern linguistic and translation theories. Different approaches in translation may include comparison between languages, analysis of cultural characteristics and context, study of the structure and semantics of texts in different languages, as well as the use of computer technology in translation. The study of this topic allows us to expand our understanding of the diversity of translation practices and ways of analyzing them, which is a key element in today's globalized world, where interaction between different cultures and languages is becoming increasingly important. This work provides valuable research materials for linguists, translators, students, and all interested in the field of intercultural exchange and language interaction, contributing to a deeper understanding of the complexity and diversity of translation activities in the modern world.
Peningkatan Hasil Belajar Menulis Surat Pribadi dengan Metode Pembelajaran Think Talk Write (TTW)

Writing is one of the most complex skills in learning Indonesian. When writing has gone through the process of listening, speaking, and reading. Therefore, the purpose of learning Indonesian is for students to be able to manage aspects of language proficiency. However, it is difficult for most students to learn Indonesian, especially by writing personal letters. Based on data from 31 students in Class VII-E of SMPN 7 Kediri, only 13 students met the minimum completeness criteria with a score of 75. The main problem in this study was the low learning achievement of students in class VII-E of SMPN 7 Kediri in writing letters. This is caused by students and teachers. Based on these factors, to overcome the problem, the researcher applied the Think Talk Write (TTW) learning method to write personal letters. The Think Talk Write (TTW) learning method is said to be able to improve letter writing skills. The average value of students after being given the Think Talk Write (TTW) treatment increased from pre-cycle 70.96, cycle 1 75.8, and cycle 2 83.5. Thus based on the results of the study it can be concluded that the application of the Think Talk Write (TTW) method can improve the ability to learn to write personal letters. This learning method should be used by teachers because it can improve student learning outcomes, especially the learning outcomes of writing personal letters.
Pengaruh Slice Thickness terhadap Signal to Noise Ratio (SNR) dari Hasil Penyinaran CT Scan di RSUP Prof. Dr. I.G.N.G Ngoerah

A research has been carried out on the effect of slice thickness on SNR values from the results of CT Scan irradiation at Prof. Dr. I.G.N.G Ngoerah Hospital. Variations in the thickness of the slices used are 1 mm, 3 mm, 5 mm, and 7 mm. The tube voltage, tube current, and FOV used are constant, namely 120 kV, 200 mA, and 380 mm. SNR value analysis was calculated using the average object value and SD background for each material. Based on the results of a simple linear regression test, the thickness of the slices obtained has an effect of 99.9% on the SNR value. These results indicate that the greater the thickness of the slice used, so the SNR value will increasesas the SNR value increases, it will improve image quality.
Perbandingan Metode Peluruhan untuk Memperkirakan Berakhirnya Gempabumi Susulan (Studi Kasus Gempabumi Signifikan di Wilayah Bali Tahun 2019-2020)

An Data collection and processing has been carried out to compare the aftershock decay method which aims to determine the assumption of aftershock ending time, how much energy, fracture length and fracture velocity occurred on the island of Bali. Data processing using several methods, namely the Omori method, the Mogi method I, the Mogi II method, and the Utsu method. The data used is based on the BBMKG Region III Denpasar earthquake catalog book, that’s is case of the Buleleng earthquake on November 14 2019, the Jembrana earthquake on July 16 2019 and the South Kuta earthquake on March 19 2020. Based on the results of data processing, for the Buleleng earthquake case the method closest to the observations was the Mogi II method with the energy is 1012 joules, the fracture length is 1.186 m and the fracture speed is 29.78 m/s. As for the Jembrana and South Kuta earthquake cases, the results of the calculation of all methods have not come close to the observations. The magnitude of the energy, the length of the fracture and the fracture speed of the Jembrana earthquake is 3.562313 joules, 32.733 m, 327.330 m/s. Whereas for the South Kuta earthquake, the energy, fracture length and fracture velocity were 3.162317 joules, 125.890 m, 70.750 m/s
Comparison of Algorithms K-Means and DBSCAN for Clustering Student Cognitive Learning Outcomes in Physics Subject

Clustering is an activity of grouping data into the same group based on similarity. The purpose of the study is to cluster and determine student cognitive learning outcomes characteristics. Cluster analysis was conducted on student cognitive learning outcomes using algorithms K-Means and DBSCAN. Both algorithms are appropriate to have been applied to the overlapping data such as student learning outcomes data. Also, their advantages are scaling large datasets and outliers. The data used in this study is student cognitive learning outcomes - final and mid-term exams grade X in physics subject. Applying the two proposed algorithms K-Means and DBSCAN, the best cluster algorithm to have been used for clustering analysis is K-Means which is based on the highest silhouette score of 0.43, while the silhouette score of DBSCAN is 0.39 respectively. Using the best cluster, the K-Means algorithm, found two types of clusters – cluster 1 consists of 132 students who have a high average score, and cluster 2 shows 183 students who have a low average score in both final and mid-term exams respectively. From the analysis results, most students still have low cognitive learning outcomes in physics subject.
Dinamika Sebaran Suhu Udara Menggunakan Citra Landsat 8 di Wilayah Denpasar Periode 2018-2021

Research has been conducted on the Dynamics of Air Temperature Distribution Using Landsat 8 Imagery for the 2018-2021 Period, in the Denpasar area study area with a location point at the Sanglah Geophysical Station. The methods used in this study were Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST), while the analysis used was correlation and validation. The results of the NDVI value study explained that from 2018 to 2021 the NDVI density value was getting higher so that the resulting temperature was low. Meanwhile, the determination of ESG on the dynamics of average air temperature explains that the average air temperature value in each year does not always rise linearly, in 2018 the highest average air temperature distribution was at 29.56 °C, in 2019 it was 35.56 °C, in 2020 it was 25.62 ° C and 2021 it was 30.72 ° C. In testing and analysis, a correlation relationship was obtained between Landsat 8 imagery (band 10 and band 11) and BMKG, which showed that the temperature between Landsat 8 imagery and BMKG was correlated in the same direction. The relationship between the distribution of average air temperatures with the implementation of PPKM in the Denpasar area has nothing to do with it as previously thought. Due to the increase in temperature, landsat 8 and BMKG images are only affected by the atmosphere
A Hardware-friendly Quantization and Model Fine Tuning with STEBC for Object Detection

This paper present a hardware-friendly quantization and model training method using dynamic fixed-point and straight through estimator with boundary constraint(STEBC). By the proposed quantization and fine-tune methods, with all bit-width of layers are set to less than 8-bit including the first layer, quantizing a model from floating-point without fine-tuning can achieve less than 5% drop in accuracy. After fine-tune using the straight-through estimator with boundary constraint, we can recover the accuracy to less than 1% drop compared to its floating-point counterpart. We have proven our method works on various detectors and various backbone, including one-stage and two-stage detector.
In-Memory-Computing (IMC) Technique in Local Difference Decision Block of an On-Board Satellite Hyperspectral Data Compression Algorithm

Due to high data acquisition rates and large data volumes, on-board hyperspectral imagery (HSI) demands data compression before transmission. The major bottleneck in on-board implementation of algorithms is the limited availability of power and resources along with the need to be less error prone while accessing and processing the data. The recently proposed simple lossless algorithm (SLA) for on-board satellite hyperspectral data compression, performs ~80% calculations by utilizing previous neighborhood pixels, which in turn requires majority of memory access. This may lead to an increase in overall power dissipation and probability of error due to bit flips in the von-Neumann architectures of SLA. Thus, to reduce the probability of error and to improve power efficiency, this work adopts in-memory-computing (IMC) technique for the local difference (LD) decision block of the SLA for non-overlapping case. The LD decision block calculates absolute difference between the neighboring pixels and requires XOR operation for the same. To perform IMC, the SRAM cell from literature is modified to get XOR between two pixels. A non-overlapping (6*16) bit memory cell is implemented using von-Neumann and IMC based architecture for LD decision block. The proposed architecture allows reduced memory overheads and instead of using more number of transistors in a cell, consumes comparable power when compared to the von-Neumann architecture. The error resilience of SLA is expected to improve, as the total memory access for processing a pixel is reduced by 21.42%.
Investigating a Quantum Cloud Paradigm with Quantum Neural Networks

In this work, we examine the interactions between an embedded processing board and cloud server performing quantum computing simulations. We examined the trade-offs in performance and complexity between using classical neural networks and quantum-hybrid neural networks on the cloud server. More specifically, we propose the use of quantum hybrid neural network (QNN) for the classification of spoken commands on live audio data acquired at the networks edge. For this application, a compact embedded processor board handles simple operations like data acquisition and pre-processing while a quantum cloud server is used to perform quantum computing simulations. This experimental setup is well suited for quantum computing as it is not feasible to embed a quantum processor at the edge, but practical to interface embedded processor boards to a quantum cloud server for processing massive audio data for a variety of recognition tasks. We found that distributing the task between the embedded processor and quantum sever enabled the application of QNN's to live audio data at the expense of system response time.