Reviewerly/exHarmony huggingface dataset:
Mocking HTTP redirects

You’ve experienced an HTTP redirect (or URL redirect, or URL forwarding) even if you haven’t noticed. We all use browsers (I assume, since you are reading this), either on a phone or laptop/desktop computer.
Differentiated Time-Series Reconstruction for Anomaly Detection of Spacecraft Using Siamese Neural Network

With the increasingly complex structure and challenging missions, anomaly detection of spacecraft becomes more and more important to ensure the operating reliability. But the abnormal events are not very common in practical applications. Therefore, it is meaningful to establish the anomaly detection model based on these abnormal samples. This research proposes a differentiated Siamese neural network-based time-series reconstruction technique. The autoencoder composed with Long short-term memory (LSTM) is established to extract the different characteristics within the samples in normal intervals and samples in abnormal intervals. The contrastive learning strategy are used to making full use of the existing anomalies. So that the trained autoencoder can enhance the difference between the normal samples and abnormal samples. With this Siamese neural network-based time-series reconstruction model, its statistical parameters the data reconstruction error can be used to clearly divide the normal and abnormal samples. The outcome of the experiment based on real spacecraft telemetry data collected by NASA illustrated that, using the existing time-series reconstruction model we constructed, the mean reconstruction error between samples in normal intervals and samples in abnormal intervals is higher than 0.117, and the mean value is 2.6706 as large as the value computed in the basic LSTM model, which means the proposed method can enhance the difference between normal samples and anomalies. This will be much helpful to realize the accurate anomaly detection for spacecraft.
Point Cloud based Motion State Estimation Method for Autonomous Driving

Generally, motion vector information primarily stems from moving objects, while static objects contribute minimally to the estimation task. Traditional approaches for motion vector estimation typically rely on scene flow methods that depend on deep models to extract features from individual points at a high cost. These methods then acquire flow information through complex matching mechanisms or feature decoding. Such approaches are computationally expensive and exhibit substantial latency. Moreover, they neglect the importance of motion objects in motion vector estimation and the interference from static objects. Therefore, this paper introduces a novel method that first performs point cloud motion segmentation and subsequently estimates motion vectors. This approach focuses on leveraging point cloud information annotated with motion objects to estimate three-dimensional scene flow more effectively. By employing motion segmentation, we can obtain annotations for moving objects, enabling greater emphasis on the estimation of motion vectors for more challenging cases. In our experiments conducted on the KITTI dataset, the proposed method demonstrates superior performance compared to existing scene flow estimation methods. Specifically, without considering motion segmentation errors, the error in the motion direction is only 0.0363 m/s, showcasing better performance. Additionally, our method achieves an error of 0.076 m for three-dimensional endpoint error (EPE3D), showcasing distinct advantages over current scene flow networks.
A hierarchical path planning algorithm based on graphical search and optimization methods

This paper proposes a hierarchical path-planning algorithm based on a combination of graphical search algorithms and optimization methods. In the global path planning layer, by using the Hybrid A* algorithm, we can quickly obtain an optimal path that can avoid all static obstacles on the map. In the local path planning layer, the global path is optimized by numerical nonlinear numerical optimization to generate a feasible path that satisfies the safety constraints. By processing global path planning and local path planning separately, the computational complexity of path planning can be effectively reduced, and the efficiency and accuracy of the path planning can be improved. Secondly, the hybrid planning algorithm can generate high-quality paths with both safety and flexibility. We simulate and verify the algorithm, and the results show that the method has practical applications in autonomous driving.
Notes on Python

It’s been interesting switching jobs with respect to programming languages. I used to write 95% R - now I write 95% Python. I have been using Python for many years, but not seriously or getting paid either. I’ve learned alot in the first 6 months.
Cooperative Computing for Mobile Crowdsensing: Design and Optimization

With the increasing number of mobile devices, mobile crowdsensing (MCS) has garnered significant attention in research. However, computing infrastructures such as edge/cloud nodes, which are necessary for processing sensor data, are not always readily available. To address this issue, we propose a cooperative computing framework that enables the offloading of sensor data to nearby mobile devices with unused computational resources (known as helpers) for processing. Our approach considers a scenario with multiple sources and multiple helpers, where computational tasks can be partially offloaded to several helpers. We jointly optimize task offloading strategy, communication resources, and computational resources to minimize the weighted sum energy consumption of mobile devices. We model the optimization problem as a mixed- integer nonlinear programming (MINLP), with the source-helper assignment solved using a distributed algorithm based on matching theory, and the joint task partition and resource allocation problem solved using an alternating optimization (AO) method. Simulation results demonstrate the efficacy of our cooperative computing framework and scheduling scheme, which offer significant advantages over local computing in terms of reducing the weighted sum energy consumption and improving the task completion ratio.
Day-Ahead Forecast of Photovoltaic Power Based on a Novel Stacking Ensemble Method

Accurate prediction of photovoltaic (PV) power is the prerequisite for the safe and stable operation of the power grid with high penetration of PV. Despite various machine learning models for forecasting PV power have been developed, their accuracies are generally unstable. Toward this end, this study proposes a novel Stacking ensemble forecast model to improve the precision of day-ahead PV power forecasts. Different from the traditional Stacking model that uses the original training dataset to train the base learners, the proposed model creates multiple sub-training sets from the original training dataset to train the base learners, so as to enhance the diversity of base models and further improve the prediction accuracy. Specifically, in the proposed Stacking ensemble model, four machine learning learners, i.e., generalized regression neural network (GRNN), extreme learning machine (ELM), Elman neural network (ElmanNN), and Long shot-term memory (LSTM) neural network are incorporated, which are trained with the diverse sub-training datasets, and a variety of candidate base models are generated. For those candidate base models, the ones with the best performance are selected and integrated through a meta-model, namely the back-propagation network work (BPNN), to produce the final PV power prediction. The proposed model is evaluated using measured data from a 15kW PV power station in Ashland, Oregon, USA. Results indicate that across three weather scenarios, the performance of the novel Stacking ensemble model consistently outperforms single models and the traditional Stacking ensemble model in terms of the errors for out-of-sample forecasting, which proves the effectiveness of the developed procedure in improving PV power forecasting accuracy.
List comprehension vs. filter vs. key lookup

I was working on a work task last week, and needed to filter out one instance of a class from a list of class instances. No matter how you do this speed doesn’t matter too much if you’re doing this operation once or a few times.
What is Your VR use Case for Educational Like: A State-Of-The-Art Taxonomy

Virtual reality has emerged as an influential technology in the educational landscape, offering learners and teachers immersive and interactive experiences that enhance traditional teaching methods. However, despite the increasing importance of virtual reality in education, a systematic description and classification of virtual reality use cases in education is still lacking. This limits the understanding and comparability of virtual reality use cases and highlights the need for a structured approach. This study asks the research question: How can virtual reality educational use cases, identified in the literature, be described, and classified? To classify these use cases, this study develops a state-of-the-art taxonomy. The taxonomy was developed in a combination of a conceptual-to-empirical and empirical-to-conceptual approach. The first stage to develop the taxonomy was based on a conceptual-to-empirical approach. Here, the concepts of virtual reality, use case and education serve as meta-characteristics and theoretical structure. To further detail the dimensions and characteristics, a systematic literature review was conducted by following a PRISMA-guided search and selection process. Therefore, the scientific databases Science Direct, AISel and Springer Link were used to search for studies between 2018 and 2023, obtaining a sample of 39 publications. As the conceptual-to-empirical approach did not richly describe the analysed virtual reality use cases from the studies, additional dimensions and characteristics were identified inductively. Therefore, a second iteration was conducted relating to the empirical-to-conceptual approach. This process explored the practical aspects of virtual reality scenarios and added applicable and practical characteristics to the initial theoretical foundation. The result is a comprehensive taxonomy of virtual reality use cases in education that contributes significantly to existing knowledge and provides a solid foundation for future research. The final taxonomy includes 17 dimensions and 37 characteristics. These findings can support educators to understand the nature of virtual reality use cases, enabling them to describe and implement such use cases effectively within educational settings.
Make It Less Complex: Autoencoder for Speckle Noise Removal—Application to Breast and Lung Ultrasound

Ultrasound (US) imaging is used in the diagnosis and monitoring of COVID-19 and breast cancer. The presence of Speckle Noise (SN) is a downside to its usage since it decreases lesion conspicuity. Filters can be used to remove SN, but they involve time-consuming computation and parameter tuning. Several researchers have been developing complex Deep Learning (DL) models (150,000–500,000 parameters) for the removal of simulated added SN, without focusing on the real-world application of removing naturally occurring SN from original US images. Here, a simpler (&lt;30,000 parameters) Convolutional Neural Network Autoencoder (CNN-AE) to remove SN from US images of the breast and lung is proposed. In order to do so, simulated SN was added to such US images, considering four different noise levels (σ = 0.05, 0.1, 0.2, 0.5). The original US images (N = 1227, breast + lung) were given as targets, while the noised US images served as the input. The Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) were used to compare the output of the CNN-AE and of the Median and Lee filters with the original US images. The CNN-AE outperformed the use of these classic filters for every noise level. To see how well the model removed naturally occurring SN from the original US images and to test its real-world applicability, a CNN model that differentiates malignant from benign breast lesions was developed. Several inputs were used to train the model (original, CNN-AE denoised, filter denoised, and noised US images). The use of the original US images resulted in the highest Matthews Correlation Coefficient (MCC) and accuracy values, while for sensitivity and negative predicted values, the CNN-AE-denoised US images (for higher σ values) achieved the best results. Our results demonstrate that the application of a simpler DL model for SN removal results in fewer misclassifications of malignant breast lesions in comparison to the use of original US images and the application of the Median filter. This shows that the use of a less-complex model and the focus on clinical practice applicability are relevant and should be considered in future studies.
Top-Down Models across CPU Architectures: Applicability and Comparison in a High-Performance Computing Environment

Top-Down models are defined by hardware architects to provide information on the utilization of different hardware components. The target is to isolate the users from the complexity of the hardware architecture while giving them insight into how efficiently the code uses the resources. In this paper, we explore the applicability of four Top-Down models defined for different hardware architectures powering state-of-the-art HPC clusters (Intel Skylake, Fujitsu A64FX, IBM Power9, and Huawei Kunpeng 920) and propose a model for AMD Zen 2. We study a parallel CFD code used for scientific production to compare these five Top-Down models. We evaluate the level of insight achieved, the clarity of the information, the ease of use, and the conclusions each allows us to reach. Our study indicates that the Top-Down model makes it very difficult for a performance analyst to spot inefficiencies in complex scientific codes without delving deep into micro-architecture details.
Salient Object Detection Based on Optimization of Feature Computation by Neutrosophic Set Theory

In recent saliency detection research, too many or too few image features are used in the algorithm, and the processing of saliency map details is not satisfactory, resulting in significant degradation of the salient object detection result. To overcome the above deficiencies and achieve better object detection results, we propose a salient object detection method based on feature optimization by neutrosophic set (NS) theory in this paper. First, prior object knowledge is built using foreground and background models, which include pixel-wise and super-pixel cues. Simultaneously, the feature maps are selected and extracted for feature computation, allowing the object and background features of the image to be separated as much as possible. Second, the salient object is obtained by fusing the features decomposed by the low-rank matrix recovery model with the object prior knowledge. Finally, for salient object detection, we present a novel mathematical description of neutrosophic set theory. To reduce the uncertainty of the obtained saliency map and then obtain good saliency detection results, the new NS theory is proposed. Extensive experiments on five public datasets demonstrate that the results are competitive and superior to previous state-of-the-art methods.
Using Distance Measure to Perform Optimal Mapping with the K-Medoids Method on Medicinal Plants, Aromatics, and Spices Export

About 80% of the world's medicinal plants grow in Indonesia. The Negeri Rempah Foundation also said that Indonesia has more kinds of spices than any other country in Southeast Asia. To figure out the best way to export plants, you need to do a study that groups them by their main destination country. This is called "clustering," and it can be done by doing regional mapping. In the clustering process, measuring deviation/distance or distance space is a key part of figuring out how similar or regular data and items are. K-Medoids is one way to group things together. K-Medoids is an algorithm that groups data based on how far apart they are. Distance Measure is a way to measure the distance between two points. It can help an algorithm sort object into groups based on how similar their variables are. The dataset used comes from the Customs Documents of the Directorate General of Customs and Excise on the official website of the Central Bureau of Statistics for the period of 2012-2021 about the Export of Medicinal, Aromatic, and Spices Plants. This study uses mixed measures (mixed euclideandistance), numerical measures (camberradistance), and bregmandivergences (generalizeddivergence). The mapping results are compared with the validation of the Davies Bouldin Index (DBI). With the help of the rapidminer software, a number of tests were done. The results showed that using mixed measures (mixed euclideandistance) with a value of k=4 gave a DBI value of 0.021. Because it gives a DBI value close to 0, the K-Medoids algorithm with mixed measures (mixedeuclideandistance) is thought to work better than other distance measures.
Data Optimization using PSO and K-Means Algorithm

Tourism is one of the industries that contribute considerably to the country's economy. Tourism helps the country's economy expand by providing and increasing jobs, living standards, and triggering the rise of other tourist-related production. The tourism industry will become a multinational industry and the primary driver of the global economy in the twenty-first century. Tourism has generated significant foreign exchange for a number of countries. Indonesia, the world's biggest archipelagic country with 17,508 islands, often known as the archipelago or maritime country, has recognized the importance of the tourist sector to the Indonesian economy because tourism growth consistently outpaces economic growth. The research's goal is to map the number of tourist visits. The mapping is in the form of clusters based on countries. The technology utilized is classification data mining with the K-Means method and Particle Swarm Optimization (PSO). The dataset came from the Central Bureau of Statistics, a government organization (abbreviated as BPS). The research outcomes in cluster mapping, with the cluster results compared to standard K-Means and K-Means + PSO. RapidMiner software is used during the analytical process. The calculation results in the form of clusters will be evaluated using the Davies-Bouldin Index (DBI) parameter. The cluster value (k) used is k = 2, 3, 4, 5. The findings show that the K-Means + PSO optimization has the minimum DBI value for k = 5. Meanwhile, the DBI value for k = 5 is 0.134.
Going native? How crypto technology may help regulators

The crypto industry suffered a prolonged crisis in 2022. It is now at a turning point, as lawmakers around the world deploy new statutes aimed at curtailing endemic fraud, inadequate risk management, and bad governance. This is a net positive for the ecosystem – without legal certainty, it cannot flourish. In this paper, I argue that some crypto-native constructs may usefully complement traditional frameworks in the regulation of both crypto itself and the broader financial system. Protected environments, such as regulatory sandboxes and innovation hubs, should be leveraged to foster creative cooperation between authorities and the crypto industry.
3D Scanner Using Infrared for Small Object

Three-Dimensional scanning is a method to convert various distances set into object visualization in 3-dimensional form. Developing a 3D scanner has various methods and techniques depending on the 3d scanner's purpose and the size of the object target. This research aims to build a prototype of a 3D scanner scanning small objects with dimensions maximum(10x7x23)cm. The study applied an a-three dimensional(3D) scanner using infrared and a motor to move the infrared upward to get Z-ordinate. The infrared is used to scan an object and visualize the result based on distance measurement by infrared. At the same time, the motor for rotating objects gets the (X, Y) ordinates. The object was placed in the center of the scanner, and the maximum distance of the object from infrared was 20cm. The model uses infrared to measure the object's distance, collect the result for each object's height, and visualize it in the graphic user interface. In this research, we tested the scanner with the distance between the object and infrared were 7 cm, 10 cm, 15 cm, and 20 cm. The best result was 80% accurate, with the distance between the object and the infrared being 10cm. The best result was obtained when the scanner was used on a cylindrical object and an object made of a non-glossy material. The design of this study is not recommended for objects with edge points and metal material.
The Impact Between on-The-Job Training and Employee Performance

Most studies are done on the teachers and nurses but few on the manufacturing employees. According to the previous study, in 2016 product, process design and manufacturing are the most requested training with a percentage of 32% and it has increased in 2017 to 41%. Therefore, there is an emerging need for companies to create formative plans in organizing training. As most of the manufacturing companies are using on-the-job training, it is essential to study the relationship between on-the-job training and employee performance. This research is aimed to investigate the relationship between on-the-job training and employee performance in manufacturing companies. The study was based on the following objectives: (i) to determine the level of employees’ perception towards on-the-job training in manufacturing companies; (ii) to identify the level of employee performance in manufacturing companies; (iii) to examine the relationship between on-the-job training and employee performance; (iv) to investigate the impact of on-the-job training towards employee performance in manufacturing companies. A sample of 101 employees from the southern region of Malaysia (Johor, Melaka & Negeri Sembilan) were selected as respondents. A purposive sampling method has been used to determine the sample. This study is quantitative study. Hence, questionnaires were being used and the data was analyzed using Statistical Package for Social Science (SPSS). To achieve the objective of this research, descriptive analysis, mean score, Spearman correlation and linear regression were used. The findings discover that both the level of employees’ perception towards on-the-job training in manufacturing companies and the level of employee performance are high. Meanwhile, the relationship between on-the-job training and employee performance showed significant medium correlation. Lastly, there is a significant positive impact of on-the-job training towards employee performance. This means that a unit change of on-the-job training could bring 58.8% changes in employee performance as well.