Reviewerly/exHarmony huggingface dataset:
Estimating the Distribution of Paddy Growth Stage in Karawang, West Java Based on Gradient Boosting Algorithm

Paddy is an important and strategic commodity to supply food needs in Indonesia. Karawang Regency in West Java Province is well known as rice producer due to its natural resources and farming system. This study aims to estimate the distribution of paddy growth stage in Karawang Regency based on the classification results using the Gradient Boosting algorithm. The classification was carried out to conduct training on monthly data so as to produce predictions of the distribution of paddy growth stage area. There are 8 classifications defined, including water bodies, early vegetative, late vegetative, generative, harvesting & land preparation, not paddy fields, others, also settlements and roads. As validation material, Area Frame Sampling (Kerangka Sampling Area or KSA) data of Karawang Regency 2020 was used and comparisons were made using the Rainy Season Paddy Cropping Calendar for the October 2020-March 2021 period of Karawang Regency as an evaluation of the results. The results of the accuracy test for estimation of the distribution of paddy growth stage using the Gradient Boosting algorithm show that this algorithm has a good level of suitability and confidence level between rows and columns in the process of estimating the paddy growth stage in Karawang Regency as evidenced by the overall accuracy dominant value is >75% and the kappa statistics dominant value is > 0.7.
Improving the Reliability of Gaze Estimation through Cross-dataset Multi-task Learning

In recent years, gaze estimation has been applied to numerous application scenarios, such as driver monitor systems and autism evaluation, to name a few. However, the current algorithms are based on an assumption: the users eyes are all open in the input images. In practice, this assumption does not always hold since people blink. Images with eyes closed will result in irregular gaze estimation results that will affect the safety of the vehicle driving and the reliability of the evaluation results. Here, we address the problem by leveraging cross-dataset training to train a multi-task network capable of simultaneously estimating the eye gaze and the blink on the gaze estimation dataset and blink estimation dataset. We further compare our networks results with that of single-task networks and evaluate our network in a simulated scenario. The experimental results demonstrate that our method is suitable for practical use and can improve the reliability of the gaze estimation results.
A secure and anonymous communication scheme based on Internet public service environment

With the continuous expansion of the Internet and the full popularization of intelligent terminal devices, the risk of a large amount of personal privacy data such as identity, location, and preferences being leaked during Internet communication is getting higher and higher. Although the existing cryptographic technologies such as encryption and authentication can realize the security of information content and can effectively prevent attackers from obtaining information illegally, they cannot prevent attackers from obtaining important information such as the location and identity of nodes through eavesdropping and traffic analysis. The secure and anonymous communication technology represented by Tor has always been the focus of researchers. It is essential to realize free and open communication on today's Internet, but it is usually only when large institutions invest huge resources to complete the construction of a secure and anonymous system. This paper proposes a method for constructing a secure and anonymous transmission network based on Internet public services (such as VPS, cloud storage services, P2P networks, etc.), and at the same time, a redundant sharding mechanism is designed to improve the transmission capacity. Then complete the construction of the system prototype by renting a virtual host, and realize the practical ability test of the safe and anonymous system. Provide small-scale and convenient secure and anonymous transmission capabilities in emergencies such as epidemics.
Increasing the Efficiency of Irrigation Systems in the Republic of Bulgaria Through New Electrical Systems and Blockchain

The quality of services determines the standard of living of people, the well-being of companies, and the formation of GDP. Such is the household and communal services, but the latter are fundamental for the existence of the household. In this aspect, maintaining the quality of all systems of the utility sector is very important. It appears that significant problems exist in the electricity supply, water supply, and communications. In water supply, they are heterogeneous given the presence of leaks, seasonality of service use, drought, reduced harvest, and others. From this point of view, the aim of the article is to analyze the origin of the problems in the water supply system. The institutions responsible for the various types of water supply systems are presented. Based on this analysis, means to increase the quality of services are proposed. A blockchain-based accident monitoring scheme has been synthesized between the responsible authorities.
Machine Learning for Plastic Waste Detection: State-of-the-art, Challenges, and Solutions

Machine learning has advanced rapidly in recent technologies of artificial intelligence. The abundant and cheap computation in machine learning has created more knowledge and reliability of this method. Therefore, more researchers have relied on machine learning to solve plastic waste pollution, which contributes to global greenhouse gas emissions. Datasets are fundamental requirements to build the foundation of the plastic waste detection system. This paper provides an overview of several state-of-the-art deep learning models, focusing on the dataset used for waste classification and identification. The performance of each deep learning model in classification accuracy is also discussed. The challenges of implementing deep learning in plastic waste detection are identified; (i) Data deficiency for training, (ii) Quality of the existing plastic waste dataset, and (iii) Color spaces. Lastly, the proposed solution to each challenge is presented.
Low-SNR Modulation Recognition based on Deep Learning on Software Defined Radio

Automatic modulation classification (AMC) and recognition (AMR) of received wireless signals have a significant role for various commercial and military areas. These methods are able to identify the modulation type and recognize the received signal by extracting discriminating features from the signals. Deep neural network (DNN) offer a great tool that assist the identification of signal modulation because of its capability to extract complex features from the received signals. In this work, we propose a convolutional network model to classify the modulation type of a wireless signal at low-SNR values. The experimental results demonstrate that the proposed model correctly classify 72% digital signals at -4 dB. The accuracy can be increased if the similarities between QAM4 and QAM64, 8PSK and QPSK is reduced.
Facial Emotions Recognition Using Vit and Transfer Learning

Facial Emotions Recognition is one of the celebrated and challenging tasks in computer vision. Different approaches (e.g., Convolutional Neural Network (CNN), Graph Convolutional Network (GCN), Vision transformer (Vit), etc.) have been proposed in the literature. In recent years, the most popular methods are based on CNNs, the fact which has increased the accuracy of FER. However, lack of large training databases has been a major issue in model training. Researchers use various deep-learning techniques to solve this issue, namely Transfer-Learning and Fine-Tuning. Moreover, Vit models have been a trend in the evolving use of computer vision tasks. Thus, to design a highly accurate FER system, we propose modeling through Transfer Learning (TL) technique, where a pre-trained Vit model is adopted through fine-tuning its last layer(s) to be compatible with the FER task. This work compares different pre-trained Vits models (Vit-B16, Vit-B32, Vit-L16, Vit-L32) and a Vit-CNN merging model in the RAF-DB dataset. Experiment results showed that combining Vit-b16 with pre-trained CNN model VGG16 is the best model, and our fine-tuning was found to improve accuracy in the RAF-DB dataset.
Golden Eagle Extreme Learning Machine for Hourly Solar Irradiance Forecasting

Nowadays, the photovoltaic system which is one of the renewable energy sources has an important role in generating electricity. To forecast photovoltaic system generation, the time-series information of solar irradiance should be considered for implementation with the computational model. Machine learning is a highly successful model that can forecast information accurately. However, the matter of machine learning model can cause the instability of forecasting; therefore, the forecasting result is unstable, which cannot apply in real forecasting electricity. To reduce the cause of instability, this paper proposes a novel machine learning model which applied the golden eagle optimization combined with the extreme learning machine model. The experiment results showed that the proposed model's minimum root mean square error was achieved at 0.0791, which was better than the comparative models.
Synthetic Training Data Generation and Domain Randomization for Object Detection in the Formula Student Driverless Framework

Today industries strive toward using data-driven machine learning wherever applicable.Consequently, they require manually or automatically labeled training data sets.Currently, synthetically generating labeled training data sets belongs to the open challenges in machine learning across multiple application fields.In this paper, we propose employing a procedural pipeline combining BlenderProc with domain randomization to create prelabeled training data sets synthetically.Randomizing the domain using uncorrelated random background images, we ensure that the neural network applied for object detection purely learns the object features and is background-independent.Our proposed pipeline yields a solution to create sizeable prelabeled training data sets.We assess the pipeline performance for the application of cone object detection for the formula student driverless competition using no real training and a small realworld training data set for fine-tuning: We show that using the synthetically generated training data fine-tuned with a limited real training data set performs best for object detection.This transfer learning-based, fine-tuned solution also outperforms the benchmark training data set in detecting knocked-over cones that are neither present in the real nor the synthetic training data set.Consequently, by combining BlenderProc and domain randomization, we provide a solution for formula student teams to generate extensive training data for cone detection and other detection problems relevant to driverless.
Logic-Based Neural Network for Pattern Correction

As the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century continues, Deep Learning (DL) has become an important part in the digital world. The use of Artificial Neural Networks (ANNs), the core of DL, has led to the development of many novel applications, like speech recognition, character recognition, autonomous cars, etc. In this paper, we present a logic-based neural network processor for pattern correction, which can detect anomalies in a certain character, and can regenerate the character after removing the anomalies. The processor constitutes of sub-networks, namely the Generator network, the Inverter network, the Locator network, the Identifier network, and the Replacer network. A test for the proposed processor was done on the dataset of numeric characters.
Policy Optimization to Learn Adaptive Motion Primitives in Path Planning With Dynamic Obstacles

This letter addresses the kinodynamic motion planning for non-holonomic robots in dynamic environments with both static and dynamic obstacles – a challenging problem that lacks a universal solution yet. One of the promising approaches to solve it is decomposing the problem into the smaller sub-problems and combining the local solutions into the global one. The crux of any planning method for non-holonomic robots is the generation of motion primitives that generates solutions to local planning sub-problems. In this work we introduce a novel learnable steering function (policy), which takes into account kinodynamic constraints of the robot and both static and dynamic obstacles. This policy is efficiently trained via the policy optimization. Empirically, we show that our steering function generalizes well to unseen problems. We then plug in the trained policy into the sampling-based and lattice-based planners, and evaluate the resultant POLAMP algorithm (Policy Optimization that Learns Adaptive Motion Primitives) in a range of challenging setups that involve a car-like robot operating in the obstacle-rich parking-lot environments. We show that POLAMP is able to plan collision-free kinodynamic trajectories with success rates higher than 92%, when 50 simultaneously moving obstacles populate the environment showing better performance than the state-of-the-art competitors.
Anomaly Detection of GAN Industrial Image Based on Attention Feature Fusion

As life becomes richer day by day, the requirement for quality industrial products is becoming greater and greater. Therefore, image anomaly detection on industrial products is of significant importance and has become a research hotspot. Industrial manufacturers are also gradually intellectualizing how product parts may have flaws and defects, and that industrial product image anomalies have characteristics such as category diversity, sample scarcity, and the uncertainty of change; thus, a higher requirement for image anomaly detection has arisen. For this reason, we proposed a method of industrial image anomaly detection that applies a generative adversarial network based on attention feature fusion. For the purpose of capturing richer image channel features, we added attention feature fusion based on an encoder and decoder, and through skip-connection, this performs the feature fusion for the encode and decode vectors in the same dimension. During training, we used random cut-paste image augmentation, which improved the diversity of the datasets. We displayed the results of a wide experiment, which was based on the public industrial detection MVTec dataset. The experiment illustrated that the method we proposed has a higher level AUC and the overall result was increased by 4.1%. Finally, we realized the pixel level anomaly localization of the industrial dataset, which illustrates the feasibility and effectiveness of this method.
Classification of News Texts by Categories Using Machine Learning Methods

In parallel with the advances in technology, digital journalism is preferred more than printed journalism day by day. Due to the fast and up-to-date sense of journalism provided by digital journalism and its ubiquitous accessibility features, it is read more by users. In addition to these advantages provided by digital journalism, it also has some difficulties compared to printed journalism. The stage of preparation and delivery of the news to the user requires more technological knowledge and equipment compared to printed journalism. The processes of title selection, text creation, photo selection and determination of the appropriate news category in the preparation phase of the news are designed to be both faster and user-friendly compared to printed publishing. The news created to be presented to the target audience may belong to one or more of different categories such as economy, politics, sports, technology, and health. The inclusion of the news in the appropriate category provides convenience in terms of reaching the right audience and archiving the news correctly. In this study, news texts were classified according to their categories based on the machine learning methods. In the study, news of five newspapers in three different categories were used. Bayesian classifier and decision tree methods were used to classify the news in the dataset including a total of 10.500 news. In the results of the study, it was observed that the Bayesian classifier classified the news more successfully according to their categories.
Competence-aware systems

Building autonomous systems for deployment in the open world has been a longstanding objective in both artificial intelligence and robotics. The open world, however, presents challenges that question some of the assumptions often made in contemporary AI models. Autonomous systems that operate in the open world face complex, non-stationary environments wherein enumerating all situations the system may face over the course of its deployment is intractable. Nevertheless, these systems are expected to operate safely and reliably for extended durations. Consequently, AI systems often rely on some degree of human assistance to mitigate risks while completing their tasks, and are hence better treated as semi-autonomous systems. In order to reduce unnecessary reliance on humans and optimize autonomy, we propose a novel introspective planning model—competence-aware systems (CAS)—that enables a semi-autonomous system to reason about its own competence and allowed level of autonomy by leveraging human feedback or assistance. A CAS learns to adjust its level of autonomy based on experience and interactions with a human authority so as to reduce improper reliance on the human and optimize the degree of autonomy it employs in any given circumstance. To handle situations in which the initial CAS model has insufficient state information to properly discriminate feedback received from humans, we introduce a methodology called iterative state space refinement that gradually increases the granularity of the state space online. The approach exploits information that exists in the standard CAS model and requires no additional input from the human. The result is an agent that can more confidently predict the correct feedback from the human authority in each level of autonomy, enabling it learn its competence in a larger portion of the state space.
Evaluation of Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation for Chronic Heart Disease Detection

This study aims to investigate the effectiveness of local interpretable model-agnostic explanation (LIME) and Shapley additive explanation (SHAP) approaches for chronic heart disease detection. The efficiency of LIME and SHAP are evaluated by analyzing the diagnostic results of the XGBoost model and the stability and quality of counterfactual explanations. Firstly, 1025 heart disease samples are collected from the University of California Irvine. Then, the performance of LIME and SHAP is compared by using the XGBoost model with various measures, such as consistency and proximity. Finally, Python 3.7 programming language with Jupyter Notebook integrated development environment is used for simulation. The simulation result shows that the XGBoost model achieves 99.79% accuracy, indicating that the counterfactual explanation of the XGBoost model describes the smallest changes in the feature values for changing the diagnosis outcome to the predefined output.
Detecting Moving Vehicles from Satellite-Based Videos by Tracklet Feature Classification

Satellite-based video enables potential vehicle monitoring and tracking for urban traffic management. However, due to the tiny size of moving vehicles and cluttered background, it is difficult to distinguish actual targets from random noise and pseudo-moving objects, resulting in low detection accuracy. In contrast to the currently overused deep-learning-based methods, this study takes full advantage of the geometric properties of vehicle tracklets (segments of moving object trajectory) and proposes a tracklet-feature-based method that can achieve high precision and high recall. The approach is a two-step strategy: (1) smoothing filtering is used to suppress noise, and then a non-parametric-based background subtracting model is applied for obtaining preliminary recognition results with high recall but low precision; and (2) generated tracklets are used to discriminate between true and false vehicles by tracklet feature classification. Experiments and evaluations were performed on SkySat and ChangGuang acquired videos, showing that our method can improve precision and retain high recall, outperforming some classical and deep-learning methods from previously published literature.
Severity Based Light-Weight Encryption Model for Secure Medical Information System

As the amount of medical images transmitted over networks and kept on online servers continues to rise, the need to protect those images digitally is becoming increasingly important.However, due to the massive amounts of multimedia and medical pictures being exchanged, low computational complexity techniques have been developed.Most commonly used algorithms offer very little security and require a great deal of communication, all of which add to the high processing costs associated with using them.First, a deep learning classifier is used to classify records according to the degree of concealment they require.Medical images that aren't needed can be saved by using this method, which cuts down on security costs.Encryption is one of the most effective methods for protecting medical images after this step.Confusion and dispersion are two fundamental encryption processes.A new encryption algorithm for very sensitive data is developed in this study.Picture splitting with image blocks is now developed by using Zigzag patterns, rotation of the image blocks, and random permutation for scrambling the blocks.After that, this research suggests a Region of Interest (ROI) technique based on selective picture encryption.For the first step, we use an active contour picture segmentation to separate the ROI from the Region of Background (ROB).5692 CMC, 2023, vol.74, no.3 Permutation and diffusion are then carried out using a Hilbert curve and a Skew Tent map.Once all of the blocks have been encrypted, they are combined to create encrypted images.The investigational analysis is carried out to test the competence of the projected ideal with existing techniques.