Reviewerly/exHarmony huggingface dataset:
LightGBM-Integrated PV Power Prediction Based on Multi-Resolution Similarity

Improving the accuracy of PV power prediction is conducive to PV participation in economic dispatch and power market transactions in the distribution network, as well as safe dispatch and operation of the grid. Considering that the selection of highly correlated historical data can improve the accuracy of PV power prediction, this study proposes an integrated PV power prediction method based on a multi-resolution similarity consideration that considers both trend similarity and detail similarity. Firstly, using irradiance as the similarity variable, similar-days were selected using grey correlation analysis to form a set of similar data to control the similarity, with the overall trend of the day to be predicted at a macro level. Using irradiance to calculate the similarity at each specific point in time via Euclidean distance, similar-times were identified to form another set of similar data to consider the degree of similarity in detail. The above approach enables the selection of similarity data for both resolutions. Then, a 1DCNN-LSTM prediction model that considers the feature correlation of different variables and the temporal dependence of a single variable was proposed. Three important features were selected by a random forest model as inputs to the prediction model, and two similar data training models with different resolutions were used to generate a photovoltaic power prediction model based on similar-days and similar-times. Ultimately, the learning of the two predictions integrated with LightGBM compensate for each other, generating highly accurate predictions that combine the advantages of multi-resolution similarity considerations. Actual operation data of a PV power station was used for verification. The simulation results show that the prediction effect of ensemble learning was better than that of the single 1DCNN-LSTM model. The proposed method was compared with other commonly used PV power prediction models. In the data case of this study, it was found that the proposed method reduced the prediction error rate by 1.48%, 11.4%, and 6.45%, compared to the LSTM, CNN, and BP, respectively. Experiments show that model prediction results considering the selection of similar data at multiple resolutions can provide more extensive information to an ensemble learner and reduce the deviation in model predictions. Therefore, the proposed method can provide a reference for PV integration into the grid and participation in market-based electricity trading, which is of great significance.
Lite-FPN for keypoint-based monocular 3D object detection

3D object detection with a single image is an essential and challenging task for autonomous driving. Multi-scale feature fusion is effective for keypoint-based monocular 3D object detectors to boost performance within a large range of scales and distances. However, the existing FPN modules inevitably increase latency owing to the further extraction and merging operations on multi-scale feature maps. In this paper, we propose a lightweight feature pyramid network called Lite-FPN for keypoint-based monocular 3D object detectors that perform multi-scale feature fusion only at sparsely distributed keypoint locations. Besides, to alleviate the misalignment between classification score and localization precision, we propose an effective regression loss named attention loss, which assigns predictions with misaligned classification score and localization precision larger weights in the training stage. Extensive experiments based on several state-of-the-art keypoint-based detectors on the KITTI and nuScenes datasets show that our proposed methods manage to achieve significant accuracy improvements. Meanwhile, the enhanced SMOKE with our Lite-FPN module surpasses the baseline enhanced by the classic FPN over 19 FPS.
<b>D</b>ense <b>o</b>r <b>S</b>parse : Elastic SPMM Implementation for Optimal Big-Data Processing

Many real-world graph datasets can be represented using a sparse matrix format, and they are widely used for various big-data applications. The multiplication of two sparse matrices (SPMM) is a major kernel for various machine learning algorithms when using a sparsely expressed dataset. Apache Spark, a general-purpose big-data processing engine, includes the SPMM operation in its linear algebra package. The default Spark SPMM implementation, however, always converts a right sparse matrix to a dense format before performing multiplication, which can result in significant performance overhead for diverse SPMM scenarios. To address a limitation of the current Spark implementation, we describe an SPMM implementation that keeps the right matrix in a Compressed Sparse Column (CSC) format and propose an SPMM task latency prediction model based on a Deep Neural Network (DNN) architecture. Using the SPMM latency prediction model, we implement an elastic SPMM implementation recommendation service, which we name DoS ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> ense <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</b> r <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> parse). The proposed DoS recommends an optimal SPMM implementation method of either transforming a right matrix to a dense format or keeping it as a sparse format during the multiplication. Through evaluation of the proposed system using a real-world graph reveals that the proposed service can improve the SPMM latency of default Spark implementation by 2.2 times while shortening the overall execution time.
JetIQ electronic ecosystem at the service of the university in the country that has undergone armed aggression

The development of modern technologies and innovations provides opportunities for higher education institutions (HEIs) and directly for teachers and students to voluntarily use tools for distance and mixed learning. However, remote access in a pandemic or a state of war is necessity and has no alternatives.The purpose of this study is to determine the role of the JetIQ electronic ecosystem for Vinnytsia National Technical University (VNTU) in wartime conditions. As in all HEI of Ukraine, the educational process at VNTU changed significantly during the year of military aggression. The need for students and teachers to hide in bomb shelters during air raids, frequent and, most importantly, unexpected interruptions of the educational process, and significant complications of access to laboratory equipment negatively affect the psychological state of all participants in educational activities. In these conditions, the JetIQ electronic ecosystem brought substantial benefits and became the primary tool for the management of the educational activities at VNTU. Reliable technological support also improved the psychological confidence of students and teachers. A comparison of the interim indicators of the 2022/2023 academic year with the trends of the three previous academic years in terms of quantitative indicators of educational resources and actions of all groups of JetIQ users showed positive trends. This testifies to its stability in the conditions of martial law.
Identification of heat transfer coefficients in continuous casting by a GPU-based improved comprehensive learning particle swarm optimization algorithm

The heat transfer coefficients (HTCs) of the secondary cooling zone (SCZ) are a critical boundary condition for numerical modeling of solidification heat transfer in the continuous casting process. However, it is difficult to identify accurately these HTCs. Due to the high computational complexity, the computation time in the identification process is not negligible, especially during the process of optimization and heat transfer model solution. To solve those problems, we first developed an improved comprehensive learning particle swarm optimization algorithm (ICL-PSO) to identify more accurate HTCs in the SCZ of continuous casting. Secondly, a two-layer parallel algorithm that implements ICL-PSO on a graphics processing unit (GPU) was then designed and tested to reduce the computation time of identifying these HTCs. Finally, the experimental results show that the accuracy of the proposed algorithm for HTC identification and the efficiency of GPU-accelerated computation have been improved.
Research on Scale Improvement of Geochemical Exploration Based on Remote Sensing Image Fusion

Both remote sensing and geochemical exploration technologies are effective tools for detecting target objects. Although information on anomalous geochemical elemental abundances differs in terms of professional attributes from remote sensing data, both are based on geological bodies or phenomena on the Earth’s surface. Therefore, exploring the use of remote sensing data with high spatial resolution to improve the accuracy of small-scale geochemical data, and fusing them to obtain large-scale geochemical layers could provide new data for geological and mineral exploration through inversion. This study provides a method of fusing remote sensing images with small-scale geochemical data based on a linear regression model that improves the resolution of geochemical elemental layers and provides reference data for mineral exploration in areas lacking large-scale geochemical data. In the Xianshuigou area of Northwest China, a fusion study was conducted using 200,000 geochemical and remote sensing data. The method provides fused large-scale regional chemical data in well-exposed areas where large-scale geochemical data are lacking and could provide potential data sources for regional mineral exploration.
Smart Home Masculinities

Existing research has shown dominant smart home imaginaries to be gendered visions of technologically deterministic lives of affluent young white men and middle-class hetero-normative white cultures.Instead, we argue, a more realistic, plausible and ethical approach to smart home design needs to originate from the different starting point of gender diversity and equality in the home.To demonstrate this, we situate smart home design within the dominant masculine and colonising narratives of industry and science.We then develop a design anthropological analysis of the everyday experiences as reported by four middle-class white Australian men living in heteronormative family households.We propose that existing masculinist imaginaries of smart home futures are not only limited because they fail to account for the diverse experiences and aspirations of heterogeneous households but because they also fail to recognise the messiness, contingency and processual nature of the social, material and experiential circumstances of the very heteronormative households they appear to be aligned with.A feminist approach to the smart home would need to be designed from and with gender-equal relations in the very site of the home.
Generative image captioning in Urdu using deep learning

Abstract Urdu is morphologically rich language and lacks the resources available in English. While several studies on the image captioning task in English have been published, this is among the pioneer studies on Urdu generative image captioning. The study makes several key contributions: (i) it presents a new dataset for Urdu image captioning, and (ii) it presents different attention-based architectures for image captioning in the Urdu language. These attention mechanisms are new to the Urdu language, as those have never been used for the Urdu image captioning task (iii) Finally, it performs quantitative and qualitative analysis of the results by studying the impact of different model architectures on Urdu’s image caption generation task. The extensive experiments on the Urdu image caption generation task show encouraging results such as a BLEU-1 score of 72.5, BLEU-2 of 56.9, BLEU-3 of 42.8, and BLEU-4 of 31.6. Finally, we present data and code used in the study for future research via GitHub ( https://github.com/saeedhas/Urdu_cap_gen ).
Research on the Construction of Grain Food Multi-Chain Blockchain Based on Zero-Knowledge Proof

As the main food source of the world's population, grain quality safety is of great significance to the healthy development of human beings. The grain food supply chain is characterized by its long life cycle, numerous and complex business data, difficulty defining private information, and difficult managing and sharing. In order to strengthen the ability of information application processing and coordination of the grain food supply chain under many risk factors, an information management model suitable for the grain food supply chain is studied based on the blockchain multi-chain technology. First, the information on key links in the grain food supply chain is analyzed to obtain privacy data classifications. Second, a multi-chain network model of the grain food supply chain is constructed, and based on this model, the hierarchical encryption and storage mode of private data as well as the relay cross-chain communication mode, are designed. In addition, a complete consensus process, including CPBFT, ZKP, and KZKP algorithms, is designed for the global information collaborative consensus under the multi-chain architecture. Finally, the model is verified through performance simulation, theory analysis, and prototype system verification in terms of its correctness, security, scalability, and consensus efficiency. The results show that this research model effectively reduces the storage redundancy and deals with problems of data differential sharing in traditional single-chain research, as well as provides a secure data protection mechanism, a credible data interaction mechanism, and an efficient multi-chain collaborative consensus mechanism. By attempting to apply blockchain multi-chain technology to the grain food supply chain, this study provides new research ideas for the trusted protection of data and information collaborative consensus in this field.
Correction and Estimation of Workout Postures with Pose Estimation using AI

Strength workouts are effective and popular ways to achieve health benefits, but they can cause injury for newcomers if performed incorrectly without prior knowledge. The purpose of this research is to examine how the recent developments in pose recognition, estimation and correction can be used to estimate workout postures and provide valuable feedback on workout techniques to detect specific technique issues associated with a high risk of injury for common exercises. To provide a user with feedback, action recognition will be responsible for collecting, labeling, and organizing the data, as well as training and integrating with real-time data. Using a validation dataset of 218 workout images from the Penn Action dataset as a validation set, our best model scored 97.25 % accurate. The results of this research proves that the pose recognition, estimation and correction algorithm is accurate and can yield useful feedbacks when it comes to estimate workout techniques.
Credit Card Fraud Detection using ML: A Survey

The popularity of credit card fraud is rising as the number of cashless transactions increases. Online transactions are expanding as more people use credit cards and mobile wallets. The growth of internet commerce has also accelerated the spread of credit card fraud. Examining the cardholder's spending patterns can help identify credit card theft. Any odd behavior is grounds for the transaction to be deemed fraudulent. There are many difficulties in detection, such as frequent profile changes between fraudulent and normal transactions. Researchers use numerous techniques to detect credit card fraud, among them are hidden markov model, naives bayes classifier, decision tree, k-nearest neighbour classifier, logistic regression. The aim of this research paper is to examine divergent approaches of credit card fraud detection.
Tuning of Fuzzy Controller by Variable Clustered Fuzzy Rules and Its Application to Overhead Crane

This paper proposed efficient rule extraction and rule reduction methods for the self-tuning fuzzy controller. The Fuzzy Clustering Method (FCM) and similarity approach are applied to extract and reduce the fuzzy gain rules. The proposed rule extraction scheme is investigated on a self-tuning fuzzy proportional plus derivative controller (STFLPDC), having 49 fuzzy rules and 49 fuzzy gain rules. The utility of the scheme is validated with various clustering validity indices. The effectiveness of the self-tuning fuzzy controller with the different reduced number of extracted fuzzy gain rules generated from clustering data is found to be quite satisfactory in comparison with the initial (49) fuzzy gain rules. The effect of gain rule variations on STFLPDC is tested to control the position and swing of an overhead crane and a comparison is made with other fuzzy and conventional controllers.
Classification of Brahmi script characters using HOG features and multiclass error-correcting output codes (ECOC) model containing SVM binary learners

A lot of research has been done in the field of character recognition, but very less work has been reported related to Indian ancient scripts. Brahmi is considered as oldest script of India and is parent of all modern-day Indic scripts. In the process of optical character recognition, feature selection and classification are very of the important and crucial stages. Selection of appropriate, unique and simple features helps in better recognition. In this paper, authors have presented a system to recognize the Brahmi script characters using HOG features and SVM classifier with ECOC model. The presented system have achieved 92.09% of accuracy in recognition of Brahmi script characters.
Dynamic Trajectory Planning and Optimization for Automated Driving on Ice and Snow Covered Road

The rapid development of 5G and Artificial Intelligence (AI) has promoted the widespread application of autonomous driving in various scenarios. Currently, autonomous vehicles (AVs) can autonomously perform operations such as turning, lane changing, and acceleration in accordance with road traffic rules. It is a challenge for autonomous vehicles (AVs) to plan a series of safe and efficient trajectories on ice and snow covered road (ISCR). This paper proposes an optimal trajectory planning algorithm based on the Frenet coordinate system, which ignores the influence of road curvature and improves the quality of trajectory. Specifically, the vehicle motion is decoupled into two dimensions using the Frenet coordinate system to build lateral and longitudinal trajectory planning models, respectively. Further, according to the information on the initial and target configuration of the vehicle, the corresponding trajectory sets of horizontal and longitudinal motion were generated by sampling. Moreover, to improve the safety of autonomous vehicles (AVs) on ice and snow covered road (ISCR), the cost of driving distance and ice-obstacle distance are used as the core indicators to evaluate the trajectory planning cost, combined with the safe speed check. Simulation results show that this algorithm can plan an optimal trajectory for autonomous vehicles (AVs) that combines safety, comfort, and stability, especially on ice and snow covered road (ISCR).
MET-iquette: enabling virtual agents to have a social compliant behavior in the Metaverse

Metaverse aims at connecting the physical and digital world in a highly immersive virtual environment that can replicate society and social practices within it. It is populated not only by avatars corresponding to users but also by nonplayer characters (NPCs). These virtual agents should be able to perceive other agents and objects in the scene, reason and act preferably by exhibiting credible and acceptable social behavior. Relying on the Social Practice theory, this work proposes an architecture for the design and development of a virtual agent able to understand social practices in the Metaverse and act by respecting the social norms that the circumstances entail. The agent is equipped with modules for recognizing elements of the scene, knowledge and rules about a set of social practices. The scene information is exploited by a reasoning module that allows the agent to evaluate the current social practice, socially interpret the events, and activate the most appropriate behaviors. The paper describes the proposed architecture, its implementation, and the realization of an experimental virtual scenario.
Computer-Assisted Evaluation of Patient Information Leaflets (PILs) for Chronic Kidney Disease Compared with Marketed PILs

Printed educational materials play an important part in the treatment of disease.The patient may use them as a reference at home or to enhance knowledge.The goal of this study was to use standard approaches, such as Baker Able Leaflet Design (BALD) and readability score, to the evaluation of patients' opinions on Patient Information Leaflets (PILs) for chronic kidney disease.Participate were enrolled randomly, and some were taken at a convenient time.The study spanned 1 year in a hospital in Assam, India.We compiled our Patient Information Leaflets (PILs) by referring to various sources of information and translating them into the Assamese language.With the help of the BALD assessment strategy and user rating, we compared our PILs with available PILs on the website and marketed them.The prepared leaflets scored "above standard" in BALD and readability evaluations, indicating that they are above the industry standard.since their average score was greater than 25, the layout and design.This shows that the issue is important from the patient's perspective, which may encourage them to read the PILs.Even though a larger percentage of Indian consumers cannot read or write English, many PILs are still written in the English language.Without considering the patient's reading levels and ensuring proper style and design, PILs may not be understood or used.Every company has to take this into account when creating leaflets and, at the absolute least, in some of the most important local languages in which PILs must be published.
ERRA: An Embodied Representation and Reasoning Architecture for Long-Horizon Language-Conditioned Manipulation Tasks

This letter introduces ERRA, an embodied learning architecture that enables robots to jointly obtain three fundamental capabilities (reasoning, planning, and interaction) for solving long-horizon language-conditioned manipulation tasks. ERRA is based on tightly-coupled probabilistic inferences at two granularity levels. Coarse-resolution inference is formulated as sequence generation through a large language model, which infers action language from natural language instruction and environment state. The robot then zooms to the fine-resolution inference part to perform the concrete action corresponding to the action language. Fine-resolution inference is constructed as a Markov decision process, which takes action language and environmental sensing as observations and outputs the action. The results of action execution in environments provide feedback for subsequent coarse-resolution reasoning. Such coarse-to-fine inference allows the robot to decompose and achieve long-horizon tasks interactively. In extensive experiments, we show that ERRA can complete various long-horizon manipulation tasks specified by abstract language instructions. We also demonstrate successful generalization to the novel but similar natural language instructions.