Reviewerly/exHarmony huggingface dataset:
SWBPose: A Lightweight and Efficient Method for Human Pose Estimation

Human pose estimation is a crucial challenge in the field of computer vision, contributing significantly to diverse domains, such as fall detection, security, and healthcare. While most existing models achieve high accuracy by constructing complex network architectures, they often overlook the issue of their impracticability for deployment on edge devices due to substantial computational requirements. In response to this, we propose SWBPose, a lightweight model tailored for efficient pose estimation. Our research demonstrates that the integration of the Swin Transformer Feature Extract (STFE) module can expedite model convergence. We also introduce the novel Dynamic Shift Max activation function, which has empirically been shown to enhance the model's Average Precision (AP) by 0.2%, without necessitating additional computational resources. We validated our model using the COCO2017 dataset, achieving a mean Average Precision (mAP) of 66.01%, with a frame rate of 96.76 FPS, computational complexity of 0.61 Gflops, and a model size of 4.37M parameters when implemented on a GPU. The experimental results clearly demonstrate that our model offers superior performance in balancing the trade-off between accuracy and latency.
Research of Electromechanical-Electromagnetic Transient Hybrid Simulation System Based on Kubernetes

The construction of new power system and the Guidelines for power system security and stability in China put forward higher requirements for the efficiency of electromechanical electromagnetic transient hybrid simulation of large power grids. In order to meet the ever-increasing demand for simulation in the future, this paper combines the current development direction of cloud services in the computer field, builds a high-performance computing platform suitable for the characteristics of hybrid simulation computing based on the Internet leading cluster management platform Kubernetes and container virtualization technology, and tests it in Power Dispatch and Control Center of China Southern Power Grid. Through the actual data test, the accuracy of the platform simulation is verified, and the efficiency improvement is basically close to the ideal value provided by the hardware computing power. The simulation time of 60 cases can be reduced to less than 1 hour, which provides a practical computing platform for the simulation analysis of new power system.
Single-Phase Grid-Connection Inverter Control Strategy Based on Echo State Network

Single-phase inverters are becoming increasingly important and popular because of the rise of distributed renewable energy. The mainstream single-phase inverter control methods include proportional resonance (PR) control and proportional integral (PI) control, which need to adjust parameters according to manual experience. In this article, an echo state network (ESN) controller is designed to control L-filter single phase inverter. Firstly, the relevant factors affecting the modulation wave required by PWM are selected by analyzing the working principle of the inverter circuit. Secondly, the nonlinear relationship between them and the modulated wave is established by ESN and gradient descent (GD) algorithm to achieve high-precision approximation of the modulated wave, which avoids the problem of artificial parameter adjustment. Finally, the simulation is carried out on MATLAB/Simulink platform. The experiments show that the ESN controller can not only accurately approximate the modulation signal required to realize the inverter control, but also compared with PR controller and PI controller, the ESN controller can realize stable grid-connection without adjustment in the initial state, and has good dynamic performance.
Comprehensive Evaluation of Photovoltaic Power Prediction Error Based on TOPSIS Considering the Influence of Cloud Motion

The comprehensive evaluation of photovoltaic power prediction errors is the basis of improving the prediction model, enhancing the prediction accuracy and providing guidance for the rational application of prediction results, which is of great significance for the safe and economic operation of the new power system containing large-scale renewable energy. Currently, the photovoltaic power prediction mainly adopts mean value evaluation indexes of the 15-minute level, which cannot reflect the influence of the sudden fluctuations on low-inertia power systems and the adaptability of prediction systems at the minute level prediction situation. Therefore, considering the influence of cloud motion on photovoltaic power generation and the corresponding characteristics, this paper proposes a comprehensive evaluation index system including random indexes and volatility indexes. Then, a comprehensive evaluation method of photovoltaic power prediction errors based on technique for order preference by similarity to ideal solution (TOPSIS) is proposed. Finally, the prediction performance of different prediction models is evaluated comprehensively, which verifies the feasibility of the proposed evaluation method. This evaluation method could provide some theoretical support for the selection of prediction model and performance evaluation of prediction system in a photovoltaic station.
A bidirectional fusion branch network with penalty term-based trihard loss for person re-identification

Person re-identification (Re-ID) is the recognition of the same person in different camera views. Because of the existence of highly similar persons and great differences of the same person in different scenes, and the fact that the features extracted by current mainstream models lose some fine-grained information, it is likely for the models to misidentify the query person. To tackle these challenges, we introduce a bidirectional fusion branch network with penalty term-based trihard loss (BFB-PTT). The BFB-PTT constructs a bidirectional fusion branch (BFB) network based on feature pyramid, where low-level features are transferred to a high-level feature space through fewer convolutional layers than most of the traditional CNN-based models have, thus retaining more local features to discriminate different pedestrians more accurately and effectively. Meanwhile, we propose using the penalty term-based trihard loss (PTT) to optimize the spatial structure of pedestrian’s samples, so that the similar samples are drawn closer together in order to reduce the variabilities of the same person in different scenes. We have conducted comprehensive experiments and analyses on the proposed method’s effectiveness on three challenging benchmarks, and the results show that our approach achieves competitive performance with the state-of-art models.
Put Your Voice on Stage: Personalized Headline Generation for News Articles

In this article, we study the problem of personalized news headline generation, which aims to produce not only concise and fact-consistent titles for news articles but also decorate these titles as personalized irresistible reading invitations by incorporating readers’ preferences. We propose an approach named PNG ( P ersonalized N ews headline G enerator) by utilizing distant supervision in readers’ past click behaviors to resolve. First, user preference representations are learned through a knowledge-aware user encoder that comprehensively captures the genuine, sequential, and flash interests of users reflected in their historical clicked news. Then, a user-perturbed pointer-generator network is devised to accomplish the headline generation in which the learned user representations implicitly affect the word prediction. The proposed model is optimized by reinforcement learning solvers where indicators on factual, personalized, and linguistic aspects of the generated headline are regarded as rewards. Extensive experiments are conducted on the real-world dataset PENS, 1 which is a large-scale benchmark collected from Microsoft News. Both the quantitative and qualitative results validate the effectiveness of our approach.
Factors influencing acceptance of Indonesian contact tracing APP: Development of the technology acceptance model

Contact tracing apps for the COVID-19 have been broadly developed worldwide and Indonesia is no exception. The Indonesian application is called PeduliLindungi. This study aimed to provide a comprehensive understading of the public’s intention to use such applications by incorporating trust, trust in government, privacy concerns, and social influence variables as an extension to the technology acceptance model (TAM). A questionnaire was distributed online through social media to attain 371 participants among Indonesian inhabitants based on the convenience sampling method. Descriptive analysis and covariance-based structural equation modeling were used to analyze the data. The results indicated that intention was predicted well by trust, attitude, and social influence. Furthermore, trust in government played a role in predicting the application’s trustworthiness. The government and decision-makers should consider this observation in promoting the PeduliLindungi application, as it could increase its effectiveness.
Do IT users behave responsibly in terms of cybercrime protection?

This study aims to analyze the behaviour of IT users regarding their personal protection against potential cybercrimes. The research data set is based on surveys conducted by the European Commission in 2020-2021 for 35 European countries. Canonical analysis revealed that 66.67% of cybercrime cases (Phishing, Pharming, Online identity theft, etc.) determine individuals' choice of personal protection method (using a security token, social media logins, electronic identification, etc.). Kohonen's self-organizing maps were used to form 9 clusters of countries depending on the attitude of IT users to personal cybersecurity. The map results showed that individuals behave less responsibly using a security token, electronic identification certificate or card, pin code list or random characters of a password, and other electronic identification procedures. Users from Denmark, the Netherlands, Iceland, Norway, the UK, Austria, and Finland were the most responsible Europeans in terms of personal protection, while people from Bulgaria, Romania, Serbia, Albania, North Macedonia, Bosnia and Herzegovina were the least conscientious about protection.
Understanding Consumers’ Visual Attention in Mobile Advertisements: An Ambulatory Eye-Tracking Study with Machine Learning Techniques

As mobile devices have become a necessity in our daily lives, mobile advertising is also prevalent. Accordingly, it is critical for practitioners to understand how consumers visually attend to mobile advertisements. One popular way of doing so is via eye-tracking methodology. However, scant eye-tracking research exists in mobile settings due to technical challenges, e.g., cumbersome data annotation. To tackle these challenges, the authors propose an object-detection machine learning (ML) algorithm—You Only Look Once (YOLO) v3—to analyze eye-tracking videos automatically. Moreover, we extend the original YOLO v3 model by developing a novel algorithm to optimize the analysis of eye-tracking data collected from mobile devices. Through a lab experiment, we investigate how two types of ad elements (i.e., textual vs. pictorial) and shopping devices (i.e., mobile vs. PC) affect consumers’ visual attention. Our findings suggest that (1) textual ad elements receive more attention than pictorial ones, and such differences are more pronounced in ads on mobile devices than those on PCs; and (2) mobile ads receive less attention than PC ads. Our findings provide managerial insights into developing effective digital advertising strategies to improve consumers’ visual attention in online and mobile advertisements.
Feature perturbation augmentation for reliable evaluation of importance estimators in neural networks

Post-hoc explanation methods attempt to make the inner workings of deep neural networks more comprehensible and trustworthy, which otherwise act as black box models. However, since a ground truth is in general lacking, local post-hoc explanation methods, which assign importance scores to input features, are challenging to evaluate. One of the most popular evaluation frameworks is to perturb features deemed important by an explanation and to measure the change in prediction accuracy. Intuitively, a large decrease in prediction accuracy would indicate that the explanation has correctly quantified the importance of features with respect to the prediction outcome (e.g., logits). However, the change in the prediction outcome may stem from perturbation artifacts, since perturbed samples in the test dataset are out of distribution (OOD) compared to the training dataset and can therefore potentially disturb the model in an unexpected manner. To overcome this challenge, we propose feature perturbation augmentation (FPA) which creates and adds perturbed images during the model training. Our computational experiments suggest that FPA makes the considered models more robust against perturbations. Overall, FPA is an intuitive and straightforward data augmentation technique that renders the evaluation of post-hoc explanations more trustworthy Reproducible codes and pre-trained models with FPA are available on Github: https://github.com/lenbrocki/Feature-Perturbation-Augmentation.
On the function approximation by trigonometric polynomials and the properties of families of function classes over harmonic intervals

The article is devoted to research on approximation theory. When approximating functions by trigonometric polynomials, the spectrum is chosen from various sets. In this paper, the spectrum consists of harmonic intervals. Devices, various processes, perception of the senses have a limited range. In the mathematical modeling of numerous practical problems and in the further study of such mathematical models, it is sufficient to find a solution in this range. It is possible to study such models to some extent with the help of harmonic intervals. To prove the main theorem, an auxiliary lemma was proved, and elements of the theory of approximations with respect to harmonic intervals were used. For the constructed families of function classes associated with the best approximations by trigonometric polynomials with a spectrum of harmonic intervals, their relationship with classical Besov spaces is shown
Global Attention-Based Approach for Substation Devices Classification and Localization

Accurately identifying various electrical devices is crucial for the safe and stable operation of substations. However, the size of devices in substations varies, and some devices have similar appearances, which often leads to inspection errors. To address these issues, we designed the path aggregation network with global attention mechanism (GAM-PAN). Considering the scale variation of electrical devices in images, the path aggregation module is introduced to fuse the multi-scale features. To address the problem of recognizing similar devices, global attention mechanism (GAM) is s embedded into the feature aggregation module. The GAM enhances the fusion features of similar devices and reconstructs the fused feature maps. In addition, a transfer learning strategy is adapted to train GAM-PAN, which speeds up the convergence rate of the network. GAM-PAN is tested on a substation multi-device dataset (SMDD) and achieves a detection accuracy of 84.32%, compared to four classical object detection methods. Furthermore, it was verified that global attention can effectively solve the problem of recognizing similar targets.
Short-Term Power Prediction for Centralized Photovoltaic Plants Based on LSTNet-Attention

Short-term photovoltaic (PV) power forecast, as a fundamental foundation for the operation of new power systems, is critical for the safe and economical operation of power systems. In order to improve the short-term power prediction accuracy of PV power plants, this paper proposes a prediction model based on a long-term and short-term time series network-attention mechanism (LSTNet-Attn), which is formed by the fusion of multiple deep learning methods. The LSTNet-Attn model is established to forecast the short-term power generation of centralized PV power plants. This model first uses a convolutional neural network to extract local dependencies between power data, then captures the long-term trends of the power time series using a long-term and short-term memory (LSTM) network, fully learns the ultra-long-term repetitive patterns of the power series using a recurrent-skip structure, and then a fully connected layer and an autoregressive (AR) model are linked together for combined prediction. Finally, the effectiveness of the proposed method is verified using actual measurement data from a central PV plant. In comparison with four PV power prediction methods, the experimental results show that the proposed prediction model outperforms other methods and has higher prediction accuracy and robustness.
Characteristic Analysis and Category Recognition of Insulating Paperboard Surface Pattern Based on Image Morphology Calculation

In order to detect the types of insulating paperboard, this paper built an insulating paperboard image acquisition platform and collected 6 different types of insulating paperboard surface images. According to the differences in the surface patterns of the images, an image morphologic feature analysis method was proposed, which eliminated the image surface noise and extracted the characteristic regions of different insulating paperboard surfaces. Three feature statistics were carried out for each type of image feature area, including feature area, roundness and rotation degree. According to the statistical information of feature area, 6 kinds of insulated paperboard with different patterns could be distinguished.
Cost Efficient Job Scheduling Scheme for Large Scale Data Center

The world's shift to digital technology has made data centers (DCs) an essential part of the country's energy infrastructure. However, as data centers continue to expand at an accelerated pace, their energy consumption and operational expenses have increased significantly. To address this problem, based on the Decima model, this paper designs a cost efficient job scheduling scheme for large scale data center. By combining the graph neural network (GNN) used to characterize the complex characteristics of large-scale DAG jobs with the policy network for efficient decision-making, we propose a joint training end-to-end model in which the deadline feature is added to the job states as the user's quality of service feedback evaluation criteria, and train the model in the real-time market environment to achieve the purpose of perceiving the energy cost. Compared with the current prevalent industrial algorithms, we have demonstrated that the proposed algorithm effectively balances energy cost and quality of service under the real-time electricity price.
Remote Sensing-Based Estimation of Potential Solar PV Power Output Considering the Effects of High Temperature, Dust and Precipitation: Case of the Philippines

Satellite-derived solar radiation data provide measurements of surface solar energy available for solar PV power production. However, as solar radiation enters the atmosphere, its interaction with atmospheric constituents causes the attenuation of a significant amount of energy. The advantages of solar PV systems as an alternative for electricity generation are in terms of their scalability and solar cell conversion efficiency. However, environmental factors such as dust accumulation can significantly affect these systems. Precipitation may act as a natural cleaning agent while high temperature reduces cell efficiency and power output. The objective of this study is to estimate the potential of solar PV power (PPV) output in the Philippines using remotely-sensed shortwave radiation (SWR) data from Advanced Himawari Imager (AHI)-8/9 considering the effects of high temperature, dust and precipitation. Results show that a minimum of 195.88 MW and a maximum of 1,147.48 MW effective PPV output on a monthly average basis can be produced, and 440.08 to 899.61 MW on an annual average basis. A decrease in PPV output of up to 38.90% annually and up to 58.85% monthly were observed due to the combined effects of high temperature and dust accumulation in the Philippines. A comparison of the SWR data considering the effects of meteorological factors and actual PV power production data suggests a high correlation in the cities of Baguio <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{R}^{2}=0.8$</tex> , in Cebu <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{R}^{2}=0.8$</tex> , and in Tagum <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{R}^{2}=0.7$</tex> located in the three major Philippine island groups of Luzon, Visayas, and Mindanao, respectively. This localized study incorporating the meteorological effects suggests that satellite-derived solar radiation measurements can be a good substitute for in-situ data in estimating the solar PV power output.
<i>Classes are not Clusters</i>: Improving Label-based Evaluation of Dimensionality Reduction

A common way to evaluate the reliability of dimensionality reduction (DR) embeddings is to quantify how well labeled classes form compact, mutually separated clusters in the embeddings. This approach is based on the assumption that the classes stay as clear clusters in the original high-dimensional space. However, in reality, this assumption can be violated; a single class can be fragmented into multiple separated clusters, and multiple classes can be merged into a single cluster. We thus cannot always assure the credibility of the evaluation using class labels. In this paper, we introduce two novel quality measures-Label-Trustworthiness and Label-Continuity (Label-T&C)-advancing the process of DR evaluation based on class labels. Instead of assuming that classes are well-clustered in the original space, Label-T&C work by (1) estimating the extent to which classes form clusters in the original and embedded spaces and (2) evaluating the difference between the two. A quantitative evaluation showed that Label-T&C outperform widely used DR evaluation measures (e.g., Trustworthiness and Continuity, Kullback-Leibler divergence) in terms of the accuracy in assessing how well DR embeddings preserve the cluster structure, and are also scalable. Moreover, we present case studies demonstrating that Label-T&C can be successfully used for revealing the intrinsic characteristics of DR techniques and their hyperparameters.
Social Sentiment Analysis of Pfizer (PFE) &amp;amp; Its Comparison with Fundamental Analysis

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