Reviewerly/exHarmony huggingface dataset:
Advancement in Physical Education Teaching Using Improved Energy Efficient Scalable Routing Algorithm-Based Wireless Network

Physical education (PE) is a crucial topic in higher coaching that individually points motor abilities in health-enhancing activities. Conventional PE in institutions struggles to pique graduates’ attentiveness in sports, proceeding in low task involvements rates, and incapacity to exercise the body. Innovative teaching concepts and methodologies, coaching techniques and procedures, and coaching assessment techniques in physical education are all accompanied to developing the physical education study hall climate and successfully boosting physical education efficacy. Each element of regular living, especially education, is being influenced by wireless internet innovations. We will provide extra help to students by predicting academic endurance or dropout. We can improve the wireless platform’s potential utility in sports applications and change the character of PE, including visualization and repetition by incorporating it into PE teaching. Based on the concept of wireless network technology, this paper proposes an Improved Energy Efficient Scalable Routing Algorithm (IEESRA) for physical education advancement. Initially, the physical education dataset is preprocessed using normalization. The aspects are removed using the scale-invariant feature transform (SIFT) method. The data is transferred using a wireless network using Improved Energy Efficient Scalable Routing Algorithm (IEESRA). The classification is done using random forest (RF) classifier. The results of the analysis reveal that wireless network-based PE may increase graduates’ strength, speed, and qualities providing a more important reference and reference for enhancing the success of PE. The proposed strategy has the potential to enhance actual attention to PE teaching to 90% with raising students’ engagement to 70%.
A Docking Mechanism Based on a Stewart Platform and Its Tracking Control Based on Information Fusion Algorithm

Aiming at the problem of unmanned reconfiguration and docking of ground vehicles under complex working conditions, we designed a piece of docking equipment composed of an active mechanism based on a six-degree-of-freedom platform and a locking mechanism with multi-sensors. Through the proposed control method based on laser and image sensor information fusion calculation, the six-dimensional posture information of the mechanism during the docking process is captured in real time so as to achieve high-precision docking. Finally, the effectiveness of the method and the feasibility of the 6-DOF platform are verified by the established model. The results show that the mechanism can meet the requirements of smooth docking of ground unmanned vehicles.
Hourly Forecasting of Solar Photovoltaic Power in Pakistan Using Recurrent Neural Networks

The solar photovoltaic (PV) power forecast is crucial for steady grid operation, scheduling, and grid electricity management. In this work, numerous time series forecast methodologies, including the statistical and artificial intelligence-based methods, are studied and compared fastidiously to forecast PV electricity. Moreover, the impact of different environmental conditions for all of the algorithms is investigated. Hourly solar PV power forecasting is done to confirm the effectiveness of various models. Data used in this paper is of one entire year and is acquired from a 100 MW solar power plant, namely, Quaid-e-Azam Solar Park, Bahawalpur, Pakistan. This paper suggests recurrent neural networks (RNNs) as the best-performing forecasting model for PV power output. Furthermore, the bidirectional long-short-term memory RNN framework delivered high accuracy results in all weather conditions, especially under cloudy weather conditions where root mean square error (RMSE) was found lowest 0.0025, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>R</mi> </math> square stands at 0.99, and coefficient of variation of root mean square error (RMSE) Cv was observed 0.0095%.
Secure machine learning against adversarial samples at test time

Abstract Deep neural networks (DNNs) are widely used to handle many difficult tasks, such as image classification and malware detection, and achieve outstanding performance. However, recent studies on adversarial examples, which have maliciously undetectable perturbations added to their original samples that are indistinguishable by human eyes but mislead the machine learning approaches, show that machine learning models are vulnerable to security attacks. Though various adversarial retraining techniques have been developed in the past few years, none of them is scalable. In this paper, we propose a new iterative adversarial retraining approach to robustify the model and to reduce the effectiveness of adversarial inputs on DNN models. The proposed method retrains the model with both Gaussian noise augmentation and adversarial generation techniques for better generalization. Furthermore, the ensemble model is utilized during the testing phase in order to increase the robust test accuracy. The results from our extensive experiments demonstrate that the proposed approach increases the robustness of the DNN model against various adversarial attacks, specifically, fast gradient sign attack, Carlini and Wagner (C&amp;W) attack, Projected Gradient Descent (PGD) attack, and DeepFool attack. To be precise, the robust classifier obtained by our proposed approach can maintain a performance accuracy of 99% on average on the standard test set. Moreover, we empirically evaluate the runtime of two of the most effective adversarial attacks, i.e., C&amp;W attack and BIM attack, to find that the C&amp;W attack can utilize GPU for faster adversarial example generation than the BIM attack can. For this reason, we further develop a parallel implementation of the proposed approach. This parallel implementation makes the proposed approach scalable for large datasets and complex models.
Solar Simulator Development for 50 WP Solar Photovoltaic Experimental Design Using Halogen Lamp

Indoor testing with a solar simulator is recommended to acquire the steady-state performance of a PV Solar Panel. Because of the simplicity, reproducibility, and dependability of data, the solar simulator allows for more efficient test findings. This can be accomplished by modifying and manipulating the test circumstances as needed. Several conditions must be met during the testing of the solar simulator, including power density, uniformity and stability of irradiance intensity (not exceeding 10%), spectral energy distribution, and angle of sunlight incident. Besides, the light source that replaces the sunlight is an important factor in developing a solar simulator. In this study, halogen lamps were chosen because of their reliability, low cost, and easy availability in the market. This solar simulator is designed for experimental testing on the development of Solar PV panels with a capacity of 50 WP. The solar simulator test performed at a distance of 75cm between the lamp and the solar panel, with five voltage variations: 100 volts, 125 volts, 160 volts, 190 volts, and 225 volts. Based on the ASTM E927 standard, the results of the solar simulator test in class B are obtained with an effective area of 16%. This result brought the best non uniformity of 2.61% at a voltage of 190 volts and the highest irradiance intensity of 1156.313 W/m2 at 225 volts.
Atomic waveguide QED with atomic dimers

Quantum emitters coupled to a waveguide is a paradigm of quantum optics, whose essential properties are described by waveguide quantum electrodynamics (QED). We study the possibility of observing the typical features of the conventional waveguide QED scenario in a system where the role of the waveguide is played by a one-dimensional subwavelength atomic array. For the role of emitters, we propose to use anti-symmetric states of atomic dimers - a pair of closely spaced atoms - as effective two-level systems, which significantly reduces the effect of free-space spontaneous emission. We solve the dynamics of the system both when the dimer frequency lies inside and when it lies outside the band of modes of the array. Along with well-known phenomena of collective emission into the guided modes and waveguide mediated long-range dimer-dimer interactions, we uncover significant non-Markovian corrections which arise from both the finiteness of the array and through retardation effects.
Research on Community Public Service Information Collaborative Governance Based on Deep Learning Model

The communities have significantly increased in number and the environment has become complex. There are problems such as poor information collection in community public service information governance, lack of relevant analysis standards and models, and unreliable prediction results. In order to forecast and manage the risk information of the community, this research analyzes the public information of the community through the collaborative deep learning model. First of all, the information characteristic factors are selected that affect social risks based on the correlation analysis theory. Secondly, the convolutional neural network is used in deep learning for simulation of the community risk prediction model. Finally, through the comparative analysis of the model prediction results, it can be concluded that the accuracy rate of the proposed prediction model reaches 92.5%. An effective collaborative deep learning model is used to govern community public service information.
Multi-Vector Attention Models for Deep Re-ranking

Large-scale document retrieval systems often utilize two styles of neural network models which live at two different ends of the joint computation vs. accuracy spectrum.The first style is dual encoder (or two-tower) models, where the query and document representations are computed completely independently and combined with a simple dot product operation.The second style is cross-attention models, where the query and document features are concatenated in the input layer and all computation is based on the joint querydocument representation.Dual encoder models are typically used for retrieval and deep re-ranking, while cross-attention models are typically used for shallow re-ranking.In this paper, we present a lightweight architecture that explores this joint cost vs.accuracy trade-off based on multi-vector attention (MVA).We thoroughly evaluate our method on the MS-MARCO passage retrieval dataset and show how to efficiently trade off retrieval accuracy with joint computation and offline document storage cost.We show that a highly compressed document representation and inexpensive joint computation can be achieved through a combination of learned pooling tokens and aggressive downprojection.Our code and model checkpoints are available on GitHub.
Behavior monitoring model of kitchen staff based on YOLOv5l and DeepSort techniques

Although the monitoring system has been widely used, the actual monitoring task still needs more manpower to complete. This paper takes yolov5l model and deep sort algorithm as the basic framework to identify and track the staff in kitchen environment. We apply a relation construction with detected items and people, then label the relation corresponding to behaviors violate the regulations of kitchen, such as the staff did not wear mask or hat. We train our model and the experimental results show that the model can correctly identify the inappropriate behaviors of staff. The model achieves the time-constrained accuracy of 95.32% in identifying whether the staff wear a hat or not, and the time-constrained accuracy of 96.32% in identifying whether the staff wear mask correctly. The result shows that the proposed model could fulfil monitoring task in this kitchen environment.
A New Class of Autopoietic and Cognitive Machines

Making computing machines mimic living organisms has captured the imagination of many since the dawn of digital computers. However, today’s artificial intelligence technologies fall short of replicating even the basic autopoietic and cognitive behaviors found in primitive biological systems. According to Charles Darwin, the difference in mind between humans and higher animals, great as it is, certainly is one of degree and not of kind. Autopoiesis refers to the behavior of a system that replicates itself and maintains identity and stability while facing fluctuations caused by external influences. Cognitive behaviors model the system’s state, sense internal and external changes, analyze, predict and take action to mitigate any risk to its functional fulfillment. How did intelligence evolve? what is the relationship between the mind and body? Answers to these questions should guide us to infuse autopoietic and cognitive behaviors into digital machines. In this paper, we show how to use the structural machine to build a cognitive reasoning system that integrates the knowledge from various digital symbolic and sub-symbolic computations. This approach is analogous to how the neocortex repurposed the reptilian brain and paves the path for digital machines to mimic living organisms using an integrated knowledge representation from different sources.
Dimensional Emotion Detection from Categorical Emotion

We present a model to predict fine-grained emotions along the continuous dimensions of valence, arousal, and dominance (VAD) with a corpus with categorical emotion annotations.Our model is trained by minimizing the EMD (Earth Mover's Distance) loss between the predicted VAD score distribution and the categorical emotion distributions sorted along VAD, and it can simultaneously classify the emotion categories and predict the VAD scores for a given sentence.We use pre-trained RoBERTa-Large and fine-tune on three different corpora with categorical labels and evaluate on EmoBank corpus with VAD scores.We show that our approach reaches comparable performance to that of the state-of-the-art classifiers in categorical emotion classification and shows significant positive correlations with the ground truth VAD scores.Also, further training with supervision of VAD labels leads to improved performance especially when dataset is small.We also present examples of predictions of appropriate emotion words that are not part of the original annotations.
Echo state network model for analyzing solar-wind effects on the AU and AL indices

Abstract. The properties of the auroral electrojets are examined on the basis of a trained machine-learning model. The relationships between solar-wind parameters and the AU and AL indices are modeled with an echo state network (ESN), a kind of recurrent neural network. We can consider this trained ESN model to represent nonlinear effects of the solar-wind inputs on the auroral electrojets. To identify the properties of auroral electrojets, we obtain various synthetic AU and AL data by using various artificial inputs with the trained ESN. The analyses of various synthetic data show that the AU and AL indices are mainly controlled by the solar-wind speed in addition to Bz of the interplanetary magnetic field (IMF) as suggested by the literature. The results also indicate that the solar-wind density effect is emphasized when solar-wind speed is high and when IMF Bz is near zero. This suggests some nonlinear effects of the solar-wind density.
MFAN: Multi-Level Features Attention Network for Fake Certificate Image Detection

Up to now, most of the forensics methods have attached more attention to natural content images. To expand the application of image forensics technology, forgery detection for certificate images that can directly represent people's rights and interests is investigated in this paper. Variable tampered region scales and diverse manipulation types are two typical characteristics in fake certificate images. To tackle this task, a novel method called Multi-level Feature Attention Network (MFAN) is proposed. MFAN is built following the encoder-decoder network structure. In order to extract features with rich scale information in the encoder, on the one hand, we employ Atrous Spatial Pyramid Pooling (ASPP) on the final layer of a pre-trained residual network to capture the contextual information at different scales; on the other hand, low-level features are concatenated to ensure the sensibility to small targets. Furthermore, the resulting multi-level features are recalibrated on channels for irrelevant information suppression and enhancing the tampered regions, guiding the MFAN to adapt to diverse manipulation traces. In the decoder module, the attentive feature maps are convoluted and unsampled to effectively generate the prediction mask. Experimental results indicate that the proposed method outperforms some state-of-the-art forensics methods.
Flexible Generation of Natural Language Deductions

An interpretable system for open-domain reasoning needs to express its reasoning process in a transparent form.Natural language is an attractive representation for this purpose -it is both highly expressive and easy for humans to understand.However, manipulating natural language statements in logically consistent ways is hard: models must cope with variation in how meaning is expressed while remaining precise.In this paper, we describe PARAPATTERN, a method for building models to generate deductive inferences from diverse natural language inputs without direct human supervision.We train BART-based models (Lewis et al., 2020) to generate the result of applying a particular logical operation to one or more premise statements.Crucially, we develop a largely automated pipeline for constructing suitable training examples from Wikipedia.We evaluate our models using out-of-domain sentence compositions from the QASC (Khot et al., 2020) and EntailmentBank (Dalvi et al., 2021) datasets as well as targeted perturbation sets.Our results show that our models are substantially more accurate and flexible than baseline systems.PARAPATTERN achieves 85% validity on examples of the 'substitution' operation from EntailmentBank without the use of any in-domain training data, matching the performance of a model fine-tuned for EntailmentBank.The full source code for our method is publicly available. 1.Substitution Premises: Staphylococcus epidermis is a microorganism.Microorganisms colonize the skin surface.Paraphrased: Staphylococcus epidermidis is a microorganism.Microbiological colonization of the skin surface."Staphylococcus Epidermidis is a Microorganism."The skin surface is colonized by micro organisms.Conclusion: Staphylococcus epidermis colonizes the skin surface.Premises: During the undergraduate years, seminarians learn the ancient language courses.Latin is an ancient language course.Paraphrased: The seminars know the ancient language courses.Latin is an old language course.Seminarians learn ancient language during
Predicting Text Readability from Scrolling Interactions

Judging the readability of text has many important applications, for instance when performing text simplification or when sourcing reading material for language learners.In this paper, we present a 518 participant study which investigates how scrolling behaviour relates to the readability of English texts.We make our dataset publicly available and show that (1) there are statistically significant differences in the way readers interact with text depending on the text level, (2) such measures can be used to predict the readability of text, and (3) the background of a reader impacts their reading interactions and the factors contributing to text difficulty. 1
Outcome and biomarker supervised deep learning for survival prediction in two multicenter breast cancer series

Prediction of clinical outcomes for individual cancer patients is an important step in the disease diagnosis and subsequently guides the treatment and patient counseling. In this work, we develop and evaluate a joint outcome and biomarker supervised (estrogen receptor expression and ERBB2 expression and gene amplification) multitask deep learning model for prediction of outcome in breast cancer patients in two nation-wide multicenter studies in Finland (the FinProg and FinHer studies). Our approach combines deep learning with expert knowledge to provide more accurate, robust, and integrated prediction of breast cancer outcomes.Using deep learning, we trained convolutional neural networks (CNNs) with digitized tissue microarray (TMA) samples of primary hematoxylin-eosin-stained breast cancer specimens from 693 patients in the FinProg series as input and breast cancer-specific survival as the endpoint. The trained algorithms were tested on 354 TMA patient samples in the same series. An independent set of whole-slide (WS) tumor samples from 674 patients in another multicenter study (FinHer) was used to validate and verify the generalization of the outcome prediction based on CNN models by Cox survival regression and concordance index (c-index). Visual cancer tissue characterization, i.e., number of mitoses, tubules, nuclear pleomorphism, tumor-infiltrating lymphocytes, and necrosis was performed on TMA samples in the FinProg test set by a pathologist and combined with deep learning-based outcome prediction in a multitask algorithm.The multitask algorithm achieved a hazard ratio (HR) of 2.0 (95% confidence interval [CI] 1.30-3.00), P < 0.001, c-index of 0.59 on the 354 test set of FinProg patients, and an HR of 1.7 (95% CI 1.2-2.6), P = 0.003, c-index 0.57 on the WS tumor samples from 674 patients in the independent FinHer series. The multitask CNN remained a statistically independent predictor of survival in both test sets when adjusted for histological grade, tumor size, and axillary lymph node status in a multivariate Cox analyses. An improved accuracy (c-index 0.66) was achieved when deep learning was combined with the tissue characteristics assessed visually by a pathologist.A multitask deep learning algorithm supervised by both patient outcome and biomarker status learned features in basic tissue morphology predictive of survival in a nationwide, multicenter series of patients with breast cancer. The algorithms generalized to another independent multicenter patient series and whole-slide breast cancer samples and provide prognostic information complementary to that of a comprehensive series of established prognostic factors.
Monocular Depth Estimation with Self-Supervised Learning for Vineyard Unmanned Agricultural Vehicle

To find an economical solution to infer the depth of the surrounding environment of unmanned agricultural vehicles (UAV), a lightweight depth estimation model called MonoDA based on a convolutional neural network is proposed. A series of sequential frames from monocular videos are used to train the model. The model is composed of two subnetworks—the depth estimation subnetwork and the pose estimation subnetwork. The former is a modified version of U-Net that reduces the number of bridges, while the latter takes EfficientNet-B0 as its backbone network to extract the features of sequential frames and predict the pose transformation relations between the frames. The self-supervised strategy is adopted during the training, which means the depth information labels of frames are not needed. Instead, the adjacent frames in the image sequence and the reprojection relation of the pose are used to train the model. Subnetworks’ outputs (depth map and pose relation) are used to reconstruct the input frame, then a self-supervised loss between the reconstructed input and the original input is calculated. Finally, the loss is employed to update the parameters of the two subnetworks through the backward pass. Several experiments are conducted to evaluate the model’s performance, and the results show that MonoDA has competitive accuracy over the KITTI raw dataset as well as our vineyard dataset. Besides, our method also possessed the advantage of non-sensitivity to color. On the computing platform of our UAV’s environment perceptual system NVIDIA JETSON TX2, the model could run at 18.92 FPS. To sum up, our approach provides an economical solution for depth estimation by using monocular cameras, which achieves a good trade-off between accuracy and speed and can be used as a novel auxiliary depth detection paradigm for UAVs.