Reviewerly/exHarmony huggingface dataset:
Short-Term PV Output Prediction Method Based on Feature Analysis and Multi-model Fusion

In the future, photovoltaic power generation will usher in a larger market. The rapid development of artificial intelligence provides a new solution for photovoltaic power generation forecasting. In this paper, combined with the current cutting-edge theoretical research in the field of artificial intelligence, a short-term photovoltaic power generation power prediction method based on multi-model fusion Stacking ensemble learning is proposed. Random Forest and correlation coefficient feature importance analysis were used to determine the important climate factors as the input characteristics of the prediction model. On this basis, multiple machine learning models with good single prediction performance and certain differences are integrated into the Stacking ensemble learning photovoltaic output prediction model. The base learner of the model includes XGBoost tree ensemble algorithm and GRU neural network algorithm. To prevent overfitting, the meta-learner consists of the LSSVM algorithm with relatively simple complexity and high accuracy. The calculation example uses the photovoltaic power and climate data provided by the Australian Solar Energy Research and Development Center to verify the effectiveness of the algorithm. The prediction results show that the Stacking model has higher prediction accuracy than the traditional single model which can better track the fluctuation of output power.
Sentiment Analysis using Ensemble Technique on Textual and Emoticon Data

Nowadays social media is very much popular as people can convey their feelings or recapitulate. Many people express through emotions. Sentiment Analysis using text and emoticons has become leading and challenging research topic in recent world. In the past, text, image, and emoticons were classified; however, there are few research on text and emoticon in combination. In this work, the classification of sentiments was done on both text and emoticons data. The data were on airlines feedback and collected from UCI repository. The data were classified as positive, negative and neutral class using machine learning (ML), deep learning (DL) and ensemble classifiers based on three types of features such as TF-IDF, Bag-of-Words, and N-grams. The comparative evaluation of results shows machine learning to be outperformer over DL and ensemble classifiers.
Pedestrian Detection Based on Gradient Direction Histogram

In the field of intelligent video surveillance, in order to realize the real-time detection of moving pedestrians in surveillance video, this paper introduces a method to realize pedestrian detection by using the gradient histogram (HOG) feature of image and support vector machine (SVM). In this method, the local gradient histogram of positive and negative sample images is extracted, and the support vector machine is used for sample training to obtain the pedestrian classifier. Finally, the trained classifier is used for pedestrian detection. Experimental results show that this method can effectively detect moving pedestrians in surveillance video, and has good detection effect.
Construction and Application of Text Entity Relation Joint Extraction Model Based on Multi-Head Attention Neural Network

Entity relationship extraction is one of the key areas of information extraction and is an important research content in the field of natural language processing. Based on past research, this paper proposes a combined extraction model based on a multi-headed attention neural network. Based on the BERT training model architecture, this paper extracts textual entities and relations tasks. At the same time, it integrates the naming entity feature, the terminology labeling characteristics, and the training relationship. The multi-attention mechanism and improved neural structures are added to the model to enhance the characteristic extraction capacity of the model. By studying the parameters of the multi-head attention mechanism, it is shown that the optimal parameters of the multi-head attention are h = 8, dv = 16, and the classification effect of the model is the best at this time. After experimental analysis, comparing the traditional text entity relationship extraction model and the multi-head attention neural network joint extraction model, the model entity relationship extraction effect was evaluated from the aspects of comprehensive evaluation index F1, accuracy rate P, and system time consumed. Experiments show: First, in the accuracy indicator, Xception performance is best, reaching 87.7%, indicating that the model extraction feature effect is enhanced. Second, with the increase of the number of iterative times, the verification set curve and the training set curve have increased to 96% and 98%, respectively, and the model has a strong generalization ability. Third, the model completes the extraction of all data in the test set in 1005 ms, which is an acceptable speed. Therefore, the model test results in this article are good, with a strong practical value.
Application Research and Analysis of Intelligent Sensors in Tennis Serving Technical Correction Equipment

The traditional tennis serving teaching correction method has been deeply rooted, and in the current teaching, the teaching method based on experience has been used for a long time, relying on the subjective consciousness of the coach to guide and supervise the technical movements of the athletes. It is said that to master the essentials of technical movements proficiently, it is necessary to carry out long-term and repetitive exercises according to the coach’s guidance. This situation seriously restricts the improvement of the level of teaching and training. To solve the above problems, the combined application of smart sensors and microsystem technology is used to Sensing identify and intelligently correct the player serve technology. This article in the presentation part initially sums up the movement catch innovation and tennis serve innovation and development; then, at that point, in the strategy part presents the objective following calculation and normal denoising handling calculation; and then, at that point, in the trial part presents the person choice, exploratory program, plan astute sensor handling framework, and trial examination. This article mainly introduces the application, research, and analysis of smart sensors in tennis server technology correction equipment, to reduce the burden on players and coaches. This article proposes target following calculations, normal denoising handling calculations, and so on, which are utilized to lead research investigating the use of savvy sensors in tennis server innovation adjustment hardware. The trial aftereffects of this paper show that the normal acknowledgment exactness of the savvy sensor is 91.14%, and the acknowledgment precision is high, which can be better applied to the tennis serve innovation revision hardware.
Topic Sentiment Analysis for Twitter Data in Indian Languages Using Composite Kernel SVM and Deep Learning

Sentiment analysis of public opinions on social networks, such as Twitter or Facebook, can provide us with valuable information, which has a wide range of applications. But the efficiency and accuracy of the automated methods for Twitter sentiment analysis are hindered by the special characteristics of the Twitter data. The Twitter data is generally noisy, high-dimensional, and it has complex syntactic and semantic structures. Sentiment analysis of Twitter data in Indian languages is more challenging because the data is multilingual and code-mixed. In this article, we propose various composite kernel functions, each of which is used with Support Vector Machines (SVM) for developing a model for topic sentiment analysis of Twitter data in Indian languages. Each composite kernel function is constructed by taking the weighted summation of multiple single kernel functions defined by us. In addition to our proposed composite kernel SVM method, we use several state-of-the-art deep learning classifiers for topic sentiment classification. Since any suitable Twitter dataset in Indian languages is not available for conducting our experiments, we have developed our own datasets by collecting tweets related to five different Twitter trending topics in India. To prove the robustness and generalization capability of the proposed models, they are also evaluated on the US airline Twitter dataset which is a publicly available benchmark English dataset. The empirical study exhibits that the proposed composite kernel SVM method is effective for the sentiment classification task. In the case of Indian language datasets, the proposed composite kernel SVM method achieves the highest average accuracy of 74% and the highest average F-score of 0.73. On the other hand, the deep learning-based method achieves the average accuracy and the average F-score of 71.31% and 0.70, respectively. In the case of the US airline Twitter dataset, the proposed composite kernel SVM method achieves the average accuracy of 83% and the average F-score of 0.82, which are higher than that of the deep learning-based method.
Bimodal Feature Propagation and Fusion for Real-time Semantic Segmentation on RGB-D Images

Semantic Segmentation is the foundation of scene understanding and automatic driving tasks. One of the challenges of semantic segmentation is the reduction of feature resolution as the network goes deep. In this paper, the low-resolution features are integrated into high-resolution features progressively, enabling high-resolution prediction. Currently, incorporating the auxiliary depth information into a semantic segmentation framework has proven to be helpful to improve accuracy. However, additional process of depth data immensely increases computational complexity, resulting in limitation for practical application. Here, a real-time bimodal semantic segmentation network that effectively extracts and fuses complementary information is investigated. Our network extends a light-weight architecture which can seamlessly take advantage of various prevalent backbone networks. Our model extracts RGB and depth features from two branches in parallel and integrates multi-level features of two modalities through bimodal feature fusion blocks. To reduce computation complexity, we employ depth-wise separable convolution while keep performance intact. Meanwhile, an attention mechanism is exploited to recalibrate the fusion modality features adaptively. Refinement components are retained to refine multi-resolution feature maps. We conduct ablative study to demonstrate the availability of our bimodal fusion blocks. Comprehensive experiments validate that the proposed network outperforms the state-of-the-arts and achieves 18 FPS on the challenging RGB-D dataset, NYUDv2.
Application of Chinese Traditional Elements in Furniture Design Based on Wireless Communication and Artificial Intelligence Decision

The traditional elements in Chinese classical furniture are constantly changing with the development of history and culture. It is not only quite artistic, but also has unique aesthetic value. Since the reform and opening up, Western culture has flocked into our country with the opening of the country, which has led to the blind pursuit of the aesthetic psychology of Europe and the United States, which is also more obvious in the furniture design. This kind of foreign-oriented mentality has caused my country’s furniture design to stagnate or even regress in the display process of traditional cultural elements. The traditional elements contained in the furniture of people’s homes turned out to be less, and some were even completely decorated in European and American styles. This tendency shows that the recognition of traditional elements in furniture design in our country is constantly decreasing, and we must pay more attention to it. In contemporary society, wireless communication and artificial intelligence decision-making have become an important reference for people to solve complex problems and have exerted more and more influence in all fields of social life. In the contemporary era when the global economy and culture are increasingly integrated, the daily life of people in various countries is inevitably affected. Furniture is a relatively important daily necessity in residents’ homes. It has the closest contact with people’s lives. It enhances the expression of traditional elements in furniture in China and wins the recognition of many people. It has a value that cannot be ignored for our work to promote traditional culture. In contemporary furniture style design, how to better integrate traditional cultural elements, so as to achieve the dual purpose of protecting cultural heritage and improving artistic quality and enriching the expression of traditional culture in contemporary furniture, has become a topic of concern to many people. The purpose of this research is to explore how to better integrate traditional elements into contemporary furniture under the background of contemporary technologies such as wireless communication and artificial intelligence decision-making. The study obtained the public’s attitude towards the integration of traditional elements into furniture through questionnaires and judged how to innovate traditional elements through wireless communication and artificial intelligence decision-making, which would be more in line with the aesthetic concept of contemporary people.
Self-Assessment of Students of Geography Education and Primary Social and Science Teaching towards the Use of Digital (Geo-) Media for Written and Oral Argumentation

This article discusses possible challenges and potentials in the use of digital geomedia in the context of written and oral argumentation in higher education by examining the perspectives of students and lecturers, especially for the subjects of geography in general and primary school science. To this end, potentials and challenges, as well as competences that have to be promoted among pupils and students in dealing with digital geomedia in the context of written and oral argumentation are first discussed. In the following, we present the results of a mixed methods approach in which we surveyed student teachers with a questionnaire (n = 150) on the one hand and, on the other hand, reflected their view of the issue by analysing qualitative expert interviews (n = 17) with lecturers who teach at the same universities in the corresponding degree programmes. In this way we contextualise the student's self-assessment in the respective location's teacher training. Our results show that there is a high degree of commonality between lecturers and students with regard to the importance of argumentation with digital geomedia. At the same time, the assessment of the students' abilities differs greatly; most students feel capable of dealing with these topics, while lecturers see deficits here.
Multimodal News Recommendation Based on Deep Reinforcement Learning

Multimodal news recommendation is a challenging problem due to the rapid expansion of Internet information, bringing different levels of knowledge expression such as text, images, audio, and video, etc. In this paper, we propose a multimodal news recommendation method based on a deep reinforcement learning framework to represent user interests as multimodal information. The proposed method feeds the multimodal fusion feature into the rainbow agent of deep reinforcement learning to learn news representation. The experiment on the MIND and IM-MIND datasets give the result of AUC, MRR, nDCG@5, and nDCG@10 scores, which outperform LSTUR, FIM, and DKN, NRMS, NPA, and DeepFM models. It shows that reinforcement learning is an excellent choice to fulfill recommendation tasks, and the multimodal fusion feature is effective for learning accurate news representations.
Compact Facial Landmark Layouts for Performance Capture

Abstract An abundance of older, as well as recent work exists at the intersection of computer vision and computer graphics on accurate estimation of dynamic facial landmarks with applications in facial animation, emotion recognition, and beyond. However, only a few publications exist that optimize the actual layout of facial landmarks to ensure an optimal trade‐off between compact layouts and detailed capturing. At the same time, we observe that applications like social games prefer simplicity and performance over detail to reduce the computational budget especially on mobile devices. Other common attributes of such applications are predefined low‐dimensional models to animate and a large, diverse user‐base. In contrast to existing methods that focus on creating person‐specific facial landmarks, we suggest to derive application‐specific facial landmarks. We formulate our optimization method on the widely adopted blendshape model. First, a score is defined suitable to compute a characteristic landmark for each blendshape. In a following step, we optimize a global function, which mimics merging of similar landmarks to one. The optimization is solved in less than a second using integer linear programming and guarantees a globally optimal solution to an NP‐hard problem. Our application‐specific approach is faster and fundamentally different to previous, actor‐specific methods. Resulting layouts are more similar to empirical layouts. Compared to empirical landmarks, our layouts require only a fraction of landmarks to achieve the same numerical error when reconstructing the animation from landmarks. The method is compared against previous work and tested on various blendshape models, representing a wide spectrum of applications.
Use words, not constructions!

Abstract The aim of collostructional analysis or, more precisely, simple collexeme analysis, is to quantify the statistical association between a construction c and a lexeme l that occurs in a particular slot of the construction. The analysis is based on 2×2 contingency tables that ought to represent a cross-classification of the units of analysis. So far, the units of analysis have been identified either as all constructions in the corpus or all instances of a class C of constructions to which construction c belongs. In practice, it is often not possible or feasible to identify these constructions. Therefore, the sample size is typically approximated by heuristic estimates. The bottom-right cell of the contingency table is most affected by these approximations. I suggest that the units of analysis be defined on the word level, instead, as the class W of word forms that satisfy the restrictions on the collexeme slot of c .
CAST: Character labeling in Animation using Self‐supervision by Tracking

Abstract Cartoons and animation domain videos have very different characteristics compared to real‐life images and videos. In addition, this domain carries a large variability in styles. Current computer vision and deep‐learning solutions often fail on animated content because they were trained on natural images. In this paper we present a method to refine a semantic representation suitable for specific animated content. We first train a neural network on a large‐scale set of animation videos and use the mapping to deep features as an embedding space. Next, we use self‐supervision to refine the representation for any specific animation style by gathering many examples of animated characters in this style, using a multi‐object tracking. These examples are used to define triplets for contrastive loss training. The refined semantic space allows better clustering of animated characters even when they have diverse manifestations. Using this space we can build dictionaries of characters in an animation videos, and define specialized classifiers for specific stylistic content (e.g., characters in a specific animation series) with very little user effort. These classifiers are the basis for automatically labeling characters in animation videos. We present results on a collection of characters in a variety of animation styles. Code and resources are available at: https://github.com/oronnir/CAST .
Texture Image Classification Based on Deep Learning and Wireless Sensor Technology

The main purpose of the object detection process is to determine the category of the scene object and use the display 3D and 3D frame size. At present, in the case of 3D object detection, we can extract more accurate features by learning a large number of data, and this deep learning network has good results, but there is a very big problem, including the error of input information, extraction error, and so on. Therefore, solving the above problems has become an important direction to promote the rapid development of 3D target detection technology. This paper mainly studies the deep learning wireless sensor technology and also studies the deep learning infrared and visible image fusion. At the same time, based on the introduction of wireless sensor technology and research status, this paper summarizes the existing algorithms. Texture image classification is a more important visual cue in life. Because it will be affected by light intensity, noise size, image scale, and so on. This makes the classification and feature extraction of image scale and texture image more difficult. To solve these problems has become a hot topic of computer vision research in recent years. The shape of the point cloud is completed by using the 3D target detection method to complete the algorithm research. The radar point cloud is extracted by the 3D target detection method, and the radar point group of the overall shape of the object is obtained. The principal component analysis algorithm is used to extract the principal features of the radar point cloud with the complete shape of the object, and the more accurate 3D target frame is obtained after feature adjustment.
A BeiDou Sweep Interference Detection Method based on Choi-Williams-Hough transform

This paper presents an interference detection method for GNSS receiver based on Hough transform and Choi-Williams transform (CWT). In order to analyze performance of the proposed method, the BDS B3I signal disturbed by the sweep interference is chosen in the GNSS interference detection test, and the proposed method is compared with the existed Wigner-Hough transform (WHT). The results have shown that improved interference detection performance can be obtained with the proposed over the conventional WHT technique, which is very promising to be used in the anti-interference GNSS receiver in the interfering scenarios.
Implementation of Omni-D Tele-Presence Robot Using Kalman Filter and Tricon Ultrasonic Sensors

The tele-presence robot is designed to set forth an economic solution to facilitate day-to-day normal activities in almost every field. There are several solutions to design tele-presence robots, e.g., Skype and team viewer, but it is pretty inappropriate to use Skype and extra hardware. Therefore, in this article, we have presented a robust implementation of the tele-presence robot. Our proposed omnidirectional tele-presence robot consists of (i) Tricon ultrasonic sensors, (ii) Kalman filter implementation and control, and (iii) integration of our developed WebRTC-based application with the omnidirectional tele-presence robot for video transmission. We present a new algorithm to encounter the sensor noise with the least number of sensors for the estimation of Kalman filter. We have simulated the complete model of robot in Simulink and Matlab for the tough paths and critical hurdles. The robot successfully prevents the collision and reaches the destination. The mean errors for the estimation of position and velocity are 5.77% and 2.04%. To achieve efficient and reliable video transmission, the quality factors such as resolution, encoding, average delay and throughput are resolved using the WebRTC along with the integration of the communication protocols. To protect the data transmission, we have implemented the SSL protocol and installed it on the server. We tested three different cases of video resolutions (i.e., 320×280, 820×460 and 900×590) for the performance evaluation of the video transmission. For the highest resolution, our TPR takes 3.5 ms for the encoding, and the average delay is 2.70 ms with 900 × 590 pixels.