Reviewerly/exHarmony huggingface dataset:
An adaptive interactive multiple-model algorithm with time-varying Markov transition matrix for maneuvering target tracking

Maneuvering target tracking technology is widely used in military scenarios. To solve the hysteresis problem of maneuvering target motion model transition in interactive multiple-model technology, an adaptive interactive multiple-model algorithm with time-varying Markov transition matrix is proposed. The proposed method constructs time-varying Markov transition matrix based on the differential between the motion model probabilities at adjacent time steps. Meanwhile, it is combined with particle filter to get a better estimation accuracy for maneuvering target tracking. The proposed algorithm is verified in experimental simulation compared with traditional interactive multiple-model algorithm, and it is shown that average tracking error decreases by 18.5% and the probability of matched motion model of maneuvering target can increase rapidly when the target maneuvers.
INNOVATIONS AND EDUCATION

Education is of vital importance in both shaping individuals and preparing them for challenges of the future life. In the era marked by rapid technological advancements and evolving social needs, it is essential to be able to explore and implement innovative approaches in education, and those approaches are supposed to be not only comprehensive, sustainable, superb and up-to-date, but to also continuously evolve, able to meet challenges of the fast-changing, unpredictable, globalized world. The evolution has to be systemic, consistent, scalable and the work is intitled to touch upon and consider some of the modern tools to enhance the qualitative aspect of education.
The Role of Artificial Intelligence in Modern Finance and Sustainable Marketing

Artificial intelligence (AI) is reshaping marketing and is becoming a capable assistant supporting many facets of the industry. Artificial intelligence plays a vital role in modern finance and sustainable marketing. AI can be utilized to improve sustainability and enhance financial services. The chapter begins with the assertion that contemporary finance and market viability are crucial components of every economy. The most recent disruptive technology is artificial intelligence (AI), which has the greatest potential to change marketing. International practitioners are trying to recognize the best AI results for their marketing functions. To build a more secure business and economic environment and lower human mistakes, the chapter demonstrates how artificial intelligence may combine contemporary finance and market sustainability with tech skills.
Organizational and pedagogical conditions of the efficiency of mixed training of it disciplines of vocational education specialists

The article describes modern approaches to mixed learning of informatics disciplines in institutions of higher education. The concept of mixed learning of informatics disciplines is singled out. The interpretation of the main approaches to integration, combining the training of informatics disciplines of professional education specialists with the use of the advantages of various technologies in the educational process is presented. The concept of combined learning is revealed, which assumes that in modern conditions, a student should optimally and in various combinations use all the opportunities provided by both classical learning and the use of distance technologies. The main directions of the development of mixed learning of informatics disciplines are characterized. The main principles of maintaining dynamic balance and unity of the pedagogical and organizational foundations of the IT training system for professional education specialists in mixed education are revealed. The organizational and pedagogical requirements for the mixed learning of informatics disciplines on the basis of electronic educational and methodological complexes were analyzed and determined. Instrumental means with the help of which mixed learning is implemented in institutions of higher education have been determined. The main organizational-pedagogical and educational-methodical aspects of the use of electronic educational-methodical complexes for the training of vocational education specialists of various forms of education are distinguished. of a specific IT discipline and ensures the implementation of a methodical system of learning in the learning environment. The organizational and pedagogical conditions for the implementation of mixed training of informatics disciplines of professional education specialists have been determined.
Athlete Training Sensor Data Detection Research Based on Optimized Convolutional Neural Network

The traditional convolutional neural network has matured in image detection and recognition, but maintaining its high accuracy requires a large memory for computation, which increases the volume of sensors and puts additional strain on athletes. Therefore, the research uses local error calculation to replace global error, and uses layer-by-layer residual unit structure to replace gradient calculation, to apply it to athlete training sensor data detection. Through the simulation experiment and the example experiment of the data set, the optimal residual unit structure and the local error joint weight of the residual network human motion detection are obtained. At the same time, in the simulation experiment and example experiment analysis of the school physical education curriculum, the accuracy of the algorithm model proposed in the study is higher than the first five CNN network structures, and its training time is the shortest. The experiment demonstrated that the local error method combined with the residual network structure had improved precise performance in motion detection, and the computational load was low, making it appropriate for wearable sensors during training.
Helmet wearing detection algorithm based on improved YOLOv5

In order to solve the problem that the existing helmet detection algorithms have low accuracy in detecting dense small targets in a complex environment, helmet wearing detection algorithm based on improved YOLOv5 is proposed. Firstly, the original CSP structure is replaced by a new redesigned DB_CSP module to improve the ability of backbone network feature extraction. Secondly, the coordinate attention mechanism is added in the backbone feature network layer to make the model pay more attention to small targets. Then, Bidirectional Feature Pyramid Network (BiFPN) is used to replace PANet to enhance the ability of feature fusion. Finally, a special detection layer for small targets is added to form four scale detection. The experimental results show that the mAP of this model is 92.28%, which is 4.9% higher than that of YOLOv5s model. The average detection time is 0.0276 seconds, which improves the detection accuracy while maintaining the model speed.
Research on intelligent algorithm for autonomous landing of quadrotor UAV

This paper takes the autonomous landing of four-rotor UAV as the specific research object. Aiming at the problem of identifying the feature points of landing signs and improving the accuracy of autonomous landing, an improved multi-objective soft subspace clustering algorithm is proposed. The anti-redundant mutation operator and forward comparison operation are designed, it is proposed to improve the population diversity and convergence speed of the algorithm. The iNSGA-II is used as the base algorithm, and the repair operator and local search operator are designed to reflect the clustering characteristics. An autonomous landing control model based on visual recognition is established. A large number of position feature points are clustered and analyzed to decouple the highly nonlinear mapping relationship in the feature space, and then the redundant information between the dimensions is stripped to achieve the maximum stability of the sampling points, the importance sequence of all features and the accurate classification.
A question generation model based on semantic enhancement and reinforcement learning

The purpose of the Question Generation (QG) task is to automatically generate a reasonable and relevant question for the given document, the goal of this paper is to improve the performance of the question generation model and thus generate higher quality questions. This paper mainly focuses on the neural network model of Seq2Seq architecture and aims to two problems existed: exposure bias caused by the difference between the training and testing processes of the current Seq2Seq architecture model, and insufficient utilization of semantic information of the input documents. This paper proposes a question generation method based on semantic enhancement and reinforcement learning, which enhances the document representation through semantic graph; Add the reinforcement learning framework, use three special indicators for question generation tasks to fine-tune the model parameters during the training process to improve the performance of the model. The experiments’ results on show that this method effectively improves the quality of generating questions.
Optimization of convolutional backpropagation operators based on domestic accelerators

Convolutional Neural Network (CNN) has always been a hot topic in deep learning. With the increasing demand for network models in daily production, the optimization of convolution calculation process is very important. This paper starts with the process of back propagation in convolutional neural network, introduces the derivation of convolutional neural network back propagation and the conversion process of im2col, uses implicit to convert the calculation of convolution on the domestic acceleration platform, and optimizes the convolution back propagation operator through a variety of general matrix multiplication optimization strategies. The final performance reaches more than 70% of the performance of NVIDIA operator, which meets the expectation of the experiment under the performance bottleneck of the platform.
A fused syntactic information tree model for aspect-level sentiment analysis

Aspect-level sentiment analysis aims to determine the sentiment polarity of different aspects contained in text sentences. To address the problem that current neural network models based on LSTM and attention mechanisms cannot effectively encode aspect features and sentiment features, thus leading to a less than reasonable representation of text information, this paper proposes a fused syntactic information tree SITM-Bi-LSTM model for aspect-level sentiment analysis. First, the text sequence is passed through a bidirectional LSTM neural network to obtain its hidden output representation containing contextual information. Then, the syntactic information is used in the syntactic path to focus on the influence of contextual words with different distances from the aspect on its sentiment polarity, which in turn achieves the effect of enhancing the aspectual feature representation. Finally, the Tan-Relu gating unit is constructed to selectively extract emotional features that match the given aspect information for determining the emotional polarity of the aspect. The experimental results on the Laptop and Restaurant datasets show that the accuracy and values of the SITM-Bi-LSTM model are better than those of the comparison models, which confirms the model's effectiveness.
Sumformer: recursive positional encoding for transformer in short text classification

In various transformers, positional encoding is used to compensate for the inability of the attention mechanism to capture positional information between words. Previous research on transformers' temporal modeling has utilized recursive and relative positional encoding based on the Recurrent Neural Network (RNN). Recursive positional encoding captures linear text structure but lacks parallelization, hindering speed. In contrast, relative positional encoding ignores linear text structure, leading to weaker performance in short text classification compared to recursive positional encoding. To address the issues, we propose a model, sumformer, which mainly includes two parts different from the other transformers: cumsum calculation and summer initialization. Cumsum calculation simplifies the feature extraction part of RNN by a substitution method, replacing the dynamic rate function of RNNs with static trainable position parameters, and preserves the recursive structure, which enables the model to capture the linear structure information of the text through cumsum calculation method and maintains a low time overhead compared to RNNs. In addition, the summer initialization method, which limits the highest standard deviation of the positional parameter, enables the model to pay attention to the multi-level information of the text during initialization, with the richer optimization space, thereby improving the convergence ability of the model. The experimental results show the sumformer achieves roughly a 3% improvement in performance and a 58% improvement in speed compared to existing transformers based on recursive positional encoding. It achieves better short text classification faster, and summer initialization also can improve the performance without increasing training and inference time.
Fused semantic information and hierarchical attention network for course recommendation

With the development of smart education, personalized course recommendations are getting more and more attention. Recently, some in-depth learning-based solutions have been proposed. The main principle is to make sequential recommendations based on the learner's historical learning records, rarely use the semantic information covered by the course profile and user profile to complete the recommendation task. User preferences change over time. To solve these problems, a course recommendation model (FIHA) that integrates semantic information and hierarchical attention network is proposed. next class. Specifically, TextCNN is used to perform semantic processing on the course introduction to generate vectors, and then the learner's historical access course records are used to capture the learner's long-term and short-term preferences using hierarchical attention mechanism, and the learner's dynamic preference representation is obtained. The two are fused and recommendations are made based on their scores to courses. Experiments on real datasets show that this method is superior to other mainstream advanced methods.
Cascade models of neural networks and machine learning algorithms for heart disease classification

Artificial Neural Networks (ANN) are now widely used in the medical field. However, on many datasets, only using ANN does not necessarily yield high prediction accuracy to some extent. Therefore, in this paper, the authors tried combinations of different models to obtain better accuracy based on the binary classification of a heart failure dataset collected from Pakistan, Faisalabad hospital. Briefly, the author considered ANN as feature extractors and then connected some statistical binary classifiers to build models, such as ANN with Support Vector Machines, ANN with Logistic Regression, and ANN with Random Forest. In particular, the authors used two different kernels of Support Vector Machines to build models in the artificial neural network with Support Vector Machines. One kernel is the radial basis function kernel; the other is the polynomial kernel. Experimental results indicate that models combining ANN and other statistical binary classifiers all get better performance than the ANN model alone. Moreover, the model with the highest accuracy is the ANN combined with Random Forest, followed by the ANN combined with Support Vector Machines and Logistic Regression.
Character recognition system based on depth residual network and YOLO V2

To improve capitation accuracy by residual network and multiscale image training, there are two methods to detect caphead characteristics. Previously based on pre-described head characteristics, and methods based on the statistical training of the models, the latter is very robust, using a neural network to train, a better effect can be obtained. Based on the presence of overlapping heads in dense multi-human images and head features with varying near and far scales, this requires more special treatment for the training sample of character avatar. For problems that cannot be detected in real time, using an alternative method of YOLO V2 detection can remedy the defects and help obtain the relevant application model.
A video keyframe retrieval algorithm based on CLIP tech

Traditional video keyframe retrieval algorithms have certain limitations in terms of retrieval accuracy and user experience, such as the inability to accurately capture the semantic information of the video and weak correlation between keyframes. To address these issues, this paper proposes a video keyframe retrieval algorithm based on CLIP technology. The algorithm extracts video keyframes using a clustering method based on SIFT features and uses a pre-trained CLIP model to extract both visual and semantic features of the keyframes, achieving more accurate retrieval. Additionally, the algorithm uses a bidirectional LSTM model to model inter-frame contexts, enhancing the correlation between keyframes and improving retrieval accuracy. Experimental results show that the proposed algorithm outperforms traditional algorithms in terms of retrieval accuracy and user experience.
KPI anomaly detection method of AIOps based on GAN

To ensure the stable running of systems and services in a data center, O&amp;M engineers need to collect and monitor Key Performance indicators (KPIs) generated during system and service running. Traditional performance anomaly detection methods based on thresholds and rules are suitable for simple KPI monitoring. Unfortunately, when there are significant differences between KPI values at different times of the day, or when there are significant fluctuations due to different usage patterns, it is difficult to find appropriate threshold levels that are gradually no longer appropriate for highly dynamic systems and businesses. With the popularity of Artificial Intelligence algorithms, machine learning and deep learning methods also begin to be applied in operation and maintenance scenarios, which is the emergence of Artificial Intelligence for IT Operations (AIOps), among which KPI anomaly detection is the research hotspot of AIOps. KPI anomaly detection refers to the effective data mining and analysis of KPI time series data through various anomaly detection methods, in order to quickly discover anomalies and avoid faults. Therefore, we propose a hybrid model of KPI anomaly detection based on GAN. This is an unsupervised learning method based on Generative adversarial network (GAN) and Encoder (Encoder), which can identify abnormal data in KPI. We train the GAN model with health data, and then complete the rapid mapping of new data to the potential space of the GAN model through the encoder, and then detect anomalies by combining the feature residuals of the discriminator and the data reconstruction error into an anomaly score. Experimental results show that, compared with the traditional KPI anomaly detection method and the general unsupervised method, the proposed method can capture the variable data characteristics of KPI more accurately and achieve better performance in the KPI anomaly detection task.
Autonomous unsupervised image inpainting networks based on structural priors

Deep convolutional networks have become a popular tool for image generation and restoration.In general, their outstanding performance is attributed to their ability to learn real image priors from a large number of example images.Many seemingly unrelated computer vision tasks can be viewed as special cases of image decomposition into independent layers.Image segmentation (divided into foreground layer and background layer); transparent layer separation (divided into reflection layer and transmission layer); image dehazing (separation into clear image and haze map), etc.Nowadays, in the field of image restoration using deep learning, there are many low-quality images, but the corresponding original pictures are fewer, which makes image restoration based on deep learning one of the more difficult problems in the field of computer vision.In order to break through the related problems, this paper abandons the supervised deep learning method based on a large amount of data training and then proposes an autonomous unsupervised image inpainting network, Fast-RDDIP, based on structure prior.This ability stems from the fact that the internal statistics of a mixture of layers are more complex than the statistics of each individual component.Consequently, this study constructs a matching network structure using the characteristics of various learning distribution rates of the network and additional convolution, which has characteristics like shared parameters. can perform end-to-end training.This essay explains how this technique improves image quality and dehazing. According to the experimental findings on the O-Haze dataset, Fast-RDDIP performs better than more established methods like SIHR, EIDBC, supervised neural networks like DehazeNet, NTDF, NLD, URIE, and BOPBL, as well as unsupervised neural networks like DoubleDIP models.
Enhancing use of BERT information in neural machine translation with masking-BERT attention

BERT has shown remarkable performance in several natural language processing tasks, but it fails to exhibit the same high performance in cross-lingual tasks, particularly machine translation. To address this issue, we propose a BERT-enhanced neural machine translation (BE-NMT) model that optimizes the use of the information contained in BERT by NMT. Our proposed model comprises three components: (1) A MASKING strategy to mitigate knowledge forgetting caused by fine-tuning of BERT on the NMT task; (2) Serial and parallel processing of multi-attention models for incorporating BERT into the NMT system; (3) Fusing multiple hidden layer outputs of BERT to supplement the missing linguistic information of its final hidden layer output. We conducted experiments on several translation tasks, and our proposed model notably outperforms the strong baseline by improving 1.93 BLEU points on the United Nations Parallel Corpus English→Chinese task. Additionally, our model also achieves remarkable performance on other translation tasks.
Improved differentiable architecture search algorithm

As a gradient-guided search method, differentiable architecture search greatly reduces computational costs and improves search speed compared with traditional reinforcement learning methods and evolutionary methods that search for network structures in discrete spaces. However, when the number of search epochs is too large, the searched architecture will contain a lot of skip connections, resulting in a sharp decline in network performance. Aiming at this phenomenon, this paper designs a phased search process. With the deepening of the search stage, different early stopping rules are designed, so that the units located in different positions of the network can present different structures, effectively solving the performance crash problem caused by skip connections. At the same time, the design of edge normalization is introduced on the connection between nodes, and the difference between the weight parameters of different operations is enlarged by improving the loss function, which effectively improves the stability of the architecture search. The experimental results show that this method trades off a small amount of parameters and search time in exchange for an improvement in accuracy, and the verification accuracy on the cifar10 and cifar100 datasets has increased by 0.15% and 1.36%, respectively.