Reviewerly/exHarmony huggingface dataset:
Flight operation intention confirmation method based on continuous visual gaze

Flight operation intention confirmation is an important function of intelligent avionics systems. In order to meet the needs of flight operation intention confirmation, a flight operation intention confirmation method based on continuous visual gaze is proposed. In this method, the CCNN+Transformer algorithm is used to calculate the visual gaze landing point of the flight operator, and then determine whether the visual gaze landing point of the flight operator is located on the confirmation button. Continuous visual gaze on the confirmation button for 1.5 seconds is considered as confirmation completion. Through 100 sets of flight operation intention confirmation experiments conducted by two flight operators, it was shown that the success rate of this method reached 98%, demonstrating good results.
New student reporting robot design based on Baidu AI face recognition

Campus service intelligence is the new trend of current social development. To integrate artificial intelligence and campus service more deeply, this paper designs a campus new student service system based on Baidu AI face recognition, verifies new students' identity information through face recognition technology, realizes campus route guidance through Gaode Map API, etc., improves the efficiency of new students' reporting and promotes artificial intelligence projects into campus service industry.
UAV image object detection based on improved YOLOv5s

As we all know, the objects in the images taken by Unmanned Aerial Vehicle (UAV) are relatively small, while our naked eyes are able to extract the information almost instantly, even from far away, image resolution and computational resources limitations make detecting smaller objects a genuinely challenging task for machines. We propose an algorithm based on YOLOv5s with small computational resources and high accuracy, so as to be applied to edge detection devices such as unmanned aerial vehicles. By simplifying the depth of the feature extraction network and adjusting the size of the feature map of the detection head, the target in the image taken by UAV can be accurately identified. In the end, we reduced the number of parameters by 70% at the expense of a little accuracy, while improving accuracy by 15.25%, or 5.2 percentage points, over the baseline.
Research on small target detection in remote sensing images based on improved YOLOv7

To solve the current target detection algorithms in remote sensing images, which have problems of small target detection accuracy is not high and easy to produce error detection and omission detection under the target dense and complex background, this paper introduces two enhancements rooted in the YOLOv7 algorithm. First, the ACmix attention mechanism is added after the backbone network of YOLOv7 algorithm, and second, drawing on the ideas of Soft-NMS algorithm and DIOU-NMS algorithm, CHSD-NMS algorithm is proposed, which improves the base NMS algorithm by softening the strategy of eliminating the candidate box and replacing the IOU with the DIOU to change the traditional NMS algorithm violent removal of candidate boxes. To evaluate the efficacy of the proposed algorithm in this study, experiments are conducted on the remote sensing image dataset DOTA and compared with the basic YOLOv7 algorithm. The experimental results indicate that the algorithm's average accuracy improves by 2.9% with the incorporation of the ACmix attention mechanism at IOU=0.5, a further enhancement of 3.2% is achieved by substituting the traditional NMS algorithm with the CHSD-NMS algorithm, and the overall average accuracy of the algorithm in this study increases by 5.2% with the implementation of both improvements compared to the conventional algorithm.
Use advantage-better action imitation for policy constraint

Offline Reinforcement Learning (RL) aims to learn an optimal policy from a fixed dataset previously collected. Unlike in the online training process, the errors in value estimation from out-of-distribution actions (OOD actions) could not be corrected by interacting with the environment, which makes offline RL difficult to train. Prior policy constraint methods mitigate the errors by minimizing the deviation from the behavior policy, which in fact makes a trade-off between RL and imitation learning. However, while using the constraint term to avoid choosing OOD actions, the learned policy may be enslaved to the unfavorable actions from the dataset. In this paper, we propose a simple solution to this problem. Our method uses an imitation learning term introduced by TD3PlusBc as the constraint and re-weights it with a function of actions' advantage value to mitigate the influence of the unfavorable actions. To make the learning process stable, we also decouple the policy evaluation and policy improvement by using implicit q-learning, which modifies the loss function in a SARSA-style TD backup. Our method (ABAI) is easy to implement, fast to train, and computationally efficient. ABAI achieves state-of-the-art performance on the D4RL dataset, a standard benchmark for offline reinforcement learning, and shows a high ability to learn robustly from various datasets.
Video anomaly detection method based on frame differential residual attention mechanism

An essential function of video surveillance systems that are widely used in fields like public safety is automatic anomaly detection. To efficiently employ spatial-temporal data, the system in this thesis combines geographical branching and temporal branching in a single network. The network has a residual self-encoder structure that, along with a DMA-based encoder and a multi-stage channel attention-based decoder, first uses a spatial-temporal wavelet analysis module for multi-frequency decomposition of video content to improve the model's understanding of high-frequency detail information and processive motion. The model learns more effectively thanks to the implementation of a channel attention mechanism that takes advantage of the channel correlations between features. Finally, training is carried out without supervision. While context dependency is retrieved from the channel attention module, the time-shifted technique is employed to take advantage of temporal aspects. The UCSD dataset is utilized to assess the system's performance. The outcomes demonstrate that our network outperforms current approaches.
Comparison of AFM with traditional FM/NFM and deep exploration of AFM: considering temperature parameters and attention size

Recommendation systems as one of the main products of today's Internet companies, as well as in view of the large number of current recommendation algorithms used in various mobile platforms. This paper focuses on the horizontal and vertical exploration of the model of the popular recommendation algorithm AFM. A cross-sectional comparison is made with two other recommendation models, FM and NFM, to demonstrate that AFM is superior in terms of some model evaluation metrics. In addition, the AFM recommendation model is improved by setting different temperature parameters and different feature interaction degrees to demonstrate the performance of AFM in terms of recommendation accuracy in different dimensions.
Energy efficiency improvement method for data centers based on hybrid proximal policy optimization algorithm

As an essential support for technology applications such as cloud computing and artificial intelligence, data centers face a pressing need to balance their demand with their own high energy consumption. Since the energy consumption of the Information Technology (IT) systems and cooling systems accounts for the majority of total energy consumption in data centers, a collaborative optimization framework called COHPPO based on a Hybrid Proximal Policy Optimization algorithm (HPPO) is proposed, which controls IT system and cooling system in a coordinated manner, fully compressing the resource redundancy of the IT system and cooling redundancy of the cooling system. The HPPO algorithm is trained and verified using a task dataset and a simulation platform that simulates the environmental state inside the data center. The experimental results demonstrate that the COHPPO collaborative optimization framework proposed in this paper can effectively improve the energy efficiency of the data center and the stability of its internal environment.
Design and implementation of gesture recognition in VR power training

In order to enrich the electric power training based on virtual reality technology, this paper focuses on the design and implementation of gesture recognition function. Through the configuration of HTC Vive locator and Gabor gesture recognition algorithm, plus data glove and VR headset for auxiliary use, to complete the natural and intuitive human-computer interaction in VR. Among them, the HTC Vive locator can locate the user's position in real time in the virtual environment, and the Gabor gesture recognition algorithm is to analyze the user's hand movements and posture. The simulation test with the students of Hainan Electric Power Training School verifies the accuracy of the design of the scheme, which can improve the training effect and learning results.
Local anomaly detection network with textural feature enhancement

In real life, image forgery techniques such as stitching, copying, moving, deleting, and enhancing operations are rampant, causing serious social harm. We propose an image forgery detection called LaTe-Net. LaTe-Net does not require additional preprocessing or postprocessing and can detect images of any size and image forgery operation type at the same time. In this paper, we designed a texture enhancement module that focuses on local texture features and amplifies subtle pseudo shadows in shallow features to detect forged images by identifying local abnormal features. In addition, we perform forgery localization through local abnormality detection to capture local abnormalities. Finally, we conducted comparative and visual experiments on different public datasets, and the experimental results demonstrate that LaTe-Net has good performance and generalization properties for different types of forgery operations.
GAN-generated face detection by color feature and ResNet

Deep generative models, particularly Generative Adversarial Networks (GANs), have the capability to generate highly realistic fake face images that can easily deceive humans. Consequently, the utilization of machine learning techniques to discern these fake faces is becoming increasingly crucial. In this study, we initially analyze the characteristics of face images generated by GANs, and subsequently propose the extraction of color features as the key discriminator between real and fake faces. Regarding the choice of classifier, this paper adopts ResNet for accurate detection. The experimental results indicate that the method presented in this paper exhibits superior efficiency in detecting fake faces.
Enhancing recommendation quality in dense data environments: an improved collaborative filtering approach

In recent years, the proliferation of smartphones and advancements in internet technology have led to the emergence of various internet applications, resulting in a surge in data volume. However, this expansion has also brought about information overload. To address this issue, recommendation systems have gained prominence, leveraging recommendation algorithms to extract user-relevant items from extensive datasets based on user interests. These systems have found applications in various domains such as e-commerce and social networks. However, most collaborative filtering algorithms are tailored for sparse user-item rating matrices, neglecting scenarios with densely rated popular items by heavy users. This paper presents an improved collaborative filtering algorithm for dense data scenarios. It utilizes the K-Nearest Neighbors (KNN) approach, focusing on user rating predictions to tackle challenges arising from dense data environments. The proposed algorithm divides user ratings into liked and disliked item sets, enhancing prediction accuracy by calculating similarities separately. Experimental evaluations are performed using a real-world dataset from the "Kuaishou" video sharing app, demonstrating the algorithm's superiority in terms of F-Score compared to traditional collaborative filtering methods. The algorithm's simplicity, scalability, and performance are highlighted, showcasing its effectiveness in addressing recommendation challenges in real-world settings. Future research directions could explore its applicability across diverse datasets and domains.
Research on two-party private sharing method of distributed digital identity based on oblivious transfer technology

In a distributed digital identity system, there are various types of credential storage entities. With the development and popularization of distributed digital identities, data silos have emerged among multiple credential storage entities. To break this dilemma, this paper designs a distributed digital identity private sharing method based on an efficient Random 1-out-of-n OT extension protocol. The method uses the data obfuscation algorithm constructed by Permutation-based Hashing to design a two-party private data sharing protocol, which outputs the intersection of private data sets to the two parties. After experimental analysis, on a scale of ten million data, this method can better meet the needs of the real world.
Enhanced grasshopper optimization algorithm for solving continuous optimization problems

Aiming at the problems of weak ability to jump out of local optimization and lack of randomness in grasshopper optimization algorithms, this paper proposes a multi-strategy Enhanced Grasshopper Optimization Algorithm (EGOA) based on Brown random walk. The EGOA incorporates multiple strategies, including a Brownian random walk, a nonlinearly decreasing strategy with perturbation, and an improved optimal value guidance step. These innovations enhance the algorithm's ability to escape local optima, increase randomness, balance exploration and exploitation, and expand individual search ranges. Experimental results demonstrate that EGOA surpasses other swarm intelligence algorithms in benchmark function tests.
Advances in abnormal human activity detection based on video surveillance

This research reviewed the rapidly developing field of abnormal behavior detection in video surveillance. The emergence of deep learning techniques, especially in video feature extraction, makes up for the shortcomings of traditional methods. In terms of abnormal behavior detection, unsupervised, supervised, and weakly supervised methods have different advantages and disadvantages. At present, the weakly supervised method is popular in this field, and the highest AUC under the UCF dataset reaches 86.98%. Benchmark datasets play a crucial role in evaluating the performance of algorithms. Future research will focus on addressing challenges related to complex scene dynamics, occlusions, and real-time processing. Integrating multiple sensing modalities, transfer learning, deep learning techniques for feature extraction, and leveraging spatiotemporal graph-based methods are important directions for improving surveillance systems in anomaly detection.
Sequential augmented attention graph neural network for session-oriented recommendation

In order to solve the problem that existing session recommendation methods ignore the temporal relationship between items and do not consider the correlation between the items of interest and all items, which leads to poor recommendation results. A novel session recommendation model based on Graph Neural Network (GNN), Long- and Short-Term Memory network (LSTM) and attention mechanism is proposed. The GNN and LSTM are used to capture the complex dependencies and temporal relationships between items to obtain item embeddings; the global embeddings are generated by combining multi-headed attention and soft attention to accurately represent global preferences; the interest attention mechanism is introduced to generate interest embeddings to activate interest item relevance; finally, the current embeddings are combined to obtain the final session representation and predict the next click. The model is compared with the existing benchmark model on three public datasets and the results are all improved, which proves that the proposed method is very effective.
Insulator defect detection algorithm based on multi-scale feature fusion optimization

The detection of insulator defects in transmission lines presents greater difficulty and challenges compared to regular object detection tasks due to the small size of defects in the image and complex background. In this paper, we propose an insulator defect detection algorithm based on multi-scale feature fusion optimization to address the problem of extracting effective information from insulator defect images with complex backgrounds. The algorithm incorporates a multi-scale feature fusion optimization module into the CornerNet network, which enhances the network's ability to capture global contextual information and local detailed information of the insulator feature map. Experimental results demonstrate that the proposed algorithm achieves an average precision of 93.65% for insulator defect detection with an inference time of 32.15ms, providing high detection accuracy and inference speed. The proposed approach has certain reference value for insulator defect detection in transmission lines.
A TBGAV-Based Image-Text Multimodal Sentiment Analysis Method for Tourism Reviews

To overcome limitations in existing methods for sentiment analysis of tourism reviews, the authors propose an image-text multimodal sentiment analysis method (TBGAV). It consists of three modules: image sentiment extraction, text sentiment extraction, and image-text fusion. The image sentiment extraction module employs a pre-trained VGG19 model to capture sentiment features. The text sentiment extraction module utilizes the tiny bidirectional encoder representations from transformers (TinyBERT) model, incorporating the bidirectional recurrent neural network and attention (BiGRU-Attention) module for deeper sentiment semantics. The image-text fusion module employs the dual linear fusion approach to correlate image-text links and the maximum decision-making approach for high-precision sentiment prediction. TBGAV achieves superior performance on the Yelp dataset with accuracy, recall rates, and F1 scores of 77.51%, 78.01%, and 78.34%, respectively, outperforming existing methods. Accordingly, TBGAV is expected to help improve travel-related recommender systems and marketing strategies.
Research on telecom network fraud crime offenses and the optimization path of basic information gathering based on grounded theory

With the rapid growth of the digital economy in the Internet era, traditional crimes have expanded into the realm of cyberspace, leading to the emergence of telecom network fraud crimes. This paper specifically focuses on telecom network fraud crimes. By utilizing NVivo software, a qualitative analysis was conducted on 53 adjudication documents in database to develop a model aimed at comprehending and combating telecom network fraud crimes. Based on the analysis of the model, the following conclusions have been drawn there is a need to strengthen the collection and utilization of evidence, enhance anti-fraud education, and improve the gathering of basic information on telecom network fraud.
pymodconn: A python package for developing modular sequence-to-sequence control-oriented deep neural networks

This paper introduces "pymodconn", a comprehensive python package developed for constructing modular sequence-to-sequence control-oriented deep neural networks. These deep neural networks (DNNs) are designed to predict the future dynamics of complex time-dependent systems for given known future data, e.g., control inputs, using past known system dynamics and control inputs. The strength of DNNs in modeling complex systems is well known, but developing an optimal deep learning-based model can be a resource-intensive task. This package streamlines this process, simplifying model architecture selection and fine-tuning. The key strength of pymodconn lies in its high-level modularity, enabling users to design their DNN architectures in a flexible manner via a simple text-based configuration file. This flexibility and the comprehensive nature of pymodconn considerably reduce the development efforts and time for applications where precise control over system dynamics is necessary.
Adaptive Regression Analysis of Heterogeneous Data Streams via Models with Dynamic Effects

Streaming data sequences arise from various areas in the era of big data, and it is challenging to explore efficient online models that adapt to them. To address the potential heterogeneity, we introduce a new online estimation procedure to analyze the constantly incoming streaming datasets. The underlying model structures are assumed to be the generalized linear models with dynamic regression coefficients. Our key idea lies in introducing a vector of unknown parameters to measure the differences between batch-specific regression coefficients from adjacent data blocks. This is followed by the usage of the adaptive lasso penalization methodology to accurately select nonzero components, which indicates the existence of dynamic coefficients. We provide detailed derivations to demonstrate how our proposed method not only fits within the online updating framework in which the old estimator is recursively replaced with a new one based solely on the current individual-level samples and historical summary statistics but also adaptively avoids undesirable estimation biases coming from the potential changes in model parameters of interest. Computational issues are also discussed in detail to facilitate implementation. Its practical performance is demonstrated through both extensive simulations and a real case study. In summary, we contribute to a novel online method that efficiently adapts to streaming data environment, addresses potential heterogeneity, and mitigates estimation biases from changes in coefficients.
Multivariable System Prediction Based on TCN-LSTM Networks with Self-Attention Mechanism and LASSO Variable Selection

Intelligent prediction of key output variables that are difficult to measure online in complex systems has important research significance. In this paper, by using the least absolute shrinkage and selection operator (LASSO) algorithm to analyze the principal elements of input variables, a temporal convolutional network fused with long short-term memory (TCN-LSTM) network and self-attention mechanism (SAM) is designed to realize dynamic modeling of multivariate feature sequences. For complex processes with multiple input variables, each variable has different effects on the output, so it is necessary to use the LASSO algorithm to perform regression analysis on the input and output data for selecting the principal component variables and reducing the redundancy and computation burden of the network. The TCN network is used to extract the features of the input variables efficiently. The long-term memory performance of time series is enhanced by applying an LSTM network. The multihead SAM is used to optimize the network, and the role of key features is enhanced by assigning weights with probability to further improve the accuracy of sequence prediction. Finally, by comparison with the existing network model, the offline data generated by the high and low converters in the synthetic ammonia industry is used to predict the CO content so as to verify the superiority and applicability of the proposed network model.