Reviewerly/exHarmony huggingface dataset:
An Energy Optimization Algorithm for Data Centers based on Deep Q-learning with Multi-Source Energy

More and more data centers are supplied by multi-source energy. However, the features of random, uncertain, and time-varying of renewable energy has made it difficult to achieve good results with traditional methods. In this paper, we research how to coordinate multiple energy sources (such as wind power, solar, and smart grids) to reduce energy costs of data centers. We propose a deep Q-learning (DQN) algorithm based on the auto encoder to control the energy consumption of data center. Our algorithm uses the auto encoder to approximate the Q-value function, learning the expected cost based on the state of current system. It solves the problem that the Q-value function in traditional Q-learning algorithm is difficultly designed under multi-constraint conditions, and it can converge by any state of the system to obtain the optimal solution. In order to further improve the convergence speed and accuracy of the algorithm. We design a parameter optimization strategy to solve the slow convergence problem of the algorithm. This strategy is based on the experience replay technology to optimize the parameters of algorithm. We conducted extensive experiments based on real- world data, and evaluated the performance of our algorithm. The experiment results show that our algorithm can average save 20% energy cost so as to bring a set of safe and highly available solution to meet the requirements of multi-Source energy for data centers.
The Algorithm Researching of Hand Pose Estimation Based on 3D Joint Regression

The RGB image is used for 3D hand pose estimation, and the Gaussian function is defined according to the kinematics principle to represent the thermal map dependency of the previous joint point of the finger on the next joint point. The convolution network is assembled through the tree structure through the branching strategy, and the tree branch hierarchical network algorithm framework is designed. The two-dimensional pose of the five single finger joint points of the hand is estimated according to the local pose characteristics, and then the three-dimensional joint normalization regression algorithm is used to estimate the full three-dimensional hand pose for the two-dimensional pose of the single finger joint points. When regularizing linear regression, all features are punished. The degree of punishment is selected by the optimization standard of the sum of squares of residuals. The heat map loss function and joint point regression loss function are set. The experiment is carried out on the CMU Panoptic Dataset and STB Dataset. The experimental results show the advantages of this method by comparing parameters with other methods.
Implementation of Parallel Acceleration for Real-time Extraction of Visual Features

Visual feature extraction is the key to target localization, reconstruction and estimation. SIFT feature extraction algorithm has good adaptability and high computational complexity. In this paper, a parallel accelerated implementation of real-time extraction of visual SIFT features is designed. A parallel pipelined computing architecture is designed for the general SIFT computing process, and the data access is accelerated by combining DDR3, Block RAM and register three-level cache. Fixed-point computing is used in the computing modules of large data streams such as Gaussian pyramid, differential pyramid and extreme value detection. Hybrid precision is used in gradient histogram, feature description and matching process. When it comes to the calculation of exponential and trigonometric functions, the series expansion and table lookup method are adopted to effectively reduce the logic and storage occupation. In this paper, the design architecture and data information flow of each SIFT calculation module are given, and the whole design is verified experimentally in XC7K325. The results show that the design for 1024X1024 pixel image can reach 100 frames of calculation speed, power consumption is not more than 12W. The SIFT descriptor error is less than 1%, which ensures the calculation error of the system while the resources are effectively utilized.
A Novel End-to-End Object Detection Model Based on Multi-scale Deformable Attention Module

Although Transformer has good performance in NLP, but it is introduced into the CV, when the processing object is an image, due to its own characteristics resulting in computational complexity and spatial complexity is very large, forcing the model to use only low-resolution image features, the present invention for the PIX2SEQ model is still used in this way, by introducing a deformable attention mechanism into the transformer, greatly reducing its complexity, This enables the model to use high-resolution image features and multi-scale feature information, thereby improving the model detection performance.
A New Convolution Neural Layer Based on Weights Constraints

With the rapid development of artificial intelligence techniques, emerging deep neural networks (DNN) is one of the most effective ways to solve many challenges. Convolution neural networks (CNNs) are considered one of the most popular AI techniques used to extract and analyze meaningful features for image datasets, especially in the medical diagnosis field. In this paper, a proposed constrained convolution layer (COCL) for the CNN model is proposed. The new layer uses a constrained number of weights in each kernel trained in the phase of learning and excludes the others weights with zero values. The proposed method is introduced to extract a special type of feature considering the local shape of a sub-image (window) and topological relations between group pixels. The features extract according to a random distribution of weights in kernels that are determined considering a particular desired percentage. Furthermore, this paper proposed a CNN model architecture that uses COCL rather than the traditional CNN layer (TCL). The efficiency of the method is evaluated using three types of medical image datasets compared with the traditional convolution layer, pre-trained deep neural networks (pre-DNNs), and state-of-art methods. The proposed model outperforms other methods in terms of accuracy and F1 score metrics and exceeds more than 98%, 89%, and 93% for the three datasets used in the evaluation, respectively.
Automatic Density Peaks Clustering based on the Cosine Similarity

Density peaks clustering (DPC) is a simple and effective clustering algorithm. However, the clustering results are greatly impacted by the chain reaction due to its special allocation strategy. To solve this problem, we proposed an automatic density peaks clustering based on cosine similarity (ADPC) in this paper. First, data points are seen as vectors and each attribute value is seen as corresponded coordinate based on cosine we proposed to measure similarity, which represents similarity by the data points’ vectors and considers both the direction and the size of points’ vectors. In theory, the cosine similarity varies from 0 to 1, and the value bigger, the result will be better. Such similarity measurement is more appropriate than direct distance. Second, based on this, the improved local density, the improved similarity from the nearest point with higher density are proposed to replace the corresponded concepts of DPC. Third, to make DPC allocation strategy simpler, the improved local similarity is created. At last, a method to specify cut-off distance is introduced. We give out the strategy based on theory analysis, which makes the strategy more convinced. Above all, ADPC could select cut-off distance automatically instead of specifying it by our experience and overcome chain reaction simultaneously. ADPC shows the promotive performance on 4 synthetic datasets and 11 real datasets.
Implements of Transformer in NLP and DKT

Transformer is a strong model proposed by Google team in 2017. It was a huge improvement that it entirely abandons the mechanism of Recurrent Neural Network (RNN) and Convolutional Neural Network (RNN). As a result, soon it became a popular choice in a diversity of scenarios. A typical implement of Transformer is for handling text-like input sequences, such as Natural Language Process (NLP) and Knowledge Tracing (KT). Although Transformer is a strong model, it still has a number of improvements. Some state-of-the-art Deep-Learning-based models (e.g., BERT in [2], SAINT in [3], etc.) are based on Transformer. In this paper, I give some examples of application of Transformer or Transformer-based models and summarize the pros and cons of Transformer.
Contrastive Graph Learning for Session-based Recommendation

In view of the problem that random deletion of graph nodes and edges may delete important item nodes when constructing graph structure enhancement samples in existing research work, which is not conducive to item node learning, this paper proposes a session-based recommendation method based on graph structure information enhancement. According to the importance of the nodes in the graph, the enhanced samples on the graph structure are constructed to enrich the representation of the item nodes. Firstly, our method constructs an item transfer graph according to the sequence of items interacted by the user, and then calculate the importance of the nodes and edges in the item transfer graph according to the in-degree and out-degree information of the user nodes in the graph. For important nodes and edges, we delete them with small probability. For the enhanced graph structure, we design contrastive learning on the graph structure to learn the representations of nodes on the graph. Finally, in the traditional conversation recommendation task, the auxiliary task of contrastive learning is added, and the multi-task learning framework is applied to learn the user's preference in session-based recommendation. Experimental results show that this method can effectively improve the performance of session sequence recommendation.
Research Advanced in Object Detection Based on Deep Learning

Object detection has always been a hot topic in the computer vision community. The task is easy for humans, while it is difficult for computers to learn the informational concepts to locate object positions, especially for the sense that multiple objects and cluttered backgrounds are mixed together. Traditional object detection methods are based on manual feature extraction. Coupled with the development in deep learning, both the detection accuracy and speed had made a breakthrough. This paper provides a review of deep learning-based object detection architectures. First, a simple summary on deep learning and its applications area are introduced. Then, this paper pay attention to the basic framework of object detection and its performance index. Then, it classify the object detection algorithms into three categories according to their structure: one-stage, two-stage and point-based. And this paper evaluates their performance as well as their practicality. Finally, several current problems and solutions are provided as suggestions for advanced research on object detection and its applications.
The Use of Toulmin's Argumentation Model in Solving The Drug Conflict Problems

The field of argumentation in Artificial Intelligence (AI) has witnessed a great increase an important cognitive to deal with uncertain information and conflicting opinions. This has led to a number of interesting lines of research in this field and related fields, giving rise to computational models of the argument as a promising research field. The remedies conflict problem is considered one of the challenges in the field of medicine the world. This paper makes use of Toulmin's argumentation model to deal with conflicting problems within the medicine field. In addition, inference rules were used for associating a patient's symptoms and patient history(premises) with remedies use, eventually leading to medications diagnosis for patient (claims). After that, several remedy features are used to compete for the support and the attack (pros and cons) for each remedy item. A decision is made during the qualifier phase in Toulmin's model about whether or not the drug should be used based on the highest value of support or attack. The dataset consists of 200 patients as samples for two heart diseases (hypertension, angina pectoris). It is collected from the Iraqi educational hospitals, annotated by a team of experts working in the medical field. The performance achieved in the proposed model in hypertension and angina pectoris diseases were 78% and 83%, respectively, using the confusion matrix method.
Research on network security behavior audit method of power industrial control system operation support cloud platform based on FP-Growth association rule algorithm

With the introduction of the national “carbon peaking and carbon neutrality” strategic goals and the accelerated construction of the new generation of power systems, cloud applications built on advanced IT technologies play an increasingly important role in meeting the needs of digital power business. In view of the characteristics of the current power industrial control system operation support cloud platform with wide coverage, large amount of log data, and low analysis intelligence, this paper proposes a cloud platform network security behavior audit method based on FP-Growth association rule algorithm, aiming at the uniqueness of the operating data of the cloud platform that directly interacts with the isolated system environment of power industrial control system. By using the association rule algorithm to associate and classify user behaviors, our scheme formulates abnormal behavior judgment standards, establishes an automated audit strategy knowledge base, and improves the security audit efficiency of power industrial control system operation support cloud platform. The intelligent level of log data analysis enables effective discovery, traceability and management of internal personnel operational risks.
Radar Emitter Signal Recognition Method Based on EEMD and Ambiguity Function Contour with EfficientNet

Radar emitter signal recognition is the key technology of electronic countermeasure. Considering the strong inherent structure characterization ability of radar signal with ambiguity function contour and the strong recognition performance of deep neural network, this paper proposes a recognition method based on EEMD and ambiguity function contour with EfficientNet. First, select the appropriate EEMD parameters, denoise the time-domain signal and build a ambiguity function contour data set, then establish a depth network based on EfficientNet-B0 model, complete the training of tag data with migration learning, and finally realize radar emitter signal recognition based on this network. Simulation results show that when the SNR is greater than - 10dB, the proposed method can achieve an average recognition accuracy of more than 99.67% for six types signals, and has strong robustness.
Head Pose Estimation In Classroom Scenes

In this paper, we propose an image classification method to recognize head pose in surveillance video of classroom scenes, which is improved on EfficientNetV2-S, called EfficientNetV2-SC. Firstly, the SENet attention mechanism in MBConv module is replaced by the ECA-Net, which can preserve the original channel features in classroom video. Then, a module combining residual structure, channel blending and convolutional attention is proposed to be added between the feature extraction network and the fully connected layer to enhance feature extraction ability and semantic association between channels. Finally, cross-entropy loss function is combined with label smoothing, which can prevent overfitting and improve generalization ability. In the homemade dataset, compared with the original EfficientNetV2-S, the accuracy of our method is improved by 2.7% which is up to 94.8%, and the number of model parameters decrease by 3.55M. In the POSE subset of CAS-PEAL-R1, EfficientNetV2-SC also achieves a significant advantage in experimental accuracy.
A Study of Reversible Data Hiding Technology with an Authentication Function

Digital signature is a very effective verification tool in checking electronic documents, but it does not apply to digital image, because digital images can fail to be verified by a digital signature with slight interference. To achieve the dual purpose of both verifying the copyright information of a digital image and restoring the original image without loss, in this paper, we propose a reversible data hiding scheme with an authentication function. The digital signature is first QR-coded, then the disrupted confidential data is embedded into the digital image. When digital signature verification is performed, it is only necessary to restore the stego image to the original image and extract the QR code according to the reversible algorithm. The experimental results show that the reversible data hiding algorithm can be embedded in each image block with up to 6 bits of data, its embedding block ratio reaches 87.42% on the average, which effectively improves the embedding capacity while the quality of the carrier image remains relatively stable, and is also robust against non-deliberate attacks on image processing; the strong error correction capability of the QR code also improves the success rate of digital signature verification.
Building a Real-Time System to Monitor Students Electronically Based on Digital Images of Face Movement

The spread of the Corona Virus pandemic on a global scale had a great impact on the trend towards e-learning. In the virtual exams the student can take his exams online without any papers, in addition to the correction and electronic monitoring of the exams. Tests are supervised and controlled by a camera and proven cheat-checking tools. This technology has opened the doors of academic institutions for distance learning to be wide spread without any problems at all. In this paper, a proposed model was built by linking a computer network using a server/client model because it is a system that distributes tasks between the two. The main computer that acts as a server (exam observer) is connected to a group of sub-computers (students) who are being tested and these devices are considered the set of clients. The proposed student face recognition system is run on each computer (client) in order to identify and verify the identity of the student. When another face is detected, the program sends a warning signal to the server. Thus, the concerned student is alerted. This mechanism helps examinees reduce cheating cases in early time. The results obtained from the face recognition showed high accuracy despite the large number of students' faces. The performance speed was in line with the test performance requirements, handling 1,081 real photos and adding 960 photos.
Flight cockpit gesture recognition based on YOLOv7

With the rise of smart mobile devices and wearable devices, emerging technologies such as gesture recognition have been widely used in reality. However, its application for specific complex scenarios (such as flight cockpit gesture recognition) is not perfect at present. In view of this, in this paper, a flight cockpit gesture simulation dataset (FCGS) is firstly proposed; secondly, a flight cockpit gesture recognition algorithm YOLOv7H is proposed, which is based on the YOLOv7 algorithm Reconstruction on the basis of: The BackBone part adopts the Hornet series network, which effectively combines the advantages of the visual Transformer and CNN, and adds a No-parameter attention mechanism (SimAM) module to the Head network, which improves the representation ability of the convolutional network, with flexibility and effectiveness. The results obtained in this experiment are: percison increased from 92.469% of YOLOv7 to 98.053%, recall increased from 97.486 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> of YOLOv7 to 99.994%, and mAP increased from 99.448% of YOLOv7 to 99.500%. In the scene of flight cockpit gesture recognition, the recognition accuracy of YOLOv7H is better than the current mainstream target detection algorithm, and the recognition speed is almost the same. It has advantages in processing natural interactive information and provides an effective means for human-computer interaction. In addition, The source code will be published in https://github.com/Dennis-Chen2021/aircraft-cockpit-gesture-recognition.
Review on Image Segmentation Methods Using Deep Learning

In recent years, the machine learning field has been inundated with a variety of deep learning methods. Different deep learning model types, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), adversarial neural networks (ANNs), and autoencoders, are successfully tackling challenging computer vision problems including image detection and segmentation in an unconstrained environment. Although image segmentation has received a lot of interest, there have been several new deep learning methods discovered with regard to object detection and recognition. An academic review of deep learning image segmentation methods is presented in this article. In this study, the major goal is to offer a sensible comprehension of the basic approaches that have already made a substantial contribution to the domain of image segmentation throughout the years. The article describes the existing state of image segmentation, and goes on to make the argument that deep learning has revolutionized this field. Afterwards, segmentation algorithms have been scientifically classified and optimized, each with their own special contribution. With a variety of informative narratives, the reader may be able to understand the internal workings of these processes more quickly.
Underwater salient object detection based on frequency-tuned

Under the environment of ocean development, underwater object detection technology has been widely used. Underwater salient object detection technology plays an important role in this process. The complex optical environment of underwater makes the underwater optical image have low contrast, colour distortion and blurred boundary, which causes great interference to the underwater visual salient object detection. Searching for saliency features of images from a frequency domain perspective is an effective means. In this paper, the frequency-tuned salient object detection method is used. After comparison with the images in the atmosphere, it is not significantly reduced in the indicators, which can obviously meet certain detection requirements.
Prediction of regional ecological security by applying deep learning methods in spatial and temporal simulation

Climate change is becoming the most important ecological and environmental issue on a global scale. Regional ecological security simulation and prediction based on computer deep learning is a way to use advanced technology to help human work and protect our ecological security issues. Computer deep learning can help regional ecological security research to achieve simulation and prediction in the areas of integrated water security, biosafety, address disaster security, cultural heritage security and recreational safety. In the specific implementation, we use the genetic algorithm that has been proved to be the most stable in practice, and through the evolution and analysis, the optimization of time-space simulation and prediction is continuously carried out. It is hoped that through the research of this paper, it has certain reference significance for the protection of regional ecological security.