Reviewerly/exHarmony huggingface dataset:
Topology-Aware Self-Adaptive Resource Provisioning for Microservices

Microservice architecture is a popular technology for deploying services in cloud computing, with benefits like loose coupling, high fault tolerance, and scalability. The heterogeneous resource requirements and complex interaction relations have increased the difficulty in provisioning resources for microservices with intricacy topology. Existing approaches allocate resources for different microservices separately, and thus cannot achieve optimal global performance. Moreover, these approaches extract features from specific microservice topologies. We propose a topology-aware self-adaptive resource provisioning approach for microservices. Firstly, we propose a microservice state graph to characterize the status of each microservice in an application. Then, we use graph neural networks and attention to extract the resource requirements and correlation features of microservices. Thirdly, we use a reinforcement learning-based approach to allocate resources for microservices uniformly. Finally, we evaluate our approach by conducting a series of experiments on three typical microservice applications deployed in a heterogeneous cluster. The results show that our approach is efficient in extracting resource and correlation features of microservices, and can guarantee QoS with efficient resource utilization. Our approach can reduce the End-to-End latency by 22%, and can improve resource utilization by 18% with guaranteed latency.
Improvement of IMODE - a Differential Evolution Algorithm - by Replacing the Third Mutation Strategy

In the field of intelligent computing, real parameter single objective optimization is an essential research direction. Recently, a new setting is employed for real parameter single objective optimization. IMODE is a DE algorithm with three mutation strategies suitable for the new setting. Although IMODE has good performance, it can be further improved. In this paper, we replace the third mutation strategy in IMODE - DE/weighted- rand-to-ϕbest/1 - by DE/rand/1. Compared with the original third mutation strategy, it has much lower probability leading to structurally bias in searching. Based on the replacement, we obtain IMODE-II. Experimental results show that our proposed algorithm is very competitive.
Representation Enhancement based Cold Start Model for Elastic Compute Service Recommendation

As the core infrastructure service of cloud computing, Elastic Compute Service (ECS) offers a diverse range of products for users to select from. However, it is challenging for users to combine numerous configurations for the appropriate products, making it critical to be recommended individually. In contrast to traditional recommendation scenarios, ECS combines Business-to-Business (B2B) and Business-to-Consumer (B2C). Compared with enterprise users with rich behaviors, individual users account for a large proportion with limited behaviors, facing a serious cold start problem. Furthermore, enterprise users have high demands and strong preferences, leading to a high concentration on popular products, resulting in long tail distribution. To address the problems, we propose a novel model named the Representation Enhancement based Cold Start Model (ReCSM). Based on dynamic relations among users, the Cold start user Contextual Attention Module (CCAM) is designed to enhance the representation of cold start users with behavior-rich users, thus improving the expressiveness of the inductive model. What’s more, the Generalized Attribute Clustering Embedding module (GACE) is proposed to maintain the clustering center representation for each user to improve the generalization ability. Additionally, we implement the Contrastive Learning based Long tail products Enhancement Module (CL-LEM) to relieve the effect of long tail distribution, ensuring the diversity and fairness of recommendation results. Extensive experiments have proved the validity of ReCSM on two real ECS datasets and one public dataset.
A recursive sorting method based on multi-station TDOA vector

With increasingly fierce electronic countermeasures, the modulation methods of radiation source signals are becoming increasingly complex. In the face of complex electromagnetic environments, current signal multi-station collaborative sorting algorithms have problems such as high difficulty in data fusion and low sorting accuracy. To address the above issues, this paper proposes a recursive sorting method based on the multi-station time difference vector. Firstly, the location information of the reconnaissance station is used for time difference constraints, and then the multi-parameter Euclidean distance of the pulses is combined to accurately match the multi-station pulses. Finally, based on the distance average of the k-nearest neighbors of the time difference vector, the pulses within the range threshold are merged, the time difference center is updated in real time, and the pulses are recursively extracted and sorted. Simulation results show that the proposed algorithm has a high sorting accuracy.
A Network Structure for Rehabilitation Training Evaluation Based on Transformer

Evaluation and analysis during rehabilitation training is a very important part for patients. The subtle changes in the rehabilitation training process cannot be captured by the naked eye. However, previous studies have been focusing on evaluating the patient's training process in the form of action recognition, which cannot capture the subtle changes in the patient's training process and give a quantitative evaluation. In order to improve the accuracy and practicality of rehabilitation training assessments, better provide patients with rehabilitation training assistance and training feedback in a home environment. We propose a Transformer-based network structure Recurrent Spatio-temporal Transformer (RSTformer). Our model extracts patient motion feature information from common RGB videos for scoring, and the proposed model can handle variable-length inputs, which allowing users to perform multiple training sessions at different speeds. We use MAD, RMS, and MAPE to evaluate our model on KIMORE and UI-PRMD datasets. The experimental results show that proposed model has achieved a 21.8% improvement in rehabilitation training evaluation compared to previous methods, which shows that we are in the Superior Performance for Rehabilitation Training Assessment.
Density peaks clustering based on shared nearest neighbors and dense degree

Density peaks clustering (DPC) is an algorithm that searches density peaks based on density and distance. It has been widely used due for requiring fewer parameters, no iteration and good robustness. Nevertheless, the internal structure of a dataset is overlooked in computing local density. In addition, the dense points between clusters are often clustered incorrectly. Therefore, an improved DPC algorithm named DPC-DDE is proposed. The calculation of local density based on shared nearest neighbors and dense degree can measure the closeness of two points. It is effective to improve the separability of clusters. We conduct experiments on 6 datasets, including 4 artificial datasets and 2 UCI datasets. Experimental results indicate that DPC-DDE outperforms three comparison algorithms.
Multi-Attention Spatio-Temporal Feature Extraction Network for Image Deraining

At present, the field of unmanned driving is facing the public's safety question, which directly hinders the further development of unmanned driving. In order to adapt to the complexity and variability of driving environment and enhance the anti-jamming ability of unmanned vehicles, we proposes a lightweight rain removal network based on temporal and spatial feature correlation (MASTF-Net). In this method, the dewatering process is divided into several stages. In each stage, spatial features are extracted by global and local feature extraction modules, and then feature spatio-temporal correlation is realized by residual memory modules integrated by multiple attention mechanisms. In this way, MASTF can restore fine images of rain noise and preserve the background information well. The MASTF also has an extremely low parameter quantity and FLOPs, making it deployable. A large number of experimental results on real datasets and synthetic datasets show that our scheme has higher performance and efficiency than some state-of-the-arts methods.
Research on Trusted Service Assurance for IoT: A Blockchain Smart Contract-Based Method

Cloud computing has become an integral component in various domains, offering users on-demand access to computing resources, software, data, and other services through networks. However, the increasing necessity for verifiability in cloud computing presents new challenges. To address these challenges while minimizing costs, this paper introduces a trust mechanism based on blockchain smart contracts. Specifically, clients allocate identical computing tasks to two Internet of Things (IoT) service providers, employing smart contracts to prevent collusion between providers. A prototype of the proposed smart contract is implemented, and a private test blockchain is established. Subsequently, this paper evaluates the cost and performance of the smart contract. The designed smart contract demonstrates reduced overhead compared to traditional smart contracts and supports a maximum transaction speed of 4 TX/s, which is relatively fast in comparison to other approaches.
A Fall Monitoring System Based On Embedded System

In today's increasingly severe aging age, the safety requirements of the elderly living alone both in indoor and outdoor activities are increasing, in response of this social phenomenon, this paper completes a lightweight embedded fall detection system by combining embedded technology, network communication technology and the behavior recognition module of the target detection algorithm using deep learning. This study used 534 images containing fallen objects as a dataset, based on the YOLOv5 (You Only Look Once) algorithm model <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> . The model was lightweighted, and the model network structure was improved by densely connected networks. In the experimental comparison with the original model, the new model achieved the best performance with fewer iterations, and the recall rate of 97.74%, which was 1% higher than the original model; the best accuracy of 97.09%, which was 0.96% higher than the original model; and the mAP value of the improved network of 0.962.
Research on Active Prevention and Control Technology for High Risk Targets in Transmission Lines

To address the issues of low accuracy, slow speed, and low fine-grained detection of hazardous targets in transmission line application scenarios, an attention mechanism based lightweight target detection algorithm for hazardous targets is proposed. Adopting a lightweight algorithm for object recognition based on attention networks, research on enhanced detection techniques for common threat targets, achieve recognition of common targets in and around transmission lines, and automatically detect size and distance of moving targets, achieving high sensitivity and quantitative detection of abnormal changes around transmission lines.
Research on Path Planning of Mobile Robot by Fusing A* and DWA Algorithms

The traditional A* algorithm has low search efficiency, many turning points in the planned path, and cannot achieve dynamic path planning when facing unknown obstacles in the environment. This article proposes a fusion algorithm that combines the A* algorithm with the Dynamic Window Approach (DWA) algorithm, which uses the DWA algorithm's dynamic window evaluation function to distinguish between known and unknown obstacles. The key points of the A* algorithm's planned path are extracted as temporary targets for the DWA algorithm, achieving the fusion of A* and DWA algorithms based on global optimality. The experimental results show that the fusion algorithm can ensure global optimality; and avoid unknown obstacles in the environment timely and effectively, achieving dynamic path planning.
Sports Video Data Classification using Yolov5 Model

The machine learning along with computer vision has helped widely in classification of sports videos. Deep learning techniques are also being used to perform research in this domain. The video frames are the most significant components of the sports classification system. There are many models that have been used to classify sports videos. The objective of this work is to develop a classifier for different sports activity recognition using video data with high accuracy and probability. Sports dataset has been used from Kaggle [10]. This framework has been created for applications related to sports, object detection, game identification, recognition, analysis, players’ tracking, and performance. This framework consists of various intermediary processes. At first, preprocessing has been carried out in this framework by converting input sports video into video frames. Then skeletonization has been carried out using computer vision. Finally, feature extraction and classification has been done using the Yolov5 model that has been trained with the dataset. Accuracy and loss graphs have been generated using prediction metrics for this model to evaluate the accuracy of this framework. The accuracy of the framework is more than 90% according to the results we have obtained.
A visual question answering method based on question intention

The input to a Visual Question Answering(VQA) is an image and a textual question related to the content of the image. The computer system needs to understand and process the image based on the input question and retrieve the answer to the question from the image. Since its emergence, VQA has naturally spanned two domains, Natural Language Processing (NLP) and Computer Vision (CV), and is a typical multimodal learning task. People often consider a variety of high-level semantic information when answering questions. For example, questions about color, type, quantity, purpose, etc. Although this information is critical to answering the VQA question, it is not directly available from the input data. Therefore, in this paper, we input the high-level semantic information of question intention into the model in the form of external knowledge, which is used to influence the process of multimodal information interaction and select the most appropriate features. The visual question answering method based on question intention designed in this paper was tested on the open datasets VQAv2, and the accuracy of the VQA is better than that of the baseline model.
MAM-YOLOv5 Based Method to Detect Underground Staff Falling Down Accidents

Most coal mining enterprises use surveillance videos to monitor the situation of underground staff. Due to the interference of underground noise, dust, and light, the quality of video images is poor, which brings difficulties to the detection of falling down accidents. At the moment, the commonly used YOLOv5 model have certain false positive detections in this case. To solve the problem, this paper proposes an improved YOLOv5 underground staff falling down accidents detection model that integrates multiple attention mechanism, called MAM-YOLOv5. Firstly, OpenCV was used to enhance the brightness and contrast of the YOLOv5 backbone network module, so that the model could notice the importance between different channels and the spatial location information during feature extraction. This improves the attention to key targets. Finally, to solve the problem where the feature information extracted by the upper module is gradually missing with the continuous increase of the network depth, the CA attention mechanism is introduced to establish the long-distance dependency between modules and improve the recognition ability of feature information. The experimental results show that compared with the YOLOv5 model, the Mean Average Precision (mAP) of the MAM-YOLOv5 model is increased by 14.5%, and the mAP value reaches 92.9%. This proves that the improved model produces more accurate result when detecting the falling down accident of underground staff, which provides a safer coal mine production environment.
A lightweight and high-precision traffic sign identification method

This paper presents a lightweight and highly accurate method for identifying traffic signs. This method adopts the object detection method without anchor frame, which greatly reduces the number of parameters of the deep learning network model, reduces the network complexity, and realizes the purpose of lightweight network. In addition, this method greatly improves the accuracy of object detection by improving the backbone network structure, neck structure and detection head, and optimizing the loss function. According to experimental simulation, the parameters of this method is only about 3.3M, which belongs to lightweight network and can be easily deployed on mobile terminals. In addition, the detection accuracy of this method mAP (0.50) is as high as 99%, which is significantly higher than the current mainstream object detection method.
Implicit Feedback Sequence Recommendation System Based on Interactive Interest Modeling

Traditional recommendation systems often rely on explicit feedback, such as user ratings and favorites. However, this approach suffers from data sparsity and inaccuracy issues. In contrast, implicit feedback data, such as user browsing history and click records, is much richer. Therefore, this paper uses implicit feedback data as the basis for recommendations, models user behavior to uncover their potential interests, and transforms them into vector representations.This paper also introduces interactive interest modeling. This approach models user interest representations based on implicit feedback user behavior sequences. For sequence recommendation problems, we use a Transformer model to extract deeper features from interactive interest representations and use an attention mechanism to capture users' real-time interests. Experimental results on Kaggle datasets demonstrate that this method outperforms traditional implicit feedback recommendation methods in terms of accuracy and personalization.
Research on Images Recognition based on Deep Convolution Neural Network

Image recognition has become a necessary component for computer visual system and widely utilized to detect objectives for downstream tasks in realistic applications. However, existing methods are concentrated on utilizing the clustering information of image features to recognize the subjects, which are unable to dispose several high correlation subjects and cost numerous computation period. In this paper, we utilize the convolution operation for images and extract the separated features. After acquiring these features, a deep neural network is established to recognize the objectives in the input images with enough iterations training procedures. Subsequently, the trained model is evaluated through the testing data-set to measure the real performance of proposed method. From our extensive experimental results, we can conclude that our proposed model can automatically realize the recognition process for input images with reasonable accuracy and acceptable computation costs. Additionally, our experimental results also indicate that the convolutional operation is more suitable to dispose the images data-set than traditional machine learning method.
Comparison Analysis and Research Between Fuzzy Self-Adaptable Prediction Method and Growth curve Prediction Method

In the past years, large scale settlement observation work has been carried out in various places, and a large amount of field measured data has been accumulated. However, the current research method is mainly based on traditional manual collection, which has a large workload and low efficiency. Especially when a large amount of data information is encountered, the processing is very difficult, which is not conducive to the management and query of data and reflects the development of the overall deformation and settlement of the subgrade with time and space. It is difficult to calculate the settlement of soft soil subgrade accurately, and there are often some differences between the engineering calculation results and the measured results. According to the principle of artificial intelligence and method of fuzzy mathematics, the fuzzy combination forecasting method of self adapt variable weight is proposed. Based on matching degree of past-recent period and actual value, the weights are automatically adjust, so that forecasting results will be accurate. The fuzzy combination forecasting method of self-adapt variable weight is applied to the subgrade settlement in expressway, which provides the prediction accuracy and reliable applicability. In order to improve the prediction accuracy, the measured sample points should be updated, and with the passage of time, the recent settlement results should be set into the original samples.
An Adaptive GLMB Filter with Unknown Environmental Parameters

In the context of multi-target tracking based on random finite sets, the main challenge lies in the random fluctuations in both the number and state of targets. In most target tracking application scenarios, model parameters such as clutter rate and detection probability are unknown and time-varying; in addition, the birth intensity of nascent targets is difficult to predict. All of this information significantly affects the tracking accuracy of the filter and is usually assumed to be known and provided by the user. In this study, we propose an adaptive generalized labeled multi-Bernoulli (GLMB) filter capable of estimating the unknown parameters and the birth intensity of nascent targets. This filter combines the measurement-driven method and uses the robust multi-Bernoulli filter to calculate the clutter rate and detection probability, which can track multiple targets without the need for priori information. The simulation results demonstrate that the proposed method significantly improves the multi-target tracking performance, outperforming the CBMeMBer and CPHD filters with known tracking scene information in terms of tracking effectiveness. Meanwhile, the proposed method achieves tracking performance that is comparable to that of the GLMB filter.
Transformer Tracker Based on Clipped SwinV2 Network

As Transformer technology continues to show its advantages in the field of computer vision, object tracking technology based on the Transformer network has also attracted much attention. In this paper, a Transformer object tracking algorithm based on an improved SwinV2 backbone network is proposed. First, the SwinV2 network is suitably trimmed to fit the object tracking application requirements by combining the requirements of feature dimensionality and computational speed of the object tracking application and retaining the advantages of the SwinV2 Transformer network in terms of the visual model. Then, the trimmed SwinV2 network is migrated to the SwinTrack base algorithm as the backbone network. Comparison experiments are conducted with the recent mainstream tracking algorithms based on three public test sets, LaSOT, GOT-10k, and OTB100, and the results show that the algorithm in this paper has better tracking performance.