Reviewerly/exHarmony huggingface dataset:
Adversarial Attack for Modulation Recognition Based on Deep Neural Networks

In recent years, Deep Neural Networks have faced the challenge of resisting example attacks, which seriously affects the security of modulation recognition models based on Deep Neural Networks. Aiming at the adversarial attack problem in signal modulation recognition, this paper uses five classic adversarial attack methods to attack three modulation recognition models based on Deep Neural Networks with different architectures. Then, the attack effectiveness of different methods is evaluated in terms of attack success rate, imperceptibility. The experimental results show that the neural network model is extremely vulnerable to adversarial attacks. The evaluation index proposed in this paper can comprehensively and accurately evaluate the signal adversarial examples, which is of great value for the research on the security and reliability of the intelligent modulation recognition model.
Roadside Object Detection Algorithm Based on Improved YOLOv7

As autonomous driving gradually enters the development direction of vehicle-road collaboration, the research on perception tasks in roadside scenes has become increasingly important. Due to the complexity of the road environment at intersections and the large number of traffic participants, roadside object detection has become a very challenging task. In this paper, we studied the practicality of the popular object detection network YOLOv7 in roadside perception from the roadside perspective. We introduced the CBAM attention mechanism to improve the network's feature extraction capability, targeting the problem of numerous categories of roadside traffic participants and the high installation angle of roadside cameras. We conducted experiments on the latest open-source large-scale vehicle-road collaboration dataset DAIR-V2X. Based on the experimental results, the YOLOv7 object detection network can be applied to the detection of roadside objects. Compared with the original YOLOv7, our improved YOLOv7 network increased the average detection accuracy mAP@50 by 1.1% to 91.3%, surpassing other mainstream object detection networks, and the detection speed reached 60FPS, meeting the requirements of environmental perception in complex intersections.
A New GNSS/INS Navigation Scheme Using Integrated Particle Filter and Extended Kalman Filter

This paper presents a new navigation scheme for the global navigation satellite system and inertial navigation system (GNSS/INS) by integrating the particle filter a nd the extended Kalman filter. In the proposed scheme, t he particle filter (PF) is used to estimate the object position and its velocity while the extended Kalman filter (EKF) is applied to predict the orientation, accelerometer error gyroscope error. In addition, the adaptive Gaussian particle swarm optimization (AGPSO) algorithm is involved into the PF to further improve the estimation accuracy. Experimental results show that the positioning and velocity errors using the proposed method are greatly reduced compared to the standard particle filter estimation method. The positioning accuracy can achieve sub-meter level, which satisfies the requirements of high-precision positioning and navigation. Finally, we analyze the sensitivity of the proposed algorithm on the number of particles, and we show that increasing the number of particles within a limited range is effective for improving the accuracy of the proposed algorithm.
Research on Recognition Method of Running Landing Action Based on Posture Sensor

Running posture is reflected in the stride frequency, gait, landing way. Wrong running posture can easily lead to lower limb injury. In this paper, we analyze the landing patterns of full-foot, heel and toe in running movement, and design and develop the hardware and software system of running monitoring system. Data acquisition, signal pre-processing, and Kalman posture angle solution are all done in the wearable device, and the processed data are sent to the running monitoring platform via WiFi wireless communication module. The running monitoring platform extracts the gait feature points from the uploaded data and uses two thin-film pressure sensors to verify whether the gait feature points are correctly marked. Finally, three running landing postures are identified using hierarchical classification-based and SVM-based running posture detection algorithms, and the classification results are above 90%, proving the method's effectiveness.
Secure Data Sharing in Internet of Vehicles Based on Blockchain and Attribute-Based Encryption

Sharing data among vehicles is one of the most important ways to provide safety-related and value-added services to connected vehicles. Nevertheless, access to the shared data must be controlled to prevent the exposure of users' privacy and data leakage or corruption. Attribute-based encryption (ABE) can provide data confidentiality and fine-grained access control. However, the complex and dynamic driving environment of vehicles may cause the attributes of vehicles to change frequently, and thus put a huge burden on the attribute management of the system or degrade the security of the system. In this paper, we propose a secure data sharing method by using ABE and blockchain for Internet of Vehicles. By using ABE, the data owner can stipulate the policy of the data access control based on the attributes of vehicles. The trusted authority is replaced by blockchain, which reduces the burden and solved the problem of single point failure of the trusted authority and increases the transparency of the whole system. An adaptive attribute revocation method is used to balance the revocation time and system cost. Moreover, the shared data are stored in a distributed Inter-Planetary File System (IPFS) to improve the efficiency and security of the data sharing system. The test results show that the proposed method can well satisfy the performance requirement of secure data sharing for Internet of Vehicles.
Crowd Density Estimation and Motion Trajectory Detection in Video Surveillance

The research on crowd density estimation and motion trajectory detection is an important part of visual surveillance and crowd management. In this paper, the threshold segmentation is introduced. Firstly, the foreground is extracted, and. Then, the ratio between the sum pixels of foreground and the sum pixels of the background is calculated. For the high density, the paper proposes a method of feature data. In the part of motion trajectory detection, a feature fusion method based on variance is proposed. For different density levels, the results show that the proposed algorithm can make estimation accurate and robust. And the method of motion trajectory detection has better robustness to color, illumination and occlusion, and it has good effect on the tracking error and stability.
A Consensus Mechanism of Blockchain Based on Traffic Condition in Internet of Vehicles

The application of blockchain technology in the Internet of vehicles has becomes an effective solution. Blockchain's consensus process is a key component that has a direct impact on the reliability and security of on-chain data. The design of the consensus mechanism is an efficient technique to encourage the integration of blockchain into IoV. Traditional Proof of Work and Proof of Stake consensus relies on the competition while wasting the majority of resources or may lead to unfair circumstances. Practical Byzantine Fault Tolerance consensus method has been proved to adapt to IoV well while rotation of primary node selection seems not suitable for IoV environment. Data in the Internet of Vehicles is highly regional and consensus with IoV is expected to be adapt to those specific data. To address the aforementioned issues to some extent, this paper suggests a consensus mechanism called Proof of Traffic Condition consensus. In order to better reflect the road data distribution, the proposed consensus mechanism suggests a new primary node election procedure based on road and historical on-chain information. Simulation experiments are carried out to prove the proposed consensus mechanism can effectively classify RSUs and appropriately describes roads.
A Hierarchical Path Planning Method Based on Key Points Generation

The traditional path planning algorithm suffers from a problem where the search complexity is positively correlated with the resolution of the grid map and the complexity of the obstacle distribution. In this paper, we propose a hierarchical path planning algorithm based on key points generation to address this issue. The algorithm utilizes a key points set to transform the global optimization problem into a parallel local optimization problem, which greatly reduces the complexity of the search space. The method mainly consists of parallel local path planning and global path planning based on Dijkstra algorithm. The parallel local path planning includes both offline prior path planning and online dynamic local path planning. The former generates key points of the global grid map using JPS (jump point search) algorithm, and completes offline prior local path planning according to the key points. The latter generates dynamic local path planning based on the start point, end point and the adjacent key points. The offline prior local path and the online dynamic local path planning together construct a global dynamic undirected graph, which enables the completion of global optimal path planning by the Dijkstra algorithm. Our simulation results demonstrate that as the map complexity and planned route length increase, the proposed method shows significantly improved planning efficiency.
Improve Data Mining Performance by Noise Redistribution: A Mixed Integer Programming Formulation

Differential privacy is a widely used privacy protection mechanism that provides strong privacy guarantees for data analysis. Typically, the Laplace mechanism is an essential method to implement differential privacy. However, the randomness nature of noise produced by the Laplace mechanism inevitably not only leads to low data quality but also result in weak performance of relevant data mining tasks. To overcome the aforementioned challenges, this paper attempts to enhance the efficiency of data mining procedures by a noise redistribution stage, which aims at minimizing the interval frequency changes between perturbed data and raw data via reallocating the noise generated by Laplace mechanism to raw data. To guarantee the optimality, we formulate the noise redistribution stage as a mixed integer programming model (Lap-MIP). Numerical experiments reveal that the performance of data mining tasks is significantly improved after embedding the noise redistribution stage in the classification framework, compared to directly adding the Laplace noise to raw data.
QoE-Driven Optimization Strategies for Dynamic Adaptive Streaming Media

Dynamic adaptive streaming over HTTP can adaptively select the most appropriate video bitrate for video users. Mobile edge computing (MEC) can effectively improve video quality by providing computing and storage capabilities. In order to improve the quality of video user experience, a QoE-driven DASH optimization scheme is proposed, which includes an adaptive bitrate algorithm deployed on video clients and a video caching strategy deployed on edge servers. First of all, we propose an adaptive bitrate algorithm that can cyclically accumulate or consume video buffer to intermittently increase video bitrate. Besides, we design a video caching and replacement strategy that works on edge servers, which treats the video blocks instead of complete videos as the basic storage unit of edge servers. The simulation results show that the proposed adaptive bitrate algorithm can effectively improve the average video bitrate and the smoothness of video playback. The proposed video caching strategy can effectively improve the cache hit rate of edge servers and reduce the average request response delay, significantly improving the quality of user experience.
SHGNet:An Improved Algorithm for Human Pose Estimation Based on a Single-Layer Hourglass Network

Human pose estimation (HPE) depends on the inter-dependence among body parts for consistency. Existing methods use images of different resolutions to extract features at various scales, which may cause context information conflicts and long-range dependency issues, leading to inaccuracies. To address this, we propose SHGNet, an improved algorithm based on a single-layer hourglass structure. SHGNet extracts local and global features using a pyramid structure and introduces a feature enhancement module for context enhancement and long-range dependency resolution. By fusing features from multiscale convolutions, improving context information representation, and employing a sparse multi-layer perceptron (sMLP) to capture long-range dependencies. Finally, by fusing the convolutional layer and the output layer with attention mechanism in a weighted manner, we achieve a better balance between local and global information, obtaining more refined global image features. Additionally, we introduce a channel purification module for more accurate feature capturing. When applied to the YOLOPOSE pose estimation model and tested on the MSCOCO keypoints validation dataset, our method achieves a 1.4% AP increase and a 1.7% APL increase compared to the original YOLOPOSE baseline, demonstrating enhanced accuracy and performance.
Cross-Modal Retrieval Method Based on Random Generative Sampling and Triplet Loss

CLIP achieves remarkable cross-modal retrieval results through self-supervised learning and distributed training. However, limited training samples per batch due to high-dimensional video samples and computational constraints hinder the improvement of CLIP network performance. And, CLIP neglecting the contrasting role of negative samples in the cross-entropy loss function makes it difficult to handle interclass similarity and intra-class differences in retrieval. Therefore, in this paper, we propose a cross-modal retrieval approach that addresses the aforementioned challenges by incorporating random expansion sampling and triplet loss. We expand text samples to generate negative sample (video, text) pairs and introduce them into both batch training and loss function design. Firstly, we generate a negative sample pool by utilizing common non-matching text samples across all videos in each batch and their respective video set. The negative sample pool is then subject to random sampling and combined with the original sample set in the batch to create the training samples. Next, the cosine similarity between the video and text depth features in the sample pairs is computed and sorted. A triplet loss function is constructed with the video as the anchor point, matching text as the positive sample, and K-nearest neighbor non-matching texts as the negative samples. This suppresses inter-class similarity by reinforcing the differences between positive and negative samples and is combined with cross-entropy to form the loss function. The simulation experiments in this paper indicate that the proposed method achieves a 2.6% improvement in top@l compared to the baseline. This improvement effectively enhances the fine-grained retrieval capability of the CLIP network, leading to a significant boost in performance.
Image Quality Guided Pollen Classification Based on Multi-Grain Image Information Content

Pollen classification is crucial for the prevention of pollen allergy diseases. Light Microscope (LM) based pollen classification methods have received more attention. However, a large number of LM images in the real world have blurring problems, which seriously affect the classifier's classification accuracy. To address these problems, an image quality assessment (IQA) method is considered to pick high-definition pollen datasets for aiding in solving classifier's low accuracy problem. Unlike existing image quality assessment methods that are commonly based on global features, we focus on the local texture information of pollen grains. We propose an image quality assessment method for small pollen grains in pollen images, which consists of three main modules. First, in the global IQA model, Siamese network is applied to focus on the coarse-grained image quality information. Secondly, in the Local IQA module, pollen grain fine-grained texture information is considered and incorporated into the image quality information. Finally, in the IQA fusion model, Global IQA and Local IQA are integrated based on a weighting strategy. On the IQA pollen dataset, this method showed a 13.8% improvement in accuracy compared to well-expressed quality assessment methods. To verify the effectiveness of this paper's quality assessment method applied to the classification problem, we compare the classification performance of introducing different quality assessment methods. The classification method with the introduction of this paper's image quality assessment improves the average accuracy by 3%.
Online voting management system based on Blockchain

Online voting is a topic that companies have been dealing with for many years. Several countries tried and are still trying to implement such voting system instead of a classical paper election system we know the most nowadays, as it brings many advantages. Blockchain technology became known to people mainly thanks to the Bitcoin cryptocurrency which was created in 2008. Blockchain presented a new solution that was almost ideal for voting purposes. With the use of blockchain, the security and reliability of online voting systems has also increased over time. Voting became transparent. The work focuses on the development of an online voting management system built on such blockchain technology. The introductory part is focused on the blockchain technology and online voting. The work focuses on the design and implementation of a system based on the blockchain technology which allows the administrator to create new votes, start them and stop them.
Reigning in GPT-4 to Tutor Java Programming Pupils and Students Instead of just Providing Answer Algorithms – a Prototype

The popular press (and the academic press to some extent) look at the newly released GPT-4 language model and ask "What’s it good forƒ" Teachers and lecturers, however, look at GPT-4 and panic saying "Pupils and students will cheat by having GPT-4 do their homework for them!". Even tech employers look at their tech-test gate keepers which screen applicants for tech positions and wonder if potential employees are having GPT-4 generate the answers to their tech-tests. These are all valid questions to ask. This paper takes another approach. This paper asks the question "Can GPT-4 be configured to tutor people without just providing complete answers to questionsƒ" Specifically, this paper looks at tutoring pupils and students in the Java programming language. The research question is "Can GPT-4 be configured to help people learn Java programmingƒ" Can GPT-4 be configured to assist pupils and students to learn how to program in Java without just generating the answer algorithms for the pupils and studentsƒ
Motion Of Rainy Cells Observed In Radar Images

The determination of the intensity and direction of rainy cloud motion is one of the problems raised when issuing weather forecasting. In this study, we examine the optical flow equation-based identification and extraction of rainy cell velocity fields from radar images. The motion of precipitation was analyzed with two different methods of optical flow: the local estimate employing a fractional variation model and the global motion model of Horn and Schunck. Both methods describe very well the trajectory and evolution of velocity fields in the radar images. Applying the differential method of 'Horn & Schunck on a rainy cell isolated from radar images, allowed us to detect the direction and the motion of the precipitation The fractional variation produces better results than the Horn & Schunck technique because it is more precise and produces a dense velocity field, although taking more time to analyze
Advancements in Code Restructuring: Enhancing System Quality through Object-Oriented Coding Practices

The implementation of object-oriented coding techniques to improve system quality is the main emphasis of this paper's presentation of developments in the field of code restructuring. The project, which builds on earlier work in this field, intends to enhance current methods for code reorganization and offer developers a complete toolkit. The research recognises the value of high-quality code in software systems as well as the difficulties in sustaining and enhancing it over time. The suggested approach intends to overcome design defects and enhance system maintainability, scalability, and reusability by adhering to object-oriented coding concepts, such as encapsulation, inheritance, and polymorphism. A variety of features are provided by the created toolset to aid in the code restructuring process. Developers may detect problem areas and tackle certain design defects thanks to its automatic code analysis and metrics collection features. Developers with varied degrees of competence may utilize the toolset's user-friendly interface to successfully traverse the restructuring process. In order to help choose the best transformations, the research also incorporates non-functional criteria called Desired Soft Goals (DSGs). The toolkit guarantees that the resultant code reorganization is in line with the required system characteristics and performance goals by taking these criteria into account. Extensive experimentation is carried out using a variety of software projects to assess the efficacy of the suggested concept and toolset. Before and after the restructuring process, different metrics including complexity, coupling, and maintainability are measured as part of the evaluation. The outcomes reveal improvements in code maintainability, performance, and adherence to object-oriented coding standards, illustrative of the model's beneficial effects on system quality. In the end, by providing an advanced framework and toolkit for enhancing system quality while adhering to object-oriented coding standards, this research makes a contribution to the field of code restructuring. The research highlights the value of proactive code restructuring in software development, encouraging scalable, long-lasting systems that can adapt to changing business requirements.
An Improved Method for the Elderly Fall Recognition System Based on YOLOV7*

With 761 million people aged 65 and over in 2021, the global population is now aging more and more severely. Falls are the leading cause of injury-related death for people over 65 years old. To ensure the safety of the elderly, our team has previously developed a fall warning system for elderly people living alone, but the average judgment accuracy rate is only 87.68%. In order to reduce the occurrence of judgment errors as much as possible and improve the detection performance of the system, this paper improves the accuracy of detection by reasonably reducing the fall judgment score and introducing the YOLOV7 for further identification of possible falling posture. Compared with the previous system, the accuracy of the improved system increased from 87.68% to 97.41%. Consequently, the improved method proposed in this paper has greater detection performance, robustness and lower error rate.