Reviewerly/exHarmony huggingface dataset:
A Garbage Recognition Algorithm Based on YoloX Using in Flood Control System

In recent years, floods in city have occurred frequently, causing great losses to the personal safety and property safety of city dwellers. One of the main factors causing flood disasters in city is that the garbage is not cleaned up in time, resulting in the blockage of the sewer manhole cover and the failure to discharge sewage in time. The daily inspection of urban environment and the garbage clean-up work are often powerless in the face of enormous pressure in sanitation work. Compared with relying on manual screening, real-time monitoring of manhole covers through road cameras, and data will be fed back timely once the garbage accumulation is identified, which can effectively improve the efficiency of urban sanitation work. This paper uses the data captured by urban surveillance cameras, identifies manhole covers and garbage using the algorithm based on the YoloX. And this paper builds a flood preventing system that can automatically monitor and identify the accumulation of garbage and send the data to the client for data display and giving early warning.
A Blockchain-Based Decentralized Insurance Platform

Blockchain technology is considered as revolutionary for its potential to revolutionize many sectors by addressing existing issues in the traditional systems. A blockchain is a digital ledger consisting of transaction records that is duplicated and distributed to every participant computer in the network. Insurance industry is one of the most significant sectors where large institutes dominate the market. The traditional insurance process is highly centralized and cost-intensive to both insurance companies and customers. The process involves a third party to handle several processes among multiple parties manually in the form of paperwork. Inefficiency in centralized, manual processes lead to large frictional costs being borne by the customers. Also, conflicts of interest between insurance companies and policyholders are very common due lack of trust, lack of transparency, and ambiguity in policy terms. To provide a solution for these issues in the traditional centralized systems, this research proposes a blockchain-based, decentralized platform for insurance. The platform aims to replace the conventional insurance companies. Using blockchain technology, the platform allows users to transact directly with each other, eliminating the need for an intermediary third party.
A Comprehensive Review on Vision-based Sign Language Detection and Recognition

Deaf or hard-hearing people's primary mode of communication is sign language. Communication between hard hearing and hearing people is greatly aided by sign language recognition technologies. With the advent of technology, many approaches proposed for sign language recognition. Among them, vision-based approaches are more convenient than sensor-based approaches. Vision-based approaches are involved five different stages where various techniques and algorithms are utilized in various approaches. The accuracy of the recognition is based on the techniques used and the quality of the input. Background invariance and lighting conditions highly affect the accuracy of the result. Simply by increasing the quality of the input, each and every method can approach a high accuracy rate. This paper provides a comprehensive introduction and comparison of the existing vision-based sign language detection and recognition approaches.
A New Quantization Deployment Method of Neural Network Models Integrating LSTM Layers

The embedded neural network accelerator deploying neural network algorithms needs to quantize the values in the neural network model into low-bit integers through quantization operations. However, the neural network accelerator doesn’t directly support performing the quantization operation for the LSTM layer. Aiming at the video classification problem based on time series inputs, a new quantization deployment method of the neural network model is proposed to integrate LSTM layers. Concretely, the calculation process of the LSTM layer is split into multiple regular layers supported by the neural network accelerator. The quantization calibration process is designed as a step-by-step combination strategy for time series image groups and diverse image groups. The LSTM layer is converted into an existing operator for single-layer quantization and deployed on the neural network accelerator. The experimental results show that the proposed method integrating LSTM layers differs only by 0.803% in terms of accuracy between the deployment model on FPGA and the benchmark model on PC. It not only quickly and efficiently supports integrating the LSTM layer on the neural network accelerator, but also guarantees the diversity, generalization, and temporal characteristics requirements of the quantization calibration sets.
A Lightweight Attribute-based Encryption Scheme for Data Access Control in Smart Grids

Smart grids are envisioned as the next-generation electricity grids. The data measured from the smart grid is very sensitive. It is thus highly necessary to adopt data access control in smart grids to guarantee the security and privacy of the measured data. Due to its flexibility and scalability, attribute-based encryption (ABE) is widely utilized to realize data access control in smart grids. However, most existing ABE solutions impose a heavy decryption overhead on their users. To this end, we propose a lightweight attribute-based encryption scheme for data access control in smart grids by adopting the idea of computation outsourcing. Under our proposed scheme, users can outsource a large amount of computation to a server during the decryption phase while still guaranteeing the security and privacy of the data. Theoretical analysis and experimental evaluation demonstrate that our scheme outperforms the existing schemes by achieving a very low decryption cost.
An Effective Method for Privacy Concerns on Digital Contact Tracing Protocols

In the post-epidemic era of normalized COVID-19, digital contact tracing will continue to be used as an efficient epidemiological investigation tool. Meanwhile, its widespread use has raised concerns about long-term data privacy and security. In this paper, an effective method for privacy concerns on digital contact tracing protocols (DCTP) is proposed. Digital contact tracing protocols are classified from both technical and architectural perspective. Then, the LINDDUN (each letter of "LINDDUN" stands for a privacy threat type) privacy threat modeling framework is used to analyze the privacy threats that may be contained in different protocols. Last, mitigation strategies are provided to advance privacy protection for future digital contact tracing protocols. The experiment show that the proposed method significantly outperforms existing methods.
ReIMOT: Rethinking and Improving Multi-object Tracking Based on JDE Approach

The multi-object tracking (MOT) algorithms of the joint detection and embedding (JDE) approach estimate bounding boxes and re-identification (re-ID) features of objects with the single network, which balance the tracking accuracy and inference speed. However, when the appearance information between different objects is highly similar, these algorithms are usually easy to cause identity switches, and the comprehensive tracking performance is poor in crowded scenes. Aiming at the above problems, we propose a stronger multi-object tracking algorithm termed as ReIMOT, based on FairMOT. A joint loss function of combining normalized Softmax Loss and the center distance penalty term is designed to supervise the re-ID branch, which increases the intra-class similarity and makes the extracted appearance features more discriminative. To further improve the tracking performance, we introduce coordinate attention to make the encoder-decoder network focus more on features of interest. The experimental results show that the proposed ReIMOT is more effective than the other advanced multi-object tracking algorithms, and decreases the number of ID switches by 13.8% compared to FairMOT on the MOT17 dataset.
A Novel Character-Word Fusion Chinese Named Entity Recognition Model Based on Attention Mechanism

Named Entity Recognition (NER) is a fundamental task in natural language processing. Compared with English NER, the difficulty of Chinese NER lies in word segmentation ambiguity and polysemy. Aiming at the issue, a novel character-word fusion Long Short-Term Memory (LSTM) model combined with the sentence-level attention mechanism (CWSA-LSTM) is proposed. Firstly, the method encodes the representations of characters and words through the pretrained models. The word information is incorporated into the character sequence by matching the potential word with a lexicon. Then the feature vectors are fed into the LSTM layer to learn contextual information. The attention mechanism is utilized to capture the tightness of the correlation in the sentence. Experiments on benchmark datasets demonstrate that CWSA-LSTM outperforms other state-of-the-art methods, and verify the effectiveness of character-word fusion. For the MSRA dataset, CWSA-LSTM achieves a 2.46% improvement in F1 score over baseline Lattice LSTM.
Rotated Object Detection of High Resolution Remote Sensing Image Based on Yolov5

Nowadays, object detection is an increasingly important technology in the field of remote sensing image processing, which is applied to locate and identify high value ground objects in high resolution remote sensing images. High resolution remote sensing images have many kinds of objects, some small objects are difficult to be detected because there are too many objects. Based on some existing technologies, this paper proposes a novel algorithm for rotated object detection problem of remote sensing images. The angle is added for detection on the original yolov5 algorithm, which is also introduce mosaic-9 to enhance the dataset and expand the number of samples. Combined with the circular smooth label technology as an independent module, it is added to the training and loss of the network to solve the discontinuous at the boundary for object detection. The detection performance of the proposed model is evaluated on the public dataset DOTA, which shows the modified yolov5 algorithm reliability by this experiment.
Efficient Object Detection with Deformable Convolution for Optical Remote Sensing Imagery

Deep learning-based object detection in remote sensing images is an important yet challenging task because of the complex background and large variations in size of the targets. Currently, many detectors have made significant progress in improving detection accuracy, but they have not shown good performance in terms of detection speed and model size. To address these issues, a lightweight and efficient object detector in remote sensing images is proposed. Specifically, we utilize MobileNetv3 with Shuffle Attention as the feature extraction backbone network to reduce the parameter of the model. Meanwhile, deformable convolution is introduced to adapt to the deformation of the target and obtain stronger geometric feature expression ability. Extensive experiments on two remote sensing public datasets (RSOD and DIOR) show good performance for the efficiency of our detector. In particular, our strategy achieves 71.3 mAP on the DIOR dataset with 3.05 parameters and a test speed of 12.1 ms, and it also has good performance on the RSOD dataset.
A Mask-Wearing Face Recognition Method Based on Low-Level Features and Deep Residual Networks

Masks will invalidate the original face recognition algorithm model and make the computer unable to recognize faces. In addition, there are many types of masks, and the degree of occlusion is different, which increases the difficulty of face recognition. This paper combines the traditional feature extraction method with deep learning, and proposes a face recognition method with masks based on low-level features and deep residual network. The method of face segmentation based on feature points is used to extract the local features of the face, using the Holistically-nested Edge Detection (HED) algorithm to extract the overall contour features of the face, fusion of local features, overall contour features and pre-processed images into a deep residual network model, realize face recognition with masks, and evaluate the face recognition method with accuracy. The experiment results show this method improves the recognition accuracy compared with Principal Component Analysis (PCA) and convolutional neural network (CNN).
Frequency Attention Module for Speaker Recognition

Speaker recognition is a task of identifying user’s individuality from their speech utterances. Most of the text-independent speaker recognition models available directly input the spectrogram into the deep neural network. However, this does not take full advantage of the spectrogram features. In this paper, we propose a frequency attention module (FAM) based on the fact that the frequency axis in the spectrogram is more representative for speaker feature extraction. It aims to assigning higher weights to important frequency bins, and making the network to extract more features to improve speaker recognition performance. Furthermore, FAM is applied on the convolution output in ResBlock of ResNet34 to capture richer features of the speaker. The experimental results under self-attention pooling (SAP) and attentive-statistics pooling (ASP) show that FAM is feasible and effective. Moreover, the extended experimental results on the Free ST Chinese Mandarin Corpus dataset indicate that the proposed FAM (equal error rate (EER) is 1.59%) outperforms the no-attention ResNet34 (EER is 1.97 %) and spatial-CBAM ResNet34 (EER is 1.88%).
Speaker Recognition Based on Ensemble Neural Network

To improve the accuracy of speaker recognition, this paper proposes a speaker recognition method based on an ensemble neural network. The main idea of our method is to fuse the three basic networks of EfficientNet, ResNet, and GoogleNet to build an EnsembleNet. A weighted average of three networks obtains the output of this new model. A weighted average of the three networks is used to produce the output of this new model. The secondary network, which is used to train the weighted average weight values, receives the output of the basic network as input, and the weights vary adaptively throughout the training process. First, the basic network is trained individually, and the training stops when the basic network's recognition accuracy is the highest. Next, the weights are trained, and the output of the basic network prediction is used as the input of the secondary network to train the weighted average weight value. Each basic network produces a feature vector whose length is the number of speakers, whereas the secondary network produces a vector whose length is the number of basic networks. The proposed method is validated on the ST-CMDS Chinese open source dataset and the ST-CMDS, AIshell, Aidatatang hybrid dataset, and Zhvoice dataset. The experimental results show that our method is better than the three separation models and the voting method. And the recognition accuracy rate reached 97.02%, 95.04% and 53.97%.
An Improved Lion Swarm Optimization Algorithm Based on Tent-map and Differential Evolution

Aiming at the poor optimization performance of traditional Lion Swarm optimization algorithm, an improved algorithm is proposed based on Tent-map and differential evolution. Firstly, to address the problem of uneven population distribution and low efficiency in the later search stage, the chaotic sequence is introduced to improve the diversity and uniform traversal of the population so as to enhance the global search capability. Secondly, owing to the algorithm is prone to local optimum and unsatisfactory convergence accuracy, the lioness position update method is improved by the differential evolution to enhance its ability to jump out of the local optimum and boost the optimization accuracy. Experiments are carried out on 8 representative multi type benchmark functions, and compared with 4 optimization algorithms. Results show that the improved algorithm has higher convergence speed, training accuracy and stability.
Aerial Object Detection by UAV Based on Improved YOLOV3

Aerial object detection is a very challenging task in the field of computer vision, because it is susceptible to complex backgrounds and sparse features of small targets. Classical object detection algorithms have low accuracy under such complex conditions. YOLOv3 is one of the most advanced methods for object detection. In this paper, an algorithm named YOLOV3-SEPP based on YOLOv3 hybridizing attention mechanism and Spatial Pyramid Pooling (SPP) network for UAV aerial image object detection is proposed. In order to fuse multiple receptive fields, SPP is introduced into the model. Attention mechanisms are introduced to automatically capture the key information of each feature channel in the backbone. According to these messages, the model extracts the features that are beneficial to small object detection and suppress other features. Experiments show that the accuracy of the YOLO-SEPP algorithm has been improved.
The Combination of Blockchain and Internet of Things - Challenges and Solutions to Security Issues

Data security is a crucial issue in the scenarios of IoT (Internet of Things) applications, which require massive data collection and sharing. The Blockchain technique, which could be a potential solution, has the characteristics of point-to-point information transmission, distributed accounting, decentralization, and so on. Services such as availability, authentication, authorization, confidentiality, and integrity can be provided by integrating Blockchain into IoT applications. This paper mainly studies how to solve the security problems of the Internet of Things through the combination of Blockchain and the Internet of Things. It summarizes the advantages and disadvantages of various methods and the difficulties faced and gives corresponding views and opinions. Besides these, it summarizes and explores the development prospects of the current Blockchain in the IoT.
FA-SSD: A Small Object Detection Algorithm Based on Feature Alignment

In order to solve the problem that the SSD algorithm has low detection accuracy for small objects, the FA-SSD algorithm is proposed. The FA-SSD algorithm improves the upsampling path, enhances the semantics of the shallow feature map, and then combines the feature alignment and SE module to design a feature fusion module to fuse features more effectively. The experimental results show that the MAP of the FA-SSD algorithm on the VOC07-test dataset reaches 79.81%, which is 2.01% higher than that of the SSD algorithm, and in our small target dataset, the MAP is 3.61% higher than that of the SSD algorithm, and the FA-SSD algorithm has better detection performance.
TinyML-Enabled Static Hand Gesture Recognition System Based on an Ultra-Low Resolution Infrared Array Sensor and a Low-Cost AI Chip

Tiny machine learning (TinyML) aims to bring machine learning to the extreme edge, i.e., the microcontroller next to the sensor, which is expected to unlock new smart applications and pave the way for the last mile of artificial intelligence. Meanwhile, gesture recognition, as an important technology for realizing human-computer interaction (HCI), is of great significance for enabling convenient interaction between people and smart devices. Currently, the more mature static hand gesture recognition solutions include the use of data gloves or vision cameras to capture static hand gestures, which have many limitations in practical applications, such as cumbersome wearing process and performance affected by lighting conditions, and the processing platform mostly relies on cloud or edge servers. Considering the above limitations, in this paper, we design and implement a static hand gesture recognition system based on an ultra-low resolution infrared array sensor and a low-cost AI chip from the perspective of tiny machine learning applications. On the one hand, a fast method of collecting and labeling sensor data is introduced, and on the other hand, an ultra-lightweight neural network model is customized for the low-cost AI chip. The experimental results show that the static hand gesture recognition system designed and implemented in this paper has 99.14% recognition accuracy for several simple static hand gestures, and its inference time at the microcontroller side is around 35ms, which can achieve accurate and real-time recognition with low cost, strong anti-interference capability and good privacy.
SWTFace: A Multi-branch Network for Masked Face Detection and Recognition

The globalization pandemic of COVID-19 has made wearing masks to become a norm in people's lives, and this preventive measure brings new challenges to face recognition algorithms. To address this problem, in this paper, a multi-branch network is proposed to simultaneously complete the task of the masked face detection and recognition. Firstly, this network improves the Swin Transformer for extraction of facial features. Secondly, a face organ attention mechanism, FOA, is proposed to make the model focus on the face organs that are not covered by masks. Then, in order to overcome the problem of inadequate masked face dataset, a data augmentation method using 3D face mesh is proposed to add face mask. Finally, the experimental results show that, compared with the benchmark model, the proposed model reduces the number of model parameters by 60.6%, while the AP of masked face recognition increases by 5.33%, which better balances speed and accuracy.
An Improved YOLOX Based Face Mask Detection Method in Complex Scenes

Proper use of masks can effectively reduce the risk of spreading the epidemic. In the task of detecting whether people wear masks, it often faces the problems of low detection rate of long-distance and close-range personnel wearing masks, high false detection rate when facing sideways, and complex and changeable real-time detection scenes, which lead to difficulties positioning. Therefore, this paper proposes a face mask detection algorithm based on the YOLOX algorithm. A Coordinate Attention (CA) mechanism is embedded in the model to enhance the network’s learning of mask target details. an Adaptively Spatial Feature Fusion (ASFF) module is also added to the model to make full use of the information of masks at the same position in feature layers of different sizes. In addition, Varifocal Loss and EIoU Loss are used to replace the original confidence loss and localization loss, which improves the ability to select the optimal bounding box and target localization. The experimental results show that the improved algorithm has good accuracy and practicability for the mask detection task, and can better meet the actual needs of the current epidemic prevention and control.
A Machine Learning Approach to Visual Insight Discovery in Multidimensional Hierarchical Data

Multidimensional hierarchical (mTree) data is very common in daily life and scientific research. However, the mTree data exploration is a laborious and time-consuming process due to its structural complexity and large attributes combination space. To solve this problem, we propose a novel machine learning approach to recommend the top important subspace from the large attribute combination space and predict the suitable design choice for their visual presentation. The Subspace Importance Assessment model, the Visualization Recommendation model, and the Rule-based Chart Encoding model are proposed, and seamlessly pipelined to fulfil the task. The comparative experiment demonstrates the usability and effectiveness of our method.