Reviewerly/exHarmony huggingface dataset:
Lossless compression for quality scores of genomic data based on high-throughput sequencing

The FASTQ format is a common genome file format generated by sequencing, mainly composed of identifiers, bases and quality scores. The quality score is an indicator representing the error rate of a base, which corresponds to the base. Although the compression of bases has achieved remarkable developments in recent years, the compression of quality scores remains challenging. This paper mainly improves ACO, a compression algorithm for quality scores. The main content of the algorithm includes the following parts. In terms of traversal quality scores, we comprehensively consider the sequencing process and the principle of FASTQ file generation. Instead of using the traditional raster scanning order, we choose a more reasonable serpentine traversal order. Base changes are introduced as contextual information. The weighted average of the quality score sequence is used as the index of the statistical characteristics of the sequence to perform clustering to improve the coding performance.
Calibration using knowledge graph attributes in recommender systems

Calibration is currently becoming a topic in machine learning. In particular, there have been many attempts to realize fair recommendation through the calibration in recommender systems. However, current research on the calibration in recommender systems has been realized by using genres in the film domain. Few studies have been conducted to generalize the calibration method to other recommendation domains. In this paper, we extend the calibration to other recommendation domains using the attributes of knowledge graphs, which are globalized and standardized knowledge source. We conducted extensive experiments using the weighted matrix factorization algorithm and the graph networks of Amazon and Yelp2018 datasets. The results show that calibration based on knowledge graph attributes improves the accuracy and diversity of the recommendation system and is applicable to all recommendation domains. This method is based on the re-ranking method and can be used with all recommended algorithms.
Research on English grammar error correction algorithm based on deep learning

Automatic grammatical correction (GEC) is one of the more difficult tasks in syntactic analysis for natural language processing. Non-native speakers often have difficulty understanding grammatical nuances, and grammatical correction in natural language contains grammatical errors and collocation errors. Compared with statistical classification-based error correction models, deep classification error correction models are a big improvement, but deep classification grammar error correction models can only correct for closed-form class errors. To address the above problems, this paper explores the automatic English grammar error correction algorithm from two ideas: using deep classification models and treating the grammar error correction problem as machine translation, and locating grammatical errors by building encoder-decoder sequence-to-sequence (seq2seq) models to understand the semantic and word expression differences between incorrect and correct sentences in order to improve the operational efficiency of model training and the basic ability to correct errors. This study uses the TensorFlow framework for the empirical validation of the used models to test the effectiveness of using the seq2seq error correction algorithm model with an attention mechanism.
Generation of trustworthy attribute knowledge graph based on semantic analysis

At present, with the rapid development of Internet information technology, the demand for enterprise-level intelligent information technology software projects has emerged in large numbers. Software trustworthiness has become an important part of the development of the software industry that cannot be ignored. For developers, software trustworthiness should be considered as an important factor from software development to the end. Software trustworthiness exists in the whole life cycle of software, at present, there is no trustworthy declaration document that can be applied in the whole development process of software, which makes it necessary for developers to repeatedly define trustworthy declaration documents in the process of developing information technology projects with the same functional attributes. Based on the above existing problems, this study takes the front and back ends of web and WeChat applets as the research objects. By obtaining the functional requirement documents of informatization projects as the data base, introducing natural language processing technology, constructing a semantic analysis model conforming to this study through semantic analysis processing technology, and finally realizing the function of automatically generating trustworthy behavior declaration files by traversing the language under the graph computing framework. In this thesis, we focus on the functional attribute extraction method based on semantic analysis technology and the knowledge graph generation and storage method of functional attribute keywords and high-frequency words, which lay the foundation for the automatic generation of trustworthy behavior statement documents afterwards.
Breast lesion detection in mammograms based on unsupervised convolutional neural network

To reduce the dependence of lesion detection algorithms on annotated data and fully leverage the advantages of medical image big data from healthy people, this paper proposes an unsupervised breast lesion detection model based on a parallel convolutional neural network. It treats breast lesion detection in mammograms as a one-class classification problem. The network comprises two parallel convolutional neural network branches. One branch utilizes normal mammographic images as training samples to extract the deep features of normal images and considersthe compactness measure of features as the training loss. Simultaneously, to prevent the lack of inter-class discrimination in features extracted from the first branch's sample learning, the other branch introduces a subset of the ImageNet dataset as training samples and employs the descriptive measure of features as the training loss. This ensures that the extracted features not only satisfy intra-class similarity but also maintain inter-class discrimination. ResNet50 is chosen by comparison as the backbone network for the parallel branch and further improved. Experiments are conducted using the INbreast and BCDR datasets, and the results indicate that the proposed unsupervised lesion detection method could achieve comparable performance with the supervised methods without requiring any lesion annotation data.
A lightweight detection algorithm for steel surface defects based on improved YOLOv5

To address the issue of imbalanced complexity and precision in series models of YOLO, this paper proposes a lightweight object detection algorithm based on YOLOv5s as the baseline model, namely YOLOv5-SFD. Firstly, the PANet structure of YOLOv5s is simplified to the basic FPN, and a Locally Dense Connected Convolution (LDCC) is proposed to improve the problem of feature information loss during downsampling by cheap feature extraction and feature reuse. Secondly, the C3 module of the original model is improved based on LDCC and combined with the C3Ghost module to reduce the weight of the model. Finally, a bottleneck residual block, BLS (Bottleneck LDCC-SE), based on LDCC and Squeeze-and-Excitation attention is introduced to enhance the feature extraction capability of the network. On the dataset NEU-DET, compared to the baseline model, the precision, recall, F1 score, mAP@.5 and mAP@.5:.95 is 73.4%, 84.7%, 77%, 91.5% and 52.5%, respectively, which represents an improvement of 3.2%, 2.2%, 2%, 11.8%, and 4.5%. In addition, the number of parameters is reduced by approximately 47%, and the model size is only 55% of the baseline model. The experimental results demonstrate that YOLOv5-SFD has lower model complexity and higher detection accuracy, which meets the requirements of lightweight networks.
Chinese knot Identification via YOLOv5

In this paper, the author collects corresponding pictures with Chinese knots on the Internet and unifies their format as .jpg. After using Colabeler to label the Chinese knot in each picture, import the xml file and the xml file in pascal-voc format respectively. Put the prepared files into the corresponding folders in the yolo-master project to complete the production of the data set. After ensuring that the running environment of yolo-master is normal, adjust the corresponding parameters, run the train.py file and use this data set to train the model for detecting Chinese knots. The authors then test the model on a dataset of images mixed with other objects and obtain corresponding detection results. Finally, the authors analyze the results using metrics such as precision, recall, and F1 score. Furthermore, we discuss the novelty and future development potential of the method for object detection and recognition in detecting classical Chinese artworks.
Research on semantic segmentation method of road scenes based on deep learning

The accuracy of image semantic segmentation directly affects the ability of autonomous driving technology to perceive the surrounding environment. To address the problems of unclear object edge segmentation and inaccurate small target object segmentation in the semantic segmentation model of road images in deep learning, this paper combines the convolutional attention mechanism module with the multiscale feature fusion module to optimize and improve the Deeplabv3+ algorithm. The convolutional attention mechanism module is added to the feature extraction network to improve the network feature extraction capability. Feature enhancement and fusion operations are introduced in the encoder part to make features of different sizes deeper and more expressive. The model was experimentally and validated on the Cityscapes dataset, and the results showed that the method designed in this paper ensures segmentation efficiency while making object edge segmentation clearer and segmenting small target objects more accurate. The segmentation accuracy of the model has been improved.
Blockchain-based supply and demand system for matching transactions

Matching supply and demand is a key link in the process of online trading information system design. In order to alleviate the problems of low efficiency of online trading , leakage of private information of buyers and sellers, inaccurate information and high cost of platform supervision, a blockchain-based matching trading model is proposed and designed based on the characteristics of multi-attribute matching trading, transferring the trading process to blockchain, making the trading decentralized, trustworthy and non-tampering, and realizing accurate matching of supply and demand between buyers and sellers based on ant colony algorithm, meeting the needs of both sides trading in the trading platform in terms of cost, efficiency and security. The experimental analysis shows that the model is compared with the traditional matching of supply and demand has timeliness, safety and effectiveness.
Revocable multi-authority attribute-based encryption from lattice in cloud storage

With the rapid development of quantum computing technology, data sharing systems in cloud storage may suffer from quantum computing attacks. Recently, many researchers have proposed encryption schemes to achieve quantum attack resistance and fine-grained data access control. However, single point of failure and secure attribute revocation are two critical problems for quantum-resistant encryption scheme in cloud storage environments. In order to deal with these problems, in this paper, we propose a revocable multi-authority attribute-based encryption from lattice (RMA-ABE-L) in cloud storage. Firstly, RMA-ABE-L scheme can achieve fine-grained data access control with quantum computing attack resistance. We design efficient anti-quantum algorithms based on R-LWE problem and merge them with ciphertext-policy attribute-based encryption (CP-ABE) structure. Secondly, RMA-ABE-L scheme can solve the single point of failure problem. We design the multi-authority structure to so that the compromise of some authorities does not affect the functioning of the whole data sharing system. Moreover, RMA-ABE-L scheme can realize secure attribute revocation. We design a secure attribute revocation mechanism and set special identity construction. The performance analysis shows that RMA-ABE-L scheme is more efficient than other related schemes.
Mixed DSP packing method for convolutional neural network on FPGA

Currently, quantized neural networks have been widely applied in edge device model inference. In model inference using FPGAs, convolution operations are typically implemented using DSP that can provide 27×18bits operations. However, there is no general method for fully utilizing DSP to compute arbitrary low-precision quantized data in parallel. In the paper, we propose MDP, a universal solution that packs multiple multiplications in one DSP based on quantization precision and convolution parameters in multiple modes. To maximize the DSP multiplication performance, we design a data pipeline architecture based on GEMM. The experimental results show that, for single convolutional layer, MDP can decrease DSP resource utilization by 0.78× while maintaining the same level of parallelism. Compared to UltraNet, MDP achieves 1.56× latency improvement.
Research and application of residual local blueprint separable network for lightweight image super-resolution

Deep learning based Single image super-resolution has achieved great success, but the computational cost is too high, making it unsuitable for real-time tasks on resource-constrained devices. In this paper, we propose Residual Local Blueprint Separable Network (RLBSN), which based on RLFN lightweight backbone network. The blueprint separable convolution was used to replace the traditional convolution, which can enhance the extraction of edge information, reduce the number of model parameters, and improve the running speed. The knowledge distillation concept is introduced to calculate loss function and the output of high-performance model is taken as ground-truth to improve the performance of the model. We design the joint loss function that combines the characteristics of various loss functions to generate high-quality super-resolution images. Inference time can be significantly accelerated by transferring post processing to the GPU.
Research on blockchain covert communication scheme based on transaction mechanism

The communication method in which both parties could modify certain types of shared resources without attracting the attention of other users is called covert communication. Generally, researchers believe that blockchain, as a distributed storage database that can still guarantee security in an unreliable network environment, has excellent characteristics such as open, decentralized, untrusted, anonymous, tamper prevention, traceability, and contract execution automation. More and more studies have applied it to covert communication technology. Due to the different transaction mechanisms of various blockchain systems, schemes based on system mechanisms are usually designed for a specific transaction mechanism of a blockchain, making the scheme more covert. The blockchain covert communication scheme based on transaction mechanism designed in this paper mainly utilizes the Bitcoin transaction mechanism in the Bitcoin system. The transaction parties generate addresses as input addresses through pre negotiated keys, construct invalid transactions through transaction withdrawal mechanisms, embed secret information in invalid transactions, and make them undetectable on the blockchain network. The receiver obtains information about invalid transactions through the negotiated keys, Decrypting and obtaining secret information completes the communication process of covert communication.
A feature integration method for Chinese chunking

Chunking is a crucial task in natural language processing. The task aims to divide a text into syntactically correlated non-overlapping chunks. The discrete model and neural model can be applied to Chinese chunking. The feature representations of them are different and both can achieve excellent performance. In this paper, we build both of the models and make a detailed comparison between the two models. Furthermore, we present the feature integration method to integrate the advantages of the two models. Experiments show the effectiveness of the feature integration method. Since the neural model utilizes pre-trained word embeddings, it can be regarded as a semi-supervised learning method. In order to make a fairer comparison between the discrete model and neural model, we incorporate word clusters into these models. Experimental results show that the word cluster information does not significantly improve chunking performance and the feature integration method still improves the performance of both the discrete model and neural model.
BC-GFL: blockchain-based graph federated learning

Graph Neural Networks (GNNs) can better handle non-euclidean space data, and when combined with Federated Learning (FL) can address their data volume shortage and privacy security issues during model training. However, the traditional centralized Graph Federated Learning architecture faces threats such as unreliable centralized servers and attacks from malicious clients, and there may be missing edge structures between subgraph data of different clients, which reduces the performance of GNN models. In this study, a blockchain platform is introduced to implement decentralized Graph Federated Learning to improve the robustness of models. An approximation evaluation mechanism for finding similar subgraphs through Transfer Learning (TL) is also proposed, which can discover subgraph clients with structural associations and aggregate models of similar subgraphs without leaking data, thus solving the structural missing problem. Simulation results show that compared with existing algorithms, the approximation evaluation mechanism in this paper can better compensate for the missing subgraph structure and significantly improve the accuracy of model prediction, and the introduction of blockchain also effectively prevents the influence of malicious nodes and ensures the stability of the training process.
Visualization of hydrophone research fields using Citespace

To investigate the research hotspots and trends of hydrophone technology around the world, this paper visualizes the literature based on authors, countries, journals, and keywords since the twenty-first century using Citespace literature analysis software, which can help related workers better understand and grasp the research dynamics and hydrophone development trend. It has been discovered that research on hydrophones has been continuously rising, with China and the United States having the most papers in this sector. Various forms of hydrophones, including piezoelectric, MEMS, and fiber optic, offer promising development potential. Furthermore, the author feels using bionic technology for fiber optic hydrophones is reliable.
Research on traffic sign detection based on improved YOLOv4

Traffic signs contain important traffic information. The traditional traffic sign detection method cannot solve the problem of low detection accuracy caused by the small, occupied area of traffic sign images. Based on this, a traffic sign detection algorithm based on improved YOLOv4 is proposed. Firstly, the 13 × 13 large receptive field detection layer is removed on the YOLOv4 structure, and the 104 × 104 detection layer is added. It obtains more global feature information and improves detection accuracy. The attention mechanism is introduced into the algorithm, that is, the backbone network extracts three feature layers and then adds the scSE module. Make the network focus on the target area and improve the algorithm detection ability. Secondly, in order to speed up the convergence of the network and improve the detection accuracy, a dynamic residual connection is added to the backbone network. It promotes the spread of well-performing signals. And use the decoupled-head detection head to use different branches to calculate classification and positioning tasks. By evaluating the average accuracy of the detection effect of CCTSDB traffic sign data set, mAP reaches 97.68 %, which is 3.78 % higher than YOLOv4. Moreover, network convergence experiments have shown that the improved model converges faster. Compared with other models, the improved model has better detection performance for smaller traffic signs and can better meet the actual needs of high-precision detection.
Multi-objective request scheduling algorithm based on cuckoo search

In enterprise level web applications, request scheduling usually aims to minimize average response time and maximize application benefits by fully utilizing existing server resources. It is necessary to prioritize the processing of critical requests and high-priority user requests to enable the enterprise to obtain more benefits. Aiming at the multiple objectives of enterprise Web request scheduling, this paper proposes a multi-objective request scheduling algorithm based on cuckoo search. Firstly, based on the characteristics of the request task and the scheduling objectives, a multi-objective evaluation model is established, and the decision variable is determined as the execution order of the request queue on the server side. Then the non-dominated sort cuckoo search algorithm with elite strategy is introduced to solve the decision variables and the global optimal Pareto solution set is found by combining the multi-objective evaluation model. From this set, an optimal solution is selected as the request execution sequence, and the server executes the request based on this sequence. Experiments show that the proposed algorithm has more obvious advantages than FIFO and SJR in achieving the goal of minimizing the average response time and maximizing the benefits.
CP-ABE access control scheme supporting data permission management in IOT

With the flourishing development of the Internet of Things (IoT), IoT devices are becoming increasingly connected with people. IoT devices are constantly collecting and transmitting large amounts of private information, and there is a need for data access control and secure data sharing. Since Ciphertext Policy Attribute-Based Encryption (CP-ABE) is an end-to-end encryption solution, CP-ABE-based access control methods can effectively meet the data security sharing requirements in the IoT environment. However, existing CP-ABE access control methods are difficult to implement data access control. There are no standardized data access control solutions, and redundant storage of data can occur. This study addresses the issue that existing CP-ABE access control methods have difficulty in achieving data permission management. A CP-ABE access control scheme that supports data permission management is designed. To support data permission management, the data is partitioned into key-value pairs. A data permission management policy is designed based on the access control policy, which not only sets the access policy but also specifies the data unit scope of authorized users. The CP-ABE algorithm is used to encrypt the set of encrypted data unit keys in the policy, replacing the redundant storage of data units with keys. This study formulates a standardized data permission management scheme based on the CP-ABE access control scheme, effectively reducing data redundancy storages.