Reviewerly/exHarmony huggingface dataset:
Toward Secure Graph Data Collaboration in a Data-Sharing-Free Manner: A Novel Privacy-Preserving Graph Pre-training Model

Graph data is a valuable data source that has been widely adopted in a variety of domains, such as bioinformatics, social networks, and finance. Such data is unique in that it increases in value through data sharing. However, sharing raw graph data can pose risks to information security and commercial confidentiality. To mitigate such risks, it is more desirable to share graph pre-trained models that contain knowledge derived from the data. Recent studies have shown that even graph pre-trained models come with some privacy risks, as an adversary with access to the model can reveal private information in the training graph. To address this issue and move towards a safer graph knowledge-sharing environment, we propose a privacy-preserving graph pre-training model for sharing graph information. In particular, we introduce a novel principle of privacy-preserving data augmentation, which can be paired with graph contrastive learning for pre-training a privacy-preserving graph neural network. The privacy-preserving data augmentation enables graph models to obfuscate private information during training. Extensive experiments suggest that our proposed model outperforms state-of-the-art approaches in reducing privacy risk while maintaining generalizability. Our further case studies offer additional insights into how graph properties affect generalizability and privacy-preserving performance.
Analysis of the Possibilities of Implementing Bitcoin and Other Cryptocurrency Payments at the University

The study aims to analyze the possibility of introducing payment gateways for payments at universities and the introduction of bitcoin as a means of payment at universities. The study describes the differences between the most used virtual currencies regarding payment options. The most widely used virtual currencies are Bitcoin, Litecoin, and Ethereum. Subsequently, we compare the advantages and disadvantages of introducing cryptocurrency as a payment option and the general advantages and disadvantages of e-payments. The study’s outcome is the selection of a payment gateway for making payments by cryptocurrencies. To achieve the set goals, we needed to examine the theoretical foundations of the issue, evaluate the current situation around payment and cryptocurrency payments, and compare the most used cryptocurrencies. Subsequently, we analyzed the payment options available in Slovakia and used cryptocurrency payments.
What Makes a Good Answer? Analyzing the Content Structure of Answers to Stack Overflow’s Most Popular Question

Stack Overflow provides a popular and practical community for software developers to ask and answer questions related to coding. These answers are ranked by users to evaluate their quality. For newcomers, participating in answering questions can be challenging, as they must learn what the expectations for answers in this online community are. In this paper, using epistemic networks, we analyze the content structure of the answers posted to Stack Overflow’s most highly ranked question with the goal of understanding characteristics of answers valued by the Stack Overflow community. Network models show that answer content is qualitatively different between high and low ranked answers, with high ranked answers including general explanations and code examples to contextualize question-specific code and explanations. We discuss how these findings could be used to better support and scaffold new participants in crafting their answers.
Design of Music Art Teaching Quality Evaluation System Based on Intelligent Optimization Algorithm

The research on big data and intelligent optimization algorithm is to propose a new method to solve problems by human brain through artificial intelligence after human brain analyzes and processes it. It transforms people from complex and unstructured information into simple and intuitive mathematical models. Big data technology can help people solve some problems. Firstly, the related concepts of music teaching courses are analyzed, then the application principle and formula of intelligent optimization algorithm are studied, and a teaching quality evaluation system based on intelligent optimization algorithm is designed. At the same time, the performance of the system is tested by simulation experiments. The test results show that the system can operate normally, achieve the expected goal, and finally meet the user’s evaluation requirements for the quality of music art teaching, so as to improve students’ music level, comprehensive quality growth and social harmony and stability.
Style Transfer with Subject-Object Separation using Masked Random Adaptive Instance Normalization

In order to improve the effect of style transfer and provide a style transfer result that matches human cognition, it is necessary to separate the subject and object of the content image and style image and perform style transfer accordingly. However, in the case of few samples, the effect of style transfer is often unsatisfactory. In order to improve the ability of few-shot style transfer and optimize the visual effect of few-shot style transfer, this paper proposes a Masked Random AdaIN network with subject-object separation. By improving the style transfer method of the AdaIN network and proposing content and style loss computation method for subject-object separation, the style transfer effect of subject-object separation is realized. In order to optimize the visual hierarchy and edge transition of the generated image, this paper also proposes an edge enhancement module. With this module, the generated image can maintain the edge area of the original image and ensure a smooth transition between subject and object, so that the image we get is more in line with human cognition. The experimental results show that images generated by our method have a better subject-object style preservation effect and are more in line with human cognition.
How can entities improve the quality of medical dialogue generation?

Studies on Medical Dialogue Generation (MDG) contribute to a highly reliable medical dialogue system. As medical dialogue involves professional entities, such as symptoms and diagnoses, recent MDG models follow the pipeline composed of entity prediction and entity-aware dialogue generation to enhance professionalism and reliability. However, existing entity-aware models have not taken full use of entities. In this paper, we propose a pre-training model of mutual reconstruction of entities and corresponding sentences to establish a strong association between entities and sentences. Our model can increase language fluency while strengthening the role of entities in professional reliability. Moreover, we found a certain pattern of entity transitions during dialogues, so we use bidirectional GRU to encode historical text information and historical entity information separately. Then we use an attention mechanism to fuse them. We performed several ablation experiments to demonstrate the validity of our two models. Finally, we propose Entity-enhanced Dialogue Generation (EDG) model and improve 63%-129% in BLEU and Distinct metrics compared to the baseline.
Towards Strong Privacy Protection for Association Rule Mining and Query in the Cloud

Efficiently mining frequent itemsets and association rules on the encrypted outsourced data remains a great challenge for the time-consuming ciphertext computations. Nowadays, it has been not well addressed for privacy-preserving frequent itemsets and association rule mining schemes with mining efficiency, dataset, and query confidentiality simultaneously. In this paper, we investigate the study of privacy issues on frequent itemset mining and association rule mining on outsourced data in a two-cloud model, where the data are encrypted and outsourced by multiple owners holding different public keys. We develop several secure computation protocols based on additively homomorphic cryptosystem and additive secret sharing, which enable the clouds could securely mine the frequent itemsets and association rules. Furthermore, we also design two kinds of frequent itemset and association rule query service models, i.e., service customers query the cloud-mined results, and service customers query with their own decided threshold. The proposed scheme not only supports the mining process on the data encrypted by multiple public keys without compromising the security of the datasets, query data and query results, but also offline users. In addition, the experimental results show that our query scheme is much more efficient than the state-of-the-art work.
DT-PBFT: A Double-Layer Group Consensus Algorithm of Credibility for IoT Blockchain

Blockchain technology is becoming more and more popular, but performance problems have always troubled it, especially the scale of distributed networks is limited. Like Practical Byzantine consensus algorithm (PBFT), the main bottlenecks are concentrated in the scope of consensus too small, and the communication is too high. In order to implement PBFT in large-scale systems such as large-scale IoT ecosystems and blockchains, this paper proposes a reputation-based two-layer improved PBFT consensus computing model DT-PBFT. In order to better combine with the large -scale Internet of Things ecosystem, we give each node regional location, communication capacity, computing power and other related attributes to each involvement, so that Client nodes can input node attributes as parameters into the reputation calculation algorithm, to select the master node of each layer in the double-layer consensus model. Determining the master node through reputation improves the credibility of the consensus algorithm. At the same time, the DTPBFT cancels the Pre-Prepare stage, reducing communication complexity. Finally, this paper transforms the existing PBFT algorithm on the Fabric platform to implement the DT-PBFT consensus algorithm and compares its performance with traditional PBFT, proving that DT-PBFT has the best balance and excellent comprehensive performance in large-scale IoT application scenarios.
Combining Event Segment Classification And Graph Self-Encoder For Event Prediction

Scripted event prediction is the automatic learning of knowledge from unstructured text to predict subsequent events based on a given sequence of events. Existing work has mainly been based on event pairs or entire event chains for learning, which may ignore much structural and semantic information. In order to learn global dependencies between nodes in event evolution graphs, this paper proposes an event prediction method combining event segment classification and graph self-encoder, which uses a variational self-encoder of the graph to obtain the global structure of the event graph, learn and represent potential evolution patterns between events, and construct a global representation of the event graph. The method combines different rules to classify event segments, learns the features of different event segments, and predicts events by determining the scores of candidate events based on the correlation between the event segments and the candidate events. Experiments on a standard dataset demonstrate the effectiveness of the method.
Improved Resnet18 Expression Recognition Algorithm

Expression recognition is a sub-task of computer vision. The accuracy of expression recognition accuracy is an important research content. However, Because facial expressions have different complexity and diversity at different times, expression recognition has always been a research difficulty. Aiming at the problem of feature extraction in expression recognition, to improve the accuracy of expression recognition, this paper proposes an expression recognition algorithm that improves the Resnet18 network model. First, in the Resnet18 network, The main branch of the residual module is embedded with the SEnet attention module. The feature layer is extracted from the channel dimension. Through the adaptive global average pooling method, the feature layer is compressed, and only the channel dimension information is taken. Then two full connection layers are used to pay attention to the channel information. Finally, the original feature layer is weighted. This method improves the accuracy of image classification tasks, Secondly, the Swish activation function is used to prevent saturation when the gradient gradually approaches zero during slow training, improve the performance of the whole neural network, and improve the accuracy of the network in classification. The experimental results show that, after improvement, compared with the original Resnet18 network, the accuracy on the public data set RAF-DB is improved by 0.59%, reaching 83.64%. Compared with the relevant classification methods, the detection accuracy is effectively improved.
Anonymous Password-Authenticated Key Exchange Protocol Based on Lattice

As the security of password authenticated key exchange protocols based on traditional public key cryptosystems is threatened by quantum computing, the research on password authenticated key exchange protocols that can resist quantum computing attacks has become a hot spot of current research. At present, the anonymity of anonymous PAKE protocol is generally linkable, and cannot completely protect the user’s identity privacy information. This paper proposes a two-party password authenticated key exchange protocol with anonymity based on SPHF from ideal lattice, and gives a strict security proofunder the BPR model. The security and performance analysis proves that the proposed protocol has good advantages in communication costs and security.
A Novel Effective Combinatorial Framework for Sign Language Translation

Sign language is a process by which people with speech and hearing disabilities to communicate with the world. It uses body movements to simulate syllables and form corresponding words to convey information. With the introduction of the CLIP model, which makes multi-modal tasks possible, there is a new solution for sign language recognition and translation. This paper proposes a novel framework for sign language translation, CL-MarianMT, which can effectively combine a sign language recognition model with a SOTA model for translation. In the part of sign language recognition, the video feature vectors are extracted by the Video Encoder of CLIP4clip architecture, and then the feature vectors are input into the Transformer model, and the recognized sentences are sent into the fine-tuned translation model MarianMT to realize the translation from English to Chinese. The experiment shows that the fine-tuned MarianMT improves the Chinese translation ability of sign language. The study in this paper can serve the purpose of enabling deaf-mute people to communicate in different languages and facilitate hearing-impaired people in language communication. At the same time, it can also allow hearing-impaired people to communicate with people with different language systems, which have a certain social value.
Visual Object Tracking Based on Double Siamese Networks

This paper deeply decouples two different tasks in the conventional Siamese-based trackers structure, so that the model can better optimize the two different tasks (classification and regression). Specifically, two heterogeneous features are extracted by building two independent feature extractors. The interaction between multiple feature maps is realized through attention mechanism, which can establish dependency relationships between multi-branches. In addition, a novel multichannel interaction method is proposed. The tracking results on multiple datasets show that the proposed tracking algorithm greatly improves the tracking accuracy of the tracker and can run at 60 FPS on GPU.
A Transformer-Based Network for Low-Light Image Enhancement

Challenging lighting conditions in the real world (low light, underexposure, and overexposure) not only create an unpleasant visual appearance, but also pollute computer vision tasks. we proposed a transformer-based network consists of global branch and local branch which is based on ISP theory to enhance low-light image quality. The key component in our network is the Window-based Self-Attention Block (WSAB) which captures non-local self-similarity and long-range dependencies. •Extensive experiments and ablation study demonstrate prove the efficiency of each part and demonstrate the superior performance of our proposed transformer-based network over SOTA methods.
BTRPP: A Rapid PGA Prediction Model Based on Machine Learning

Peak ground acceleration (PGA) is a critical parameter in the postearthquake intensity snapshot. In this article, we propose a Bagged Tree for Rapid PGA Prediction (BTRPP) based on velocity waveforms recorded at stations around the epicenter, as well as a feature pruning algorithm based on BTRPP to predict PGA and analyze the relationship between PGA and manually predefined features. The research not only aims to discover the best model for forecasting PGA but also examines the impact of features using the interpretability of some models. As inputs to the model, BTRPP uses and mixes features in the time and frequency domains. Bayesian optimization (BO) is used to find the optimum hyperparameters of BTRPP. The results demonstrate that, using the data within 5 s after the first arrival of the P-wave, the BTRPP with BO can reduce the RMSE to 0.2076 and enhance the R-square to 0.91. It is, finally, found that the PGA is most closely related to the average positive amplitude, average negative amplitude, and spectral slope through the feature pruning algorithm in this article, which indicates that the results are plausible.
RscaNet:a novel attention mechanism for image classification model

In computer vision technology, image classification task is an important technical progress. In view of the fact that the current garbage classification cannot effectively classify these different kinds of garbage, resulting in resource waste, environmental pollution and other problems, we propose to improve the deep learning model of ResNet to achieve classification. We added CBAM attention mechanism module and FocalLoss function to improve classification accuracy. We evaluate the performance of this model in different types of garbage classification data sets, and the results show that compared with other models, our model improved 0.16, 0.07, 0.06, 0.17 in fl-score, accuracy, percsion, and recall, respectively. Experimental results show that this method can effectively improve the image classification effect.
3D reconstruction of PCB based on ICP point cloud optimization

Printed Circuit Board (PCB) was the basic carrier for a large number of components in electronic devices. The quality of PCB will directly affect the performance of electronic devices, so it is crucial to conduct defect detection during PCB assembly. Traditional defect detection methods are inefficient and costly, thus failing to satisfy the requirements of large-scale PCB inspection at this stage. Three-dimensional reconstruction has the advantages of non-contact and fast, and is gradually applied to PCB defect detection, but the general point cloud generation method is difficult to identify defects highly correlated with components, which is easy to produce missed detection. The Iterative Closet Point (ICP) point cloud alignment optimization method proposed in this paper can effectively identify such height defects. Firstly, the point cloud is matched in a two-by-two alignment, and the third piece is aligned on the basis of the completion of the first two pieces of matching, and so on. Then the duplicate points are removed to ensure the 3D morphological features of the point cloud while effectively filtering out the redundant point cloud data points, and finally the non-overlapping parts are superimposed to optimize the point cloud model. The experimental results show that the average alignment error of the algorithm in this paper is less than 0.05% on the PCB board, which can be better applied to detect defects in the PCB assembly process.
Optimization Algorithm of Learning Quality Evaluation Based on Cognition in Cloud Computing

In the cloud computing environment, the optimization algorithm of learning quality evaluation based on cognition has strong practicability. Cloud computing based learning quality evaluation optimization algorithm provides realtime feedback to cognitive users in the virtual network, which can improve the model design and improvement work, thus improving the system operation efficiency. This paper mainly uses the analytic hierarchy process and experimental design to design and test the learning quality evaluation system. The experimental data shows that the system designed in this paper takes no more than 3s and can be used by students’ schools normally.
Restoration Algorithm of Lijiang Baisha Murals Based on Structure Tensor and Gradient Priority Calculation

Aiming at the problems of falling off and crack of Lijiang Baisha murals in Yunnan Province with different areas and irregular shapes, a repair algorithm of Lijiang Baisha murals based on priority calculation of structural tensor and gradient is proposed. First, the data item in the Criminisi algorithm is changed to the calculation of eigenvalue based on the structure tensor, and then the gradient item is introduced to reconstruct the priority function, making the order of mural repair more reasonable; Secondly, the similarity measure in the best matching block is changed from the original sum of squares distance (SSD) to the absolute value distance (ABSD) to avoid large area of matching errors or garbage blocks due to error accumulation; Finally, the digital repair simulation experiments of simulated damage and real damage are carried out for Baisha murals, and the repair results of this paper are compared with those of Criminisi algorithm and its improved two algorithms. The experimental results show that the proposed algorithm has better visual effect and stronger robustness, and is equivalent to the Criminisi algorithm in time complexity, while improving the peak signal to noise ratio and structure similarity.
Research on Screen Shooting Resilient Watermarking based on Dot-Matric

Aiming at the problems of low efficiency, poor accuracy and inability to extract screen dot-matrix watermark information under some specific circumstances, this paper proposes a method of information embedding and automatic recognition of screen dot-matrix watermark. In this method, some specific patterns formed by vector dot-matrix graphics arrayed regularly are designed as the information encoding units. According to the watermark information to be embedded, the watermarked image is generated by selecting the corresponding dot-matrix patterns,and superimposed on the screen display area to complete the watermark information embedding process. In addition, an automatic extraction method of watermark information from the screen captured or screen shoot watermarked image is proposed. The experimental results show that the proposedalgorithm has strong robustness, and can effectively resist lens distortion, illumination distortion, Moiré distortion and other attacks, and it is simple to implement with high extraction efficiency.