Reviewerly/exHarmony huggingface dataset:
Get A Sense of Accomplishment in Doing Exercises: A Reinforcement Learning Perspective

Doing exercises is vital to improving students’ learning effects. Setting reasonable rewards in the process of doing exercises can promote students’ problem-solving interests. Reinforcement learning has been employed on optimal instructional sequencing in education for decades. Existing reinforcement-learning-induced policies in instructional sequencing mainly pay attention to how students take actions in learning activities while attention to the setting of rewards in doing exercises has been little. To redress this imbalance, we first propose a set of calculation formulas of reward function closely associated with the difficulty of each exercise item and the consuming-time of each student. We put forward the confidence index of students as another important parameter in our reward function, which represents how sure each student is that he/she could do the exercises right. We then propose and prove a reward shaping scheme based on dynamic potential function, which can not only ensure invariance of the optimal policy of acquiring exercise-doing rewards but also accelerate the convergence of the exercise-doing process. This scheme provides a new paradigm for motivating students to do exercises in the e-learning environment, which helps to enhance students’ learning enthusiasm, so as to improve their learning efficiencies impressively.
Object Detection of Few-Shot Thangka Images by Contrastive Proposal Coding

As one of China's intangible cultural heritage, Thangka is the main carrier of Tibetan culture. The application of modern technology in the digital protection of thangka images is of great significance to promote cultural exchange and inheritance. In this paper, the headdress, seat and handheld objects of the thangka images are taken as the detection targets, and they are classified and recognized by using the method,which is few-shot object detection by contrastive proposal coding. This method introduces contrastive learning into the few-shot object detection method. That takes Faster R-CNN as the baseline model, adds a contrastive branch parallel to the classification and prediction branch at the end of the model. In the contrastive branch, the problem of misclassification is alleviated by calculating the similarity score between the object proposal boxes. The experiment shows that this method can achieve good results on the thangka data set, with mAP reaching 38.1% and AP50 reaching 51.3%.
A Large-Scale Privacy Measurement of Novel TLS 1.3 Protocol

With the continuous improvement of people's awareness of network security, also the increasing needs of network security and privacy protection, more and more websites begin to use full encryption technologies (such as TLS1.3, DoT, ESNI/ECH). From the perspective of passive traffic, network behavior is completely invisible, which greatly protects user privacy. However, the destination IP accessed by the user is still visible, which still bring the risk. In this paper, we aim to measure the privacy leakage of the novel protocols by studying the relationship between Domain and IP. We have collected a large number of domain and IP datasets from China's major academic backbone networks, with a scale of 53.4 billion and a time range of July 2021. We use IP anonymity, domain popularity, and Domain-IP dynamics to quantify the degree of privacy protection. Through a comprehensive analysis of large-scale real-world DNS data, we conduct an assessment of the privacy leakage risks of the novel protocols in current open networks. We find that 0.64% of the domains have not gained any privacy benefit since they have a one-to-one mapping between domain and IP, 1.4% of IP (with anonymity greater than 10k) host 83% of domains, which can obtain high privacy protection. Less popular sites get higher privacy protections. 0.02% (10 million) of domains change IP every day, and these websites get better privacy protection. Finally, we discuss the potential methods of privacy protection in the novel protocol.
Identifying Substantial Changes for AIP Projects Using RF and SVM

The budget for the Airport Improvement Program (AIP) in FY21 is $3.35 billion. AIP’s contractual guidelines and policies are outlined in Federal Aviation Administration (FAA) 5100.38D. Substantial contractual changes within AIP projects are likely to create risks that can greatly impact cost, time, and quality. To implement improved change management, identification and evaluation of contractual change are essential. Thus, there is a need to create an automated model to identify and evaluate contractual changes in AIP projects. This paper fills this knowledge gap. Using 876 contractual changes made to FAA 5100.38D, the authors utilized an interrelated multi-step methodology. Firstly, the data is prepared, and the changes in the FAA 5100.38D are manually adapted. Secondly, the data is manipulated using NLP techniques. Thirdly, before building the machine learning (ML) models, the data is preprocessed. Finally, hyperparameter tuned random forest (RF) and support vector machine (SVM) ML models are developed to predict contractual substantial changes, and the optimal model is selected and evaluated. Results show that RF presented the optimal hyperparameter tuned model with an AUC value of 0.928. Ultimately, this research presents an AIP decision support tool that predicts substantial contractual changes effectively and efficiently, and thus proactively provides risk assessment for AIP projects.
Time-varying PHD filter for multi-target tracking without the detection probability

In Bayesian multi-target filtering, knowledge of detection probability is of critical importance. Significant mismatches in detection probability parameter result in biased estimates. In this paper, we propose a time-varying probability hypothesis density (PHD) filter to deal with unknown detection probability. The detection probability for each target is assured when the predicted states associate with the measurements, and closed-form solutions to the PHD filter are derived by some modifications of the measurement updated formula. Unlike other methods for this challenging class of the PHD filters, the novel method can deal with the unknown and time-varying detection probability effectively, without the complicated process of parameter estimation. Simulations show that the proposed algorithm is suitable to solve the unknown detection probability problems and have better performance, implying good application prospects.
Interpretation of run-of-mine comminution and recovery parameters using multi-element geochemical data clustering

Multi-element (ME) datasets provide comprehensive geochemical signatures of an orebody and are commonly used to gain insight into the mineralogy, lithology, alteration patterns and to identify target-pathfinders. However, little effort is made in using these data to explain comminution or recovery characteristics. This paper describes an agglomerative hierarchical clustering approach applied to ME data from the Tropicana Gold Mine, Australia, and investigates the relationship between the resultant classes and run-of-mine comminution and recovery parameters. First, it is demonstrated how an industry scale ME dataset is prepared for clustering. The preparation consists of verifying the absence of interlaboratory and intralaboratory bias between measurements, centred log-ratio transformation (clr), normalisation and principal component analysis (PCA). Afterwards, the first case study indicate that the clustering separation is primarily driven by geochemical differences caused by major rock-forming mineral signatures (felsic vs mafic, alteration vs no alteration, chert or quartz lithologies, unmineralised vs mineralised material). This case study separates the ME dataset into five unmineralised and two Au-mineralised material classes. The second case study continues with the two identified mineralised material classes and further separates these samples into five new classes. These classes are explored geochemically and by using the spatial context (within domains) better matched with metallurgical test results. It is found that domain-related material class proportions assist in interpreting different processing proxies such as the Equotip hardness (Leeb), Bond Work index (BWi), Axb, and processing recovery and reagent consumption. Knowledge of the processing parameters per domain and class composition can be used to infer such characteristics in the absence of standard metallurgical tests. This new approach of gaining insights into comminution and recovery parameters through geochemical analysis demonstrates the benefit of the conceptualised material fingerprinting concept.
A LUNet based on large kernel attention mechanism for image denoising

U-shaped networks are widely used in the field of image denoising with their multiscale and jump connection structures in recent years. The feature extraction structures mainly used in previous works are convolutional neural networks (CNNs), but their ability to extract information at a distance is poor. In this paper, we propose a U-shaped network structure combined with large kernel attention structure for image denoising, in which the CNNs structure can effectively extract local information while the large kernel attention structure has better extraction of global information and lower computational cost compared with Transformer, through which local-global features are learned on the feature maps of the input noisy images at different scales, which can effectively enhance the performance of the network and improve the image denoising task. The performance of the network can be enhanced to improve the performance of the image denoising task.
NRNet: Retinex Decomposition with Realistic Noise

Computer vision tasks fail in low-light environments, so high-quality restoration of low-light images is of great significance to improve the robustness of computer vision. Traditional low-light enhancement algorithms use statistical methods or need to manually adjust parameters, which will fail in poor enhancement effect and robustness. Based on the deep learning method and the Retinex method, this paper proposes a self-supervised low-light enhancement algorithm based on the attention mechanism. We decouple the illumination component and reflection component of Retinex, and then synthesize it into an input RGB image, and finally use the estimated components to adaptively adjust different areas of the image. In experiments, we use several different datasets to test our method. Experiments show that our method is able to enhance low-light images under various scene conditions.
QianXun: A Novel Enterprise File Search Framework

With the deepening of the digital transformation, enterprises have accumulated plenty of electronic files. For users, how to quickly find their desired files from the massive resources is a big challenge, which is also a critical concern for enterprises. Although a series of enterprise search engines have emerged in the market, there is a lack of search systems for enterprise files. In this paper, we design a novel enterprise file search framework, named QianXun, which supports searching in both Chinese and English. By introducing technologies such as automatic text summarization, online file previewing, and Knowledge Graph-enhanced search, our proposed framework improves the efficiency of file search as well as the user experience, which is of great significance to enterprises.
Multi-Objective Optimization based on Role in Mentorship Mode

In this paper, we propose a Multi-Objective Optimization problem with Mentorship Mode (MM-MOOP) and formalize the real-world collaboration problem using group role assignment (GRA) framework. The GRA with budget constraints, which is a typical Multi-Objective Nonlinear Optimization problem, and it can be transformed into linear optimization problems and solved by IBM ILOG CPLEX optimization package (CPLEX). Through simulation experiment, the optimal proportion of novice and veteran in the group is obtained. The experiments also verifie that the group performance in Mentorship Mode can be improved under the same budget constraints.
Research on the Development Process and Construction of Domain-specific Knowledge Graph

Knowledge graph is the core content of artificial intelligence research in the new era. With the rapid development of artificial intelligence in science and various fields of technology and society, it has attracted the attention of academia and industry. Various fields are actively constructing the Domain-specific Knowledge Graph (DKG). The construction of Domain-specific Knowledge Graph has great application value in intelligent search, intelligent question and answer, intelligent recommendation and other information services. This paper taking the artificial intelligence system development as background, systematically elaborated the origin and composition of Domain-specific Knowledge Graph, finding out that the Domain-specific Knowledge Graph has characteristics of extending the depth and detailing granularity of the knowledge representation of Domain-specific Knowledge Graph. And the development process of Domain-specific Knowledge Graph technology: developing from the symbol and the reasoning model to knowledge engineering, then to an important approach to knowledge representation of big data. The key steps and methods of Domain-specific Knowledge Graph construction are described, including entity recognition and attribute information recognition, and extraction of relationships between entities. The entity recognition technology and relation extraction technology are systematically described to provide some useful references for the construction of Domain-specific Knowledge Graph.
Differentially Private Data Synthesis: State of the Art and Challenges

Differential privacy has been accepted as the de facto notion for protecting privacy. Companies and government agencies use differential privacy for privacy-preserving data analysis. For example, the US census bureau applied differential privacy in the 2020 census. One important approach to use a private dataset is to generate a synthetic dataset that is similar to the private dataset in a way that satisfies differential privacy. This enables data analysts to directly apply existing algorithms for performing data analysis. Furthermore, as additional data analysis tasks performed on the published dataset are post-processing, they do not incur additional privacy cost. In recent years, US National Institutes of Standards and Technology ran two competitions on differentially private data synthesis, which drove the development of practically effective data synthesis algorithms.
An LSTM-based Framework for Motion Pattern Recognition of Handling Robots

With the development of the construction industry, construction work more and more tends to mechanization, automation, and intelligence. The appearance of handling robots greatly improves the handling efficiency and reduces the cost. Even though great efforts have been made on motion pattern recognition for handling robots, the existing methods still have some shortcomings. Previous studies are all used for motion pattern recognition, each input is processed separately, which cuts off the time continuity between the latter data and the former data. To this end, in this paper, we first emphasize the importance of temporal correlation between data in the field of motion pattern recognition and devise an LSTM based method to ease the issue above. Finally, we use Softmax to effectively calculate the classification results of data potential features and temporal information. Compared with traditional machine learning models and popular neural network models, extensive experiments on real-world datasets demonstrate the effect of our framework is superior to the baselines, thus providing novel perspectives to the building automation industry.
The handling trolley based on the RT-Thread and gesture control

With the rapid development of sensors and machinery manufacturing, handling trolley has also gradually become a hot topic. This paper introduces the design of a single-handed control handling trolley based on real-time operating system RT-Thread, which has high application value, including using for daily life assistance of disabled people and non-contact small goods transmission and other fields. The trolley consists of a gesture acquisition terminal, a server transmission network and a trolley control terminal. The gesture acquisition terminal uses wearable devices to realize the gesture recognition. The uploaded data are sent to the trolley control terminal through the server transmission network. The trolley control terminal collects the video stream and transmits it to the server network auxiliary operator to implement the remote control. The system shows good control performance on the experimental platform.
Context-based Operation Merging in Real-Time Collaborative Programming Environments

Real-time collaborative programming environments support a team of programmers to edit shared source code at the same time, where each local editing operation is captured and immediately transmitted to remote sites in a fine-grained manner. However, under real-world network conditions, collaborators are usually plagued by data congestion and transmitted errors. In this study, we define and analyze the content relationships among operations, and propose a Context-based Operation Merging Algorithm (COMA). Technically, the COMA examines the content relationships among a series of editing operations and merges content-related operations. Powered by the COMA, real-time collaborative programming environments can significantly compress editing operations to cope with complex network situations (such as network fluctuation and interruption) and improve the user experience of real-time collaboration. The proposed COMA has been implemented in a real-time collaborative programming environment prototype, namely CoEclipse. Preliminary user evaluations, correctness analysis and performance evaluations have demonstrated the effectiveness, correctness, and efficiency of the algorithm.
Efficiently Obfuscating Proxy Signature for Protecting Signing Keys on Untrusted Cloud Servers

Nowadays, with the development and massive use of cloud services, protecting the information security on untrusted cloud servers is becoming an important issue. Since the signing key is one of the most important parts of secret information and the Schnorr signature is frequently used in on-cloud systems, this study proposes an obfuscatable proxy signature scheme based on the Schnorr signature and the Elgamal encryption, and presents a provably secure obfuscator for the scheme. Moreover, the application scenario of the proposed scheme for untrusted cloud servers has been discussed. Experimental results have indicated that the proposed scheme is computationally efficient.
Sentiment Analysis Model Based on Adaptive Multi-modal Feature Fusion

Recent years, people tend to use text coupling with images to express and share their views on social media. At present, most multi-modal research work only adopts a simple stitching approach for different modal data fusion, failing to consider the correlation and complementarity between different modalities. Fully exploiting the correlation and complementarity of text and images is essential for user sentiment analysis. In this paper, we propose a sentiment analysis model based on adaptive multi-modal feature fusion strategy. This model can effectively explore the bi-directional association relationship between text and picture and fuse each modal feature through the multi-modal deep adaptive feature fusion model. In addition, we add Label Smoothing to the loss function to enhance the generalization ability of the model. The experimental results on a public dataset show that the proposed model achieves the best performance compared to the baseline model, with a 2.0% improvement in accuracy. Moreover, to demonstrate the generalization capability of the proposed adaptive fusion feature module, we test it on other tasks Multi-Modal Fake News Detection achieving accuracy with 2.5% improvement.
A Unified Normalization Method for Point and Line-Based Homography Estimation

It is well accepted that data normalization is an essential and imperative step in using the direct linear transformation (DLT) method for homography estimation. Nevertheless, the existing data normalization methods are either point or line-based, thus making them inapplicable in scenarios where both point and line correspondences are available. To fill this gap and further widen the applicability of the DLT method, this study proposed a unified data normalization method for homography estimation using combined point and line correspondences. In this method, the existing point-based normalization method was exploited and then extended to normalize the line coefficients by conducting extra data processing created by the authors. Finally, a unified normalization method was developed by fusing the normalization principles implemented on the point coordinates and line coefficients alone, which allows for choices of using different combinations of point and line correspondences for homography estimation. The field results showed that the proposed method can significantly improve the accuracy and offer higher reliability for homography estimation.
Autonomous obstacle avoidance assistant system for unmanned surface vehicle based on Intelligent Vision

In view of the current situation of ship borne automatic identification system, automatic radar and vision based technology applied in the field of ship collision avoidance at home and abroad, there are common shortcomings, that is, it is difficult to accurately identify in the poor navigation environment with low visibility such as night and fog, or in the complex navigation environment with dense targets such as narrow channel and heavy traffic, and the contrast of collected image information is lowDue to the problems of low signal-to-noise ratio and easy missing detection and loss, the intelligent obstacle avoidance system of unmanned surface vehicle based on intelligent vision constructed in this project can not only overcome the adverse environmental factors of fog and improve the perception accuracy;It can also solve the detection problem when the targets are dense and the navigation environment is complex, and obtain more intuitive external navigation environment information.The intelligent obstacle avoidance system of unmanned surface vehicle based on intelligent vision constructed in this project is mainly composed of three parts: visual perception enhancement, obstacle target autonomous recognition and unmanned boat autonomous obstacle avoidance.Firstly, the obtained video image is processed;Secondly, yolov4 target detection network is used to identify and enhance obstacles in video images.Then, the obstacle blocking angle in the image is obtained by computer technology, and the obstacle distance is measured by millimeter wave radar to realize the fusion of vision and radar information.Finally, the intelligent collision avoidance of unmanned craft is realized based on VFH obstacle avoidance algorithm.While solving the problems of the current ship borne identification system, it enhances the type identification effect of obstacles, realizes the effect of accurate identification, and greatly reduces the impact of external interference factors on the safe navigation of unmanned craft. It has high application value and prospect.