Reviewerly/exHarmony huggingface dataset:
A Contrastive Framework for Improving Multi-turn Response Selection

In retrieval-based dialogue systems, multi-round dialogue response selection is a major task of research and has received a lot of attention from academia and industry. BERTbased Dense Vector Retrieval employs an interactive encoder architecture to learn representations of dialogue context and candidate responses for semantic matching. However, in training session, the interaction between the context and the candidate responses exists only on the loss calculation phase. In the inference session, the prediction of the relevance score between the context and each candidate response is independent, and there is no interaction between the responses. At the same time, the context involves too many conversation rounds, resulting in the model’s inability to capture the relationship between the context and the candidate responses at a fine-grained level. To address the above issues, we propose a fine-grained pre-training and fine-tuning framework with a list-aware encoder overlaid on top of the interactive encoder. We evaluated our model on public datasets from three different domains. With our proposed framework, MRR has an average improvement of 1.1% and the average improvement of R <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</inf> © 1, R <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</inf> © 2 and R <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</inf> © 5 increased by 3.4%, 2.9% and 1.0% respectively, showing the effectiveness of this approach.
Hybrid Recommendation Algorithm Combining User Similarity And Item Similarity

Aiming at the problem that the traditional recommendation algorithm cannot accurately recommend the target user in the case of sparse data, this paper proposes a collaborative filtering recommendation algorithm that improves user similarity and item similarity.Firstly,the traditional item-based collaborative filtering algorithm just takes into account the similarity between items. It also neglects the importance of auxiliary information about items. In particular, time has a great impact on the recommendation results.Aiming at this problem, this paper introduces the time function into the traditional item similarity, and then solves the problem that the number of users’ common scores in sparse data is very small and their differences in dimensions. In this paper, the traditional user similarity is improved, and the Pearson similarity and Jacarrd coefficient is weighted and combined. Finally, the recommendation results are weighted and combined to get the final recommendation results. The experimental results show that the average value of the MAE of the fusion recommendation algorithm is 2.08% lower than the traditional UserBasedCF on the Movielens dataset, 2.89 % lower than the ItemBasedCF, and 1.09 % lower than the improved algorithm proposed in literature [7].
Prediction of Advertising Click-Through Rate Based on Dual Attention Network

Click-through rate (CTR) prediction refers to whether users will click on the content distributed by manufacturers. In the field of computing advertising and recommendation systems, click-through rate prediction has always been a mainstream hot topic. The relationship between features is often hidden in the data, detailed enumeration will lead to a waste of resources and manpower, and meaningless features will also interfere with and influence the final prediction results. Therefore, the key challenge in CTR task is how to effectively model feature interaction. In this paper, we continue the framework of the traditional deep click rate prediction model, considering the sparsity of the effective feature interaction, we add a feature generation module in the feature input part, and a dual attention network is proposed to further interact and filter the input features. The experiments are conducted on Criteo and Avazu datasets, and the results are improved compared with other models.
A method of Named Entity Recognition in Classical Chinese based on Bert-Ancient-Chinese

At present, in the field of Natural Language Processing (NLP), the accuracy of pre-trained models for downstream tasks such as Named Entity Recognition (NER) is significantly improved. Named Entity Recognition is a key technology in Knowledge Graphs (KG), but there are relatively few studies on this technology in the field of Classical Chinese. This paper proposes a Classical Chinese Named Entity Recognition model based on bert-ancient-chinese, which is based on bert-ancient-chinese+Recurrent Long Short-Term Memory+Conditional Random Field (BAC+RLSTM+CRF) for Named Entity Recognition and tested on the C-CLUE dataset. Compared with the baseline of the dataset, the F1 value is improved by about 22.11%.
Reliability of Air-Rail Hypernetwork under the Disturbance of Flight Delays based on Big Dada

In order to measure the reliability of air-rail network under the disturbance of flight delays, the air-rail network of China Eastern Airlines was taken as the research object. First, the air-rail hypernetwork model was established. the disturbance mechanism of flight delays on the air-rail hypernetwork under flight delays was analyzed. Then, the airrail network data of China Eastern Airlines were selected based on the big data. The global efficiency and the maximum relative size of connected subgraph were taken as the measurement indexes of reliability. And two groups of reliability of air-rail hypernetwork under different attack modes of flight delays were analyzed. The results shown that under the random attack mode of flight delays, the reliability of air-rail hypernetwork was stronger than that of the selective attack mode. When the proportion of airport nodes attacked was low, the reliability of air-rail hypernetwork under the selective attack mode was significantly lower than that of the random attack mode. However, when the proportion of airport nodes attacked reached a certain degree, the two groups of reliability of air-rail hypernetwork under different attack modes were gradually close to each other. No matter under the random attack mode or under the selective attack mode, the reliability of the air-rail hypernetwork was better than that of its airport network.
An improved density peaks clustering based on a new local density

Density Peaks(DP) clustering algorithm is shown to be a novel and effective clustering approach. It has been widely used in a range of applications. DP requires computing density $\rho$ and distance $\delta$ of each point. The clustering result is determined by $\rho$ and $\delta$. The $\delta$ of each point is relative to its $\rho$. The local density $\rho$ is computed by counting the number of points within a cutoff distance $d c$. Finally, the clustering result of DP depends on the value of $d c$, i.e., DP is sensitive to the cut-off distance $d c$. We found that a tiny change in $d c$ always results in different clustering results, which decreases the applicability of DP. This is because simply counting the number of points within the $d c$ range is not able to describe the local density of each point very well. To solve the above problem, in this paper, we proposed DP-NLD, an improved density peaks clustering algorithm based on a new local density measurement method. In DP-NLD, we not only count the number of points within $d c$, but also take the distribution of neighbors into account when computing the density of each point. Compared with the original DP, DP-NLD has better robustness. Our experimental results show that DP-NLD has better clustering results than DP and other clustering algorithms.
An application of XGBoost algorithm for online transaction fraud detection based on improved sailfish optimizer

Fraud detection is relevant in this context because of the large scale of online transactions, which conceal a small number of fraudulent transactions.Machine learning (ML) fraud detection models have numerous hyperparameters of the eXtreme Gradient Boosting (XGBoost) algorithm, which are important for the performance impact of fraud detection models. In this paper, the propose an improved version based on Sailfish optimizer(SFO) applied to online transaction fraud detection to optimize the hyperparameters of the XGBoost algorithm. Firstly, the optimization-seeking and local-optimal-skipping capabilities of Sailfish optimizer are improved, and Improved Sailfish optimizer (ISFO) is compared with other classical optimization algorithms, and the results show that the algorithm has outstanding. The Improved Sailfish optimizer is then used to optimize the parameters of XGBoost (ISFO-XGBoost) and compared with several other machine learning classifiers such as: Decision Tree (DT), Random Forest (RF), XGBoost, and several optimization algorithms optimized for the XGBoost model. To validate the performance, a fraud dataset of online transactions is used for evaluation. The experimental results demonstrate that the proposed improved algorithm is highly promising in solving machine learning hyperparameter optimization challenges, and the ISFO-XGBoost algorithm achieves the best results.
Virtual Machine Placement in Bigdata based Cloud Center

With wide application of information systems and mobile devices, vast amount of big data is generated and always managed in a bigdata cloud center. In the cloud center, virtualization is used as a basic resource allocation method and virtual machine placement techniques are applied based on predefined rules. However, some virtual machine placement rules do not consider data replication and I/O requirement of the cloud data center, causing sub-optimal data management outcome. In this paper we propose an optimized virtual machine placement algorithm considering data transfer velocity, cloud storage performance, and network bandwidth. The algorithm is based on heuristics of different tasks data load. Compared with existing virtual machine placement algorithms over extensive experiments indicates that, our proposed solution reduces great energy consumption and needs less migration.
Performance optimization algorithm for CRF layer of Chinese sequence labeling model based on deep learning

The “deep learning model + CRF” model architecture such as Bi-LSTM-CRF has outstanding performance| in the tasks of part-of-speech tagging, named entity recognition, and semantic role tagging in the field of natural language processing, and has become the most popular algorithm for sequence tagging tasks. However, in practical applications, due to the strong fitting ability of the feature extractor in the deep learning model, the use efficiency of the CRF layer is relatively low; at the same time, the calculation cost of the CRF layer is relatively large. This paper proposes a performance optimization algorithm for the CRF layer in the sequence annotation task based on the deep learning model. The algorithm introduces five rules. First of all, the tag result of the feature extractor is checked by the five rules, if the adjacent tag is found to be logically incorrect, then use the CRF layer for logic correction. The experimental results show that the algorithm shows great advantages in the process of word segmentation, part-of-speech tagging and named entity recognition, a lot of computing time has been saved.
U-SSLN: A U-Net based and optimization preferred semi-supervised learning network for medical image segmentation

In the medical field, image analysis is an important and complex task for clinical diagnosis. While image analysis can provide an important diagnostic basis for clinical treatment, image analysis also needs to determine its different analysis purposes (i.e., purpose preference for image analysis) according to different treatment requirements. Such purpose-preference image analysis will help improve the diagnostic capability of clinical care. In recent years, with the rapid development of deep learning for semantic segmentation, this technique has become an important auxiliary aid for medical image analysis. However, the expensive manual labeling cost makes the training data for semantic segmentation insufficient. This may lead to models that exhibit poor segmentation ability, while increasing the likelihood of models providing misleading information. To solve the problem of insufficient training data, many improvement methods have been proposed. For example, transfer learning, unsupervised learning, and semi-supervised learning. However, some of the methods have been poorly optimized on medical images. The semantic segmentation network UA-MT provides a semi-supervised approach based on the V-Net architecture to alleviate the data shortage problem, but it rarely integrates the segmentation performance with the purpose preference of image analysis. In this paper, we propose a semi-supervised learning network (U-SSLN) based on the U-Net architecture, which can be integrated with the purposeful preference of image analysis by different optimization combination strategies. U-SSLN integrates the advantages of UA-MT and U-Net architecture, and its combination strategies are mainly through residual concatenation (RC) on the segmentation model and batch normalization (BN) on the up-down sampling layer to implementation. These combined strategies can further optimize the segmentation performance of the segmentation network on different metrics, allowing the segmentation network to better assist in different stages of medical analysis. In order to verify the feasibility and effectiveness of U-SSLN, we conducted a large number of comparative experiments. The results of the experiments show that the segmentation models based on USSLN outperform the supervised segmentation models in all of them. Moreover, the U-SSLN without RC and BN achieves optimal performance (87.91% and 78.71%) on the area metrics Dice and Jaccard. And on the geometric metrics HD95 and Asd, the U-SSLN with RC and BN reached the optimal performance (10.27 and 1.99).
Application of YOLOv7 and Transformer structures to small object (license plate) detection in complex traffic scenes

YOLO is a family of widely used one-stage object detectors, and YOLOv7 is the latest one of the series. In traffic application scenarios, such as night scenes, the images or videos often appear to be blurred, too dark or bright, or the distance might be too far away or too close. These situations may pose great challenges to the detection. In this paper, we examined the detection ability of YOLOv7 to small targets such as license plates in complex traffic scenarios by training and testing it on the specific dataset, and made changes to YOLOv7 with Transformer structures to see the performance of the modified YOLOv7 on the same task. We have confirmed that YOLOv7 performs very well in the task of license plate detection, and got accuracy significantly better than the object detectors of the previous generation. When we apply Transformer-style module to the network, we got lower FLOPs than the original YOLOv7, and successfully kept the accuracy at the same level.
Research on Web Components and Fingerprint Intelligent Mining Methods

Web component fingerprint library is the basis to solve the problem of Web component identification. A complete and accurate Web component fingerprint library can effectively improve the Web component identification capability. At present, the expansion mode of Web component fingerprint database is still mainly based on expert experience for manual mining, which is difficult to expand and update. Therefore, there is an urgent need for a method to efficiently extend the Web component fingerprint library. To solve this problem, an intelligent method for mining Web components and fingerprints is proposed. This method uses the idea of manual mining new components for reference, and uses the search result characteristics of Web components in search engines to intelligently mine new Web components. At the same time, the fingerprint of Web components can be obtained automatically through data mining on the websites where new components are applied. The experimental results show that 22 new components and 102 component fingerprints have been found in a short time by using intelligent mining methods, which can efficiently mine Web components and fingerprints. Compared with the current mainstream manual mining methods, the efficiency of this method is greatly improved, which proves the feasibility of this method.
Prediction of New Technical Terms Based on Machine Learning Algorithms

New technical terms (NTTs), the precursors of technical terminology, are vehicles for new ideas contained in innovative technologies. Term identification research tends to mature, but little research has been done on the prediction of NTTs, which mines promising technical terms from voluminous technological data to help R&D teams rapidly form new ideas. In this study, we design predictor variables by constructing a prior co-word network and construct multiple machine learning models that predict the formation of NTTs. To validate the above approach, this paper focuses on the formation of some NTTs in the neural network domain. The prediction model constructed by the stacking model produced the best results with a prediction accuracy of 78.6%.
Research on Chinese Keyword Extraction Based on the ALBERT Pre-Training Model

The classic word embedding-based Chinese keyword extraction techniques have a number of drawbacks, including poor word segmentation and an inability to recognize word ambiguity. An efficient ALBERT-BiGRU-CRF-based Chinese keyword extraction model has been presented to address the aforementioned issues. The model first obtains dynamic Chinese character vector representation based on ALBERT pre-training model, fuses lexical information by lexical enhancement method, then extracts contextual long-range semantic features using bidirectional gated recurrent units, and finally introduces conditional random fields to calculate the global optimal solution to obtain the final keywords. The experimental findings indicate that the model’s Fl value is 81.36%, which is higher than that of the conventional keyword extraction models and demonstrates that the suggested technique can successfully enhance Chinese keyword extraction performance.
Constraint-based and hybrid structure learning of multidimensional continuous-time Bayesian network classifiers

Learning the structure of continuous-time Bayesian networks directly from data has traditionally been performed using score-based structure learning algorithms. Only recently has a constraint-based method been proposed, proving to be more suitable under specific settings, as in modelling systems with variables having more than two states. As a result, studying diverse structure learning algorithms is essential to learn the most appropriate models according to data characteristics and task-related priorities, such as learning speed or accuracy. This article proposes alternative algorithms for learning multidimensional continuous-time Bayesian network classifiers, introducing, for the first time, constraint-based and hybrid algorithms for these models. Nevertheless, these contributions also apply to the simpler one-dimensional classification problem for which only score-based solutions exist in the literature. More specifically, the aforementioned constraint-based structure learning algorithm is first adapted to the supervised classification setting. Then, a novel algorithm of this kind, specifically tailored for the multidimensional classification problem, is presented to improve the learning times for the induction of multidimensional classifiers. Finally, a hybrid algorithm is introduced, attempting to combine the strengths of the score- and constraint-based approaches. Experiments with synthetic and real-world data are performed not only to validate the capabilities of the proposed algorithms but also to conduct a comparative study of the available competitors.
Entangled state engineering in the 4-coupled qubits system

This article studies the behavior of the avoided level crossing in the 4-coupled qubit to each other and mainly focuses on how to engineer it. This phenomenon occurs due to the two transitions out of the ground state in a two-coupled qubit, contributing to the entangled states. This essential and unique behavior can be engineered in a quantum circuit. For this reason, a quantum circuit containing 4 qubits is designed, and its quantum Hamiltonian and dynamic equation of the motion are theoretically derived. Analysis of the entanglement between each coupled qubit using the entanglement metric reveals that the strength of the qubit-qubit coupling factor and the qubit's non-linearity play an essential role in engineering the photonic mode entanglement. The results show that the avoided level crossing appears in the photonic mode entanglement. In other words, two or more transitions from the ground state to the multiple excited states for each bias current. However, the interesting point is that the avoided level crossing just occurs for the qubits connected capacitively to the driven field (the first qubit in this work), not for all.
The use of the output states generated by the broadcasting of entanglement in quantum teleportation

In this article, we find a theorem that gives a relation between the maximal fidelity of teleportation and the concurrence of the inseparable X state used as a quantum channel in this process. Furthermore, we evaluate the concurrence of the output states generated by the local and nonlocal asymmetric broadcasting of entanglement and prove that the concurrence is greater in the case of nonlocal broadcasting. We analyze the possibility of using the output states obtained by the broadcasting of entanglement as quantum channels in quantum teleportation. We prove, with the help of the above-mentioned theorem, that all the inseparable states given by the local and nonlocal asymmetric broadcasting of entanglement are useful for quantum teleportation. Finally, we show that the maximal fidelity of teleportation is greater in the case when the second scenario is used, i.e., when the quantum channel is generated by the nonlocal asymmetric broadcasting of entanglement.
Multi-Feature Classification of Breast Cancer Histopathology Images: An Experimental Investigation in Machine Learning and Deep Learning Paradigm

HIGHLIGHTS The proposed approach performs the classification of breast tumors in histopathology images. The proposed approach evaluates a multi-feature classifier for risk stratification. The performance of different classifiers is compared under different data division protocols. The highest classification accuracy of 99.85% after feature selection is reported.