Reviewerly/exHarmony huggingface dataset:
Detecting Entanglement-Generating Circuits in Cloud-Based Quantum Computing

Entanglement, which is a direct consequence of elementary quantum gates such as controlled-NOT and Toffoli, is a key resource that provides quantum advantages. In this work, we establish a framework for certifying entanglement generation in cloud-based quantum computing services and present the construction of quantum circuits that certify entanglement generation in a circuit-based quantum computing model. The framework relaxes the assumption of qubit allocation, which, in cloud services, relates the physical qubits in hardware to a circuit proposed by a user. Consequently, certification is valid in cloud computing regardless of the success or trustworthiness of qubit allocation. The certification of entanglement generation was demonstrated on 2 and 3 qubits in the IBMQ and IonQ services. Remarkably, entanglement generation was successfully certified in the IonQ service, which does not offer manual qubit allocation. The capabilities of entanglement generation in IBMQ and IonQ circuits were also quantified. We envisage the application of the proposed framework in cloud-based quantum computing services for practical computation and information tasks, with the results determining whether it is possible to achieve quantum advantages.
An Item–Item Collaborative Filtering Recommender System Based on Item Reviews: An Approach with Deep Learning

Item−item collaborative filtering is a sub-type of a recommender system that applies the items’ similarities for recommending a new set of items to the user. Usually, a traditional recommender system utilizes items’ ratings given by the user for deducing their preferences for recommending items. However, for the popularity of social platforms, users are now more familiar to write textual comments known as reviews about items based on their experiences rather than giving a rating, because rating any item limits a user to manifest the degree of satisfaction towards the item. As a result, the items’ reviews become a precious source of information that could enhance the system’s performance. In this paper, a novel recommendation approach has been proposed by applying a recurrent neural network to incorporate items’ reviews with the recommender system. The recurrent neural network is a deep learning-based approach that can distribute the text to the relevant classes. Thus, the proposed approach has applied long short-term memory which is a modern formation of recurrent neural network that is applied to compute items’ rating scores from the items’ reviews. Then, the score is used to define the uniformity of items by using the Jaccard and Pearson correlation coefficient. The proposed approach has been evaluated by two familiar datasets named Yelp &amp; Amazon datasets. Also, it is found that the proposed approach surpasses the traditional techniques and also improved the accuracy of prediction for the Yelp dataset by approximately in respect of 1.37% mean absolute error, 2.17% precision, 2.08% recall, and 2.11% f-measure. Furthermore, the proposed approach increased the recommendation performance for Amazon dataset on average in term of 1.34% mean absolute error, 2.09% precision, 2.53% recall, and 2.32% f-measure, respectively.
Analysis of Regional Differences in the Distribution of Higher Education Opportunities in the Concept of Educational Equity

Cultivating top-notch innovative talents and promoting China's social progress require attention to the distribution of higher education opportunities. Therefore, the distribution of higher education opportunities has important research value for the development of education in China in the 21st century. Through literature survey, this study finds that the current distribution of higher education opportunities in terms of regional differences has problems with the entrance examination system, educational resources allocation, differences in the teaching force, educational policy input, and inequitable access to higher education due to differences in educational information technology. And accordingly, five recommendations are proposed: to add diversified subjects, to carry out diversified admission methods according to the characteristics of the west, to increase the investment in the allocation of educational resources in the west, to increase the capacity and improve the quality of the teaching force in the west, to enhance policy support and financial investment, and to strengthen infrastructure construction and promote information sharing of educational resources.
Violation of Bell Inequality by Four‐Photon Greenberger‐Horne‐Zeilinger State with a Phase from a Warm Atomic Ensemble

Abstract A Greenberger–Horne–Zeilinger (GHZ) entangled state with a phase is crucial for realizing desired multipartite quantum states for practical applications. Here, it is reported that the violations of the general Bell inequality (GBI) introduced in using the four‐photon polarization‐entangled phase‐GHZ state realized via intrinsic polarization correlation and collective two‐photon coherence in the 5S 1/2 –5P 3/2 –5D 5/2 transition of 87 Rb atoms. The phase‐GHZ state can be achieved by the unitary transformation of only one local phase of the four photons. Theoretically, the GHZ state with the π/4 phase affords maximal violation of the GBI of 2√2 at the local measurement settings of the Pauli operators σ x and σ y . Strong violations of the GBI of the phase‐GHZ state by 47 standard deviations are experimentally demonstrated. In addition to the entanglement witness for the phase‐GHZ state, the results represent a genuine four‐photon entanglement of the phase‐GHZ state, thereby providing a novel resource for realizing photonic quantum computation, magic‐state distillation from entangled states, and quantum networks based on atom–photon interactions.
A multi-layer composite identification scheme of cryptographic algorithm based on hybrid random forest and logistic regression model

Abstract Cryptographic technology can effectively defend against malicious attackers to attack sensitive and private information. The core of cryptographic technology is cryptographic algorithm, and the cryptographic algorithm identification is the premise of in-depth analysis of cryptography. In the cryptanalysis of unknown cryptographic algorithm, the primary task is to identify the cryptographic algorithm used in the encryption and then carry out targeted analysis. With the rapid growth of Internet data, the increasing complexity of communication environment, and the increasing number of cryptographic algorithms, the single-layer identification scheme of cryptographic algorithm faces great challenges in terms of identification ability and stability. To solve these problems, on the basis of existing identification schemes, this paper proposes a new cluster division scheme CMSSBAM-cluster, and then proposes a multi-layer composite identification scheme of cryptographic algorithm using a composite structure. The scheme adopts the method of cluster division and single division to identify various cryptographic algorithms. Based on the idea of ensemble, the scheme uses the hybrid random forest and logistic regression (HRFLR) model for training, and conducts research on a data set consisting of 1700 ciphertext files encrypted by 17 cryptographic algorithms. In addition, two ensemble learning models, hybrid gradient boosting decision tree and logistic regression (HGBDTLR) model and hybrid k-neighbors and random forest (HKNNRF) model are used as controls to conduct controlled experiments in this paper. The experimental results show that multi-layer composite identification scheme of cryptographic algorithm based on HRFLR model has an accuracy rate close to 100% in the cluster division stage, and the identification results are higher than those of the other two models in both the cluster division and single division stages. In the last layer of cluster division, the identification accuracy of ECB and CBC encryption modes in block cryptosystem is significantly higher than that of the other two classification models by 35.2% and 36.1%. In single division, the identification accuracy is higher than HGBDTLR with a maximum of 9.8%, and higher than HKNNRF with a maximum of 7.5%. At the same time, the scheme proposed in this paper has significantly improved the identification effect compared with the single division identification accuracy of 17 cryptosystem directly and the 17 classification accuracy of 5.9% compared with random classification, which indicates that multi-layer composite identification scheme of cryptographic algorithm based on HRFLR model has significant advantages in the accuracy of identifying multiple cryptographic algorithms.
DNN controlled adaptive front-end for replay attack detection systems

Developing robust countermeasures to protect automatic speaker verification systems against replay spoofing attacks is a well-recognized challenge. Current approaches to spoofing detection are generally based on a fixed front-end, typically a time-invariant filter bank, followed by a machine learning back-end. In this paper, we propose a novel approach whereby the front-end comprises an adaptive filter bank with a deep neural network-based controller, which is jointly trained along with a neural network back-end. Specifically, the deep neural network-based adaptive filter controller tunes the selectivity and sensitivity of the front-end filter bank at every frame to capture replay-related artefacts. We demonstrate the effectiveness of the proposed framework in spoofing attack detection on a synthesized dataset and ASVSpoof 2019 and ASVSpoof 2021 challenge datasets in terms of equal error rate and its ability to capture artefacts that differentiate replayed signals from genuine ones in comparison to conventional non-adaptive front-end.
Incremental learning for an evolving stream of medical ultrasound images via counterfactual thinking

Despite the fact that traditional deep learning (DL) approaches provide promising accuracy and efficiency in medical ultrasound image analysis, they cannot replace the physician in making a diagnosis since the DL model is only appropriate in static application scenarios. Currently, most DL-based models are incapable of learning new tasks in the dynamic clinical environments due to the catastrophic forgetting of old tasks. To address the above problem, we propose an incremental classifier that is sequentially trained on evolving tasks for medical ultrasound images by counterfactual thinking. Specifically, the proposed model consists of a feature extractor and a classifier that can add new classes at any time during training. Toward a more discriminative model in the continual learning setting, a contrastive strategy is designed to leverage fine-grained information by generating a series of counterfactual regions. For model optimization, we design a multi-task loss made up of a knowledge distillation loss, a cross-entropy loss, and a contrasting loss. This objective jointly enjoys the merits of less forgetting, better accuracy, and fine-grained information utilization. A newly collected dataset with 52 medical ultrasound classification tasks is used to demonstrate the effectiveness of our method. The proposed approach achieves 76.59%, 11.67%, and 7.93% in terms of the average incremental accuracy, forgetting rate, and feature retention, respectively.
Deep Learning-based End-to-End Address Recognition Solution on Chinese Courier Order Forms

The courier industry in China has grown quickly due to the rise of online shopping. However, courier notes can unavoidably become smudged or damaged during the delivery process, making it difficult to read the printed Chinese address information or recognize the barcodes. To solve this problem, this paper proposes an end-to-end solution to recognize damaged Chinese addresses: the CRNN model is trained for address recognition for damaged Chinese courier orders using a large Chinese address dataset generated via data augmentation and manual collection. And an address association algorithm is proposed to reduce the recognition errors at the provincial and municipal levels of the addresses. By applying this algorithm, the final accuracy is increased by 2% to 98.7%.
Recognition of Beat-Motion Gestures of Orchestra Conductor using DTW and Nearest Neighbor Method

The conductor is responsible for controlling speed, emotion, instruments, and other musical information in music performances. Using hand gestures, facial expressions, and body movements, the conductor communicates with each member of the band; the conductor primarily uses hand movements to reflect the different beats of different music pieces. In this study, Kinect was used to capture the gestural trajectory of the conductor. Additionally, the three-dimensional spatial data of the obtained motion trajectory were adaptively smoothed. The motion timing data were subsequently segmented, and a dynamic time-warping algorithm was used to match the timing data of the template library with the to-be-classified data.
An agent motion model construction method based on sequential attention neural network

Agent motion model is the research basis of agent motion control. At present, due to the high cost and time consumption of using physical platforms, utilizing the particle motion equation or other dynamic equations to construct the agent motion model is a popular method in the research of agent motion control. However, these construction methods cannot involve the internal nonlinear characteristics and external environmental factors of the actual agent, which makes it difficult for the constructed motion model to accurately match the real world. In order to construct a more realistic agent motion model, a sequential attention neural network (SANN) based method is proposed to construct the agent motion model. By feeding the motion data of the actual agent into the designed SANN, a motion model closed to the actual one can be learned. In our motion model, the influence of the historical state on the current state of the agent is taken into account, and an attention mechanism is introduced into the network to measure the influence of historical states on the motion state transition of agent. Experiment results verify the feasibility of our construction method, and the motion model learned by SANN is closer to the actual motion model compared to the dynamic equation based motion models.
Research on Control Strategy of Manipulator Based on Deep Reinforcement Learning

Abstract: Today, the traditional control algorithm of the manipulator is difficult to meet in the complex and changing working environment, and the development of artificial intelligence has achieved remarkable results in the field of manipulator control. As a branch of artificial intelligence, deep reinforcement learning combines the perception ability of deep learning with the decision-making ability of reinforcement learning, which can greatly improve the work efficiency and adaptability of the manipulator. This paper expounds on the principle of deep reinforcement learning, compares and analyzes the application examples of the single manipulator and multi-manipulator control algorithms based on deep reinforcement learning, and demonstrates the achievements of deep reinforcement learning in the motion control of manipulators. The intelligent control research provides a reference.
Pipeline-based Optimization Method for Large-Scale End-to-End Inference

Enhancing the utilization of computing resources is a crucial technical challenge within the realm of deep learning model deployment and application. It holds significant importance in effectively leveraging various deep learning models. However, when it comes to actual deployment and operation, deep learning models face an urgent task—processing large-scale data. This processing flow is an end-to-end procedure that typically involves three essential steps: preprocessing, model inference, and postprocessing. Presently, existing research mainly focuses on the optimization of deep learning model algorithms, and rarely considers the coordinated utilization of CPU and accelerator resources after model deployment, resulting in low resource utilization and execution efficiency. In order to solve this problem, in this study, we comprehensively analyzed the demand for computing resources and the mutual adaptation relationship between the end-to-end processing flow in the model application and designed a general algorithm based on the pipeline idea to Realize the overlapping of CPU processing and accelerator operation process. Through this scheme, the serial execution flow of the end-to-end processing can be performed in parallel, resulting in a significant reduction in accelerator latency. We extensively conducted experiments on two specific tasks, and the outcomes demonstrated that our proposed method considerably enhances the accelerator's utilization rate and program execution efficiency. Specifically, the utilization rate of the accelerator surged from 26% to over 97%, while the program's execution efficiency witnessed a remarkable improvement of 3.41 to 5.54 times.
RHC Method Based 2D-equal-step Path Generation for UAV Swarm Online Cooperative Path Planning in Dynamic Mission Environment

This paper first mathematically models the UAV swarm online cooperative path planning problem based on the prerequisite assumptions of transparent posture and dynamic mission environment. Then the receding horizon control (RHC) and 2D-equal-step path generation method are briefly introduced and combined with the improved firefly optimization algorithm to solve the UAV swarm online cooperative path planning problem modeled in the previous. Simulations show that the improved firefly algorithm combining the RHC and 2D-equal-step path generation methods can be used to optimally solve the UAV swarm online cooperative path planning problem for moving mission targets in dynamic environments, and the improved firefly algorithm is more powerful and more efficient than the original algorithm in this process of application.
Word-Constrained Response Generation for International Chinese Language Education based on Decoder Backward Attention

Dialogue systems are a valuable technology in the field of natural language processing to improve work, learning, and daily life. Currently, dialogue systems are employed as an educational technology for mentoring, evaluation, and personalized learning. To make dialogue teaching achieve the purpose of training vocabulary at the primary level of international Chinese learning education, we first collect the entire dialogue corpus from textbooks to create the dataset, and then we propose a dialogue response generation model, Seq2BF-Attention, containing a specific word based on the sequence to backward and forward sequences framework by adding an attention to enhance the modeling of dialogue posts. We also provide two decoder connection strategies, backward hidden connection and backward attention, to handle the problems of not sharing parameters and incoherent generation separately. It has been experimentally proven that our suggested models perform well in both the ICLE and Weibo datasets across all metrics.
Multi-Classification Data Stream Algorithm Based on One-Vs-Rest Strategy

Abstract: In the data stream learning scenario, the whole picture of the data can't be observed, and the data may change dynamically, thus increasing the complexity and imbalance of the data. Aiming at the characteristics of data class changes (appearance, disappearance, and reappearance) of multi-class data stream, a Matthews Adaptive XGBoost algorithm based on One-Vs-Rest strategy is proposed. For the three cases of class change, the One-Vs-Rest strategy is used to build the model, and a binary classification model is created for each class. Aiming at the problem that the model gradually forgets the knowledge of the class after the class disappears, a management mechanism based on class frequency is proposed. In view of the problem that the restarted model affects the overall performance of the ensemble after the class is reproduced, the sliding window is used to initialize it. Aiming at the problem of ensemble classifiers, a mechanism to adapt the number of base classifiers is proposed. Experiments on real and synthetic datasets show that the improved algorithm improves Kappa and PMAUC indicators by 1.03% and 0.82%, respectively.
Adaptive weighted ensemble classifier for improving breast tumors classification based on ultrasound RF data

In the classification of benign and malignant breast tumors, in addition to the extraction of features, the selection of classifier is also an important factor affecting the accuracy of classification. At present, the ensemble classifier has limitations of local optimization and weak scalability in the integration process, which affect the accuracy and stability of classification. In this study, an ensemble classifier based on adaptive weighted ensemble is proposed to classify benign and malignant breast tumors. First, the original ultrasonic RF signal is preprocessed by down-sampling, dilation, and adaptive decomposition. Then, based on the intrinsic modal function obtained by decomposition, the ring region of interest (ROI) containing the tissue surrounding the tumor is determined. Next, texture features are extracted based on ring ROIs. Weighted k-nearest neighbors (KNN), bagged trees, and Gaussian Naive Bayes (NB) are integrated by bagging method, and each classifier is adaptively weighted based on genetic algorithms during the integration process. Finally, benign and malignant breast tumors are classified by an ensemble classifier based on adaptive weights. Experiments based on the Open Access Series of Breast Ultrasound Data database show that the classification accuracy of the proposed adaptive weight-based ensemble classifier is 90%, which is improved by 10.77%, 8.43%, 1.12% and 5.88% compared with the weighted KNN, bagged trees and Gaussian NB alone, and voting-based ensemble classifier. The conclusion is that the proposed ensemble classifier based on adaptive weighting can effectively improve the classification accuracy of breast tumors, which is helpful for the diagnosis of clinical breast tumors.
An Evidential Classifier with Multiple Pre-trained Language Models for Nested Named Entity Recognition

Nested named entity recognition (NER) is an important and challenging task in information extraction. One effective approach is to detect regions in sentences that are later classified by neural networks. Since pre-trained language models (PLMs) were proposed, nested NER models have benefited a lot from them. However, it is common that only one PLM is utilized for a given model, and the performance varies with different PLMs. We note that there exist some conflicting predictions which lead to the final variation. Thus, there is still room for investigation as to whether a model could achieve even better performance by conducting a comprehensive analysis of results from various PLMs. In this paper, we propose an evidential classifier with multiple PLMs for nested NER. First, the well-known deep exhaustive model is trained separately with different PLMs, whose predictions are then treated as pieces of evidence that can be represented in the framework of Dempster-Shafer theory. Finally, the pooled evidence is obtained using a combination rule, based on which the inference is performed. Experiments are conducted on the GENIA dataset, and detailed analysis demonstrates the merits of our model.