Reviewerly/exHarmony huggingface dataset:
Polarization‐Entangled Photon Pairs from Warm Atomic Ensemble with Magnetic Background Noise

Abstract Atomic ensembles are important quantum resources for the generation, manipulation, and quantum memory of entangled photons. In photonic quantum information based on atom–photon interactions, high‐quality entangled‐photon‐pair sources are essential for realizing quantum information networks consisting of channels to connect the nodes through atomic ensembles. Here, a proof‐of‐concept for controlling polarization‐entangled photon‐pair sources from atomic ensembles by an external magnetic field under a magnetic noise environment is demonstrated. In the unshielded magnetic field, the polarization entangled state of the photon pair could be optimized to the target state by adjusting the magnetic field in an atomic vapor cell. The polarization‐interference fringe, Bell's inequality value, quantum state tomography, and Hong–Ou–Mandel interference of the polarization entangled photon pairs from the cascade‐type 5S 1/2 –5P 3/2 –5D 5/2 transition of 87 Rb according to the direction of the external magnetic field. Accordingly, a magnetic field is found to be a promising means for controlling entangled two‐qubit states based on atom–photon.
Agriculture Food Supply Chain Management System based on BlockChain and IOT

In agriculture supply chain management block chain distributed ledger underpinning cryptocurrncies bit coin, represent, new innovative technological approach to realizing system. they provide fault-tolerance, immutability, transparency and full tractability of stored transaction record, as well as coherent Digital representation of physical assets and autonomous transaction execution. Trace-ability is critical in managing food quality and safety. Traditional Internet of Things (IoT) trace-ability systems offer viable solutions for food supply chain quality monitoring and trace-ability. However, majority of IOT solution rely on the centralized server-client paradigm, which makes is difficult for consumers to obtain all transaction information and track the origins of product. Block chain is an innovative technology that has potential to significance improve tracebility performance by providing security and complete transparency. However, the current literature does not fully explore benefit ,challenges and development method block chain based food traceability systems. As result ,the to identify block chain and IOT based solutions for food tracebility.
Credit Card Fraud Detection using Big Data Technologies

The use of credit cards is prevalent in modern day society. But it is obvious that the number of credit card fraud cases is constantly increasing in spite of the chip cards worldwide integration and existing protection systems. This is why the problem of fraud detection is very important now. Credit card fraud detection is the most frequently occurring problem in the present world. This is due to the rise in both online transactions and e-commerce platforms. Credit card fraud generally happens when the card was stolen for any of the unauthorized purposes or even when the fraudster uses the credit card information for his use. In the present world, we are facing a lot of credit card problems. To detect the fraudulent activities the credit card fraud detection system was introduced. This project aims to focus mainly on validating various Business Rules to Identify whether the transaction happened is Fraud/Genuine and report the same accordingly.
Heterogeneous Network Representation Learning Guided by Community Information

Network representation learning usually aims to learn low-dimensional vector representations for nodes in a network. However, most existing methods often ignore community information of networks. Community structure is an important topology feature in complex networks. Nodes belonging to a community are more densely connected and tend to share more common attributes. Preserving community structure of network during network representation learning has positive effects on learning results. This paper proposes a community-enhanced heterogeneous network representation learning algorithm. It introduces the community information of a heterogeneous network into its node representation learning, so that the learned results can maintain both the properties of the micro-structure and the community structure. The experiment results show that our algorithm can greatly improve the quality of heterogeneous network representation learning.
MOOC Resources Recommendation Based on Heterogeneous Information Network

Aiming at the problem that the existing MOOC recommendation mechanism cannot meet the dynamic and diversified learning needs of different individuals, a MOOC resource recommendation model based on heterogeneous information network is proposed. First by capturing MOOC platform of the heterogeneity between multiple entities in building its corresponding heterogeneous information network, and then through the node level attention and meta-path level fusion of attention, will learn to the user and the knowledge incorporated into the extended matrix factorization framework, to predict user preferences for knowledge, to carry on the personalized recommendation service. Experimental results show that this model has better recommendation performance than other commonly used models, and effectively solves the problem of personalized recommendation for learners.
Combining AST Segmentation and Deep Semantic Extraction for Function Level Vulnerability Detection

The explosive growth of software vulnerabilities poses a serious threat to computer system security and has become one of the urgent problems of the day. Yet, most existing vulnerability detection methods generally fail to capture the deep semantic features of code fragments, leading to the problem of high false negative rate easily. To this end, this paper proposes TrFVD (abstract syntax Tree based Function Vulnerability Detector), which mines deep semantics implied in source code fragments for accurate function level vulnerability detection. To ease the capture of fine-grained subtle semantic features, TrFVD converts the AST of a function into sequentially ordered sub-trees by splitting it in accordance with statements. The semantics of each sub-tree is then extracted with the Tree-LSTM, and a Text-RNN based model is utilized to summarize them up into a dense numerical vector to get the function represented. The experimental evaluations conducted on two C program vulnerability datasets show the effectiveness of TrFVD, which achieves 98.44% and 98.32% accuracy respectively. The averagely 12% more performance promotion against other vulnerability detection methods also indicates the superiority of TrFVD in capturing deeper subtle yet significant code semantics.
Multiple Layers Global Average Pooling Fusion

We propose for deep convolutional neural network (CNN) a simple but effective feature fusion technique called multiple layers global average pooling fusion (MLGAPF). It adds a branch at each CNN layer or module which uses global average pooling to extract global features, and these features are then fused for classification. Empirical experiments show that this technique can effectively improve the accuracy of ResNet, GoogleNet, SqueezeNet, MobileNetv2 and others. On average, MLGAPF brought additional performance enhancement of 2.62% and 2.49% on CIFAR100 and Tiny-ImageNet respectively.
Deep Embedded Clustering with Random Projection Penalty

Despite being studied for decades, current clustering approaches still face difficulties in handling high-dimensional datasets. For high-dimensional data, it is essential to get a good feature representation for the clustering algorithm to conduct on. Most of the clustering algorithms do not explicitly encourage the preservation of pairwise distance within the input data when learning feature representation. We proposed an improvement over the Deep Embedded Clustering (DEC) by including a penalty term in the loss function for the differences between the input data and the random projection of its corresponding feature embedding. The idea behind this penalty term is one of the properties of random projection, that is the pairwise distance is preserved between the low-dimensional manifold and the data space. In this way, the network encourages the learning towards preserving data similarities in the feature space. We named the proposed method as DEC-RPP. The experiments show significant improvements on clustering metrics over four datasets compared with the baseline DEC and another work IDEC that improves DEC by preserving local structure.
Multi-scale Prototypical Network for Few-shot Anomaly Detection

Few-shot anomaly detection needs to solve the problem of anomaly detection when the training samples are scarce. The previous anomaly detection methods showed incompatibility in the case of lack of samples, and the current few-shot anomaly detection methods were not satisfactory. So, we hope to solve this problem from the perspective of few-shot learning. We utilize a pre-trained model for feature extraction and construct multiple sub-prototype networks in multi-scale features to compute anomaly maps corresponding to each scale. The final anomaly map can be used for anomaly detection. Our method does not need to be trained for each category and can be plug-and-play when there are a small number of normal class samples as the support set. Experiments show that our method achieves excellent performance on MNIST, CIFAR10, and MVTecAD datasets.
Hierarchical Medical Services Data Sharing Scheme Based on Searchable Attribute Encryption

In the referral process of hierarchical medical services, it involves the problem of two-way medical data sharing services. In order to solve the problem of data security in this process, the article first uses an attribute proxy re-encryption scheme that supports keyword search to encrypt data, which enables both data sharing and data forwarding services, and also supports flexible access control, so that patients can flexibly control the target hospitals they want to refer to through attribute sets and initiate request services to the target hospitals, and the target hospitals that meet the patient's expectations can decrypt and view these requests and obtain the corresponding patient medical information at the time of treatment. In addition, using the decentralized feature of blockchain, we build a federation chain node in major hospitals and run smart contracts as a trusted authority center, which serves to store key information and perform cryptographic algorithms (e.g., public parameter generation, token generation, and other algorithms) and attribute verification, etc. The innovation of this paper is to create a safe and efficient data sharing model by taking hospital as node and combining searchable encryption and attribute encryption.
Multimodal Incremental Learning via Knowledge Distillation in License Classification Tasks

Most current license classification algorithms are based on handcrafted rules or single visual feature for image classification, which cannot make full use of the multi-modal attributes existed in the licenses, resulting in limited accuracy. Moreover, the update and abolishment of licenses have put forward requirements for the rapid response and learning ability of license classification models, so that the model can learn new knowledge incrementally, and finding a balance between stability and plasticity has always been very important. However, most of the existing incremental learning algorithms are designed for a single modality. In this paper, we propose a incremental multimodal classification algorithm to make full use of the multimodal properties of licenses, and integrate multimodality and incremental learning algorithm based on knowledge distillation. Compared to single visual feature solution, the proposed algorithm has better performance, which is 11% higher than that of fine-tuning, and about 7% higher than that of single-modal incremental learning.
Few-Shot Learning for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task. However, the lack of annotated sequence data in many domains hinders the effectiveness of monitoring methods. In order to address this issue, we explored the cross-domain ABSA tasks with only a few labeled support sentences. In our work, we exploited three benchmark datasets from different domains (Laptop, Restaurant, and Service), and used one of them as a source domain for training, used other domains as the target domain for testing. Our system uses a supervised sequence labeling model as a feature extractor, and construct a prototype representation of each tag, and then we calculate the distance between the tokens of the target domain and the prototype representation to determine the label type. We further use the Viterbi decoder to determine dependencies of the labels. We show that our method achieves state-of-the-art performance on the cross-domain ABSA task, the F1 scores of the latest model is 57.03 and 30.34 under the 5-shot and 1-shot settings, respectively, which are 9.88 and 1.43 higher than the strongest baseline under the same settings.
Unsupervised Concept Drift Detectors: A Survey

Concept drift mainly refers to the change of the current data distribution in the data streams due to the dynamic evolution of the external environment, which leads to the failure of machine learning or data mining models to show the desired effect. As a result, the forecast or decision model needs to be constantly updated. To find the appropriate time for model updating, a large number of studies have proposed methods for detecting concept drift, which can be divided into supervised detection methods and unsupervised detection methods. Because an unsupervised concept drift detector does not make strong assumptions about the availability of data annotations for real application scenarios, it has more robust availability and generality, and there is less summary work related to unsupervised detectors. Therefore, in this paper, we will summarize all the existing unsupervised concept drift detection methods and give a new classification basis to classify the unsupervised concept drift detection methods.
Multi-modal Scene Recognition Based on Global Self-attention Mechanism

With the rapid development of deep neural network and the emergence of multi-modal acquisition devices, multi-modal scene recognition based on deep neural network has been known as a research hotspot. In view of the characteristics of various objects and complex spatial layout in scene images, and the complementarity of multi-modal data, this paper proposes an end-to-end trainable network model based on global self-attention mechanism for multi-modal scene recognition. This model, which is named MSR-Trans, is mainly consisted of two transformer-based branches for extracting feature from RGB image and depth data, respectively. Then, a fusion layer is used to fuse these two features for final scene recognition. To further explore the relationship between multi-modal information, the lateral connections are added on some layers between the two branches. And, a dropout layer is embedded in transformer block for preventing the model from overfitting. Extensive experiments are conducted to test the performance of the proposed method on SUN RGB-D and NYUD2 datasets, and the recognition accuracies of multi-modal scene recognition can be achieved at 69.0% and 74.1%, respectively.
DeSG: Towards Generating Valid Solidity Smart Contracts with Deep Learning

Solidity being a young yet the most widely used programming language to develop smart contracts, which are programs that run on the blockchains, is frequently exposed of bugs and under continues improvement. To this end, this work presents DeSG to facilitate the fuzz testing of the fast-evolving Solidity compiler, by automatically generating massive solidity smart contracts of diversity in a deep learning based manner. An encoder-decoder model is designed to ensure the production of high-quality and valid smart contracts, by equipping with the powerful representation learning ability from the Transformer, as well as three carefully-designed generation strategies that well-match the features of the Solidity language. The experimental evaluations conducted show that, DeSG can effectively generate syntactically valid and highly compilable smart contracts. The impacts of different encoding neural networks and generation strategies to DeSG are also evaluated, with the best performance reaching 91.2% validity score.
Self-Attention SSD Network Detection Method of X-ray Security Images

For the manual omission of X-ray security images inspection, SSD network extracts image features with a large number of convolutional layers, and some object feature information is missed in the deep feature layer after multiple convolutions and pooling. The self-attention mechanism can obtain images global features, and reduce the missing of image feature information. This paper proposes two new target detection algorithm BoT-SSD and ResT-SSD based on self-attention neural network, they apply BoTNet and ResT as the backbone network to extract more important features. The multi-scale network structure of SSD is combined with FPN and CBAM, which can use underlying and high-level feature and strengthen target feature information. Moreover, the self-attention modules are used to enhance the global under-standing of the image by high-level features layers. The X-ray images detection results show that the targets with complex background can identified more accurately. Compared with the original SSD network, the proposed BoT-SSD and ResT-SSD networks improve MAP value by 5.63% and 7.52% respectively.
Rotated Ship Detection with Improved YOLOv5X

Ship detection in optical remote sensing images is a vital yet challenging task. Now, more attention has been focused on increasing detection accuracy, while the detection speed is ignored. However, detection speed is as important as detection precision for ship detection. In this paper, we propose a new model, named ImYOLOv5X, which is based on YOLOv5X combined with a Squeeze-and-Excitation Module for fast and accurate rotated ship detection. Firstly, we incorporate a Squeeze-and-Excitation (SE) module into backbone of YOLOv5X, which enables the model to focus on detection objects, thus improving detection accuracy. Then we design an easy-to-insert module, containing a Convolution Set and Squeeze-and-Excitation Module (CS-SE), which can extract features and weigh the channels of features for prediction. Finally, we introduce the Gaussian Wasserstein Distance (GWD) loss as the regression loss of the model. The GWD loss resolves the boundary discontinuity and inconsistency in training and final detection metric. Extensive experiments on the HRSC2016 dataset show that our model can achieve highest detection accuracy and still maintain fastest detection speed compared with some other models, which proves the effectiveness of our model.
A Synchronous Secondary Index Framework Based on Elasticsearch for HBase

In the era of big data, HBase has been widely used in big data applications due to its fast write throughput and fast rowkey lookup. However, HBase only optimizes the index for rowkeys, and the query performance of non-rowkey columns is low. To solve this problem, we propose a synchronous secondary index framework based on Elasticsearch (SSIFE). In the framework, Elasticsearch is used to build a secondary index of HBase’s non-rowkey columns, using the efficient and multi-condition retrieval function of Elasticsearch to achieve multi-conditional queries on the vast amount of data stored in HBase. Furthermore, an index maintenance strategy based on HBase replication is designed to reduce the cost of index data synchronization. Finally, a parallel query mechanism is designed to achieve higher query speed. Experimental results on the real-world power dataset show that the performance of SSIFE is better than other secondary index schemes and index maintenance strategies.
From Source Code to Model Service: A Framework’s Perspective

In the current IT world, machine learning has become a powerful driving force for the development of computer science. The entire life cycle of machine learning includes data processing, code development, model training, model release, and other processes. Although there are currently mature machine learning development frameworks such as TensorFlow and PyTorch, help algorithm engineers develop quickly. However, data import, code deployment, model release and other links in the machine learning process still need to be done manually. These engineering problems distract the energy of algorithm engineering and reduce the iterative efficiency of the model. At the same time, cloud-native has become the development direction of current software systems. If machine learning can be based on cloud-native technology, it will significantly liberate productivity. Therefore, there is an urgent need to develop a one-stop machine learning platform based on cloud-native technologies.
MOOC Courses Recommendation Algorithm Based on Attention Mechanism Enhanced Graph Convolution Network

With the widespread popularity of massive online courses, facing the problem of information overload and users’ difficulty in choosing suitable courses for themselves. Although the recommendation algorithm based on graph convolution network can extract the high-order correlation between users and items, but it ignores the influence of different neighbor nodes on the target node and cannot assign different weights to neighbor nodes. We propose a MOOC Courses Recommendation Algorithm Based on Attention Mechanism Enhanced Graph Convolution Network (AEGCN), apply it to MOOC courses recommendations areas, high-order association between nodes is extracted by graph convolution network; according to the importance of different neighbor nodes, the attention mechanism is used to assign different neighbor weights to the neighbor nodes of each node, so that the weights of nodes depend on the feature expression between nodes, and finally fuse the embedding representation of each layer to predict the user’s preference. The recommendation results on two real datasets show that the performance indicators of this algorithm are better than other recommendation algorithms. Compared with the optimal recommendation algorithm, its Recall indicators are increased by 1.09% and 0.96% respectively, and it shows that the algorithm has better recommendation performance.
Design and Implementation of Vehicle Density Detection Method Based on Deep Learning

With the development of society, more and more people rely on cars to travel and cause traffic congestion. Vehicle density detection can provide intelligent decision-making assistance for traffic video surveillance system and improve traffic efficiency. The camera view can cause the scale difference between vehicles in the same scene. To solve the multi-scale problem, a vehicle density detection method based on deep learning semantic segmentation is proposed. VGG-16 is used to extract vehicle features at the front end of the network, and the output feature image is 1/8 of the original image, which can improve the accuracy of the prediction density map. A dilated convolution module is designed at the back end to capture multi-scale features of the vehicle, enabling the network to capture more scale details and edge information. Finally, the network cascades the output with $$1 \times 1$$ convolution to get the prediction density map. And in the output characteristics of the last layer of the network, the quantity is predicted by adding the fully connected layer, and then the final predicted quantity is obtained by summing up the counting results of the network output density map. The performance of the network is tested on the TRANCOS dataset, and the test results show that the MAE of our network is 34.1% higher than that of the Hydra-3s network.