Reviewerly/exHarmony huggingface dataset:
Research on Intelligent Scheduling Algorithm Based on Cloud Computing

With the rapid development of the information age, computer technology and network technology are more and more mature, and the application of Internet technology is more and more extensive. On the basis of the gradual development and maturity of distributed computing, parallel computing and grid computing, cloud computing based on big data came into being. The core idea of cloud computing is to connect a large number of computing resources, storage resources and service resources through the network to form a resource pool, and then conduct unified scheduling and management of resources according to user needs. This paper mainly studies the resource scheduling method based on neural network and genetic algorithm to dynamically provide data centric computing service system for users, and maximize the reliability, customization and security of the services provided.
Named Entity Recognition Based on BERT-BiLSTM-SPAN in Low Resource Scenarios

The task of named entity recognition (NER) is crucial in the creation of knowledge graphs. With the advancement of deep learning, the pre-training model BERT has become the mainstream solution for NER. However, lack of corpus leads to poor performance of NER models using BERT alone. In low resource scenarios, previous work has focused on merging complex information to model or transfer learning from high resource corpora. Therefore, a simple but effective strategy for fully utilizing the corpus is required. In this paper, we focus on recognizing entities under resource constraints. We propose BERT-BiLSTM-SPAN for low resource scenarios, where BERT is used as an embedding layer, combined with BiLSTM and a decoding layer using a span pointer decoding algorithm. To make our model more robust, we employ adversarial training and data augmentation techniques. We conduct experiments on the marine news dataset. The BERT-BiLSTM-SPAN achieves an 80.11% F1-score. Furthermore, experimental results of data augmentation and adversarial training are both encouraging. Therefore, our proposed solutions show suitability in low resource scenarios.
An Energy and Memory Efficient Speaker Verification System Based on Binary Neural Networks

Speaker verification based on deep neural network is one of the most prospective biometric authentication methods. But massive computation and parameters make it difficult to deploy speaker verification systems on memory and power constrained embedded devices. Binary neural networks with weights and activations compressed to 1 bit can reduce computational and memory cost significantly. However, speaker verification system based on binary neural networks suffers performance degradation problem. In this paper, to mitigate this problem and facilitate information flow across layers, we optimize binary neural network structure by adding a shortcut to connect layers. We propose an energy and memory efficient speaker system based on the optimized binary networks. Experimental results on text-dependent and text-independent datasets show that performance of proposed speaker verification system outperforms conventional binary neural networks significantly. Compared with full precision networks, the proposed system can largely maintain the performance while reducing memory and computational costs.
3D Face Reconstruction Based on Fine-Grained Optimization

The objective of this paper is to investigate fine-grained 3D face reconstruction. Recently, many methods based on 3DMM and mixed multiple low-level losses for unsupervised or weak supervised learning have achieved some results. Based on this, we propose a more flexible framework that can reserve original structure and directly learn detail information in 3D space, which associates 3DMM parameterized model with an end-to-end self-supervised system. To encourage high-quality reconstruction, residual learning is introduced. Evaluations on popular benchmarks show that our approach can attain comparable state-of-the-art performance. In addition, our framework is applied to a reconstruction system for interaction.
Enviroment Representations with Bisimulation Metrics for Hierarchical Reinforcement Learning

Hierarchical reinforcement learning has achieved good results in solving complex learning tasks. Hierarchical reinforcement learning mainly includes on-policy and off-policy methods. On-policy cannot be applied to actual scenarios due to low data utilization. Therefore, off-policy reinforcement learning methods have become the main development direction. However, in the off-policy method, because the data in the replay buffer comes from different policy, the upper-layer policy gives the same goal, and the lower-layer policy is constantly updated and shifts to a different state, so the upper-layer policy cannot be stably trained. Aiming at the above problems, we propose a hierarchical reinforcement learning algorithm for environment representation based on the mutual bisimulation Metrics. When training the upper policy, the lower policy is used as a part of the environment, which is called virtual representation environment. The output is used for feature extraction, and the feature value is used as the state value of the upper-level policy net. Using our proposed method to compare a variety of complex tasks with the current main hierarchical reinforcement learning has achieved the stability and training effect improvement.
Fully Homomorphic Encryption Accelerator Using DSP Embedded Multiplier

Encryption has an important role in cloud computing, as it secures the data into a ciphertext which is then protected with public key. But, how to encrypt massive cloud data? Do we have an encryption scheme that allow computation on the ciphertext, thus make the ciphertext dynamically encrypted? A recent breakthrough Fully Homomorphic Encryption (FHE) by Gentry is so much fulfilled the requirement of cloud encryption. However, it requires a huge key size, thus encryption process takes long and impractical for real-time deployment. Hence, this work proposed FHE accelerator on hardware platform to speed up the encryption process so that practical encryption time could be achieved. We proposed an optimization on Digital Signal Processing (DSP) utilization on modern Field Programmable Gate Array (FPGA), Virtex7. Our proposed Karatsuba multiplier shows 15.94 times better Throughput vs Area than Comba Multiplier. With the significance performance, the proposed Karatsuba multiplier is then employed in the multiplication building block for FHE encryption process. A nearly practical encryption time is achieved between 0.005 s to 39.08 s for Toys to Large key size. In addition, our proposed multiplier has significantly small DSP usage, from 48 to 192 slices for Toys to Large key size.
Personalized Microblog Recommendation System Integrates Following and Reposting Relationship

At present, the research on personalized recommendation methods mainly uses user tags and friends to explore user interests, and does not effectively utilize the user's active interaction information such as following relationship and reposting relationship. Additionally, data will be more sparse after increasing relationships, only using methods based solely on binary relations, such as collaborative filtering, cannot achieve the ideal recommendation effect. To deal with such problems, this paper proposes a microblog personalized recommendation system based on lightweight graph convolutional neural network that integrates attention relationship and forwarding relationship. First, the user's following relationship and reposting relationship are integrated into the user and microblog embedding to enhance the user representation vector and the microblog representation vector, and then the microblogs will be accurately recommended to the user through recommendation score ranking. The experimental results show that the performance of the model has been significantly improved after integrating following information of users and reposting relationship of microblogs, and the cold start problem can be alleviated at the same time.
Research on Ship Wake Detection Method Based on Composite Mode

Ship wake is an important military target, while the ocean background data in satellite images is huge, and ship wake targets are few. How to quickly and accurately find targets in large amounts of data is a valuable research topic. The ship wake target has a different detection method from the general target, as it has the characteristics of uniform background and prominent line characteristics. In this paper, we design a ship wake detection method which can quickly screen out easily detected samples by using traditional algorithms including Gaussian filtering, Canny edge detection and probabilistic Hough transform. We used typical representative YOLOv5 and Faster-R-CNN target detection frameworks to train single-stage and two-stage target detection models. In the process of integrating traditional algorithms into ship wake detection, we have built a ship wake target detection system that combines traditional algorithms with neural network methods. The experiment shows that the improved traditional wake detection algorithm can effectively improve the efficiency of ship wake detection system in target detection task.
Parallel Acceleration of Real-time Feature Extraction Based on SURF Algorithm

Visual feature extraction is the key to target registration calculation. Compared with SIFT, SURF feature extraction algorithm has less computational complexity and better applicability when image signal-to-noise ratio is low. In this paper, an FPGA-based SURF algorithm for real-time feature extraction and parallel acceleration is designed for large-field scene registration applications of space targets. Firstly, the application scenario of space target is analyzed, and the grayscale features of the data containing the target used for registration are counted. Then, the five main parts of SURF algorithm were analyzed, and the algorithm was simplified based on the gray feature of the image. Finally, according to the simplified results, the parallel pipeline architecture of each module is designed based on FPGA. SURF computing modules are also given in this paper the design of the architecture and data flow of information, in XC7K325 completed the whole design of experiment, the results show that the design for 1024 × 1024 pixel image, single frame image processing time need only 51 us, resource usage is not more than 18.6%, power consumption is not more than 7.5 W, The computational efficiency is 87% higher than the previous design. Low signal-to-noise ratio (SNR) was used to simulate space target scene data for simulation test calculation, and the SURF descriptor error was less than 3%, ensuring the system's calculation error while effectively utilizing resources.
Enhancing the Feature Learning with Phonetic Control Module for Speaker Verification

In this paper, we propose to enhance the feature learning of feature-level layers in a deep residual network based speaker verification system using phonetic information from auxiliary network. Specially, the proposed method adopts a multi-task learning strategy with the primary task classifying the speakers and the auxiliary task learning the phonemes. The features from the auxiliary task is used to guide the feature learning of the backbone through a phonetic control module. Experiments on common condition 5 (CC5) of the core-core and the 10s-10s tests of NIST SRE10 evaluation set show that the proposed algorithm achieves consistent improvements over the baseline.
The Latest Progress in Human Pose Estimation

Human pose estimation focuses on locating and finding the key characteristics of human body in images or videos. It is often used in many scenes as an auxiliary part. This paper firstly classifies and summarizes the latest research on human pose estimation, dividing them based on their achievements in 3D pose estimation, multi-person posture estimation and occlusion problem. Then, we summarize the up-to-date methods which can solve the existing problems. The details of different models and solutions used in different problems are also introduced. Finally, we point out the current challenges encountered in human pose estimation field.
Architecture Design of Flash Sale System Based on Apache Dubbo Technology

The flash buying system has a high instantaneous request volume, but a low effective request volume, which requires a high timeliness. It is very difficult to develop a highly concurrent and highly available flash sale system in a distributed scenario. It is a huge challenge for some small and medium-sized teams to rely on a highly concurrent communication framework. For large teams, it also requires a large number of R&D personnel to develop flash sale systems. Therefore, developing a distributed flash sale system requires a well integrated open source communication framework, which requires out of the box features and simple design patterns, as well as support from the open source community. Apache Dubbo encapsulates a series of functions of remote service invocation on the basis of Netty, and provides a stable and reliable communication framework between server and server. This paper analyzes and uses Apache Dubbo and Netty communication frameworks to design a distributed flash buying system. This distributed architecture has important guiding significance for the design of large-scale distributed flash buying systems.
Decentralized Attribute-Based Access Control with Attribute Revocation and Outsourced Decryption

Decentralized ciphertext-policy attribute-based encryption (CP-ABE) is considered a promising cryptographic primitive to enable fine-grained access control over encrypted data. The revocation is a necessary mechanism in real-world access control systems. However, existing revocation mechanisms in CP-ABE either are triggered periodically and cannot revoke users in a timely manner, or require a trusted third-party proxy to assist in revocation. In this work, we present a decentralized CP-ABE scheme that supports periodic attribute-level revocation as well as immediate attribute-level revocation, simultaneously. It means that once an attribute key of a user naturally expires or is identified as leaked, that attribute will be revoked and then become unavailable instantly, remaining the users' other attributes still active. Moreover, we provide optional outsourced decryption capabilities. Resource-constrained users can choose to outsource partial decryption to any third-party proxy without disclosing the underlying plaintext. The performance analysis demonstrates that our proposal is better in functionality compared with existing schemes. Our scheme is proven secure against chosen-plaintext attacks in the random oracle model.
A comparative analysis of ANN based time series models for predicting PV output

Due to uncertainty in PV power output, predictions are important information for promoting the application of PV. In this paper a novel time series artificial neural network (ANN) model is developed for prediction of PV energy output, and utilized to enhance the accuracy of three time series models: (1) Nonlinear auto regression with external input (NARX); (2) Nonlinear auto regression (NAR); (3) Nonlinear input-output (NIOP). Based on measured data from the PV plant, firstly, the performance of three artificial neural network training algorithms were estimated in terms of error statistics: Mean Square Error (MSE), Root Mean Square Error (RMSE) and normalized Root Mean Square Error (nRMSE). Secondly, the performance of three time series model were analyzed through error statistics with the training algorithm of highest accuracy. A comparative study shows that the NARX with Bayesian regularization training algorithm (BRTA) gave highest accuracy and minimum error (15.5%) as compared to other models.
Face Recognition Fairness Assessment based on Data Augmentation: An Empirical Study

Deep learning models are affected by the training data when classifying, leading to discrimination in prediction output or disparity in prediction quality. We need to test the model adequately using a large amount of data. However, data for certain combinations of attributes occur less frequently in reality and are more difficult to obtain. Data augmentation is one of the methods to alleviate this problem. In this paper, we conduct a preliminary study on whether changes in these features(hair, glasses, bangs, etc.) could affect classification accuracy. This study provides some conclusions, (1) there is a fairness problem in the depth model (2) the fairness of the model can be well tested by auamentation against Image attributes.
A Quantum Information and Cybernetics Based Algorithm for Creativity Applying in Cultural Heritage Products Design

Creative products based on cultural heritage are high value-added products relying on wisdom, skills and talents, creating and promoting cultural resources and cultural products. Cultural heritage creative products are the organic integration of content creativity and cultural heritage carrier. At the technical level, the design algorithm has not been widely applied, but it has been studied. The output design algorithm fails to take into account both intelligent design and creative design. Computer cannot make creativity even based on the most robust algorithm. As the computing logic is 0 or 1. Quantum information theory can solve the problem. In this research, we proposed the creativity generation scheme inspired by multi data and information entanglement. Application scene is the culture products design with traditional elements based on world culture heritage, the Forbidden City. In this research, it is proposed the creativity generation scheme inspired by multi data and information entanglement.
Software Defect Prediction via GIN with Hybrid Graphical Features

Software defect prediction (SDP) is one of the determining factors in software development cycle, enabling developers to estimate the quality of delivery and thus improve the user experience. Generally, efforts have opted for building the abstract syntax tree (AST). However, AST is limited in its capacity of providing adequate semantic and structural information. This paper proposes a framework, which trains hybrid features combined from AST and control flow graph (CFG) via Graph Isomorphism Network (GIN) (H-GIN): The framework parses the CFG of the source code and converts redundant blocks to a concise representation by extracting AST. In addition, the contextual relationships between each block in the CFG is carefully preserved, after which the graphical representation of the code is obtained and then fed into the GIN to conduct a defect prediction. The experiments were performed on a large python dataset called PyTraceBus with Precision, Recall, and F1-score as evaluation criteria. Our approach outperforms the previous state-of-the-art methods, which clearly demonstrates the effectiveness of the proposed method.
Image-to-Graph Transformation via Superpixel Clustering to Build Nodes in Deep Learning for Graph

In recent years, convolutional neural network (CNN) becomes the mainstream image processing techniques for numerous medical imaging tasks such as segmentation, classification and detection. Nonetheless, CNN is limited to processing of fixed size input and demonstrates low generalizability to unseen features. Graph deep learning adopts graph concept and properties to capture rich information from complex data structure. Graph can effectively analyze the pairwise relationship between the target entities. Implementation of graph deep learning in medical imaging requires the conversion of grid-like image structure into graph representation. To date, the conversion mechanism remains underexplored. In this work, image-to-graph conversion via clustering has been proposed. Locally group homogeneous pixels have been grouped into a superpixel, which can be identified as node. Simple linear iterative clustering (SLIC) emerged as the suitable clustering technique to build superpixels as nodes for subsequent graph deep learning computation. The method was validated on knee, call and membrane image datasets. SLIC has reported Rand score of 0.92±0.015 and Silhouette coefficient of 0.85±0.02 for cell dataset, 0.62±0.02 (Rand score) and 0.61±0.07 (Silhouette coefficient) for membrane dataset and 0.82±0.025 (Rand score) and 0.67±0.02 (Silhouette coefficient) for knee dataset. Future works will investigate the performance of superpixel with enforcing connectivity as the prerequisite to develop graph deep learning for medical image segmentation.
Quantum Random Access Codes with Mutually Unbiased Bases in Three-Dimensional Hilbert Space

Quantum random access codes (QRACs) are key tools for a variety of protocols in quantum information theory. This paper gives an upper bound on the guessing success probability in the classical case of random access codes using mutually unbiased bases as measurement bases in a 3-dimensional Hilbert space and gives an encoding strategy capable of exceeding the classical bound. This encoding strategy holds for both 3-1 and 4-1 QRACs. This result is useful in areas such as random number expansion.
Hyperledger Fabric-Based Copyright Management System for Clothing design drawings

The growth of the Internet has accelerated the distribution of digital content such as graphics and audio, and its copyright issues have gained attention. One particular industry worth noting, apparel design, has no explicit legal constraints, making piracy even easier. In this paper, we combine the decentralized and tamper-evident features of blockchain to design and implement a federated chain-based copyright management system for apparel design diagrams using the Hyperledger Fabric platform. In this paper, the copyright checking model uses perceptual hash algorithm and difference hash algorithm to calculate the graph similarity of garment effect and garment plan respectively, and calculate the mean value of similarity between them to determine whether they are plagiarized. The design diagrams are stored on IPFS, which makes up for the drawbacks of blockchain's difficulty in scaling and expensive storage space. Simulation experiments show that the blockchain system can maintain a high throughput and the originality checking model proposed in this paper can meet the practical requirements.
Machine Learning based Re-identification of Location Histories using Contact Information Collected from Social Networking Services

Large amounts of location history information are being collected and used for various services. Although the collected location histories are anonymized, are-identification attack can be used to de-anonymize the information by combining it with certain external information. Investigation of re-identification method and measurement of the risk of privacy leakage have thus become important. Two models have been developed that supplement the small-fine, large-coarse, and time-aware re-identification models of Ohka et al. and improve re-identification performance. The proposed small-contact and large-contact models fully utilize time information, which is essential for re-identification because it enables more accurate distinction between an original person and another person. They use the locations of contacts in the original person's location history. Experiments demonstrated that the re-identification rate with all five models is higher than that with the models of Ohka et al.; they are effective to re-identify an original person from among 100,000 people.