Reviewerly/exHarmony huggingface dataset:
An Attribute Word Extraction Model Incorporating RoBERTa and CRF

The accuracy of attribute word extraction directly affects the effects of aspect-based sentiment analysis. Traditional attribute word extraction methods disregard the impact of polysemy on extraction results. This paper proposes an attribute word extraction model based on RoBERTa-CRF. First, the RoBERTa model is used to encode each word of Chinese comment text. Then, in the Conditional Random Field layer, the relations between attribute words are learned using the transfer score matrix. The most logical prediction results in the paths are calculated, reducing the number of invalid labels and improving prediction accuracy. The test results on the SemEval-2016 Task dataset reveal that the RoBERTa-CRF model outperforms other comparison models by 2.2 % in terms of optimal results.
Construction and Application of Cigarette Supply and Marketing Relationship Network Based on Knowledge Graph

Knowledge graphs describe knowledge in the form of triples, and a large number of entities can be closely related to each other through several semantic relationships, thus forming a directed graph containing multiple relationships. Based on the tobacco knowledge graph, this paper studies the prediction method to improve and supplement the tobacco monopoly knowledge graph. This research closely combines the needs of the tobacco monopoly business to build a vertical knowledge graph of the tobacco monopoly industry. At the same time, it is the first time to conduct inference algorithm research based on the knowledge graph of the tobacco monopoly industry. Information retrieval and recommendation systems. It is a major innovation in the informatization of the tobacco monopoly industry, and it is also an expansion of the application field of knowledge graph.
A Multi-factor Activity Arrangement Method Based on EBSN for Different Users

Event based social networks is a new type of complex heterogeneous social networks. The existing activity recommendation algorithm in EBSN cannot be applied to activity scheduling because it does not consider the conflict between activities. This paper proposes a multi-factor activity scheduling method based on EBSN for different users. This paper presents different methods for new and old users in EBSN. For new users, an improved UCB activity scheduling algorithm with user feedback is proposed. It arranges activities with high interest value for them and receives feedback on whether users accept the activity. For historical users, this paper proposes a historical user event arrangement based on a variety of factors. By calculating potential friendship, activity-based collaborative filtering, E-UCB algorithm calculates the value of interest, the total value of interest is calculated by integrating these three factors. It is arranged to users in order from large to small and receives user feedback. A large number of experiments on real and simulated datasets have verified that the proposed method can effectively implement online activity scheduling on EBSN.
A Privacy-Preserving Blockchain-based Energy Supply Chain System Supporting Supervision

With the continuous development of science and technology, more and more energy has poured into human life and production activities. The energy supply chain is responsible for managing the entire life cycle of energy, but there are still some problems in the current energy supply chain. On the one hand, energy data lacks an effective sharing mechanism, and on the other hand, the privacy of user data cannot be guaranteed. In addition, different entities in the current energy supply chain system manage their data, and there will be data barriers, so it is difficult to effectively supervise and track energy data. Based on the consortium blockchain, this paper proposes an energy management system to provide a credible data collaboration environment for each entity in the energy supply chain. Data encryption ensures the privacy of data, better realizes the sharing of energy data, and breaks the data barriers between participants. The system uses smart contracts to set a unified data operation specification, and at the same time designs an energy management mechanism that is friendly to regulators, so as to better realize the supervision of the whole process of energy supply. Experiments show that the proposed system is practical.
BIM Credibility: A Framework of Blockchain-enabled BIM Data

The construction of buildings needs to rely on Building Information Modeling (BIM) model data. If we want to finally form a legally effective BIM database, we must know the source of the BIM data to ensure the security and traceability of the BIM data. Blockchain technology, assuring the immutability and provenance of data, can play an important role in the credibility of BIM data. The application of blockchain to BIM can solve these problems currently existing in BIM technology. This article establishes and proposes a framework of Blockchain-enabled BIM data. By the way of a software plugin or a new App, we can record the changes of the BIM model on the Blockchain. Furthermore, the designers can select some components and properties as core data and put these dadas on Blockchain by the BIM data services (BDS). This way can improve the efficiency of BIM data comparison. Thus, all stakeholders of the project can trace the BIM models to verify the credibility and ensure the safety of data.
A New Hybrid Steganography Scheme Employing A Time-Varying Delayed Chaotic Neural Network

A secure hybrid audio steganography algorithm using Discrete Wavelet Transform (DWT) and Hopfield Chaotic Neural Network is presented in this paper. An uncompressed audio file is used as a cover medium and a greyscale image is used as secret data. The pixels of the secret image are reordered using cyclic shifting to increase the system security, then the permutated pixels are encoded by applying Hamming code (7,4) before embedding them in the DWT coefficients of the stereo audio signal. The chaotic neural network is applied here to generate a random sequence to choose the embedding locations of hidden image pixels. Regarding the system’s quality, the Peak Signal to Noise Ratio of stego-audio files is above 60 dB, which is close to the original audio quality. Furthermore, the algorithm has an improved embedding payload than previously proposed algorithms and high-security performance, as proved by the results obtained.
Multi-task Assignment Research for Heterogeneous UAVs based on Improved Simulated Annealing Particle Swarm Optimization Algorithm

Task assignment problem of unmanned aerial vehicles (UAVs) based on the artificial intelligent algorithms has been widely explored in recent years. UAVs’ heterogeneity including velocity, range, number of weapons is studied in this paper. Mathematical model is constructed based on the total distance objective function and complex constrains of UAVs, such as the multiple tasks, specified task sequence and time window. To solve the problem, the improved simulated annealing particle swarm optimization (SAPSO) algorithm is applied. In addition, the relationship between the particle swarm and the feasible task allocation scheme is established. The reasonable and efficient task assignment schemes are obtained based on the coding and repair- based methods. Large numbers of experimental simulations show that the improved SAPSO algorithm is more reliable and provides a reference for multi-task assignment problem of heterogenous multi-UAVs.
Big data encrypting transmission framework for a multi-AGV system

This project has developed a data transmission framework including Automated Guided Vehicles (AGVs), a cloud platform, edge computers and a master server. This framework is designed to provide a solution for protecting user privacy in the data transmission process of AGV in practical applications. The system divides the information collected by AGVs into messages and big data, where messages are transmitted through Message Queuing Telemetry Transport (MQTT) protocol and big data is transmitted through socket. Big data is collected by different sensors equipped in AGVs. Messages are processed by the AGV’s main chip. Meanwhile, public key encryption based on RSA algorithm is performed in the transmission process to ensure the privacy and safety of information and users.
A Relation Enhanced Model For Abstractive Dialogue Summarization

Traditional document summarization models perform less satisfactorily on dialogues due to the complex personal pronouns referential relationships and insufficient modeling of conversation. To address this problem, we propose a novel end-to-end Transformer-based model for abstractive dialogue summarization with Relation Enhanced method based on BART named RE-BART. Our model leverages local relation and global relation in a conversation to model dialogue and to generate better summaries. In detail, we consider that the verb and related arguments in a single utterance contribute to the local event for encoding the dialogue. And coreference information in a whole conversation represents the global relation which helps to trace the topic and information flow of the speakers. Then we design a dialogue relation enhanced model for modeling both information. Experiments on the SAMsum dataset show that our model outperforms various dialogue summarization approaches and achieves new state-of- the-art ROUGE results.
A Fusion Network for Non-Uniform Deblurring<sup>*</sup>

In the field of computer vision, non-uniform image deblurring is a crucial and difficult task. By learning features from receptive fields, existing image deblurring algorithms have made advanced progress. However, non-local feature representations, which depict the global data distribution of blurry images are not taken into account. In this paper, we investigate non-uniform task by integrating local and non-local features. Specifically, we develop a DRDDBU (Dense-in-Residual Dense Dilation Blocks Unit) and a SAB (Scale Attention Block) to implement local and non-local, respectively. DRDDBU has the virtue of dense connections embodied in locally dense blocks and globally dense connections, which reuses and enhances all intermediate features. SAB is developed to preserve significant and suppress irrelevant responses for generating latent images. In addition, multiple loss functions are proposed to enhance network training and encourage convergence. Subjective and objective comparison experiments on various datasets are done to illustrate the efficiency of the suggested strategy. On synthetic datasets and real images, our non-uniform deblurring method outperforms state-of-the-art (SOTA) methods.
A CVD Critical Level-aware Scheduling Model Based on Reinforcement Learning for ECG Service Request

In the cardiovascular disease (CVD) diagnosis scenario, the number of electrocardiogram (ECG) service request data is large and the severity of CVD is different. Efficient task scheduling is the key to large cluster computer-aided CVD diagnosis. Therefore, in task scheduling, the workload changes and the critical condition of CVD must be paid attention to. We propose a CVD critical level-aware scheduling model based on reinforcement learning (CLS-RL) to optimize ECG service request scheduling. To solve the problem that there is no publicly available ECG service request data, this paper proposes a method of composing it. Then, we utilize RL with Actor-Critic to improve the efficiency of scheduling. Finally, we define the new objective functions for ECG service request scheduling. The experimental results show that the proposed CLS-RL is the best in comprehensive performance.
A Quadtree Based Piecewise Poisson Seamless Composition Algorithm for Image Stitching

A piecewise composition algorithm is proposed for large-scale stitching image, which solves several low-dimensional Poisson equations based on gradient domain to achieve seamless panorama. This piecewise processing strategy, on the one hand, greatly reduces the requirements for computer memory and computing performance, which provides a new way for consumer computers to process large-scale image composition, and also supports parallel processing to improve the efficiency of image seamless composition. The algorithm guarantees piecewise continuous of the whole stitched image, effectively eliminates seams, and is suitable for ultra-wide image stitching such as ultra-high resolution or aerial photography.
A 3-dimensional Histogram Publishing Algorithm for Spatiotemporal Data Based on Differential Privacy

Geospatial data collected from location-based services (LBS) systems provide a wealth of location information when analyzing spatial information. However, publishing location data directly can lead to the disclosure of sensitive personal information. Aiming at the uneven distribution of location data and the correlation between the plane region and the time dimension, as well as the problem of lower availability of the published results of the existing differential privacy location data histogram publishing algorithms, this paper proposes a 3-dimensional Histogram Publishing Algorithm based on differential privacy (SC_DPHP). First, considering the influence of time while dividing the plane region, a pretreatment method combining the time range bisection iterative cutting and plane region meshing is proposed to obtain a 3-dimensional histogram of the spatiotemporal data; secondly, to improve the availability of the published results, we combine the spectral clustering method to cluster and group the count values of the spatial decomposition cell, and then each group is added to the noise by the Laplacian mechanism. Theoretical analysis and experimental results verify that the method improves the accuracy of published data range queries while protecting privacy.
Integration learning oriented click anti-fraud prediction

Predicting whether an ad click is a normal click or a cheating click is of great importance to digital marketing. In order to solve the problem of information loss in the numerical process of small-scale data in feature engineering, this paper proposes a click anti-fraud prediction method based on SVD (Singular Value Decomposition) and integrated learning in the process of click anti-fraud data exploration. The method uses SVD to mine information on category features in the dataset while increasing the category data dimensionality; Xgboost is used as a feature converter for integrated learning, and the information of each leaf node in the tree is fed into a logistic regression model for click anti-fraud prediction as a feature vector. The experimental results show that both SVD and integrated learning methods can improve the accuracy of click-to-fraud prediction with an accuracy of 89.19%, and to a certain extent avoid the loss of data information in feature engineering.
MDAEN: Multi-Dimensional Attention-based Ensemble Network in Deep Reinforcement Learning Framework for Portfolio Management

Reinforcement Learning algorithms are widely applied in many diverse fields, including portfolio management. Ensemble of Identical Independent Evaluators (EIIE) framework proposed by Jiang et al. achieved portfolio management based on their deep reinforcement learning algorithm. In the implementation of EIIE framework, a neural network such as the Convolutional Neural Network is applied as the policy network, to uncover more patterns in the data. However, this network typology is inefficient due to its simple structure. To overcome the shortcoming of EIIE framework, this paper introduces a novel algorithm, the Multi-Dimensional Attention-based Ensemble Network (MDAEN) strategy, which consists of a features-attention module and an assets-attention module. The MDAEN applies different types of attention mechanisms to extract information from the assets. Having adopted the reinforcement learning framework from Jiang et al., the agent is able to process transactions through MDAEN in a market. In our portfolio establishment, Bitcoin together with eleven other cryptocurrencies is selected to validate the performance of MDAEN against seven traditional portfolio strategies and EIIE. The experimental result demonstrates the efficacy of our strategy outperforming all other strategies by at least 35% in profitability and at least 30% in Sharpe Ratio.
Analysis of Zero-Key Authentication and Zero-Knowledge Proof

Recently passwordless authentication such as zero-key authentication or zero-knowledge access control is becoming popular among businesses prioritizing their users' and employees' security and digital experience. A challenge-response mechanism and public key infrastructure (PKI) cryptography are employed to perform the zero-key authentication or zero- knowledge access control that authorizes user access to an online service without a password or any shared secret required. Using a large quantum computer, a quantum algorithm could break the hard mathematical problems underlying PKI. The National Institute of Standards and Technology (NIST) has launched a program and competition to standardize one or more post-quantum cryptographic (PQC) algorithms to fight against quantum attacks. In this paper, we have conducted the first-ever mathematical analysis of lattice-based and polynomial-based PQC by introducing the relationship between automorphism and homomorphism. This analysis can help enterprises and organizations leverage NIST-selected PQC algorithms to safeguard their online services from quantum attacks. We performed the simulation to illustrate brute force broken probability for polynomial-based or multivariate-based PQC to validate our mathematical analysis of PQC.
Dangerous Action Recognition for Ship Sailing to Limited Resource Environment

Action recognition is a comprehensive application prospect in the surveillance video source but most of the well-known models have significant computational cost feature. Take a bottom-up algorithm named OpenPose as an example, it consumes a lot of time to solve a task. If dangerous action monitoring is deployed on overseas ships, which can not afford such a large amount of computation and storage cost. Meanwhile, a oversea ship is mostly offline during ocean voyages and cannot upload and download data. This paper addresses this issue by improving on the PyTorch-OpenPose model, adjusting the resolution of the input images and removing some of the unneeded skeletal key-point information from the model. Experimental results demonstrate that the execution time is reduced by about 71%, while the memory footprint is reduced by about 62%.
Research on Multiple Targets Pedestrian Reidentification with Night Scene Image Enhancement

Pedestrian reidentification is a popular research topic in the field of computer vision in recent years, and is a technique that uses computer vision techniques to determine whether a specific pedestrian is present in an image or video. After research and experiment, we found that YOLOv3-based pedestrian reidentification in practice has the problem of low accuracy rate of recognizing pedestrian pictures taken at night and cannot recognize multiple pedestrians at one time. In this paper, we improve the above problems by introducing a picture enhancement module to improve the brightness and defogging of night pictures before recognition, and improve the practice of averaging the distance values of multiple results for the same pedestrian to enable multiple targets pedestrian recognition. The experimental results demonstrate that the average accuracy rate of recognizing pedestrian pictures taken at night has improved from 6.85% to 80%, while the average accuracy rate of multiple targets pedestrian recognition has reached 85.9%, which is competent for multiple targets pedestrian recognition tasks at night.
Kick-off Your Reliability Program with a Requirements Failure Modes and Effects Analysis

SUMMARYThoroughly checking requirements in the very early stages of development is extremely important to reducing the risk of out-of-phase rework later in the program’s lifecycle. Therefore, one of a development program’s first goals is to converge on achievable system design requirements that satisfy the customer’s functional and performance needs. Part of this system design requirements development activity is quantifying risks and risk mitigations associated with the requirements, where risk mitigation may include creating new requirements or changing existing requirements. Conducting a Requirements Failure Modes and Effects Analysis (FMEA) during this early stage of development is an excellent way of reviewing and cutting potential gaps or ambiguous requirements that lead to out-of-program-phase rework. This paper explains what a Requirements FMEA is and its key steps using practical examples. Furthermore, this paper will show the reader how to perform a Requirements FMEA using Model Based Systems Engineering (MBSE) methods for even greater benefits.
LwF4IEE: An Incremental Learning Method for Interactive Event Extraction

Albeit great progress has been witnessed in event extraction, the accuracies achieved up to now by various automatic models still can not meet the performance requirements of some special applications such as disaster monitoring and rescue. It motivates us to introduce a new human-in-loop extraction mode called interactive event extraction (IEE), which works iteratively. Each iteration consists of three main steps: "model recommending candidate results → manual selecting and correcting → model re-training and updating". For candidate recommendation, we build an MRC (Machine Reading Comprehension)-based model that can output several most likely candidate elements, i.e., candidate triggers and arguments, by confidence evaluation. For model re-training and updating, we proposed an incremental learning method named LwF4IEE (Learning without Forgetting for IEE), which employs manual selected and corrected results as hard label and prediction of original model as soft label to avoid catastrophic forgetting. We conduct extensive experiments on datasets constructed from real-world Chinese texts. The results show that when setting the number of candidates to be 5, recalls of triggers and arguments reach 93.80% and 90.58% respectively, which is 11.51% and 11.33% higher compared with the basic MRC-based automatic extraction model. Moreover, LwF4IEE increases the recall of triggers by 2.71% on specific event types and only decreases by 0.24% on other types, achieving the purpose of learning without forgetting.
Quantum-Enhanced Reliability Assessment of Power Networks in Response to Wildfire Events

As the risk associated with wildfires in the western United States increases every year, it becomes fundamental to research alternatives to both improve the resilience and reliability of engineering systems against those types of events, as well as mitigate the effects that wildfire emergencies may have on human population and critical infrastructure. An often- overlooked cause of wildfire emergencies are failures in power line systems and electricity distribution networks. Given the immense number of factors that can affect the probability of a power line ignition occurring, the analysis quickly becomes intractable for traditional probabilistic inference methodologies. In this paper, a quantum-enhanced probabilistic sampling and inference application is proposed for improving the acceptance ratio of queries of the form P(Q|E = e) when compared to traditional Monte-Carlo approaches. For this purpose, the system of interest is first modeled as a Bayesian network. Then, quantum computing is used to translate that Bayesian network into an equivalent quantum circuit using a set of basic quantum gates. Finally, the sampling process is enhanced by applying a quantum protocol called Amplitude Amplification to the resulting quantum circuit. Empirical results are presented using as a case study a non-trivial six-node Bayesian network.