Reviewerly/exHarmony huggingface dataset:
A New Approach for Text Style Transfer Based on Deletion and Generation

We propose using the Sequence Classification modeling, SHAP algorithm and masked-language modeling (MLM) for the task of text style transfer. To tackle cases when no parallel source-target pairs are available, we train Sequence Classification model based on Bert model with SST-2 task of GLUE for both source and target domain; and we use SHAP values, which are computed based on Sequence Classification model we gained, to detect and then delete words associated with original attributes. The deleted tokens are replaced by MLM trained with the target domain to retrieve new phrases associated with the target attributes. Based on this, we detect the part of speech (POS) of each word in the sentence in order to replace the suitable positions without much impact on the semantics. Additionally, we use GloVe to determine semantic similarity between the word generated by MLM and the original word so that we can trade off content versus attribute by using grid search to gain their weighting percentage. The experiments show that our methods improve style conversion rate by 9.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and get a semantic similarity compared to original contents 28.2% on average higher than best previous system.
Research on Intellectualization of Cost Management System of Power Transmission and Transformation Project

With the reform of China's power system, power has become a pillar industry of China's national economy, which needs to improve the competitiveness of the power industry. However, in the actual power engineering, the cost management of power transmission and transformation project (hereinafter referred to as PTATP) runs through the whole process of project construction, involving the processes of feasibility study, design, construction, change, completion and final accounts, which is a systematic problem. With the application of intelligent technology, collaborative office, BIM, smart site and big data have been applied to project management and cost management, which has been fully introduced into the digital and intelligent era. Therefore, the construction of PTATP cost management system has become the trend in the future. Through the cost management system of PTATP, we can collect, store, calculate and optimize the cost of PTATP life cycle, which can predict and warn the project cost. Therefore, this paper first analyses the application technology and construction principles of the cost management system for power transmission and transformation projects, as well as the main problems existing in the current cost management model. Then, some prediction methods, standardised data elements, validation data and integration data are analysed for the retrieved key cost data to realise the management of key cost data before the cost data collection template for transmission and substation projects, and effectively improve the key cost data matching ratio in the data collection template. Finally, this paper constructs and analyses the intelligent research results of the system, and introduces how to build a professional intelligent application platform for cost management in terms of platform architecture, technical route selection, application architecture design and data integration design. It is hoped to provide a more systematic and professional solution for the pricing management of power transmission and transformation projects.
Design of college student employment service platform based on cloud computing

Traditional recruitment methods are no longer suitable for the current situation. With the rapid development of online recruitment, the employment situation of college students is becoming more and more severe. Based on this situation, this paper studies data crawler, visualization and analysis technology, and designs a cloud-based employment service platform for college students to help them understand the recruitment information and current situation. This project is written by python Web application framework Django, the purpose is to research the establishment of a convenient college students in the shortest time to find a suitable job platform; The main content includes five functional modules: data crawler and processing, data visualization, cloud computing and job recommendation. The design structure of this platform makes the recruitment data more clear and intuitive for job seekers, universities and enterprises, and has guiding significance for the job hunting and employment of contemporary college students.
Heterogenous Graph Network for Explainable Medical Event Identification

The explainability is critical for claim-decision making in medical insurance scenario, which is a challenging task due to various disease. To remedy this dilemma, this paper sets forth a novel architecture of claim-decision making via exploring the historical medical clues and identifying explainable medical events. Specifically, we introduce a heterogeneous graph construction to nourish the information of medical events and conduct text classification. Then, a knowledge fusion mechanism consumes the dynamic relation between events to eliminate the error-prone impact of graph neural networks, and a new witness loss facilitates the consistency between the distributions over the values of graph edges. Our proposed method is evaluated on actual medical insurance claim cases. Improvements of 2.6% F1-Score for claim-decision making and 16.6% Jaccard Similarity Coefficient compared to other counterpart models indicate the superiority of our work.
Design of Experiments with Imputable Feature Data

Selection of experiments to estimate an underlying model is an innate task across various fields. Since experiments have costs associated with them, selecting statistically significant experiments becomes necessary. Classic linear experimental design deals with experiment selection so as to minimize (functions of) variance in estimation of regression parameter. Typically, standard algorithms for solving this problem assume that data associated with each experiment is fully known. This is not often true since missing data is a common problem. Hence experiment selection under such scenarios is a widespread but challenging task. Standard design of experiments is an NP hard problem, and the additional objective of imputing for missing data amplifies the computational complexity. In this paper, we propose a maximum-entropy-principle based framework that simultaneously addresses the problem of design of experiments as well as the imputation of missing data. Our algorithm exploits homotopy from a suitably chosen convex function to the non-convex cost function; hence avoiding poor local minima. Further, our proposed framework is flexible to incorporate application specific constraints. Simulations on various datasets show improvement in the cost value by over 60% in comparison to benchmark algorithms applied sequentially to the imputation and experiment selection problems.
DUKE: Distance Fusion and Knowledge Enhanced Framework for Chinese Spelling Check

Chinese Spelling Check (CSC) is a task that eliminates spelling errors in Chinese, which is important for downstream NLP tasks. Misuse of semantically, glyphically, or phonologically similar characters, as well as unseen fixed expressions, are typical causes of Chinese spelling errors. Previous methods neglect the fact that characters' relative positions in multimodal spaces are not well-aligned. To address these issues, we propose the DUKE CSC framework, which fuses multimodal features with relative distances between characters retained, and reduce errors from unseen fixed expressions with a knowledge enhancement mechanism. Specifically, to transfer multimodal candidate information into semantic space, we capture relative distances between character and vocabulary in multimodal space and then construct a semantic feature with a similar distance relationship. By reconstructing input sentences, our method is able to capture extra knowledge and has more flexibility and controllability during prediction. Experiments on SIGHAN benchmarks demonstrate our method improves detection F1 by 1.0%, 0.7%, 5.0%, correction F1 by 1.4%, 2.6%, 4.9% previous models.
A Study of Pediatric Epilepsy Question Answering System

Pediatric epilepsy has a serious impact on patients. It affects about 50 million people worldwide. Providing accurate and effective information is urgent for patients' family members. In this paper, we briefly discuss the construction of the Pediatric Epilepsy Knowledge Graph (PEKG), based on which, the Pediatric Epilepsy Question Answering system (PEQA) is studied. Question parsing is crucial to PEQA, which contains entity recognition and intent classification. For entity recognition, we integrate AC automata and T-RoBERTa-BiLSTM-CRF model to recognize ambiguous medical entities. For intent classification, we propose a hierarchical relation classification method under the guidance of entity type. The online test shows that the system answers users' questions efficiently.
Sentiment identification on Twitter using machine learning

The analysis of feelings is a task belonging to the field of Natural Language Processing, which has become more relevant in recent years due to the large amount of subjective information that is generated daily through social networks specifically Twitter, same information that is of great value to organizations. Because it allows them to have tools that support decision making and even perform digital marketing. In particular, the focus of this paper is to use some techniques within the area Natural Language Processing and Machine Learning that allow us to improve the performance of the accuracy that is had when classifying tweets.
Research on Operation and Maintenance Data Synchronization Method of Substation Monitoring System based on SCD Model

With the continuous development of substation automation technology, some typical problems have been exposed in the construction and operation of smart substation based on IEC 61850 protocol, such as complex data model mapping, lack of model control means and low efficiency of engineering debugging. In view of the above problems, this paper studies the data architecture and data source of substation monitoring system, and proposes a method of data synchronization based on model, which effectively reduces the difficulty of operation and maintenance personnel debugging, and greatly improves the operation and maintenance efficiency of intelligent substation monitoring system.
Event-Oriented Visual Question Answering: The E-VQA Dataset and Benchmark

Visual question answering (VQA) is a challenging task that reasons over questions on images with knowledge. A prerequisite for VQA is the availability of annotated datasets, while the available datasets have several limitations. 1) The diversity of questions and answers are limited to a few question categories and certain concepts (e.g., objects, relations, actions.) with somewhat mechanical answers. 2) The availability of background knowledge or context information has been disregarded with just images, questions and answers being provided. 3) The timeliness of knowledge has not been examined, though some works may introduce factual or commonsense knowledge bases, e.g., ConceptNet, DBPedia. In this paper, we provide an Event-oriented Visual Question Answering (E-VQA) dataset including free-form questions and answers for real-world event concepts, which provides context information of events as domain knowledge in addition to images. E-VQA consists of 2,690 social media images, 9,088 questions, 5,479 answers, and 1,157 news media articles for references being annotated to 182 real-world events, covering a wide range of topics, such as armed conflicts and attacks, disasters and accidents, law and crime. For comparisons, we investigate 10 state-of-the-art VQA methods as benchmarks.
Deep Learning-based Algorithm for Detecting Counterfeit Domain Names

To address the two major problems of machine learning: the high cost of manually extracting features and the requirement for reasonable and high relevance of feature set input, this paper will use the ability of deep learning to automatically characterize and learn data to build a lightweight counterfeit domain name deep learning grid-based detection model based only on domain name strings, and conduct comparison experiments with edit distance-based detection models and visual feature-based detection models by testing on public datasets to verify the effectiveness of this deep learning-based counterfeit domain name detection model for counterfeit domain name detection.
Research on Text Simplification Method Based on BERT

Text simplification is to transform complex text into simple and understandable text without changing sentence meaning. It can serve people with low language literacy, cognitive or language barriers, and can also be deployed to natural language processing systems for understanding many text contents. In order to solve the problem that the existing methods ignore the role of context in word simplification, a word simplification method based on Bert is studied. The iterative editing sentence simplification method of Bert is used to solve the problem that the existing methods rely on high-quality training corpus and the simplification is uncontrollable. Finally, a sentence simplification prototype system based on web is designed. The system includes three modules, system configuration module Sentence module and sentence simplification module directly simplify the output text and show the specific simplified content.
BP Neural Network-Based Security Management System Design for College Students

With the continuous development of network technology and the continuous expansion of network scale, the security of the network has suffered more threats, and the attacks faced are becoming more and more extensive. The frequent occurrence of network security incidents has caused huge losses, facing more and more severe situation, it is necessary to adopt various network security technologies to solve the problem. In network security, the most commonly used technology is firewall. The firewall has a certain blocking effect on attacks from outside the network, but it has a weak defense effect on the attacks in the internal network, and it is easy to be bypassed. Intrusion detection technology can detect both internal and external network attacks. Responses are generated before the intrusion behavior occurs, and alarm information is issued for timely and effective processing. In recent years, China's campus security incidents are still happening, seriously threatening the lives of students and disrupting the normal teaching order of schools. At present, there are still many loopholes in campus security operations. Campus security management system has become an important task in campus security construction. On this basis, relevant personnel are required to analyze the existing problems of campus safety and the needs of the safety management system, and find the main technology of a more advanced intelligent safety management system.
A Long-Term Recurrent Convolutional Network for SNR Estimation of Cone-Shaped Target

Signal-to-noise ratio (SNR) is an important prior information for many micro-motion echo processing approaches and techniques. Estimating SNR in advance can effectively enhance the performance of such technologies. This letter proposes a long-term recurrent convolutional network (LRCN)-based SNR estimation method for cone-shaped target. First, mathematical expression of the echo is derived by studying the cone-shaped target micro-motion model, and hence the characteristics of the echo and its short-time fourier transform are analyzed. Second, the motivation for the proposed are analyzed, and the echo is converted as an time-frequency graph with short-time Fourier transform (STFT), so that an estimation technique based on LRCN (a hybrid of convolutional neural network and recurrent neural networks) is designed to estimate the SNR. Experiments indicate that the proposed method outperforms other already existing methods with better accuracy and robustness.
Lightweight Privacy and Security Computing for Blockchained Federated Learning in IoT

The development of Internet of Things (IoT) makes human life more intelligent, and the interconnection of all things has become a reality. However, the surge in the number of devices and centralized management brings severe challenges to IoT, such as single point of failure, poor security, privacy leakage, and low reliability. Due to the decentralization, verifiability, and privacy protection of blockchain federated learning (BFL), some BFL schemes have been proposed to solve these problems, but bring new challenges, such as device privacy leakage and heavy security computing load. In this article, we propose a new decentralized, secure and verifiable consortium BFL privacy protection scheme, named LPBFL, which realizes lightweight computing while ensuring the privacy of the local model and data set of the device. To achieve lightweight privacy protection, LPBFL adopts the Paillier encryption and the newly designed lightweight digital signature and batch verification algorithm. Additionally, considering that devices upload invalid or even toxic local models intentionally or unintentionally, we design a device reputation selection mechanism to make BFL more efficient. Finally, the theoretical analysis proves the security of LPBFL and verifies the unforgeability of the proposed digital signature. Comprehensive comparisons and extensive experiments demonstrate that our LPBFL has significant advantages in multiple aspects.
Automatic Arabic Grading System for Short Answer Questions

The era of technology and digitalization has been advantageous to the educational sector. The examination system is one of the most important educational pillars that have been affected. As automatic exam grading is a revolution in the history of exam development, and therefore the automatic grading system has started to replace the traditional assessment system. The automatic grading system allows the examiners to automatically assign grades for students” answers compared to the model answers. And, generate results based on the examiners’ answers. In this paper, we especially address the short answer questions. Most research has been done on the English language. On the other side, few research works have been conducted on Arabic. Moreover, Arabic is considered one of the rare resource languages. This paper is aimed to build an Automatic Arabic Short Answer Grading (AASAG) model using semantic similarity approaches. It is used to measure the semantic similarity between the student and model answer. The proposed model is applied to one of the Arabic scarce publicly available datasets which is called (AR-ASAG). It contains 2133 pairs of models and student answers in several versions such as txt, xml, and db. The efficiency of the proposed model was evaluated through two conducted experiments using two weighting schemas local, and hybrid local and global weighting schema. The developed approach with hybrid local and global weight-based LSA achieved better results than using local weight-based LSA with (82.82%) as F1-score value, and 0.798 as an RMSE (Root-Mean-Square Error) value using hybrid local and global weight-based LSA.
Sponge-Based Parallel Authenticated Encryption With Variable Tag Length and Side-Channel Protection

Authenticated Encryption (AE) protects confidentiality and integrity at the same time. The sponge construction is based on an iterated permutation or transformation that can be used to implement hashing, and AE schemes, among others. Sponge-based AE schemes offer desirable characteristics like parallelizability and incrementality. In addition, they provide security features such as protection against Chosen Plaintext Attacks, Chosen-Ciphertext Attacks, and Side-Channel Attacks (SCAs). Traditionally AE schemes assume the tag length, also called the stretch, as a fixed parameter per key, and the security is proved according to that assumption. However, the variable tag length per key could happen due to misconfiguration or misuse. In that case, the security would be violated, so it is vital to accommodate variable tag length without sacrificing other desirable features. Reyhanitabar et al. proposed Key Equivalent Separation by Stretch feature and concretized it for protection against tag length misuse attacks in block cipher-based AE schemes. However, the problem remains unresolved for sponge-based constructions, where current sponge-based schemes are vulnerable to tag length variation under the same key attacks. This work aims to bridge this gap by proposing a parallel, sponge-based AE scheme with a variable tag length per key that protects against SCAs and suggesting a lower bound for the recommended tag length. Finally, the security of the proposed scheme is discussed, and its performance is analyzed after implementing the proposed AE scheme in the C programming language.
Generalized Weights of Convolutional Codes

In 1997 Rosenthal and York defined generalized Hamming weights for convolutional codes, by regarding a convolutional code as an infinite dimensional linear code endowed with the Hamming metric. In this paper, we propose a new definition of generalized weights of convolutional codes, that takes into account the underlying module structure of the code. We derive the basic properties of our generalized weights and discuss the relation with the previous definition. We establish upper bounds on the weight hierarchy of MDS and MDP codes and show that, depending on the code parameters, some or all of the generalized weights of MDS codes are determined by the length, rank, and internal degree of the code. We also prove an anticode bound for convolutional codes and define optimal anticodes as the codes which meet the anticode bound. Finally, we classify optimal anticodes and compute their weight hierarchy.
Simultaneous Perturbation Method for Multi-task Weight Optimization in One-Shot Meta-learning

Meta-learning methods aim to build learning algorithms capable of quickly adapting to new tasks in low-data regime. One of the most difficult benchmarks of such algorithms is a one-shot learning problem. In this setting many algorithms face uncertainties associated with limited amount of training samples, which may result in overfitting. This problem can be resolved by providing additional information to the model. One of the most efficient ways to do this is multi-task learning. In this paper we investigate the modification of a standard meta-learning pipeline. The proposed method simultaneously utilizes information from several meta-training tasks in a common loss function. The impact of these tasks in the loss function is controlled by a per task weight. Proper optimization of the weights can have big influence on training and the final quality of the model. We propose and investigate the use of methods from the family of Simultaneous Perturbation Stochastic Approximation (SPSA) for optimization of meta-train tasks weights. We also demonstrate superiority of stochastic approximation in comparison to gradient-based method. The proposed Multi-Task Modification can be applied to almost all meta-learning methods. We study applications of this modification on Model-Agnostic Meta-Learning and Prototypical Network algorithms on CIFAR-FS, FC100, miniImageNet and tieredImageNet one-shot learning benchmarks. During these experiments Multi-Task Modification has demonstrated improvement over original methods. SPSA-Tracking algorithm first adapted in this paper for multi-task weight optimization shows the largest accuracy boost that is competitive to the state-of-the-art meta-learning methods.