Reviewerly/exHarmony huggingface dataset:
EPICpy: A Python Interface for EPIC Computational Cognitive Modeling

Executive-Process Interactive Control (EPIC) is a computational cognitive architecture that specifies a theory of human performance and a facility to create behavioral simulations using that theory (Meyer & Kieras, 1997a, 1997b).EPIC compacts decades of psychological theory and findings into a rich set of processors, including those that model sensory, perceptual, motor, cognitive, and memory subsystems of the human mind.EPIC is particularly suited to stimulus-response type human performance tasks including those involving multitasking and task-switching.Although many research articles, technical reports, talks, and conference presentations have been published using EPIC, these have been produced by a relatively small number of researchers.One reason EPIC has not been used more widely is due to its complex design and basis in the C++ programming language.Another challenge is the complete lack of documentation on how to set up and use its development environment.A final limitation is that EPIC is only available for MacOS.None of these issues are a problem for an experienced C++ developer, but they can be significant hurdles for those with limited C++ expertise, a state that is exceedingly common in the cognitive science community most likely to be interested in using EPIC.
Audit Scheme of University Scientific Research Funds Based on Consortium Blockchain

In the current process of university scientific research funding audits, the auditors do not obtain audit evidence in a timely manner, resulting in a lag in audit time; at the same time, there is the possibility that the audit evidence may be tampered with, and the sensitive expenditure cannot be effectively protected. Based on this, this paper proposes an audit scheme for university scientific research funds based on a consortium chain. The scheme saves relevant audit evidence through blockchain and IPFS, which changes the storage method of traditional audit evidence and effectively ensures the integrity, auditability of audit evidence and verifiability, and facilitates real-time auditing. We use Pedersen Commitment and Zero-Knowledge Range Proof to ensure the auditability of sensitive data; use smart contracts to automate auditing to further ensure that auditors can find relevant audit issues in a timely manner; and separate different departments and construct an audit tree through multi-channel technology to ensure the integrity and reliability of data and improve audit efficiency and accuracy. Finally, functional comparison analysis and security analysis show that the scheme in this paper has certain feasibility and robustness.
DRIB: Interpreting DNN with Dynamic Reasoning and Information Bottleneck

The interpretability of deep neural networks has aroused widespread concern in the academic and industrial fields. This paper proposes a new method named the dynamic reasoning and information bottleneck (DRIB) to improve human interpretability and understandability. In the method, a novel dynamic reasoning decision algorithm was proposed to reduce multiply accumulate operations and improve the interpretability of the calculation. The information bottleneck was introduced to the DRIB model to verify the attribution correctness of the dynamic reasoning module. The DRIB reduces the burden approximately 50% by decreasing the amount of computation. In addition, DRIB keeps the correct rate at approximately 93%. The information bottleneck theory verifies the effectiveness of this method, and the credibility is approximately 85%. In addition, through visual verification of this method, the highlighted area can reach 50% of the predicted area, which can be explained more obviously. Some experiments prove that the dynamic reasoning decision algorithm and information bottleneck theory can be combined with each other. Otherwise, the method provides users with good interpretability and understandability, making deep neural networks trustworthy.
Classroom Teaching Effect Monitoring and Evaluation System with Deep Integration of Artificial Intelligence

With the progress and development of the times, education informatization has become the primary content to promote the progress of education. However, with the rapid advancement of technology, many problems have arisen in the integration of online education and information technology. From a practical point of view, advanced information technology should be fully integrated with school education activities to promote education reform and development, and students will definitely obtain a better learning experience. This study aimed to introduce a classroom teaching effect monitoring and evaluation system that incorporates artificial intelligence. In view of the background conditions for the development of this system, reforming measures and specific implementation methods for the integration of information technology and online classroom education are proposed.
Intelligent Industrial Auxiliary System Based on AR Technology

The traditional industrial assembly method is inefficient, and it is difficult to meet the needs of rapid production in modern society. Although many virtual assembly systems based on augmented reality technology have appeared in recent years, most of these assembly systems are noninteractive, nonintelligent, and inefficient. In response to this problem, we combined AR technology and AI technology to design and implement a strong interactive, high-intelligence virtual assembly guidance system in four levels. Finally, we took a UAV assembly as an example to show our design results. Operators can use this system to quickly master the assembly process. Finally, according to some problems encountered in the process of system implementation, the improvement direction of the virtual assembly system is proposed, and the future development of the AR auxiliary industry is proposed. This article describes the functions and implementation process of the four levels of the AR assembly system in detail, which can help readers quickly understand the connection between AR technology and AI technology and understand the principle of the virtual assembly guidance system.
Multirelationship Aware Personalized Recommendation Model

The existing methods using social information can alleviate the data sparsity issue in collaborative filtering recommendation, but they do not fully tap the complex and diverse user relationships, so it is difficult to obtain an accurate modeling representation of the user. To solve this, we propose a multirelationship aware personalized recommendation(MrAPR) model, which aggregates the various relationships between social users from two aspects of the user's personal information and interaction sequence. Based on the comprehensive and accurate relationship graphs established, the graph neural network and attention network are used to adaptively distinguish the importance of different relationships and improve the aggregation reliability of multiple relationships. The MrAPR model better describes the characteristics of user interest and can be compatible with the existing sequence recommendation methods. The experimental results on two real-world datasets clearly show the effectiveness of the MrAPR model.
KPH: A Novel Blockchain Privacy Preserving Scheme Based on Paillier and FO Commitment

Blockchain is a shared database with excellent characteristics, such as high decentralization and traceability. However, data leakage is still a major problem for blockchain transactions. To address this issue, this work introduces KPH (Paillier Homomorphic Encryption with Variable k), a privacy protection strategy that updates the transaction amount using the enhanced Paillier semihomomorphic encryption algorithm and verifies the transaction using the FO commitment. Unlike the typical Paillier algorithm, the KPH scheme's Paillier algorithm includes a variable k and combines the L function and the Chinese remainder theorem to reduce the time complexity of the algorithm from O $$({|n|}^{2+e})$$ to O $$({log}n)$$ , making the decryption process more efficient.
Factorization Machine Based on Bitwise Feature Importance for CTR Prediction

Click-through-rate (CTR) prediction is a crucial task in recommendation systems. The accuracy of CTR prediction is strongly influenced by the precise extraction of essential data and the modeling strategy chosen. The data of the CTR task are often very sparse, and Factorization Machines (FMs) are a class of general predictors working effectively with it. However, the performance of FMs can be limited by the fixed feature representation and the same weight of different features. In this work, we propose an improved Bitwise Feature Importance Factorization Machine (BFIFM) to improve the accuracy. The necessity of learning the degree of effect of the same feature under various situations is learned through the low-order intersection method, and the deep neural network (DNN) in our model is used in parallel to study high-order intersections. According to the final results obtained, the BFIFM model significantly outperforms other state-of-the-art models.
Collaborative Learning Method for Natural Image Captioning

We propose a collaborative learning method to solve the natural image captioning problem. Numerous existing methods use pretrained image classification CNNs to obtain feature representations for image caption generation, which ignores the gap in image feature representations between different computer vision tasks. To address this problem, our method aims to utilize the similarity between image caption and pix-to-pix inverting tasks to ease the feature representation gap. Specifically, our framework consists of two modules: 1) The pix2pix module (P2PM), which has a share learning feature extractor to extract feature representations and a U-net architecture to encode the image to latent code and then decodes them to the original image. 2) The natural language generation module (NLGM) generates descriptions from feature representations extracted by P2PM. Consequently, the feature representations and generated image captions are improved during the collaborative learning process. The experimental results on the MSCOCO 2017 dataset prove the effectiveness of our approach compared to other comparison methods.
Automatic Generation of Graduation Thesis Comments Based on Multilevel Analysis

In the evaluation of graduation theses, teachers' evaluation criteria for graduation theses are inconsistent, subjective and not completely reasonable and fair. This paper proposes using the BERT model to analyze the existing graduation papers in colleges and universities and make quantitatively evaluate students' graduation projects according to the given relevant parameters. The purpose of this method is to use standards to make comprehensive, systematic and accurate evaluations and avoid the phenomenon of high repetition and similarity caused by a large number of teachers' comments. This can not only effectively improve the efficiency of graduation design evaluation but also improve the fairness of evaluation. In this paper, changing the review work of the graduation thesis from pure manual operation to machine review combined with manual operation can not only reduce manpower consumption but also make the review work more objective and fair, making it more objective on the basis of traditional subjective review.
Decentralized Counterparty Matches and Automatic Settlement of Interest Rate Swap Through Blockchain’s Smart Contracts

Interest Rate Swap (IRS) is the most vivid application of the principle of comparative advantage in the financial field. The effectiveness of interest rate swap in managing interest rate risk has been widely recognized. However, the traditional interest rate swap transaction is complicated. Meanwhile, there usually exist market risks and credit risks. To alleviate risks and cost, and improve liquidity of interest rate swap, this paper proposes a smart contract based matching platform for interest rate swap of real fiat currency. Smart contracts play a key role for sharing data among participants, which can not be forged or tampered with. In our design, an efficient peer-to-peer counterparty matching method on the chain is proposed. The whole trading process of interest rate swap is carried out on the blockchain, which has higher security. A prototype based on smart contracts running on Ethereum is implemented and validates our design.
Anomaly Detection of Multivariate Time Series Based on Metric Learning

Most of the current methods for anomaly detection in time series are unsupervised. However, unsupervised learning assumes the distribution of the data and cannot obtain satisfactory results in some scenarios. In this paper, we design a semisupervised time series anomaly detection algorithm based on metric learning. The algorithm model mines the features in the time series from the perspectives of the time domain and frequency domain. Furthermore, we design a loss function for anomaly detection. Different from the two-class loss function, in the scenario of the loss function we designed, the normal data will be clustered and distributed in the embedding space, and the abnormal data will be far from the normal data distribution. Furthermore, we extend our designed metric learning model to a semisupervised learning model, extending the labeled dataset with the unlabeled dataset by setting different confidence levels. We conduct experiments on different public datasets and compare them with commonly used time series anomaly detection algorithms. The results show that our model has a good effect. At the same time the semisupervised setting does improve the accuracy of model detection.
Unsupervised Style Control for Image Captioning

We propose a novel unsupervised image captioning method. Image captioning involves two fields of deep learning, natural language processing and computer vision. The excessive pursuit of model evaluation results makes the caption style generated by the model too monotonous, which is difficult to meet people's demands for vivid and stylized image captions. Therefore, we propose an image captioning model that combines text style transfer and image emotion recognition methods, with which the model can better understand images and generate controllable stylized captions. The proposed method can automatically judge the emotion contained in the image through the image emotion recognition module, better understand the image content, and control the description through the text style transfer method, thereby generating captions that meet people's expectations. To our knowledge, this is the first work to use both image emotion recognition and text style control.
A Preliminary Study of Interpreting CNNs Using Soft Decision Trees

Decision trees can be used to enhance the interpretability of neural networks. In this work, we compare the classification and interpretability performance of the normal decision tree and a type of soft decision tree when they are used to interpret the decision paths of CNN networks. With the help of feature visualization and human-labeled features, we demonstrate that the soft decision trees identify more consistent features while maintaining much higher classification performance than the normal decision tree.
LSN: Long-Term Spatio-Temporal Network for Video Recognition

Although recurrent neural networks (RNNs) are widely leveraged to process temporal or sequential data, they have attracted too little attention in current video action recognition applications. Therefore, this work attempts to model the long-term spatio-temporal information of the video based on a variant of RNN, i.e., higher-order RNN. Moreover, we propose a novel long-term spatio-temporal network (LSN) for solving this video task, the core of which integrates the newly constructed high-order ConvLSTM (HO-ConvLSTM) modules with traditional 2D convolutional blocks. Specifically, each HO-ConvLSTM module consists of an accumulated temporary state (ATS) module as well as a standard ConvLSTM module, and several previous hidden states in the ATS module are accumulated to one temporary state that will enter the standard ConvLSTM to determine the output together with the current input. The HO-ConvLSTM module can be inserted into different stages of the 2D convolutional neural network (CNN) in a plug-and-play manner, thus well characterizing the long-term temporal evolution at various spatial resolutions. Experiment results on three commonly used video benchmarks demonstrate that the proposed LSN model can achieve competitive performance with the representative models.
Association Strategy Graph Convolutional Neural Network for Human Skeletal Behavior Recognition

Aiming at the problem that the joint point partition strategy expresses the important information of the human body in the human body behavior recognition of bones cannot fully express the behavior, an RCTR-GCN human bone behavior recognition model of the correlation strategy is proposed. First, by adding an association strategy of a refined graph convolutional network model (CTR-GCN) of the smart channel topology, it can dynamically learn different topological structures and efficiently amplify the characteristics of the connection points in different channels while improving the key joint points of associated characteristics. Then, the network model redefines each channel by learning a shared topology and uses a specific channel relationship to unify the model through theoretical analysis; finally, redefining the model structure effectively reflects the associated information of local nodes within the channel. Action recognition has stronger aggregation capabilities. The results show that the recognition accuracy in the commonly used NTU RGB + D and NW-UCLA datasets reaches 93.6% (X-View), 97.6% (X-Sub), and 97.2%, respectively. The experimental results show that the accuracy rate is improved.
A Multi-event Extraction Model for Nursing Records

Nursing records contain information on patients' treatment processes, which reflect the changes in patients' conditions and have legal effects. However, some of the written records of intensive care unit (ICU) nurses are incomplete according to our observations. This paper proposes an approach extracting structured nursing events from unstructured nursing records for detecting the missing items automatically. According to the PIO (problem, intervention, outcome) principle in the field of medical care, we propose event schemas for nursing records and annotate a Chinese nursing event extraction dataset (CNEED) on ICU nursing records. We find that several events may occur in a nursing record. Therefore, we present a multi-event extraction model for the nursing records. The experimental results demonstrate that our model achieves good results on CNEED and outperforms competitive methods on the multi-event argument attribution problem. By observing the results of automatic event extraction by our model, we detect missing items in the existing nursing records. This proves that our model can be used to help nurses check and improve the method of recording nursing processes.
A Complex Background Image Registration Method Based on the Optical Flow Field Algorithm

An effective nonrigid image registration method is developed based on the optical flow field (OFF) framework for the complex registration of structure images. In our method, a new force is modeled and integrated into the original optical flow equation to jointly drive the motion direction of pixels. At any point in the offset field, in addition to the force generated by the OFF model derived from local gradient information to drive the pixels in the floating image to infiltrate into the reference pixel set, a new "guiding force" derived from the global grayscale overall trend in a given neighborhood system helps the pixels to more properly spread into the corresponding reference pixel set, particularly when the gradient field of the reference image is unstable. In the experiment, a data set containing several images with complex structures was employed to validate the performance of our registration model. The test results show that our method can quickly and efficiently register complex images and is robust to noise in images.
Effective and Lightweight Defenses Against Website Fingerprinting on Encrypted Traffic

Recently, website fingerprinting (WF) attacks that eavesdrop on the web browsing activity of users by analyzing the observed traffic can endanger the data security of users even if the users have deployed encrypted proxies such as Tor. Several WF defenses have been raised to counter passive WF attacks. However, the existing defense methods have several significant drawbacks in terms of effectiveness and overhead, which means that these defenses rarely apply in the real world. The performance of the existing methods greatly depends on the number of dummy packets added, which increases overheads and hampers the user experience of web browsing activity. Inspired by the feature extraction of current WF attacks with deep learning networks, in this paper, we propose TED, a lightweight WF defense method that effectively decreases the accuracy of current WF attacks. We apply the idea of adversary examples, aiming to effectively disturb the accuracy of WF attacks with deep learning networks and precisely insert a few dummy packets. The defense extracts the key features of similar websites through a feature extraction network with adapted Grad-CAM and applies the features to interfere with the WF attacks. The key features of traces are utilized to generate defense fractions that are inserted into the targeted trace to deceive WF classifiers. The experiments are carried out on public datasets from DF. Compared with several WF defenses, the experiments show that TED can efficiently reduce the effectiveness of WF attacks with minimal expenditure, reducing the accuracy by nearly 40% with less than 30% overhead.
Data Hiding in the Division Domain: Simultaneously Achieving Robustness to Scaling and Additive Attacks

Data hiding plays an important role in privacy protection and authentication, but most data hiding methods fail to achieve satisfactory performance in resisting scaling attacks and additive attacks. To this end, this paper proposes a new quantization index modulation (QIM) variant based on division domains (D-QIM). It can not only resist the above two attacks well, but also adjust the performance trade-offs by controlling the parameters. Simulation results confirm the performance gain of D-QIM in terms of the bit error rate (BER).