Reviewerly/exHarmony huggingface dataset:
Construction and Practice of the Hybrid Teaching Mode of Nursing under the Normalized Epidemic Prevention and Control: ----Takes rehabilitation Care for Stroke Patients as an Example

Objectives: To discuss the construction of hybrid teaching mode of nursing under new situation and evaluate its effect. Methods: Using information technology to build a blended teaching mode, nursing students in Class 2 Grade 2017 were randomly selected as the observation group, and students in the other class as the control group. The hybrid teaching mode was used in the observation group, and the traditional teaching method was used in the control group. Observe and compare the teaching effect. Results: The observation group got higher marks in theoretical knowledge mastery, course preference degree, critical thinking ability and self-learning ability, which had statistically significant differences from the control group. Conclusion: Under the epidemic prevention and control situation, the blended teaching based on information technology not only improves the teaching quality and teaching efficiency, but also can mobilize students' enthusiasm for learning and promote the improvement of students' communication ability.
Personalized Recommendation System of Innovation and Entrepreneurship Course Based on Collaborative Filtering

This article will use the process and structure of personalized recommendation to design a recommendation system suitable for innovation and entrepreneurship course platform. It also conducts a comparative analysis of the existing innovation and entrepreneurship platforms to clarify the characteristics and attributes of the innovation and entrepreneurship platforms. Through comparative research on the use process, application scenarios, advantages and disadvantages of traditional personalized recommendation algorithms. On the basis of making full use of the characteristics of the innovation and entrepreneurship platform, a collaborative filtering algorithm was selected to complete the recommendation system design.
Quality Evaluation of Dissertation Based on DBSCAN Clustering

The quality of dissertations is an important reference for evaluating the quality of graduate degree-awarding. Most of the methods evaluate the quality of the dissertation from four types of dimentions, topic and review, innovation and paper value, scientific research ability and basic knowledge, and the standard expression of the paper. However, the experts' comments often have great value for evaluating the quality of the dissertations. Thus, this paper proposes an innovative method of quality evaluation of the dissertations, which integrates the experts' comments and four types of evaluation criteria mentioned above. The results show that the deviation of the dissertations' score becomes larger. So, it is easier to distinguish between excellent papers and problematic papers, as the score of low-level papers decrease, while the score of high-level papers increase. And, our proposed method has a strong generalization ability for all levels of the paper. Finally, the paper compares the indicators and comments of the eliminated papers and excellent papers in the engineering discipline, analyzes the typical characteristics of the two kinds, and gives corresponding suggestions on how to write high-level dissertations.
Application of Mathematical Expression Rules in Train Door Fault Diagnosis Expert System

As the key subsystem of rail transit vehicles, the reliability of train door subsystem directly affects the safety of vehicles' operation. Therefore, it is very necessary to obtain the operation state of train door in advance. The train door is a complex system that drives the movement of the train door leaf through the rotation of the motor driven screw rod. It is difficult to diagnose and predict its fault by establishing a mathematical model directly. It is also very difficult to collect a large number of sample data with fault labels for machine learning. Therefore, the expert system combined with domain expert knowledge is a better choice for train door fault diagnosis. By using mathematical expression as the rule of train door fault diagnosis expert system, this paper makes train door fault diagnosis more flexible, more scalable and easier to popularize.
Separating Explorer for Task Inference Based Meta Reinforcement Learning Algorithm

Traditional meta reinforcement learning based on task inference separates task inference with task control, but ignores the importance of exploration during task inference. The agent uses the same policy for both task exploration process and task control process, which leads to low task inference efficiency. To solve this problem, this paper proposes a task inference based meta reinforcement learning framework (Separating Explorer from Task Inference based Meta-Reinforcement Learning, SETIMRL). In this framework, an explorer agent is specially designed for task inference. The explorer takes the task exploration fully, and transits the collected data to the inference network. And the actor will adapt to the new tasks rapidly with the received inference information, which helps improve the model’s performance. Experimental results show that the proposed algorithm has better efficiency in multi-dimensions and sequential control tasks, compared to traditional meta reinforcement learning based on task inference.
Secured Steganographic Scheme Utilizing Fuzzy Threshold with Weighted Matrix

The idea of similarity measure, also known as entropy measurement, is used to discriminate between dissimilar objects. In many cases, mathematical, psychological, and fuzzy approaches have been utilized to explore it. Taking these principles into consideration, a new fuzzy threshold-based steganographic system is designed to choose pixel blocks for data embedding based on the degree of the pixel category. Then the secret data has been embedded through sum-of-entry-wise multiplication operation using predefined weighted matrix and selected pixel block. The secret data has been successfully recovered during the extraction phase using the required pixel range, fuzzy threshold, and weighted matrix, which are responsible for shared secret key to improve security and robustness. Finally, the proposed technique is tested using steganographic attacks and several types of analysis to determine its imperceptibility and robustness. Moreover, various experimental tests have been carried out to demonstrate the efficacy and effectiveness of the proposed method. From a security standpoint, it has been observed that unknown weighted matrix, threshold value and reference table have failed to retrieve the secret from watermarked image. The anticipated consequence highlighted certain outstanding magnificent aspects in the fields of image authentication, tamper detection, and digital forgery detection, all of which are essential to the technological life. This system benefits a wide range of government and business sectors, including health care, commercial security, defense, and intellectual property rights.
Detecting Fake Reviews via Verbal and Nonverbal Features

Online reviews are important to help customers to make purchasing decisions. However, opinion spamming exists in online review hosting websites, which has biased consumers' understanding of targeted products and service. Artificial Intelligence algorithms have been designed and adopted to automatically detect fake reviews. In this study, we evaluate the detection performance of various classic algorithms in a dataset crawled from one of largest online review hosting websites. We generate both verbal features from review text and nonverbal features from review related attributes. Experiment results show nonverbal features have higher impacts on fake review detection. Besides, GBDT could achieve stable detection performance in terms of four common metrics but using fewer features.
A firefly optimization fault-tolerant control algorithm for 4500m human occupied vehicle

In this paper, a new fault-tolerant control method for 4500m human occupied vehicle thrusters based on firefly algorithm is proposed. The firefly algorithm has a simple structure and few parameters. Meanwhile, it also has fast convergence and good global optimization search ability. To show the efficiency of firefly algorithm, the fault-tolerant control method based on particle swarm optimization is studied as a comparison research. Then, simulations results illustrate the performance of the proposed firefly optimization thruster fault-tolerant control method for 4500m human occupied vehicle is effective and advantageous.
Radar Signal Recognition under Impact Noise Based on Convolutional Neural Network

To solve the problem of radar signal recognition under the impact noise along with the conventional Gaussian white noise, we propose a method for radar signal recognition based on Choi-Williams Distribution (CWD) time-frequency transform and convolutional neural network. The &#x03B1; distribution is employed to model the impact noise in radar signals. The proposed method firstly performs CWD time-frequency analysis on the radar signal. Then, two-dimensional time-frequency images obtained by time-frequency transform are fed to a lightweight convolutional neural network for deep feature extraction. Finally, a softmax classifier is used to classify and recognize the radar signals. The simulation results show that the proposed method performs well in the signal classification task, and the lightweight convolutional neural network model provides convenience for realizing FPGA hardware acceleration.
High-Resolution Polymerization Network For Multi-Person Pose Estimation

In this paper, we propose a High-Resolution Polymerization Network (HPN) for Multi-Person Pose Estimation. First, we use the separate channel convolution module High-Resolution Polymerization Block(HPB) in HPN to optimize the input features. The HPB module can segment local features on the channel multiple times and fuse different scales, learn more refined features to extract reliable keypoint coordinate representations. In addition, we observe that there is only a simple vertical identity mapping relationship among the features in the network structure. In order to further utilize the high-resolution features that contain rich spatial information, we extend the horizontal mapping of network characteristics, use parallel convolutional to link high and low resolution features across scales, enhance feature information by maintaining high-resolution representation, to achieve the purpose of precise keypoint positioning. Our method is evaluated on the COCO data set, and the current method has reached an advanced level.
ENIOSTYLE AS AN INTERDISCIPLINARY CONCEPT

Eniostyle is the most accessible, powerful, fast and objective system of human cognition. A person who lives in harmony with his nature is the strongest and most resourceful one. Man is an energy and informational being. Each of us consists of a special type of information and energy that we exchange with the world around us and with other people. And this exchange is unique for each of us. That is why we study eniostyle system – the style of energy and information exchange. Within the framework of eniostyle, a successful person is one who "got on its own tracks." A person who lives in accordance with his nature is the most resourceful and strongest. Eniostyle allows you not to rebuild yourself, but to understand what you are strong in and what your weaknesses are. It has clear criteria for determining the eniotype (East, West, South and North). All basic settings are the same: NORTH – preservation and development of systems; EAST – creativity, idea generation; SOUTH creating connections between people; WEST development of society. Returning to your type is fast and fun. It is enough just to study eniostyle and recognize yourself in one of the types. You become interested in the surrounding people, you begin to speak with other people in their language, you select the most suitable environment for yourself for quick professional and social realization. The quality of your life changes, while you strengthen yourself many times over and create the most favorable environment for self-realization.
The Digital Economy and Usury in Indonesia

This study examines the impact of internet literacy on interest rates, consumption and economic growth.This study analyzes 21 years of data, from 2000 to 2020, to create "autoregressive vectors" to evaluate the causal relationship between variables. The following multivariate regression model was used to analyze the causal relationship between internet literacy, interest rates, consumption, and economic development in Indonesia, based on secondary data from the World Bank. We found that internet literacy has an impact on consumption and economic growth in Indonesia but does not have a significant impact on interest rates. This shows that internet technology penetration has no significant effect on interest rates but does affect consumption and economic growth. The level of interest rates itself encourages domestic consumption but suppresses economic growth. This becomes rational because the ease of credit makes it easier for people to consume but becomes a burden from economic growth.
Transition from LibSys to KOHA: A Case Study

This paper explains the procedure adopted and the issues encountered in transiting from LibSys to KOHA at the Dr. B. R. Ambedkar National Institute of Technology, Jalandhar. Open Source ILMS KOHA has all the modules required for managing library housekeeping operations. All the data has been successfully migrated from LibSys to KOHA. The paper provides guidelines for transition from LibSys to KOHA.
Pest Identification System based on YOLOv5

Forest pests and diseases are a global problem. The key to control forestry diseases is to accurately identify the species and severity of pests. How to use artificial intelligence and image recognition technology to detect forestry pests is an important challenge and opportunity. This paper presents a new method for forestry pest identification based on YOLOv5 algorithm. In addition, in order to unify the system and expand the flexibility of the future system, we adopted the B/S/S structure to develop the pest identification system. The system uses the camera to shoot images and transmits the data to the background recognition. The experimental results show that our system can detect the target pests more accurately and conveniently, which is helpful for the actual prevention and control of forestry pests.
On the Cognitive Foundations of Autonomous Systems and General AI

It is recognized that autonomous systems are advanced intelligent systems towards general AI that may think and infer. This work investigates into basic research on the cognitive foundations of autonomous systems. Key challenges to real-time intelligence generation are analyzed in classic stored-programmed-controlled computing and data-regressed ion AI, which lead to a general intelligence generation methodology of knowledge-inferred AI. A theoretical framework of autonomous AI systems is elaborated towards autonomous systems for enabling deep machine thinking, knowledge learning, cognitive computing, and mission-critical intelligent systems.
Research on YOLOv4-tiny traffic sign detection algorithm with attention mechanism

In the process of traffic sign detection, the small and dense traffic signs which are influenced by bad weather, similar interference and other natural environment, lead to poor detection performance. To solve these problems, this paper proposes a target detection network based on improved YOLOv4-tiny, which improves the original YOLOv4-tiny backbone extraction network through the attention mechanism based on channel, and obtains a new backbone extraction network to increase the interpretability of neural network. K-means clustering algorithm is used to calculate the anchor value which is suitable for the experimental dataset. The experiment results show that, compared with the original model, the mAP value of the improved model is increased by 1.81% and our model can effectively improve the performance of small target detection. It only needs 0.8s to get the traffic sign detection results, which can meet the real-time requirements of practical application scenarios.
The Logic of “Must” and “Have to”

It is proposed that “must” and “have to” express aggregative modalities, i.e., they represent the combined effects of various types of inducements, including legal and moral obligations, the individual’s own commitments and decisions, practical necessity, etc. The logic of “must” and “have to” is shown to differ from that of pure deontic modalities, such as “have an obligation to”. Aggregative predicates can be used to clarify the processes in which we combine different types of requirements in order to determine what to do. This analysis also makes it possible to avoid the common deontic paradoxes, such as Ross’s paradox, for some of the deontic predicates.
FERNIE-ViL: Facial Expression Enhanced Vision-and-Language Model

Visual cognition requires analyzing actions, intentions, and emotions of persons in a given image. Visual Commonsense Reasoning (VCR) is a task that selects rationales and answers to questions for given images. In VCR, facial expressions are important nonverbal signals because they convey emotions and intentions in human interactions. However, ERNIE-ViL and UNITER, which are vision-and-language models to get image and text representations, do not learn them. We find that ERNIE-ViL and UNITER are vulnerable to the problem of identifying emotions. In this paper, therefore, we propose facial expression recognition FERNIE-ViL, which adapts a facial expression recognition module to the existing vision-and-language model. Experimental results (2.4% point improvement on VCR Q&#x2192;A and 0.3% point improvement on VCR QA&#x2192;R) demonstrate that our method can enhance visual commonsense reasoning by understanding human interactions.
Influence of Different Pooling Operation Functions on Image Classification and Recognition Rate of Convolutional Neural Network

The convolution neural network has proved to be an efficient method for the image processing. It can optimize the convolution of the convolutional neural network and the filter in the poolinglayer Function, and optimize the performance of the role, and the number of parameters. It can construct a certain structure of the convolution neural network, and then the image data set is classified and processed to obtain the image model of the desired classification result.
Representation of the Problem-Solving Process of the Tower of Hanoi using Fuzzy Cognitive Maps

Fuzzy cognitive maps represent the knowledge system of both an individual and a group, wherein the data collected from study participants are used to develop quantitative dynamic models from qualitative static models. The objective of this paper was to construct a dynamic representation model of the Tower of Hanoi problem using fuzzy cognitive maps. The methodology of this paper included five main phases: application of the Stroop test, warm-up, the experiment itself, data analysis, and creating a representation of the Tower of Hanoi problem-solving process. The results obtained show that fuzzy cognitive maps allow the researchers to identify the degree of influence between specific verbal instructions and their causal relationship concerning the categories of executive processes and subprocesses developed by the participants. It is necessary to continue deepening this research to design future studies that identify new functions at the cognitive and metacognitive levels in these specific problem-solving contexts.
Measurement System of the Interpersonal Proximity using a Wearable Eye-Tracker

In this study, we propose the measurement system of the interpersonal proximity using a wearable eye tracker. The proposed system uses the area occupied by the other person in the visual field, head tilt and gazing at the face as distance indicators. Measuring the interpersonal proximity compares with the measuring the physical distance between the two persons for the development of the quantitatively measurement. In this paper, we conducted a preliminary experiment using the proposed system. Results shows that the ratio of face gazing decreases in the approached participant. The face gazing does not occur when the approached participant in sitting.
Research on Teaching Design of "UAV Teaching Experiment Platform" Based on Image Recognition

In the innovation and development of modern UAV technology, the actual research is mainly focused on rotorcraft UAV, and it meets the requirements of navigation control with too low speed. In this paper, the design and construction of a four-rotor UAV teaching experimental platform with photoelectric image processing as the core is the key point of this study. This experimental platform uses attitude sensor MPU 6050 and camera OV 7725, both of which are core airborne sensors, while ARM embedded system chip STM32F4 series belongs to the main control chip. R5F100 LEA series are auxiliary control chips. By using the optimized single loop PID control algorithm and accurately identifying the specific target material according to the photograph image processing algorithm, the automatic control experiment operation of the quadrotor UAV can be completed. The final experimental results show that the constructed airborne control system can effectively carry out flight control experiments, and can walk according to the set route, accurately deliver the expected set of substances, and search for specific substances. These functions are the basic contents of the practical operation of uav teaching experiment platform.
Design of Data Sharing Architecture for Integrated Online Electric Power Service Applications Based on Blockchain

With the continuous advancement of the informatization process of the power information system, the electronic level of the power company's business has continued to deepen. However, the Integrated Online Electric Power Service Applications (IOEPSA) has not yet been implemented. There are many problems in this scenario, such as low real-time data exchanges, difficult data life cycle management, data accuracy and security low-level issues. This paper proposed a blockchain-based data sharing architecture for IOEPSA, which effectively improves the security, convenience and reliability of data sharing.
Synchrony-Based State Representation for Classification by Liquid State Machines

The Liquid State Machine (LSM) models usually ignore the influence of the liquid state representation on performance, with the assumption that the latter depends only on the readout circuit. The typical decoding of the liquid&#x2019;s spike trains is achieved with spike rate-based vectors that are input into the readout circuit. This occults the spike timing, a central aspect of biological neural coding, with potentially detrimental consequences on performance. We propose a model of liquid state representation that builds the feature vectors from the temporal information about the spike trains, hence using spike synchrony instead of rate. Using Poisson-distributed spike trains in noisy conditions, we show that such model outperforms a rate-only model in distinguishing spike train pairs, regardless of the frequency chosen to sample the liquid state or the noise level. In the same vein, we suggest a synchrony-based measure of the Separation Property (SP), a core feature of LSMs regarding classification performance, for a more robust and biologically plausible interpretation.