Reviewerly/exHarmony huggingface dataset:
How to Use Artificial Intelligence to Improve Entrepreneurial Attitude in Business Simulation Games: Implications From a Quasi-Experiment

Business simulation games (BSGs) have been widely used in entrepreneurship education with positive effects. However, there are still some deficiencies in the BSGs, such as limited guidance, low uncertainty and limited simulation environment, which make it impossible to exert the maximum effect. Artificial intelligence (AI) can solve the above shortcomings. The combination of AI and BSGs is the possible development direction of BSGs. But how to effectively combine BSGs with AI is still an open question. Using a quasi-experimental design, this study uses fuzzy-set qualitative comparative analysis to analyze how participants' entrepreneurial attitude changes in BSGs. The results show that BSGs can effectively improve entrepreneurial attitude, and there are four types of promotion configurations. These four configurations consist of five antecedent conditions. According to the above conclusions, AI can improve entrepreneurial attitude in BSGs in various ways, such as simulating competitors, providing targeted feedback for failures, and improving game experience. The contribution of this paper is to highlight the possibility of combining AI with BSGs, and to provide suggestions on how AI can intervene in BSGs.
Mind the Remainder: Taylor’s Theorem View on Recurrent Neural Networks

Recurrent neural networks (RNNs) have gained tremendous popularity in almost every sequence modeling task. Despite the effort, these kinds of discrete unstructured data, such as texts, audio, and videos, are still difficult to be embedded in the feature space. Studies in improving the neural networks have accelerated since the introduction of more complex or deeper architectures. The improvements of previous methods are highly dependent on the model at the expense of huge computational sources. However, few of them pay attention to the algorithm. In this article, we bridge the Taylor series with the construction of RNN. Training RNN can be considered as a parameter estimate for the Taylor series. However, we found that there is a discrete term called the remainder in the finite Taylor series that cannot be optimized using gradient descent, which is part of the reason for the truncation error and the model falling into the local optimal solution. To address this, we propose a training algorithm that estimates the range of remainder and introduces the remainder obtained by sampling in this continuous space into the RNN to assist in optimizing the parameters. Notably, the performance of RNN can be improved without changing the RNN architecture in the testing phase. We demonstrate that our approach is able to achieve state-of-the-art performance in action recognition and cross-modal retrieval tasks.
A Survey on Fuzzy Systems Optimization Using Evolutionary Algorithms and Swarm Intelligence

Humans are capable to make good decisions in an uncertain and linguistic environment. The implementation of human decisions into machines is a crucial research aspect to work better and accurate decision-making by the machines. Fuzzy rule-based systems are important mechanisms to implement such decision-making into machines. Mamdani and Takagi–Sugeno-Kang models are the most commonly used fuzzy rule-based systems. The optimization of fuzzy rule-based systems is an effort towards making it more precise and capable of learning. Evolutionary algorithms and swarm intelligence approaches are used to optimize fuzzy rule-based systems. This paper gives an overview of fuzzy rule-based systems, evolutionary algorithm, and swarm intelligence approaches along with a recent review on the optimization of fuzzy systems. The performance parameters of the fuzzy rule-based systems are interpretability and accuracy. Interpretability and accuracy have a trade-off relation, which means if one is improved then another is compromised. Multi-objective optimization plays a vital role to deal with the interpretability-accuracy trade-off in fuzzy models. This aspect is also addressed and reviewed in this paper.
Anomaly Detection based on Pose Estimation and GRU-FFN

Anomaly Detection has recently received significant attention as an efficient concept for surveillance. The main challenges of anomaly detection mainly come from three aspects:1) Anomaly behavior always occurs suddenly with unknown. 2) One class anomaly has a completely different feature from another. 3)Anomaly datasets are rare. Most current approaches learn features of normal samples for reconstructing video frames, from which anomaly is detected by reconstruction error. However, they ignored the representation capacity of CNNs, and abnormal behavior can still be reconstructed. Moreover, feature representation-based methods are difficult to distinguish between signal and noise. To solve these problems, we propose a novel framework for anomaly detection in this paper, which is equipped with human motion prediction ability. Special emphasis is placed on skeleton key points prediction which relies on deep learning. The experimental results show that the proposed approach outperforms current state-of-the-art techniques quantitatively and qualitatively.
AVSDA: Autonomous Vehicle Security Decay Assessment

Security practices become weaker over time as attackers’ capabilities evolve. Security decay within vehicle software systems can have devastating consequences as it can pose a direct threat to people’s lives. Thus, it is crucial to monitor the changing threat level on vehicles during their full lifespan. We present an Autonomous Vehicle Security Decay Assessment (AVSDA) framework that analyzes and predicts the system’s security risk over vehicles’ lifespan. The framework analyzes vulnerable software components periodically and estimates the security risk level to identify security decay. AVSDA employs several metrics specifically designed for autonomous vehicle systems to automatically identify potentially weak components and quantify security risk. We evaluate the framework on OpenPilot, an autonomous driving system. The case study demonstrates the effectiveness of the AVSDA framework in identifying security decay over time. The results show an accuracy rate of 94% and a recall rate of 78%, outperforming all other known metrics by at least 50%.
Study on the Organization and Governance of Bigdata for Lifelong Education

The open university is carrying out various types of academic and non-academic education to serve the city's citizens and usually has gained a good reputation in the local area. However, previously, due to its inadequate hardware system, fragmented information system, and incomplete data collection, it carried out various operations more by empirical judgment and lacked objective data support, thus making it impossible to promote the development of a lifelong education system accurately. By collecting, integrating, and analyzing these lifelong education projects, converting them into lifelong education big data, and establishing various lifelong education data visualization and analysis models, we can promote the transformation of life-long education from discrete demand to education big data precision-driven development mode, provide support for the supply-side structural reform of lifelong education, and at the same time improve the leading role of the open university in the construction of lifelong education system, thus promoting the high-quality development of lifelong education for citizens.
Analysis of Real Time Video Summarization using Subtitles

With the rapid growth of user-generated videos, being able to browse them effectively is becoming increasingly vital. Video summarization is seen to be a potential method for effectively realizing video content by identifying and selecting descriptive frames from the video. An automatic video summary would be advantageous for everyone who wants to save time and learn more in less time as video content continues to grow at a rapid rate. In this paper, essential components of a video can be summarized by the summation of subtitles in a video using text summarization and video mapping algorithms. The audio-generated version of the summary subtitle file will be played with the summarized video. The system&#x0027;s final output would be a summary video accompanied by a summarized audio-generated version.
Applications of Hand Gesture Recognition

Hand gestures, as the name suggests, are different gestures made by the use of hands. Historically, hand gestures have been created to communicate with people who were unable to speak, but as new technologies have emerged, hand gestures have been used widely in different fields such as medicine, defense, IT industry. Hand gestures are being used to create TVs without remotes. Face recognition is also used to verify the user and to change channels, increase/decrease volume. To switch on or off the lights in the house, hand gesture recognition devices are being developed. Hand gesture recognition (HGR) is a natural kind of human-machine interaction that has been used in a variety of settings. In this chapter, the authors have described the application of HGR in various sectors. They have also explained the tools and techniques used for HGR.
Hybrid Improved Models Combined SR3 Module for Animal Recognition in Electric Car’s Actual Vision

With the development of information technology including artificial intelligence and image recognition technology, has gradually entered the application of electric cars. Especially real-time images monitoring in the field of electric car has become the eyes of them. In this paper, we proposed a hybrid improved intelligent recognition system based on SR3 module for animal recognition in the case of electric cars. The proposed hybrid improved system mainly used the actual image information collected by the monitor of the cars, which information acquired by the front-end monitors of electricity cars. And then the backend recognition model will train and run the classification algorithm to identify the animal species walk through so that the result can stop the vehicle by applying the brake order. The number of the test and train data set is about 2,000 animal images respectively, some of them were downloaded by Python crawler, they are all clear pictures under ideal conditions, and another 1000 pictures was taken by real cars monitor which are blurred picture under actual conditions. In terms of experimental results, several models of traditional and Deep Convolution Neural Networks by research and data analysis are compared, such as LBPH and VGG etc., under the real situation of the blur animal pictures which were taken by the real monitor, the accuracy of 88% of the hybrid improved model as a test set could be the best choice from several models. It could be the best electric cars' assistant visual model based on our findings.
Parabolic Airdrop Trajectory Planning for Multirotor Unmanned Aerial Vehicles

In this paper we present a motion planning method for the parabolic airdrop using multirotor UAVs.The first step is to determine a set of parabolic trajectories that can reach a desired target.Next, we plan a path to the payload launch point, and based on that path we plan a collision-free trajectory that safely executes both launch and stopping motions.The described motion planning method is extensively tested in the simulation environment: by executing many trajectories through repeatability analysis; planning in a large city-like environment; and planning in a dense office environment.Furthermore, the method is tested through real-world experiments in indoor and outdoor environments.In both environments, we performed the repeatability analysis and the obstacle avoidance experiments while delivering the payload to the target.The video demonstrating our simulation and experimental analyses is available at: https://youtu.
Quantum Gaussian process model of potential energy surface for a polyatomic molecule

With gates of a quantum computer designed to encode multi-dimensional vectors, projections of quantum computer states onto specific qubit states can produce kernels of reproducing kernel Hilbert spaces. We show that quantum kernels obtained with a fixed ansatz implementable on current quantum computers can be used for accurate regression models of global potential energy surfaces (PESs) for polyatomic molecules. To obtain accurate regression models, we apply Bayesian optimization to maximize marginal likelihood by varying the parameters of the quantum gates. This yields Gaussian process models with quantum kernels. We illustrate the effect of qubit entanglement in the quantum kernels and explore the generalization performance of quantum Gaussian processes by extrapolating global six-dimensional PESs in the energy domain.
GP-DMD: a genetic programming variant with dynamic management of diversity

The proper management of diversity is essential to the success of Evolutionary Algorithms. Specifically, methods that explicitly relate the amount of diversity maintained in the population to the stopping criterion and elapsed period of execution, with the aim of attaining a gradual shift from exploration to exploitation, have been particularly successful. However, in the area of Genetic Programming, the performance of this design principle has not been studied. In this paper, a novel Genetic Programming method, Genetic Programming with Dynamic Management of Diversity (GP-DMD), is presented. GP-DMD applies this design principle through a replacement strategy that combines penalties based on distance-like functions with a multi-objective Pareto selection based on accuracy and simplicity. The proposed general method was adapted to the well-established Symbolic Regression benchmark problem using tree-based Genetic Programming. Several state-of-the-art diversity management approaches were considered for the experimental validation, and the results obtained showcase the improvements both in terms of mean square error and size. The effects of GP-DMD on the dynamics of the population are also analyzed, revealing the reasons for its superiority. As in other fields of Evolutionary Computation, this design principle contributes significantly to the area of Genetic Programming.
Performance of Post-Training Two-Bits Uniform and Layer-Wise Uniform Quantization for MNIST Dataset from the Perspective of Support Region Choice

This paper contributes to the goal of finding an efficient compression solution for post-training quantization from the perspective of support region choice under the framework of low-bit uniform quantization. The decision to give preference to uniform quantization comes from the fact that support region choice is the most sensitive in the uniform quantization of nonuniform sources (e.g., Laplacian sources). Therefore, in this paper, we analyse in detail how the choice of the support region influences the performance of two-bits uniform quantization, measured with signal to quantization noise ratio (SQNR), and the accuracy of the compressed neural network (NN) model. We provide experimental and theoretical results for a few significant cases of two-bits uniform quantizer design, where we assume that Laplacian source models the distribution of weights in our fully connected NN. We opt for Laplacian distribution since it models well weights of NNs. Specifically, we analyse whether it is possible to apply the simplest uniform quantization in trained NN model weight representation with a bit rate of R = 2 bit/sample while preserving the accuracy of the model to a great extent. Also, our goal is to determine whether the choice of the key parameter of two-bits uniform quantizer (support region threshold) equally reflects on both, SQNR and accuracy. Moreover, we extend our analysis to the application of layer-wise two-bits uniform quantization in order to examine whether it is possible to achieve an additional improvement of the accuracy of our NN model for the MNIST dataset. We believe that the detailed analysis of post-training quantization described and conducted in this paper is very useful for all further research studies of this very current topic, especially due to the fact that the problem regarding post-training quantization is addressed from a particularly important perspective of choosing the support region.
Detecting Violent Arm Movements Using CNN-LSTM

With the increasing rate of surveillance camera utilization in household residences, intelligent security systems capable of automatically identifying violent activity has already become a significant research issue in recent times. Indoor violence mainly comes in the form of punching, beating and scuffling which primarily involve arm movements. In this paper, an end-to-end deep learning framework for detecting violent arm movements has been proposed. To consider the motion information of the violent movements from the surveillance video clips, a combination of Convolutional Neural Network (CNN) and Long-Short-Term-Memory (LSTM) model is used. The CNN model extracts the spatial features which are passed to the LSTM model for temporal feature learning. The spatio-temporal features are then fed to the Softmax classifier for binary classification and outputs violent or non-violent label. Experimental results obtained after evaluating the proposed model on the benchmark Hockey Fight dataset exhibit higher classification accuracy than some of the existing state-of-the-art methods, thus, proving superior performance in the context of detecting violent arm movements.
Automated Acute Lymphocytic Leukemia (ALL) Detection Using Microscopic Images: An Efficient CAD Approach

Sumi, Tahmina Akter Hossain, Mohammad Shahadat Andersson, KarlLeukemia, which is caused by the excessive and aberrant reproduction of white blood cells, completely destroys the immune system of our body and leads to death. Among four different types of leukemia, the progress of acute lymphocytic leukemia is rapid and becomes fatal even in weeks if it is kept untreated. So, early diagnosis of ALL is quite necessary. In manual methods, pathologists diagnose ALL by the microscopic test of blood specimen or bone marrow test. Though this is the most efficient process to diagnose ALL, it is a time-consuming matter. In this case, computer-aided diagnosis (CAD) may be considered as a great associative diagnostic tool for ALL identification. Numerous supervised and unsupervised machine learning algorithms have been proposed for ALL detection for years. This paper concerns with establishing a CNN- based CAD system for automated ALL detection from the microscopic blood images which is collected from ALL-IDB dataset. In this regard, at first the images have been preprocessed applying median filter and histogram equalization for the purpose of reducing the noise and enhancing the image. Being smaller in size, data augmentation has been applied on the dataset which increases the images by including slightly modified copies of images that already exist in the dataset. Finally, the modified images are passed through a CNN model for training purpose where feature extraction and classification are performed by convolution, ReLU, pooling layer, and a fully connected layer. Here, the dataset is also trained using some pretrained model to show a comparison of our model with other models. It is observed that our proposed model results as a well fitting model with 100% training accuracy and 97.89% testing accuracy which is promising.
Validation of the Indonesian translated Zoom Exhaustion and Fatigue (ZEF-I) scale: a RASCH Model and Factor Analysis

Background The Coronavirus Disease pandemic has led to exponentially greater screen time and technology utilization. The Zoom Exhaustion and Fatigue (ZEF) scale is a diagnostic measure developed to identify zoom fatigue arising from excessive screen time. The language barrier, however, has prevented its implementation in Indonesia. This paper thus investigated the validity and reliability of a Bahasa Indonesia diagnostic instrument, ZEF-I.Methods This study generated a ZEF-I translation through the World Health Organization (WHO)-Euro-Reves technique. RASCH and confirmatory factor analysis (CFA) of the scale based on data from 300 participants were undertaken. The reliability and validity of the ZEF-I were specifically assessed with the separation index, Cronbach’s alpha, content validation index as well as Pearson and corrected-item correlations.Results The model showed adequate reliability and validity of the ZEF-I as well as favorable goodness-of-fit indexes and fit indices. The fit-misfit statistics also preserved all 15 items on the translated instrument. Among fatigued students, the blurred vision item was the most frequently endorsed, while the need for self-time was the least endorsed item.Conclusion This study developed and validated an Indonesian translation of the zoom fatigue questionnaire. ZEF-I is an appropriate zoom fatigue assessment instrument for Indonesian university students.
Lightweight Single Image Super-Resolution With Similar Feature Fusion Block

Convolutional neural network-based image super-resolution methods have achieved great success in recent years. However, the huge memory and computational costs make most of the existing methods difficult to be applied to resource-constrained scenarios such as edge devices. To tackle this problem, we propose a generic, lightweight and efficient feature fusion block to replace the commonly used 1&#x002A;1 convolution. In addition, we propose the enhanced shallow residual blocks to improve the super-resolution performance. By combining these two novel blocks, we design an efficient similar feature fusion network for single image super-resolution, based on the observation that cross-layer features of the same channel usually have high similarities. The similar feature fusion block utilizes similarity as a guide for feature clustering, enabling efficient and high-performance cross-layer feature fusion. On the other hand, the enhanced shallow residual blocks are used as the base feature extraction model for the network to improve super-resolution performance in conjunction with the feature fusion module. In the enhanced shallow residual blocks, we combine convolution with identity connection to maintain the similarity of cross-layer features that are fed into the similar feature fusion block. The spatial attention mechanism is also introduced to reinforce the useful spatial features. Experimental results on the benchmark datasets show that the proposed method can achieve comparable results to state-of-the-art methods with a small number of parameters.
Jointly Learning Sentimental Clues and Context Incongruity for Sarcasm Detection

Sarcasm is widely used in social communities and e-commerce platforms, failing to detect it in natural language processing tasks leads to false positives, e.g., opinion mining and sentiment classification.Recent works have indicated that the two linguistic characteristics, sentiment and incongruity information are beneficial to sarcasm detection.However, sarcasm datasets with sentiment labels are usually unavailable, and researchers consider little semantic information while modeling incongruity.In this paper, we propose a multi-task learning framework that incorporates sentiment clues by soft sentiment labels and integrates semantic information while modeling context incongruity.Experimental results on datasets show that the model we proposed yields better performance for the sarcasm detection task with the help of sentiment clues and incongruity information.