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| 1 | ChatGPT as an Educational Tool: Opportunities, Challenges, and Recommendations for Communication, Business Writing, and Composition Courses显示文摘This empirical study examines ChatGPT as an educational and learning tool.It investigates the opportunities and challenges that ChatGPT provides to the students and instructors of communication,business writing,and composition courses.It also strives to provide recommendations.After conducting 30 theory-based and application-based ChatGPT tests,it is found that ChatGPT has the potential of replacing search engines as it provides accurate and reliable input to students.For opportunities,the study found that ChatGPT provides a platform for students to seek answers to theory-based questions and generate ideas for application-based questions.It also provides a platform for instructors to integrate technology in classrooms and conduct workshops to discuss and evaluate generated responses.For challenges,the study found that ChatGPT,if unethically used by students,may lead to human unintelligence and unlearning.This may also present a challenge to instructors as the use of ChatGPT negatively affects their ability to differentiate between meticulous and automation-dependent students,on the one hand,and measure the achievement of learning outcomes,on the other hand.Based on the outcome of the analysis,this study recommends communication,business writing,and composition instructors to(1)refrain from making theory-based questions as take-home assessments,(2)provide communication and business writing students with detailed case-based and scenario-based assessment tasks that call for personalized answers utilizing critical,creative,and imaginative thinking incorporating lectures and textbook material,(3)enforce submitting all take-home assessments on plagiarism detection software,especially for composition courses,and(4)integrate ChatGPT generated responses in classes as examples to be discussed in workshops.Remarkably,this study found that ChatGPT skillfully paraphrases regenerated responses in a way that is not detected by similarity detection software.To maintain their effectiveness,similarity detection software providers need to upgrade their software to avoid such incidents from slipping unnoticed. | Mohammad Awad AlAfnan Samira Dishari Marina Jovic Koba Lomidze | 2023 | Journal of Artificial Intelligence and Technology2023，3，2： | 5 |
| 2 | UAV Maneuvering Decision-Making Algorithm Based on Twin Delayed Deep Deterministic Policy Gradient Algorithm显示文摘Aiming at intelligent decision-making of unmanned aerial vehicle(UAV)based on situation information in air combat,a novelmaneuvering decision method based on deep reinforcement learning is proposed in this paper.The autonomous maneuvering model ofUAV is established byMarkovDecision Process.The Twin DelayedDeep Deterministic Policy Gradient(TD3)algorithm and the Deep Deterministic Policy Gradient(DDPG)algorithm in deep reinforcement learning are used to train the model,and the experimental results of the two algorithms are analyzed and compared.The simulation experiment results show that compared with the DDPG algorithm,the TD3 algorithm has stronger decision-making performance and faster convergence speed and is more suitable for solving combat problems.The algorithm proposed in this paper enables UAVs to autonomously make maneuvering decisions based on situation information such as position,speed,and relative azimuth,adjust their actions to approach,and successfully strike the enemy,providing a new method for UAVs to make intelligent maneuvering decisions during air combat. | Bai Shuangxia Song Shaomei Liang Shiyang Wang Jianmei Li Bo Neretin Evgeny | 2022 | Journal of Artificial Intelligence and Technology2022，2，1： | 3 |
| 3 | A Fully Convolutional Neural Network-based Regression Approach for Effective Chemical Composition Analysis Using Near-infrared Spectroscopy in Cloud显示文摘As one chemical composition,nicotine content has an important influence on the quality of tobacco leaves.Rapid and nondestructive quantitative analysis of nicotine is an important task in the tobacco industry.Near-infrared(NIR)spectroscopy as an effective chemical composition analysis technique has been widely used.In this paper,we propose a one-dimensional fully convolutional network(1D-FCN)model to quantitatively analyze the nicotine composition of tobacco leaves using NIR spectroscopy data in a cloud environment.This 1D-FCN model uses one-dimensional convolution layers to directly extract the complex features from sequential spectroscopy data.It consists of five convolutional layers and two full connection layers with the max-pooling layer replaced by a convolutional layer to avoid information loss.Cloud computing techniques are used to solve the increasing requests of large-size data analysis and implement data sharing and accessing.Experimental results show that the proposed 1D-FCN model can effectively extract the complex characteristics inside the spectrum and more accurately predict the nicotine volumes in tobacco leaves than other approaches.This research provides a deep learning foundation for quantitative analysis of NIR spectral data in the tobacco industry. | Daiyu Jiang Gang Hu Guanqiu Qi Neal Mazur | 2021 | Journal of Artificial Intelligence and Technology2021，1，1： | 3 |
| 4 | A Hybrid CNN for Image Denoising显示文摘Deep convolutional neural networks(CNNs)with strong learning abilities have been used in the field of image denoising.However,some CNNs depend on a single deep network to train an image denoising model,which will have poor performance in complex screens.To address this problem,we propose a hybrid denoising CNN(HDCNN).HDCNN is composed of a dilated block(DB),RepVGG block(RVB),feature refinement block(FB),and a single convolution.DB combines a dilated convolution,batch normalization(BN),common convolutions,and activation function of ReLU to obtain more context information.RVB uses parallel combination of convolution,BN,and ReLU to extract complementary width features.FB is used to obtain more accurate information via refining obtained feature from the RVB.A single convolution collaborates a residual learning operation to construct a clean image.These key components make the HDCNN have good performance in image denoising.Experiment shows that the proposed HDCNN enjoys good denoising effect in public data sets. | Menghua Zheng Keyan Zhi Jiawen Zeng Chunwei Tian Lei You | 2022 | Journal of Artificial Intelligence and Technology2022，2，3： | 2 |
| 5 | Single-Image Dehazing Based on Two-Stream Convolutional Neural Network显示文摘The haze weather environment leads to the deterioration of the visual effect of the image,and it is difficult to carry out the work of the advanced vision task.Therefore,dehazing the haze image is an important step before the execution of the advanced vision task.Traditional dehazing algorithms achieve image dehazing by improving image brightness and contrast or constructing artificial priors such as color attenuation priors and dark channel priors.However,the effect is unstable when dealing with complex scenes.In the method based on convolutional neural network,the image dehazing network of the encoding and decoding structure does not consider the difference before and after the dehazing image,and the image spatial information is lost in the encoding stage.In order to overcome these problems,this paper proposes a novel end-to-end two-stream convolutional neural network for single-image dehazing.The network model is composed of a spatial information feature stream and a highlevel semantic feature stream.The spatial information feature stream retains the detailed information of the dehazing image,and the high-level semantic feature stream extracts the multi-scale structural features of the dehazing image.A spatial information auxiliary module is designed and placed between the feature streams.This module uses the attention mechanism to construct a unified expression of different types of information and realizes the gradual restoration of the clear image with the semantic information auxiliary spatial information in the dehazing network.A parallel residual twicing module is proposed,which performs dehazing on the difference information of features at different stages to improve the model’s ability to discriminate haze images.The peak signal-to-noise ratio(PSNR)and structural similarity are used to quantitatively evaluate the similarity between the dehazing results of each algorithm and the original image.The structure similarity and PSNR of the method in this paper reached 0.852 and 17.557dB on the HazeRD dataset,which were higher than existing comparison algorithms.On the SOTS dataset,the indicators are 0.955 and 27.348dB,which are sub-optimal results.In experiments with real haze images,this method can also achieve excellent visual restoration effects.The experimental results show that the model proposed in this paper can restore desired visual effects without fog images,and it also has good generalization performance in real haze scenes. | Meng Jun Li Yuanyuan Liang HuaHua Ma You | 2022 | Journal of Artificial Intelligence and Technology2022，2，3： | 2 |
| 6 | Thirty years of graph matching in pattern recognition 显示文摘 | CONTE D FOGGIA P SANSONE C | 2004 | International Journal of Pattern Recognition and Artificial Intelligence2004，18，3： | 2 |
| 7 | Technologies Supporting Artificial Intelligence and Robotics Application Development显示文摘Artificial intelligence(AI)and robotics have gone through three generations of development,from Turing test,logic theory machine,to expert system and self-driving car.In the third-generation today,AI and robotics have collaboratively been used in many areas in our society,including industry,business,manufacture,research,and education.There are many challenging problems in developing AI and robotics applications.We launch this new Journal of Artificial Intelligence and Technology to facilitate the exchange of the latest research and practice in AI and technologies.In this inaugural issue,we first introduce a few key technologies and platforms supporting the third-generation AI and robotics application development based on stacks of technologies and platforms.We present examples of such development environments created by both industry and academia.We also selected eight papers in the related areas to celebrate the foundation of this journal. | Yinong Chen Gennaro De Luca | 2021 | Journal of Artificial Intelligence and Technology2021，1，1： | 2 |
| 8 | Wear particle texture classification using artificial neural networks 显示文摘 | Laghapi M S Boujarwah A | 1999 | International Journal of Pattern Recognition and Artificial Intelligence1999，13，3： | 2 |
| 9 | Key Radar Signal Sorting and Recognition Method Based on Clustering Combined with PRI Transform Algorithm显示文摘In this paper,we investigate the problem of key radar signal sorting and recognition in electronic intelligence(ELINT).Our major contribution is the development of a combined approach based on clustering and pulse repetition interval(PRI)transform algorithm,to solve the problem that the traditional methods based on pulse description word(PDW)were not exclusively targeted at tiny particular signals and were less time-efficient.We achieve this in three steps:firstly,PDW presorting is carried out by the DBSCAN(Density-Based Spatial Clustering of Applications with Noise)clustering algorithm,and then PRI estimates of each cluster are obtained by the PRI transform algorithm.Finally,by judging the matching between various PRI estimates and key targets,it is determined whether the current signal contains key target signals or not.Simulation results show that the proposed method should improve the time efficiency of key signal recognition and deal with the complex signal environment with noise interference and overlapping signals. | Kai Kang Yi-xiao Zhang Wen-pu Guo Luo-geng Tian | 2022 | Journal of Artificial Intelligence and Technology2022，2，2： | 2 |
| 10 | A model of inductive bias learning 显示文摘 | Baxter J | 2000 | Journal of Artificial Intelligence Research2000，12，： | 1 |
| 11 | The FF planning system: Fast plan generation through heuristic search 显示文摘 | Hoffmann J Nebel B | 2001 | Journal of Artificial Intelligence Res2001，，14： | 1 |
| 12 | Wrapper induction: efficiency and expressiveness 显示文摘 | | 2000 | Artificial Intelligence Journal2000，118，12： | 1 |
| 13 | Wrappers for feature subset selection 显示文摘 | KOHAVI R JOHN G H | 1997 | artificial Intelligence Journal1997，97，1： | 1 |
| 14 | Smote: Synthetic Minority Over-sampling Technique显示文摘 | Chawla N V Bower K W Hall L O | 2002 | Journal of Artificial Intelligence Research2002，16，3： | 1 |
| 15 | Improved Heterogeneous Distance Functions显示文摘 | Wilson T M | 1997 | Journal of Artificial Intelligence Research1997，6，1： | 1 |
| 16 | A proven three-tiered architecture for programming autonomous robots显示文摘 | R P Bonasso R J Firby E Gat D Kortenkamp D Miller and M Slack | 1997 | Journal of Experimental and Theoretical Artificial Intelligence1997，9，2： | 1 |
| 17 | Detection of sea surface small targets in infrared images based on multilevel filter and minimum risk bayes test显示文摘 | ZHANG Tian-xu ZUO Zheng-rong | 2000 | International Journal of Pattern Recognition and Artificial Intelligence2000，14，7： | 1 |
| 18 | Metalinks:authoring and affordances for conceptual and narrative flow in adaptive hyperbooks显示文摘 | MURRAY T | 2003 | International Journal of Artificial Intelligence in Education2003，13，24： | 1 |
| 19 | The Harvard binocular head显示文摘 | Ferrier N J Clark J J | 1993 | International Journal of Pattern Recognition and Artificial Intelligence1993，7，1： | 1 |
| 20 | Graph classification based on vector space embedding 显示文摘 | Riesen K Bnnke H | 2009 | International Journal of Pattern Recognition and Artificial Intelligence (S1793-6381)2009，23，06： | 1 |
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