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the automated Future: How AI and Automation Are Revolutionizing Online Services 8

The Automated Future: How AI and Automation Are Revolutioniz...

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8th IEEE international conference on engineering Technologies and Applied Sciences, ICETAS 2023

作者： Desmal, Abdulla Jaafar Alsaeed, Mohamed Hamid, Suraya Zulait, Ali Hussain College of Computer Studies University of Technology Bahrain Salmabad Bahrain Universiti Malaya Department of Information System Kuala Lumpur Malaysia College of Information Technology University of Bahrain Sakhair Bahrain

ISBN: (纸本)9798350327090

Artificial intelligence (AI) and automation are rapidly transforming the landscape of online services. From intelligent chatbots to hyper-personalized recommendations, AI-driven technologies are enabling online providers to deliver more customized, streamlined, and human-like digital experiences. this paper examines the growing role and impacts of AI and automation across a range of online industries, products, and interactions. It analyzes key applications like natural language processing, computer vision, predictive analytics, and robotic process automation that are automating everything from customer service to supply chain logistics. the benefits of these technologies, including 24/7 availability, scalability, and cost savings, are discussed along with challenges around data privacy, transparency, and workforce impacts. the paper underscores how AI and automation will shape the future of how companies engage and serve digital consumers. As online providers continue to collect more data and refine predictive algorithms, they edge closer to delivering truly individualized services tailored to customers' ever-evolving preferences and needs. However, realizing the promise of AI will require addressing critical issues around bias, ethics, and accountability. this paper provides a comprehensive look at the current landscape and future trajectory of AI and automation in online services. © 2023 IEEE.

关键词： Machine learning

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Improving automated Evaluation of Student Text Responses Using GPT-3.5 for Text data Augmentation 24th

Improving Automated Evaluation of Student Text Responses Usi...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Cochran, Keith Cohn, Clayton Rouet, Jean Francois Hastings, Peter DePaul Univ Chicago IL 60604 USA Vanderbilt Univ 221 Kirkland Hall Nashville TN 37235 USA Univ Poitiers F-86073 Poitiers 9 France

ISBN: (纸本)9783031362712;9783031362729

In education, intelligent learning environments allow students to choose how to tackle open-ended tasks while monitoring performance and behavior, allowing for the creation of adaptive support to help students overcome challenges. Timely feedback is critical to aid students' progression toward learning and improved problem-solving. Feedback on text-based student responses can be delayed when teachers are overloaded with work. automated evaluation can provide quick student feedback while easing the manual evaluation burden for teachers in areas with a high teacher-to-student ratio. Current methods of evaluating student essay responses to questions have included transformer-based natural language processing models with varying degrees of success. One main challenge in training these models is the scarcity of data for studentgenerated data. Larger volumes of training data are needed to create models that perform at a sufficient level of accuracy. Some studies have vast data, but large quantities are difficult to obtain when educational studies involve student-generated text. To overcome this data scarcity issue, text augmentation techniques have been employed to balance and expand the data set so that models can be trained with higher accuracy, leading to more reliable evaluation and categorization of student answers to aid teachers in the student's learning progression. this paper examines the text-generating AI model, GPT-3.5, to determine if prompt-based text-generation methods are viable for generating additional text to supplement small sets of student responses for machine learning model training. We augmented student responses across two domains using GPT-3.5 completions and used that data to train a multilingual BERT model. Our results show that text generation can improve model performance on small data sets over simple self-augmentation.

关键词： data augmentation text generation BERT GPT-3.5 educational texts natural language processing

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Improving Classroom Quality by Analyzing Student Behaviors through Computer Vision 4

Improving Classroom Quality by Analyzing Student Behaviors T...

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4th international conference on Information Science and Education, ICISE-IE 2023

作者： Chen, Xiaopan Li, Boyuan Zhu, Xiaoke Zhang, Tao School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9798350394610

this paper aims to enhance the efficiency of teaching and learning by leveraging computer vision technology for automated analysis of student behavior in the classroom. We propose a feature-enhanced method for recognizing student behavior, which integrates an Upsampling module (UPS) and a convolutional block layer (CBL) module into the YOLOv5 model. this innovative approach significantly improves the detection and feature extraction capabilities for student targets, effectively addressing challenges associated with small-sized targets and lowresolution images. through algorithm optimization, it achieves a balance in sample diversity, enhancing the model's adaptability and generalizability in complex classroom environments. Building upon these technological advancements, we have developed a Classroom Student Posture Perception System (CSP2S). this system integrates functions for data collection, student behavior detection, and result visualization. the CSP2S operates in realtime by capturing and analyzing students' behavioral dynamics while providing teachers with immediate feedback. Experimental results demonstrate that this system substantially reduces teachers' workload in student assessment, offering significant potential to improve teaching effectiveness. these robust technical advancements provide strong support for future applications in real educational environments. © 2023 IEEE.

关键词： Students

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Receiver-Agnostic Radio Frequency Fingerprint Identification via Feature Disentanglement 24

Receiver-Agnostic Radio Frequency Fingerprint Identification...

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24th IEEE international Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Liu, Hongli Zhu, Shaoxian Yang, Liu Li, Qiang Lin, Jingran University of Electronic Science and Technology of China School of Information and Communication Engineering China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing100089 China

ISBN: (纸本)9781665496261

the Radio Frequency Fingerprint Identification (RFFI) technique utilizes the subtle and unintentional modulation present in the transmitted RF waveform, caused by the non-ideal characteristics of the device, to uniquely identify an emitter. RFFI has recently gained significant attention, and deep learning (DL) approach has achieved superior performance. In this paper, we study the DL-based RFFI with an emphasis on the fingerprint contamination by receivers. Specifically, during reception, the receivers' fingerprints are inevitably introduced, and different receivers affect emitters' fingerprints differently, thereby degrading recognition performance when the RFFI recognition model is deployed at different receivers. To mitigate the receivers' effect, we propose a receiver-independent recognition model based on the idea of feature disentanglement. With the aid of some carefully designed model structure, loss functions and training strategy, the proposed model learns a feature space, in which the emitters' features are disentangled from the receivers', through multi-receiver's training data. When deploying the model on a new receiver, the emitter-dependent features are exploited to achieve receiver-independent identification. Experiments on two real-world datasets demonstrate that our method significantly outperforms the baseline method without considering fingerprint contamination. © 2023 IEEE.

关键词： Deep learning

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Math Information Retrieval with Contrastive learning of Formula Embeddings 24th

Math Information Retrieval with Contrastive Learning of Fo...

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24th international conference on Web Information Systems engineering, WISE 2023

作者： Wang, Jingyi Tian, Xuedong School of Cyber Security and Computer Hebei University Baoding071002 China Institute of Intelligent Image and Document Information Processing Hebei University Baoding071002 China Hebei Machine Vision Engineering Research Center Hebei University Baoding071002 China

ISBN: (纸本)9789819972531

the core and hard part of Mathematical Information Retrieval (MathIR) is formula retrieval. the datasets used for formula retrieval are usually scientific documents containing formulas. However, there is a lack of labeled datasets specifically for formula similarity. Contrastive learning can learn general features of datasets from unlabeled data and autonomously discover latent structures in the data. Furthermore, dense retrieval methods based on bi-encoders have gained increasing attention. therefore, we propose CLFE, a simple framework for contrastive learning of formula embeddings. It can learn the latent structure and content information of formulas from unlabeled formulas and generate formula embeddings for formula retrieval. We design two frameworks, Contrastive Presentation MathML-Content MathML learning and Contrastive LaTeX-MathML learning, which initialize the encoders using transformer-based models and can produce superior LaTeX, Presentation MathML, and Content MathML embeddings for each formula. the combination of these three embeddings results in the Add embedding and Concat embedding. Finally, the formula retrieval task is achieved by finding the k nearest formulas in the vector space through nearest neighbor search. Experimental results show that when applying our proposed method on the NTCIR-12 dataset to retrieve 20 non-wildcard formulas and scoring the top-10 results, the highest achieved scores for mean P@10, mean nDCG@10, and MAP@10 are 0.6250, 0.9792, and 0.9381. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Information retrieval

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3D fuzzy GIST to analyze emotional features in movies

3D fuzzy GIST to analyze emotional features in movies

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13th international conference on intelligent data engineering and automated learning, ideal 2012

作者： Kwon, Mingu Lee, Minho School of Electronics Engineering and Computer Science Kyungpook National University 1370 Sankyuk-Dong Puk-Gu Taegu 702-701 Korea Republic of

ISBN: (纸本)9783642326387

In this paper, we propose a 3D fuzzy GIST to effectively describe the visual dynamic features related to the emotional characteristics in a movie clip. Unlike the previous fuzzy approaches, which use images, the proposed method employs movie clips and can dynamically extract the features to classify the emotional characteristics in a movie clip. the 3D fuzzy GIST based on 3D tensor data including L*C*H color (L: Lightness, C: Chroma, H: Hue) and orientation information in a movie clip can extract the visual dynamic features related to the emotional characteristics in a movie clip. the extracted visual dynamic features obtained by the proposed 3D fuzzy GIST are used as inputs to an adaptive neuro-fuzzy inference classifier. the classifier is provided with the mean opinion scores as the teaching signals. Experimental results show that the system with the proposed 3D fuzzy GIST feature extractor not only discriminates the positive emotional features from the negative ones but also identifies the changes of emotional features in movie clips successfully. © 2012 Springer-Verlag.

关键词： Motion pictures

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Enhancing Noisy Binary Search Efficiency through Deep Reinforcement learning

Enhancing Noisy Binary Search Efficiency through Deep Reinfo...

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IEEE international conference on Information Reuse and Integration (IRI)

作者： Rui Ma Yudong Tao Mohamed M. Khodeiry Karam A. Alawa Mei-Ling Shyu Richard K. Lee Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA Bascom Palmer Eye Institute University of Miami Miller School of Medicine Miami FL USA School of Science and Engineering University of Missouri-Kansas City Kansas City MO USA

Noisy binary search (NBS) aims to find the closest element to a target value within a sorted array through erroneous queries. In an ideal NBS environment where the error rate remains constant, and the costs of all queries are the same, the maximum likelihood estimation (MLE) procedure has been proven to be the optimal decision strategy. However, in some non-ideal NBS problems, both the error rates and the costs are dependent on the queries, and in some cases, finding the optimal decision strategies can be intractable. We propose to use deep reinforcement learning to approximate the optimal decision strategy in the NBS problem, in which an intelligent agent is used to interact with the NBS environment. A dueling double deep Q-network guides the agent to take action at each step, either to generate a query or to stop the search and predict the target value. An optimized policy will be derived by training the network in the NBS environment until convergence. By evaluating our proposed algorithm on a non-ideal NBS environment, visual field test, we show that the performance of our proposed algorithm surpasses baseline visual field testing algorithms by a large margin.

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Traffic Pattern Sharing for Federated Traffic Flow Prediction with Personalization 24

Traffic Pattern Sharing for Federated Traffic Flow Predictio...

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24th IEEE international conference on data Mining, ICDM 2024

作者： Zhou, Hang Yu, Wentao Wan, Sheng Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China SKLSDE Beihang University China Jinan University Engineering Research Center of Trustworthy AI Ministry of Education China

ISBN: (纸本)9798331506681

Accurate Traffic Flow Prediction (TFP) is crucial for enhancing the efficiency and safety of transportation systems, so it has attracted intensive researches by exploiting spatial-temporal dependencies within road networks. However, existing works only consider the case of centralized data collection with all traffic data observed, which may raise privacy concerns as each region of a city may have its own traffic administration department and the traffic data is not allowed to distribute. therefore, this paper proposes to use Federated learning (FL) to address this issue by allowing all clients (i.e., traffic administration departments in all regions in our problem) to collaboratively train TFP models without exchanging raw data, thereby offering a solution in maintaining data privacy. Nevertheless, most existing FL methods aim to learn a global model that performs well universally, so they cannot well handle the non-Independent and Identically Distributed (non-IID) traffic data naturally over different regions. To cope with this problem, this paper develops a new FL framework termed 'personalized Federated learning with Traffic Pattern Sharing' (FedTPS) to solve federated TFP problem. Our FedTPS critically exploits the underlying common traffic patterns (e.g., morning and evening rush hours) shared across different city regions and meanwhile maintaining the region-specific data characteristics in a personalized FL manner. Specifically, to extract the common traffic patterns, we decompose the traffic data in each client via using discrete wavelet transform, where the low-frequency components uncover the stable traffic dynamics of different regions and thus can be considered as the common traffic patterns. these common patterns are then shared among different clients through traffic pattern repositories on the server side to aid the global collaborative traffic flow modeling. Moreover, the model components capturing spatial-temporal dependencies in traffic data are r

关键词： Highway administration

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Reinforcement learning for Microwave Windows Tuning

Reinforcement Learning for Microwave Windows Tuning

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international conference on Vacuum Electronics

作者： Li Liao Zheng Tan HaiYing Yuan YuLu Hu LuanFeng Gao GuoQun Cao National Key Laboratory of Science and Technology on Vacuum Electronic School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu P.R. China Shenzhen Institute for Advanced Study UESTC Shenzhen P.R. China

An intelligent tuning method of microwave window based on reinforcement learning is proposed in this paper. Using Python script to drive HFSS, the method can obtain the structural parameters and simulation response data of the microwave window. this method takes the structural parameters and simulation response data as input and output to establish the forward prediction model and the reverse prediction model of the microwave window. HFSS environment, action, state, reward and other elements of reinforcement learning are constructed in this method. Based on this method, the S-parameter curves of microwave window can be rapidly optimized, and the microwave window with reflection coefficient less than −26.444 dB in the working frequency band of $\boldsymbol{135\sim 145}\mathbf{GHz}$ is realized.

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automated Scoring of Logical Consistency of Japanese Essays 24th

Automated Scoring of Logical Consistency of Japanese Essays

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Nakamoto, Sayaka Shimada, Kazutaka Kyushu Inst Technol Iizuka Japan

ISBN: (纸本)9783031363351;9783031363368

automated essay scoring (AES) is to estimate the scores of essays automatically. Two types of AES models are commonly used: handcrafted feature-based and neural-based models. In this paper, we introduce AES systems based on the two types for evaluating the logical consistency of Japanese essays. In addition, to enhance the performance of models, we integrate the neural-based model with the handcrafted features: a hybrid AES system. In the experiment, we show the effectiveness of our hybrid AES system. Besides, most of our AES models obtained higher QWK scores than human evaluators.

关键词： automated Essay Scoring Machine learning Education Technology

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