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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Mallapragada, Srivatsa Xie, Ying Chawan, Varsha Rani Hailat, Zeyad Wang, Yuanbo Kennesaw State University School of Data Science and Analytics MariettaGA United States Kennesaw State University MariettaGA United States The Home Depot Inc AtlantaGA United States

ISBN: (纸本)9798350362480

E-commerce click-stream data and product catalogs offer critical user behavior insights and product knowledge. This paper propose a multi-modal transformer termed as PINCER, that leverages the above data sources to transform initial user queries into pseudo-product representations. By tapping into these external data sources, our model can infer users' potential purchase intent from their limited queries and capture query relevant product features. We demonstrate our model's superior performance over state-of-the-art alternatives on e-commerce online retrieval in both controlled and real-world experiments. Our ablation studies confirm that the proposed transformer architecture and integrated learning strategies enable the mining of key data sources to infer purchase intent, extract product features, and enhance the transformation pipeline from queries to more accurate pseudo-product representations. © 2024 IEEE.

关键词： Sales

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Stress prediction through sleep parameters using a feature engineering approach 4

Stress prediction through sleep parameters using a feature e...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Singh, Krishna Kumar Mahapatra, Madhumita Dixit, Ganesh Kumar Symbiosis Centre for Information Technology Dept of Data Science & Analytics Pune India Delhi Technical Campus Dept of Cse Greater Noida India Starex University School of Cs & Technology Haryana India

ISBN: (纸本)9798350307757

Stress is a high growing problem in today's age. Stress affects physical and emotional well-being and also affects the normal working of a person. It can have long- lasting health hazards, which can turn into diseases if not treated promptly. Detecting Stress and measuring Stress is the first step toward curing it. Stress can be predicted by using sleep parameters. A sleeping person's snoring range, eye movement, limb movement, hours of Sleep, heart rate, etc., can be used to measure stress levels. Stress can be predicted with the help of these parameters by using Machine Learning models. This study is on the detection of Stress and aims to develop a machine-learning model that can precisely determine stress levels by looking at sleep patterns. In this study, the researcher aims to estimate the amount of Stress using machine learning algorithms using particular human body parameters recorded during Sleep. Among the sleep parameters are snoring intensity, limb movement rate, eye movement rate, and sleep duration. Other metrics include heart rate, respiration rate, body temperature, and oxygen level while sleeping. In order to assess the precision of stress level prediction, the study performs multiclass classification using machine learning techniques. In this research, multiple multiclass classification techniques were used to predict the stress levels of a sleeping person with the highest accuracy of 99.5%. The techniques used in performing the predictions are less in time complexity and code complexity and thus can be used in devices with less computational resources, such as smart wearables. This research will therefore help in detecting and measuring Stress quickly. © 2024 IEEE.

关键词： Forecasting

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Pretrained Sentence Embedding and Semantic Sentence Similarity Language Model for Text Classification in NLP 3

Pretrained Sentence Embedding and Semantic Sentence Similari...

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3rd International Conference on Artificial Intelligence and Signal Processing, AISP 2023

作者： Valli Mayil, V. Ratha Jeyalakshmi, T. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Jain University School of Sciences Department of Data Science & Analytics Karnataka Banglore India

ISBN: (纸本)9798350320749

A key task in the field of Natural Language Processing (NLP) is determining semantic similarity between text sentences. Sentence pair modeling, Textual similarity and language modeling are few important tasks in NLP. Traditional machine learning algorithms require an enormous quantity of training data, but it is a timeconsuming process. Pre-trained models can be modified for a variety of downstream applications since they use methods for generically learning the characteristics of neural network topologies and language representations. Bidirectional Encoder Representations from Transformers- BERT & GPT are the popular architectures in NLP which enable to use minimal fine-tuning effort to produce effective results. In this work a fine-tuned BERT model that is suitable for semantic sentence similarity which predicts the entailment, neutral and contradictory categories of sentence pairs is presented. The fine-tuning feature promotes the training phase of model whichis widely effective across different types of semantic similarity models. The performance analysis of our system shows that the fine-tuned model reduces the number of neurons in the neural network there by reducing storage and time spent in expensive training task to create deep learning model. © 2023 IEEE.

关键词： Semantics

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DE-ConvGraph 3D UNet: A Novel Deep Learning Model for Optimizing Radiotherapy Treatment Plans in Oropharyngeal Cancer

DE-ConvGraph 3D UNet: A Novel Deep Learning Model for Optimi...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Murugadoss, Bhuvanashree Amudha, J. Sugumaran, Vijayan Amrita Vishwa Vidyapeetham School of Computing Department of Computer Science and Engineering Karnataka Bengaluru India Oakland University Center for Data Science and Big Data Analytics RochesterMI United States Oakland University Department of Decision and Information Sciences RochesterMI United States

ISBN: (纸本)9798350359527

Automated radiotherapy treatment planning aims to improve treatment accuracy and efficiency. However, the prevalent Knowledge-Based Planning (KBP) method faces issues like lengthy manual problem formulation and challenges in accurately modeling human anatomy and processing high-dimensional data. This work focuses on treatment plan optimization and considers deep learning as a potential solution. Deep learning algorithms, by virtue of their ability to learn from large quantities of data and model complex relationships, can automate the formulation of the optimization problem in KBP, saving significant time and effort. However, despite these compelling advantages, it should be noted that the current convolution-based encoder-decoder models used for radiotherapy treatment plan optimization have a limited capability in capturing long-range dependencies between distant voxels. This work aims to introduce 'DE-ConvGraph 3D UNet', a novel deep learning model, to address these limitations and optimize radiotherapy treatment plans for oropharyngeal cancer. The proposed 'DE-ConvGraph 3D UNet' model includes a Graph Convolutional Network (GCN) component to capture long-range dependencies between distant voxels. Furthermore, the dual-encoder structure of the model combines the strengths of GCN and 3D Convolutional U-Net, enabling global relationships and local pattern capturing in a 3D patient volume. Experiments were conducted using the benchmark dataset-OpenKBP-Opt. The proposed model shows improvements in performance in comparison to state-of-The-Art U-Net variants in terms of mean squared voxel-wise error, dose volume histogram points and clinical criteria satisfaction. © 2023 IEEE.

关键词： Learning algorithms

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Efficient Inference and Computation of Optimal Alternatives for Preference Languages Based On Lexicographic Models Longer Version of IJCAI-17 Submission

arXiv

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arXiv 2024年

作者： Wilson, Nic George, Anne-Marie Insight Centre for Data Analytics School of Computer Science and IT University College Cork Ireland

We analyse preference inference, through consistency, for general preference languages based on lexicographic models. We identify a property, which we call strong compositionality, that applies for many natural kinds of preference statement, and that allows a greedy algorithm for determining consistency of a set of preference statements. We also consider different natural definitions of optimality, and their relations to each other, for general preference languages based on lexicographicmodels. Based on our framework,we show that testing consistency, and thus inference, is polynomial for a specific preference language L' pqT , which allows strict and non-strict statements, comparisons between outcomes and between partial tuples, both ceteris paribus and strong statements, and their combination. Computing different kinds of optimal sets is also shown to be polynomial;this is backed up by our experimental results. © 2024, CC BY.

关键词： Polynomials

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Explaining Classifications to Non-experts: An XAI User Study of Post-Hoc Explanations for a Classifier When People Lack Expertise 1

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26th International Conference on Pattern Recognition, ICPR 2022

作者： Ford, Courtney Keane, Mark T. School of Computer Science University College Dublin Dublin Ireland Insight Centre for Data Analytics UCD Dublin Ireland ML Labs SFI CRT Dublin Ireland

ISBN: (数字)9783031377310

ISBN: (纸本)9783031377303

Very few eXplainable AI (XAI) studies consider how users’ understanding of explanations might change depending on whether they know more/less about the to-be-explained domain (i.e., whether they differ in their expertise). Yet, expertise is a critical facet of most high-stakes, human decision-making (e.g., understanding how a trainee doctor differs from an experienced consultant). Accordingly, this paper reports a novel, user study (N = 96) on how people’s expertise in a domain affects their understanding of post-hoc explanations-by-example for a deep-learning, black-box classifier. The results show that people’s understanding of explanations for correct and incorrect classifications changes dramatically, on several dimensions (e.g., response times, perceptions of correctness and helpfulness), when the image-based domain considered is familiar (i.e., MNIST) as opposed to unfamiliar (i.e., Kannada-MNIST). The wider implications of these new findings for XAI strategies are discussed. © 2023, Springer Nature Switzerland AG.

关键词： Deep learning

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Computation and Complexity of Preference Inference Based on Hierarchical Models

arXiv

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arXiv 2024年

作者： Wilson, Nic George, Anne-Marie O’Sullivan, Barry Insight Centre for Data Analytics School of Computer Science and IT University College Cork Ireland

Preference Inference involves inferring additional user preferences from elicited or observed preferences, based on assumptions regarding the form of the user’s preference relation. In this paper we consider a situation in which alternatives have an associated vector of costs, each component corresponding to a different criterion, and are compared using a kind of lexicographic order, similar to the way alternatives are compared in a Hierarchical Constraint Logic Programming model. It is assumed that the user has some (unknown) importance ordering on criteria, and that to compare two alternatives, firstly, the combined cost of each alternative with respect to the most important criteria are compared;only if these combined costs are equal, are the next most important criteria considered. The preference inference problem then consists of determining whether a preference statement can be inferred from a set of input preferences. We show that this problem is coNP-complete, even if one restricts the cardinality of the equal-importance sets to have at most two elements, and one only considers non-strict preferences. However, it is polynomial if it is assumed that the user’s ordering of criteria is a total ordering;it is also polynomial if the sets of equally important criteria are all equivalence classes of a given fixed equivalence relation. We give an efficient polynomial algorithm for these cases, which also throws light on the structure of the inference. © 2024, CC BY.

关键词： Logic programming

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QUALYST: data Quality Assessment System for Thailand Open Government data 20

QUALYST: Data Quality Assessment System for Thailand Open Go...

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20th International Joint Conference on Computer science and Software Engineering, JCSSE 2023

作者： Samakit, Tanapat Anutariya, Chutiporn Buranarach, Marut School of Engineering and Technology Asian Institute of Technology Computer Science & Information Management Ict Department Thailand National Electronics and Computer Technology Center Data Science and Analytics Research Group Thailand

ISBN: (纸本)9798350300505

Open Government data (OGD) refers to the provision of data produced by the government to the general public, in a format that is readily readable and can be used by machines with ease. It can also promote transparency, improve decision-making, enhance accountability, create economic opportunities, and encourage civic engagement. The OGD can help citizens understand the government and its legitimacy and transparency. Thus, when the government shares its data with people, it helps to create trust by being transparent, accountable, and promoting innovative solutions that benefit everyone. However, each published dataset has no indication of its quality assessment at all;thus, making it difficult for citizens to assess the reliability of the data from the OGD. Therefore, a data quality assessment for OGD should be developed. This will help create effective datasets which in turn help users understand the data. This study proposes QUALYST, a system that assesses Thailand's OGD dataset and validates it for analytic and visualization purposes. The study focuses on designing the data storage and implementing the assessment system. Furthermore, the proposed data quality dimensions, the developed data pipeline, and the assessment process are elaborated. Finally, the prototype system is demonstrated using Thailand's OGD datasets with examples in a visualized format. © 2023 IEEE.

关键词： Transparency

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A Style and Attention Mechanism Based Generative Adversarial Neural Network (Style-Atten GAN) for Flute Music Composition 9

A Style and Attention Mechanism Based Generative Adversarial...

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9th IEEE International Conference on Computational Intelligence and Applications, ICCIA 2024

作者： Guo, Dan Zhang, Dian Fyr, Kyle Yu, Sha College of Music Guizhou University China Hainan University Trusted Data and Intelligent Analytics Research Group China College of Music Mahidol University Thailand School of Computer Science and Technology Guizhou University China

ISBN: (纸本)9798350352214

This study delves into the task of automated flute music composition by leveraging deep learning methods to automatically generate flute music with single and multiple tracks. To enhance musical complexity and diversity, we propose a new music generation model called Style-Atten GAN, with the integration of a generative adversarial network (GAN) model, self-attention mechanism, and stylistic characteristics of the flute. This directs the music generative process to focus on critical musical elements and structures in typical flute audios, thereby fostering the production of compositions that are not only novel, but also rich in musicality and expressiveness. Through a comparative experiment and analysis, this study evaluated the effectiveness of the proposed Style-Atten GAN model against recent music composition methods of the same type. Quantitative and qualitative analytical indicators highlight the superiority of the proposed neural network approach in producing stylistically diverse and creatively rich flute music. The findings reveal that GAN models, when augmented with the attention mechanism and unique stylized characteristics of the flute instrument, can outperform conventional methods in terms of fidelity, adherence to musical aesthetics, and the ability to capture the nuanced expressiveness of flute music. © 2024 IEEE.

关键词： Generative adversarial networks

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Towards Fast Algorithms for the Preference Consistency Problem Based on Hierarchical Models

arXiv

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arXiv 2024年

作者： George, Anne-Marie Wilson, Nic O'Sullivan, Barry Insight Centre for Data Analytics School of Computer Science IT University College Cork Ireland

In this paper, we construct and compare algorithmic approaches to solve the Preference Consistency Problem for preference statements based on hierarchical models. Instances of this problem contain a set of preference statements that are direct comparisons (strict and non-strict) between some alternatives, and a set of evaluation functions by which all alternatives can be rated. An instance is consistent based on hierarchical preference models, if there exists an hierarchical model on the evaluation functions that induces an order relation on the alternatives by which all relations given by the preference statements are satisfied. Deciding if an instance is consistent is known to be NP-complete for hierarchical models. We develop three approaches to solve this decision problem. The first involves a Mixed Integer Linear Programming (MILP) formulation, the other two are recursive algorithms that are based on properties of the problem by which the search space can be pruned. Our experiments on synthetic data show that the recursive algorithms are faster than solving the MILP formulation and that the ratio between the running times increases extremely quickly. © 2024, CC BY.

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