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2022 International Conference on Emerging Trends in computing and Engineering Applications, ETCEA 2022

作者： Al-Omoush, Razan Fraihat, Salam Al-Naymat, Ghazi Awad, Mohammed Princess Sumaya University for Technology King Hussein School of Computing Sciences Amman Jordan Ajman University Artificial Intelligence Research Center College of Engineering and Information Technology United Arab Emirates American University of Ras Al Department of Computer Science and Engineering Khaimah United Arab Emirates

ISBN: (纸本)9781665477093

Due to the intense competition in today's online retail environment, companies seek to enhance their strategies by adopting effective analytical techniques and infrastructure, allowing them to quickly analyze critical information that supports decision-making. A Business intelligence (BI) framework can promptly fulfill such needs by processing massive amounts of collected data from multiple sources and representing them in a way companies can utilize in their strategic decisions. This research paper presents a detailed design and implementation of a BI framework for the online retail business industry. It includes requirement analysis, data modeling, BI framework design, and the implementation of descriptive and predictive analytic tools to provide insights and decision support for retail businesses. Moreover, the paper details the implementation of various machine learning algorithms used in sales predictive analytics, such as Linear Regression, Lasso Regression, XGBoost, Random Forest, and LSTM. Interactive charts are provided to assist decision-makers in carrying informed decisions. © 2022 IEEE.

关键词： Predictive analytics

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Hybrid Variational Autoencoder for Collaborative Filtering 25

Hybrid Variational Autoencoder for Collaborative Filtering

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Liu, Jinxin Xiao, Yingyuan Zhu, Ke Zheng, Wenguang Hsu, Ching-Hsien Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Asia University Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9781665405270

In recent years, Variational AutoEncoder (VAE) based methods have made many important achievements in the field of collaborative filtering recommendation system. VAE is a kind of Bayesian model which combines latent variable model with variational inference, but its optimization is often troubled by posterior collapse. By comparing the optimization process of VAE and ordinary autoencoder, we observe that the mismatch between poorly optimized encoder and decoder with too strong characterization capabilities makes it difficult to learn the mapping from the data manifold to the parameterized graph. Since the learning of a posteriori network corresponds to the encoder, we think that the problem of a posteriori collapse can be alleviated by balancing the encoder and decoder better. Therefore, we proposed Hy-VAE, which combines conventional VAE with deterministic autoencoder, and has the advantages of both VAE and deterministic autoencoder. Experiments on three real-world recommendation data sets show that our method alleviates the posterior crash problem in VAE and improves the recommendation performance. © 2022 IEEE.

关键词： Collaborative filtering

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Image Retargeting Techniques Identification Using Supervised Deep Learning

Image Retargeting Techniques Identification Using Supervised...

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Intelligent Data science Technologies and Applications (IDSTA), International Conference on

作者： Ruba Kharsa Rouaa Alzoubi Mohammad Alsmirat Mahmoud Al-Ayyoub Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Artificial Intelligence Research Center (AIRC) College of Engineering and Information Technology Ajman University Ajaman UAE

Image retargeting involves the adjustment of an image's dimensions to ensure that its content and visual quality are preserved when the image is resized to fit various screens or devices. This process retains all essential visual elements and details. Different techniques have been developed for this purpose, including cropping (CR), scaling (SCL), seam carving (SC), warping (WARP), scale-and-stretch (SNS), multi-operator (MULTI), and shift-map (SM). However, determining the most suitable method for retargeting a specific image with particular dimensions remains a challenge. Therefore, this research introduces initial work on developing CNN based deep learning model and a transfer learning model based on InceptionV3,to predict the optimal retargeting method for a given image and resolution. The study employed a dataset consisting of 46,716 images with varying resolutions, created using different retargeting techniques, categorized into six groups. Results demonstrates a promising effectiveness of the proposed approach for selecting the appropriate retargeting techniques.

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Towards automatic speech to sign language generation

arXiv

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arXiv 2021年

作者： Kapoor, Parul Mukhopadhyay, Rudrabha Hegde, Sindhu B. Namboodiri, Vinay Jawahar, C.V. Department of Computer Science and Engineering IIT Kanpur Center for Visual Information Technology IIIT Hyderabad Department of Computer Science University of Bath

We aim to solve the highly challenging task of generating continuous sign language videos solely from speech segments for the first time. Recent efforts in this space have focused on generating such videos from human-annotated text transcripts without considering other modalities. However, replacing speech with sign language proves to be a practical solution while communicating with people suffering from hearing loss. Therefore, we eliminate the need of using text as input and design techniques that work for more natural, continuous, freely uttered speech covering an extensive vocabulary. Since the current datasets are inadequate for generating sign language directly from speech, we collect and release the first Indian sign language dataset comprising speech-level annotations, text transcripts, and the corresponding sign-language videos. Next, we propose a multi-tasking transformer network trained to generate signer’s poses from speech segments. With speech-to-text as an auxiliary task and an additional cross-modal discriminator, our model learns to generate continuous sign pose sequences in an end-to-end manner. Extensive experiments and comparisons with other baselines demonstrate the effectiveness of our approach. We also conduct additional ablation studies to analyze the effect of different modules of our network. A demo video containing several results is attached to the supplementary material. Copyright © 2021, The Authors. All rights reserved.

关键词： Speech

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Neural P3M: a long-range interaction modeling enhancer for geometric GNNs 24

Neural P3M: a long-range interaction modeling enhancer for g...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yusong Wang Chaoran Cheng Shaoning Li Yuxuan Ren Bin Shao Ge Liu Pheng-Ann Heng Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University University of Illinois Urbana-Champaign Department of Computer Science and Engineering The Chinese University of Hong Kong University of Science and Technology of China Microsoft Research AI4Science

ISBN: (纸本)9798331314385

Geometric graph neural networks (GNNs) have emerged as powerful tools for modeling molecular geometry. However, they encounter limitations in effectively capturing long-range interactions in large molecular systems due to the localization assumption of GNN. To address this challenge, we introduce Neural P3M, a versatile enhancer of geometric GNNs to expand the scope of their capabilities by incorporating mesh points alongside atoms and reimaging traditional mathematical operations in a trainable manner. Neural P3M exhibits flexibility across a wide range of molecular systems and demonstrates remarkable accuracy in predicting energies and forces, outperforming on benchmarks such as the MD22 dataset. It also achieves an average improvement of 22% on the OE62 dataset while integrating with various architectures. Codes are available at https://***/OnlyLoveKFC/Neural_P3M.

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Justification in Case-Based Reasoning 1

Justification in Case-Based Reasoning

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1st International Workshop on Argumentation for eXplainable AI, ArgXAI 2022

作者： van Woerkom, Wijnand Grossi, Davide Prakken, Henry Verheij, Bart Department of Information and Computing Sciences Utrecht University Netherlands Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Netherlands Amsterdam Center for Law and Economics University of Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Netherlands Faculty of Law University of Groningen Netherlands

The explanation and justification of decisions is an important subject in contemporary data-driven automated methods. Case-based argumentation has been proposed as the formal background for the explanation of data-driven automated decision making. In particular, a method was developed in recent work based on the theory of precedential constraint which reasons from a case base, given by the training data of the machine learning system, to produce a justification for the outcome of a focus case. An important role is played in this method by the notions of citability and compensation, and in the present work we develop these in more detail. Special attention is paid to the notion of compensation;we formally specify the notion and identify several of its desirable properties. These considerations reveal a refined formal perspective on the explanation method as an extension of the theory of precedential constraint with a formal notion of justification. © 2022 Copyright for this paper by its authors.

关键词： Case based reasoning

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Auxiliary Informatics System: An Advancement Towards A Smart Home Environment

TechRxiv

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TechRxiv 2022年

作者： Gupta, Ishu Yadav, Preetesh K. Pareek, Sourav Shakeel, Saif Singh, Ashutosh Kumar Cloud Computing Research Center Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan Department of Computer Applications National Institute of Technology Kurukshetra136119 India

The main problem that we face today is not about getting services, but how efficiently we manage them. We are living in an era where we cannot neglect the fact that we are now depending on various electronic devices which we use daily in our life. These devices have become an inseparable part of our life, in the present scenario technology is evolving so rapidly that it’s a fact if we do not accept any upcoming trend then we lag behind in the eye of others. Managing all these devices and remembering to tune them is quite a hectic task, as we have to do other main tasks too. Using every device efficiently at the same time is not possible. Hence, we came up with a solution, with a system that will manage all these devices according to predefined rules or instructions. Current smart home systems work in a way, to establish a connection between nodes and a hub and are controlled by smartphones, computers, remote, etc. In this research paper, we have proposed a model that will not only establish a connection but will work smartly according to instructions and algorithms saved earlier. This Auxiliary Informatics System (AuxIS) will serve the requirement of managing all the devices in a parallel fashion with very little human intervention and hence, will make a smarter home environment. © 2022, CC BY.

关键词： Internet of things

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Landmarks in Case-Based Reasoning: From Theory to Data 1

Landmarks in Case-Based Reasoning: From Theory to Data

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1st International Conference on Hybrid Human-Artificial Intelligence, HHAI 2022

作者： Van Woerkom, Wijnand Grossi, Davide Prakken, Henry Verheij, Bart Department of Information and Computing Sciences Utrecht University Netherlands Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Netherlands Amsterdam Center for Law and Economics University of Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Netherlands Faculty of Law University of Groningen Netherlands

ISBN: (纸本)9781643683089

Widespread application of uninterpretable machine learning systems for sensitive purposes has spurred research into elucidating the decision making process of these systems. These efforts have their background in many different disciplines, one of which is the field of AI & law. In particular, recent works have observed that machine learning training data can be interpreted as legal cases. Under this interpretation the formalism developed to study case law, called the theory of precedential constraint, can be used to analyze the way in which machine learning systems draw on training data - or should draw on them - to make decisions. These works predominantly stay on the theoretical level, hence in the present work the formalism is evaluated on a real world dataset. Through this analysis we identify a significant new concept which we call landmark cases, and use it to characterize the types of datasets that are more or less suitable to be described by the theory. © 2022 The authors and IOS Press.

关键词： Case based reasoning

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iCOIL: Scenario Aware Autonomous Parking Via Integrated Constrained Optimization and Imitation Learning

iCOIL: Scenario Aware Autonomous Parking Via Integrated Cons...

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International Conference on Distributed computing Systems Workshop

作者： Lexiong Huang Ruihua Han Guoliang Li He Li Shuai Wang Yang Wang Chengzhong Xu Center for Cloud Computing Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Department of Computer Science University of Hong Kong IOTSC University of Macau

Autonomous parking (AP) is an emerging technique to navigate an intelligent vehicle to a parking space without any human intervention. Existing AP methods based on mathematical optimization or machine learning may lead to potential failures due to either excessive execution time or lack of generalization. To fill this gap, this paper proposes an integrated constrained optimization and imitation learning (iCOIL) approach to achieve efficient and reliable AP. The iCOIL method has two candidate working modes, i.e., CO and IL, and adopts a hybrid scenario analysis (HSA) model to determine the better mode under various scenarios. We implement and verify iCOIL on the Macao Car Racing Metaverse (MoCAM) platform. Results show that iCOIL properly adapts to different scenarios during the entire AP procedure, and achieves significantly larger success rates than other benchmarks.

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MLRM: A MULTIPLE LINEAR REGRESSION BASED MODEL FOR AVERAGE TEMPERATURE PREDICTION OF A DAY

arXiv

引用

arXiv 2022年

作者： Gupta, Ishu Mittal, Harsh Rikhari, Deepak Singh, Ashutosh Kumar Cloud Computing Research Center Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan Department of Computer Applications National Institute of Technology Kurukshetra136119 India

Weather is a phenomenon that affects everything and everyone around us on a daily basis. Weather prediction has been an important point of study for decades as researchers have tried to predict the weather and climatic changes using traditional meteorological techniques. With the advent of modern technologies and computing power, we can do so with the help of machine learning techniques. We aim to predict the weather of an area using past meteorological data and features using the Multiple Linear Regression Model. The performance of the model is evaluated and a conclusion is drawn. The model is successfully able to predict the average temperature of a day with an error of 2.8 degrees Celsius. © 2022, CC BY.

关键词： Weather forecasting

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