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25th Pacific-Asia conference on knowledge discovery and data mining (PAKDD)

作者： Shang, Tianqi Li, Xinxin Shi, Xiaoyu Wang, Qingxian Sichuan Univ Coll Comp Sci Chengdu 610207 Peoples R China Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 610054 Peoples R China Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing Key Lab Big Data & Intelligent Comp Chongqing 400714 Peoples R China

ISBN: (纸本)9783030757656;9783030757649

The emerging of sequential recommender (SR) has attracted increasing attention in recent years, which focuses on understanding and modeling the temporal dynamic of user behaviors hidden in the sequence of user-item interactions. However, with the tremendous increase of users and items, SR still faces several challenges: (1) the hardness of modeling user interests from spare explicit feedback;(2) the time and semantic irregularities hidden in the user's successive actions. In this study, we present a neural network-based sequential recommender model to learn the temporal-aware user preferences and item popularity jointly from reviews. The proposed model consists of the semantic extracting layer and the dynamic feature learning layer, besides the embedding layer and the output layer. To alleviate the data sparse issue, the semantic extracting layer focuses on exploiting the enriched semantic information hidden in reviews. To address the time and semantic irregularities hidden in user behaviors, the dynamic feature learning layer leverages convolutional fitters with varying size, integrating with a time-ware controller to capture the temporal dynamic of user and item features from multiple temporal dimensions. The experimental results demonstrate that our proposed model outperforms several state-of-art methods consistently.

关键词： Sequential recommendation Preference modeling Temporal dynamic Semantic extracting Deep learning

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Empowering data mining Sciences by Habitual Domains theory, Part ii: Reaching Wonderful Solutions

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Annals of data Science 2020年第4期7卷 549-580页

作者： Larbani, Moussa Yu, Po Lung School of Mathematics and Statistics Carleton University Ottawa Canada School of Business University of Kansas LawrenceKS66045 United States Institute of Information Management National Chiao Tung University Hsinchu30010 Taiwan

In the Part I of this paper, we presented the main concept of the proposed comprehensive decision model based on Habitual Domains theory, the concept of wonderful solution for solving challenging decision problems that we called decision making in changeable spaces problem (DMCS). In this Part ii of the paper, we complete the construction of the model and show that it is operational and effectively empowers DMs in facing challenges. For this purpose, we present the mental principles "7–8–9 principles" that can be used to restructure decision parameters so that new solutions or alternatives could emerge. Then we provide procedures for finding wonderful solutions as sequences of the 7–8–9 principles by solving optimization in changeable spaces (OCS) problems, a new paradigm in optimization. Finally, we present applications of the model to post data mining analysis and decision making. In fact, the proposed model can be used in any area involving decision making and knowledge discovery such as management, politics, health care, technology and research. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： data mining

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Machine Learning-Driven Blockchain for Enhanced Drug discovery and Development in Pharmaceutical Research

Machine Learning-Driven Blockchain for Enhanced Drug Discove...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： V Asha Shaik Anjimoon Meenu Rani Verma Atul Singla Irfan Khan Mohammed Ayad Alkhafaji Department of Computer Applications New Horizon College of Engineering Bangalore Institute of Aeronautical Engineering Dundigal Hyderabad DoCSBS GyanGanga Institute of Technology and Sciences Jabalpur M.P India Lovely Professional University Phagwara Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh College of Technical Engineering National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

Machine learning (ML) and blockchain technologies have made huge steps forward in the pharmacy business by being used in the process of creating and discovering new medicines. Both breakthroughs were made possible by using both tools together. With the help of a blockchain design powered by machine learning, the study’s main goal is to come up with a new way to make drug research more effective and efficient. This method makes it possible to make molecules with a wide range of structure features that have the right pharmacological qualities. You can also make these molecules, improve the plans of clinical trials, and guess how drugs will interact with their targets. Technologies like generative adversarial networks, reinforcement learning methods, and deep learning algorithms have made it possible for these things to be used. If blockchain technology is used throughout the whole process of making medicines, it improves global openness, traceability, and data security. This is because it makes sure that managing data is safe. The tests show that the suggested method works better than the others when it comes to the process of finding and developing new drugs. This is better because the creation process is more precise, accurate, and efficient.

关键词： Drugs Deep learning Technological innovation data security Reinforcement learning Production Clinical trials

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Building Continuous Integration Services for Machine Learning 20

Building Continuous Integration Services for Machine Learnin...

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26th ACM SIGKDD International conference on knowledge discovery and data mining (KDD)

作者： Karlas, Bojan Interlandi, Matteo Renggli, Cedric Wu, Wentao Zhang, Ce Mukunthu, Deepak Babu, Iyappan Edwards, Jordan Lauren, Chris Xu, Andy Weimer, Markus Microsoft Redmond WA 98052 USA Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781450379984

Continuous integration (CI) has been a de facto standard for building industrial-strength software. Yet, there is little attention towards applying CI to the development of machine learning (ML) applications until the very recent effort on the theoretical side. In this paper, we take a step forward to bring the theory into practice. We develop the first CI system for ML, to the best of our knowledge, that integrates seamlessly with existing ML development tools. We present its design and implementation details.

关键词： data management machine learning continuous integration testing overfitting prevention

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Insurance Contract for High Renewable Energy Integration

Insurance Contract for High Renewable Energy Integration

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2022 IEEE International conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2022

作者： Zhao, Dongwei Wang, Hao Huang, Jianwei Lin, Xiaojun Mit Energy Initiative Massachusetts Institute of Technology United States Monash University Department of Data Science and Artificial Intelligence Australia School of Science and Engineering The Chinese University of HongKong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Elmore Family School of Electrical and Computer Engineering Purdue University United States

ISBN: (数字)9781665432542

ISBN: (纸本)9781665432542

The increasing penetration of renewable energy poses significant challenges to power grid reliability. There have been increasing interests in utilizing financial tools, such as insurance, to help end-users hedge the potential risk of lost load due to renewable energy variability. With insurance, a user pays a premium fee to the utility, so that he will get compensated in case his demand is not fully satisfied. A proper insurance design needs to resolve the following two challenges: (i) users' reliability preference is private information;and (ii) the insurance design is tightly coupled with the renewable energy investment decision. To address these challenges, we adopt the contract theory to elicit users' private reliability preferences, and we study how the utility can jointly optimize the insurance contract and the planning of renewable energy. A key analytical challenge is that the joint optimization of the insurance design and the planning of renewables is non-convex. We resolve this difficulty by revealing important structural properties of the optimal solution, using the help of two benchmark problems: the no-insurance benchmark and the social-optimum benchmark. Compared with the no-insurance benchmark, we prove that the social cost and users' total energy cost are always no larger under the optimal contract. Simulation results show that the largest benefit of the insurance contract is achieved at a medium electricity-bill price together with a low type heterogeneity and a high renewable uncertainty. © 2022 IEEE.

关键词： Insurance

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GeneSum: Large Language Model-based Gene Summary Extraction

GeneSum: Large Language Model-based Gene Summary Extraction

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IEEE International conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhijian Chen Chuan Hu Min Wu Qingqing Long Xuezhi Wang Yuanchun Zhou Meng Xiao Computer Network Information Center Chinese Academy of Sciences China University of Chinese Academy of Sciences China Institute for Infocomm Research (I2R) Agency for Science Technology and Research (A*STAR) Singapore Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Emerging topics in biomedical research are continuously expanding, providing a wealth of information about genes and their function. This rapid proliferation of knowledge presents unprecedented opportunities for scientific discovery and formidable challenges for researchers striving to keep abreast of the latest advancements. One significant challenge is navigating the vast corpus of literature to extract vital gene-related information, a time-consuming and cumbersome task. To enhance the efficiency of this process, it is crucial to address several key challenges: (1) the overwhelming volume of literature, (2) the complexity of gene functions, and (3) the automated integration and generation. In response, we propose GeneSum, a two-stage automated gene summary extractor utilizing a large language model (LLM). Our approach retrieves and eliminates redundancy of target gene literature and then fine-tunes the LLM to refine and streamline the summarization process. We conducted extensive experiments to validate the efficacy of our proposed framework. The results demonstrate that LLM significantly enhances the integration of gene-specific information, allowing more efficient decision-making in ongoing research.

关键词： Navigation Biological system modeling Large language models Redundancy Decision making Complexity theory data mining Bioinformatics

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Graph Representation Learning: Foundations, Methods, Applications and Systems 21

Graph Representation Learning: Foundations, Methods, Applica...

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27th ACM SIGKDD International conference on knowledge discovery and data mining (KDD)

作者： Jin, Wei Ma, Yao Wang, Yiqi Liu, Xiaorui Tang, Jiliang Cen, Yukuo Qiu, Jiezhong Tang, Jie Shi, Chuan Ye, Yanfang Zhang, Jiawei Yu, Philip S. Michigan State Univ Comp Sci & Engn E Lansing MI 48824 USA Michigan State Univ Comp Sci & Engn Dept E Lansing MI 48824 USA Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Case Western Reserve Univ Cleveland OH 44106 USA Case Western Reserve Univ Dept Comp & Data Sci Cleveland OH 44106 USA Florida State Univ Tallahassee FL 32306 USA Florida State Univ Comp Sci Tallahassee FL 32306 USA Univ Illinois Chicago IL USA Univ Illinois Comp Sci Chicago IL USA

ISBN: (纸本)9781450383325

Graphs such as social networks and molecular graphs are ubiquitous data structures in the real world. Due to their prevalence, it is of great research importance to extract meaningful patterns from graph structured data so that downstream tasks can be facilitated. Instead of designing hand-engineered features, graph representation learning has emerged to learn representations that can encode the abundant information about the graph. It has achieved tremendous success in various tasks such as node classification, link prediction, and graph classification and has attracted increasing attention in recent years. In this tutorial, we systematically review the foundations, techniques, applications and advances in graph representation learning. We first introduce the foundations on graph theory and graph Fourier analysis. We then cover the key achievements of graph representation learning in recent years. Concretely, we discuss the six topics: 1) network embedding theories and systems;2) foundations of graph neural networks (GNNs);3) CogDL toolkit for GNNs;4) scalable GNNs;5) self-supervised learning in GNNs and 6) heterogeneous graphs and heterogeneous GNNs. Finally, we will introduce the applications of graph representation learning with a focus on recommender systems.

关键词： heterogeneous graphs graph neural networks graph representation learning

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A Proximity Weighted Evidential k Nearest Neighbor Classifier for Imbalanced data 24th

A Proximity Weighted Evidential k Nearest Neighbor Classifie...

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24th Pacific-Asia conference on knowledge discovery and data mining (PAKDD)

作者： Kadir, Md Eusha Akash, Pritom Saha Sharmin, Sadia Ali, Amin Ahsan Shoyaib, Mohammad Univ Dhaka Inst Informat Technol Dhaka Bangladesh Islamic Univ Technol Gazipur Bangladesh Independent Univ Dhaka Bangladesh

ISBN: (纸本)9783030474362;9783030474355

In k Nearest Neighbor (kNN) classifier, a query instance is classified based on the most frequent class of its nearest neighbors among the training instances. In imbalanced datasets, kNN becomes biased towards the majority instances of the training space. To solve this problem, we propose a method called Proximity weighted Evidential kNN classifier. In this method, each neighbor of a query instance is considered as a piece of evidence from which we calculate the probability of class label given feature values to provide more preference to the minority instances. This is then discounted by the proximity of the neighbor to prioritize the closer instances in the local neighborhood. These evidences are then combined using Dempster-Shafer theory of evidence. A rigorous experiment over 30 benchmark imbalanced datasets shows that our method performs better compared to 12 popular methods. In pairwise comparison of these 12 methods with our method, in the best case, our method wins in 29 datasets, and in the worst case it wins in least 19 datasets. More importantly, according to Friedman test the proposed method ranks higher than all other methods in terms of AUC at 5% level of significance.

关键词： Classifier Imbalanced learning kNN Evidence theory

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Event Logs Pre-processing for Configurable Process discovery: Ontology-Based Approach

Event Logs Pre-processing for Configurable Process Discovery...

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International conference on Information Science and technology (ICIST)

作者： Aicha Khannat Hanae Sbai Laila Kjiri AlQUALSADI Rsearch team ENSIAS Mohammed V University of Rabat Rabat Morocco FST Mohammedia University Hassan II of Casablanca Casablanca Morocco

ISBN: (数字)9781728166469

ISBN: (纸本)9781728166476

Nowadays, process mining offers key tools for companies to discover and develop continuously their processes. Moreover, many research works focus on the application of process mining techniques to manage configurable process models. To provide configurable process models with high quality and less complexity using process mining techniques, the preparation of event logs before discovering processes is strongly required. Therefore, various approaches have introduced semantics to derive knowledge from process execution traces. Although, this knowledge is used for discovering variability rules or deriving process variants. However, existing works lack the discovery of semantically enriched configurable process models from event logs. Since event logs are usually combined with semantics and domain ontology for optimizing process mining applications, it seems to be important to introduce a new approach of semantically enriched configurable process discovery. It will be helpful during configuration, validation, and enhancement of configurable processes that are extracted from event logs. In this paper, we present a new framework for event logs preprocessing using ontologies. Our aim is to enrich a collection of event logs with concepts from ontologies as a first step towards discovering a semantically enriched configurable process.

关键词： Information science Semantics Companies Ontologies tools Complexity theory

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Implementation of Reduct and Core features of Rough Set theory on FPGA

Implementation of Reduct and Core features of Rough Set Theo...

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International conference on Signal and Information Processing (IConSIP)

作者： Shubhangee Kishan Varma Chinmay Dhole Vibha Vyas Vanita Agarwal Department of Electronics and Telecommunication Cusrow Wadia Institute of Technology Pune India

ISBN: (纸本)9781728168869

As a result of recent breakthroughs in data mining, RST has emerged as one of the most essential techniques for feature selection. With the aid of confidence and strength of a relationship, this research provides a mathematical technique for selecting the most essential feature. The finding of attribute relationships is one of the most significant parts of database analysis. Our technique focuses on removing unnecessary characteristics and forming the core of the attribute set to obtain the effective reduct set (i.e., a smaller collection of required attributes). Rough set algorithms have proven beneficial in a variety of fields, including artificial intelligence, medical, instrumentation, and guidance systems, among many others, therefore having specialist rough set hardware can reduce time and save energy. The paper focuses on creating RTL for the rough set theory application, also leveraging this RTL program to obtain rough set theory's Reduct and Core features. RST detects partial or entire data dependencies, removes redundant data, and provides a solution to null values, missing data, dynamic data, and other types of data. Reduction and Core are two important parameters in signal processing as unimportant data can be removed and only necessary parameters can be processed for specific application. Hence this paper focused on Reduct and core features of RST.

关键词： Redundancy Rough sets Signal processing algorithms Null value Very large scale integration Signal processing knowledge discovery

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