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检索条件"丛书名=Lecture Notes in Networks and Systems"

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A Multi-clustering Unbiased Relative Prediction Recommendation Scheme for Data with Hidden Multiple Overlaps

A Multi-clustering Unbiased Relative Prediction Recommendati...

引用

Computing Conference

作者： Levy, Avivit Chalamish, Michal Shalom, B. Riva Sharir, Guy Peltzman, Opal Salzmann, Sivan Shenkar Coll Engn Design & Art Ramat Gan Israel

ISBN: (纸本)9783031622793;9783031622779;9783031622762

This paper presents a recommendation system designed for shortening and streamlining hiring processes on both candidate and recruiting company parts, which is specialized in Hi-Tech companies. In this case, the hidden multiple overlaps challenge arises, where the trivial matching of data items to types does not necessarily reflect a ground truth labelling and other hidden multiple matches may exist. To this end, we propose a multi-clustering unbiased relative prediction recommendation scheme. Our mechanism enables to efficiently deal with data containing hidden multiple overlaps. The multi-clustering allows a many-to-many matching of items to types, enabling to expose hidden item-user matches. The mechanism's efficiency is vital for supporting a dynamic modeling implementation, which is required for the Hi-Tech recruiting application. Moreover, we design an unbiased relative prediction scheme for providing recommendations. Our prediction scheme is two-sided (but asymmetric): ranking the multiple items for a given user and (differently) ranking the multiple users for a given item. Though the underlying problem of overlapping clustering and multi-label learning is complicated, our scheme enables to bypass this difficulty in a conceptually exquisite and efficient way for recommender systems, in particular, for the Hi-Tech recruiting process.

关键词： Overlapping clustering Recommender system Unbiased prediction Hi-Tech recruiting process

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Human-Created and AI-Generated Text: What's Left to Uncover?

Human-Created and AI-Generated Text: What's Left to Uncover?

引用

Computing Conference

作者： Salter, Steven Teh, Phoey Lee Hebblewhite, Richard Wrexham Univ Fac Art Comp & Engn Wrexham Wales

ISBN: (纸本)9783031622793;9783031622779;9783031622762

The advent of generative Artificial Intelligence (AI) has brought about profound changes in society, education, and the professional realm. Machine Learning models have created remarkably sophisticated language generators, blurring the line between human-authored content and AI-generated text. This poses a challenge for educators and professionals in distinguishing authentic work from instances of plagiarism. This study investigates the fundamental distinctions between human and AI texts by analyzing perspectives from human points of view. It aims to present the outcome of those five questions that we examined about human perceptions of text composition and contrasting themwith computer-generated text. By exploring this, it aids in upholding academic integrity and contributes to advancing our comprehension of Natural Language Processing. In essence, this research strives to maintain academic credibility in a landscape transformed by AI and nurtures the growth of more equitable AI technologies.

关键词： Artificial intelligent Chat GPT Human generated-text AI generated text

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BipartiteJoin: Optimal Similarity Join for Fuzzy Bipartite Matching 12th

BipartiteJoin: Optimal Similarity Join for Fuzzy Bipartite M...

引用

12th World Conference on Information systems and Technologies (WorldCIST)

作者： Rozinek, Ondrej Borkovcova, Monika Mares, Jan Univ Pardubice Dept Proc Control Studentska 95 Pardubice 53210 Czech Republic Univ Pardubice Dept Informat Technol Studentska 95 Pardubice 53210 Czech Republic Univ Chem & Technol Prague Dept Math Informat & Cybernet Tech 5 Prague 16628 Czech Republic

ISBN: (纸本)9783031603273;9783031603280

Set similarity join, crucial for data cleaning, integration, and recommendation systems, identifies set pairs exceeding a similarity threshold. Our approach combines a count Q-gram filter with maximum weighted bipartite matching, balancing accuracy and efficiency. The Q-gram filter, based on the relationship between Q-gram similarity and edit distance, reduces the number of comparisons, operating in constant time on a pre-built index. This enables real-time processing, as only a minimal number of pairs are verified through Fuzzy Bipartite Matching, significantly enhancing the efficiency of similarity joins.

关键词： similarity join Q-gram filter record linkage entity resolution similarity space bipartite matching

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Development of Selective Electrodes for Drug Detection 13th

Development of Selective Electrodes for Drug Detection

引用

13th Computer Science Online Conference

作者： Abdurakhmanova, Zamira Abdurakhmanov, Ergashboy Murodova, Zulfiya Salimova, Shokhista Ismoilov, Eldor Suvonkulov, Shokhjakhon Shukrulloyev, Beknazar Samarkand Med Univ Samarkand Uzbekistan Samarkand State Univ Samarkand 140104 Uzbekistan

ISBN: (纸本)9783031705175;9783031705182

Methods for obtaining plasticized membranes have been developed based on synthesized ionophores. The physico-chemical properties of EAC, the effect of membrane and solution composition on the Potentiometric characteristic of ISE have been determined. The main electrochemical indicators of the developed ISE are studied: such parameters as the linear range of the signal (electrode function), the slope of the electrode function, the lowest limit of ion detection, and the dynamics of the electrode signal (response time).

关键词： Medicinal Substances Novocaine Phosphoromolibdate Phosphorovolframate Electrode Function

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Improving Passband in Cosine-Based CIC Decimation Filter with Improved Worst-Case Aliasing Attenuation 10th

Improving Passband in Cosine-Based CIC Decimation Filter wit...

引用

International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Dolecek, Gordana Jovanovic Karabegovic, Isak Inst Natl INAOE Dept Elect Puebla Mexico Univ Bihac Tech Fac Bihac St Irfana Ljubijankica Bb Bihac 77000 Bosnia & Herceg

ISBN: (纸本)9783031662706;9783031662713

This paper presents the design of a compensator for the cosine-based CIC (Cascade-Integrator-Comb) decimation filter proposed in the literature for even decimation factors. This decimation filter improves the worst-case aliasing rejection in the first folding band. Additionally, the aliasing rejection is increased in all odd folding bands using a modified cosine filter. However, as a result, the passband droop is increased compared with the CIC filter. We aimed here to design a compensator to decrease passband droop. We consider the wideband case. The magnitude characteristic of the compensator is composed of two sinusoidal functions. The design parameters are the amplitudes of sinusoidals and the parameter of the modified cosine filter. The designs with multipliers and without multipliers are presented and illustrated with examples. For orders of CIC filter less than six, the maximum passband deviation of the compensated filter is less than 0.05 dB. The complexity of the overall design is expressed in the number of APOS (Adders per Output Sample). The proposed design is compared with the compensated decimation filters from the literature.

关键词： decimation aliasing decimation filter CIC filter compensator APOS

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Designing Converged Middleware for HPC, AI, and Big Data: Challenges and Opportunities 6th

Designing Converged Middleware for HPC, AI, and Big Data: Ch...

引用

6th International Conference on Cloud Computing and Artificial Intelligence-Technologies and Applications

作者： Panda, Dhableswar K. Subramoni, Hari Abduljabbar, Mustafa Shafi, Aamir Alnaasan, Nawras Xu, Shulei Ohio State Univ Columbus OH 43210 USA

ISBN: (纸本)9783031786976;9783031786983

The field of computing has been evolving over the years with the need for High-Performance Computing (HPC), Deep Learning (DL), and Machine Learning (ML) on heterogeneous architectures. These modern computing environments are spread over edges to clouds/HPC centers. This chapter focuses on challenges and opportunities in designing HPC and Artificial Intelligence (AI) middleware on these systems with both scale-up and scale-out strategies. The first part of the chapter focuses on the broad approaches/solutions to address these challenges. Next, we present an overview of the High-Performance MVAPICH Message Passing Interface (MPI) library, its features, and sample solutions on current generation CPU and GPU on-premise systems. Next, we present an overview of the MPI-driven framework to provide high-performance training and inference for emerging AI workloads. An overview of the MPI-driven approach towards Big Data stacks (Spark and Dask) is presented next. Finally, we demonstrate how these solutions are being transitioned into public cloud environments. The results demonstrate that an MPI-driven converged middleware stack can be used on current and emerging on-premise and cloud systems for diverse HPC, AI, and Big Data workloads and frameworks.

关键词： High-Performance Computing (HPC) Cloud Message Passing Interface (MPI) AI Big Data Data Science

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Handling Overfitting and Imbalance Data in Modelling Convolutional Neural networks for Astronomical Transient Discovery

Handling Overfitting and Imbalance Data in Modelling Convolu...

引用

2024 International Conference on Computing, Communication, Cybersecurity and AI

作者： Boongoen, Tossapon Iam-On, Natthakan Aberystwyth Univ Dept Comp Sci Adv Reasoning Res Grp Aberystwyth Ceredigion Wales

ISBN: (纸本)9783031744426;9783031744433

Given development in telescope and image acquisition technology, efficient processing and timely discovery of events have become challenges for astronomers and data scientists around the globe. Among modern sky survey projects, GOTO (Gravitational-wave Optical Transient Observer) is searching for transient events with new breed of optical survey telescopes, which allow a faster and deeper assessment. As compared to conventional machine learning techniques that have been sup-optimal to identify new sources from a large pool of candidates, this works presents the application of convolutional neural networks, with different data augmentation methods being explored to handle issues of overfitting and imbalance data.

关键词： Astronomy Transient event Classification CNN Data augmentation

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Enhancing Domain Generalization with Auto-encoders

Enhancing Domain Generalization with Auto-encoders

引用

Intelligent systems Conference

作者： Goerttler, Thomas Obermayer, Klaus Tech Univ Berlin Chair Neural Informat Proc Berlin Germany Bernstein Ctr Computat Neurosci Berlin Berlin Germany

ISBN: (纸本)9783031663284;9783031663291

In deep learning, generalization under distribution shift is still a challenge. Domain generalization is an example of domain shift where the task is to train models to perform effectively on unseen data from different but related domains. In this paper, we introduce novel solutions for domain generalization and integrate a sparse auto-encoder before the classifier for multi-class image classification. Our three approaches focus on reconstructing class-specific features while neglecting domain-specific information. They all increase the model performance on a synthetic dataset, and one approach slightly increases the performance on the iWildCam 2020 dataset. This underscores the importance of exploring techniques to support deep learning models' resilience against distribution shifts.

关键词： Auto-encoders Domain generalization Domain shift

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Detecting and Predicting Smart Car Collisions in Hybrid Environments from Sensor Data 1

引用

11st World Conference on Information systems and Technologies (WorldCIST)

作者： Menendez, Hector D. Kelly, David Kings Coll London London England

ISBN: (数字)9783031456428

ISBN: (纸本)9783031456411;9783031456428

Self-driving or smart cars are becoming the next step forward for safety and autonomy of driving, aiming to provide the best travelling experience. Guaranteeing their trustworthiness requires understanding of different critical situations, such as collisions. Smart cars need to detect collisions to perform recovery actions and, even in some cases, call an ambulance. Ideally, smart cars need to predict collisions, not merely detect them after the event. This prediction is difficult, especially when human drivers are part of the environment. This work proposes a system for collision prediction and detection based on machine learning: it focuses only on the information that the car receives through its sensors. We evaluate our method using a multi-car hybrid system designed to generate realistic and complex scenarios for the smart cars, thereby providing sensory information that is close to a realistic road. Our methodology detects collisions with 99% accuracy and predicts 5 different levels of collision risk with up to 43% accuracy.

关键词： Smart Cars Collision Detection Collision Prediction Carla Simulator Machine Learning

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Machine Learning and Ensemble Learning Techniques for Intrusion Detection systems: A Performance Analysis Based on Feature Selection Methods

Machine Learning and Ensemble Learning Techniques for Intrus...

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International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Basarslan, Muhammet Sinan Turgut, Zeynep Istanbul Medeniyet Univ TR-34700 Istanbul Turkiye

ISBN: (纸本)9783031671913;9783031671920

Anomaly-based intrusion detection systems rely on analyzing the behavior of data within the network. In such systems, the imperative lies in crafting designs that employ a minimal yet effective set of features. This approach facilitates the creation of systems that operate with enhanced speed and accuracy rates. This study utilized the UNR-IDD dataset, a representative intrusion detection dataset employing Network Port Statistics. UNR-IDD encompasses six distinct data classes, including five different attack types and normal data samples. The identification of the most significant features in the dataset are carried out through the application of Least Absolute Shrinkage and Selection Operator (LASSO), Laplacian Score, Correlation Based Feature Selection (CFS), and ReliefF feature selection techniques. Subsequently, based on the identified features, a variety of machine learning classifiers, namely Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Naive Bayes (NB), as well as ensemble learning techniques including CatBoost, Gradient Boosting, XGBoost, AdaBoost (ADA), Voting, and Stacking, are employed. The performance of related machine learning and ensemble learning techniques are compared, and metrics such as accuracy, precision, recall, and F1-score are presented considering 80% training rate. The highest accuracy, reaching 97.1% for an 80% training rate, is achieved when 25 of 34 features are selected using the Laplacian Score feature selection technique, coupled with the application of the Stacking ensemble learning approach.

关键词： intrusion detection system feature selection ensemble learning machine learning

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