Anomaly detection of sensor data is crucial to ensure the stability and effectiveness of IoT system. The task requires high accuracy and low latency, which makes distributed anomaly detection gradually become a resear...
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Automated crack recognition has achieved remarkable progress in the past decades as a critical task in structure health monitoring, to ensure safety and durability in many industrial scenarios. However, imbalanced cra...
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Zero-shot anomaly detection (ZSAD) identifies anomalies without needing training samples from the target dataset, essential for scenarios with privacy concerns or limited data. Vision-language models like CLIP show po...
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Modern data collection processes and applications provide the ability to gather detailed spending patterns of large numbers of consumers. Additionally, modern on-line marketing programs allow for highly specific targe...
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ISBN:
(数字)9798331520403
ISBN:
(纸本)9798331520410
Modern data collection processes and applications provide the ability to gather detailed spending patterns of large numbers of consumers. Additionally, modern on-line marketing programs allow for highly specific targeting of advertising to demographic cohorts of interest. Advertisers know the sales performance characteristics of the consumers they wish to target, but they do not know the demographic characteristics of those consumers that exhibit the desired performance characteristics. This paper proposes a machine learning driven approach to determine, via feature-value selection, the specific demographics to target with online marketing when the aim is to focus that marketing spend on specific consumers of interest. In this experiment, a particular use case is examined to identify the key demographic features and values of high spenders in the grocery sector for a specific region. Specifically, a multilayer perceptron model and explainer is deployed to determine the feature values that distinguish high spenders in grocery retail that can subsequently be used to optimize online marketing spend. It is shown that this approach does show the key demographic feature values associated with the high spending cohort targeted. This can be the basis on which to further develop a generalized approach that can be used to determine the target demographic groups for other cohorts of interest.
With the rapid advancement of industrial structures, many factories are deploying anomaly detection systems. However, most existing anomaly detection algorithms are unsuitable for high-noise industrial control system ...
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Runtime smell detection in software systems, particularly through system call analysis, has garnered significant attention in recent years. Although various machine learning techniques have been employed to enhance de...
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ISBN:
(纸本)9798400711305
Runtime smell detection in software systems, particularly through system call analysis, has garnered significant attention in recent years. Although various machine learning techniques have been employed to enhance detection accuracy and reduce false positives, limited focus has been given to their practical application in early real-time anomaly detection. To address this gap, we propose a deep learning-based approach, called TraceLens, designed for the early detection of performance-related issues in software systems. Unlike traditional methods that rely on system call data, our approach leverages critical path analysis, enabling more efficient and targeted anomaly detection. Experimental results demonstrate that this approach achieves detection performance comparable to methods that use system calls, while significantly improving data collection efficiency. In addition, the critical path dataset highlights software dependencies, both internal and external, providing deeper insight into the dynamic behavior of software systems.
This paper addresses the problem of deploying complex systems in Kubernetes clusters. It discusses using the OperatorSDK framework supported by RedHat as a basis for implementing the Kubernetes operator for Lightweigh...
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Rapid growth of digital educational content necessitates efficient and accurate methods for organizing and mapping resources to ensure well-alignment with targeted learning outcomes, academic standards, and competency...
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In mobile app development, user reviews are a significant source of requirements. Users frequently report bugs, request new features, or suggest enhancements. Mobile app vendors aim to maximize user satisfaction by ad...
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ISBN:
(数字)9798350355468
ISBN:
(纸本)9798350355475
In mobile app development, user reviews are a significant source of requirements. Users frequently report bugs, request new features, or suggest enhancements. Mobile app vendors aim to maximize user satisfaction by addressing these continuous comments and requests as early as possible. Typically, they prioritize delivering the most promising features, extracted from user reviews, in early releases while deferring fewer promising ones to later stages. However, due to the massive volume of reviews, redundancy, and conflicts among them, manually extracting requirements is inefficient and often challenging, making requirement prioritization even more difficult. Therefore, automating this process is essential. This paper presents a conceptual framework for the requirements prioritization process's automation, continuity, and scalability. The proposed framework follows a hybrid approach that integrates multiple advanced techniques: generative artificial intelligence, active learning, ontologies, and optimization algorithms. Generative artificial intelligence enables the identification of important patterns and the automatic extraction of requirements and their properties, which aids in assessing properties to determine requirement priorities. The generative artificial intelligence framework integrates with an active learning system to improve annotation efficiency. Ontologies help comprehend relationships, properties, and dependencies among requirements, aligning them with domain-specific knowledge. Optimization methods playa crucial role in the requirements prioritization process by computing the weights of various properties to identify the most effective combination of requirements and determine the optimal order for implementation. Consequently, this research presents a smart theoretical framework for enhancing user-driven maintenance and development of mobile applications. Researchers are tackling several critical challenges that remain unresolved in the field, with
In October 2023, 23andMe, a prominent provider of personal genetic testing, ancestry, and health information services, suffered a significant data breach orchestrated by a cybercriminal known as "Golem." Ini...
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