The outbreak of the COVID-19 pandemic has resulted in a significant impact on global health and economy. Forecasting the spread of COVID-19 cases is essential for policymakers to make informed decisions and allocate r...
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The importance of federated information retrieval (FIR) is growing in humanities research. Unlike traditional centralized information retrieval methods, where searches are conducted within a logically centralised coll...
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User preference learning has been around for many years. This is a common problem arise in e-commerce system, where the companies need to understand their customers in order to sell the correct products to their targe...
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Today, malicious users are widespread and are frequently lengthening worldwide. So, network security becomes crucial in the domain of education, government, business, and other sectors with related network connections...
Today, malicious users are widespread and are frequently lengthening worldwide. So, network security becomes crucial in the domain of education, government, business, and other sectors with related network connections. The firewall filtering rules itself might cause network vulnerability due to the misconfiguration and order them. The system builds a network testbed using a firewall, and Intrusion Detection System (IDS) and then implements a dataset using DoS traffic and normal traffic from that testbed environment. It is needed to be tested various requirements as features, false positive rates, and accuracy based on datasets apply and built for DoS. The importance of features in the proposed dataset was tested using attribute evaluators and methods. The focus of this work is to improve the performance with two classifiers as Logistic Regression and Support Vector Machine. The system also selects the important features by classifying traffics according to times by machine learning methods.
We investigate the phenomenon of norm inconsistency: where LLMs apply different norms in similar situations. Specifically, we focus on the high-risk application of deciding whether to call the police in Amazon Ring ho...
We investigate the phenomenon of norm inconsistency: where LLMs apply different norms in similar situations. Specifically, we focus on the high-risk application of deciding whether to call the police in Amazon Ring home surveillance videos. We evaluate the decisions of three state-of-the-art LLMs - GPT-4, Gemini 1.0, and Claude 3 Sonnet - in relation to the activities portrayed in the videos, the subjects' skin-tone and gender, and the characteristics of the neighborhoods where the videos were recorded. Our analysis reveals significant norm inconsistencies: (1) a discordance between the recommendation to call the police and the actual presence of criminal activity, and (2) biases influenced by the racial demographics of the neighborhoods. These results highlight the arbitrariness of model decisions in the surveillance context and the limitations of current bias detection and mitigation strategies in normative decision-making.
Indoor understanding is currently a topic that is widely studied in the field of machine learning. Furniture is the most common object in indoor scenes, just as various vehicles are most commonly seen in street scenes...
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The public’s health is seriously at risk from the coronavirus pandemic. Millions of people have already died as a result of this devastating illness, which affects countless people daily worldwide. Unfortunately, no ...
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Visual relation detection (VRD) aims to identify relationships (or interactions) between object pairs in an image. Although recent VRD models have achieved impressive performance, they are all restricted to pre-define...
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Predicting crime using machine learning and deep learning techniques has gained considerable attention from researchers in recent years, focusing on identifying patterns and trends in crime occurrences. This review pa...
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User Satisfaction is a crucial factor to the success of the information System which will have extensive impact on the benefits for individuals and organizations as well as users' continuance intention. Paperless ...
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