The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can qu...
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The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can quickly fabricate comments and news on social *** most difficult challenge is determining which news is real or ***,tracking down programmed techniques to recognize fake news online is *** an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media *** study shows past,current,and possible methods that can be used in the future for fake news *** different publicly available datasets containing political news are utilized for performing *** supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text *** contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer ***,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.
The unprecedented growth in video conferencing usage is accompanied by multiple security and privacy threats. Importantly, protecting users' privacy is not always in their own hands. Posting meeting images affects...
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The unprecedented growth in video conferencing usage is accompanied by multiple security and privacy threats. Importantly, protecting users' privacy is not always in their own hands. Posting meeting images affects all participants, leading to an easy collection of personal data including age, gender and linkage with participation in other meetings. Here, we explored privacy issues that may be at risk by attending virtual meetings. We extracted private information from collage images of meeting participants that are publicly posted online. We used image processing, text recognition tools, as well as social network analysis to explore our curated dataset of over 15 700 collage images, and over 142 000 face images of meeting participants. We demonstrate that video conference users are facing prevalent security and privacy threats. Our results indicate that it is relatively easy to collect thousands of publicly available images of video conference meetings and extract personal information about the participants, including their face images, age, gender, usernames, and even full names. This type of data can vastly and easily jeopardize people's security and privacy both in the online and real-world, affecting not only adults but also more vulnerable segments of society, such as children and older adults. Finally, we show that cross-referencing facial image data with social network data may put participants at additional privacy risks they may not be aware of and that it is possible to identify users that appear in several video conference meetings, thus providing a potential to maliciously aggregate different sources of information about a target individual.
Fault classification of mechanical equipment is a vital issue in modern industrial production. Mechanical equipment typically relies on multiple sensors to collect the operational data, which is represented as multiva...
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Fault classification of mechanical equipment is a vital issue in modern industrial production. Mechanical equipment typically relies on multiple sensors to collect the operational data, which is represented as multivariate time-series data. However, existing methods for analyzing mechanical faults often overlook the causal relationships between sensors and struggle with the scarcity of labeled training samples. To address these challenges, we propose a graph neural network model leveraging sensor causality and meta-learning for mechanical fault classification (SCML-GNN). Specifically, we use transfer entropy to represent multivariate time-series data as a graph, with each sensor as a node and their causal relationships as edges. We then extract the node features using temporal convolutional layers and apply a graph neural network to learn the low-dimensional features. Additionally, graph pooling methods are used to obtain global embeddings. To further tackle the issue of limited labeled training samples, we introduce a metric-based class prototype attention mechanism within SCML-GNN. Extensive experiments conducted on three real-world mechanical equipment datasets demonstrate the superior effectiveness and efficiency of SCML-GNN in mechanical fault classification compared to the other existing methods.
4D millimeter-wave radar demonstrates considerable potential in the field of autonomous driving. It facilitates stable perception in adverse weather conditions and complex lighting environments, in addition to featuri...
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4D millimeter-wave radar demonstrates considerable potential in the field of autonomous driving. It facilitates stable perception in adverse weather conditions and complex lighting environments, in addition to featuring low cost and high data-processing efficiency. The realization of Novel view synthesis (NVS) for 4D radar is of significant practical importance. However, due to the substantial disparities in the principles of point cloud generation between LiDAR and 4D radar, existing methods for NVS of LiDAR point clouds are not applicable to 4D radar. Specifically, during the process of projecting radar point clouds into cylindrical coordinates, radar produces irregular point distributions, in contrast to LiDAR's regular angular resolution. This phenomenon results in range map that are filled with numerous empty pixels. Furthermore, the intrinsic irregularity in imaging and the prevalence of empty pixels compromise inter-frame geometric consistency, presenting a challenge that is distinct from LiDAR-based systems. To address these challenges, we associate ray-drop probability with viewpoints and utilize a U-Net architecture to learn the distribution of radar point clouds, effectively addressing data sparsity in a viewpoint-dependent manner. Extensive experiments demonstrate that our method achieves superior reconstruction results on both the publicly available VOD dataset, K-RaDAR dataset and Dual-Radar dataset.
The rapid development of the internet of things (IoT) prompts organizations and developers to seek innovative approaches for future IoT device development and research. Leveraging advanced artificial intelligence (AI)...
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The rapid development of the internet of things (IoT) prompts organizations and developers to seek innovative approaches for future IoT device development and research. Leveraging advanced artificial intelligence (AI) models such as ChatGPT holds promise in reshaping the conceptualization, development, and commercialization of IoT devices. Through real-world data utilization, AI enhances the effectiveness, adaptability, and intelligence of IoT devices and wearables, expediting their production process from ideation to deployment and customer assistance. However, integrating ChatGPT into IoT-based devices and wearables poses ethical concerns including data ownership, security, privacy, accessibility, bias, accountability, cost, design, quality, storage, model training, explainability, consistency, fairness, safety, transparency, trust, and generalizability. Addressing these ethical principles necessitates a comprehensive review of the literature to identify and classify relevant principles. The author identified 14 ethical principles from the literature using a systematic literature review (SLR) with a criteria of frequency >= 50% based on similarities. Four categories emerge based on the identified ethical principles, culminating in the application of Fuzzy-TOPSIS for analyzing, categorizing, ranking, and prioritizing these ethical principles. From the Fuzzy-TOPSIS technique results, the principle of data security and privacy is the highly ranked ethical principle for IoT-based software wearable devices with the ranking value of "0.925" as a consistency coefficient index. This method, well-established in computer science, effectively navigates fuzzy and uncertain decision-making scenarios. The pioneer outcomes of this study provide a taxonomy-based valuable insight for software manufacturers, facilitating the analysis, ranking, categorization, and prioritization of ethical principles amid the integration of ChatGPT in IoT-based devices and wearables' research and de
Imbalance data processing is one of big issues for machine learning. There are some proposed approaches. On the other hand, Maharanobis-Taguchi System (MTS) is a well-known approach in quality engineering. Although th...
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In the Edge Coloring problem, we are given an undirected graph G with n vertices and m edges, and are tasked with finding the smallest positive integer k so that the edges of G can be assigned k colors in such a way t...
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In the Edge Coloring problem, we are given an undirected graph G with n vertices and m edges, and are tasked with finding the smallest positive integer k so that the edges of G can be assigned k colors in such a way that no two edges incident to the same vertex are assigned the same color. Edge Coloring is a classic NP-hard problem, and so significant research has gone into designing fast exponential-time algorithms for solving Edge Coloring and its variants exactly. Prior work showed that Edge Coloring can be solved in 2m poly(n) time and polynomial space, and in graphs with average degree d in 2(1−Εd)m poly(n) time and exponential space, where Εd = (1/d)Θ(d3). We present an algorithm that solves Edge Coloring in 2m−3n/5 poly(n) time and polynomial space. Our result is the first algorithm for this problem which simultaneously runs in faster than 2m poly(m) time and uses only polynomial space. In graphs of average degree d, our algorithm runs in 2(1−6/(5d))m poly(n) time, which has far better dependence in d than previous results. We also consider a generalization of Edge Coloring called List Edge Coloring, where each edge e in the input graph comes with a list Le ⊆ {1, . . ., k} of colors, and we must determine whether we can assign each edge a color from its list so that no two edges incident to the same vertex receive the same color. We show that this problem can be solved in 2(1−6/(5k))m poly(n) time and polynomial space. The previous best algorithm for List Edge Coloring took 2m poly(n) time and space. Our algorithms are algebraic, and work by constructing a special polynomial P based off the input graph that contains a multilinear monomial (i.e., a monomial where every variable has degree at most one) if and only if the answer to the List Edge Coloring problem on the input graph is YES. We then solve the problem by detecting multilinear monomials in P. Previous work also employed such monomial detection techniques to solve Edge Coloring. We obtain faster algor
In recent years, the electromagnetic scattering characteristics of large ground equipment have attracted more and more attention, and how to accurately and efficiently obtain the electromagnetic scattering characteris...
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Most enterprises adopted social media for marketing purposes. Traditional media resources about one decade ago are now a day's costly and less productive than social media. Companies that are using social media li...
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Reinforcement learning has made great progress in solving single agent problems in recent years. However, the development of multi-agent reinforcement learning is much slower. Many existing algorithms perform not very...
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