This research was aimed to assess the severity of emotional and motivational processes during the perception of chess patterns of varying contrast and registration of visual evoked potentials (vEPs) with subsequent an...
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ISBN:
(纸本)9783031619625;9783031619632
This research was aimed to assess the severity of emotional and motivational processes during the perception of chess patterns of varying contrast and registration of visual evoked potentials (vEPs) with subsequent analysis of personal and cognitive processes on this basis. An increase in the intensity of the contrast of a reversible chess field, regardless of the emotional sphere is accompanied by an increase in the amplitude of the amplitude of the N150 wave in persons with a high degree of tension and reduced emotional stability was traced. For persons with a low degree of tension and high emotional stability in the occipital-parietal leads, an inverse relationship between the magnitude of contrast and amplitude N150 is noted. Comparison of behavioral investigation results with electrophysiological data enabled to find that the first variant of reaction was characterized by lower degree of visual images identification, under condition hampering their identification, irrespective of test objects modality.
Sentiment analysis represents an emerging field of study in both natural language processing and computational intelligence with consumer evaluations of products and services are advancing in autonomy. Multimodal sent...
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Multimedia art is a comprehensive audio-visual art type, which utilises a variety of elements such as graphics, text, sound and image to support the spatial representation of the work. Therefore, the virtual space for...
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In the Indian scenario, the use of geophysical surveys in mineral exploration was still in the infancy stages. However, there is a decrease in exploration success, despite increased company expenditure on geophysical ...
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The emerging growth in communication technology, created a path for innovation of various consumer products that support peoples in different arena. The problem faced by visually impaired people on communicating the e...
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This paper demonstrates a holistic approach for conducting multi-platform and multilingual sentiment analysis of a main stream football club in the United Arab Emirates. The paper reports first iterations of the resea...
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IoT devices, constrained by limited resources and weak security measures, are highly vulnerable to malware at- tacks. This review examines malware detection methods using textual, visual, and network traffic features,...
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In order to explore the research frontiers and hot topics of blended learning, the research on blended learning in China during the past two decades from 2004 to 2023 was visualized and analyzed with the help of CiteS...
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Many real-world tasks require an agent to reason jointly over text and visual objects, (e.g., navigating in public spaces), which we refer to as context-sensitive text-rich visual reasoning. Specifically, these tasks ...
Many real-world tasks require an agent to reason jointly over text and visual objects, (e.g., navigating in public spaces), which we refer to as context-sensitive text-rich visual reasoning. Specifically, these tasks require an understanding of the context in which the text interacts with visual elements within an image. However, there is a lack of existing datasets to benchmark the state-of-the-art multimodal models' capability on context-sensitive text-rich visual reasoning. In this paper, we introduce CONTEXTUAL, a novel dataset featuring human-crafted instructions that require context-sensitive reasoning for text-rich images. We conduct experiments to assess the performance of 14 foundation models (GPT-4v, Gemini-Provision, LLavA-Next) and establish a human performance baseline. Further, we perform human evaluations of the model responses and observe a significant performance gap of 30.8% between GPT-4v (the current best-performing Large Multimodal Model) and human performance. Our fine-grained analysis reveals that GPT-4v encounters difficulties interpreting time-related data and infographics. However, it demonstrates proficiency in comprehending abstract visual contexts such as memes and quotes. Finally, our qualitative analysis uncovers various factors contributing to poor performance including lack of precise visual perception and hallucinations. Our dataset, code, and leaderboard can be found on the project page https://***/. Copyright 2024 by the author(s)
Social media platforms are inundated with an extensive volume of unverified information, most of which originates from heterogeneous data from a variety of diverse sources, spreading rapidly and widely, thereby posing...
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ISBN:
(纸本)9789819722648;9789819722624
Social media platforms are inundated with an extensive volume of unverified information, most of which originates from heterogeneous data from a variety of diverse sources, spreading rapidly and widely, thereby posing a significant threat to both individuals and society. An existing challenge in multimodal fake news detection is its limitation to acquiring textual and visualdata exclusively from a single source, which leads to a high level of subjectivity in news reporting, incomplete data coverage, and difficulties in adapting to the various forms and sources of fake news. In this paper, we propose a fake news detection model (MHDF) for multi-source heterogeneous data progressive fusion. Our approach begins with gathering, filtering, and cleaning data from multiple sources to create a reliable multi-source multimodal dataset, which involved obtaining reports from diverse perspectives on each event. Subsequently, progressive fusion is achieved by combining features from diverse sources. This is achieved by inputting the features obtained from the textual feature extractor and visual feature extractor into the news textual and visual feature fusion module. We also integrated sentiment features from the text into the model, allowing for multi-level feature extraction. Experimental results and analysis indicate that our approach outperforms other methods.
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