1,720,966 research outputs found
Zn, Pb and Hg contents of Pistacia lentiscus L. grown on heavy metal-rich soils: implications for phytostabilization
No full textIn this study, we determined the metal (Zn, Pb and Hg) contents in epigean and hypogean organs of Pistacia lentiscus L., a Mediterranean native plant grown on heavy metal-rich soils of Iglesiente (southwestern Sardinia, Italy), in view of its perspective use for revegetation and phytostabilization of mine waste piles. Plant samples were collected from four different areas in the district. Metal contents in the different plant tissues are roughly dependent on their total and mobile (diethylene triamine penta acetic acid (DTPA)-extractable) contents in soil and are shown in the following ranges: 48–628 mg kg−1 (Zn), 2–354 mg kg−1 (Pb) and 13–530 μg kg−1 (Hg) and usually decrease in the following order: roots>stems>leaves; the apparent exception for Hg, with an order of leaves>stems, is ascribed to foliar absorption of this element. The biological concentration factors are consistently low (≤0.05) for all metals and support the concept that the strategy of metal tolerance of P. lentiscus is based on exclusion. These results are consistent with most previous literature data, confirming that P. lentiscus is well suited for revegetation actions and could decrease metal mobility through the soil stabilization strategy
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Quality-based Artifact Modeling for Facial Deepfake Detection in Videos
Full text linkFacial deepfakes are becoming more and more realistic, to the point that it is often difficult for humans to distinguish between a fake and a real video. However, it is acknowledged that deepfakes contain artifacts at different levels; we hypothesize a connection between manipulations and visible or non-visible artifacts, especially where the subject’s movements are difficult to reproduce in detail. Accordingly, our approach relies on different quality measures, No-Reference (NR) and Full-Reference (FR), over the detected faces in the video. The measurements allow us to adopt a frame-by-frame approach to build an effective matrix-based representation of a video sequence. We show that the results obtained by this basic feature set for a neural network architecture constitute the first step that encourages the empowerment of this representation, aimed to extend our investigation to further deepfake classes. The FaceForensics++ dataset is chosen for experiments, which allows the evaluation of the proposed approach over different deepfake generation algorithms
Generalized Deepfake Detection Algorithm Based on Inconsistency Between Inner and Outer Faces
Full text linkDeepfake refers to using artificial intelligence (AI) and machine learning techniques to create compelling and realistic media content, such as videos, images, or recordings, that appear real but are fake. The most common form of deepfake involves using deep neural networks to replace or superimpose faces in existing videos or images on top of other people’s faces. While this technology can be used for various benign purposes, such as filmmaking or online education, it can also be used maliciously to spread misinformation by creating fake videos or images. Based on the classic deepfake generation process, this paper explores the Inconsistency between inner and outer faces in fake content to find synthetic defects and proposes a general deepfake detection algorithm. Experimental results show that our proposed method has certain advantages, especially regarding cross-method detection performance
Texture and artifact decomposition for improving generalization in deep-learning-based deepfake detection
No full textThe harmful utilization of DeepFake technology poses a significant threat to public welfare, precipitating a crisis in public opinion. Existing detection methodologies, predominantly relying on convolutional neural networks and deep learning paradigms, focus on achieving high in-domain recognition accuracy amidst many forgery techniques. However, overseeing the intricate interplay between textures and artifacts results in compromised performance across diverse forgery scenarios. This paper introduces a groundbreaking framework, denoted as Texture and Artifact Detector (TAD), to mitigate the challenge posed by the limited generalization ability stemming from the mutual neglect of textures and artifacts. Specifically, our approach delves into the similarities among disparate forged datasets, discerning synthetic content based on the consistency of textures and the presence of artifacts. Furthermore, we use a model ensemble learning strategy to judiciously aggregate texture disparities and artifact patterns inherent in various forgery types, thereby enabling the model’s generalization ability. Our comprehensive experimental analysis, encompassing extensive intra-dataset and cross-dataset validations along with evaluations on both video sequences and individual frames, confirms the effectiveness of TAD. The results from four benchmark datasets highlight the significant impact of the synergistic consideration of texture and artifact information, leading to a marked improvement in detection capabilities
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Texture and artifact decomposition for improving generalization in deep-learning-based deepfake detection
Full text linkThe harmful utilization of DeepFake technology poses a significant threat to public welfare, precipitating a crisis in public opinion. Existing detection methodologies, predominantly relying on convolutional neural networks and deep learning paradigms, focus on achieving high in-domain recognition accuracy amidst many forgery techniques. However, overseeing the intricate interplay between textures and artifacts results in compromised performance across diverse forgery scenarios. This paper introduces a groundbreaking framework, denoted as Texture and Artifact Detector (TAD), to mitigate the challenge posed by the limited generalization ability stemming from the mutual neglect of textures and artifacts. Specifically, our approach delves into the similarities among disparate forged datasets, discerning synthetic content based on the consistency of textures and the presence of artifacts. Furthermore, we use a model ensemble learning strategy to judiciously aggregate texture disparities and artifact patterns inherent in various forgery types, thereby enabling the model’s generalization ability. Our comprehensive experimental analysis, encompassing extensive intra-dataset and cross-dataset validations along with evaluations on both video sequences and individual frames, confirms the effectiveness of TAD. The results from four benchmark datasets highlight the significant impact of the synergistic consideration of texture and artifact information, leading to a marked improvement in detection capabilities
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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