View Of Text Research Articles

Why thermal images are blurry.

The resolution of optical imaging is limited by diffraction as well as detector noise. However, thermal imaging exhibits an additional unique phenomenon of ghosting which results in blurry and low-texture images. Here, we provide a detailed view of thermal physics-driven texture and explain why it vanishes in thermal images capturing heat radiation. We show that spectral resolution in thermal imagery can help recover this texture, and we provide algorithms to recover texture close to the ground truth. We develop a simulator for complex 3D scenes and discuss the interplay of geometric textures and non-uniform temperatures which is common in real-world thermal imaging. We demonstrate the failure of traditional thermal imaging to recover ground truth in multiple scenarios while our thermal perception approach successfully recovers geometric textures. Finally, we put forth an experimentally feasible infrared Bayer-filter approach to achieve thermal perception in pitch darkness as vivid as optical imagery in broad daylight.

Enhancing Arabic E-Commerce Review Sentiment Analysis Using a hybrid Deep Learning Model and FastText word embedding

The usage of NLP is shown in sentiment analysis (SA). SA extracts textual views. Arabic SA is challenging because of ambiguity, dialects, morphological variation, and the need for more resources available. The application of convolutional neural networks to Arabic SA has shown to be successful. Hybrid models improve single deep learning models. By layering many deep learning ensembles, earlier deep learning models should achieve higher accuracy. This research successfully predicted Arabic sentiment using CNN, LSTM, GRU, BiGRU, BiLSTM, CNN-BiGRU, CNN-GRU, CNN-LSTM, and CNN-biLSTM. Two enormous datasets, including the HARD and BRAD datasets, are used to evaluate the effectiveness of the proposed model. The findings demonstrated that the provided model could interpret the feelings conveyed in Arabic. The proposed procedure kicks off with the extraction of Arabert model features. After that, we developed and trained nine deep-learning models, including CNN, LSTM, GRU, BiGRU, BiLSTM, CNN-BiGRU, CNN-GRU, CNN-LSTM, and CNN-biLSTM. Concatenating the FastText and GLOVE as word embedding models. By a margin of 0.9112, our technique surpassed both standard forms of deep learning.

A Formal Language for Modelling and Verifying Systems-of-Systems Software Architectures

Systems of systems (SoS) are a set of independent systems that are constantly evolving. These systems cooperate in order to achieve a common goal, known as an SoS mission. It is difficult to capture their behaviours while operating in a hierarchical, cooperative and evolutionary structure. Thus, a software architecture based approach is defined in order to model and analyze these systems, reducing their increasing complexity. It ought to describe an SoS on both structural and behavioural levels. In this paper, ArchSoS, an ADL dedicated to describe SoS software architectures, is extended with a syntax-driven formal description, based on Bigraphic Reactive Systems (BRS). SoS defined via ArchSoS have both a graphical and a textual view. For this syntax to be executable, and to verify SoS evolutions, an operational semantic for ArchSoS is defined. It is implemented in Maude language to permit ArchSoS behavioural simulations, and its formal analysis is done through Linear Temporal Logic (LTL). The proposed approach is illustrated through a Crisis Response SoS as a case study.

When the Peace Generation is a Hundred Years Away… Youth, Citizenship, and Democracy in Times of Transition to Peace in Colombia

Efforts to consolidate a democratic citizenry are salient in societies transitioning to peace. These endeavors have spawned educational approaches to promote democratic skills, behaviors, and values in young citizens. Yet normative civic framings do not always align with students’ lived experiences, nor with their everyday decisions and expectations. Drawing from in-depth interviews with students in Leticia, Colombia, this study discusses youth understandings about citizenship in relation to the current political process. Findings provide a textured view of how young citizens participate in their civic subjectification through negotiated meanings and values they grant to the peace process and the prospects for sustained peacebuilding. Students’ perspectives prompt us to consider more comprehensive civic practices and frameworks that resonate with their everyday lives and the increasingly challenges of disrupting protracted violence and its legacies.

Individual Acceptance of Using Health Data for Private and Public Benefit: Changes During the COVID-19 Pandemic

While the COVID-19 pandemic has been devastating, data collected in this context has unprecedented opportunities for data scientists. The stunning breadth of data obtained through new gathering systems put in place to manage the pandemic offers a richly textured view of a transformed world. Looking forward, privacy researchers worry that these new data-gathering systems risk running afoul of societal norms regarding the flow of information. Looking back at pre-pandemic public preferences with respect to data sharing may provide us some idea of what to expect in the future. In July of 2019, we happened to conduct a vignette study in Germany to examine the public’s willingness to share data for fighting an outbreak of an infectious disease. In April of 2020, during the first peak of the pandemic, we repeated the study to examine crisis-driven changes in respondents’ willingness to share data for public health purposes with three different samples. Public acceptance of the use of individual health data to combat an infectious disease outbreak increased notably between the two measurements, while acceptance of data use in several other scenarios barely changed over time. This shift aligns with the predictive framework of contextual integrity theory, and the data presented here may serve as a good reminder for policymakers to carefully consider the intended purpose of and appropriate limitations on data use.

Sexuality: Homosexual and Heterosexual Conceptions in the Selected Poems of Walt Whitman

This literary research aims to analyze the selected poems of Walt Whitman: “Not Heat Flames Up and Consumes,” “When I Heard at the Close of the Day,” “A Woman Waits for Me,” and “Spontaneous Me.” This study assumes that the selected poems depict sexuality such as homosexual and heterosexual nuances. The text of the poems supporting the study’s assumptions is descriptively analyzed based on archetypal theory and formalistic theories. This in-depth study further utilizes a qualitative method of discourse analysis in scrutinizing the representative poems. Findings revealed three phases: a) Signifier and signified transaction; b) conceptualization of imagery; and c) Author's Insightful textual view. It is concluded that Walt Whitman’s selected poems reveal homosexual and heterosexual manifestations.

Women, gender and sexuality: Changing historiographies of colonial India—A bibliographical essay

Drawing on fragmentary examples from women’s histories in colonial India, this paper underlines the problems and possibilities in historiographies of modern India. Feminist scholars argue that the three terms—women, gender and sex—have often been used interchangeably. However, the commonsensical term woman is neither a natural category (of non-men) nor a homogeneous community (of sisterhood), for there are historically many ways of being a woman in different times. Further, gender is not merely a natural or biological identity of a person. It is a historical, social and political construction of how to be a man or a woman. Even sex is no longer seen as the biological ground upon which gender is constructed, as sexualities too are socially produced and regulated by dominant discourses, which establish one kind of sexuality as normal and relegate others into the domain of deviance, perversion or criminality. Through selective readings from discourses around women’s education and conceptualisation of the modern women in colonial India, the paper reflects on how a gender-sensitive perspective produces a more complex and textured view of historical processes. While patriarchies were recast in more powerful, though subtle ways, they were also subverted, or at least questioned, in colonial India.

PFAN++: Bi-Directional Image-Text Retrieval With Position Focused Attention Network

Bi-directional image-text retrieval and matching attract much attention recently. This cross-domain task demands a fine understanding of both modalities for learning a measure of different modality data. In this paper, we propose a novel position focused attention network to investigate the relation between the visual and the textual views. This work integrates the prior object position to enhance the visual-text joint-embedding learning. The image is first split into blocks, which are treated as the basic position cells, and the position of an image region is inferred. Then, we propose a position attention to model the relations between the image region and position cells. Finally, we generate a valuable position feature to further enhance the region expression and model a more reliable relationship between the visual image and the textual sentence. Experiments on the popular datasets Flickr30K and MS-COCO show the effectiveness of the proposed method. Besides the public datasets, we also conduct experiments on our collected practical large-scale news dataset (Tencent-News) to validate the practical application value of the proposed method. As far as we know, this is the first attempt to test the performance on the practical application. Our method achieves the competitive performance on all of these three datasets.

A textual framework for contour retrieval using sub-multiple contour striking spread position learning

In edge-based retrieval systems, a learning model to contour processing is required to enhance the retrieval process. This paper presents a textual view of contour retrieval problem by feature similarity learning (FSL). The proposed model detects the number of contours in the image to process in an image spread area of interest that strikes its local directions of trajectories and lines of map levels by preserving its feature characteristics by FSL. Experiments on database images is learning with several query image, show that the proposed model can provide better results. They have illustrated the performance of the proposed learning model in various numerical experiments.

Sentiment analysis of extremism in social media from textual information

Uncertainty in political, religious, and social issues causes extremism among people that are depicted by their sentiments on social media. Although, English is the most common language used to share views on social media, however, other vicinity based languages are also used by locals. Thus, it is also required to incorporate the views in such languages along with widely used languages for revealing better insights from data. This research focuses on the sentimental analysis of social media multilingual textual data to discover the intensity of the sentiments of extremism. Our study classifies the incorporated textual views into any of four categories, including high extreme, low extreme, moderate, and neutral, based on their level of extremism. Initially, a multilingual lexicon with the intensity weights is created. This lexicon is validated from domain experts and it attains 88% accuracy for validation. Subsequently, Multinomial Naïve Bayes and Linear Support Vector Classifier algorithms are employed for classification purposes. Overall, on the underlying multilingual dataset, Linear Support Vector Classifier out-performs with an accuracy of 82%.

Self-learning residual model for fast intra CU size decision in 3D-HEVC

As an extension of the High Efficiency Video Coding (HEVC) standard, 3D-HEVC requires to encode multiple texture views and depth maps, which inherits the same quad-tree coding structure as HEVC. Due to the distinct properties of texture views and depth maps, existing fast intra prediction approaches were presented for the coding of texture views and depth maps, respectively. To further reduce the coding complexity of 3D-HEVC, a self-learning residual model-based fast coding unit (CU) size decision approach is proposed for the intra coding of both texture views and depth maps. Residual signal, which is defined as the difference between the original luminance pixel and the optimal prediction luminance pixel, is firstly extracted from each CU. Since residue signal is strongly correlated with the optimal CU partition, it is used as the feature of each CU. Then, a self-learning residual model is established by intra feature learning, which iteratively learns the features of the previously encoded coding tree unit (CTU) generated by itself. Finally, a binary classifier is developed with the self-learning residual model to early terminate CU size decision of both texture views and depth maps. Experimental results show the proposed fast intra CU size decision approach achieves 33.3% and 49.3% encoding time reduction on average for texture views and depth maps with negligible loss of overall video quality, respectively.

Assessing the Impact of Corneal Refraction and Iris Tissue Non-Planarity on Iris Recognition

Assumptions regarding eye morphology are implicit in iris recognition algorithms. The cornea is assumed to have little to no effect on the view of the iris texture when the eye gaze is non-frontal, the iris is assumed flat, and the eye is assumed to be imaged via an orthographic projection. If these assumptions hold, affine transformations may be used to rectify non-frontally posed images to a frontal view and the rubber sheet model may be accurately used to normalize the iris annulus to a rectangular image. This paper examines how iris recognition performance degrades when the first two assumptions are violated. Using a computer renderable eye model, a data set is created varying the presence of the cornea and a parameterized non-planarity of the iris shape across a large range of eye gaze angles. Matching scores are created using a commercial matcher. When comparing the relative impact of each assumption violation, it is observed that iris non-planarity presents a more significant problem than corneal refractive distortion with regard to iris recognition accuracy in non-frontal images.

Hybrid stopping model-based fast PU and CU decision for 3D-HEVC texture coding

As an extension of High-Efficiency Video Coding (HEVC) standard, 3D-HEVC needs to encode multiple texture views and depth maps, which further increases the computational complexity. To reduce the complexity of dependent texture view coding, a fast prediction unit (PU) and coding unit (CU) decision method is proposed for 3D-HEVC based on hybrid stopping model. The inter-view correlation is used as a priori information to roughly predict the possible optimal PU and CU sizes. Then, by exploiting the encoded posterior information, the rate distortion cost correlation and the code block flag, the optimal PU and CU are further examined as being optimal or not. Experimental results show that the proposed fast PU and CU decision method achieves 52.7% encoding time saving on average with negligible loss of coding efficiency for 3D-HEVC-dependent texture view coding.

Surface Roughness and Gravitational Slope Distributions of Vesta and Ceres

AbstractHeavily cratered terrains dominate the surfaces of asteroid 4 Vesta and dwarf planet 1 Ceres. The data from the Dawn spacecraft allowed reconstruction of high‐resolution shape models of these bodies. We used the stereophotoclinometric shape models to compute gravitational slopes and topographic roughness of Vesta and Ceres. We compute the slope distributions of Vesta and Ceres and compare them to those of the other bodies with heavily cratered terrains. The distribution of slopes of Vesta and Ceres has a kink at ≈34°. The slope distribution is steeper for slopes higher than ≈34°. The Moon and Mars have a similar kink but at a lower (shallower) slope (≈29°–30°). We hypothesize that this kink corresponds to the angle of repose, which is higher on the two minor bodies compared to that of Mars and the Moon. We have also analyzed the topographic roughness of Vesta and Ceres. Vesta's topography is characterized by lower roughness in the southern hemisphere that was resurfaced by giant impacts. The roughness of Ceres is mostly controlled by regional‐scale geology with no significant large‐scale variations. Large, fresh craters have smooth ejecta blankets on both Vesta and Ceres unlike previously observed rough ejecta on the Moon, Mars, and Mercury. On Ceres, the smooth ejecta craters also have smooth floors explained by impact slurry. Lineaments of elevated roughness are abundant on Ceres, whereas are nearly absent on Vesta. The roughness maps we produced provide a synoptic view of the surface texture and could be used to aid geologic mapping.

Motion and disparity vectors early determination for texture video in 3D-HEVC

3D-HEVC is the state-of-the-art video coding standard for 3D video, and it is an extension of high efficiency video coding (HEVC) standard. Besides the original HEVC coding tools, 3D-HEVC adopts some advanced coding tools, such as disparity vector (DV), inter-view prediction and inter-component prediction. However, these advanced tools lead to extremely high encoding complexity at the same time, thus it cannot be well applied in real-time multimedia systems. In this paper, we propose a motion and disparity vectors early determination algorithm to reduce 3D-HEVC computational complexity. First, based on the statistical analyses, the spatial and temporal motion vector (MV) candidates are adaptively reduced for the prediction unit (PU) with the Merge mode. Then, for the PU with the Inter mode, the combination of spatial and temporal candidates is used to early determine the final MV. Finally, an adaptive optimization algorithm is adopted to select the valid inter-view disparity vectors (DV) candidates. Moreover, if the difference between candidate vectors is within a conditional range, current PU will be encoded with the Merge mode to skip unnecessary coding process. Experimental results show that for the texture views encoding, the proposed algorithm achieves an average of 33.03% encoding time saving, and an average of 0.47% BD-Rate increases.

Probability Model-Based Early Merge Mode Decision for Dependent Views Coding in 3D-HEVC

As a 3D extension to the High Efficiency Video Coding (HEVC) standard, 3D-HEVC was developed to improve the coding efficiency of multiview videos. It inherits the prediction modes from HEVC, yet both Motion Estimation (ME) and Disparity Estimation (DE) are required for dependent views coding. This improves coding efficiency at the cost of huge computational costs. In this article, an early Merge mode decision approach is proposed for dependent texture views and dependent depth maps coding in 3D-HEVC based on priori and posterior probability models. First, the priori probability model is established by exploiting the hierarchical and interview correlations from those previously encoded blocks. Second, the posterior probability model is built by using the Coded Block Flag (CBF) of the current coding block. Finally, the joint priori and posterior probability model is adopted to early terminate the Merge mode decision for both dependent texture views and dependent depth maps coding. Experimental results show that the proposed approach saves 45.2% and 30.6% encoding time on average for dependent texture views and dependent depth maps coding while maintaining negligible loss of coding efficiency, respectively.

Factorization machines and deep views-based co-training for improving answer quality prediction in online health expert question-answering services.

In online health expert question-answering (HQA) services, it is significant to automatically determine the quality of the answers. There are two prominent challenges in this task. First, the answers are usually written in short text, which makes it difficult to absorb the text semantic information. Second, it usually lacks sufficient labeled data but contains a huge amount of unlabeled data. To tackle these challenges, we propose a novel deep co-training framework based on factorization machines (FM) and deep textual views to intelligently and automatically identify the quality of HQA systems. More specifically, we exploit additional domain-specific semantic information from domain-specific word embeddings to expand the semantic space of short text and apply FM to excavate the non-independent interaction relationships among diverse features within individual views for improving the performance of the base classifier via co-training. Our learned deep textual views, the convolutional neural networks (CNN) view which focuses on extracting local features using convolution filters to locally model short text and the dependency-sensitive convolutional neural networks (DSCNN) view which focuses on capturing long-distance dependency information within the text to globally model short text, can then overcome the challenge of feature sparseness in the short text answers from the doctors. The developed co-training framework can effectively mine the highly non-linear semantic information embedded in the unlabeled data and expose the highly non-linear relationships between different views, which minimizes the labeling effort. Finally, we conduct extensive empirical evaluations and demonstrate that our proposed method can significantly improve the predictive performance of the answer quality in the context of HQA services.

QUALITY IN WIRELESS TRANSMISSIONS OF 3D VIDEO WITH THE HEVC STANDARD

Objectives: In this work, the assessment of 3D video quality is performed with the HEVC (High Efficiency Video Coding) standard over a wireless transmission considering the diversity MIMO schemes. Methods/Statistical Analysis: The video sequences in both Multiview Extension and 3D-Extension with its depth map using the HEVC standard were encoded. In addition, three different diversity MIMO schemes and a turbo codec in the system were considered. The BER results obtained using the Montecarlo simulation for packet loss were implemented to evaluate the 3D video sequence at the output of the system, it based in the objective and subjective assessment. Findings: In 3D video encoding with multiple views using HEVC standard with GOP All-Intra coding structure, packets of different texture views can be lost, but in 3D video encoding with DIBR and depth data, you cannot miss any package of the texture views, because the video cannot be decoded. The coding structures of GOP type IPBB and IPPP is more susceptible to the package loss, so the transmission protocol TCP is the suitable. Therefore, this scheme in real- time transmission is not recommended. The increase of diversity in the MIMO schemes decreases the package loss, in this way, the increasing of the video quality that is verified using the objective assessment, but it with a cost of velocity transmission due that is required more intervals of transmission in the MIMO scheme i.e. (with redundant information). The packet loss in wireless transmissions with MIMO 3D video schemes encoded with HEVC standard it has greater impact on the degradation of the video sequences of high mobility than the low mobility. Application/Improvements: The assessment of 3D Video quality using the HEVC standard and its transmission through a wireless channel is using the diversity MIMO schemes which takes advantage of spatial dimension. Keywords: Video Quality, 3D Video, HEVC, MIMO

Iconicity of syntax and narrative in Amerindian prosaic texts

AbstractThe notion that the form of a word bears an arbitrary relation to its meaning accounts only partly for the attested relations between form and meaning in the world's languages. Recent research suggests a more textured view of syntactic and narrative structure in Amerindian prosaic texts, in which arbitrariness is complemented by iconicity (aspects of form resemble aspects of meaning) and systematicity (statistical regularities in forms predict function).

Judicial Independence – French and European Perspectives

Judicial independence within the framework of the rule of law is a broad, complex and controversial subject, which is especially true of France, because of the country’s particular attitude to justice since the French Revolution, and even before this critical and fundamental period. The first part of this article deals, on a broad constitutional scale, with the configuration of judicial independence, its guarantees (material and organic) and its interaction with the rule of law in the constitutions of the member states of the EU. The main purpose is to draw an objective map of the situation from a pure formal and textual (and therefore a slightly partial and narrow) view point. The second part of this contribution considers the specificity of the independence of the French judiciary in relation to the French constitutional principle of the rule of law, which is called the “Etat de Droite” in the French legal tradition.