773 research outputs found

    A Review of Deep Learning Models for Twitter Sentiment Analysis: Challenges and Opportunities

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    Microblogging site Twitter (re-branded to X since July 2023) is one of the most influential online social media websites, which offers a platform for the masses to communicate, expresses their opinions, and shares information on a wide range of subjects and products, resulting in the creation of a large amount of unstructured data. This has attracted significant attention from researchers who seek to understand and analyze the sentiments contained within this massive user-generated text. The task of sentiment analysis (SA) entails extracting and identifying user opinions from the text, and various lexicon-and machine learning-based methods have been developed over the years to accomplish this. However, deep learning (DL)-based approaches have recently become dominant due to their superior performance. This study briefs on standard preprocessing techniques and various word embeddings for data preparation. It then delves into a taxonomy to provide a comprehensive summary of DL-based approaches. In addition, the work compiles popular benchmark datasets and highlights evaluation metrics employed for performance measures and the resources available in the public domain to aid SA tasks. Furthermore, the survey discusses domain-specific practical applications of SA tasks. Finally, the study concludes with various research challenges and outlines future outlooks for further investigation

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    A corpus-based CDA study of ideological mediation through translation shifts: an analysis of the official Chinese-English translation of the governance of China

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    This study aims to explore the extent to which President Xi’s ideological message is mediated in the official Chinese-English translation of The Governance of China via various translation shifts and analyze the possible ideological reasons behind it. Unlike previous studies whose interpretation of translation shifts has been restricted to either the linguistic level or the speech situation, this research project focuses on exploring the translation shifts’ ideological significance within the broader sociopolitical context. It adopts a mixed-methods approach, merging critical discourse analysis (CDA) and corpus-based translation studies. A parallel corpus based on the source and target texts of President Xi’s domestic speeches to officials and Party members, published in The Governance of China, was built to ensure a quantitative and qualitative analysis. It is also noteworthy that this study concentrates on the key Chinese modality markers, transitivity processes, metaphorical expressions, and referring terms that stand out in the present research corpus compared to general Chinese discourse instead of all the existing or the most frequent ones. The overall results suggest that translation shifts in modality, transitivity, metaphor, and reference have slightly increased the ideological significance of strengthening the government and the Party’s self-discipline compared to other national issues, and exhibited a tendency to contextualize considering the foreign audiences’ ideological positions. Such shifts may be related to the translation agency’s commitment and the state’s current foreign policy. Ultimately, this study reveals subtle ideological translation shifts that will be buried if researchers treat source and target texts separately. It calls for translators to raise awareness of textual features’ ideological potential and encourages audiences to pay attention to the institutional and sociopolitical background of translated texts

    The Socio-Technical Dynamics of Renewable Energy Policies in Germany

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    Growing environmental concerns and human-caused climate change increase the pressure on policymakers for rapid action to transform how societies convert energy, produce goods, or transport freight. Innovation and technological progress may contribute to such transitions. However, technological change is hard to predict, requires time, and may be laden with political conflicts. Although more sustainable technologies are available, incentivizing demand and deployment are crucial to accelerate transitions. As transformations develop over decades, understanding the temporal dynamics of policies is critical for governance. In Germany, the renewable energy act incentivizes the deployment of renewable energy technologies by remunerating electricity fed into the common grid. This dissertation assesses how socio-technical developments of solar and wind energy conversion technologies and the renewable energy act interactively shaped each other. Drawing on frameworks such as technological innovation systems, legitimacy, framing, and policy feedback, the contents of 16,485 newspaper articles and additional empirical studies were scrutinized. Combining methods from natural language processing, machine learning, and statistics, this thesis develops text models to assess changes in content and sentiment in large corpora over time. Three studies focus on the shifts in media framing of the German renewable energy act, the underlying co-evolution of technological and policy processes, and the development of the legitimacy of wind power. The results confirm that renewable energy deployment and policy are contested with varying intensity over time. Where change ought to occur, non-linear dynamics of innovation and technology uptake, growing policy costs, economic interests of incumbents, and technology side effects increasingly complicate policymaking over time. The early phases of the renewable energy act were shaped by positive expectations toward renewable energy technologies, which later shifted towards an emphasis on policy costs. The findings highlight the importance of the prosperity of underlying innovation systems as supporters of policy ambition and maintenance over time. However, policy costs and side effects must be managed effectively to withstand increasing contestation. These results may contribute to advancing the successful governance of sectoral transitions likely to unfold over several decades

    Complexity Science in Human Change

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    This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience

    Izolirana i nestala banijska naselja

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    IN CROATIAN: Ruralna naselja u kontinentalnoj Hrvatskoj obilježena su snažnom depopula[1]cijom, prometnom izoliranošću i velikim brojem napuštenih i praznih mjesta. Područje Banije desetljećima je posebno izloženo snažnoj depopulaciji. Od 195 nenaseljenih mjesta u Hrvatskoj 2021. godine, čak njih 13 je na području Ba[1]nije. Banijska napuštena naselja, u kojima nitko više ne živi pored ruševina ili tragova kuća i okućnica, s devastiranom infrastrukturom, nepostojećim jav[1]nim prijevozom i, na kraju, u potpuno zapuštenim ruralnim krajolicima, svje[1]doče demografskom slomu, izumiranju i nestajanju. Raznoliki razlozi (migra[1]cije, ratovi i katastrofe, ekonomski i politički razlozi) doveli su do društvene i kulturne izolacije. U ovome radu prikazat će se prazna i napuštena naselja (Baturi, Bišćanovo, Brubno, Donja Trstenica, Donji Selkovac, Gornje Jame, Turčenica, Kobiljak, Ostojići, Zut, Bukovica i Mala Vranovina) pomoću doku[1]mentiranja devastiranih i danas jedva vidljivih ostataka materijalne kulture. Uz to analizirat će se „svakodnevica u prošlosti“ korištenjem arhivske građe o stanovnicima i životu u prošlosti te pomoću iskustava, doživljaja, sjećanja i privatne arhive (sačuvani predmeti, fotografije) njihovih bivših stanovnika. --------------- IN ENGLISH: Rural settlements in Continental Croatia are characterized by severe depopulation, traffic isolation, and a large number of abandoned and empty settlements. The Banija Region has been particularly affected by severe depopulation for decades. Of the 195 uninhabited settlements in Croatia in 2021, as many as 13 are in the Banija Region. Uninhabited and abandoned settlements, where no one lives anymore next to the ruins or traces of houses and gardens, with devastated or destroyed infrastructure, non-existent public transport, and completely neglected rural landscapes testify to demographic collapse, extinction, and desertion. Various reasons (migrations, wars and disasters, economic and political reasons) have led to social and cultural isolation. In this paper, we will show empty and abandoned settlements (Baturi, Bišćanovo, Brubno, Donja Trstenica, Donji Selkovac, Gornje Jame, Turčenica, Kobiljak, Ostojići, Zut, Bukovica, and Mala Vranovina) by documenting the devastated and barely visible remains of material culture. In addition, we will analyse “everyday life in the past” using archival material about life in the past, and through the experiences, memories, stories, and private archives (preserved objects, photos) of their former residents

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Topological data analysis of organoids

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    Organoids are multi-cellular structures which are cultured in vitro from stem cells to resemble specific organs (e.g., colon, liver) in their three- dimensional composition. The gene expression and the tissue composition of organoids constantly affect each other. Dynamic changes in the shape, cellular composition and transcriptomic profile of these model systems can be used to understand the effect of mutations and treatments in health and disease. In this thesis, I propose new techniques in the field of topological data analysis (TDA) to analyse the gene expression and the morphology of organoids. I use TDA methods, which are inspired by topology, to analyse and quantify the continuous structure of single-cell RNA sequencing data, which is embedded in high dimensional space, and the shape of an organoid. For single-cell RNA sequencing data, I developed the multiscale Laplacian score (MLS) and the UMAP diffusion cover, which both extend and im- prove existing topological analysis methods. I demonstrate the utility of these techniques by applying them to a published benchmark single-cell data set and a data set of mouse colon organoids. The methods validate previously identified genes and detect additional genes with known involvement cancers. To study the morphology of organoids I propose DETECT, a rotationally invariant signature of dynamically changing shapes. I demonstrate the efficacy of this method on a data set of segmented videos of mouse small intestine organoid experiments and show that it outperforms classical shape descriptors. I verify the method on a synthetic organoid data set and illustrate how it generalises to 3D to conclude that DETECT offers rigorous quantification of organoids and opens up computationally scalable methods for distinguishing different growth regimes and assessing treatment effects. Finally, I make a theoretical contribution to the statistical inference of the method underlying DETECT

    International Academic Symposium of Social Science 2022

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    This conference proceedings gathers work and research presented at the International Academic Symposium of Social Science 2022 (IASSC2022) held on July 3, 2022, in Kota Bharu, Kelantan, Malaysia. The conference was jointly organized by the Faculty of Information Management of Universiti Teknologi MARA Kelantan Branch, Malaysia; University of Malaya, Malaysia; Universitas Pembangunan Nasional Veteran Jakarta, Indonesia; Universitas Ngudi Waluyo, Indonesia; Camarines Sur Polytechnic Colleges, Philippines; and UCSI University, Malaysia. Featuring experienced keynote speakers from Malaysia, Australia, and England, this proceeding provides an opportunity for researchers, postgraduate students, and industry practitioners to gain knowledge and understanding of advanced topics concerning digital transformations in the perspective of the social sciences and information systems, focusing on issues, challenges, impacts, and theoretical foundations. This conference proceedings will assist in shaping the future of the academy and industry by compiling state-of-the-art works and future trends in the digital transformation of the social sciences and the field of information systems. It is also considered an interactive platform that enables academicians, practitioners and students from various institutions and industries to collaborate
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