Public emotional dynamics toward AIGC content generation across social media platform

Given the widespread popularity of interactive AI models like ChatGPT, public opinion on emerging artificial intelligence generated content(AIGC) has been extensively debated. Pessimists believe that AIGC will replace humans in the future, and optimists think that it will further liberate productivity. Public emotions play a crucial role on social media platforms. They can provide valuable insights into the public's opinions, attitudes, and behaviors. There is a lack of research on the analysis of social group emotions triggered by AIGC content, and even more on the cross-platform differences of group emotions. This study fills the research gap by connecting the theory of group dynamics with emotions in social media. Specifically, we develop a scientific group emotion calculation and visualization system based on chains of communication. The system is capable of crawling data in real time and presenting the current state of group emotions in a fine-grained manner. We then analyze which group dynamic factors drive different public emotions towards nine AIGC products on the three most popular social media platforms in China. Finally, we obtain four main findings. First, Douyin is the only platform with negative group emotion on emerging AI technologies. Second, Weibo users prefer extreme emotions more than others. Third, the group emotion varies by education and age. It is negatively correlated with senior high school or lower and 25 or younger, and positively correlated with bachelor's degree or higher and 26-35. Fourth, the group emotion polarization increases with more posts without comments and celebrity publishers. By analyzing the key dynamic factors of group emotions to AIGC on various social media platforms, we can improve our products and services, develop more effective marketing strategies, and create more accurate and effective AI models to solve complex problems.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Culpa e desejo em Uma abelha na chuva: o livro e o filme

Mestrado em Línguas, Literaturas e CulturasO presente trabalho pretende investigar a relação entre o romance Uma Abelha na Chuva de Carlos de Oliveira e a reinterpretação do romance no filme homónimo, realizada por Fernando Lopes. Propomos comparar as semelhanças e as diferenças que existem entre os mundos objetivos apresentados nas referidas obras e ainda tentarmos explorar os dois mundos subjetivos de Álvaro Silvestre e de D. Maria dos Prazeres, que são os focos de representação nas duas obras. Para atingir os objetivos propostos, em primeiro lugar, investigamos os percursos dos dois autores. Posteriormente, analisamos as paisagens naturais e sociais e as relações humanas em ambas as obras. Por diante, escolhendo duas cenas que aparecem tanto no romance como no filme, e utilizando a filosofia existencialista sartriana e a psicanálise freudiana, analisamos a figura de Álvaro Silvestre, dominada pelos sentimentos de culpa e pelo receio de morte e, ao mesmo tempo, contemplamos a representação da figura de D. Maria dos Prazeres, bem como o seu desejo recalcado. Em ambas as obras, concluímos existir uma vontade de expressão contra a opressão e as limitações político-sociais e culturais do Estado Novo: no romance, o escritor expõe-nos um mundo objetivo árido, pobre e caraterizado pela exploração entre os homens; no filme, organizando os diálogos e os gestos corporais dos atores, bem como os adereços, Fernando Lopes diminui a dimensão do mundo objetivo e intensifica os conflitos entre o casal. Apesar do romance e do filme se concentrarem em temas diferentes (culpa e morte no romance e impossibilidade de realização do desejo sexual e amoroso, no filme), as duas obras, complementarmente, mostram a relação conjugal dura e infeliz que ocorria realmente entre muitos dos casais que viviam sob o paradigma ideológico-cultural do Estado Novo. O nosso trabalho tenta justificar que as duas obras não só se complementam, mas até são o reverso uma da outra. São ambas, cada uma à sua maneira, testemunhos de um tempo e de um povo que sofreu consequências amargas e severas sob o domínio da ditatura salazarista.The present dissertation aims to investigate the relation between the novel A Bee in the Rain, written by Carlos de Oliveira and its adaption to the homonymous film, directed by Fernando Lopes. The dissertation proposes to compare the similarities and the differences between the two objective worlds, which are shown separately in these works in study. Also, the dissertation attempts to explore the two subjective worlds of Álvaro Silvestre and his wife, Maria dos Prazeres, presented by these works as their main focuses respectively. To achieve these goals, first, the careers of the author and the director are investigated. Then the natural and social landscapes, as well as human relationships demonstrated in both of the works are analyzed. Moreover, under the instruction of Sartre’s existentialism and Freud’s psychoanalysis, by choosing two plots that appear in both of the works, the character of Álvaro Silvestre dominated by feelings of fault and fears of death is demonstrated. Our investigation concentrates on the representation of the character of Maria dos Prazeres, as well as on the interpretation of her frustrated desires. In conclusion, it turns out that in these works, there is a will to fight against the political, social and cultural oppression and restrictions that were applied by the Estado Novo: in the novel, the writer exhibits for us an arid world that was full of poverty and human exploitation; in the film, by organizing the actors’ dialogs and body languages, as well as others props, the director reduces the range of the objective world, meanwhile, intensifies the conflicts between the couple Silvestre. Even though the novel and the film put their focuses on the representation of different topics (fault and death in the novel and the impossibility to realize the sexual and amorous desire in the film), together, these works show the harsh and unhappy marital relationship that really happened among many of the couples who lived under the control of the ideological and cultural paradigm, set by the Estado Novo. Under this circumstance, we intend to justify that these two works in discussion, each one by its own way, are the witnesses of their era and the life of the Portuguese people, who was suffering from the dictatorship ruled by Salazar

A China e Macau a partir de duas “navegações” portuguesas do século XX : O Caminho do Oriente (1932) de Jaime do Inso e Nocturno em Macau (1991) de Maria Ondina Braga

Tendo como corpus O Caminho do Oriente (1932) e Nocturno em Macau (1991), o presente trabalho foca-se em duas “navegações” portuguesas ligadas à China e a Macau, aproximadamente empreendidas nos finais dos anos 20 e na primeira metade da década de 60 do século XX, e que são ficcionalizadas pelos escritores Jaime do Inso (1880-1967) e Maria Ondina Braga (1932-2003), respetivamente. O objetivo é perscrutar a autoperceção dos viajantes portugueses, a sua atitude em relação à realidade de Macau, o seu modo de interagir com a população chinesa local e a representação da China e das vivências chinesa e portuguesa em vivo contraste, tudo isto que se interpreta nos dois romances. Numa perspetiva mais ampla, e a partir da demonstração de Edward W. Said sobre o orientalismo e o imperialismo, visa-se dar visibilidade ao complexo e dinâmico panorama que existe por trás das duas obras literárias e que consiste, sobretudo, nos seguintes fatores, a saber: as contemporâneas conjunturas histórico-políticas de Portugal e da China, os vieses ideológicos ocorridos nestes dois universos, bem como as singulares experiências sociais dos dois escritores, que se veem espelhadas nas suas criações literárias. Enfim, pretende-se contribuir para o entendimento da coexistência das comunidades portuguesa e chinesa em Macau enquanto território chinês sob administração portuguesa, ou seja, no âmbito do império colonial português.With O Caminho do Oriente (1932) and Nocturno em Macau (1991) as the corpus, the present work focuses on two Portuguese “navigations” linked to China and Macao. These two “navigations” were undertaken in the late 1920s and in the first half of the 1960s and were fictionalized respectively by Jaime do Inso (1880-1967) and Maria Ondina Braga (1932-2003). The objective of this study is to examine the self-perception of the Portuguese travelers, their attitude towards the reality of Macao, their way of interacting with the local Chinese, as well as the depiction of China and of the contrastive living conditions of the Portuguese and the Chinese in Macao, which are fully interpreted in the two novels. Based on Edward W. Said’s elaboration of orientalism and imperialism, the present work aims to give visibility to the complex and dynamic panorama illustrated by the two literary works, which is mainly composed by the following factors: the contemporary historical and political conjunctures of Portugal and China, the ideological tendencies that occurred in these two countries and the unique social experiences of the two writers that are reflected in their literary creations. Also, the present work is meant to contribute to the understanding of the coexistence of the Portuguese and Chinese communities in Macao, part of the Chinese territory under the Portuguese rule until 1999.Fundação Orient

Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning

Transfer learning leverages feature representations of deep neural networks (DNNs) pretrained on source tasks with rich data to empower effective finetuning on downstream tasks. However, the pretrained models are often prohibitively large for delivering generalizable representations, which limits their deployment on edge devices with constrained resources. To close this gap, we propose a new transfer learning pipeline, which leverages our finding that robust tickets can transfer better, i.e., subnetworks drawn with properly induced adversarial robustness can win better transferability over vanilla lottery ticket subnetworks. Extensive experiments and ablation studies validate that our proposed transfer learning pipeline can achieve enhanced accuracy-sparsity trade-offs across both diverse downstream tasks and sparsity patterns, further enriching the lottery ticket hypothesis.Comment: Accepted by DAC 202

LLM for Patient-Trial Matching: Privacy-Aware Data Augmentation Towards Better Performance and Generalizability

The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care. However, current approaches face challenges such as data standardization, ethical considerations, and a lack of interoperability between Electronic Health Records (EHRs) and clinical trial criteria. In this paper, we explore the potential of large language models (LLMs) to address these challenges by leveraging their advanced natural language generation capabilities to improve compatibility between EHRs and clinical trial descriptions. We propose an innovative privacy-aware data augmentation approach for LLM-based patient-trial matching (LLM-PTM), which balances the benefits of LLMs while ensuring the security and confidentiality of sensitive patient data. Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%. Additionally, we present case studies to further illustrate the effectiveness of our approach and provide a deeper understanding of its underlying principles

NetBooster: Empowering Tiny Deep Learning By Standing on the Shoulders of Deep Giants

Tiny deep learning has attracted increasing attention driven by the substantial demand for deploying deep learning on numerous intelligent Internet-of-Things devices. However, it is still challenging to unleash tiny deep learning's full potential on both large-scale datasets and downstream tasks due to the under-fitting issues caused by the limited model capacity of tiny neural networks (TNNs). To this end, we propose a framework called NetBooster to empower tiny deep learning by augmenting the architectures of TNNs via an expansion-then-contraction strategy. Extensive experiments show that NetBooster consistently outperforms state-of-the-art tiny deep learning solutions

Setting the Trap: Capturing and Defeating Backdoors in Pretrained Language Models through Honeypots

In the field of natural language processing, the prevalent approach involves fine-tuning pretrained language models (PLMs) using local samples. Recent research has exposed the susceptibility of PLMs to backdoor attacks, wherein the adversaries can embed malicious prediction behaviors by manipulating a few training samples. In this study, our objective is to develop a backdoor-resistant tuning procedure that yields a backdoor-free model, no matter whether the fine-tuning dataset contains poisoned samples. To this end, we propose and integrate a honeypot module into the original PLM, specifically designed to absorb backdoor information exclusively. Our design is motivated by the observation that lower-layer representations in PLMs carry sufficient backdoor features while carrying minimal information about the original tasks. Consequently, we can impose penalties on the information acquired by the honeypot module to inhibit backdoor creation during the fine-tuning process of the stem network. Comprehensive experiments conducted on benchmark datasets substantiate the effectiveness and robustness of our defensive strategy. Notably, these results indicate a substantial reduction in the attack success rate ranging from 10\% to 40\% when compared to prior state-of-the-art methods

Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint

Clinical trials are indispensable in developing new treatments, but they face obstacles in patient recruitment and retention, hindering the enrollment of necessary participants. To tackle these challenges, deep learning frameworks have been created to match patients to trials. These frameworks calculate the similarity between patients and clinical trial eligibility criteria, considering the discrepancy between inclusion and exclusion criteria. Recent studies have shown that these frameworks outperform earlier approaches. However, deep learning models may raise fairness issues in patient-trial matching when certain sensitive groups of individuals are underrepresented in clinical trials, leading to incomplete or inaccurate data and potential harm. To tackle the issue of fairness, this work proposes a fair patient-trial matching framework by generating a patient-criterion level fairness constraint. The proposed framework considers the inconsistency between the embedding of inclusion and exclusion criteria among patients of different sensitive groups. The experimental results on real-world patient-trial and patient-criterion matching tasks demonstrate that the proposed framework can successfully alleviate the predictions that tend to be biased