在多方对话中有效地发现发言者的情绪状态是设计人类类似的会话代理商的重要性。在谈话期间，扬声器的认知状态通常由于某些过去的话语而改变，这可能导致他们的情绪状态的翻转。因此，在对话期间发现扬声器情感翻转背后的原因（触发）对于解释个人话语的情感标签至关重要。在本文中，除了解决对话中的情感认可的任务（ERC），我们介绍了一种新的任务 - 情感 - 翻转推理（EFR），旨在识别过去的话语，这引发了一个人的情绪状态以在一定时间翻转。我们提出了一个掩蔽的存储器网络来解决前者和基于变换器的网络的后一种任务。为此，我们考虑融合的基准情感识别数据集，用于ERC任务的多方对话，并使用EFR的新地基标签增强它。与五个最先进的模型进行了广泛的比较，表明我们对两个任务的模型的表现。我们进一步提出了轶事证据和定性和定量误差分析，以支持与基线相比模型的优势。

Intelligently extracting and linking complex scientific information from unstructured text is a challenging endeavor particularly for those inexperienced with natural language processing. Here, we present a simple sequence-to-sequence approach to joint named entity recognition and relation extraction for complex hierarchical information in scientific text. The approach leverages a pre-trained large language model (LLM), GPT-3, that is fine-tuned on approximately 500 pairs of prompts (inputs) and completions (outputs). Information is extracted either from single sentences or across sentences in abstracts/passages, and the output can be returned as simple English sentences or a more structured format, such as a list of JSON objects. We demonstrate that LLMs trained in this way are capable of accurately extracting useful records of complex scientific knowledge for three representative tasks in materials chemistry: linking dopants with their host materials, cataloging metal-organic frameworks, and general chemistry/phase/morphology/application information extraction. This approach represents a simple, accessible, and highly-flexible route to obtaining large databases of structured knowledge extracted from unstructured text. An online demo is available at http://www.matscholar.com/info-extraction.

Face swapping technology used to create "Deepfakes" has advanced significantly over the past few years and now enables us to create realistic facial manipulations. Current deep learning algorithms to detect deepfakes have shown promising results, however, they require large amounts of training data, and as we show they are biased towards a particular ethnicity. We propose a deepfake detection methodology that eliminates the need for any real data by making use of synthetically generated data using StyleGAN3. This not only performs at par with the traditional training methodology of using real data but it shows better generalization capabilities when finetuned with a small amount of real data. Furthermore, this also reduces biases created by facial image datasets that might have sparse data from particular ethnicities.

The internet has had a dramatic effect on the healthcare industry, allowing documents to be saved, shared, and managed digitally. This has made it easier to locate and share important data, improving patient care and providing more opportunities for medical studies. As there is so much data accessible to doctors and patients alike, summarizing it has become increasingly necessary - this has been supported through the introduction of deep learning and transformer-based networks, which have boosted the sector significantly in recent years. This paper gives a comprehensive survey of the current techniques and trends in medical summarization

Unavailability of parallel corpora for training text style transfer (TST) models is a very challenging yet common scenario. Also, TST models implicitly need to preserve the content while transforming a source sentence into the target style. To tackle these problems, an intermediate representation is often constructed that is devoid of style while still preserving the meaning of the source sentence. In this work, we study the usefulness of Abstract Meaning Representation (AMR) graph as the intermediate style agnostic representation. We posit that semantic notations like AMR are a natural choice for an intermediate representation. Hence, we propose T-STAR: a model comprising of two components, text-to-AMR encoder and a AMR-to-text decoder. We propose several modeling improvements to enhance the style agnosticity of the generated AMR. To the best of our knowledge, T-STAR is the first work that uses AMR as an intermediate representation for TST. With thorough experimental evaluation we show T-STAR significantly outperforms state of the art techniques by achieving on an average 15.2% higher content preservation with negligible loss (3% approx.) in style accuracy. Through detailed human evaluation with 90,000 ratings, we also show that T-STAR has up to 50% lesser hallucinations compared to state of the art TST models.

Predicting stock market movements has always been of great interest to investors and an active area of research. Research has proven that popularity of products is highly influenced by what people talk about. Social media like Twitter, Reddit have become hotspots of such influences. This paper investigates the impact of social media posts on close price prediction of stocks using Twitter and Reddit posts. Our objective is to integrate sentiment of social media data with historical stock data and study its effect on closing prices using time series models. We carried out rigorous experiments and deep analysis using multiple deep learning based models on different datasets to study the influence of posts by executives and general people on the close price. Experimental results on multiple stocks (Apple and Tesla) and decentralised currencies (Bitcoin and Ethereum) consistently show improvements in prediction on including social media data and greater improvements on including executive posts.

多种业务场景需要从结构化输入数据中自动生成描述性的人类可读文本。因此，已经开发了针对各种下游任务的事实到文本的系统主要是由于相关数据集的高可用性。直到最近，提出了跨语言事实与文本（XF2T）的问题，该问题是针对多种语言的生成，以及一个数据集，Xalign的八种语言。但是，实际上XF2T生成问题没有严格的工作。我们使用另外四种语言的注释数据扩展了Xalign数据集：旁遮普语，马拉雅拉姆语，阿萨姆语和Oriya。我们在扩展的多语言数据集上使用基于变压器的流行文本生成模型进行了广泛的研究，我们称之为Xalignv2。此外，我们研究了不同文本生成策略的性能：预处理，事实感知的嵌入和结构意识的输入编码的多种变化。我们的广泛实验表明，使用具有结构意识的输入编码的事实感知的嵌入式的多语言MT5模型可以平均在十二种语言中获得最佳结果。我们将代码，数据集和模型公开可用，并希望这将有助于进一步在此关键领域进行进一步的研究。

Covid-19已遍布全球，已经开发了几种疫苗来应对其激增。为了确定与社交媒体帖子中与疫苗相关的正确情感，我们在与Covid-19疫苗相关的推文上微调了各种最新的预训练的变压器模型。具体而言，我们使用最近引入的最先进的预训练的变压器模型Roberta，XLNet和Bert，以及在CoVID-19的推文中预先训练的域特异性变压器模型CT-Bert和Bertweet。我们通过使用基于语言模型的过采样技术（LMOTE）过采样来进一步探索文本扩展的选项，以改善这些模型的准确性，特别是对于小样本数据集，在正面，负面和中性情感类别之间存在不平衡的类别分布。我们的结果总结了我们关于用于微调最先进的预训练的变压器模型的不平衡小样本数据集的文本过采样的适用性，以及针对分类任务的域特异性变压器模型的实用性。

我们提出了跨模式的细心连接，这是一种从可穿戴数据中学习的新型动态和有效技术。我们的解决方案可以集成到管道的任何阶段，即在任何卷积层或块之后，以在负责处理每种模式的单个流之间创建中间连接。此外，我们的方法受益于两个属性。首先，它可以单向共享信息（从一种方式到另一种方式）或双向分别。其次，可以同时将其集成到多个阶段中，以进一步允许以几个接触点交换网络梯度。我们对三个公共多模式可穿戴数据集（Wesad，Swell-KW和案例）进行了广泛的实验，并证明我们的方法可以有效地调节不同模式之间的信息，以学习更好的表示。我们的实验进一步表明，一旦整合到基于CNN的多模式溶液（2、3或4模态）中，我们的方法就会导致卓越或竞争性的性能，而不是最先进的表现，并且表现优于各种基线模式和经典的多模式方法。

In this paper, we propose dictionary attacks against speaker verification - a novel attack vector that aims to match a large fraction of speaker population by chance. We introduce a generic formulation of the attack that can be used with various speech representations and threat models. The attacker uses adversarial optimization to maximize raw similarity of speaker embeddings between a seed speech sample and a proxy population. The resulting master voice successfully matches a non-trivial fraction of people in an unknown population. Adversarial waveforms obtained with our approach can match on average 69% of females and 38% of males enrolled in the target system at a strict decision threshold calibrated to yield false alarm rate of 1%. By using the attack with a black-box voice cloning system, we obtain master voices that are effective in the most challenging conditions and transferable between speaker encoders. We also show that, combined with multiple attempts, this attack opens even more to serious issues on the security of these systems.