Neither the hype exemplified in some exaggerated claims about deep neural networks (DNNs), nor the gloom expressed by Bowers et al. do DNNs as models in vision science justice: DNNs rapidly evolve, and today's limitations are often tomorrow's successes. In addition, providing explanations as well as prediction and image-computability are model desiderata; one should not be favoured at the expense of the other. We agree with Bowers et al. (2022) that some of the quoted statements at the beginning of their target article about DNNs as "best models" are exaggerated--perhaps some of them bordering on scientific hype (Intemann, 2020). However, only the authors of such exaggerated statements are to blame, not DNNs: Instead of blaming DNNs, perhaps Bowers et al. should have engaged in a critical discussion of the increasingly widespread practice of rewarding impact and boldness over carefulness and modesty that allows hyperbole to flourish in science. This is unfortunate as the target article does mention a number of valid issues with DNNs in vision science and raises a number of valid concerns. For example, we fully agree that human vision is much more than recognising photographs of objects in scenes; we also fully agree there are still a number of important behavioural differences between DNNs and humans even in terms of core object recognition (DiCarlo et al., 2012), i.e. even when recognising photographs of objects in scenes, such as DNNs' adversarial susceptibility (section 4.1.1)

Public Wiki systems are collaborative knowledge bases More specifically, a research question is that, given a query, that anyone can contribute. This open model is user-friendly whether strategic revisions can be made on selected Wiki and powerful, which reduces participation barriers and allows articles (which we call adversarial revisions) to ensure people with different backgrounds to contribute. As that the following goals are achieved simultaneously: 1) a prominent example of public Wiki systems, Wikipedia the ranks of the revised articles are significantly improved is instrumental in making open knowledge that millions of among query results, 2) the revisions cannot be detected people use, redistribute, and contribute to [28]. In another by Wiki vandalism detection even when the detector is instance, Wikidata [83] is a free and open knowledge base blackbox to the adversary, and 3) the content of revisions with 0.1 billion data items that can be read and edited does not arouse any suspicion from Wiki users but can still by both humans and machines. Public Wiki systems have capture their attention by keeping the semantic consistency already served as key knowledge sources in people's daily and topic relevancy of the revised articles.

Internet links enable users to deepen their understanding of a topic by providing convenient access to related information. However, the majority of links are unanchored -- they link to a target webpage as a whole, and readers may expend considerable effort localizing the specific parts of the target webpage that enrich their understanding of the link's source context. To help readers effectively find information in linked webpages, we introduce the task of anchor prediction, where the goal is to identify the specific part of the linked target webpage that is most related to the source linking context. We release the AuthorAnchors dataset, a collection of 34K naturally-occurring anchored links, which reflect relevance judgments by the authors of the source article. To model reader relevance judgments, we annotate and release ReaderAnchors, an evaluation set of anchors that readers find useful. Our analysis shows that effective anchor prediction often requires jointly reasoning over lengthy source and target webpages to determine their implicit relations and identify parts of the target webpage that are related but not redundant. We benchmark a performant T5-based ranking approach to establish baseline performance on the task, finding ample room for improvement.

Holzenberger et al. 2020 has modeled statutory Legal Judgment Prediction (LJP) has recently reasoning by classifying US tax law provisions gained considerable attention in the mainstream concatenated with textual case descriptions. We NLP community (e.g., Aletras et al. 2016; build on this prior work in two ways. First, we Chalkidis et al. 2019, 2021, 2022b; Santosh et al. develop and evaluate our model on a public dataset 2022, 2023). In LJP, the outcome of a case should (Chalkidis et al., 2022b) of cases by the European be classified/predicted based on a textual description Court of Human Rights (ECtHR), which hears complaints of case facts. In actual legal reasoning, legal by individuals about possible infringements practitioners (e.g., advocates, judges) determine relevant of their rights enshrined in the European Convention rules from the sources of law (e.g., statutes, on Human Rights (ECHR) by states. To the regulations, precedent) that are relevant to the case best of our knowledge, this is the first work applying at hand. They then carry out an analysis to determine article-aware case outcome prediction setting which rules apply to the case at hand, and to human rights adjudication.

Opinion prediction is an emerging research area with diverse real-world applications, such as market research and situational awareness. We identify two lines of approaches to the problem of opinion prediction. One uses topic-based sentiment analysis with time-series modeling, while the other uses static embedding of text. The latter approaches seek user-specific solutions by generating user fingerprints. Such approaches are useful in predicting user's reactions to unseen content. In this work, we propose a novel dynamic fingerprinting method that leverages contextual embedding of user's comments conditioned on relevant user's reading history. We integrate BERT variants with a recurrent neural network to generate predictions. The results show up to 13\% improvement in micro F1-score compared to previous approaches. Experimental results show novel insights that were previously unknown such as better predictions for an increase in dynamic history length, the impact of the nature of the article on performance, thereby laying the foundation for further research.

Therefore, to mitigate such problems, researchers have developed state-of-the-art (SOTA) models to autodetect fake news on social media using sophisticated data science and machine learning techniques. In this work, then, we ask "what if adversaries attempt to attack such detection models?" and investigate related issues by (i) proposing a novel attack scenario against fake news detectors, in which adversaries can post malicious comments toward news articles to mislead SOTA fake news detectors, and (ii) developing Malcom, an end-to-end adversarial comment generation framework to achieve such an attack. Through a comprehensive evaluation, we demonstrate that about 94% and 93.5% of the time on average Malcom can successfully mislead five of the latest neural detection models to always output targeted real and fake news labels. Furthermore, Malcom can also fool black box fake news detectors to always output real news labels 90% of the time on average. We also compare Real Comment: admitting im not going to read this (...) our attack model with four baselines across two real-world Malcom: hes a conservative from a few months ago datasets, not only on attack performance but also on generated Prediction Change: Real News Fake News quality, coherency, transferability, and robustness. We release the source code of Malcom at https://github.com/lethaiq/MALCOM

The Bayesian brain hypothesis, predictive processing and variational free energy minimisation are typically used to describe perceptual processes based on accurate generative models of the world. However, generative models need not be veridical representations of the environment. We suggest that they can (and should) be used to describe sensorimotor relationships relevant for behaviour rather than precise accounts of the world. In the target article, Brette questions the use of the neural coding metaphor in the neurosciences. One of the main arguments is related to the criticism of approaches that overemphasise the role of perception as opposed to motor control for accounts of cognition.

In this paper, we describe our work on discovering missing links in Wikipedia articles. This task is important for both readers and authors of Wikipedia. The readers will benefit from the increased article quality with better navigation support. On the other hand, the system can be employed to support the authors during editing. This study combines the strengths of different approaches previously applied for the task, and adds its own techniques to reach satisfactory results. Because of the subjectivity in the nature of the task; automatic evaluation is hard to apply. Comparing approaches seems to be the best method to evaluate new techniques, and we offer a semi-automatized method for evaluation of the results. The recall is calculated automatically using existing links in Wikipedia. The precision is calculated according to manual evaluations of human assessors. Comparative results for different techniques are presented, showing the success of our improvements. We employ Turkish Wikipedia, we are the first to study on it, to examine whether a small instance is scalable enough for such purposes.