Keeping up with research and finding related work is still a time-consuming task for academics. Researchers sift through thousands of studies to identify a few relevant ones. Automation techniques can help by increasing the efficiency and effectiveness of this task. To this end, we developed CRUISE-Screening, a web-based application for conducting living literature reviews - a type of literature review that is continuously updated to reflect the latest research in a particular field. CRUISE-Screening is connected to several search engines via an API, which allows for updating the search results periodically. Moreover, it can facilitate the process of screening for relevant publications by using text classification and question answering models. CRUISE-Screening can be used both by researchers conducting literature reviews and by those working on automating the citation screening process to validate their algorithms. The application is open-source: https://github.com/ProjectDoSSIER/cruise-screening, and a demo is available under this URL: https://citation-screening.ec.tuwien.ac.at. We discuss the limitations of our tool in Appendix A.

A much-demanded feature is finally coming to Threads very soon, Meta CEO Mark Zuckerberg has confirmed. A keyword search function is on the way. Until now, the search box has only been useful for looking up user accounts. In fact, the company is already testing the function in Australia and New Zealand. It plans to roll out keyword search more broadly to English-speaking territories in short order.

Quality-sensitive applications of machine learning (ML) require quality assurance (QA) by humans before the predictions of an ML model can be deployed. QA for ML (QA4ML) interfaces require users to view a large amount of data and perform many interactions to correct errors made by the ML model. An optimized user interface (UI) can significantly reduce interaction costs. While UI optimization can be informed by user studies evaluating design options, this approach is not scalable because there are typically numerous small variations that can affect the efficiency of a QA4ML interface. Hence, we propose using simulation to evaluate and aid the optimization of QA4ML interfaces. In particular, we focus on simulating the combined effects of human intelligence in initiating appropriate interaction commands and machine intelligence in providing algorithmic assistance for accelerating QA4ML processes. As QA4ML is usually labor-intensive, we use the simulated task completion time as the metric for UI optimization under different interface and algorithm setups. We demonstrate the usage of this UI design method in several QA4ML applications.

The scale and complexity of workloads in modern cloud services have brought into sharper focus a critical challenge in automated index tuning -- the need to recommend high-quality indexes while maintaining index tuning scalability. This challenge is further compounded by the requirement for automated index implementations to introduce minimal query performance regressions in production deployments, representing a significant barrier to achieving scalability and full automation. This paper directs attention to these challenges within automated index tuning and explores ways in which machine learning (ML) techniques provide new opportunities in their mitigation. In particular, we reflect on recent efforts in developing ML techniques for workload selection, candidate index filtering, speeding up index configuration search, reducing the amount of query optimizer calls, and lowering the chances of performance regressions. We highlight the key takeaways from these efforts and underline the gaps that need to be closed for their effective functioning within the traditional index tuning framework. Additionally, we present a preliminary cross-platform design aimed at democratizing index tuning across multiple SQL-like systems -- an imperative in today's continuously expanding data system landscape. We believe our findings will help provide context and impetus to the research and development efforts in automated index tuning.

Joint entity and relation extraction is the fundamental task of information extraction, consisting of two subtasks: named entity recognition and relation extraction. Most existing joint extraction methods suffer from issues of feature confusion or inadequate interaction between two subtasks. In this work, we propose a Co-Attention network for joint entity and Relation Extraction (CARE). Our approach involves learning separate representations for each subtask, aiming to avoid feature overlap. At the core of our approach is the co-attention module that captures two-way interaction between two subtasks, allowing the model to leverage entity information for relation prediction and vice versa, thus promoting mutual enhancement. Extensive experiments on three joint entity-relation extraction benchmark datasets (NYT, WebNLG and SciERC) show that our proposed model achieves superior performance, surpassing existing baseline models.

For visual document understanding (VDU), self-supervised pretraining has been shown to successfully generate transferable representations, yet, effective adaptation of such representations to distribution shifts at test-time remains to be an unexplored area. We propose DocTTA, a novel test-time adaptation method for documents, that does source-free domain adaptation using unlabeled target document data. DocTTA leverages cross-modality self-supervised learning via masked visual language modeling, as well as pseudo labeling to adapt models learned on a \textit{source} domain to an unlabeled \textit{target} domain at test time. We introduce new benchmarks using existing public datasets for various VDU tasks, including entity recognition, key-value extraction, and document visual question answering. DocTTA shows significant improvements on these compared to the source model performance, up to 1.89\% in (F1 score), 3.43\% (F1 score), and 17.68\% (ANLS score), respectively. Our benchmark datasets are available at \url{https://saynaebrahimi.github.io/DocTTA.html}.

Accurately evaluating the similarity of object vector embeddings is of critical importance for natural language processing, information retrieval and classification tasks. Popular similarity scores (e.g cosine similarity) are based on pairs of embedding vectors and disregard the distribution of the ensemble from which objects are drawn. Human perception of object similarity significantly depends on the context in which the objects appear. In this work we propose the $\textit{surprise score}$, an ensemble-normalized similarity metric that encapsulates the contrast effect of human perception and significantly improves the classification performance on zero- and few-shot document classification tasks. This score quantifies the surprise to find a given similarity between two elements relative to the pairwise ensemble similarities. We evaluate this metric on zero/few shot classification and clustering tasks and typically find 10-15 % better performance compared to raw cosine similarity. Our code is available at https://github.com/MeetElise/surprise-similarity.

Multi-modal search engines have experienced significant growth and widespread use in recent years, making them the second most common internet use. While search engine systems offer a range of services, the image search field has recently become a focal point in the information retrieval community, as the adage goes, "a picture is worth a thousand words". Although popular search engines like Google excel at image search accuracy and agility, there is an ongoing debate over whether their search results can be biased in terms of gender, language, demographics, socio-cultural aspects, and stereotypes. This potential for bias can have a significant impact on individuals' perceptions and influence their perspectives. In this paper, we present our study on bias and fairness in web search, with a focus on keyword-based image search. We first discuss several kinds of biases that exist in search systems and why it is important to mitigate them. We narrow down our study to assessing and mitigating occupational stereotypes in image search, which is a prevalent fairness issue in image retrieval. For the assessment of stereotypes, we take gender as an indicator. We explore various open-source and proprietary APIs for gender identification from images. With these, we examine the extent of gender bias in top-tanked image search results obtained for several occupational keywords. To mitigate the bias, we then propose a fairness-aware re-ranking algorithm that optimizes (a) relevance of the search result with the keyword and (b) fairness w.r.t genders identified. We experiment on 100 top-ranked images obtained for 10 occupational keywords and consider random re-ranking and re-ranking based on relevance as baselines. Our experimental results show that the fairness-aware re-ranking algorithm produces rankings with better fairness scores and competitive relevance scores than the baselines.

Recognizing software entities such as library names from free-form text is essential to enable many software engineering (SE) technologies, such as traceability link recovery, automated documentation, and API recommendation. While many approaches have been proposed to address this problem, they suffer from small entity vocabularies or noisy training data, hindering their ability to recognize software entities mentioned in sophisticated narratives. To address this challenge, we leverage the Wikipedia taxonomy to develop a comprehensive entity lexicon with 79K unique software entities in 12 fine-grained types, as well as a large labeled dataset of over 1.7M sentences. Then, we propose self-regularization, a noise-robust learning approach, to the training of our software entity recognition (SER) model by accounting for many dropouts. Results show that models trained with self-regularization outperform both their vanilla counterparts and state-of-the-art approaches on our Wikipedia benchmark and two Stack Overflow benchmarks. We release our models, data, and code for future research.

Multi-turn textual feedback-based fashion image retrieval focuses on a real-world setting, where users can iteratively provide information to refine retrieval results until they find an item that fits all their requirements. In this work, we present a novel memory-based method, called FashionNTM, for such a multi-turn system. Our framework incorporates a new Cascaded Memory Neural Turing Machine (CM-NTM) approach for implicit state management, thereby learning to integrate information across all past turns to retrieve new images, for a given turn. Unlike vanilla Neural Turing Machine (NTM), our CM-NTM operates on multiple inputs, which interact with their respective memories via individual read and write heads, to learn complex relationships. Extensive evaluation results show that our proposed method outperforms the previous state-of-the-art algorithm by 50.5%, on Multi-turn FashionIQ -- the only existing multi-turn fashion dataset currently, in addition to having a relative improvement of 12.6% on Multi-turn Shoes -- an extension of the single-turn Shoes dataset that we created in this work. Further analysis of the model in a real-world interactive setting demonstrates two important capabilities of our model -- memory retention across turns, and agnosticity to turn order for non-contradictory feedback. Finally, user study results show that images retrieved by FashionNTM were favored by 83.1% over other multi-turn models. Project page: https://sites.google.com/eng.ucsd.edu/fashionntm