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When training and evaluating machine reading comprehension models, it is very important to work with high-quality datasets that are also representative of real-world reading comprehension tasks. This requirement includes, for instance, having questions that are based on texts of different genres and require generating inferences or reflecting on the reading material. In this article we turn our attention to RACE, a dataset of English texts and corresponding multiple-choice questions (MCQs). Each MCQ consists of a question and four alternatives (of which one is the correct answer). RACE was constructed by Chinese teachers of English for human reading comprehension and is widely used as training material for machine reading comprehension models. By construction, RACE should satisfy the aforementioned quality requirements and the purpose of this article is to check whether they are indeed satisfied. We provide a detailed analysis of the test set of RACE for high-school students (1045 texts and 3498 corresponding MCQs) including (1) an evaluation of the difficulty of each MCQ and (2) annotations for the relevant pieces of the texts (called "bases") that are used to justify the plausibility of each alternative. A considerable number of MCQs appear not to fulfill basic requirements for this type of reading comprehension tasks, so we additionally identify the high-quality subset of the evaluated RACE corpus. We also demonstrate that the distribution of the positions of the bases for the alternatives is biased towards certain parts of texts, which is not necessarily desirable when evaluating MCQ answering and generation models.

In traditional machine learning, it is trivial to conduct model evaluation since all data samples are managed centrally by a server. However, model evaluation becomes a challenging problem in federated learning (FL), which is called federated evaluation in this work. This is because clients do not expose their original data to preserve data privacy. Federated evaluation plays a vital role in client selection, incentive mechanism design, malicious attack detection, etc. In this paper, we provide the first comprehensive survey of existing federated evaluation methods. Moreover, we explore various applications of federated evaluation for enhancing FL performance and finally present future research directions by envisioning some challenges.

When faced with a polar question, speakers often provide overinformative answers going beyond a simple "yes" or "no". But what principles guide the selection of additional information? In this paper, we provide experimental evidence from two studies suggesting that overinformativeness in human answering is driven by considerations of relevance to the questioner's goals which they flexibly adjust given the functional context in which the question is uttered. We take these human results as a strong benchmark for investigating question-answering performance in state-of-the-art neural language models, conducting an extensive evaluation on items from human experiments. We find that most models fail to adjust their answering behavior in a human-like way and tend to include irrelevant information. We show that GPT-3 is highly sensitive to the form of the prompt and only achieves human-like answer patterns when guided by an example and cognitively-motivated explanation.

We introduce the Collection Space Navigator (CSN), a browser-based visualization tool to explore, research, and curate large collections of visual digital artifacts that are associated with multidimensional data, such as vector embeddings or tables of metadata. Media objects such as images are often encoded as numerical vectors, for e.g. based on metadata or using machine learning to embed image information. Yet, while such procedures are widespread for a range of applications, it remains a challenge to explore, analyze, and understand the resulting multidimensional spaces in a more comprehensive manner. Dimensionality reduction techniques such as t-SNE or UMAP often serve to project high-dimensional data into low dimensional visualizations, yet require interpretation themselves as the remaining dimensions are typically abstract. Here, the Collection Space Navigator provides a customizable interface that combines two-dimensional projections with a set of configurable multidimensional filters. As a result, the user is able to view and investigate collections, by zooming and scaling, by transforming between projections, by filtering dimensions via range sliders, and advanced text filters. Insights that are gained during the interaction can be fed back into the original data via ad hoc exports of filtered metadata and projections. This paper comes with a functional showcase demo using a large digitized collection of classical Western art. The Collection Space Navigator is open source. Users can reconfigure the interface to fit their own data and research needs, including projections and filter controls. The CSN is ready to serve a broad community.

By virtue of technology and benefit advantages, cloud computing has increasingly attracted a large number of potential cloud consumers (PCC) plan to migrate the traditional business to the cloud service. However, trust has become one of the most challenging issues that prevent the PCC from adopting cloud services, especially in trustworthy cloud service selection. Besides, due to the diversity and dynamic of quality of service (QoS) in the cloud environment, the existing trust assessment methods based on the single constant value of QoS attribute and the subjective weight assignment are not good enough to provide an effective solution for PCCs to identify and select a trustworthy cloud service among a wide range of functionally-equivalent cloud service providers (CSPs). To address the challenge, a novel assessment and selection framework for trustworthy cloud service, FASTCloud, is proposed in this study. This framework facilitates PCCs to select a trustworthy cloud service based on their actual QoS requirements. In order to accurately and efficiently assess the trust level of cloud services, a QoS-based trust assessment model is proposed. This model represents a trust level assessment method based on the interval multiple attributes with an objective weight assignment method based on the deviation maximization to adaptively determine the trust level of different cloud services provisioned by candidate CSPs. The advantage of the proposed trust level assessment method in time complexity is demonstrated by the performance analysis and comparison. The experimental result of a case study with an open-source dataset shows that the trust model is efficient in cloud service trust assessment and the FASTCloud can effectively help PCCs select a trustworthy cloud service.

Driven by the need for larger and more diverse datasets to pre-train and fine-tune increasingly complex machine learning models, the number of datasets is rapidly growing. audb is an open-source Python library that supports versioning and documentation of audio datasets. It aims to provide a standardized and simple user-interface to publish, maintain, and access the annotations and audio files of a dataset. To efficiently store the data on a server, audb automatically resolves dependencies between versions of a dataset and only uploads newly added or altered files when a new version is published. The library supports partial loading of a dataset and local caching for fast access. audb is a lightweight library and can be interfaced from any machine learning library. It supports the management of datasets on a single PC, within a university or company, or within a whole research community.

We develop a method for hybrid analyses that uses external controls to augment internal control arms in randomized controlled trials (RCT) where the degree of borrowing is determined based on similarity between RCT and external control patients to account for systematic differences (e.g. unmeasured confounders). The method represents a novel extension of the power prior where discounting weights are computed separately for each external control based on compatibility with the randomized control data. The discounting weights are determined using the predictive distribution for the external controls derived via the posterior distribution for time-to-event parameters estimated from the RCT. This method is applied using a proportional hazards regression model with piecewise constant baseline hazard. A simulation study and a real-data example are presented based on a completed trial in non-small cell lung cancer. It is shown that the case weighted adaptive power prior provides robust inference under various forms of incompatibility between the external controls and RCT population.

We present The Vault, an open-source, large-scale code-text dataset designed to enhance the training of code-focused large language models (LLMs). Existing open-source datasets for training code-based LLMs often face challenges in terms of size, quality (due to noisy signals), and format (only containing code function and text explanation pairings). The Vault overcomes these limitations by providing 40 million code-text pairs across 10 popular programming languages, thorough cleaning for 10+ prevalent issues, and various levels of code-text pairings, including class, function, and line levels. Researchers and practitioners can utilize The Vault for training diverse code-focused LLMs or incorporate the provided data cleaning methods and scripts to improve their datasets. By employing The Vault as the training dataset for code-centric LLMs, we anticipate significant advancements in code understanding and generation tasks, fostering progress in both artificial intelligence research and software development practices.

Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB). Recently, a large number of studies focus on semantically or syntactically complicated questions. In this paper, we elaborately summarize the typical challenges and solutions for complex KBQA. We begin with introducing the background about the KBQA task. Next, we present the two mainstream categories of methods for complex KBQA, namely semantic parsing-based (SP-based) methods and information retrieval-based (IR-based) methods. We then review the advanced methods comprehensively from the perspective of the two categories. Specifically, we explicate their solutions to the typical challenges. Finally, we conclude and discuss some promising directions for future research.

Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. As the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning researches, as well as to summarize and interpret the mechanisms and the strategies in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Different from previous surveys, this survey paper reviews over forty representative transfer learning approaches from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, twenty representative transfer learning models are used for experiments. The models are performed on three different datasets, i.e., Amazon Reviews, Reuters-21578, and Office-31. And the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice.