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Scientific papers, as pivotal tools for academic communication, should be articulated with clarity and precision to ensure the effective conveyance of scholarly ideas and to prevent reader confusion. Yet, many such papers conspicuously lack in-depth research, and their core content is often ambiguously presented. This pattern poses a significant impediment to the progressive evolution of science and technology. While numerous researchers have recognized this widespread challenge, a holistic theoretical or methodological solution remains elusive in the academic realm. To bridge this gap, we introduce the INTEGrity vERification (INTEGER) task. This task aids researchers in assessing the integrity of their papers by verifying the clarity of each knowledge unit. To implement this task on text, we propose a multi-task learning model that utilizes the Tucker decomposition and span-level attention mechanism to identify terms and their integrity precisely. More specifically, to provide insights into the INTEGER task and validate the effectiveness of the proposed model, we collect 8076 sentences and construct three new datasets containing various types of terms and descriptions in different domains. Extensive experimental results show that our proposed model has an average performance improvement of 1.1% F1 over the three datasets compared to a series of state-of-the-art baseline methods. [ABSTRACT FROM AUTHOR]

This article is a follow-up contribution that extends the conference paper (Nxumalo, in: Laarman, Sokolova (eds)Model Checking Software - 27th International Symposium, 2021) which presented the spotlight abstraction method to enable an efficient model checking of schedulability properties of real-time operating systems. Our approach is based on iterative spotlight abstraction whereby the real-time task queue is divided into a so-called spotlight and a shade. Initially, an abstract state space model is generated from the spotlight, which contains a small number of tasks that appear at the front of the queue and the shade contains the remaining tasks. The schedulability of the spotlight tasks is model checked, whereas the behavior of the shade is summarized, and the result is saved for later re-use. If this result is still inconclusive, more tasks are iteratively brought from the shade into the spotlight, with which the model checker can proceed. These steps are repeated until a decisive schedulability result is obtained. In this article, we enrich the encoding of real-time systems into timed automata by separating operations over clock variables from operations based on other variables such as Boolean and integers. We enforce to maintain a reasonable size of the spotlight model by applying counter abstraction to the tasks that were previously confirmed schedulable in the previous iterations. We compare our approach against Timestool and RTLib, an Uppaal template, in the analysis of the schedulability of real-time tasks for the First-In-First-Out and Shortest-Deadline-First scheduling policies. Empirical results show that our approach can handle more tasks than Timestool and RTLib. [ABSTRACT FROM AUTHOR]