TP -Detector: Detecting Turning Points in the Engineering Process of Large-scale Projects (2023)

Abstract: This paper introduces a novel task of detecting turning points in the engineering process of large-scale projects, wherein the turning points signify significant transitions occurring between phases. Given the complexities involving diverse critical events and limited comprehension in individual news reports, we approach the problem by treating the sequence of related news streams as a window with multiple instances. To capture the evolution of changes effectively, we adopt a deep Multiple Instance Learning (MIL) framework and employ the multiple instance ranking loss to discern the transition patterns exhibited in the turning point window. Extensive experiments consistently demonstrate the effectiveness of our proposed approach on the constructed dataset compared to baseline methods. We deployed the proposed mode and provided a demonstration video to illustrate its functionality. The code and dataset are available on GitHub.

## introduction large-scale projects are intricate and extensive endeavors requiring substantial resources, effort, and coordination to achieve specific objectives, often involving multiple stakeholders and phases with a significant impact on organizations, communities, and society. they encompass diverse fields, such as aerospace engineering, water resources facilities, and transportation infrastructure, and hold paramount importance in driving economic growth, enhancing infrastructure, and addressing societal needs, while also fostering innovation and sustain-ability. typically, their successful execution requires careful planning, collaboration, and a longterm vision to maximize their positive impact on communities and society at large. in order to tackle the intricacies and difficulties of carrying out large-scale projects, the engineering process employs a systematic and structured approach to design, plan, and execute complex endeavors efficiently and effectively @xcite . the life cycle of large-scale projects consists of several phases that cover the entire journey from initiation to closure @xcite and each phase dedicats to accomplishing different objectives. figure 1 depicts the seven phases involved in nasa's insight mission engineering process, wherein each phase comprises a series of subtasks or key events represented by the gray diamonds, which can occur simultaneously or have interdependencies. during the engineering process of large-scale projects, there are significant moments that deserve attention, such as when the project reaches a new milestone, e.g., transitioning from the conceptual phase to on-ground implementation on the ground (as shown in the green diamond h of figure 1 ). the moments or events which bring about critical changes in direction, course, or outcome are referred to turning points in this paper, signifying transitions occurring between adjacent phases. the identification of these turning points provides stakeholders, government agencies, and the general public with valuable insights, empowering them to navigate challenges, capitalize on opportunities, and effectively adjust their strategies in response to changing circumstances. for example, an analyst can assess boeing's development status and offer design suggestions for products at airbus. however, detecting turning points in the engineering processes of large-scale projects, particularly ongoing projects, is a non-trivial undertaking. first, limited public data sharing and potential lack following the insights, this paper delves into the intricate task of detecting turning points in the engineering process of large-scale projects by utilizing multiple instance learning techniques. to facilitate this study, we collect 80 large-scale projects to construct the turning point detection dataset. we structure the sequence of related news streams into a window with multiple instances through a deep mil framework and identify turning points via convolutional transformer encoder @xcite . we then employ the multiple instance ranking loss to push the positive instances and negative instances far apart in terms of the extent of change or shift that occurs between phases. additionally, we develop a website for detection and visualization with our deployed model, provid-ing users with transition timeline and news related to single or multiple large-scale projects. multiple instance learning multiple instance learning (mil) is a form of weakly supervised learning where training instances are organized into sets, called bags @xcite . only the label for the bag is provided. due to this characteristic, mil has found extensive applications in domains with large amounts of weakly labeled data @xcite tp bag roberta convolutional transformer encoder instance regressor linear head mil ranking loss p 0.6 scores in tp bag scores in non-tp bag tp window non-tp window non-tp bag instance class token 0.3 0.9 0.5 0.7 0.3 0.1 0.4 0.3 0.5 instance encoder class token bag classifier linear head bag classifier linear head p bce loss bce loss instance of k news 2017; tian et al., 2020), such as video classification (ding et al., 2013; sultani et al., 2018; li et al., 2022), image classification(sudharshan et al., 2019; li et al., 2021; yang et al., 2023), and text classification(he and wang, 2009; liu et al., 2018), among others. in the context of weakly supervised video classification, typically only video-level category labels are provided. sultani et al. (sultani et al., 2018) proposed the mil ranking model. they utilized the mil ranking model to compute the highest-scoring instance within the bag for video classification. li et al. (li et al., 2022) introduced a mil ranking model based on transformer for video classification. in the realm of text classification, he et al.(he and wang, 2009) proposed a knn algorithm-based multi-instance chinese text classifier. liu et al.(liu et al., 2018) introduced selective multi-instance transfer learning to address the issue of knowledge-safe transfer in multi-instance learning for text classification. ## demonstration we provide our services in a web-based. we deployed the proposed model foot_3 and provided a demonstration video foot_4 to illustrate its functionality. we offer two detection options: single project and multiple project. in the "single project" mode, the system performs tp detection on an individual project. the input entails a json file containing the news stream, while the output encompasses tp detection results image and the highest-scoring instance within tp windows. the explanation for a tp window is provided by showcasing the highestscoring instance. on the other hand, the "multiple projects" mode enables simultaneous tp detection across multiple projects. similar to the single project mode, the input comprises json files containing news streams for multiple projects, and the output is similar to the "single project" mode. figure 3 illustrates the results of multiple project detection. ## conclusion to investigate phase transitions in engineering processes, we propose the turning point detection task on large-scale projects. for this task, we introduce a deep multi-instance learning model. this model initially performs feature extraction on input windows using a pre-trained language model. subsequently, it employs an instance encoder to capture instance-level features. following this, a convolutional transformers encoder is employed to detect phase transition features. ultimately, a linear head is employed to provide prediction outcomes. we collected a new dataset specifically for this task. extensive experiments demonstrated that our model outperforms strong baselines. we have deployed our model online to assist in detecting phase transitions in news streams. we hope that our work will contribute to further research in this emerging task and benefit relevant stakeholders. the dataset we have constructed currently only includes publicly available news reports. in the future, we aim to incorporate officially released information into the dataset as an essential supplement to enhance our understanding of the projects with more comprehensiveness.