The economic crisis created major problems for a successful, hi-tech Chinese company -Tonsan. They already had in place a performance management system based around the balanced scorecard which worked successfully in times of growth and high demand. However, with the world downturn they suddenly found that their current system was not able to cope with the demands placed on it. The authors were called in and decided to design a new, strategic performance management system to overhaul all the key business processes. The approach taken to develop the PM system was based around soft systems methodology (SSM), a well established systems-based approach to problem solving and organizational design. The methodology progressed from the development of key strategic objectives (using the BSC and strategy maps), through a structured decomposition of necessary organizational activities, the construction of key performance indicators, the specification of targets, to communication and future planning. It involved significant levels of participation and communication throughout the organization. The results were judged by senior management to have been very successful, and the company has grown significantly.2
Measuring the performance of organizations in both the private and public sectors is an evergrowing phenomena and this is increasingly true of academic universities and research institutes. The results can have major financial and reputational consequences. Methods have been developed for generating performance indicators particularly in the private sector but these have limitations, especially when applied to public sector organizations which often have a diversity of missions, values and stakeholders. This paper describes a new methodology for constructing a set of indicators that was developed as part of a project to evaluate the performance of the Chinese Academy of Sciences (CAS). The methodology has several important characteristics: it works from the mission and values of the particular organization rather than a predefined template; it has a logical and transparent method for moving from high-level missions down to low-level indicators; and it is based on discussion and agreement with stakeholders at all stages. The methodology is illustrated through the CAS study but it is generalisable to any organization. It has a sound theoretical base from Soft Systems Methodology.
Grounded Situation Recognition (GSR), i.e., recognizing the salient activity (or verb) category in an image (e.g.,buying) and detecting all corresponding semantic roles (e.g.,agent and goods), is an essential step towards “human-like” event understanding. Since each verb is associated with a specific set of semantic roles, all existing GSR methods resort to a two-stage framework: predicting the verb in the first stage and detecting the semantic roles in the second stage. However, there are obvious drawbacks in both stages: 1) The widely-used cross-entropy (XE) loss for object recognition is insufficient in verb classification due to the large intra-class variation and high inter-class similarity among daily activities. 2) All semantic roles are detected in an autoregressive manner, which fails to model the complex semantic relations between different roles. To this end, we propose a novel SituFormerfor GSR which consists of a Coarse-to-Fine Verb Model (CFVM) and a Transformer-based Noun Model (TNM). CFVM is a two-step verb prediction model: a coarse-grained model trained with XE loss first proposes a set of verb candidates, and then a fine-grained model trained with triplet loss re-ranks these candidates with enhanced verb features (not only separable but also discriminative). TNM is a transformer-based semantic role detection model, which detects all roles parallelly. Owing to the global relation modeling ability and flexibility of the transformer decoder, TNM can fully explore the statistical dependency of the roles. Extensive validations on the challenging SWiG benchmark show that SituFormer achieves a new state-of-the-art performance with significant gains under various metrics. Code is available at https://github.com/kellyiss/SituFormer.
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