Learning institutes are unique places for innovation, technical transformations, and social changes, which are the main pillars for sustainable development. The purpose of this study was to examine the innovation capacity building through the impact of transformational leadership on followers’ satisfaction and output in two engineering colleges: one in a public university in the United States and the other in an International Branch Campus in Qatar. The Multifactor Leadership Questionnaire was used to assess leadership style, and three output indicators were chosen to represent innovative outputs. Innovation-driven systems and Intrinsic motivation were other innovation drivers assessed through the designed survey. The Statistical Package of Social Science was used to identify the correlated constructs of leadership styles and outcomes. The explanatory sequential mixed method helped explain the underlying reasons for the quantitative results through interviews with faculty. The study showed that leaders (deans) exhibited different ranges of transformational leadership styles, yet were lower than the norm. Moreover, transformational leadership traits, in addition to contingent rewards from transactional leadership, were highly correlated with followers’ satisfaction with the leader and the system. As this was a cross-cultural study, context affected the participation rate and response results, as hesitation to evaluate the dean was common in a high power–distance context.
The main purpose of this paper is to compare clustering (region growing) and gradient based techniques for detecting regions of interest in digital mammograms. Such regions of interest form the basis of applying shape and texture techniques for detecting cancerous masses. In addition, the paper proposes a two-stage method, in which gradient based techniques are applied first, followed by a region growing method that will yield lesser numbers of regions for analysis. For this purpose, we first use histogram equalization and fuzzy enhancement techniques to improve the quality of the images and to compare their utility on our mammogram data. Image-enhanced mammograms are then subjected to clustering or to gradient operations (masking) for segmentation purposes. The segmented image is then analyzed for estimating the regions of interest, and the results are compared against the previously known diagnosis of the radiologist. A total of 30 mammograms from the University of South Florida database were used, for which the radiologist's hand-sketched boundaries of the masses were known. The results show that when compared with histogram equalization, fuzzy enhancement techniques are better suited for mammogram analysis, and when compared with gradient based segmentation, region growing segmentation will give a lesser number of regions for analysis without compromising on quality.
Explainability has become an essential requirement for safe and effective collaborative Human-AI environments, especially when generating recommendations through black-box modality. One goal of eXplainable AI (XAI) is to help humans calibrate their trust while working with intelligent systems, i.e., avoid situations where human decision-makers over-trust the AI when it is incorrect, or under-trust the AI when it is correct. XAI, in this context, aims to help humans understand AI reasoning and decide whether to follow or reject its recommendations. However, recent studies showed that users, on average, continue to overtrust (or under-trust) AI recommendations which is an indication of XAI's failure to support trust calibration. Such a failure to aid trust calibration was due to the assumption that XAI users would cognitively engage with explanations and interpret them without bias. In this work, we hypothesize that XAI interaction design can play a role in helping users' cognitive engagement with XAI and consequently enhance trust calibration. To this end, we propose friction as a Nudge-based approach to help XAI users to calibrate their trust in AI and present the results of a preliminary study of its potential in fulfilling that role.
The essentiality of the universities’ roles in enhancing economies and transforming societies is a global mantra. However, when it comes to wealthy and oil-dependent states such as Texas in the United States and Qatar in the Middle East, the impact of universities on sustainable economic development is questionable. This article discusses the transformational efforts within engineering colleges at two public universities in Texas and in Qatar to support their states’ visions in moving toward innovative and knowledge-based economies. The study examined the innovation capacity building of both institutions through measuring the transformational leadership styles in engineering colleges and its impact on the faculty’s innovative production of technical articles, patents, and sustainable development-related courses. The cultural impact of the two contexts on the leader–follower relationship was addressed in the discussion using Hofstede’s cultural dimension framework. The results showed that leaders in both colleges possess a transformational leadership style, albeit lower than the norm. This study disclosed that, in the high-power distance contexts, the idealized image of the leader contributed positively toward higher satisfaction of the followers with their leaders and current governance systems, while acknowledgment and rewards were the sources of satisfaction in low-power distance societies. Followers in a low uncertainty avoidance, individualistic, and short-term-oriented context achieved higher technical production. Both public universities expressed the need for government involvement in supporting the culture of innovation.
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