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In this article we present a novel multimodal gender recognition system, which successfully integrates the head and mouth motion information with facial appearance by taking advantage of a unified probabilistic framework. In fact, we develop a temporal subsystem that has an extended feature space consisting of parameters related to head and mouth motion; at the same time, we introduce a complementary spatial subsystem based on a probabilistic extension of the eigenface approach. In the end, we implement an integration step to combine the similarity scores of the two parallel subsystems, using a suitable opinion fusion (or score fusion) strategy. The experiments show that not only facial appearance but also head and mouth motion possess a potentially relevant discriminatory power, and that the integration of different sources of biometric information from video sequences is the key strategy to develop more accurate and reliable recognition systems. I. INTRODUCTIONHuman face contains a variety of information for adaptive social interactions amongst people. In fact, individuals are able to process a face in a variety of ways to categorize it by its identity, along with a number of other demographic characteristics, such as gender, ethnicity, and age. In particular, recognizing human gender is important since people respond differently according to gender. In addition, a successful gender classification approach can boost the performance of many other applications, including person recognition and smart human-computer interfaces.In this article, we address the problem of automatic gender recognition by exploiting the physiological and behavioural aspects of the face at the same time. We have already investigated the use of the head and mouth motion information for person recognition in an earlier research study [1]. Currently, comforted by the promising results obtained by this previous approach, we explore the possibility of using head motion, mouth motion and facial appearance in a gender recognition scenario. Hence, we propose a multimodal recognition approach that integrates the temporal and spatial information of the face through a probabilistic framework.The remainder of this article is organised as follows: in section II we propose a short review of related works, and then in section III we detail our recognition system; afterwards we report and comment the experiments in section IV, and finally we conclude this paper with remarks and future work in section V.

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Corporate governance is considered as environment of trust, set of processes, policies and laws affecting the way corporations are administrated and directed. The previous literature in context of the corporate governance relationship with firm financial performance shows controversial findings; similarly literature shows lack of studies in context of developing countries as Pakistan. Therefore, this research explores the relationship of the corporate governance and the firm financial performance in context of developing country as Pakistan. The data has been collected from the sugar sector listed in KSE (Pakistan Stock Exchange), 20 corporations are selected as sample from sugar sector on basis of outstanding shares. Corporate governance taken as independent variable and measured as CEO biformity (CB), board size (BS), firm age (FA), firm size (FS). Financial performance of firms taken as dependent variable and measured as return on asset (ROA), return on equity (ROE), net profit margin (NPM). Data is collected for period of 2000-2013 from reports of the sugar companies listed in KSE (Pakistan Stock Exchange) issued annually and analysis of balance sheet given by State Bank of Pakistan (SBP). Result shows that CEO biformity significantly affecting firm financial performance. Board size (BS) shows partially significant impact on firm financial performance. Firms age (FA) show partially significant impact on firm financial performance. Firm size (FS) shows partially significant impact on firm financial performance. Therefore, conclusion has been drawn based on the results of analysis that this study adds new knowledge to the existing body of knowledge of corporate governance impact on firm financial performance and in context of developing countries as Pakistan. Keywords: Corporate governance, firm financial performance, sugar sector, Pakistan.

Artificial intelligence (AI) is creating a revolution in business and society at large, as well as challenges for organizations. AI‐powered social bots can sense, think and act on social media platforms in ways similar to humans. The challenge is that social bots can perform many harmful actions, such as providing wrong information to people, escalating arguments, perpetrating scams and exploiting the stock market. As such, an understanding of different kinds of social bots and their authors’ intentions is vital from the management perspective. Drawing from the actor‐network theory (ANT), this study investigates human and non‐human actors’ roles in social media, particularly Twitter. We use text mining and machine learning techniques, and after applying different pre‐processing techniques, we applied the bag of words model to a dataset of 30,000 English‐language tweets. The present research is among the few studies to use a theory‐based focus to look, through experimental research, at the role of social bots and the spread of disinformation in social media. Firms can use our tool for the early detection of harmful social bots before they can spread misinformation on social media about their organizations.