Enhancing Facial Emotion Recognition Using Image Processing with CNN

Facial expression recognition (FER) has been a challenging task in computer vision for decades. With recent advancements in deep learning, convolutional neural networks (CNNs) have shown promising results in this field. However, the accuracy of FER using CNNs heavily relies on the quality of the input images and the size of the dataset. Moreover, even in pictures of the same person with the same expression, brightness, backdrop, and stance might change. These variations are emphasized when comparing pictures of individuals with varying ethnic backgrounds and facial features, which makes it challenging for deep-learning models to classify. In this paper, we provide a simple yet efficient way for recognizing facial expressions that combines a CNN with certain image pre-processing techniques. We conducted our experiments on a combination of MUG, JAFFE, and CK+ datasets. To improve the performance of CNN, we experimented with various image pre-processing techniques such as face detection and cropping, image sharpening using Unsharp Mask, and normalization techniques like Global Contrast Normalization, Histogram Equalization, and Adaptive Histogram Equalization. Furthermore, we also examined data augmentation techniques such as image translations and adding noise to images to enhance performance of the deep learning model. Our custom CNN-based FER model achieved a maximum average accuracy of 93.3% (6 classes) and 91% (7 classes) after cross-validation. Our experimental results show that our proposed method can effectively enhance the accuracy of facial expression recognitio

A Computational Framework for Designing Interleaved Workflow and Groupware Tasks

Organizations are adopting a variety of process coordination tools such as groupware and workflow management systems to support seamless process execution and streamline individual and group knowledge worker activities. Such process support systems are being deployed in organizations in an ad hoc manner without any overall guiding process design principles leading to additional costly overheads of systems modeling and software maintenance without the requisite benefits. This paper presents a conceptual framework illustrating a structured approach to organizational process design, providing effective task coordination and information management to address some of the relevant issues. Contributions of the research discussed in this paper include: a) a declarative AI planning based representation formalism to describe both individual and group activities, b) a structured top-down design process that enables the design of group and individual activities in an explicit manner, c) computational procedures to automate the generation of process design alternatives, role assignment to tasks, and support the detailed design of group activities. The feasibility of the integrated representation is evaluated based on extant literature on process models and case studies. The benefits of the formalism are evaluated by prototyping intelligent build-time tools for process design, and utilize the same in the design of processes for tasks such as new product development, requirements analysis, and drug discovery. This paper summarizes the work done so far as well as ongoing work by the author as a part of his doctoral dissertation

Towards a Design Theory for Process-Based Knowledge Management Systems

In today’s knowledge economy, organizations need to effectively manage their knowledge to support an increasing number of knowledge intensive business processes. Such knowledgeintensive business processes could be more effectively supported by Process-based knowledge management (PKM) systems that integrate knowledge management services with process management systems. However, currently there are minimal design guidelines for developing such PKM systems. In this research-in-progress paper, we highlight this research problem, and propose a preliminary framework that can be extended to serve as a design theory for developing process-based knowledge management systems. Specifically, we identify kernel theories governing the design and development of PKM systems, and propose a design process for developing PKM systems. We also identify future research opportunities for further extending the framework and its evaluation

Electron beam evaporated high mobility thin films of indium antimonide

Electron beam evaporation and recrystallization of InSb thin films yielding high Hall mobilitie

A Design Process for Process-based Knowledge Management Systems

In order to gain sustainable competitive advantage in today’s knowledge economy, organizations are looking beyond routine transactional workflow processes to support knowledge-intensive processes. Traditional business process management systems are effective in providing coordination support, but are not geared towards providing relevant knowledge support as well. Also, knowledge management systems are used in an ad hoc manner without explicitly linking them to the underlying organizational processes. Process-based knowledge management (PKM) systems have emerged as a potential solution to support knowledge-intensive processes. However, design guidelines for developing PKM systems are minimal. This paper highlights this research problem, identifies kernel theories governing the design and development of PKM systems, and synthesizes various kernel theories to propose a comprehensive design theory for PKM systems. Feasibility and a comparative evaluation of the proposed design theory are also discussed

Toward an Understanding of Preference for Agile Software Development Methods from a Personality Theory Perspective

This paper presents the results of an exploratory research study that investigates factors contributing to preference for the agile software development approaches. The initial exploration revolves around the Five Factor Model of personality and the premise that these personality factors provide a partial explanation of preference for an agile approach. A survey instrument for measuring the preference for agile methods was developed and validated. The results from the quantitative data collected from the survey study indicate that three out of the five personality factors from the Five Factor Model show a correlation with above average preference for agile methods. These factors are extra version, openness and neuroticism. The first two have a positive relationship with agile preference while neuroticism (emotional instability) has a negative relationship with agile methodology preference. To further investigate the results, an exploratory factor analysis was performed on the data, which identified three factors that may also contribute to a preference for agile methods

Text Mining for Studying Management’s Confidence in IPO Prospectuses and IPO Valuations

Understanding pricing strategies in the context of the Initial Public Offering (IPO) process has been receiving much attention. Most prior studies have however focused on information sources from post issuance periods, and understanding such strategies from the management’s perspective during the IPO process is still an open research issue. Form 424 variants, as finalized IPO prospectus approved by Security Exchange Committee (SEC), contain rich and genuine information about the issuing firms. In this study, we analyze the inter-relationships between the management’s confidence (through the proxy of sentiments expressed in textual contents in the Management’s Discussion & Analysis (MD&A) sections in the prospectus) and the pre-/post-IPO valuations. We develop an analytical framework namely FOCAS-IE (Feature-Oriented, Context-Aware, Systematic Information Extraction) to derive sentiments from the MD&A sections. Further, we construct predictive models using information extracted using FOCAS-IE to predict IPO pricings. The results have shown to outperform results from prior related studies

Collaboration Process Patterns and Integrated Assessment in E-Learning Environments

Collaboration activities are particularly difficult in e-learning environments, where the intention is to provide students with valuable learning experiences through working in teams and sharing a common goal. These activities are often conducted in an ad hoc manner with lack of proper assessment and control over learning outcomes. In this article, we propose the idea of enhancing the effectiveness of collaborative e-learning practices through structured collaborative e-learning processes and integrated assessment mechanisms. The structuring of collaboration processes is suggested through the application of successful collaboration process patterns, while the integrated assessment is suggested through assessing not just the end learning outcomes, but also the process leading to those learning outcomes. These structured templates are regarded as collaborative e-learning templates (CET) that may be instantiated using common collaboration tools to generate desired collaboration patterns among elearners. Thus, the research objective involves improving the learning outcomes as well as the collaboration process dynamics through novel application of collaboration process patterns and integrated assessment techniques. This research is currently in progress and we are conducting a pilot study to test the feasibility of the proposed ideas