Emotion Detection Using Noninvasive Low Cost Sensors

Emotion recognition from biometrics is relevant to a wide range of application domains, including healthcare. Existing approaches usually adopt multi-electrodes sensors that could be expensive or uncomfortable to be used in real-life situations. In this study, we investigate whether we can reliably recognize high vs. low emotional valence and arousal by relying on noninvasive low cost EEG, EMG, and GSR sensors. We report the results of an empirical study involving 19 subjects. We achieve state-of-the- art classification performance for both valence and arousal even in a cross-subject classification setting, which eliminates the need for individual training and tuning of classification models.Comment: To appear in Proceedings of ACII 2017, the Seventh International Conference on Affective Computing and Intelligent Interaction, San Antonio, TX, USA, Oct. 23-26, 201

A Call to Promote Soft Skills in Software Engineering

We have been thinking about other aspects of software engineering for many years; the missing link in engineering software is the soft skills set, essential in the software development process. Although soft skills are among the most important aspects in the creation of software, they are often overlooked by educators and practitioners. One of the main reasons for the oversight is that soft skills are usually related to social and personality factors, i.e., teamwork, motivation, commitment, leadership, multi-culturalism, emotions, interpersonal skills, etc. This editorial is a manifesto declaring the importance of soft skills in software engineering with the intention to draw professionals’ attention to these topics. We have approached this issue by mentioning what we know about the field, what we believe to be evident, and which topics need further investigation. Important references to back up our claims are also included. In summary, technical people tend to overlook the importance of soft skills as it is unrelated to their technical area and because their training is in dealing with technical issue; thus considering the soft skills in the software development process to be foreign to them, since the field deals with human factors and touches social sciences. These are topics that software professionals do not have expertise in. We believe that it is high time for the software development community to realize that the human element is pivotal to success in the engineering of software. We have to recognize that software engineering is a people-intensive discipline, hence requires appropriate treatment. Therefore, human aspects of software engineering are important subjects to teach, study and research. We urge software engineers to take on this challenge

Eye-tracking for IS Research: A Literature Review

In this literature review, we describe the Information Systems (IS) research using eye-tracking. Based on a sample of 60 papers published since 2008 in journals and conference proceedings, we examine i) what is the trend in eye-tracking IS research, ii) what types of experimental design have been used, iii) what types of metrics have been collected and iv) what constructs and topics have been investigated. We found that IS research using eye-tracking is broad in its research themes but concentrated in its methods of analysis. All the research is quantitative and mostly use fixation counts on computer desktop. A limited number of articles take advantage of pupil dilation measure or mobile eye-tracking. We call for broadening the methods of collection and analysis in eye-tracking IS research

Towards a Continuous Assessment of Cognitive Workload for Smartphone Multitasking Users

The intermeshing of Smartphone interactions and daily activities depletes the availability of cognitive resources. This excessive demand may lead to several undesirable cognitive states, which can be avoided by continuously assessing the user cognitive workload. Recently, many attempts have emerged to assess this workload by using psycho physiological signals. This paper provides evidence that it is possible to train models that accurately identify in short time windows such cognitive workload by processing heart rate and blood oxygen saturation signals. This assessment could be applied in Smartphone notification delivery, interface adaptations or cognitive capabilities evaluation

Probes and Sensors: The Design of Feedback Loops for Usability Improvements

The importance of user-centric design methods in the design of programming tools is now well accepted. These methods depend on creating a feedback loop between the designers and their users, providing data about developers, their needs and behaviour gathered through various means. These include controlled experiments, field observations, as well as analytical frameworks. However, whilst there have been a number of experiments detailed, quantitative data is rarely used as part of the design process. Part of the reason for this might be that such feedback loops are hard to design and use in practice. Still, we believe there is potential in this approach and opportunities in gathering this kind of ‘big data’. In this paper, we sketch a framework for reasoning about these feedback loops - when data gathering may make sense and for how to incorporate the results of such data gathering into the programming tool design process. We illustrate how to use the framework on two case studies and outline some of the challenges in instrumentation and in knowing when and how to act on signals

Predicting Java Computer Programming Task Difficulty Levels Using EEG for Educational Environments

Understanding how difficult a learning task is for a person allows teaching material to be appropriately designed to suit the person, especially for programming material. A first step for this would be to predict on the task difficulty level. While this is possible through subjective questionnaire, it could lead to misleading outcome and it would be better to do this by tapping the actual thought process in the brain while the subject is performing the task, which can be done using electroencephalogram. We set out on this objective and show that it is possible to predict easy and difficult levels of mental tasks when subjects are attempting to solve Java programming problems. Using a proposed confidence threshold, we obtained a classification performance of 87.05% thereby showing that it is possible to use brain data to determine the teaching material difficulty level which will be useful in educational environments

Rocks Coding, Not Development--A Human-Centric, Experimental Evaluation of LLM-Supported SE Tasks

Recently, large language models (LLM) based generative AI has been gaining momentum for their impressive high-quality performances in multiple domains, particularly after the release of the ChatGPT. Many believe that they have the potential to perform general-purpose problem-solving in software development and replace human software developers. Nevertheless, there are in a lack of serious investigation into the capability of these LLM techniques in fulfilling software development tasks. In a controlled 2 x 2 between-subject experiment with 109 participants, we examined whether and to what degree working with ChatGPT was helpful in the coding task and typical software development task and how people work with ChatGPT. We found that while ChatGPT performed well in solving simple coding problems, its performance in supporting typical software development tasks was not that good. We also observed the interactions between participants and ChatGPT and found the relations between the interactions and the outcomes. Our study thus provides first-hand insights into using ChatGPT to fulfill software engineering tasks with real-world developers and motivates the need for novel interaction mechanisms that help developers effectively work with large language models to achieve desired outcomes.Comment: The paper has been accepted by FS