Explore the vast range of titles available.

Effective requirements elicitation is crucial for the success of large-scale software projects. However, challenges arise when customers are unavailable or unable to express their needs clearly. This research presents a cost-effective model to address these challenges and facilitate efficient requirements elicitation in such scenarios. The survey is used to investigate the significance of the requirements elicitation process and its impact on software project outcomes. The key themes that emerged from the survey analysis are the importance of the elicitation process, the value of prior experience, the impact of poor requirements definition, customer engagement and communication, schedule adherence, and previous success and confidence. Based on these findings, the proposed model provides a systematic framework for requirements elicitation. It encompasses essential components such as determining customer availability, gathering domain understanding, defining project scope and objectives, conducting personal and collective introspection, consolidating requirements, refining and prioritizing requirements, developing an initial Software Requirements Specification (SRS) version, and validating requirements. This research article contributes valuable insights into the requirements elicitation process and presents a practical model that enhances understanding and capturing stakeholder needs when customer involvement is challenging, accelerates elicitation and analysis processes, improves requirements documentation accuracy and completeness, and offers competitive market advantages.

Expert opinion and judgment enter into the practice of statistical inference and decision-making in numerous ways. Indeed, there is essentially no aspect of scientific investigation in which judgment is not required. Judgment is necessarily subjective, but should be made as carefully, as objectively, and as scientifically as possible. Elicitation of expert knowledge concerning an uncertain quantity expresses that knowledge in the form of a (subjective) probability distribution for the quantity. Such distributions play an important role in statistical inference (for example as prior distributions in a Bayesian analysis) and in evidence-based decision-making (for example as expressions of uncertainty regarding inputs to a decision model). This article sets out a number of practices through which elicitation can be made as rigorous and scientific as possible. One such practice is to follow a recognized protocol that is designed to address and minimize the cognitive biases that experts are prone to when making probabilistic judgments. We review the leading protocols in the field, and contrast their different approaches to dealing with these biases through the medium of a detailed case study employing the SHELF protocol. The article ends with discussion of how to elicit a joint probability distribution for multiple uncertain quantities, which is a challenge for all the leading protocols. Supplementary materials for this article are available online.

Introduction: Visual methodologies are a collection of methods used to understand and interpret images. These methods have been used for a long time in anthropology and sociology; however, they are a relatively new way to research for the majority of disciplines, especially health research. Two effective visual methodologies that could be used in health research are autophotography and photo elicitation. Autophotography: Autophotography is asking participants to take photographs of their environment and then using the photographs as actual data. Autophotography captures the world through the participant’s eyes with subsequent knowledge production. Photo Elicitation: Photo elicitation is using photographs or other visual mediums in an interview to generate verbal discussion to create data and knowledge. Different layers of meaning can be discovered as this method evokes deep emotions, memories, and ideas. Photo elicitation interviews contribute to trustworthiness and rigor of the findings through member checking. Mental Health Research: This article aims to describe the use of autophotography and photo elicitation to compare people with clinically diagnosed depression and people without depression and their ideas about sources of meaning in life and beliefs about the meaning of life. The analytical approach incorporates eight steps. Firstly, data analysis began during the interviews, then came organizing the data, coding the data, structured analysis, detailed analysis, interpretative analysis, creating themes, and the write-up. The steps taken to ensure trustworthiness were Shenton’s credibility, transferability, confirmability, and dependability. This method is a new, innovative, and viable method for mental health researchers.

Depression has become a global concern, and COVID-19 also has caused a big surge in its incidence. Broadly, there are two primary methods of detecting depression: Task-based and Mobile Crowd Sensing (MCS) based methods. These two approaches, when integrated, can complement each other. This paper proposes a novel approach for depression detection that combines real-time MCS and task-based mechanisms. We aim to design an end-to-end machine learning pipeline, which involves multimodal data collection, feature extraction, feature selection, fusion, and classification to distinguish between depressed and non-depressed subjects. For this purpose, we created a real-world dataset of depressed and non-depressed subjects. We experimented with: various features from multi-modalities, feature selection techniques, fused features, and machine learning classifiers such as Logistic Regression, Support Vector Machines (SVM), etc. for classification. Our findings suggest that combining features from multiple modalities perform better than any single data modality, and the best classification accuracy is achieved when features from all three data modalities are fused. Feature selection method based on Pearson’s correlation coefficients improved the accuracy in comparison with other methods. Also, SVM yielded the best accuracy of 86%. Our proposed approach was also applied on benchmarking dataset, and results demonstrated that the multimodal approach is advantageous in performance with state-of-the-art depression recognition techniques.

Affecting computing is an artificial intelligence area of study that recognizes, interprets, processes, and simulates human affects. The user’s emotional states can be sensed through electroencephalography (EEG)-based Brain Computer Interfaces (BCI) devices. Research in emotion recognition using these tools is a rapidly growing field with multiple inter-disciplinary applications. This article performs a survey of the pertinent scientific literature from 2015 to 2020. It presents trends and a comparative analysis of algorithm applications in new implementations from a computer science perspective. Our survey gives an overview of datasets, emotion elicitation methods, feature extraction and selection, classification algorithms, and performance evaluation. Lastly, we provide insights for future developments.

This research arose from a moment when one of the author’s interviewees said, “Wait, where was I?” Through 60–90 minutes, in-depth phone interviews, shared highly detailed accounts of their personal stories of entering underemployment in South Korea. However, the first six interviewees talked about many different topics during their interviews, and confused the chronological sequences of multiple episodes. The interviewer recognized these instances as emergent chaotic moments, so she decided to incorporate various methods into her interviews. Among the many elicitation strategies that can be employed in qualitative interviewing, the author applied timeline elicitation to more accurately document their life events. Timeline elicitation is useful for extracting narratives of individual journeys and delineating the meaning of specific events. This technique proved the most effective strategy for minimizing confusions and successfully completing the interviews. This study used comparative analysis to assess the transcripts of two different groups (with/without applying timeline drawing elicitation), and presents findings showing their different reactions to dealing with the transition of time. This study demonstrated the potential of visual timeline approaches to reflect and illustrate the complexity of women participants’ experiences of underemployment. Moreover, the efficacy of this timeline drawing elicitation method will be critically discussed, along with its limitations within the context of innovative qualitative methodologies.

The paper performs an in-depth comparison of four incentivised risk elicitation tasks. We show by means of a simulation exercise that part of the often observed heterogeneity of estimates across tasks is due to task-specific measurement error induced by the mere mechanics of the tasks. We run a replication experiment in a homogeneous subject pool using a between subjects one-shot design. Results shows that the task estimates vary over and above what can be explained by the simulations. We investigate the possibility the tasks elicit different types of preferences, rather than simply provide a different measure of the same preferences. In particular, the availability of a riskless alternative plays a prominent role helping to explain part of the differences in the estimated preferences.

This paper presents the Bomb Risk Elicitation Task (BRET), an intuitive procedure aimed at measuring risk attitudes. Subjects decide how many boxes to collect out of 100, one of which contains a bomb. Earnings increase linearly with the number of boxes accumulated but are zero if the bomb is also collected. The BRET requires minimal numeracy skills, avoids truncation of the data, allows the precise estimation of both risk aversion and risk seeking, and is not affected by the degree of loss aversion or by violations of the Reduction Axiom. We validate the BRET, test its robustness in a large-scale experiment, and compare it to three popular risk elicitation tasks. Choices react significantly only to increased stakes, and are sensible to wealth effects. Our experiment rationalizes the gender gap that often characterizes choices under uncertainty by means of a higher loss rather than risk aversion.

Emotions play a critical role in our daily lives, so the understanding and recognition of emotional responses is crucial for human research. Affective computing research has mostly used non-immersive two-dimensional (2D) images or videos to elicit emotional states. However, immersive virtual reality, which allows researchers to simulate environments in controlled laboratory conditions with high levels of sense of presence and interactivity, is becoming more popular in emotion research. Moreover, its synergy with implicit measurements and machine-learning techniques has the potential to impact transversely in many research areas, opening new opportunities for the scientific community. This paper presents a systematic review of the emotion recognition research undertaken with physiological and behavioural measures using head-mounted displays as elicitation devices. The results highlight the evolution of the field, give a clear perspective using aggregated analysis, reveal the current open issues and provide guidelines for future research.