Abstract The worldwide software ecosystem is a trust-rich part of the world. Throughout the software life cycle, software engineers, end-users, and other stakeholders collaboratively place their trust in major hubs in the ecosystem, such as package managers, repository services, and software components. However, as our reliance on software grows, this trust is frequently violated by bad actors and crippling vulnerabilities in the software supply chain. This study aims to define software trust in the worldwide SECO, that is, to determine what signifies a trustworthy system, actor, or hub. We conduct a systematic literature review on the concept of trust in the software ecosystem. We acknowledge that trust is something between two actors in the software ecosystem, and we examine what role trust plays in the relationships between end-users and (1) software products, (2) package managers, (3) software producing organizations, and (4) software engineers. Two major findings emerged from the systematic literature review. To begin, we define trust in the software ecosystem by examining the definition and characteristics of trust. Second, we provide a list of trust factors that can be used to assemble an overview of software trust. Trust is critical in the communication between actors in the worldwide software ecosystem, particularly regarding software selection and evaluation. With this comprehensive overview of trust, software engineering researchers have a new foundation to understand and use trust to create a trustworthy software ecosystem. 

/pdf/a-systematic-literature-review-on-trust-in-the-software-3aeo0o4j.pdf

A systematic literature review on trust in the software ecosystem

Mid-air gesture-based systems are becoming ubiquitous. Many mid-air gestures control different kinds of interactive devices, applications, and systems. They are, however, still targeted at specific devices in specific domains and are not necessarily consistent across domain boundaries. A comprehensive evaluation of the transferability of gesture vocabulary between domains is also lacking. Consequently, interaction designers cannot decide which gestures to use for which domain. In this systematic literature review, we contribute to the future research agenda in this area, based on an analysis of 172 papers. As part of our analysis, we clustered gestures according to the dimensions of an existing taxonomy to identify their common characteristics in different domains, and we investigated the extent to which existing mid-air gesture sets are consistent across different domains. We derived a consensus gesture set containing 22 gestures based on agreement rates calculation and considered their transferability across different domains.

Towards a Consensus Gesture Set: A Survey of Mid-Air Gestures in HCI for Maximized Agreement Across Domains

Multimodal emotion recognition has gained much traction in the field of affective computing, human–computer interaction (HCI), artificial intelligence (AI), and user experience (UX). There is growing demand to automate analysis of user emotion towards HCI, AI, and UX evaluation applications for providing affective services. Emotions are increasingly being used, obtained through the videos, audio, text or physiological signals. This has led to process emotions from multiple modalities, usually combined through ensemble-based systems with static weights. Due to numerous limitations like missing modality data, inter-class variations, and intra-class similarities, an effective weighting scheme is thus required to improve the aforementioned discrimination between modalities. This article takes into account the importance of difference between multiple modalities and assigns dynamic weights to them by adapting a more efficient combination process with the application of generalized mixture (GM) functions. Therefore, we present a hybrid multimodal emotion recognition (H-MMER) framework using multi-view learning approach for unimodal emotion recognition and introducing multimodal feature fusion level, and decision level fusion using GM functions. In an experimental study, we evaluated the ability of our proposed framework to model a set of four different emotional states (Happiness, Neutral, Sadness, and Anger) and found that most of them can be modeled well with significantly high accuracy using GM functions. The experiment shows that the proposed framework can model emotional states with an average accuracy of 98.19% and indicates significant gain in terms of performance in contrast to traditional approaches. The overall evaluation results indicate that we can identify emotional states with high accuracy and increase the robustness of an emotion classification system required for UX measurement.

/pdf/a-hybrid-multimodal-emotion-recognition-framework-for-ux-tcvht6wg.pdf

A Hybrid Multimodal Emotion Recognition Framework for UX Evaluation Using Generalized Mixture Functions

Multiple and varied domains can benefit from automated Facial Expression Recognition (FER) like human computer interfaces or health applications. New approaches using Machine learning (ML) are achieving successful results, but its use raises concerns related with biases, fairness or explainability, which can undermine the trust of the users. This work aims to study how gender biased training datasets alter fairness in FER. The main outcomes show which facial expressions recognition are more impacted by gender bias.

Facial Expression Recognition: Impact of Gender on Fairness and Expressions∗

/pdf/analysis-of-gender-differences-in-facial-expression-dz3rotgd.pdf

Analysis of Gender Differences in Facial Expression Recognition Based on Deep Learning Using Explainable Artificial Intelligence

This work presents the design and analysis of an Adaptive User Interface (AUI) for a desktop application that uses a novel solution for the recognition of the emotional state of a user through both facial expressions and body posture from an RGB-D sensor. Six basic emotions are recognized through facial expressions in addition to the physiological state, which is recognized through the body posture. The facial expressions and body posture are acquired in real-time from a Kinect sensor. A scoring system is used to improve recognition by minimizing the confusion between the different emotions. The implemented solution achieves an accuracy rate of above 90%. The recognized emotion is then used to derive an Automatic AUI where the user can use speech commands to modify the User Interface (UI) automatically. A comprehensive user study is performed to compare the usability of an Automatic, Manual, and a Hybrid AUI. The AUIs are evaluated in terms of their efficiency, effectiveness, productivity, and error safety. Additionally, a comprehensive analysis is performed to evaluate the results from the viewpoint of different genders and age groups. Results show that the hybrid adaptation improves usability in terms of productivity and efficiency. Finally, a combination of both automatic and hybrid AUIs result in significantly positive user experience compared to the manual adaptation.

/pdf/adaptive-user-interface-design-and-analysis-using-emotion-2qyo7bvyp8.pdf

Adaptive user interface design and analysis using emotion recognition through facial expressions and body posture from an RGB-D sensor.

This article presents a new natural user interface to control and manipulate a 3D animation using the Kinect. The researchers design a number of gestures that allow the user to play, pause, forward, rewind, scale, and rotate the 3D animation. They also implement a cursor-based traditional interface and compare it with the natural user interface. Both interfaces are extensively evaluated via a user study in terms of both the usability and user experience. Through both quantitative and the qualitative evaluation, they show that a gesture-based natural user interface is a preferred method to control a 3D animation compared to a cursor-based interface. The natural user interface not only proved to be more efficient but resulted in a more engaging and enjoyable user experience.

A Natural User Interface for 3D Animation Using Kinect

Design and Analysis of an Automatic UI Adaptation Framework from Multimodal Emotion Recognition using an RGB-D Sensor

Open source software (OSS) has recently become very important due to the rapid expansion of the software industry. In order to determine whether the quality of the software can achieve the intended purposes, the components of OSS need to be assessed as they are in closed source (conventional) software. Several quality in-use models have been introduced to evaluate software quality in various fields. The banking sector is one of the most critical sectors, as it deals with highly sensitive data; it therefore requires an accurate and effective assessment of software quality. In this article, two pieces of banking software are compared: one open source and one closed source. A new quality in use model, inspired by ISO/IEC 25010, is used to ensure concise results in the comparison. The results obtained show the great potential of OSS, especially in the banking field.

/pdf/application-of-quality-in-use-model-to-evaluate-the-user-7g5ks79n8w.pdf

Application of Quality in Use Model to Evaluate the User Experience of Online Banking Software

This work presents a new comparative study for manipulating animated 3D content on a large scale display using two user interaction mechanisms. The first interaction mechanism relies on a traditional cursor-based user interface (UI) with onscreen interaction elements. The second interaction mechanism adopts a gesture-based UI without any visible interaction elements. The user’s distance from the display is significantly farther compared to a traditional desktop to correctly gauge the impact of a cursor-based UI compared to a gesture-based UI on a large scale display. Both interaction mechanisms are extensively evaluated via a user study in terms of usability, user experience (UX), and ergonomics. Through both quantitative and the qualitative evaluation, the results show that a gesture-based interface is a preferred method to manipulate animated 3D content compared to a cursor-based UI on a large scale display in terms of usability and UX. The results also demonstrate that using a gesture-based UI does not rank higher in terms of physical and cognitive ergonomics compared to a cursor-based UI.

A Comparative Study to Assess Human Machine Interaction Mechanisms for Large Scale Displays

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