Understanding the Role of Interactivity and Explanation in Adaptive Experiences

Adaptive experiences have been an active area of research in the past few decades, accompanied by advances in technology such as machine learning and artificial intelligence. Whether the currently ongoing research on adaptive experiences has focused on personalization algorithms, explainability, user engagement, or privacy and security, there is growing interest and resources in developing and improving these research focuses. Even though the research on adaptive experiences has been dynamic and rapidly evolving, achieving a high level of user engagement in adaptive experiences remains a challenge. %????? This dissertation aims to uncover ways to engage users in adaptive experiences by incorporating interactivity and explanation through four studies. Study I takes the first step to link the explanation and interactivity in machine learning systems to facilitate users\u27 engagement with the underlying machine learning model with the Tic-Tac-Toe game as a use case. The results show that explainable machine learning (XML) systems (and arguably XAI systems in general) indeed benefit from mechanisms that allow users to interact with the system\u27s internal decision rules. Study II, III, and IV further focus on adaptive experiences in recommender systems in specific, exploring the role of interactivity and explanation to keep the user “in-the-loop” in recommender systems, trying to mitigate the ``filter bubble\u27\u27 problem and help users in self-actualizing by supporting them in exploring and understanding their unique tastes. Study II investigates the effect of recommendation source (a human expert vs. an AI algorithm) and justification method (needs-based vs. interest-based justification) on professional development recommendations in a scenario-based study setting. The results show an interaction effect between these two system aspects: users who are told that the recommendations are based on their interests have a better experience when the recommendations are presented as originating from an AI algorithm, while users who are told that the recommendations are based on their needs have a better experience when the recommendations are presented as originating from a human expert. This work implies that while building the proposed novel movie recommender system covered in study IV, it would provide a better user experience if the movie recommendations are presented as originating from algorithms rather than from a human expert considering that movie preferences (which will be visualized by the movies\u27 emotion feature) are usually based on users\u27 interest. Study III explores the effects of four novel alternative recommendation lists on participants’ perceptions of recommendations and their satisfaction with the system. The four novel alternative recommendation lists (RSSA features) which have the potential to go beyond the traditional top N recommendations provide transparency from a different level --- how much else does the system learn about users beyond the traditional top N recommendations, which in turn enable users to interact with these alternative lists by rating the initial recommendations so as to correct or confirm the system\u27s estimates of the alternative recommendations. The subjective evaluation and behavioral analysis demonstrate that the proposed RSSA features had a significant effect on the user experience, surprisingly, two of the four RSSA features (the controversial and hate features) perform worse than the traditional top-N recommendations on the measured subjective dependent variables while the other two RSSA features (the hipster and no clue items) perform equally well and even slightly better than the traditional top-N (but this effect is not statistically significant). Moreover, the results indicate that individual differences, such as the need for novelty and domain knowledge, play a significant role in users’ perception of and interaction with the system. Study IV further combines diversification, visualization, and interactivity, aiming to encourage users to be more engaged with the system. The results show that introducing emotion as an item feature into recommender systems does help in personalization and individual taste exploration; these benefits are greatly optimized through the mechanisms that diversify recommendations by emotional signature, visualize recommendations on the emotional signature, and allow users to directly interact with the system by tweaking their tastes, which further contributes to both user experience and self-actualization. This work has practical implications for designing adaptive experiences. Explanation solutions in adaptive experiences might not always lead to a positive user experience, it highly depends on the application domain and the context (as studied in all four studies); it is essential to carefully investigate a specific explanation solution in combination with other design elements in different fields. Introducing control by allowing for direct interactivity (vs. indirect interactivity) in adaptive systems and providing feedback to users\u27 input by integrating their input into the algorithms would create a more engaging and interactive user experience (as studied in Study I and IV). And cumulatively, appropriate direct interaction with the system along with deliberate and thoughtful designs of explanation (including visualization design with the application environment fully considered), which are able to arouse user reflection or resonance, would potentially promote both user experience and user self-actualization

Deeper understanding of cobalt-doped SiC nanowires as excellent electromagnetic wave absorbers

Doping is a facile and effective technique that plays a key role in the function of many semiconductor materials. Unraveling the regulatory mechanism of doping can offer useful guidance for the design of the material structure and fabricating novel functional composites invalid in pure phase structures, which extents their applications in catalysts, light emitting devices, and environmental protection. Especially, transition metal doping related to the spin and charge introduces foreign states to the electronic structures in the host materials and endows the composites with intriguing properties. However, most of the reported papers are limited on the fabrication of the doped composites with enhanced performance. Little progress has been made to clarify the underlying mechanism for those improvements. Herein, Co-doped SiC nanowires with different Co contents were successfully fabricated by a simple carbothermal reduction method. The Co-doped SiC nanowires were characterized in terms of microstructure, electronic structure, and electromagnetic (EM) parameters to study the effects of doping on enhancing the EM wave absorption ability. Both the microstructure analysis and density functional theory calculations indicated that the incorporation of Co into SiC nanowires inhibited the formation of defective structures but increased their conductivity. Thus, the improved electronic transportation ability was dominant in enhancing the dielectric loss. The Co dopants also imparted the Co-doped SiC nanowires with magnetic property, which could generate magnetic resonance to attenuate EM wave and achieve superior impedance matching. The induced synergistic effects between Co dopants and SiC nanowires endowed Co-doped SiC nanowires with excellent EM wave absorption ability. Their minimum reflection loss was -50 dB, and the effective absorption bandwidth was up to 4.0 GHz at 1.5 mm sample thickness. Therefore, the fabricated Co-doped SiC nanowires are potential candidates for high-efficiency EM wave absorption materials. The findings of this research provide a guideline for other doped functional composites. Please click Additional Files below to see the full abstract

A Hybrid Process Monitoring and Fault Diagnosis Approach for Chemical Plants

Given their potentially enormous risk, process monitoring and fault diagnosis for chemical plants have recently been the focus of many studies. Based on hazard and operability (HAZOP) analysis, kernel principal component analysis (KPCA), wavelet neural network (WNN), and fault tree analysis (FTA), a hybrid process monitoring and fault diagnosis approach is proposed in this study. HAZOP analysis helps identify the fault modes and determine process variables monitored. The KPCA model is then constructed to reduce monitoring variable dimensionality. Meanwhile, the fault features of the monitoring variables are extracted, so then process monitoring can be performed with the squared prediction error (SPE) statistics of KPCA. Then, multiple WNN models are designed through the use of low-dimensional sample data preprocessed by KPCA as the training and test samples to detect the fault mode online. Finally, FTA approach is introduced to further locate the fault root causes of the fault mode. The proposed approach is applied to process monitoring and fault diagnosis in a depropanizer unit. Case study results indicate that this approach can be applicable to process monitoring and diagnosis in large-scale chemical plants. Accordingly, the approach can serve as an early and reliable basis for technicians’ and operators’ safety management decision-making

Effects of Inoculants (Chlorobium limicola and Rhodopseudo-monas palustris) on Nutrient Uptake and Growth in Cucumber

Rhizobacteria is a prosperous for promoting plant growth for the superiority of reducing environmental damages. Two Strains of Chlorobium limicola and Rhodopseudomonas palustris were supplied in the experiment as potential inoculants for cucumber. Significant enhancement of the availability of macronutrient elements N, P and K were observed in soil, and further improvement on the uptake of them was also obtained in cucumber plants. Accumulation of essential micronutrients of Fe and Zn were detected both in roots and in shoots. The two stains increased chlorophyll and carotinoid synthesis, plant height, stem diameter, wet weight and dry weight. Various dose has significantly effect on plant growth stimulation, C. Limicola with 107 cells mL-1 and R. Palustris with 108 cells mL-1 seem to be better on the whole

Scene Graph Modification as Incremental Structure Expanding

A scene graph is a semantic representation that expresses the objects, attributes, and relationships between objects in a scene. Scene graphs play an important role in many cross modality tasks, as they are able to capture the interactions between images and texts. In this paper, we focus on scene graph modification (SGM), where the system is required to learn how to update an existing scene graph based on a natural language query. Unlike previous approaches that rebuilt the entire scene graph, we frame SGM as a graph expansion task by introducing the incremental structure expanding (ISE). ISE constructs the target graph by incrementally expanding the source graph without changing the unmodified structure. Based on ISE, we further propose a model that iterates between nodes prediction and edges prediction, inferring more accurate and harmonious expansion decisions progressively. In addition, we construct a challenging dataset that contains more complicated queries and larger scene graphs than existing datasets. Experiments on four benchmarks demonstrate the effectiveness of our approach, which surpasses the previous state-of-the-art model by large margins.Comment: In COLING 2022 as a long paper. Code and data available at https://github.com/THU-BPM/SG

The Pursuit of Transparency and Control: A Classification of Ad Explanations in Social Media

Online advertising on social media platforms has been at the center of recent controversies over growing concerns regarding users\u27 privacy, dishonest data collection, and a lack of transparency and control. Facing public pressure, some social media platforms have opted to implement explanatory tools in an effort to empower consumers and shed light on marketing practices. Yet, to date research shows significant inconsistencies around how ads should be explained. To address this issue, we conduct a systematic literature review on ad explanations, covering existing research on how they are generated, presented, and perceived by users. Based on this review, we present a classification scheme of ad explanations that offers insights into the reasoning behind the ad recommendation, the objective of the explanation, the content of the explanation, and how this content should be presented. Moreover, we identify challenges that are unaddressed by either current research or explanatory tools deployed in practice, and we discuss avenues for future research to address these challenges. This paper calls attention to and helps to solidify an agenda for interdisciplinary communities to collaboratively approach the design and implementation of explanations for online ads in social media

Modal analysis of micro wind turbine blade using COSMOSWorks

In this paper, vibration modal analysis of a horizontal axis micro wind turbine blade of different rotational speeds was carried out by using the finite element analysis software COSMOSWorks. The dynamic stiffening phenomenon and its effect on the vibration mode of the wind turbine blade were taken into account. Numerical results were analyzed and compared. The analysis can help not only to ensure the reliability of system operation but also to improve the structural performance

Synthesis, characterization, and antifungal evaluation of novel 1,2,3-triazolium-functionalized starch derivative

1,2,3-Triazolium-functionalized starch derivative was obtained by straightforward quaternization of the synthesized starch derivative bearing 1,2,3-triazole with benzyl bromide by combining the robust attributes of cuprous-catalyzed azide-alkyne cycloaddition. These novel starch derivatives were characterized by FTIR, UV-vis, H-1 NMR, C-13 NMR, and elemental analysis. Their antifungal activities against Colletotrichum lagenarium, Watermelon fusarium, and Phomopsis asparagi were investigated by hypha measurement in vitro. The fungicidal assessment revealed that compared with starch and starch derivative bearing 1,2,3-triazole with inhibitory indices of below 15% at 1.0 mg/mL, 1,2,3-triazolium-functionalized starch derivative had superior antifungal activity with inhibitory rates of over 60%. Especially, the best inhibitory index of 1,2,3-triazolium-functionalized starch derivative against Colletotrichum lagenarium attained 90% above at 1.0mg/mL. The results obviously showed that quaternization of 1,2,3-triazole with benzyl bromide could effectively enhance antifungal activity of the synthesized starch derivatives. The synthetic strategy described here could be utilized for the development of starch as novel antifungal biomaterial. (C) 2017 Elsevier B.V. All rights reserved