Reviewerly/exHarmony huggingface dataset:
An Empirical Study on Data Distribution-Aware Test Selection for Deep Learning Enhancement

Similar to traditional software that is constantly under evolution, deep neural networks need to evolve upon the rapid growth of test data for continuous enhancement (e.g., adapting to distribution shift in a new environment for deployment). However, it is labor intensive to manually label all of the collected test data. Test selection solves this problem by strategically choosing a small set to label. Via retraining with the selected set, deep neural networks will achieve competitive accuracy. Unfortunately, existing selection metrics involve three main limitations: (1) using different retraining processes, (2) ignoring data distribution shifts, and (3) being insufficiently evaluated. To fill this gap, we first conduct a systemically empirical study to reveal the impact of the retraining process and data distribution on model enhancement. Then based on our findings, we propose DAT, a novel distribution-aware test selection metric. Experimental results reveal that retraining using both the training and selected data outperforms using only the selected data. None of the selection metrics perform the best under various data distributions. By contrast, DAT effectively alleviates the impact of distribution shifts and outperforms the compared metrics by up to five times and 30.09% accuracy improvement for model enhancement on simulated and in-the-wild distribution shift scenarios, respectively.
Who needs explanation and when? Juggling explainable AI and user epistemic uncertainty

• AI explainability (XAI) plays opposing roles on human decision-making. • Users’ epistemic uncertainty matters when understanding XAI's impacts. • As users’ uncertainty increases, only providing prediction rationale is beneficial. • Providing alternative advice may hinder users’ advice acceptance. • Showing prediction confidence scores may also hinder users’ advice acceptance. In recent years, AI explainability (XAI) has received wide attention. Although XAI is expected to play a positive role in decision-making and advice acceptance, various opposing effects have also been found. The opposing effects of XAI highlight the critical role of context, especially human factors, in understanding XAI's impacts. This study investigates the effects of providing three types of post-hoc explanations (alternative advice, prediction confidence scores, and prediction rationale) on two context-specific user decision-making outcomes (AI advice acceptance and advice adoption). Our field experiment results show that users’ epistemic uncertainty matters when understanding XAI's impacts. As users’ epistemic uncertainty increases, only providing prediction rationale is beneficial, whereas providing alternative advice and showing prediction confidence scores may hinder users’ advice acceptance. Our study contributes to the emerging literature on the human aspects of XAI by clarifying XAI and showing that XAI may not always be desirable. It also contributes by highlighting the importance of considering user profiles when predicting XAI's impacts, designing XAI, and providing professional services with AI.
Drone Forensics and Machine Learning: Sustaining the Investigation Process

Drones have been increasingly adopted to address several critical challenges faced by humanity to provide support and convenience . The technological advances in the broader domains of artificial intelligence and the Internet of Things (IoT) as well as the affordability of off-the-shelf devices, have facilitated modern-day drone use. Drones are readily available for deployment in hard to access locations for delivery of critical medical supplies, for surveillance, for weather data collection and for home delivery of purchased goods. Whilst drones are increasingly beneficial to civilians, they have also been used to carry out crimes. We present a survey of artificial intelligence techniques that exist in the literature in the context of processing drone data to reveal criminal activity. Our contribution also comprises the proposal of a novel model to adopt the concepts of machine learning for classification of drone data as part of a digital forensic investigation. Our main conclusions include that properly trained machine-learning models hold promise to enable an accurate assessment of drone data obtained from drones confiscated from a crime scene. Our research work opens the door for academics and industry practitioners to adopt machine learning to enable the use of drone data in forensic investigations.
A Practical Cross-Device Federated Learning Framework over 5G Networks

The concept of federated learning (FL) was first proposed by Google in 2016. Since then, FL has been widely studied for the feasibility of application in various fields due to its potential to make full use of data without compromising privacy. However, limited by the capacity of wireless data transmission, the employment of FL on mobile devices has been making slow progress in practice. The development and commercialization of the 5th generation (5G) mobile networks has shed some light on this. In this article, we analyze the challenges of existing FL schemes for mobile devices and propose a novel cross-device FL framework that utilizes the anonymous communication technology and ring signature to protect the privacy of participants while reducing the computation overhead of mobile devices participating in FL. In addition, our scheme implements a contribution-based incentive mechanism to encourage mobile users to participate in FL. We also give a case study of autonomous driving. Finally, we present the performance evaluation of the proposed scheme and discuss some open issues in FL.
Blockchain technology in the construction industry: mapping current research trends using social network analysis and clustering

Blockchain represents an evolving technology for distributed and secure recording and sharing of information. Meanwhile, blockchain has thrived in banking, finance, and supply chain; its usage within the construction industry is still in its infancy. To this end, the existing literature falls short in providing comprehensive quantitative understanding, within a systems-based analytic context, of the factors affecting blockchain utilization in construction applications. This paper fills this knowledge gap. The authors: (1) conducted an extensive literature review on blockchain implementation in the construction domain; (2) identified a list of 41 factors affecting blockchain implementation in construction projects categorized in four categories: challenges, needs, requirements, and capabilities; (3) utilized a social network analysis (SNA) approach on a database of 111 publications to quantitatively analyze the literature as related to the aforementioned factors; and (4) performed clustering analysis on the SNA graphs to determine the combinations of factors that are most likely co-occurring in research publications. SNA results indicate that while the most investigated factor was “increased trust and transparency between project parties”, the least studied factors included: “cash upfront funding system”, “change payment processes and procedures”, “smart contracts design errors”, “cryptocurrency fluctuations”, “lack of sufficiently skilled personnel”, and “increased awareness and capabilities of personnel”. Also, clustering outcomes highlight that some combinations of factors are not well-represented in current scholarly efforts. Such imbalance and consequent knowledge gaps may contribute to the actual implementation rate of blockchain in construction applications. Ultimately, this paper provides a roadmap for potential future directions of blockchain construction-related research.
Application of Internet of Things in Image Processing

Image processing and IoT technology use various sensors and camera-based sensors for processing image data with help of a variety of IoT applications. So far, the IoT and image processing concepts have been used for our real-life applications. Their individual use in the sphere of industries is possible and has had some success. However, a combination of the two technologies has yet to be developed. In this paper, we have purposed architecture that provides an overview of the function of image processing in the field of the Internet of Things. In this paper, we have used the ESP32 CAM WiFi Module with OV2640 Camera Module and the Arduino Uno R3 that open-source microcontroller board. We capture the plant image and use the cloud and big data-based prediction model for data analysis. We have used the ThingSpeak open-source platform to show the camera Received Signal Strength.
Disentangling Features for Fashion Recommendation

Online stores have become fundamental for the fashion industry, revolving around recommendation systems to suggest appropriate items to customers. Such recommendations often suffer from a lack of diversity and propose items that are similar to previous purchases of a user. Recently, a novel kind of approach based on Memory Augmented Neural Networks (MANNs) has been proposed, aimed at recommending a variety of garments to create an outfit by complementing a given fashion item. In this article we address the task of compatible garment recommendation developing a MANN architecture by taking into account the co-occurrence of clothing attributes, such as shape and color, to compose an outfit. To this end we obtain disentangled representations of fashion items and store them in external memory modules, used to guide recommendations at inference time. We show that our disentangled representations are able to achieve significantly better performance compared to the state of the art and also provide interpretable latent spaces, giving a qualitative explanation of the recommendations.
Semantic schema based genetic programming for symbolic regression

Despite the empirical success of Genetic programming (GP) in various symbolic regression applications, GP is not still known as a reliable problem-solving technique in this domain. Non-locality of GP representation and operators causes ineffectiveness of its search procedure. This study employs semantic schema theory to control and guide the GP search and proposes a local GP called semantic schema-based genetic programming (SBGP). SBGP partitions the semantic search space into semantic schemas and biases the search to the significant schema of the population, which is gradually progressing towards the optimal solution. Several semantic local operators are proposed for performing a local search around the significant schema. In combination with schema evolution as a global search, the local in-schema search provides an efficient exploration–exploitation control mechanism in SBGP. For evaluating the proposed method, we use six benchmarks, including synthesized and real-world problems. The obtained errors are compared to the best semantic genetic programming algorithms, on the one hand, and data-driven layered learning approaches, on the other hand. Results demonstrate that SBGP outperforms all mentioned methods in four out of six benchmarks up to 87% in the first set and up to 76% in the second set of experiments in terms of generalization measured by root mean squared error.
Dual Space Graph Contrastive Learning

Unsupervised graph representation learning has emerged as a powerful tool to address real-world problems and achieves huge success in the graph learning domain. Graph contrastive learning is one of the unsupervised graph representation learning methods, which recently attracts attention from researchers and has achieved state-of-the-art performances on various tasks. The key to the success of graph contrastive learning is to construct proper contrasting pairs to acquire the underlying structural semantics of the graph. However, this key part is not fully explored currently, most of the ways generating contrasting pairs focus on augmenting or perturbating graph structures to obtain different views of the input graph. But such strategies could degrade the performances via adding noise into the graph, which may narrow down the field of the applications of graph contrastive learning. In this paper, we propose a novel graph contrastive learning method, namely \textbf{D}ual \textbf{S}pace \textbf{G}raph \textbf{C}ontrastive (DSGC) Learning, to conduct graph contrastive learning among views generated in different spaces including the hyperbolic space and the Euclidean space. Since both spaces have their own advantages to represent graph data in the embedding spaces, we hope to utilize graph contrastive learning to bridge the spaces and leverage advantages from both sides. The comparison experiment results show that DSGC achieves competitive or better performances among all the datasets. In addition, we conduct extensive experiments to analyze the impact of different graph encoders on DSGC, giving insights about how to better leverage the advantages of contrastive learning between different spaces.
Cross Pairwise Ranking for Unbiased Item Recommendation

Most recommender systems optimize the model on observed interaction data, which is affected by the previous exposure mechanism and exhibits many biases like popularity bias. The loss functions, such as the mostly used pointwise Binary Cross-Entropy and pairwise Bayesian Personalized Ranking, are not designed to consider the biases in observed data. As a result, the model optimized on the loss would inherit the data biases, or even worse, amplify the biases. For example, a few popular items take up more and more exposure opportunities, severely hurting the recommendation quality on niche items — known as the notorious Mathew effect.
Representation learning using deep random vector functional link networks for clustering

Random Vector Functional Link (RVFL) Networks have received a lot of attention due to the fast training speed as the non-iterative solution characteristic. Currently, the main research direction of RVFLs has supervised learning, including semi-supervised and multi-label. There are hardly any unsupervised research results for RVFLs. In this paper, we propose the unsupervised RVFL (usRVFL), and the unsupervised framework is generic that can be used with other RVFL variants, thus we extend it to an ensemble deep variant, unsupervised deep RVFL (usdRVFL). The unsupervised method is based on the manifold regularization while the deep variant is related to the consensus clustering method, which can increase the capability and diversity of RVFLs. Our unsupervised approaches also benefit from fast training speed, even the deep variant offers a very competitive computation efficiency. Empirical experiments on several benchmark datasets demonstrated the effectiveness of the proposed algorithms.
Weaving Stories: Toward Repertoires for Designing Things

While much work is underway within the context of posthuman design, this research is often described from a dominantly human perspective. It rarely accounts for the creative capacities of nonhumans in design, such as materials, tools, and software. There is a need to further engage with posthuman theories conceptually, materially, and methodologically. We approach this challenge through Ron Wakkary's concept of repertoires: actions the human designer can take to increase participation of nonhumans in design research practice. This paper reports on potential repertoires' development by exploring three approaches from outside of HCI: describing the landscape, noticing, and translations. We use these methods to account for weaving events that the first author was engaged in. Through critical reflection of these accounts, we contribute three repertoires and an example of applying the theoretical framework of Designing Things.
When Confidence Meets Accuracy: Exploring the Effects of Multiple Performance Indicators on Trust in Machine Learning Models

Previous research shows that laypeople’s trust in a machine learning model can be affected by both performance measurements of the model on the aggregate level and performance estimates on individual predictions. However, it is unclear how people would trust the model when multiple performance indicators are presented at the same time. We conduct an exploratory human-subject experiment to answer this question. We find that while the level of model confidence significantly affects people’s belief in model accuracy, both the model’s stated and observed accuracy generally have a larger impact on people’s willingness to follow the model’s predictions as well as their self-reported levels of trust in the model, especially after observing the model’s performance in practice. We hope the empirical evidence reported in this work could open doors to further studies to advance understanding of how people perceive, process, and react to performance-related information of machine learning.
Does AI control or support? Power shifts after AI system implementation in customer relationship management

Many companies are currently investing in artificial intelligence (AI) because of its potential to increase customer satisfaction or financial performance. However, the success rates in implementing AI systems are low, partly due to technology-centric approaches that neglect work practices. This study draws on Bourdieu’s theory of practice to highlight the potential power shift related to AI in customer relationship management, based on the concepts field, capital, and habitus. Two longitudinal case studies were conducted to understand the power shift related to AI implementation. These two AI systems were designed with the objective to support employees. However, subsequently, their implementation changed the balance of power with a significant shift towards more management control, resulting in a devaluation of employees’ work practices. The paper discusses implications for theory and practice in terms of the discrepancies and power shifts following the introduction of AI systems to support customer relationship management.
FSGANv2: Improved Subject Agnostic Face Swapping and Reenactment

We present Face Swapping GAN (FSGAN) for face swapping and reenactment. Unlike previous work, we offer a subject agnostic swapping scheme that can be applied to pairs of faces without requiring training on those faces. We derive a novel iterative deep learning-based approach for face reenactment which adjusts significant pose and expression variations that can be applied to a single image or a video sequence. For video sequences, we introduce a continuous interpolation of the face views based on reenactment, Delaunay Triangulation, and barycentric coordinates. Occluded face regions are handled by a face completion network. Finally, we use a face blending network for seamless blending of the two faces while preserving the target skin color and lighting conditions. This network uses a novel Poisson blending loss combining Poisson optimization with a perceptual loss. We compare our approach to existing state-of-the-art systems and show our results to be both qualitatively and quantitatively superior. This work describes extensions of the FSGAN method, proposed in an earlier conference version of our work (Nirkin et al. 2019), as well as additional experiments and results.
The Role of Staff in Facilitating Immersive Virtual Reality for Enrichment in Aged Care: An Ethic of Care Perspective

Immersive virtual reality (VR) is being used as an enriching experience for people living in residential aged care, or nursing homes, where care staff play a critical role supporting clients to use VR. In HCI research concerned with technology use in aged care, however, the role of formal caregivers has received limited attention. We conducted interviews with 11 caregivers working in care homes that have implemented VR as part of the social program offered to residents. Our findings highlight tensions between the opportunities created by the immersive VR experience and the risks and challenges full immersion presents for people in aged care. In this paper, we draw on an ethics of care framework to make visible the care practices involved in facilitating VR in aged care homes, highlighting the care required to ensure that older adults experience benefits when using immersive VR, while risks and challenges are carefully managed.
Towards Understanding Diminished Reality

Diminished reality (DR) refers to the concept of removing content from a user’s visual environment. While its implementation is becoming feasible, it is still unclear how users perceive and interact in DR-enabled environments and what applications it benefits. To address this challenge, we first conduct a formative study to compare user perceptions of DR and mediated reality effects (e. g., changing the color or size of target elements) in four example scenarios. Participants preferred removing objects through opacity reduction (i. e., the standard DR implementation) and appreciated mechanisms for maintaining a contextual understanding of diminished items (e. g., outlining). In a second study, we explore the user experience of performing tasks within DR-enabled environments. Participants selected which objects to diminish and the magnitude of the effects when performing two separate tasks (video viewing, assembly). Participants were comfortable with decreased contextual understanding, particularly for less mobile tasks. Based on the results, we define guidelines for creating general DR-enabled environments.
Human Activity Recognition Data Analysis: History, Evolutions, and New Trends

The Assisted Living Environments Research Area-AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist, medical care and rehabilitation to older adults, to increase the time in which these people can live. independently, whether they suffer from neurodegenerative diseases or some disability. This important area is responsible for the development of activity recognition systems-ARS (Activity Recognition Systems), which is a valuable tool when it comes to identifying the type of activity carried out by older adults, to provide them with assistance. that allows you to carry out your daily activities with complete normality. This article aims to show the review of the literature and the evolution of the different techniques for processing this type of data from supervised, unsupervised, ensembled learning, deep learning, reinforcement learning, transfer learning, and metaheuristics approach applied to this sector of science. health, showing the metrics of recent experiments for researchers in this area of knowledge. As a result of this article, it can be identified that models based on reinforcement or transfer learning constitute a good line of work for the processing and analysis of human recognition activities.