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Review of Psychological Barriers to AI Adoption

In this study, the authors delve into psychological factors influencing consumer attitudes toward AI systems. They categorize barriers into opacity, emotionlessness, rigidity, autonomy, and non-humanness, examining how each relates to human cognition. The study emphasizes the need for targeted, context-specific interventions based on system capabilities and user inclinations, providing insights for improving acceptance and mitigating risks in the adoption of AI technologies.

26 Nov 2023

Enhancing Demolition Waste Management with Neural Network Forecasting

Researchers introduce artificial neural networks (ANNs) as a powerful tool for forecasting the generation of ten common types of demolition waste from buildings. Analyzing data from 150 buildings in South Korean redevelopment zones, the research develops specialized ANN prediction models for each waste category, achieving significant performance gains over past studies.

26 Nov 2023

Machine Learning for Precision Broiler Weight Estimation

This article introduces a novel machine learning approach for non-invasive broiler weight estimation in large-scale production. Utilizing Gaussian mixture models, Isolation Forest, and OPTICS algorithm in a two-stage clustering process, the researchers achieved accurate predictions of individual broiler weights. The comprehensive methodology, combining polynomial fitting, gray models, and adaptive forecasting, offers a promising and cost-effective solution for precise broiler weight monitoring in large-scale farming setups, as evidenced by considerable accuracy in evaluations across 111 datasets.

26 Nov 2023

Autoencoders in Molecular Design: A Comprehensive Overview

This article explores the algorithmic foundations and applications of autoencoders in molecular informatics and drug discovery, with a focus on their role in data-driven molecular representation and constructive molecular design. The study highlights the versatility of autoencoders, especially variational autoencoders (VAEs), in handling diverse molecular data types and their applications in tasks such as dimensionality reduction, preprocessing, and generative molecular design.

26 Nov 2023

Machine Learning Enhances Bulk Modulus Prediction in High Entropy Alloys

Researchers introduce machine learning (ML) models for predicting the bulk modulus in High Entropy Alloys (HEA), a crucial property for aerospace and high-pressure applications. The Gradient Boosting Classifier (GBC) excels in HEA classification, while the LASSO Regression model predicts bulk modulus values, accelerating the discovery and design of HEAs with superior mechanical traits. This pioneering study addresses a significant gap in HEA research and offers a pathway for optimized alloy compositions in diverse applications.

24 Nov 2023

Revolutionizing Investigative Interview Training: AI-Powered Virtual Reality with Child Avatars

Researchers unveil a groundbreaking virtual reality (VR) system utilizing child avatars for immersive investigative interview training. The AI-driven prototype, featuring a lifelike 6-year-old avatar, outperforms 2D alternatives, showcasing superior realism, engagement, and training efficacy. The system's AI capabilities, including automatic performance evaluation and tailored feedback, present a promising avenue for scalable and personalized training, potentially transforming competencies in handling child abuse cases globally.

24 Nov 2023

Enhanced Speech-Emotion Analysis Using Multi-Stage Machine Learning

Researchers introduce an innovative approach for speech-emotion analysis employing a multi-stage process involving spectro-temporal modulation, entropy features, convolutional neural networks, and a combined GC-ECOC classification model. Evaluating against Berlin and ShEMO datasets, the method showcases remarkable performance, achieving average accuracies of 93.33% and 85.73%, respectively, surpassing existing methods by at least 2.1% in accuracy and showing significant potential for improved emotion recognition in speech across various applications.

24 Nov 2023

Decentralized Cooperation: Blockchain-Powered Economics in Robot Swarms

Researchers introduce a pioneering approach to swarm robotics, leveraging blockchain technology for decentralized economic incentives. The study demonstrates the feasibility and advantages of a blockchain-based information marketplace, fostering cooperation and penalizing misinformation among robots with conflicting interests in open swarms. This innovation holds promise for real-world applications beyond robotics, ushering in a new era of decentralized, trustless networks inspired by the intersection of swarm robotics and blockchain technology.

24 Nov 2023

Introducing CNSABC Algorithm for Enhanced Convergence and Solution Quality

Researchers unveil the Chaotic and Neighborhood Search-based Artificial Bee Colony (CNSABC) algorithm, a groundbreaking variant addressing limitations in traditional Artificial Bee Colony (ABC) for optimization problems. Demonstrating superior convergence speed and solution quality, CNSABC surpasses other algorithms in extensive experiments, showcasing its potential for practical problem-solving, particularly in complex engineering optimization scenarios.

24 Nov 2023

Efficient Dairy Cow Behavior Recognition: A Two-Pathway X3DFast Model

Researchers introduce the X3DFast model, a spatiotemporal behavior recognition model for dairy cows. This efficient and lightweight model, with a two-pathway architecture, demonstrates high accuracy (over 97%) in recognizing common dairy cow behaviors such as walking, standing, lying, and mounting, outperforming existing models in both speed and accuracy, offering a promising solution for real-world agricultural environments.

24 Nov 2023

Automation of Sediment Core Analysis: A Deep Learning Approach

Researchers propose a novel deep learning (DL) method utilizing convolutional neural networks (CNNs) for automatic sediment core analysis. The DL-based approach employs semantic segmentation on digital images of sediment cores, demonstrating high accuracy in interpreting sedimentary facies, offering a precise, efficient tool for subsurface stratigraphic modeling in geoscience applications.

23 Nov 2023

Enhancing Dissolved Oxygen Prediction in Rivers Using Metaheuristic Algorithms and Neural Networks

Researchers introduced and evaluated four metaheuristic algorithms—teaching–learning-based optimization, sine cosine algorithm, water cycle algorithm, and electromagnetic field optimization—integrated with a multi-layer perceptron neural network for predicting dissolved oxygen concentration in the Klamath River. These algorithms optimized computational variables, improving DO prediction accuracy in water quality assessment.

23 Nov 2023

European Artificial Intelligence Act: A Framework for Responsible AI Governance

This study conducts a systematic literature review to categorize critiques and challenges of the proposed European Artificial Intelligence Act (AIA). As AI governance becomes crucial, the AIA aims to regulate AI development and deployment, considering potential harms. The interdisciplinary Information Systems (IS) field's attention to societal AI dimensions highlights the need for a thorough analysis of the AIA, guiding responsible innovation amidst rapid advancements.

23 Nov 2023

Decoding Masterpieces and Unveiling Cultural Heritage Using AI

This paper explores the profound impact of artificial intelligence (AI) on art history, showcasing how algorithms decode intricate details in art compositions. The study reveals AI's role in analyzing poses, color palettes, brushwork, and perspectives, contributing to the understanding of artists' use of optical science. Additionally, AI aids in art restoration, uncovering hidden layers, reconstructing missing elements, and disproving theories.

23 Nov 2023

Expertise Framing Effects on User Behavior: A Study on Algorithm Aversion in AI Advisory

This study investigates the impact of expertise framing on user behavior in distinguishing between human and algorithmic advisors. Experiments on regression tasks revealed a significant increase in advice utilization when human advisors were framed as experts, while a comparable effect was not observed for algorithmic advisors.

23 Nov 2023

Navigating Challenges in AI Regulation: Insights from the European AI Act

This publication analyzes challenges in the European AI Act (AIA), offering insights applicable to subsequent versions. The research, focused on the April 2021 draft, categorizes critiques into regulation, compliance, and anticipated impact themes. Notable concerns include the AIA's broad scope, ambiguous wording, unrealistic provider requirements, and potential negative effects on innovation and industry, providing valuable guidance for further AI regulation research.

23 Nov 2023

Advancing Deep Learning with Differentiable Visual Computing

A recent article in Nature Machine Intelligence delves into the progress and challenges of Differentiable Visual Computing (DVC). The study proposes a unified DVC pipeline, integrating differentiable geometry, physics, and animation, enhancing data efficiency, accuracy, and speed in machine learning applications for real-world physical systems. The authors review key aspects, including rendering, animation, and geometry, highlighting the potential of DVC to bridge the gap between visual computing and deep learning.

22 Nov 2023

Emotional Pathways in User Experiences: Integrating ChatGPT into Knowledge Work

This study, published in AISeL, explores the user experience of integrating AI technologies like ChatGPT into knowledge work. Through interviews with 31 users, distinct phases were identified, ranging from pre-use curiosity and anxiety to the establishment of a tight intertwinement with ChatGPT as a collaborative assistant. The findings emphasize the emotional dimensions of AI adoption and raise important considerations for individuals, organizations, and society regarding potential dependencies, deskilling, and the evolving role of AI in the workplace.

22 Nov 2023

Navigating the Dynamic Landscape of AI Skills in the Workforce

The AI-driven transformation of the labor market demands a nuanced approach to skill measurement. Researchers, in a recent AISeL publication, illuminate the dynamic nature of AI skills and propose a novel dynamic co-occurrence method, addressing limitations in static approaches. Leveraging empirical analysis, they reveal the evolving landscape of AI skills and demonstrate the superior performance of the dynamic method in capturing emergence and evolution.

22 Nov 2023

Creativity through Human-AI Collaboration: The Role of Automated Feedback

This paper explores the pivotal role of generative AI in providing automated feedback to foster human creativity in innovation. The researchers conducted a series of experiments, utilizing generative AI to offer visual and numeric feedback in real-time. Preliminary insights indicate that visual feedback enhances perceived originality, imagination, and task competence, shedding light on the potential of AI-driven feedback in augmenting creative endeavors.

22 Nov 2023