Hasil untuk "Technology"

G. Hoogers

H. Herman

D. MacKenzie, J. Wajcman

D. Steigerwald, J. Bhat, D. Collins et al.

J. Fried

P. Siegel

K. Buschow, R. Cahn, M. Flemings et al.

C. Garbisu, I. Alkorta

K. Byrappa, T. Adschiri

B. Wilson, M. Patterson

Kelkar

W. Bogaerts, R. Baets, P. Dumon et al.

Bin Xu

Yong Zhao, Kevin J. Pugh, Stephen G. Sheldon et al.

This article reports on a study of the complex and messy process of classroom technology integration. The main purpose of the study was to empirically address the large question of “why don't teachers innovate when they are given computers?” rather than whether computers can improve student learning. Specifically, we were interested in understanding the conditions under which technology innovation can take place in classrooms. For a year, we followed a group of K–12 teachers who attempted to carry out technology-rich projects in their classrooms. These teachers were selected from more than 100 recipients of a technology grant program for teachers. The study found 11 salient factors that significantly impact the degree of success of classroom technology innovations. Some of these factors have been commonly mentioned in the literature, but our study found new dimensions to them. Others have not been identified in the literature. Each factor can be placed in one of three interactive domains, the teacher, the innovation, and the context. The article discusses the 11 factors in detail and proposes a model of the relationship among the different factors and their domains.

J. Eaton, Samuel Kortum

John C. McCarthy, Peter C. Wright

C. Auth, C. Allen, A. Blattner et al.

Zefan Sramek, Koji Yatani

The history of information technology development has been characterized by consecutive waves of boom and bust, as new technologies come to market, fuel surges of investment, and then stabilize towards maturity. However, in recent decades, the acceleration of such technology hype cycles has resulted in the prioritization of massive capital generation at the expense of longterm sustainability, resulting in a cascade of negative social, political, and environmental consequences. Despite the negative impacts of this pattern, academic research, and in particular HCI research, is not immune from such hype cycles, often contributing substantial amounts of literature to the discourse surrounding a wave of hype. In this paper, we discuss the relationship between technology and capital, offer a critique of the technology hype cycle using generative AI as an example, and finally suggest an approach and a set of strategies for how we can counteract such cycles through research as resistance.

Wonyoung Kim, Sujeong Seo, Juhyun Lee

Technology opportunities are critical information that serve as a foundation for advancements in technology, industry, and innovation. This paper proposes a framework based on the temporal relationships between technologies to identify emerging technology opportunities. The proposed framework begins by extracting text from a patent dataset, followed by mapping text-based topics to discover inter-technology relationships. Technology opportunities are then identified by tracking changes in these topics over time. To enhance efficiency, the framework leverages a large language model to extract topics and employs a prompt for a chat-based language model to support the discovery of technology opportunities. The framework was evaluated using an artificial intelligence patent dataset provided by the United States Patent and Trademark Office. The experimental results suggest that artificial intelligence technology is evolving into forms that facilitate everyday accessibility. This approach demonstrates the potential of the proposed framework to identify future technology opportunities.

Karim Alghoul, Hussein Al Osman, Abdulmotaleb El Saddik

Human computer interaction has become integral to modern life, driven by advancements in machine learning technologies. Affective computing, in particular, has focused on systems that recognize, interpret, and respond to human emotions, often using wearable devices, which provide continuous data streams of physiological signals. Among various physiological signals, the photoplethysmogram (PPG) has gained prominence due to its ease of acquisition from widely available devices. However, the generalization of PPG-based emotion recognition models across individuals remains an unresolved challenge. This paper introduces a novel hybrid architecture that combines Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and Temporal Convolutional Networks (TCNs) to address this issue. The proposed model integrates the strengths of these architectures to improve robustness and generalization. Raw PPG signals are fed into the CNN for feature extraction. These features are processed separately by LSTM and TCN. The outputs from these components are concatenated to generate a final feature representation, which serves as the input for classifying valence and arousal, the primary dimensions of emotion. Experiments using the Photoplethysmogram Dataset for Emotional Analysis (PPGE) demonstrate that the proposed hybrid model achieves better model generalization than standalone CNN and LSTM architectures. Our results show that the proposed solution outperforms the state-of-the-art CNN architecture, as well as a CNN-LSTM model, in emotion recognition tasks with PPG signals. Using metrics such as Area Under the Curve (AUC) and F1 Score, we highlight the model's effectiveness in handling subject variability.