arXiv subjects

Octavian Machidon

Publications and source records attributed to Octavian Machidon.

3 recordsLinked to original sources

arXiv2026

A Scoping Review of the Negative Effects of Digital Technology on Cognition

The rapid integration of digital technology into daily life has prompted sustained concern regarding its impact on human cognition. To characterize documented negative effects and the conditions under which they arise, we conducted a scoping review isolating the documented negative effects of digital technology use on cognition. Using a hybrid automated and manual search strategy, we identified foundational seed papers via Scopus and executed an algorithmic citation snowballing process via the OpenAlex API to capture relevant empirical and non-empirical literature. The resulting synthesis of 937 papers (584 empirical, 353 non-empirical) spans legacy screens, multitasking, smartphones, and the nascent work on generative artificial intelligence (AI). Evidence suggests an evolution in the nature of cognitive risk: while research on earlier technologies predominantly describes disruptions to resource allocation, early findings on AI point toward a hypothesized erosion of higher-order cognition. We analyze these risks across cognitive domains through four mechanisms: functional interference, neurochemical dysregulation, structural neuroplasticity, and psychosocial displacement. Effects are frequently moderated by socioeconomic status and environmental factors, suggesting that cognitive decline is often mediated by the displacement of activities rather than direct technological toxicity. Finally, the paper examines how habitual digital offloading could theoretically deplete cognitive reserve, creating downstream risks for long-term health. The collective evidence suggests an efficiency-atrophy paradox, where digital tools optimize short-term performance at the potential expense of long-term unassisted cognition.

cs.CY↗

arXiv2023

Mobiprox: Supporting Dynamic Approximate Computing on Mobiles

Runtime-tunable context-dependent network compression would make mobile deep learning (DL) adaptable to often varying resource availability, input "difficulty", or user needs. The existing compression techniques significantly reduce the memory, processing, and energy tax of DL, yet, the resulting models tend to be permanently impaired, sacrificing the inference power for reduced resource usage. The existing tunable compression approaches, on the other hand, require expensive re-training, do not support arbitrary strategies for adapting the compression and do not provide mobile-ready implementations. In this paper we present Mobiprox, a framework enabling mobile DL with flexible precision. Mobiprox implements tunable approximations of tensor operations and enables runtime-adaptable approximation of individual network layers. A profiler and a tuner included with Mobiprox identify the most promising neural network approximation configurations leading to the desired inference quality with the minimal use of resources. Furthermore, we develop control strategies that depending on contextual factors, such as the input data difficulty, dynamically adjust the approximation levels across a mobile DL model's layers. We implement Mobiprox in Android OS and through experiments in diverse mobile domains, including human activity recognition and spoken keyword detection, demonstrate that it can save up to 15% system-wide energy with a minimal impact on the inference accuracy.

cs.LG↗

arXiv2022

Context-aware adaptation of mobile video decoding resolution

While the evolution of mobile computing is experiencing a considerable growth, it is at the same time seriously threatened by the limitations of the battery technology, which does not keep pace with the evergrowing increase in energy requirements of mobile applications. A novel approach for reducing the energy appetite of mobile apps comes from the approximate computing field, which proposes techniques that in a controlled manner sacrifice computation accuracy for higher energy savings. Building on this philosophy we propose a context-aware mobile video quality adaptation that reduces the energy needed for video playback, while ensuring that a user's quality expectations with respect to the mobile video are met. We confirm that the decoding resolution can play a significant role in reducing the overall power consumption of a mobile device and conduct two user studies to investigate how the context in which a video is played, its content, and the user's personality, modulate a user's quality expectations. We discover that a user's physical activity, the spatial/temporal properties of the video, and the user's personality traits interact and jointly influence the minimal acceptable playback resolution, paving the way for context-adaptable approximate mobile computing.

cs.MM↗