Examining Recognition of Occupants’ Cooking Activity Based on Sound Data Using Deep Learning Models

In today’s society, where people spend over 90% of their time indoors, indoor air quality (IAQ) is crucial for sustaining human life. However, as various indoor activities such as cooking generate diverse types of pollutants in indoor spaces, IAQ has emerged as a serious issue. Previous studies have employed methods such as CO2 sensors, smart floor systems, and video-based pattern recognition to distinguish occupants’ activities; however, each method has its limitations. This study delves into the classification of occupants’ cooking activities using sound recognition technology. Four deep learning-based sound recognition models capable of recognizing and classifying sounds generated during cooking were presented and analyzed. Experiments were carried out using sound data collected from real kitchen environments and online data-sharing websites. Additionally, changes in performance according to the amount of collected data were observed. Among the developed models, the most efficient is found to be the convolutional neural network, which is relatively unaffected by fluctuations in the amount of sound data and consistently delivers excellent performance. In contrast, other models exhibited a tendency for reduced performance as the amount of sound data decreased. Consequently, the results of this study offer insights into the classification of cooking activities based on sound data and underscore the research potential for sound-based occupant behavior recognition classification models

Enforcing Last-Level Cache Partitioning through Memory Virtual Channels

© 2019 IEEE.Ensuring fairness or providing isolation between multiple workloads with different characteristics that are colocated on a single, shared-memory system is a challenge. Recent multicore processors provide last-level cache (LLC) hardware partitioning to provide hardware support for isolation, with the cache partitioning often specified by the user. While more LLC capacity often results in higher performance, in this work we identify that a workload allocated more LLC capacity result in worse performance on real-machine experiments, which we refer to as MiW (more is worse). Through various controlled experiments, we identify that another workload with less LLC capacity causes more frequent LLC misses. The workload stresses the main-memory system shared by both workloads and degrades the performance of the former workload even if the LLC partitioning is used (a balloon effect). To resolve this problem, we propose virtualizing the datapath of main-memory controllers and dedicating the memory virtual channels (mVCs) to each group of applications, grouped for LLC partitioning. mVC can further fine-Tune the performance of groups by differentiating buffer sizes among mVCs. It can reduce the total system cost by executing latency-critical and throughput-oriented workloads together on shared machines, of which performance criteria can be achieved only on dedicated machines if mVCs are not supported. Experiments on a simulated chip multiprocessor show that our proposals effectively eliminate the MiW phenomenon, hence providing additional opportunities for workload consolidation in a datacenter. Our case study demonstrates potential savings of machine count by 21.8% with mVC, which would otherwise violate a service level objective (SLO).N

High5: Promoting interpersonal hand-to-hand touch for vibrant workplace with electrodermal sensor watches

Interpersonal touch is our most primitive social language strongly governing our emotional well-being. Despite the positive implications of touch in many facets of our daily social interactions, we find wide-spread caution and taboo limiting touch-based interactions in workplace relationships that constitute a significant part of our daily social life. In this paper, we explore new opportunities for ubicomp technology to promote a new meme of casual and cheerful interpersonal touch such as high-fives towards facilitating vibrant workplace culture. Specifically, we propose High5, a mobile service with a smartwatch-style system to promote high-fives in everyday workplace interactions. We first present initial user motivation from semi-structured interviews regarding the potentially controversial idea of High5. We then present our smartwatch-style prototype to detect high-fives based on sensing electric skin potential levels. We demonstrate its key technical observation and performance evaluation.

Lithium metal storage in zeolitic imidazolate framework derived nanoarchitectures

© 2020 Elsevier B.V. Due to the increasing demands for energy storage devices with higher energy density, lithium (Li) metal is considered to be the ultimate choice as an anode material because it has a high theoretical capacity (3860 mAh g−1) and the lowest reduction potential (−3.04 V versus standard hydrogen electrode) among all the alkali metals. Despite these advantages, repeated Li plating/stripping during cell operation leads to dendritic Li and the formation of irreversible Li (dead Li), leading to internal short-circuits and capacity fading. These fundamental problems cause safety issues and cell failure, so they must be resolved to commercialize Li-metal anode. Many in-depth studies are ongoing to solve these drawbacks through a variety of approaches, such as the formation of artificial solid-electrolyte interphase (SEI), inserting an interfacial layer between the electrolyte and electrode, demonstrating three-dimensional structured electrodes, and using stable host structures to store Li-metal. In this Review, we focus on using host materials to store Li-metal among various strategies, which may be regarded as an alternative method but is very feasible. Also, we propose porous carbon materials derived from zeolitic imidazolate frameworks (ZIFs) as the host materials due to their suitable properties for Li-metal storage. To advance progress towards practical application, the Li-metal storage capacity of porous materials is mathematically inferred, and further strategies are discussed for improving the storage capacity in this regard. Finally, we presented a perspective that paves the way for applying host materials to anodes of practical Li-metal battery

Hooked on Smartphones: An Exploratory Study on Smartphone Overuse among College Students

The negative aspects of smartphone overuse on young adults, such as sleep deprivation and attention deficits, are being increasingly recognized recently. This emerging issue motivated us to analyze the usage patterns related to smartphone overuse. We investigate smartphone usage for 95 college students using surveys, logged data, and interviews. We first divide the participants into risk and non-risk groups based on self-reported rating scale for smartphone overuse. We then analyze the usage data to identify between-group usage differences, which ranged from the overall usage patterns to appspecific usage patterns. Compared with the non-risk group, our results show that the risk group has longer usage time per day and different diurnal usage patterns. Also, the risk group users are more susceptible to push notifications, and tend to consume more online content. We characterize the overall relationship between usage features and smartphone overuse using analytic modeling and provide detailed illustrations of problematic usage behaviors based on interview data

Design of cobalt catalysed carbon nanotubes in bimetallic zeolitic imidazolate frameworks

Carbon nanotubes are the most effective way to enhance electrical conductivity between particles, although the growth mechanism in zeolitic imidazolate frameworks still remains elusive. According to our density functional theory calculations and experimental studies, the role of cobalt nanoparticles as catalysts was proved to induce graphitization during pyrolysis. In particular, the sizes and exposed facets of the Co particles play an important role in triggering a hierarchical carbon nanotubes. This work paves the way to control the growth of carbon nanotubes toward various applications

Structurally stabilized lithium-metal anode via surface chemistry engineering

Dendrite-free lithium (Li) has been the primary issue for the practical application of metallic Li anode. Repeated Li plating/stripping is known to inevitably lead to severe volume changes and gradual Li dendrite growth, eventually resulting in irreversible Li (called dead-Li) as an unexpected feature. In order to avoid the dead-Li, a lithiophilic surface is highly desirable and a nanoarchitectured host for metallic Li is also required. Herein, cobalt-embedded, mesoporous, nitrogen-doped graphite (N-doped graphite) is strategically proposed as a new innovative Li-metal storage host. After tuning the surface chemistry, the material shows high Li ion affinity as well as a highly lithiophilic surface, which is attributed to the low formation energy of N-doped graphite, strongly supported by density functional theory calculations. As a result, the desirable anode shows excellent electrochemical performance with high Li-metal reversible capacity and even stable long-term cyclability with no dead-Li formation. Our findings pave the way to optimize the Li-metal host up to the limit of the theoretical capacity.11Nsciescopu