69 research outputs found
Occupancy estimation in smart buildings using audio-processing techniques
In the past few years, several case studies have illustrated that the use of occupancy information in buildings leads to energy-efficient and low-cost HVAC operation. The widely presented techniques for occupancy estimation include temperature, humidity, CO2 concentration, image camera, motion sensor and passive infrared (PIR) sensor. So far little studies have been reported in literature to utilize audio and speech processing as indoor occupancy prediction technique. With rapid advances of audio and speech processing technologies, nowadays it is more feasible and attractive to integrate audio-based signal processing component into smart buildings. In this work, we propose to utilize audio processing techniques (i.e., speaker recognition and background audio energy estimation) to estimate room occupancy (i.e., the number of people inside a room). Theoretical analysis and simulation results demonstrate the accuracy and effectiveness of this proposed occupancy estimation technique. Based on the occupancy estimation, smart buildings will adjust the thermostat setups and HVAC operations, thus, achieving greater quality of service and drastic cost savings
Refining Wi-Fi Based Indoor Localization with Li-Fi Assisted Model Calibration in Smart Buildings
In recent years, there has been an increasing number of information
technologies utilized in buildings to advance the idea of "smart buildings".
Among various potential techniques, the use of Wi-Fi based indoor positioning
allows to locate and track smartphone users inside a building, therefore,
location-aware intelligent solutions can be applied to control and of building
operations. These location-aware indoor services (e.g., path finding, internet
of things, location based advertising) demand real-time accurate indoor
localization, which is a key issue to guarantee high quality of service in
smart buildings. This paper presents a new Wi-Fi based indoor localization
technique that achieves significantly improvement of indoor positioning
accuracy with the help of Li-Fi assisted coefficient calibration. The proposed
technique leverages indoor existing Li-Fi lighting and Wi-Fi infrastructure,
and results in a cost-effective and user-convenient indoor accurate
localization framework. In this work, experimental study and measurements are
conducted to verify the performance of the proposed idea. The results
substantiate the concept of refining Wi-Fi based indoor localization with Li-Fi
assisted computation calibration.Comment: International Conference on Computing in Civil and Building
Engineering (ICCCBE) 201
Coded Computing for Half-Duplex Wireless Distributed Computing Systems via Interference Alignment
Distributed computing frameworks such as MapReduce and Spark are often used
to process large-scale data computing jobs. In wireless scenarios, exchanging
data among distributed nodes would seriously suffer from the communication
bottleneck due to limited communication resources such as bandwidth and power.
To address this problem, we propose a coded parallel computing (CPC) scheme for
distributed computing systems where distributed nodes exchange information over
a half-duplex wireless interference network. The CPC scheme achieves the
multicast gain by utilizing coded computing to multicast coded symbols
{intended to} multiple receiver nodes and the cooperative transmission gain by
allowing multiple {transmitter} nodes to jointly deliver messages via
interference alignment. To measure communication performance, we apply the
widely used latency-oriented metric: \emph{normalized delivery time (NDT)}. It
is shown that CPC can significantly reduce the NDT by jointly exploiting the
parallel transmission and coded multicasting opportunities. Surprisingly, when
tends to infinity and the computation load is fixed, CPC approaches zero
NDT while all state-of-the-art schemes achieve positive values of NDT. Finally,
we establish an information-theoretic lower bound for the NDT-computation load
trade-off over \emph{half-duplex} network, and prove our scheme achieves the
minimum NDT within a multiplicative gap of , i.e., our scheme is order
optimal.Comment: 17 pages, 6 figure
Coded Caching Scheme for Partially Connected Linear Networks Via Multi-antenna Placement Delivery Array
In this paper, we study the coded caching scheme for the
partially connected linear network, where
there are files each of which has an equal size, transmitters and
users; each user and transmitter caches at most and
files respectively; each user cyclically communicates with
transmitters. The goal is to design caching and delivery schemes to reduce the
transmission latency measured by the metric normalized delivery time (NDT). By
delicately designing the data placement of the transmitters and users according
to the topology, we show that a combinatorial structure called multiple-antenna
placement delivery array (MAPDA), which was originally proposed for the
multiple-input single-output broadcast channels, can be also used to design
schemes for the partially connected linear network. Then, based on existing
MAPDAs and our constructing approach, we propose new schemes that achieve the
optimal NDT when and smaller NDT than that
of the existing schemes when (,
) or (). Moreover, our schemes operate in one-shot
linear delivery and significantly reduce the subpacketizations compared to the
existing scheme, which implies that our schemes have a wider range of
applications and lower complexity of implementation.Comment: 13 page
Bayesian Robust Tensor Ring Model for Incomplete Multiway Data
Robust tensor completion (RTC) aims to recover a low-rank tensor from its
incomplete observation with outlier corruption. The recently proposed tensor
ring (TR) model has demonstrated superiority in solving the RTC problem.
However, the existing methods either require a pre-assigned TR rank or
aggressively pursue the minimum TR rank, thereby often leading to biased
solutions in the presence of noise. In this paper, a Bayesian robust tensor
ring decomposition (BRTR) method is proposed to give more accurate solutions to
the RTC problem, which can avoid exquisite selection of the TR rank and penalty
parameters. A variational Bayesian (VB) algorithm is developed to infer the
probability distribution of posteriors. During the learning process, BRTR can
prune off slices of core tensor with marginal components, resulting in
automatic TR rank detection. Extensive experiments show that BRTR can achieve
significantly improved performance than other state-of-the-art methods
Malicious Package Detection in NPM and PyPI using a Single Model of Malicious Behavior Sequence
Open-source software (OSS) supply chain enlarges the attack surface, which
makes package registries attractive targets for attacks. Recently, package
registries NPM and PyPI have been flooded with malicious packages. The
effectiveness of existing malicious NPM and PyPI package detection approaches
is hindered by two challenges. The first challenge is how to leverage the
knowledge of malicious packages from different ecosystems in a unified way such
that multi-lingual malicious package detection can be feasible. The second
challenge is how to model malicious behavior in a sequential way such that
maliciousness can be precisely captured. To address the two challenges, we
propose and implement Cerebro to detect malicious packages in NPM and PyPI. We
curate a feature set based on a high-level abstraction of malicious behavior to
enable multi-lingual knowledge fusing. We organize extracted features into a
behavior sequence to model sequential malicious behavior. We fine-tune the BERT
model to understand the semantics of malicious behavior. Extensive evaluation
has demonstrated the effectiveness of Cerebro over the state-of-the-art as well
as the practically acceptable efficiency. Cerebro has successfully detected 306
and 196 new malicious packages in PyPI and NPM, and received 385 thank letters
from the official PyPI and NPM teams
DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing
Graph Neural Networks (GNNs) have shown great power for learning and mining
on graphs, and Graph Structure Learning (GSL) plays an important role in
boosting GNNs with a refined graph. In the literature, most GSL solutions
either primarily focus on structure refinement with task-specific supervision
(i.e., node classification), or overlook the inherent weakness of GNNs
themselves (e.g., over-squashing), resulting in suboptimal performance despite
sophisticated designs. In light of these limitations, we propose to study
self-supervised graph structure-feature co-refinement for effectively
alleviating the issue of over-squashing in typical GNNs. In this paper, we take
a fundamentally different perspective of the Ricci curvature in Riemannian
geometry, in which we encounter the challenges of modeling, utilizing and
computing Ricci curvature. To tackle these challenges, we present a
self-supervised Riemannian model, DeepRicci. Specifically, we introduce a
latent Riemannian space of heterogeneous curvatures to model various Ricci
curvatures, and propose a gyrovector feature mapping to utilize Ricci curvature
for typical GNNs. Thereafter, we refine node features by geometric contrastive
learning among different geometric views, and simultaneously refine graph
structure by backward Ricci flow based on a novel formulation of differentiable
Ricci curvature. Finally, extensive experiments on public datasets show the
superiority of DeepRicci, and the connection between backward Ricci flow and
over-squashing. Codes of our work are given in https://github.com/RiemanGraph/.Comment: Accepted by IEEE ICDM 2023, Full paper, 10 page
Seasonal variations of C-1-C-4 alkyl nitrates at a coastal site in Hong Kong: Influence of photochemical formation and oceanic emissions
Five C-1-C-4 alkyl nitrates (RONO2) were measured at a coastal site in Hong Kong in four selected months of 2011 and 2012. The total mixing ratios of C-1-C-4 RONO2 (Sigma 5RONO2) ranged from 15.4 to 143.7 pptv with an average of 65.9 +/- 33.0 pptv. C-3-C-4 RONO2 (2-butyl nitrate and 2-propyl nitrate) were the most abundant RONO2 during the entire sampling period. The mixing ratios of C-3-C-4 RONO2 were higher in winter than those in summer, while the ones of methyl nitrate (MeONO2) were higher in summer than those in winter. Source analysis suggests that C-2-C-4 RONO2 were mainly derived from photochemical formation along with biomass burning (58.3-71.6%), while ocean was a major contributor to MeONO2 (53.8%) during the whole sampling period. The photochemical evolution of C-2-C-4 RONO2 was investigated, and found to be dominantly produced by the parent hydrocarbon oxidation. The notable enrichment of MeONO2 over C-3-C-4 RONO2 was observed in a summer episode when the air masses originating from the South China Sea (SCS) and MeONO2 was dominantly derived from oceanic emissions. In order to improve the accuracy of ozone (O-3) prediction in coastal environment, the relative contribution of RONO2 from oceanic emissions versus photochemical formation and their coupling effects on O-3 production should be taken into account in future studies. (C) 2017 Elsevier Ltd. All rights reserved
Genomic surveillance indicates clonal replacement of hypervirulent Klebsiella pneumoniae ST881 and ST29 lineage strains in vivo
The emergence of hypervirulent Klebsiella pneumoniae (hvKp) poses a significant public health threat, particularly regarding its carriage in the healthy population. However, the genomic epidemiological characteristics and population dynamics of hvKp within a single patient across distinct infection episodes remain largely unknown. This study aimed to investigate the clonal replacement of hvKp K2-ST881 and K54-ST29 lineage strains in a single patient experiencing multiple-site infections during two independent episodes. Two strains, designated EDhvKp-1 and EDhvKp-2, were obtained from blood and cerebrospinal fluid during the first admission, and the strain isolated from blood on the second admission was named EDhvKp-3. Whole-genome sequencing, utilizing both short-read Illumina and long-read Oxford Nanopore platforms, was conducted. In silico multilocus sequence typing (MLST), identification of antimicrobial resistance and virulence genes, and the phylogenetic relationship between our strains and other K. pneumoniae ST881 and ST29 genomes retrieved from the public database were performed. Virulence potentials were assessed through a mouse lethality assay. Our study indicated that the strains were highly susceptible to multiple antimicrobial agents. Plasmid sequence analysis confirmed that both virulence plasmids, pEDhvKp-1 (166,008 bp) and pEDhvKp-3 (210,948 bp), belonged to IncFIB type. Multiple virulence genes, including rmpA, rmpA2, rmpC, rmpD, iroBCDN, iucABCD, and iutA, were identified. EDhvKp-1 and EDhvKp-2 showed the closest relationship to strain 502 (differing by 51 SNPs), while EDhvKp-3 exhibited 69 SNPs differences compared to strain TAKPN-1, which all recovered from Chinese patients in 2020. In the mouse infection experiment, both ST881 EDhvKp-1 and ST29 EDhvKp-3 displayed similar virulence traits, causing 90 and 100% of the mice to die within 72 h after intraperitoneal infection, respectively. Our study expands the spectrum of hvKp lineages and highlights genomic alterations associated with clonal switching between two distinct lineages of hvKP that successively replaced each other in vivo. The development of novel strategies for the surveillance, diagnosis, and treatment of high-risk hvKp is urgently needed
- …