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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Audio-Visual Automatic Speech Recognition Towards Education for Disabilities
Education is a fundamental right that enriches everyoneâs life. However, physically challenged people often debar from the general and advanced education system. Audio-Visual Automatic Speech Recognition (AV-ASR) based system is useful to improve the education of physically challenged people by providing hands-free computing. They can communicate to the learning system through AV-ASR. However, it is challenging to trace the lip correctly for visual modality. Thus, this paper addresses the appearance-based visual feature along with the co-occurrence statistical measure for visual speech recognition. Local Binary Pattern-Three Orthogonal Planes (LBP-TOP) and Grey-Level Co-occurrence Matrix (GLCM) is proposed for visual speech information. The experimental results show that the proposed system achieves 76.60 % accuracy for visual speech and 96.00 % accuracy for audio speech recognition
Construction of radon chamber to expose active and passive detectors
In this research and development, we present the design and manufacture of a radon chamber
(PUCP radon chamber), a necessary tool for the calibration of passive detectors, verification
of the operation of active radon monitors as well as diffusion chamber calibration used in
radon measurements in air, and soils. The first chapter is an introduction to describe radon
gas and national levels of radon concentration given by many organizations. Parameters that
influence the calibration factor of the LR 115 type 2 film detector are studied, such as the
energy window, critical angle, and effective volumes. Those are strongly related to the etching
processes and counting of tracks all seen from a semi-empirical approach studied in the second
chapter. The third chapter presents a review of some radon chambers that have been reported
in the literature, based on their size and mode of operation as well as the radon source they use.
The design and construction of the radon chamber are presented, use of uranium ore (autunite)
as a chamber source is also discussed. In chapter fourth, radon chamber characterization
is presented through leakage lambda, homogeneity of radon concentration, regimes-operation
modes, and the saturation concentrations that can be reached. Procedures and methodology
used in this work are contained in the fifth chapter and also some uses and applications of the
PUCP radon chamber are presented; the calibration of cylindrical metallic diffusion chamber
based on CR-39 chips detectors taking into account overlapping effect; transmission factors of
gaps and pinhole for the same diffusion chambers are determined; permeability of glass fiber
filter for 222Rn is obtained after reach equilibrium through Ramachandran model and taking
into account a partition function as the rate of track density. The results of this research have
been published in indexed journals. Finally, the conclusion and recommendations that reflect
the fulfillment aims of this thesis are presented
Open Set Classification of GAN-based Image Manipulations via a ViT-based Hybrid Architecture
Classification of AI-manipulated content is receiving great attention, for
distinguishing different types of manipulations. Most of the methods developed
so far fail in the open-set scenario, that is when the algorithm used for the
manipulation is not represented by the training set. In this paper, we focus on
the classification of synthetic face generation and manipulation in open-set
scenarios, and propose a method for classification with a rejection option. The
proposed method combines the use of Vision Transformers (ViT) with a hybrid
approach for simultaneous classification and localization. Feature map
correlation is exploited by the ViT module, while a localization branch is
employed as an attention mechanism to force the model to learn per-class
discriminative features associated with the forgery when the manipulation is
performed locally in the image. Rejection is performed by considering several
strategies and analyzing the model output layers. The effectiveness of the
proposed method is assessed for the task of classification of facial attribute
editing and GAN attribution
Information-Theoretic GAN Compression with Variational Energy-based Model
We propose an information-theoretic knowledge distillation approach for the
compression of generative adversarial networks, which aims to maximize the
mutual information between teacher and student networks via a variational
optimization based on an energy-based model. Because the direct computation of
the mutual information in continuous domains is intractable, our approach
alternatively optimizes the student network by maximizing the variational lower
bound of the mutual information. To achieve a tight lower bound, we introduce
an energy-based model relying on a deep neural network to represent a flexible
variational distribution that deals with high-dimensional images and consider
spatial dependencies between pixels, effectively. Since the proposed method is
a generic optimization algorithm, it can be conveniently incorporated into
arbitrary generative adversarial networks and even dense prediction networks,
e.g., image enhancement models. We demonstrate that the proposed algorithm
achieves outstanding performance in model compression of generative adversarial
networks consistently when combined with several existing models.Comment: Accepted at Neurips202
On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective
ChatGPT is a recent chatbot service released by OpenAI and is receiving
increasing attention over the past few months. While evaluations of various
aspects of ChatGPT have been done, its robustness, i.e., the performance to
unexpected inputs, is still unclear to the public. Robustness is of particular
concern in responsible AI, especially for safety-critical applications. In this
paper, we conduct a thorough evaluation of the robustness of ChatGPT from the
adversarial and out-of-distribution (OOD) perspective. To do so, we employ the
AdvGLUE and ANLI benchmarks to assess adversarial robustness and the Flipkart
review and DDXPlus medical diagnosis datasets for OOD evaluation. We select
several popular foundation models as baselines. Results show that ChatGPT shows
consistent advantages on most adversarial and OOD classification and
translation tasks. However, the absolute performance is far from perfection,
which suggests that adversarial and OOD robustness remains a significant threat
to foundation models. Moreover, ChatGPT shows astounding performance in
understanding dialogue-related texts and we find that it tends to provide
informal suggestions for medical tasks instead of definitive answers. Finally,
we present in-depth discussions of possible research directions.Comment: Technical report; code is at:
https://github.com/microsoft/robustlear
LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretability
EEG-based recognition of activities and states involves the use of prior
neuroscience knowledge to generate quantitative EEG features, which may limit
BCI performance. Although neural network-based methods can effectively extract
features, they often encounter issues such as poor generalization across
datasets, high predicting volatility, and low model interpretability. Hence, we
propose a novel lightweight multi-dimensional attention network, called
LMDA-Net. By incorporating two novel attention modules designed specifically
for EEG signals, the channel attention module and the depth attention module,
LMDA-Net can effectively integrate features from multiple dimensions, resulting
in improved classification performance across various BCI tasks. LMDA-Net was
evaluated on four high-impact public datasets, including motor imagery (MI) and
P300-Speller paradigms, and was compared with other representative models. The
experimental results demonstrate that LMDA-Net outperforms other representative
methods in terms of classification accuracy and predicting volatility,
achieving the highest accuracy in all datasets within 300 training epochs.
Ablation experiments further confirm the effectiveness of the channel attention
module and the depth attention module. To facilitate an in-depth understanding
of the features extracted by LMDA-Net, we propose class-specific neural network
feature interpretability algorithms that are suitable for event-related
potentials (ERPs) and event-related desynchronization/synchronization
(ERD/ERS). By mapping the output of the specific layer of LMDA-Net to the time
or spatial domain through class activation maps, the resulting feature
visualizations can provide interpretable analysis and establish connections
with EEG time-spatial analysis in neuroscience. In summary, LMDA-Net shows
great potential as a general online decoding model for various EEG tasks.Comment: 20 pages, 7 Figure
Norsk rÄ kumelk, en kilde til zoonotiske patogener?
The worldwide emerging trend of eating ânaturalâ foods, that has not been
processed, also applies for beverages. According to Norwegian legislation, all
milk must be pasteurized before commercial sale but drinking milk that has
not been heat-treated, is gaining increasing popularity. Scientist are warning
against this trend and highlights the risk of contracting disease from milkborne
microorganisms. To examine potential risks associated with drinking
unpasteurized milk in Norway, milk- and environmental samples were
collected from dairy farms located in south-east of Norway. The samples
were analyzed for the presence of specific zoonotic pathogens; Listeria
monocytogenes, Campylobacter spp., and Shiga toxin-producing Escherichia
coli (STEC). Cattle are known to be healthy carriers of these pathogens, and
Campylobacter spp. and STEC have a low infectious dose, meaning that
infection can be established by ingesting a low number of bacterial cells. L.
monocytogenes causes one of the most severe foodborne zoonotic diseases,
listeriosis, that has a high fatality rate. All three pathogens have caused milk
borne disease outbreaks all over the world, also in Norway.
During this work, we observed that the prevalence of the three examined
bacteria were high in the environment at the examined farms. In addition, 7%
of the milk filters were contaminated by STEC, 13% by L. monocytogenes and
4% by Campylobacter spp. Four of the STEC isolates detected were eaepositive,
which is associated with the capability to cause severe human
disease. One of the eae-positive STEC isolates were collected from a milk
filter, which strongly indicate that Norwegian raw milk may contain potential
pathogenic STEC.
To further assess the possibilities of getting ill by STEC after consuming raw
milk, we examined the growth of the four eae-positive STEC isolates in raw milk at different temperatures. All four isolates seemed to have ability to multiply in raw milk at 8°C, and one isolate had significant growth after 72 hours. Incubation at 6°C seemed to reduce the number of bacteria during the
first 24 hours before cell death stopped. These findings highlight the
importance of stable refrigerator temperatures, preferable < 4°C, for storage
of raw milk.
The L. monocytogenes isolates collected during this study show genetic
similarities to isolates collected from urban and rural environmental
locations, but different clones were predominant in agricultural
environments compared to clinical and food environments. However, the
results indicate that the same clone can persist in a farm over time, and that
milk can be contaminated by L. monocytogenes clones present in farm
environment.
Despite testing small volumes (25 mL) of milk, we were able to isolate both
STEC and Campylobacter spp. directly from raw milk. A proportion of 3% of
the bulk tank milk and teat milk samples were contaminated by
Campylobacter spp. and one STEC was isolated from bulk tank milk. L
monocytogenes was not detected in bulk tank milk, nor in teat milk samples.
The agricultural evolvement during the past decades have led to larger
production units and new food safety challenges. Dairy cattle production in
Norway is in a current transition from tie-stall housing with conventional
pipeline milking systems, to modern loose housing systems with robotic
milking. The occurrence of the three pathogens in this project were higher in
samples collected from farms with loose housing compared to those with tiestall
housing.
Pasteurization of cowâs milk is a risk reducing procedure to protect
consumers from microbial pathogens and in most EU countries, commercial
distribution of unpasteurized milk is legally restricted. Together, the results
presented in this thesis show that the animal housing may influence the level
of pathogenic bacteria in the raw milk and that ingestion of Norwegian raw
cowâs milk may expose consumers to pathogenic bacteria which can cause
severe disease, especially in children, elderly and in persons with underlying
diseases. The results also highlight the importance of storing raw milk at low
temperatures between milking and consumption.Ă
spise mat som er mindre prosessert og mer «naturlig» er en pÄgÄende
trend i Norge og i andre deler av verden. Interessen for Ă„ drikke melk som
ikke er varmebehandlet, sÄkalt rÄ melk, er ogsÄ Þkende. I Norge er det pÄbudt
Ă„ pasteurisere melk fĂžr kommersielt salg for Ă„ beskytte forbrukeren mot
sykdomsfremkallende mikroorganismer. Fagfolk advarer mot Ä drikke rÄ
melk, og pÄpeker risikoen for Ä bli syk av patogene bakterier som kan finnes i
melken.
I denne avhandlingen undersĂžker vi den potensielle risikoen det medfĂžrer Ă„
drikke upasteurisert melk fra Norge. I tillegg til Ă„ samle inn tankmelk- og
speneprÞver fra melkegÄrder i sÞrÞst Norge, samlet vi ogsÄ miljÞprÞver fra
de samme gÄrdene for Ä kartlegge forekomst og for Ä identifisere potensielle
mattrygghetsrisikoer i melkeproduksjonen. Alle prĂžvene ble analysert for de
zoonotiske sykdomsfremkallende bakteriene Listeria monocytogenes,
Campylobacter spp., og Shiga toksin-produserende Escherichia coli (STEC).
Kyr kan vĂŠre friske smittebĂŠrere av disse bakteriene, som dermed kan
etablere et reservoar pÄ gÄrdene. Bakteriene kan overfÞres fra gÄrdsmiljÞet
til melkekjeden og dermed utfordre mattryggheten. Disse bakteriene har
forÄrsaket melkebÄrne sykdomsutbrudd over hele verden, ogsÄ i Norge.
Campylobacter spp. og STEC har lav infeksiĂžs dose, som vil si at man kan bli
syk selv om man bare inntar et lavt antall bakterieceller. L. monocytogenes
kan gi sykdommen listeriose, en av de mest alvorlige matbÄrne zoonotiske
sykdommene vi har i den vestlige verden.
Resultater fra denne oppgaven viser en hĂžy forekomst av de tre patogenene i
gÄrdsmiljÞet. I tillegg var 7% av melkefiltrene vi testet positive for STEC, 13%
positive for L. monocytogenes og 4% positive for Campylobacter spp.. Fire av
STEC isolatene bar genet for Intimin, eae, som er ansett som en viktig
virulensfaktor som Ăžker sjansen for alvorlig sykdom. Ett av de eae-positive
isolatene ble funnet i et melkefilter, noe som indikerer at norsk rÄ melk kan
inneholde patogene STEC. For Ă„ videre vurdere risikoen for Ă„ bli syk av STEC
fra rÄ melk undersÞkte vi hvordan de fire eae-positive isolatene vokste i rÄ
melk lagret ved forskjellige temperaturer. For alle isolatene Ăžkte antall
bakterier etter lagring ved 8°C, og for et isolat var veksten signifikant. Etter
lagring ved 6°C ble antallet bakterier redusert de fÞrste 24 timene, deretter
stoppet reduksjonen i antall bakterier. Disse resultatene viser hvor viktig det
er Ä ha stabil lav lagringstemperatur for rÄ melk, helst < 4°C.
L. monocytogenes isolatene som ble samlet inn fra melkegÄrdene viste
genetiske likheter med isolater samlet inn fra urbane og rurale miljĂžer rundt
omkring i Norge. Derimot var kloner som dominerte i landbruksmiljĂžet
forskjellige fra kliniske isolater og isolater fra matproduksjonslokaler. Videre
sÄ man at en klone kan persistere pÄ en gÄrd over tid og at melk kan
kontamineres av L. monocytogenes kloner som er til stede i gÄrdsmiljÞet.
Til tross for smÄ testvolum av tankmelken (25 mL) fant vi bÄde STEC og
Campylobacter spp. i melkeprĂžvene. 3% av tankmelkprĂžvene og
speneprĂžvene var positive for Campylobacter spp. og ett STEC isolat ble
funnet i tankmelk. L. monocytogenes ble ikke funnet direkte i melkeprĂžvene.
Landbruket i Norge er i stadig utvikling der besetningene blir stĂžrre, men
fĂŠrre. Melkebesetningene er midt i en overgang der tradisjonell oppstalling
med melking pÄ bÄs byttes ut med lÞsdriftssystemer og melkeroboter.
Forekomsten av de tre patogenene funnet i denne studien var hĂžyere i
besetningene med lĂžsdrift sammenliknet med besetningene som hadde
melkekyrne oppstallet pÄ bÄs.
Pasteurisering er et viktig forebyggende tiltak for Ă„ beskytte konsumenter fra
mikrobielle patogener, og i de fleste EU-land er kommersielt salg av rÄ melk
juridisk begrenset. Denne studien viser at oppstallingstype kan pÄvirke
nivÄene av patogene bakterier i gÄrdsmiljÞet og i rÄ melk. Inntak av rÄ melk
kan eksponere forbruker for patogene bakterier som kan gi alvorlig sykdom,
spesielt hos barn, eldre og personer med underliggende sykdommer.
Resultatene underbygger viktigheten av Ă„ pasteurisere melk for Ă„ sikre
mattryggheten, og at det er avgjÞrende Ä lagre rÄ melk ved kontinuerlig lave
temperaturer for Ă„ forebygge vekst av zoonotiske patogener
Model Diagnostics meets Forecast Evaluation: Goodness-of-Fit, Calibration, and Related Topics
Principled forecast evaluation and model diagnostics are vital in fitting probabilistic models and forecasting outcomes of interest. A common principle is that fitted or predicted distributions ought to be calibrated, ideally in the sense that the outcome is indistinguishable from a random draw from the posited distribution. Much of this thesis is centered on calibration properties of various types of forecasts.
In the first part of the thesis, a simple algorithm for exact multinomial goodness-of-fit tests is proposed. The algorithm computes exact -values based on various test statistics, such as the log-likelihood ratio and Pearson\u27s chi-square. A thorough analysis shows improvement on extant methods. However, the runtime of the algorithm grows exponentially in the number of categories and hence its use is limited.
In the second part, a framework rooted in probability theory is developed, which gives rise to hierarchies of calibration, and applies to both predictive distributions and stand-alone point forecasts. Based on a general notion of conditional T-calibration, the thesis introduces population versions of T-reliability diagrams and revisits a score decomposition into measures of miscalibration, discrimination, and uncertainty. Stable and efficient estimators of T-reliability diagrams and score components arise via nonparametric isotonic regression and the pool-adjacent-violators algorithm. For in-sample model diagnostics, a universal coefficient of determination is introduced that nests and reinterprets the classical in least squares regression.
In the third part, probabilistic top lists are proposed as a novel type of prediction in classification, which bridges the gap between single-class predictions and predictive distributions. The probabilistic top list functional is elicited by strictly consistent evaluation metrics, based on symmetric proper scoring rules, which admit comparison of various types of predictions
Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review
Globally, the external Internet is increasingly being connected to the
contemporary industrial control system. As a result, there is an immediate need
to protect the network from several threats. The key infrastructure of
industrial activity may be protected from harm by using an intrusion detection
system (IDS), a preventive measure mechanism, to recognize new kinds of
dangerous threats and hostile activities. The most recent artificial
intelligence (AI) techniques used to create IDS in many kinds of industrial
control networks are examined in this study, with a particular emphasis on
IDS-based deep transfer learning (DTL). This latter can be seen as a type of
information fusion that merge, and/or adapt knowledge from multiple domains to
enhance the performance of the target task, particularly when the labeled data
in the target domain is scarce. Publications issued after 2015 were taken into
account. These selected publications were divided into three categories:
DTL-only and IDS-only are involved in the introduction and background, and
DTL-based IDS papers are involved in the core papers of this review.
Researchers will be able to have a better grasp of the current state of DTL
approaches used in IDS in many different types of networks by reading this
review paper. Other useful information, such as the datasets used, the sort of
DTL employed, the pre-trained network, IDS techniques, the evaluation metrics
including accuracy/F-score and false alarm rate (FAR), and the improvement
gained, were also covered. The algorithms, and methods used in several studies,
or illustrate deeply and clearly the principle in any DTL-based IDS subcategory
are presented to the reader
- âŠ