20,560 research outputs found
Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control
This paper provides an overview of the current state-of-the-art in selective
harvesting robots (SHRs) and their potential for addressing the challenges of
global food production. SHRs have the potential to increase productivity,
reduce labour costs, and minimise food waste by selectively harvesting only
ripe fruits and vegetables. The paper discusses the main components of SHRs,
including perception, grasping, cutting, motion planning, and control. It also
highlights the challenges in developing SHR technologies, particularly in the
areas of robot design, motion planning and control. The paper also discusses
the potential benefits of integrating AI and soft robots and data-driven
methods to enhance the performance and robustness of SHR systems. Finally, the
paper identifies several open research questions in the field and highlights
the need for further research and development efforts to advance SHR
technologies to meet the challenges of global food production. Overall, this
paper provides a starting point for researchers and practitioners interested in
developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic
Security and Privacy Problems in Voice Assistant Applications: A Survey
Voice assistant applications have become omniscient nowadays. Two models that
provide the two most important functions for real-life applications (i.e.,
Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR)
models and Speaker Identification (SI) models. According to recent studies,
security and privacy threats have also emerged with the rapid development of
the Internet of Things (IoT). The security issues researched include attack
techniques toward machine learning models and other hardware components widely
used in voice assistant applications. The privacy issues include technical-wise
information stealing and policy-wise privacy breaches. The voice assistant
application takes a steadily growing market share every year, but their privacy
and security issues never stopped causing huge economic losses and endangering
users' personal sensitive information. Thus, it is important to have a
comprehensive survey to outline the categorization of the current research
regarding the security and privacy problems of voice assistant applications.
This paper concludes and assesses five kinds of security attacks and three
types of privacy threats in the papers published in the top-tier conferences of
cyber security and voice domain.Comment: 5 figure
Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASR
Automatic speech recognition (ASR) has gained a remarkable success thanks to
recent advances of deep learning, but it usually degrades significantly under
real-world noisy conditions. Recent works introduce speech enhancement (SE) as
front-end to improve speech quality, which is proved effective but may not be
optimal for downstream ASR due to speech distortion problem. Based on that,
latest works combine SE and currently popular self-supervised learning (SSL) to
alleviate distortion and improve noise robustness. Despite the effectiveness,
the speech distortion caused by conventional SE still cannot be completely
eliminated. In this paper, we propose a self-supervised framework named
Wav2code to implement a generalized SE without distortions for noise-robust
ASR. First, in pre-training stage the clean speech representations from SSL
model are sent to lookup a discrete codebook via nearest-neighbor feature
matching, the resulted code sequence are then exploited to reconstruct the
original clean representations, in order to store them in codebook as prior.
Second, during finetuning we propose a Transformer-based code predictor to
accurately predict clean codes by modeling the global dependency of input noisy
representations, which enables discovery and restoration of high-quality clean
representations without distortions. Furthermore, we propose an interactive
feature fusion network to combine original noisy and the restored clean
representations to consider both fidelity and quality, resulting in even more
informative features for downstream ASR. Finally, experiments on both synthetic
and real noisy datasets demonstrate that Wav2code can solve the speech
distortion and improve ASR performance under various noisy conditions,
resulting in stronger robustness.Comment: 12 pages, 7 figures, Submitted to IEEE/ACM TASL
Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective
This paper introduces a comprehensive, multi-stage machine learning
methodology that effectively integrates information systems and artificial
intelligence to enhance decision-making processes within the domain of
operations research. The proposed framework adeptly addresses common
limitations of existing solutions, such as the neglect of data-driven
estimation for vital production parameters, exclusive generation of point
forecasts without considering model uncertainty, and lacking explanations
regarding the sources of such uncertainty. Our approach employs Quantile
Regression Forests for generating interval predictions, alongside both local
and global variants of SHapley Additive Explanations for the examined
predictive process monitoring problem. The practical applicability of the
proposed methodology is substantiated through a real-world production planning
case study, emphasizing the potential of prescriptive analytics in refining
decision-making procedures. This paper accentuates the imperative of addressing
these challenges to fully harness the extensive and rich data resources
accessible for well-informed decision-making
UniverSeg: Universal Medical Image Segmentation
While deep learning models have become the predominant method for medical
image segmentation, they are typically not capable of generalizing to unseen
segmentation tasks involving new anatomies, image modalities, or labels. Given
a new segmentation task, researchers generally have to train or fine-tune
models, which is time-consuming and poses a substantial barrier for clinical
researchers, who often lack the resources and expertise to train neural
networks. We present UniverSeg, a method for solving unseen medical
segmentation tasks without additional training. Given a query image and example
set of image-label pairs that define a new segmentation task, UniverSeg employs
a new Cross-Block mechanism to produce accurate segmentation maps without the
need for additional training. To achieve generalization to new tasks, we have
gathered and standardized a collection of 53 open-access medical segmentation
datasets with over 22,000 scans, which we refer to as MegaMedical. We used this
collection to train UniverSeg on a diverse set of anatomies and imaging
modalities. We demonstrate that UniverSeg substantially outperforms several
related methods on unseen tasks, and thoroughly analyze and draw insights about
important aspects of the proposed system. The UniverSeg source code and model
weights are freely available at https://universeg.csail.mit.eduComment: Victor and Jose Javier contributed equally to this work. Project
Website: https://universeg.csail.mit.ed
The impact of innovative technologies in construction activities on concrete debris recycling in China : a system dynamics-based analysis
As construction activities become more intensive in developing countries, increasing improperly managed construction and demolition waste (CDW) brings serious environmental impacts. Recycling is a beneficial way to dispose of CDW that reduces environmental impact and brings economic benefits, especially for concrete. China is the country that generates the most CDW in the world, but its domestic recycling rate is much lower than that of developed countries. While the efficient technologies in developed regions have helped them to achieve a well-established recycling industry, whether these innovative technologies can be used to improve the concrete debris recycling targets in developing regions is unclear. This study examines whether innovations currently widely used in construction activities and materials can have a positive effect on the recycling of End-of-Life concrete materials in China. Results from modeling system dynamics imply that the introduction of innovative technologies in the recycling system of concrete debris can probably contribute to CO2 reduction (3.6% reduction) and economic benefits (2.6 times increase, but mainly from landfill charges and fines) from 2022 to 2030. Prefabrication and 3D printing significantly impact recycled concrete production and CDW recycling, and they are recommended as a priority for promotion. In contrast, carbonation is not suggested for application due to its minor role. Nevertheless, since the market share of innovative technologies and the basic CDW recycling rates are currently low in China, fluctuations in their usage are hardly to have a substantial positive impact. We suggest that financial support from the government is needed for upcycling by recyclers and technology providers to improve the base recycling rate in order for innovative technologies to make an effective contribution to the sustainable construction industry, creating a winâwin situation for both the economy and the environment of the recycling system
Food biodiversity: Quantifying the unquantifiable in human diets
Dietary diversity is an established public health principle, and its measurement is essential for studies of diet quality and food security. However, conventional between food group scores fail to capture the nutritional variability and ecosystem services delivered by dietary richness and dissimilarity within food groups, or the relative distribution (i.e., evenness or moderation) of e.g., species or varieties across whole diets. Summarizing food biodiversity in an all-encompassing index is problematic. Therefore, various diversity indices have been proposed in ecology, yet these require methodological adaption for integration in dietary assessments. In this narrative review, we summarize the key conceptual issues underlying the measurement of food biodiversity at an edible species level, assess the ecological diversity indices previously applied to food consumption and food supply data, discuss their relative suitability, and potential amendments for use in (quantitative) dietary intake studies. Ecological diversity indices are often used without justification through the lens of nutrition. To illustrate: (i) dietary species richness fails to account for the distribution of foods across the diet or their functional traits; (ii) evenness indices, such as the Gini-Simpson index, require widely accepted relative abundance units (e.g., kcal, g, cups) and evidence-based moderation weighting factors; and (iii) functional dissimilarity indices are constructed based on an arbitrary selection of distance measures, cutoff criteria, and number of phylogenetic, nutritional, and morphological traits. Disregard for these limitations can lead to counterintuitive results and ambiguous or incorrect conclusions about the food biodiversity within diets or food systems. To ensure comparability and robustness of future research, we advocate food biodiversity indices that: (i) satisfy key axioms; (ii) can be extended to account for disparity between edible species; and (iii) are used in combination, rather than in isolation
Semantic Segmentation Enhanced Transformer Model for Human Attention Prediction
Saliency Prediction aims to predict the attention distribution of human eyes
given an RGB image. Most of the recent state-of-the-art methods are based on
deep image feature representations from traditional CNNs. However, the
traditional convolution could not capture the global features of the image well
due to its small kernel size. Besides, the high-level factors which closely
correlate to human visual perception, e.g., objects, color, light, etc., are
not considered. Inspired by these, we propose a Transformer-based method with
semantic segmentation as another learning objective. More global cues of the
image could be captured by Transformer. In addition, simultaneously learning
the object segmentation simulates the human visual perception, which we would
verify in our investigation of human gaze control in cognitive science. We
build an extra decoder for the subtask and the multiple tasks share the same
Transformer encoder, forcing it to learn from multiple feature spaces. We find
in practice simply adding the subtask might confuse the main task learning,
hence Multi-task Attention Module is proposed to deal with the feature
interaction between the multiple learning targets. Our method achieves
competitive performance compared to other state-of-the-art methods
GETT-QA: Graph Embedding based T2T Transformer for Knowledge Graph Question Answering
In this work, we present an end-to-end Knowledge Graph Question Answering
(KGQA) system named GETT-QA. GETT-QA uses T5, a popular text-to-text
pre-trained language model. The model takes a question in natural language as
input and produces a simpler form of the intended SPARQL query. In the simpler
form, the model does not directly produce entity and relation IDs. Instead, it
produces corresponding entity and relation labels. The labels are grounded to
KG entity and relation IDs in a subsequent step. To further improve the
results, we instruct the model to produce a truncated version of the KG
embedding for each entity. The truncated KG embedding enables a finer search
for disambiguation purposes. We find that T5 is able to learn the truncated KG
embeddings without any change of loss function, improving KGQA performance. As
a result, we report strong results for LC-QuAD 2.0 and SimpleQuestions-Wikidata
datasets on end-to-end KGQA over Wikidata.Comment: 16 pages single column format accepted at ESWC 2023 research trac
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
- âŠ