7,322 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
Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite Rules
We target the problem of automatically synthesizing proofs of semantic
equivalence between two programs made of sequences of statements. We represent
programs using abstract syntax trees (AST), where a given set of
semantics-preserving rewrite rules can be applied on a specific AST pattern to
generate a transformed and semantically equivalent program. In our system, two
programs are equivalent if there exists a sequence of application of these
rewrite rules that leads to rewriting one program into the other. We propose a
neural network architecture based on a transformer model to generate proofs of
equivalence between program pairs. The system outputs a sequence of rewrites,
and the validity of the sequence is simply checked by verifying it can be
applied. If no valid sequence is produced by the neural network, the system
reports the programs as non-equivalent, ensuring by design no programs may be
incorrectly reported as equivalent. Our system is fully implemented for a given
grammar which can represent straight-line programs with function calls and
multiple types. To efficiently train the system to generate such sequences, we
develop an original incremental training technique, named self-supervised
sample selection. We extensively study the effectiveness of this novel training
approach on proofs of increasing complexity and length. Our system, S4Eq,
achieves 97% proof success on a curated dataset of 10,000 pairs of equivalent
programsComment: 30 pages including appendi
Examples of works to practice staccato technique in clarinet instrument
Klarnetin staccato tekniğini güçlendirme aşamaları eser çalışmalarıyla uygulanmıştır. Staccato
geçişlerini hızlandıracak ritim ve nüans çalışmalarına yer verilmiştir. Çalışmanın en önemli amacı
sadece staccato çalışması değil parmak-dilin eş zamanlı uyumunun hassasiyeti üzerinde de
durulmasıdır. Staccato çalışmalarını daha verimli hale getirmek için eser çalışmasının içinde etüt
çalışmasına da yer verilmiştir. Çalışmaların üzerinde titizlikle durulması staccato çalışmasının ilham
verici etkisi ile müzikal kimliğe yeni bir boyut kazandırmıştır. Sekiz özgün eser çalışmasının her
aşaması anlatılmıştır. Her aşamanın bir sonraki performans ve tekniği güçlendirmesi esas alınmıştır.
Bu çalışmada staccato tekniğinin hangi alanlarda kullanıldığı, nasıl sonuçlar elde edildiği bilgisine
yer verilmiştir. Notaların parmak ve dil uyumu ile nasıl şekilleneceği ve nasıl bir çalışma disiplini
içinde gerçekleşeceği planlanmıştır. Kamış-nota-diyafram-parmak-dil-nüans ve disiplin
kavramlarının staccato tekniğinde ayrılmaz bir bütün olduğu saptanmıştır. Araştırmada literatür
taraması yapılarak staccato ile ilgili çalışmalar taranmıştır. Tarama sonucunda klarnet tekniğin de
kullanılan staccato eser çalışmasının az olduğu tespit edilmiştir. Metot taramasında da etüt
çalışmasının daha çok olduğu saptanmıştır. Böylelikle klarnetin staccato tekniğini hızlandırma ve
güçlendirme çalışmaları sunulmuştur. Staccato etüt çalışmaları yapılırken, araya eser çalışmasının
girmesi beyni rahatlattığı ve istekliliği daha arttırdığı gözlemlenmiştir. Staccato çalışmasını yaparken
doğru bir kamış seçimi üzerinde de durulmuştur. Staccato tekniğini doğru çalışmak için doğru bir
kamışın dil hızını arttırdığı saptanmıştır. Doğru bir kamış seçimi kamıştan rahat ses çıkmasına
bağlıdır. Kamış, dil atma gücünü vermiyorsa daha doğru bir kamış seçiminin yapılması gerekliliği
vurgulanmıştır. Staccato çalışmalarında baştan sona bir eseri yorumlamak zor olabilir. Bu açıdan
çalışma, verilen müzikal nüanslara uymanın, dil atış performansını rahatlattığını ortaya koymuştur.
Gelecek nesillere edinilen bilgi ve birikimlerin aktarılması ve geliştirici olması teşvik edilmiştir.
Çıkacak eserlerin nasıl çözüleceği, staccato tekniğinin nasıl üstesinden gelinebileceği anlatılmıştır.
Staccato tekniğinin daha kısa sürede çözüme kavuşturulması amaç edinilmiştir. Parmakların
yerlerini öğrettiğimiz kadar belleğimize de çalışmaların kaydedilmesi önemlidir. Gösterilen azmin ve
sabrın sonucu olarak ortaya çıkan yapıt başarıyı daha da yukarı seviyelere çıkaracaktır
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
Machine learning for managing structured and semi-structured data
As the digitalization of private, commercial, and public sectors advances rapidly, an increasing amount of data is becoming available. In order to gain insights or knowledge from these enormous amounts of raw data, a deep analysis is essential. The immense volume requires highly automated processes with minimal manual interaction. In recent years, machine learning methods have taken on a central role in this task. In addition to the individual data points, their interrelationships often play a decisive role, e.g. whether two patients are related to each other or whether they are treated by the same physician. Hence, relational learning is an important branch of research, which studies how to harness this explicitly available structural information between different data points. Recently, graph neural networks have gained importance. These can be considered an extension of convolutional neural networks from regular grids to general (irregular) graphs.
Knowledge graphs play an essential role in representing facts about entities in a machine-readable way. While great efforts are made to store as many facts as possible in these graphs, they often remain incomplete, i.e., true facts are missing. Manual verification and expansion of the graphs is becoming increasingly difficult due to the large volume of data and must therefore be assisted or substituted by automated procedures which predict missing facts. The field of knowledge graph completion can be roughly divided into two categories: Link Prediction and Entity Alignment. In Link Prediction, machine learning models are trained to predict unknown facts between entities based on the known facts. Entity Alignment aims at identifying shared entities between graphs in order to link several such knowledge graphs based on some provided seed alignment pairs.
In this thesis, we present important advances in the field of knowledge graph completion. For Entity Alignment, we show how to reduce the number of required seed alignments while maintaining performance by novel active learning techniques. We also discuss the power of textual features and show that graph-neural-network-based methods have difficulties with noisy alignment data. For Link Prediction, we demonstrate how to improve the prediction for unknown entities at training time by exploiting additional metadata on individual statements, often available in modern graphs. Supported with results from a large-scale experimental study, we present an analysis of the effect of individual components of machine learning models, e.g., the interaction function or loss criterion, on the task of link prediction. We also introduce a software library that simplifies the implementation and study of such components and makes them accessible to a wide research community, ranging from relational learning researchers to applied fields, such as life sciences. Finally, we propose a novel metric for evaluating ranking results, as used for both completion tasks. It allows for easier interpretation and comparison, especially in cases with different numbers of ranking candidates, as encountered in the de-facto standard evaluation protocols for both tasks.Mit der rasant fortschreitenden Digitalisierung des privaten, kommerziellen und öffentlichen Sektors werden immer größere Datenmengen verfügbar. Um aus diesen enormen Mengen an Rohdaten Erkenntnisse oder Wissen zu gewinnen, ist eine tiefgehende Analyse unerlässlich. Das immense Volumen erfordert hochautomatisierte Prozesse mit minimaler manueller Interaktion. In den letzten Jahren haben Methoden des maschinellen Lernens eine zentrale Rolle bei dieser Aufgabe eingenommen. Neben den einzelnen Datenpunkten spielen oft auch deren Zusammenhänge eine entscheidende Rolle, z.B. ob zwei Patienten miteinander verwandt sind oder ob sie vom selben Arzt behandelt werden. Daher ist das relationale Lernen ein wichtiger Forschungszweig, der untersucht, wie diese explizit verfügbaren strukturellen Informationen zwischen verschiedenen Datenpunkten nutzbar gemacht werden können. In letzter Zeit haben Graph Neural Networks an Bedeutung gewonnen. Diese können als eine Erweiterung von CNNs von regelmäßigen Gittern auf allgemeine (unregelmäßige) Graphen betrachtet werden.
Wissensgraphen spielen eine wesentliche Rolle bei der Darstellung von Fakten über Entitäten in maschinenlesbaren Form. Obwohl große Anstrengungen unternommen werden, so viele Fakten wie möglich in diesen Graphen zu speichern, bleiben sie oft unvollständig, d. h. es fehlen Fakten. Die manuelle Überprüfung und Erweiterung der Graphen wird aufgrund der großen Datenmengen immer schwieriger und muss daher durch automatisierte Verfahren unterstützt oder ersetzt werden, die fehlende Fakten vorhersagen. Das Gebiet der Wissensgraphenvervollständigung lässt sich grob in zwei Kategorien einteilen: Link Prediction und Entity Alignment. Bei der Link Prediction werden maschinelle Lernmodelle trainiert, um unbekannte Fakten zwischen Entitäten auf der Grundlage der bekannten Fakten vorherzusagen. Entity Alignment zielt darauf ab, gemeinsame Entitäten zwischen Graphen zu identifizieren, um mehrere solcher Wissensgraphen auf der Grundlage einiger vorgegebener Paare zu verknüpfen.
In dieser Arbeit stellen wir wichtige Fortschritte auf dem Gebiet der Vervollständigung von Wissensgraphen vor. Für das Entity Alignment zeigen wir, wie die Anzahl der benötigten Paare reduziert werden kann, während die Leistung durch neuartige aktive Lerntechniken erhalten bleibt. Wir erörtern auch die Leistungsfähigkeit von Textmerkmalen und zeigen, dass auf Graph-Neural-Networks basierende Methoden Schwierigkeiten mit verrauschten Paar-Daten haben. Für die Link Prediction demonstrieren wir, wie die Vorhersage für unbekannte Entitäten zur Trainingszeit verbessert werden kann, indem zusätzliche Metadaten zu einzelnen Aussagen genutzt werden, die oft in modernen Graphen verfügbar sind. Gestützt auf Ergebnisse einer groß angelegten experimentellen Studie präsentieren wir eine Analyse der Auswirkungen einzelner Komponenten von Modellen des maschinellen Lernens, z. B. der Interaktionsfunktion oder des Verlustkriteriums, auf die Aufgabe der Link Prediction. Außerdem stellen wir eine Softwarebibliothek vor, die die Implementierung und Untersuchung solcher Komponenten vereinfacht und sie einer breiten Forschungsgemeinschaft zugänglich macht, die von Forschern im Bereich des relationalen Lernens bis hin zu angewandten Bereichen wie den Biowissenschaften reicht. Schließlich schlagen wir eine neuartige Metrik für die Bewertung von Ranking-Ergebnissen vor, wie sie für beide Aufgaben verwendet wird. Sie ermöglicht eine einfachere Interpretation und einen leichteren Vergleich, insbesondere in Fällen mit einer unterschiedlichen Anzahl von Kandidaten, wie sie in den de-facto Standardbewertungsprotokollen für beide Aufgaben vorkommen
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Brain signal recognition using deep learning
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityBrain Computer Interface (BCI) has the potential to offer a new generation of applications independent of
muscular activity and controlled by the human brain. Brain imaging technologies are used to transfer the
cognitive tasks into control commands for a BCI system. The electroencephalography (EEG) technology
serves as the best available non-invasive solution for extracting signals from the brain. On the other hand,
speech is the primary means of communication, but for patients suffering from locked-in syndrome, there
is no easy way to communicate. Therefore, an ideal communication system for locked-in patients is a
thought-to-speech BCI system.
This research aims to investigate methods for the recognition of imagined speech from EEG signals
using deep learning techniques. In order to design an optimal imagined speech recognition BCI, variety
of issues have been solved. These include 1) proposing new feature extraction and classification
framework for recognition of imagined speech from EEG signals, 2) grammatical class recognition of
imagined words from EEG signals, 3) discriminating different cognitive tasks associated with speech in
the brain such as overt speech, covert speech, and visual imagery. In this work machine learning, deep
learning methods were used to analyze EEG signals.
For recognition of imagined speech from EEG signals, a new EEG database was collected while the
participants mentally spoke (imagined speech) the presented words. Along with imagined speech, EEG
data was recorded for visual imagery (imagining a scene or an image) and overt speech (verbal speech).
Spectro-temporal and spatio-temporal domain features were investigated for the classification of imagined
words from EEG signals. Further, a deep learning framework using the convolutional network
and attention mechanism was implemented for learning features in the spatial, temporal, and spectral
domains. The method achieved a recognition rate of 76.6% for three binary word pairs. These experiments
show that deep learning algorithms are ideal for imagined speech recognition from EEG signals
due to their ability to interpret features from non-linear and non-stationary signals. Grammatical classes
of imagined words from EEG signals were also recognized using a multi-channel convolution network
framework. This method was extended to a multi-level recognition system for multi-class classification
of imagined words which achieved an accuracy of 52.9% for 10 words, which is much better in
comparison to previous work.
In order to investigate the difference between imagined speech with verbal speech and visual imagery
from EEG signals, we used multivariate pattern analysis (MVPA). MVPA provided the time segments
when the neural oscillation for the different cognitive tasks was linearly separable. Further, frequencies
that result in most discrimination between the different cognitive tasks were also explored. A framework
was proposed to discriminate two cognitive tasks based on the spatio-temporal patterns in EEG signals.
The proposed method used the K-means clustering algorithm to find the best electrode combination and
convolutional-attention network for feature extraction and classification. The proposed method achieved
a high recognition rate of 82.9% and 77.7%.
The results in this research suggest that a communication based BCI system can be designed using
deep learning methods. Further, this work add knowledge to the existing work in the field of communication
based BCI system
AIUCD 2022 - Proceedings
L’undicesima edizione del Convegno Nazionale dell’AIUCD-Associazione di Informatica Umanistica ha per titolo Culture digitali. Intersezioni: filosofia, arti, media. Nel titolo è presente, in maniera esplicita, la richiesta di una riflessione, metodologica e teorica, sull’interrelazione tra tecnologie digitali, scienze dell’informazione, discipline filosofiche, mondo delle arti e cultural studies
SYSTEMS METHODS FOR ANALYSIS OF HETEROGENEOUS GLIOBLASTOMA DATASETS TOWARDS ELUCIDATION OF INTER-TUMOURAL RESISTANCE PATHWAYS AND NEW THERAPEUTIC TARGETS
In this PhD thesis is described an endeavour to compile litterature about Glioblastoma key molecular mechanisms into a directed network followin Disease Maps standards, analyse its topology and compare results with quantitative analysis of multi-omics datasets in order to investigate Glioblastoma resistance mechanisms. The work also integrated implementation of Data Management good practices and procedures
Industry 4.0: product digital twins for remanufacturing decision-making
Currently there is a desire to reduce natural resource consumption and expand circular business principles whilst Industry 4.0 (I4.0) is regarded as the evolutionary and potentially disruptive movement of technology, automation, digitalisation, and data manipulation into the industrial sector. The remanufacturing industry is recognised as being vital to the circular economy (CE) as it extends the in-use life of products, but its synergy with I4.0 has had little attention thus far. This thesis documents the first investigating into I4.0 in remanufacturing for a CE contributing a design and demonstration of a model that optimises remanufacturing planning using data from different instances in a product’s life cycle.
The initial aim of this work was to identify the I4.0 technology that would enhance the stability in remanufacturing with a view to reducing resource consumption. As the project progressed it narrowed to focus on the development of a product digital twin (DT) model to support data-driven decision making for operations planning. The model’s architecture was derived using a bottom-up approach where requirements were extracted from the identified complications in production planning and control that differentiate remanufacturing from manufacturing. Simultaneously, the benefits of enabling visibility of an asset’s through-life health were obtained using a DT as the modus operandi. A product simulator and DT prototype was designed to use Internet of Things (IoT) components, a neural network for remaining life estimations and a search algorithm for operational planning optimisation. The DT was iteratively developed using case studies to validate and examine the real opportunities that exist in deploying a business model that harnesses, and commodifies, early life product data for end-of-life processing optimisation. Findings suggest that using intelligent programming networks and algorithms, a DT can enhance decision-making if it has visibility of the product and access to reliable remanufacturing process information, whilst existing IoT components provide rudimentary “smart” capabilities, but their integration is complex, and the durability of the systems over extended product life cycles needs to be further explored
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