413 research outputs found
Brain Music : Sistema generativo para la creación de música simbólica a partir de respuestas neuronales afectivas
gráficas, tablasEsta tesis de maestría presenta una metodología de aprendizaje profundo multimodal innovadora que fusiona un modelo de clasificación de emociones con un generador musical, con el propósito de crear música a partir de señales de electroencefalografía, profundizando así en la interconexión entre emociones y música. Los resultados alcanzan tres objetivos específicos:
Primero, ya que el rendimiento de los sistemas interfaz cerebro-computadora varía considerablemente entre diferentes sujetos, se introduce un enfoque basado en la transferencia de conocimiento entre sujetos para mejorar el rendimiento de individuos con dificultades en sistemas de interfaz cerebro-computadora basados en el paradigma de imaginación motora. Este enfoque combina datos de EEG etiquetados con datos estructurados, como cuestionarios psicológicos, mediante un método de "Kernel Matching CKA". Utilizamos una red neuronal profunda (Deep&Wide) para la clasificación de la imaginación motora. Los resultados destacan su potencial para mejorar las habilidades motoras en interfaces cerebro-computadora.
Segundo, proponemos una técnica innovadora llamada "Labeled Correlation Alignment"(LCA) para sonificar respuestas neurales a estímulos representados en datos no estructurados, como música afectiva. Esto genera características musicales basadas en la actividad cerebral inducida por las emociones. LCA aborda la variabilidad entre sujetos y dentro de sujetos mediante el análisis de correlación, lo que permite la creación de envolventes acústicos y la distinción entre diferente información sonora. Esto convierte a LCA en una herramienta prometedora para interpretar la actividad neuronal y su reacción a estímulos auditivos.
Finalmente, en otro capítulo, desarrollamos una metodología de aprendizaje profundo de extremo a extremo para generar contenido musical MIDI (datos simbólicos) a partir de señales de actividad cerebral inducidas por música con etiquetas afectivas. Esta metodología abarca el preprocesamiento de datos, el entrenamiento de modelos de extracción de características y un proceso de emparejamiento de características mediante Deep Centered Kernel Alignment, lo que permite la generación de música a partir de señales EEG.
En conjunto, estos logros representan avances significativos en la comprensión de la relación entre emociones y música, así como en la aplicación de la inteligencia artificial en la generación musical a partir de señales cerebrales. Ofrecen nuevas perspectivas y herramientas para la creación musical y la investigación en neurociencia emocional. Para llevar a cabo nuestros experimentos, utilizamos bases de datos públicas como GigaScience, Affective Music Listening y Deap Dataset (Texto tomado de la fuente)This master’s thesis presents an innovative multimodal deep learning methodology that combines an emotion classification model with a music generator, aimed at creating music from electroencephalography (EEG) signals, thus delving into the interplay between emotions and music. The results achieve three specific objectives:
First, since the performance of brain-computer interface systems varies significantly among different subjects, an approach based on knowledge transfer among subjects is introduced to enhance the performance of individuals facing challenges in motor imagery-based brain-computer interface systems. This approach combines labeled EEG data with structured information, such as psychological questionnaires, through a "Kernel Matching CKA"method. We employ a deep neural network (Deep&Wide) for motor imagery classification. The results underscore its potential to enhance motor skills in brain-computer interfaces.
Second, we propose an innovative technique called "Labeled Correlation Alignment"(LCA) to sonify neural responses to stimuli represented in unstructured data, such as affective music. This generates musical features based on emotion-induced brain activity. LCA addresses variability among subjects and within subjects through correlation analysis, enabling the creation of acoustic envelopes and the distinction of different sound information. This makes LCA a promising tool for interpreting neural activity and its response to auditory stimuli.
Finally, in another chapter, we develop an end-to-end deep learning methodology for generating MIDI music content (symbolic data) from EEG signals induced by affectively labeled music. This methodology encompasses data preprocessing, feature extraction model training, and a feature matching process using Deep Centered Kernel Alignment, enabling music generation from EEG signals.
Together, these achievements represent significant advances in understanding the relationship between emotions and music, as well as in the application of artificial intelligence in musical generation from brain signals. They offer new perspectives and tools for musical creation and research in emotional neuroscience. To conduct our experiments, we utilized public databases such as GigaScience, Affective Music Listening and Deap DatasetMaestríaMagíster en Ingeniería - Automatización IndustrialInvestigación en Aprendizaje Profundo y señales BiológicasEléctrica, Electrónica, Automatización Y Telecomunicaciones.Sede Manizale
How ECS Improve Creative Use of Employees’ Knowledge?
Recently, organizations are using crowdsourcing systems (CSs) to collect innovative ideas from their employees harnessing their insights of companies’ products, processes, customers, and competitors. While crowd workers in third-party CSs are a diverse and multifaceted population with a range of motives and experience, and yet few researchers have grappled with the facilitators of the employees’ behavior comprising the creative application of their knowledge using enterprise CSs. This study develops a theoretical framework to identify enterprise CSs role and to provide the way how CSs are related to creative behavior via knowledge sharing. In this research, we used a survey to collect data from organizational employees and conducted data analysis to understand how enterprise CSs affect employees’ creative knowledge application. The findings of this study can help organization refine their ECSs and innovative initiatives
State of The Art and Hot Aspects in Cloud Data Storage Security
Along with the evolution of cloud computing and cloud storage towards matu-
rity, researchers have analyzed an increasing range of cloud computing security
aspects, data security being an important topic in this area. In this paper, we
examine the state of the art in cloud storage security through an overview of
selected peer reviewed publications. We address the question of defining cloud
storage security and its different aspects, as well as enumerate the main vec-
tors of attack on cloud storage. The reviewed papers present techniques for key
management and controlled disclosure of encrypted data in cloud storage, while
novel ideas regarding secure operations on encrypted data and methods for pro-
tection of data in fully virtualized environments provide a glimpse of the toolbox
available for securing cloud storage. Finally, new challenges such as emergent
government regulation call for solutions to problems that did not receive enough
attention in earlier stages of cloud computing, such as for example geographical
location of data. The methods presented in the papers selected for this review
represent only a small fraction of the wide research effort within cloud storage
security. Nevertheless, they serve as an indication of the diversity of problems
that are being addressed
Posets with Interfaces for Concurrent Kleene Algebra
We introduce posets with interfaces (iposets) and generalise the serial
composition of posets to a new gluing composition of iposets. In partial order
semantics of concurrency, this amounts to designate events that continue their
execution across components. Alternatively, in terms of decomposing concurrent
systems, it allows cutting through some events, whereas serial composition may
cut through edges only.
We show that iposets under gluing composition form a category, extending the
monoid of posets under serial composition, and a 2-category when enriched with
a subsumption order and a suitable parallel composition as a lax tensor. This
generalises the interchange monoids used in concurrent Kleene algebra.
We also consider gp-iposets, which are generated from singletons by finitary
gluing and parallel compositions. We show that the class includes the
series-parallel posets as well as the interval orders, which are also well
studied in concurrency theory. Finally, we show that not all posets are
gp-iposets, exposing several posets that cannot occur as induced substructures
of gp-iposets
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RADICAL POLYMERIZATION: CHEMISTRIES, APPLICATIONS, DEVELOPMENTS, AND PERSPECTIVES
Radical polymerization is one of most versatile and easily implemented chain-growth polymerization methods for obtaining polymers, copolymers and polymer composites. As a synthetic process with over seventy years of investigation, it has enabled the production of materials that enriched the daily lives of humankind. The polymerization mechanism involves the fundamental steps of initiation, propagation, and termination events. This radical-based synthetic route provides many advantages, such as the reaction conditions are usually not as demanding as ionic and coordination-insertion polymerizations regarding the tolerance of water, chemical functionalities and impurities. This polymerization technique can be applied to a wide variety of monomers.
The major challenge during the early development of controlled radical polymerization resulted from the presence of radical combination, atom transfer and abstraction reactions, which bring difficulties in understanding polymerization kinetics and achieving well-defined polymer structures. Thereby, industrial and academic effort has been focusing on developing techniques that offered the prospect of control over radical polymerization. The seeds were laid for the major growth of controlled radical polymerization techniques in the 1990s. These approaches allow for the facile production of polymer architectures with complexities, from simple chains with narrow dispersity to di-block, tri-block and multi-block copolymers.
In Chapters 2 and 4 of this dissertation, radical addition fragmentation chain transfer (RAFT) polymerization was utilized to investigated well-defined polymer structures, enabling subsequent structure-property relationship investigations of polyelectrolyte solutions and multi-block copolymer membranes. In Chapter 3, nitroxide mediated polymerization (NMP) was performed to prepare polyisoprene that was successfully chain extended with chloromethyl styrene. The resulting diblock copolymer was quaternized for ionomer preparation. The analysis of their bulk as well as surface morphology was investigated.
Cyclic ketene acetals (CKA) can be polymerized through concomitant radical rearrangement and ring-opening mechanisms, to yield ester-based scission points on the resultant polymer backbone. An aliphatic and an aromatic CKAs were investigated in Chapter 5 to develop a fundamental understanding of CKA radical-mediated polymerization and charge transfer as a main competitive reaction.
Chapter 6 concludes on the areas of research and development that I believe will lead to further progress in the future
Comparative analysis of molecular fingerprints in prediction of drug combination effects
bbab291Application of machine and deep learning methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel computational solutions in relation to established techniques. To this end, we compare rule-based and data-driven molecular representations in prediction of drug combination sensitivity and drug synergy scores using standardized results of 14 high-throughput screening studies, comprising 64 200 unique combinations of 4153 molecules tested in 112 cancer cell lines. We evaluate the clustering performance of molecular representations and quantify their similarity by adapting the Centered Kernel Alignment metric. Our work demonstrates that to identify an optimal molecular representation type, it is necessary to supplement quantitative benchmark results with qualitative considerations, such as model interpretability and robustness, which may vary between and throughout preclinical drug development projects.Peer reviewe
Relational geometry modelling execution of structured programs
We discuss some twists around Concurrent Kleene Algebra (CKA). First, a new model of CKA represents a trace of a concurrent program as a diagram in a two-dimensional non-metric finite geometry, namely, program actions by points, objects and threads by vertical lines, transactions by horizontal lines, communications and resource sharing by sloping lines. While we had already sketched this earlier, we fully formalise it here in terms of the algebra of binary relations. Second, we present a new definition technique for partial operators, namely an assume/claim style akin to rely/guarantee program specification. This admits a general refinement order with Top and Bottom as well as proofs of the CKA laws. Finally, we give a short perspective on the geometric representation of some standard concurrent programming concepts
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