An architecture for network acceleration as a service in the cloud continuum

The pervasive availability of connected devices in any industrial and societal sector is pushing for an evolution of the well-established cloud computing model. The emerging paradigm of the cloud continuum embraces this decentralization trend and envisions virtualized computing resources physically located between traditional datacenters and data sources. By totally or partially executing closer to the network edge, applications can have quicker reactions to events, thus enabling advanced forms of automation and intelligence. However, these applications also induce new data-intensive workloads with low-latency constraints that require the adoption of specialized resources, such as high-performance communication options (e.g., RDMA, DPDK, XDP, etc.). Unfortunately, cloud providers still struggle to integrate these options into their infrastructures. That risks undermining the principle of generality that underlies the cloud computing scale economy by forcing developers to tailor their code to low-level APIs, non-standard programming models, and static execution environments. This thesis proposes a novel system architecture to empower cloud platforms across the whole cloud continuum with Network Acceleration as a Service (NAaaS). To provide commodity yet efficient access to acceleration, this architecture defines a layer of agnostic high-performance I/O APIs, exposed to applications and clearly separated from the heterogeneous protocols, interfaces, and hardware devices that implement it. A novel system component embodies this decoupling by offering a set of agnostic OS features to applications: memory management for zero-copy transfers, asynchronous I/O processing, and efficient packet scheduling. This thesis also explores the design space of the possible implementations of this architecture by proposing two reference middleware systems and by adopting them to support interactive use cases in the cloud continuum: a serverless platform and an Industry 4.0 scenario. A detailed discussion and a thorough performance evaluation demonstrate that the proposed architecture is suitable to enable the easy-to-use, flexible integration of modern network acceleration into next-generation cloud platforms.

Extinction of affective learning: No evidence for dual process accounts of human learning

The acquisition and extinction of affective valence to neutral geometrical shape conditional stimuli was investigated in three experiments. Experiment 1 employed a differential conditioning procedure with aversive shock USs. Differential electrodermal responding was evident during acquisition and lost during extinction. As indexed by verbal ratings, the CS1 acquired negative valence during acquisition,which was reduced after extinction. Affective priming, a reaction time based demand free measure of stimulus valence, failed to provide evidence for affective learning. Experiment 2 employed pictures of happy and angry faces as USs.Valence ratings after acquisitionweremore positive for theCS paired with happy faces (CS-H) and less positive for the CS paired with angry faces (CS-A) than during baseline. Extinction training reduced the extent of acquired valence significantly for both CSs, however, ratings of the CS-A remained different from baseline. Affective priming confirmed these results yielding differences between CS-A and CS-H after acquisition for pleasant and unpleasant targets, but for pleasant targets only after extinction. Experiment 3 replicated the design of Experiment 2, but presented the US pictures backwardly masked. Neither rating nor affective priming measures yielded any evidence for affective learning. The present results confirm across two different experimental procedures that, contrary to predictions from dual process accounts of human learning, affective learning is subject to extinction.

IEEE 802.15.4: a Federating Communication Protocol for Time-Sensitive Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have been attracting increasing interests for developing a new generation of embedded systems with great potential for many applications such as surveillance, environment monitoring, emergency medical response and home automation. However, the communication paradigms in WSNs differ from the ones attributed to traditional wireless networks, triggering the need for new communication protocols. In this context, the recently standardised IEEE 802.15.4 protocol presents some potentially interesting features for deployment in wireless sensor network applications, such as power-efficiency, timeliness guarantees and scalability. Nevertheless, when addressing WSN applications with (soft/hard) timing requirements some inherent paradoxes emerge, such as power-efficiency versus timeliness, triggering the need of engineering solutions for an efficient deployment of IEEE 802.15.4 in WSNs. In this technical report, we will explore the most relevant characteristics of the IEEE 802.15.4 protocol for wireless sensor networks and present the most important challenges regarding time-sensitive WSN applications. We also provide some timing performance and analysis of the IEEE 802.15.4 that unveil some directions for resolving the previously mentioned paradoxes.

Desenvolvimento do currículo do 4º ano de escolaridade: análise da utilização de uma plataforma de e-learning em contexto escolar

Partindo de um sentido de inovação pedagógica assente numa concepção de clara ruptura com abordagens pedagógicas tradicionalistas ligadas a uma certa perspectiva transmissiva/reprodutiva do saber, o presente trabalho de investigação analisa a utilização, em contexto curricular escolar, de uma plataforma de e-learning, considerando um enquadramento referencial de natureza construtivista, no qual, o aluno possa assumir um papel central e activo, na criação do seu próprio conhecimento. Consistindo numa abordagem de natureza qualitativa, o estudo de caso apresentado releva um paradigma interpretativo decorrente da análise da utilização, pelos alunos, de uma turma do 4º Ano de escolaridade, da plataforma de e-learning ―Escola Virtual™‖. Para tal intento, a diversidade de técnicas de recolha de dados — observação participante, entrevistas , e análise documental — ao permitirem uma triangulação dos mesmos, fundamentam um aprofundado sentido de comprensão do fenómeno analisado. Deste modo, a investigação centra-se em 3 componentes referenciais: Componente Pedagógica, Componente de Conteúdos e Componente de Interface. Os resultados obtidos revelam que, no âmbito da primeira componente, surgem especialmente facilitados os processos colaborativos entre os alunos, embora num contexto de interacção presencial e com carácter essencialmente pontual; a actuação docente, mantendo o seu carácter imprescindível, adquire uma dimensão de maior proximidade e orientação dos alunos enquanto, por seu turno, o nível motivacional dos alunos é estimulado, focalizando o aluno nas tarefas de aprendizagem. A natureza do feedback presente, no entanto, revelou-se um factor com efeitos prejudiciais para os processos de reflexão e metacognição dos alunos. No respeitante aos conteúdos, foi evidente alguma ligeireza e fragilidade de concepção em muitos deles, comprovada por uma quantidade expressiva de erros e lapsos de diversa natureza; enquanto que a natureza pré-formatada dos conteúdos — estruturando-os em ―Objectos de Aprendizagem‖ — limitou significativamente a possibilidade de desenvolvimento de procressos activos e criativos por parte do aluno, bem como, anulando a capacidade de abordagem de competências de natureza prática previstas no Programa Nacional do Ensino Básico. Por fim, o interface que, embora caracterizando-se pela sua intuitividade e simplicidade, suporta um limitado grau de controlo, pelos alunos, sobre os fluxos interactivos, reduzidos às suas expressões mais elementares. No seu aspecto global podemos considerar que, num contexto curricular escolar, a plataforma ―Escola Virtual‖ não se constitui como uma ferramenta de aprendizagem capaz de se enquadrar num paradigma de inovação pedagógica congruente com o sentido atrás defendido,não obstante possamos entender, que a mesma, constitua uma potencial mais-valia no âmbito da consolidação e testagem de conhecimentos préviamente adquiridos.

Evolvable hardware applied to voice recognition

This paper presents some results of the application on Evolvable Hardware (EHW) in the area of voice recognition. Evolvable Hardware is able to change inner connections, using genetic learning techniques, adapting its own functionality to external condition changing. This technique became feasible by the improvement of the Programmable Logic Devices. Nowadays, it is possible to have, in a single device, the ability to change, on-line and in real-time, part of its own circuit. This work proposes a reconfigurable architecture of a system that is able to receive voice commands to execute special tasks as, to help handicapped persons in their daily home routines. The idea is to collect several voice samples, process them through algorithms based on Mel - Ceptrais theory to obtain their numerical coefficients for each sample, which, compose the universe of search used by genetic algorithm. The voice patterns considered, are limited to seven sustained Portuguese vowel phonemes (a, eh, e, i, oh, o, u).

Mining recurring concepts in a dynamic feature space

Most data stream classification techniques assume that the underlying feature space is static. However, in real-world applications the set of features and their relevance to the target concept may change over time. In addition, when the underlying concepts reappear, reusing previously learnt models can enhance the learning process in terms of accuracy and processing time at the expense of manageable memory consumption. In this paper, we propose mining recurring concepts in a dynamic feature space (MReC-DFS), a data stream classification system to address the challenges of learning recurring concepts in a dynamic feature space while simultaneously reducing the memory cost associated with storing past models. MReC-DFS is able to detect and adapt to concept changes using the performance of the learning process and contextual information. To handle recurring concepts, stored models are combined in a dynamically weighted ensemble. Incremental feature selection is performed to reduce the combined feature space. This contribution allows MReC-DFS to store only the features most relevant to the learnt concepts, which in turn increases the memory efficiency of the technique. In addition, an incremental feature selection method is proposed that dynamically determines the threshold between relevant and irrelevant features. Experimental results demonstrating the high accuracy of MReC-DFS compared with state-of-the-art techniques on a variety of real datasets are presented. The results also show the superior memory efficiency of MReC-DFS.

Concit-Corpus: Context Citation Analysis to learn Function, Polarity and Influence

Citation corpus composed by 85 articles taken randomly from ACL Anthology with a total of 2195 bibliography cites.

Action research at the Sea Fish Industry Authority:improving the quality of our knowing- how do practices achieve organisational learning and renewal and how does this change overtime?

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

Dimensões formais, informais e não-formais em diversos contextos de aprendizagem da dança

Preprint. Título do artigo editado: "Dimensões formais, informais e não-formais em diversos contextos de aprendizagem da dança". Publicação na Revista Portuguesa de Educação Artística, 2015 (5), pp. 61-72.

Movement Sonification: Effects on Motor Learning beyond Rhythmic Adjustments

Motor learning is based on motor perception and emergent perceptual-motor representations. A lot of behavioral research is related to single perceptual modalities but during last two decades the contribution of multimodal perception on motor behavior was discovered more and more. A growing number of studies indicates an enhanced impact of multimodal stimuli on motor perception, motor control and motor learning in terms of better precision and higher reliability of the related actions. Behavioral research is supported by neurophysiological data, revealing that multisensory integration supports motor control and learning. But the overwhelming part of both research lines is dedicated to basic research. Besides research in the domains of music, dance and motor rehabilitation, there is almost no evidence for enhanced effectiveness of multisensory information on learning of gross motor skills. To reduce this gap, movement sonification is used here in applied research on motor learning in sports. Based on the current knowledge on the multimodal organization of the perceptual system, we generate additional real-time movement information being suitable for integration with perceptual feedback streams of visual and proprioceptive modality. With ongoing training, synchronously processed auditory information should be initially integrated into the emerging internal models, enhancing the efficacy of motor learning. This is achieved by a direct mapping of kinematic and dynamic motion parameters to electronic sounds, resulting in continuous auditory and convergent audiovisual or audio-proprioceptive stimulus arrays. In sharp contrast to other approaches using acoustic information as error-feedback in motor learning settings, we try to generate additional movement information suitable for acceleration and enhancement of adequate sensorimotor representations and processible below the level of consciousness. In the experimental setting, participants were asked to learn a closed motor skill (technique acquisition of indoor rowing). One group was treated with visual information and two groups with audiovisual information (sonification vs. natural sounds). For all three groups learning became evident and remained stable. Participants treated with additional movement sonification showed better performance compared to both other groups. Results indicate that movement sonification enhances motor learning of a complex gross motor skill-even exceeding usually expected acoustic rhythmic effects on motor learning.

Päätöspuun käyttö tutkamaalien initialisoinnissa

Suomen ilmatilaa valvotaan reaaliaikaisesti, pääasiassa ilmavalvontatutkilla. Ilmatilassa on lentokoneiden lisäksi paljon muitakin kohteita, jotka tutka havaitsee. Tutka lähettää nämä tiedot edelleen ilmavalvontajärjestelmään. Ilmavalvontajärjestelmä käsittelee tiedot, sekä lähettää ne edelleen esitysjärjestelmään. Esitysjärjestelmässä tiedot esitetään synteettisinä merkkeinä, seurantoina joista käytetään nimitystä träkki. Näiden tietojen puitteissa sekä oman ammattitaitonsa perusteella ihmiset tekevät päätöksiä. Tämän työn tarkoituksena on tutkia tutkan havaintoja träkkien initialisointipisteessä siten, että voitaisiin määritellä tyypillinen rakenne sille mikä on oikea ja mikä väärä tai huono träkki. Tämän lisäksi tulisi ennustaa, mitkä Irakeista eivät aiheudu ilma- aluksista. Saadut tulokset voivat helpottaa työtä havaintojen tulkinnassa - jokainen lintuparvi ei ole ehdokas seurannaksi. Havaintojen luokittelu voidaan tehdä joko neurolaskennalla tai päätöspuulla. Neurolaskenta tehdään neuroverkoilla, jotka koostuvat neuroneista. Päätöspuu- luokittelijat ovat oppivia tietorakenteita kuten neuroverkotkin. Yleisin päätöpuu on binääripuu. Tämän työn tavoitteena on opettaa päätöspuuluokittelija havaintojen avulla siten, että se pystyy luokittelemaan väärät havainnot oikeista. Neurolaskennan mahdollisuuksia tässä työssä ei käsitellä kuin teoreettisesti. Työn tuloksena voi todeta, että päätöspuuluokittelijat ovat erittäin kykeneviä erottamaan oikeat havainnot vääristä. Vaikka tulokset olivat rohkaiseva, lisää tutkimusta tarvitaan määrittelemään luotettavammin tekijät, jotka parhaiten suorittavat luokittelun.

Complex-valued B-spline neural networks for modeling and inverting Hammerstein systems

Many communication signal processing applications involve modelling and inverting complex-valued (CV) Hammerstein systems. We develops a new CV B-spline neural network approach for efficient identification of the CV Hammerstein system and effective inversion of the estimated CV Hammerstein model. Specifically, the CV nonlinear static function in the Hammerstein system is represented using the tensor product from two univariate B-spline neural networks. An efficient alternating least squares estimation method is adopted for identifying the CV linear dynamic model’s coefficients and the CV B-spline neural network’s weights, which yields the closed-form solutions for both the linear dynamic model’s coefficients and the B-spline neural network’s weights, and this estimation process is guaranteed to converge very fast to a unique minimum solution. Furthermore, an accurate inversion of the CV Hammerstein system can readily be obtained using the estimated model. In particular, the inversion of the CV nonlinear static function in the Hammerstein system can be calculated effectively using a Gaussian-Newton algorithm, which naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first order derivative recursions. The effectiveness of our approach is demonstrated using the application to equalisation of Hammerstein channels.

Uma arquitetura baseada em agentes para configuração dinâmica de aplicações inteligentes em ambiente foundation fieldbus

The industrial automation is directly linked to the development of information tecnology. Better hardware solutions, as well as improvements in software development methodologies make possible the rapid growth of the productive process control. In this thesis, we propose an architecture that will allow the joining of two technologies in hardware (industrial network) and software field (multiagent systems). The objective of this proposal is to join those technologies in a multiagent architecture to allow control strategies implementations in to field devices. With this, we intend develop an agents architecture to detect and solve problems which may occur in the industrial network environment. Our work ally machine learning with industrial context, become proposed multiagent architecture adaptable to unfamiliar or unexpected production environment. We used neural networks and presented an allocation strategies of these networks in industrial network field devices. With this we intend to improve decision support at plant level and allow operations human intervention independent