A Voice for the Voiceless: Peer-to-peer Mobile Phone Networks for a Community Radio Service

We propose a new application for mobile ad-hoc networks (MANETs) – community radio. We argue how MANETS help overcome important limitations in how community radio is currently operationalized. We identify critical design elements for a MANET based community radio service and propose a broad architecture for the same. We then investigate a most critical issue– the choice of the network wide broadcast protocol for the audio content. We identify desired characteristics of a community radio broadcasting service. We choose and evaluate eight popular broadcasting protocols on these characteristics, to find the protocols most suited for our application.

Artificial Neural Network (ANN) in a Small Dataset to determine Neutrality in the Pronunciation of English as a Foreign Language in Filipino Call Center Agents

Artificial Neural Networks (ANNs) have continued to be efficient models in solving classification problems. In this paper, we explore the use of an ANN with a small dataset to accurately classify whether Filipino call center agents’ pronunciations are neutral or not based on their employer’s standards. Isolated utterances of the ten most commonly used words in the call center were recorded from eleven agents creating a dataset of 110 utterances. Two learning specialists were consulted to establish ground truths and Cohen’s Kappa was computed as 0.82, validating the reliability of the dataset. The first thirteen Mel-Frequency Cepstral Coefficients (MFCCs) were then extracted from each word and an ANN was trained with Ten-fold Stratified Cross Validation. Experimental results on the model recorded a classification accuracy of 89.60% supported by an overall F-Score of 0.92

Artificial Neural Network (ANN) in a Small Dataset to determine Neutrality in the Pronunciation of English as a Foreign Language in Filipino Call Center Agents

Artificial Neural Networks (ANNs) have continued to be efficient models in solving classification problems. In this paper, we explore the use of an A NN with a small dataset to accurately classify whet her Filipino call center agents’ pronunciations are neutral or not based on their employer’s standards. Isolated utterances of the ten most commonly used words in the call center were recorded from eleven agents creating a dataset of 110 utterances. Two learning specialists were consulted to establish ground truths and Cohen’s Kappa was computed as 0.82, validating the reliability of the dataset. The first thirteen Mel-Frequency Cepstral Coefficients (MFCCs) were then extracted from each word and an ANN was trained with Ten-fold Stratified Cross Validation. Experimental results on the model recorded a classification accuracy of 89.60% supported by an overall F-Score of 0.92

On the performance of probabilistic flooding in wireless mobile ad hoc networks

Broadcasting in MANET’s has traditionally been based on flooding, but this can induce broadcast storms that severely degrade network performance due to redundant retransmission, collision and contention. Probabilistic flooding, where a node rebroadcasts a newly arrived one-to-all packet with some probability, p, was an early suggestion to reduce the broadcast storm problem. The first part of this thesis investigates the effects on the performance of probabilistic flooding of a number of important MANET parameters, including node speed, traffic load and node density. It transpires that these parameters have a critical impact both on reachability and on the number of so-called “saved rebroadcast packets” achieved. For instance, across a range of rebroadcast probability values, as network density increases from 25 to 100 nodes, reachability achieved by probabilistic flooding increases from 85% to 100%. Moreover, as node speed increases from 2 to 20 m/sec, reachability increases from 90% to 100%. The second part of this thesis proposes two new probabilistic algorithms that dynamically adjust the rebroadcasting probability contingent on node distribution using only one-hop neighbourhood information, without requiring any assistance of distance measurements or location-determination devices. The performance of the new algorithm is assessed and compared to blind flooding as well as the fixed probabilistic approach. It is demonstrated that the new algorithms have superior performance characteristics in terms of both reachability and saved rebroadcasts. For instance, the suggested algorithms can improve saved rebroadcasts by up to 70% and 47% compared to blind and fixed probabilistic flooding, respectively, even under conditions of high node mobility and high network density without degrading reachability. The final part of the thesis assesses the impact of probabilistic flooding on the performance of routing protocols in MANETs. Our performance results indicate that using our new probabilistic flooding algorithms during route discovery enables AODV to achieve a higher delivery ratio of data packets while keeping a lower routing overhead compared to using blind and fixed probabilistic flooding. For instance, the packet delivery ratio using our algorithm is improved by up to 19% and 12% compared to using blind and fixed probabilistic flooding, respectively. This performance advantage is achieved with a routing overhead that is lower by up to 28% and 19% than in fixed probabilistic and blind flooding, respectively

A expansão do universo comunicacional: A inteligência artificial como impulsionadora da comunicação digital

O ser humano está profundamente imerso em tecnologia, especialmente as gerações mais recentes. Com a contínua implementação de inteligência artificial em ambientes digitais, as ferramentas de comunicação estão em constante metamorfose. A tecnologia tem, portanto, um papel massivo nas indústrias contemporâneas e os mais recentes desenvolvimentos permitem agora que comuniquemos verbalmente com dispositivos e interagir através deles. O reconhecimento de voz, pesquisa por voz e os assistentes digitais revolucionam continuamente o quotidiano, reformando as conjunturas sociais e impulsionando experiências pessoais. Porém, a interação humana com sistemas de pesquisa por voz (Siri, Alexa, Cortana, Assistente Google) e chatbots tem ainda pouca investigação em Portugal. O presente estudo contempla as aplicações e possibilidades da inteligência artificial na comunicação digital como estratégia do marketing digital. O mundo empresa-consumidor e os seus processos de comunicação são o foco da investigação já que se vê ser uma área em que as aplicações de IA se fazem sentir. Desde mascotes digitais e chatbots a e-mail marketing, a comunicação digital inteligente está percetível no quotidiano, sendo alvo de análise deste estudo o prisma do consumidor sito em Portugal. O chatbot revolucionou o modo como as empresas e consumidores interagem e se relacionam. Assim, com esta pesquisa pretende-se conhecer a relevância dos meios de comunicação digitais com IA, em Portugal, em contexto marca-consumidor. Para melhor compreender a presença da IA, foi efetuada uma análise da presença de chatbots e pesquisa de voz em empresas localizadas em Portugal (Leroy Merlin, CP-Comboios de Portugal, Allianz Seguros, Segurança Social e Nespresso), tendo sido aplicado também um inquérito por questionário à população para maior aprofundamento da temática. Concluiu-se, com a presente investigação, que em Portugal, a Inteligência Artificial está ainda pouco desenvolvida e com pouca presença no âmbito empresarial, bem como tem um baixo grau de utilização e importância na vida quotidiana.Humans are deeply immersed in technology, especially the younger generations. With the continuous implementation of artificial intelligence in digital environments, communication tools are in constant metamorphosis. Technology, therefore, plays a massive role in contemporary industries and the latest developments now allow us to communicate verbally with devices and interact through them. Voice recognition, voice search and digital assistants continually revolutionize everyday life, reshaping social environments and boosting personal experiences. However, human interaction with voice search systems (Siri, Alexa, Cortana, Google Assistant) and chatbots has little research in Portugal. The present study contemplates the applications and possibilities of artificial intelligence in digital communication as a digital marketing strategy. The businessconsumer world and its communication processes are the focus of the investigation as it is seen to be an area where AI applications make themselves felt. From digital mascots and chatbots to email marketing, intelligent digital communication is perceptible in everyday life and the focus of this study is the consumer's perspective in Portugal. The chatbot revolutionized the way companies and consumers interact and relate. Thus, with this study it is intended to learn the relevance of digital media with AI in Portugal, in a business-consumer context. To better understand the presence of AI, an analysis of the presence of chatbots in companies located in Portugal has been made (Leroy Merlin, CP-Comboios de Portugal, Allianz, Segurança Social and Nespresso), also having a questionnaire survey been applied to the population to further deepen the theme. It was concluded with this study that, in Portugal, artificial intelligence is not very developed or has current use within the companies and additionally has a low level of use and a small importance in the daily life of the population

Alignement du chant par rapport à une référence audio en temps réel

Dans l'optique de créer un système de karaoké qui modifie une interprétation chantée à capella en temps réel, il est nécessaire de pouvoir localiser l'interprète par rapport à une référence afin de pouvoir déterminer quelle serait la cible d'un algorithme de modification de la voix. Pour qu'un tel système fonctionne bien, il est nécessaire que l'algorithme d'alignement exploite au maximum les spécificités de la voix, qu'il utilise l'information liée au texte prononcé plutôt qu'aux aspects artistiques du chant, qu'il soit à temps réel et qu'il offr la plus faible latence possible. Afin d'atteindre ces objectifs, un système d'alignement basé sur le Dynamic Time Warping (DTW) a été développé. Une adaptation temps réel simple de l'algorithme ordinaire de la DTW qui permet d'atteindre les objectifs énumérés est proposée et comparée à d'autres approches répertoriées dans la littérature. Cette adaptation a permis d'obtenir de meilleurs résultats que les autres techniques testées. Une étude comparative de trois types d'analyses spectrales couramment utilisées dans des systèmes de reconnaissance automatique de la voix a été réalisée, dans le cadre spécifique d'un algorithme d'alignement de la voix chantée. Les coefficients évalués sont les Mel-frquency Cepstrum Coefficients (MFCC), les Warped Discrete Cosine Transform Coefficients (WDCTC) et les coefficients de l'analyse Perceptual Linear Prediction (PLP). Les résultats obtenus indiquent une meilleure performance pour l'analyse PLP. L'utilisation d'une fonction de transformation linéaire par morceaux, appliquée aux matrices de coûts instantanés obtenues, permet de rendre l'alignement le plus facilement distinguable dans les matrices de coûts cumulés calculées. Les paramètres de la fonction de transformation peuvent être obtenus par l'optimisation en boucle fermée par recherche directe par motif. Une fonction-objectif permettant d'éviter les discontinuités de l'écart quadratique moyen sur l'alignement est développée. Plusieurs matrices de coûts peuvent être combinées entre elles en effectuant une somme pondérée des matrices de coûts instantanées transformées de chacun des paramètres considérés. La pondération est également obtenue par optimisation. Plusieurs assemblages sont comparés : les meilleurs résultats sont obtenus avec une combinaison de l'analyse PLP et du niveau d'énergie et des dérivées de ceux-ci. L'écart moyen sur l'alignement de référence est de l'ordre de 50 ms, avec un écart-type d'environ 75 ms pour les séquences testées. Des perspectives permettant d'améliorer la convergence de l'algorithme pour les paires de séquences audio difficiles à aligner, d'obtenir de meilleures matrices de coûts en utilisant d'autres contraintes locales, en considérant l'intégration de nouveaux paramètres tels le pitch ou en utilisant une base de données de voix chantée segmentée pour optimiser une mesure de distance sont données