Recent Advances of Local Mechanisms in Computer Vision: A Survey and Outlook of Recent Work

Inspired by the fact that human brains can emphasize discriminative parts of the input and suppress irrelevant ones, substantial local mechanisms have been designed to boost the development of computer vision. They can not only focus on target parts to learn discriminative local representations, but also process information selectively to improve the efficiency. In terms of application scenarios and paradigms, local mechanisms have different characteristics. In this survey, we provide a systematic review of local mechanisms for various computer vision tasks and approaches, including fine-grained visual recognition, person re-identification, few-/zero-shot learning, multi-modal learning, self-supervised learning, Vision Transformers, and so on. Categorization of local mechanisms in each field is summarized. Then, advantages and disadvantages for every category are analyzed deeply, leaving room for exploration. Finally, future research directions about local mechanisms have also been discussed that may benefit future works. To the best our knowledge, this is the first survey about local mechanisms on computer vision. We hope that this survey can shed light on future research in the computer vision field

The text classification pipeline: Starting shallow, going deeper

An increasingly relevant and crucial subfield of Natural Language Processing (NLP), tackled in this PhD thesis from a computer science and engineering perspective, is the Text Classification (TC). Also in this field, the exceptional success of deep learning has sparked a boom over the past ten years. Text retrieval and categorization, information extraction and summarization all rely heavily on TC. The literature has presented numerous datasets, models, and evaluation criteria. Even if languages as Arabic, Chinese, Hindi and others are employed in several works, from a computer science perspective the most used and referred language in the literature concerning TC is English. This is also the language mainly referenced in the rest of this PhD thesis. Even if numerous machine learning techniques have shown outstanding results, the classifier effectiveness depends on the capability to comprehend intricate relations and non-linear correlations in texts. In order to achieve this level of understanding, it is necessary to pay attention not only to the architecture of a model but also to other stages of the TC pipeline. In an NLP framework, a range of text representation techniques and model designs have emerged, including the large language models. These models are capable of turning massive amounts of text into useful vector representations that effectively capture semantically significant information. The fact that this field has been investigated by numerous communities, including data mining, linguistics, and information retrieval, is an aspect of crucial interest. These communities frequently have some overlap, but are mostly separate and do their research on their own. Bringing researchers from other groups together to improve the multidisciplinary comprehension of this field is one of the objectives of this dissertation. Additionally, this dissertation makes an effort to examine text mining from both a traditional and modern perspective. This thesis covers the whole TC pipeline in detail. However, the main contribution is to investigate the impact of every element in the TC pipeline to evaluate the impact on the final performance of a TC model. It is discussed the TC pipeline, including the traditional and the most recent deep learning-based models. This pipeline consists of State-Of-The-Art (SOTA) datasets used in the literature as benchmark, text preprocessing, text representation, machine learning models for TC, evaluation metrics and current SOTA results. In each chapter of this dissertation, I go over each of these steps, covering both the technical advancements and my most significant and recent findings while performing experiments and introducing novel models. The advantages and disadvantages of various options are also listed, along with a thorough comparison of the various approaches. At the end of each chapter, there are my contributions with experimental evaluations and discussions on the results that I have obtained during my three years PhD course. The experiments and the analysis related to each chapter (i.e., each element of the TC pipeline) are the main contributions that I provide, extending the basic knowledge of a regular survey on the matter of TC.An increasingly relevant and crucial subfield of Natural Language Processing (NLP), tackled in this PhD thesis from a computer science and engineering perspective, is the Text Classification (TC). Also in this field, the exceptional success of deep learning has sparked a boom over the past ten years. Text retrieval and categorization, information extraction and summarization all rely heavily on TC. The literature has presented numerous datasets, models, and evaluation criteria. Even if languages as Arabic, Chinese, Hindi and others are employed in several works, from a computer science perspective the most used and referred language in the literature concerning TC is English. This is also the language mainly referenced in the rest of this PhD thesis. Even if numerous machine learning techniques have shown outstanding results, the classifier effectiveness depends on the capability to comprehend intricate relations and non-linear correlations in texts. In order to achieve this level of understanding, it is necessary to pay attention not only to the architecture of a model but also to other stages of the TC pipeline. In an NLP framework, a range of text representation techniques and model designs have emerged, including the large language models. These models are capable of turning massive amounts of text into useful vector representations that effectively capture semantically significant information. The fact that this field has been investigated by numerous communities, including data mining, linguistics, and information retrieval, is an aspect of crucial interest. These communities frequently have some overlap, but are mostly separate and do their research on their own. Bringing researchers from other groups together to improve the multidisciplinary comprehension of this field is one of the objectives of this dissertation. Additionally, this dissertation makes an effort to examine text mining from both a traditional and modern perspective. This thesis covers the whole TC pipeline in detail. However, the main contribution is to investigate the impact of every element in the TC pipeline to evaluate the impact on the final performance of a TC model. It is discussed the TC pipeline, including the traditional and the most recent deep learning-based models. This pipeline consists of State-Of-The-Art (SOTA) datasets used in the literature as benchmark, text preprocessing, text representation, machine learning models for TC, evaluation metrics and current SOTA results. In each chapter of this dissertation, I go over each of these steps, covering both the technical advancements and my most significant and recent findings while performing experiments and introducing novel models. The advantages and disadvantages of various options are also listed, along with a thorough comparison of the various approaches. At the end of each chapter, there are my contributions with experimental evaluations and discussions on the results that I have obtained during my three years PhD course. The experiments and the analysis related to each chapter (i.e., each element of the TC pipeline) are the main contributions that I provide, extending the basic knowledge of a regular survey on the matter of TC

Telecom Churn Prediction: An approach Towards Big Data

Churn prediction is a crucial subject in telecom companies. Acquiring a new customer is more expensive than retaining a customer. Identifying such customers requires the address of multiple challenges. The first is caused by telecom datasets. These tend to be high-dimensional and at the same time very sparse, bringing multicollinearity and overfitting issues. Another challenge concerns variable data types. There are static variables and dynamic variables over time. The nature of the use case creates another adversity. The goal is to predict who is leaving the service, but, in any successful company, there are much more clients staying than leaving. This creates what an unbalanced dataset, where the binary target variable has an unbalanced distribution between its’ classes. In this work, a pipeline is proposed targeting the telecom industry. This pipeline aims to address the churn problem, i.e., to identify the clients that have a high propensity to leave the service. The pipeline is designed to deal with the multiple challenges identified and to be adaptable to other telecom datasets. This pipeline is composed of multiple steps, the first step was to restructure data, this was done by realigning all clients by its last month, in an active state, stored in the system. The multiple observations per client were compressed into one using statistics like median and standard deviation, after that feature selection method was applied but multiple options were considered and evaluated at the end of this document. Models were then used to predict variable. These models were adapted to handle unbalance challenge. This work demonstrated the ability to achieve reasonable results using a restructuring proccess and compressing statistics. This work also demonstrated the ability to achieve reasonable good results using a feature selection algorithm.A previsão de churn é crucial nas empresas de telecomunicações. Adquirir um novo cliente é mais dispendioso do que reter um cliente. A identificação de tais clientes requer a abordagem de múltiplos desafios. O primeiro deve-se aos dataset de telecomunicações. Estes tendem a ter uma dimensionalidade elevada sendo, no entanto, esparsos. Isto traz problemas de multicolinearidade e de churn. Outro desafio diz respeito aos tipos de dados. Os dados presentes nestes dataset por norma ou são estáticos ou dinâmicos ao longo do tempo. A natureza do estudo caso em si cria outra adversidade. O objectivo é prever clientes que vão deixar o serviço, mas, em qualquer empresa bem sucedida, existem consideravel- mente mais clientes que ficam do que os que desistem. Isto queria um desequilíbrio na variável objectiva, tendo esta uma distribuição desequilibrada entre classes. Neste trabalho, é proposto uma pipeline à luz da indústria telecom. Esta pipeline visa identificar clientes que com uma elevada propensão para desistir de um determinado serviço. A pipeline foi concebida para lidar com os desafios apresentados e sendo adaptável a outros datasets de telecomincações. O primeiro passo da pipeline foi reestruturar os dados. Realinhou-se todos os dados dos clientes pelo o seu último mês activo existenete no sistema. As múltiplas observações por cliente foram comprimidas numa só usando estatísticas como a mediana e o desvio padrão. Depois, foram aplicados metodos de selecção de variáveis, no entanto foram consideradas e avaliados múltiplos cenários no final deste documento. Por fim a variável objetivo foi modelada usando os múltiplos scenários sendo que modelos usados foram adaptados para lidar com o desequilíbrio da variável objetivo. Este trabalho demonstrou resultados razoáveis ao utilizar um processo de reestruturação e estatísticas de compressão. No mesmo trabalho foram de alcançados bons resultados razoáveis, filtrando algumas variáveis usando um algoritmo de seleção de variáveis

Monitoring of power system dynamics using a hybrid state estimator

Modern power systems are undergoing a transformation process where distributed energy re-sources together with complex load technologies are increasingly integrated. This, in addition to a sustained growth in electricity consumption and a lack of significant investment in trans-mission infrastructure, leads power systems to face with new stochastic operating behavior and dynamics and to operate under stressed conditions. Under such operating conditions, the occurrence of a potential disturbance may cause a partial or a total collapse. Therefore, in order to minimize the risk of collapses and their impact, new monitoring tools must be adopted, capable of providing the right conditions for dynamic wide-area monitoring. The thesis presents a hybrid state estimator, that is a monitoring tool that combines fast synchronized phasor measurements with traditional measurements into a single scheme. It has the ability to estimate at high speed power system dynamics associated to slow and fast transient phenomena considering a reduced amount of phasor measurement units (PMUs). The developed scheme consists of two phases depending on the power system operating regime. In phase one the system is in stationary regime and bus voltages (magnitude and angle) together with related variables like power flows, current through lines, etc. are estimated by a static estimator at a low speed, which is determined by the supervisory control and data acquisition (SCADA) system. When a physical disturbance happens and the system is in transient regime phase two comes into operation. This time, two estimators work in sequence at high speed. First, a static state estimator is used to estimate bus voltages as soon as the synchronized phasor measurement set arrives. Then, a dynamic estimator is in charge of estimating dynamic states of all generators and motors in the system, even if the unit is not observed by a PMU. Full observability is re-stored through a novel data-mining based methodology, which defines, first, a PMU topology that allows monitoring the post-contingency bus voltage dynamics of the entire power system and, second, generates a number of bus voltage pseudo-measurements to extend the observability to the whole system

Photovoltaic potential in building façades

Tese de doutoramento, Sistemas Sustentáveis de Energia, Universidade de Lisboa, Faculdade de Ciências, 2018Consistent reductions in the costs of photovoltaic (PV) systems have prompted interest in applications with less-than-optimum inclinations and orientations. That is the case of building façades, with plenty of free area for the deployment of solar systems. Lower sun heights benefit vertical façades, whereas rooftops are favoured when the sun is near the zenith, therefore the PV potential in urban environments can increase twofold when the contribution from building façades is added to that of the rooftops. This complementarity between façades and rooftops is helpful for a better match between electricity demand and supply. This thesis focuses on: i) the modelling of façade PV potential; ii) the optimization of façade PV yields; and iii) underlining the overall role that building façades will play in future solar cities. Digital surface and solar radiation modelling methodologies were reviewed. Special focus is given to the 3D LiDAR-based model SOL and the CAD/plugin models DIVA and LadyBug. Model SOL was validated against measurements from the BIPV system in the façade of the Solar XXI building (Lisbon), and used to evaluate façade PV potential in different urban sites in Lisbon and Geneva. The plugins DIVA and LadyBug helped assessing the potential for PV glare from façade integrated photovoltaics in distinct urban blocks. Technologies for PV integration in façades were also reviewed. Alternative façade designs, including louvers, geometric forms and balconies, were explored and optimized for the maximization of annual solar irradiation using DIVA. Partial shading impacts on rooftops and façades were addressed through SOL simulations and the interconnections between PV modules were optimized using a custom Multi-Objective Genetic Algorithm. The contribution of PV façades to the solar potential of two dissimilar neighbourhoods in Lisbon was quantified using SOL, considering local electricity consumption. Cost-efficient rooftop/façade PV mixes are proposed based on combined payback times. Impacts of larger scale PV deployment on the spare capacity of power distribution transformers were studied through LadyBug and SolarAnalyst simulations. A new empirical solar factor was proposed to account for PV potential in future upgrade interventions. The combined effect of aggregating building demand, photovoltaic generation and storage on the self-consumption of PV and net load variance was analysed using irradiation results from DIVA, metered distribution transformer loads and custom optimization algorithms. SOL is shown to be an accurate LiDAR-based model (nMBE ranging from around 7% to 51%, nMAE from 20% to 58% and nRMSE from 29% to 81%), being the isotropic diffuse radiation algorithm its current main limitation. In addition, building surface material properties should be regarded when handling façades, for both irradiance simulation and PV glare evaluation. The latter appears to be negligible in comparison to glare from typical glaze/mirror skins used in high-rises. Irradiation levels in the more sunlit façades reach about 50-60% of the rooftop levels. Latitude biases the potential towards the vertical surfaces, which can be enhanced when the proportion of diffuse radiation is high. Façade PV potential can be increased in about 30% if horizontal folded louvers becomes a more common design and in another 6 to 24% if the interconnection of PV modules are optimized. In 2030, a mix of PV systems featuring around 40% façade and 60% rooftop occupation is shown to comprehend a combined financial payback time of 10 years, if conventional module efficiencies reach 20%. This will trigger large-scale PV deployment that might overwhelm current grid assets and lead to electricity grid instability. This challenge can be resolved if the placement of PV modules is optimized to increase self-sufficiency while keeping low net load variance. Aggregated storage within solar communities might help resolving the conflicting interests between prosumers and grid, although the former can achieve self-sufficiency levels above 50% with storage capacities as small as 0.25kWh/kWpv. Business models ought to adapt in order to create conditions for both parts to share the added value of peak power reduction due to optimized solar façades.Fundação para a Ciência e a Tecnologia (FCT), SFRH/BD/52363/201

Enhancing heart disease prediction using a self-attention-based transformer model

Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million mortalities worldwide. The early detection of heart failure with high accuracy is crucial for clinical trials and therapy. Patients will be categorized into various types of heart disease based on characteristics like blood pressure, cholesterol levels, heart rate, and other characteristics. With the use of an automatic system, we can provide early diagnoses for those who are prone to heart failure by analyzing their characteristics. In this work, we deploy a novel self-attention-based transformer model, that combines self-attention mechanisms and transformer networks to predict CVD risk. The self-attention layers capture contextual information and generate representations that effectively model complex patterns in the data. Self-attention mechanisms provide interpretability by giving each component of the input sequence a certain amount of attention weight. This includes adjusting the input and output layers, incorporating more layers, and modifying the attention processes to collect relevant information. This also makes it possible for physicians to comprehend which features of the data contributed to the model's predictions. The proposed model is tested on the Cleveland dataset, a benchmark dataset of the University of California Irvine (UCI) machine learning (ML) repository. Comparing the proposed model to several baseline approaches, we achieved the highest accuracy of 96.51%. Furthermore, the outcomes of our experiments demonstrate that the prediction rate of our model is higher than that of other cutting-edge approaches used for heart disease prediction

Bushing diagnosis using artificial intelligence and dissolved gas analysis

This dissertation is a study of artificial intelligence for diagnosing the condition of high voltage bushings. The techniques include neural networks, genetic algorithms, fuzzy set theory, particle swarm optimisation, multi-classifier systems, factor analysis, principal component analysis, multidimensional scaling, data-fusion techniques, automatic relevance determination and autoencoders. The classification is done using Dissolved Gas Analysis (DGA) data based on field experience together with criteria from IEEEc57.104 and IEC60599. A review of current literature showed that common methods for the diagnosis of bushings are: partial discharge, DGA, tan- (dielectric dissipation factor), water content in oil, dielectric strength of oil, acidity level (neutralisation value), visual analysis of sludge in suspension, colour of the oil, furanic content, degree of polymerisation (DP), strength of the insulating paper, interfacial tension or oxygen content tests. All the methods have limitations in terms of time and accuracy in decision making. The fact that making decisions using each of these methods individually is highly subjective, also the huge size of the data base of historical data, as well as the loss of skills due to retirement of experienced technical staff, highlights the need for an automated diagnosis tool that integrates information from the many sensors and recalls the historical decisions and learns from new information. Three classifiers that are compared in this analysis are radial basis functions (RBF), multiple layer perceptrons (MLP) and support vector machines (SVM). In this work 60699 bushings were classified based on ten criteria. Classification was done based on a majority vote. The work proposes the application of neural networks with particle swarm optimisation (PSO) and genetic algorithms (GA) to compensate for missing data in classifying high voltage bushings. The work also proposes the application of fuzzy set theory (FST) to diagnose the condition of high voltage bushings. The relevance and redundancy detection methods were able to prune the redundant measured variables and accurately diagnose the condition of the bushing with fewer variables. Experimental results from bushings that were evaluated in the field verified the simulations. The results of this work can help to develop real-time monitoring and decision making tools that combine information from chemical, electrical and mechanical measurements taken from bushings

Through space and time

Modern fishing vessels have a wide range of instruments and sensors on board that are used for active fishing operations, with sonar equipment and echo sounders being among the most common. Sonar allows horizontal observation of the water column, while echo sounders provide more precise underwater environment monitoring. These instruments are useful as they are used today but require a lot of user experience for effective use. Estimating biomass den- sity, fish size, and species is highly demanding, and the existing systems have significant uncertainties. In this thesis, we propose a novel approach to hydroacoustic data analysis that capitalizes on catch reports as annotations for hydroacoustic transects. Com- bining catch messages with the positional attribute of echo data allows us to obtain annotated echo examples that describe the biota within a given loca- tion. The thesis leverages EchoBERT, a BERT-inspired model, as the underlying architecture. To assess the capabilities of the annotations, we evaluate the model using dif- ferent types of models. Both classification and regression tasks are employed, wherein the classification task aims to predict the presence of a species based on catch messages. In contrast, the regression tasks attempt to fit the model to the catch data and generate a distribution of the species. Furthermore, we assess the model considering timestamps. Since the catch messages may not necessarily correspond to the same date as the echo data, we incorporate weighted loss functions that account for the temporal proximity. This approach allows for a closer association during the training process, where the outcome is weighted more heavily for temporally closer labels. Our results provide insight into the characteristics of catch reports as anno- tations, shedding light on their usefulness and limitations. We also uncover potential bias present in the labelled data, where a seasonal fishing activity can be uncovered in the dataset. We also experiment and find the magnitude of difference in collation criterion when finding catch data based on the haversines formula

Managing Distributed Information: Implications for Energy Infrastructure Co-production

abstract: The Internet and climate change are two forces that are poised to both cause and enable changes in how we provide our energy infrastructure. The Internet has catalyzed enormous changes across many sectors by shifting the feedback and organizational structure of systems towards more decentralized users. Today’s energy systems require colossal shifts toward a more sustainable future. However, energy systems face enormous socio-technical lock-in and, thus far, have been largely unaffected by these destabilizing forces. More distributed information offers not only the ability to craft new markets, but to accelerate learning processes that respond to emerging user or prosumer centered design needs. This may include values and needs such as local reliability, transparency and accountability, integration into the built environment, and reduction of local pollution challenges. The same institutions (rules, norms and strategies) that dominated with the hierarchical infrastructure system of the twentieth century are unlikely to be good fit if a more distributed infrastructure increases in dominance. As information is produced at more distributed points, it is more difficult to coordinate and manage as an interconnected system. This research examines several aspects of these, historically dominant, infrastructure provisioning strategies to understand the implications of managing more distributed information. The first chapter experimentally examines information search and sharing strategies under different information protection rules. The second and third chapters focus on strategies to model and compare distributed energy production effects on shared electricity grid infrastructure. Finally, the fourth chapter dives into the literature of co-production, and explores connections between concepts in co-production and modularity (an engineering approach to information encapsulation) using the distributed energy resource regulations for San Diego, CA. Each of these sections highlights different aspects of how information rules offer a design space to enable a more adaptive, innovative and sustainable energy system that can more easily react to the shocks of the twenty-first century.Dissertation/ThesisDoctoral Dissertation Sustainability 201