Review on Intrusion Detection System Based on The Goal of The Detection System

An extensive review of the intrusion detection system (IDS) is presented in this paper. Previous studies review the IDS based on the approaches (algorithms) used or based on the types of the intrusion itself. The presented paper reviews the IDS based on the goal of the IDS (accuracy and time), which become the main objective of this paper. Firstly, the IDS were classified into two types based on the goal they intend to achieve. These two types of IDS were later reviewed in detail, followed by a comparison of some of the studies that have earlier been carried out on IDS. The comparison is done based on the results shown in the studies compared. The comparison shows that the studies focusing on the detection time reduce the accuracy of the detection compared to other studies

High-dimensional hierarchical models and massively parallel computing

This work expounds a computationally expedient strategy for the fully Bayesian treatment of high-dimensional hierarchical models. Most steps in a Markov chain Monte Carlo routine for such models are either conditionally independent draws or low-dimensional draws based on summary statistics of parameters at higher levels of the hierarchy. We construct both sets of steps using parallelized algorithms designed to take advantage of the immense parallel computing power of general-purpose graphics processing units while avoiding the severe memory transfer bottleneck. We apply our strategy to RNA-sequencing (RNA-seq) data analysis, a multiple-testing, low-sample-size scenario where hierarchical models provide a way to borrow information across genes. Our approach is solidly tractable, and it performs well under several metrics of estimation, posterior inference, and gene detection. Best-case-scenario empirical Bayes counterparts perform equally well, lending support to existing empirical Bayes approaches in RNA-seq. Finally, we attempt to improve the robustness of estimation and inference of our RNA-seq model using alternate hierarchical distributions

Uavs path planning under a bi-objective optimization framework for smart cities

Unmanned aerial vehicles (UAVs) have been used extensively for search and rescue operations, surveillance, disaster monitoring, attacking terrorists, etc. due to their growing advantages of low-cost, high maneuverability, and easy deployability. This study proposes a mixed-integer programming model under a multi-objective optimization framework to design trajectories that enable a set of UAVs to execute surveillance tasks. The first objective maximizes the cumulative probability of target detection to aim for mission planning success. The second objective ensures minimization of cumulative path length to provide a higher resource utilization goal. A two-step variable neighborhood search (VNS) algorithm is offered, which addresses the combinatorial optimization issue for determining the near-optimal sequence for cell visiting to reach the target. Numerical experiments and simulation results are evaluated in numerous benchmark instances. Results demonstrate that the proposed approach can favorably support practical deployability purposes

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.Open access funding provided by FCT|FCCN (b-on). This work has been supported by FCT— Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021 The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/ MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).info:eu-repo/semantics/publishedVersio

Stochastic Methods for Fine-Grained Image Segmentation and Uncertainty Estimation in Computer Vision

In this dissertation, we exploit concepts of probability theory, stochastic methods and machine learning to address three existing limitations of deep learning-based models for image understanding. First, although convolutional neural networks (CNN) have substantially improved the state of the art in image understanding, conventional CNNs provide segmentation masks that poorly adhere to object boundaries, a critical limitation for many potential applications. Second, training deep learning models requires large amounts of carefully selected and annotated data, but large-scale annotation of image segmentation datasets is often prohibitively expensive. And third, conventional deep learning models also lack the capability of uncertainty estimation, which compromises both decision making and model interpretability. To address these limitations, we introduce the Region Growing Refinement (RGR) algorithm, an unsupervised post-processing algorithm that exploits Monte Carlo sampling and pixel similarities to propagate high-confidence labels into regions of low-confidence classification. The probabilistic Region Growing Refinement (pRGR) provides RGR with a rigorous mathematical foundation that exploits concepts of Bayesian estimation and variance reduction techniques. Experiments demonstrate both the effectiveness of (p)RGR for the refinement of segmentation predictions, as well as its suitability for uncertainty estimation, since its variance estimates obtained in the Monte Carlo iterations are highly correlated with segmentation accuracy. We also introduce FreeLabel, an intuitive open-source web interface that exploits RGR to allow users to obtain high-quality segmentation masks with just a few freehand scribbles, in a matter of seconds. Designed to benefit the computer vision community, FreeLabel can be used for both crowdsourced or private annotation and has a modular structure that can be easily adapted for any image dataset. The practical relevance of methods developed in this dissertation are illustrated through applications on agricultural and healthcare-related domains. We have combined RGR and modern CNNs for fine segmentation of fruit flowers, motivated by the importance of automated bloom intensity estimation for optimization of fruit orchard management and, possibly, automatizing procedures such as flower thinning and pollination. We also exploited an early version of FreeLabel to annotate novel datasets for segmentation of fruit flowers, which are currently publicly available. Finally, this dissertation also describes works on fine segmentation and gaze estimation for images collected from assisted living environments, with the ultimate goal of assisting geriatricians in evaluating health status of patients in such facilities

Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

Identifying the Genetic Mechanism of Ethylene-Inducible Fruit Abscission in Sweet Cherry

Sweet cherry (Prunus avium, L.) belongs to the Amygdaloideae subfamily in the Rosaceae family and is related to Peach (Prunus persica, L.) which presents a classical example of climacteric ripening along with members in other sub-families such as Apple (Malus x domestica Borkh). While the fruit ripening process in sweet cherry is non-climacteric, a novel response to ethylene has been observed in a subset of cultivars, e.g., 'Bing', where a pre-harvest canopy-wide application of exogenous ethylene results in the formation of a distinct pedicel-fruit abscission zone. This decreases the force required to separate fruit from pedicel, presenting the possibility of harvesting the fruit mechanically. Some cultivars, e.g., 'Chelan', maintain a high pedicel fruit retention force, while the abscission zone in 'Skeena' develops without exogenous ethylene. This natural spectrum of phenotypes for ethylene-inducible abscission zone formation presents an interesting forward genetics model to unravel the underlying molecular mechanism. This is critical for a perennial crops species where reverse genetics approaches are not feasible due to the lack of genetic resources available in annual model system crops. Understanding the causal molecular and genetic underpinnings of the inducible abscission zone formation offers the potential to expand the basic knowledge of ethylene's role in a non-climacteric fruit crop. Practical applications from this work include precision in timing the application of chemicals that induce abscission zone formation in other non-climacteric horticultural crops to enable mechanical harvesting, efficiently manage labor and enhance safety. My research used a genomics approach to refine limited genomic data for the 'Stella' genotype by resequencing five additional genotypes: 'Bing', 'Kimberly', 'Glory', 'Staccato' and 'Sweetheart'. Genes involved in ethylene inducible fruit abscission were then characterized using a developmental time-course transcriptomic approach. The results are presented in the following chapters. Chapter 1: A international collaboration established the reference genome for sweet cherry and outlines the rationale for strategies used. Chapter 2: Multiple approaches to identify polymorphisms in genomic data were evaluated. Chapter 3: The results of the developmental time course, genotypic, and tissue specific transcriptome analysis that enabled the identification of co-expressed sequences during ethylene-induced abscission are described.; This is an abstract

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Doctor of Philosophy

dissertationThe MAKER genome annotation and curation software tool was developed in response to increased demand for genome annotation services, secondary to decreased genome sequencing costs. MAKER currently has over 1000 registered users throughout the world. This wide adoption of MAKER has uncovered the need for additional functionalities. Here I addressed moving MAKER into the domain of plant annotation, expanding MAKER to include new methods of gene and noncoding RNA annotation, and improving usability of MAKER through documentation and community outreach. To move MAKER into the plant annotation domain, I benchmarked MAKER on the well-annotated Arabidopsis thaliana genome. MAKER performs well on the Arabidopsis genome in de novo genome annotation and was able to improve the current TAIR10 gene models by incorporating mRNA-seq data not available during the original annotation efforts. In addition to this benchmarking, I annotated the genome of the sacred lotus Nelumbo Nucifera. I enabled noncoding RNA annotation in MAKER by adding the ability for MAKER to run and process the outputs of tRNAscan-SE and snoscan. These functionalities were tested on the Arabidopsis genome and used MAKER to annotate tRNAs and snoRNAs in Zea mays. The resulting version of MAKER was named MAKER-P. I added the functionality of a combiner by adding EVidence Modeler to the MAKER code base. iv As the number of MAKER users has grown, so have the help requests sent to the MAKER developers list. Motivated by the belief that improving the MAKER documentation would obviate the need for many of these requests, I created a media wiki that was linked to the MAKER download page, and the MAKER developers list was made searchable. Additionally I have written a unit on genome annotation using MAKER for Current Protocols in Bioinformatics. In response to these efforts I have seen a corresponding decrease in help requests, even though the number of registered MAKER users continues to increase. Taken together these products and activities have moved MAKER into the domain of plant annotation, expanded MAKER to include new methods of gene and noncoding RNA annotation, and improved the usability of MAKER through documentation and community outreach