9,290 research outputs found

    An advanced deep learning models-based plant disease detection: A review of recent research

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    Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation

    Modular lifelong machine learning

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    Deep learning has drastically improved the state-of-the-art in many important fields, including computer vision and natural language processing (LeCun et al., 2015). However, it is expensive to train a deep neural network on a machine learning problem. The overall training cost further increases when one wants to solve additional problems. Lifelong machine learning (LML) develops algorithms that aim to efficiently learn to solve a sequence of problems, which become available one at a time. New problems are solved with less resources by transferring previously learned knowledge. At the same time, an LML algorithm needs to retain good performance on all encountered problems, thus avoiding catastrophic forgetting. Current approaches do not possess all the desired properties of an LML algorithm. First, they primarily focus on preventing catastrophic forgetting (Diaz-Rodriguez et al., 2018; Delange et al., 2021). As a result, they neglect some knowledge transfer properties. Furthermore, they assume that all problems in a sequence share the same input space. Finally, scaling these methods to a large sequence of problems remains a challenge. Modular approaches to deep learning decompose a deep neural network into sub-networks, referred to as modules. Each module can then be trained to perform an atomic transformation, specialised in processing a distinct subset of inputs. This modular approach to storing knowledge makes it easy to only reuse the subset of modules which are useful for the task at hand. This thesis introduces a line of research which demonstrates the merits of a modular approach to lifelong machine learning, and its ability to address the aforementioned shortcomings of other methods. Compared to previous work, we show that a modular approach can be used to achieve more LML properties than previously demonstrated. Furthermore, we develop tools which allow modular LML algorithms to scale in order to retain said properties on longer sequences of problems. First, we introduce HOUDINI, a neurosymbolic framework for modular LML. HOUDINI represents modular deep neural networks as functional programs and accumulates a library of pre-trained modules over a sequence of problems. Given a new problem, we use program synthesis to select a suitable neural architecture, as well as a high-performing combination of pre-trained and new modules. We show that our approach has most of the properties desired from an LML algorithm. Notably, it can perform forward transfer, avoid negative transfer and prevent catastrophic forgetting, even across problems with disparate input domains and problems which require different neural architectures. Second, we produce a modular LML algorithm which retains the properties of HOUDINI but can also scale to longer sequences of problems. To this end, we fix the choice of a neural architecture and introduce a probabilistic search framework, PICLE, for searching through different module combinations. To apply PICLE, we introduce two probabilistic models over neural modules which allows us to efficiently identify promising module combinations. Third, we phrase the search over module combinations in modular LML as black-box optimisation, which allows one to make use of methods from the setting of hyperparameter optimisation (HPO). We then develop a new HPO method which marries a multi-fidelity approach with model-based optimisation. We demonstrate that this leads to improvement in anytime performance in the HPO setting and discuss how this can in turn be used to augment modular LML methods. Overall, this thesis identifies a number of important LML properties, which have not all been attained in past methods, and presents an LML algorithm which can achieve all of them, apart from backward transfer

    Towards A Practical High-Assurance Systems Programming Language

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    Writing correct and performant low-level systems code is a notoriously demanding job, even for experienced developers. To make the matter worse, formally reasoning about their correctness properties introduces yet another level of complexity to the task. It requires considerable expertise in both systems programming and formal verification. The development can be extremely costly due to the sheer complexity of the systems and the nuances in them, if not assisted with appropriate tools that provide abstraction and automation. Cogent is designed to alleviate the burden on developers when writing and verifying systems code. It is a high-level functional language with a certifying compiler, which automatically proves the correctness of the compiled code and also provides a purely functional abstraction of the low-level program to the developer. Equational reasoning techniques can then be used to prove functional correctness properties of the program on top of this abstract semantics, which is notably less laborious than directly verifying the C code. To make Cogent a more approachable and effective tool for developing real-world systems, we further strengthen the framework by extending the core language and its ecosystem. Specifically, we enrich the language to allow users to control the memory representation of algebraic data types, while retaining the automatic proof with a data layout refinement calculus. We repurpose existing tools in a novel way and develop an intuitive foreign function interface, which provides users a seamless experience when using Cogent in conjunction with native C. We augment the Cogent ecosystem with a property-based testing framework, which helps developers better understand the impact formal verification has on their programs and enables a progressive approach to producing high-assurance systems. Finally we explore refinement type systems, which we plan to incorporate into Cogent for more expressiveness and better integration of systems programmers with the verification process

    Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques

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    The rapid growth of demanding applications in domains applying multimedia processing and machine learning has marked a new era for edge and cloud computing. These applications involve massive data and compute-intensive tasks, and thus, typical computing paradigms in embedded systems and data centers are stressed to meet the worldwide demand for high performance. Concurrently, the landscape of the semiconductor field in the last 15 years has constituted power as a first-class design concern. As a result, the community of computing systems is forced to find alternative design approaches to facilitate high-performance and/or power-efficient computing. Among the examined solutions, Approximate Computing has attracted an ever-increasing interest, with research works applying approximations across the entire traditional computing stack, i.e., at software, hardware, and architectural levels. Over the last decade, there is a plethora of approximation techniques in software (programs, frameworks, compilers, runtimes, languages), hardware (circuits, accelerators), and architectures (processors, memories). The current article is Part I of our comprehensive survey on Approximate Computing, and it reviews its motivation, terminology and principles, as well it classifies and presents the technical details of the state-of-the-art software and hardware approximation techniques.Comment: Under Review at ACM Computing Survey

    Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse

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    This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses. This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups. In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena

    The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

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    The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution

    On the Principles of Evaluation for Natural Language Generation

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    Natural language processing is concerned with the ability of computers to understand natural language texts, which is, arguably, one of the major bottlenecks in the course of chasing the holy grail of general Artificial Intelligence. Given the unprecedented success of deep learning technology, the natural language processing community has been almost entirely in favor of practical applications with state-of-the-art systems emerging and competing for human-parity performance at an ever-increasing pace. For that reason, fair and adequate evaluation and comparison, responsible for ensuring trustworthy, reproducible and unbiased results, have fascinated the scientific community for long, not only in natural language but also in other fields. A popular example is the ISO-9126 evaluation standard for software products, which outlines a wide range of evaluation concerns, such as cost, reliability, scalability, security, and so forth. The European project EAGLES-1996, being the acclaimed extension to ISO-9126, depicted the fundamental principles specifically for evaluating natural language technologies, which underpins succeeding methodologies in the evaluation of natural language. Natural language processing encompasses an enormous range of applications, each with its own evaluation concerns, criteria and measures. This thesis cannot hope to be comprehensive but particularly addresses the evaluation in natural language generation (NLG), which touches on, arguably, one of the most human-like natural language applications. In this context, research on quantifying day-to-day progress with evaluation metrics lays the foundation of the fast-growing NLG community. However, previous works have failed to address high-quality metrics in multiple scenarios such as evaluating long texts and when human references are not available, and, more prominently, these studies are limited in scope, given the lack of a holistic view sketched for principled NLG evaluation. In this thesis, we aim for a holistic view of NLG evaluation from three complementary perspectives, driven by the evaluation principles in EAGLES-1996: (i) high-quality evaluation metrics, (ii) rigorous comparison of NLG systems for properly tracking the progress, and (iii) understanding evaluation metrics. To this end, we identify the current state of challenges derived from the inherent characteristics of these perspectives, and then present novel metrics, rigorous comparison approaches, and explainability techniques for metrics to address the identified issues. We hope that our work on evaluation metrics, system comparison and explainability for metrics inspires more research towards principled NLG evaluation, and contributes to the fair and adequate evaluation and comparison in natural language processing

    A Practical Framework for Storing and Searching Encrypted Data on Cloud Storage

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    Security has become a significant concern with the increased popularity of cloud storage services. It comes with the vulnerability of being accessed by third parties. Security is one of the major hurdles in the cloud server for the user when the user data that reside in local storage is outsourced to the cloud. It has given rise to security concerns involved in data confidentiality even after the deletion of data from cloud storage. Though, it raises a serious problem when the encrypted data needs to be shared with more people than the data owner initially designated. However, searching on encrypted data is a fundamental issue in cloud storage. The method of searching over encrypted data represents a significant challenge in the cloud. Searchable encryption allows a cloud server to conduct a search over encrypted data on behalf of the data users without learning the underlying plaintexts. While many academic SE schemes show provable security, they usually expose some query information, making them less practical, weak in usability, and challenging to deploy. Also, sharing encrypted data with other authorized users must provide each document's secret key. However, this way has many limitations due to the difficulty of key management and distribution. We have designed the system using the existing cryptographic approaches, ensuring the search on encrypted data over the cloud. The primary focus of our proposed model is to ensure user privacy and security through a less computationally intensive, user-friendly system with a trusted third party entity. To demonstrate our proposed model, we have implemented a web application called CryptoSearch as an overlay system on top of a well-known cloud storage domain. It exhibits secure search on encrypted data with no compromise to the user-friendliness and the scheme's functional performance in real-world applications.Comment: 146 Pages, Master's Thesis, 6 Chapters, 96 Figures, 11 Table

    Acoustic modelling, data augmentation and feature extraction for in-pipe machine learning applications

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    Gathering measurements from infrastructure, private premises, and harsh environments can be difficult and expensive. From this perspective, the development of new machine learning algorithms is strongly affected by the availability of training and test data. We focus on audio archives for in-pipe events. Although several examples of pipe-related applications can be found in the literature, datasets of audio/vibration recordings are much scarcer, and the only references found relate to leakage detection and characterisation. Therefore, this work proposes a methodology to relieve the burden of data collection for acoustic events in deployed pipes. The aim is to maximise the yield of small sets of real recordings and demonstrate how to extract effective features for machine learning. The methodology developed requires the preliminary creation of a soundbank of audio samples gathered with simple weak annotations. For practical reasons, the case study is given by a range of appliances, fittings, and fixtures connected to pipes in domestic environments. The source recordings are low-reverberated audio signals enhanced through a bespoke spectral filter and containing the desired audio fingerprints. The soundbank is then processed to create an arbitrary number of synthetic augmented observations. The data augmentation improves the quality and the quantity of the metadata and automatically creates strong and accurate annotations that are both machine and human-readable. Besides, the implemented processing chain allows precise control of properties such as signal-to-noise ratio, duration of the events, and the number of overlapping events. The inter-class variability is expanded by recombining source audio blocks and adding simulated artificial reverberation obtained through an acoustic model developed for the purpose. Finally, the dataset is synthesised to guarantee separability and balance. A few signal representations are optimised to maximise the classification performance, and the results are reported as a benchmark for future developments. The contribution to the existing knowledge concerns several aspects of the processing chain implemented. A novel quasi-analytic acoustic model is introduced to simulate in-pipe reverberations, adopting a three-layer architecture particularly convenient for batch processing. The first layer includes two algorithms: one for the numerical calculation of the axial wavenumbers and one for the separation of the modes. The latter, in particular, provides a workaround for a problem not explicitly treated in the literature and related to the modal non-orthogonality given by the solid-liquid interface in the analysed domain. A set of results for different waveguides is reported to compare the dispersive behaviour against different mechanical configurations. Two more novel solutions are also included in the second layer of the model and concern the integration of the acoustic sources. Specifically, the amplitudes of the non-orthogonal modal potentials are obtained using either a distance minimisation objective function or by solving an analytical decoupling problem. In both cases, results show that sources sufficiently smooth can be approximated with a limited number of modes keeping the error below 1%. The last layer proposes a bespoke approach for the integration of the acoustic model into the synthesiser as a reverberation simulator. Additional elements of novelty relate to the other blocks of the audio synthesiser. The statistical spectral filter, for instance, is a batch-processing solution for the attenuation of the background noise of the source recordings. The signal-to-noise ratio analysis for both moderate and high noise levels indicates a clear improvement of several decibels against the closest filter example in the literature. The recombination of the audio blocks and the system of fully tracked annotations are also novel extensions of similar approaches recently adopted in other contexts. Moreover, a bespoke synthesis strategy is proposed to guarantee separable and balanced datasets. The last contribution concerns the extraction of convenient sets of audio features. Elements of novelty are introduced for the optimisation of the filter banks of the mel-frequency cepstral coefficients and the scattering wavelet transform. In particular, compared to the respective standard definitions, the average F-score performance of the optimised features is roughly 6% higher in the first case and 2.5% higher for the latter. Finally, the soundbank, the synthetic dataset, and the fundamental blocks of the software library developed are publicly available for further research
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