Climatic classification: the semi-arid tropics and its environment a review.

This paper analyses the advantages and limitations in using the Troll, Hargreaves and modified Thornthwaite approaches for the demarcation of the semi-arid tropics. Data from India, Africa, Brazil, Australia and Thailand, were used for the comparison of these three methods. The modified Thornthwaite approach provided the most relevant agriculturally oriented demarcation of the semi-arid tropics. This method in not only simple, tut uses input data that are avaliable for a global network of stations. Using this method the semi-arid tropics include major dryland or rainfed agricultural zones with annual rainfall varying from about 400 to 1,250 mm. Major dryland crops are pearl millet, sorghum, pigeonpea and groundnut. This paper also presents the brief description of climate, soils and farming systems of the semi-arid tropics.

Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN

Different types of sentences express sentiment in very different ways. Traditional sentence-level sentiment classification research focuses on one-technique-fits-all solution or only centers on one special type of sentences. In this paper, we propose a divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type. Specifically, we find that sentences tend to be more complex if they contain more sentiment targets. Thus, we propose to first apply a neural network based sequence model to classify opinionated sentences into three types according to the number of targets appeared in a sentence. Each group of sentences is then fed into a one-dimensional convolutional neural network separately for sentiment classification. Our approach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines. Experimental results show that: (1) sentence type classification can improve the performance of sentence-level sentiment analysis; (2) the proposed approach achieves state-of-the-art results on several benchmarking datasets.

Ranganathan’s Layers of Classification Theory and the FASDA Model of Classification

Describes four waves of Ranganathan’s dynamic theory of classification. Outlines components that distinguish each wave, and porposes ways in which this understanding can inform systems design in the contemporary environment, particularly with regard to interoperability and scheme versioning. Ends with an appeal to better understanding the relationship between structure and semantics in faceted classification schemes and similar indexing languages.

Some Temporal Aspects of Indexing and Classification: Toward a Metrics for Measuring Scheme Change

In this paper we discuss the temporal aspects of indexing and classification in information systems. Basing this discussion off of the three sources of research of scheme change: of indexing: (1) analytical research on the types of scheme change and (2) empirical data on scheme change in systems and (3) evidence of cataloguer decision-making in the context of scheme change. From this general discussion we propose two constructs along which we might craft metrics to measure scheme change: collocative integrity and semantic gravity. The paper closes with a discussion of these constructs.

A Kaleidoscope Perspective: Change in the Semantics and Structure of Facets and Isolates in Analytico-Synthetic Classification

Examines the limitations of the dynamic theory of classification in accommodating the changes and rapid growth of new topics in the universe of knowledge. Change in an analytico-synthetic scheme for classification is much more a web of connections and mapping these changes is a complex process. Suggests that there is need for exploration of this complexity for both improving systems, and revisiting our theory.

Constructs in Knowledge Organization Systems: Rhythm in Time, Intention, and Form

In the context of the International Society for Knowledge Organization, we often consider knowledge organization systems to comprise catalogues, thesauri, and bibliothecal classification schemes – schemes for library arrangement. In recent years we have added ontologies and folksonomies to our sphere of study. In all of these cases it seems we are concerned with improving access to information. We want a good system.And much of the literature from the late 19th into the late 20th century took that as their goal – to analyze the world of knowledge and the structures of representing it as its objects of study; again, with the ethos for creating a good system. In most cases this meant we had to be correct in our assertions about the universe of knowledge and the relationships that obtain between its constituent parts. As a result much of the literature of knowledge organization is prescriptive – instructing designers and professionals how to build or use the schemes correctly – that is to maximize redundant success in accessing information.In 2005, there was a turn in some of the knowledge organization literature. It has been called the descriptive turn. This is in relation to the otherwise prescriptive efforts of researchers in KO. And it is the descriptive turn that makes me think of context, languages, and cultures in knowledge organization–the theme of this year’s conference.Work in the descriptive turn questions the basic assumptions about what we want to do when we create, implement, maintain, and evaluate knowledge organization systems. Following on these assumptions researchers have examined a wider range of systems and question the motivations behind system design. Online websites that allow users to curate their own collections are one such addition, for example Pinterest (cf., Feinberg, 2011). However, researchers have also looked back at other lineages of organizing to compare forms and functions. For example, encyclopedias, catalogues raisonnés, archival description, and winter counts designed and used by Native Americans.In this case of online curated collections, Melanie Feinberg has started to examine the craft of curation, as she calls it. In this line of research purpose, voice, and rhetorical stance surface as design considerations. For example, in the case of the Pinterest, users are able and encouraged to create boards. The process of putting together these boards is an act of curation in contemporary terminology. It is describing this craft that comes from the descriptive turn in KO.In the second case, when researchers in the descriptive turn look back at older and varied examples of knowledge organization systems, we are looking for a full inventory of intent and inspiration for future design. Encyclopedias, catalogues raisonnés, archival description, and works of knowledge organization in other cultures provide a rich world for the descriptive turn. And researchers have availed themselves of this.

Experientialist Epistemology and Classification Theory: Embodied and Dimensional Classification

What theoretical framework can help in building, maintaining and evaluating networked knowledge organization resources? Specifically, what theoretical framework makes sense of the semantic prowess of ontologies and peer-to-peer sys- tems, and by extension aids in their building, maintenance, and evaluation? I posit that a theoretical work that weds both for- mal and associative (structural and interpretive) aspects of knowledge organization systems provides that framework. Here I lay out the terms and the intellectual constructs that serve as the foundation for investigative work into experientialist classifi- cation theory, a theoretical framework of embodied, infrastructural, and reified knowledge organization. I build on the inter- pretive work of scholars in information studies, cognitive semantics, sociology, and science studies. With the terms and the framework in place, I then outline classification theory s critiques of classificatory structures. In order to address these cri- tiques with an experientialist approach an experientialist semantics is offered as a design commitment for an example: metadata in peer-to-peer network knowledge organization structures.

Fringe Types and KOS Systematics: Examining the Limits of the Population Perspective of Knowledge Organization Systems

We find ourselves, after the close of the twentieth century, looking back at a mass of responses to the knowledge organization problem. Many institutions, such as the Dewey Decimal Classification (Furner, 2007), have grown up to address it. Increasingly, many diverse discourses are appropriating the problem and crafting a wide variety of responses. This includes many artistic interpretations of the act and products of knowledge organization. These surface as responses to the expressive power or limits of the Library and Information Studies institutions (e.g., DDC) and their often primarily utilitarian gaze.One way to make sense of this diversity is to approach the study from a descriptive stance, inventorying the population of types of KOS. This population perspective approaches the phenomenon of types and boundaries of Knowledge Organization Systems (KOS) as one that develops out of particular discourses, for particular purposes. For example, both DDC and Martianus Capella, a 5th Century encyclopedist, are KOS in this worldview. Both are part of the population of KOS. Approaching the study of KOS from the population perspective allows the researcher a systematic look at the diversity emergent at the constellation of different factors of design and implementation. However, it is not enough to render a model of core types, but we have to also consider the borders of KOS. Fringe types of KOS inform research, specifically to the basic principles of design and implementation used by others outside of the scholarly and professional discourse of Library and Information Studies.Four examples of fringe types of KOS are presented in this paper. Applying a rubric developed in previous papers, our aim here is to show how the conceptual anatomy of these fringe types relates to more established KOS, thereby laying bare the definitions of domain, purpose, structure, and practice. Fringe types, like Beghtol’s examples (2003), are drawn from areas outside of Library and Information Studies proper, and reflect the reinvention of structures to fit particular purposes in particular domains. The four fringe types discussed in this paper are (1) Roland Barthes’ text S/Z which “indexes” a text of an essay with particular “codes” that are meant to expose the literary rhythm of the work; (2) Mary Daly’s Wickedary, a reference work crafted for radical liberation theology – and specifically designed to remove patriarchy from the language used by what the author calls “wild women”; (3) Luigi Serafini’s Codex Seraphinianus a work of book art that plays on the trope of universal encyclopedia and back-of- the book index; and (4) Martinaus Capella – and his Marriage of Mercury and Philology, a fifth century encyclopedia. We compared these using previous analytic taxonomies (Wright, 2008; Tennis, 2006; Tudhope, 2006, Soergel, 2001, Hodge, 2000).

A regulation-based classification system for marine protected areas (MPAs)

Marine protected areas (MPAs) are a global conservation and management tool to enhance the resilience of linked social-ecological systems with the aim of conserving biodiversity and providing ecosystem services for sustainable use. However, MPAs implemented worldwide include a large variety of zoning and management schemes from single to multiple-zoning and from no-take to multiple-use areas. The current IUCN categorisation of MPAs is based on management objectives which many times have a significant mismatch to regulations causing a strong uncertainty when evaluating global MPAs effectiveness. A novel global classification system for MPAs based on regulations of uses as an alternative or complementing, the current IUCN system of categories is presented. Scores for uses weighted by their potential impact on biodiversity were built. Each zone within a MPA was scored and an MPA index integrates the zone scores. This system classifies MPAs as well as each MPA zone individually, is globally applicable and unambiguously discriminates the impacts of uses. (C) 2016 The Authors. Published by Elsevier Ltd.

Above ground biomass estimation in Mediterranean multiple use systems with high spatial resolution satellite images

Forest biomass has been having an increasing importance in the world economy and in the evaluation of the forests development and monitoring. It was identified as a global strategic reserve, due to its applications in bioenergy, bioproduct development and issues related to reducing greenhouse gas emissions. The estimation of above ground biomass is frequently done with allometric functions per species with plot inventory data. An adequate sampling design and intensity for an error threshold is required. The estimation per unit area is done using an extrapolation method. This procedure is labour demanding and costly. The mail goal of this study is the development of allometric functions for the estimation of above ground biomass with ground cover as independent variable, for forest areas of holm aok (Quercus rotundifolia), cork oak (Quercus suber) and umbrella pine (Pinus pinea) in multiple use systems. Ground cover per species was derived from crown horizontal projection obtained by processing high resolution satellite images, orthorectified, geometrically and atmospheric corrected, with multi-resolution segmentation method and object oriented classification. Forest inventory data were used to estimate plot above ground biomass with published allometric functions at tree level. The developed functions were fitted for monospecies stands and for multispecies stands of Quercus rotundifolia and Quercus suber, and Quercus suber and Pinus pinea. The stand composition was considered adding dummy variables to distinguish monospecies from multispecies stands. The models showed a good performance. Noteworthy is that the dummy variables, reflecting the differences between species, originated improvements in the models. Significant differences were found for above ground biomass estimation with the functions with and without the dummy variables. An error threshold of 10% corresponds to stand areas of about 40 ha. This method enables the overall area evaluation, not requiring extrapolation procedures, for the three species, which occur frequently in multispecies stands.

Learning representations for graph-structured socio-technical systems

The recent widespread use of social media platforms and web services has led to a vast amount of behavioral data that can be used to model socio-technical systems. A significant part of this data can be represented as graphs or networks, which have become the prevalent mathematical framework for studying the structure and the dynamics of complex interacting systems. However, analyzing and understanding these data presents new challenges due to their increasing complexity and diversity. For instance, the characterization of real-world networks includes the need of accounting for their temporal dimension, together with incorporating higher-order interactions beyond the traditional pairwise formalism. The ongoing growth of AI has led to the integration of traditional graph mining techniques with representation learning and low-dimensional embeddings of networks to address current challenges. These methods capture the underlying similarities and geometry of graph-shaped data, generating latent representations that enable the resolution of various tasks, such as link prediction, node classification, and graph clustering. As these techniques gain popularity, there is even a growing concern about their responsible use. In particular, there has been an increased emphasis on addressing the limitations of interpretability in graph representation learning. This thesis contributes to the advancement of knowledge in the field of graph representation learning and has potential applications in a wide range of complex systems domains. We initially focus on forecasting problems related to face-to-face contact networks with time-varying graph embeddings. Then, we study hyperedge prediction and reconstruction with simplicial complex embeddings. Finally, we analyze the problem of interpreting latent dimensions in node embeddings for graphs. The proposed models are extensively evaluated in multiple experimental settings and the results demonstrate their effectiveness and reliability, achieving state-of-the-art performances and providing valuable insights into the properties of the learned representations.

Mind the gaps: cognitive-inspired AI for high-level visual sensemaking. Towards abstract concept image classification

The abundance of visual data and the push for robust AI are driving the need for automated visual sensemaking. Computer Vision (CV) faces growing demand for models that can discern not only what images "represent," but also what they "evoke." This is a demand for tools mimicking human perception at a high semantic level, categorizing images based on concepts like freedom, danger, or safety. However, automating this process is challenging due to entropy, scarcity, subjectivity, and ethical considerations. These challenges not only impact performance but also underscore the critical need for interoperability. This dissertation focuses on abstract concept-based (AC) image classification, guided by three technical principles: situated grounding, performance enhancement, and interpretability. We introduce ART-stract, a novel dataset of cultural images annotated with ACs, serving as the foundation for a series of experiments across four key domains: assessing the effectiveness of the end-to-end DL paradigm, exploring cognitive-inspired semantic intermediaries, incorporating cultural and commonsense aspects, and neuro-symbolic integration of sensory-perceptual data with cognitive-based knowledge. Our results demonstrate that integrating CV approaches with semantic technologies yields methods that surpass the current state of the art in AC image classification, outperforming the end-to-end deep vision paradigm. The results emphasize the role semantic technologies can play in developing both effective and interpretable systems, through the capturing, situating, and reasoning over knowledge related to visual data. Furthermore, this dissertation explores the complex interplay between technical and socio-technical factors. By merging technical expertise with an understanding of human and societal aspects, we advocate for responsible labeling and training practices in visual media. These insights and techniques not only advance efforts in CV and explainable artificial intelligence but also propel us toward an era of AI development that harmonizes technical prowess with deep awareness of its human and societal implications.

Addressing challenges in heterogeneous embedded systems programming: security and performance

Embedded systems are increasingly integral to daily life, improving and facilitating the efficiency of modern Cyber-Physical Systems which provide access to sensor data, and actuators. As modern architectures become increasingly complex and heterogeneous, their optimization becomes a challenging task. Additionally, ensuring platform security is important to avoid harm to individuals and assets. This study primarily addresses challenges in contemporary Embedded Systems, focusing on platform optimization and security enforcement. The initial section of this study delves into the application of machine learning methods to efficiently determine the optimal number of cores for a parallel RISC-V cluster to minimize energy consumption using static source code analysis. Results demonstrate that automated platform configuration is not only viable but also that there is a moderate performance trade-off when relying solely on static features. The second part focuses on addressing the problem of heterogeneous device mapping, which involves assigning tasks to the most suitable computational device in a heterogeneous platform for optimal runtime. The contribution of this section lies in the introduction of novel pre-processing techniques, along with a training framework called Siamese Networks, that enhances the classification performance of DeepLLVM, an advanced approach for task mapping. Importantly, these proposed approaches are independent from the specific deep-learning model used. Finally, this research work focuses on addressing issues concerning the binary exploitation of software running in modern Embedded Systems. It proposes an architecture to implement Control-Flow Integrity in embedded platforms with a Root-of-Trust, aiming to enhance security guarantees with limited hardware modifications. The approach involves enhancing the architecture of a modern RISC-V platform for autonomous vehicles by implementing a side-channel communication mechanism that relays control-flow changes executed by the process running on the host core to the Root-of-Trust. This approach has limited impact on performance and it is effective in enhancing the security of embedded platforms.

Motor Unit Spike Trains Recontruction from sEMG Signals for Gesture Classification on a Low-Power Processing Platform

Hand gesture recognition based on surface electromyography (sEMG) signals is a promising approach for the development of intuitive human-machine interfaces (HMIs) in domains such as robotics and prosthetics. The sEMG signal arises from the muscles' electrical activity, and can thus be used to recognize hand gestures. The decoding from sEMG signals to actual control signals is non-trivial; typically, control systems map sEMG patterns into a set of gestures using machine learning, failing to incorporate any physiological insight. This master thesis aims at developing a bio-inspired hand gesture recognition system based on neuromuscular spike extraction rather than on simple pattern recognition. The system relies on a decomposition algorithm based on independent component analysis (ICA) that decomposes the sEMG signal into its constituent motor unit spike trains, which are then forwarded to a machine learning classifier. Since ICA does not guarantee a consistent motor unit ordering across different sessions, 3 approaches are proposed: 2 ordering criteria based on firing rate and negative entropy, and a re-calibration approach that allows the decomposition model to retain information about previous sessions. Using a multilayer perceptron (MLP), the latter approach results in an accuracy up to 99.4% in a 1-subject, 1-degree of freedom scenario. Afterwards, the decomposition and classification pipeline for inference is parallelized and profiled on the PULP platform, achieving a latency < 50 ms and an energy consumption < 1 mJ. Both the classification models tested (a support vector machine and a lightweight MLP) yielded an accuracy > 92% in a 1-subject, 5-classes (4 gestures and rest) scenario. These results prove that the proposed system is suitable for real-time execution on embedded platforms and also capable of matching the accuracy of state-of-the-art approaches, while also giving some physiological insight on the neuromuscular spikes underlying the sEMG.

Automatic classification of stellar orbits in axisymmetric potentials

Within the classification of orbits in axisymmetric stellar systems, we present a new algorithm able to automatically classify the orbits according to their nature. The algorithm involves the application of the correlation integral method to the surface of section of the orbit; fitting the cumulative distribution function built with the consequents in the surface of section of the orbit, we can obtain the value of its logarithmic slope m which is directly related to the orbit’s nature: for slopes m ≈ 1 we expect the orbit to be regular, for slopes m ≈ 2 we expect it to be chaotic. With this method we have a fast and reliable way to classify orbits and, furthermore, we provide an analytical expression of the probability that an orbit is regular or chaotic given the logarithmic slope m of its correlation integral. Although this method works statistically well, the underlying algorithm can fail in some cases, misclassifying individual orbits under some peculiar circumstances. The performance of the algorithm benefits from a rich sampling of the traces of the SoS, which can be obtained with long numerical integration of orbits. Finally we note that the algorithm does not differentiate between the subtypes of regular orbits: resonantly trapped and untrapped orbits. Such distinction would be a useful feature, which we leave for future work. Since the result of the analysis is a probability linked to a Gaussian distribution, for the very definition of distribution, some orbits even if they have a certain nature are classified as belonging to the opposite class and create the probabilistic tails of the distribution. So while the method produces fair statistical results, it lacks in absolute classification precision.