Social media mining for veterinary epidemiological surveillance

Extensive records are kept in the UK regarding large-scale farms, which include information on farm sizes, locations, disease outbreaks, and the movement of animals. This data enables a nuanced understanding of the disease risks associated with commercial farms. Unfortunately, there is a lack of documented data on small-scale farms, making it difficult to evaluate the risks linked with them, despite literature inferring that they play a crucial part in epidemiological surveillance. The primary aim of this project was to evaluate the viability of using social media data as an instrument of passive surveillance for both identifying smallholding communities and early disease detection. This includes assessing the availability and quality of sufficient data, in addition to deriving meaningful inferences about the animal health population within the United Kingdom. Through the use of numerous data science techniques, such as text classification, topic modelling, social network analysis, and spatio-temporal analysis, it was possible to gain insights into the demographics, concerns, and interactions of these communities. Offering a new perspective on disease surveillance and control for policymakers, veterinarians, and agricultural experts, social media platforms have great potential to supplement traditional surveillance, as indicated by the findings. While the research faced limitations, such as the rapidly evolving nature of social media and the specific focus on English-language platforms only, it still added valuable insights to the growing body of knowledge. With the ever-increasing integration of digital and physical domains in today’s world, this research points towards new opportunities for interdisciplinary research in data science and livestock farming. Main contributions from this work: • Digital Surveillance Mechanism: Formulated an innovative methodology for monitoring and analysing smallholder discussions, concerns and actions on the internet in niche fora. • Predictive Modelling: Machine learning models have been introduced that can classify smallholding users based on their profile descriptions, providing a valuable tool for rapid identification. • Disease Outbreak Analysis: Leveraged spatio-temporal analysis to link online discussions with real-world events, providing a potential early warning system for disease outbreaks. • Network Analysis: Unveiled the complex social dynamics of the smallholder community, pinpointing crucial nodes and pathways of information diffusion

Discrete Automation - Eyes of the City

Observing people’s presence in physical space and deciphering their behaviors have always been critical actions to designers, planners and anyone else who has an interest in exploring how cities work. It was 1961 when Jane Jacobs, in her seminal book “The Death and Life of Great American Cities”, coined a famous expression to convey this idea. According to Jacobs, “the natural proprietors” of a certain part of the metropolis – the people who live, work or spend a substantial amount of time there – become the “eyes on the street.” Their collective, distributed, decentralized gaze becomes the prerequisite to establishing “a marvelous order for maintaining the safety of the streets and the freedom of the city.” Almost half a century later, we find ourselves at the inception of a new chapter in the relationship between the city and digital technologies, which calls for a reexamination of the old “eyes on the street” idea. In the next few years, thanks to the most recent advances in Artificial Intelligence, deep learning and imaging, we are about to reach an unprecedented scenario, the most radical development in the evolution of the Internet-of-Things: architectural space is acquiring the full ability to “see.” Imagine that any room, street or shop in our city can recognize you, and autonomously respond to your presence. With Jacobs’s “eyes on the street,” it was people who looked at other people or the city and interpreted its mechanisms. In this new scenario, buildings and streets similarly acquire the ability to observe and react as urban life unfolds in front of them. After the “eyes on the street,” we are now entering the era of the “Eyes of the City.” What happens, then, to people and the urban landscape when the sensor-imbued city is able to gaze back? What we are currently facing is an “utopia or oblivion” crossroads, to say it with the words of one of the most notable thinkers of the past century, Richard Buckminster Fuller. We believe that one of the fundamental duties of architects and designers today is to grapple with this momentous shift, and engage people in the process. “Eyes of the City” aims to experiment with these emerging scenarios to better comprehend them, deconstructing the potential uses of new technologies in order to make them accessible to everyone and inspire people to form an opinion. Using critical design as a tool, the exhibition seeks to create experiences that will encourage people to get involved in defining the ways in which new technologies will shape their cities in years to come. For this reason, it recognizes in Shenzhen’s Futian high-speed railway station its natural home – a place where to reach a broad, diverse audience of intentional visitors and accidental passersby, and a space where, just like in most other liminal transportation hubs, the impact of an “Eyes of the City” scenario is likely going to be felt the most

Sensing the Cultural Significance with AI for Social Inclusion

Social Inclusion has been growing as a goal in heritage management. Whereas the 2011 UNESCO Recommendation on the Historic Urban Landscape (HUL) called for tools of knowledge documentation, social media already functions as a platform for online communities to actively involve themselves in heritage-related discussions. Such discussions happen both in “baseline scenarios” when people calmly share their experiences about the cities they live in or travel to, and in “activated scenarios” when radical events trigger their emotions. To organize, process, and analyse the massive unstructured multi-modal (mainly images and texts) user-generated data from social media efficiently and systematically, Artificial Intelligence (AI) is shown to be indispensable. This thesis explores the use of AI in a methodological framework to include the contribution of a larger and more diverse group of participants with user-generated data. It is an interdisciplinary study integrating methods and knowledge from heritage studies, computer science, social sciences, network science, and spatial analysis. AI models were applied, nurtured, and tested, helping to analyse the massive information content to derive the knowledge of cultural significance perceived by online communities. The framework was tested in case study cities including Venice, Paris, Suzhou, Amsterdam, and Rome for the baseline and/or activated scenarios. The AI-based methodological framework proposed in this thesis is shown to be able to collect information in cities and map the knowledge of the communities about cultural significance, fulfilling the expectation and requirement of HUL, useful and informative for future socially inclusive heritage management processes

Human-Computer Interaction: Security Aspects

Along with the rapid development of intelligent information age, users are having a growing interaction with smart devices. Such smart devices are interconnected together in the Internet of Things (IoT). The sensors of IoT devices collect information about users' behaviors from the interaction between users and devices. Since users interact with IoT smart devices for the daily communication and social network activities, such interaction generates a huge amount of network traffic. Hence, users' behaviors are playing an important role in the security of IoT smart devices, and the security aspects of Human-Computer Interaction are becoming significant. In this dissertation, we provide a threefold contribution: (1) we review security challenges of HCI-based authentication, and design a tool to detect deceitful users via keystroke dynamics; (2) we present the impact of users' behaviors on network traffic, and propose a framework to manage such network traffic; (3) we illustrate a proposal for energy-constrained IoT smart devices to be resilient against energy attack and efficient in network communication. More in detail, in the first part of this thesis, we investigate how users' behaviors impact on the way they interact with a device. Then we review the work related to security challenges of HCI-based authentication on smartphones, and Brain-Computer Interfaces (BCI). Moreover, we design a tool to assess the truthfulness of the information that users input using a computer keyboard. This tool is based on keystroke dynamics and it relies on machine learning technique to achieve this goal. To the best of our knowledge, this is the first work that associates the typing users' behaviors with the production of deceptive personal information. We reached an overall accuracy of 76% in the classification of a single answer as truthful or deceptive. In the second part of this thesis, we review the analysis of network traffic, especially related to the interaction between mobile devices and users. Since the interaction generates a huge amount of network traffic, we propose an innovative framework, GolfEngine, to manage and control the impact of users behavior on the network relying on Software Defined Networking (SDN) techniques. GolfEngine provides users a tool to build their security applications and offers Graphical User Interface (GUI) for managing and monitoring the network. In particular, GolfEngine provides the function of checking policy conflicts when users design security applications and the mechanism to check data storage redundancy. GolfEngine not only prevents the malicious inputting policies but also it enforces the security about network management of network traffic. The results of our simulation underline that GolfEngine provides an efficient, secure, and robust performance for managing network traffic via SDN. In the third and last part of this dissertation, we analyze the security aspects of battery-equipped IoT devices from the energy consumption perspective. Although most of the energy consumption of IoT devices is due to user interaction, there is still a significant amount of energy consumed by point-to-point communication and IoT network management. In this scenario, an adversary may hijack an IoT device and conduct a Denial of Service attack (DoS) that aims to run out batteries of other devices. Therefore, we propose EnergIoT, a novel method based on energetic policies that prevent such attacks and, at the same time, optimizes the communication between users and IoT devices, and extends the lifetime of the network. EnergIoT relies on a hierarchical clustering approach, based on different duty cycle ratios, to maximize network lifetime of energy-constrained smart devices. The results show that EnergIoT enhances the security and improves the network lifetime by 32%, compared to the earlier used approach, without sacrificing the network performance (i.e., end-to-end delay)

Representation Learning for Natural Language Processing

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

Voices from the South

This volume captures the status of digital humanities within the Arts in South Africa. The primary research methodology falls within the broader tradition of phenomenological hermeneutics, with a specific emphasis on visual hermeneutics. Some of the tools utilised as part of the visual hermeneutic methods are geographic information system (GIS) mapping, sensory ethnography and narrative pathways. Digital humanities is positioned here as the necessary engagement of the humanities with the pervasive digital culture of the 21st century. It is posited that the humanities and arts, in particular, have an essential role to play in unlocking meaning from scientific, technological and data-driven research. The critical engagement with digital humanities is foregrounded throughout the volume, as this crucial engagement works through images. Images (as understood within image studies) are not merely another form of text but always more than text. As such, this book is the first of its kind in the South African scholarly landscape, and notably also a first on the African continent. Its targeted audience include both scholars within the humanities, particularly in the arts and social sciences. Researchers pursuing the new field of digital humanities may also find the ideas presented in this book significant. Several of the chapters analyse the question of dealing with digital humanities through representations of the self as viewed from the Global South. However, it should be noted that self-representation is not the only area covered in this volume. The latter chapters of the book discuss innovative ways of implementing digital humanities strategies and methodologies for teaching and researching in South Africa

Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico

Conference proceedings info: ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies Raleigh, HI, United States, March 24-26, 2023 Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologías de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clínicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la Secretaría de Salud, el Centro de Comando, Comunicaciones y Control Informático. de la Secretaría del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-