85 research outputs found
Machine-Learning-Powered Cyber-Physical Systems
In the last few years, we witnessed the revolution of the Internet of Things (IoT) paradigm and the consequent growth of Cyber-Physical Systems (CPSs). IoT devices, which include a plethora of smart interconnected sensors, actuators, and microcontrollers, have the ability to sense physical phenomena occurring in an environment and provide copious amounts of heterogeneous data about the functioning of a system. As a consequence, the large amounts of generated data represent an opportunity to adopt artificial intelligence and machine learning techniques that can be used to make informed decisions aimed at the optimization of such systems, thus enabling a variety of services and applications across multiple domains. Machine learning processes and analyses such data to generate a feedback, which represents a status the environment is in. A feedback given to the user in order to make an informed decision is called an open-loop feedback. Thus, an open-loop CPS is characterized by the lack of an actuation directed at improving the system itself. A feedback used by the system itself to actuate a change aimed at optimizing the system itself is called a closed-loop feedback. Thus, a closed-loop CPS pairs feedback based on sensing data with an actuation that impacts the system directly. In this dissertation, we propose several applications in the context of CPS. We propose open-loop CPSs designed for the early prediction, diagnosis, and persistency detection of Bovine Respiratory Disease (BRD) in dairy calves, and for gait activity recognition in horses.These works use sensor data, such as pedometers and automated feeders, to perform valuable real-field data collection. Data are then processed by a mix of state-of-the-art approaches as well as novel techniques, before being fed to machine learning algorithms for classification, which informs the user on the status of their animals. Our work further evaluates a variety of trade-offs. In the context of BRD, we adopt optimization techniques to explore the trade-offs of using sensor data as opposed to manual examination performed by domain experts. Similarly, we carry out an extensive analysis on the cost-accuracy trade-offs, which farmers can adopt to make informed decisions on their barn investments. In the context of horse gait recognition we evaluate the benefits of lighter classifications algorithms to improve energy and storage usage, and their impact on classification accuracy. With respect to closed-loop CPS we proposes an incentive-based demand response approach for Heating Ventilation and Air Conditioning (HVAC) designed for peak load reduction in the context of smart grids. Specifically, our approach uses machine learning to process power data from smart thermostats deployed in user homes, along with their personal temperature preferences. Our machine learning models predict power savings due to thermostat changes, which are then plugged into our optimization problem that uses auction theory coupled with behavioral science. This framework selects the set of users who fulfill the power saving requirement, while minimizing financial incentives paid to the users, and, as a consequence, their discomfort. Our work on BRD has been published on IEEE DCOSS 2022 and Frontiers in Animal Science. Our work on gait recognition has been published on IEEE SMARTCOMP 2019 and Elsevier PMC 2020, and our work on energy management and energy prediction has been published on IEEE PerCom 2022 and IEEE SMARTCOMP 2022. Several other works are under submission when this thesis was written, and are included in this document as well
Broadband Power Line Communication in Railway Traction Lines: A Survey
Power line communication (PLC) is a technology that exploits existing electrical transmission and distribution networks as guiding structures for electromagnetic signal propagation. This facilitates low-rate data transmission for signaling and control operations. As the demand in terms of data rate has greatly increased in the last years, the attention paid to broadband PLC (BPLC) has also greatly increased. This concept also extended to railways as broadband traction power line communication (BTPLC), aiming to offer railway operators an alternative data network in areas where other technologies are lacking. However, BTPLC implementation faces challenges due to varying operating scenarios like urban, rural, and galleries. Hence, ensuring coverage and service continuity demands the suitable characterization of the communication channel. In this regard, the scientific literature, which is an indicator of the body of knowledge related to BTPLC systems, is definitely poor if compared to that addressed to BPLC systems installed on the electrical transmission and distribution network. The relative papers dealing with BTPLC systems and focusing on the characterization of the communication channel show some theoretical approaches and, rarely, measurements guidelines and experimental results. In addition, to the best of the author's knowledge, there are no surveys that comprehensively address these aspects. To compensate for this lack of information, a survey of the state of the art concerning BTPLC systems and the measurement methods that assist their installation, assessment, and maintenance is presented. The primary goal is to provide the interested readers with a thorough understanding of the matter and identify the current research gaps, in order to drive future research towards the most significant issues
An exploration of virtual criminal investigations in Ghana : legal issues and challenges
The widespread cybercrime has caused changes and brought about a need for new investigative skills, laws and enforcement procedures to attack these obstacles. Since technological crimes committed through the information superhighway or the internet is evolving very rapidly, efficacious enforcement of cybercrime is becoming extremely challenging. Cybercrime is both a national and international issue and local legislation alone cannot be able to combat the menace. Digital evidence permeates every aspect of the average person's life in today's society and no matter what you are doing these days, a digital footprint is probably being created and contains some type of digital evidence that can be recovered through digital forensic investigation It requires stringent laws, skilled personnel, well-established institutions, and transnational response. To efficaciously combat cybercrime, countries, states or governments must establish an independent anti-cybercrime unit and design national guidelines for digital evidence collections to combat the canker. This thesis, therefore, presents an examination of the virtual crime or cybercrime investigation challenges and legal issues on electronic evidence in Ghana. The study examines the existing cybercrime laws and practices in Ghana and makes a comparative study from other jurisdictions. Also, the study draws a survey from the international legal framework on cybercrime and electronic evidence on various methods and procedures that can be used to conduct digital forensic search and seizure of electronic evidence and investigation when cybercrimes occur. Recommendations were made which include formulation of stringent laws, establishing the national Cybercrime investigation Strategy and policies, the establishment of national guidelines for digital evidence collections, develop anti-cybercrime tool-kit for the collection of digital evidence, the establishment of digital forensic training institutions in all regions of Ghana for hands-on skilled based training for law enforcement officers and judges to ensure efficiency in the process of digital forensic investigation and prosecution of cybercrimes in Ghana are given.Police PracticeD. Phil. (Criminal Justice
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-
An enhanced fuzzy commitment scheme in biometric template protection
Biometric template protection consists of two approaches; Feature Transformation (FT) and Biometric Cryptography (BC). This research focuses on Key-Binding Technique based on Fuzzy Commitment Scheme (FCS) under BC approach. In FCS, the helper data should not disclose any information about the biometric data. However, literatures showed that it had dependency issue in its helper data which jeopardize security and privacy. Moreover, this also increases the probability of privacy leakage which lead to attacks such as brute-force and cross-matching attack. Thus, the aim of this research is to reduce the dependency of helper data that can caused privacy leakage. Three objectives have been set such as (1) to identify the factors that cause dependency on biometric features (2) to enhance FCS by proposing an approach that reduces this dependency, and (3) to evaluate the proposed approach based on parameters such as security, privacy, and biometric performance. This research involved four phases. Phase one, involved research review and analysis, followed by designing conceptual model and algorithm development in phase two and three respectively. Phase four, involved with the evaluation of the proposed approach. The security and privacy analysis shows that with the additional hash function, it is difficult for adversary to perform brute‐force attack on information stored in database. Furthermore, the proposed approach has enhanced the aspect of unlinkability and prevents cross-matching attack. The proposed approach has achieved high accuracy of 95.31% with Equal Error Rate (EER) of 1.54% which performs slightly better by 1.42% compared to the existing approach. This research has contributed towards the key-binding technique of biometric fingerprint template protection, based on FCS. In particular, this research was designed to create a secret binary feature that can be used in other state-of-the-art cryptographic systems by using an appropriate error-correcting approach that meets security standards
Routledge Handbook of Public Policy in Africa
This Handbook provides an authoritative and foundational disciplinary overview of African Public Policy and a comprehensive examination of the practicalities of policy analysis, policymaking processes, implementation, and administration in Africa today. The book assembles a multidisciplinary team of distinguished and upcoming Africanist scholars, practitioners, researchers and policy experts working inside and outside Africa to analyse the historical and emerging policy issues in 21st-century Africa. While mostly attentive to comparative public policy in Africa, this book attempts to address some of the following pertinent questions: • How can public policy be understood and taught in Africa? • How does policymaking occur in unstable political contexts, or in states under pressure? • Has the democratisation of governing systems improved policy processes in Africa? • How have recent transformations, such as technological proliferation in Africa, impacted public policy processes? • What are the underlying challenges and potential policy paths for Africa going forward? The contributions examine an interplay of prevailing institutional, political, structural challenges and opportunities for policy effectiveness to discern striking commonalities and trajectories across different African states. This is a valuable resource for practitioners, politicians, researchers, university students, and academics interested in studying and understanding how African countries are governed
Measuring knowledge sharing processes through social network analysis within construction organisations
The construction industry is a knowledge intensive and information dependent industry. Organisations risk losing valuable knowledge, when the employees leave them. Therefore, construction organisations need to nurture opportunities to disseminate knowledge through strengthening knowledge-sharing networks. This study aimed at evaluating the formal and informal knowledge sharing methods in social networks within Australian construction organisations and identifying how knowledge sharing could be improved. Data were collected from two estimating teams in two case studies. The collected data through semi-structured interviews were analysed using UCINET, a Social Network Analysis (SNA) tool, and SNA measures. The findings revealed that one case study consisted of influencers, while the other demonstrated an optimal knowledge sharing structure in both formal and informal knowledge sharing methods. Social networks could vary based on the organisation as well as the individuals’ behaviour. Identifying networks with specific issues and taking steps to strengthen networks will enable
to achieve optimum knowledge sharing processes. This research offers knowledge sharing good practices for construction organisations to optimise their knowledge sharing processes
Harnessing Human Potential for Security Analytics
Humans are often considered the weakest link in cybersecurity. As a result, their potential has been continuously neglected. However, in recent years there is a contrasting development recognizing that humans can benefit the area of security analytics, especially in the case of security incidents that leave no technical traces. Therefore, the demand becomes apparent to see humans not only as a problem but also as part of the solution. In line with this shift in the perception of humans, the present dissertation pursues the research vision to evolve from a human-as-a-problem to a human-as-a-solution view in cybersecurity. A step in this direction is taken by exploring the research question of how humans can be integrated into security analytics to contribute to the improvement of the overall security posture. In addition to laying foundations in the field of security analytics, this question is approached from two directions. On the one hand, an approach in the context of the human-as-a-security-sensor paradigm is developed which harnesses the potential of security novices to detect security incidents while maintaining high data quality of human-provided information. On the other hand, contributions are made to better leverage the potential of security experts within a SOC. Besides elaborating the current state in research, a tool for determining the target state of a SOC in the form of a maturity model is developed. Based on this, the integration of security experts was improved by the innovative application of digital twins within SOCs. Accordingly, a framework is created that improves manual security analyses by simulating attacks within a digital twin. Furthermore, a cyber range was created, which offers a realistic training environment for security experts based on this digital twin
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