894 research outputs found

    The Cryptic Communication Function of Anagrams in Specific Literary Contexts

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    The concept of anagrams as a form of cryptic communication has not gained much currency of usage in our contemporary world, particularly in the global South. Apart from its usage as a pastime in leisure and entertainment activities in games and puzzles, its usage has not transcended these pedantic forms and formats into more professional communicative utilities. This study is interested in investigating the communicative forms and formats inherent in the use of anagrams, especially in cryptic communication. It also hopes to emphasise the historical use of such cryptic systems as anagrams, secret codes, and underground symbolic and literary forms of communication, deliberate misinformation, disinformation and propaganda in our contemporary world. The study is nestled within the “Diffusion of Innovations theory” of mass media, which traces how people adopt a new idea or practice based on the available information. The study avers that much utility is inherent in the adoption of a cryptic communication system that can be derived from anagrams and other forms and systems of discreet, secret or crypto-communication, both for deific, ritualistic, medical, psycho-therapeutic, civilian and militaristic uses.   

    A Literature Review of Fault Diagnosis Based on Ensemble Learning

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    The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

    Modern meat: the next generation of meat from cells

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    Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community. The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World. The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia

    Cryptography: Recent Advances and Research Perspectives

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    Cryptography is considered as a branch of both mathematics and computer science, and it is related closely to information security. This chapter explores the earliest known cryptographic methods, including the scytale, Caesar cipher, substitution ciphers, and transposition ciphers. Also, explains the evolution of these methods over time. The development of symmetric and asymmetric key cryptography, hash functions, and digital signatures is also discussed. The chapter highlights major historical events and technological advancements that have driven the need for stronger and more efficient encryption methods. In addition, the chapter explores the potential for integrating artificial intelligence tools with cryptographic algorithms and the future of encryption technology

    Undergraduate and Graduate Course Descriptions, 2023 Spring

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    Wright State University undergraduate and graduate course descriptions from Spring 2023

    Reinforcement Learning Empowered Unmanned Aerial Vehicle Assisted Internet of Things Networks

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    This thesis aims towards performance enhancement for unmanned aerial vehicles (UAVs) assisted internet of things network (IoT). In this realm, novel reinforcement learning (RL) frameworks have been proposed for solving intricate joint optimisation scenarios. These scenarios include, uplink, downlink and combined. The multi-access technique utilised is non-orthogonal multiple access (NOMA), as key enabler in this regime. The outcomes of this research entail, enhancement in key performance metrics, such as sum-rate, energy efficiency and consequent reduction in outage. For the scenarios involving uplink transmissions by IoT devices, adaptive and tandem rein forcement learning frameworks have been developed. The aim is to maximise capacity over fixed UAV trajectory. The adaptive framework is utilised in a scenario wherein channel suitability is ascertained for uplink transmissions utilising a fixed clustering regime in NOMA. Tandem framework is utilised in a scenario wherein multiple-channel resource suitability is ascertained along with, power allocation, dynamic clustering and IoT node associations to NOMA clusters and channels. In scenarios involving downlink transmission to IoT devices, an ensemble RL (ERL) frame work is proposed for sum-rate enhancement over fixed UAV trajectory. For dynamic UAV trajec tory, hybrid decision framework (HDF) is proposed for energy efficiency optimisation. Downlink transmission power and bandwidth is managed for NOMA transmissions over fixed and dynamic UAV trajectories, facilitating IoT networks. In UAV enabled relaying scenario, for control system plants and their respective remotely deployed sensors, a head start reinforcement learning framework based on deep learning is de veloped and implemented. NOMA is invoked, in both uplink and downlink transmissions for IoT network. Dynamic NOMA clustering, power management and nodes association along with UAV height control is jointly managed. The primary aim is the, enhancement of net sum-rate and its subsequent manifestation in facilitating the IoT assisted use case. The simulation results relating to aforesaid scenarios indicate, enhanced sum-rate, energy efficiency and reduced outage for UAV-assisted IoT networks. The proposed RL frameworks surpass in performance in comparison to existing frameworks as benchmarks for the same sce narios. The simulation platforms utilised are MATLAB and Python, for network modeling, RL framework design and validation

    Environmentally-Aware and Energy-Efficient Multi-Drone Coordination and Networking for Disaster Response

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    In a Disaster Response Management (DRM) Scenario, Communication and Coordination Are Limited, and Absence of Related Infrastructure Hinders Situational Awareness. Unmanned Aerial Vehicles (UAVs) or Drones Provide New Capabilities for DRM to Address These Barriers. However, There is a Dearth of Works that Address Multiple Heterogeneous Drones Collaboratively Working Together to Form a Flying Ad-Hoc Network (FANET) with Air-To-Air and Air-To-Ground Links that Are Impacted By: (I) Environmental Obstacles, (Ii) Wind, and (Iii) Limited Battery Capacities. in This Paper, We Present a Novel Environmentally-Aware and Energy-Efficient Multi-Drone Coordination and Networking Scheme that Features a Reinforcement Learning (RL) based Location Prediction Algorithm Coupled with a Packet Forwarding Algorithm for Drone-To-Ground Network Establishment. We Specifically Present Two Novel Drone Location-Based Solutions (I.e., Heuristic Greedy, and Learning-Based) in Our Packet Forwarding Approach to Support Application Requirements. These Requirements Involve Improving Connectivity (I.e., Optimize Packet Delivery Ratio and End-To-End Delay) Despite Environmental Obstacles, and Improving Efficiency (I.e., by Lower Energy Use and Time Consumption) Despite Energy Constraints. We Evaluate Our Scheme with State-Of-The-Art Networking Algorithms in a Trace-Based DRM FANET Simulation Testbed Featuring Rural and Metropolitan Areas. Results Show that Our Strategy overcomes Obstacles and Can Achieve 81-To-90% of Network Connectivity Performance Observed under No Obstacle Conditions. in the Presence of Obstacles, Our Scheme Improves the Network Connectivity Performance by 14-To-38% While Also Providing 23-To-54% of Energy Savings in Rural Areas; the Same in Metropolitan Areas Achieved an Average of 25% Gain When Compared with Baseline Obstacle Awareness Approaches with 15-To-76% of Energy Savings

    Forensics from trusted computing and remote attestation

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    Abstract. The demand for forensics tools is ever-increasing as cyber-attacks become more frequent and devastating. The only way to maintain the system’s trusted state is to keep the mechanisms to uncover malware more competitive than cyber-attackers’ abilities to create them. We provide a digital forensics tool and procedures specifically tailored for integration with remote attestation. Example Root Cause Analysis investigations are performed, where digital forensics plays the main role of evidence provider.Rikostekninen tieto tietoturvallisesti käyttäen etätodennusta. Tiivistelmä. Tietoturvahyökkäysten yleistyessä tarve rikostodennustyökaluille lisääntyy. Ainut keino digitaalisten järjestelmien turvaamiseksi, on olla askeleen edellä tietoturvahyökkääjiä. Onnistuakseen tässä tavoitteessa tietoturvatutkijoiden on kehitettävä jatkuvasti tehokkaampia menetelmiä haittaohjelmien havaitsemiseksi. Tämä työ tarjoaa uuden digitaalisen rikostutkinnan työkalun, jota voidaan hyödyntää etätodennuksen kanssa. Työssä esitellään tutkintatapausesimerkkejä, joiden lopputuloksiin päästään hyödyntäen perussyyanalyysiä ja digitaalisen rikostutkinnan työkalua todistuaineiston tarjoajana

    Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts-Volume II

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    The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications, such as hybrid and microgrid power systems based on the Energy Internet, Blockchain technology, and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above
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