The Imitation Game: Detecting Human and AI-Generated Texts in the Era of Large Language Models

The potential of artificial intelligence (AI)-based large language models (LLMs) holds considerable promise in revolutionizing education, research, and practice. However, distinguishing between human-written and AI-generated text has become a significant task. This paper presents a comparative study, introducing a novel dataset of human-written and LLM-generated texts in different genres: essays, stories, poetry, and Python code. We employ several machine learning models to classify the texts. Results demonstrate the efficacy of these models in discerning between human and AI-generated text, despite the dataset's limited sample size. However, the task becomes more challenging when classifying GPT-generated text, particularly in story writing. The results indicate that the models exhibit superior performance in binary classification tasks, such as distinguishing human-generated text from a specific LLM, compared to the more complex multiclass tasks that involve discerning among human-generated and multiple LLMs. Our findings provide insightful implications for AI text detection while our dataset paves the way for future research in this evolving area

From the Internet of Things to the web of things-enabling by sensing as-A service

© 2016 IEEE. Sensing as a Service (SenaaS) is emerging as a prominent element in the middleware linking together the Internet of Things (IoT) and the Web of Things (WoT) layers of future ubiquitous systems. An architecture framework is discussed in this paper whereby things are abstracted into services via embedded sensors which expose a thing as a service. The architecture acts as a blueprint to guide software architects realizing WoT applications. Web-enabled things are eventually appended into Web platforms such as Social Web platforms to drive data and services that are exposed by these things to interact with both other things and people, in order to materialize further the future social Web of Things. Research directions are discussed to illustrate the integration of SenaaS into the proposed WoT architectural framework

Suitability of Blockchain for Collaborative Intrusion Detection Systems

© 2020 IEEE. Cyber-security is indispensable as malicious incidents are ubiquitous on the Internet. Intrusion Detection Systems have an important role in detecting and thwarting cyber-attacks. However, it is more effective in a centralized system but not in peer-to-peer networks which makes it subject to central point failure, especially in collaborated intrusion detection systems. The novel blockchain technology assures a fully distributed security system through its powerful features of transparency, immutability, decentralization, and provenance. Therefore, in this paper, we investigate and demonstrate several methods of collaborative intrusion detection with blockchain to analyze the suitability and security of blockchain for collaborative intrusion detection systems. We also studied the difference between the existing means of the integration of intrusion detection systems with blockchain and categorized the major vulnerabilities of blockchain with their potential losses and current enhancements for mitigation

Collect, scope, and verify big data - A framework for institution accreditation

© 2016 IEEE. Institutions in higher education generate terabytesof data that has great value to shape future of nations. This Big Data is in heterogeneous formats, very current, and in large volumes. We propose a framework to collect, scope and verify this large amount of data. Although the framework is explained in the context of institution accreditation in higher education, the framework can be applied in the fields of health-care, finance, marketing etc. Our framework is used to reduce human involvement in the collection and analysis of data, for the purpose of accreditation. The framework extends the scope of data collected to target the heterogeneous nature of the data. Finally, our framework helps to verify the data against a standard set by an accreditation body

Facial image pre-processing and emotion classification: A deep learning approach

© 2019 IEEE. Facial emotion detection and expressions are vital for applications that require credibility assessment, evaluating truthfulness, and detection of deception. However, most of the research reveal low accuracy in emotion detection mainly due to the low quality of images under consideration. Conducting intensive pre-processing activities and using artificial intelligence especially deep learning techniques are increasing accuracy in computational predictions. Our research focuses on emotion detection using deep learning techniques and combined preprocessing activities. We propose a solution that applies and compares four deep learning models for image pre-processing with the main objective to improve emotion recognition accuracy. Our methodology includes three major stages in the data value chain, pre-processing, deep learning and post-processing. We evaluate the proposed scheme on a real facial data set, namely Facial Image Data of Indian Film Stars for our study. The experimentation compares the performance of various deep learning techniques on the facial image data and confirms that our approach enhanced significantly the image quality using intensive pre-processing and deep-learning, improves accuracy in emotion prediction

CrowdPower: A Novel Crowdsensing-as-a-Service Platform for Real-Time Incident Reporting

Crowdsensing using mobile phones is a novel addition to the Internet of Things applications suite. However, there are many challenges related to crowdsensing, including (1) the ability to manage a large number of mobile users with varying devices’ capabilities; (2) recruiting reliable users available in the location of interest at the right time; (3) handling various sensory data collected with different requirements and at different frequencies and scales; (4) brokering the relationship between data collectors and consumers in an efficient and scalable manner; and (5) automatically generating intelligence reports after processing the collected sensory data. No comprehensive end-to-end crowdsensing platform has been proposed despite a few attempts to address these challenges. In this work, we aim at filling this gap by proposing and describing the practical implementation of an end-to-end crowdsensing-as-a-service system dubbed CrowdPower. Our platform offers a standard interface for the management and brokerage of sensory data, enabling the transformation of raw sensory data into valuable smart city intelligence. Our solution includes a model for selecting participants for sensing campaigns based on the reliability and quality of sensors on users’ devices, then subsequently analysing the quality of the data provided using a clustering approach to predict user reputation and identify outliers. The platform also has an elaborate administration web portal developed to manage and visualize sensing activities. In addition to the architecture, design, and implementation of the backend platform capabilities, we also explain the creation of CrowdPower’s sensing mobile application that enables data collectors and consumers to participate in various sensing activities

Building sustainable parking lots with the Web of Things

Peak-time traffic woes create considerable amount of stress and environmental pollution resulting in an economic loss. Research innovations in areas such as the Web of Things are able to curtail some of these issues by creating scalable and sustainable environments like parking lots, which provide motorists with access to convenient parking spots. We present a scalable parking lot network infrastructure that exposes parking management operations through a judicious mashup of physical things\u27 services within a parking lot. Our system uses service-oriented architecture, allowing motorists to reserve parking spots in advance. In doing so, our proposed system leverages the use of HTTP and Wi-Fi for the Web enablement and interoperability of things within a parking spot and elevates it as a Smart Parking Spot on the Web. Our suggested semantic Web-based structure for representing things makes it possible to query physical things\u27 states and services depending on their capabilities and other relevant parking-related parameters. Our performance evaluation reveals that a maximum of 40 % time is saved to find parking spots and also 40 % reduction in air pollution is observed. © 2013 Springer-Verlag London

Securing the internet of things: A worst-case analysis of trade-off between query-anonymity and communication-cost

© 2017 IEEE. Cloud services are widely used to virtualize themanagement and actuation of the real-world the Internet ofThings (IoT). Due to the increasing privacy concerns regardingquerying untrusted cloud servers, query anonymity has becomea critical issue to all the stakeholders which are related toassessment of the dependability and security of the IoT system. The paper presents our study on the problem of query receiver-anonymityin the cloud-based IoT system, where the trade-offbetween the offered query-anonymity and the incurred communicationis considered. The paper will investigate whether theaccepted worst-case communication cost is sufficient to achieve aspecific query anonymity or not. By way of extensive theoreticalanalysis, it shows that the bounds of worst-case communicationcost is quadratically increased as the offered level of anonymityis increased, and they are quadratic in the network diameter forthe opposite range. Extensive simulation is conducted to verifythe analytical assertions

A Novel Quality and Reliability-Based Approach for Participants\u27 Selection in Mobile Crowdsensing

© 2013 IEEE. With the advent of mobile crowdsensing, we now have the possibility of tapping into the sensing capabilities of smartphones carried by citizens every day for the collection of information and intelligence about cities and events. Finding the best group of crowdsensing participants that can satisfy a sensing task in terms of data types required, while satisfying the quality, time, and budget constraints is a complex problem. Indeed, the time-constrained and location-based nature of crowdsensing tasks, combined with participants\u27 mobility, render the task of participants\u27 selection, a difficult task. In this paper, we propose a comprehensive and practical mobile crowdsensing recruitment model that offers reliability and quality-based approach for selecting the most reliable group of participants able to provide the best quality possible for the required sensory data. In our model, we adopt a group-based approach for the selection, in which a group of participants (gathered into sites) collaborate to achieve the sensing task using the combined capabilities of their smartphones. Our model was implemented using MATLAB and configured using realistic inputs such as benchmarked sensors\u27 quality scores, most widely used phone brands in different countries, and sensory data types associated with various events. Extensive testing was conducted to study the impact of various parameters on participants\u27 selection and gain an understanding of the compromises involved when deploying such process in practical MCS environments. The results obtained are very promising and provide important insights into the different aspects impacting the quality and reliability of the process of mobile crowdsensing participants\u27 selection

Continuous health monitoring using smartphones-A case-study for monitoring diabetic patients in UAE

© 2016 IEEE. With diabetes patients doubling every year especially in the UAE there is a need to curb this epidemic and help those who are affected to live an active life. Continuous monitoring of health indicators ensures prompt medical attention and reduction in fatalities. The primary challenge to continuously monitor diabetes is that glucose level measurement requires invasive methods. Moreover, continuous monitoring must happen remotely and therefore would require computing and networking technologies that is seamless, real-Time, high speed, and with large storage capacities. With the increasing penetration of smartphones, especially in the UAE, we propose a framework for continuous monitoring of diabetes on the smartphone platform. We also survey the state-of-The-Art in continuous health monitoring