6,605 research outputs found

    Radio frequency fingerprint identification for Internet of Things: A survey

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    Radio frequency fingerprint (RFF) identification is a promising technique for identifying Internet of Things (IoT) devices. This paper presents a comprehensive survey on RFF identification, which covers various aspects ranging from related definitions to details of each stage in the identification process, namely signal preprocessing, RFF feature extraction, further processing, and RFF identification. Specifically, three main steps of preprocessing are summarized, including carrier frequency offset estimation, noise elimination, and channel cancellation. Besides, three kinds of RFFs are categorized, comprising I/Q signal-based, parameter-based, and transformation-based features. Meanwhile, feature fusion and feature dimension reduction are elaborated as two main further processing methods. Furthermore, a novel framework is established from the perspective of closed set and open set problems, and the related state-of-the-art methodologies are investigated, including approaches based on traditional machine learning, deep learning, and generative models. Additionally, we highlight the challenges faced by RFF identification and point out future research trends in this field

    An Optimal House Price Prediction Algorithm: XGBoost

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    An accurate prediction of house prices is a fundamental requirement for various sectors, including real estate and mortgage lending. It is widely recognized that a property’s value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighborhood. Meeting the diverse housing needs of individuals while balancing budget constraints is a primary concern for real estate developers. To this end, we addressed the house price prediction problem as a regression task and thus employed various machine learning (ML) techniques capable of expressing the significance of independent variables. We made use of the housing dataset of Ames City in Iowa, USA to compare XGBoost, support vector regressor, random forest regressor, multilayer perceptron, and multiple linear regression algorithms for house price prediction. Afterwards, we identified the key factors that influence housing costs. Our results show that XGBoost is the best performing model for house price prediction. Our findings present valuable insights and tools for stakeholders, facilitating more accurate property price estimates and, in turn, enabling more informed decision making to meet the housing needs of diverse populations while considering budget constraints

    A Trust Management Framework for Vehicular Ad Hoc Networks

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    The inception of Vehicular Ad Hoc Networks (VANETs) provides an opportunity for road users and public infrastructure to share information that improves the operation of roads and the driver experience. However, such systems can be vulnerable to malicious external entities and legitimate users. Trust management is used to address attacks from legitimate users in accordance with a user’s trust score. Trust models evaluate messages to assign rewards or punishments. This can be used to influence a driver’s future behaviour or, in extremis, block the driver. With receiver-side schemes, various methods are used to evaluate trust including, reputation computation, neighbour recommendations, and storing historical information. However, they incur overhead and add a delay when deciding whether to accept or reject messages. In this thesis, we propose a novel Tamper-Proof Device (TPD) based trust framework for managing trust of multiple drivers at the sender side vehicle that updates trust, stores, and protects information from malicious tampering. The TPD also regulates, rewards, and punishes each specific driver, as required. Furthermore, the trust score determines the classes of message that a driver can access. Dissemination of feedback is only required when there is an attack (conflicting information). A Road-Side Unit (RSU) rules on a dispute, using either the sum of products of trust and feedback or official vehicle data if available. These “untrue attacks” are resolved by an RSU using collaboration, and then providing a fixed amount of reward and punishment, as appropriate. Repeated attacks are addressed by incremental punishments and potentially driver access-blocking when conditions are met. The lack of sophistication in this fixed RSU assessment scheme is then addressed by a novel fuzzy logic-based RSU approach. This determines a fairer level of reward and punishment based on the severity of incident, driver past behaviour, and RSU confidence. The fuzzy RSU controller assesses judgements in such a way as to encourage drivers to improve their behaviour. Although any driver can lie in any situation, we believe that trustworthy drivers are more likely to remain so, and vice versa. We capture this behaviour in a Markov chain model for the sender and reporter driver behaviours where a driver’s truthfulness is influenced by their trust score and trust state. For each trust state, the driver’s likelihood of lying or honesty is set by a probability distribution which is different for each state. This framework is analysed in Veins using various classes of vehicles under different traffic conditions. Results confirm that the framework operates effectively in the presence of untrue and inconsistent attacks. The correct functioning is confirmed with the system appropriately classifying incidents when clarifier vehicles send truthful feedback. The framework is also evaluated against a centralized reputation scheme and the results demonstrate that it outperforms the reputation approach in terms of reduced communication overhead and shorter response time. Next, we perform a set of experiments to evaluate the performance of the fuzzy assessment in Veins. The fuzzy and fixed RSU assessment schemes are compared, and the results show that the fuzzy scheme provides better overall driver behaviour. The Markov chain driver behaviour model is also examined when changing the initial trust score of all drivers

    An innovative network intrusion detection system (NIDS): Hierarchical deep learning model based on Unsw-Nb15 dataset

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    With the increasing prevalence of network intrusions, the development of effective network intrusion detection systems (NIDS) has become crucial. In this study, we propose a novel NIDS approach that combines the power of long short-term memory (LSTM) and attention mechanisms to analyze the spatial and temporal features of network traffic data. We utilize the benchmark UNSW-NB15 dataset, which exhibits a diverse distribution of patterns, including a significant disparity in the size of the training and testing sets. Unlike traditional machine learning techniques like support vector machines (SVM) and k-nearest neighbors (KNN) that often struggle with limited feature sets and lower accuracy, our proposed model overcomes these limitations. Notably, existing models applied to this dataset typically require manual feature selection and extraction, which can be time-consuming and less precise. In contrast, our model achieves superior results in binary classification by leveraging the advantages of LSTM and attention mechanisms. Through extensive experiments and evaluations with state-of-the-art ML/DL models, we demonstrate the effectiveness and superiority of our proposed approach. Our findings highlight the potential of combining LSTM and attention mechanisms for enhanced network intrusion detection

    Driving venture capital funding efficiencies through data driven models. Why is this important and what are its implications for the startup ecosystem?

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    This thesis aims to test whether data models can fit the venture capital funding process better, and if they do fit, can they help improve the venture capital funding efficiency? Based on the reported results, venture capitalists can only see returns in 20% of their investments. The thesis argues that it is essential to help venture capital investment as it can help drive economic growth through investments in innovation. The thesis considers four startup scenarios and the related investment factors. The scenarios are a funded artificial intelligence startup seeking follow-on funding, a new startup seeking first funding, the survivability of a sustainability-focused startup, and the importance of patents for exit. Patents are a proxy for innovation in this thesis. Through quantitative analysis using generalized linear models, logit regressions, and t-tests, the thesis can establish that data models can identify the relative significance of funding factors. Once the factor significance is established, it can be deployed in a model. Building the machine learning model has been considered outside the scope of this thesis. A mix of academic and real-world research has been used for the data analysis of this thesis. Accelerators and venture capitalists also used some of the results to improve their own processes. Many of the models have shifted from a prediction to factor significance. This thesis implies that it could help venture capitalists plan for a 10% efficiency improvement. From an academic perspective, this study focuses on the entire life of a startup, from the first funding stage to the exit. It also links the startup ecosystem with economic development. Two additional factors from the study are the regional perspective of funding differences between Asia, Europe, and the US and that this study would include the recent economic sentiment. The impact of the funding slowdown has been measured through a focus on first funding and longitudinal validations of the data decision before the slowdown. Based on the results of the thesis, data models are a credible alternative and show significant correlations between returns and factors. It is advisable for a venture capitalist to consider these

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Pragmatic randomised controlled trial of guided self-help versus individual cognitive behavioural therapy with a trauma focus for post-traumatic stress disorder (RAPID)

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    This is the final version. Available on open access from the NIHR Journals Library via the DOI in this recordData availability: All available data can be obtained from the corresponding author.BACKGROUND: Guided self-help has been shown to be effective for other mental conditions and, if effective for post-traumatic stress disorder, would offer a time-efficient and accessible treatment option, with the potential to reduce waiting times and costs. OBJECTIVE: To determine if trauma-focused guided self-help is non-inferior to individual, face-to-face cognitive-behavioural therapy with a trauma focus for mild to moderate post-traumatic stress disorder to a single traumatic event. DESIGN: Multicentre pragmatic randomised controlled non-inferiority trial with economic evaluation to determine cost-effectiveness and nested process evaluation to assess fidelity and adherence, dose and factors that influence outcome (including context, acceptability, facilitators and barriers, measured qualitatively). Participants were randomised in a 1 : 1 ratio. The primary analysis was intention to treat using multilevel analysis of covariance. SETTING: Primary and secondary mental health settings across the United Kingdom's National Health Service. PARTICIPANTS: One hundred and ninety-six adults with a primary diagnosis of mild to moderate post-traumatic stress disorder were randomised with 82% retention at 16 weeks and 71% at 52 weeks. Nineteen participants and ten therapists were interviewed for the process evaluation. INTERVENTIONS: Up to 12 face-to-face, manualised, individual cognitive-behavioural therapy with a trauma focus sessions, each lasting 60-90 minutes, or to guided self-help using Spring, an eight-step online guided self-help programme based on cognitive-behavioural therapy with a trauma focus, with up to five face-to-face meetings of up to 3 hours in total and four brief telephone calls or e-mail contacts between sessions. MAIN OUTCOME MEASURES: Primary outcome: the Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, at 16 weeks post-randomisation. Secondary outcomes: included severity of post-traumatic stress disorder symptoms at 52 weeks, and functioning, symptoms of depression, symptoms of anxiety, alcohol use and perceived social support at both 16 and 52 weeks post-randomisation. Those assessing outcomes were blinded to group assignment. RESULTS: Non-inferiority was demonstrated at the primary end point of 16 weeks on the Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [mean difference 1.01 (one-sided 95% CI -∞ to 3.90, non-inferiority p = 0.012)]. Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, score improvements of over 60% in both groups were maintained at 52 weeks but the non-inferiority results were inconclusive in favour of cognitive-behavioural therapy with a trauma focus at this timepoint [mean difference 3.20 (one-sided 95% confidence interval -∞ to 6.00, non-inferiority p = 0.15)]. Guided self-help using Spring was not shown to be more cost-effective than face-to-face cognitive-behavioural therapy with a trauma focus although there was no significant difference in accruing quality-adjusted life-years, incremental quality-adjusted life-years -0.04 (95% confidence interval -0.10 to 0.01) and guided self-help using Spring was significantly cheaper to deliver [£277 (95% confidence interval £253 to £301) vs. £729 (95% CI £671 to £788)]. Guided self-help using Spring appeared to be acceptable and well tolerated by participants. No important adverse events or side effects were identified. LIMITATIONS: The results are not generalisable to people with post-traumatic stress disorder to more than one traumatic event. CONCLUSIONS: Guided self-help using Spring for mild to moderate post-traumatic stress disorder to a single traumatic event appears to be non-inferior to individual face-to-face cognitive-behavioural therapy with a trauma focus and the results suggest it should be considered a first-line treatment for people with this condition. FUTURE WORK: Work is now needed to determine how best to effectively disseminate and implement guided self-help using Spring at scale. TRIAL REGISTRATION: This trial is registered as ISRCTN13697710. FUNDING: This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 14/192/97) and is published in full in Health Technology Assessment; Vol. 27, No. 26. See the NIHR Funding and Awards website for further award information.National Institute for Health and Care Research (NIHR

    Fairness-aware Machine Learning in Educational Data Mining

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    Fairness is an essential requirement of every educational system, which is reflected in a variety of educational activities. With the extensive use of Artificial Intelligence (AI) and Machine Learning (ML) techniques in education, researchers and educators can analyze educational (big) data and propose new (technical) methods in order to support teachers, students, or administrators of (online) learning systems in the organization of teaching and learning. Educational data mining (EDM) is the result of the application and development of data mining (DM), and ML techniques to deal with educational problems, such as student performance prediction and student grouping. However, ML-based decisions in education can be based on protected attributes, such as race or gender, leading to discrimination of individual students or subgroups of students. Therefore, ensuring fairness in ML models also contributes to equity in educational systems. On the other hand, bias can also appear in the data obtained from learning environments. Hence, bias-aware exploratory educational data analysis is important to support unbiased decision-making in EDM. In this thesis, we address the aforementioned issues and propose methods that mitigate discriminatory outcomes of ML algorithms in EDM tasks. Specifically, we make the following contributions: We perform bias-aware exploratory analysis of educational datasets using Bayesian networks to identify the relationships among attributes in order to understand bias in the datasets. We focus the exploratory data analysis on features having a direct or indirect relationship with the protected attributes w.r.t. prediction outcomes. We perform a comprehensive evaluation of the sufficiency of various group fairness measures in predictive models for student performance prediction problems. A variety of experiments on various educational datasets with different fairness measures are performed to provide users with a broad view of unfairness from diverse aspects. We deal with the student grouping problem in collaborative learning. We introduce the fair-capacitated clustering problem that takes into account cluster fairness and cluster cardinalities. We propose two approaches, namely hierarchical clustering and partitioning-based clustering, to obtain fair-capacitated clustering. We introduce the multi-fair capacitated (MFC) students-topics grouping problem that satisfies students' preferences while ensuring balanced group cardinalities and maximizing the diversity of members regarding the protected attribute. We propose three approaches: a greedy heuristic approach, a knapsack-based approach using vanilla maximal 0-1 knapsack formulation, and an MFC knapsack approach based on group fairness knapsack formulation. In short, the findings described in this thesis demonstrate the importance of fairness-aware ML in educational settings. We show that bias-aware data analysis, fairness measures, and fairness-aware ML models are essential aspects to ensure fairness in EDM and the educational environment.Ministry of Science and Culture of Lower Saxony/LernMINT/51410078/E

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Machine learning applications in search algorithms for gravitational waves from compact binary mergers

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    Gravitational waves from compact binary mergers are now routinely observed by Earth-bound detectors. These observations enable exciting new science, as they have opened a new window to the Universe. However, extracting gravitational-wave signals from the noisy detector data is a challenging problem. The most sensitive search algorithms for compact binary mergers use matched filtering, an algorithm that compares the data with a set of expected template signals. As detectors are upgraded and more sophisticated signal models become available, the number of required templates will increase, which can make some sources computationally prohibitive to search for. The computational cost is of particular concern when low-latency alerts should be issued to maximize the time for electromagnetic follow-up observations. One potential solution to reduce computational requirements that has started to be explored in the last decade is machine learning. However, different proposed deep learning searches target varying parameter spaces and use metrics that are not always comparable to existing literature. Consequently, a clear picture of the capabilities of machine learning searches has been sorely missing. In this thesis, we closely examine the sensitivity of various deep learning gravitational-wave search algorithms and introduce new methods to detect signals from binary black hole and binary neutron star mergers at previously untested statistical confidence levels. By using the sensitive distance as our core metric, we allow for a direct comparison of our algorithms to state-of-the-art search pipelines. As part of this thesis, we organized a global mock data challenge to create a benchmark for machine learning search algorithms targeting compact binaries. This way, the tools developed in this thesis are made available to the greater community by publishing them as open source software. Our studies show that, depending on the parameter space, deep learning gravitational-wave search algorithms are already competitive with current production search pipelines. We also find that strategies developed for traditional searches can be effectively adapted to their machine learning counterparts. In regions where matched filtering becomes computationally expensive, available deep learning algorithms are also limited in their capability. We find reduced sensitivity to long duration signals compared to the excellent results for short-duration binary black hole signals
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