Miracle-dz/UniFAR-FTUs huggingface dataset:
A Real-time Machine Learning-based GPS Spoofing Solution for Location-dependent UAV Applications

In this paper, a three-class machine learning (ML) model is implemented on an unmanned aerial vehicle (UAV) with a Raspberry Pi processor for classifying two global positioning system (GPS) spoofing attacks (i.e., static, dynamic) in real-time. First, several models are developed and tested utilizing a dataset collected in a previous work. This dataset conveys GPS-specific features, including location information. Models evaluations are carried out using the detection rate, F-score, false alarm rate, and misdetection rate, which all showed an acceptable performance. Then, the optimum model is loaded to the processor and tested for real-time detection and classification. Location-dependent applications, such as fixed-route public transportations are expected to benefit from the methodology presented herein as the longitude, latitude, and altitude features are characterized in the developed model.
Semiparametric Slepian-Bangs Formula for Complex Elliptically Symmetric Distributions.

This letter aims at deriving a Semiparametric Slepian-Bangs (SSB) formula for Complex Elliptically Symmetric (CES) distributed data vectors. The Semiparametric Cram\'{e}r-Rao Bound (SCRB), related to the proposed SSB formula, provides a lower bound on the Mean Square Error (MSE) of \textit{any} robust estimator of a parameter vector parameterizing the mean vector and the scatter matrix of the given CES-distributed vector in the presence of an unknown, nuisance, density generator.
Ergodic Rate Analysis and Optimization of Full-Duplex STAR-RIS Communication Systems

Recently, the concept of simultaneously transmitting and reflecting based reconfigurable intelligent surface (STAR-RIS) has attracted research interest due to its capability of serving both half-spaces unlike conventional reflecting-only RIS. In this paper, we perform ergodic rate (ER) analysis for full-duplex based STAR-RIS communication and then maximize the same subject to quality-of-service constraints of the users. We first present closed-form expressions of ER for both uplink and downlink users over generalized Nakagami - m fading channels. Further, to minimize the effects inter-user interference and residual self-interference, particle swarm optimization algorithm is employed in jointly optimizing the transmission amplitude, element partitioning and user transmit powers. Extensive Monte-Carlo simulations are carried out over different system param-eters to verify accuracy of the results and useful inferences are obtained.
Deep Learning Based Algae Detection Method

The ocean is an important part of the ecosystem and is closely related to our lives. Detecting the status of algae in the ocean contributes to the protection of the marine environment. With the continuous development of target detection technology, small target detection tasks are gradually applied to the task of monitoring marine organisms. We use two-stage cascade RCNN with Res2Net, ResNeSt, CBNet, ConvNeXt and DetectoRS backbone. Secondly, data pre-processing was used with blur, motion blur, MixUp, random rotation and other data enhancements. Then the pseudo label training model is used as a pre-training model. And model ensemble is used to improve the inference results. Finally Post-processing is performed using reduced bbox. We conduct extensive experiments on the dataset and achieve the performance of 0.562.
An Easy-to-install Rear-Mountable Intelligent Street Light with Companion App for Mitigations of Urban Traffic Problems

As one of the essential parts of the smart city concept, the realization of intelligent transportation has become a trendy topic, and one of the fundamental ways to realize intelligent transportation is to introduce intelligent street lights. This paper mainly presents a design of a rear-mountable intelligent street light which can be installed on light posts easily. On one hand, based on the original street lights, it can fulfill all the functions of a street light after installation without the costs of replacing existing street lights. On the other hand, our approach can eliminate a lot of disassembly and installation procedures, which typically involve many engineering costs. On top of the functions provided by traditional street lights, we propose to include a AI-supported camera for traffic and parking monitoring, a full-coverage Wi-Fi access point, a laser sensor for intelligent monitoring of pedestrians crossing the road, and an electric car charging point. The co-development of the companion app can operate selected functions, for example, parking reservation and electric car charging, of the intelligent street lights. This proposal has been tested in a controlled lab environment which shows the feasibility of hardware selection, welding approaches, and the companion app design. Our future work aims to test our proposal against real-world environments and actual road conditions.
Autoencoder via DCNN and LSTM Models for Intrusion Detection in Industrial Control Systems of Critical Infrastructures

Industrial Control Systems (ICS) are widely used to carry out the fundamental functions of a society and are frequently employed in Critical Infrastructures (CIs). Consequently, protection against cyber-attacks is essential for these systems. Over the years, numerous cyber-attack detection system concepts have been proposed, each employing a distinct set of processes and methodologies. Despite this, there is a significant gap in the field of techniques for detecting cyber-attacks on ICS. Most existing studies used device logs, which require considerable pre-processing and understanding before they can be utilized for intrusion detection in an ICS environment. In this paper, we proposed an intrusion detection using an autoencoder for feature dimensionality reduction trained on network flow data via a Deep Convolutional Neural Network (DCNN) and Long Short-Term Memory (LSTM), which does not require prior knowledge of the underlying architecture and network's topology. The experimental analysis was performed on the ICS dataset and gas pipeline data given by Mississippi State University (MSU). The LSTM model achieved an accuracy greater than 99% and an AUC-ROC of 99.50% on the ICS data, whereas the DCNN model achieved an accuracy of 96.0% and an AUC-ROC of 97.20% on the gas pipeline network data, with extremely low false negatives and false positives. The results of the study showed that LSTM is superior to DCNN in detecting anomalies in ICS. In addition, the results disclosed that LSTM and DCNN are effective at time series prediction tasks. This observation is encouraging, as DCNN and LSTM are smaller, faster, and more straightforward than the deep neural network and recurrent neural networks utilized in previous research. The proposed IDS architecture is a low-cost, network-based solution that requires minimal processing, performs unsupervised, and is straightforward to implement in a real-world environment.
Philosophical Framework for Business System Modeling

As an infrastructure, the information system should be based on a comprehensive understanding of the business system that it is intended to support. Therefore, one of the essential principles of information systems development methodologies is the Principle of Modeling. This principle, expressing that the information system is always a model of the Real World business system, persists despite the abrupt development of new and popular approaches and methods that often, unfortunately, cannot be considered an evolutionary step forward. Current ideas like the digital transformation or business-IT alignment that dismantle the border between a business system and its information system make the importance of this principle even stronger. In the paper, the comprehensive four-dimensional information model of the business system is defined based on the MMABP Philosophical Framework for Business System Modeling. The two basic phenomena that form the business system and two points of view on the business system are explained together with their manifestation in the modeling methods in informatics. Finally, we also shortly discuss emerging challenges for these methods following the framework.
Distributed Consensus Control of Homogeneous Vehicle Platoons With Bidirectional Communication

Vehicle platooning is deemed a promising solution to improve traffic safety, reduce fuel consumption and increase traffic throughput and road capacity. Road throughput can be increased by driving at small inter-vehicle distances. Adjusting the spacing with both the preceding and the succeeding vehicles using bidirectional communication improves the cohesion of a vehicle string. In this paper, a distributed consensus controller is presented which relies on bidirectional platoon communication. Along with the consensus-based controller synthesis, vehicle stability, and an extended string stability analysis, the effectiveness of the controller is demonstrated in a simulation study.
Bayesian sparse regularization for multiple force identification and location in time domain

ABSTRACT In this work, reconstruction and location in time domain of multiple forces acting on a linear elastic structure are achieved through a Bayesian approach to solve an inverse problem. The Bayesian solution of the inverse problem is provided in the form of a posterior probability density function. The unknown forces are determined through Markov chain Monte Carlo method, the Gibbs algorithm. This posterior density integrating both the likelihood and prior information was considered for the particular case of a linear elastic structure. The measurements are affected by an additive random noise. Two particular cases were analysed: unperturbed and uncertain model representing the structure. The unperturbed model was used to identify a single force. When the model is uncertain, compressed sensing technique was used to provide an adequate sparse representation of the inverse problem through a wavelet basis. With this sparse representation, the possibility of achieving automatic location of the forces was investigated. This requires to identify all the degrees of freedom along with the identified actions are not vanishing. Also, the possibility of force identification with less sensors than forces was studied. The proposed approach is illustrated and validated on numerical examples. This proposed approach is compared with classical approach of force identification based on Tikhonov regularization associated with the GCV criterion.
Inverted Beta Lindley Distribution

In this paper, a three-parameter continuous distribution, namely, Inverted Beta-Lindley (IBL) distribution is proposed and studied. The new model turns out to be quite flexible for analyzing positive data and has various shapes of density and hazard rate functions. Several statistical properties associated with this distribution are derived. Moreover, point estimation via method of moments and maximum likelihood method are studied and the observed information matrix is derived. An application of the new model to real data shows that it can give consistently a better fit than other important lifetime models.
Extreme-Aware Local-Global Attention for Spatio-Temporal Urban Mobility Learning

The occurrence of special contexts or events (e.g., extreme weather conditions, festival events, other urban anomalies) can significantly influence the movement patterns of urban mobility (e.g., human crowds, transportation systems). Accurate mobility modeling and prediction under the occurrences of such anomaly events is therefore imperative for city management and urban resource allocation. In this study, we propose EALGAP, a novel Extreme-Aware Local-Global Attention urban mobility Prediction model. Specifically, EALGAP models the spatio-temporal global and local impacts of mobility in different regions and time steps for mobility prediction at various city regions. EALGAP takes into account the global impacts by extracting the overall or regular spatial dependencies and temporal patterns of mobility systems for different regions. We have designed a temporally-varying normalization and data-driven technique to quantify the extreme degrees, i.e., how significantly the extreme events have impacted the local mobility trend, of the patterns within different regions and time steps. We have conducted ex-tensive experimental studies upon four different mobility datasets (over 13 million trips in total) harvested from two metropolitan cities in U.S. with anomalous natural or social events (e.g., hurricane events, other extreme weather conditions, and the Federal holidays). Our results have demonstrated the accuracy, effectiveness, and extreme-awareness of our proposed EALGAP with more than 44.12% error reduction on average compared with other state-of-the-art approaches.
Domain Modelling For A Lightweight Convolutional Network Focused On Automated Exudate Detection in Retinal Fundus Images

Retinal Fundus images contain important signs implying various stages of diabetic retinopathy. One of the early signs of diabetic retinopathy is exudates. The timely detection of this sign from fundus images can prevent or at least suppress the advancement of this condition. To this effect, this research presents a CNN-based automated exudate detection architecture for the timely detection of this early sign. In order to overcome the challenges of a limited dataset several domain specific augmentations are proposed for improving the generalization capacity of the developed architecture. The lightweight architecture consists of only 6.42 million parameters, compared to Resnet-18 (11.69 million) whilst achieving an overall F1 score of 89%
A 300-GHz 52-mW CMOS Receiver With On-Chip LO Generation

A fully integrated receiver employs a heterodyne architecture with 270- and 27-GHz local oscillators to alleviate phase mismatch issues. The system incorporates three on-chip phase-locked loops (PLLs) to generate the local oscillator phases for both downconversions. Realized in 28-nm CMOS technology, the prototype exhibits a noise figure of 16–20 dB, a gain of 17–21 dB, and a 1-dB compression point of −17.3 dBm. The phase noise (PN) of the 270-GHz PLL is −105 dBc/Hz at 10-MHz offset, amounting to an integrated jitter of 106 fs from 10 kHz to 10 MHz.
A Multipronged Approach for Modeling Menopausal Health Using Ensemble Learning

This study strives to investigate the effectiveness of ensemble learning methods in analyzing the sentiments of menopausal experiences as manifested on Twitter. To achieve this objective, we leveraged data crawling techniques to collect pertinent Twitter data from two different time periods i.e., February 2023 and March 2023, which we analyzed using ensemble learning approaches. By doing so, we aimed to augment the precision and robustness of our sentiment analysis results. Specifically, we collected relevant Twitter data relating to menopause by using specific keywords and applied the term frequency-inverse document frequency (TF-IDF) algorithm to extract features. However, our dataset exhibited class imbalance, which we addressed using the Synthetic Minority Over-sampling Technique (SMOTE). Subsequently, we trained several ensemble learning models, including bagging, boosting, and random forest, using the sci-kit-learn library in Python. We evaluated the efficacy of each model using accuracy, recall and precision as the performance metric. Our analysis demonstrated that the Random Forest algorithm outperformed other ensemble learning approaches in terms of accuracy, attaining an accuracy of 0.96 and 0.89 respectively for the two datasets collected. Our research is of paramount importance as it provides a comprehensive understanding of the emotional experiences of women during menopause. We underscore the significance of mood changes in comprehending the emotional experiences of menopausal women. Our results can inform the development of personalized interventions for managing menopausal symptoms, which can significantly enhance the quality of life of women undergoing menopause.
Lightweight Group Pre-Handover Authentication Scheme for Aviation 5G Air-To-Ground Networks

With the emergence of fifth-generation (5G) technology, the air-to-ground (ATG) communication system based on 5G public mobile communication technology makes it possible to provide data services for in-flight communication. Customer premises equipment (CPE) is connected to ATG base stations to provide network access for user devices in-cabin. However, the handover authentication performed by CPEs during a handover between ATG base stations poses security risks and incurs a lot of overhead. Furthermore, frequent handovers between base stations and the limited computational resources of the CPE make the protection of security properties even more challenging. In this paper, we propose a lightweight group pre-handover authentication scheme for aviation 5G ATG networks. By leveraging the predictability of flight paths, all CPEs on the same aircraft can complete handover authentication before arriving at the next base station, and handover authentication delay can be ignored, providing seamless communication services for user devices in the cabin. The scheme utilizes the extended Chebyshev chaotic map and symmetric encryption technology to reduce computational overhead and adapt to the limited computing resources of CPE. Security and performance analysis show that our scheme achieves lightweight and efficiency while ensuring security performance and outperforms other relevant schemes.
Research Report: Synthesizing Intrusion Detection System Test Data from Open-Source Attack Signatures

Intrusion Detection Systems (IDS) act as a first line of defense for network infrastructure by identifying malicious traffic and reporting it to administrators. Signature-based IDS identify this traffic by attempting to parse packets according to user-supplied rules based on well-known examples of bad traffic. However, test data can be difficult to come by (due to its sensitive nature) which makes evaluating new rules difficult. In this work we discuss the limitations of an existing SMT-based synthesis approach to automatically generating malicious network traffic. We then present a survey of how IDS rules are written in practice using an open-source corpus of over 30,000 rules and discuss a road-map towards extending the existing approach with the goal of generating security test data characterizing a broad range of threats, as well as ancillary uses assisting users in writing IDS rules and identifying IDS implementation bugs. Finally, we share early results from an evaluation of one such extension which successfully generated IDS test data for over 90% of the rules evaluated.
M2G4RTP: A Multi-Level and Multi-Task Graph Model for Instant-Logistics Route and Time Joint Prediction

Instant-logistics (e.g., food delivery and package pick-up) is increasingly calling for Route and Time Prediction (RTP), which aims to predict both future route and arrival time of a courier’s unvisited locations. Accurate RTP can greatly benefit the platform, such as optimizing order dispatching and improving user experience. Although recent years have witnessed various works for solving the RTP problem, they still suffer from the following three limitations: i) Failing to consider the high-level transfer mode of couriers between AOIs (Areas Of Interest, such as residential quarters or office buildings), which can help to build more accurate RTP. ii) Failing to simultaneously make the route and time prediction. Existing works either separately predict route/time or predict them in a two-step way. However, since route and time are strongly correlated (nearby locations in the route should have similar arrival times), jointly predicting them should be more effective. iii) The widely adopted tree-based or sequence-based architecture fails to fully encode the spatial relationship between different locations. To address the above limitations, we propose a multi-level and multi-task graph model, named M2G4RTP, for instant-logistics route and time joint prediction. Specifically, we propose a multi-level graph encoder equipped with a newly-designed GAT-e encoding module to capture couriers’ both high-level transfer modes between AOIs and low-level transfer modes between locations. Moreover, a multi-task decoder is presented to jointly predict the route and time at different levels. Finally, a loss weighting method based on homoscedastic uncertainty is designed to balance the two tasks adaptively. Extensive experiments on an industry-scale real-world dataset, as well as the online deployment on Cainiao Alibaba, demonstrate the superiority of our proposed model.
Optimization of the Internet of remote things data acquisition based on satellite UAV integrated network

In order to achieve dependable and efficient data acquisition and transmission in the Internet of Remote Things (IoRT), we investigate the optimization scheme of IoRT data acquisition under the unmanned aerial vehicle (UAV)-low earth orbit (LEO) satellite integrated space-air-ground network, in which the UAV acquires data from massive Internet of Things (IoT) devices in special scenarios. To combine with the actual scenario, we consider two different data types, that is, delay-sensitive data and delay-tolerant data, the transmission mode is accordingly divided into two types. For delay-sensitive data, the data will be transmitted via the LEO satellite relay to the data center (DC) in real-time. For delay-tolerant data, the UAV will store and carry the data until the acquisition is completed, and then return to DC. Due to non-convexity and complexity of the formulated problem, a multi-dimensional optimization Rate Demand based Joint Optimization (RDJO) algorithm is proposed. The algorithm first uses successive convex approximation (SCA) technology to solve the non-convexity, and then based on the block coordinate descent (BCD) method, the data acquisition efficiency is maximized by jointly optimizing UAV deployment, the bandwidth allocation of IoRT devices, and the transmission power of the UAV. Finally, the proposed RDJO algorithm is compared with the conventional algorithms. Simulation consequences demonstrate that the efficiency of IoRT data acquisition can be greatly improved by multi-parameter optimization of the bandwidth allocation, UAV deployment and the transmission power.
Efficiently Learning Spatial Indices

Learned indices can leverage the high prediction accuracy and efficiency of modern deep learning techniques. They are capable of delivering better query performance than traditional indices over one-dimensional data. Recent studies demonstrate that we can also achieve query-efficient learned in-dices for spatial data by partitioning and subsequently transforming spatial data to one-dimensional values, after which existing techniques can be applied. While enabling efficient querying, building and rebuilding learned spatial indices efficiently remains largely unaddressed. As the model training needed to learn a spatial index is costly, efficient building and rebuilding of learned spatial indices on large data sets is challenging if performed by means of model training and retraining.To advance the practicality of learned spatial indices, we propose a system named ELSI that enables the efficient building and rebuilding of a class of learned spatial indices that follow two simple design principles. The core idea is to reduce the model (re-)building times by engineering reduced training sets that preserve key data distribution patterns. ELSI encompasses a suite of methods for constructing small and distribution-preserving training sets from input data sets. Further, given an input data set, ELSI can adaptively select a method that produces a learned index with high query efficiency. Experiments on real data sets of 100+ million points show that ELSI can reduce the build times of four different learned spatial indices consistently (by up to two orders of magnitude) without jeopardizing query efficiency.