Miracle-dz/UniFAR-FTUs huggingface dataset:
A Traceable Location Privacy Preserving Scheme for Data Collection in Vehicular Fog Computing

Vehicular fog computing (VFC) has emerged as a popular paradigm in the Internet of Vehicles (IoV) by replacing cloud servers with edge fog servers, thereby reducing communication latency and improving efficiency. However, data collection in VFC poses significant security challenges, particularly with respect to the privacy of location data, which is essential for effective vehicle data collection. While some traditional location privacy protection schemes have been developed, they fail to address the availability of location data and the traceability of special situations. To overcome these limitations, we propose a traceable vehicle location privacy protection scheme in VFC that ensures the availability of location data by using fuzzy location information and achieves the traceability of location data through a secret sharing scheme. The simulation results demonstrate its effectiveness and feasibility.
Unsupervised cognitive monitoring in a mixed-reality flight simulator for smart debriefing

Monitoring cognitive performance during virtual flight training sessions is important to prevent negative experiences and ensure proper training. Due to the demanding nature of the task and reluctance of student pilots to report their cognitive state, we propose using Long Short-Term Memory Autoencoder networks for anomaly detection to monitor cognitive decline. This application adds automatic processing capabilities to the flight simulator, and the resulting information can be used by flight instructors for enhanced debriefing.
Predictive Analysis of Mental Stress using Machine Learning Techniques

Many stress-related health problems can be avoided if mental stress is identified early. It is critical to have reliable techniques for detecting human stress quickly and accurately. This study aims to measure physiological indicators and recognize mental fatigue using a wrist-worn sensor gadget. A group of healthy individuals were exposed to three distinct stresses. The electrocardiogram, breathing, skin conductivity, and EMG of the trapezius muscles were all monitored throughout the operation. These data were used to calculate 19 physiological parameters. A subset of nine characteristics was chosen for further analysis once the feature values had been normalized, and correlations between these features were examined. Through the use of principal component analysis, these nine characteristics were reduced to seven principal components. It was discovered that by using these primary components in conjunction with a number of classifiers, a classification accuracy of approximately 90% could be maintained between stressful and non-stressful conditions. This indicates that a potentially useful feature subset may have been discovered, which can be used in the future to construct a personalized stress monitor. When using these strategies, obtaining the best characteristics is never going to be an easy task. As it focuses on the binary classification problem, this work will employ the Bernoulli Naive Bayes method, one of the best classification algorithms. An attempt is made to identify the optimal set of features that maximizes classification accuracy.
Automated Metamorphic-Relation Generation with ChatGPT: An Experience Report

This paper reports on a pilot study of using ChatGPT, a language model based on GPT-3.5 architecture, for automatic generation of metamorphic relations (MRs), in the context of testing of autonomous driving systems (ADSs). The oracle problem is a major challenge in testing such systems, where it is difficult to determine whether or not the output of a system is correct. Metamorphic testing (MT) can alleviate this problem by checking the consistency of the system’s outputs under various transformations. However, manual generation of MRs is often a time-consuming and error-prone process. Automated MR generation can yield several benefits, including enhanced efficiency, quality, coverage, scalability, and reusability in software testing, thereby facilitating a more comprehensive and effective testing process. In this paper, we investigate the effectiveness of using ChatGPT for automatic generation of MRs for ADSs. We provide a detailed methodology for generating MRs using ChatGPT and evaluate the generated MRs using our domain knowledge and existing MRs. The results of our study indicate that our proposed approach is effective at generating high-quality MRs, and can significantly reduce the manual effort required for MR generation. Furthermore, we discuss the practical implications and limitations of using ChatGPT for MR generation and provide recommendations for future research. Our study contributes to the advancement of automated testing of ADSs, which is crucial for ensuring their safety and reliability in real-world scenarios.
Near-Source Attack for Isolated Networks with Covert Channel Transmission

This paper investigates a new attack method called "near-source attack". It leverages the broadcast frames of the 802.11 protocol to establish a hidden tunnel and bypass physical isolation networks or air-gapped networks. We first analyze and implement a common technology known as Ghost Tunnel, which allows the attacker to control the target host and transmit information without being detected. However, this method suffers from frame loss, repeated frame, and attack transparency. We then propose an improved solution to deliver malicious programs to the target host using a modified BadUSB hardware device. Once the attackers successfully get in the isolated networks, they can bypass security protection devices and exploit vulnerabilities of communication protocols, so that they can remote control of target devices and hidden data transmission. We further conducted experiments to verify the feasibility and effectiveness of this attack scheme. The results indicate that the attack logic is capable of inducing the target host to engage in covert communication. Finally, we give some defense measures for such attacks.
AI based Real-Time Traffic Signal Control System using Machine Learning

This study presents a novel system that utilizes computer vision and machine learning approaches to address the problem of traffic congestion in urban areas. The proposed system leverages the advanced object detection algorithm, You Only Look Once (YOLO), to detect and track vehicles in live camera footage from traffic junctions. The system then calculates the traffic density in real-time by analyzing the number and speed of vehicles passing through the intersection. The proposed system utilizes an intelligent algorithm that optimizes traffic flow by switching traffic lights based on the calculated traffic density. This approach reduces congestion and minimizes delays, resulting in faster transit times and reduced fuel consumption and air pollution. To assess the performance of the proposed system, experiments are carried on real-world traffic data. The results demonstrate that the system can accurately detect and track vehicles with high precision and recall rates. The real-time traffic density calculations produced by the system were found to be highly reliable, and the traffic light switching algorithm led to a significant reduction in traffic congestion and improved traffic flow. The proposed system has several advantages over traditional traffic management systems, including lower implementation and maintenance costs, improved accuracy and efficiency, and the ability to adapt to changing traffic conditions in real-time.
Evaluation of the Relevance of Adverse Drug Reactions Based on ERNIE-DPCNN

Adverse drug reactions (ADR) have become a common and serious problem faced by drug users worldwide, posing a significant threat to human life and health safety. How to achieve automatic evaluation of the quality of ADR reports, and how to mine and evaluate the relevance between drugs and adverse drug reactions, has become an urgent problem that needs to be solved at present. In this study, a text classification technology based on deep learning were employed to establish an automated system to evaluate the information quality and relevance of ADR reports, using ADR reports from cooperative medical institutions and case studies in the literature as samples. The ERNIE+DCGNN model was used to train the ADR relevance evaluation model, and its effects were compared with other mainstream models. Comparative experimental results demonstrated that the ADR relevance evaluation model constructed in this paper had better experimental results.
Towards a Smart Operation - Novel Grey-box Modelling of Ultra-low Temperature Freezing Chambers

This study develops a novel grey-box approach for modelling the heat dynamics of the ULT freezing chamber. The modelling approach is based on stochastic differential equations and fed only with costless data measured by embedded temperature sensors in the particular freezer under study. The unknown parameters are estimated using the maximum likelihood method based on data from regular operation periods. A novel model that describes the nonlinear heat transfer between the local evaporator and chamber is proposed. This nonlinear model is able to compensate for the biased measurements from the embedded temperature probe and adapt to changes in the local cooling intensity. Two freezers with different operation patterns are modelled. The results prove that the established models perform well in predicting chamber temperature (RMSE < 0.19°C), which is the backbone of reliable applications. Despite compromised physical interpretability, the developed modelling approach eases the modelling procedure and is expected to greatly promote the potential of practical implementations of grey-box models in future intelligent surveillance and operation of ULT freezers.
Identifying Coincidental Correct Test Cases with Multiple Features Extraction for Fault Localization

Spectrum-Based Fault Localization (SBFL) technique is widely applied for fault localization, identifying faulty statements potentially resulting in unexpected faulty programs’ behavior. However, researchers have approved that Coincidental Correct (CC) test cases contained in test suites can negatively affect the accuracy of SBFL. Previous researchers sought to identify CC test cases through machine learning algorithms, but the feature representation is insufficient, leading to limited accuracy. To address this challenge, we propose the Machine Learning-based CC test cases Identification approach (MLCCI), which leverages multiple features extracted from the program under test to identify CC test cases and map the CC identification task to a learning problem. To evaluate the performance of MLCCI, we conduct experiments in the well-known dataset Defects4J. The experimental results compared with state-of-the-art baselines indicate that: (1) MLCCI achieves higher CC identifying accuracy, with the average Recall, P recision, and F -measure values of MLCCI are 65.93%, 71.69%, and 53.74%, respectively; (2) The fault localization accuracy of MLCCI with the Jaccard formula outperforms baselines, where the values of Accuracy@ 1, 3, and 5 are 347, 369, and 393, achieving the maximum 137.67%, 67.73%, and 47.74% improvement against baselines, respectively. Besides, we perform ablation analysis to reveal the effectiveness of features utilized in this study.
Efficient Direction of Arrival Estimation in mMIMO Systems with Antenna Array Imperfections

Direction of arrival (DOA) estimation in 5G massive MIMO mm Wave systems, is crucial for effective beamforming. A high-resolution DOA estimate is required to avoid interferences and steer the beam to the corresponding user. The accuracy of DOA estimation deteriorates significantly due to the antenna array imperfections, channel fluctuations, and noise. This research investigates the performance of DOA estimation with a uniform linear array of 128 elements at a 5G Frequency Range FR2 of 26GHz and a single antenna user. The antenna array imperfections considered are antenna position perturbations, inconsistent gain, and phases. To cope with these array imperfections, the pre-processing technique is done by evaluating the compensation matrix with the help of the Least Square (LS) approach and Orthogonal Matching Pursuit (OMP). The compensation matrices are estimated with the steering vectors of ideal and practical array manifolds. Then the received signals are compensated for imperfections with the compensation matrix. The compensated signals are then fed into the DOA estimator, and the performance analysis of Multiple Signal Classifier (MUSIC), Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), Root-MUSIC (RMUSIC), and Beam Scan algorithms are compared. The quantitative results and analytical formulation of the estimation technique are discussed based on the results of the DOA estimation techniques. Simulation results show that resolution of the DOA estimation of Root-MUSIC and ESPRIT with OMP achieves better performance-complexity trade-off at low SNR region, whereas MUSIC and Beam scan achieves better performance-complexity trade-off at high SNR region than LS compensation technique.
SQL Synthesis with Input-Output Example Based on Deep Learning

In recent years, an increasing number of people rely on data manipulation tasks to complete their work. However, many of these users lack programming background and find it challenging to write complex programs, especially SQL. As a result, the automatic synthesis of SQL has become a hot research topic. This process, called Query Reverse Engineering (QRE), involves automatically synthesizing SQL based on input-output tables provided by users. While most SQL synthesizers focus on structures that do not use deep learning, we propose a new SQL synthesis method that lever-ages the superiority of deep learning. Our method uses a deep neural network (DNN) to predict the correlation between the input-output table and DSL operators, eliminate irrelevant operators, and improve the efficiency of SQL synthesis. We have implemented the SQL synthesis system, Solid, based on this method. The system introduces the Deep Neural Network (DNN) based on SQUARES, one of the best query synthesizers. To verify the effectiveness of our proposed method, we conducted experiments using a simple neural network structure. The results show that our method outperforms SQUARES, with an increased success rate of stereo synthesis from 80% to 89.1%, and a reduced average synthesis time from 251s to 130s.
An Expert System for Insulin Dosage Prediction using Machine Learning & Deep Learning Algorithms

Diabetes is a long-term metabolic condition. This study describes an expert system for predicting diabetes and insulin dosage. A deep learning approach is used in the proposed system to predict diabetes and a machine learning algorithm is used to predict insulin dosage based on several characteristics like blood sugar levels, body mass, age, insulin, and diabetic pedigree function. A deep learning and machine learning algorithm was trained on a PIMA Indian diabetes dataset& related properties to create the system. To improve the model parameters, the model is trained using a Convolutional Neural Network (CNN) technique. Once trained, the algorithm can accurately calculate insulin dosage. The result reveals that the model exceeds existing insulin dosage prediction systems in terms of accuracy. The proposed approach can potentially improve the accuracy of insulin dosage prediction, reduce the risk of hypoglycemia and hyperglycemia, & ultimately improve the quality of life for diabetic patients.
Design, Control and Simulation of a Leg-Wheel Robot: STransleg

A versatile leg-wheel robot called STransleg is proposed in this paper. STransleg is propelled by four identical leg-wheel mechanisms, which can transform between the legged and wheeled modes. The leg has three active joints in the legged mode, and the wheel is active in the wheeled mode. The waist yaw and pitch joints are introduced to STransleg to improve the locomotion flexibility and stability. In this paper, the design and kinematics modeling methods of STransleg are described. Additionally, the control method is proposed for STransleg to make it do the four-wheeled locomotion, walking locomotion, and carriage-like locomotion. The simulations are carried out to make the dynamical model of STransleg do the different locomotion, and the results verify the feasibility of the robot design method and the proposed control method.
Autoimmune Atrial Fibrillation or Atrial Fibrillation–Induced Autoimmunity? A New Atrial Fibrillation Begets Atrial Fibrillation Pathway?

499 The opinions expressed in this article are not necessarily those of the editors or of the American Heart Association. Correspondence to: Vincent Algalarrondo, MD, PhD, Service de Cardiologie, Unité de Rythmologie, Hôpital Bichat Claude Bernard, 46 Bd Henri Huchard, 75018, Paris, France. Email vincent.algalarrondo@aphp.fr For Sources of Funding and Disclosures, see page 501. © 2023 American Heart Association, Inc. EDITORIAL
B LUE S APPHIRES FROM M OGOK , M YANMAR : A G EMOLOGICAL R EVIEW

GEMS & GEMOLOGY WINTER 2021 Mogok is the most legendary gemstone source in Myanmar; its fabled rubies have been highly desired for centuries. Perhaps equally sought-after by connoisseurs but less known to the general public are its blue sapphires (figure 1). Mogok is one of the oldest sapphire sources, but its sapphires have always faced challenges in gemological circles. Their beauty was often not acknowledged in older days, but this changed dramatically in recent decades. In light of the increasing demand for origin determination services, separating blue sapphire sources has become important for gemological laboratories. Collecting reliable samples had been extremely challenging due to limited access to the mines and the rarity of blue sapphire in Mogok. This paper aims to summarize the current knowledge of Mogok sapphires based on extensive gemological analysis of samples collected during field expeditions in the last decade, combined with data available in the gemological literature.
Green Synthesis of 7,8-Difluoro-6, 11-Dihydrodibenz[B,E]Thiophene-11-One of Baloxavir Marboxil -- Reducing Environmental Pollution

Baloxavir marboxil is a novel anti-influenza drug with a unique mechanism of action that can effectively treat both type A and type B influenza. Key intermediate compound 7,8-difluoro-6,11-dihydrodibenz[b,e]thiophene-11-one is not only an important component of the chemical structure of baloxavir marboxil but also an essential intermediate in the synthetic route of the drug. However, conventional synthesis methods for key intermediate 7,8-difluoro-6,11-dihydrodibenz[b,e]thiophene-11-one usually utilize dangerous and harmful chemicals that can cause significant environmental pollution during large-scale production. This article proposes a new synthesis route for 7,8-difluoro-6,11-dihydrodibenz[b,e]thiophene-11-one to facilitate large-scale production while integrating the principles of green chemistry. This new route feature mild reaction conditions and simpler steps that result in an overall yield increase to 64.9%. More significantly, the new synthesis route reduces the use of hazardous chemicals, thus limiting the generation of hazardous waste during industrial production. This research offers significant potential for environmental pollution control.
A Comparison of Genetic versus Non-Genetic Contribution of Serotonin to Suicide

Suicide is a complex and multifaceted public health issue that has been intensively studied to identify its contributing factors. Serotonin, a neurotransmitter essential for affective regulation and mood control, has been linked to suicidal propensity. Understanding the relative contribution of serotonin’s genetic versus non-genetic influences is essential for the development of effective preventive measures, given that the etiology of suicide involves both genetic and non-genetic factors. This review seeks to compare the influence of genetic and non-genetic factors on the association between serotonin and suicide risk. Examining serotonin-related gene polymorphisms, with a focus on the serotonin transporter gene, the serotonin receptor 1A, and the serotonin receptor 2A, genetic contributions are investigated. This review emphasizes the complex interplay between genetic and non-genetic contributions to serotonin’s role in suicide by synthesizing existing literature. Understanding these complex interactions can provide a comprehensive framework for targeted interventions and individualized methods of suicide prevention and mental health promotion. Future research should incorporate large-scale genetic studies, genetic and non-genetic interaction analyses, and longitudinal designs in order to further elucidate the complex relationship between serotonin and suicide risk.
Analytical algorithms for direct and inverse problems pertaining to the electromagnetic excitation of a layered medium by N dipoles

The direct problem of exciting a multilayered spherical medium by a distribution of arbitrarily located (internal and external) N$$ N $$ electric dipoles is considered. A formulation based on excitation operators is adopted. The solution of the direct problem is obtained by means of an overall superposition method that combines elements of the T‐matrix approach, the Sommerfeld's method, and Green's functions' techniques. Approximations are extracted for the obtained far‐field patterns in the long‐wavelength (low‐frequency) regime. A set of source‐localization inverse problems is considered. Analytical algorithms are devised in which nonlinear systems are obtained from specifically selected low‐frequency far‐field measurements. These systems are subsequently solved explicitly including all special cases. The developed algorithms are noninvasive and can determine accurately the characteristics of internal sources.
Aesthetic evaluation of handwritten Chinese characters based on deep learning

The aesthetic evaluation of handwritten Chinese characters has important due value in primary and secondary education. This paper proposes to use deep learning to solve this problem. Firstly, using the public handwritten Chinese character aesthetic scoring dataset, training is carried out in various deep learning network models, and finally the network model with better scoring effect is determined. The experimental results show that the deep learning network model used in this paper has an accuracy rate as high as 83% in the aesthetic evaluation of Chinese characters, and the error performance is better than that of human evaluation.
Estimating the Gumbel-Barnett copula parameter of dependence

In this paper, we developed an empirical evaluation of four estimation procedures for the dependence parameter of the Gumbel-Barnett copula obtained from a Gumbel type I distribution. We used the maximum likelihood, moments and Bayesian methods and studied the performance of the estimates, assuming three dependence levels and 20 different sample sizes. For each method and scenario, a simulation study was conducted with 1000 runs and the quality of the estimator was evaluated using four different criteria. A Bayesian estimator assuming a Beta(a,b) as prior distribution, showed the best performance regardless the sample size and the dependence structure.