Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of a Manual Response Requirement on Early and Late Correlates of Auditory Awareness

In hearing, two neural correlates of awareness are the auditory awareness negativity (AAN) and the late positivity (LP). These correlates of auditory awareness are typically observed with tasks in which subjects are required to report their awareness with manual responses. Thus, the correlates may be confounded by this manual response requirement. We manipulated the response requirement in a tone detection task (N = 52). Tones were presented at each subject’s individual awareness threshold while high-density electroencephalography (EEG) activity was recorded. In one response condition, subjects pushed a button if they were aware of the tone and withheld responding if they were unaware of the tone. In the other condition, subjects pushed a button if they were unaware of the tone and withheld responding if they were aware of the tone. To capture AAN and LP, difference waves were computed between aware and unaware trials, separately for trials in which responses were required and trials in which responses were not required. Results suggest that AAN and LP are unaffected by the response requirement. These findings imply that in hearing, early and late correlates of awareness are not confounded by a manual response requirement. Furthermore, the results suggest that AAN originates from bilateral auditory cortices, supporting the view that AAN is a neural correlate of localized recurrent processing in early sensory areas.
A New Effective-Conducting-Path-Driven Subthreshold Behavior Model for Junctionless Dual-Material Omega-Gate Nano-MOSFETs

In this paper, a generalized analytical subthreshold behavior model for junctionless omega-gate (JLΩG) MOSFETs combining dual-material gate (DMG) is built. Through the effective conducting path effects (ECPEs), we propose a new concept of off-center ratio (δ) dominating the effective scaling length to derive our model based on the effective conducting central potential $(\Phi _{eff,C})$, obtained by quasi-3D potential method. It provides a more accurate expression of scaling equation for JLΩGFETs and leads to an improved subthreshold behavior prediction. Accounting for these renewed potential and current models, we utilize dual materials to achieve better immunity to short channel effects. The proposed modeling can be extensively applied to other multiple-gate (MG) nano-transistors as well.
Enabling Cybersecurity Incident Reporting and Coordinated Handling for Maritime Sector

The maritime industry is experiencing a new era of digital transformation. At the same time as the number of cyberattacks and cybersecurity incidents are increasing, cybersecurity awareness and incident reporting in this sector remains low. In this paper, we describe a cybersecurity incident reporting system for the maritime industry that aims to address this issue. The work focuses on autonomous and unmanned vessels, but can be equally applied to other areas of the maritime industry. The proposed approach has been evaluated experimentally and the results demonstrate its applicability and feasibility.
Privacy and Integrity Protection of Metering Data in Smart Grid

As a new generation of power system, smart grid significantly improves the efficiency, reliability and sustainability. However, frequent collection of real-time metering data will lead to sensitive information leakage. To guarantee the privacy and security of metering data in smart grid, we propose an identity-based aggregation scheme (PIP) in bilinear groups. Security analysis shows that our scheme offers privacy and integrity guarantee for metering data in the random oracle model. Performance analysis demonstrates that our scheme enjoys higher efficiency than related scheme in terms of communication and computation costs.
Application of Functional Data Analysis for the Prediction of Maximum Heart Rate

Maximum heart rate (MHR) is widely used in the prescription and monitoring of exercise intensity, and also as a criterion for the termination of sub-maximal aerobic fitness tests in clinical populations. Traditionally, MHR is predicted from an age-based formula, usually 220–age. These formulae, however, are prone to high predictive errors that potentially could lead to inaccurately prescribed or quantified training or inappropriate fitness test termination. In this paper, we used functional data analysis (FDA) to create a new method to predict MHR. It uses heart rate data gathered every 5 seconds during a low intensity, sub-maximal exercise test. FDA allows the use of all the information recorded by monitoring devices in the form of a function, reducing the amount of information needed to generalize a model, besides minimizing the curse of dimensionality. The functional data model created reduced the predictive error by more than 50% compared to current models within the literature. This new approach has important benefits to clinicians and practitioners when using MHR to test fitness or prescribe exercise.
Integrated Route Planning Algorithm Based on Spot Price and Classified Travel Objectives for EV Users

This paper presents an integrated route planning algorithm to provide optimal routes and corresponding charging schemes for EVs (Electric vehicles) users with different travel objectives based on spot price and traffic conditions. With the development of EVs, more users are facing difficulties to find a charging route that satisfy their demands. To solve the problem, the route planning algorithm is improved based on classified travel objectives, meanwhile the spot price forecast model is established to provide the user’s economic assessment. Firstly, the classification of user’s travel objectives is completed and the evaluation indicators are proposed. Secondly, a time-window electricity price forecasting based on the GRU (Gated recurrent unit) neural network is established to generate pricing information for route planning algorithm. Finally, SAA (Simulated annealing algorithm) is combined with A* (A-star) algorithm and Dijkstra algorithm to gain the integrated route planning algorithm. Also, the algorithm provides the users with optimal charging paths considering travel objective and price prediction. The results of the optimal routes are displayed on App (application) so that the users can choose their own charging paths and control EV’s charging at any time. The simulations prove the effectiveness and accuracy of the proposed algorithm.
3-Sector Cell vs. Omnicell: Cell Sectorization Impact on the Performance of Side-by-Side Unlicensed LTE and 802.11ac Air Interfaces

Due to the benefits of networks coexistence, it is common nowadays to equip mobile phones with two types of network interfaces: LTE and 802.11ac. However, using the same 5GHz bandwidth by 802.11ac and LTE in unlicensed spectrum, along with the structural differences of the two networks, result in multiple coexistence limitations and implementation issues. Considering the potential benefits of cell sectorization over the conventional omnicells for improving the performance of LTE users, can they achieve similar improvements in coexisting networks. Moreover, can LTE signals interfere and affect the performance of 802.11ac users coexisting with LTE. In this case, which LTE cell deployment, either omnicell or sectorized cell, has the most impact. Toward addressing these issues, this work proposes a link-level and physical-level model. The model consists of two distinct LTE sites: a conventional omnicell site (360 degrees) and a three-sector site ( $3\times120$ degrees). In addition, the model contains two similar 802.11ac networks, one for each site, to coexist 802.11ac Wi-Fi stations with LTE users. The model is further optimized to include a pure 802.11ac network, dedicated as the baseline. Subsequently, the model is verified in NS3 through various simulation scenarios by means of measuring and quantifying the three-sector, omnicell, and pure 802.11ac networks performances to facilitate resolving any doubt of mobile operators and developers regarding the cell sectorization and coexistence issues. The simulation results indicate that in coexisting networks, LTE users in omnicell sites attain better performance than users in 3-sector sites, while the performance of 802.11ac users varies when different features are combined.
Energy-Latency Aware Offloading for Hierarchical Mobile Edge Computing

Mobile edge computing (MEC) enhances the computing capacity of resources-poor user equipment (UE) by computational offloading. However, edge clouds suffer from a limited computation capacity, and thus cannot cater for a large amount of offloading requests in periods of high load. To tackle this issue, the hierarchical MEC network is proposed and can utilize the vast resources in the backhaul and backbone networks. Previous studies describe the network layout with a three-tier tree which is not suitable for the realistic implementation. Meanwhile, the influences brought by network congestion on backhaul and backbone links are omitted. Thus, we generalize the assumption on the network layout and propose topology-independent offloading algorithms which can balance the workload over the entire region of the MEC network. In order to relieve the congestion on the backhaul and backbone networks, the task routing is incorporated into the offloading optimization, along with the offloading decision, the transmission power control, and the cloud selection. In order to jointly conduct the offloading optimization, we convert the offloading problem into a multi-source single-destination routing. A distributed offloading approach (i.e., BROA) is developed based on the game theory, in which UE collaborates with each other to minimize the network cost in terms of energy consumption and latency. We theoretically analyze the efficiency of UE collaboration and prove that BROA can achieve the globally optimal solution. Furthermore, an approximate offloading algorithm (i.e., FCOA) is developed which can give a quick solution to adapt to time-varying environments. We theoretically demonstrate the convergence, the accuracy, and the time complexity of FCOA. Numerical results show that the proposed algorithms are superior to conventional offloading schemes.
On the Inspection of Glass Reinforced Epoxy Pipes using Microwave NDT

Glass Reinforced Epoxy (GRE) pipes are rapidly replacing their steel counterparts in pipeline infrastructures owing to their inherent advantages like corrosion resistance, lightweight, and low maintenance costs. However, GRE pipes suffer from a variety of defects which occur during manufacturing or in-service. These defects can have disastrous consequences if undetected and treated. Therefore, regular inspection of GRE pipes is mandatory to ensure their structural integrity and avoid catastrophic failures. Non-destructive testing (NDT) of GRE pipes poses various challenges to inspection modalities which include thickness variation, rough or uneven surface conditions and lowdensity material defect. Microwave and millimeter wave based non-destructive evaluation has shown great potential in inspecting non-metallic material structures such as GRE pipes. In this paper, microwave imaging of a GRE pipe with embedded defects such as holes and notches is presented. In order to image these defects, various probes operating at different frequencies are employed herein. The images produced by the probes above are presented, and the effectiveness of microwave NDT for GRE inspection is demonstrated.
Wearable Koch Pre-Fractal Antennas for Ultrahigh Frequency Band

In this paper is presented a rectangular Koch pre-fractal patch antenna until three iterations on wearable flexible polyamide dielectric operating in the ultrahigh frequency band for medical wireless applications. The antennas were implemented by MATLAB, with generated images simulated in commercial software ANSYS. The polyamide presents characteristics of mechanical and thermal resistance suitable for wearable applications that require high-temperature resistance and constant movement. The use of Koch pre-fractal geometries from square patch antenna provides a decrease of 20.3 % in resonance frequency, and 30° in half-power beamwidth, making it more directional (first iteration). The wearable patch antenna with Koch pre-fractal for the third iteration shows maximum a gain at the resonance frequency of 5.57 dBi, and a current density of 41 A/m2.
A TDR-Based Method for Pre-bond Testing of the Silicon Interposer in 2.5D ICs

Since the passive silicon interposer is the least expensive component in interposer-based 2.5-dimensional integrated circuits (2.5D ICs), it is necessary to test the interposer before die stacking, in order to minimize the yield loss caused by the stacking of defect-free dies on a faulty interposer. This paper presents the application of time domain reflectometry (TDR) to pre-bond testing of silicon interposer. The proposed test structure utilizes e-fuses to connect separated horizontal and vertical interconnects into one test path during testing. Thus, by applying the test stimulus pulse of TDR at a C4 bump which is available to be probed directly, the horizontal and vertical interconnect test can be implemented simultaneously. Once there exists some defects in the test path, the emission pulse will be reflected on the fault point because the impedance of it differs from that of transmission line. The oscillogram of simulation result shows that the reflection signals are in accordance with the defect and impedance mismatch points as expected. Further analysis with feature extraction and support vector machine (SVM) can get the fault location accurately and distinguish the type successfully.
Prevention of Data Leakage due to Implicit Information Flows in Android Applications

Dynamic Taint Analysis (DTA) technique has been developed for analysis and understanding behavior of Android applications and privacy policy enforcement. Meanwhile, implicit information flows (IIFs) are major concern of security researchers because IIFs can evade DTA technique easily and give attackers an advantage over the researchers. Some researchers suggested approaches to the issue and developed analysis systems supporting privacy policy enforcement against IIF-accompanied attacks; however, there is still no effective technique of comprehensive analysis and privacy policy enforcement against IIF-accompanied attacks. In this paper, we propose an IIF detection technique to enforce privacy policy against IIF-accompanied attacks in Android applications. We developed a new analysis tool, called Smalien, that can discover data leakage caused by IIF-contained information flows as well as explicit information flows. We demonstrated practicability of Smalien by applying it to 16 IIF tricks from ScrubDroid and two IIF tricks from DroidBench. Smalien enforced privacy policy successfully against all the tricks except one trick because the trick loads code dynamically from a remote server at runtime, and Smalien cannot analyze any code outside of a target application. The results show that our approach can be a solution to the current attacker-superior situation.
Adaptive Sparse Representation for Kronecker Compressive Sensing

Kronecker compressive sensing (KCS) technique is used for compressively sampling multi-dimensional signals, and reconstructing them from their measurements. In order to obtain more accurate reconstruction, the learned dictionaries are usually employed for Tucker-decomposition-based sparse representation of original signals. Such dictionaries are learned in advance by using a set of multi-dimensional training samples which contain similar structures with the original signals. However, the prior information of the original signals may be unknown in advance. In such case, it is infeasible to select proper samples for dictionary learning. To overcome this limitation, in this paper, we propose an adaptive approach for sparse representation of KCS. The proposed approach achieves dynamic update of dictionaries without requiring the prior information of original signals. As a result, the reconstruction accuracy can be continually improved as the number of input signals increases, which is verified through the simulations on real images.
Automatic feature extraction method for crack detection in eddy current testing

Eddy current testing is one of the most extensively used methods for defect detection. In eddy current testing, the trajectory of the impedance data when scanning a crack is presented as a Lissajous curve (LC) in the complex plane. This paper has proposed a novel analytical model for describing a LC. Further, a new feature extraction method is implemented which automatically compute four geometric features (amplitude, width, angle and symmetry) from Lissajous figures. In addition, some experiments are conducted to verify the proposed method.
Boosting Fuzzing Performance with Differential Seed Scheduling

Fuzzing is a common technique used to perform automated vulnerability discovery. Fuzzing performance could be improved by various means. In this paper, we discuss the impacts of seed scheduling, and propose differential seed scheduling to maximize fuzzing performance by increasing the number of crashes identified within a limited time. Differential seed scheduling works for grey-box fuzzers that generate seeds based on runtime code coverage measurement. It attempts to evaluate the value of fuzzing seeds and selectively pick the best one to achieve balance between fuzzing effectiveness and efficiency. Our contribution is four-fold. First, we proposed differential seed scheduling to improve overall fuzzing performance. Second, we implemented AFLExplorer by integrating differential seed scheduling with the open-source American Fuzzy Lop (AFL) fuzzer. Third, we conducted in-depth experiments with AFLExplorer to show the effectiveness and the efficiency of seed scheduling. Our evaluations showed that AFLExplorer can discover up to 90% more unique crashes compared with a vanilla fuzzer. Last, we reported newly identified vulnerabilities to the authors of the tested applications, had them fixed, and 15 common vulnerabilities and exposures (CVE) numbers were assigned as of writing of this paper.
An Improved Algorithm for Selecting IMF Components in Ensemble Empirical Mode Decomposition for Domain of Rub-Impact Fault Diagnosis

A rubbing fault is a complex non-linear and non-stationary fault that frequently occurs in rotating machinery such as turbines. One of the most frequently applied signal processing techniques for the analysis of rub-impact faults in rotating machines is ensemble empirical mode decomposition (EEMD). Despite the advantages of using EEMD in analyzing non-linear and non-stationary signals, it is crucial to determine which of the extracted intrinsic mode functions (IMFs) carry the most valuable and significant information about the mechanical faults under investigation. In this paper, an improvement in the IMF selection technique is introduced, which is based on the recent ratio of degree-of-presence (DPR) to the Kullback-Leibler divergence (DPR/KLdiv). The number of selected IMFs in the DPR/KLdiv-based technique is subjective with a constant threshold, whereas we apply an adaptive thresholding technique to select the most meaningful IMFs that are relevant to a rubbing fault. The experimental results demonstrate that the proposed enhanced IMF selection algorithm allows for better signal denoising properties than the original technique while preserving significant features evidencing the presence of rubbing faults in rotating machinery.
A Comprehensive Survey of Control Strategies for Autonomous Quadrotors

Over the past several decades, there has been a constant increase in the use of unmanned aircraft systems (UASs). Hence, there has also been a growth in the number of control algorithms to service many applications embodied by these vehicles. Initially, UASs were made popular by the military for reconnaissance, intelligence, surveillance, and target acquisition (RISTA) applications. Nowadays, UASs are used for everything from crop surveys to tourism. Nowhere is this more evident than with a multi-rotor unmanned aerial vehicle (UAV). This paper presents a survey of control methods for multi-rotor systems, namely quadrotors. In doing so, we hope to visualize a clear path to what additional capabilities might be needed in the future. In our examination, we review many of the notable research organizations and their efforts to expand the utility of multi-rotor aircraft. We also summarize the basic literature definitions and control strategies for autonomous quadrotors.
Hybrid Multi-frequency Attitude Estimation Based on Vision/Inertial Integrated Measurement System

To ddetermine the accurate attitude in vision/inertial integrated measurement system, a hybrid measurement strategy termed as long short-term memory (LTSM) neural networks-based multi-frequency cubature Kalman filter (MFCKF) is proposed to achieve accurate attitude information and reduce angle errors. The process of the proposed LSTM-MFCKF contains two innovation steps: (1) firstly, inertial information which comprises of angular velocity and angle are considered as the inputs in the MFCKF loop, and visual attitudes are calculated out of the filter loop when the sampling frequency of MEMS-based gyroscope device match the vision sensor; (2) LSTM starts to train matched vision data in training mode and output predicted vision information to match inertial sensors in prediction mode; (3) the predicted vision results and high frequency inertial information are fused in next MFCKF estimation, and the vision results are stored as the observation values. The proposed integrated attitude determination system is tested on semi-physical simulation platform. Experiment results with superior precision and reliability show the feasibility and effectiveness of the proposed method for attitude measurement system.
Conditional Generative Adversarial Network- Based Data Augmentation for Enhancement of Iris Recognition Accuracy

Presently, lots of previous studies on biometrics employ convolutional neural networks (CNN) which requires a large amount of labeled training data. However, biometric data are considered as important personal information, and it is difficult to obtain large amounts of data due to individual privacy issues. Training with a small amount of data is a major cause of overfitting and low testing accuracy. To resolve this problem, previous studies have performed data augmentation that are based on geometric transforms and the adjustment of image brightness. Nevertheless, the data created by these methods have high correlation with the original data, and they cannot adequately reflect individual diversities. To resolve this problem, this study proposes iris image augmentation based on a conditional generative adversarial network (cGAN), as well as a method for improving recognition performance that uses this augmentation method. In our method, normalized iris images that are generated through arbitrary changes in the iris and pupil coordinates are used as input in the cGAN-based model to generate iris images. Due to the limitations of the cGAN model, data augmentation, which uses the periocular region, was found to fail with regard to the improvement of performance. Based on this information, only the iris region was used as input for the cGAN model. The augmentation method proposed in this paper was tested using NICE.II training dataset (selected from UBIRS.v2), MICHE database, and CASIA-Iris-Distance database. The results showed that the recognition performance was improved compared to existing studies.