Miracle-dz/UniFAR-FTUs huggingface dataset:
A cross-sectional study to assess knowledge, attitude, and awareness of forensic odontology among medical students: An emergency concern

Background: Medical professionals are important in treating and management of victims of mass disasters, abuse, and organized crimes. Although the forensic odontologist has a pivotal role in the investigation, health-care provider in the emergency medicine should have the potential to detect, inform, and interact with the dentist for further applications in legal issues. We aimed to conduct the present study on the knowledge, awareness, and attitude among the medical students. Materials and Methods: With informed consent, we conducted a cross-sectional study among 194 4th-year medical students. A structured questionnaire consisting of 17 items relating to forensic odontology to assess the knowledge, awareness, and attitude among medical students and data were collected and reviewed. Results: On analyzing the collected data of 194 participants, the percentage of participants who knew: the meaning of forensic odontology, forensic odontology a part of forensic medicine, and role of forensic odontology in the criminal analysis were 172 (87%), 192 (99%), and 181 (93%), respectively. A total of 183 (94%) were aware of bite mark patterns, 171 (88%) would examine for bite marks in child abuse, 89 (46%) the lip prints, DNA as accurate and sensitive method of comparison and teeth as source of DNA was 128 (66%) and 122 (63%), respectively. A total of 127 (65%) agreed that forensic odontologist as experts, 101 (52%) had opted for dental evidence, and 139 (72%) had agreed that postmortem unit should include a dental laboratory facility. However, only 79 (41%) knew the tooth as the choice of evidence in mass disaster. 101 (52%) stated dentist as expert witness. The awareness of the role of teeth in age and gender estimation was 147 (76%) and 79 (41%), respectively. Forty-nine (25%) had handled forensic dentistry case in emergency medicine and importance of maintaining dental records were known to 85 (44%). Conclusions: Medical students had inadequate knowledge of forensic odontology. As a health-care provider, medical professionals should understand the implications of forensic odontology.
A Compact MEMS Chip for a Rapid and Highly Accurate Picoliter Calorimetry

We introduce a compact MEMS-based microcalorimeter ($\mu \mathrm{C}$) for rapid ultrasmall-volume sample analysis. The chip features a time constant of 4 ms, with the possibility of achieving high scanning rate and sensitivity without noticeably distorting the thermograms. We demonstrated the chip performance in differential scanning calorimeter (DSC) mode and measured the melting temperature ($T_{\mathrm{m}}$) of bovine serum albumin (BSA) at 75 K·min−1, one of the fastest scan rate reported. The measured melting point of 77.4 °C is practically identical to that measured by a conventional MEMS-based DSC. Our system exhibits an unprecedented accuracy at such a high scanning rate having the capability of performing a flash DSC analysis for a liquid phase sample.
Acoustic Features Characterization of Autism Speech for Automated Detection and Classification

The verbal children affected with autism spectrum disorder (ASD) often shows some notable acoustic patterns. This paper represents the classification of autism speech, i.e., the speech signal of children affected with ASD. In addition, this work specifically aims to classify the speech signals of non-native Indo English speakers (children) affected with ASD. Previous studies, however, have focused only on native English speakers. Hence, for this study purpose a speech signal dataset of ASD children and a speech signal dataset of normal children were recorded in English, and all the children selected for the data collection were non-native Indo English speakers. Here, for the ASD and the normal children, the acoustic features explored for classification are namely, fundamental frequency (FO), strength of excitation (SoE), formants frequencies (F1 to F5), dominant frequencies (FD1, FD2), signal energy (E), zero-crossing rate (ZCR), mel-frequency cepstral coefficients (MFCC), and linear prediction cepstrum coefficients (LPCC). Further, these feature sets are classified by utilizing different classifiers. The KNN classifier model achieves the highest 96.5% accuracy with respect to other baseline models explored here.
MobiLogLeak: A Preliminary Study on Data Leakage Caused by Poor Logging Practices

Logging is an essential software practice that is used by developers to debug, diagnose and audit software systems. Despite the advantages of logging, poor logging practices can potentially leak sensitive data. The problem of data leakage is more severe in applications that run on mobile devices, since these devices carry sensitive identification information ranging from physical device identifiers (e.g., IMEI MAC address) to communications network identifiers (e.g., SIM, IP, Bluetooth ID), and application-specific identifiers related to the location and the users' accounts. This preliminary study explores the impact of logging practices on data leakage of such sensitive information. Particularly, we want to investigate whether log-related statements inserted into an application code could lead to data leakage. While studying logging practices in mobile applications is an active research area, to our knowledge, this is the first study that explores the interplay between logging and security in the context of mobile applications for Android. We propose an approach called MobiLogLeak, an approach that identifies log statements in deployed apps that leak sensitive data. MobiLogLeak relies on taint flow analysis. Among 5,000 Android apps that we studied, we found that 200 apps leak sensitive data through logging.
Network Intrusion Detection System Using Voting Ensemble Machine Learning

Due to increasing amount of cyber attack, there is a growing demand for Network intrusion detection systems (NIDSs) which are necessary for defending from potential attacks. Detecting and preventing cyber attacks is one of the key research areas. Existing NIDSs use traditional machine learning algorithms with low accuracy and are also not suitable for the new unknown cyber attacks. In this paper, we propose a NIDS model with ensemble machine learning methods. Ensemble machine learning methods have the potential to detect and prevent different types of attacks compared to traditional machine learning methods. Our proposed system can detect known attacks as well as can prevent unknown attacks. Our proposed system uses ensemble machine learning methods with Voting. We used the full NSL-KDD dataset to evaluate the performance of multiclass classification and we also compare the performance with deep learning as well as traditional base level machine learning techniques. Experimental results show that the proposed NIDS system is superior to the performance of existing methods. Our model improves the detection rate of the IDS which is vital for network intrusion detection systems.
Bi-Level Optimal Allocation of Flexible Resources for Distribution Network Considering Different Energy Storage Operation Strategies in Electricity Market

Due to the random volatility, a large amount of renewable energy will bring challenges to the security and stability of distribution network. Comprehensive consideration of system economics, security and flexibility has become the focus of research on distribution network optimization planning under the new situation. For the flexible resource allocation problem of distribution network, this paper analyzes the supply and demand relationship of the flexibility of the distribution network, and establishes a bi-level operation-planning joint optimization model for flexible resources. In the operational layer, we not only introduce the insufficient flexibility rate as the evaluation index of system flexibility, but also introduce the network loss, the abandoned wind and solar energy in the economic penalty, aiming at optimizing the annual operating cost of the system. In the planning layer, we evaluate the system security by introducing the comprehensive security index of the system, aiming at optimizing the annual comprehensive cost of the system. In addition, this paper also considers the third-party companies’ investment in energy storage system in the electricity market, and further analyses the impact of energy storage operation strategies under different investors on flexible resource allocation. In this paper, the particle swarm optimization algorithm is used to solve the bi-level allocation model. Finally, the IEEE 33-bus test system is used for verification and analysis of the case. The results verify the validity and rationality of the proposed bi-level allocation model.
The Component-Interleaved Golden Code and Its Low-Complexity Detection

In this paper, component interleaving for the Golden code is investigated. The proposed component-interleaved Golden code (CI-Golden code) not only preserves the full rate and full diversity of the conventional Golden code, but also achieves signal space diversity. The maximum-likelihood detection complexity of the CI-Golden code is proportional to <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(M^{8})$ </tex-math></inline-formula> for <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>-ary quadrature amplitude modulation (<inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>QAM) and is extremely high. Hence, two low-complexity detection schemes, a QR decomposition-based detection scheme and sphere decoding with detection subset, are proposed. Furthermore, the bound on average bit error probability (ABEP) for the <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>QAM CI-Golden code, which is valid for more than 2 receive antennas is formulated. Simulation results show that the two proposed low-complexity detection schemes can converge to the ABEP for more than two receive antennas. Both the ABEP and simulation results show that the proposed CI-Golden code with three and four receive antennas can achieve a signal-to-noise ratio gain of more than 1.6 dB at a bit error rate of <inline-formula> <tex-math notation="LaTeX">$1\times 10^{-5}$ </tex-math></inline-formula> for 16QAM and 64QAM, respectively, compared to the conventional Golden code. The computational complexity for the proposed schemes is further discussed.
Machine Learning Classifiers for Autism Spectrum Disorder: A Review

Autism Spectrum Disorder (ASD) is a brain development disorder affects the ability to communicate and interact socially. There have been many studies using machine learning methods to classify autism including support vector machines, decision trees, naïve Bayes, random forests, logistic regression, K-nearest Neighbors and others. In this study provides a review of autism spectrum disorder using the machine learning algorithm is supervised learning. The initial study of the article was collected from website provided articles were in according to this study. The articles were collected from online databases and 16 research articles met the requirements in this study. Based on the results obtained, the most widely used algorithm in the literature study in this study is support vector machine (SVM) of 6S.75%. With the application of machine learning in the case of ASD expected to be able to accelerate and improve accuracy in determining a diagnosis.
Comparing BERT and XLNet from the Perspective of Computational Characteristics

Exploiting attention mechanism, Transformer provides superior performance compared to traditional CNN and RNN models on various NLP (Natural Language Processing) tasks. BERT and XLNet are two popular models utilizing Transformer. In this paper, we compare the computational characteristics of the inference of BERT and XLNet using MPRC (Microsoft Research Paraphrase Corpus), one of the popular language understanding benchmarks. Through evaluation, we observe that the both models exhibit similar computational characteristics except the target-position-aware representation and relative position encoding features of XLNet, leading to a better benchmark score at the cost of $\mathit{1.2}\times$ arithmetic operations and $\mathit{1.5}\times$ execution time on a modern CPU.
Explosion Characteristics of a Premixed Methane-Air Vapor Cloud Induced by a Jet Flame

Explosion characteristics of premixed methane/air vapor cloud ignited by a jet flame are estimated numerically using CFD software FLACS to comprehensively understand the explosion propagation mechanism of mixed gas at quiescent ambient air condition. Simulation scenarios were constructed to investigate the effect of methane concentration on the explosive properties of the freely dispersed vapor cloud. The premixed jet flame was ejected from a cuboid tube filled with varying concentrations of methane, and the mushroom shaped flame front was mainly attributed to the combustion of the ejected unburnt gas. The numerical simulations match the empirical and analytical formulas well, indicating the numerical model provides acceptable accuracy in assessing the explosion behaviors. The research results show that the methane concentration plays an important role in determining the jet flame length and temperature which reach respectively a peak value, 3.3 m, and a minimum value, 779.12K, at 11 % methane concentration. The dependence of the critical ignition distance Dc on the methane concentration is similar to that of the flame length, but the ignition temperature exhibits an opposite tendency. Additionally, the critical ignition distance is found much smaller than the jet flame length. The flame geometry is significantly enlarged due to the ignited vapor cloud, which may cause severe secondary disaster in practical scenarios.
A Novel Pitch-Frequency-Based ECG Signal Classification Approach for Abnormality Detection

Electrocardiogram (ECG) has a significant role for measuring the electric activity of the heart to discover heart diseases. Accurate classification of the ECG signals is used to detect the heart abnormalities. The present work is an efficient approach for the classification of normal and abnormal ECG signals based on pitch frequency estimation of these signals. Two time-domain methods, namely the auto-correlation function (ACF), and average magnitude difference function (AMDF) are used for pitch detection from ECG signals. The receiver operating characteristic (ROC) curve is used to measure the accuracy of the proposed method for ECG signal classification. The results report 100% classification accuracy of the ECG signals.
Outage Analysis of Adaptive Combining Scheme for Hybrid FSO/RF Communication

In this paper, we analyse the outage performance of an adaptive combining scheme for hybrid free space optics (FSO) / radio frequency (RF) system considering both terrestrial communication and uplink satellite communication (SATCOM) scenarios. Adaptive combining involves keeping the FSO link active all the time and activating the RF link based on the quality of the FSO link. The outage analysis has been carried out by modeling the atmospheric turbulence of FSO link using Gamma-Gamma distribution. Further, Nakagami-m and Ricean distributions are used for modeling the small-scale fading of RF link for terrestrial communication and SATCOM scenarios, respectively. The exact expression of outage probability for the adaptive-combining-based hybrid FSO/RF system has been derived. Further, a range for optimum switching threshold has been determined. Asymptotic analysis along with the calculation of diversity gain has also been carried out for both scenarios. The outage performance of adaptive combining scheme is compared with that of hard switching scheme and single-link FSO system.
Recurrent Deep Learning for EEG-based Motor Imagination Recognition

Deep Learning has grasped great attention for recognition of Electroencephalography. For the analysis of brain dynamics, non-stationary motor imagery signals are used. Although a number of studies have been carried out for the extraction of hidden patterns and classification of EEG signals, temporal information has rarely been incorporated. In this paper, we propose a spatio-temporal energy maps generation scheme followed by deep learning classification model. Common spatial pattern filters and Fast Fourier Transform Energy Maps are deployed to obtain discriminative and spatio-temporal features. Long-Short-Term-Memory (LSTM) based neural network has been proposed to classify the temporal series of energy maps. This research also investigates preprocessing techniques to obtain optimal parameters which include frequency bands selection and temporal segmentation. The proposed model is evaluated on BCI Competition IV dataset 2a and achieved 0.64 mean kappa for multi-class EEG classification, which is the current state of the art. Furthermore, several empirical findings are also presented, that may be of significant interest to the BCI community.
Simultaneous Control Framework for Humanoid Tracking Human Movement With Interacting Wearable Assistive Device

Instead of human subjects, humanoid robots can be used as human dummies to test the human-designed products. We propose a controller that uses wearable assistive devices (also referred to as exoskeletons) to reproduce human movement in the evaluation. The proposed control scheme consists two components: one is the torque controller designed for a simplified interaction model with the device, and the other is the tracking controller based on a vector field to reproduce human motion. We implemented the proposed controller on the human-sized humanoid HRP-4 and validated the feasibility of the human motion reproduction by wearing the assistive device. In the experiment, we tested the commercially available device “Muscle Suit” by using our control scheme. The experimental results showed that while the device applies its supporting strength, the humanoid robot could reproduce human movements. The assistive effect of the device was visualized effectively in our evaluation framework.
Average Rate of Optimal Incremental Relaying with Selection: Analysis and Insights

Incremental relaying (IR) has been widely studied in the cooperative communications literature in order to tradeoff between the improved signal-to-noise-ratio (SNR) and spatial diversity provided by a relay, and the additional time required by it to forward data to a destination. When multiple relays are present, several variants of IR with relay selection (RS) have been proposed and analyzed. These select one among the available relays to forward data only if the SNR of the source-to-destination (SD) link is either less than a threshold or less than the end-to-end SNR of at least one of the relays. However, an in-depth analysis of the average rate of the rate-optimal RS rule for IR, which turns out to be a non-linear function of the SNR of the SD link, and insights into its behavior are not available in the literature. We derive novel, closed-form expressions for this important performance metric. We further develop an insightful asymptotic analysis that helps to quantify the rate gain over direct transmission and characterizes the effect of various system parameters. We also extensively benchmark the performance of the rate-optimal RS rule against several IR variants proposed in the literature. We present numerical results to verify the analysis and show the impact of imperfect channel state information.
Enhancement of Secrecy Multicast Capacity over k - µ Shadowed Fading Channel

This paper illustrates a secure multicast scenario over k- µ shadowed fading channel in the appearance of multiple eavesdroppers. In order to achieve perfect secrecy, it must be ensured that no eavesdropper can decipher any transmitted information from the multicast channels. The secrecy analysis incorporates deriving mathematical expressions for two secrecy matrices such as the ergodic secrecy multicast capacity and the secure outage probability for multicasting. It is observed that proposed k - µ shadowed model unifies secrecy analysis of some well-known classical fading models and the derived expressions can help to quantify the significant impact of shadowing as-well-as other system parameters on secrecy performance. Finally, a close match between the analytical and Monte-Carlo simulation results is shown which validates the correctness of the derived expressions.
Characterizing the Propagation of Situational Information in Social Media During COVID-19 Epidemic: A Case Study on Weibo

During the ongoing outbreak of coronavirus disease (COVID-19), people use social media to acquire and exchange various types of information at a historic and unprecedented scale. Only the situational information are valuable for the public and authorities to response to the epidemic. Therefore, it is important to identify such situational information and to understand how it is being propagated on social media, so that appropriate information publishing strategies can be informed for the COVID-19 epidemic. This article sought to fill this gap by harnessing Weibo data and natural language processing techniques to classify the COVID-19-related information into seven types of situational information. We found specific features in predicting the reposted amount of each type of information. The results provide data-driven insights into the information need and public attention.
Adaptive Energy Harvesting with Relay Selection Schemes in an Ordered NOMA network

An adaptive energy harvesting relay based Non-Orthogonal Multiple Access (NOMA) network is investigated where three schemes of energy harvesting adaptations utilizing power splitting relaying (PSR) are proposed. A comparative study of proposed adaptive PSR schemes, employing partial relay selection in NOMA relaying network is carried out. The proposed adaptive energy harvesting schemes outperform fixed PSR based energy harvesting in NOMA network as per priority of signals. It is observed that the imperfection of successive interference cancellation can significantly degrade the performance of strong signal (x2). The statistics of power splitting coefficients in proposed adaptive energy harvesting is also presented. A closed-form expressions on outage performance of two users NOMA network is developed which is verified by MATLAB based simulation.
An Edge-Based Framework for Enhanced Road Safety of Connected Cars

In this paper, we present an enhanced Collision Avoidance (eCA) service that leverages vehicle connectivity through a cellular network to avoid vehicle collisions and increase road safety at intersections. The eCA service is assumed to be deployed at the edge of the network, thus curbing the latency incurred by the communication process. The core of the eCA service is composed of a Collision Avoidance Algorithm (CAA), and a Collision Avoidance Strategy (CAS). The former predicts the vehicle’s future trajectory through the positional information advertised by periodic beacons and detects if two vehicles are on a collision course. The latter decides which of the vehicles potentially involved in a collision should yield. The vehicles are then notified of both the impending danger and of the actions needed to avoid it. We have simulated our solution using SUMO (Simulation of Urban MObility) and ns-3 (network simulator 3) with the LENA (LTE-EPC Network simulAtor) framework on a Manhattan-grid road topology, and observed its good performance in terms of avoided collisions percentage as a function of vehicle speed and different vehicles densities.