Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing the Modification Space of Signature IDS Rules

Signature-based Intrusion Detection Systems (SIDSs) are traditionally used to detect malicious activity in networks. A notable example of such a system is Snort, which compares network traffic against a series of rules that match known exploits. Current SIDS rules are designed to minimize the amount of legitimate traffic flagged incorrectly, reducing the burden on network administrators. However, different use cases than the traditional one–such as researchers studying trends or analyzing modified versions of known exploits–may require SIDSs to be less constrained in their operation. In this paper, we demonstrate that applying modifications to real-world SIDS rules allow for relaxing some constraints and characterizing the performance space of modified rules. We develop an iterative approach for exploring the space of modifications to SIDS rules. By taking the modifications that expand the ROC curve of performance and altering them further, we show how to modify rules in a directed manner. Using traffic collected and identified as benign or malicious from a cloud telescope, we find that the removal of a single component from SIDS rules has the largest impact on the performance space. Effectively modifying SIDS rules to reduce constraints can enable a broader range of detection for various objectives, from increased security to research purposes.
A New Strategy for Combining Nonlinear Kalman Filters With Smooth Variable Structure Filters

Bayesian filters exemplified by the celebrated Kalman Filter (KF), and its non-linear variants rely on a fairly accurate state-space model of the system under study. To address the issue of modelling uncertainty in state estimation, the Smooth Variable Structure Filter (SVSF) was proposed in 2007. Since then, several SVSF variants have been proposed to extend its domain of applicability. In some of these algorithms, SVSF has been viewed as a complementary approach alongside the well-established nonlinear Kalman Filters. This paper seeks a general framework for SVSF formulation to unify some of the recent developments in SVSF literature under one umbrella. In this way, the SVSF variants are revisited as special cases of the proposed framework. This paper proposes a new strategy to combine SVSF filters with other nonlinear filters and puts existing SVSF filters under one umbrella. Six filters are formulated based on the proposed method of combining filters. The proposed filters relax limitations of existing SVSF variants, making the proposed filters more universal. In simulations, the new filters outperform state-of-the-art nonlinear KFs and some existing SVSF filters. To demonstrate the merits of the proposed framework, the new filters are applied to target tracking and are comparatively evaluated.
Study on Interface Trap and Fixed Charge Generation under Channel Hot Carrier Stressing at Low Temperature Using Device Simulation

We investigated the degradation mechanism on nMOSFETs that is regarding the interface-state and the fixed charge generations at low temperature. The research of the mechanism is conducted to ensure effective and large-scale quantum computing and make space electronics cost-effective. In the paper, both of ΔDit and ΔDfix were demonstrated to be exponential degradation with stress time and the fixed charge generation is found to be a strong acceleration at lower temperature, suggesting that the degradation mechanism of MOSFETs at low temperature is different from that at room temperature.
Modelled effects of temperature gradients and waves on the hydroxyl rotational distribution in ground-based airglow measurements

Abstract. Spectroscopy of the hydroxyl (OH) airglow has been a commonly used way to remotely sense temperatures in the mesopause region for many decades. This technique relies on the OH rotational state populations to be thermalized through collisions with the surrounding gas into a Boltzmann distribution characterized by the local temperature. However, deviations of the rotational populations from a Boltzmann distribution characterized by a single temperature have been observed and attributed to an incomplete thermalization of the OH from its initial, non-thermodynamic-equilibrium distribution. Here we address an additional cause for the apparent amount of excess population in the higher rotational levels of the OH airglow brought about by integrating these OH emissions through vertical gradients in the atmospheric temperature. We find that up to 40 % of the apparent excess population, currently attributed to incomplete thermalization, can be due to the vertical temperature gradients created by waves. Additionally, we find that the populations of the different upper vibrational levels are affected differently. These effects need to be taken into account in order to assess the true extent of non-thermodynamic-equilibrium effects on the OH rotational populations.
Application of Image Classification Based on Improved LSTM in Internet Reading Therapy Platform

Reading therapy is an effective approach for improving mental states or addressing disabilities associated with individuals’ dyslexia. In traditional approaches, this process is performed through the intervention and supervision of an expert, which incurs time and cost. However, by utilizing artificial intelligence technologies, the reading therapy process can be automated. This article focuses on presenting an internet-based automated platform for reading therapy. In this method, audio and visual features during the reading therapy are processed using deep learning techniques to identify the individual’s emotional state based on their reading status. In this state, two separate convolutional neural networks are used to describe the facial image features and speech characteristics of the individual. Then, the described features from these two models are merged to determine the individual’s mental states using LSTM layers. Finally, a reinforcement learning model is used to provide feedback and design subsequent exercises. This reinforcement approach leads to continuous improvement of the evaluated process and plays a significant role in enhancing the efficiency of the internet-based reading therapy system. The performance of the proposed method has been evaluated based on information from 20 volunteers. According to the results, the proposed method can effectively improve individuals’ mental states and compete with the conventional supervisor-based approaches. The performance of the proposed deep learning models in identifying emotional states has also been investigated. The results indicate that this model achieves a minimum improvement of 9.71% in emotional state recognition compared to previous research, with an average correlation coefficient of 0.64485.
Expected Probability of Radiometric Detection by Channelized Radiometer

Low probability of detection (LPD) communication techniques are often used in tactical environments, and the introduction of commercial-grade cognitive radios has made them relevant in civilian environments. In highly mobile networks, updating the probability of detection often enough to ensure accurate predictions can be computationally expensive. This paper derives the expected probability of radiometric detection by a multi-channel radiometer and presents a flexible framework for approximating that probability which allows the user to balance accuracy and time complexity. This approximation provides a MiniMax solution under the condition that the probability of detection is never underestimated. An example is given which has relative error less than 10−5 and runs more than 50 times faster than numerical integration and 10 times faster than solutions derived from Q function approximations.
Mapping T-cell furin cleavage site epitopes in SARS-CoV-2 spike protein sequences from the Philippines and global variants

A recently identified novel epitope, which maps to the furin cleavage site, a crucial virulence factor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has shown that its corresponding antibody may be able to neutralize the virus before it enters a host cell. This study aimed to identify conserved and distinct epitopes that map to the furin cleavage site of the SARS-CoV-2 spike protein across various SARS-CoV-2 variants, focusing on the top two most frequent human leukocyte antigen (HLA) alleles across Southeast Asia. In this study, we predicted potential epitopes that map to the furin cleavage site within the immune epitope database thresholds. Multiple sequence alignment was performed to determine the conservation of the epitopes, and different epitopes were docked to their corresponding HLA molecules using AutoDock Vina. The resulting peptide-major histocompatibility complex (pMHC) complexes were then docked to prospect T-cell receptors (TCRs), and the interactions and binding energies of the pMHC and TCR-pMHC model complexes were identified. Our results revealed potential epitopes that may be suitable for vaccine design, including SQSIIAYTM (−7.1 kcal/mol) of HLA-B40:01, VASQSIIAY (−8.2 kcal/mol), and SIIAYTMSL (−7.2 kcal/mol) of HLA-B46:01, IAQYTSALL (−8.0 kcal/mol) of HLA-C01:02, and VASQSIIAY (−8.2 kcal/mol) of HLA-C07:02, as well as SQSIIAYTMSLGAEN, ASQSIIAYTMSLGAE, EMIAQYTSALLAGTI, and NFNGLTGTGVLTESN (HLA-DRB1*09:01). The pMHC complexes formed dominant hydrogen bonds and hydrophobic interactions. TCR-pMHC docking revealed that SQSIIAYTM (−16.0 kcal/mol) has the lowest binding energy and the highest number (128) of binding interactions, primarily consisting of charged-polar and charged-nonpolar interactions. Our findings suggest that epitopes mapping to the furin cleavage site of the SARS-CoV-2 spike protein could be potential targets for vaccine design. Our identified epitopes could serve as a basis for the development of effective vaccines that provide broad coverage across different SARS-CoV-2 variants, particularly in Southeast Asia, where the top two most frequent HLA alleles are prevalent.
H-Band High-Power and High-Efficiency Voltage-Controlled Oscillator IC and Waveguide Module

We present a high-power and high-efficiency voltage-controlled oscillator (VCO) integrated circuit (IC) and waveguide module operating at H-band (around 230 GHz) using a 250-nm InP HBT technology. The Colpitts oscillator with an LC emitter degeneration is adopted to reduce supply voltage and improve efficiency, where the inductance is implemented by the shorted transmission line. Capacitive feedback at the emitter is realized by the varactor consisting of the collector–base junction of the HBT. A common-base buffer amplifier is added to reduce the load-pulling effect of the Colpitts oscillator and increase output power. The optimum load impedance for high output power is determined from the load-pull simulations based on the large-signal oscillation analysis. To further increase output power, a low-loss on-chip balun is employed to combine output powers from each branch of the differential VCOs. On-wafer measurement of the fabricated VCO shows a peak output power of 6.8 dBm at 230 GHz with dc-to-radio-frequency (RF) efficiency of 6.0%. To build a stable and practical H-band source module, the fabricated VCO is mounted in a WR-3.4 waveguide. For this purpose, the on-chip balun is replaced with the dipole transition, which is also integrated into the InP substrate. The fabricated VCO module exhibits a peak output power of 6.4 dBm and dc-to-RF efficiency of 5.5% at 229.3 GHz. Oscillation frequency is tuned from 221.6 to 232.9 GHz. The phase noise is also measured to be −101.9 dBc/Hz at a 10 MHz offset at 230.3 GHz. To the best of the authors’ knowledge, these results demonstrate the best performance in terms of output power and efficiency among the reported H-band VCO ICs and modules.
Palmitate Stimulates Expression of the von Willebrand Factor and Modulates Toll-like Receptors Level and Activity in Human Umbilical Vein Endothelial Cells (HUVECs)

An increased concentration of palmitate in circulation is one of the most harmful factors in obesity. The von Willebrand factor (vWF), a protein involved in haemostasis, is produced and secreted by the vascular endothelium. An increased level of vWF in obese patients is associated with thrombosis and cardiovascular disease. The aim of this study was to investigate a palmitate effect on vWF in endothelial cells and understand the mechanisms of palmitate-activated signalling. Human umbilical vein endothelial cells (HUVECs) incubated in the presence of palmitate, exhibited an increased VWF gene expression, vWF protein maturation, and stimulated vWF secretion. Cardamonin, a Nuclear Factor kappa B (NF-κB) inhibitor, abolished the palmitate effect on VWF expression. The inhibition of Toll-like receptor (TLR) 2 with C29 resulted in the TLR4 overactivation in palmitate-treated cells. Palmitate, in the presence of TLR4 inhibitor TAK-242, leads to a higher expression of TLR6, CD36, and TIRAP. The silencing of TLR4 resulted in an increase in TLR2 level and vice versa. The obtained results indicate a potential mechanism of obesity-induced thrombotic complication caused by fatty acid activation of NF-κB signalling and vWF upregulation and help to identify various compensatory mechanisms related to TLR4 signal transduction.
Secure Beamforming in DLA-Based CAP-MIMO

This work proposes a novel secure beamforming design for discrete lens array (DLA)-based continuous aperture phased (CAP) multiple-input multiple-output wiretap channels. The base station exploits a switching network to connect a subset of its analog beams or DLA feed antennas to the available radio frequency chains. The switching network and transmit beamformers are jointly designed to maximize the weighted secrecy sum-rate for this setting. The principal design problem reduces to an NP-hard mixed-integer non-linear programming. We invoke the fractional programming technique and the penalty dual decomposition method to develop a tractable iterative algorithm that effectively approximates the optimal design. Our numerical investigations validate the effectiveness of the proposed algorithm and its superior performance compared with the benchmark.
Application of machine learning in chemical engineering: outlook and perspectives

Chemical engineers' formulation, development, and stance processes all heavily rely on models. The physical and economic consequences of these decisions can have disastrous effects. Attempts to employ a hybrid form of artificial intelligence for modeling in various disciplines. However, they fell short of expectations. Due to a rise in the amount of data and computational resources during the previous five years. A lot of recent work has gone into developing new data sources, indexes, chemical interface designs, and machine learning algorithms in an effort to facilitate the adoption of these techniques in the research community. However, there are some important downsides to machine learning gains. The most promising uses for machine learning are in time-critical tasks like real-time optimization and planning that require extreme precision and can build on models that can self-learn to recognize patterns, draw conclusions from data, and become more intelligent over time. Due to their limited exposure to computer science and data analysis, the majority of chemical engineers are potentially vulnerable to the development of artificial intelligence. But in the not-too-distant future, chemical engineers' modeling toolbox will include a reliable machine learning component.
Deep convolutional neural networks for cardiovascular vulnerable plaque detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.
PrivDroid: Android Security Code Smells Tool for Privilege Escalation Prevention

Privilege Escalation (PE) attacks are common security issues in Android ecosystem. They typically involve the exploitation of vulnerabilities to gain unauthorized access to sensitive data. Preventing their related vulnerabilities is complex and hard to be understood and mitigated by developers. In a previous research, we performed an empirical study to investigate the effectiveness of existing IDE plugins in detecting known Android related vulnerabilities. We found that most of PE vulnerabilities are not covered by these IDE plugins. In order to assist developers to evade these issues, we present in this paper PrivDroid, an up to date and available IDE plugin for secure Android development. The tool combines static analysis techniques on the Android project source files to identify security code smells related to PE. Finally, PrivDroid is tested against more than 200 real Android applications and demonstrates that it gives additional capabilities to prevent Privilege Escalation related vulnerabilities.
Optical-Waveguide Based 3-Axial Tactile Sensor for Minimally Invasive Surgical Instruments

Force feedback is of importance in Minimally Invasive Surgery (MIS) as it reduces surgical risks and enhances surgical safety. However, equipping force sensing to the tip of surgical instruments presents challenges due to their diminutive dimensions and often curved shapes. To address this issue, a novel and compact optical-based 3-Axial force sensor used for the surgical forceps is proposed. Based on the extent of disruption to the total internal reflection (TIR), the magnitude and the direction of the force can be detected by measuring the light intensity patterns from three intersecting channels. The calibration experiments validate the capability of the proposed sensor to accurately measure forces within the range of 0 to 3 N, achieving an average measurement error of 0.089N. Subsequently, the sensor, along with the detection circuit, are integrated onto a surgical forcep, and verification experiments are conducted. The results indicate that the proposed sensor can provide effective 3-Axial force sensing during the surgical process such as grasping, manipulation, and pulling. The characteristics of compact size, high precision, and integrability of the sensor make it highly promising for providing force feedback in MIS.
Seismicity patterns (1963-1983) as stress indicators in the Shumagin seismic gap, Alaska

Earthquakes (1963 to 1983) of magnitude 5.0 to 6.5 form a rim of high seismic activity around a central region of relative seismic quiescence that coincides with the Shumagin seismic gap. This pattern, which can be inferred both from local network and teleseismic earthquake locations, shows high activity near the eastern and western ends of the seismic gap and along the down-dip end of the main thrust zone. The rim of seismicity surrounding an area of seismic quiescence may reflect strong couplign along the elastic-brittle part of the plate boundary where the great earthquakes occur. The temporal behavior of the microseismicity recorded by the Shumagin network is characterized by a burst of activity in 1978 and 1979, and a low or average level of activity from 1980 to January 1983. The increased microearthquake activity during 1978-1979 is located mainly near the down-dip end of the main thrust zone and along the Benioff zone down to 100 to 200 km depth. Composite fault plane solutions of earthquakes occurring in 1978-1979 show down-dip tension between 50 to 120 km depth in the upper plane of the Benioff zone. Composite fault plane solutions of 1981 earthquakes, however, indicate in-plate compression in the same region. Hence, the rate of occurrence and focal mechanisms of microearthquakes located in the Benioff zone below the main thrust both show coincident temporal and spatial variations that may reflect fluctuations in local stresses.
Inherited arrhythmias and gene therapy: Are there any ethical considerations to take into account?

Interventional electrophysiology represents a relatively recent subspecialty within the field of cardiology. In the past half-century, there has been significant advancement in the development and implementation of innovative ablation treatments and approaches. However, the treatment of arrhythmias continues to be inadequate. Several arrhythmias, such as ventricular tachycardia and atrial fibrillation, pose significant challenges in terms of therapeutic efficacy, whether through interventional procedures or the administration of antiarrhythmic drugs. Cardiologists are engaged in ongoing research to explore innovative methodologies, such as genome editing, with the purpose of effectively managing arrhythmias and meeting the growing needs of patients afflicted with rhythm disturbances. The field of genome editing has significant promise and has the potential to serve as a highly effective personalized therapy for rhythm disorders in patients. However, several ethical issues must be considered.
Deep learning method for lung cancer identification and classification

Lung cancer (LC) is calming many lives and is becoming a serious cause of concern. The detection of LC at an early stage assists the chances of recovery. Accuracy of detection of LC at an early stage can be improved with the help of a convolutional neural network (CNN) based deep learning approach. In this paper, we present two methodologies for Lung cancer detection (LCD) applied on Lung image database consortium (LIDC) and image database resource initiative (IDRI) data sets. Classification of these LC images is carried out using support vector machine (SVM), and deep CNN. The CNN is trained with i) multiple batches and ii) single batch for LC image classification as non cancer and cancer image. All these methods are being implemented in MATLAB. The accuracy of classification obtained by SVM is 65%, whereas deep CNN produced detection accuracy of 80% and 100% respectively for multiple and single batch training. The novelty of our experimentation is near 100% classification accuracy obtained by our deep CNN model when tested on 25 Lung computed tomography (CT) test images each of size 512×512 pixels in less than 20 iterations as compared to the research work carried out by other researchers using cropped LC nodule images.