Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimized Multi - Layer Hierarchical Network Intrusion Detection System with Genetic Algorithms

The number of connected devices on the Internet has exceeded 31 billion devices in 2018 and it is forecasted that this number will exceed 50 billion by the year 2020. One the other hand, malicious software and network attacks are raising on an alarming rate. It is estimated that more than 230,000 new malware are produced daily and over 53,000 new Cryptoware malware engines are detected as well. This proliferation in security attacks constitutes a great challenge for Intrusion Detection Systems (IDS), in particular, in detecting modern attacks. In this paper, a multi-layer hierarchical Network Intrusion Detection System (NIDS) is proposed with the aim to improve the overall detection performance of the IDS for detecting modern attack types. The proposed multi-layer NIDS utilizes multiple models of machine learning algorithms in a hierarchical architecture in addition to using evolutionary computing, namely Genetic Algorithms, to tune the configurations of the neural network models used in the first layer. A modern dataset (i.e. CICIDS-2017) is used to evaluate the proposed approach, which contains several modern attacks. The results showed that the proposed multi-layer system significantly improved on the error generalization metrics.
A Generative Machine Learning Approach to RFI Mitigation for Radio Astronomy

Radio astronomy is a vital tool for astronomers to study the Universe and has seen a wave of renewed interest and advancement over recent years. Next-generation radio telescope arrays like the SKA, ALMA and VLA are developed to be significantly more sensitive compared to older telescopes, which as a result also make them more susceptible to radio frequency interference (RFI). This highlights the need for effective RFI mitigation techniques in radio astronomy. We present a machine learning-based RFI mitigation approach that aims to separate RFI-corrupted spectrogram observations into signal of interest and RFI components in an unsupervised manner using a modified generative adversarial network (GAN) framework. We show that this unsupervised source separation approach is able to achieve performance comparable to a fully supervised approach.
Liver Cancer Prediction Using Synthetic Minority based on Probabilistic Distribution (SyMProD) Oversampling Technique

Liver cancer is challenging to diagnose in general. Moreover, liver cancer prediction can be hindered by skewed data between majority and minority classes, and missing values. Many existing prediction models do not address these two limitations that can make classification results ignore minority instances (i.e., patients with liver cancer are not detected). In this paper, we present a liver cancer prediction model with a new oversampling technique called Synthetic Minority based on Probabilistic Distribution (SyMProD) to handle skewed patients’ data from Chulabhorn hospital. SyMProD removes noisy data based on z-score normalization value and adaptively selects referenced data using probability distribution from the ratio of minority and majority closeness factor. The proposed method oversamples minority instances from several minority nearest neighbors to cover the distribution. We employ Random Forest (RF) and Gradient Boosted Tree (GBT) to generate prediction models with stratified five-fold cross-validation. Results demonstrate that GBT with our proposed oversampling technique achieves a better result than other techniques. These results from our technique generate new instances in the minority distribution, avoid the majority region, remove the overgeneralization problem, and reduce possibilities of creating noise and overlapping classes. Our prediction model may help prompt high-risk patients to get a proper diagnosis and treatments in time.
A Novel Learning Model Based on Trust Diffusion and Global Item for Recommender Systems

Recommender systems can provide users with an ordered list of various items, which greatly assists users to purchase products that they are satisfied with. However, item recommendation has been confronted with some inherent problems, such as sparse ratings and long-tail distribution, resulting in low accuracy of recommendations and insignificant marketing. In this paper, we propose a novel learning model based on trust diffusion and global item (TDGIL) to improve the accuracy of item rating prediction for recommender systems. Specifically, first, the rating information on items is mined and aggregated to the greatest extent based on trust diffusion characteristics among users. The benchmark prediction of item recommendation is updated by a user trust neighbor set and its item ratings, which are obtained by a trust diffusion algorithm. Then, the difference weights and compensation coefficients for all items are defined to learn users’ potential preferences in the proposed global item model. Finally, the TDGIL learning algorithm is presented to train and learn the target networks by random gradient descent. The extensive experiments and results on two real-world datasets demonstrated that our proposed model can achieve significant improvements in the accuracy of rating prediction compared with some state-of-the-art methods.
A Mixed-Signal Offset-Compensation System for Multi-Gbit/s Optical Receiver Frontends

Offset compensation (OC) systems are indispensable parts of multi-Gbit/s optical receiver (RX) frontends. Effects of offset are addressed in this paper. The analytical expression for the highest lower-cut-off frequency of the OC with minimum impact on the sensitivity is found. Existing OC solutions are discussed. Then, a novel mixed-signal (MS) architecture is introduced which uses digital filtering of the signal, and current-digital-to-analog converters (IDACs) to compensate the static offset in the limiting amplifier (LA) and transimpedance amplifier (TIA), as well as continuously track and compensate the TIA offset. By using two feedback loops and a continuous tracking the presented solution offers more functionality than other existing MS architectures. Three RX implementations, with RC, switched-capacitor (S-C) and with the MS-OC architectures, in the same 28 nm bulk-CMOS are compared quantitatively with measurements. The presented MS design reaches a lower-cut-off frequency of under 9 kHz, a dynamic range of over 1 mA, 3. 2$\mu$A residual input offset-current and it is compensating the RX via two feedback loops. These are achieved using an area of only 1345 $\mu \mathrm{m}^{2}$, nearly half of RC-filter based architecture. Although the SC implementation needs less area, its residual offset is 8 times higher. Both conventional implementations have a higher high-pass characteristic of about 20 kHz and can compensate only the offset of the TIA. It is concluded, that the presented system offers a higher flexibility and functionality in implementation, as well as a very good compromise between area, precision and performance over the commonly used RC-filter and S-C filter based solutions.
A fuzzy-evaluated gain scheduling approach for the PID control of a pressure plant

A fuzzy gain scheduling routine is combined with the well-known PID algorithm to allow the new structure to deal with non-linearities of the process. It is shown that both process gain and integral time change along the operation range, due to non-linear behavior of the process. In current gain scheduling applications, the gain and integral time commutation is abrupt, producing internal disturbances to the closed loop. The issue of these variations and their implication on the controller performance is discussed and solved. Fuzzy modeling is used here as an useful tool for solving the commutation problem. Simulation studies and experimental data show satisfactorily how the proposed algorithm improves the performance of the controller over a large operation range.
Patient care experiences and perceptions of the patient-provider relationship: A mixed method study

Abstract Improving interpersonal continuity of careprovider (PCP) over time—is often considered a goal of primary care. Continuity of care is frequentlyof longitudinal continuity, or the proportion of encounters with one practitioner, overlooking aspects of the patientprovider relationship that are key to interpersonal continuity of care. Further, few studies explore patients’ perspectives regarding which care experiences enhance or detract from the patientgroup interviews, a patient experience CAHPexperiences at 10 primary care clinics influenced their perceptions of their relationship with their PCPs. Focus group interviews with 63 participants indicated that patients’ experienceperceptions of the patient-provider relationship. The relationship between patient experience and interpersonal continuity was empirically assessed using survey responses and medical records (n=645). Wthat their provider knows them as a person as a measure of interpersonal continuity. Logistic regression results indicated that being seen within 15 minutes, receiving visit reminders, effective provider communication, and satipositively influenced patient perceptions of the patientshaped their perceptions of the patient-provider relationship independent of their satisfaction with care. The mixed methods d esign adds depth to our understanding of patients’ care experiences, and illustrates that these experiences are critical for understanding the patient-provider relationship. Future research on interpersonal continuity should take patient experiences into account.
Hybrid Feature Selection Models for Machine Learning Based Botnet Detection in IoT Networks

Timely detection of intrusions is essential in IoT networks, considering the massive attacks launched by the huge-sized botnets which are composed of insecure devices. Machine learning methods have demonstrated promising results for the detection of such attacks. However, the effectiveness of such methods may greatly benefit from the reduction of feature set size as this may prevent the impeding impact of unnecessary features and minimize the computational resources required for intrusion detection in such networks having several limitations. This paper elaborates on feature selection methods applied to machine learning models which are induced for botnet detection in IoT networks. A particular attention is devoted to the use of wrapper methods and their combination with filter methods. While filter-based feature selection methods provide a computationally light approach to select the most informative features, it is shown that their utilization in combination with wrapper methods boosts up the detection accuracy.
Density Imaging Using a Compressive Sampling DBIM approach

Density information has been used as a property of sound to restore objects in a quantitative manner in ultrasound tomography based on backscatter theory. In the traditional method, the authors only study the distorted Born iterative method (DBIM) to create density images using Tikhonov regularization. The downside is that the image quality is still low, the resolution is low, the convergence rate is not high. In this paper, we study the DBIM method to create density images using compressive sampling technique. With compressive sampling technique, the probes will be randomly distributed on the measurement system (unlike the traditional method, the probes are evenly distributed on the measurement system). This approach uses the l1 regularization to restore images. The proposed method will give superior results in image recovery quality, spatial resolution. The limitation of this method is that the imaging time is longer than the one in the traditional method, but the less number of iterations is used in this method.
Identifying Influential Nodes with a Community Structure Measure

Locating significant spreaders in social networks is an important issue for optimizing network structures so as to enhance the robustness of a system. While traditional methods are helpful in identifying influential nodes, they have some obvious limitations in overlooking the properties of social networks. For example, local metrics usually only account for neighbor size without reflecting the interactions between the nodes, though they are simple to use. Therefore, the topological connections among neighbors are neglected. On the other hand, global metrics are difficult to be applied in large social networks because of the high computational complexity. In order to take advantage of network properties, this paper introduces a new measure to identify influential nodes based on the combination of the influences of the community and the neighbors. SIR (Susceptible-Infected-Recovered) model is simulated to evaluate the nodes' spreading capabilities. In our experiment, we have demonstrated that the proposed measure can identify nodes with strong spreading influence, by comparing to the widely-used baseline measures.
Bidirectional Mean Distance Estimation: A New Gap filling Method

Time Series Analysis has been used in several areas, such as pattern recognition, machine learning, data mining, andforecasting. However, before applying any method to model a timeseries, it is important to preprocess its observations aiming at, forexample, removing outliers, normalizing its values, and dealingwith missing data. In this context, this work proposes a newpreprocessing method to replace missing data with valid values, respecting the general time series behavior. In our experiments, we demonstrate BMDE provides impressive results, reducingthe memory usage and the complexity by analyzing a numbersignificantly lower of implicit components. The most commonlyused method to replace missing data is SSA (Singular SpectrumAnalysis). However, its memory usage limits its adoption in bigtemporal data. To overcome this situation, we present in thismanuscript a new gap filling method called Bidirectional MeanDistance Estimation (BMDE).
Rebalancing the Car-Sharing System: A Reinforcement Learning Method

With the boom of sharing economy, more and more car-sharing corporations sprout up, providing more travel options and convenience. Due to similar travel patterns of urban dwellers, the car-sharing system results in an imbalance of shared cars in spatial distribution, especially during the rush hours. To redress this imbalance faces many challenges, such as insufficient data and the enormous state space. In this study, we propose a new reward method called Double P (Picking & Parking) Bonus (DPB). We model the research problem as a Markov Decision Process (MDP) problem and introduce Deep Deterministic Policy Gradient, a state-of-the-art reinforcement learning framework, to find a solution. The results show that the rewarding mechanism embodied in the DPB method can indeed guide the users' behaviors through price leverage, increase user stickiness, cultivate user habits, and thus boost the service provider's long-term profit.
A High Frequency 40kW Step-Up Converter for Fuel Cell Electrical Vehicles Based on SiC MOSFET

With the development of fuel cell electrical vehicles, a class of step-up DC-DC converters with high efficiency, high power density and low input current ripple is needed. For this application, a high frequency 40kW DC-DC converter is proposed in this paper. By using SiC MOSFETs, operating frequency of the converter is up to 100 kHz, thus reducing the volume and weight of inductors and capacitors. Besides, the power loss is reduced effectively by replacing the diode in boost circuit by a SiC MOSFET. Meanwhile, the interleaved 6 half-bridge power modules achieve much smaller input current ripple than that of single channel circuit. The parameter design and power losses distribution and optimization are analysis in this paper. Finally, a natural cooling prototype converter was built, the power density is about 62W/in3, and experiment results show that current ripples are less than 10%, and efficiency is about 98.4% at rated power.
Model Predictive Control for Three-Phase Four-Wire Power Quality Control under Unbalanced Power Grid

In the three-phase four-wire power distribution system under unbalanced power grid, the neutral line current generates zero sequence current due to increasing nonlinear and unbalanced loads. A model predictive control (MPC) strategy is proposed and applied to the split-capacitor-type structure. To compensate the zero-sequence current, an optimal voltage vector which can minimize the cost function is selected and then applied to the next period. Finally, the simulation results show that the proposed control strategy has good practicability.
Experiment Designs to Minimize Input Peak and Crest Factor in MIMO System Identification

The quality of the data for system identification is of utmost of importance. Ideally, the output data should satisfy requirements regarding variance, and in the case of multiple-input multiple-output (MIMO) systems, correlation between the outputs. Usually, it is desired to achieve this by input perturbations with peak values as small as possible. A related measure is the crest factor, which is a measure of the (inverse) power of an input perturbation with given peak value. In this paper, the effect of minimizing the input peak and the crest factor, subject to desired output variances/covariances, is studied for various types of perturbation signals. The design procedure is completely data based; data are obtained by one or more preliminary experiments with the system to be identified. A model of an ill-conditioned distillation column is used for illustration.
Listen to My Heart? How Privacy Concerns Shape Users’ Acceptance of e-Health Technologies

Due to the ageing population healthcare systems face monumental economic and social challenges. To respond to them, e-Health technologies and services are developed that seek to improve the quality of care and to support the monitoring and management of chronic diseases. One decisive factor for the widespread use of these technologies is user acceptance. As many e-Health technologies are essentially based on the storage, analysis, and transmission of large amounts of health-related data, privacy concerns can be a serious impediment to the successful adoption. However, privacy concerns are not included in traditional technology acceptance models. In this study, the impact of privacy concerns on the acceptance of e-Health technologies is studied. Also benefits and the formation of privacy concerns through trust in data protection, perceived data sensitivity, and the individual disposition to value privacy are examined. N = 253 participants with chronic health conditions evaluated two exemplary e- Health technologies: fitness trackers and remote monitoring of implanted cardiac devices. Structural equation modelling shows that privacy concerns are indeed an important barrier to the adoption of e-Health technologies and are influenced by the perceived data sensitivity, trust in data protection, and the individual disposition to value privacy. Users’ privacy perceptions and concerns should be taken into account when designing e-Health technologies.
Improvement of AM–PM in a 33-GHz CMOS SOI Power Amplifier Using pMOS Neutralization

This letter presents two highly efficient two-stage power amplifiers (PAs) for 5G applications, implemented in a 22-nm slilicon on insulator (SOI) CMOS technology. High efficiency is achieved by carefully designing the power cells and optimizing the layout. Capacitive neutralization is used to improve the stability and the gain. Both PAs are similar except for the use of nMOS neutralization capacitors in the first one. In the second PA, we propose the use of pMOS capacitors instead to enhance significantly both stability and AM–PM linearity at the same time. For both PAs, the saturated output power is 12.7 dBm and <inline-formula> <tex-math notation="LaTeX">$\text{P}_{\mathrm {1\,dB}}$ </tex-math></inline-formula> is 11.9 dBm from a 0.9-V supply at 33 GHz with a power-added efficiency (PAE) at <inline-formula> <tex-math notation="LaTeX">$\text{P}_{\mathrm {1\,dB}}$ </tex-math></inline-formula> of more than 36%. The PAE at <inline-formula> <tex-math notation="LaTeX">$\text{P}_{\mathrm {sat}}$ </tex-math></inline-formula> is 38% and 40% for the PA with nMOS and pMOS neutralizations, respectively. The AM–PM up to <inline-formula> <tex-math notation="LaTeX">$\text{P}_{\mathrm {3\,dB}}$ </tex-math></inline-formula> for the PA with nMOS neutralization is 7°, and for the one with pMOS neutralization, it is less than 1.3° thanks to the proposed technique.
Large-Signal Stability Analysis and Shunt Active Damper Compensation for DC Microgrid with Multiple Constant Power Loads

The various impedances of DC microgrid are easy to cause the deterioration of system stability, especially the influence of the negative incremental resistance characteristic of constant power load (CPL). In this paper, the stability of large-signal disturbances in DC microgrid with multiple constant power loads is studied. The Takagi-Sugeno (T-S) fuzzy model and the Lyapunov stability theorem are used to analyzed the large-signal stability of the system, and the region of asymptotic stability (RAS) and the power boundary of each load are estimated. To enhance the stability of DC microgrid, a shunt active damper is introduced to serve as a virtual passive element by controlling the active damper, which can be set either at the bus-side capacitor or the input-side of CPL or the source converter side. The effect of compensation positions of the active damper in a DC microgrid are analyzed and compared, and as a result the active damper set on the input-side of CPL is more efficient than set on the others. Finally, the effectiveness of the active damper to improve the stability of DC microgrid is verified by simulation.
Analyzing a Five-Year Failure Record of a Leadership-Class Supercomputer

Extreme-scale computing systems are required to solve some of the grand challenges in science and technology. From astrophysics to molecular biology, supercomputers are an essential tool to accelerate scientific discovery. However, large computing systems are prone to failures due to their complexity. It is crucial to develop an understanding of how these systems fail to design reliable supercomputing platforms for the future. This paper examines a five-year failure and workload record of a leadership-class supercomputer. To the best of our knowledge, five years represents the vast majority of the lifespan of a supercomputer. This is the first time such analysis is performed on a top 10 modern supercomputer. We performed a failure categorization and found out that: i) most errors are GPUrelated, with roughly 37% of them being double-bit errors on the cards; ii) failures are not evenly spread across the physical machine, with room temperature presumably playing a major role; and iii) software errors of the system bring down several nodes concurrently. Our failure rate analysis unveils that: i) the system consistently degrades, being at least twice as reliable at the beginning, compared to the end of the period; ii) Weibull distribution closely fits the mean-time-between-failure data; and iii) hardware and software errors show a markedly different pattern. Finally, we correlated failure and workload records to reveal that: i) failure and workload records are weakly correlated, except for certain types of failures when segmented by the hours of the day; ii) several categories of failures make jobs crash within the first minutes of execution; and iii) a significant fraction of failed jobs exhaust the requested time with a disregard of when the failure occurred during execution. Index Terms-Fault tolerance, resilience, failure analysis, high performance computing.