Miracle-dz/UniFAR-FTUs huggingface dataset:
Concatenation of SC-LDGM Codes with Outer BCH Codes

Systematic convolutional low density generator matrix (SC-LDGM) codes are a class of codes with flexible construction and predictable performance. However, the SC-LDGM codes suffer from error floors. In this paper, we propose to lower down the error floors of the SC-LDGM codes by the use of concatenated codes with the outer Bose-Chaudhuri-Hocquenghem (BCH) codes. Performance bounds are derived for the proposed concatenated codes based on genie-aided (GA) bounds on the inner SC-LDGM codes. As a byproduct, we show that the outer BCH codes can also be used as a constraint to stop early the iterative decoding. The simulation results confirm our analysis.
Harmonic-resonance enhanced third-harmonic generation and Kerr-effect in mid-infrared quantum-cascade lasers

We study mid-infrared quantum-cascade lasers (QCL's) with a pair of triply harmonic resonant levels. Potential applications of such designs are discussed based on the resonant third-order nonlinear susceptibility χ(3) at the third-harmonic (TH) frequency, χ(3)(3ω; ω, ω, ω), and that at fundamental mode (FM) frequency χ(3)( ω; ω, ω,-ω). Mode overlap and the phase mismatch effect are considered in the TH power evaluation. In addition to single-photon processes, resonant two-photon processes are included in the evaluation of χ(3)( ω; ω, ω,-ω), which results in the enhancement of the positive nonlinear (Kerr) refractive index, and thus induce stronger self-phase modulation (SPM). It is concluded that QCL's with multiple-resonance design are not only favorable for tunable light emission in the near- to mid-infrared region, but are also promising candidates for spectrum broadening by SPM.
Experimental Tests of Mediation Models

Experimental tests of assumed mediation models have been recommended by numerous methodologists. However, they may prove problematic for a number of reasons. Thus, this article considers (a) experimental designs that can be used in tests of mediation, (b) problems that may arise in manipulating mediator variables, (c) distributional issues in experimental tests of mediation, (d) statistical approaches for testing mediation models, including strategies for testing for direct versus indirect effects in experimental tests of mediation models, and (e) a set of conclusions stemming from our analysis.
Web APIs: Features, Issues, and Expectations – A Large-Scale Empirical Study of Web APIs From Two Publicly Accessible Registries Using Stack Overflow and a User Survey

With the increasing adoption of services-oriented computing and cloud computing technologies, web APIs have become the fundamental building blocks for constructing software applications. Web APIs are developed and published on the internet. The functionality of web APIs can be used to facilitate the development of software applications. There are numerous studies on retrieving and recommending candidate web APIs based on user requirements from a large set of web APIs. However, there are very limited studies on the features of web APIs that make them more likely to be used and the issues of using web APIs in practice. Moreover, users’ expectations on the development and management of web APIs are rarely investigated. In this paper, we conduct a large-scale empirical study of 20,047 web APIs published at two popular and publicly accessible web API registries: ProgrammableWeb and APIs.guru. We first extract the questions posted in Stack Overflow (SO) that are relevant to the web APIs. We then manually analyze 1,885 randomly sampled SO questions and identify 24 web API issue types (e.g., authorization error) that are encountered by users. Afterwards, we conduct a user survey to investigate the features of web APIs that users often consider when shortlisting a web API for testing before they adopt it, validate the identified types of web API issues, and understand users’ expectations on the development and management of web APIs. From the 191 received responses, we extract 14 important features for users to decide whether to use a web API (e.g., well-organized documentation). We also gain a better understanding of web API issue types and summarize 11 categories of user expectations on web APIs (e.g., documentation and SDK/library). As the result of our study, we provide guidelines for web API developers and registry managers to improve web APIs and promote the use of web APIs.
Feature dimensionality in CNN acceleration for high-throughput network intrusion detection

With the ever increasing need for better cybersecurity, and due to the continuous growth of network traffic bandwidths, there is a continuous pursuit of faster and smarter network intrusion detection systems. Neural network-based solutions on FPGAs are very effective in detecting different types of attacks, but have problems with analyzing network traffic online at line speed. One important bottleneck that limits the throughput in raw traffic-based existing systems, is the input shape of the features that are extracted from the raw data. In this work, we propose new methods for extracting and representing features based on raw network traffic in online network intrusion detection systems. We show that feature dimensionality has a significant influence on the classification accuracy and the throughput. Our experiments are based on FPGA-based neural networks accelerated through FINN. We compare three newly proposed input shapes to the traditional 2D-based approach, and we show that two of the presented techniques greatly surpass the state-of-the-art with regards to accuracy and throughput. Our best architecture reaches a maximum bandwidth of 23.09 Gbps, while maintaining over 99% accuracy on both the UNSW-NB15 and CICIDS2017 datasets.
Spatiotemporal LiDAR-IMU-Camera Calibration: A Targetless and IMU-Centric Approach Based on Continuous-time Batch Optimization

Sensor calibration is one of the basic tasks for a multimodal sensing system. In this paper, we present a two-stage spatiotemporal calibration method for a common-used sensor suite, i.e. the LiDAR-IMU-camera sensor combination. In the first stage, the proposed method combines correlation analysis with hand-eye calibration to acquire the initial values of the time offset and the extrinsic rotation parameters for IMU-LiDAR and IMU-camera respectively, and refines the time offset through aligning the rotational trajectories. In the second stage, continuous-time batch optimization framework is employed to jointly estimate the extrinsic parameters of IMU-LiDAR and the trajectory of the IMU, and the optimized trajectory is utilized to calibrate the IMU-camera extrinsic parameters. In addition, the data association scheme is designed according to the modality of the LiDAR and the camera rather than the assistance of artificial targets. We validate the proposed method on both public dataset and self-record data. Experimental results show the feasibility and the performance of the proposed method through comparision with the state-of-the-art calibration methods for IMU-LiDAR and IMU-camera calibration.
Measuring Google Classroom's Usability Level from an Expert Perspective

The demand for the use of distance learning media, especially at the beginning of the covid-19 pandemic, led to the high use of one of the long-known distance learning media, namely Google Classroom. This study aims to provide empirical evidence of the effect of perceived usability on the use of Google Classroom recently. We use heuristic evaluation method to evaluate user interface of e-Learning with four expert participants who are also instructor/teacher in some courses, the data is presented in form of quantitative and in-depth in the form of qualitative. Result of the study showed that the highest severity level was 1 (cosmetic usability problem) in the first heuristic (visibility of system status): 2 from 4 expert said that Google Classroom does not provide visuals that help the user understand in which screen they are standing; and sixth heuristic (recognition rather than recall): 2 from 4 expert said that Google Classroom does not indicate which controls are currently inactive in clear visual way. This study introduces a new approach, risk opportunity measurement which is based on risk-based thinking of ISO 9001:2015 (standard of quality management system), to provide recommendations for improvement of problems found by evaluators to stakeholders based on principles/heuristics that have the highest to lowest risk opportunities for improvement and further development.
a Combined Modal/finite Element Analysis Technique for the Dynamic Response of a Non-Linear Beam to Harmonic Excitation

Abstract In this paper, a method is proposed for modelling large deflection beam response involving multiple vibration modes. Significant savings in computational time can be obtained compared with the direct integration non-linear finite element method. The deflections from a number of static non-linear finite element test cases are transformed into modal co-ordinates using the modes of the underlying linear system. Regression analysis is then used to find the unknown coupled non-linear modal stiffness coefficients. The inclusion of finite element derived modal masses, and an arbitrary damping model completes the governing non-linear equations of motion. The response of the beam to excitation of an arbitrary nature may then be found using time domain numerical integration of the reduced set of equations. The work presented here extends upon the work of previous researchers to include non-linearly coupled multi-modal response. The particular benefits of this approach are that no linearization is imposed, and that almost any commercial finite element package may be employed without modification. The proposed method is applied to the case of a homogeneous isotropic beam. Fully simply supported and fully clamped boundary conditions are considered. For the free vibration case, results are compared to those of previous researchers. For the case of steady-state harmonic excitation, results are compared with the direct integration non-linear finite element method using ABAQUS. In all cases, excellent agreement is obtained.
Numerical Modelling of Cell Distribution in Blood Flow

Properties of blood cells and their interaction determine their distribution in flow. It is observed experimentally that erythrocytes migrate to the flow axis, platelets to the vessel wall, and leucocytes roll along the vessel wall. In this work, a three-dimensional model based on Dissipative Particle Dynamics method and a new hybrid (discrete-continuous) model for blood cells is used to study the interaction of erythrocytes with platelets and leucocytes in flow. Erythrocytes are modelled as elastic highly deformable membranes, while platelets and leucocytes as elastic membranes with their shape close to a sphere. Separation of erythrocytes and platelets in flow is shown for different values of hematocrit. Erythrocyte and platelet distributions are in a good qualitative agreement with the existing experimental results. Migration of leucocyte to the vessel wall and its rolling along the wall is observed.
Evaluation of Gradient Boosting Algorithms on Balanced Home Credit Default Risk

Default risk divides customers into two groups-those who pay on time (non-defaulters) and those who don’t (defaulters). Recently, banks and data scientists, have employed different machine learning classification algorithms to categorize default risks. Among many algorithms, using imbalanced credit risk datasets, gradient boosting methods such as XGBoost, LightGBM, and CatBoost have been used to determine credit defaulters. However, this research aims to examine and compare the effectiveness of the three algorithms in terms of ROC AUC, and F1 performance scores on a balanced dataset using SMOTE and ADASYN oversampling techniques providing higher reliability in real-time with decreased wait time. Additionally, this research extracted the top 40 features shortlisted by each model resulting in lesser convergence time and decreased computational overhead during prediction. This study confirms that XGBoost outperforms both LightGBM and CatBoost in terms of AUC score and F1 score which is significantly higher compared to results from earlier studies on the same dataset.
Active Constraints Inspired Zero-Forcing Precoder Design under Per-Antenna Power Constraints

This paper investigates the problem of linear zero-forcing (ZF) precoder design for sum rate maximization under per-antenna power constraints (PAPCs). A conventional method to tackle this problem is to lift it into a convex determinant maximization program with respect to the transmit covariance matrices, whose complexity, however, increases dramatically as the problem size increases. A suboptimal alternative is to adopt a semi-closed-form pseudo-inverse solution, whose performance, however, becomes dramatically inferior to the optimal design as the number of transmit antennas goes large. A new low-complexity high-performance ZF precoder design under PAPCs is proposed in this paper. Specifically, we first investigate the power usage at each transmit antenna for ZF precoding under PAPCs and identify a lower bound on the number of power constraints that should be active (i.e., satisfied with equality) at the optimal ZF precoder. Then, we devise a low-complexity iterative algorithm to find a ZF precoder satisfying PAPCs, at which the number of active power constraints meets the lower bound requirement for optimality. Simulation results show that the proposed ZF precoder achieves the near-optimal sum rate with significantly reduced computational complexity.
Multi-label Classification of Steel Surface Defects Using Transfer Learning and Vision Transformer

Steel is the most widely used metal in various industries, for instance, automotive, buildings, packaging, etc. Defect detection on steel surfaces is crucial for steel production companies. This paper proposes a multi-label classification algorithm based on deep learning methods. Several models were trained based on the Severstal dataset, namely MobileNet-V2, Xception, DenseNet121, ViT, and ResNet-50 with transfer learning. In addition, a base model is trained from scratch based on convolutional layers such as ResNet. Moreover, a ViT model is constructed according to the attention and transformer layers concept. At first, 91% for the weighted F1 score is obtained. By exploiting the weighted loss method, the weighted F1 score increased to 92%.
Short-term Traffic Prediction Based-on Support Vector Regression

Realizing the short-term traffic prediction under various situations is important for the traffic to continue in a certain pattern or normal conditions. Most of the designed traffic forecasting models aim to present results for the future by taking into account all traffic conditions (accidents, weather conditions, road works, events, etc.). In this study, a traffic model has been developed in order for intelligent transportation systems to predict the traffic that will occur and make it easier to take action accordingly. In order to minimize the computational complexity in training phase, the Support Vector Regression (SVR) method, which is a Machine Learning (ML) method, has been preferred. The SVR model was trained using data collected from a busy intersection for Denizli province, and then short-term traffic prediction was performed. The hyper-parameter optimization of the model trained with four different directions and thirty-day data was made and the model accuracy was presented with several criteria. The results obtained by SVR model for traffic prediction are satisfactory in terms of prediction accuracy, similar to the typical traffic prediction models in the literature.
The minimum principle for deterministic impulsive control systems

We prove the minimum principle for an optimal impulsive control problem. This result is a generalization of the well-known Pontryagin minimum principle, and yields a necessary condition for an optimal impulsive control strategy that minimizes an associated cost. Furthermore, we establish an explicit connection between the value function arising from the dynamic programming principle approach and the costate arising from the minimum principle approach for the impulsive optimal control problem.
Shearing flow over a wavy boundary

A theoretical study is made of shearing flows bounded by a simple-harmonic wavy surface, the main object being to calculate the normal and tangential stresses on the boundary. The type of flow considered is approximately parallel in the absence of the waves, being exemplified by two-dimensional boundary layers over a plane. Account is taken of viscosity; but, as the Reynolds number is assumed to be large, its effects are seen to be confined within narrow ‘friction layers’, one of which adjoins the wave and another surrounds the ‘critical point’ where the velocity of flow equals the wave velocity. The boundary conditions are made as general as possible by including the three cases where respectively the boundary is rigid, flexible yet still solid, or completely mobile as if it were the interface with a second fluid. The theory is developed on the model of stable laminar flow, although it is proposed that the same theory may usefully be applied also to examples of turbulent flow considered as ‘pseudo-laminar’ with velocity profiles corresponding to the mean-velocity distribution. Use is made of curvilinear co-ordinates which follow the contour of the wave-train. This admits a linearized form of the problem whose validity requires only that the wave amplitude be small in comparison with the wavelength, even when large velocity gradients exist close to the boundary. The analysis is made largely without restriction to particular forms of the velocity profile; but eventually consideration is given to the example of a linear profile and the example of a boundary-layer profile approximated by a quarter-period sinusoid. In § 7 some general methods are set out for the treatment of disturbed boundary-layer proses: these apply with greatest precision to thin boundary layers, but are also useful for the initially very steep but on the whole fairly diffuse profiles which occur in most practical instances of turbulent flow over waves. The phase relationships found between the stresses and the wave elevation are discussed for several examples, and their interest in connexion with problems of wave generation by wind is pointed out. It is shown that in most circumstances the stresses are distributed in much the same way as if the leeward slopes of the waves were sheltered. For instance, the pressure distribution often has a substantial component in phase with the wave slope, just as if a wake were formed behind each wave crest—although of course actual separation effects are outside the scope of the present theory. In this aspect, the analysis amplifies the work of Miles (1957).
Sharp Metal-Insulator Transition in a Random Solid

We have measured zero temperature metallic conductivities above and below Mott's minimum value σ_(MIN) in bulk crystals of P doped Si. Studies of lattice heating, electronic heating and macroscopic inhomogeneities support the finding that conductivities below σ_(MIN) increase by over 10 as the P density is increased by 1%, and that over a wider density range the data can be fit t o a scaling form with a characteristic length that tends to diverge with the same exponent (ν = 0.55±0.10) in the metal and insulator.
Nonlinear magnetohydrodynamic detonation: Part I

The sudden release of magnetic free energy, as occurs in spectacular solar flare events, tokamak disruptions, and enigmatic magnetospheric substorms, has long defied any acceptable theoretical explanation. Usual attempts at explaining these explosive events invoke magnetic reconnection and/or ideal magnetohydrodynamic (MHD) instability. However, neither of these two mechanisms can explain the fast time scales without nonlinear destabilization. Recently, Cowley et al. [Phys. Plasmas 3, 1848 (1996)] have demonstrated a new mechanism for nonlinear explosive MHD destabilization of a line tied Rayleigh–Taylor model. In this paper, this picture is generalized to arbitrary magnetic field geometries. As an intermediate step, the ballooning equation in a general equilibrium is derived including the effects of magnetic field curvature, shear, and gravity. This equation determines the linear stability of the plasma configuration and the behavior of the plasma displacement along the magnetic field line. The nonlinear.
Exploring Factors Influencing The Acceptance of Community Service Web Application

A learning management system (LMS) is commonly used in universities and higher education institutions to monitor and manage electronic learning and teaching. There are no known characteristics that influence community service web apps. By presenting an integrated technology acceptance model (TAM), the current work contributes to this research path. This research is then carried out to study the success of this web application development. The proposed model is tested using statistical data from 400 web application users from Indonesia and verified using structured equation modeling (SEM) partial least squares (PLS). This study's findings reveal that the application's presentation and navigation encourage ease of use and ultimately encourage users to use the web application. The study also found that content quality affects the usability of web applications, and eventually, users use web applications.
Carbon Monoxide in Jupiter after Comet Shoemaker-Levy 9

Abstract Observations of the carbon monoxide fundamental vibration–rotation band near 4.7 μm before and after the impacts of the fragments of Comet Shoemaker–Levy 9 showed no detectable changes in the R5 and R7 lines, with one possible exception. Observations of the G-impact site 21 hr after impact do not show CO emission, indicating that the heated portions of the stratosphere had cooled by that time. The large abundances of CO detected at the millibar pressure level by millimeter wave observations did not extend deeper in Jupiter's atmosphere. Predicted upwelling of shocked, O-rich material from below also did not occur. Combined with evidence for upwelling of N- and S-rich gas, our observations indicate that the comet fragments may not have penetrated to the H 2 O cloud. We find that CO concentrations in Jupiter's stratosphere may be higher than previously suspected, suggesting that some of the CO detected after the impacts may already have been present in Jupiter's stratosphere.
Fast Driver Distraction and Fatigue Detection Systems based on Multiple Facial Behavior

Driver distraction and fatigue detection systems can effectively reduce car accidents and ensure the safety of traffic participants. Most of the existing vision-based approaches use facial landmarks as driver’s states. However, facial landmark detection is inaccurate under the large angle of head posture, which impacts the accuracy of further processing. This paper presents an effective method using convolution neural networks (CNNs). The method firstly deploys a modified MTCNN to detect the face region. Then, a lightweight multi-task CNN is proposed to detect eye regions, mouth landmarks and 3D head pose, and a simple CNN is used to detect eye closure independently. An angle-adapted loss function is applied to improve the landmark detection accuracy under the large posture. Finally, multiple abnormal behaviors are recognized to determine distraction and fatigue driving. Experiments show that our proposed method is superior to existing methods in both accuracy and running speed.