Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Learning based Self-updating Incremental Malware Detection Model

With the rapid evolution of machine learning technology, ML-based malware detection is widely accepted as a panacea towards effective malware de-tection. However, facing with the great number of detecion system, malware can always breakthrough. It is chanllenging for the train models to detect a malware that newly show up. This phenomenon is widely known as concept drift. To address this chal-lenge, we proposed a online learning based malware detection system, which is based on the API sequences generated by the processes when it is running and also able to recognize concept drift. The sustainbility of detection system can be significantly improved with online learning algorithms. Lastly, in order to detect malware as much as possible, we use the incremental model structure.
UWB-based Multi-source Fusion Positioning for Cooperative UAVs in Complex Scene

This paper investigates the high accuracy three-dimensional (3D) positioning technology by using ultra-wide-band (UWB) based multi-source fusion algorithm for cooperative unmanned aerial vehicles (UAVs) in hybrid outdoor and indoor scenario. Firstly, the system architecture of cooperative UAVs is proposed, and the core idea is that each UAV can act as either reconnaissance node or positioning anchor through a low-cost integrated communication and positioning module (ICPM), providing an effective cooperative positioning scheme among nodes for drone swarm. Secondly, the UWB-based positioning optimization of two-way is designed. Moreover, a multi-source fusion positioning method is proposed to realize increasing the 3D positioning accuracy and seamless handover. Based on the low-cost prototype system, the real flight experiment results show that both horizontal and vertical positioning accuracy of UAV landing point (1σ) can be better than 30cm; besides, the successful rate of seamless handover is approaching 96% in hybrid complex scene. Furthermore, the multi-source fusion positioning scheme based on cooperative UAVs can be also utilized in some practical applications since the cumulative positioning error is satisfied by sub-meter level.
Massive Star Evolution: What we do (not) know

The modelling of massive star evolution is a complex task, and is very sensitive to the way physical processes (such as convection, rotation, mass loss, etc.) are included in stellar evolution code. Moreover, the very high observed fraction of binary systems among massive stars makes the comparison with observations difficult. In this paper, we focus on discussing the uncertainties linked to the modelling of convection and rotation in single massive stars.
Psychophysical Model of the Signal Detection Theory in the Categorization Task of Textual and Pictographic Visual Stimuli

A number of studies showed that the form of presentation of the stimuli affects the perception of these stimuli and their processing by the brain. In categorization tasks, the processing of stimuli of different categories in visual and verbal forms was compared. However, the mechanisms of categorization of stimuli of various forms and categories require additional research. The paper considers the application of the psychophysical model of signal detection theory (SDT) to the problem of categorizing textual and pictographic visual stimuli. SDT is used to measure sensitivity, biases and discriminability performed in categorization tasks. An experimental study involved 55 participants (14 males aged 18 to 23 years and 41 females aged 18 to 24 years). Stimulus material was developed by the authors of this study and consisted of two recognizable natural categories of objectified nouns: fruits and dishes. Graphical stimulus material was presented in the form of pictograms. Each category consisted of eight objects, so a total of sixteen words (names of the objects) and sixteen corresponding pictograms were selected. Pictographic stimuli were categorized faster than textual stimuli. However, the ability to recognize text was higher than the ability to recognize pictograms. When making a categorization error, most of the subjects demonstrated liberalism. SDT has shown its productivity in assessing discriminability of visual stimuli and expanded our knowledge of categorization mechanisms.
Spectroscopy of Star Cluster Candidates and H II Regions in NGC 6822

We present optical spectroscopy of four star clusters and six H II regions in the nearby, dwarf irregular galaxy NGC 6822. From the data, we estimate ages of 2.5 × 107, 2.0 × 108, 1.4 × 109, and 10 ± 2 × 109 yr for clusters H VI, C21, H VIII, and H VII, respectively. For clusters H VI and H VII we estimate [Fe/H] ≈ -1.46 ± 0.26 and ∼-2.0, lower than Large and Small Magellanic Cloud clusters at similar ages. Mass estimates for H VI, H VII, and H VIII demonstrate that clusters with typical globular cluster masses (>104 M⊙) have formed over the lifetime of NGC 6822. For six H II regions, ionic abundances are derived for element species of N, O, S, and Ne. These show that there is a correlation of abundance with position, the highest oxygen abundance H II regions being located off the main body of the galaxy.
A Privacy-preserving Approach to Distributed Set-membership Estimation over Wireless Sensor Networks

This paper focuses on the system on wireless sensor networks. The system is linear and the time of the system is discrete as well as variable, which named discrete-time linear time-varying systems (DLTVS). DLTVS are vulnerable to network attacks when exchanging information between sensors in the network, as well as putting their security at risk. A DLTVS with privacy-preserving is designed for this purpose. A set-membership estimator is designed by adding privacy noise obeying the Laplace distribution to state at the initial moment. Simultaneously, the differential privacy of the system is analyzed. On this basis, the real state of the system and the existence form of the estimator for the desired distribution are analyzed. Finally, simulation examples are given, which prove that the model after adding differential privacy can obtain accurate estimates and ensure the security of the system state.
Joint Passive Beamforming and Elevation Angle-Dependent Trajectory Design for RIS-aided UAV-enabled Wireless Sensor Networks

This paper investigates a reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV)-enabled wireless sensor network (WSN) under the probabilistic line-of-sight (LoS) channel in urban areas, where a UAV is dispatched to collect data from spatially distributed sensor nodes (SNs) with the aid of an RIS to enhance the communication quality. With the objective of maximizing the minimum average data collection rate from all the SNs for the UAV, we jointly design the communication scheduling, the phase shift of the RIS, and the UAV trajectory. However, due to its non-convexity, the formulated problem is difficult to solve. Therefore, we propose an efficient algorithm to attain its suboptimal solution by leveraging alternating optimization (AO), successive convex approximation (SCA), and semidefinite relaxation (SDR). Simulation results reveal new insights on the elevation angle-distance trade-off for the RIS-assisted UAV-enabled WSN, and the data collection rate of our proposed algorithm is significantly improved compared with other benchmark schemes.
The Use of Many-valued Logics to Improve the Elements and Devices of Computer Technology

To estimate the measure of proximity and distance in various tasks (clustering, classification, etc.), one of the most common ways to represent information about objects is a binary indications vector. At the same time, to estimate the measure of proximity, a sufficiently large number of relationships are used that describe binary indicators of similarity and distance between objects with qualitative characteristics. At the same time, during the real functioning of objects, their characteristics can change over time, which can lead to uncertainties when comparing binary vectors of qualitative indications that describe these objects. Therefore, it is advisable to use many-valued logics that can take into account such uncertainties. The paper describes approaches to the application of such logics, and also considers their modifications to determine the measures of proximity and distances for objects whose characteristics are described by vectors using many-valued logics under uncertainty.
Asset Administration Shell as Integration Layer for the Orchestration of Mixed Process and Manufacturing Plants

Modular automation is a recent concept for industrial automation offering a more flexible production with less engineering effort. Historically, incompatible standards for modular automation exist in different industrial segments, e.g., in process automation (Module Type Package) and discrete automation (PackML). The incompatibility of these standards has a particular negative impact on so called hybrid or mixed-equipment production facilities where process and discrete manufacturing modules co-exist in a physical installation. In this contribution, we propose an integration approach of both standards which is based on Industry 4.0 technologies (e.g., Asset Administration Shell). Our approach is validated on a simulated process plant involving commercial soft-PLCs of multiple vendors for MTP modules and a BPMN-based process orchestrator.
AC-DC Bidirectional Converter Based on DAB with Flyback Modulation

Distributed generation systems require a power conversion stage that allows the generated energy to be in conditions to be used by the grid users. In this document, a twostage power conversion, comprised of a synchronous rectifier and a dual-active-bridge (DAB) converter, is used as a link between a distributed generation system with the low voltage ac mains. The DAB, driven as a flyback converter, draws current in the direct power flow mode, acting as a resistive load, and delivers current, as an inverter, in the reverse power flow mode. The system features simplicity while achieving a high power factor and low THD. In order to control the power flow, a constant displacement angle is utilized for both operation modes. Simulation results are presented.
Modified MultiResUNet for Left Ventricle Segmentation from Echocardiographic Images

An accurate assessment of heart function is crucial in diagnosing the cardiovascular disease. One way to evaluate or detect the disease can use echocardiography, by detecting systolic and diastolic volumes. However, manual human assessments can be time-consuming and error-prone due to the low resolution of the image. One way to detect heart failure on echocardiogram is by segmenting the left ventricle on the echocardiogram using deep learning. In this study, we modified the MultiResUNet model for left ventricle segmentation in echocardiography images by adding Atrous Spatial Pyramid Pooling block and Attention block. The use of multires blocks from MultiResUnet is able to overcome the problem of multi-resolution segmentation objects, where the segmentation objects have different sizes. This problem has similar characteristics to echocardiographic images, where the systole and diastole segmentation objects have different sizes from each other. Performance measure were evaluated using Echonet-Dynamic dataset. The proposed model achieves dice coefficient of 92%, giving an additional 2% performance result compared to the MultiResUNet.
Functional Smart Grid Application Development

Due to the increasing complexity of power grids, the development complexity of smart grid applications has continued to rise in recent years. Smart grid applications are currently being developed for specific power grids and not in a grid-independent way causing significant repetitive engineering effort. This paper gives an overview of the state of the art of application development in the smart grid domain, as well as approaches that try to cope with the mentioned problem. In particular, solutions are examined where the grid-independent application is modeled using a functional description. These approaches are then reviewed for limitations and weaknesses in order to make suggestions on how an integrated framework for the development of such applications could look like. Finally, an overview of research questions to reach this goal is presented.
Layout-invariant license plate detection and recognition

Many current automatic license plate (LP) recognition systems are designed to handle a fixed form of LPs. In the present work, we develop an effective system using deep convolutional neuron network (CNN) that can process LPs with different layouts (e.g., variable character lengths, diverse colors, square-like and rectangular shapes). Firstly, we make an attempt of gathering a sufficient large and diverse Vietnamese LP dataset and manually creating the annotations for images. Secondly, a CNN model is derived to detect the LPs in images and predict the LP’s shape (i.e., one-row or two-row form). Thirdly, we design an efficient and unified CNN model to predict the characters of an input LP image patch. The proposed system has been extensively validated on two datasets (Vietnamese and Chinese LPs), demonstrating promising accuracy (e.g., 95% – 99%) and real-time CPU inference in comparison with the state-of-the-art approaches.
Cosmological Studies from Radio Source Samples

I review some recent cosmological studies based on redshift surveys of radio sources selected at low frequencies. The accretion rate onto the central black hole is identified as the basis of a crude physical division of the low-frequency population into two sub-populations: the first comprises `Eddington-tuned' (high accretion rate) quasars and their torus-hidden counterparts; the second comprises `starved quasars' like M87. There exist remarkable similarities between the shapes and evolutionary behaviours of the luminosity functions of radio sources and radio-quiet quasars; all luminous AGN are $\sim300-$times rarer at epochs corresponding to $z \sim 0$ than at $z \sim 2.5$. I argue that any evidence that quasars were intrinsically rarer at $z \sim 5$ than at $z \sim 2.5$ is as yet both tentative and indirect. A simple calculation suggests that the radio source population has been over-looked as a potentially important contributor to the entropy budget of the Universe. A recent sub-mm survey of radio sources is used to demonstrate a connection between the events which trigger jets and intense bursts of star formation, and a close link between the histories of star formation and AGN activity is proposed. I discuss the aims and methods of future large redshift surveys of radio sources, emphasising the importance of dovetailing these with the development of robust physical models for radio sources and their epoch-dependent environments.
Collaborative Programming for Work-Relevant Learning: Comparing Programming Practice With Example-Based Reflection for Student Learning and Transfer Task Performance

Computer science pedagogy, especially in the higher education and vocational training context, has long-favored the hands-on practice provided by programming tasks due to the belief that this leads to better performance on hands-on tasks at work. This assumption, however, has not been experimentally tested against other modes of engagement such as worked example-based reflection. While theory suggests that example-based reflection could be better for conceptual learning, the concern is that the lack of practice will leave students unable to implement the learned concepts in practice, thus leaving them unprepared for work. In this article, therefore, we experimentally contrast programming practice with example-based reflection to observe their differential impact on conceptual learning and performance on a hands-on task in the context of a collaborative programming project. The industry paradigm of Mob Programming, adapted for use in an online and instructional context, is used to structure the collaboration. Keeping with the prevailing view held in pedagogy, we hypothesize that example-based reflection will lead to better conceptual learning but will be detrimental to hands-on task performance. Results support that reflection leads to conceptual learning. Additionally, however, reflection does not pose an impediment to hands-on task performance. We discuss possible explanations for this effect, thus providing an improved understanding of prior theory in this new computer science education context. We also discuss implications for the pedagogy of software engineering education, in light of this new evidence, that impacts student learning as well as work performance in the future.
Fault-Tolerant Ad Hoc On-Demand Routing Protocol for Mobile Ad Hoc Networks

Mobile ad hoc networks (MANETs) are particularly suited for scenarios that demand rapid deployment of a communication system without any existing network resources. For instance, a MANET can facilitate the intercommunication process between members of a rescue party in a natural disaster, where the underlying routing protocol is crucial to maintaining the dissemination capability of data-critical packets. However, the backbone of every MANET, i.e., their routing protocol, is limited by the communication range of nodes, their high-speed mobility, and the capacity constraints of energy. This study proposed a fault-tolerant ad hoc on-demand routing protocol (FT-AORP) that relies on these characteristics of MANET nodes to determine reliable paths for data transmission. Subsequently, two of the discovered paths were used to transmit the duplicates of an original data packet to maximize fault tolerance. Further, using the OMNeT++ network simulator, the performance of the proposed system was evaluated through extensive simulation experiments against three simulation parameters: the number of network nodes, node speed, and data packet sending rate. The simulation results demonstrated that FT-AORP greatly improved the packet delivery ratio, reduced end-to-end delay, and maintained a higher residual energy level of the transmission path, compared to other baseline routing protocols.
Guan-fuzz: Argument Selection With Mean Shift Clustering for Multi-argument Fuzzing

Recently, fuzzers have become more important in software security. Various fuzzer strategies have been proposed continuously in order to improve the efficiency of fuzzer exploration. In order to find more program vulnerabilities, multi-parameter fuzzing has been proposed in recent years. For example, SQ-Fuzz and CRFuzz use multi-argument fuzzing to find many program vulnerabilities that were not found by single-argument fuzzer. However, there is no relevant research on optimizing parameter-based fuzzing at present. To make multi-argument fuzzer more efficient, selecting suitable combinations of command arguments is necessary. Therefore, in this paper, we propose Guan-fuzz, which uses the MeanShift algorithm to group the execution coverage of different program parameters. It can reduce the number of execution of similar parameters. The experimental results show that Guan-fuzz has 84% and 14% higher program coverage than AFL and SQ-Fuzzer. Guan-fuzz's improvement in multi-argument fuzzing is significant. Guan-fuzz can find more vulnerabilities that SQ-Fuzzu did not find, and in the real world programs, Guan-fuzz found 41 new bugs, of which 32 have been fixed, and eight have been assigned CVE IDs.
Analyse mathématique d'équations de semi-conducteurs avec mobilités non constantes et identification des frontières libres dans les jonctions PN

La description des mecanismes de conduction dans les dispositifs semi-conducteurs par le modele derive-diffusion (DD) mene a un systeme de trois equations aux derivees partielles non lineaires fortement couplees. Cette these est composee de trois parties. La premiere est consacree a la mise en equations et a la presentation des regimes de fonctionnement ainsi que la simplification du modele dans le cas d'une jonction pn. La deuxieme partie consiste a identifier la zone de depletion dans une jonction PN. En formulant le probleme en un probleme d'inequations variationnelles, nous demontrons que le probleme admet une solution. L'originalite numerique de cette partie est l'utilisation des noeuds sur la frontiere libre comme inconnus. Nous proposons deux algorithmes de resolution que nous testons en utilisant la methode des elements finis et la methode des equations integrales. Dans la troisieme partie, nous nous interessons a l'etude mathematique du modele DD a l'etat stationnaire dans les semi-conducteurs ecrit avec les variables de Slotboom. Nous demontrons l'existence d'une solution, dans le cas ou les lois de mobilites dependent du champ electrique, en appliquant les techniques de l'analyse convexe. Ensuite, nous considerons que le terme d'avalanche est non nul, nous donnons des estimations a priori et nous prouvons un theoreme d'existence. Afin d'etudier l'unicite de solutions de notre modele, nous exposons tout d'abord une condition pour que le systeme possede au plus une solution. Nous en deduisons des resultats d'unicite dans des cas specifiques tels que le domaine soit suffisamment petit ou la permittivite soit assez grande. Nous donnons un theoreme d'unicite locale dans les cas ou le terme d'avalanche est non nul et les changements de conditions aux limites se font a angles droits.
Bolometric luminosity black hole growth time and slim accretion discs in active galactic nuclei

We investigate the accretion rate, bolometric luminosity, black hole (BH) growth time and BH spin in a large AGN sample under the assumption that all such objects are powered via thin or slim accretion discs (ADs). We use direct estimates of the mass accretion rate, Mdot, to show that many currently used values of Lbol and Ledd are either under estimated or over estimated because they are based on bolometric correction factors that are adjusted to the properties of moderately accreting active galactic nuclei (AGN) and do not take into account the correct combination of BH mass, spin and accretion rate. The consistent application of AD physics to our sample of Sloan Digital Sky Survey (SDSS) AGN leads to the following findings: 1. Even the most conservative assumption about the radiative efficiency of fast accreting BHs shows that many of these sources must contain slim ADs. We illustrate this by estimating the fraction of such objects at various redshifts. 2. Many previously estimated BH growth times are inconsistent with the AD theory. In particular, the growth times of the fastest accreting BHs were over estimated in the past by large factors with important consequences to AGN evolution. 3. Currently used bolometric correction factors for low accretion rate very massive SDSS BHs, are inconsistent with the AD theory. Applying the AD set of assumptions to such objects, combined with standard photoionization calculations of broad emission lines, leads to the conclusion that many such objects must contain fast spinning BHs.
W-Band GB-SAR for 3D Imaging

This paper presents a W-band Ground-based Synthetic Aperture Radar system able to acquire high resolution 3D images in just about 10 minutes. Resolution in both the horizontal plane and in elevation are obtained through mechanical scanning of the radar head along two orthogonal directions. In this paper, authors illustrate the system design and performances. The results of some experimental tests performed both in controlled and in urban scenario are shown.