Miracle-dz/UniFAR-FTUs huggingface dataset:
IoT Network Intrusion Detection with Ensemble Learners

Protecting information systems against intruders' attacks requires utilising intrusion detection systems. Over the past several years, many open-source intrusion datasets have been made available so that academics and researchers can analyse and assess various detection classifiers' effectiveness. These datasets are made available with a full complement of illustrative network features. In this research, we investigate the issue of Network Intrusion Detection (NID) by utilising an Internet of Things (IoT) dataset called Bot-IoT to evaluate the detection efficiency and effectiveness of five different Ensemble Learning Classifiers (ELCs). Our experiment's results showed that despite all ELCs recording high classification metric scores, CatBoost emerged as the ELC that performed the best in our experiment in terms of Accuracy, Precision, F1-Score, Training and Test Time.
Automated Single Particle Growth Measurement using Segmentation

Fine-grain imaging is revealing secrets of nature with every passing day and artificial intelligence is reducing the manual effort required for detailed analysis. This work proposes an automated growth measurement of a particle in electron microscopic images in real-time. The particle selected in this study is an Au spiky nanoparticle (SNP) that develops spikes over the course of its growth. In this study, multiple techniques from conventional and sophisticated algorithms are used to segment the particle using supervised and unsupervised learning techniques. A comprehensive analysis of the automated techniques is presented with qualitative and quantitative results.
Genotype Imputation Using K-Nearest Neighbors and Levenshtein Distance Metric

With several new genome sequencing methods such as Next Generation Sequencing (NGS) and nanopore technologies, there exists a wide range of techniques to explore different genetic variants and their impacts. However, these sequences can become degraded as some genotypes are not detected, leading to missing base pair values. Imputing these gaps in the data is essential to analyze the data properly. Some past studies have shown that certain machine learning models have, to some extent, been able to accurately impute the missing values in genotypes. This paper aims to outline an imputation approach created using the K-Nearest Neighbors algorithm and Levenshtein Distance parameters on the Mus genus. This approach involved imputing randomly masked nucleotide bases in any given gene sequence in Mus musculus by using data of the same genes from similar species in the Phylogenetic tree, namely Mus pahari and Mus caroli. Predictions for the missing spaces were generated by comparing a set number of bases before and after a given sequence of missing nucleotide bases in the target species, Mus musculus, to the same number of bases occurring before and after every possible prediction in the similar species using the Levenshtein distance metric. We found that using our proposed algorithm, we were able to predict over 500,000 individual missing bases in the gene sequences of Mus musculus with accuracies up to 87%. The model maintained an accuracy greater than 80% when all the blank spaces (sequences of consecutive blank spaces) were less than 200 characters long.
Class D Power Amplifier Based on Waveform Predistortion Technology

With the rapid development of wireless communication technology, out of the pursuit of higher spectrum efficiency and higher power efficiency, the linearization technology of class D power amplifiers has become an important research direction. The purpose of this article is to study the design and implementation of Class D power amplifiers based on waveform predistortion technology. It introduces in detail the causes and theoretical analysis of several non-linear distortions in the class D audio amplifier, and proposes corresponding solutions, and introduces the modulation methods of several popular class D audio amplifiers, as well as each method. At the same time, several main performance indicators and design points of Class D audio amplifiers are explained. The nonlinear distortion in the open-loop and closed-loop class-D audio amplifiers was modeled and analyzed separately, and some parameters that had a greater impact on the system performance were extracted. Although the frequency of the oscillator is less than 500kHz, it is still very close to the design index, which is low for the design. The distortion class D audio amplifier has great reference value.
New Drone's Flight Path Proposal to Reduce Information Delivery Time in DTN

In the event of a major disaster, the communication infrastructure might be collapsed. In this paper, we focus on deploying a drone-based temporary communication network introducing the concept of DTN (Delay Tolerant Networking). The conventional drone's flight path, rebounding flight path, has been proposed, and it has the problem that the average information delivery time from sender to receiver might be longer for a smaller number of drones. Moreover, it might not meet 100% coverage of the target disaster area. In this paper, we propose a new concentric circle flight path for drones and evaluate it by computer simulation. As a result, the proposed flight path can reduce the information delivery time compared to that of the rebounding flight path while keeping 100% coverage.
Hybrid Logic Computing of Binary and Stochastic

Binary logic is applied internally to almost all digital signal processing and computer systems, because binary logic is direct implemented in CMOS circuits. Stochastic logic is achieved through its particular representation of data, which uses the probability of the logic level being ON to represent data. Stochastic logic computing is a type of logic computation based on stochastic bit stream instead of the binary numbers. This letter proposes a hybrid computing system of binary logic and stochastic logic, called hybrid logic. The study discusses how to generate hybrid logic circuits, and demonstrates the properties of hybrid logic circuits.
Continuum and C iii] Microlensing in Q2237+0305 and the Quasar Geometry

New photometry of Q2237+0305 in V and C III] light obtained with HRCam at the CFHT on 1991 October 10 is combined with earlier data to show that, at that epoch, component B was amplified by 0.5 mag in the continuum, with a concurrent change of its (V - C III]) color, and that component A had undergone a U-shape amplification event in 1988-1989. The light curves and color change indicate that, for an assumed transverse velocity of 600 km/s, the continuum region of the quasar is 10 times larger
Numerical Solver for an Ultrafast Laser Heating on Different 3-D Microscale Metallic Films

Ultrafast laser heating on microscale metals has become an important process over the last several decades. The small physical domain and time scale mean conventional models cannot obtain accurate results. Thus, the dual-phase-lag method is studied and implemented in this article. Heat conductions in a finite slab with raised temperatures at both ends and ultrafast laser heating on a microscale gold film are simulated as verifications. Results show the dual-phase-lag model is more accurate by comparing with experiments. Four different metals with an ultrafast laser heating are then simulated to study the effects of the microscale thermal properties.
Homogenization of degenerate wave equations with periodic coefficients

In this paper the authors discuss homogenization of hyperbolic equations involving periodic coefficients which are degenerate relative to a certain direction. The general scheme by which effective equations are obtained is as the reiterated homogenization. The first step of the process leads to equations describing the oscillatory behavior in the direction of the propagation. Next, space averaging in the degenerate direction gives the result. The process in the second step produces nonlocal effects.
Reconfigurable Intelligent Surface Assisted Multi-User Orbital Angular Momentum Communications

Orbital angular momentum (OAM) has been recently the attractive wireless communication scheme that can enhance the channel capacity by using mode multiplexing. However, the OAM performs well in short-range in free space, where the line of sight (LoS) condition is dominant. In this paper, we proposed the reconfigurable intelligent surface (RIS) assisted multi-user OAM communication system to improve the capacity and overcome the non-LOS (NLoS) condition. Simulation results show that the proposed system outperforms the conventional OAM in terms of capacity.
Comparative Analysis of GPU Stream Processing between Persistent and Non-persistent Kernels

Employing GPU to accelerate stream data processing has shown to be a considerable success in recent research. The stream processing engines need to process continuous data. Persistent Thread (PT), where GPU threads remain in a loop throughout executions, rather than non-Persistent Thread (nonPT) kernels can give several advantages. PT provides high-performance low-latency data processing by reducing kernel launch overhead and hiding memory copy overhead to the GPU. In this paper, we comparatively analyze the performances of PT and nonPT in various aspects. We also figure out the cause of advantages and what needs to be considered in order to obtain actual performance gain. The evaluation was done with four distinct application scenarios and the result shows that PT yields at most x4.4 higher performance over nonPT under desirable conditions.
Automatic Gait Analysis and Classification in Video Sequences

Gait abnormalities can be related to locomotion injuries or to various musculoskeletal and neurological pathologies. In this paper an automatic method for gait analysis and evaluation is proposed. It can be used for rehabilitation assessment but also to detect the gait abnormalities. The proposed system is noninvasive and it is based on the analysis of the video sequences captured in the sagittal plane. The MoveNet pose estimation model is used to detect the joints positions of the body segments. Then the knee and thigh-trunk angles on both left and right side are computed in each frame of the video sequence to study their variation. The video sequences can have different lengths and frame rates, therefore the analysis will be done at stride level by using statistical parameters. Experiments were made on a reduced number of video sequences that contain pathological gait and normal gait. Data augmentation was applied to increase the volume of data which is then used for gait classification.
Distribution Network Topology Planning Using Life Cycle Cost driven Cost Surfaces in the Internodal Parameter Computation

This paper presents a method for using the most accurate available geographic data in network topology planning. The process is an iterative process, where an initial network topology is first generated in order to acquire certain topology related parameters for each node-to-node connection. Then, node-to-node optimal routes are considered through individual cost surfaces, generated with varying topological and static geographic inputs. A near-optimal network topology is then constructed, now also considering the geographical cost drivers and constraints. In the topology the back-up connections and network sectionalization devices are also considered from the point of view of cost-optimality. A study from a real network proves the potential of the developed methodology to be able to cope with realistically sized networks
Testing strategy for new ocean sensors

The definition of a sensor monitoring strategy is based on the location for water monitoring, sensor performances, data storage and transmission. For any new sensor, available instruments currently used in oceanographic studies are identified to perform comparisons. Suitable transmission technology is selected according to the test conditions: open sea, coastal areas, remote locations, etc. Sensitivity and stress tests are designed to establish confidence limits under different environmental situations, so that the results obtained in planned testing exercises are enabled to certify the performance of the new instruments. In this paper, we will address three key phases to test and certify the performance of new sensors: (1) RD basis for cost-effective sensor development, (2) sensor development, sensor web platform and integration, and (3) field testing
Automatic Rule Generation Method for User Action Detection from Traffic Data

User action detection is an important role in network management and security. To identify and detect user action accurately, much research has been conducted. In this paper, we propose an automatic rule generation method that improves the existing rule generation process. In our previous research, we proposed the rule-based user action detection method that shows high detection accuracy. However, there are several problems in that the rule generation process is too complicated and it takes a lot of time and effort. We conducted several comparative experiments to verify the proposed method with the previous rule generation method. Rule generation time is greatly reduced without performance degradation compared to the previous rule generation method.
Graph Query Language (GQL)-structured Algorithms for Geospatial Intelligence on Public Transportation

Massive data are generated and processed every day. Insights from this big data make significant difference in one's way of living. But insight is one thing and intelligence is another. To draw out an intelligence, one must be able to discern patterns and deeper relationships among data. Intelligence on the road comes in various forms and applies in different segments of the public transportation. This study focused on geospatial intelligence that will benefit the passengers in their commuting experience with the intention of restoring their confidence in using the mass transit. A bi-directional unweighted path cost search and geodesic distance priority algorithms were structured using graph query language (GQL) to query the framework developed using TigerGraph database for: public transit routes from a given source and destination locations, and facilities within the specified distance from a location, respectively. Graph database was selected because it naturally represents geospatial data, and it focuses on relationships. To visualize the intelligence obtained, output was overlaid into maps and exhibited through Jupyter notebook. The goal of this study is to empower the public transport sector with geospatial intelligence by introducing and proving the viability of developing GQL-structured algorithms in answering its persistent priority areas.
Influence of External Electric Field on Dielectric Properties of Si–C60–InGa Structures in the Wagner-Koops Model

The article deals with the influence of an external electric field on the maximum dielectric loss tangent <tex>$(\text{tg}\delta)_{max}$</tex> and relaxation time <tex>$\tau$</tex> in Si(p)–C<inf>60</inf>–InGa structures. The results of the description of the dependencies <tex>$(\text{tg}\delta)_{max}(\mathrm{U}_{bias})$</tex> and <tex>$\tau(\mathrm{U}_{bias})$</tex> using the Wagner-Koops «grain-layer» model and the Debye equations are given. According to the above model, the experimental results of <tex>$(\text{tg}\delta)_{max}$</tex> are well approximated at an electric field strength of <tex>$F > 2.4\ \text{MV}/\mathrm{m}$</tex>. The maximum relaxation time is <tex>$\tau=22$</tex> ms and is observed at <tex>$F=2.8\text{MV}/\mathrm{m}$</tex>.
Towards Robust Combination of Neural Networks for Fingerprint Presentation Attack Detection

To resolve security threats on fingerprint authentication systems, a number of fingerprint Presentation Attack Detection (fingerprint PAD) methods have been proposed. However, existing methods still provide limited performance in terms of detection accuracy and generalization performance. In this paper, we propose a new fingerprint PAD method that ensembles Feature-based Neural Network (FNN) and Convolutional Neural Network (CNN) models based on a weighted voting mechanism. We also designed a new FNN architecture and a new CNN architecture, respectively, which provide improved performance for PAD. To verify the effectiveness of our method, we performed experimental evaluations using real-world datasets, LivDet 2015. The results showed that our method provided improved finger-print PAD accuracy compared to existing methods.
Traffic Classification using Deep Learning Approach for End-to-End Slice Management in 5G/B5G

Network slicing is a key role in future networks. 5G networks are intended to meet different service demands of an application offered to users. 5G architecture is used to match the requirement of the Quality of Service (QoS) by addressing different scenarios in terms of latency, scalability and throughput with different service types. Using machine learning with network slicing allows network operators to create multiple virtual networks or slices on the same physical infrastructure. These slices are independent and customized. Precisely, These slices will be managed dynamically according to the requirements defined between the network operators and the users. For this research, multi-machine learning algorithms are used to train our model, classify network traffic and predict accurate slice type for each user. After the traffic classification, we compared and analysed the performance of various machine learning algorithms in terms of learning percentage, accuracy, precision and F1 score.
Analytic Results in the Position-Dependent Mass Schrödinger Problem

We investigate the Schrödinger equation for a particle with a nonuniform solitonic mass density. First, we discuss in extent the (nontrivial) position-dependent mass V(x) = 0 case whose solutions are hypergeometric functions in tanh2 x. Then, we consider an external hyperbolic-tangent potential. We show that the effective quantum mechanical problem is given by a Heun class equation and find analytically an eigenbasis for the space of solutions. We also compute the eigenstates for a potential of the form V(x) = V0 sinh2 x.
Beamforming Design for Cache-enabled Integrated Access and Backhaul Networks

In this paper, we consider a cache-enabled integrated access and backhaul (IAB) network. We first derive the sum achievable data rate. Then, we formulate the sum achievable data rate maximization problem with respect to (w.r.t.) the beamforming designs of the macro base station (MBS) and small base stations (SBSs). By using successive convex approximation (SCA), we obtain a stationary point of the non-convex problem. Finally, by using numerical results, we show the proposed solution achieves a higher sum achievable data rate than the baseline schemes. Moreover, We show the impacts of the number of content types and Zipf exponent.
Underwater mussel culture grounds: precision technologies for management purposes

The exploitation of offshore mussel farms is becoming relevant almost everywhere, even though it lags way behind other food sectors that are already supported by mature monitoring, modeling, prediction, and analysis tools. New technologies and sensors could indeed boost this sector and alleviate key challenges facing the aquaculture industry. However, experiences and usable solutions are still scarce. Here, we propose a first attempt to introduce Augmented Reality (AR) technologies - compositing real environments and simulated objects - to increase the human perception of a scene in real time and support the management of a mussel culture ground. The case study offshore farm, located in the central Adriatic Sea, is structured in submerged mussel long-lines and, partly, in innovative submerged fixed poles. Object detection in video with deep learning is used to geo-locate buoys in real time, while a multibeam echosounder allowed geo-referenced observations of the fixed mussel poles not otherwise visible by the onboard camera. The AR prototype supports farmers to accomplish their every-day offshore tasks and have real-time visual access to the whole farm and related geographical information system.
Monologue versus Conversation: Differences in Emotion Perception and Acoustic Expressivity

Advancing speech emotion recognition (SER) depends highly on the source used to train the model, i.e., the emotional speech corpora. By permuting different design parameters, researchers have released versions of corpora that attempt to provide a better-quality source for training SER. In this work, we focus on studying communication modes of collection. In particular, we analyze the patterns of emotional speech collected during interpersonal conversations or monologues. While it is well known that conversation provides a better protocol for eliciting authentic emotion expressions, there is a lack of systematic analyses to determine whether conversational speech provide a “better-quality” source. Specifically, we examine this research question from three perspectives: perceptual differences, acoustic variability and SER model learning. Our analyses on the MSP-Podcast corpus show that: 1) rater's consistency for conversation recordings is higher when evaluating categorical emotions, 2) the perceptions and acoustic patterns observed on conversations have properties that are better aligned with expected trends discussed in emotion literature, and 3) a more robust SER model can be trained from conversational data. This work brings initial evidences stating that samples of conversations may provide a better-quality source than samples from monologues for building a SER model.
Axisymmetric Impingement Heat Transfer with a Nonlinear k-e Model

A nonlinear κ-e model is applied to predict local convective heat transfer in impinging turbulent axisymmetric jets onto a flat plate. Both the nonlinear constitutive law for the turbulent stresses and the e transport equation improve the results. Accurate results are obtained for different geometrical setups (in particular a different nozzle-plate distance), both in terms of heat transfer and of flowfield predictions. Reynolds-number dependence of the results is discussed. Second, it is illustrated that the second-order terms in the constitutive law have a negligible influence on the results, except for the turbulent normal stress profiles. Consequently, a first-order model, with locally flow-dependent eddy viscosity, is sufficient to obtain accurate results for the complex test case under study
Effects of Signal Blockage by a Road Bridge on mmWave Vehicular Communications

Millimeter-wave (mmWave) communication has re-cently attracted substantial interest owing to its abundant bandwidth availability and low-latency capability that facilitates meeting the requirements of vehicle-to-everything (V2X) services. However, performance degradation due to signal blockage has been raised as one of the critical technical challenges. This paper investigates the signal blockage effect of a road bridge in mmWave vehicular communication by performing a ray-tracing simulation for highway scenarios. The main purpose of this study is to help the researchers understand the signal blockage effect and present considerations for cell planning of mm Wave vehicular communications in the presence of a road bridge.