Miracle-dz/UniFAR-FTUs huggingface dataset:
Web User Interaction Speed Study

Web applications often suffer from interaction problems, causing users to struggle to fill out forms or find information. To diagnose these problems without incurring large costs, a popular solution is to analyze interaction events automatically to find problematic behavior patterns. However, these analyses often do not take into account the user's interaction style, such as speed. In this paper we present a study on the web users'speed by using recordings of real sessions to find how early this characteristic, measured in terms of mouse, scroll and keyboard speed, can be estimated. Preliminary results suggest that it takes approximately 50 seconds or 500 events to obtain a close estimate (>0.85) of the benchmark speed, calculated from the total number of events in the session.
Verifying the differences of the action selections for the action planning problem of AGVs by applying the reinforcement learning

Reinforcement learning is often used to solve AGVs problems, such as luggage transportation. Softmax policy and $\epsilon$-greedy policy are typical policies for action selection in reinforcement learning. In this study, we verify the transport efficiency of AGVs for the two types of action selection and verify a method that combines the two types of action selection.
Classification of Mental Effort vs. Relaxation States Using EEG Signals

Electroencephalographic (EEG) signals accurately describe the neural activity of a subject. This paper proposes a classification of EEG signals acquired from 10 subjects into two classes: relaxation and mental effort states. Each subject performs a predefined battery of eight scenario tests, switching between tasks (relaxation and mental effort). To classify the raw EEG signals collected, 12 representative statistical features were extracted and used as inputs for a SVM classifier. To assess the performance of the proposed algorithm, four metrics were computed: Accuracy, Precision, Recall and F1-score. The results confirmed the experimental expectations from a medical perspective and proved that the data are consistent, related to the series of tests, showing that the proposed method can serve as a useful binary classifier, which can discriminate between the relaxation and mental effort states.
DeepSkinNet: A Deep Learning Model for Skin Cancer Detection

In this article, an intelligent skin cancer detection system is proposed. It is common knowledge that skin cancer is one of the deadliest diseases in the world. Therefore, in order to save human lives, it must be detected correctly at the early stage, for which a fully automatic system using Deep Learning techniques can be used. In this work, the HAM10000 dataset is used for skin cancer image classification. A specific model for skin cancer classification, DeepSkinNet model, is proposed and tested. For skin lesion classification, the proposed DeepSkinNet model is compared to contemporary models such as AlexNet, VGG-16, and InceptionV3. The same dataset is trained with all the above models and the confusion matrix of the models is determined. Known quantitative measures such as accuracy, precision and recall are also evaluated. Finally, the experimental studies shows that the proposed model achieves better accuracy than the other models. The DeepSkinNet model has an accuracy, precision, recall and F1-score of 97.354%, 98%, 97% and 97% respectively.
The Impact of IT on Knowledge Sharing Environment and Management Practice

Knowledge-sharing environment have increasingly become an important tool in sustaining best management practices in an organization. Consequently, as the need to implement an effective management practice increases with the innovation in information technology (IT), many channels are available for improving knowledge sharing in organizations. This suggests that the more knowledge is shared in an environment the more rapidly it enhances best management practices which will ultimately lead to a high-quality performance in organizations. Thus, efficient management practice in a knowledge-sharing environment motivates workers to collectively participate in improving organizational performance. Therefore, to investigate the impact of IT on knowledge sharing environment and management practice and its relation to organizational performance, this study analyzed survey data collected from an IT organization. Findings show that different levels of experience improve with the effectiveness of knowledge sharing. Thus, management practice, IT effectiveness, knowledge-sharing environment, and organizational performance make organizations more efficient and effective. The study concludes with recommendations to organizations that aim to achieve high-quality performance to improve their management practices through effective IT implementation and knowledge sharing using important management attributes highlighted in the study.
A Proactive Algorithm for the Mitigation of Fragmentation Losses in Elastic Links

This paper presents simulations of a novel algorithm for spectrum assignment on an elastic link that achieves reductions of nearly 50% of the losses incurred under the first-fit algorithm with the same random sequence of requests. Instead of trying to reduce the fragmentation itself, the new algorithm aims to optimize the matching between each request and the spectral void chosen for its assignment. For this purpose, it prioritizes functional voids over dysfunctional ones, which are deferred in order to maximize their chance of recovering functionality through coalescence with neighbouring voids brought about by connection terminations. The simulations were performed for a 2-class traffic with requests for bitrates of 400 Gb/s and 1 Tb/s, with uniform and non-uniform traffic profiles, and with slot numbers optimized for short (400 km), intermediate (1600 km) and long (8000 km) distances.
Fault-tolerance Aware Green Computing Scheme in Software-Defined Vehicular Social Network

The Vehicular Social Network (VSN) is an advanced social communication system with intelligent sensing objects and vehicular communication in smart city applications. Fault-tolerance aware green computing is an essential concern in the VSN environment considering vehicle mobility. Therefore, this work proposes a Random Way-Point (RWP) mobility model to simulate the VSN environment. Further, it is difficult to maintain global network stability with communication costs in a VSN. Therefore, fault-tolerance aware green traffic data dissemination is modelled as an optimization problem considering transmission distance, residual energy, relative load-imbalance, and mobility factor performance metrics considering Software-Defined VSN (SDVSN) architecture. To solve this problem, a Particle Swarm Optimization (PSO), called Fault-tolerance-aware Energy-efficient Particle Swarm Optimization (FEPSO) algorithm, is proposed in SDVSN. Extensive simulation studies are carried out to compare the performance of the proposed FEPSO to state-of-the-art methods. The comprehensive analysis of the compared findings attest with fault-tolerant aware green computing benefits in a real VSN environment.
3D ARCNN: An Asymmetric Residual CNN for Decreasing False Positive Rate of Lung Nodules Detection

Lung cancer is with the highest morbidity and mortality, and early detection of cancerous changes is essential to reduce the risk of death. To achieve this, it is necessary to reduce the false positive rate of detection. In this paper, we propose a novel asymmetric residual network, called 3D ARCNN, to reduce false positive rate of lung nodules detection. 3D ARCNN consists of asymmetric convolutional and multilayer cascaded residual network structures. To solve the problem of deep neural network with large amounts of parameters and poor reproduction ability, the proposed model uses asymmetric convolution to reduce model parameters and enhance the generalization ability of the model. In addition, the model uses an internally cascaded multi-stage residual to prevent the gradient vanishing and exploding problems of deep networks. Experiments are performed on the public dataset LUNA16. Our method achieved high detection sensitivity of 91.6%, 92.7%, 93.2% and 95.8% at 1, 2, 4 and 8 false positives per scan, respectively, which got an average CPM index of 0.912. Experimental results show that the proposed 3D ARCNN is very useful for reducing the false positive rate of lung nodules in the clinic.
Design of a four-channel wave demultiplexer based on two-dimensional photonic crystals

Using the coupling principle between photonic crystal microcavity and ring cavity defects, a four-channel two-dimensional photonic crystal wave decomposition multiplexer based on square lattice is designed. The device uses different radii of the central dielectric column of the annular cavity to control different resonance wavelengths. Further, only by adjusting the radius of the dielectric column in the microcavity, a four-channel WDM with wavelengths of 1496, 1513, 1599 and 1625 nm is realized. By increasing the radius of the dielectric pillar at the edge of the channel and adding three dielectric pillars at the input edge to optimize the structure, the final transmittance is over 90%, the channel crosstalk is less than − 18.2 dB, and the device size is only 11.55 Jlmx16.5 urn, The designed device has the advantages of low channel crosstalk, high coupling efficiency and high integration, and has potential application value in optical device integration.
Bandwidth Extension of GSCPW-to-Waveguide Transition in Multi-Layer Dielectric Substrate by Corrugation Structures in THz Band

This paper proposes the bandwidth extension of a grounded suspended coplanar waveguide (GSCPW)-towaveguide transition in a multi-layer dielectric substrate by use of corrugation structures in the THz band. A rectangular patch element is used to transmit to the waveguide. Concurrently, the corrugation structures are applied at the edges of the aperture on the middle layer to improve the transmission characteristics and extend the bandwidth. The bandwidths of S11 below -10 dB and S21 higher than -3 dB of the proposed multi-layer GSCPWto-waveguide transition were 58.9 GHz and 52.8 GHz, respectively. The corrugation structures can extend the bandwidth up to 12.2% compared to the transition without corrugation.
Poster: Reliable On-Ramp Merging via Multimodal Reinforcement Learning

The recent success of Artificial Intelligence (AI) has enabled autonomous driving with better perception capabilities. However, on-ramp merging remains one of the main challenging scenarios for reliable autonomous driving. Within the limited onboard sensing range, a merging vehicle can hardly observe and predict the main road conditions properly, restricting appropriate merging maneuvers. In this poster, we outline ongoing research ideas for reliable and autonomous on-ramp merging assisted by vehicular communications. By jointly leveraging the basic safety messages (BSM) from neighboring vehicles and the surveillance images, a merging vehicle can perform reliable driving via robust multimodal reinforcement learning. Some experimental results are provided to evaluate our idea under the Simulation of Urban MObility (SUMO) platform.
High resolution biomedical imaging—multimodal ultrasound microscope and combination with optics

High frequency ultrasound imaging has evolved from classical acoustic microscopy to the multimodal ultrasound microscope which is available for quantitative C-mode, surface acoustic impedance mode and 3D-mode imaging. The evolution has realized both quantitative parametric imaging and easier observation. Quantitative C-mode representing two-dimensional distribution of attenuation or sound speed is realized by frequency-domain analysis of a single pulse by a high speed digitizer. Because the square of sound speed is proportional to the tissue elasticity, sound speed imaging provides biomechanical information of the tissues which is especially important in cardiology and orthopedic surgery. The data also help understanding clinical echo features, especially important in grading of liver fibrosis. Surface acoustic impedance mode without thin-slicing has been applied for imaging of fresh brain tissues and real time observation of high-intensity focused ultrasound procedures. High frequency 3D-mode imaging has.
Hydrogen-based Hybrid Renewable Energy System Sizing Optimization using HOMER

Conventional power systems are mostly based on fossil fuels. These constitute a substantial source of pollution in addition to being quickly depleted. Isolated areas and communities cannot profit from this type of electric energy for the reason that they are far from the grid. The hybrid renewable energy systems (HRES) have demonstrated to be an excellent solution for such a situation. The purpose of this paper is to design and optimize a standalone hybrid energy system to satisfy the electricity demand for a remote drilling site in Adrar province located in the South of Algeria. To cure the problem of intermittence of the solar and wind sources, a hydrogen-based set up is appended to the hybrid renewable energy system. The simulations are done using HOMER software to determine the optimal the HRES configurations considering techno-economic criteria.
Near-field measurement and far-field characterization of antennas in microwave, millimeter-wave and THz wave band based on photonics

We show antenna far-field characterization using photonics-based near-field visualization system. The photonics technology enables near-field visualization in the range from 1 GHz to at least 600 GHz with the same system configuration. As an example, antenna characterization at 300 GHz band based on planar and cylindrical scanning will be discussed.
A concept to solve Bin Picking problem in the robotics based on CAD Model and Measurement data

Bin picking is one of the core tasks in robotics. In terms of automating industrial processes, bin-picking shows to be a fundamental step. Although highly researched, it is still a challenging task as it combines 3D object recognition and pose estimation, handling and path planning. This paper focuses on recognition and pose estimation. Here a concept for picking Lego blocks laying randomly in a bin using CAD models of objects to be handles and 3D point cloud is presented. The approach relies on aligning the point cloud captured by a Cognex camera with the CAD models, then uses a best-fit method to determine the gripping position and orientation of the object. The concept has been validated in an experimental setup and compared to a state-of-the-art tool. Results demonstrate the efficiency and robustness of the proposed approach.
Shallow Sparse Autoencoder Based Epileptic Seizure Prediction

Epileptic patients ’ quality of life can be significantly improved by epileptic seizure prediction based on scalp electroencephalogram (EEG). With the advancement of brain e-health technologies, there is an essential need for a method that accurately predicts seizures while running on computing platforms with very low computing resources. Moreover, existing methods do not provide EEG analysis on an individual channel basis to identify the abnormalities in the data. In order to address this issue, we propose an efficient framework for patient-specific seizure prediction. A hybrid model comprising of a shallow autoencoder (AE) with only one hidden layer and a support vector machine (SVM) classifier has been developed. Both multi-channel and single channel EEG signal processing schemes have been developed. Generating a lower dimensional sparse signal with AE in the first stage and classifying the signal using SVM in the second stage are the two stages that the model separates into when processing EEG data. We initially train the AE to provide an optimum sparse signal and then use this sparse signal as input for an SVM classifier to categorize the EEG data. Using the 10-fold cross validation strategy, the proposed model tests 13 patients from the CHB-MIT dataset and achieves an average sensitivity of 98% and an average area under the curve (AUC) of 99%. We have compared our hybrid approach ’s performance with both deep learning models and traditional techniques. The proposed methodology outperforms state of the art seizure prediction methods, demonstrating its effectiveness.
Noncontact Doppler Radar-based Heart Rate Detection on the SVD and ANC

In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This study is aimed at extracting heartbeat signal via the technology combined with Short Time Fourier Transform (STFT), Singular Value Decomposition (SVD) and Adaptive Noise Canceller (ANC) from radar recording. The simulated data and the data collected by Doppler radar biosensor realized in laboratory are employed to validate the proposed method. The results show that the proposed method has the ability of detection for the heart rate and heart rate variability indexes in rest state, it has certain advantages in time-consuming and detection accuracy. Therefore, the current method provides another way to process vital sign signals recorded by Doppler radar.
Prediction of miRNA and Disease Association based on Graph Convolution Network using Latent Feature Vector by Positive Samples

The traditional approach of wet biological experiments tends to reveal whether there is an association between specific miRNA molecules and diseases, resulting in a lack of reliable negative samples of miRNA-disease associations in existing databases. To deal with the problem that many current computational methods treat unknown miRNA-disease associations in benchmark datasets as negative samples directly, we propose a graph convolutional neural network model, named DNMFGCN-MDA, based on feature extractions of positive samples. Firstly, by only using a dynamic matrix with positive samples, we extracted the potential feature vectors U and V of miRNAs and diseases in low-dimensional space. Then, we combined known miRNA-disease associations with potential feature vectors U and V to construct heterogeneous graph neural networks. Finally, we adopted graph convolutional neural networks to learn the structural features of the heterogeneous graph network and predicted the potential miRNA-disease associations by using linkage prediction. We use a 5-fold cross-validation experiment to evaluate the performance of our mode, and it turns out that our model has an average AUC of 95.23% and 96.07% on the HMDD v2.0 and HMDD v3.2 datasets, respectively, and achieves better-associated performance than that of the comparison method.
Design Method of a High-Directivity Corner Fabry-Perot Cavity Antenna Using a Conformal Mapping

We propose a new method to design the high-directivity corner Fabry-Perot cavity (FPC) antenna by utilizing a conformal mapping. It is well known that both an FPC antenna and a corner reflector antenna provide very high gain despite their simple configurations. Accordingly, if we devise the corner reflector antenna designed in an FPC structure, the antenna will undoubtedly obtain extremely high directivity. However, until now, there has been no design method because it is so difficult to analyze very complicated multiple reflections between the ground plane and the superstrate. To solve this problem, we present an unprecedented design method using the coordinate transformation. Consequently, our novel design method considerably increases the antenna directivity by 3.5 dB maintaining the antenna size, which proves the validity of our design approach.
Optimization of Stable Robotic Grasp in Industrial Pick and Place Application

The goal of the proposed work is to develop an algorithm to optimize a manipulation task in industrial pick and place process. A proposed algorithm for stable grasping is presented. It takes into consideration the number of robotic hand fingertips, an Finite Element Model (FEM) generated by a 3D CAD system and restricted areas (RA) that cannot be reached. The grasping configuration is determined under several criteria that ensure the object stability. One criterion is based on Static Stability Margin (SSM) that takes into consideration the position of object center of mass in the polygon of support and a second criterion based on the Force Closure Grasping (FCG) taking into consideration the contact forces applied by the robotic hand fingertips. The proposed optimization problem is based on Genetic Algorithms. The simulation results of the proposed work, applied to an industrial product, are presented for different number of contact points.
Robust $H_{\infty}$ performance of uncertain system based on Lyapunov functions using non–monotonic terms

This work concerns the problem of robust performance by analyzing the $H_{\infty}$ norm of one-dimensional (1D) uncertain linear time-invariant systems with external disturbance (noise) while the uncertainty is described as a polytope. The contribution of this work consists of giving a more general methodology, by combining Lyapunov function with Finsler's lemma technique. The objective is to develop a new sufficient conditions by checking an optimal $H_{\infty}$ performance of uncertain system using a number of Lyapunov functions with derivative of higher-order of the state vector in continuous-time (difference of higher-order of the state vector in the discrete-time), moreover some slack and free matrices to eliminate the product between state matrix and Lyapunov matrices, these conditions in this paper are providing in terms of a set of linear matrix inequalities defined at each vertex. Finally, numerical examples are used to demonstrate the benefits of this proposed methodology.
DLmeta: a deep learning method for metagenomic identification

Metagenome takes the genome of microbial communities in a specific environment as the research object. It is of great significance in analyzing microbial diversity and exploring the relationship of microbial communities. This paper mainly discusses metagenomic identification, especially the identification of viruses in metagenomes. The identifications of viruses and other sequences are the first step in microbial analysis, and their validity may have implications for downstream work. We develop DLmeta, a deep learning method for accomplishing metagenomic identification. DLmeta obtains domains through gene prediction and protein domain prediction, and uses a model that combines Convolutional Neural Network (CNN) and Transformer to complete the metagenomic identification. We benchmarked DLmeta on data of three species that were viruses, bacteria, and plasmids. The results showed that DLmeta was able to accurately identify species sequences from the metagenomes simultaneously, outperforming other state-of-the-art methods. In the ablation experiment, we demonstrated that our model uses CNN to capture local features and Transformer to capture global features, which can greatly improve the performance of metagenomic identification. DLmeta also can be applied to other metagenomic environments. DLmeta is available at https://github.con xiaozhangzhangl23/DLmeta.