Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancement of Performance of K-Nearest Neighbors Classifiers for the Prediction of Diabetes Using Feature Selection Method

For any classification or prediction problem, machine learning algorithm plays an important role in analysis of data. It enhances the performance capability of classifiers and ease the difficulties. In this research analysis, a prediction model is developed for diabetes disease using K-nearest neighbors classification algorithm. The model is implemented in Python using PIMA indian diabetes dataset.The objective of research work is to enhance the prediction capability of KNN classifier with the help of feature selection and normalization of data. A study is also performed to find the optimal number of neighbors on which KNN returns its best result. The F1 score is considered as the main performance metrics for this work.
Honeybees swarming detection approach by sound signal processing

Swarming of honeybees is one of the biggest problems for the hobbyist-beekeepers. This is a state when old queen bee leaves the hive with worker bees. The aim of this work is to find relation between sound and swarming. The data was collected during the late spring and early summer using microphone that was placed inside the hive. Using the Welch method, implemented in Python, it was proven the relation between the power and swarming.
Secure Cognitive Communication via Cooperative Jamming

Nowadays, unmanned aerial vehicles (UAVs) are widely used to ensure the security of wireless communications. In this paper, we propose a cooperative jamming scheme to secure cognitive radio (CR) networks. In the proposed scheme, the source UAV transmits confidential information to ground users. Because of the presence of an eavesdropper (Eve), the UAV jammer sends friendly interference signals to protect private information. It should be noted that the primary transmission of CR networks cannot be disturbed. Therefore, we formulate an optimization problem to maximize the average secrecy rate under the constraint of primary interference. Since trajectory planning and power control are coupled, we propose the alternative optimization (AO) algorithm. Furthermore, as the sub-problems are non-convex, sequential convex approximation (SCA) algorithm is a good choice, which makes our scheme converge to the Karush-Kuhn-Tucker point. Simulation results show that the joint optimization of power and trajectory of our proposed scheme is effective for improving the systematic secure performance.
Probing cardiomyocyte mobility with multi-phase cardiac diffusion tensor MRI

Purpose Cardiomyocyte organization and performance underlie cardiac function, but the in vivo mobility of these cells during contraction and filling remains difficult to probe. Herein, a novel trigger delay (TD) scout sequence was used to acquire high in-plane resolution (1.6 mm) Spin-Echo (SE) cardiac diffusion tensor imaging (cDTI) at three distinct cardiac phases. The objective was to characterize cardiomyocyte organization and mobility throughout the cardiac cycle in healthy volunteers. Materials and methods Nine healthy volunteers were imaged with cDTI at three distinct cardiac phases (early systole, late systole, and diastasis). The sequence used a free-breathing Spin-Echo (SE) cDTI protocol (b-values = 350s/mm2, twelve diffusion encoding directions, eight repetitions) to acquire high-resolution images (1.6x1.6x8mm3) at 3T in ~7 minutes/cardiac phase. Helix Angle (HA), Helix Angle Range (HAR), E2 angle (E2A), Transverse Angle (TA), Mean Diffusivity (MD), diffusion tensor eigenvalues (λ1-2-3), and Fractional Anisotropy (FA) in the left ventricle (LV) were characterized. Results Images from the patient-specific TD scout sequence demonstrated that SE cDTI acquisition was possible at early systole, late systole, and diastasis in 78%, 100% and 67% of the cases, respectively. At the mid-ventricular level, mobility (reported as median [IQR]) was observed in HAR between early systole and late systole (76.9 [72.6, 80.5]° vs 96.6 [85.9, 100.3]°, p<0.001). E2A also changed significantly between early systole, late systole, and diastasis (27.7 [20.8, 35.1]° vs 45.2 [42.1, 49]° vs 20.7 [16.6, 26.4]°, p<0.001). Conclusion We demonstrate that it is possible to probe cardiomyocyte mobility using multi-phase and high resolution cDTI. In healthy volunteers, aggregate cardiomyocytes re-orient themselves more longitudinally during contraction, while cardiomyocyte sheetlets tilt radially during wall thickening. These observations provide new insights into the three-dimensional mobility of myocardial microstructure during systolic contraction.
Nonlinear Dynamical Systems Modelling and Identification Using Type-2 Fuzzy Logic. Metaheuristic Algorithms Based Approach.

This paper presents a novel type-2 fuzzy model for nonlinear dynamical systems. This method can deal with the curve fitting and computational time problems of type-2 fuzzy systems. It is based on interval type-2 fuzzy systems and it is comprised of a parallel interconnection of two type-2 sub fuzzy models. The first sub fuzzy model is the primary model, which represents an ordinary model with low resolution for the nonlinear dynamical system under consideration. To overcome resolution quality problem, and obtain a model with higher resolution, we will introduce a second type-2 fuzzy sub model called error model which will represent a model for the error modelling between the primary model and the real nonlinear dynamical system. As the error model represents uncertainty in the primary model, it’s suitable to minimize this uncertainty by simple subtraction of the error model output from the primary model output, which will lead to a parallel interconnection between them, giving then a unique whole final model possessing higher resolution. To apply this approach successfully, the model’s representation and identification are considered in this investigation using type-2 fuzzy auto regressive (T2FAR) and type-2 fuzzy auto regressive moving average (T2FARMA) models. Identification is achieved by innovative metaheuristic optimization algorithms, like as firefly and biogeography-based optimization algorithms. To evaluate the effectiveness of the proposed method, it will be tested on three types of nonlinear dynamical systems. Computer investigations indicate that the proposed model may significantly improves convergence and resolution.
Novel FGFR4-Targeting Single-Domain Antibodies for Multiple Targeted Therapies against Rhabdomyosarcoma

Simple Summary Rhabdomyosarcoma (RMS) accounts for more than 50% of all soft tissue sarcomas in childhood and adolescence. Despite progress and intensified multimodality treatment, prognoses are extremely poor with an overall survival rate of approximately 20% in the advanced stage. Therefore, there is an urgent need for targeted treatment options to improve overall survival rates, and to limit long-term side effects. The fibroblast growth factor receptor 4 (FGFR4) is overexpressed in RMS and other tumors as well. The goal of this work was to select FGFR4 specific single-domain antibodies (sdAb) and to develop FGFR4-targeted therapies. We could show that FGFR4-targeted liposomes have the potential to deliver drugs specifically to FGFR4-positive tumor cells and that chimeric antigen receptor T cells built with the selected antibodies can kill specifically FGFR4-expressing RMS cells. Abstract The fibroblast growth factor receptor 4 (FGFR4) is overexpressed in rhabdomyosarcoma (RMS) and represents a promising target for treatments based on specific and efficient antibodies. Despite progress, there is an urgent need for targeted treatment options to improve survival rates, and to limit long-term side effects. From phage display libraries we selected FGFR4-specific single-domain antibodies (sdAb) binding to recombinant FGFR4 and validated them by flow cytometry, surface plasmon resonance, and fluorescence microscopy. The specificity of the selected sdAb was verified on FGFR4-wild type and FGFR4-knock out cells. FGFR4-sdAb were used to decorate vincristine-loaded liposomes and to generate chimeric antigen receptor (CAR) T cells. First, incubation of RMS cells with FGFR4-sdAb revealed that FGFR4-sdAb can block FGF19-FGFR4 signaling via the MAPK pathway and could therefore serve as therapeutics for FGFR4-dependent cancers. Second, FGFR4-targeted vincristine-loaded liposomes bound specifically to RMS cells and were internalized by the receptor, demonstrating the potential for active drug delivery to the tumor. Third, FGFR4-CAR T cells, generated with one sdAb candidate, demonstrated strong and specific cytotoxicity against FGFR4 expressing RMS cells. We selected novel FGFR4-sdAb with high specificity and nano- to picomolar affinities for FGFR4 which have the potential to enable multiple FGFR4-targeted cancer therapy approaches.
Optimization of Electricity Consumption using Grey Wolf Algorithm

This paper offers optimized electricity consumption values of a smart home around the year using Grey Wolf Algorithm. The results were studied in different temperature slabs and their accuracy was found using Support Vector Machines. The motivation of doing this analysis lies behind a survey done on the same dataset using PSO and Cuckoo algorithm. The results were studied and further juxtaposed with the calculation of PSO and Cuckoo algorithm and the accuracy of these algorithms was found by taking Grey Wolf Algorithm as the reference. The cumulative results were further supplemented by drawing conclusions. The dataset of smart homes has been taken from Kaggle and the features were selected using pearson coefficient. In this paper, the results have been extended by further comparing the optimized results of the PSO and Cuckoo with Grey Wolf Optimization algorithm (GWO). The accuracy is predicted by using SVM. It was concluded that both PSO and Cuckoo have similar accuracies but due to different reasons.
A study of measurement technology based on Structured Light Detection and Deep Learning

This paper revealed an effective method to judge the quality levels of apples with optical images which could avoid the damages caused by touching measurement. When only using photographs of the apples, the quality levels cannot be effectively identified via auto optical inspection methods. We proposed a structured light projection system for detecting the apple surface topography which is corresponding to the small changes during storage periods. This system uses cosine waves with different phases to project onto the apple surface and then uses three phases to reconstruct the object. In order to get the most suitable spatial frequency for this system, we tested six spatial frequencies: f = 0.001, f = 0.003, f = 0.005, f = 0.008, f = 0.01, and f = 0.08. Test results show that the spatial frequency f = 0.008 is best for our samples and hardware equipment. A convolutional neural network modified from LeNet is used to classify both the pictures: directly captured from apples and the reconstructed pictures from the structured lighting system. Experiments were performed on 30 fresh apples which then storage for a period of 7 days, 14 days and 21 days. The results of the convolutional neural network trained on structured light system samples showed that the accuracy was 87%, but the accuracy of the results of only using photography of apples was just 46%.
Improving Customer Relationship Management based on Intelligent Analysis of User Behavior Patterns

A set of regression, cluster analysis, and association rule mining models, is proposed to search for patterns in user behavior regarding marketing campaigns taking into account user characteristics and financially significant metrics. Using the example of two organizations, it was possible to accurately predict the cost of customer acquisition (CPA, cost-per-action). The use of the models proposed allows organizations to improve advertisement settings to increase online advertising efficiency.
LSTM Neural Network Model with Feature selection for Financial Time series Prediction

The case of features selection plays an important role in fine-tuning the prediction capacity of machine learning models. This paper reviews the different scenarios with three sets of features in each case and evaluate the training and validation data performance with and without these features. How the prediction results change can be seen as and when the different features are included or excluded and Recursive feature elimination, Correlation, Random forest algorithm is used for feature importance and evaluate the results with LSTM networks.
Modal Network Representation for Broadband SI/PI-Analysis of Interconnection Structures in Multilayer PCBs

A modal equivalent circuit for interconnection structures within multilayer PCBs including the rigorous incorporation of dissipation, dispersion and skin effect is presented for the first time. Based on the discrete field formulation of the three-dimensional boundary value problem an exact network representation is obtained from the corresponding eigenvalue problem employing the lossless modes. In contrast to a recently published general approach, the mutual coupling of the modes can be neglected, such that the network complexity is minimized. The modal network includes the physical-based rational approximation of the intrinsic system dynamic, which is independent of the port positions. These in turns determine the excitation of the individual modes and can be simply accounted for by a transducer network. The model order is safely estimated in advanced and significantly reduced by the approximation of all sub-resonant (higher) modes by lumped elements. The accuracy and advantages of the proposed SPICE-compatible equivalent circuit are demonstrated and compared to the conventional overall vector fitting approach in frequency and time-domain examples including non-linear terminations.
Two-Stage Optimized Trajectory Planning for ASVs Under Polygonal Obstacle Constraints: Theory and Experiments

We propose a method for energy-optimized trajectory planning for autonomous surface vehicles (ASVs), which can handle arbitrary polygonal maps as obstacle constraints. The method comprises two stages: The first is a hybrid A⋆ search that finds a dynamically feasible trajectory in a polygonal map on a discretized configuration space using optimal motion primitives. The second stage uses the resulting hybrid A⋆ trajectory as an initial guess to an optimal control problem (OCP) solver. In addition to providing the OCP with a warm start, we use the initial guess to create convex regions encoded as halfspace descriptions, which converts the inherent nonconvex obstacle constraints into a convex and smooth representation. The OCP uses this representation in order to optimize the initial guess within a collision-free corridor. The OCP solves the trajectory planning problem in continuous state space. Our approach solves two challenges related to optimization-based trajectory planning: The need for a dynamically feasible initial guess that can guide the solver away from undesirable local optima and the ability to represent arbitrary obstacle shapes as smooth constraints. The method can take into account external disturbances such as wind or ocean currents. We compare our method to two similar trajectory planning methods in simulation and have found significant computation time improvements. Additionally, we have validated the method in full-scale experiments in the Trondheim harbor area.
Digital Beamforming Techniques for Future Communications Systems

5G is the next mobile generation that will support the need for more data applications, higher speeds, more capacity, lower latency and increased performance. New cellular architectures and technologies are being investigated in order to meet the growing demand for wireless services. One example is the use of beamforming. Beamforming is a signal processing technique used to direct the transmission or reception of signals. Directional beamforming is produced by adjusting the elements of each antenna vector, until an optimal gain is obtained. The main contributions of this paper consist on the evaluation of the performance of a 5G communication system, using massive MIMO technology embedded with digital beamforming techniques. A comparison of two digital beamforming methods proposed for future 5G systems, EBB and GoB beamforming, is assessed.
A Fast Design Space Exploration Framework for the Deep Learning Accelerators: Work-in-Progress

The Capsule Networks (CapsNets) is an advanced form of Convolutional Neural Network (CNN), capable of learning spatial relations and being invariant to transformations. CapsNets requires complex matrix operations which current accelerators are not optimized for, concerning both training and inference passes. Current state-of-the-art simulators and design space exploration (DSE) tools for DNN hardware neglect the modeling of training operations, while requiring long exploration times that slow down the complete design flow. These impediments restrict the real-world applications of CapsNets (e.g., autonomous driving and robotics) as well as the further development of DNNs in life-long learning scenarios that require training on low-power embedded devices. Towards this, we present XploreDL, a novel framework to perform fast yet high-fidelity DSE for both inference and training accelerators, supporting both CNNs and CapsNets operations. XploreDL enables a resource-efficient DSE for accelerators, focusing on power, area, and latency, highlighting Pareto-optimal solutions which can be a green-lit to expedite the design flow. XploreDL can reach the same fidelity as ARM's SCALE-sim, while providing 600x speedup and having a 50x lower memory-footprint. Preliminary results with a deep CapsNet model on MNIST for training accelerators show promising Pareto-optimal architectures with up to 0.4 TOPS/squared-mm and 800 fJ/op efficiency. With inference accelerators for AlexNet the Pareto-optimal solutions reach up to 1.8 TOPS/squared-mm and 200 fJ/op efficiency.
Classifying Protein Structures by Using Protein Ray Based Descriptor, KNN and FuzzyKNN Classification Methods

Bioinformatics community continuously studies the protein molecules and the processes in which they are involved. To understanding these processes, the researches need to understand the characteristics and functions of proteins. Therefore, classification of protein structures is one of the most studied problems in proteomics. Although many methods are developed for this purpose, still there is a need for fast and accurate methods that would provide classification of protein structures. In this paper, our aim is to develop a method that would provide accurate classification of proteins. First, we extract feature vectors with the geometrical characteristics of the protein structures. For that purpose we use our protein ray based descriptor, which represents how the amino acid residues are placed with respect to the center of mass. After feature extraction, next we apply two instance-based learning methods for making predictions. Besides the KNN classifier, which is based on classical set theory, we also use the FuzzyKNN classification method that is based of fuzzy set theory. For evaluation, we use a part of the SCOP database. The paper presents the experimental results achieved by using the two classification methods by considering various number of nearest neighbors.
Exploiting R-CNN for video smoke/fire sensing in antifire surveillance indoor and outdoor systems for smart cities

This work presents a video-camera-based fire/smoke sensing technique for early warning in antifire surveillance systems. By exploiting R-CNN (Region Convolutional Neural Network), a detection technique is developed for the measurement of the smoke and fire characteristics in restricted video surveillance environments, both indoor (e.g. a railway carriage, container, bus wagon, homes, offices), or outdoor (e.g. storage or parking areas). The considered application scenario, to reduce costs, is composed of a single, fixed camera per scene, working in the visible spectral range already installed in a closed-circuit television system for surveillance purposes. The training phase is done with indoor and outdoor image sets, with both smoke and non-smoke scenarios to assess the capability of true-positive/true-negative detection and false-positive/false-negative rejection. To generate the training set, a Ground Truth Labeler app is used and applied to the open-access Firesense dataset, including tens of indoor and outdoor fire/ smoke scenes developed as the output of an FP7 project, plus other videos not publicly available, provided by Trenitalia during specific fire/smoke tests on railway wagons performed at their testing facility in Osmannoro, Italy. The achieved results show that the proposed R-CNN technique is suitable for the creation of a smart video-surveillance system for fire/smoke detection.
Asymptotic Solution for Hybrid Waves in a Circular Waveguide with a Helical Corrugation

Propagation of electromagnetic waves in a circular waveguide with helical corrugation is examined. Bragg coupling of rotated or azimuthally symmetric modes is investigated. Coupling coefficients are obtained by the asymptotic the Krylov-Bogoliubov-Mitropolsky method. Perturbed longitudinal wavenumbers are calculated for a case of periodic perturbation.
Trans-Ancestral Fine-Mapping and Epigenetic Annotation as Tools to Delineate Functionally Relevant Risk Alleles at IKZF1 and IKZF3 in Systemic Lupus Erythematosus

Background: Prioritizing tag-SNPs carried on extended risk haplotypes at susceptibility loci for common disease is a challenge. Methods: We utilized trans-ancestral exclusion mapping to reduce risk haplotypes at IKZF1 and IKZF3 identified in multiple ancestries from SLE GWAS and ImmunoChip datasets. We characterized functional annotation data across each risk haplotype from publicly available datasets including ENCODE, RoadMap Consortium, PC Hi-C data from 3D genome browser, NESDR NTR conditional eQTL database, GeneCards Genehancers and TF (transcription factor) binding sites from Haploregv4. Results: We refined the 60 kb associated haplotype upstream of IKZF1 to just 12 tag-SNPs tagging a 47.7 kb core risk haplotype. There was preferential enrichment of DNAse I hypersensitivity and H3K27ac modification across the 3′ end of the risk haplotype, with four tag-SNPs sharing allele-specific TF binding sites with promoter variants, which are eQTLs for IKZF1 in whole blood. At IKZF3, we refined a core risk haplotype of 101 kb (27 tag-SNPs) from an initial extended haplotype of 194 kb (282 tag-SNPs), which had widespread DNAse I hypersensitivity, H3K27ac modification and multiple allele-specific TF binding sites. Dimerization of Fox family TFs bound at the 3′ and promoter of IKZF3 may stabilize chromatin looping across the locus. Conclusions: We combined trans-ancestral exclusion mapping and epigenetic annotation to identify variants at both IKZF1 and IKZF3 with the highest likelihood of biological relevance. The approach will be of strong interest to other complex trait geneticists seeking to attribute biological relevance to risk alleles on extended risk haplotypes in their disease of interest.