Miracle-dz/UniFAR-FTUs huggingface dataset:
On a method for determining limit cycles in nonlinear circuits

A theory for determining the existence, number and position of limit cycles in perturbed Hamiltonian systems, that depends on a small parameter, is presented. The limit cycle emerges from the periodic trajectories surrounding a centre of the unperturbed system. By means of this theory a sinusoidal oscillator with a nonlinear element has been analysed. It is shown that only the terms with odd degrees in the approximation polynomial of the nonlinear element's characteristic exert an influence on the initiation of limit cycles.
Two days, twenty outfits: Coachella attendees’ visual presentation of self and experience on Instagram

Abstract This study uses a visual discourse analysis to explore the self-construction of presence and experience at events such as a music festival, theoretically guided by presentation of self and how users curate this via multimodal, visual-focussed social media posts. Real-time, location-based, public posts from events act as a new form of visually-embedded culture in how individuals present themselves and their experiences. An analysis of 200 location-tagged Instagram posts from attendees at the 2017 Coachella music festival reveals attendees care less about sharing photos of the music and more about curating a visual sense of taste, embrace and place. This focus on self-presentation is explored through themes such as seeing more full-body images than selfies in order to emphasise festival fashion, use of the ‘plandid’ (planned candid) to add a sense of spontaneity, and use of chronological and geographic tags to establish physical presence in time and place.
Superpixel-Based Multiscale CNN Approach Toward Multiclass Object Segmentation From UAV-Captured Aerial Images

Unmanned aerial vehicles (UAVs) are promising remote sensors capable of reforming remote sensing applications. However, for artificial-intelligence-guided tasks, such as land cover mapping and ground-object mapping, most deep-learning-based architectures fail to extract scale-invariant features, resulting in poor performance accuracy. In this context, the article proposes a superpixel-aided multiscale convolutional neural network (CNN) architecture to avoid misclassification in complex urban aerial images. The proposed framework is a two-tier deep-learning-based segmentation architecture. In the first stage, a superpixel-based simple linear iterative cluster algorithm produces superpixel images with crucial contextual information. The second stage comprises a multiscale CNN architecture that uses these information-rich superpixel images to extract scale-invariant features for predicting the object class of each pixel. Two UAV-image-based aerial image datasets: 1) NITRDrone dataset and 2) urban drone dataset (UDD), are considered to perform the experiment. The proposed model outperforms the considered state-of-the-art methods with an intersection of union of 76.39% and 86.85% on UDD and NITRDrone datasets, respectively. Experimentally obtained results prove that the proposed architecture performs superior by achieving better performance accuracy in complex and challenging scenarios.
Using Machine Learning to Explore Shared Genetic Pathways and Possible Endophenotypes in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a heterogeneous condition, characterized by complex genetic architectures and intertwined genetic/environmental interactions. Novel analysis approaches to disentangle its pathophysiology by computing large amounts of data are needed. We present an advanced machine learning technique, based on a clustering analysis on genotypical/phenotypical embedding spaces, to identify biological processes that might act as pathophysiological substrates for ASD. This technique was applied to the VariCarta database, which contained 187,794 variant events retrieved from 15,189 individuals with ASD. Nine clusters of ASD-related genes were identified. The 3 largest clusters included 68.6% of all individuals, consisting of 1455 (38.0%), 841 (21.9%), and 336 (8.7%) persons, respectively. Enrichment analysis was applied to isolate clinically relevant ASD-associated biological processes. Two of the identified clusters were characterized by individuals with an increased presence of variants linked to biological processes and cellular components, such as axon growth and guidance, synaptic membrane components, or transmission. The study also suggested other clusters with possible genotype–phenotype associations. Innovative methodologies, including machine learning, can improve our understanding of the underlying biological processes and gene variant networks that undergo the etiology and pathogenic mechanisms of ASD. Future work to ascertain the reproducibility of the presented methodology is warranted.
Edukasi Pemanfaatan Tumbuhan Berkhasiat Obat menjadi Sediaan Herbal

Desa Babakan dihuni oleh 2.323 kepala keluarga (KK) dan pendatang pelajar atau santri karena fasilitas pendidikan lengkap dari tingkat dasar (SD) sampai Madrasah Aliyah (MA). Namun, masih ditemukan data siswa putus sekolah, pengangguran dan data keluarga pra sejahtera. Potensi wilayah desa ini meliputi persawahan 96 Ha, perkebunan 20 Ha dan pekarangan 47 Ha yang menjadi peluang untuk budidaya tumbuhan berkhasiat obat. Sebagai upaya mencegah pengangguran akibat putus sekolah dan untuk memaksimalkan potensi desa maka dilakukan survey terkait tumbuhan yang bekerja sama dengan 70 siswa MAN 2 Cirebon. Hasil survey menunjukkan 46,04% responden (286 KK) telah menggunakan 69 jenis tumbuhan untuk mengobati beberapa penyakit. Beberapa penyakit yang sering dikeluhkan yaitu penyakit degeneratif seperti darah tinggi, diabetes, kolesterol; penyakit menular seperti batuk, diare; dan penyakit tidak menular seperti gastritis, pegal-pegal, asam urat, rematik, demam, sakit perut. Selanjutnya dilakukan edukasi cara pembuatan sediaan herbal dan demonstrasi proses penyulingan minyak atsiri sebagai bekal ilmu kepada mitra yaitu 100 siswa MAN 2 Cirebon dan 40 siswa MA Tunas Pertiwi. Hasil analisis kuisioner pre test dan post test terkait materi ceramah terhadap mitra menunjukkan bahwa sebagian besar siswa mampu menguasai materi dengan baik. Mayoritas siswa (83,67%) mengalami kenaikan poin nilai sebesar 32,57 dari nilai pre test mereka. Hanya sedikit siswa yang nilainya tetap (4,08%) dan nilainya turun (12,24%). Hasil post test juga menunjukkan jumlah siswa yang mampu menjawab benar semua pertanyaan (poin 100) meningkat dari 6,26% menjadi 17,54%. Selain itu, para siswa mampu mengulangi dan menunjukkan alur proses pembuatan minyak atsiri dari peralatan yang diberikan. Kata kunci: Desa Babakan, edukasi, herbal, survey, tanaman
A predictive model for pressure fluctuations on sloping channels using support vector machine

In this study, support vector machine (SVM) is proposed as a new predictive model for pressure fluctuations beneath free jump occurring on sloping channels. The proposed model reproduces the pressure fluctuation intensity C′p in terms of normalized flow and channel section characteristics. The model variables were derived from dimensional analysis. The proposed model is calibrated and validated by using a wide range of experimental data. The SVM predicted C′p with a correlation of coefficient (CC) of 0.989 and a root mean square error (RMSE) of 0.004. Also, linear and nonlinear regression analyses are applied on the same experimental data set, and the SVM model is compared to the equations obtained from these regression analyses. CC, RMSE and average absolute deviation (Di) are used in the evaluation of performance of each model. The SVM model predicted the measured pressure fluctuations better than conventional regression equations. The results of this study reveal that the proposed SVM model can be effectively used in predicting the pressure fluctuation beneath free jump. Copyright © 2010 John Wiley & Sons, Ltd.
Applications of Adaptive Controllers to Systems With Nonlinear Parametrization

Nonlinear parametrizations occur in dynamic models of several complex engineering problems. The theory of adaptive estimation and control has been applicable, by and large, to problems where parameters appear linearly. We have recently developed an adaptive controller that is capable of estimating parameters that appear nonlinearly in dynamic systems in a stable manner. In this paper, we present this algorithm and its applicability to two problems, temperature regulation in chemical reactors and precise positioning using magnetic bearings both of which contain nonlinear parametrizations. It is shown in both problems that the proposed controller leads to a significantly better performance than those based on linear parametrizations or linearized dynamics.
Coronaviruses Are Abundant and Genetically Diverse in West and Central African Bats, including Viruses Closely Related to Human Coronaviruses

Bats are at the origin of human coronaviruses, either directly or via an intermediate host. We tested swabs from 4597 bats (897 from the Democratic Republic of Congo (DRC), 2191 from Cameroon and 1509 from Guinea) with a broadly reactive PCR in the RdRp region. Coronaviruses were detected in 903 (19.6%) bats and in all species, with more than 25 individuals tested. The highest prevalence was observed in Eidolon helvum (239/733; 39.9%) and Rhinolophus sp. (306/899; 34.1%), followed by Hipposideros sp. (61/291; 20.9%). Frugivorous bats were predominantly infected with beta coronaviruses from the Nobecovirus subgenus (93.8%), in which at least 6 species/genus-specific subclades were observed. In contrast, insectivorous bats were infected with beta-coronaviruses from different subgenera (Nobecovirus (8.5%), Hibecovirus (32.8%), Merbecovirus (0.5%) and Sarbecovirus (57.6%)) and with a high diversity of alpha-coronaviruses. Overall, our study shows a high prevalence and genetic diversity of coronaviruses in bats and illustrates that Rhinolophus bats in Africa are infected at high levels with the Sarbecovirus subgenus, to which SARS-CoV-2 belongs. It is important to characterize in more detail the different coronavirus lineages from bats for their potential to infect human cells, their evolution and to study frequency and modes of contact between humans and bats in Africa.
Frequency Estimation Mechanisms Under ϵδ-Utility-Optimized Local Differential Privacy

Frequency estimation mechanisms are widely applied in domains such as machine learning and cloud computing, where it is desirable to provide statistical information. As a fundamental operation in these domains, frequency estimation utilizes personal data which contains sensitive information while it is necessary to protect sensitive information from others. Motivated by this, we preserve user's privacy with local differential privacy by obfuscating personal data on the user side. In this paper, we propose frequency estimation mechanisms under utility-optimized local differential privacy (ULDP), which allow the data collector to obtain some non-sensitive values to improve data utility while protecting sensitive values from leaking sensitive information. We propose three frequency estimation mechanisms under <inline-formula><tex-math notation="LaTeX">$(\epsilon,\delta)$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>(</mml:mo><mml:mi>ε</mml:mi><mml:mo>,</mml:mo><mml:mi>δ</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="zhu-ieq3-3238839.gif"/></alternatives></inline-formula>-ULDP (uRFM-GRR, uRFM-RAPPOR, uRFM-OLH) to preserve user's sensitive information. Our proposed mechanisms protect sensitive data with the same privacy guarantee and they are suitable for different scenarios. Besides, in theory, we compare the estimation errors of our proposed mechanisms with existing LDP based mechanisms and show that ours are lower than theirs. Finally, we conduct experiments on synthetic and real-world datasets to evaluate the performance of the three mechanisms. The experimental results demonstrate that our proposed mechanisms are better than the existing LDP based solutions over the same privacy level, while uRFM-OLH frequently performs the best.
A Nonstandard Method for Estimating a Linear G rowth Model in LISREL

A structural equation modelling approach for estimating a linear growth curve model is presented. This method can be used for a wide variety of models based on the General Linear Mixed Model. This model is a simple example of a multilevel or random coefficients model. The logic of the method is described, and then a LISR E L implementation is presented. A s an example of the individual linear growth curve model, growth data presented by Pothoff and Roy (1964) is analysed.
Optimization of Intrusion Detection Using Likely Point PSO and Enhanced LSTM-RNN Hybrid Technique in Communication Networks

The intrusion detection system (IDS) is considered an essential sector in maintaining communication network security and has been desirably adopted by all network administrators. Several existing methods have been proposed for early intrusion detection systems. However, they experience drawbacks that make them subsequently inefficient against new/distinct attacks. To overcome these drawbacks, this paper proposes the enhanced long-short term memory (ELSTM) technique with recurrent neural network (RNN) (ELSTM-RNN) to enhance security in IDS. Intrusion detection technology has been associated with various problems, such as gradient vanishing, generalization, and overfitting issues. The proposed system solves the gradient-clipping issue using the likely point particle swarm optimization (LPPSO) and enhanced LSTM classification. The proposed method was evaluated using the NSL-KDD dataset (KDD TEST PLUS and KDD TEST21) for validation and testing. Many efficient features were selected using an enhanced technique, namely, the particle swarm optimization. The selected features serve for effective classification using an enhanced LSTM framework, where it is used to efficiently classify and detect the attack data from the normal data. The proposed system has been applied to the UNSW-NB15, CICIDS2017, CSE-CIC-IDS2018, and BOT _DATASET datasets for further verification. Results show that the training time of the proposed system is much less than that of other methods for different classes. Finally, the performance of the proposed ELSTM-RNN framework is analyzed using various metrics, such as accuracy, precision, recall, and error rate. Our proposed method outperformed LPBoost and DNNs methods.
Ray-density normalization for ray-optical wave propagation modeling in arbitrarily shaped tunnels

This work is concerned with the calculation of natural electromagnetic (EM) wave propagation and the determination of the propagation channel characteristics in highway or railway tunnels in the ultrahigh-frequency (UHF) range and above (>300 MHz). A novel ray-tracing technique based on geometrical optics (GO) is presented. Contrary to classical ray tracing, where the one ray representing a locally plane wave front is searched, the new method requires multiple representatives of each physical EM wave at a time. The contribution of each ray to the total field at the receiver is determined by the proposed ray-density normalization (RBN). This technique has the further advantage of overcoming one of the major disadvantages of GO, the failure at caustics. In contrast to existing techniques, the new approach does not use ray tubes or adaptive reception spheres. Consequently, it does not suffer their restrictions to planar geometries. Therefore, it allows one to predict the propagation of high-frequency EM waves in confined spaces with curved boundaries, like tunnels, with an adequate precision. The approach is verified theoretically with canonical examples and by various measurements at 120 GHz in scaled tunnel models.
Deep Learning-Based Multiclass Instance Segmentation for Dental Lesion Detection

Automated dental imaging interpretation is one of the most prolific areas of research using artificial intelligence. X-ray imaging systems have enabled dental clinicians to identify dental diseases. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, as well as machine and deep learning models for dental disease diagnoses using X-ray imagery. In this regard, a lightweight Mask-RCNN model is proposed for periapical disease detection. The proposed model is constructed in two parts: a lightweight modified MobileNet-v2 backbone and region-based network (RPN) are proposed for periapical disease localization on a small dataset. To measure the effectiveness of the proposed model, the lightweight Mask-RCNN is evaluated on a custom annotated dataset comprising images of five different types of periapical lesions. The results reveal that the model can detect and localize periapical lesions with an overall accuracy of 94%, a mean average precision of 85%, and a mean insection over a union of 71.0%. The proposed model improves the detection, classification, and localization accuracy significantly using a smaller number of images compared to existing methods and outperforms state-of-the-art approaches.
An FPGA-based Accelerator for Regular Path Queries over Edge-labeled Graphs

Edge-labeled directed graphs are commonly used to represent various information in different applications, such as social networks, knowledge graphs, etc., and regular path queries (RPQs) allow us to extract pairs of nodes that are reachable from one to another through a labeled path matching with the query pattern represented as a regular expression. It is useful for us to extract complicated or semantically meaningful information from a graph, but it gives rise to a challenge when dealing with large graphs. This is due to the long execution time caused by the explosive growth of intermediate results, but, on the other hand, some applications require fast query executions. To address this problem, we propose an FPGA-based RPQ accelerator. The idea is to exploit FPGA’s parallelism in traversing the target graph and matching the regular path expression in parallel with the pipeline manner. To validate the performance of the proposed method, we conducted a set of experiments. From the results, we observed that the proposed method achieves shorter elapsed times for RPQs against social graphs extracted from the real world, up to three orders of magnitude compared with baseline methods.
A Hybird Method with Gravity Model and Nearest-Neighbor Search for Trip Destination Prediction in New Metropolitan Areas

As the urban population rises, so does the pressure on the city’s transportation system. Most of the existing methods for passenger destination selection focus on processing the historical behaviors and travel trajectories of passengers. However, the existing methods face the generalization issue, the trained model cannot be applied to predict destinations in new metropolitan areas as the destination information is totally unseen and different from the training sets. To deal with the issue faced in IEEE BigData Cup 2022 – Trip Destination Prediction, in this work, we present a hybrid method. The main idea of our method is four-fold. The first is to implement the gravity model to capture human mobility between zones. The second contains two novel features to depict zones, including human traffic flow and feature class ratio. The third is to initialize the destinations in the new metropolitan area using the origin zones of multi-trip individuals. The last is to perform the nearest-neighbor search on both individuals and trips. The final destination prediction is produced by combining the gravity model and the nearest-neighbor search. Performance comparison reported by the competition leaderboard exhibits the superiority of our hybrid method, which also brings us to the fifth place in the competition.
Sutureless Technique for Surgical Castration in Adult Boars: A Feasibility Study

Simple Summary The use of boars for breeding purposes is common on small farms in rural regions of Italy. These animals are butchered at the end of their reproductive life for the production of charcuterie. In this regard, orchiectomy is required to remove boar taint caused by testosterone metabolites. The purpose of this study is to validate the efficacy of a safe, rapid and inexpensive castration procedure. To accomplish this objective, 91 boars were orchiectomised using the suture ligation technique. Following castration, characteristics of the severed ductus deference were evaluated in order to understand how much force would be required to tie knots without breaking the deferens and achieving proper haemostasis. After identifying a correlation between tensile strength and the age and weight of boars, a sutureless technique was successfully applied to 20 animals. Abstract The heterogeneity of Italian manufacturing processes results in the production of a large variety of pork products. In Sardinia, boars are raised and butchered to produce charcuterie. These animals are castrated before slaughter as androstenone would otherwise taint the meat, rendering it unfit for human consumption. However, to date, the literature concerning surgical orchiectomy in adult boars is limited. The goal of this study is to assess whether a sutureless swine orchiectomy procedure is feasible. Additionally, this study aims to determine the appropriate traction force needed to tie knots in the deferens duct of pigs of different weights and ages. Two groups were created: the first (n = 91) underwent orchiectomy by suture ligation; the second (n = 20) was castrated using the sutureless technique. Deferens ducts of animals in the first group (n = 182) were collected following castration, and their tensile strength was measured. Pearson’s linear correlation was used to determine the relationship between the maximum traction force and weight and age groups. A correlation of 0.99 and 0.96 was shown between traction force and age and traction force and weight, respectively. In accordance with these results, sutureless castration was performed on 20 animals, calibrating the pulling force needed according to the age and weight of the boars. No complications were observed during the feasibility study, thus validating sutureless orchiectomy in adult boars.
Pedestrian Detection Algorithm using a Gabor Filter Bank

Abstract: A Gabor filter is a linear filter used for edge detectionas frequency and orientation representations of Gabor filters are similar to those of the human visual system. In this thesis, we propose a pedestrian detection algorithm using a Gabor filter bank. In order to extract the features of the pedestrian, we use various image processing algorithms and data structure algorithms. First, color image segmentation is performed to consider the information of the RGB color space. Second, histogram equalization is performed to enhance the brightness of the input images. Third, convolution is performed between a Gabor filter bank and the enhanced images. Fourth, statistical values are calculated by using the integral image (summed area table) method. The calculated statistical values are used for the feature matrix of the pedestrian area. To evaluate the proposed algorithm, the INRIA pedestrian database and SVM (Support Vector Machine) are used, and we compare the proposed algorithm and the HOG (Histogram of Oriented Gradient) pedestrian detector, presentlyreferred to as the methodology of pedestrian detection algorithm. The experimental results show that the proposed algorithm is more accurate compared to the HOG pedestrian detector. Keywords: pedestrian detection, Gabor filter, Gabor filter bank