Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Raga Identification in Indian Classical Music with FCN-based Models

In this study, we provide a novel method for improving raga identification in the field of Indian classical music by making use of Fully Connected Convolutional Networks (FCNs). The broad variety of ragas in this musical tradition are distinguished by subtle melodic and structural differences, making raga identification a difficult task. This study has proposed a novel FCN-based approach, which utilizes deep learning to provide more reliable and effective answers to the problem. The model shows a high degree of accuracy in recognizing and classifying ragas, which bodes well for the potential of automating this difficult activity. This study has performed an extensive review, which included a thorough comparison to prior methods, to prove that our methodology was superior. Our FCN-based model has been shown to perform better than its competitors in this evaluation, demonstrating its potential for real-world use. The rich cultural history of Indian classical music depends on such measures being taken to preserve and disseminate it.
AlphaNet: Single Morphing Attack Detection Using Multiple Contributors

This paper proposes and explores a Single Morphing Attack Detection (S-MAD) method of morphed face images created from different numbers of contributors. We selected facial images from K subjects and their accomplices for developing morphing face images from K = 2, 4, 8 and 16 subjects. Morphing images with more contributors appear as smothered faces. We developed a multiclass Convolutional Neural Network (CNN) based on three levels of alpha filters applied to the RGB channels based on MobileNetV3 (AlphaNet). This AlphaNet was evaluated on intra-dataset and cross-dataset scenarios. Our method tested in synthetic images obtained an BPCER10of 4.41 % and BPCER20of 4.56% on cross-dataset.
Mechanical Properties of 3D-Printed Liquid Crystalline Polymers with Low and High Melting Temperatures

Additive manufacturing allows for the production of complex components using various types of materials such as plastics, metals and ceramics without the need for molding tools. In the field of high-performance polymers, semi-crystalline polymers such as polyetheretherketone (PEEK) or amorphous polymers such as polyetherimide (PEI) are already successfully applied. Contrary to semi-crystalline and amorphous polymers, thermotropic liquid crystalline polymers (LCPs) do not change into an isotropic liquid during melting. Instead, they possess anisotropic properties in their liquid phase. Within the scope of this work, this special group of polymers was investigated with regard to its suitability for processing by means of fused filament fabrication. Using an LCP with a low melting temperature of around 280 °C is compared to processing an LCP that exhibits a high melting temperature around 330 °C. In doing so, it was revealed that the achievable mechanical properties strongly depend on the process parameters such as the direction of deposition, printing temperature, printing speed and layer height. At a layer height of 0.10 mm, a Young’s modulus of 27.3 GPa was achieved. Moreover, by employing an annealing step after the printing process, the tensile strength could be increased up to 406 MPa at a layer height of 0.15 mm. Regarding the general suitability for FFF as well as the achieved uniaxial mechanical properties, the LCP with a low melting temperature was advantageous compared to the LCP with a high melting temperature.
Encircling Skin Structure for Covering the Palmar and Dorsal Side of Robot Hand Joints with Thick 3-Axis Tactile Sensors

Tactile sensing is crucial for robot fingers to achieve dexterous manipulation tasks. A wide coverage with tactile sensors increases the adaptability of the hand for various tasks. However, covering certain parts of the hand such as the joints with sensors is challenging. This can cause undetected contact with the object. This paper presents a novel structure to cover a robot finger with thick tactile sensor skin. By encircling the joint and finger phalanges with the skin, the robot finger can have a vast sensing area on both the palmar and dorsal side of the finger, including the joints at the same time. Adaptive joints were implemented into the joints so that the robot finger can conform to various object shapes, which expands the contact areas for more stability. uSkin tactile sensors, which are distributed 3-axis tactile sensors for flat and curved surfaces, are implemented for evaluation. To validate the proposed structure, we confirmed that continuous tactile sensing on both sides of each joint is possible. The proposed finger also stopped its motion when touching an obstacle while the finger held an object. Finally, this paper also shows that tactile sensing on the palmar and dorsal sides can be useful to improve manipulation stability.
Simultaneous Multifrequency Demodulation for Single-Shot Multiple-Path ToF Imaging

Indirect Time-of-Flight (iToF) sensors measure the received signal's phase shift or time delay to calculate depth. In realistic conditions, however, recovering depth is challenging as reflections from secondary scattering areas or translucent objects may interfere with the direct reflection, resulting in inaccurate 3D estimates. We propose a new measurement concept including a single-shot on-chip multifrequency demodulation method with periodically-repeated ultrashort-pulsed illumination using a novel pixel array architecture to address a main limitation of conventional iToF, the Multi-Path Interference (MPI). Due to the careful hardware/software codesign, the proposed single-shot architecture provides close-to-optimal Fourier measurements to a spectral estimation algorithm that retrieves the unknown parameters of the interfering return paths in a closed form. Electrical simulations of the on-chip multifrequency demodulation circuit demonstrate the feasibility of distance retrieval in double and triple bounce conditions in a single shot with high accuracy. Furthermore, we propose a set of methods for processing the resulting sensor measurements that exploit valuable a priori information and structural constraints of the data and observe that they yield a substantial increase in accuracy.
sEMG based hand gesture recognition for rehabilitation prosthetics based on machine learning methods

The human hand plays a crucial role in daily activities, and individuals experiencing dysfunction due to strokes or accidents often require therapy to enhance their condition. The usage of surface electromyographic signals to control has become a important research area, being rehabilitation one of it’s applications. The present work propose a CNN architecture that is able to predict six hand gestures with three channels. We enrolled 8 subjects that generated 14,400 samples in our dataset, each one with a hand gesture label. This CNN could work as a control system of a rehabilitation device, for which a database was prepared that accounted for possible hand movements to serve this purpose. The proposed solution shows an accuracy of 90.56% with an inference time of 1.515 ms.
Obstacle detection by multi-sensor fusion of a laser scanner and depth camera

Reliable sensor systems are essential to detect and track the human operator in a human-robot collaborative environment. This work proposes a low-cost obstacle warning system by fusing 2D LiDAR and 3D vision data. Some regions of the workspace can be inaccessible to a single sensor, defined as blind spots. Therefore, by using a 2D LiDAR on the robot's base and a vision sensor on top of the workspace, we ensure coverage of the entire workspace of the manipulator. For a more efficient system, first human operator has been detected using an object detection algorithm then the laser points are segmented. Further, to obtain more accurate results, the data from both sensors has been fused using Kalman Filter. This fusion not only provides accurate and fast distance information on the position of a human worker without leaving any blind spots but is also significantly more affordable than the more common 3D LiDAR plus vision approach.
Design and Modifications of Rotor Flux Barrier in Synchronous Reluctance Motor for E-Bike

Electric bicycles have become progressively popular due to their environmentally friendly and cost-effective nature. However, choosing a suitable motor is very much needed to attain better performance and energy efficiency. This research paper explores the design and modification of rotor flux barrier in synchronous reluctance motor (SynRM) for an electric bicycle by comparing its Hyperbolic-curve and Hyperbolic-polyline rotor designs. The aim of this study is to determine which design produces more efficiency in terms of power consumption and performance. Both designs were analyzed using finite element analysis, and their performance was evaluated through simulations by Ansys rmxprt. The results revealed that the hyperbolic-curve design had a higher efficiency and better performance compared to the hyperbolic-polyline design. This suggests that the hyperbolic-curve design is a better choice for electric bicycles.
Analysis of Lsm Protein-Mediated Regulation in the Haloarchaeon Haloferax mediterranei

The Sm protein superfamily includes Sm, like-Sm (Lsm), and Hfq found in the Eukarya, Archaea, and Bacteria domains. Archaeal Lsm proteins have been shown to bind sRNAs and are probably involved in various cellular processes, suggesting a similar function in regulating sRNAs by Hfq in bacteria. Moreover, archaeal Lsm proteins probably represent the ancestral Lsm domain from which eukaryotic Sm proteins have evolved. In this work, Haloferax mediterranei was used as a model organism because it has been widely used to investigate the nitrogen cycle and its regulation in Haloarchaea. Predicting this protein’s secondary and tertiary structures has resulted in a three-dimensional model like the solved Lsm protein structure of Archaeoglobus fulgidus. To obtain information on the oligomerization state of the protein, homologous overexpression and purification by means of molecular exclusion chromatography have been performed. The results show that this protein can form hexameric complexes, which can aggregate into 6 or 12 hexameric rings depending on the NaCl concentration and without RNA. In addition, the study of transcriptional expression via microarrays has allowed us to obtain the target genes regulated by the Lsm protein under nutritional stress conditions: nitrogen or carbon starvation. Microarray analysis has shown the first universal stress proteins (USP) in this microorganism that mediate survival in situations of nitrogen deficiency.
PHYTOCHEMICAL SCREENING, ANTIBACTERIAL AND INSECTICIDAL ACTIVITIES OF STEM BARK EXTRACTS OF BOSWELLIA DALZIELII HUTCHIN FROM KALTUNGO, NIGERIA

The importance of traditional herbal medicinal system has now gained attention in developed and developing countries. Boswellia dalzielii is a tree grows in the wild usually in the Savannah region of West Africa. These work assessed the phytochemical constituents, antibacterial and insecticidal activities of the stem bark extracts of Boswellia dalzielii. The plant extracts were obtained using soxhlet method with petroleum ether, ethyl acetate, acetone, ethanol and water as solvents for 10 hours while the phytochemicals and insecticidal activities were determined using standard methods. The phytochemical screening showed significant presence of carbohydrate in the ethanol and water extracts while significant amount of tannin was detected only in the ethanol extract. The petroleum ether and ethyl acetate showed absence of carbohydrate, tannins, phlobotannin, cyanogenic glycoside, chlorogenic acid and alkaloid. Significant amount of flavonoid was seen in the water extract, while only the ethanolic extract showed significant amount of cardiac glycoside and alkaloid. The acetone extract showed significant quantity of alkaloid and terpenes and steroids. The antibacterial screening showed that ethanol extract had the highest inhibitory activity. The petroleum ether extract had the least active components with no activity on the test organisms. The insecticidal activity of the plant extracts showed that acetone extract had the highest insecticidal activity on both the test insects. Camponotus consobrinus shows the highest percentage mortality of 75% at a concentration of 500µg and LC50 of 1. 64x102µg followed by the Acanthscelides obtectus with the percentage mortality of 53% at a concentration of 250µg and.
Optimizing Curvature and Clearance of Piecewise Bézier Paths

Path optimization typically involves finding a smooth and short path that satisfies motion constraints of a given vehicle or simulated agent. In this paper, we propose a new path optimization scheme based on convex optimization of a piecewise quadratic Bézier path. Our method addresses $C^{1}$ continuity and a target minimum clearance to obstacles while minimizing path length and maximum curvature. The user is able to customize the objective function by assigning different weights to the clearance, length, and curvature terms. As a result our method is versatile and suitable for a variety of path planning applications in robotics and autonomous vehicles. We present several results demonstrating the ability to achieve paths according to different values for the curvature, length, and clearance parameters. Our benchmark scenarios apply our method directly to low-quality paths generated by a sampling-based planner, and as well in comparison to a heuristic shortcut-based smoothing method. Our results show that the quality of the paths produced by our method outperforms other approaches, and that our method is responsive to the weights chosen by the user. Our method is able to control the main path properties of interest in a unified fashion, and therefore represents an excellent option for several path planning applications in robotics and autonomous vehicles.
A Decentralised Model for Pricing of Competitive Electric Vehicle Charging Stations Considering the Distribution System

The growing number of electric vehicles (EVs) has led to an increasing number of privately owned EV charging stations (EVCSs). They will engage in competition to maximise their revenues. Also, the prices offered at these EVCSs will influence the charging demands, affecting the operation of the distribution system (DS). This work presents a pricing model for the EVCSs considering their competitive interactions and the DS. The interactions between the EVCSs to maximise their profit is formulated as an equilibrium problem (EP) in the upper level, while the DS market clearing problem is in the lower level. These subproblems are solved in a decentralised way between the EVCSs and the DS by exchanging the charging demand data only. The proposed pricing model is studied for a 15-bus DS with two EVCSs and compared with three different model types to verify its effectiveness in achieving higher profits for them.
Leveraging Deep Convolutional Neural Networks for Accurate Discrimination between Benign and Malignant Skin Lesions

In the field of dermatology, accurately distinguishing between benign and malignant skin lesions is a critical challenge. In current circumstances, when the incidence of skin cancer is increasing, expensive devices for diagnosis is prevalent. AI is an emerging technology that provides solutions in the medical domain. Machine Learning (ML) along with Deep Learning (DL) techniques are used in AI to create intelligence similar to that of humans. The suggested method also includes a classification of skin cancers based on DL. The dataset, which got from Kaggle, consists of several skin lesion types that have been thoroughly processed to produce a collection of roughly 750 photos for discrimination. It applies a Blackhat filter, and then uses an in-painting approach to improve categorization. We also use the ABCD rule, which doctors frequently use to evaluate skin lesions and measure asymmetry, border irregularity, color variation, and diameter. Our suggested method uses a CNN model that performs better than previous classifiers and achieves a remarkable accuracy of 99.31%. A validation of our methodology comes from experimental testing performed across 150 epochs. Our results show that CNNs are more effective at classifying skin lesions than SVM, logistic regression, decision trees, XG Boost, and bagging classifiers. In this way, skin cancer detects very smoothly and perform in a very precise manner.
Safe Explorative Bayesian Optimization - Towards Personalized Treatments in Plasma Medicine

This paper considers the problem of Bayesian optimization (BO) for systems with safety-critical constraints. Recent work has shown that a theoretically consistent way to account for constraints in BO is to relax the constraint functions such that the feasible region has a high probability of containing the global solution. However, by construction, these approaches are unable to ensure safe/feasible operation at every query, which is unacceptable in safety-critical applications. Alternatively, safe BO methods force the query points to remain in the interior of a partially-revealed safety region, which may result in unacceptable (and unquantified) performance losses. This paper presents a new safe BO method that avoids these performance losses by systematically incorporating potential performance gains from enlargement of the safety region. The proposed method avoids getting stuck at suboptimal points based on a potentially small initial safety region due to limited initial exploration of the safety boundary. The performance of the proposed method is demonstrated for safe control of a cold atmospheric plasma jet towards personalized plasma medicine.
The Tipping Point of Edge-Cloud Data Management

Edge and Internet of Things (IoT) applications have attracted significant attention from both industry and academia due to their immense potential. As a result, the database community—through communications such as the recent database Seattle reports—has recognized the criticality of developing a new breed of data management systems specifically tailored for IoT and edge applications. These systems need to be distributed across edge locations to effectively handle the unique challenges posed by these environments. However, the development of such databases remains largely minimal in both industry and academia. Over the past five years, our team has conducted extensive research and collaborated with industry partners to bring an edge-cloud database to market and to investigate the reasons behind the limited progress and adoption of distributed edge-cloud databases. As part of our efforts, we have developed a distributed edge-cloud database called AnyLog and deployed it with numerous industry partners in different IoT and smart city sectors. Our interactions with many technical teams from diverse industries have provided invaluable insights into the challenges and opportunities associated with distributed edge-cloud data management. In this paper, we present a comprehensive summary of our findings, drawing from our five-year experience in the field. We high-light the real challenges faced in distributed edge-cloud data management and discuss the opportunities that lie within. Furthermore, we showcase the capabilities of edge-cloud databases using AnyLog, illustrating our insights about the challenges and opportunities of edge-cloud databases.
An updated sediment source areas inventory in the Rio Cordon catchment (Dolomites)

This study presents an inventory of sediment source areas updated to 2016 of the Rio Cordon catchment, a small headwater basin located in the Eastern Italian Alps. The aim of the 2016 inventory was to update an old sediment source areas dataset dating back to 2006, built using LiDAR-derived geomorphometric parameters interpretation and field survey, in order to evaluate the geomorphic changes occurred in the last 10 years in the catchment. The new inventory wasrealized by means of an extensive field survey campaign conducted with a mobile GIS mounted on a rugged notebook integrating a GPS receiver. Pictures of individual or collective sediment source areas were also acquired in order to integrate the GIS dataset and to better define their current status of activity. The images were also used for a comparison with the images acquired in 2006 to identify the evolution or stabilization of each sediment source area. The identification and delimitation of large and/or unreachable areas was carried out in the office by interpreting aerial photo images (Bing satellite images and Web Map Services for AGEA 2009-2012 aerial photographs,compared with the old LiDAR-derived high resolution images used for the 2006 sediment source inventory). The comparison with the previous inventory shows that several old sediment sources resulted totally stabilized indicating a general decrease of erosion processes in the catchment. In contrast, some new sediment source areas were identified. They were mainly related to a very recent rock fall that took place in July 2016 in the Lastoni di Formin group and many new shallow landslides were found in the upper part of small Rio Cordon tributary where a small debris flow event occurred in 2012.
Chinese consumers’ attribute preference prediction for table grapes

This paper aims to understand intrinsic attribute preferences of Chinese consumers for table grapes, analyze the influencing factors, and build consumer preference prediction models. In this study, 4324 consumers from various regions of China were investigated. We analyzed consumer preferences and the influencing factors. Finally, binary logistic regression was used to construct prediction models of consumers’ intrinsic attribute preferences for table grapes. The results showed that grapes popular with Chinese consumers had fixed characteristics, including moderate size, spherical or near-spherical shape, purple-red color, strawberry flavor, light aroma, soft flesh and juicy, sweet taste, seedlessness, thin skin powder, and easy to peel. The results of the prediction models showed that age, annual consumption of grapes, and other factors of consumers had significant effects on consumer preferences. The prediction models achieved 80% accuracy in predicting consumer preferences for taste, seedless and peeling degrees. Analyzing the latest attribute preferences of Chinese table grapes, consumers can provide on the one hand, breeding direction reference for breeders, and on the other, marketing suggestions for marketers in the table grape industry. This study comprehensively investigated the intrinsic attribute preferences of Chinese table grape consumers, mastered the latest results of consumer preferences, added the indicators of the intrinsic attributes from the perspective of consumer demand, and conducted relatively more complete prediction research on the preferences of Chinese consumers.