Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Buffer Replacement Policies for Multi-Tenant Cloud Services

Cloud services such as multi-tenant content delivery networks (CDN) have become a trend because they can offer personalized business support for tenants. However, the business modes of different tenants are usually different. Thus, it is not appropriate to use a single buffering strategy on cloud servers because it cannot adapt to various access patterns of tenants. In this paper, we propose to develop adaptive buffer management for multi-tenant cloud services. Particularly, we present an adaptive buffer replacement policy called ARP (Adaptive Replacement Policy) for each tenant. ARP can automatically choose a recency-based or a frequency-based replacement policy according to the workload characteristics. Further, to solve the problem that ARP needs to manually set a parameter $p$, we present an optimization for ARP, called ARP+, to automatically adjust the value of parameter $p$ according to the workload characteristics. We conduct experiments on multiple tailor-made workloads. The results show that both ARP and ARP+ outperform conventional buffering policies, including LRU, LFU, LIRS, and LRU-2. In addition, ARP+ can automatically adapt to the change of access patterns, which is more suitable for multi-tenant cloud scenarios.
Recurrent Soft Actor Critic Reinforcement Learning for Demand Response Problems

Demand response problems are typically solved with rule-based or model predictive control solutions. While rule-based controls do not take into account the future development of uncertain variables, model predictive control solutions often require very accurate predictions. To solve demand response problems, which incorporate the inaccuracies of predictions in their decision making, deep reinforcement learning methods have become popular. For their implementation, current literature defines the demand response problem as a fully observable Markov decision process. However, the assumption of full observability is usually not satisfied in reality. An alternative idea is to describe the problem as partially observable and to use recurrency in the policy function. In this paper, we adapt this idea and propose a novel deep reinforcement learning control solution for demand response problems, based on the soft actor critic framework. Controlling a heat pump with a mixture of discrete and continuous action capabilities, we show that significant performance improvement can be achieved by using recurrency in the policy compared to a non recurrent policy function.
Design and Fabrication Challenges of Integrated Optical Circuits for Quantum Computing Applications

The paper presents current design issues, limitations, and fabrication challenges towards the development of hybrid integrated optical circuits with embedded single photon sources, which are critical components for future quantum computing, sensing, and communication systems. Optical nanowires with embedded quantum dots (NWQD) are very promising single photon sources, with several reported results of integration in high index platforms like silicon or silicon nitride. Silica on silicon is a favorable platform for optical computing, providing the high degree of customization when combined with direct laser writing techniques for definition of optical structures with refractive index modification. However, the low ~10-3 achievable refractive index contrast imposes strict limitations on the compatibility with NWQD resulting in general in low coupling efficiency as shown in this work. Consideration of several design and fabrication issues demonstrates the potential for NQWD integration in laser written silica based optical circuits with adequate efficiency for practical applications.
A Hand Exoskeleton Using a Multi-Segment Mechanism with Two Degrees-of-Freedom for Each Finger

This paper presents the development and experimental validation of a novel multi-degrees-of-freedom hand rehabilitation exoskeleton system designed for stroke patients to restore their grasping function. The exoskeleton system includes a two-degrees-of-freedom finger mechanism, an upper computer, and a lower electromechanical subsystem. The fully integrated system makes the exoskeleton more convenient to use and easy to control for grasping assistance with objects used in daily activities. The exoskeleton fingers are powered by a two-degrees-of-freedom driven unit, which allows the exoskeleton to perform movements with a larger range of motion and adequate grasping force. Through four experiments, the exoskeleton system's fingertip force, grasping ability and ability to lift objects were verified.
Recursive Spatial Covariance Estimation with Sparse Priors for Sound Field Interpolation

Recent advances have shown that sound fields can be accurately interpolated between microphone measurements when the spatial covariance matrix is known. This matrix may be estimated in various ways; one promising approach is to use a plane wave formulation with sparse priors, although this may require the use of a many microphones to suppress the noise. To overcome this, we introduce a time domain formulation exploiting multiple time samples, posing the problem as an identification problem of a recursively estimated sample covariance matrix. A computationally efficient method is proposed to solve the resulting identification problem. Using both numerical experiments and anechoic data, the proposed method is shown to yield preferable performance as compared to current state of the art methods, notably for high frequencies sources and/or in cases when using few microphones.
Frequency Oscillation Control in a Microgrid System using Binary PSO based PID Controller

Renewable sources of energy are the prime mover of the microgrid in the present period because they are non-conventional sources of energy. With the implementation of renewable units in the power system, the frequency deviation becomes unstable. To fulfil the rising load demand in the power system, the number of renewable sources has been integrated in the power system. Here, a number of sources like wind turbine, diesel engine generator, aqua electrolyzer, fuel cell, battery energy storage is implemented in a microgrid system and frequency deviation control is examined. PID controller is considered in this article for frequency deviation control in microgrid incorporated with any renewable sources. The existing PSO techniques have been modified to form binary PSO to tune the constraints of the PID controller. In order to adjust the factors of the PID controller an abrupt load deviation of 3% is applied in the microgrid system. Considering ITAE as fitness function the binary PSO is employed to tune the values of the control parameters. Here, the dominance of binary PSO is established over PSO techniques in contrast to many time domain indices such as settling time, undershoot and overshoot. Further, the sensitivity of the microgrid system is examined with the existing controller constraints by amplifying the loading of the microgrid system.
Török hódoltság kori idegen etnikumok összehasonlító embertani elemzése = Comparative anthropological analysis of foreign populations in Turkish occupation of Hungary

A 3 eves kutatasi periodus ideje alatt 7 - antropologiai szempontbol kevesse ismert - 16-17. szazadi temető embertani vizsgalatat vegeztuk el. A koponya metrikus adataira alapozva klaszteranalizist alkalmaztunk, melynek eredmenyeiből kiderult, hogy a torok hodoltsag idejen betelepult nepessegek az osszehasonlitashoz felhasznalt keső kozepkori magyar temetőktől jol elkulonulő csoportot alkotnak. A vizsgalatba bevont romaniai es delszlav temetők kozul csupan a del-boszniai Raąka Gora mutatott parhuzamot a Magyarorszagon letelepedett idegen etnikumokkal. A csontvazleletek patologiai vizsgalatanak eredmenye ramutatott, hogy a vizsgalt nepessegek eletmodjukat tekintve 2 csoportba sorolhatok: egy bekes korulmenyek kozt elő foldművelő-allattenyesztő es egy időnkent harci tevekenyseget is folytato csoportba. Az elemzett lelet egyuttesek koreben megfigyelt nagyszamu, fertőzeses eredetre utalo kezdeti stadiumu csontelvaltozas legyengult immunrendszerre utal. A magyar teruleten elő idegen eredetű nepessegek elterő nyelvi es vallasi kulturaja kovetkezmenyekent feltetelezett endogamiat, a koponyacsontokon manifesztalodott fejlődesi rendellenessegek felhalmozodasaval, a vizsgalt temetők kozul kettőnel igazoltuk. A kutatomunka eredmenyei alapul szolgalnak egy magyar, a paleohisztologiai resz pedig egy nemetorszagi PhD disszertacio elkeszitesehez, tovabba egy, a jovőben benyujtando nemzetkozi (magyar-nemet) palyazathoz. | A comparative anthropological examination of skeletal material dating from the Turkish occupation of Hungary (16th-17th centuries AD) belonging to 7 foreign ethnic groups was carried out during a 3-year research period. Cluster analyses were applied to find out the relationships between these non-Hungarian groups and the late medieval Hungarian populations, as well as the origin of the immigrated groups. Southern Slav and Romanian series were also used for comparison. Our results indicated that the foreign populations of this study are separated in a distinct group and revealed a close relationship with a southern Bosnian Raąka Gora. According to the results of our palaeopathological examinations, the analyzed populations could be classified into two groups: a quiet agro-pastoralist population and another group with a more violent lifestyle. The large number of chronic infectious bony lesions refers to the poor state of health of these populations. Endogamy was proved in two of the examined series by the high frequency of developmental anomalies on the skull. The main results of our study served as a basis for a PhD dissertation, and the palaeohistological results provided an important source for another PhD thesis. A new international project is also planned on the basis of our results.
Estimating Unobservable Machines in Multi-Area Power Systems Considering Model Imperfections

Control room applications for multi-areas systems, e.g. oscillations detection, are dependent on models and measurements of neighbouring areas. Given inherent limitations in state-of-the-art data-sharing architectures, such dependencies can suffer from model imperfections and low observability. This paper presents a study on the use of the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) for centralised estimation of rotor angles, in support of applications such as oscillation detection. The study focused on the Kalman filter variants ability to overcome low observability and model imperfections, implemented on the Kundur two-area four-machine network and the Nordic-44 bus network. To study the effect of low observability and model imperfection limitations, the study included three different data sharing architectures as proposed by ENTSO-E. The simulated test cases demonstrated that the EKF is unable to capture the dynamics under low observability conditions during model imperfections, while UKF captures the dynamics for all presented cases.
Arc Fault Detection for Improved Electrical Safety with ADC and Relay

Electrical fires and accidents continue to cause considerable property damage and fatalities in India, raising serious concerns about electrical safety. Electrical safety in India can be greatly increased by implementing real-time arc fault detection systems employing C++, Analog-to-Digital converter, and relay technologies. The technology can be tailored to meet various electrical systems, locations, and infrastructure. Using cutting-edge electrical safety solutions, such as real-time arc fault detection systems, is essential to reducing electrical dangers and ensuring the safety of people and property in India due to the country's expanding electrical infrastructure and rising demand for power. To reduce electrical mishaps, the Indian government should give priority to implementing such systems and raising awareness of the significance of electrical safety.
Analysis of Juggling Data Registration of Functional Data Subject to Biomechanical Constraints

: We illustrate how physical constraints of a biomechanical sys- tem can be taken into account when registering functional data from juggling trials. We deﬁne an idealized model of juggling, based on a periodic joint movement in a low-dimensional space and a periodic position vector (from an undeﬁned joint to the ﬁnger tip) of approximately constant length along the observed trajectory. Our registration procedure ﬁrst warps the cycles in the trial to each other and computes a periodic average, and then estimates the joint movement and the position vector of the abovemen-tioned model.
Influência de diferentes condições da ionosfera no posicionamento por ponto com GPS : avaliação na região brasileira

In the SA absence, the ionosphere is the largest error source in GPS positioning. The error due to the ionosphere in the GPS observables depends on the signal frequency and Total Electron Content (TEC) in the ionospheric layer. The TEC varies regularly in time and space in relation to the sunspot number, the season, the local time, the geographic position, and others. The Brazilian region is one of the regions of the Earth that presents largest values and space variations of the TEC, being influenced by the equatorial anomaly of ionization and ionospheric scintillation. Therefore, it is important to study the TEC behavior in the Brazilian region. Due to the ionosphere dispersive nature, the TEC behavior in Brazil can be studied using GPS data from RBMC (Rede Brasileira de Monitoramento Contínuo – Brazilian Network for Continuous Monitoring of GPS). Additionally, GPS data from IGS (International GNSS Service) network of the South America can also be used in the experiments. The goal of this research is to study the ionospheric error behavior in the Brazilian region, considering different ionosphere situations, using TEC values computed by GPS data from RBMC and IGS network. Other goal is to evaluate the performance and limitations of Global Ionospheric Map of IGS applied in the GPS point positioning in Brazil.
Selective Noise Cancellation using Machine Learning

Research on Adaptive Noise Cancellation has concentrated on cancelling all audio signals other than the one the user desires to hear. This paper proposes a method for using machine learning in signal processing to train the model in a manner that can cancel the noise signals. Active noise cancellation has existed for quite some time, but it completely canceled out all the frequencies. But with adaptive noise cancellation, the user should be free to choose the frequencies they want to hear. The goal of this system is to improve and take care of hearing health in noisy cities, such as for example, noise from traffic, engines from factories, reckless driving from drivers, and noise from street vendors, allwhile making sure the important sounds can be still audible by the listener. Previous researches have focused only on training audios based on available datasets, whereas an entire dataset of noise and its features was constructed in order to train the model. Further, a comparison is analysed for the results achieved through the selected algorithms which helps state that KNN provides which a higher accuracy of 86% over Logistic Regression.
A Comparative Study of Object Detection Techniques for Automatic License Plate Recognition for Indian License Plates

Automatic License Plate Recognition (ALPR) is a technology that allows for the automatic detection and recognition of license plates on vehicles. Deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been applied to enhance license plate detection and character recognition capabilities, improving performance in situations involving distorted or skewed license plates. This paper compares the accuracy of various object detection techniques for License Plate Detection step in ALPR, and their results and performance are analyzed. These object detection techniques include traditional canny edge and contour segmentation, YOLOv5, Single Shot Detector (SSD), FasterRCNN, and RetinaNet. After comparison, it is found that YOLOv5 performs the best in accuracy and inference speed out of all the above-stated techniques.
Selected Lanthanide Complexes for Nonlinear Optical Applications

In the present work, the second- and third-order nonlinear optical (NLO) properties of composite materials based on Europium (Eu) and Gadolinium (Gd) complexes and poly (methyl methacrylate) (PMMA) in thin film form were investigated. The thin films were prepared by using the spin coating technique. The Maker fringe technique was used to study the second and third harmonic generation (SHG and THG) in the picosecond regime at the incident wavelength of 1064 nm. After applying the corona poling approach, the second-order nonlinear susceptibility χ(2) and third-order nonlinear susceptibility χ(3) were estimated using the comparative models of Lee and Kubodera-Kobayashi, respectively. These compounds have good nonlinear properties, according to the data, which makes them a viable candidate for optoelectronic applications.
Late Cretaceous sea level rise and inversion: Their influence on the depositional environment between Aachen and Antwerp

Des recherches pluridisciplinaires dans les depots du Cretace superieur (Santonien a Maastrichtien) entre Aachen et Antwerpen (NE Belgique, SE Pays-Bas et region d'Aachen en Republique federale allemande) ont demontre leur histoire sedimentaire relativement complexe. Des correlations entre differentes lithologies sont basees sur des bioclastes, des Foraminiferes, des Ostracodes, des Belemnites et des logs petrophysiques des sondages. Le depot a ete controle par une montee continue du niveau de la mer du Santonien a la fin du Maastrichtien superieur et par des mouvements tectoniques (inversion de subsidence a partir du Campanien inferieur, relâchement de l'inversion a partir de la partie moyenne du Maastrichtien superieur, gauchissement differentiel des blocs au sud de la region de la vallee de la Rur, en particulier du Campanien superieur au debut du Maastrichtien superieur). Un changement majeur dans les assemblages fossiles, a l'entree de la partie moyenne du Maastrichtien superieur, est marquee par l'apparition d'elements mediterraneens, par une modification brusque de la composition quantitative des assemblages de bioclastes, et par une diversification plutot brutale et accentuee de la plupart des groupes fossiles. Ce changement correspond au debut du relâchement de l'inversion dans la region de la vallee de la Rur et il est interprete comme un des evenements ecostratigraphiques regionaux induits par la tectonique.
Study of Converters for Energy Storage Systems in Parallel Operation of Modules or Elements

Energy storage systems use electrical converters for charging and discharging energy storage elements. In order to obtain greater power of the converters, parallel operation of units is used. For application at high input voltages, converters connected in series along the input are used. The converters are most often resonant converters operating on a common load and with dephased controls - multiphase systems. The paper presents investigate different configurations of connecting converters or elements to obtain a certain power output by using elements or units with less power. Simulation results are shown.
Optimizing Charging Time Periods of Electric Vehicles with Reinforcement Learning

The increasing integration of electric vehicles into the grid leads the focus of research to Demand Side Management and thus the control of charging processes. Mainly complex and hardly realizable coordination strategies have been investigated so far. They often involve high efforts in data measurement, collection and communication. We therefore investigate the optimal design of fixed charging time periods as a counter approach and apply them in a grid simulation. In this paper we present a Reinforcement Learning Agent to derive these charging time periods. Our case study shows that today's curtailment strategies of distribution grid operators can increase asset utilization up to 145 % while our optimized strategy can decrease it to 40 % compared to the unregulated case.
ML-Aided SOP Compensation to Increase Key Exchange Rate in QKD Systems

Secure communications have become a requirement for virtually all kind of applications. Currently, two distant parties can generate shared random secret keys by using public key cryptography. However, quantum computing represents one of the greatest threats for the finite complexity of the mathematics behind public key cryptography. In contrast, Quantum Key Distribution (QKD) relies on properties of quantum mechanics, which enables eavesdropping detection and guarantees the security of the key. Among QKD systems, polarization encoded QKD has been successfully tested in laboratory experiments and recently demonstrated in closed environments. In this paper, we propose a Machine Learning (ML) -based polarization tracking and compensation that is able to keep shared secret key exchange to high rates even under large fiber stressing events. Exhaustive results using both synthetic and experimental data show remarkable performance, which can simplify the design of both quantum transmitter and receiver, as well as enable the use of aerial optical cables, thus reducing total QKD system cost.
CNN-Powered Monument Detection Webapp for Preserving India’s Cultural Heritage

Particularly in India, where a wide variety of monuments and historical sites are dispersed throughout the nation, cultural heritage protection has recently gained importance. This study introduces a web application for monument discovery powered by Convolutional Neural Networks (CNN) that provides a variety of elements that will aid in protecting India’s cultural heritage. CNNs are used by the app as the deep learning technology to precisely recognize and classify monuments based on photographs that user’s input. The web app’s user-friendly layout makes it accessible to those with little to no technological knowledge. It also provides a number of helpful features, such as local weather predictions and temperature information for users at the monument site. Additionally, the app has an interactive map that shows the monument’s precise location and address. When visiting historical places, these aspects help to provide guests a more thorough and immersive experience. The outcomes demonstrate how effective the CNN-based web application is at classifying monuments. A sizable collection of images of famous sites in India were used to test the proposed method. The system may be used for cultural preservation, tourism, and education. Overall, this study provides a novel approach for locating monuments and significantly contributes to preserving India’s rich cultural history. The accuracy of the proposed model is 92.14% in predicting monuments.
Impact of Concentration Variations in P3HT: PCBM on Power Conversion Efficiency of Inverted Organic Solar Cell

In this paper, the solution dependent study on polymer: fullerene based bulk hetero-junction (BHJ) inverted organic solar cell (OSC) has been presented. It has been found that, in fabricated inverted OSC the power conversion efficiency (PCE) is strongly influenced by the weight ratio of donor and acceptor materials. Experimental results show that by modifying the weight ratio of poly (3-hexylthiophene) and phenyl C61 butyric acid methyl ester (P3HT: PCBM) in BHJ solution from 1.0:1.0 to 1.0:0.8, the PCE has been improved by ~40%. It has also been observed that, changes in blending ratio substantially increases the fill factor, open circuit voltage, and the short circuit current density of the device. Furthermore, with a weight ratio of 1.0:0.8, external quantum efficiency and the absorption spectrum also improves.