nils-herrmann/clean_scirepeval huggingface dataset:
On the Use of Low Cost Sensors for the Implementation of a Real Time Air Pollution Monitoring System Using Wireless Sensor Networks
Venue: 2018 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)
Year: 2018

In this paper, we investigate the use of low cost wireless sensor networks for the development of an environmental monitoring system that can be used to visualize air pollutants in real time by means of a web application. Measuring nodes were implemented, using low cost analog and digital sensors, for collecting data about air pollutants such as CO2 and dust particles concentrations present in the environment. The system works in such a way that the values measured by the sensor nodes are encoded and sent via Wireless to the Gateway (Raspberry pi) by means of Xbee pro S1 modules. The data are decoded and stored in a local database. Afterwards, a web application makes a PHP connection with the database, and data are plotted in real time, using the HighChart JavaScript library with AJAX methods and JSON notation. To check the functioning of the system and to compare the behavior of the sensors, measurements of the concentrations of CO2 and dust particles were taken outdoors and inside an underground parking lot.
Behavior of concrete beams reinforced with GFRP BARS
Year: 2011

The use of FRP bars in place of steel rods in concrete structures plays a significant role due to non corrosive nature. These materials possess high tensile strength and low modulus of elasticity and exhibit a linear stress strain response up to the failure. The present study focuses on gaining an insight into the behavior of concrete beams reinforced with GFRP bars subjected to two point loading system by varying the grade of the concrete and diameter of the bar. The modes of failure and the crack width at each stage of loading were observed. The results of load deflection and load crack width characteristics are discussed. The crack pattern of the GFRP beams under loading is also reported. The comparisons between the flexural capacity of GFRP beams and steel beams from theoretical analysis and GFRP beams with that of the experimental were done. The structural behavior of tested GFRP beams is validated by developing FE models using the ANSYS software.
The deformation and failure mechanism of cylindrical shell and square plate with pre formed holes under blast loading
Year: 2020

The deformation and failure mechanism of cylindrical shells and square plate with pre formed holes under blast loading were investigated numerically by employing the Ansys 17.0 and Ls Dyna 971. To calibrate the numerical model, the experiments of square plates with pre formed circle holes were modeled and the numerical results have a good agreement with the experiment data. The calibrated numerical model was used to study the deformation and failure mechanism of cylindrical shells with pre formed circle holes subjected to blast loading. The structure response and stress field changing process has been divided into four specific stages and the deformation mechanism has been discussed systematically. The local and global deformation curves, degree of damage, change of stress status and failure modes of cylindrical shell and square plate with pre formed circular holes are obtained, compared and analyzed, it can be concluded as: (1) The transition of tensile stress fields is due to the geometrical characteristic of pre formed holes and cylindrical shell with arch configuration; (2) The existence of pre formed holes not only lead to the increasing of stress concentration around the holes, but also release the stress concentration during whole response process; (3) There are three and two kinds of failure modes for square plate and cylindrical shell with pre formed holes, respectively. and the standoff distance has a key influence on the forming location of the crack initiating point and the locus of crack propagation; (4) The square plate with pre formed holes has a better performance than cylindrical shell on blast resistant capability at a smaller standoff distance, while the influence of pre formed holes on the reduction of blast resistant capability of square plate is bigger than that of cylindrical shell.
VOICE2TUBA: transforming singing voice into a musical instrument
Venue: Multimedia Tools and Applications
Year: 2016

In this paper, a scheme to synthesize and convert singing voice into tuba sound is presented. First, our method estimates the fundamental frequency (F0) and the aperiodicity of a monophonic audio signal, in order to obtain the pitch and volume variations of human voice. Then, the parameters extracted are used to generate a musical excerpt emulating a certain musical instrument (tuba) in such a way that the melody resembles the original sung song. To this end, two different generation approaches are devised. One of them is based on additive signal synthesis from harmonic amplitudes. The other one converts the F0 curve into a MIDI stream, in order to allow the play back with a virtual tuba.
YOLO ASC: You Only Look Once And See Contours
Venue: 2020 International Joint Conference on Neural Networks (IJCNN)
Year: 2020

YOLO is a useful, one stage tool for object detection and classification. In this paper, we consider the application of grocery product detection. The grocery stores have a significant amount of product classes, so it is beneficial to postpone the classification into a second, specialized neural network with a higher capacity. Extracting bounding boxes for a classification network is not precise enough as the detected area includes redundant information about the background. We propose YOLO ASC, which, for rectangular based objects, detects bounding boxes together with object contour using a quadrangular. This approach allows detecting objects more accurately and without the background. For the quadrangular detection functionality, YOLO ASC shares the feature maps that are already present in the network, and therefore its inference time is almost identical to the original YOLO. YOLO reaches high detection precision by using YOLO apriori knowledge, anchors extracted from data. In this work, we present two experiments where we demonstrate that YOLO ASC training converges faster due to the symbiosis between the bounding box detection and quadrangular detection. Finally, we propose a tool for generating synthetic datasets with quadrangular labels that is helpful for transfer learning.
Integrating LEO Satellites and Multi UAV Reinforcement Learning for Hybrid FSO/RF Non Terrestrial Networks
Venue: ArXiv
Year: 2020

A mega constellation of low altitude earth orbit (LEO) satellites (SATs) and burgeoning unmanned aerial vehicles (UAVs) are promising enablers for high speed and long distance communications in beyond fifth generation (5G) systems. Integrating SATs and UAVs within a non terrestrial network (NTN) in this article we investigate the problem of forwarding packets between two faraway ground terminals through SAT and UAV relays using either millimeter wave (mmWave) radio frequency (RF) or free space optical (FSO) link. Towards maximizing the communication efficiency, the real time associations with orbiting SATs and the moving trajectories of UAVs should be optimized with suitable FSO/RF links, which is challenging due to the time varying network topology and a huge number of possible control actions. To overcome the difficulty, we lift this problem to multi agent deep reinforcement learning (MARL) with a novel action dimensionality reduction technique. Simulation results corroborate that our proposed SAT UAV integrated scheme achieves 1.99x higher end to end sum throughput compared to a benchmark scheme with fixed ground relays. While improving the throughput, our proposed scheme also aims to reduce the UAV control energy, yielding 2.25x higher energy efficiency than a baseline method only maximizing the throughput. Lastly, thanks to utilizing hybrid FSO/RF links, the proposed scheme achieves up to 62.56x higher peak throughput and 21.09x higher worst case throughput than the cases utilizing either RF or FSO links, highlighting the importance of co designing SAT UAV associations, UAV trajectories, and hybrid FSO/RF links in beyond 5G NTNs.
A Thorough Study of LoRaWAN Performance Under Different Parameter Settings
Venue: IEEE Internet of Things Journal
Year: 2020

LoRaWAN is an emerging low power wide area network (LPWAN) technology, which is gaining momentum thanks to its flexibility and ease of deployment. Conversely to other LPWAN solutions, LoRaWAN indeed permits the configuration of several network parameters that affect different network performance indexes, such as energy efficiency, fairness, and capacity, in principle making it possible to adapt the network behavior to the specific requirements of the application scenario. Unfortunately, the complex and sometimes elusive interactions among the different network components make it rather difficult to predict the actual effect of a certain parameters setting, so that flexibility can turn into a stumbling block if not deeply understood. In this article, we shed light on such complex interactions for a single gateway (GW) system by analyzing the effect of some built in features and configurations, including the GW's limitations in terms of duty cycle and the number of parallel reception paths, the number of allowed retransmissions for confirmed traffic, and the preconfigured data rate used in downlink transmissions. The simulation based analysis reveals various tradeoffs and highlights some inefficiencies in the design of the LoRaWAN standard. Furthermore, we show how significant performance gains can be obtained by wisely setting the system parameters, possibly in combination with some novel network management policies (e.g. enabling selective prioritization of downlink transmissions at the GW)
Determination of Heavy Metal Ions and Organic Pollutants in Water Samples Using Ionic Liquids and Ionic Liquid Modified Sorbents
Venue: Journal of analytical methods in chemistry
Year: 2019

Water pollution, especially by inorganic and organic substances, is considered as a critical problem worldwide. Several governmental agencies are listing an increasing number of compounds as serious problems in water because of their toxicity, bioaccumulation, and persistence. In recent decades, there has been considerable research on developing analytical methods of heavy metal ions and organic pollutants from water. Ionic liquids, as the environment friendly solvents, have been applied in the analytical process owing to their unique physicochemical properties. This review summarizes the applications of ionic liquids in the determination of heavy metal ions and organic pollutants in water samples. In addition, some sorbents that were modified physically or chemically by ionic liquids were applied in the adsorption of pollutants. According to the results in all references, the application of new designed ionic liquids and related sorbents is expected to increase in the future
PENDIDIKAN KARAKTER DALAM PROGRAM ASRAMA BAHASA ARAB MENGHADAPI ERA REVOLUSI INDUSTRI 4.0 DI MAN 3 PALEMBANG
Year: 2020

Pada Era 4.0, Indonesia dihadapkan dengan derasnya arus globalisasi yang merupakan proses tatanan yang dimiliki masyarakat secara mendunia tidak mengenal batas wilayah. Hal ini berlangsung juga dibidang Pendidikan di Indonesia yang dianggap sebagai penyebab keterpurukan bangsa dengan alasan pendidikan kita tidak menghasilkan SDM yang baik.karakter bangsa merupakan suatu yang penting untuk memajukan suatu bangsa. Untuk mengatasi hal tersebut perlu adanya perubahan dalam pembelajaran tidak terkecuali dalam proses program pembelajaran bahasa arab. tujuan penelitian ini untuk mendeskripsikan penerapan pendidikan karakter dalam program pembelajaran bahasa arab.adapun metode menggunakan kualitatif deskriptif dengan melakukan pengumpulan data melalui observasi, wawancara. Sedangkan analisis data menggunakan teori milles dan huberman yaitu reduksi data, penyajian data dan verifikasi. Di Madrasah Aliyah Negeri (MAN) 3 Palembang melakukan penerapan pendidikan karakter di asrama melalui bahasa arab melalui bebera kegiatan kajian kitab, muhadoroh, al barzanji, tahfidz al qur'an, membaca surah yasin, bimbel malam, dan pentas seni. Adapun karakter yang dapat dihasilkan dari kegiatan kegiatan tersebut adalah religious, bertanggung jawab, disiplin, percaya diri, kreatif, komunikatif, rasa ingin tahu, sopan dan santun, sabar, ikhlas, rendah hati, mandiri, jujur, menghargai, kreatif dan inovatif
Design of Environment Temperature and Humidity Acquisition System of Internet of Things Based on Cloud Platform
Year: 2019

With the rapid development of science and technology, the collection of temperature and humidity has been widely used in various industries. The transportation and storage of special medical supplies, seafood products and dairy products are strict to the temperature and humidity. This paper introduces a cloud platform based on STM32 microcontroller to control chip acquisition system design scheme, the data of temperature and humidity temperature and humidity acquisition system is described in detail design, hardware design, software design. Paper the temperature and humidity acquisition system development present situation, through to the existing collection system was studied, and identify the feasibility scheme to ensure data accuracy, real time detection system of collecting the data of temperature and humidity, easy to check. The alarm function of the acquisition system is designed according to the research requirements and practical analysis. Finally, the design requirements are achieved through systematic testing and multiple optimization.
IGNNITION: Bridging the Gap Between Graph Neural Networks and Networking Systems
Venue: ArXiv
Year: 2021

Recent years have seen the vast potential of Graph Neural Networks (GNN) in many fields where data is structured as graphs (e.g. chemistry, recommender systems) In particular, GNNs are becoming increasingly popular in the field of networking, as graphs are intrinsically present at many levels (e.g. topology, routing) The main novelty of GNNs is their ability to generalize to other networks unseen during training, which is an essential feature for developing practical Machine Learning (ML) solutions for networking. However, implementing a functional GNN prototype is currently a cumbersome task that requires strong skills in neural network programming. This poses an important barrier to network engineers that often do not have the necessary ML expertise. In this article, we present IGNNITION, a novel open source framework that enables fast prototyping of GNNs for networking systems. IGNNITION is based on an intuitive high level abstraction that hides the complexity behind GNNs, while still offering great flexibility to build custom GNN architectures. To showcase the versatility and performance of this framework, we implement two state of theart GNN models applied to different networking use cases. Our results show that the GNN models produced by IGNNITION are equivalent in terms of accuracy and performance to their native implementations in TensorFlow.
A K means Clustering Algorithm based on Hierarchy
Year: 2010

Probabilistic hierarchical clustering based on document information quantity.From an information theory angle,we study a K means clustering algorithm based on hierarchy in this paper.Firstly,this algorithm classifies documents into one or more predefined categories using hierarchical methods,the total classified number is taken for the number of clusters.Secondly,it uses k means to modify the clustering results.Experimental results showed that these algorithms have higher mining efficiency in execution time,memory usage and CPU utilization than most current ones like k means.
Green and Facile Synthesis of Metal Organic Framework Cu BTC Supported Sn (II) Substituted Keggin Heteropoly Composites as an Esterification Nanocatalyst for Biodiesel Production
Venue: Frontiers in Chemistry
Year: 2020

In the present study, metal organic framework Cu BTC supported Sn (II) substituted Keggin heteropoly nanocomposite (Sn1.5PW/Cu BTC) was successfully prepared by a simple impregnation method and applied as a novel nanocatalyst for producing biodiesel from oleic acid (OA) through esterification. The nanocatalyst was characterized by Fourier transform infrared spectrometry (FTIR) wide angle X ray diffraction (XRD) scanning electron microscopy (SEM) transmission electron microscopy (TEM) nitrogen adsorption desorption, thermogravimetrics (TG) and NH3 temperature programmed desorption (NH3 TPD) Accordingly, the synthesized nanocatalyst with a Sn1.5PW/Cu BTC weight ratio of 1 exhibited a relatively large specific surface area, appropriate pore size, and high acidity. Moreover, an OA conversion of 87.7% was achieved under optimum reaction conditions. The nanocatalyst was reused seven times, and the OA conversion remained at more than 80% after three uses. Kinetic study showed that the esterification reaction followed first order kinetics, and the activation energy (Ea) was calculated to be 38.3 kJ/mol.
Fuzzy image fusion algorithm based on SGNN
Year: 2009

The optimized order and number for current M L optimization algorithms are presented;and the composite processing method of clipping first and optimizing again for the generated SGNN network is proposed.Then,after clustering the original image element,the proposed composite processing method is added to get better element clustering.Finally,aiming at the large grayscale differences of every class's center and the different class number because of the different grayscale characteristics of images acquired from different sensors,a modified fusion method is proposed.Simulation result verifies the superiority of the proposed fuzzy fusion algorithm.
Learning traffic as a graph: A gated graph wavelet recurrent neural network for network scale traffic prediction
Year: 2020

Network wide traffic forecasting is a critical component of modern intelligent transportation systems for urban traffic management and control. With the rise of artificial intelligence, many recent studies attempted to use deep neural networks to extract comprehensive features from traffic networks to enhance prediction performance, given the volume and variety of traffic data has been greatly increased. Considering that traffic status on a road segment is highly influenced by the upstream/downstream segments and nearby bottlenecks in the traffic network, extracting well localized features from these neighboring segments is essential for a traffic prediction model. Although the convolution neural network or graph convolution neural network has been adopted to learn localized features from the complex geometric or topological structure of traffic networks, the lack of flexibility in the local feature extraction process is still a big issue. Classical wavelet transform can detect sudden changes and peaks in temporal signals. Analogously, when extending to the graph/spectral domain, graph wavelet can concentrate more on key vertices in the graph and discriminatively extract localized features. In this study, to capture the complex spatial temporal dependencies in network wide traffic data, we learn the traffic network as a graph and propose a graph wavelet gated recurrent (GWGR) neural network. The graph wavelet is incorporated as a key component for extracting spatial features in the proposed model. A gated recurrent structure is employed to learn temporal dependencies in the sequence data. Comparing to baseline models, the proposed model can achieve state of the art prediction performance and training efficiency on two real world datasets. In addition, experiments show that the sparsity of graph wavelet weight matrices greatly increases the interpretability of GWGR.
Studies on Isolation of Cellulose Fibres from Waste Plant Biomass
Year: 2012

Instytut Biopolimers and Chemical Fibres ul. M. Sklodowskiej Curie 19/27, 90 570 Lodz, Poland E mail: celuloza@ibwch.lodz.pl  This paper presents the results of cellulose fibre extraction from plant biomass including: rape, hemp and flax straws. The selected materials were treated by thermal/mechanical/ chemical method in order to remove non cellulosic components such as lignin, hemicellulose, and pectin as well as to obtain cellulosic material with suitable structure and properties necessary for the manufacture of nanofibres. It has been shown that the use of multistage chemical treatment using oxygen and peroxide compounds as delignifing agents for hemp and flax straws, allowed the safe removal of lignin and other non cellulosic components without degradation of the cellulose fibres obtained, while maintaining a polymerization degree above 1000 units. It was found out that it is possible to obtain cellulose fibres from hemp and flax straws (type retted flax straw fibre variety, flax straw oil variety) which can be processed into cellulose micro and nanofibres.
Candida albicans infection leads to barrier breakdown and a MAPK/NF kB mediated stress response in the intestinal epithelial cell line C2BBe1
Venue: Cellular microbiology
Year: 2016

Intestinal epithelial cells (IEC) form a tight barrier to the gut lumen. Paracellular permeability of the intestinal barrier is regulated by tight junction proteins and can be modulated by microorganisms and other stimuli. The polymorphic fungus Candida albicans, a frequent commensal of the human mucosa, has the capacity of traversing this barrier and establishing systemic disease within the host. Infection of polarized C2BBe1 IEC with wild type C. albicans led to a transient increase of transepithelial electric resistance (TEER) before subsequent barrier disruption, accompanied by a strong decline of junctional protein levels and substantial, but considerably delayed cytotoxicity. Time resolved microarray based transcriptome analysis of C. albicans challenged IEC revealed a prominent role of NF kB and MAPK signalling pathways in the response to infection. Hence, we inferred a gene regulatory network based on differentially expressed NF kB and MAPK pathway components and their predicted transcriptional targets. The network model predicted activation of GDF15 by NF kB was experimentally validated. Furthermore, inhibition of NF kB activation in C. albicans infected C2BBe1 cells led to enhanced cytotoxicity in the epithelial cells. Taken together our study identifies NF kB activation as an important protective signalling pathway in the response of epithelial cells to C. albicans.
Neural Correlates of Control of a Kinematically Redundant Brain Machine Interface*
Venue: 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)
Year: 2019

Brain machine interfaces (BMIs) use signals from the brain to control cursors or robotic arms, with potential applications for restoring the ability for users to interact with the physical world around them. BMIs that are kinematically redundant allow for many viable solutions for the same task. While natural motor control involves the coordinated movements of kinematically redundant limbs, it is unclear how the brain might control the redundant degrees of freedom (DOF) in a BMI. In this study, we analyze a previously collected dataset where a macaque controlled a 4 DOF virtual arm in 2D space. A Kalman filter was used to decode neural signals from motor cortices into the four joint angle velocities. The monkey was instructed to move the virtual arm from a center target to eight peripheral targets, distributed evenly around a circle in a self initiated center out task. The monkey was able to achieve high accuracy in the task in the first day, but reach times continued to decrease over learning and endpoint trajectories became more stereotyped. We found that the neural activity fired in more correlated patterns over days with increased firing rates, suggesting a consolidation of neural activity into a high level representation of the joint angles, optimizing endpoint control.