Miracle-dz/UniFAR-FTUs huggingface dataset:
Ontology Based Information Extraction -A Review

The indexing based by ontology us not in biomedicine and used to retrieve the data in an efficient manner. By using ontology based method it improves the excellence of the effect and also make simpler for the user interaction and aware of the complexity. Ontology creation is done by using many tools. The most recently used tools are Protege, OWLGrED, TopBraid and SWOOP. Ontology can be used in many of the fields such as enterprise system, health, tourism, agriculture, education, agent processing system and biomedical domain, etc. Many researchers designed ontology for their research either manually, automatic or semi-automatic way to improve the retrieval or extraction accuracy. This paper presents the overview of these three types of ontologies designed for the domains such as Agriculture, Biomedical and Health, Education and Tourism. This paper also presents the tremendous growth of ontology based research in the last five years and reviewed the most cited research papers which really pave the way to the budding researchers for capturing the knowledge in ontology.
Designing Communication Topologies for Optimal Synchronization Trajectories of HomogeneousLinear Multi-Agent Systems

In this paper the synchronization of homogeneous linear multi-agent systems is considered. In this process, all agents are required to converge to a common trajectory called synchronization trajectory. Without synchronization every sin- gle agent would have followed its own autonomous trajectory determined by its initial state. Hence, we interpret the initial states of the agents as their respective preferences. We define a cost function that penalizes the compromise each agent has to make when the synchronization trajectory differs from the agent's preferred trajectory. Using classical synchronization controllers, the synchronization trajectory essentially depends on the communication topology and the initial states of the agents. Therefore, we pose optimization problems concerned with finding the communication topology yielding minimal cost, i.e. the synchronization trajectory that constitutes an optimal compromise forall agents. In this respect, we minimize the cost for a given initial state, for the worst-case as well as for the average over all admissible initial states of the agents. The introduced minimization problems are reformulated as semidefinite programs so that they can be efficiently solved. A numerical example illustrates the results.
Development and Operation Control of a Switched-Reluctance Motor Driven Flywheel

This paper presents the development of a switched-reluctance machine (SRM) driven flywheel system, its charging/discharging controls, and performance evaluations. By properly setting and tuning the winding commutation instant, the same SRM can be operated as a motor and a generator in the SRM-driven flywheel system with satisfactory performances. Moreover, the hysteresis current controlled pulsewidth modulation scheme with hard freewheeling and the commutation angle dynamic shift controller are proposed to enhance the flywheel discharging generating performance. For interfacing the flywheel system to the dc microgrid, a half-bridge bidirectional dc–dc interface converter is developed. It is operated in a buck mode under flywheel charging and in a boost mode under flywheel discharging. The dynamic modeling and controller design are conducted to yield well-regulated dc output voltage under varying flywheel-driven switched-reluctance generator (SRG) speed. Finally, the measured results are given to verify the performances of the established flywheel and its interconnected operation to a wind SRG-based dc microgrid.
Heterogeneous Map Reduce Scheduling Using First Order Logic

The objective of the research is to implement big data for a Map Reduce technique that helps to process queries from the user in an efficient way. The main theme of the map reduce is to optimize the time factor. First Data collection of the documents, secondly mapping to the systems, third tries to do the shuffling and at least reduce the information. Here the same Map Reduce is used to process large amount of data through the servers in a parallel and distributed manner. In the research work, this is the first time, The First order logic method is applied for map reduces scheduling for heterogeneous data. The results are obtained in an optimized way through the above said methodology. The paper concludes that the resources are utilized very efficiently and work has done in a faster mode and with greater accuracy.
Weakly- and Semi-supervised Faster R-CNN with Curriculum Learning

Object detection is a core problem in computer vision and pattern recognition. In this paper, we study the problem of learning an effective object detector using weakly-annotated images (i.e., only the image level annotation is given) and a small proportion of fully-annotated images (i.e., bounding box level annotation is given) with curriculum learning. Our method is built upon Faster R-CNN. Different from previous weakly-supervised object detectors which rely on hand-craft object proposals, the proposed method learns a region proposal network using weakly- and semi-supervised training data. And the weakly-labeled images are fed into the deep network in a meaningful order which illustrates from easy to gradually more complex examples with curriculum learning. We name the Faster R-CNN trained using Weakly- And Semi-Supervised data with Curriculum Learning as WASSCL R-CNN. The WASSCL R-CNN is validated on the PASCAL VOC 2007 benchmark, and obtains 90% of a fully-supervised Faster R-CNN's performance (measured using mAP) with only 15% of fully-supervised annotations together with weak supervision. The results show that the proposed learning framework can significantly reduce the labeling efforts for obtaining reliable object detectors.
A Control Strategy for Variable-Speed Variable-Pitch Wind Turbines within the regions of partial-and full-load operation without wind speed feedback

In this paper, the control of a variable-speed variable-pitch wind turbine in the whole wind speed range is addressed, without any feedback measurement of wind speed. An aerodynamic torque observer is adopted to ensure the tracking of the Maximum Delivered Power in the partial-load region, while a novel wind speed observer is proposed for power regulation in the full-load region, along with a novel sliding surface ensuring finite-time set-point stabilization of the speed tracking error. Transitions between regions can be seen as changes in the reference to be tracked by the generator electrical torque and by the blades pitch angle. The proposed control solution has been validated on the National Renewable Energy Laboratory (NREL) $5-MW$ three-blade wind turbine model.
Find it! Fraud Detection Contest Report

This paper describes the ICPR2018 fraud detection contest, its data set, its evaluation methodology, as well as the different methods submitted by the participants to tackle the predefined tasks. Forensics research is quite a sensitive topic. Data are either private or unlabeled and most of related works are evaluated on private datasets with a restricted access. This restriction has two major consequences: results cannot be reproduced and no benchmarking can be done between every approach. This contest was conceived in order to address these drawbacks. Two tasks were proposed: detecting documents containing at least one forgery in a flow of documents and spotting and localizing these forgeries within documents. An original dataset composed of images and texts of French receipts was provided to participants. The results they obtained are presented and discussed.
Malware Detection using Opcode Trigram Sequence with SVM

Malicious software also known as “Malware” is software that uses legitimate instructions or code to perform malicious actions. Malware poses a major threat for computer security and information security in general. Over the years, malware has evolved to the point that a single malware specimen can have hundreds or maybe thousands of variants using polymorphic and metamorphic transformation to change the signature of the malware variant in propagation. The common signature-based malware detection methods are no longer robust to detect these variants due to the alteration of code. Static analysis is required to obtain these signatures and anti-virus companies are required to propagate these signature updates to their software. A faster detection method is needed to compensate the exponentially increasing number of malware variants. Machine learning is a trending approach for malware detection. This removes the need to use signature-based detection and is also faster. Software companies do not need to propagate signatures as often. Machine learning algorithms using opcode sequences can recognise patterns in the malicious code that are not present in common signatures and classify them more efficiently. Therefore, a machine learning approach for malware detection should be adopted for faster and more efficient detection. Most research in malware detection using machine learning used static attributes such as network connections, processes spawned, hashes, etc., that were not that robust to changes. In this paper we introduced our novel approach in using trigrams and PE file attributes as features for malware detection. We took a text mining approach to make our detection method more robust to polymorphism and metamorphism. The instruction sequence for critical code in malware on the assembly level is basically the same across malware families. We used opcode trigram sequences as the main feature for our machine learning algorithm. We used Support Vector Machine(SVM) as our classifying algorithm which is a discriminative classifier model that gives a definite decision whether the predicted outcome belongs to the learned class or not. The above shows our novel approach that enabled us to get higher detection rates with less features.
Robonomics Based on Blockchain as a Principle of Creating Smart Factories

The paper describes work organization methods for manufacturing with plenty of autonomous agents with the help of market mechanisms what authors call robot economy. As a technological basis, the application of decentralized technologies is presented and justified: Blockchain technologies and smart contracts of the Ethereum network. The paper is an extension of the article “Blockchain-based protocol of autonomous business activity for multi-agent systems of UAVs” and describes in more detail the architecture of the robots economy protocol. The main attention is paid to the protocol functionality and its basic elements. There is a description of the architecture of an autonomous agent working on the principles of robot economy in the article. The article shows how the work of a smart factory is organized. As a result, the experience of approbation of the presented methods in various projects and experiments is presented.
Engineering Complex Safety Critical Software Systems: Abstraction and Concreteness

Concrete approaches and Formal methodologies are required to achieve the quality and assurance level in engineering complex safety critical systems. The specificity and opportunity that inspired this work is the development of a new-generation Air Traffic Control System- called 4-Flight. The paper will give the context, the key factors, the challenges and methods to achieve concrete and safe results in a large scale project.
Deep Learning Based Reliable and Intelligent Chaotic OFDM Communications for Cognitive Radio System

In this paper, we present a deep learning based modulation scheme for chaotic orthogonal frequency division multiplex (OFDM) transmissions over non-contiguous frequency bands of cognitive radio systems. In cognitive radio systems, the users access the spectrum bands dynamically and the corresponding channel characteristics also changes. Different from the traditional modulation scheme that uses the fixed mapping pattern to modulate the signals, we propose to apply the deep learning method to build up the constellations intelligently. Based on the autoencoder architecture of deep learning, we construct the constellation mapping and demapping patterns adaptively with the aim to minimize the bit error rate (BER) over the dynamically changing non-contiguous channels. Simulation results over additive white Gaussian noise (AWGN) channel and Rayleigh fading channel show that our proposed system achieves better BER performances for legitimate receivers when compared with the conventional modulation schemes. In addition, the presented scheme remains the high security performance thanks to the usage of the chaotic sequences.
Anacardium microcarpum Promotes Neuroprotection Dependently of AKT and ERK Phosphorylation but Does Not Prevent Mitochondrial Damage by 6-OHDA

Parkinson's disease is a degenerative and progressive illness characterized by the degeneration of dopaminergic neurons. 6-hydroxydopamine (6-OHDA) is a widespread model for induction of molecular and behavioral alterations similar to Parkinson and has contributed for testing of compounds with neuroprotective potential. The Brazilian plant Anacardium microcarpum is used in folk medicine for treatment of several illnesses; however, the knowledge about toxicology and biological effects for this plant is very rare. The neuroprotective effect from hydroalcoholic extract and methanolic and acetate fraction of A. microcarpum on 6-OHDA-induced damage on chicken brain slices was investigated in this study. 6-OHDA decreased cellular viability measured by MTT reduction assay, induced lipid peroxidation by HPLC, stimulated Glutathione-S-Transferase and Thioredoxin Reductase activity, and decreased Glutathione Peroxidase activity and the total content of thiols containing compounds. The methanolic fraction of A. microcarpum presented the better neuroprotective effects in 6-OHDA-induced damage in relation with hydroalcoholic and acetate fraction. The presence of AKT and ERK1/2 pharmacological inhibitors blocked the protective effect of methanolic fraction suggesting the involvement of survival pathways in the neuroprotection by the plant. The plant did not prevent 6-OHDA autoxidation or 6-OHDA-induced mitochondrial dysfunction. Thus, the neuroprotective effect of the methanolic fraction of A. microcarpum appears to be attributed in part to chelating properties of extract toward reactive species and is dependent on ERK1/2 and AKT phosphorylation. This study contributes to the understanding of biochemical mechanisms implied in neuroprotective effects of the vegetal species A. microcarpum.
Joint Transceiver Design for Decoupled Full-Duplex Wireless Powered Communication Networks with Lens Antenna Arrays

In this paper, we consider the joint design of power allocation, beam selection and precoding matrices for decoupled full-duplex (FD) wireless powered communication networks (WPCN) with lens antenna arrays. Firstly, we formulate the optimization problem to maximize the minimum rate of all users. The formulated problem contains complicated discrete binary constraints, as well as the highly coupled nonconvex objective function. Then we introduce auxiliary variables and transform the original problem into a more tractable form. Finally, we propose an efficient algorithm to solve the transformed problem based on the penalty dual decomposition (PDD) method. Simulation results demonstrate that our proposed algorithm approaches the optimal performance of the full-duplex fully digital transceiver scheme, and outperforms the competing scheme.11This work was supported by the National Science Foundation of China (NSFC) under Grant 91538103 and the Fundamental Research Funds for the Central Universities 2018FZA5006.
Saving Groups and Social Structure in a Rural Village in Kurunegala, Sri Lanka

The purpose of this paper is twofold: (1) to analyse how the activities of self-help group (SHG)-type ‘Saving Groups’, a type of microfinance, have changed the social structure of a village in rural Sri Lanka and (2) to evaluate whether these activities have reached the poorest of the village as microfinance sometimes fails to include the least privileged. To achieve the aforementioned objectives, the researcher surveyed a whole village in Polpithigama Divisional Secretariat Division, Kurunegala District, Sri Lanka, where ‘Saving Groups’ have been active for a long time under the auspices of a non-government organisation. The implementing organisation of the ‘Saving Groups’ claims that exclusion does not exist in the village as it has tried to carefully include the poorest of the village during the formation of the ‘Saving Groups’. I used social network analysis to visualise the social structure of the village and conducted qualitative interviews of key informants. The survey results reveal that the structure of the village has changed as a result of the implementation of the studied interventions in the village. It was found that, although the poorest were unable to voice their opinions at meetings organised by the local community organisations, their financial situation improved as a result of the activities; their social position in the village improved and discrimination against them reduced. Nonetheless, there are two distinct geographic spaces in the village, and different castes live separately from one another. Because of these spaces, the lower caste had been excluded from availing of the services provided by ‘Saving Groups’ through their activities on a long-term basis. These effects were unforeseen and unintended by the implementing agency of the programme. The success of the activities has been confirmed by other surveys conducted by the author in this area of study.
Development of conductive back-sheet for manufacture of PV modules with back-contact cells

Modules with back-contact cells are considered to be amongst the modules with the highest power output when compared to modules manufactured with more conventional cells [1]. Back-contact cells include MWT and IBC, with IBC cells showing the highest performance in the record tables [2]. Manufacturing modules with back-contact cells can be simplified by using a conductive back-sheet [3]. In this paper development of the conductive back-sheet with the necessary properties for high quality module manufacture is introduced. Innovative solutions for improving the quality of the conductive back-sheet will be discussed. A road-map for further developments will be presented.
Development of close-to-zero damage rear heterojunction stripe contacts and compatible front metallization approaches for hybrid heterojunction solar cells

A close-to-zero damage rear-side heterojunction stripe contact has been realized using laser ablation, which involves the optimization of (1) laser fluence and (2) wet-chemical damage removal processes. The saturation current density (j0) values underneath the heterojunction stripe contacts as well as underneath the passivated regions have been extracted using intensity-dependent photoluminescence (PL) imaging and PL image analysis using Griddler AI. Thus the area-fraction and pitch of rear-side heterojunction stripe-contacts can be optimized by means of numerical computer simulation. The measured implied open circuit voltages and extracted saturation current densities of the heterojunction stripe-contacts (j0,cont ≈ 20 fA cm−2) show that close-to-zero damage rear-side heterojunction stripe contacts can be realized under optimized conditions. For the front-side metallization scheme, a low-temperature metallization approach, i.e. light-induced plating thickened by electroplating, is investigated to avoid degradation of the heterojunction layers by conventional high-temperature screen printing. It’s demonstrated that plating can achieve comparable efficiencies with respect to conventional screen printing and can be applied directly to hybrid heterojunction solar cells using a rear-emitter configuration. For front-emitter configurations, a thin evaporated metal contact layer may be used as the seed layer for subsequent electroplating.
A Moving Horizon Estimator for the Speed and Rotor Position of a Sensorless PMSM Drive

In this paper, a systematic model-based approach for the state estimation of permanent magnet synchronous motors to build up sensorless drives is presented. A moving horizon estimation algorithm, an optimization-based scheme that yields excellent performance, is applied for the speed and position estimation. Under mild assumptions, an optimal problem of equality constrained quadratic programing type has been solved each iteration. The steady state and transient performance of the proposal are evaluated considering different horizon lengths. The influence of the latter is analyzed in terms of estimation error and computational burden. Besides, the behavior of the scheme is analyzed with different current control loops, i.e., with conventional proportional integral regulators and with model predictive control, highlighting its adaptability and good response in control schemes with completely different features. The performance is compared with that of an extended Kalman filter and the results prove the effectiveness of the proposed control scheme in terms of the estimation accuracy and robustness even at low speed operation. The algorithm has been efficiently implemented showing the real-time feasibility of the proposed approach up to 10-kHz sampling rate.
Comparative Analysis of Temperature Prediction Using Regression Methods and Back Propagation Neural Network

Weather forecasting is very important for our day to day life as the prediction of climatic parameters like temperature, humidity, rainfall, etc. is important for agriculture, forestry, commercial companies etc. It is known that the climatic conditions are irregular in nature and may be often unpredictable. In previous years most of the work was done on finding a linear equation to predict climatic parameters. It is now known that climatic conditions are nonlinear in nature and thus Artificial Neural Network is now considered to be an efficient nonlinear method for weather prediction. In this paper a comparative analysis has been done between regression methods and nonlinear method like artificial neural network to analyse the difference between the performance of linear and nonlinear models for prediction of temperature.
Control of an induction motor under uncertainty conditions

During operation of the induction electric drive, its parameters included in the description of the mathematical model can change significantly. These processes involve heating of squirrel cage windings of the rotor, the saturation of the magnetic circuits of the rotor and the stator, and changes the given moment of inertia. The article considers the problem of tracking for a given angular velocity of the rotor under conditions of the uncertainty of parameters of the mathematical model, describing the behavior of the electric motor, and influence of uncontrolled external disturbances. The using of nonlinear discontinuous control law provides exponential convergence of tracking error to zero.
Modelling and FEA Analysis of Partial Discharge Phenomena in a Spherical Void within Solid Dielectric Material

Partial Discharge (PD) phenomena is always related to degradation of insulation; affecting the insulation performance within an electrical equipment. Modelling of PD permits an improved consideration of the occurrences. In this review paper, a simulation model has been considered for spherical cavity within a dielectric material which is homogenous. The model is simulated using method called as Finite Element Method (FEM) with the help of ANSYS-software. PD action inside a void is subjective to several parameters. Here applied voltage is considered as one of its parameter. Different PD patterns are obtained when subjected to different applied volatages and leads to failure of insulation. The applied voltage that is considered here is AC sinusoidal voltage. Results obtained improves the understanding on behaviour of partial discharge under applied voltage; also assist in insulation diagnostics.