Reviewerly/exHarmony huggingface dataset:
Learning Framework for Pronoun Resolution Using Convolution Neural Network

The task of understanding grammatical entities in sentences is a very important part of preprocessing in natural language processing tasks. Named entity recognition is a subtask of information extraction. The fundamental goal of NER is to induce and classify the defined categories such as person names, organizations, locations, and other entities which might be the requirement of the application. Parts of speech are another important aspect of preprocessing required by many tasks in NLP. The main challenge in most reference resolution systems is having a pre-labeled dataset that has entities that are rich in features such as NER and POS tags.This research study proposes a learning-based outcome resolution of grammatical entities in a self-curated data set in this study. The suggested model, which includes six classes, learns from a hand-annotated corpus and determines different classes of input entities. This system is the first learning-based model for the provided corpus to approach comparable performance. It pledges and achieves a high performance as compared to a non-learning outcome. The proposed model's the main challenge is to anticipate the references among the entities which are the base for understanding the relationship between each other. Towards this, the proposed model curate sentences that have entities that require references to be resolved to help build the mentions for coreference resolution systems. To test the proposed hypothesis, a learning-based approach with word embedding and position embedding techniques is proposed to classify various types of nouns and pronouns. The pronouns in the dataset are linked with the nouns and their types by following the approach of the binding theory. The evaluation of the model's accuracy is robust, with an F1 score of 97.45, recall of 99.20, and precision of 95.75, and it identifies the correct references and compare it to a state-of-the-art model.
Secure, Scalable and Privacy Aware Data Strategy in Cloud

The enterprises today are faced with the tough challenge of processing, storing large amounts of data in a secure, scalable manner and enabling decision makers to make quick, informed data driven decisions. This paper addresses this challenge and develops an effective enterprise data strategy in the cloud. Various components of an effective data strategy are discussed and architectures addressing security, scalability and privacy aspects are provided.
Medium-Term Forecasting of Global Horizontal Solar Radiation in Brasov Using Multivariate Transformer

In solar radiation forecasting, changes in a variable such as diffused horizontal solar radiation, temperature, sunny days, and time may provide important details about global horizontal solar radiation behavior. Thus, multivariate global solar radiation forecasting is more attractive in renewable energy integration into the electric grid. With this motivation, we devoted this paper to propose a multivariate transformer-based forecasting model for global solar radiation forecasting. The proposed transformer-based global solar radiation forecasting model effectiveness was validated with the comparative investigation. The proposed multivariate Transformer forecasting model obtained an MSE of 0.3422. The prior existing forecasting models such as persistent, numerical weather prediction (NWP), Autoregressive integrated moving average (ARIMA), Backpropagation Network (BPN), Support Vector Machines (SVM), Recurrent Neural Network (RNN), Elman Neural Network (ENN), Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) based forecasting of global solar radiation is lack behind the multivariate transformer based forecasting in term of accuracy and convergence for the medium-term horizon based forecasting. Hence, the proposed forecasting model proves that it surpasses the prior forecasting model and aids in power system planning and energy marketing.
Advanced Controller for Single Axis Solar Tracking System

Society is more reliant on traditional energy sources, and the percentage of power use is rising every day. Continuation of this trend could lead to the demise of traditional energy sources within a few years. So, now would be the moment to use renewable energies. The most environmentally friendly and long-term renewable energy is the solar energy. It is possible to capture the energy of the sun by employing a solar panel. Many factors influence the rate at which a solar panel generates energy, like the irradiance of sunlight and the temperature of the material. Hence more sunshine the solar panel receives, the more power it generates. Because a sun's location in the sky varies throughout the day, a fixed solar panel can't detect the greatest amount of sunlight throughout the daylight hours. The solar panel must have an automatic tracking mechanism to guarantee that it receives the maximum amount of sunlight. This paper aims to design an autonomous solar tracking system and make the solar panel rotate depending on the sunlight direction. As an advanced controller for the tracking system, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is selected. The solar panel is tilted to the desired angle by this controller. The conventional Proportional Integral Derivative controller is also employed and compared with the ANFIS controller. The controller's performance is evaluated by comparing the controller's time characteristics and errors. In both set point tracking and disturbance rejection, the simulated results suggest that the ANFIS controller is the optimum choice for a solar tracking system.
Novel methods for measuring the shutter speed of film cameras using image processing

In photography, there are three exposure parameters: the shutter speed, the aperture, and the film sensitivity (ISO). In modern cameras, the camera body is responsible for setting the shutter speed (i.e., exposure time) and the ISO, while film cameras set only the shutter speed, therefore it is their main performance parameter. The shutter mechanism in most film cameras is mechanical, involving the use of springs. The springs wear in time, leading to losing the calibration of the shutter speed, thus the exposure time becomes longer than the designed value. The accurate measurement of the shutter speed is thus a very important performance evaluation process that allows a proper estimation of the camera's market value. This paper presents two innovative methods that allows the precise measurement of a camera's shutter speed using image processing. They involve the recording of the mechanical shutter during the exposure process using a high-speed camera, then applying the corresponding image processing algorithm on each of the recorded frames to automatically determine the exposure time. The paper presents the experimental setup, the image processing algorithms and the results obtained during testing.
MR-KClust: An efficient Map Reduce based clustering Technique

The world is rich in data, but efficient storage and processing of the data are of the utmost importance. As the data grows, processing it and applying algorithms to extract meaningful and hidden patterns efficiently becomes a challenge for the research community. Traditional clustering approaches, such as K Means and its variations, may not perform well when working with big data, that is, if the input data is large and has a high computational cost and slow speed Keeping this in mind, an efficient map-reduced clustering algorithm, named MR-KClust, has been designed It is a variant of the well-known k-means clustering, where a polynomial regression has been applied for the initial selection of centroids and algorithms. In the Map Reduce design of the algorithm, the data has been split, and each input split is provided to the mappers, where the map function calculates the distances from each sample to each centroid point and emits relabeled cluster ID as a key and data ID coordinates as a value. The combiner function combines samples with the same key for each machine and loads what the mapper emits. The performance of the algorithm as compared with the existing variants of K - Means outperformed the others with respect to the mean squared error and has a decent runtime.
High-speed Camera Motion Capture and Essence Extraction Algorithm in the Context of Feature Extraction

High-speed camera motion capture and essence extraction algorithm of regional dance in the context of the intangible cultural heritage feature extraction is designed and implemented in this paper. Painting can be freed from pictorial distractions, but photography only seems to be an image. Traditional photography is based on photochemical processes that are difficult to control. Therefore, since its invention, photography has been recognized ontologically as a “transparent” cognition and a “technical” image. Hence, the camera motion capture and essence extraction algorithm are combined. The key of generative algorithm is to accurately define the algorithm function, so as to compare specific assumptions with image information. This research study considers the novel regional dance in the context of intangible cultural heritage feature extraction to finalize the framework. Through the systematic testing, the effectiveness of the proposed model has been proved.
Securing Biometric Data with Optimized Share Creation and Visual Cryptography Technique

Biometric security is the fastest growing area that receives considerable attention over the past few years. Digital hiding and encryption technologies provide an effective solution to secure biometric information from intentional or accidental attacks. Visual cryptography is the approach utilized for encrypting the information which is in the form of visual information for example images. Meanwhile, the biometric template stored in the databases are generally in the form of images, the visual cryptography could be employed effectively for encrypting the template from the attack. This study develops a share creation with improved encryption process for secure biometric verification (SCIEP-SBV) technique. The presented SCIEP-SBV technique majorly aims to attain security via encryption and share creation (SC) procedure. Firstly, the biometric images undergo SC process to produce several shares. For encryption process, homomorphic encryption (HE) technique is utilized in this work. To further improve the secrecy, an improved bald eagle search (IBES) approach was exploited in this work. The simulation values of the SCIEP-SBV system are tested on biometric images. The extensive comparison study demonstrated the improved outcomes of the SCIEP-SBV technique over compared methods.
High Voltage Circuit Breaker Speed Measurement Method Based on Camera Calibration

The measurement result of opening and closing speed of high voltage circuit breaker is an important reference to evaluate the working performance of high voltage circuit breaker. At present, the measurement methods based on machine vision technology do not consider the problems of camera field distortion and target point tracking instability under different working conditions, and the measurement accuracy is greatly reduced. To solve this problem, this paper proposes a correction method of speed characteristic measurement of high voltage circuit breaker based on camera calibration technology. The opening and closing speed parameters of 220kV high-voltage circuit breaker have been measured for many times. The results show that when this method meets the requirements of measurement accuracy, the opening and closing speed characteristic curve of circuit breaker can be calculated within 30min of field test, which is very easy to be tested on site and improves the operation efficiency of maintenance personnel.
MDCLCG with Square Root Carry Select Adder Technique for Hardware Security

Three-operand adder performs the modular arithmetic operations like addition, multiplication and exponentiation by utilizing various cryptography algorithms. Here, the pseudo-random bit generator algorithm is used to perform addition operation. It performs in binary format and reduces the area, delay, power and increases the speed. In this adder application purpose, the MDCLCG is used to increase the hardware security and light-weighted core. In comparison to all other LCGs and present PBRG techniques, MDCLCG is the more secure and extremely random PRBG approach. The existing MDCLCG can work on the increased operand size but it does not perform well with the three-operand adder to improve the performance. Hence, the SRCSA in integrated to the MDCLCG to obtain better result than existing architectures. Square-Root Carry Save Adder (SRCSA) is one of the simplest techniques used to sequentially add all N partial products by using N-1 adders. This research study intends to improve the implementation of MDCLCG with three-operand binary adder technique for hardware security. The novel MDCLCG architecture can execute 32-bits in Xilinx tool to obtain a better simulation and synthesis result. This architecture is used in addition to the LCGs, FIR and IIR filters, ALU processor, randomness-based verification, etc. These adders execute 8-bits, 16-bits, 32-bits, 64-bits and 128-bits architecture and it is then implemented by using Verilog-HDL and further the synthesis shows the report of less area, less power consumption and high speed with reduced delay than existing architectures.
Design and Analysis of a Nano-photonic Processing Unit for Low-Latency Recurrent Neural Network Applications

Recurrent neural networks (RNNs) have achieved high performance in inference processing that handles time-series data. Among them, hardware acceleration for fast processing RNNs is helpful for tasks where real-time performance is es-sential, such as speech recognition and stock market prediction. The nano-photonic neural network accelerator is an approach that takes advantage of the high speed, high parallelism, and low power consumption of light to achieve high performance in neural network processing. However, existing methods are inefficient for RNNs due to significant overhead caused by the absence of recursive paths and the immaturity of the model to be designed. Therefore, architectural considerations that take advantage of RNN characteristics are essential for low latency. This paper proposes a fast and low-power processing unit for RNNs that introduces activation functions and recursion processing using optical devices. We clarified the impact of noise on the proposed circuit's calculation accuracy and inference accuracy. As a result, the calculation accuracy deteriorated significantly in proportion to the increase in the number of recursions, but the effect on inference accuracy was negligible. We also compared the performance of the proposed circuit to an all-electric design and a hybrid design that processes the vector-matrix product optically and the recursion electrically. As a result, the performance of the proposed circuit improves latency by 467x, reduces power consumption by 93.0% compared with the all-electrical design, improves latency by 7.3x, and reduces power consumption by 58.6% compared with the hybrid design.
Bio-Inspired Optimization Algorithm on Cloud based Image Retrieval System using Deep Features

With the tremendous improvement of cloud computing (CC) technique, increasing count of users choosing to outsource image data to the clouds. Cloud resource provider decreases the storing problem on local hardware by outsourcing massive image database to cloud server and exploiting the cloud computation ability to image processing application. Learning effective feature representation and similarity measures are essential for the performance of content-based image retrieval (CBIR). Despite wide-ranging research efforts for decades, it remains a challenge that significantly hinders the success of real-time CBIR system. Since the trail and error hyperparameter selection is a tedious process, metaheuristic optimization can be used. This paper introduces an Evolutionary Optimization Algorithm for Cloud Based Image Retrieval System (EOA-CIRS) technique. The presented EOA-CIRS technique derives a new CBIR model for retrieving highly related images. To obtain this, the EOA-CIRS technique initially employs MobileNetv3 model as a feature extractor, which derives features from the query and database images. Next, the bamboo forest growth optimizer (BFGO) approach was applied as a hyperparameter optimizer to appropriately tune the hyperparameters of the MobileNetv3 approach. Finally, Euclidean distance based similarity measurement is utilized for retrieving the related images. The experimental validation of the EOA-CIRS model on Corel10K dataset demonstrates the effectual efficacy of the EOA-CIRS model over other recent approaches.
A TinyML based Residual Binarized Neural Network for real-time Image Classification

Image processing is an important requirement in IoT applications such as robotics, augmented reality, computer vision, Industry 4.0 etc. The capabilities of IoT devices for image processing are limited to sensing the environment, processing and communicating the results. Tiny machine learning (TinyML) is a new paradigm that takes advantage of the IoT to deploy deep learning models to perform complex tasks in resource constrained embedded devices. Image classification is an important task in IoT to interpret images of a particular scene or class. Currently, this task is performed in embedded devices using Binarized Neural Networks (BNNs), which can be converted to a set of weights using a one-hot encoding process. These networks integrated with hardware accelerators can be trained to perform image processing tasks in real-time. This paper proposes a BNN for image classification based on residual learning paradigm, called Tiny-BNN which exploits the skip connections to reduce information loss, and improve the training time and accuracy. Experimental results show that the model achieves a classification accuracy of 90.1 % and 91.6% on the on CIFAR-10 and MNIST datasets respectively.
Integrating Data-Oriented Intelligent Evaluation Framework Based Image Detection System

Integrating data-oriented intelligent evaluation framework for English guiding quality based on teacher status image detection system is designed in the paper. The content-based dynamic key frame extraction algorithm is used to obtain the key frames in the moving video stream, so as to reduce the processing amount of subsequent operations and achieve the purpose of automatic segmentation of the continuous actions. Inspired by this, the designed model is based on the further analysis on the motion status image detection system. The designed model is based on modern new media and multi-terminal interactive network platform, it completes the collection of multi-source data, and designs and constructs a core sub-database including entertainment life sub-database. With the designed model, the data-oriented intelligent evaluation framework for English guiding quality is implemented. Through the testing focusing on the detection performance, the results are proven to be effect.
Comparison of Cryptographic Techniques: Classical, Quantum and Neural

Now days, the necessity of highly secured and reliable network is tremendously increased in the wireless communication network. There are various routing attacks occurs in wireless communication network there for secure routing is one of the most challenging research area in a mobile ad-hoc network-MANETs. Several methods are available for providing safety of the MANET, still various attacks are there which reduces network performance. Hence a strong cryptography technique is required to secure communication in MANET. An efficient cryptographic method is required, which will not only generate and maintain key also distribute it safely to the nodes which are not malicious. The method proposed here detects the nodes which are malicious and keeps them away from communication in the network so that packet delivery rate is increased by reducing delay in the network. The reliable communication in MANET is achieved by applying strong cryptography methods. In this paper comparison of classical, quantum and neural cryptography are given.
Framework for Designing a Multiband Ultra-Wide Band (UWB) Antenna with its Functions

Technology known as multiband ultra-wideband (UWB) has the potential to provide a response to the rapid expansion of wireless communication thanks to its high data rate, large bandwidth, and greater resilience to interference from many paths. The spectral form of the pulses is altered as a result of the Multiband UWB antenna's function as a band pass filter. This gives it a unique character. As a consequence of this, the development of it is essential. UWB antennas are required to function throughout the entire FCC -designated UWB band (3.1-10.6 GHz ultra-wide bandwidth), have directional or omnidirectional radiation patterns, maintain constant gain and group delay throughout the entirety of the band, have exceptional radiation efficiency, and be compact. A number of different designs for UWB antennas are investigated, and after looking at the present status of fundamental approaches to UWB antennas, a unique class of UWB microstrip patch antennas is suggested as a solution.
Ensemble Learning Model for Object Detection in Image and Videos

Object detection is a very difficult task in many applications. Presently many authors are trying to develop new research applications to find the objects in Images and videos. In images, static objects are identified and in videos, dynamic objects are identified which are called moving objects. Deep Learning and Artificial intelligence playa major role in finding the objects in Images and also in Videos. So many existing methods are developed for the detection of objects from various sources. In real-time applications, obj ect detection can be used to find malicious objects also. In this paper, an ensemble model is developed to find the accurate objects from the given inputs. The ensemble model is the combination of YOLOV3 (You Only Look Once) and a Convolutional neural network (CNN). The dataset used in this paper is COCO-2017 collected from online sources. The performance of the proposed approach is analyzed by comparing it with the several existing approaches.
Handwriting Recognition using Convolutional Neural Network and Support Vector Machine Algorithms

The goal of the Handwritten Text Recognition system is to transform human handwritings into digital text. Handwriting Recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. Going back to the history of handwriting recognition, the first step created was a special pen device which was also known as'S RI pen’ established in 1964. From that, the technology has evolved so much better. This technology will improve further in the future; one among such trials is this proposed system. The proposed research employs Convolutional N eural Network and Support Vector Machine as classifiers, EMNIS T as dataset with suitable parameters for training and testing for Handwriting Recognition and providing 95.41 % accuracy with lesser computational time to recognize English alphabets from A-Z, along with digits from 0–9. The algorithms, Convolutional Neural Network and Support Vector Machine are used here in this research.