Reviewerly/exHarmony huggingface dataset:
Cyber Security: Issues and Challenges in Covid - 19

A technology enhances a man&rsquo;s life in every aspect whether it is healthcare, transport, communication, education etc. During this Covid &ndash; 19 pandemic, the growths of technology usage have increased enormously. There are various issues and challenges which we face during the use of any technology. Security is the most important aspect, when we are using in any digital platform during Covid &ndash; 19. When we think about Cyber Security, the first thing that comes to our mind is &ldquo;Cyber Crime&rdquo; which is increasing immensely day by day. Cyber Security refers to the practice of ensuring the integrity, confidentiality and availability (ICA) of information. Basically Cyber security is the state or process of protecting and recovering networks, devices and programs from any type of Cyber Attack. It is comprised of an evolving set of tools, risk management approaches, technologies, training, and best practices designed to protect networks, devices, programs, and data from attacks or unauthorized access. Cyber-attacks in India have risen up to such an extent that our country ranks fourth out of the tenth targeted countries in the world. In this Covid &ndash; 19 pandemic Cyber-attacks are an evolving danger to many organizations, employees and consumers in different sectors. They may be designed to access or destroy sensitive data, change any kind of data or extort money. According to a Research, more than 50% of onliners are victim of some of cybercrime every year, which includes computer viruses, malware, credit card fraud, online scams, phishing, and identity theft and so on. These crimes will lead the country to lose millions of rupees or dollars, also time and expenses to put back the things in right directions. This paper mainly focuses on the different aspects of cyber security and the challenges faced in the implementation and by using latest technologies during Covid - 19. The paper also focuses on the India&rsquo;s legal framework for Cyber Security.
Carbon Footprint Analysis Using Knowledge Graph

Climate change is a global issue which needs to be addressed. Identifying and analyzing the data of one of the root elements, i.e., carbon footprint, and hence implementing required solutions serves as a good way to solve this issue. Although there is a lot of data available, there exists an information gap because of which we are not able to derive solutions. Knowledge graph is a possible solution where one can access information related to carbon footprint and thereby take actions to address the problem. In this paper, we discuss a knowledge graph approach where we discuss how can we connect scattered information that is present everywhere on the Web and create one knowledge graph which can talk about problem as well as the solution. We further complete this knowledge graph with node embedding and cluster the nodes. This further helps in getting the understanding on discussion of the problem and solution if present or can be derived. We can further query the graph and try and understand what policies can be build or measures can be taken to reduce carbon footprint.
Content-based brain tumor retrieval for MR images with joint deep and handcrafted visual features

A novel feature extraction framework is proposed to improve the performance of the brain tumor retrieval system. To extract information that radiologists pay attention to when diagnosing brain tumors, not only features describing the location and layout of the tumor in the brain but also those for texture variations of the tumor region and tumor-surrounding tissues are extracted. The gray level co-occurrence matrix (GLCM) and Fisher vector (FV) are calculated as handcrafted features for the augmented tumor regions. Two deep features are extracted respectively from the whole brain MR images and the augmented tumor regions by fine-tuning the Xception model. Then the handcrafted and deep features are fused together after implementing a feature selection procedure based on roulette wheel selection (RWS) method. Extensive experiments are conducted on the brain CE-MRI dataset. The results show that the proposed system can achieve average mAP of 98.17±0.88% and Prec@10 of 97.56±1.16%, which outperforms the state-of-the-art retrieval systems by a large margin on the same dataset.
Art Installation Design and Algorithm Research Oriented to Heterogeneous Computing Architecture and Particle Swarm Algorithm

Traditional high-performance computers generally use commercial general-purpose processors. When constructing large-scale parallel computing systems, they will face many challenges in system efficiency, power consumption, system maintenance, and cost. Heterogeneous architectures have begun to become the key to constructing supercomputer systems. In order to improve the optimization efficiency of the particle swarm algorithm, based on the simplified particle swarm algorithm, an improved strategy for fusion of population information is proposed. Only using the optimal position of the individual particle and the optimal position of the population to update the particle position makes the algorithm description simpler, construct a random term that depends on the population information to increase the diversity of the population, and design an adaptive control function equation to balance the algorithm's global detection and local detection. This article aims to study the design and algorithm of art installations for heterogeneous computing architecture and particle swarm algorithm. Aiming at the two heterogeneous system components of GPU and FPGA in heterogeneous computing systems to solve the dual heterogeneous problem between various computing components and applications, a task flow model for heterogeneous computing systems is proposed, which adopts heterogeneous systems. The structure focuses on the deterministic and nondeterministic calculation methods of particle swarm algorithm and carries out parallel research algorithms of particle transport data to analyze the design of art installations. The experimental results show that by making full use of the GPU hardware storage structure to improve memory access speed and reduce cache failure, the combination of particle swarm algorithm and heterogeneous computing system enables the calculation of the carrier components to obtain 3 times the acceleration effect; through the energy analysis model, Heterogeneous computing system components can obtain at least 1.5 times performance improvement.
Efficient Cloaked Face Recognition Methodology throughout The Covid-19 Pandemic

The COVID-19 is a partner in Nursing unequaled disaster resulting in a huge range of casualties and protection issues. to cut back the unfold of coronavirus, individuals typically wear masks to guard themselves. This makes face popularity a truly tough project because bound components of the face rectangular measure hidden. A primary awareness of researchers for the duration of the continuing coronavirus pandemic is to come back up with hints to handle this downside thru fast and reasonably-priced solutions. during this paper, we tend to endorse a dependable technique supported by discard cloaked region and deep learning-based options to deal with the matter of the cloaked face recognition technique. the number one step is to discard the cloaked face vicinity. next, we tend to apply pre-trained deep Convolutional neural networks (CNN) to extract the only options from the received areas (in general eyes and forehead regions). in the end, the Bag-of-features paradigm is carried out on the function maps of the last convolutional layer to quantize them and to induce small illustration scrutiny to the simply related layer of classical CNN. in the end, Multilayer Perceptron (MLP) is implemented for the class approach. Experimental effects on real-global-Masked-Face-Dataset display high popularity overall performance.
The Transformation of Three Colleges to Institutes of Technology and Advanced Learning

"The Transformation of Three Colleges to Institutes of Technology and Advanced Learning." Journal of Vocational Education & Training, 73(4), p. 609
Multi-level Hierarchical Controller Assisted Task Scheduling and Resource Allocation in Large Cloud Infrastructures

The high-pace emergence in Cloud Computing technologies demands and alarmed academia-industries to attain Quality-of-Service (QoS) oriented solutions to ensure optimal network performance in terms of Service Level Agreement (SLA) provision as well as Energy-Efficiency.Majority of the at-hand solutions employ Virtual Machine Migration to perform dynamic resource allocation which fails in addressing the key problem of SLA-sensitive scheduling where it demands timely and reliable task-migration solution.Undeniably, VM consolidation may help achieve energy-efficiency along with dynamic resource allocation where the classical heuristic methods which are often criticized for its local minima and premature convergence doesn't guarantee the optimality of the solution, especially over large cloud infrastructures.Considering these key problems as motivation, in this paper a highly robust and improved metaheuristic model based on Ant Colony System is developed to achieve Task Scheduling and Resource Allocation.CloudSim based simulation over different PlanetLab cloud traces exhibited superior performance by the proposed task-scheduling model in terms of negligible SLA violence, minimum downtime, minimum energy-consumption and higher number of migrations over other heuristic variants, which make it suitable towards realistic Cloud Computing purposes.
Designing and Implementing a Method of Data Augmentation Using Machine Learning

Efficiency of neural network (NN) models depend on the parameters given and the input data. Due to the complexity of environmental conditions and limitations the data for NN models, especially for the case of images, can be insufficient. To overcome this problem data augmentation has been used to enlarge the dataset. The task is to generate diverse set of images from a small set of images for NN training. Due to data augmentation transformation, 3105 new images out of 345 input data were created for classification, detection and image segmentation.
Reinforcement Learning-Based Safe Path Planning for a 3R Planar Robot

Path planning is an essential topic of robotics studies. Robotic researchers have suggested some methods such as particle swarm optimization, A*, and reinforcement learning (RL) to obtain a path. In the current study, it was aimed to generate RL-based safe path planning for a 3R planar robot. For this purpose, firstly, the environment was performed. Later, state, action, reward, and terminate functions were determined. Lastly, actor and critic artificial neural networks (ANN), which are basic components of deep deterministic policy gradients (DDPG), were formed in order to generate a safe path. Another aim of the current study was to obtain an optimum actor ANN. Different ANN structures that have 2, 4, and 8-layers and 512, 1024, 2048, and 4096-units were formed to get an optimum actor ANN. These formed ANN structures were trained during 5000 episodes and 200 steps and the best results were obtained by 4-layer, 1024, and 2048-units structures. Owing to this reason, 4 different ANN structures were performed utilizing 4-layer, 1024, and 2048-units. The proposed structures were trained. The NET-M2U-4L structure generated the best result among 4 different proposed structures. The NET-M2U-4L structure was tested by using 1000 different scenarios. As a result of the tests, the rate of generating a safe path was calculated as 93.80% and the rate of colliding to the obstacle was computed as 1.70%. As a consequence, a safe path was planned and an optimum actor ANN was obtained for a 3R planar robot.
DICE: Dynamically Induced Cross Entropy for Robust Learning with Noisy Labels

Image classification has lead to the revolution of artificial intelligence in the past five years. However, image classification algorithms are significantly affected by the inherent variance in sensory input and noise in the labelled data in a real-world situation. Also, the class count of items in the real world is significantly larger than that of the typical experiment setup. How to speedily train/fine-tune a large scale model in giant label space with considerable noise is a recent interest of the machine learning community. This paper proposed a multi-stage training algorithm to fine-tune a pre-trained EfficientNet model on AliProducts large scale product classification dataset, which has a large label space (50030 classes) and severe label noise. Our method can generalize well on such a dataset while keeping the prior knowledge gained in the large-scale pretraining stage. With our novel Dynamically Induced Cross-Entropy(DICE) network loss and several other methods to tackle unbalanced datasets and improve model convergence, the model achieved 76.67% of Top-1 Accuracy and 85.42% of Top-5 accuracy, which are 3.63%/2.9% higher than symmetric cross-entropy and are significantly higher than the usual fine-tuning method with categorical cross-entropy loss(CCE).
Real Time Distributed and Decentralized Peer-to-Peer Protocol for Swarm Robots

This contribution proposes an approach to enhance the capability of robotic agents to join the Internet of Things (IoT) and act autonomously in extreme and hostile environment.This capability will help in the development in environments where the connectivity, availability, and responsivity of the devices are subject to variations and noises.A real time distributed and decentralized Peer-to-Peer protocol was designed to allow Autonomous Unmanned Surface Vessels (AUSV) extend their context awareness.The developed Middleware allows a real time communication and is designed to run on top of a Real Time Operating System (RTOS).Furthermore, the proposed Middleware will give researchers access to a large amount of data collected by sensors, and thus solve one of the major problems encountered while training artificial intelligence models which is the lack of sufficient data.
Defining the Internet of Value

This chapter begins by examining the development and evolution of the Internet and the rise of blockchain. It then moves on to the various definitions of the Internet of Value (IoV), noting that blockchain is the commonality between all of them. A definition of the IoV is suggested as the “instant transfer of assets that can be expressed in monetary terms over the Internet between peers without the need for intermediaries”. The attributes of an effective IoV are described along with the potential enablers of its development. Finally, the chapter concludes with a high-level examination of the economic impact that the successful enablement of the IoV could have.
The Survey of Joint Entity and Relation Extraction

In general, people use the pipeline method for named entity recognition and relationship extraction, which, though, cannot take full advantage of the benefits in between. To solve this problem, people turned their attention to the method of joint entity and relation extraction. This review surveys several ways in joint entity and relation extraction based neural networks. It systematically summarizes the advantages and disadvantages of the models. After comparing these models’ performance of various datasets, this paper found that the model using BERT achieve the state-of-the-art result. The reasons and possible future development directions are analyzed and discussed.
Fuzzy-Multiple Model of Adjustment of the “Smart House” Mobile Application Under the User Level

The methodological foundations for adapting the Smart Home application to the user level have been developed for its most efficient operation and optimization of learning to work with the system. We propose to use three levels of mastering a custom mobile application: Beginner, Basic, Classic. At each level, we use our own sequence of work with the system, permissible actions and opportunities for working with the Smart Home, as well as training moments in work. It is assumed that training in working with the system is organized in such a way that the user can eventually move from one level to another. To recognize the level of the user, a system of productive rules, fuzzy inferences, based on the inference of Mamdani, was used. The creation of productive rules is based on the statistics of the user’s daily actions when working with the Smart Home system.
Practical application of pedestrian intelligent detection technology based on computer vision

Pedestrian detection based on computer vision technology uses camera equipment mounted in vehicles to obtain driving image data, and then calculates whether pedestrians exist from the image or sequence and gives accurate positioning. It improves the safety of driverless vehicles to a large extent and greatly reduces the probability of accidents Pedestrian detection is the use of computer vision technology to determine whether there is a pedestrian in the image or sequence, and at the same time to locate the pedestrian technology. In this paper, first of all, the threshold method, image segmentation technology is described, and then the feature extraction method is divided into two different categories of bottom features and machine learning to make an introduction, and then the classifier construction method is summarized. Finally, the future research of pedestrian detection technology is prospected through the existing problems.
Variazioni in tema di linguaggio e giustizia amministrativa di fronte al regime

The paper focuses on the issue of the impact of the Fascist regime’s own language on the activity of the administrative judge. After recalling the problem of the context in which the language of the administrative judge is framed, the investigation highlights some possible “symptoms” of a “sensibility” to be assessed on the level of stylistic and expressive formulas. If, in some cases, this language has been the subject of a particular criticism and attack, in others cases it is possible to highlight how a certain assonance emerges indirectly with the typical speech of the regime. In this perspective, it can be recognized that the linguistic “charm” of the regime, while not affecting the independence of judgment of the administrative judge, had a certain impact on the style of some decisions of the Italian Council of State. An analysis conducted on some paradigmatic cases is able to identify, with regard to a part of the jurisprudence developed after the Thirties, a real “climax” on the linguistic and expressive level.
Filosofia e dibattito. Le Meditazioni Cartesiane come esempio di deliberazione filosofica

This essay relates the content of an educational experience that applies the methodology of debate to the closer examination of an argumentative philosophical text, in the form of a deliberation with oneself. The text in question is Descartes’ Meditations on First Philosophy, specifically the first, the second, the sixth meditation and respective Objections. The class involved in the activity is the IV B of the Liceo classico Alessandro Manzoni in Lecco.
Prediction of cardiac disease using online extreme learning machine

This paper presents an automated Machine Learning (ML) algorithm to detect the Coronary Disease like Congestive Heart Failure (CHF) and Coronary Artery Disease (CAD). The proposed method has been employed as a combination of non-linear feature extraction methods: Online Sequential machine (OS-ELM) and Linear Discriminate Analysis (LDA) as well as Generalised Discriminate Analysis (GDA) as feature reduction algorithms. For training and validation of ML, 12 non-linear features were extracted from Heart Rate Variablility (HRV) signal. The numerical experiments were carried out on the sets as CAD-CHF, YOUNG-ELDERLY-CAD and YOUNG-ELDERLY-CHF subjects. The numerical simulation results clearly have shown that GDA combined with OS-ELM gives better detection performance compared to OSELM. To test the robustness of proposed method the classification performance like accuracy, positive prediction value, sensitivity and specificity were calculated on 100 trials and it achieved average performance accuracy of 99.77% for YOUNG-ELDERLY-CAD and 100% for CAD-CHF and YOUNG-ELDERLY CHF subjects.
Statistical Inference Through Variable Adaptive Threshold Algorithm in Over-Sampling the Imbalanced Data Distribution Problem

Classification of an imbalanced data is a major problem in many real-time systems. Classifiers classify the majority class samples with lesser misclassification and the minority class samples with more misclassification. The biased decisions of a classifier are due to the less availability of samples in the minority class. To solve this problem, new re-sampling method for over-sampling is proposed. The synthetic samples for the minority class are generated by statistically analysing the features of a minority class samples. Here, the samples are generated in double the number of minority samples to reduce its misclassification. It helps the classifier to have a balanced focus on majority and minority classes. The samples over-sampled through this approach are compared against the over-sampling approaches such as SMOTE and ADASYN. The results obtained through the proposed work show better classification accuracy and reduced misclassification rate in both majority and minority classes. Result is evaluated using the statistical evaluation metrics. It is also observed that over-sampling with the proposed approach is better for the small and medium imbalanced ratio datasets.
AttnGAN++: Enhencing the Edge of Images on AttnGAN

The text-to-image synthesis has made great progress at present, but the extraction of key semantic information and the restoration of the edge details of the generated image are still not perfect. In this paper, we proposed Attentional Generative Adversarial Network++ (AttnGAN++) model based on AttnGAN that allows to effectively solve the problem of missing the edge information of the generated image and extracting insufficient text features. First, we introduced Bi-directional Gated Recurrent Unit model (BiGRU), which can still ensure sufficient extraction of contextual information in processing long texts. This model combined with the attention mechanism, to achieve higher weights for the important words of the text. Then, we proposed the edge enhancement network consists four modules: Edge extraction, Residual Dense Block (RDB), Edge enhancement fusion, and Up-sampling model. Edge extraction obtains the edge of the final generated image. RDB to form a continuous memory mechanism, adaptively learn from previously more effective features to enhance feature propagation. Edge enhancement fusion and up-sampling modules to fuse the edge information and global information to generate high-resolution images with clearer edges. Thorough experiments on CUB dataset demonstrate that Attentional Generative Adversarial Network++ model significantly outperforms Attentional Generative Adversarial Network, boosting the best reported inception score by 3.78% and R-precision by 9.71% on CUB dataset, which can generate clearer image edges and improve the quality of the image.
Recommendation System with Different Algorithms

In this world, we have a vast set of data availability. To find our interesting information we need the recommendation system. Recommendation systems that will predict what users would want by analyzing their behavior which contains information on past preferences. This will bring a wide scope of utilization including online shopping, e-business, retail, media, banking etc., The recommendation system helps us guiding the right path to customers even before they are looking forward to it. And mostly all recommendation systems use the collaborative filtering method. This paper simulates the accuracy of the movie dataset by comparing two algorithms like the SVD algorithm and the Pearson correlation algorithm with RMSE metric.