Reviewerly/exHarmony huggingface dataset:
A Pipeline for Business Intelligence and Data-Driven Root Cause Analysis on Categorical Data

Business intelligence (BI) is any knowledge derived from existing data that may be strategically applied within a business. Data mining is a technique or method for extracting BI from data using statistical data modeling. Finding relationships or correlations between the various data items that have been collected can be used to boost business performance or at the very least better comprehend what's going on. Root cause analysis (RCA) is discovering the root causes of problems or events to identify appropriate solutions. RCA can show why an event occurred and this can help in avoiding occurrences of an issue in the future. This paper proposes a new clustering + association rule mining pipeline for getting business insights from data. The results of this pipeline are in the form of association rules having consequents, antecedents, and various metrics to evaluate these rules. The results of this pipeline can help in anchoring important business decisions and can also be used by data scientists for updating existing models or while developing new ones. The occurrence of any event is explained by its antecedents in the generated rules. Hence this output can also help in data-driven root cause analysis.
Interactive Image Generation Using Cycle GAN Over AWS Cloud

In today’s world, most problems could be solved with the help of modern technology. One such situation that requires the help of technology is designing and building infrastructures. For example, an architect or infrastructure designer always needs to know the future outlook of his/her blueprint and does changes accordingly before the construction. This can be done by hand sketching the infrastructure design and inputting it into a Deep Learning technique that generates images showing the building's future outlook. The paper shows the use of Cycle Generative Adversarial Network (Cycle GAN) as the Deep Learning model in the above case where the application is deployed on the Cloud as a ready-to-use service for the architect or designer.
Visualizing Abaqus output database in ParaView: A universal converter in Python and C++

Abaqus is a commercial software suite for numerical simulation and ParaView is an open-source software application for 3D scientific data visualization. While the Abaqus suite provides Abaqus/Viewer for post-processing, it is not open-source, and custom visualization features can only be implemented using Abaqus official APIs (e.g., Python APIs). ParaView has a robust rendering pipeline, such that complicated post-processing operations can be implemented using filters. Due to ParaView’s openness and plugin architectures, highly tailored data processing and visualization are made possible. However, visualizing Abaqus output database (.odb extension) in ParaView is a nontrivial task. ODB files are proprietary binary files that can only be read by Abaqus. Without reverse engineering, the option is to extract data from ODB files using Abaqus’ official APIs (C++ or Python) and write them to a file format compatible with ParaView. There have been some efforts to achieve this, but none of them are satisfactory in terms of extensibility and generality. Our converter is designed to address this issue by providing a compact, robust, and extensible solution for converting Abaqus ODB files to a format that can be read by ParaView. Our converter stands out due to the following benefits: (1) It can handle a variety of element types, including linear elements, high-order elements, and elements with multiple integration or section points; (2) It extracts data at integration and section points; (3) It includes a separate script that utilizes Python multiprocessing; (4) A C++ version is also provided, which offers a significant increase in speed compared to the Python version.
SLA Framework and Management under Multi-Cloud Infrastructure

Cloud computing technology plays a vital role in business and individual users to access on-demand and flexible resources over the internet. The time-critical applications need guarantees from service providers in terms of availability of service and response times. Recent studies show that most enterprises are started adopting a multi-cloud system to prevent lock-in, and cost-saving, mitigate the risk of using a single point of failure and utilize the unique capabilities across different service providers. Multi-cloud is a multi-vendor environment that can be complex especially when the problem is impacting multiple service providers in hundreds or more. The problem becomes more analytical or disapproving when all vendors have their different root cause analysis metrics and SLAs. Most of the service providers have their own tools and terminology for dashboards and maintaining the Key performance indicators. Multi-cloud management process becomes more fragile during the different phases. This paper presents a blockchain-based multi-cloud infrastructure that provides end to end common platform to show real-time progress and a dashboard that provides a tracking management system to address the issues.
Blockchain-based Crowdfunding for Cyber-Product Insurance

In order to reduce the expense of cyberthreats, organisations are considering to outsource their cyber-risk control to insurers. However, a number of barriers have prevented widespread adoption of cyber-insurance. First, it is challenging to determine the insurance premium due to the dearth of valid data used to assess cyber-risks. Second, the legal and administrative barriers that prevent insurers from examining an organization's security posture. Contrasted with, the blockchain technology has gained a lot of popularity because of its capacity to offer security and transparency. Blockchain uses the distributed ledger in order to store transaction details, and instead of keeping the data on a single server, the data is kept across a network of computers. This study suggests a fresh framework to cover a cyber-product utilising blockchain technology in order to enhance the use of cyber-insurance. An insurance request for a cyber-product is first made by a Manager. After then, interested insurers submit their preferred pricing for the insurance system to take part in a sealed-bid auction. The insurers will be chosen from among the auction winners, and in exchange for their responsibilities, they will be given tokens. Among the instance the legitimacy of an indemnity request is verified before calling the claim function to locate the appropriate sum in the money received from the insurers. In addition, we provide a fresh approach for integrating a sealed-bid auction into smart contracts for insurance crowdfunding.
Comparison of Machine Learning Methods for Cryptocurrency Price Prediction

In recent years, cryptocurrencies have received much attention due to their recent price surge and crash. In fact, their prices have been volatile, making them very difficult to predict. Accordingly, various machine learning methods have been used by researchers to investigate factors that affect cryptocurrencies prices and the patterns behind their fluctuations. From various machine learning and deep learning methods, this study aims to find an efficient and accurate model for predicting Bitcoin, Ethereum, and Binance Coin prices. Our experiments show that the Ridge regression model outperforms more complicated prediction models, such as RNNs and LSTM, in predicting the exact closing price. On the other hand, LSTM can anticipate the direction of the cryptocurrency price better than others.
Adaptive Frame Selection In Two Dimensional Convolutional Neural Network Action Recognition

We presented a technique in this research for dynamic frame selection to achieve robust features. This situation results in less redundancy and useful input for the network. Because it uses fewer processing resources and offers adequate accuracy, the suggested technique is appropriate for real-time applications. The network becomes more efficient and maintains adequate accuracy when informative frames are chosen and computation is minimized. The framework is tested on UCFIOI as one of the large and realistic datasets. The experiments show acceptable results employing both Resnet-50 and Mobilenet pretrained features.
Cloud Resource Demand Prediction to Achieve Efficient Resource Provisioning

Cloud computing is a paradigm that uses techniques such as virtualization, time sharing and distributed computing to provide a wide range of hardware and software services for users based on a pay-as-you-go model, on-demand and via ubiquitous network access. Due to the scarcity of important cloud resources such as compute, network and storage, cloud service providers should carefully select, apply and distribute resources within the predefined boundaries of the cloud system meanwhile satisfy the requirements of running applications. One of the most critical components of the cloud computing architecture is resource allocation, where its effectiveness significantly affects the functionality of the entire cloud system. Implementing efficient resource allocation schemes and algorithms in cloud systems is not straightforward and faces several challenges including compromising between the required implementation costs and the utilization of resources, satisfying delay requirements of running applications, effective reallocation of resources, reducing computational complexity and scheduling workloads and tasks appropriately across a data center or in a heterogeneous multicloud environment. One of the goals of the consumers of cloud computing services is to complete tasks at minimal cost. In terms of cost efficiency, the goal of cloud providers is to maximize the resource utilization and thus the profits while cloud users are aimed at achieving the alternative goal which is to reduce the cost without compromising the quality of delivered services. There are various approaches of implementing resource allocation to compromise between maximizing the resource utilization and minimizing the consumer’s costs. In this article, we survey different scheduling algorithms and resource allocation approaches in the cloud computing environment, which include the prediction of resource requests in virtual machines and optimal allocation of resources to reduce costs and increase efficiency.
Face Detection and Recognition Using Digital Image Processing: "State of the Art"

With growing techniques and computer technologies emerging, biometric recognition system has gained immense popularity among the masses. Amongst the entire biometric recognition system, face detection has been one of the most important goals of computer vision, but only in the recent years, has it been prevalent by means of social tagging and image processing. Face recognition refers to the technique of identifying one’s biometrics using their facial features, for example, eye color, nose and lips. In this paper, we shall discuss the various methodologies and approaches which can be used to implement the face detection system [1]. Due to the hike of video and image databases, it is the need of the hour for automatic understanding and examination of information by intelligent systems. It is the face through which we convey, identify feelings from person to person. Therefore, face recognition plays an vital role in our day-to-day life. Since sensitive data such as face id, biometrics are easily accessible to hackers so Digital Image Processing plays a very important role in Biometric recognition technique. Face recognition has the potential in a real time surveillance system.
Predicting Bitcoin Fluctuations Using Deep Neural Networks

Bitcoin as a decentralized cryptocurrency is the most popular cryptocurrency in the world today. Such that, a lot of people know it as a way to invest their money, which shows that is vital to predict the price trend of this cryptocurrency. In this article, we are trying to predict the price of Bitcoin using a machine learning approach. To do so, we apply collected data from trading sources to train an artificial deep neural nenvork model, and then use the model to predict Bitcoin fluctuation. Our deep model results show that it achieves the best performance among other methods.
A Reinforcement Learning-based Iterated Local Search for Software Modularization

Modularization algorithms are used to recover software structure. These algorithms divide the source code artifacts of the program into smaller and more understandable modules. Since software modularization is an NP-hard problem, heuristics/meta-heuristics methods are usually used to solve this problem. Iterated Local Search (ILS) is a meta-heuristic having two key components, namely a local search method and a perturbation method. Most current ILS models are formulated manually by selecting these components among the different combinations. This paper presents a modified ILS to solve the software modularization problem whose components are selected intelligently by utilizing a reinforcement learning (RL) technique, i.e., ILS is designed automatically at each iteration. More specifically, Q-learning is used to guide the proposed method in selecting the appropriate components in each search state. To demonstrate the applicability of the proposed algorithm, eleven software systems are selected. The experimental results show that the proposed method achieves higher or equal quality modularizations compared with other techniques.
Automatic Separation of Effective Frames in the Analysis of Voluminous Videos Using Human-Object Interactions

In recent years, machine learning algorithms and deep neural networks have provided very suitable answers to solve complex problems, including the analysis of human-object relationships in videos, but one of the main problems in the operational use of these algorithms is the time-consuming processing, Especially in bulky videos. In this research, a dynamic approach is presented by combining the use of deep neural networks along with scene evaluation function with different coefficients, during which the people and objects present in the scene are identified and the effective changes are investigated, focusing on the origin of the changes. In this method, the changes of the different parts of the body are checked separately and with different coefficients. The importance of this matter is that many of the changes in the scene are not related to human-object interactions and a significant part of the processing time in the usual methods is devoted to the investigation of these changes. In this situation, most ineffective frames in the main process of the video are automatically detected and excess frames are discarded even with changes in some areas such as the background or neutral objects. The results show that this method, while reducing frames and summarizing videos with human-object relationships, helps to use deep learning algorithms for online processing of these videos.
A Hybrid MFWT Technique for Denoising Audio Signals

Any unwanted background sound is considered noise. The perception of a sound at the same decibel level is determined by whether it is considered pleasant music or irritating noise. The removal of such noise is necessary to increase the quality of the audio signal as it travels through the communication system.A microphone is used in this study to collect real-time audio signals. A real audio signal is collected and combined with salt, pepper and white Gaussian noise for this experimental study. To remove disturbances from an audio signal, this study integrates linear and nonlinear spatial filtering mechanisms such as the median low pass filter, finite impulse response low pass filter, and wavelet transform approach. The suggested hybrid filter technique is compared to individual filtering methods, and its performance is measured using fundamental metrics such as mean square error and signal-to-noise ratio. When compared to other individual nonlinear filtering approaches, the hybrid method has a high signal to noise ratio (SNR - 88.04%) and a lower mean square error (MSE - 0.003).
Traffic Captioning: Deep Learning-based Method to Understand and Describe Traffic Images

The world around us is full of pictures. Although it seems easy for humans to compress much visual information, it is still a problem for computer systems with low output accuracy. In this paper, a method is introduced to convert traffic images into their descriptions. The presented description is grounded on prominent objects from images with deep learning models and includes three fundamental ways. First, data processing is performed on training images. Second, functional features are extracted by two deep neural networks named EfficientNet and InceptionV3. Finally, two neural networks, Gated Recurrent Unit and Transformer, are used to convert image features into text. Eventually, the optimal solution will be introduced, significantly increasing the quality of the output sentences. The MS-COCO dataset is used to evaluate the proposed methods. For this purpose, a subset including 8000 images and ten classes of traffic objects in the MS-COCO dataset are used and pre-processed. The accuracy of proposed model using BLEU evaluation is 66.9%.
A Lightweight Cryptographic Algorithm with a Multi-Purpose Encipher Key for IoT

Internet of Things (IoT) is a rapidly developing technology that has the potential to change how individuals and businesses interact. However, the actual realization of IoT technology has been hampered by numerous security and privacy concerns. Increased number of CPU clock cycles among the traditional error sensitive cryptographic algorithms is an increasing concern in IoT devices. As most IoT devices rely on batteries for power, new low power consumption algorithms are required. As a result, we have created an algorithm that uses an encipher key for more than one security concern with the goal of reducing power consumption while maintaining security. Using a synthetic dataset, this study compared the proposed algorithm’s results against a synthetic algorithm. It results in less CPU time, fewer clock cycles, and higher throughput than the synthetic algorithm. Furthermore, during the encryption process, our algorithm was able to establish an encipher key that was later used to maintain message integrity. In this study, we have addressed the question of multi-purpose and power consumption.
Deep Multi-scale Dilated Convolution Neural Network with Attention Mechanism: A Novel Method for Earthquake Magnitude Classification

The magnitude of an earthquake influences the amount of damage and casualties it causes in any society. Although reliable and timely prediction of magnitude earthquakes can help reduce damage, it requires advanced algorithms to extract relevant information from raw seismic data. Because of its high strength in better representation of features, Convolutional Neural Network (CNN) has demonstrated success in earthquake prediction among deep learning algorithms. The features of different received fields obtained by various sizes of convolution kernels can give comprehensive seismic information. Conventional CNN uses a one-size convolution kernel to learn complete information from complex inputs. Furthermore, conventional CNNs have a limited capacity to learn local and global features simultaneously. To address the issues above, we suggested a novel strategy based on a multi-scale dilated convolutional neural network with an attention mechanism (MSDCA). While the multi-scale module automatically obtains the rich features of different time scales, the attention mechanism decides the weight of distinct scales, improving the model’s dynamic adjustment performance and adaptable capacity. The purpose of this study is to classify the magnitude of Himalayan earthquakes into different categories based on their importance. The outcomes and comparisons with state-of-the-art techniques demonstrate the proposed method’s superiority.
Real-Time Product Visualization using Augmented Reality

In today's world technology is taking over everything and making our life easy day by day, that’s why we are able to do our stuff more easily. It is hard to visualize how the furniture item would look in reality and complement the already existing items in the room. This can be easily solved using Augmented Reality. With augmented reality, real-life and virtual environments are combined. We can see in real-time what the item will look like in your room.To help customers who buy interior products online, an application for smart phones is proposed to help them virtually see how their room will look after purchasing the product. It uses Google AR-Core for augmented reality. AR-Core will take as input, the image from the device camera. The software identifies planes and then overlays the 3D model of the product onto the image in real-time.This will give an almost realistic experience of placing that product in the user's home. Products like wall paintings, furniture, accessories, and electrical appliances can be placed and viewed. Intelligence within the application will make relevant suggestions to the user to help buy related items. This will help a person to decorate even an empty room from scratch. For association mining, the Apriori algorithm was used to recommend products to the user. This algorithm will find the most frequently bought sets of products so that it can provide suggestions.
Profile face recognition based on elements by normalizing global and local features

Despite recent advances in face recognition using deep learning, pose changes are still one of the challenging problems. In this paper, we presented a method to normalize the image in the feature space by capturing local consistency during training. we used facial elements such as eyes, lips, nose, and hairstyle in addition to its general state for recognition. For this purpose, all the elements were detected and localized by semantic segmentation and then normalized. Specifically, an Attention-based convolutional neural network is introduced to integrate local features with their long-range dependencies yielding better feature representations and hence generate representation that preserves identities better, especially for larger pose angles. We used the face/head segmentation dataset for training and the FERET dataset for testing, and the results were good for images with the most difficult angular conditions. The performance of this model on the +90 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> images of the FERET dataset for 980 samples was 49.6%compared to VGGFace which was only 2.6% accurate.
Research on Image Classification Based on Convolutional Neural Network

In the field of image research, aiming at the problems of complexity, large amount of calculation and low accuracy in the traditional image classification process, a variety of machine learning algorithms can be used. By extracting image features, the computer can effectively manage and classify different types of images. In recent years, convolutional neural networks have gradually become the mainstream of image classification applications, and performed very well in the field of image classification. Based on the TensorFlow deep learning framework, a 9-layer convolutional neural network was designed in this study, we applied the Modified National Institute of Standards and Technology (MNIST) image dataset to train the network model and optimize model parameters, and compared the classification effect with the Support Vector Machine (SVM) model. The results show that the classification accuracy of convolutional neural network is 4% higher than that of SVM model.