Reviewerly/exHarmony huggingface dataset:
Hardware based Analysis of Deep Neural Networks

The advent of the Machine Learning (ML) era is now evident and its inculcation into early education requires students to have feasible options to work with the field. The proposed comparative analysis tests different frameworks namely sci-kit with Keras, Tensor-flow and PyTorch on various available processors and discussed further using a range of standard metrics to evaluate the model and well as the underlying hardware that they will run upon. Based on the study, the most viable combination of framework and hardware for educational purpose shall be found out.
Hand Gesture to Character Recognition using Convolutional Neural Network

Communication serves as the medium to interact with each other, be it verbal or nonverbal. Sign Language is a structured set of visual gestures and movements with specific meanings. It is a communication tool for people having hearing impairments. According to current statistics, over 5 per cent of the world's population suffers from hearing loss, that is 432 million adults and 34 million children. While the UN predicts that by 2050 every 4th person will suffer some hearing loss, the need to at least reduce this communication gap is a near-future reality. Sign langauges are often hard to learn and understand. The paper presents an optimal approach to providing a medium for effective communication for people having vocal and hearing disabilities using hand gestures. The proposed model interprets American Sign Language (ASL) from hand gestures. The paper focuses on using Convolutional Neural Networks to predict ASL efficiently. This includes the translation of the 26 static sign language alphabets. Phase one involves preprocessing the visual input gesture, followed by its computation. Phase two consists of recognizing the letters with an accuracy of 99.52% and a Cohen Kappa score of 0.995. The paper further presents the statistical result evaluation of existing and proposed techniques.
Motion Estimating Optical Flow for Action Recognition : (FARNEBACK, HORN SCHUNCK, LUCAS KANADE AND LUCAS-KANADE DERIVATIVE OF GAUSSIAN)

Motion estimating is one of the methods which determines the movement from one frame to another in the videos. For an application of action recognition, choosing the optical flow can be an essential feature for recognizing actions. The optical flow consists of the information of the moving subject and objects in the video frames. This paper analyzes four motion estimating optical flow methods (Farneback, Horn Schunck, Lucas Kanade, and Lucas-Kanade Derivative of Gaussian explored based on visualization and PSNR. The NTURGB+D dataset uses for the analysis of experimental results.
A Novel Approach for Product Recommendation using XGBOOST

Sentiment analysis is the most trending research area. Generally, most purchase decisions and price predictions are made based on product reviews. Sentiment analysis helps in understanding the product better. The sentiment analysis of a product summarizes whether the product has a positive, negative or neutral rating. Existing machine learning algorithms like logistic Regression, Decision Tree are used to determine sentiment for product reviews. This work includes XGBOOST and a hybrid model XGBOOST - RF used to observe sentiment on product reviews. The model that gives best performance is used to build a system that recommends products to users.
Weight based Load Balancing in Kubernetes using AWS

Kubernetes is a container orchestration system to automate software deployment,scaling and management of containers. Kubernetes offers Load Balancer service to distribute external traffic to the pods.Load balancing is the process of distributing network traffic across multiple servers and prevent a single server from being overloaded. This article has discussed about the existing load balancing algorithms and different performance met that affects the load balancing. Canary releases helps to deploy applications or services to a small percentage of users which minimizes the scope, impact, and risk associated with introducing new software assets into production.To implement a weight-based load balancer in Kubernetes using AWS, an healthy Kubernetes cluster is deployed in the AWS. An automated canary deployment approach is utilized, to distribute traffic based on weights assigned to the services. Linkerd and Flagger traffic split functionality helps to dynamically switch the traffic across services. Deployed sample applications, primary version and canary version in the cluster and configured weights of the application. The load balancer shifts the traffic from 10% ,100% with the step size of 10% to Canary version. All the target environment can be updated with small increments such as 10%, 25%, 75%, and 100% traffic load increments.
Cloud Resources Forecasting based on Server Workload using ML Techniques

Provisioning of resources, automatically, according to the service demand allows cloud service providers (CSPs) to implement elastic services. It is highly essential to guarantee QoS (Quality of Service) and to fulfill SLA (Service Level Agreement), in particular for the services having strict QoS needs like web servers getting a heavy load. Over-provisioning occurs when there is low demand, under-provisioning when there is a high demand and both of these situations result in poor quality of Experience (QoE) for the users. So, an approach is required to forecast future workload accurately prior to the resource allocation so that the right amount of resources can be provisioned. Such a system can help CSPs to achieve efficient resource allocation that maximizes their economic growth and also gains users’ satisfaction. This article presents an efficient workload forecasting mechanism built with the Support Vector Regression (SVR) technique to evaluate the number of resources required using web server workload time series data and queuing theory. The main goal of this technique is to forecast the future workload of the web server and predict the resources required to minimize the latency while reducing infrastructure costs and energy consumption. Normalization and outlier removal are performed for better forecasting. Various other techniques are also compared in estimating workload. Forecasting of load and prediction of resources is done with good accuracy by our proposed model better than many other models.
Smart Security System using Shuffling Keypad with SOS System

Security systems for lockers or vaults these days have been largely compromised. As technology advances, the possibility of robbery also increases. One of the most secure and widely used security systems is password protected. As with any devices that use a keypad for authentication or access, there is a risk that the password will be compromised by unscrupulous individual may deliberately observe your finger motions on the keyboard and attempt to predict your password or use the powder method to recognize fingerprints. This paper presents a smart security system with a shuffling keyboard to overcome the issues and reduce the thefts. This system also comprises solutions for robberies in case of hostage situations, attack on the large organizations, home thefts, and unauthorized vehicle access by activating smooth silent alert system. This system is built by using Micro controller with touch screen. The screen displays every message for the users understanding.
Corneal Localization for Discerning Faces using Advanced Machine Learning Algorithms of GAN

The field of Deep Learning (DL) and Machine Learning (ML) has evolved over the years, these models generally consist of biometric recognition systems which need an extensive dataset, high-performance computing systems, and a time- consuming process approach. With existence of modern frameworks such as Generative Adversarial Networks, numerous features in biometric systems which are employed for data security can be spoofed into, as a result it creates unexplored vulnerability. These models are extremely sophisticated and are now able to manufacture artificial human features which are difficult to differentiate from the authentic ones. The work in this paper helps to recognize and explore various GAN-synthesized corneal features with the use of style generative adversarial network 2 (StyleGAN-2). The model presented extract features of eyes from the facial images passed into it, Specifically, the cornea which consists of specular highlights of an individual. The training method used for the model is employed by generating multiple unique data instances, which synthesize enormous high quality dataset containing features resembling the training data. Thus, simplifying the process of training the model in an efficient manner by making use of progressive generators in StyleGAN-2 with CNN algorithms. Generated images and real images are further provided to the discriminator for discerning the model-synthesized eyes from real eyes. The generation of synthesized images, to the extraction of corneal specular highlights was experimented using three different processing methods: Central Processing Unit (CPU), Graphical Processing Unit (GPU) and a Tensor Processing Unit (TPU). Comparative analysis on these three methods was performed based on performance and time taken when tested with varying quantity of datasets and diverse environment.
Blockchain based Certificateless Privacy Preserving Public Auditing for Cloud Storage Systems

With the increasing popularity of cloud storage systems, users often outsource their data to the cloud. Cloud-servers provide storage space at a low cost and reduce the burden of the users. Once the users outsource data to the cloud, they lose control over their data. Hence, the integrity of outsourced data is the main challenge. To address this issue, many public auditing schemes were proposed, based on third-party auditors (TPA). However, traditional public auditing schemes are vulnerable to malicious TPA who may not provide correct auditing results on time. To resist malicious TPA, blockchain-based public auditing schemes have been proposed recently. Although, blockchain-based auditing schemes avoid malicious auditors, they are suffering from the key escrow problem, because their design is based on identity-based signatures. To overcome the key escrow problem and malicious auditors, in this paper, we propose a Blockchain based Certificateless Public Auditing scheme for cloud storage systems. In security analysis, we prove that the proposed scheme is secure against the type I/II/III/IV adversaries. In experimental analysis, we show that the proposed scheme outperforms than existing schemes.
Searching Parameterized Retrieval &amp; Verification Loss for Re-Identification

The goal of re-identification (re-ID) is to find an object (e.g., person or vehicle) of interest across cameras. In re-ID, designing suitable and effective loss functions plays an essential and imperative role in learning identifiable features. Regardless of the significant success achieved by using retrieval- or verification-based loss functions due to re-ID can be formulated as a retrieval or verification task, the model performance might be degraded owing to the inconsistency between the loss functions and evaluation metrics. Moreover, current hand-designed loss functions based on evaluation metrics require great expertise and a significant workforce, which is often sub-optimal and laborious. To this end, we propose to automatically design loss functions with specific evaluation metrics for re-ID. Specifically, we propose Parameterized Retrieval & Verification (RV) Loss, which jointly optimizes RV tasks while introducing parameterized functions to replace non-differentiable operations in RV evaluation metrics. Different evaluation metric approximations are thus represented in a single formula by a family of parameterized functions. Then, an automated parameter search algorithm is used to conduct the parameter search. Experimental results indicate that the proposed Parameterized RV Loss can improve the performance of the state-of-the-art re-ID methods, thus demonstrating its effectiveness and superiority over other relevant loss functions on the public person re-ID and vehicle re-ID benchmarks.
An empirical analysis of Cyber Crimes, their prevention measures, and laws in India

Cybercrime is pervasive, widespread, and becoming more intertwined with other elements of the criminal ecosystem. The Theft of a person's identity to the destruction of a nation's Internet connectivity as a result of a large attack on that nation's networking and computer capabilities are just a few examples. The term "Cybercrime" refers to any criminal activity involving computers or networks. Moreover, Cybercrime includes criminal acts committed online. Cybercrime is a new area of crime, and with each day that goes by, more and more new types of Cybercrime are becoming visible. This study examines the various types of Cybercrime, including those committed against people, property, and the government, and the steps taken to prevent these crimes. The rate of crime has increased along with digitization. Such crimes must be prevented by law and activities.According to observations, there are numerous cyber laws and acts framed to prevent Cybercrimes, including The IT ACT of 2000 and cyber security regulations. This paper illustrates an analysis of Cybercrime and their emergence, prevention measures, and laws that have become a need and necessity in India.
A Comparative Study on Improving Word Embeddings Beyond Word2Vec and GloVe

NLP or Natural Language Processing in Machine Learning forms a subarea with linguistic roots that has applications in analyzing and predicting natural language data, namely speech and text. Deep neural networks are used in cutting-edge NLP, and the process includes several steps: data collection, preprocessing, language modeling, parsing, and prediction. This research will focus on the aspect of language modeling where words must be represented using embeddings. The study emphasizes improving post-embedding model performance, particularly for unsupervised models, although the whole word embeddings used by these models have remained relatively unchanged. Word2Vec, for example, employs an unsupervised shallow 2-layered network that has suited its function thus far, but making this network denser and adding supervised elements appears to be a viable means of increasing spatial-semantic embedding. A better representation early in the model pipeline will significantly help any computer understand the semantic similarity of the data it is examining, which will considerably enhance the performance of any model that employs the stated representation.
Enabling Cost-Effective Inspection and Failure Detection of Spare Parts in Industry 4.0

Industry 4.0 delves into how a modern manufacturing plant adopts technology to become a part of an intelligent system that intends to make processes simpler and smart. The advancements in technology in the fields of Artificial Intelligence, Machine Learning, and Robotic Process Automation have lifted the entire subject. In this era of the continuous evolution of various industrial equipment, it is essential to detect and prepare fallback mechanisms for different spare parts involved in a factory. This paper discusses Enterprise-grade solutions available for the manufacturing industry involving a network of Original Equipment Manufacturers. This research highlights techniques like Image Segmentation, Centre of Mass Matching, Template Matching, and others with an existing Industry solution like an Asset Intelligence Network offered by SAP for inspecting equipment and their subsequent spare parts. It delves into concepts of image recognition of Look-alike parts for their inspection and segmentation. This study paper further explores the business implications and how different ERP modules are connected to provide a seamless experience for separate solutions to result in an integrated Autonomous Enterprise of various functions and technologies.
Design and Development of Protected Services in Cloud Computing Environment

In quantum-based computing, the communication uses quantum bits rather than digital bits to address the information. This work proposes to employ encrypted communication for sharing the keys called bits in the Quantum framework. Cloud computing is one of the services, which supports both communication and quantum registering. For small to huge computing tasks, providing any software, platform, or infrastructure as a service, pay-n-use technique should be possible in distributed computing. Based on the duration and amount of administration utilization, the payment will be determined. The principle aim of the distributed computing is to create a resource where anybody can get to any help, whatsoever from anyplace. Protection and security are the fundamental dangers and issues of distributed computing. To overcome such problems in distributed computing, multi-tenure and framework sub-contracting method is used. The proposed encoding technique is far speedier and more effective than traditional encryption, for example, DNA-based encryption computing. To protect the information from intruders, better safety efforts are required. This theoretical investigation recommends such a strategy to defend against certain attacks.
Randomized Matrix-based Double-Key Cryptographic System

Cryptography plays an important role in information security and in secure communication, which defines the security of a particular system. Many modern cryptographic algorithms exist today however they face difficulties in uniform implementation. In addition, different types of cryptographic techniques target different types of text lengths for encryption. Ancient Indian literature has many robust encoding schemes which include Siri Bhoovalaya, Ekakshara Kosha, and Mlecchita Vikalpa. This research work presents a Randomized Matrix-based Double-key Cryptographic System (RMDCS) to encode small to large amounts of information in a matrix. This work is inspired by matrix-based encryption used in one of the ancient Indian techniques called "Siri Bhoovalaya" RMDCS aims to bring forth a modern cryptographic technique that coalesced a randomized matrix approach with Caesar cipher. RMDCS intends to achieve one-time padding by generating a unique key every time while encrypting the same text.
Investigation of Accelerated Life Test on the Conductive Slip Ring of Ship’s Platform Inertial Navigation Equipment

The conductive slip ring is an important part of the frame-type turntable, which is mainly used for signal transmission between continuously-rotating frames. The service life of the conductive slip ring is a key technical indicator of the frame-type turntable, and it directly determines the reliability of the turntable. In this investigation, the conductive slip ring was designed optimally, improved and innovated by applying the computing simulation and simulation technology of the simulation cloud platform, thereby the rapid analogy verification on the cloud computing simulation analysis model and the physical model was realized, and the service life of the conductive slip ring was verified. In this paper, the key factors affecting the service life of the conductive slip ring are studied; and an experimental verification method for the accelerated life of the conductive slip ring is proposed based on the calculation simulation of the simulation cloud platform.
Hierarchical Analysis of Intelligent Fusion Data of College Training Theory based on Virtual Cloud Classroom

Hierarchical analysis of intelligent fusion data of the college training theory based on virtual cloud classroom is studied in the paper. AJAX and Silverlight are not mutually replaceable technologies, but complementary technologies, each with its own strengths, so Microsoft's strategy is to combine the strengths of both. Hence, in this paper, the MVC is integrated for the implementations of the virtual cloud classroom. The ViewModel class encapsulates the data classes in the Model, and also contains the processing code for the business logic of the View. In the system, the business logic of the sub-function modules under the same main module is similar, we then apply the data fusion ideas into the model for the optimization. Through the verification of the experiment, the proposed model is then proven to be efficient.
Detecting Fraudulent Bank Transactions Using Deep Learning Enhanced with Genetic Programming

Today, the huge amount of credit card transactions make detecting fraudulent ones increasingly difficult. This paper explores the idea of building a Neural Networks-based solution for detecting the fraudulent (i.e., unauthorized) transactions. The aim is to provide robust outputs and a reasonable tradeoff between Precision and Recall, while complying with the constraints imposed by working in a real time payment environment. The solution relies on a applying Genetic Programming on two stages: evolving network architecture and fine tuning network parameters for enhancedg network performance. This is combined with the idea of a so-called “Four-eyes Check” system, which tries to address the customer specific needs in terms of precision and recall. The approach was validated on a publicly available dataset which contains real life credit card transactions on a span of two days. It contains only numerical input variables which are the result of a PCA transformation, with no information on the original features or background information available. With no feature engineering except scaling, the system’s performance hit 80% on both precision and recall. Using the Four-eyes check lead to an increase of about 3%, depending on the specific functioning mode selected by the user.
A Semi-Supervised Approach to Cell Nuclei Segmentation

Medical image analysis is one of the most important applications in the fields of computer vision and medicine. Developing specific models that extract information from biomedical images could help doctors provide faster and more accurate diagnosis. One highly significant problem in this domain is called cell nuclei segmentation and it consists in identifying if a pixel from a medical image is a part of cell nucleus or not. Building such a model would help specialists detect several biomarkers of tumors. Nowadays, it is a well-known fact that the best tools for computer vision tasks are using deep neural networks, but they have one vulnerability - the data. Accurate and robust models rely on numerous labeled samples, but obtaining such a large, various annotated data set for cell nuclei segmentation is very difficult. One solution to tackle this problem is to use semisupervised learning which extends the data set with unlabeled samples during training. The aim of this paper is to build a model that performs cell nuclei segmentation in a semi-supervised manner by using Cross Pseudo-Supervision. We have run our experiments on 2018 Data Science Bowl dataset and we have achieved an IoU of 0.9077 using a fully-supervised setting, an IoU of 0.8493 using the same architecture, but in a semi-supervised setting, and an IoU of 0.7734 with a U-Net trained only on the labeled samples fed to the semi-supervised model. These values indicate the potential of machine learning, especially in those cases when only a small amount of labeled data are available.