Scrambling Keypad for Secure Pin Entry to Defeat Shoulder Surfing and Inference Attacks

Personal identification number (PIN) is a common user authentication method widely used especially for automated teller machines and point-of-sales devices. The user's PIN entry is susceptible to shoulder-surfing and inference attacks, where the attacker can obtain the PIN by looking over the user's shoulder. The conventional keypad with a fixed layout makes it easy for the attacker to infer the PIN entered by casual observation. This paper proposes a method of authentication to address these challenges. The paper develops a prototype numeric keypad with a layout akin to the conventional keypad, with the keys randomized for each PIN entry. The shuffle algorithm, Durstenfeld shuffle algorithm, is implemented in an application developed using JavaScript, which is a prototype-based object-oriented programming application that conforms to the ECMAScript specification. The prototype is implemented on three computing platforms for evaluation. The test proves the effectiveness of the system to mitigate shoulder-surfing and inference attacks.

COVID-19

The world is going through one of the worst pandemics ever seen. After concurrent lockdowns, as the government is easing out, more people are on the verge of risking their lives. This leads to a need for a system that not only provides a user with relevant updates regarding this disease but is essentially a useful tool that can be used to provide a safest path between a source and a destination. Most of the people now are equipped with smart devices. Since the spread is nowhere near its termination and the world is having a lot of breakdowns be it in the form of economic disruption or sociological imbalance due to this, though the government is already working hard on detecting and declaring hotspot zones, there is no real-time evaluation of potentially crowded zones that can be a source of disease synthesis too. There is a need for a system that can notify its users regarding any kind of potentially harmful zones, and since getting on the road is more than a necessity now, a safe route provisioning system is also a dire need of the situation in order to stop the spread.

Performance Comparison of Image Processing Techniques on Various Filters

Image processing plays a crucial role in a large number of applications including fields of medical, watermarking in images, spatial data analysis applications. When images are static, generally, users can get good performance, though processing of real-time images are dependent on various parameters like efficacy of algorithm and filtering techniques. Researchers have observed high variation in performance during processing of real-life images; therefore, efficient filtering techniques play a vital role in determining the implemented processing algorithm's performance as well as the quality of captured images taken into consideration. Thus, the focus of this study is to discuss various widely used filtering techniques and efficient performance analysis in outdoor environmental scenarios. A real-time efficiency system is made to conclude each filter type's effectiveness in different environmental conditions with comparison and evaluation, highlighting merits and demerits of different algorithms based on application needs along with external factors.

Isolated Site Wind System Start-Up System

The abundant energy available in nature can be harnessed and converted to electricity in a sustainable way to supply the necessary power to elevate the living standards of the people without access to the electricity grid. Wind power is one of the cleanest and safest of all the renewable commercial methods of generating electricity. However, wind energy is difficult to use due to its stochastic variability. Energy storage can overcome the main drawback. This article consists of studying a wind starting system based on DFIG and operating in isolated mode. This system is formed by a bank of batteries and a bidirectional DC/DC converter charging a DC bus voltage as well as these batteries. The control of this system required a cascade control. Such control needs two loops: the inside loop to control the inductive current and the outside one for continuous voltage bus. The theoretical study of this command has been validated using PSIM software.

Low Power Restoration Circuits Reduce Swing Voltages of SRAM Cell With Improved Read and Write Margins

This paper examines the factors that affect the static noise margin (SNM) of static random access memories which focus on optimizing read and write operation of 8T SRAM cell which is better than 6T SRAM cell using swing restoration for dual node voltage. New 8T SRAM technique on the circuit or architecture level is required. In this paper, comparative analysis of 6T and 8T SRAM cells with improved read and write margin is done for 130nm technology with cadence virtuoso schematics tool.

Optimal Cloud Service Provider Selection Based on QoS Metrics

There are thousands of providers obtainable in the market, and more and more are being added to the service list every day. All of these providers claim that the services given to them are distinctive and hassle-free. To check their claim, the cloud service broker (CSB) verifies the quality of service (QoS) of the cloud service providers (CSPs) and the level of user needs. Depending on the requirements of the cloud consumer (CC), CSB allocates a CSP to it. This paper proposed optimal cloud service provider selection (OCSPS) based on QoS metrics. The CC would handovers the demand to the CSB for optimal CSP selection using QoS. Once the CSP selection process is complete, the outcomes would become back to the CC. Entire demands created to CCs are saved in the request buffer (RB) in the CSB. When a specific request is fulfilled, the subsequent demand would take from this RB. The experimental result shows that the proposed OCSPS algorithm takes less time for CSP ranking and optimal CSP selection.

Detection and Tracking Cows by Computer Vision and Image Classification Methods

In this paper, the cow recognition and traction in video sequences is studied. In the recognition phase, this paper does some discussion and analysis which aim at different classification algorithms and feature extraction algorithms, and cow's detection is transformed into a binary classification problem. The detection method extracts cow's features using a method of multiple feature fusion. These features include edge characters which reflects the cow body contour, grey value, and spatial position relationship. In addition, the algorithm detects the cow body through the classifier which is trained by Gentle Adaboost algorithm. Experiments show that the method has good detection performance when the target has deformation or the contrast between target and background is low. Compared with the general target detection algorithm, this method reduces the miss rate and the detection precision is improved. Detection rate can reach 97.3%. In traction phase, the popular compressive tracking (CT) algorithm is proposed. The learning rate is changed through adaptively calculating the pap distance of image block. Moreover, the update for target model is stopped to avoid introducing error and noise when the classification response values are negative. The experiment results show that the improved tracking algorithm can effectively solve the target model update by mistaken when there are large covers or the attitude is changed frequently. For the detection and tracking of cow body, a detection and tracking framework for the image of cow is built and the detector is combined with the tracking framework. The algorithm test for some video sequences under the complex environment indicates the detection algorithm based on improved compressed perception shows good tracking effect in the changing and complicated background.

Analysis of Big Data Using Two Mapper Files in Hadoop

Nowadays, a large amount of valuable uncertain data is easily available in many real-life applications. Many industries and government organizations can exploit this data to extract valuable information. This information can help the managers to enhance their strategies and optimize their plans in making decisions. In fact, various private companies and governments have launched programs with investments and funds in order to maximize profits and optimize resources. This vast amount of data is called big data. The analysis of big data is important for future growth. This paper depicts big data analytics through experimental results. In this paper, data for New York stock exchange has been analyzed using two mapper files in Hadoop. For each year, the calculation of maximum and minimum price of every stock exchange and the average stock price is done.

A Comprehensive Comparison of Hybrid Mobile Application Development Frameworks

The number of users of smart mobile devices is growing every day. Because of the popularity of using mobile devices, it is important for business stakeholders to develop mobile applications targeting all mobile platforms in order to ensure that the number of users is as large as possible. One possible solution is the creation of hybrid mobile applications. These are applications that combine the properties of web and native mobile applications, and their main advantage is compatibility with multiple mobile operating systems. This paper presents the results of very comprehensive experiments that involved the use of various hybrid mobile development frameworks that were tested under different scenarios. Experiments revealed that the performance of hybrid applications in different scenarios varies considerably, although the results of these applications were comparable to those that were achieved in the experiment with the native application.

A Study

Chikungunya, an infection which is difficult to treat, took a toll on Delhi in year 2016. In that scenario, detection and prevention of vector-borne diseases outbreak in Delhi have been a major cause of concern for government. For analyzing this epidemic outbreak, the authors have utilized the unstructured data generated through Twitter. Twitter is a social media platform that generates vast amount of epidemic-related information every day. This information is used to analyze the effect of epidemic outbreak in Delhi region. In this paper, the authors discussed an associated study of various machine learning techniques for analyzing and mining social media information. In this, the authors have also categorized and explore the steps involved in social media textual data to provide a pictorial view of the ongoing outbreak. Finally, the article discussed the challenges faced for mining social media data.