Reviewerly/exHarmony huggingface dataset:
Women safety by real-time eye tracking for password testimony

The process which includes to fulfill the credentials, which involves educating and authenticating actual users each time they log in. The educating and authenticating strategy involves gathering and tracking human eyeballs for storage in the database as an identity. The program aims to protect privacy by offering security. The action is part of automating the process by getting rid of the manual labor. The major focus of study is on PIN entry utilizing the blinking approach. As part of the authentication procedure, users are requested to enter their personal identification numbers. PIN based password verification requires users to enter a physical PIN, which puts them at danger of having their password cracked or stolen via methods like thermal scanning or shoulder surfing. A more secure option for inputting a password is PIN authentication using eye blinks input techniques, which do not leave any physical imprints behind. The method of generating the PIN after identifying eye blinks in a string of related image frames is known as “eye blinks-based authentication.” The paper showcases a real-time application that shields users from dangers like thermal tracking and shoulder surfing.-Economic Empowerment, Skill Based Education, Women Empowerment, Rural Development.
A two-stage insulator cap recognition for automatic insulator defect inspection

With the wide usage of UAVs, people can use aerial images to inspect the power equipment and find insulators’ faults. However, there are still a lot of challenges in automatic inspection, such as the different orientations of targets, complex backgrounds, and the condition of the block. To deal with the challenges and increase the accuracy of insulator and fault detection, a two-stage insulator cap recognition method is proposed. In the first stage, the rotated bounding box and an attention module are used to locate caps. And then a refinement algorithm is proposed to increase the accuracy in the second stage. The experimental results show that the proposed method has a mAP of 99.1% and an accuracy rate of 99.3%, which outperform the others.
Image Generation from Text and Segmentation

Although Text-to-Image tasks have been developing in recent years, the controlled generation of images that represent the layout of multiple complex objects remains a challenging problem. Specifically, challenges for the GLIDE model of image generation from text are: controlling the number of objects, scale conversion, instability of generation (objects indicated in the text do not appear in the image), and unnatural object structure. In addition, the task of generating images from textual information alone is so flexible that it is difficult to derive the relevance and requires a large number of data for training. In fact, the training of current generative models often uses hundreds of millions of text-image pairs. The large scale of the generative model itself makes the training cost enormous. In this study, we propose and validate a new image generation method that uses segmentation and text as input. That is, image generation from text is assisted by segmentation information. This should solve issues that GLIDE had difficulty with, such as controlling the number of objects, scale conversion, unstable generation, and unnatural object structure. Furthermore, the goal is to reduce the number of data needed for training by making it easier to find the relationship between textual information and image generation. As a result of the verification, our model achieved FID-10k score of 17.12 after training with only about 120,000 training data, and it was confirmed that it is capable of handling complex layouts and maintaining natural object structure even with a large number of objects.
Performances of Symmetric Loss for Private Data from Exponential Mechanism

This study explores the robustness of learning by symmetric loss on private data. Specifically, we leverage exponential mechanism (EM) on private labels. First, we theoretically re-discussed properties of EM when it is used for private learning with symmetric loss. Then, we propose numerical guidance of privacy budgets corresponding to different data scales and utility guarantees. Further, we conducted experiments on the CIFAR-10 dataset to present the traits of symmetric loss. Since EM is a more generic differential privacy (DP) technique, it being robust has the potential for it to be generalized, and to make other DP techniques more robust.
Review on Intrusion Detection in Edge Based IOT

A smart environment is essential in today's society. The term “smart environment” is used to describe an environment that is advanced in several ways. These may include technological advancements, improved infrastructure, improved modes of transportation, improved health care delivery, and other developments. With the help of the Internet of Things, smart environments may achieve their main objective of improving the quality of people's daily lives (IoT). In this context, “things” means any Internet-enabled device. IoT's total reliance on the Internet raises serious issues about security and privacy. Traditional methods of addressing security and privacy threats cannot be used with IoT devices because of their limited storage, processing capability, and dependence on external power sources. That's why it's crucial to develop a sophisticated IDS that can perform reliably in an IoT environment. Intruder detection systems (IDS) might be signature-based, anomaly-based, or hybrid. The delay introduced by IoT devices is particularly problematic for use in time-sensitive scenarios. To solve for this delay issue, edge computing was created. Machine learning is one approach that may be used to implement IDS. The purpose of this research is to learn everything that can be learned about various machine learning-based models for vulnerability scanning in edge-based Internet of Things (IoT) networks.
DL Based Genometric Image Retrieval

Retrieving images based on their characteristics has been a hot topic for decades. Deep learning (DL) methods are currently considered state-of-the-art for image processing, classification, and retrieval. Rotation is a frequent way to change the geometric shape of an image. However, due to the usage of anisotropic filters for convolution, Convolutional Neural Networks (CNNs) do not support rotation invariance characteristics. Therefore, the ability to rotate images without changing their essential qualities is a promising new area of research in the field of image processing. In this research, we suggest using feature-pretrained CNN model combinations for classification and comparable image retrieval; these models were trained on a large picture database that included images that had been rotated and scaled. This method yields excellent classification and retrieval results for the Corel dataset, both for the original query images and when rotated and scaled.
Threat Analysis of Cyber Security Exercise for Reservoir Testbed Based on Attack Tree

As operational technology (OT) and information technology (IT) are getting closer in industrial control systems (ICS) nowadays, the security of critical infrastructure (CI) has become an important issue, which may have a great influence on people's livelihood, economy, and even safety. Therefore, governments around the world have conducted a series of cyber security exercises, which is one of the most effective ways to maintain and optimize the security, so as to cultivate the information security capabilities of government agencies and enterprises in the face of cyber attacks. In this paper, we present the framework and situations of the cyber security exercise with two scenarios on our reservoir testbed, and then analyze the threats based on the attack tree model and give the security recommendations to protect the critical infrastructure according to the results of the practical exercise and analysis.
The smart secured harvesting technique for embedded wireless sensor networking using network deep learning model

The well-established term wireless sensor network (WSN) refers to a new class of wireless systems, which are networks of distributed, self-organizing and fault-tolerant miniature electronic devices. Autonomic sources of nutrients. Intelligent nodes of such a network are capable of sending messages along the chain, providing a significant system coverage area with low transmitter power, and as a result, the high energy efficiency of the system becomes a potential component. To ensure high reliability and security of information transmitted in WSN, it is necessary to develop native radio protocols that are resistant to changes in the characteristics of the communication channel, radio congestion, interception and data imitation. In this paper the secured harvesting model was proposed for embedded wireless sensor networking. In this case, it is better to use spread spectrum technologies DSSS (direct number sequence) and FHSS (frequency hopping) methods. Regarding the means of accessing the data transmission medium, mutually exclusive requirements appear for the high energy efficiency of the system and the minimum time delay for data propagation in the WSN. Using CSMA/CA (Carrier Sense Multiple Media Access with Collision Avoidance) as the underlying mechanism has its drawback - network devices must be constantly listening over the air, which leads to increased power consumption.
An efficient Artificial Intelligence based data detection model for complex data communication networks

In general, approaches that fall somewhere between encrypting data and transferring it over communication lines between two computers are applicable. However, these communication lines differ in their characteristics from the lines inside the computer. Information about objects and environmental phenomena, their parameters, properties and conditions, reduces uncertainty and incompleteness of Artificial Intelligence about them. Even considering a simple network of two machines, many problems associated with the physical transmission of signals over communication lines can be identified.. Data transformation and processing allows you to obtain information. In this paper an artificial intelligence based data detection model was proposed to enhance the data quality in complex communication network. The main difference between external communication lines and internal connections is their greater length and the fact that they pass through places often exposed to strong electromagnetic interference outside the shielded case.
A System Build on Block chain technology to Identify and Fight Bogus check scams

One of the most common forms of consumer fraud is the use of a bogus check.There must be currently no method for rapidly authenticating checks and detecting bogus ones. Instead, banks must wait for a longer length of time to uncover the fraud. More specifically, our solution enables banks to communicate with customers while protecting the personal information of the banks' clients. Fake checks come in a variety of shapes and sizes. They could seem as company or personal cheques, cashier's cheques, money orders, or an electronic check. These schemes work because bogus checks resemble actual checks in appearance, even to bank workers. The names and addresses of reputable financial institutions are frequently printed on them.. Banks are maintaining security services for money transactions for customer security purposes directly to check with customers and require their permission. It also charity trust their have Register login and approved by main branch and charity needed send to user and if user need to help charity they can help by give check to charity which passed to bank and amount is transaction to charity and the details about charity amount transaction will send to main branch and maintained by main branch. If a response is received from a concerned customer only, a transaction will happen.
High Level Learning Approach for High Level Collision Avoidance Using Sensors

Motion planning for many unmanned aircraft is challenging because they have a larger configuration space than self-driving automobile development (Automated guided vehicles). Additionally, there are more significant uncertainties and disruptions in UAV environments, which makes inter automatic navigation more difficult. In this letter, I proposed a 2 recurrent neural networks (RL) oriented multi-UAV collision avoidance technique by modeling the unpredictability or noise in the environment. Our objective is to create a strategy that can construct a path without clashing with anything using local noisy data. But unlike supervised algorithms, RL lacks a steady training data set with floor labels, thus its collision avoidance strategies often show significant fluctuation and are difficult to reproduce. To solve these issues, we created a two-stage training method for RL-based collision avoidance. We first optimize the policy, and then we utilize a supervised learning methodology with a loss function that encourages the agents to adopt the well-known reciprocity obstacle detection mechanism. In the second step, we use transmission to fine-tune the policy. The complete computer simulation findings demonstrate that this approach can handle noisy local views with erratic sound levels and can design moment & accident paths under inadequate sensing. We review the impact of our policies in a variety of ways.
The Intelligent Monitoring Model for Resistance Standards of Grounding Devices Installation in Wired Communication

This standard applies to station and line structures of wire communication installations, radio relay stations, and radio broadcasting nodes, selected railway communication installations and antenna systems. Collective Reception Television (SKPT), for which fixed grounding devices are equipped, and establishes resistance standards for grounding devices that ensure the normal operation of the structures and installations listed above, as well as ensure the safety of maintenance personnel. As a protective foundation device, it is allowed to use magnesium protectors used to protect PUP metal tanks from soil erosion. In cases where it is not necessary to protect metal LLP tanks from soil erosion, as well as when using non-metallic hulls. The NUP must be equipped with functional and integrated safety grounding devices. Unobserved amplifier points (NUP) and regeneration points (RP), operated remotely according to the “wire-wire” scheme, as well as the NUP. When the remote power circuit is operated according to the unterminated “wire-to-ground” scheme, two separate grounding devices must be fitted. As grounding conductors for a protective grounding device, it is allowed to use magnesium protectors used to protect metal tanks of LNP or RP from soil erosion.
Multilingual sentiment analysis for android mobile application (Bandhu) for aged people

Over the years, technology has changed our environment and become a necessary component of our daily life. The senior population must also be considered in this widespread digitization. The older population is often not included in digital society, but despite the many and varied challenges they face, they are nonetheless motivated, involved, and willing to try to integrate new digital technology into their everyday lives. As a result, this project focuses on evaluating and identifying the feelings of elderly persons by using machine learning methods based on natural language processing and creating an application called “BANDHU - YOUR FOREVER FRIEND” that they may access and utilize. This suggested solution also includes a number of additional features, such as language learning and listening to regional music that would meet the needs of a country with a diverse population like India, recording sound narratives to preserve ethnic culture and motivate others, and, finally, becoming tech-savvy by watching simple video tutorials. In addition to keeping them entertained, these elements would help them overcome some of their feelings of loneliness and exclusion and increase their digital independence and inclusion. Given that Hindi is the most widely used spoken language of India, the whole programme has been created in this language.
Web Sensor Network For Health Monitoring Systems

Patient checking is an urgent piece of the medical care framework these days, either at clinics or home. By utilizing IoT innovation, it is feasible to defeat the issues looked at by truly unsteady patients and needy individuals in counseling a specialist consistently. The essential thought behind the task is, that it suggests that whether an individual is at home, out traveling, or at his workplace, he/she can remain associated with the specialists in the emergency clinic or project that offers an IoT-based patient observation platform that includes wearable sensors that collect information on many factors affecting patients' welfare. The sensors are bridged to the Node MCU Microcontroller which has a Wi-Fi Module with a built-in TCP\IP protocol stack that can connect any microcontroller to your Wi-Fi module. The specialists bring the well-being data of patients using a UI (PDA). Additionally from the information put away in the cloud server the well-being boundaries, for example, temperature and moistness, pulse and SPO2 levels of the patients and needy individuals for general medical care.
Evolving Journey of Chatbots: Insights into Business Decisions

Rapid digitization and the emergence of the internet and mobile devices have altered how individuals communicate with businesses. Chatbots are specially designed and developed computer programs that interact with humans by simulating a conversation. Chatbots engage with humans using a variety of frameworks, ranging from a simple text interface to speech recognition capabilities. Conversational Agents are an evolved version of chatbots, where Natural Language Processing (N.L.P.) makes the interaction between the bot and humans much more realistic. This paper answers frequently asked questions about chatbots, bringing out the differences in mundane versus the complex tasks and business intelligence performance of chatbots, discussing the historical backdrop and background for chatbots and conversational agents, two types of dialogue systems. A timeline has been drawn to comprehend their advancement better. Next, a deep dive into how small and medium-sized businesses can reap the benefits of A.I. Chatbots by looking at various ways that chatbots can add value to businesses is carried out, along with linguistic differentiation and how it affects service and customer satisfaction. Finally, we analyse how companies in various industries use Chatbots for conversational marketing. The methodology followed starts with looking into the evolution of chatbots and the core decision-making changes with each step. Next, two types of chatbots have been defined based on technology and use and discover the complexity of these technologies. A deep dive into how businesses can reap the benefits of A.I., followed by the various ways that chatbots add value to businesses, is investigated, followed by reviewing the areas of applications in different industries. Lastly, analysis into how companies use chatbots for conversational marketing is done.
The commercial hard disk backup system for quick recovery operating system in cloud storage system

Drive Backup is an application for backing up data, including creating copies of partitions for quick recovery in case of an accident, virus attack or, if necessary, replacing all data, including the operating system and installed ones. Software, plus a new hard drive. Reinstalling the operating system and applications after a hardware failure or virus attack does not take you much time and effort. The best way to protect your computer is to create a backup of the system partition with the operating system installed on it and all the necessary applications. In this paper, The commercial hard disk backup system for quick recovery operating system in cloud storage system. Copies can be made to hard drives and removable media as well as network-connected drives. If you need a disk management program, check out the corporate version of this package. A multicast function for transferring copies of an image to multiple computers at the same time, well suited to the needs of corporate offices (for example, to create or restore multiple workstations). But for home backup, you may need to think about other programs - simpler and faster.
Bitcoin Price Prediction Using Deep Learning and Neural Networks

This work explains the role of tree regressions, long-term short-term memory models and ARIMA models in predicting Bitcoin values. In this study, we used Bitcoin Sets data to test and train the Ml and the Ai models. The data filtration process was completed with the help of the Python Library. When we understand the data, we adjust it for using those characteristics or attributes that are best suited for model. Here this model was implemented by recording the results. The accuracy of the decision tree regression model was found to be high when comparing with other machine learning models; R flat is found that is equal to 0.67.On the other hand, the R of the LSTM model is flat 0.49, the R of the ARIMA model is flat. Ninety-four. By this, we can see that deep learning model is highly improved model when compared with machine learning model.
Analyses of Software Testing Approaches

Software testing is an essential part of the Software Development Lifecycle because of its importance both before and after code is written. As such, it necessitates the use of well-developed, well-executed processes and procedures. This article attempts to discuss both existing testing techniques and possible enhancements to those procedures in the sake of higher quality assurance. Extensive testing, including validation and verification, is required to guarantee that the software satisfies all clients' requirements. As a result, software development processes and testing strategies are made. However, software testing strategies provide a well-thought-out sequence of actions that should reliably yield results, as opposed to software testing approaches, which focus on how to test a specific feature or function. In this analysis, the advantages and disadvantages of various software testing strategies are explored. Furthermore, it specifies the basic life cycles that must be observed throughout quality assurance testing.
Security Implementation of Wifi Password Asset Sharing With One Way Hash Cryptography Method Sha256 And QR Code

The study was conducted to model Wi-Fi password resource assets in a platform that can be shared and collaborated publicly securely and support dynamic data changes with online repository shortcuts documented in access support via dedicated search engines and QR CODE. This study uses 3 methods, namely the one-way SHA-256 HASH function which was formulated with the addition of sowing techniques and reversing techniques so as to produce a long String named Keycode, the second method uses modern cryptographic techniques, namely Digital Signature, which collects keycodes in Path the identity of the resource asset, and the last one is a QR CODE used as an access shortcut that collects digital signature access data. This study used testing data from comparator application contributors, namely wifimap.io randomly, the results of which the formulation results were then disassembled using the brute force technique using hashcat. The results of the research of password data from resources were successfully formulated into a combination of Strings with high confidentiality that cannot be disassembled to the original data but are still accessible to the owner and holder of the keycode that is balanced with the limitations of access control.