Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based Model to Improve Patent Authentication and Management Process

Patent authentication and management is a vital activity for protecting individuals' and organizations' intellectual property rights around the world. As patents are territorial and data management for patent applications is primarily handled by local intellectual property offices, the patent authentication and management procedure are inefficient and time-consuming on a worldwide scale. Furthermore, data management concerns in the patent process frequently result in conflicts and legal actions among competing parties. Blockchain technology is generally recognized for its potential application in the development of decentralized, secured, and transparent systems with immutable records. As a result, it appears to be a viable solution for overcoming issues in the patent domain. The purpose of this research is to demonstrate how blockchain technology may help with patent authentication and management. To explore the process of the existing system and its shortcomings, interviews with relevant stakeholders and a comprehensive literature review are conducted. To overcome the highlighted barriers and limitations, a novel architecture and prototype of a consortium blockchain system are proposed, with regulated bodies as nodes such as the World Intellectual Property Office and Regional IP Offices handling the consensus mechanism. The proposed consortium blockchain design is based on the Ethereum architecture and is equipped with smart contracts. To arrive at conclusions, it is tested in a simulated environment with generated actors and data. The present study demonstrates that blockchain can ensure the reliability of patent data and real-time updating of records efficiently and at a low cost. Furthermore, patents that have been registered to this network become reliable across IP offices. Without relying on a single patent office, patent applications can be approved or rejected with the proposed architecture. The prototype model shows considerable improvement in establishing evidence of first ownership which helps minimize a significant number of litigations. The study further shows how Blockchain Serves as a distributed database which is very much useful in cross-validated reliable systems like secure document management for government processes.
An Implementation of Coding Rule Checking Function for C Programming Study

Presently, C programming is essential for information technology (IT) students to study programming languages, algorithms, and computer architecture. It has been taught as a basic programming language in many universities. Due to the limited lecture time, coding rules to make readable codes are rarely educated, although they are essential in improving programming abilities of students. In this paper, we implement a coding rule checking function for C programming study of novice students. It helps a teacher to check the correctness of the names of variables and functions and the coding styles in the source codes submitted by the students and to give feedback to them. As preliminary evaluation, we verify the correctness of the implemented function through testing 109 source codes from one student.
A Search-Based Approach on Metaheuristic Algorithm for Software Modularization to optimize Software Modularity

Software refactoring is a process to maintain software quality that will further improvise the software's internal structure without amending its external behaviour. The software refactoring is implemented in the software maintenance phase by modularizing the source code of the original Software clustering is a modularization approach that is used to modularize source code objects with the purpose of boosting the code's reusability and readability. Owing to the clustering issue being NP-hard, evolutionary methods like the genetic algorithm were utilized to address it. There is no search-based technique for modularization that uses a hierarchical method in the structural refactoring literature. It is an unsubstantiated learning technique utilized to cluster software objects (for example files, classes, or modules) with similar structures. The attained clusters can be utilized for a research study, analysis, and comprehending the software objects&#x2019; structure and behaviour. While one observation is that executing software module clustering with optimum consequences is thought-provoking. In the research paper, we have utilized the local and global methods wherein a metaheuristic hierarchal search-based clustering approach has been introduced that can help in modularizing the software system. In the algorithm, the outcome is a tree that has nodes including artifacts wherein sub trees collate these artifacts and it is a cluster that is a candidate solution. This algorithm will help in extracting a newer model of the source code which will further help in deciding the correct artifacts which will redirect to obtain documents, packages, and components. The source code can be fetched from GitHub.
Lexical Ambiguity in Natural Language Processing Applications

Natural language processing (NLP) has long been regarded as one of the most essential areas of AI research. However, in comparison to other fields, development in natural language processing has been modest. The goal of this research is to undertake a comprehensive literature evaluation in order to identify the most popular NLP applications, approaches, and tough difficulties. This article gives a broad overview as well as a discussion of lexical ambiguity in NLP applications such as Machine Translation, Speech Recognition, Text Mining, and Document Retrieval. The goal of this work is to explain several NLP applications, as well as lexical ambiguity and important approaches for dealing with it.This article is to discuss about one of the algorithm Simple 1-nearest-neighbour which deals with Lexical Ambiguity.
A study on the effect of wavelet basis function selection on image denoising

The contamination caused by noise to the image leads to the image missing key information or reflect the role of the real situation. Wavelet denoising based on the spatial domain has a certain denoising effect on the noise. When doing wavelet decomposition on the image, because the wavelet function has the feature of flexibility in base selection, different choices of wavelet basis functions have different denoising effects on the noisy image. Simulation experiments are conducted by Python for several commonly used wavelet basis functions using the control variables method. The final experimental results are evaluated by PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity).
A study of AES and RSA algorithms based on GPUs

Combining the characteristics of GPUs and CPUs can greatly enhance the performance and bandwidth, though their design philosophies are inherently different. This research paper focuses on the GPU parallel computing technologies for encryption of AES and RSA algorithms. With the arrival of NVIDIA Technology’s Compute Unified Device Architecture (CUDA), the graphics processing unit (GPU) has shifted from graphics-capable devices to universal sequence streamline units. Cryptography is about the security techniques. The Advanced Encryption Standard (AES) is widely used in the file encryption, wireless security, SSL/TLS, etc. However, its execution efficiency can still be improved to provide better security. Similarly, improvements can also be made in serial implementations of the RSA algorithm. Parallel implementation of security algorithms has been shown to improve execution efficiency in comparison to serial implementation. This research paper focuses on parallel implementation of AES and RSA algorithms on GPU to enhance performance and energy efficiency in comparison to CPU
Untethered: Cataloging In the Mobile Age

In the rapidly changing work climate, it is vital that catalogers learn to contribute their skills in novel ways that expand beyond the traditional boundaries. Working a 9–5 schedule in an office area is no longer a sustainable, or even desirable model. This article will look at the infrastructure, the technology, the tools, the expertise, and the logistics needed to perform cataloging and maintenance in the field or from a remote location, away from the library or the cataloging department. Additionally, the authors will explore some of the challenges and opportunities associated with cataloging remotely during a pandemic and beyond.
Exponentially damped harmonics wavelet series and generic analogue implementation

The exponentially damped harmonics (EDH) wavelet series related to RLC damped system and their generic implementation using simple OTA and grounded capacitor are presented in this work. The EDH wavelet is derived from second-order RLC circuit and generalized to arbitrary order wavelet function, which will be considered as a wavelet base series. Then, wavelet filter is synthesized using a generic method based on the simple OTA-grounded capacitor circuit. The designed wavelet filters with different scale wavelets can be employed to implement EDH wavelet transform. The simulation results demonstrate the proposed approach is efficient. Furthermore, the generic method could be used to implement other wavelet filter and wavelet transform.
Cardiac Left Ventricle Segmentation using Recurrent U-Net and Level Set with Short Axis MRI Images

Semantic segmentation methods based on Deep Learning (DL). There are a number of techniques that exist to accomplish this task but Level Set (LS) consider to be the most popular one to perform MRI Segmentation. Also notice that the result of the segmentation results depends on two factors one is parameter initializations and set of iterations. To solve these problems and propose a hybrid method that combines classic LS methods with deep learning approaches (ie Recurrent U- Net Model) called Recurrent U-Net Level Set (RULS). First, a U-Net architecture allows for training. Next, the recurrent networks are used to allow to get best features to complete the segmentation of MRI images. At final, the level set method is used to refine the segmentation process. To test the segmentation result for Left Ventricle MRI images, real database is to be considered the MICCAI database.
A Novel Semismooth Newton Algorithm for PCA-Twin Support Vector Machine

TWSVM is an algorithm model with good classification performance in machine learning. By finding two non-parallel hyperplanes, the model makes any hyperplane as close as possible to one kind of data and as far as possible to the other kind of data. In order to alleviate the influence of quadratic coefficient on the operation time during iteration, PCA dimensionality reduction algorithm is added in this paper. The semismooth newton algorithm is applied to solve dimensionality reduction L2-CSVM and L2-TWSVM. The results show that the proposed algorithm can not only significantly reduce the computational complexity, but also improve the training accuracy.
Scene captioning with deep fusion of images and point clouds

Recently, the fusion of images and point clouds has received appreciable attentions in various fields, for example, autonomous driving, whose advantage over single-modal vision has been verified. However, it has not been extensively exploited in the scene captioning task. In this paper, a novel scene captioning framework with deep fusion of images and point clouds based on region correlation and attention is proposed to improve performances of captioning models. In our model, a symmetrical processing pipeline is designed for point clouds and images. First, 3D and 2D region features are generated respectively through region proposal generation, proposal fusion, and region pooling modules. Then, a feature fusion module is designed to integrate features according to the region correlation rule and the attention mechanism, which increases the interpretability of the fusion process and results in a sequence of fused visual features. Finally, the fused features are transformed into captions by an attention-based caption generation module. Comprehensive experiments indicate that the performance of our model reaches the state of the art.
Need of Mitigating Bias in the Datasets using Machine Learning Algorithms

Need of mitigating the bias that is present in the data is an emerging area in any field of research. In order to improve the efficiency of the system that was designed to analyze which will help in decision making from the results, data play an important role. This made the bias to be a critical factor to consider and mitigating it to ensure that AI systems perform well in making decisions without any discrimination. Various datasets are considered for feeding the machine learning algorithms which holds bias as well as other noises which are not accurate in case of supervised learning. While considering the features to identify the bias in the databases, geography-based, gender-based and object-based are few metrics that are been considered in computer vision is object-based metrics This study helps us to understand the need of mitigating the bias to the maximum possible using ensemble learning algorithm which has the ability to train the data and extract the new set of data which are never seen by applying learning algorithms. This paper also proposes the need of eliminating the skewness present in the data.
Face Recognition System Using Image Enhancement With PCA and LDA

Face recognition has numerous applications in the modern world. With recent developments in IoT devices, security, and biometric systems, many applications of face recognition are being used on devices like a raspberry pi. In this paper, we propose an efficient face recognition system that uses face detection and extraction of the face from image based on Single Shot Multibox Detector (SSD) and uses image enhancement techniques like bilateral filtering and histogram equalization to enhance the quality of face image after which Principal Component Analysis (PCA) is used for feature extraction and Linear Discriminant Analysis (LDA) is used as classifier. The experiments have been conducted on the Faces95 and Faces96 datasets to test the proposed system and the performance of the system is also compared with two other methods for face recognition namely LBPH and PCA with SVM classifier. The testing of the system in real-time shows great results while recognizing faces.
Online Multiple Object Tracking using Physical Location Prediction

Tracking-by-detection is a commonly used paradigm for multiple-object tracking. This paper presents a method that incorporates the prediction of physical locations of people into the tracking-by-detection paradigm. The proposed method predicts the physical locations of people on an estimated ground plane and applies a learning-based framework to extract the appearance features of people across frames in a video stream. The method combines the prediction of physical locations with appearance features to realize online pedestrian tracking. Experimental results show that the proposed method improves multi-object tracking in terms of the Number of Identity Switches (IDSW) and the fragmentations (Frag).
Notice of Violation of IEEE Publication Principles: Deep Learning based Forensic Face Sketch Recognition

Notice of Violation of IEEE Publication Principles <br><br>“Deep Learning based Forensic Face Sketch Recognition” <br>by R. Naga Sravana Jyothi; G. Bhargavi; M. Gowthami; B. Dhana Lakshmi; V. Tejaswi <br>in the Proceedings of the International Conference on Electronics and Renewable Systems (ICEARS), March 2022 <br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. <br><br>This paper copied content from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. <br><br> “Forensic Face Photo-Sketch Recognition Using a Deep Learning-Based Architecture” <br> by Christian Galea and Reuben A. Farrugia <br>in the IEEE Signal Processing Letters, Vol 24, No 11, November 2017 <br><br> <br/> The necessity towards technology that can distinguish, discover and recognize culprits is skyrocketing nowadays. For crime investigations and forensics experts in Machine Vision, analyzing and interpreting how individuals recognize face portraits created by forensics illustrators is critical. In government validation and surveillance, connecting the drawing to the photographs is critical since it has been used to monitor culprits or perpetrators. Suspects have been known to wear customized masks to conceal common facial traits such as the nose, eyes, lips, and color. However, the bottom - level aspects of facial biometrics cannot be eliminated. For this, it was focused on a few human face geometry elements that may be utilized to determine correspondent rates between the prototype Photographic databases and the analytical artworks. The construction of a system for forensic facial drawing authentication using deep learning algorithms is described in this work.
Leveraging Disentangled Representations to Improve Vision-Based Keystroke Inference Attacks Under Low Data Constraints

Keystroke inference attacks are a form of side-channel attacks in which an attacker leverages various techniques to recover a user's keystrokes as she inputs information into some display (e.g., while sending a text message or entering her pin). Typically, these attacks leverage machine learning approaches, but assessing the realism of the threat space has lagged behind the pace of machine learning advancements, due in-part, to the challenges in curating large real-life datasets. We aim to overcome the challenge of having limited number of real data by introducing a video domain adaptation technique that is able to leverage synthetic data through supervised disentangled learning. Specifically, for a given domain, we decompose the observed data into two factors of variation: Style and Content. Doing so provides four learned representations: real-life style, synthetic style, real-life content and synthetic content. Then, we combine them into feature representations from all combinations of style-content pairings across domains, and train a model on these combined representations to classify the content (i.e., labels) of a given datapoint in the style of another domain. We evaluate our method on real-life data using a variety of metrics to quantify the amount of information an attacker is able to recover. We show that our method prevents our model from overfitting to a small real-life training set, indicating that our method is an effective form of data augmentation, thereby making keystroke inference attacks more practical.
ICH 3-party model for claims distribution on the blockchain and their application to the e-KYC-e model

Various services based on blockchain technology are reported daily. There are many unfortunate proposals to use blockchain as a distributed database merely, and a flowchart that allows you to self-confirm whether blockchain is really necessary there has been announced. Under these circumstances, as an example of making good use of blockchain technology, digital issuance of claims corresponding to physical certificates such as diplomas is considered to be one of the suitable applications, and there are cases where it was actually put into service.In this paper, we consider a new model that assumes that IDs are given ERC735 claim-style credentials in the Ethereum blockchain. We can think of "cotton candy" as a metaphor that expresses how multiple claims cling to a specific ID, that we can entangle various attributes by an identifier (a stick of the cotton candy). We will introduce a use case in which such credentials are stored in the blockchain and can confirm official certificates such as proof of enrollment in the digital space, and propose a new concept called e-KYC-e. e-KYC-e is considered to be socially acceptable because in the last few years multiple vendors and consortia have been able to manage their own DIDs (Decentralized Identifiers) and concepts such as SSI (Self-Sovereign Identity) to control their "cotton candy".
How do non experts think about cyber attack consequences?

Purpose Nonexperts do not always follow the advice in cybersecurity warning messages. To increase compliance, it is recommended that warning messages use nontechnical language, describe how the cyberattack will affect the user personally and do so in a way that aligns with how the user thinks about cyberattacks. Implementing those recommendations requires an understanding of how nonexperts think about cyberattack consequences. Unfortunately, research has yet to reveal nonexperts’ thinking about cyberattack consequences. Toward that end, the purpose of this study was to examine how nonexperts think about cyberattack consequences. Design/methodology/approach Nonexperts sorted cyberattack consequences based on perceived similarity and labeled each group based on the reason those grouped consequences were perceived to be similar. Participants’ labels were analyzed to understand the general themes and the specific features that are present in nonexperts’ thinking. Findings The results suggested participants mainly thought about cyberattack consequences in terms of what the attacker is doing and what will be affected. Further, the results suggested participants thought about certain aspects of the consequences in concrete terms and other aspects of the consequences in general terms. Originality/value This research illuminates how nonexperts think about cyberattack consequences. This paper also reveals what aspects of nonexperts’ thinking are more or less concrete and identifies specific terminology that can be used to describe aspects that fall into each case. Such information allows one to align warning messages to nonexperts’ thinking in more nuanced ways than would otherwise be possible.
Instigating Decentralized Apps with Smart Contracts

Blockchain is regarded as a game-changing core technology. Blockchain is a popular technology these days, and all of the large internet businesses, such as Google, Facebook, and Amazon, are seeking for blockchain developers. Despite the fact that many researchers have achieved the benefits of blockchain, blockchain research is still in its infancy. This paper examines the current state of blockchain in India, particularly in the domain of Decentralize Applications (Dapps). The paper will discuss the importance of blockchain in digital currency and its use in development of decentralized apps across the world. The paper also discusses the concept of smart contracts which allows us to communicate with blockchain. The paper has also identified the reason of scarcity of blockchain developers in India and how it can digitally transform the future. Finally the paper will present a single-page web application using JavaScript to demonstrate the concept of decentralized apps using blockchain technology.