Reviewerly/exHarmony huggingface dataset:
Design and Development of a Cross-Platform Application: MSCW App

This paper presents and elaborates about a mobile application being developed for a Higher Education Institution with the aim to get timely notifications of events, without missing the deadlines and receive job/internship opportunities on time floated by the Placement Team. This application has features for notifying and registering students for any upcoming college event and also makes it possible for the alumni to register themselves in the app so that their work/career profiles can be recorded. Both alumni and Placement Team can add any new internship and placement opportunity, visible to other users only after the admin validates it. There are many technologies available for developing any app that is cross-platform compatible; one such technology is flutter framework. Flutter is an open-source UI software development kit (SDK) used to develop cross-platform applications for Android, iOS, Linux, macOS, Windows, etc. We used flutter SDK for the frontend, NodeJS for backend, and MongoDB for database. A prototype is presented in this paper showing the main features of the application. The results of a preliminary study with the application are positive and reveal that students are excited to use the application and they will use the application on a regular basis when it becomes available.
Deep Learning for Classification of Encrypted Images

Encryption is a very important aspect in the world of information technology and its importance has only kept on increasing as every year passes and with every new attack. In this paper, we selected two image encryption algorithms to obtain image ciphers that are strong enough to resist image cryptanalysis. The encryption effect of both the algorithm in terms of information entropy, sensitivity analysis and correlation analysis make the scheme secure. The objective of this work is to attack the combination of permutation and chaotic based encryption algorithms. The deep learning model was trained with these two ciphers. Given the encrypted images to the learned model, we were able to predict the testing accuracy up to 95%. The training accuracy was observed to be 99%.
Version Control System for Auto Generation of Question Papers

In this COVID pandemic, whole academics is shifted to online mode. Along with teaching and learning, even the whole examination system along with question paper setting and evaluation needs to be automated. Quality of question paper generation is an important aspect of examination system. Courses and course structures need to be updated regularly to keep up with the recent trends in the subject domain. It is very difficult to track the course and question paper versions as the versions of courses changes frequently. Another important aspect here is to maintain secrecy in paper setting which requires identifying roles and responsibilities of individuals in the existing system. The proposed system is designed to keep track of course structure pertaining to different courses, subjects and academic years and each question paper is tagged with the appropriate version of course structure tag which contains a nested tag consisting of internal and external examiner codes. The motive for tagging each question paper with the version information is for reducing table lookups.
An Optimized Textual Document Categorization Based on Fast Fourier Transform

Introduction: This research on textual document categorization has experimented with a novel method using Fast Fourier Transform. Topic Modelling is a much-explored area in automatic text categorization as there is huge number of text generation in the current era. In state-of-the art review, it is noticed that machine learning and deep learning algorithms are extensively used in this field; even evolutionary algorithms can also be found as an application over here but in less number. Method: In this research, a new concept of Fast Fourier Transform is used for text classification. Power spectrum involvement of Fast Fourier Transform has been used for the first time in this application. This Fast Fourier Transform and its power spectrum calculation take a very short time for its computation. Result: It has been observed that this research method is very suitable for any type of text, especially for large text documents where the accuracies of deep learning methods are less than the proposed research. Moreover, the time complexity of the algorithm is very less, which can be suitably applied to any large text in this age of big data. Conclusion: The research come out with 8% increased accuracy on 20 Newsgroup data than any other method present in the literature. Also, it is suitable for short one sentence text that has been tested on BBC news data.
A Systematic Review: Imbalanced Network Traffic Classification Using Machine Learning Techniques

The major purpose of technique of classifying the traffic in network is to identify various types of systems or traffic data. The analysis of received data packets is performed due to its necessity in communication networks nowadays. There are diverse phases to classify the network traffic such as to pre-process the data, extract the attributes, and perform the classification. The dataset is utilized for the input in the classification stage. This paper studies diverse ML techniques in order to classify the network traffic.
Audio Compression Using Quantum Neural Network

Data compression is a common feature of current information processing and has a broad range of uses. Audio compression is critical in database applications such as storage and transmission. The difficulty of lowering the number of bits required to represent digital audio is addressed via audio compression. It is used to reduce redundancy by reducing irrelevant and redundant data. Recent developments in deep learning have motivated researchers to investigate challenges involving highly structured data utilizing unified deep network models. Due to the necessity for discrete representations that are difficult to train, the construction and design of such models for compressing audio signals have proven difficult. The purpose of this study is to concentrate on quantum neural networks for compressing audio files. As a result, a quantum-based autoencoder capable of compressing audio data into a low-dimensional space is critical for achieving automatic audio compression, and in the decoder part, it decompresses the quantum audio signals by using deconvolution layers. Finally, the quantum representation of audio signals retrieves the original signals.
Single Image Dehazing Using Haze Veil Analysis and Contrast Stretching

Images of the outdoor scene appear hazy due to degradation occurred by atmospheric particles (water droplets, dust, etc.) while capturing the image. Dehazing such images is desired in digital photography and computer vision. Hazing creates lots of problems in the areas of surveillance, tracking and navigation, and other applications. Thus, to remove it from an image, various defogging methods (single image and multiple images) have been proposed the in literature. Therefore, a single-image dehazing approach using haze veil analysis is proposed. Adaptive contrast stretching and contrast limited histogram equalization (CLAHE) are applied to enhance the visibility level of defogged image. The results are found to be better for CLAHE. The proposed system gave better results in terms of preserving the finer details and the color quality of the images.
Development of Augmented Reality Book for Preschool Education

Augmented Reality (AR) is an effective immersive technology. We have applied it in preschool education to make it more engaging and interesting. We have developed a demo AR application for English alphabets to enhance the traditional learning. Augmented Reality (AR) makes use of smart phones. When user holds the smart phone on the alphabet in the book, 3D model relevant to it will pop up. It is bringing virtual 3D model in real environment. The scripts were added to the models to interact with them. We have used Unity 3D game engine, Vuforia and Unity Asset store for development of this AR Book. For scripting, we have used C# as a programming language. We have implemented AR as marker-base application which can be further developed as marker-less AR.
Classification of Sentiment Analysis of Tweets on Twitter

The technique of categorizing messages based on the emotions they represent is the focus of this project's issue of sentiment classification on Twitter. Twitter is a networking site and microblogging service where users can post status updates with a character limit of 140. More than 200 million people have registered on the website, among which, 100 million were active users, and 50% of those logs in daily, resulting in the publication of more than 250 million tweets daily. By examining the emotions expressed in tweets, which are widely used, we hope to express the sentiment of a broader public. There are many applications that call for the analysis of public opinion, including businesses trying to predict how the marketplace will respond to their products, forecasting political outcomes, and the analysis of social processes like the stock exchange.
HVS-inspired adversarial image generation with high perceptual quality

Abstract Adversarial images are able to fool the Deep Neural Network (DNN) based visual identity recognition systems, with the potential to be widely used in online social media for privacy-preserving purposes, especially in edge-cloud computing. However, most of the current techniques used for adversarial attacks focus on enhancing their ability to attack without making a deliberate, methodical, and well-researched effort to retain the perceptual quality of the resulting adversarial examples. This makes obvious distortion observed in the adversarial examples and affects users’ photo-sharing experience. In this work, we propose a method for generating images inspired by the Human Visual System (HVS) in order to maintain a high level of perceptual quality. Firstly, a novel perceptual loss function is proposed based on Just Noticeable Difference (JND), which considered the loss beyond the JND thresholds. Then, a perturbation adjustment strategy is developed to assign more perturbation to the insensitive color channel according to the sensitivity of the HVS for different colors. Experimental results indicate that our algorithm surpasses the SOTA techniques in both subjective viewing and objective assessment on the VGGFace2 dataset.
A Perspective View of Image and Video Forgery Techniques

In today’s digital world all kind of enhancement is becoming possible and at the same time the usage of images and videos have been growing day by day in our lives, the enthusiasm to make manipulation of images also increases concurrently. In this study, the most recent technical analysis and observations of various copy-move image and video forgery techniques were carried out. Image splicing, copy-move forgery, and image resampling are the three basic types of image counterfeiting. Copy-Move forgery is commonly used for making tampered photographs. As the forgery of images and video is increasing, it is much essential to develop tools for the detection of such forgeries. This study examines several forms of digital image and video forgeries as well as detection techniques.
A Denoising Network Based on Frequency-Spectral- Spatial-Feature for Hyperspectral Image

The quality of hyperspectral images seriously impedes subsequent high-level vision tasks such as image segmentation, image encoding and target detection.However, the frequency, spectral, and spatial properties of the hyperspectral noise pictures are not utilized fully by existing image denoising algorithms.To address this issue, a novel convolutional network based on united octave and attention mechanism (UOANet) is proposed to extract the frequency-spectral-spatial-feature for denoising the actual noise of HSIs.In particular, the negative residual mapping embedded in Unet is proposed for multi-scale abstract representation and two modules are designed for modeling global noisy HSI features in the frequency-spectralspatial domain.Firstly, with the use of residual octave convolution module (ResOct), our model can focus on the intrinsic properties of HSI noise distribution for desirable noise removal.Next, a parallel spatial-spectral attention module (SSAT) is used to fully utilize the rich spectrum data and the various spatial data of each band in HSI, which improves the richness of HSI details after denoising.Experimental results on both synthetic and real HSIs demonstrate the validity and superiority of UOANet compared with the state-of-the-arts under various noise settings.
Detection of Spam in SMS Using Machine Learning Algorithms

The Growth of electronic gadget usage has been rapidly increasing day by day. Around 80% of people are dependent on mobile devices for accessing, storing, and sharing information. This requires security which is critical to managing. In this regard, security is missing due to the presence of spam messages in mobility devices. It is important to detect spam messages because it contains unnecessary information and data leakage could be possible. It is used to develop two different solutions to this problem by using machine learning algorithms, for finding the Greek word we use Snowball Stemmer Algorithm, Porter Stemmer, and use some machine learning techniques like the TF_IDF algorithm to increase accuracy. To combine these two, we use a multinational Naive Bayes algorithm, a Support Vector Machine. A vectorized model is obtained then we can classify the messages. Upon completion of the project, the society can be secure from hackers, protect society against viruses in their devices, and also unwanted malicious behavior can be identified which will be affecting the social network users.
Violence Detection in Drone Surveillance Videos

Drones are increasing in popularity for many applications in the private and government sectors. New applications such as land surveying and wildlife movement monitoring have been made simpler with the advent of drones in these domains. A majority of the drones are fitted with some sort of vision capture system—such as a video camera and are capturing the activity in an area as dictated by the application domain. In this work, we have added analytics functionality to the drone video stream—in particular, we have created a machine learning model which is capable of detecting physical violence between individuals. With the increase in the application domains of drones, their numbers are set to increase dramatically. An important capability is that of vision, and it can be put to good use if analytics are used to detect certain anomalous events in the drone’s field of vision. While there is an intended application and journey, this can be altered if such events are detected. For example, in the case of physical violence, if any activity of this nature is detected, the drone can stop and continue to capture the activity, instead of continuing on its usual route. This will have the beneficial effect of both capturing the activity and will also have an indirect effect of the perpetrators ceasing to continue the activity, as they are being watched. The proposed solution applied deep learning models for the detection of events of interest. Deep learning has emerged as the model of choice for classification applications. A primary reason for their adoption is because they obviate the complicated feature engineering process, due to their capability of learning features. From our experiments, we see that we are able to achieve a validation accuracy of over 92% on our dataset.
UML Design Modeling of Smart Contracts

Blockchain is a linked data structure for storage of information that is distributed between the devices in a large computer network. A Smart Contract is an automatically occurring contract in which the governing rules of the agreement in the middle of the buyer and seller are directly put into code. The implementation and the agreement bundled in the contract are disseminated all through a blockchain network. For the successful development and deployment of a blockchain application and network, the underlying design and modeling of Smart Contract is needed. As a result, the proposed work assists blockchain developers in the construction of Smart Contracts using a methodology known as Unified Modeling Language that promotes flexible, highly maintainable, and reusable applications.
Stacked LSTM Model for Shakespeare Style Poem Generation

Natural Language Generation has gained immense popularity in the field of Natural Language Processing. The use of deep learning algorithms for text generation has various applications across domains. One such application is the ability to synthesize poetry from a collection of poems written by a particular poet. In this paper, a stacked character-level LSTM model has been proposed to generate Shakespeare-style sonnets. The model is trained on the complete set of sonnets written by Shakespeare and is able to generate a new sonnet. To judge the novelty of the generated sonnet, the cosine similarity between a generated sonnet and each sonnet in the corpus has been computed. The qualitative validation of the sonnet has been done by manual methods. The sonnets are judged based on fluency, coherency, meaning and poeticness.
Gestural migration as a pedagogical tool in violin learning: a case study

This article explores the potential of gestural migrations as a novel approach to violin pedagogy, allowing us to trace enlightening parallelisms between the learning of musical performative gestures and the gestural dimension of cinema as a time-shaped/shaping artistic discipline. As a case study, I take the first movement of Claude Debussy's Violin Sonata L. 140, composed in 1917, following an empirically grounded quantitative analytical approach. I attempt to trace gestural correspondences with selected scenes from the contemporary French cinematic movement that fascinated the composer in the early years of the twentieth century.
Image Forgery Detection Using Deep Learning

Since cameras are so widely available, taking pictures has become more and more common. In order to gain more information, it is frequently necessary to enhance photographs, which are crucial in our daily lives as memories or sources of wealth of information. There are many tools accessible to enhance the quality of photos; however, some of them are also widely used to alter images, leading to the dissemination of false information. This makes picture forgeries more severe and frequent, which is now a major cause of concern. To identify fake images, many conventional methods have been developed over time. Convolutional neural networks (CNNs) have drawn a lot of interest recently, and they have had an impact on the area of picture fraud detection as well. However, the majority of CNN-based picture forgery detection methods currently used in the literature are restricted to identifying a certain kind of fraud (either image splicing or copymove). As a result, a method that can quickly and precisely identify any hidden forgeries in a picture is needed. We present a powerful deep learning-based approach for detecting picture forgeries in this study. The RGB format images converted to error level analysis (ELA) are used to train our model. The suggested model is compact, and its effectiveness shows that it outperforms cutting-edge methods in terms of speed. The experiment's findings are promising, with a 91.37% total validation accuracy.
Data Sharing Approach for Personal Health Record Using Modified Attribute-Based Encryption Based on Searchable Encryption Scheme

In this digital environment, the most important domain that requires a cloud infrastructure is the medical sector, where hospitals hold vast amounts of data on patient health records. Since personal health records are stored on the cloud, the framework requires some form of encryption to keep the information safe. The proposed Modified Attribute-Based Encryption (MABE) data exchange method based on searchable encryption scheme uses the combination of Key Policy Attribute-Based Encryption (KPABE) and Cypher Text Policy Attribute-Based Encryption (CPABE) along with searchable attribute-based encryption. Through the use of attributes, the patient's medical files are decrypted. Prior to encryption, eigendecomposition is applied to reduce the attribute set. The identities of the patients are employed as attributes in this model, which fortifies and safeguards the system besides being rigorously established under various conditions. In addition, as compared to standard data exchange methods, projected scheme requires not as much of rate, period, then storage. Access cost, time, and storage requirements for the proposed system are 2100 bits, 24 ms and 1212 bytes. It has a number of security features, such as tampering protection, visibility and scalability.
A Small-Scale Ontology to Represent Knowledge About Question Items

Generation of quizzes or examination papers by using a question bank is an important activity supported by many e-learning platforms. This chapter presents an ontology to represent the knowledge associated with question items. The ontology is described through UML notations. The ontology presented in this chapter is practical and sufficient in expressing various properties related to question items. These properties capture the meta-data necessary for effectively describing question items. The ontology’s primary applications include designing question banks and generating question papers based on constraints such as graded topic coverage, broad question types, multiple difficulty levels, and rules.
Hostility Analysis for Defence Using Empirical Surveillance

The legacy COTS systems currently used to achieve battlefield transparency, need to be continuously manned and monitored. This poses a problem of inefficient management of manpower, for resource manipulation. Thus, an innovative adaptation is required to help manage human resources, without compromising the level of security deployed. A solution to tackle this has been proposed, by deploying AI algorithms to continuously monitor the live video feed. In this way, the aim is not to completely replace the workforce, but rather, integrate intelligent human and object detection algorithms into the systems processing the Live feed. This acts as a helping hand to better deduce threats that the average human eye can’t and eliminates any error that might have resulted from fatigue or carelessness. Hence, the load on the workforce is reduced, thus tackling the inefficient use of manpower in constant visual surveillance. The proposed solution brings out the prowess of Transfer Learning using Masking Techniques in Region-based Neural Networks. Despite the lack of adequate military data, this algorithm has been able to reach accuracies of 0.98 and 0.99 with minimal loss and error even in the most camouflaged situations.
Abductive Inference of Conclusions with Check of Additional Premises Literals Correctness Interpretation

At the present stage of the development of computer technology and information technology, the theory and methods of reasoning modeling play an important role in creating artificial intelligence systems. Reasoning modeling can be used, for example, in managing the functioning of complex intelligent information and control systems in order to explain and justify the decisions they recommend. The article considers a special method of abductive logical inference with checking the correctness of the interpretation of literals of additional premises. The method under consideration, in addition to explaining the course of inference with the help of schemes, allows you to check the correctness of the interpretation of new “facts” formed in the process of inference, taking into account the ontology of a particular subject area. The main advantage of the proposed method is the parallel execution of disjunctive division operations in the inference procedure.