Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Network based Digital Image Forensics using Random Forest and SVM Classifier

Recent fast advances in computer graphics and information technology have increased the quality of created digital material. Social media platforms such as Instagram, Facebook, and others, as well as movies and games, have taken use of this phenomenon; nonetheless, these artificial improvements have had severe detrimental consequences for society, such as those produced by phony photographs created with evil purpose. Hardly some strategies attain excellent accuracies for all genre of alterations such as copy-move, compression, splicing, rotation, etc. For ascertaining tampering efficiently, it is utmost of interest to devise a feature extraction mechanism based on deep learning, that ascertain correlation among pixels and evaluate more proficiently. Contrasting with recent divergent surveys, this paper enfolds notable developments in techniques of passive image forensic analysis employing deep learning techniques. Furthermore, a certain feature can only handle one type of forgeries. To overcome these challenges, this research proposes a technique based on Convolution Neural Networks (CNN) as a feature extractor and Random Forest (RF) and Support Vector Machine (SVM) as classifiers. The suggested technique was built and tested using two distinct CNNs, AlexNet and VGG19. CNN is trained on the CASIA dataset, then classify using RF and SVM individually and compare the results. Because the CNN is trained to deal with images of varying sizes, the suggested system is resistant to operations such as noise addition, blurring, and JPEG compression. The CNN-RF and CNN-SVM frameworks are assessed on the CASIA dataset and outperform state-of-the-art approaches.
AI-Powered Student Assistance Chatbot

To address the issues and queries of Class 12 students while joining college, this research study has developed a chatbot to help the students with enrollment process. A chatbot (or chatterbot) is a software technology that performs an online chat discussion and is built leveraging NLP techniques and frameworks in general, rather than allowing a regular dialogue with a real human agent. Here, the bot is developed using BotPress. This study has incorporated NLU into the proposed chatbot so that it can identify the user’s intent and respond appropriately. This chatbot is then deployed into a WordPress Website using Javascript. The plugin used here is the ‘Header and Footer Scripts Plugin’ of WordPress. Depending on the outcomes, the working of the proposed student helper chatbot system is quite remarkable in providing appropriate response to the users. The results indicate that the student helper chatbot is approximately 90.6% accurate in giving responses to users.
Blockchain in Indian Agriculture to Disrupt the Food Supply Chain

The Supply chain was always a complex task to manage the integrity, accountability, traceability, and quality of the products. The food supply chain is more important than any other supply chain. India is the second largest producer of fruits and vegetables in the world succeeding China. Although having a low level of processing, the quality of the final produced goods is not properly matched. Due to the lack of proper management in the food supply chain, many unauthorized small food organizations produce false products that are harmful to health. The modern supply chain is useful to prevent potential fraud a guarantees trade compliance. Blockchain technology solves the problem of complexity in the food supply chain. Blockchain uses a decentralized ledger to store the information, and this information cannot be changed or modified. Blockchain technology provides traceability, transparency, trust, and an immutable decentralized ledger to store data. In the existing blockchain based models, database consumption is very high. So, the proposed model introduces the concept of two parallel blockchain servers to optimize database usage.
Secure Land Registration Management via Ethereum Blockchain

Land is an asset and the transfer of ownership of a piece of land from one party to another is an important as well as tedious task. Not only it involves hectic paperwork but also several middlemen and multiple verifications at various levels of the process. This makes the whole process less reliable and increases the chances of forgery multifold. Also, it’s difficult to find out the previous transfers. This problem can be easily taken care of by using blockchain for the land registration system. This will decrease corruption in the process by removing middlemen from the process. Also, it will increase the speed, and create trust in the system without even the involvement of any central agency. Blockchain is a decentralized, immutable ledger that keeps track of every legitimate transaction. It uses cryptographic techniques, consensus mechanisms, and hashing algorithms to maintain the chain of transactions. This keeps the whole system transparent and immutable. The aim of this work is to develop a user-friendly, blockchain-based land registration system to ease the process while maintaining security and trust. In places like India where lacs of transfers are done on a regular basis, the quantity of paper used to maintain the records is also very high. Not only will this system bring common people closer to technology, but it will also help protect the environment by removing manual paperwork.
Malicious URL Detection and Classification Analysis using Machine Learning Models

One of most frequent cybersecurity vulnerabilities is malicious websites or malicious uniform resource location (URL). Each year, people are losing billions of rupees by hosting gratuitous material (spam, malware, unsuitable adverts, spoofing etc.) and tempting naïve visitors to fall for scams. Email, adverts, web searches, or connections from other websites can all encourage people to visit these websites. Users click on the malicious URL in each instance, a trustworthy system that can categorize and identify dangerous URLs is needed due to rise in phishing, spamming, and malware occurrences. Due to the enormous amount of data, changing patterns and technologies, as well as the complex relationships between characteristics, non-availability of training data, non-linearity and the presence of outliers made classification challenging. In the proposed work, malicious URLs are detected for various applications. Dataset has been categorized into four types i.e., Phishing, Benign, Defacement and Malware. Totally 6,51,191 URLs have been used for proposed implementation. Three machine learning algorithms such as random forest, LightGBM and XGBoost were implemented to detect and classify malicious URLs.
An Efficient Genetic Algorithm based Auto ML Approach for Classification and Regression

In recent years, AutoML is booming as the time-consuming and iterative tasks involved in developing a machine learning model can be automated using AutoML. It aims to lessen the requirement for skilled individuals to create the ML model. Additionally, it helps to increase productivity and advance machine learning research. Hence, this paper focusses on developing an AutoML model using genetic algorithm to automatically fulfill the function of network architecture search. The proposed methodology has been evaluated in different scenarios such as binary classification and regression. From the results it is observed that the accuracy achieved for binary classification and regression is 98%.
Hand Gesture-based Virtual Mouse using Open CV

In a world full of technologies, everyone is trying to make human life easier with the help of new techniques. Therefore, for the sake of humanitarian relief, this paper has developed a technology that can help the user to use laptops/computers easily with less physical contact. The proposed system consists of functions like controlling the system with the help of hand gestures and blinking of eyes, scrolling with the help of yellow object detection and mouth opening, and volume control with the help of fingertips. For this project, media pipe, an Open CV application has been used. This system will be helpful for people with disabilities for doing their work more easily and with simplicity. Moreover, due to significant restrictions during pandemic, it can be said that contactless is a priority in today's world, and one of the best way to achieve this is provided in this prototype.
Enigmas in Emotion Detection Techniques Tools: Challenges and Concerns

Emotion detection is a branch of sentiment analysis that focuses on the identification and study of emotions. Text mining, analysis of extracted information, and in-depth research are now at the centre of organisational success due to the development of web 2.0. It enables service providers to provide their clients with specialized services. Due to the simplicity of obtaining data and the enormous advantages its deliverables give, text-based extraction and analysis are now the topics of much research. The notion of ED from texts is examined in this article, which also highlights the major techniques that academics have taken to develop ED systems that employ texts. The proposals' key contributions, methods used, datasets consulted, outcomes attained, and strengths and limitations are described. Additionally, emotion-labelled data sources are offered to give beginners access to appropriate text datasets for ED. The essay concludes by outlining several unresolved problems and potential lines of inquiry for text-based ED. Due to its numerous potentials uses in business, cultural studies, cognitive science, human-computer interaction, cognitive computing, etc., emotion recognition in the text has recently gained popularity. The availability of a wealth of textual material, particularly opinionated and self-expressive writing, also considerably increased public awareness of this issue.
Artificial intelligence and robotics on the frontlines of the pandemic response: the regulatory models for technology adoption and the development of resilient organisations in smart cities

Smart cities do not exist without robotics and Artificial Intelligence (AI). As the case of the COVID-19 pandemic shows, they can assist in combating the novel coronavirus and its effects, and preventing its spread. However, their deployment necessitate the most secure, safe, and efficient use. The purpose of this article is to address the regulatory framework for AI and robotics in the context of developing resilient organisations in smart cities during the COVID-19 pandemic. The study provides regulatory insights necessary to re-examine the strategic management of technology creation, dissemination, and application in smart cities, in order to address the issues regarding the strategic management of innovation policies nationally, regionally, and worldwide. To meet these goals, the article analyses government materials, such as strategies, policies, legislation, reports, and literature. It also juxtaposes materials and case studies, with the help of expert knowledge. The authors emphasise the imminent need for coordinated strategies to regulate AI and robots designed for improving digital and smart public health services globally.
Harmonic Tutte polynomials of matroids

In the present paper, we introduce the concept of harmonic Tutte polynomials of matroids and discuss some of their properties. In particular, we generalize Greene’s theorem, thereby expressing harmonic weight enumerators of codes as evaluations of harmonic Tutte polynomials.
Kinematics of platform stabilization using a 3-PRS parallel manipulator

Abstract In this paper, a 3-PRS (prismatic, revolute, and spherical) parallel manipulator for platform stabilization is designed. The main purpose of this device is to stabilize visual equipment, which is placed on top of a car to inspect electrical transmission cables, as part of routine maintenance. Due to the bulky and heavy infrared cameras used during inspections, a stabilizer platform has been designed to handle the weight of camera equipment up to 10 kg. This device consists of two major mechanisms. The first mechanism is able to adjust the angle of the camera. Thus, the user can focus the camera along the electric transmission lines. The second mechanism is stabilization. The mechanism serves to stabilize the orientation and position of the camera in the roll, pitch, and heave directions. To test the performance of the stabilization mechanism, the device is fed with the known value of the angle with regard to the input. As such, the device is trying to compensate for the change in angle. The results show that the errors between the input angles and compensated angles are in the range of 0.4–3%. Errors are seen to be within an acceptable range. It is significant that the resultant errors do not affect the orientation of the camera.
An efficient and effective text spotter for characters in natural scene images based on an improved YOLOv5 model

Traditional scene text spotters aim to detect and recognize entire words or sentences in natural scene images, however, the detection and recognition of every single character is also as important as the spotting of unifying words or sentences in one image. There are few specialized methods to spot single character in scene text spotting, and some word-based methods can not recognize a series of characters in images if they can not be spelled as a correct word. In addition, some early models can only detect or recognize texts which are horizontal and distinctive. We realize that it is necessary to improve some existing models for achieving the goal of spotting characters, therefore, we propose a novel method based on an improved YOLOv5 model to accomplish the character-level spotting. It's worth noting that this method can spots characters not only in regular texts but also in irregular texts (curved texts and oriented texts).
Symmetric searchable encryption with supporting search pattern and access pattern protection in multi‐cloud

Summary Symmetric searchable encryption (SSE) enables users to search the ciphertext stored on the untrusted cloud without revealing the search keywords, effectively protecting users' privacy. However, most of the existing SSE schemes reveal the search or access pattern during the keyword query, which can be used by the adversary to infer the sensitive information in ciphertext, thus posing a great threat to users' privacy. To address this, we propose an SSE scheme supporting search pattern and access pattern protection in multi‐cloud, called SAPM‐SSE. In our scheme, an index shuffle protocol is proposed to change the content and location of the index after each query, which helps to achieve the protection of search and access pattern. Furthermore, with the purpose of improving the efficiency of shuffling, we construct a shuffling algorithm based on index cache, the number of index entries for shuffling reduced from γ to γ / n ( n ≥1). Besides, our scheme supports the dynamic update of documents and achieves the forward security in update. Finally, security analysis and experimental results show that our scheme can achieve the protection of search pattern and access pattern with high efficiency.
A Compositional Transformer Based Autoencoder for Image Style Transfer

Image style transfer has become a key technique in modern photo-editing applications. Although significant progress has been made to blend content from one image with style from another image, the synthesized image may have a hallucinatory effect when the texture from the style image is rich when processing high-resolution image style transfer tasks. In this paper, we propose a novel attention mechanism, named compositional attention, to design a compositional transformer-based autoencoder (CTA) to solve this above-mentioned issue. With the support from this module, our model is capable of generating high-quality images when transferring from texture-riched style images to content images with semantics. Additionally, we embed region-based consistency terms in our loss function for ensuring internal structure semantic preservation in our synthesized image. Moreover, information theory-based CTA is discussed and Kullback–Leibler divergence loss is introduced to preserve more brightness information for photo-realistic style transfer. Extensive experimental results based on three benchmark datasets, namely Churches, Flickr Landscapes, and Flickr Faces HQ, confirmed excellent performance when compared to several state-of-the-art methods. Based on a user study assessment, the majority number of users, ranging from 61% to 66%, gave high scores on the transfer effects of our method compared to 9% users who supported the second best method. Further, for the questions of realism and style transfer quality, we achieved the best score, i.e., an average of 4.5 out of 5 compared to other style transfer methods.
Towards Mobile Federated Learning with Unreliable Participants and Selective Aggregation

Recent advances in artificial intelligence algorithms are leveraging massive amounts of data to optimize, refine, and improve existing solutions in critical areas such as healthcare, autonomous vehicles, robotics, social media, or human resources. The significant increase in the quantity of data generated each year makes it urgent to ensure the protection of sensitive information. Federated learning allows machine learning algorithms to be partially trained locally without sharing data, while ensuring the convergence of the model so that privacy and confidentiality are maintained. Federated learning shares similarities with distributed learning in that training is distributed in both paradigms. However, federated learning also decentralizes the data to maintain the confidentiality of the information. In this work, we explore this concept by using a federated architecture for a multimobile computing case study and focus our attention on the impact of unreliable participants and selective aggregation in the federated solution. Results with Android client participants are presented and discussed, illustrating the potential of the proposed approach for real-world applications.
DENet: Detection-driven Enhancement Network for Object Detection Under Adverse Weather Conditions

Recently, the deep learning-based object detection methods have achieved a great success. However, the performance of such techniques deteriorates on the images captured under adverse weather conditions. To tackle this problem, a detection-driven enhancement network (DENet) which consists of three key modules for object detection is proposed. By using Laplacian pyramid, each input image is decomposed to a low-frequency (LF) component and several high-frequency (HF) components. For the LF component, a global enhancement module which consists of four parallel paths with different convolution kernel sizes is presented to well capture multi-scale features. For HF components, a cross-level guidance module is used to extract cross-level guidance information from the LF component, and affine transformation is applied in a detail enhancement module to incorporate the guidance information into the HF features. By cascading the proposed DENet and a common YOLO detector, we establish an elegant detection framework called DE-YOLO. Through experiments, we find that DENet avoids heavy computation and faithfully preserves the latent features which are beneficial to detection, and DE-YOLO is effective for images captured under both the normal condition and adverse weather conditions. The codes and pre-trained models are available at: https://github.com/NIvykk/DENet .
Novel Block Sorting and Symbol Prediction Algorithm for PDE-Based Lossless Image Compression: A Comparative Study with JPEG and JPEG 2000

In this paper, we present a novel compression method based on partial differential equations complemented by block sorting and symbol prediction. Block sorting is performed using the Burrows–Wheeler transform, while symbol prediction is performed using the context mixing method. With these transformations, the range coder is used as a lossless compression method. The objective and subjective quality evaluation of the reconstructed image illustrates the efficiency of this new compression method and is compared with the current standards, JPEG and JPEG 2000.
HDP-TSRRT*: A Time–Space Cooperative Path Planning Algorithm for Multiple UAVs

This paper proposes a fast cooperative path planning algorithm for multiple UAVs that satisfies the time–space cooperative constraints, namely, the RRT* algorithm based on heuristic decentralized prioritized planning (HDP-TSRRT*), which takes into account the simultaneous arrival time variables of each UAV as well as the avoidance of conflicts and threats. HDP-TSRRT* is a hierarchical decoupling algorithm. First, all UAV pre-paths are planned simultaneously at the synchronous decentralized planning level. Second, at the coordination path level, the heuristic decentralized prioritized planning algorithm (HDP) is proposed to quickly complete the coordination process of the path planning sequence. This strategy assigns reasonable and robust priority to all UAVs based on the performance evaluation function composed of the number of potential collisions and the violation of collaboration time of the pre-planned path. Third, the time–space cooperative constraints-based RRT* algorithm (TSRRT*) is proposed at the single-machine cooperative path planning level. Based on this, the algorithm uses multiple sampling and cost evaluation strategies to guide the expansion of new nodes, and then optimizes neighborhood nodes based on the time coordination cost function so as to improve the efficiency of coordination path planning. Simulation and comparison show that HDP-TSRRT* has certain advantages in algorithm performance.
Fusion Model for Classification Performance Optimization in a Highly Imbalance Breast Cancer Dataset

Accurate diagnosis of breast cancer using automated algorithms continues to be a challenge in the literature. Although researchers have conducted a great deal of work to address this issue, no definitive answer has yet been discovered. This challenge is aggravated further by the fact that most available datasets have imbalanced class issues, meaning that the number of cases in one class vastly outnumbers those of the others. The goal of this study was to (i) develop a reliable machine-learning-based prediction model for breast cancer based on the combination of the resampling technique and the classifier, which we called a ‘fusion model’; (ii) deal with a typical high-class imbalance problem, which is posed because the breast cancer patients’ class is significantly smaller than the healthy class; and (iii) interpret the model output to understand the decision-making mechanism. In a comparative analysis with three well-known classifiers representing classical learning, ensemble learning, and deep learning, the effectiveness of the proposed machine-learning-based approach was investigated in terms of metrics related to both generalization capability and prediction accuracy. Based on the comparative analysis, the fusion model (random oversampling techniques dataset + extreme gradient boosting classifier) affects the accuracy, precision, recall, and F1-score with the highest value of 99.9%. On the other hand, for ROC evaluation, the oversampling and hybrid sampling techniques dataset combined with extreme gradient boosting achieved 100% performance compared to the models combined with the undersampling techniques dataset. Thus, the proposed predictive model based on the fusion strategy can optimize the performance of breast cancer diagnosis classification.