Reviewerly/exHarmony huggingface dataset:
Multilingual Toxic Comment Classifier

This research study’s primary goal is to identify all the toxic comments on social media including hate, abusive, obscene, threat and insulting comments in a dataset where the entries are stored in more than one language. In the dataset with more than one language there are many problems faced in writing the pseudo-code of the Deep learning algorithms as the first part for each and every algorithm was to detect the language and then search/detect the word as to a toxic or a non-toxic comment.
Distributed representation learning with skip-gram model for trained random forests

The random forest family has been extensively studied due to its wide applications in machine learning and data analytics. However, the representation abilities of forests have not been explored yet. The existing forest representation is mainly based on feature hashing on the indices of leaf nodes. Feature hashing typically disregards the information from tree structures, i.e., the relationships between leaf nodes. Furthermore, the visualisation abilities of feature hashing are limited. On the contrary, the Skip-Gram model has been widely explored in word and node embedding due to its excellent representation ability. This paper proposes distributed representation learning for trained forests (DRL-TF) to extract co-occurrence relationships of samples and tree structures, and further boost the representation abilities of the trained forest using the Skip-Gram model. The experimental results demonstrate that the proposed DRL-TF outperforms the challenging baselines. To the best of the authors’ knowledge, the visualisation by DRL-TF is the first tool to analyse the trained forests. The code is available at: https://github.com/machao199271/DRL-TF.
Digital Contact Tracing for COVID-19

The spread of the COVID-19 virus across the globe has permanently changed life for billions of people. Manual contact tracing has been utilized to assist in limiting the spread of contagious diseases for many years. The ubiquitous use of smartphones and similar wireless devices has allowed this process to become digital in nature through contact tracing applications installed on these devices. Various countries, and even various regional units within those countries, developed contact tracing applications. Such applications relied on location and short-range communication technologies associated with wireless devices and found varying degrees of success. This work reviews research conducted by universities, governmental organizations, and other entities with respect to the adoption, use, and ultimate success of, digital contact tracing applications across various countries and points to their rather limited success in fighting the spread of the disease. The authors also briefly discuss some implications regarding privacy and security that affected their use in certain countries.
Real-Time Stereo Image Depth Estimation Network with Group-Wise L1 Distance for Edge Devices Towards Autonomous Driving

Depth estimation is an essential element to constitute 3D perceiving ability of autonomous vehicles. Real-time inference on power- or memory-constrained devices would expedite the progress of autonomous driving. In this paper, a lightweight stereo matching network is proposed to simultaneously achieve high accuracy and fast inference time. A novel feature extractor using patch embeddings for downsampling together with a peculiar pyramidal strategy is proposed to obtain accurate disparity maps with less computational resources. By constructing the cost volume with the proposed group-wise L1 distance, the measured feature similarity is represented faster and more efficiently compared to the group-wise correlation. A lightweight 3D aggregation network with less 3D convolutions is proposed to further improve the accuracy and inference time. In addition, TensorRT optimizer approach is employed to improve the inference speed of the model. Exhaustive experiments on KITTI 2012 and KITTI 2015 datasets demonstrate that our model has the ability to execute in real-time on resource-constrained devices, achieving higher frame rates than contemporary state-of-the-art networks. Our pipeline can process 1232 × 368 resolution images within the speed range of 33.8-73.5 frames per second on NVIDIA Jetson Nano with TensorRT optimization, while maintaining comparable accuracy. The achieved state of the art trade-off between accuracy and runtime is significant for edge devices in autonomous driving.
Unsupervised Anomaly Detection Approach Based on Adversarial Memory Autoencoders for Multivariate Time Series

The widespread usage of Cyber Physical Systems (CPSs) generates a vast volume of time series data, and precisely determining anomalies in the data is critical for practical production. Autoencoder is the mainstream method for time series anomaly detection, and the anomaly is judged by reconstruction error. However, due to the strong generalization ability of neural networks, some abnormal samples close to normal samples may be judged as normal, which fails to detect the abnormality. In addition, the dataset rarely provides sufficient anomaly labels. This research proposes an unsupervised anomaly detection approach based on adversarial memory autoencoders for multivariate time series to solve the above problem. Firstly, an encoder encodes the input data into low-dimensional space to acquire a feature vector. Then, a memory module is used to learn the feature vector’s prototype patterns and update the feature vectors. The updating process allows partial forgetting of information to prevent model overgeneralization. After that, two decoders reconstruct the input data. Finally, this research uses the Peak Over Threshold (POT) method to calculate the threshold to determine anomalous samples from normal samples. This research uses a two-stage adversarial training strategy during model training to enlarge the gap between the reconstruction error of normal and abnormal samples. The proposed method achieves significant anomaly detection results on synthetic and real datasets from power systems, water treatment plants, and computer clusters. The F1 score reached an average of 0.9196 on the five datasets, which is 0.0769 higher than the best baseline method.
An Analytical Study of Regression Techniques towards H-1B Visa Prediction

The most popular non-immigrant visa is the H1-B, which allows foreign employees to work in specific professions in the United States of America. Each year, millions of individuals file for H1B visas. It comprises of fresh H1B visa applications, transfers, and renewals. Nevertheless, the United States Citizenship and Immigration Services only select 85,000 candidates. The individual’s occupation as well as legal identity are uncertain because of the challenging H1-B visa filing procedure, that additionally results in substantial legal as well as immigration handling expenses over the employer while the employee is on employment. Utilizing data science methods, we plan to use the anonymized dataset of H1B candidates from 2020 to 2022 to search for trends and enhance the likelihood of receiving an H1B visa.
A Survey on Various Algorithms to Improve the Efficiency and Features of Text to Image Generation

Generation of realistic images is a versatile domain that has several applications in picture editing, video games, and computer-aided design. These applications include creating realistic visuals that semantically match the provided text descriptions. Text-to-image generator is used for generating images by collecting all the related images. It has a significant influence on many domains and different sets of applications. Existing solutions only help in generating an image from the given text with low accuracy and do not contain many features. Animators are in need to view their creative ideas in efficient manner. Considering these issues, the design for the proposed system is carried out. The proposed solution takes images from the internet according to the text provided for generation and enhances the generated image quality. 3D model and animated video construction are added as the additional features. Deep neural network model helps in creating images from text captions and constructing 3D model for an extensive range of topics that can be expressed using natural language. By combining various models, a new algorithm is proposed for generating images with high resolution from the given text prompt. Animated video is generated from different frames of an image. The proposed system stands as a unique solution for the users to envision their ideas.
3D human pose estimation in video with temporal and spatial transformer

Previous works on 3D human pose estimation have concentrated on predicting the 3D pose of the human body from a single image, ignoring correlation between adjacent frames in video. We design a transformer network structure that can extract video temporal information, and enhance the accuracy of human pose prediction by encoding relative position with temporal fusion transformer structure to enhance local feature learning capability. On Human3.6M, we quantitatively and qualitatively analyze our method. Research suggests that our TSFormer achieves state-of-the-art performance.
Hourly Journal: A Mobile App for Optimizing Your Daily Routines

There are certain apps to record how people spent their day and whether they lived the day as per their goals. But these only make people feel guilty for having wasted the day or happy on being productive, but no app shows what is the pattern for success and failure. But with Hourly Journal it is possible to understand the pattern of daily time investments and fix it, empower it and grow in life overall. Mobile application development is becoming more popular due to the expanding popularity of smartphones and tablets. Traditional software development has had a significant influence on the development of mobile apps. However, the ultimate product is software designed to take use of mobile devices’ specific capabilities and technology. To solve the problem of performance on each specific device, you must first design an app for that device. This implies writing code that is tailored to the exact hardware of a certain device. In the case of iOS devices, this is simple since mobile developers simply need to create apps for the iPhone and iPad to make their apps universally usable. For Android devices, on the other hand, the hardware and operating system versions vary from device to device. With the Hourly Journal available in the palm of your hand, people can use it as a modern-day diary, with revolutionary futuristic insights into the patterns of people’s activities and tendencies. Based on tag tracking there is scope for human behavioral prediction in future too.
Robot Navigation With Reinforcement Learned Path Generation and Fine-Tuned Motion Control

In this letter, we propose a novel reinforcement learning (RL) based path generation (RL-PG) approach for mobile robot navigation without a prior exploration of an unknown environment. Multiple predictive path points are dynamically generated by a deep Markov model optimized using an RL approach for the robot to track. To ensure safety when tracking the predictive points, the robot's motion is fine-tuned by a motion fine-tuning module. Such an approach, using a deep Markov model with RL algorithm for planning, focuses on the relationship between adjacent path points. We analyze the benefits of our proposed approach and show it is more effective and has higher success rates than the RL-based approach DWA-RL (Patel et al. 2021) and a traditional navigation approach APF (Chen et al. 2021). We deploy our model on both simulation and physical platforms and demonstrate our model performs robot navigation effectively and safely.
Cost Optimisation on Cloud by Adoption of Rust Programming

“Cloud computing” or “Compute over the cloud” is the on-demand delivery of IT resources like Computational power, networking infrastructure and various types of storage over the internet, which is scalable and elastic on demand. One only pays for the amount he uses. The traditional approach is to invest in on-premise hardware, buying, provisioning, and maintaining the same. Instead of the huge capital expenditure, operational expense on the cloud can be considered. One can save on costs, as maintenance and provisioning cost is transferred to the hyperscalers. Almost every other organization in the industry is adopting cloud computing as the go-to option. The flexibility and convenience are immense. Cloud is agile: Hyperscalers like AWS, Azure & GCP innovate continuously to bring about technological disruptions and support every reinvent in the techno space. This aids the developer communities worldwide not being bound by resource scarcity. Complex apps can be deployed in minutes and can be accessed from every part of the world. Developers gain the freedom to experiment with every possible technology, test new plans, and essentially transform and move towards a better future. With the increased popularity of Cloud Computing, multiple Tech giants have jumped into this space to provide cloud services and hence generate revenue. With the availability of a gamut of services and resources, multiple pricing tiers and a multitude of minuscule decisions at every step of implementation, cost optimization becomes a very crucial aspect to focus on. Every decision affects the cost one pays, from designing, resource provisioning and deployment. Hence this study tries to address various methods of cost optimization on the cloud. The narrative literature review approach has been adopted for the conceptualization of the model. The study provides implications to practitioners and developers for designing and developing cloud solutions at par with their requirements with optimal pricing.
A Robust and Secure Image Steganography using Convolutional Neural Networks and Transform Methods

The act of obscuring the existence of data in images is commonly referred to as steganography. Contrary to cryptography, there is no data scrambling. The original and the cover data carrying the hidden confidential data are so similar that human perceptions cannot differentiate. Steganography is an ancient and efficient way to hide and communicate confidential information. The effectiveness is measurable using various parameters. The steganography analysis method examines the captured data with various parameters in an attempt to identify the steganography. This research study presents an appropriate way to perform image steganography using a combination of techniques like the least significant bit, discrete cosine transforms, and a combined model of convolutional neural network (CNN)-Convolutional Autoencoder with Encoder-Decoder based Latent Features (CAEDLF). LSB-based steganography is an in-demand technique for concealing confidential information in the LSBs of pixel values without creating any noticeable deformity. DCT helps provide robustness by changing the structure of the secret image. The Convolution Neural Network (CNN) based convolutional autoencoder with encoder-decoder-based latent features (CAEDLF) autoencoder is used for optimizing steganography. The proposed steganography shows the best results in terms of robustness, security, and imperceptibility capabilities and also helps optimize steganography analysis.
Hybrid Cryptographic Algorithm to Secure Internet of Things

Small IoT devices can only support light protocols since they lack significant processing or storage power. Lightweight encryption standards or techniques will be utilized for encryption and decryption to prevent hackers from accessing the data in order to increase the security of IoT devices’ network connections and commands. The encryption has to be robust enough to safeguard and lightweight enough to enable light processing hardware. There aren’t many standards for lightweight cryptography that are used to safeguard devices in an existing network. If the network is not secured properly, an intruder can get in and take control of the devices without making a visible appearance. A hybrid lightweight cryptographic methodology have been proposed to address this problem. LWC algorithms are designed to provide strong security while minimizing the computational and memory resources required for encryption and decryption. This is particularly important for IoT devices, which often have limited processing power, storage capacity, and battery life. LWC algorithms offer a range of benefits for IoT devices, including improved security, reduced power consumption, and increased battery life. They are also designed to be easy to implement and integrate with existing IoT platforms and protocols. Proposed system’s analytical work implementation will combine Salsa20 and AES algorithms to provide greater security and making it well-suited for real-time cryptography.
Application of Deep Learning in Monitoring and Warning of Network Public Opinion in Universities

This paper uses the deep learning technology of convolutional neural network (CNN) and Word2vec to improve the accuracy of network public opinion monitoring in universities. This paper uses crawler technology to obtain data from Sina Weibo, Zhihu, Baidu Tieba and other websites, preprocesses hot topics related to public opinion in colleges and universities, uses Word2vec model to generate word vectors, uses CNN to extract features and classify emotional tendencies, and establishes a more effective monitoring and early warning system of public opinion in colleges and universities. This study has good theoretical value and practical significance to further strengthen the work of news and public opinion in Colleges and universities, and to enhance the ability of governance and the modernization level of governance system in Colleges and universities.
Sentiment Analysis Tools for Movie Review Evaluation - A Survey

Online movie review platforms present a new and effective way for users to share feedback about movies and television shows. These platforms provide information to help the prospective audience make informed choices based on their personal preferences. By analyzing these reviews, production companies can also understand customers’ opinions on their projects. The analysis of sentiments in these reviews is used to generate an overall score. This score shows how the public perceives a movie. This research study identifies user sentiments by analyzing online user feedback on some movie review platforms. This research study considers the IMDB and Rotten Tomatoes dataset. The user sentiments are classified as either Positive or Negative by computing the Polarity score of the reviews. Finally, this research study examines the performance of different lexicon-based sentiment analysis tools like Textblob, VADER Sentiment Intensity Analyzer, SpaCy, and Textblob Naive Bayes Analyzer in detecting movie sentiments. Based on the accuracy of the predicted sentiment scores, this study prefers Textblob’s Naive Bayes Analyzer since it has delivered the best performance. The proposed model generates an accuracy of 73% and an F1-score of 0.78 for the IMDB movie reviews.
Target detection of UAV aerial photography based on improved YOLOv5

In view of the traditional aerial image of UAV, the target detection method has many shortcomings, such as inaccurate detection and positioning, low detection accuracy and so on. Based on the original YOLOv5 framework, this paper proposes a new target recognition algorithm and an improved feature extraction method, and uses ultra-lightweight convolution neural network MobileV3 to replace the main feature extraction network of YOLOv5 network; In order to improve the attention of the model to other regions, the attention mechanism GAM Attention module is introduced. This mechanism embeds the location information into the channel attention, realizes multi-scale processing and feature fusion, and makes the model have higher detection accuracy. The experiment shows that the size of the improved lightweight network model is only 81.7% of the original YOLOv5 model, and the mAP on the data set reaches 96.8%. This method greatly reduces the model parameters and the amount of computation on the basis of ensuring the detection accuracy, and also greatly improves the detection speed and accuracy. Therefore, the method proposed in this paper can provide strong support for the application of UAV target detection.
Exploring Explainability and Transparency in Deep Neural Networks: A Comparative Approach

Explainability in Artificial Intelligence (AI) helps define model accuracy, clarity, transparency, and results in decision-making backed by AI. A business needs to establish reliability and trust when implementing AI models. Explainability provides a responsible approach to AI development. This paper’s primary objective is to examine the visual explanation property of various interpretability methods. An attempt is made to understand how a convolutional neural network’s output can be explained. It was common practice to refer to the deep learning models as black boxes; however, our primary concern is determining precisely what takes place within them. Explainability will give us an understanding of which all elements lead to a prediction. A few techniques for providing visual explanations from Convolutional Neural Network (CNN)-based models are the focus of our investigation. The transparency that can be achieved through visualization receives the most attention. Along with this, attention is given to class discrimination and whether the model takes into account the smallest of details. There have been several proposals for creating deep learning models that are intuitive. In this paper, an attempt is made to determine the effectiveness and drawbacks of Grad-CAM, Ablation-CAM, Score-CAM, and Eigen-CAM.
Optimizing the KYC Verification System using Ethereum Blockchain

People utilise their personal identity documents on a regular basis with current increase in digitization, which are shared with third parties without their explicit authorization and kept in numerous random locations. Financial organizations that include government institutions, banks, credit agencies, etc. store such identification information in their own databases. Having such sensitive information at multiple locations increases the chances of vulnerabilities. The financial sector has been on a search for solutions for such problems for a long time and one viable solution to this is blockchain. By keeping a single safe database on blockchain, the Know Your Customer (KYC) verification procedure reduces the repetitive KYC checks that banks presently do. Because blockchain is immutable and unalterable by definition, illegal modifications to data are instantly invalidated. Blockchain’s decentralised design will enable the collection of data from different authoritative service providers into a single immutable, safe, and verified database. The Blockchain KYC system uses a secure, public digital ledger to provide rapid and fully secure identity verification.
Generative Model for Image Classification based on Hybrid Adversarial Auto Encoder

Generative model used widely in machine learning due to its ability to learn the feature from the input and able to regenerate the same. However, for different application different models are used. Hybrid model gives more precise results in some of the area. One such model is designed for specific application of anomaly detection. Here adversarial auto encoder model is used as base model and added adversarial effect from generative adversarial network to generate image from features. The results show that the proposed model can leverage less reconstruction loss in image regeneration and its compressed representation as latent space can classify each class differently. This research study discusses about the proposed model architecture and various methods used for hyper parameter tuning.