Reviewerly/exHarmony huggingface dataset:
Unified Parsing Script using Machine Learning

Gathering noiseless data is an important phase in fields such as machine learning and target advertising and is also one of the most tedious processes. The evolution of the Internet has made it the largest source of data, in every domain. Web scraping can be defined as the process of extracting and parsing information from unstructured web pages. Advancement in web scraping has provided us with tools such as BeautifulSoup, lxml, and Selenium, which precisely extract information from webpages based on the knowledge of the HTML document to be parsed. Nevertheless, one major inconvenience associated with such primitive web scrapers is that they are site-dependent. They have to be adapted for each new website. In this paper, we propose a machine learning based approach that searches for patterns in the semantics of the webpages of a single domain, capable of acting as a site-independent web scraper. The strategy proposed involves an algorithm that utilizes the results of three independent machine learning models to extract key information from web pages belonging to a specific domain. The experimental analysis of the simulation demonstrates that the solution proposed enhances the process of web scraping by enabling a single web scraper to extract data accurately from multiple websites, thus improving productivity.
Imperceptibility and robustness study in different transform domain for copyright protection in digital image watermarking in hybrid SVD-domain

Copyright protection of digital images through watermarking technique is one of the viable approaches for industrial and commercial applications. Sustaining a watermark’s robustness during attack and preserving the image visual quality with watermark capacity is most challenging. In this work, we show the impact of attacks on the image imperceptibility and watermark robustness for a watermarking technique in hybrid transform domain with various wavelets. We observed that (i) the excellent multi-resolution features of wavelet coefficients help to enhance imperceptibility; (ii) the stability property of SVD ensures adequate robustness; (iii) Shuffling the keys have addressed the rising of FPP occurring in the conventional SVD based systems. The performance of the technique has been enumerated through various signal processing attacks. Performance examining has been done in terms of state-of-the-art measures like PSNR, NCC, SSIM, NSR. A subjective investigation of the experimental results validates the performance of the proposed technique in each transform domain.
SAMF-TLD Based Pedestrian Multi-Target Tracking

Aiming at the tracking loss problem caused by target scale change and occlusion in pedestrian multi-target tracking, a scale adaptive kernel correlation filter with tracking-learning-detection (SAMF-TLD) based multi-target tracking method is proposed. This method combines a short-time tracker with the traditional track-detect-learn (TLD) multi-target tracking algorithm, which improves the real-time performance of the algorithm and overcomes the tracking failure problem of the TLD algorithm under illumination change and large scale change. Secondly, by judging the confidence of the tracking results of the scale adaptive kernel correlation filter (SAMF), the problem of updating the filter template when the target is occluded is solved, therefore, in order to improve the tracking efficiency, effectively avoid the tracking failure problems caused by the target being occluded and the tracking target scale change. In the experiment, 2DMOT-15 and MOT-16 data sets were selected for tracking verification, and the common evaluation indicators (MOTA, MOTP, MP, MT, and IDS) were compared and analyzed. The results show that the proposed method is superior to the TLD pedestrian multi-target tracking algorithm in the accuracy, and the SAMF tracking algorithm in terms of anti-occlusion.
The Application of Target Embedding AutoEncoder based on Knowledge Distillation in Multi-View Multi-Label Classification

In multi-view learning, how to effectively integrate features among multiple views becomes a key challenge. For multi-label tasks, it is extremely important that the relationship between features and labels. This paper proposes a Target Embedding Autoencoder framework based on knowledge distillation (tea-mvml), which explores the correlation between features and labels in Multi-View Multi-Label (MVML) learning. The framework transfers knowledge from a teacher model based on a Target Embedding Autoencoder (TEA) to a small student model through knowledge distillation. The teacher model of tea-mvml learns the relationship between features and labels in the potential space, while the student model has the generalization ability of the teacher model. Experimental results on multiple real-world datasets show that tea-mvml not only reduces the complexity of the model, but also outperforms other state-of-the-art multi-view multi-label classification approaches.
Enhanced Password Manager using Hybrid Approach

Password management is essential for keeping data safe and secure. With increasing threats from cyber criminals, it is essential to have secure and unique passwords for each online account. Password management software helps users to store passwords securely. Password management also helps users easily remember complex passwords and reduces the risk of their accounts being hacked. Additionally, password management can help organizations to manage and protect confidential data, ensuring that only authorized personnel have access to sensitive information. This paper presents a secure password management extension for web browsers. The extension is designed to provide users with an easy-to-use interface for securely storing and managing website passwords. It allows users to log into websites with a single click quickly and incorporates an advanced encryption algorithm to ensure data security and privacy. The extension is available for some major web browsers and is compatible with multiple operating systems. The extension is designed to be lightweight and efficient, using minimal system resources. Furthermore, the extension is regularly updated with the latest security protocols and updates, ensuring that users' passwords remain secure.
Time Series Anomaly Detection for ICS via Multidimensional Features and Spectral Residuals

As the development of the Internet of Things, industrial control network security issues have become more prominent. Anomaly detection technology is an important detection technology for ensuring the network security of industrial control systems, which can issue alarms and block responses before the system is compromised. However, most models currently suffer from problems such as low detection rate, high false positive rate, and inability to locate the anomalies. To address these issues, this paper proposes a multidimensional feature and spectral residual-based industrial control time series anomaly detection method (SRMCAD) to improve the accuracy of anomaly detection while reducing the false positive rate. First, this paper applies the spectral residual method to multidimensional time series anomaly detection in the industrial control domain, and uses the residual data obtained by spectral residual processing instead of the original data as input to the model. Secondly, to address the problem of imprecise device attack localization and high false positive rate in existing industrial control anomaly detection models, this paper designs and implements an industrial control anomaly detection model based on an encoding-decoding architecture, incorporating the idea of system state modeling and using the Mahalanobis distance as the loss function. Experimental results show that SRMCAD has significant advantages over current advanced methods.
A Real-time Person Identity Detection System Using Machine Learning

This research study presents an automated real-time background face recognition system for a large dataset of human faces. This is very difficult because background subtraction is still an issue in live images. Addition to this there are huge features in human face image in terms of eye, nose, head, lip, etc. The proposed system simplifies many of the facial recognition features. It utilizes AdaBoost with cascade to detect human faces in real-time. The matched face is then used to Identify a person. The real-time security and automation system is based on human face recognition, and it uses a simple and fast algorithm that achieves high accuracy. We have accuracy of 92% by using Adaboost Algorithm.
Dissecting Distribution Inference

A distribution inference attack aims to infer statistical properties of data used to train machine learning models. These attacks are sometimes surprisingly potent, but the factors that impact distribution inference risk are not well understood and demonstrated attacks often rely on strong and unrealistic assumptions such as full knowledge of training environments even in supposedly black-box threat scenarios. To improve understanding of distribution inference risks, we develop a new black-box attack that even outperforms the best known white-box attack in most settings. Using this new attack, we evaluate distribution inference risk while relaxing a variety of assumptions about the adversary's knowledge under black-box access, like known model architectures and label-only access. Finally, we evaluate the effectiveness of previously proposed defenses and introduce new defenses. We find that although noise-based defenses appear to be ineffective, a simple re-sampling defense can be highly effective.
Comparative Analysis of Oversampling Techniques on Small and Imbalanced Datasets Using Deep Learning

In imbalanced datasets, certain classes have a larger number of samples compared to others, leading to an unequal distribution of samples across the classes. Since many crucial real-world classification problems, like medical diagnosis, involve imbalanced data, the research community places a high priority on understanding how to use this data. If machine learning is performed directly on the imbalanced data, the disparity between the majority and minority classes will cause bias towards the majority class and lead to inaccurate results. There is growing interest in this field of study, and several algorithms have been created. This study aims to evaluate the effectiveness of five oversampling strategies that are intended to address data imbalance, namely random oversampling, SMOTE, borderline SMOTE, ADASYN, and Deep SMOTE. A comparative analysis is carried out, and the effectiveness of each strategy is examined in terms of evaluation metrics. Experimental results demonstrated that DeepSMOTE outperformed all other oversampling techniques on small and imbalanced datasets.
Counterfactual Sentence Generation with Plug-and-Play Perturbation

Generating counterfactual test-cases is an important backbone for testing NLP models and making them as robust and reliable as traditional software. In generating the test-cases, a desired property is the ability to control the test-case generation in a flexible manner to test for a large variety of failure cases and to explain and repair them in a targeted manner. In this direction, significant progress has been made in the prior works by manually writing rules for generating controlled counterfactuals. However, this approach requires heavy manual supervision and lacks the flexibility to easily introduce new controls. Motivated by the impressive flexibility of the plug-and-play approach of PPLM, we propose bringing the framework of plug-and-play to counterfactual test case generation task. We introduce CASPer, a plug-and-play counterfactual generation framework to generate test cases that satisfy goal attributes on demand. Our plug-and-play model can steer the test case generation process given any attribute model without requiring attribute-specific training of the model. In experiments, we show that CASPer effectively generates counterfactual text that follow the steering provided by an attribute model while also being fluent, diverse and preserving the original content. We also show that the generated counterfactuals from CASPer can be used for augmenting the training data and thereby fixing and making the test model more robust.
Comfort assessment of stereo images considering edge objects

Many people often feel visually uncomfortable when viewing stereo images. In the field of stereo display, it is an important research topic to assess comfort of stereo images accurately. However, the actual stereo image scene is more complex, and it is ineffective to establish the objective comfort assessment model of those images only by few factors. The current related research has shown that edge objects have some impact on visual comfort. Therefore, in order to assessment the visual comfort more accurately, a stereo image comfort assessment model considering edge objects is proposed. The influence of edge objects were analysed, and features of them were analyzed and computed, such as parallax, edge conflict, color and contrast features. In addition, region of interest features were computed by considering the visual characteristics of human eyes. Finally, an objective assessment model of stereo images was constructed by the support vector regression(SVR) algorithm based on the these features. The experimental results had shown that the performance assessment indexes PLCC, SROCC and RMSE are improved by 0.9%, 1.16%, and 10.6%, separately, on the IVY dataset.
Multi-activation stochastic configuration network

Stochastic configuration network (SCN) utilize a unique supervisory mechanism to construct hidden layer nodes to ensure the universal approximation capability, constituting a neural network with advantages such as fast convergence and guaranteed generalization error. However, the disadvantage of the original SCN is that the single activation function used could cause premature overfitting of the model as the number of hidden layer nodes continues to increase, in order to ensure the model perform better. To address the issue, a kind of improvement strategy aiming to the constructure of the hidden layer of original SCN, a multi-activation stochastic configuration network (MA-SCN) architecture is proposed in this paper. That means, the selection of the activation functions deeply dependent on data, namely, the increase progressively progress of hidden node by selecting the activation function with the best performance, so the better generalization capability of the model could obtain with compacter structure of the hidden layer. Simulation results demonstrate that the strategy of multiple activation function selection by merit can improve the accuracy of the SCN model in solving regression problems while reducing computational effort. The proposed model has been used in real industrial data cases and has fully demonstrated its feasibility and effectiveness.
Multi-Scale Network for Object Re-Identification

A high-performance re-identification model requires focusing on both the global profile and local details of the object, which means that feature extraction needs to be performed at different granularities of the image. In this paper, we propose a multi-scale network for object re-identification, using the proposed BoT2Net as the backbone and introducing feature pyramid branches after the second and third layers. The feature maps in the bottleneck block of the last layer of BoT2Net are equally divided into four parts according to the number of channels to form the residual structure, and a double-layer self-attention is introduced to capture the global dependent features. Cross-scale connectivity and double-layer self-attention are used within the feature pyramid branches, and finally the importance of weights on channels is judged using a normalization operation. Comprehensive experiments on multiple datasets show that the method achieves good performance.
Character and Word Level Recognition from Ancient Manuscripts using Tesseract

In uses adapti ve Gaussian thresholding and pre -processing techniques like binarization to improve the quality of the images. Additionally, segmentation is carried out at the line-and word-levels utilizing the blob approach and regional zoning. Recognizing the characters and creating the database are the last steps. The efficiency of the suggested strategy for successfully identifying and digitizing Malayalam manuscripts is demonstrated by the achievement of recognition accuracy of above 93%. The goal of this project is to contribute to the creation of a comprehensive Malayalam database that will aid in the advancement of natural language processing research and development.
Dual mode Sign Language Recognizer-An Android Based CNN and LSTM Prediction model

Communication is varied important part of our life. By communicating with each other, we can interact, share our ideas and emotions. However, mute and deaf people of our society face lots of problem while communicating with normal people. Previous researches were focusing only one-sided communication i.e., Speech to Sign Conversion using only Static dataset like for Alphabets, Digits and only for specific languages. While keeping in mind the lacunae of current system, this research is about an application-based system that will serve as an interpreter for sign language, enabling two-ways communication between hearing-impaired people and normal people while working on dynamic gestures and centralized system for everyone. This system will work on two modules. One will be sign gestures to speech and other will be speech to 3-D animated sign gestures. For this purpose, dataset is collected from deaf and mute School, Dhule Maharashtra and ISL official site of size approx. 1800 words. Sign gestures to speech conversion is implemented using CNN+LSTM deep learning algorithm while Speech to 3D sign gestures is implemented by extracting keywords from live speech and developing 3D animated gestures using extracted keywords. This system will bridge the gap between deaf, mute and normal peoples.
Neural Lower Bounds for Verification

Recent years have witnessed the deployment of branch-and-bound (BaB) frameworks for formal verification in deep learning-proving or disproving a desirable property of a neural network. The main computational bottleneck of BaB is the estimation of lower bounds via convex relaxations. Past work in this field has relied on traditional optimization algorithms whose inefficiencies have limited their scope. To alleviate this deficiency, we propose a novel graph neural network (GNN) based approach. Our GNN architecture closely resembles the network we wish to verify. During inference, it performs forward-backward passes through the GNN layers to compute a feasible dual solution of the convex relaxation, thereby providing a valid lower bound. During training, its parameters are estimated via a loss function that encourages large lower bounds over a time horizon. Using standard publicly available data sets, we show that our approach provides a significant speedup for formal verification compared to the state of the art solvers. Moreover, the GNN achieves good generalization performance on unseen networks.
Vehicle Detection of Bangladesh using YOLOv7 with Hyper-parameter Tuning

In recent years, infrastructure development in Bangladesh has advanced at a remarkable rate. Road transportation has grown the most for passenger and freight transportation in the last 50 years. As a result, there are now numerous land-based transportation options. However, the traffic in Bangladesh is getting worse due to rapid unplanned expansion, diverse land use, inadequate public transport vehicles, and more private car users. In this paper, we sought to determine the application of Al in this challenging context. Object detection and classification can help distinguish different types of vehicles and determine traffic density in a given area at a given time, which can facilitate vehicle flow control. The study trains a Deep Neural Network to detect local traffic objects in Bangladesh in real-time using the object detection algorithm YOLOv7 with hyperparameter tuning. The experimental data exhibits an improved precision of up to 13.52% by tuning the hyperparameters.
Classification of Phishing Websites using Machine Learning Models

Phishing attacks have become increasingly and it is a serious threat to security of the user information. It targets unsuspecting individuals through deceptive websites that appear to be legitimate. These attacks can compromise sensitive information and also have serious consequences. Machine learning has a potential solution to address the growing threat of phishing. By training algorithms to recognize the tell-tale signs of phishing websites, it may be possible to detect and prevent these attacks before they can cause harm. In our paper, we explored the features of detecting the phishing url with the use of ML algorithms such as SVM, Logistic Regression, Naive Bayes classifier, and also ensembling algorithm such as Random Forest Classifier. We examine the features that are commonly used in these algorithms and compare their effectiveness in accurately classifying websites as either Phishing or Legitimate
Improved YOLOv5 for Stroller and Luggage Detection

With economic development and social progress, urban infrastructure is constantly improving. In public places such as shopping malls and subways, escalators are very common and provide great convenience for citizens. However, some irregular behaviours on the escalator have brought great safety hazards. Carrying large luggage and strollers onto the escalator is a typical risky behaviour. How to detect large luggage and strollers in time and issue an alarm is an important part of abnormal detection of escalators. This paper introduces the CBAM module based on YOLOv5, and conducts experiments on the collected data sets. Experimental results show that our proposed model improves the object detection performance well.
Medical Mask Detection using advanced MobileNetV2

According to data from the World Health Organization (WHO), personal contact and respiratory droplets are the two main ways that the COVID-19 virus is spread. One of the prophylactic strategies that can stop the spread of COVID-19 and other respiratory viral illnesses is wearing a surgical mask. Therefore, it is essential to correctly wear a mark in public settings like supermarkets and shopping centers. In this study, we suggest a Face Mask Detection system to determine whether or not someone is correctly donning a mask. Face mask identification in video streams has only been the subject of a few studies. It uses OpenCV, TensorFlow-Keras, MobileNetV2, and Deep Learning to recognize face masks in a two-phase design and achieved an excellent accuracy.
Video recommendation method based on multi-attention mechanism and heterogeneous information network

The online learning platform has rich video resources. To recommend video resources suitable for learners' knowledge background and learning interests, it is necessary to know the details of each entity object related to learners. To this end, a video recommendation method based on multi-attention mechanism and heterogeneous information network is proposed. The heterogeneous information network learning method is used to obtain the representation of node feature vectors, metapath and attribute embedding; Implicit feedback matrix is used to obtain the embedding of nodes and neighbors; Use convolutional neural network to embed neighbor nodes and aggregate meta-path pairs; Multi-attention mechanism is used to modify node embedding and meta-path embedding; The above embedding is spliced and input into the fully connected neural network for learning and recommendation. The experiment was carried out on the MOOCCube and MOOCdata dataset, and the evaluation model was carried out using indicators such as accuracy, recall rate, average reciprocal ranking, and area under the working characteristic curve of subjects. Experimental results show that the proposed method achieves better recommendation performance than common recommendation methods.