Reviewerly/exHarmony huggingface dataset:
Advancing Multimodal Fusion in Human-Computer Interaction: Integrating Eye Tracking, Lips Detection, Speech Recognition, and Voice Synthesis for Intelligent Cursor Control and Auditory Feedback

In the realm of human-computer interaction (HCI), the integration of multiple modalities has emerged as a promising approach to enhance user experiences and enable more natural and intuitive interaction paradigms. This paper explores the advancements in multimodal fusion techniques and their application in HCI, focusing on the integration of eye tracking, lips detection, speech recognition, and voice synthesis. The proposed framework aims to leverage the combined power of these modalities to achieve intelligent cursor control and provide enhanced auditory feedback to users. By seamlessly integrating eye tracking, lips detection, and speech recognition, the system can accurately interpret user intentions and gestures. The eye tracking component enables precise tracking of gaze movements, while the lips detection module analyses lip movements for additional input signals. Concurrently, the speech recognition module captures user commands and converts them into actionable instructions. The synthesized voice feedback component plays a crucial role in providing auditory responses to user actions, ensuring a seamless and engaging interaction experience. Through the integration of voice synthesis, the system can effectively communicate feedback, confirm user inputs, and guide users through the interface. The combined functionality of intelligent cursor control and auditory feedback opens up new possibilities for more efficient and immersive HCI. Experimental evaluations demonstrate the effectiveness of the proposed multimodal fusion framework. User studies reveal improved cursor manipulation accuracy and reduced cognitive load compared to traditional input methods. The system's ability to adapt to individual user preferences and environmental conditions further enhances the overall user experience. This paper contributes to the advancement of multimodal fusion techniques in HCI by showcasing the integration of eye tracking, lips detection, speech recognition, and voice synthesis. It highlights the potential of this comprehensive approach to enable intelligent cursor control and provide sophisticated auditory feedback, thereby paving the way for more intuitive and immersive human-computer interaction paradigms.
Comparative analysis of the effectiveness of ML and DL techniques for classifying Face Masks

In 2020, COVID has grown to be a significant public health concern. The virus's contact-transparent properties caused it to propagate quickly. Wearing a mask that stops the transmission is the most effective strategy to prevent this. The first step in lowering the danger is to wear a mask, which cuts the likelihood of contracting the infection by 70%. To resolve this problem, we need the system to keep an eye on the facial mask. This work can be facilitated by a face mask detector based on deep learning (DL) and machine learning (ML) models can make this task easier. Here, we do face mask categorization utilizing several ML and DL techniques. Here, we experimented with various methods such as CNN, Resnet50, Gabor filter, GLCM, SVM, KNN for extracting features and classifying faces with mask and without mask. From the accuracy we got for each model it says CNN performs well on the dataset we took.
Optimization Scheduling Techniques based on Resource Provisioning in Cloud Computing: Compressive Survey

The era of cloud computing has arisen as a technology utilising the internet in the last few decades to realise the "utility" approach of technology for providing services with a high computational demand. The information technology and company resources, including as storage, networks, servers, and programmes, can be automatically allocated to cloud demands presented by individuals under the scenario of cloud computing. The trend towards virtualization technology used by the cloud computing infrastructure offers on-demand availability of system assets, including processing and storage. In cloud computing, emulation is used to distribute computing resources through an alternate communications network. The main goal of the scheduling is to reduce the requirement and impairment time and increase performance. In order to do a task successfully and precisely, scheduling is crucial. The present paper provides information on several work scheduling techniques utilised by researchers in the cloud computing environment. Therefore, it is open study to determine an approach by examining present challenges. The limitations. benefits, and prospects associated with various resource provisioning approaches are therefore addressed in the present research along with several methods developed for resource provisioning in cloud computing working with various scheduling algorithms.
Augmented Reality based Smart Surgical Glass for Medical Surgery

Surgeons are always looking for technologies to make their working environment better. They frequently take the lead in implementing innovations that help their industry deliver better surgical and patient experiences. The ongoing advancement of the surgical environment in the digital era has resulted in several breakthroughs being observed as possible disruptive technologies in the surgical work place As augmented reality (AR) grows in popularity, availability, and cost, it is critical to evaluate how technology might be leveraged to better utilize medical data in healthcare. Applications in surgical anatomy, surgical processes, and surgical recovery are being researched all the time. The use of computer-generated sensory input to improve performance on a specific task is referred to as "augmented reality". A technique that combines the view of the real world with important information for doctors displayed on semi-transparent glasses attached to an augmented reality headset. For this investigation, sensors are being utilized to monitor patients in a hospital and gather data in real time. The data is then processed by a computer and delivered immediately to the augmented reality glasses used by doctors. The alarm on these spectacles notifies doctors if something is wrong. Taking the patient's current state of health into account, the physician is able to make wise decisions and carry out the necessary procedures.
Inception Inspired U-Net for Effective Segmentation of Microscopy Images

Biomedical image analysis has vital role in medical diagnosis. Computerized tools for automatic analysis of biomedical images helps the radiologist for the disease identification. This work explores a custom designed deep learning architecture for automatic segmentation of microscopy images. The deep learning architecture uses Inception modules in U-Net type structure for effective segmentation. This newly designed framework is tested on different types of microscopy data to assess segmentation performance. The proposed model’s dice similarity index for the DIC-C2DH-HELA and Fluo-C2DL-MSC datasets are 0.9559 and 0.9167 respectively. Further intersection over union for the DIC-C2DH-HELA and Fluo-C2DL-MSC datasets are 0.8783 and 0.74779 respectively.
Blockchain Based Property Deeds Repository System

Property deeds convey ownership of properties to parties purchasing them from the owners. They give us information regarding the property’s details like who is the owner, the worth of the property, and who has the most privileges. The traditional ways of handling and management of title deeds included the use of brokers. Brokers frequently take a long time to finish procedures as they become the middlemen. We thereby propose a solution that will allow several property owners to store the land details digitally on a decentralized platform and buyers can request the owners to buy the property as well as confirm the authenticity of the title deeds of the seller using the application. Ultimately this system will improve efficiency, reduce errors, eliminate the need of a middleman (broker) and broaden the accessibility of the system that is now more representative of the digital age.
NetMiner: Identifying Failure-Inducing Patterns in the Logs Generated by Network Monitoring Software

To ensure uninterrupted network communication among multiple sources, the network administrator needs to do real-time monitoring of network logs. Due to its huge volume and streaming nature, it is almost impossible to do a manual analysis of the logs generated by different network monitoring software. Moreover, these logs may contain a limited amount of data regarding the identity, location, capacity, etc. of a user due to privacy issues. Therefore, we propose an automated system named NetMiner for performing log analysis that requires only insensitive data such as the network’s current status and messages related to that status. We collect recent 30 days logs using the network monitoring tool PRTG (Paessler Router Traffic Grapher) and perform data analysis by leveraging a data mining technique called Fpmax. Our proposed technique NetMiner can identify patterns that may lead to failure and warn the network administrator about upcoming failures. Our experimental results show that NetMiner is both effective and efficient in identifying failure-inducing patterns in logs generated by network monitoring software.
Use of Propositional Logic in building AI Logic and Game Development

Logic is the foundation of most computer programming and now is an integral part of the development of New Age Artificial Intelligence. Logic helps AI and ML in fields with very little or more data. As logic is something very close to human behavior, it is safe to say that logic-integrated AI systems are of High-level programming. Their main aim is to encode human knowledge and input it into machine-understandable data. In this paper, we have discussed deeply the use of propositional logic in the development of games, solutions to these games, and also how important they are in AI. Considering games such as Sudoku, Knights and Knaves, and Game of Bluff.
Breast Cancer Histopathology Image Classification Using Ensemble Feature Set

Breast cancer accounts for 30% of newly diagnosed tumours due to lifestyle and genetic causes. According to WHO data, breast cancer is the second most frequent form of cancer globally. It was even predicted that by 2020 there will be a rise of 2.3 million more instances. Histopathologists manually quantify mitosis, which is time-consuming and subjective. CAD, a secondary reader system, improves diagnosis and therapy. Recent technology advances have focused on mitosis detection utilising hand-crafted or CNN-learned characteristics. CNN-based techniques are computationally intensive, yet hand crafted features are inaccurate. Our technique combines Convolutional Neural Network-learned deep features with handcrafted features and employs an ensemble machine learning model to classify. The model was trained using MITOS-ATYPIA-14 grand challenge mitotic images. The model fed HC and deep features has an F1 score of 96%. Thus, this model is more reliable and computationally efficient than current methods.
Identification Of Users with Social Behavioural Biometrics On Twitter

Online Social Network (OSN) platforms provide a valuable source of information, which includes user profiles and social connections that can be used to develop behavioural models of individuals. Tweets of four Twitter users are analysed and a method is proposed to identify users. The Twitter text data is pre-processed to get meaningful words to extract features of every user. Term Frequency-Inverse Document Frequency (TF-IDF) is employed to eliminate features with lower weights, extract essential features from the text, and retrieve the corresponding word vectors using a pre-trained Word2Vec model. These word vectors are input to Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and CNN-LSTM for classification to generate a model to predict the user. The experimental results achieved an accuracy of 64.41% in identifying a user in a particular category. When the behavioural patterns of users change over time, it could be challenging to develop an accurate model calling for frequent updating of models. The proposed model can identify users on social media platforms by analyzing behavioural patterns from text data. This can enhance the security and trustworthiness of social media platforms. It can also identify attempts at impersonating other users by analysing behavioural patterns.
Malicious URL Detection Using Machine Learning

Phishing costs Internet users a lot of money every single year. It means taking advantage of user-side vulnerabilities, which are susceptible to such assaults. The phishing issue is severe, and there isn't just one way to reduce everything. Several strategies are used to address security flaws successfully. By using various methods, we analyze URLs by extracting their features. We evaluate these different URL properties using machine learning approaches and algorithms. In this paper, an outline of these strategies is provided.
Pashto Isolated Digits Sign Language Recognition

Effective communication involves the ability to hear and listen without any hindrances. However, communicating with the deaf community can be challenging, even when one is making a sincere effort to do so. In such situations, sign language has proven to be the most effective means of initiating and ending a conversation. Unfortunately, members of the deaf community are often treated with less respect and may be ignored due to their inability to communicate. This leaves them feeling isolated and prevents them from accessing vital information or exercising their right to make choices. Recent technological developments in machine learning, computer vision, and sign language recognition have facilitated the bridging of communication barriers between deaf and hearing individuals. The present study endeavours to make a contribution to the field by constructing a system that employs convolutional neural networks and computer vision techniques to identify manual gestures utilised in Pashto sign language and convert them into a numerical representation.
An Image Enhancement and Data augmentation of Alzheimer’s MRI Data using modified SRGAN

Machine learning (ML) models, Deep Learning (DL) Models rely on labeled data for classification and prediction. Automated or computer-aided medical diagnosis requires a large dataset to maintain clinical accuracy and precision. High-resolution images of different clinical and symptomatic stages are essential for implementing ML /DL algorithms for medical diagnosis. High resolution MRI image aids in better performance of DL Models. Here we propose a model based on Super Resolution Generative Adversarial Networks (SRGAN) with Transfer learning to enhance the MR images for Alzheimer’s disease (AD). These images can also be used as data augmentation and increase dataset size for better model performance. AD, an irreversible cognitive impairment disorder, is one of the significant causes of death in the elderly. No cure is yet developed, and diagnosis at an early stage will enable the elderly to make informed decisions. The model enhance low resolution image of lower size to high resolution higher size. The model trained for 5000 epochs resulted in a final G loss of 0.001 and a D loss of 1.006, which is a significantly good result. The high-resolution images generated by the proposed model are validated by the pixel-wise loss function, with a comparable average MSE of 0.009.
Security Analysis and Threat Modelling of Mobile Banking Applications

Technology revolution has transformed the way consumers interact with financial institutions. With SMS and applications, the usage of mobile banking has exploded. With 5.22 billion mobile banking customers worldwide, the financial transactions made through mobile banking crosses into trillions. Though this has eased tedious banking processes, threats are rampant. Threats range from simple malwares to serious crimes threatening safety of financial assets and causing irreversible losses. This paper aims to collate the 5 major threats to banking applications (and also in the context of SMS), provide an objectified view of how vulnerabilities in mobile banking applications could cause these threats and ways to address them. This comprehensive review provides a threat modelling framework that help in detection and mitigation strategies for these threats.
Threshold Based Image Compression Scheme for Improved Compressibility

In this paper, we introduce an improved performance of well-known lossy data compression techniques, namely Huffman and Arithmetic coding, using a customized scheme that involves threshold selection and conditional pixel probability mapping. The scheme shows efficiency in terms of compressibility with minimal information and similarity loss in the process. In addition to this, flexibility is exhibited by the proposed scheme to select required compression using a threshold. The outcomes of the research are evaluated and compared based on a variety of parameters such as compression ratio, MSE, SSIM, and PSNR. The proposed approach is capable of effectively handling information-varying images of different quality levels.
Enhancing Multi-Label Image Recognition through Class-Specific Residual Attention

Researchers in the field of computer vision encounter challenges like label sparsity, label dependency, imbalanced label distribution, and feature representation when trying to address the problem of label recognition inside a single image containing several labels. Class-specific residual attention (CSRA) aims to highlight discriminative areas connected to each label by recording attention maps specific to each label. We improved the CSRA model by including two additional representative characteristics to describe the connection between labels and the various spatial regions occupied by entities from different categories. This helps to get more nuanced descriptions of the connections between the labels and the multiple locations in which things from different categories reside could be made. These enhancements have been incorporated into the updated model. Experimental results suggest that the representative matrices with the least variation have the greatest impact when it comes to expressing features. The least-variance feature is second only to the channel-average mean in terms of significance. Getting an accuracy of 90.03% when adding the minimum variance feature while accuracy is reduced to 89% when adding other features.
A systematic review of NLP methods for Sentiment classification of Online News Articles

Natural language processing (NLP) has developed into an essential field that includes sentiment analysis of news articles. It consists of the automatic identification of sentiment in articles using NLP models, which might give businesses, decision-makers, and the general public information that is helpful. Many NLP models have been published in this field for the sentiment analysis of online news articles. Some of the models in this area include VADER, TextBlob, Support Vector Machines (SVM) and Naive Bayes, Recurrent Neural Networks (RNNs), and Transformer-based models. The rule-based technology VADER provides sentiment scores to the text’s words. TextBlob classifies a text’s sentiment using machine learning methods. The methods for probabilistic machine learning Text are categorized using Naive Bayes and SVM based on the frequency of phrases. RNNs analyze text one word at a time and use the previous words to predict how the next word will sound. Transformer-based models can be customized for sentiment analysis and come pre-trained on a lot of text, including BERT, RoBERTa, and GPT-2. The model selection depends on the project’s unique requirements. Overall, NLP models for sentiment analysis of online news articles have the potential to offer insightful information for decision-making across a range of fields. In this paper, the review of different approaches and models for performing sentiment classification on online news articles is provided and the reader may be able to find the application of sentiment analysis in the domains of their interest.
Human Fall Detection System using Long-Term Recurrent Convolutional Networks for Next-Generation Healthcare: A Study of Human Motion Recognition

Falls can cause serious injuries and even death, especially in older adults. Therefore, fall detection systems have the potential to save lives and improve healthcare outcomes. Security and healthcare issues are of utmost concern today in a dynamic world. Accidents from falling can cause serious injury to elderly people or children, and in some cases, death can also occur. A deep learning-based Human Motion Recognition approach has been proposed to detect human falling from video, even in low resolution, and send an alert message to the other end-user without using any costly wearable device. In this paper, the Long-term Recurrent Convolutional Network (LRCN) model is practiced on our proposed FD503 video dataset. A 90% accuracy was attained using the suggested strategy. Precision and recall are 0.93 and 0.85 respectively. The LRCN model is flexible for vision-related problems involving time-varying sequences. The proposed system is highly capable of real-time monitoring to provide healthcare with an alert message.
Analytical Study and Recommendations for Computer Vision Methods

The purpose of this comparative study is to determine the effectiveness of various computer vision techniques for image analysis tasks. This research aims to investigate different computer vision models for object detection and recognition. Using computer vision models for object detection and recognition, this research aims to replace the cumbersome manual system currently in use. This is accomplished by comparing popular models such as YOLO and Haar Cascade based on their precision, speed, and efficiency. In addition, facial recognition methods, such as the Siamese Model and the face-recognition library, are analyzed based on their performance using metrics such as time, and similarity scores. The challenges and limitations of each approach are examined to identify the most suitable model for specific tasks. As a proof of concept, this study concludes by implementing its recommendation on a popular use case.
Reviving Black and White Images: Enhancing Colorization with Generative Adversarial Networks (GANs)

One of the more intriguing deep learning applications is colourizing black and white photographs. The restoration of old or damaged images is one application that has drawn a lot of interest in the automatic image colorization technology in the decade since its introduction. Prior to AI and deep learning, this work required a lot of human intervention and hardcoding, but today, the entire process has been entirely automated from beginning to end. Using GANs to restore and recolorize historical photos is one answer to this problem. The objective of this project is to showcase the effectiveness and advantages of GANs Networks by implementing a GAN model capable of transforming fixed-size black and white images into colorized images of identical dimensions. Concerning the dataset, I have approximately 3000 rgb photographs from diverse domains such as mountains, forests, and cities, which we will convert to grayscale and use as labels for our model. I used binary cross entropy as the discriminator's loss function and mean squared error as the generator's loss function, & then I used Adam to optimise the generator and discriminator. Moving on to the results, the colourized output from the generator was significantly closer to the original rgb image.