Reviewerly/exHarmony huggingface dataset:
Hybrid CNN-LSTM Architecture for Bilingual Next-Word Prediction in Punjabi-English Social Media Texts

This paper presents a novel hybrid Convolutional Neural Network (CNN) - Long Short-Term Memory (LSTM) architecture for next-word prediction in Punjabi-English bilingual social media texts. The proposed model aims to capitalize on the strengths of CNNs for detecting local contextual features and LSTMs for capturing long-range dependencies in a sentence, offering a more comprehensive understanding of the text. Extensive preprocessing tasks, including noise removal, text normalization, and script identification, were performed on a large dataset of Punjabi-English code-mixed social media texts to prepare them for the model. The model displayed considerable promise, achieving a training accuracy of 92.83%. These results demonstrate the effectiveness of this hybrid CNN-LSTM model for bilingual next-word prediction tasks, paving the way for more nuanced language model development for mixed-script and mixed-language scenarios prevalent in social media texts.
Recent Trends, Issues and Challenges in Container and VM Migration

The enabling of businesses to move between multiple cloud environments, even when there is a need to recreate applications on different clouds, is facilitated by Inter Cloud (C2C) migration. During C2C migration, both Virtual Machines (VMs) and Containers get transferred from one remote environment to another. However, in traditional migration strategies, the VMs are temporarily halted, resulting in the unavailability of services operating on several VMs and Containers during the Cloud-to- Cloud transfer. In Dynamic Resource Management (DRM), VM and container migration play essential roles as components. This paper aims to review recent trends in migration for both forms of virtualization. Surprisingly, there is currently no comprehensive review that explores the dynamics influencing the adoption of Virtual Machine and container technology for virtualization. The lack of overview hampers the quick and informed selection of technology. To address the existing knowledge gap and identify the most recent advancements in Virtual Machine and Container technologies and research trends. This paper presents a comprehensive literature review and valuable insights that facilitate informed decision-making in the realm of Virtual Machine and Container migration.
Hand Gesture Controlled White Board

The human-computer interaction field entered a new phase with the advent of hand gesture recognition technology. The purpose of this research is to develop a virtual whiteboard that can be controlled by hand gestures and study the effects of different environment conditions on the performance of the system. At first the hand gesture controlled white board is implemented using machine learning models. Then performance of the model is evaluated in varying light conditions, and distance. Effects of fps (frames per second) on the system also studied.
Online Fitting Room: A Mobile Shopping Application using Augmented Reality (AR) Technology

Shopping clothes online inevitably involves a bit of guesswork from customers as they cannot see how the item will look on their bodies, which can lead to unsatisfied customers and company loss through increased return rates. However, implementing an Augmented Reality: AR online fitting room mobile application will have a huge impact on how customers buy clothes online. AR makes it possible to see virtual clothes not only in your actual environment but also on your own body even with the knowledge of your fitting measurements, it’s still challenging to envision how it will look on your body beyond a simple eyeball test. Implementing this application would provide comfortability and save shoppers time and money as they will be able to shop and try clothes online without driving to the actual stores in the congested streets.
Detection of Objects in High-Definition Videos for Disaster Management

This paper presents a object detection quality assessment (QA) algorithm for ultra-high definition (UHD) videos that is both efficient and precise. The proposed method is based on the use of a single-stream deep learning model, which allows for the automatic extraction of spatio-temporal information from video clips. It is made up of two subcomponents: a network for extracting spatial distortion features and a recurrent network for modifying the quality score in the continuous time dimension, both of which depend on long short-term memory (LSTM). When applied to databases of UHD-quality, extensive testing demonstrates that the recommended model performs better than alternative BVQA methods.
Emotion-based Analysis of Reviews using Knowledge Graph

This study introduces a novel approach to sentiment analysis, combining the finesse of Knowledge Graphs with the unique perspective offered by the SenticNet and the Hourglass Model of Emotions. We aim to interpret, quantify, and categorize emotional responses in various reviews and offer, at the same time, a more nuanced understanding of user emotions than traditional sentiment analysis methods. Our process begins with extracting emotional indicators from reviews by linking their descriptive words with states of different emotions and further expressing them using terms and phrases defined in the Hourglass Model of Emotions. All the data is incorporated into a Knowledge Graph, where each review is connected to all its related aspects, description words, and their synonyms and emotions. We enhance the results with linguistic terms describing emotions of identified aspects to learn more about the justification behind the determined sentiments of the reviews.
<scp>PowerPulse</scp>: Power energy chat model with <scp>LLaMA</scp> model fine‐tuned on Chinese and power sector domain knowledge

Abstract Recently, large‐scale language models (LLMs) such as chat generative pre‐trained transformer and generative pre‐trained transformer 4 have demonstrated remarkable performance in the general domain. However, inadaptability in a particular domain has led to hallucination for these LLMs when responding in specific domain contexts. The issue has attracted widespread attention, existing domain‐centered fine‐tuning efforts have predominantly focused on sectors like medical, financial, and legal, leaving critical areas such as power energy relatively unexplored. To bridge this gap, this paper introduces a novel power energy chat model called PowerPulse. Built upon the open and efficient foundation language models (LLaMA) architecture, PowerPulse is fine‐tuned specifically on Chinese Power Sector Domain Knowledge. This work marks the inaugural application of the LLaMA model in the field of power energy. By leveraging pertinent pre‐training data and instruction fine‐tuning datasets tailored for the power energy domain, the PowerPulse model showcases exceptional performance in tasks such as text generation, summary extraction, and topic classification. Experimental results validate the efficacy of the PowerPulse model, making significant contributions to the advancement of specialized language models in specific domains.
Unveiling the Potential: Harnessing Deep Metric Learning to Circumvent Video Streaming Encryption

Encryption on the internet with the shift to HTTPS has been an important step to improve the privacy of internet users. However, there is an increasing body of work about extracting information from encrypted internet traffic without having to decrypt it. Such attacks bypass security guarantees assumed to be given by HTTPS and thus need to be understood. Prior works showed that the variable bitrates of video streams are sufficient to identify which video someone is watching. These works generally have to make trade-offs in aspects such as accuracy, scalability, robustness, etc. These trade-offs complicate the practical use of these attacks. To that end, we propose a deep metric learning framework based on the triplet loss method. Through this framework, we achieve robust, generalisable, scalable and transferable encrypted video stream detection. First, the triplet loss is better able to deal with video streams not seen during training. Second, our approach can accurately classify videos not seen during training. Third, we show that our method scales well to a dataset of over 1000 videos. Finally, we show that a model trained on video streams over Chrome can also classify streams over Firefox. Our results suggest that this side-channel attack is more broadly applicable than originally thought. We provide our code alongside a diverse and up-to-date dataset for future research.
IoT Applications in Blockchain Technology

Securing the data through blockchain might be crucial to enhancing the IoT network, since data is an especially important part of the system. Secure storage of the information generated by the IoT network's intelligent gadgets is made possible via the integration of blockchain technology. For blockchain to be widely used in the IoTs, it must first conquer technological and financial barriers. To make the most of the IoT, blockchain technology may require some tweaking. The upcoming Internet of Things (IoT) technical breakthrough could involve this. This work emphasizes on bringing on board the recent trends of IoT applications utilizing Blockchain technology. The challenges faced in doing so are also outlined. We're hoping this work might help elucidate the state of blockchain studies and provide a road map for what comes next.
Social Media Login Authentication with Unity and Web Sockets

User tracking and activity monitoring have become widespread practices in digital applications across many sectors. As a result, most applications now require users to create accounts and log in to facilitate this process and improve security. Many developers are finding easy and efficient techniques to accomplish this. In the web 2.0 era, popular social networking platforms such as Google, GitHub, and LinkedIn provide their own APIs that follow OAuth 2.0 protocols to enhance the user experience. This paper presents an innovative approach that efficiently connects Unity applications with popular social media platforms such as Google and LinkedIn. Key aspects of the approach include integrating the social network login functionality into the Unity application's user interface, handling the authentication process through the respective social web APIs, and securely transmitting user data between the client and the server.
Performance Based Features for Classification of Cancer Using Images of Breast Mass Through Fine Needle Aspirate

Breast cancer is commonly seen in women. types of cancer specially seen in women. It is proved to be the second most reason for death. The early detection of such cancer saves the life of the patients and stops spreading. Even though the country’s medical infrastructure has eventually grown, there is a need to address this problem. In search of solution to the problem, this article presents a method to classify breast cancer through fine needle aspiration features into benign and malignant. The data required for the proposed method is collected from Breast Cancer Wisconsin (Diagnostic) dataset. Eight best performing features are identified. Grouping of these features is carried out in a group of two, three, four, five, six, seven and eight to know the behavior of eight classifiers. Classifiers, namely, Decision Tree (DT), K-nearest neighbor (k-NN), Support Vector Machine (SVM), Ensemble Learning (EL), Discriminant Analysis (DA), Logistic Regression (LR), Naïve Bayes (NB), Kernel Approximation (KA) are used in the work. Results reveal that SVM gave an average accuracy of 98.2% with balanced dataset. Results are compared with state-of-the-art methods.
The Dark Side of the Language: Syntax-Based Neural Networks Rivaling Transformers in Definitely Unseen Sentences

Syntax-based methods have been largely used as key components of Natural Language Processing systems for solving a variety of tasks. Yet, pre-trained Transformers are challenging all these pre-existing methods and even humans in nearly all tasks. The massive datasets used for pre-training seem to be the key to their success on existing tasks. In this paper, we show that syntax-based neural networks rival Transformers models on tasks over definitely unseen sentences even after fine-tuning and domain adaptation. Experiments on tasks over definitely unseen sentences, provided by classification tasks over a DarkN et corpus, show that syntactic and lexical neural networks perform on par with pre-trained Transformers even after fine-tuning and domain adaptation. Only after what we call extreme domain adaptation, that is, allowing BERT to retraining on the test set with the masked language model task, pre-trained Transformers reach their standard high results. Hence, in normal conditions where sentences are really unseen, syntax-based models are a viable alternative that is more transparent and has fewer parameters than transformer-based approaches.
Detection of Breast Tumor Using Various Machine Learning Techniques

Breast tumor is among the world's most typically malignancies tumors is a condition when the breast's cells develop quickly and uncontrolled. Breast tumors can begin in a variety of locations within the breast and spread to other areas of the body. Breast tumor diagnosis and prognosis have attracted the attention of numerous researchers. Every technique has a different precision rate, which varies based on the conditions, resources, and datasets employed. Our main objective is to evaluate and contrast the many Machine Learning methods now in use in order to decide which one is the most efficient and will produce high prediction accuracy for the large dataset. The Kaggle Dataset has been applied in this paper. Our objective is to examine the data and evaluate how well various algorithm techniques identify breast tumor. The algorithm like Decision Tree, Random Forest, XGBoost, MLP, and Ridge in this case allows for the classification of cell type, including whether it is normal or malignant. To determine the best algorithm, the accuracy of each is calculated and contrasted. Two of the most popular techniques, Random Forest and XGBoost that have been used to the breast tumor sample in this study based on the analysis, and the results are compared using performance metrics. With this approach, dataset accuracy is greater than 95% and precision is 97%, making it much superior to previous algorithms.
Investigation of Data Augmentation Techniques for Assamese-English Language Pair Machine Translation

Machine translation can deliver impressive results when a substantial parallel corpus is accessible. However, in a country like India, characterized by its linguistic diversity and numerous languages with distinct origins and scripts, most languages face a scarcity of resources, making it challenging to create translation models of high quality. This work investigates a neural machine translation system with data augmentation techniques to boost the translation quality for an extremely resource-constrained language pair, i.e., English–Assamese. We experiment with back-translation, tagged back-translation, iterative back-translation, and iterative tagged back-translation. A qualitative and quantitative analysis performed on the various data augmentation techniques is performed. Furthermore, human evaluation is carried out to evaluate the adequacy and fluency of the translation. Empirical results show that data augmentation via iterative back-translation methods and tagged approach enhances translation performance in extremely low-resource settings.
Computational Drug Discovery For Blood Cancer Classification using Deep Convolutional Neural Network

Blood cancer, arising from DNA mutations, presents a significant challenge in terms of early detection. To extract crucial physiological and morphological information from cancer cells, a combination method including machine learning algorithms and human image processing is required due to the disease’s complexity. In this research, we propose a system that leverages sophisticated Convolutional Neural Network (CNN) algorithms, along with transfer learning and fine-tuning techniques, to achieve accurate prediction of cancer subtypes, enabling early detection and facilitating timely treatment interventions. It also incorporates a user-friendly web-based chatbot interface,which is integrated to a sophisticated drug discovery module, enabling the identification of potential targets for novel drugs and potential drug combinations that are specifically effective against corresponding subtypes of blood cancer considering the patient’s unique circumstances along with the accuracy of the prediction by selecting the model. This paper aims to improve the precision of blood cancer detection and therapy by merging cutting-edge machine learning algorithms, user-friendly user interfaces, and cutting-edge drug discovery techniques. The potential impact of this research is immense, as improved detection and treatment can ultimately lead to saving lives and improving the prognosis of individuals affected by blood cancer.
A Simulation &amp; Technical Feasibility Study for Removing Blur Artefacts from Emission Tomography Images Using Generative Adversarial Networks

Nuclear imaging holds significant importance, offering valuable insights into the physiological and functional aspects of organs and tissues, aiding in the diagnosis, treatment planning, and monitoring of various medical conditions. However, there artefacts arise when the subject immobility during data acquisition is violated. Such artefacts can result in the misinterpretation of images, leading to incorrect diagnoses or missed abnormalities. Generative Adversarial Networks (GANs), a type of deep neural network, have demonstrated their ability to effectively model underlying patterns and outperform traditional methods in various tasks, including image deblurring. In this study, we propose an Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) based method to address the issue of blur in SPECT and PET images. We applied blur effect including rigid motion effect to SPECT and PET images with minimal involuntary motion. The input to the GAN model was blurred SPECT and PET images, downscaled by a factor of four. The objective of the GAN model was to generate the original, unblurred image. Our proposed model achieved a 0.95 SSIM score on the test dataset. The proposed method for motion correction in SPECT and PET images not only addresses the pressing issue of motion artefacts but also holds the potential to improve the accuracy and reliability of diagnostic imaging significantly.
Generative AI Frameworks for Web for Good: Food Pantry Information Seeking as an Example

This paper introduces two generative AI frameworks, ISum and IConvo, for information seeking on the web. ISum, an intent-based web page summarizer, consists of five major components, including website selection, web page selection, web page segmentation, chunk selection and summary generation. Its application to social good on food pantry is demonstrated. Structure-aware segmentation identifies chunks based on HTML structure to preserve semantics. Chunk classification selects those chunks relevant to a specific intent for the later summary generation by InstructGPT. Besides ISum, this paper also presents a conversational agent IConvo. IConvo accepts user's question and reports the answer if the question is answerable based on the current context. If it is unanswerable, follow-up questions will be generated to acquire more information from information source including human and the web. The conversation is iterated and context is updated until information is sought.
Helping Hand Platform for the Students to Provide Multiple Services: Using Web Development

Most of the times it becomes challenging for parents, when they sent their ward away from them for higher studies and hence they concerned about things like the place, environment, food, services etc. at that area. Same way in that area its challenging for Landlords (those who rent out rooms and their services) and room seekers (those who occupy the room and its services) to find suitable matches. This platform will provide the authenticity of the service owner and the facilities provided by them such as Room service, Mess and tiffin services, Laundry Services, and other secondary services like restaurant services, hospital Services, sports and yoga facilities etc. In this paper I developed the model of customer and owner relationship management system for multiple services provided for Students which can solve the real life problems up to the large extent. However this platform gives a user and owner an experience of how the variation of room rents with other services in college Area is going, although accordingly they can manage their services and enhance their facilities to attract the customers at very reasonable price with better services. It might be the very good option for the customers to save their time in search of rooms and other facilities, as the room owners can too show their facilities they offer and customer can choose the suitable one according to their requirements.
B-on-Track: A Novel Evaluation Metric for Multiple Object Tracking

Multi-object tracking (MOT) is essential in various applications. Still, more effective evaluation metrics are often needed to measure a tracker’s ability to consistently follow an object without losing track and minimize re-identification issues. Addressing this precise problem, we present B-on-Track, a novel evaluation metric formulated to gauge a tracker’s competence in maintaining continuous tracking without loss and reducing re-identification challenges. By implementing and evaluating the B-on-Track metric on the MOTChallenge Benchmark, this paper thoroughly compares with existing evaluation metrics, elucidating the distinctive benefits and potential applications of the B-on-Track metric in the MOT field. Detailed interpretations of the B-on-Track metric further demonstrate its robustness and innovation in filling the existing gaps in MOT evaluation.
Predictive Behavior Modeling Through Web Graphs: Enhancing Next Page Prediction Using Dynamic Link Repository

The goal of this experiment is to improve user interaction predictability for better search interface design and efficient search intent satisfaction. In this work, we optimize the Web Usage Mining (WUM) process reconstructing a web session for predicting navigation paths specifically on fragile websites. Our research is based on the most current algorithmic approach called the Complete Session Reconstruction Algorithm (CSRA), which outperforms state-of-the-art session reconstruction algorithms for predicting the possible single next pages. In this work, we extend the newest session reconstruction problem-solving algorithm and improve its next page prediction performance by extending the web topology taking into account dynamic targets. We implement a Bayesian network, integrate and evaluate it in our evaluation framework for the prediction of the next visited pages. The presented experiments show how our approach outperforms the compared next page prediction state-of-the-art methods.