Reviewerly/exHarmony huggingface dataset:
Food Addressing and Consultation System Using Deep Learning By Applying Design Thinking Framework : An exploration of health recommendation system

A daily commitment to our health and exercise routines helps us to become more energized and effective in our lives. But merely having the intention won't ever be sufficient to get to point of the aim. Health and fitness routine is always a mandatory segment of life, which helps us to become more productive and energetic in our day to day life. But that intention alone can never be enough to achieve the destination. In our project, we're creating a novel tool that can assist people in ensuring their general health, simply by collecting their food data and making dietary recommendations for doing the exercises to reach their objectives. By consulting a nutritionist, anybody could possibly get the guidance to reach their objective of getting healthier and stronger. Our model which has the aim to solve the daily life style problems caused by the unselective foods. Every sports person and the people in health and fitness area are aware of their foods by the consultation of nutritionist. But in the rapid moving times, normal people in day to day life, don't have the needed time and space to meet the nutritionist every time. Our model which tries to replace the space of the nutritionist by giving consultation and addressing the nutritional values of foods to the end users
An Efficient Class Room Teaching Learning Method Using Augmented Reality

Augmented Reality (AR), a unique method of integrating the virtual world into the real world, has the potential to increase academic attainment in the classroom. This research work focuses on developing and evaluating a strategy for enhancing student education with AR in the classroom. AR enables unique human-computer interactions in real time between the physical and digital worlds. The effectiveness of AR in the classroom will depend on its development, deployment, and integration into both standard and nontraditional teaching environments. Throughout the creation and implementation of an AR classroom, collaborative learning practices and other methodologies were taken into account. Collaboration occurs when two or more individuals work together, share information, and gain insights from one another. This research offers a succinct summary of the promise and challenges of adopting AR to transform the classroom.
Density Map Based Estimation of Crowd Counting Using Vgg-16 Neural Network

The system is to evolve a method that can precisely evaluate the count of crowds from a raw image obtained with random crowd size and perspective. In the current world scenario, in many places, they are using the old traditional ways, like preserving registers and using sensors, in the entrance for counting the number of people in the crowd. These approaches are ineffective. Three methods namely counting by Regression, Detection and Density Estimation can be used to perform crowd counting. In our approach, we are counting by Density Map based Estimation. It is a VGG-16- based Convolutional Neural Network that is trained to categorize the input images into various density classes. We are using an architecture that contains multi-column based CNN, each column CNN learns can adapt to changes in people or heads sizes caused by perspective effects or differences in the image by using filters based on the sizes. It aims to create high-dimensional maps from the given image which can be combined with the provisional data obtained from the density classes. Additionally, a real density map is precisely calculated using different kernels, which don't require any knowledge about the input image's outlook map. In this case, a Gaussian filter was used to create the ground truth density map, and when it was submitted to the neural network, it created the equivalent density map. We have collected a dataset that contains 1199 photos with over 2, 20,000 heads annotated because the existing dataset does not capture all the circumstances. We conduct experiments on the collected dataset as well as the old dataset in order to validate the current model and method used. In particular, our strategy beats all currently used methods for the proposed simple MCNN model.
Alerts and A Digital Library Based on RFID

The act of keeping paper records, especially in a larger firm with more employees, is time-consuming and complicated. We propose a solution employing two methods to the previously mentioned issue. RFID technology with face detection. Face detection systems are used in security systems to identify people from face databases or from live webcam sources, whereas RFID technology is based on the idea of retrieving secret data from passive or active RFID tags. This system's primary goal is to shorten the real amount of time it takes to manage library attendance in any organization without making a mistake due to impersonation. Face detection systems are used in security systems to identify people from face databases or from live webcam sources, whereas RFID technology is based on the idea of retrieving secret data from passive or active RFID tags. This system's primary goal is to shorten the real amount of time it takes to manage library attendance in any organization without making a mistake due to impersonation.
Cloud Simulator Search Framework The Vigilant Fetcher

Availing cloud computing services is the need of time. In contrast to being sold as a product, pooled resources, software, and data are made available to users as a network-based utility in cloud computing. Real-world experimentation is expensive and challenging to conduct. As a result, a virtual cloud environment that mimics the real cloud was required. As a result, a lot of effort is put into creating cloud modeling frameworks that only include a small portion of the various components. In order to choose the best tool, it is necessary to thoroughly compare the available simulators based on a number of criteria. Large number of simulators available online. Manual simulator selection that meets all the user requirements is complex and time-consuming process. In this post, a framework will be introduced that enables researchers to quickly and easily choose the best simulator for their purposes. The best tool selection advice is then provided for researchers, cloud environment providers, and managers by offering with the simulator list, best matches with their requirements. Several scenarios are utilized effectively to evaluate the commonly used simulator's performance. The remaining issues and potential challenges for future study are then discussed.
Certain Investigations on Cognitive based Movie Recommendation system using Pairwise Cosine Similarity

The Internet serves as a major source in various sectors to communicate and share data. It is being used by every individual across the world for several purposes. The main objective of the recommender system is to provide users with suitable products and information. The movie recommendation system also helps in providing customized decisions to the user, thus improving the experience of customer or user. Content-based filtering manipulates item attributes to suggest other items that are relevant to user likes, this suggestion is made using past actions of customers or users. Cosine Similarity is an evaluation metric that helps in finding how similar the items are regardless of their dimensions. The proposed study, furnish users with suggestions that rest on the prevalence and category of a movie. Content based recommendation system is built using cosine similarity. The movie recommendation system has its separate algorithms for making suggestions by examining users’ past data. Many attributes are considered like rating of each movie, name of the director and genre of the film. The system make recommendation by considering one attribute or mixture of attributes. For the proposed system, two different datasets used namely, the credits dataset and the movie dataset. The analysis of data is done with help of Python.
Crime Data Analysis and Safety Recommendation System Using Machine Learning

Crime is the intentional commission of an act that is often seen as risky or socially damaging, specifically prohibited by law, and punishable. [1] Judicial rulings used to be the main method of defining crimes in the common-law system. [2] Today, most common-law offenses are governed by statutes. Many people hold the opinion that a crime cannot exist without a law. Behavior and an associated state of mind are the usual elements of a crime. Criminal offences include things like arson, assault, bribery, burglary, child exploitation, counterfeiting, embezzlement, extortion, forgery, fraud, hijacking, murder, kidnapping, perjury, piracy, rape, sedition, smuggling, treason, theft, and usury. [5] The major goal of this initiative is to assist people who often visit new locations by teaching them about the region's criminal history so that they may exercise caution and avoid being a victim of any kind of crime. [7] The user may clearly comprehend the most frequent crimes in the area and how often each crime occurs in different parts of the state given the extensive analysis of the crime data.
Design of Hybrid UAV With Multi Rotor Propulsion System

when a disaster takes place, plenty of humans die by getting trapped inside the rubble. Drones are very beneficial for assisting in the recovery after a disaster, but the maximum requirement is that individual pilots want to use remote control to fly unmanned aircraft. This slows rescuers’ ability to see the entire affected region and causes actual relief to be delayed. This article presents an intensive look at the trends in miniature hybrid Unmanned Aerial Vehicles (UAVs) with integrated image processing APIs that have an extended flight time as a way to help in early detection for rescue teams. The newest form, known as a hybrid UAV, combines the best attributes of both conventional and non-conventional UAVs. Following that, the hybrid UAV’s flight dynamics model and flight management tactics are discussed. Furthermore, applicable observations, existing issues, and decisive conclusions based on the data are included.
A Study on Digital Pathology Image Segmentation Using Deep Learning Methods

Medical images must be extensively examined in Digital Pathology image analysis. Many researchers have proposed utilizing various Deep Learning Methods to segment and classify Digital Pathology images. The Deep Learning technique is well suited for image analysis in Digital Pathology, since it efficiently performs tasks like detection and tissue categorization. Deep learning has the potential to help human pathologists with tasks such as tumor area identification, metastasis detection, and patient diagnosis. A systematic review was conducted to discover the different research publications accessible in the literature on all forms of image pathology in order to assess the key contributions, advantages, and numerous obstacles that impact Deep Learning performance. In addition, this research suggests ways to enhance image pathology segmentation performance for classification.
Natural Language Processing Techniques in Diagnostic Digital Cytopathology for Cervical Intraepithelial Neoplasia Detection

Cervical cancer is the third most common cancer in women diagnosed, according to figures from developing nations. One of the well-known risk factors for cervical cancer, HPV 16 or HPV 18, is responsible for more than 70% of new instances of the disease. The human papillomavirus (HPV) changed cervical cancer screening, and researchers started seeking for alternatives to conventional cytological exams that would be helpful in underdeveloped areas. A key step in the pathophysiology of cervical cancer is natural language processing (NLP), which is essential for retrieving structured data from electronic health records (EHRs). It is essential to improving healthcare. In the past ten years, the area of pathology has made a number of advancements as a result of the use of NLP to pathology reports. This work focuses on the integration of natural language processing into the creation of digital cytopathology systems for the identification of cervical intraepithelial neoplasia.
Depth Estimation for a Single Omnidirectional Image with Reversed-Gradient Warming-up Thresholds Discriminator

Depth estimation for single image using deep learning requires a large labelled depth dataset with various scenes for training. However, currently published omnidirectional depth datasets cover limited types of scenes and are not suitable for depth estimation for various real-world scenes. With the challenge of labelled real-world datasets generation and stability of the performance, we propose an architecture with the Reverse-gradient Warming-up Threshold Discriminator (RWTD) to estimate real-world depth maps from the synthetic ground truth. It takes labelled synthetic scenes of a source domain and unlabelled real-world scenes of a target domain as inputs to predict the corresponding depth maps. Compared with state-of-the-art encoder-decoder models, the proposed architecture shows an 11% points improvement on the testing dataset for depth accuracy.
Covariance Regularization for Probabilistic Linear Discriminant Analysis

Probabilistic linear discriminant analysis (PLDA) is commonly used in speaker verification systems to score the similarity of speaker embeddings. Recent studies improved the performance of PLDA in domain-matched conditions by diagonalizing its covariance. We suspect such a brutal pruning approach could eliminate its capacity in modeling dimension correlation of speaker embeddings, leading to inadequate performance with domain adaptation. This paper explores two alternative covariance regularization approaches, namely, interpolated PLDA and sparse PLDA, to tackle the problem. The interpolated PLDA incorporates the prior knowledge from cosine scoring to interpolate the covariance of PLDA. The sparse PLDA introduces a sparsity penalty to update the covariance. Experimental results demonstrate that both approaches outperform diagonal regularization noticeably with domain adaptation. In addition, in-domain data can be significantly reduced when training sparse PLDA for domain adaptation.
An Overview Of Interpretability Techniques For Explainable Artificial Intelligence (XAI) In Deep Learning-Based Medical Image Analysis

In the past two years, technology has undergone significant changes that have had a major impact on healthcare systems. Artificial intelligence (AI) is a key component of this change, and it can assist doctors with various healthcare systems and intelligent health systems. AI is crucial in diagnosing common diseases, developing new medications, and analyzing patient information from electronic health records. However, one of the main issues with adopting AI in healthcare is the lack of transparency, as doctors must interpret the output of the AI. Explainable AI (XAI) is extremely important for the healthcare sector and comes into play in this regard. With XAI, doctors, patients, and other stakeholders can more easily examine a decision's reliability by knowing its reasoning due to XAI's interpretable explanations. Deep learning is used in this study to discuss explainable artificial intelligence (XAI) in medical image analysis. The primary goal of this paper is to provide a generic six-category XAI architecture for classifying DL-based medical image analysis and interpretability methods.The interpretability method/XAI approach for medical image analysis is often categorized based on the explanation and technical method. In XAI approaches, the explanation method is further sub-categorized into three types: text-based, visual-based, and examples-based. In interpretability technical method, it was divided into nine categories. Finally, the paper discusses the advantages, disadvantages, and limitations of each neural network-based interpretability method for medical imaging analysis.
Digital Watermarking Analysis Using Data Security

The study recommends a blind-spread spread spectral technique for water imaging in which the multilayer wavelet coefficients of the cover image permanently include watermark information. The creation of a system that combines encryption standards with watermarking techniques to safeguard transmitted medical photographs is the main emphasis of this research. One of the more often used musical effects is an echo. IN This paper proposes watermarking solution based on reverberation theory. This embed method is like the one used to reverberate recordings made in music studios, so they sound more authentically like they were made in a performance hall.
Classifying Pathological Images Based on Multi-Instance Learning and End-to-End Attention Pooling

In order to address the issue that previous deep learning methods for classifying pathological images cannot adaptively learn features, we propose an end-to-end attention pooling method based on a multi-instance learning patch scoring model. Our method integrates feature extraction and classification into a unified framework that is conducive to extracting the most valuable features. In this model, a patch scoring method is constructed by a multi-instance learning method firstly and then the partial patches selected by the patches scoring model are classified using an end-to-end classification model that incorporates an attention pooled mechanism. To make the pathological image classification mechanism more compatible with the pathologist diagnosis method, we use the squared average normalization function instead of the softmax function to optimize the feature extraction and fusion process, so that the high score patches in positive pathological images receive more attention weights, thus giving better interpretability to the classification results. Experiments on publicly available datasets TCGA_BRCA show a significant improvement in the performance of our approach over other work.
Sequential Invariant Information Bottleneck

Previous approaches to the problem of generalization for out-of-distribution (OOD) data usually assume that data from each environment is available simultaneously, which is unrealistic in real-world applications. In this paper, we develop a new framework termed the sequential invariant information bottleneck (seq-IIB) to improve the generalization ability of learning agents in sequential environments. Our main idea is to combine the merits of the famed Information Bottleneck (IB) principle with the Invariant Risk Minimization (IRM), such that the learning agent can gradually remove spurious features and remain invariant and compact task-relevant information in a sequential manner. Experimental results on three MNIST-like datasets show the effectiveness of our method.
A Logical Agent Approach to Solving the Wumpus World Problem: An Analysis of Game Trees

The Wumpus World problem is a classic AI challenge in which an agent must navigate a maze-like environment to find and retrieve gold while avoiding obstacles such as pits and a monster called the Wumpus. This project aims to implement a logical agent to solve the Wumpus World problem using a combination of first-order logic and the Minimax algorithm. The environment is represented as a set of logical propositions. The agent's knowledge base is expressed as a set of first-order logic rules describing the relationships between the propositions. The Minimax algorithm is used to generate a game tree and compute the minimax values of the nodes in the tree, allowing the agent to make informed decisions about which actions to take. The implementation demonstrates the versatility and expressiveness of logical agents for solving complex AI problems and highlights the potential benefits of using first-order logic in AI systems.
Joint Modelling of Spoken Language Understanding Tasks with Integrated Dialog History

Most human interactions occur in the form of spoken conversations where the semantic meaning of a given utterance depends on the context. Each utterance in spoken conversation can be represented by many semantic and speaker attributes, and there has been an interest in building Spoken Language Understanding (SLU) systems for automatically predicting these attributes. Recent work has shown that incorporating dialogue history can help advance SLU performance. However, separate models are used for each SLU task, leading to an increase in inference time and computation cost. Motivated by this, we aim to ask: can we jointly model all the SLU tasks while incorporating context to facilitate low-latency and lightweight inference? To answer this, we propose a novel model architecture that learns dialog context to jointly predict the intent, dialog act, speaker role, and emotion for the spoken utterance. Note that our joint prediction is based on an autoregressive model and we need to decide the prediction order of dialog attributes, which is not trivial. To mitigate the issue, we also propose an order agnostic training method. Our experiments show that our joint model achieves similar results to task-specific classifiers and can effectively integrate dialog context to further improve the SLU performance. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
PRRD: Pixel-Region Relation Distillation For Efficient Semantic Segmentation

Current state-of-the-art semantic segmentation methods usually require high computational resources for accurate segmentation. Knowledge distillation has been one promising way to achieve a good trade-off between accuracy and efficiency. However, current distillation methods focus on transferring the spatial relations and ignore the multi-scale context interaction. This paper proposes one novel pixel- region relation distillation (PPRD) to transfer the multi-scale pixel-region relation (PRR) from the teacher to the student. We get the multi-scale regions with pyramid pooling and characterize the multi-scale PRR between the feature and the multi-scale regions. Transferring such PRR from the teacher to the student is beneficial for the student to mimic the teacher better in terms of multi-scale context interaction. Experimental results on two challenging datasets, Cityscapes and Pascal VOC 2012, show that the proposed approach outperforms state-of-the-art distillation methods.
A Contrastive Framework to Enhance Unsupervised Sentence Representation Learning

Recently, significant progress has been made in generating high-quality sentence representations through contrastive learning. SimCSE-like models improve the uniformity of the representation space by pulling in positive examples and pushing out negative examples. However, these models often suffer from semantic monotonicity, sampling bias, and training effect dependent on batch size. In order to solve these problems, this paper proposes a contrastive framework (CEUR) to enhance unsupervised sentence representation learning. CEUR adopts a linguistic knowledge-based sample augmentation method. Positive samples are generated by the method of synonym repetition, and negative samples are generated by the method of antonym replacement. To improve the consistency of representation space, CEUR uses an instance weighting method to reduce sampling bias. Going a step further, CEUR uses momentum contrast to increase the number of trainable negative samples. Extensive experimental results show that CEUR outperforms existing baseline models in comprehensive performance on seven semantic text similarity tasks.