Reviewerly/exHarmony huggingface dataset:
Automated Weapon Detection System in CCTV’s Through Image Processing

In our study, we aim to detect various weapons through image processing using a combination of traditional machine learning algorithms and deep learning techniques. This would prove beneficial in anticipating unusual occurrences. The utilization of Closed-Circuit Televisions (CCTVs) has become widespread in the security and surveillance industry; however, it requires constant human monitoring to extract useful information, which is both tedious and prone to errors. The base paper deals with identifying only firearms. We intend to utilize machine learning and deep learning methods to recognize different weapons automatically, not only firearms and alert the CCTV monitor to that specific area.
Deep Generative Models for Automated Dehazing Remote Sensing Satellite Images

Remote Sensing (RS) is the process of observing and measuring the physical features of an area from a distance by monitoring its reflected and emitted radiation, usually from a satellite or aircraft. The application of RS spans a wide range of fields, including precision agriculture, disaster management, military operations, environmental monitoring, and weather assessment, among others. Haze or pollution in the satellite images, makes satellite images unsightly and makes valuable information useless. Sometimes satellites must capture images in haze-filled atmospheres, rendering them unusable for study. This proposed work is implemented using the Modern Deep Learning techniques to dehaze the satellite images. We have proposed two GAN architectures, INC-Pix2Pix and RNX-Pix2Pix. A publicly available dataset was used for training our proposed approaches. To eliminate haze from images, we have suggested Deep Generative models by employing the best developments in the field of image processing. By using generative models, images can be dehazed without information loss, supporting the paper’s objective. It has the capacity to learn any kind of underlying data distribution using its learning mechanism. Therefore, it can dehaze satellite images that have been corrupted by haze using the approach automated dehazing remote sensing satellite images using deep learning models . Existing systems can be made more efficient by integrating this approach.
TEX – True Exchange (Decentralized Crypto Currency Exchange for Indian Markets)

Cryptocurrencies have been gaining popularity in India, but the lack of reliable and safe platforms for trading has been a significant concern. This research paper proposes a decentralized exchange (DEX) platform that enables Indian users to trade cryptocurrencies for Indian Rupees (INR). The system uses the Chainlink decentralized oracle network to convert INR to the equivalent ETH, the base currency for trading on the DEX platform. The proposed platform aims to provide secure and transparent trading to users, ensuring that transactions are executed instantly and at a fair market price. The paper outlines the technical details of the proposed DEX platform, including the integration with Chainlink oracles for price feeds and the development of smart contracts for trading. The platform utilizes the Ethereum blockchain and is built using Solidity, a programming language for smart contracts. Finally, the paper concludes with an evaluation of the proposed DEX platform, highlighting its potential benefits for Indian users and its potential impact on the broader cryptocurrency ecosystem in India. This research paper’s findings can guide the development of more secure and reliable DEX platforms in India and beyond.
Automated Handwritten Text Recognition

A computer’s capacity to recognize and convert handwritten inputs from sources like photographs and paper documents into digital format is known as Automated Handwritten Text Recognition (AHTR). Systems for reading handwriting are frequently employed in a variety of fields, including banking, finance, and the healthcare industry. In this paper, we took on the problem of categorizing any handwritten artwork, whether it be in block lettering or cursive. There are many different types of handwritten characters, including digits, symbols, and scripts in both English and other languages. This makes the evolution of handwriting more complex. It is difficult to train an Optical Character Recognition (OCR) system using these requirements. In order to convert handwritten material into digital form, this work aims to categorize each unique handwritten word. Because Convolutional Neural Networks (CNNs) are so good at this task, they are the best method for handwriting recognition system. The method will be used to identify writings in various formats.
Beyond Binary: The Capabilities of Classical and Quantum Computing for Securing Data Transmission

In the present times, the demand for sophisticated encryption methods has escalated, especially for securing data transmission in vulnerable environments. These methods leverage diverse algorithms to fortify the integrity of communication channels. Quantum mechanics plays a pivotal role in two specific areas: quantum key distribution and property-based cryptography, both of which contribute to establishing secure communication protocols. This study focuses on conducting a comparative evaluation of classical and quantum cryptography, employing various cryptography algorithms. The objective is to ascertain the optimal algorithm within each realm classical and quantum cryptography for ensuring robust security.
Automated Abstractive Text Summarization using Deep Learning

A class of neural networks known as Recurrent Neural Networks (RNNs) are capable of processing sequential input, including time series and plain language. In the shortest possible time to find relevant and useful information, it is for sure very helpful if the information is summarized, but it typically requires a lot of effort, dedication, patience, and attention to detail for humans to go through and summarize the lengthy texts. This can be done using automated abstractive text summarization techniques by using deep learning which is the way of selecting the most significant information in a text, then condensing it while maintaining its underlying meaning. The objective is to construct an abstractive text summarizer using deep learning. It takes large meaning-full text data as input and gives the summary of the data as output. The algorithm which is been used here is the Long Short Term Memory model (LSTM) which is a type of RNN model. And then next the model which is been used is the Sequence to Sequence model (Seq2Seq). Seq2Seq learning is the training model that can modify the sequences of one input into the sequences of another output. The dataset used is News Summary dataset which is taken from Kaggle. Experimental results on the news summary dataset show that the proposed method results are appropriate and match with the other methods.
Feasible Trend Prediction for 2019 Indian General Elections

The popularity and accessibility of social media applications, such as Twitter, has increased over the past few years at a rapid pace. Users from different parts of the world use it to share thoughts with each other. Politicians use these platforms at every viable opportunity and increase support and following for themselves and their parties. Sentiment analysis has become a key methodology to gain insight from social networks. We perform sentiment analysis using a lexicon-based sentiment analyser and sentiment trend prediction for a short interval of time to model the sentiment trend towards the top contenders of the Indian General Election 2019 on twitter. Sentiment Analysis is done on the tweets posted during the campaign period of the Indian General Elections of 2019. This is done using a pre-trained sentiment analyser called VADER of the NLTK library. VADER is optimized for social media data and can produce useful results. The dataset used for this paper is created using web scraping modules written in Python. In addition to that, sentiment trend prediction was done for a period of 10 days from the day of result announcement using Linear Regression, to achieve sustainable reports.
Bilateral network with rich semantic extractor for real-time semantic segmentation

Abstract Recently, owing to the requirements of inference speed, most real-time semantic segmentation networks often have shallow network depth, which limits the receptive field size of the model, leading to the limited acquisition of semantic information and resulting in intraclass inconsistency and ultimately a decrease in segmentation accuracy. Additionally, the shallow network depth also restricts the feature extraction capability of the network, reducing its robustness and ability to adapt to complex scenes. To address these issues, a bilateral network with a rich semantic extractor (RSE) for real-time semantic segmentation (BRSeNet) is presented to perform real-time semantic segmentation. First, to solve the problem of insufficient semantic feature information extraction, an RSE is proposed, which includes a multiscale global semantic extraction module (MGSEM) and a semantic fusion module (SFM). The MGSEM can extract rich global semantics and expand the effective receptive field. Simultaneously, the SFM efficiently integrates multiscale local semantics with multiscale global semantics, resulting in more comprehensive semantic information for the network. Finally, based on the characteristics of detail and semantic branches, a bilateral reconstruction aggregation module is designed to reconstruct the contextual information of detail features, model the interdependencies on semantic feature channels, and enhance feature representation. Comprehensive experiments on the challenging Cityscapes and ADE20K datasets are conducted. The experimental results show that the proposed BRSeNet achieves mean intersection over union of 74.9% and 35.7% at inference speeds of 74 and 65 frames per second, respectively, and ensures a favorable balance between segmentation accuracy and inference speed.
Exploration of visual variable guidance in outdoor augmented reality geovisualization

The visual perception of augmented reality (AR) geovisualization is significantly different from traditional controllable 2D and 3D visualization. In this study, we extended the rendering styles of color variables to include natural material color (NMC) and illuminating material color (IMC) and extended the size to include linear size (LS) and angular size (AS). Outdoor AR geovisualization user experiments were conducted, examining the guidance characteristics of five static variables (NMC, IMC, shape, AS, LS) and two dynamic variables (vibration, flicker). The results showed that the two dynamic variables provided the highest guidance, and among all the static variables, the order of guidance was shape, IMC, AS, NMC, and finally LS. This is a new finding that is different from the color, size, and shape guidance order in 2D visualization and the color, shape, and size order in 3D visualization. The results could be utilized to guide the selection of visual variables for symbol design in AR geovisualization.
Remote data collection in sociolinguistics: lessons from the COVID-19 pandemic

This article reports on our experience of collecting language data from informants in video-conferencing settings under a research design originally developed with face-to-face interactions in mind. We had set out to investigate whether individual stylistic features persist in different modes of textual production and designed a complex set of data-collection procedures, which we then adopted for use in a fully virtual environment with 112 informants, who were asked to provide language samples in eight discourse types. We conclude that the unintended shift to virtual settings had only a minimal impact on the volume and quality of the data. While the process was occasionally afflicted by IT-related technical issues and made extra demands on the data collector, it also created opportunities, notably around the management of interactional power asymmetries. Additional benefits included instant troubleshooting during data handover sessions and the ability to recruit participants who would not have been able to travel to face-to-face sessions.
Self-Supervised Super-Resolution for Anisotropic MR Images with and Without Slice Gap

Magnetic resonance (MR) images are often acquired as multi-slice volumes to reduce scan time and motion artifacts while improving signal-to-noise ratio. These slices often are thicker than their in-plane resolution and sometimes are acquired with gaps between slices. Such thick-slice image volumes (possibly with gaps) can impact the accuracy of volumetric analysis and 3D methods. While many super-resolution (SR) methods have been proposed to address thick slices, few have directly addressed the slice gap scenario. Furthermore, data-driven methods are sensitive to domain shift due to the variability of resolution, contrast in acquisition, pathology, and differences in anatomy. In this work, we propose a self-supervised SR technique to address anisotropic MR images with and without slice gap. We compare against competing methods and validate in both signal recovery and downstream task performance on two open-source datasets and show improvements in all respects. Our code publicly available at https://gitlab.com/iacl/smore .
Learned Local Attention Maps for Synthesising Vessel Segmentations from T2 MRI

Magnetic resonance angiography (MRA) is an imaging modality for visualising blood vessels. It is useful for several diagnostic applications and for assessing the risk of adverse events such as haemorrhagic stroke (resulting from the rupture of aneurysms in blood vessels). However, MRAs are not acquired routinely, hence, an approach to synthesise blood vessel segmentations from more routinely acquired MR contrasts such as T1 and T2, would be useful. We present an encoder-decoder model for synthesising segmentations of the main cerebral arteries in the circle of Willis (CoW) from only T2 MRI. We propose a two-phase multi-objective learning approach, which captures both global and local features. It uses learned local attention maps generated by dilating the segmentation labels, which forces the network to only extract information from the T2 MRI relevant to synthesising the CoW. Our synthetic vessel segmentations generated from only T2 MRI achieved a mean Dice score of $$0.79 \pm 0.03$$ in testing, compared to state-of-the-art segmentation networks such as transformer U-Net ( $$0.71 \pm 0.04$$ ) and nnU-net( $$0.68 \pm 0.05$$ ), while using only a fraction of the parameters. The main qualitative difference between our synthetic vessel segmentations and the comparative models was in the sharper resolution of the CoW vessel segments, especially in the posterior circulation.
Parameter Estimation of the Dirichlet Distribution Based on Entropy

The Dirichlet distribution as a multivariate generalization of the beta distribution is especially important for modeling categorical distributions. Hence, its applications vary within a wide range from modeling cell probabilities of contingency tables to modeling income inequalities. Thus, it is commonly used as the conjugate prior of the multinomial distribution in Bayesian statistics. In this study, the parameters of a bivariate Dirichlet distribution are estimated by entropy formalism. As an alternative to maximum likelihood and the method of moments, two methods based on the principle of maximum entropy are used, namely the ordinary entropy method and the parameter space expansion method. It is shown that in estimating the parameters of the bivariate Dirichlet distribution, the ordinary entropy method and the parameter space expansion method give the same results as the method of maximum likelihood. Thus, we emphasize that these two methods can be used alternatively in modeling bivariate and multinomial Dirichlet distributions.
FABSA: An aspect-based sentiment analysis dataset of user reviews

Aspect-based sentiment analysis (ABSA) aims at automatically extracting aspects of entities and classifying the polarity of each extracted aspect. The majority of available ABSA systems heavily rely on manually annotated datasets to train supervised machine learning models. However, the development of such manually curated datasets is a labour-intensive process and therefore existing ABSA datasets cover only a few domains and they are limited in size. In response, we present FABSA (Feedback ABSA), a new large-scale and multi-domain ABSA dataset of feedback reviews. FABSA consists of approximately 10,500 reviews which span across 10 domains. We conduct a number of experiments to evaluate the performance of state-of-the-art deep learning models when applied to the FABSA dataset. Our results demonstrate that ABSA models can generalise across different domains when trained on our FABSA dataset while the performance of the models is enhanced when using a larger training dataset. Our FABSA dataset is publicly available.1
Introspective Capabilities in Large Language Models

This paper considers the kind of introspection that large language models (LLMs) might be able to have. It argues that LLMs, while currently limited in their introspective capabilities, are not inherently unable to have such capabilities: they already model the world, including mental concepts, and already have some introspection-like capabilities. With deliberate training, LLMs may develop introspective capabilities. The paper proposes a method for such training for introspection, situates possible LLM introspection in the 'possible forms of introspection' framework proposed by Kammerer and Frankish, and considers the ethical ramifications of introspection and self-report in AI systems.
Local Path Planning for Mobile Robots Based on Fuzzy Dynamic Window Algorithm

Due to the increased employment of robots in modern society, path planning methods based on human-robot collaborative mobile robots have been the subject of research in both academia and industry. The dynamic window approach used in the research of the robot local path planning problem involves a mixture of fixed weight coefficients, which makes it hard to deal with the changing dynamic environment and the issue of the sub-optimal global planning paths that arise after local obstacle avoidance. By dynamically modifying the combination of weight coefficients, we propose, in this research, the use of fuzzy control logic to optimize the evaluation function's sub-functions and enhance the algorithm's performance through the safe and dynamic avoidance of obstacles. The global path is introduced to enhance the dynamic window technique's ability to plan globally, and important points on the global path are selected as key sub-target sites for the local motion planning phase of the dynamic window technique. The motion position changes after local obstacle avoidance to keep the mobile robot on the intended global path. According to the simulation results, the enhanced dynamic window algorithm cuts planning time and path length by 16% and 5%, respectively, while maintaining good obstacle avoidance and considering a better global path in the face of various dynamic environments. It is difficult to achieve a local optimum using this algorithm.
Improving the Students’ Vocabulary through Short Story of Eight Grade Students MTs. Nurul Falah Kefamenanu

This research was to obtain the improvement of students’ vocabulary through short story as learning media for students at MTs. Nurul Falah Kefamenanu. This research used classroom action research (CAR). It has been conducted in to 2 cycles. Each cycle consisted of four steps: planning, action, observation and reflecting. The technique of this research used test of short story that consists of multiple choice, essay and questionnaire. The test consisted of cycle I and cycle II. The researchers analysed the average score of each test obtain the improvement of students’ vocabulary by using short story. The result of research showed that Short Story significantly improved the students’ vocabulary. It can be seen in the result of the students’ average score in the Cycle I was 64,81 and in the Cycle II was 70. It can be concluded that short story as one of the techniques to improve the students’ vocabulary.
Virtual Reality Application for the 17th International Architecture Exhibition Organized by La Biennale di Venezia

This paper aims to investigate the use of Virtual Reality (VR) as a support for expositions and cultural events through the presentation of a case of study related to the 17th International Architecture Exhibition organized by La Biennale di Venezia. The idea for this experimentation was born during the period of Covid-19 pandemic, in which it was impossible to travel freely. The goal was to make part of the exposition available to be visited virtually all over the world, in hubs equipped with VR headsets. Thanks to a collaboration with the organizers of the exposition, a VR app has been developed in order to allow people to visit the Giardino delle Vergini, which for several years has hosted the Italian Pavilion and where this year were placed the installations of prof. Giuseppe Fallacara and his research team together with the works of other international firms. Ethic matters have been taken into consideration during the app development. The VR app has been developed non to be a mere reproduction of the original site, but to be an alternative experience of visit. This work can bring two apparently contradictory advantages: on one hand the differences between virtuality and reality can encourage people to travel and visit the exposition in Venice; on the other hand, barriers of place and time are overcome. Therefore, everyone can visit the Giardino delle Vergini, even people who can’t move.
Virtual, Augmented and Mixed Reality as Communication and Verification Tools in a Digitized Design and File-To-Factory Process for Temporary Housing in CFS

This work presents a research project in which Cold-Formed Steel building components for temporary post-emergency housing are developed and realized with a digitalized workflow. This starts from early design ideas (peacetime), includes file-to-factory production and assembly processes (emergency relief/early recovery) and leads to the disassembly of building components and their reuse (reconstruction). The key element of the entire process is the Information Model. This is the place of the interoperability that, during the different stages, interfaces with different devices including Virtual, Augmented and Mixed Reality tools as well as file-to-factory processes for the industrial production. Aim of the paper is to show how visualization tools (like interactive Whiteboard, Tablet, Cardboard, Oculus Rift, Hololens 2, and Cave) can be used not only to realistically and immersively represent the project, but also to optimize design, production and construction processes. Indeed, these devices can also be used to improve the communication between the involved stakeholders, to enhance participatory processes, to help in decision-making, to verify a digitalized design and manufacturing process and to train workers. To achieve this goal, the innovative workflow is presented in chronological order, highlighting the purposes for which the selected tools were applied, analyzing their characteristics, potential, limits, software, interfaces, involved users and costs. The results comprise not only the application itself, but in particular the advantages and challenges evaluation of the use of the selected tools in a design project in order to improve future applications.