Reviewerly/exHarmony huggingface dataset:
Automatic Short Answer Grading Using a LSTM Based Approach

Short Answer Grading is an emerging application of Natural Language Processing and text processing. Automated Short Answer Grading (ASAG) is the process of evaluating student-written short responses using computer techniques like Machine learning. The ASAG task has been studied for a long time, but because of the difficulties in the research, it still attracts attention. One of the primary limitations of ASAG is the scarcity of domain-relevant training data. The job of ASAG can be approached using a variety of methods, which can be broadly divided between traditional methods using hand-crafted features and methods based on deep learning. Due to the growing popularity of this field, researchers have been using Deep Learning Approaches to address this challenge over the past five years. This paper explores the methods of creating an LSTM model, to test how close this approach will bring the machine score to that of the Human Score.
A Post-Quantum Consortium Blockchain Based Secure EHR Framework

Electronic health records (EHR) form a digitalized medical history of a person. In the era of Healthcare 4.0, such records can play a crucial role. However, these records require high security and confidentiality, to ensure patient privacy is maintained. A system that provides data security, confidentiality, integrity and access control is needed to maintain such records. The system must also be fast and scalable, as health records are present in enormous numbers. Blockchain based EHR systems have been researched for quite some time. These systems work well, but have some limitations, such as low scalability. Advent of quantum computers threatens their ability to provide security and confidentiality. In this paper, we present a post-quantum, consortium blockchain framework which is used to store and access EHRs. The design uses CRYSTALS Kyber-768 public key cryptosystem to provide security against quantum attacks. The system is designed to give persons absolute authority on their personal health data.
Deep Feature Learning for Wireless Spectrum Data

In recent years, the traditional feature engineering process for training machine learning models is being automated by the feature extraction layers integrated in deep learning architectures. In wireless networks, many studies were conducted in automatic learning of feature representations for domain-related challenges. However, most of the existing works assume some supervision along the learning process by using labels to optimize the model. In this paper, we investigate an approach to learning feature representations for wireless transmission clustering in a completely unsupervised manner, i.e. requiring no labels in the process. We propose a model based on convolutional neural networks that automatically learns a reduced dimensionality representation of the input data with 99.3% less components compared to a baseline principal component analysis (PCA). We show that the automatic representation learning is able to extract fine-grained clusters containing the shapes of the wireless transmission bursts, while the baseline enables only general separability of the data based on the background noise.
Adaptive reppoint: an efficient approach for orientated object detection in remote sensing imagery

With the improvement of the remote sensing technology, more and more remote sensing imagery data can be obtained easily. Therefore, the object detection in remote sensing imagery becomes an important task in computer vision area. However, the object of remote sensing imagery are presented with multi-scales, arbitrary orientation and complex background, which make the task becomes challenging. Currently, the mainstream object detection methods of multi-scales and oriented objects, such as RepPoint, which focus on learning a set of adaptive points and generating the point set to the bounding box (with angle information) of the objects. However, the diversity of categories and complex background always make the learned points (in adaptive points set) deviating outside the ground-truth bounding box, which will yield inaccurate location of the objects and decrees the detection result. To deal with this problem, this paper proposes a spatial constraint method that can enforce penalties on outlier points within the point set. Firstly, a reasonable evaluation metric is proposed for quantifying the distance between the outlier points and the ground-truth; Secondly, an spatial constraint parameter is defined for the objects in different scales. Extensive testing was performed on the challenging DOTA and HRSC 2016 datasets. According to the results, the proposed method demonstrated a performance improvement of 1.84% and 0.4% for the respective datasets.
Investigating Tree Family Machine Learning Algorithm for Solar Power Prediction of 150 KW PV Array System

This work presents the solar power prediction of 150 KW solar photovoltaic (PV) system installed in The NorthCap University in Gurugram, India. The PV system consists of 600 PV modules with each module containing 60 solar cells. The system is monitored for six months and temperature, irradiance and power were recorded every 3 hrs daily from 1 August 2021 to 31 January 2022. The study of the Decision Tree Algorithm and Random Forest Algorithm has been done for their applications to predict maximum solar power output. In terms of the overall power prediction results, the Random Forest Algorithm outperformed the Decision Tree Algorithm. However, the Decision Tree Algorithm gives superior power prediction results for the month of January.
CTW-Net: A Deeper Multiscale Feature Fusion W-Shaped Network for Medical Image Segmentation

Nowadays, convolutional neural networks (CNNs) are widely adopted in medical image analysis. Nevertheless, the inherent local nature of the convolution operator leads to limitations in capturing long remote dependencies. Fortunately, the self-attentive mechanism in Transformers can effectively model long remote dependencies. Therefore, we construct a hybrid network (CTW-Net) for medical image segmentation that combines the benefits of CNN and Transformer more effectively. We first construct a pure convolutional multiscale transformer (PCM Transformer) to multiscale the features obtained from the hierarchical encoder and decoder. It utilizes Hadamard product for second-order spatial interactions to fuse features at multiple scales, enabling the network to concentrate on more significant regions of lesion characteristics. Besides, we developed an efficient maximum pooling residual block (EMPR) based on convolution and maximum pooling. The EMPR block enables the complementation of locally important features in the downsampling process, which raises the performance of lesion segmentation. Experimental outcomes reveal that our network has achieved excellent segmentation performance in breast ultrasound and colon polyp lesion segmentation.
A Study of Deep Learning and Blockchain-Federated Learning Models for Covid-19 Identification Utilizing CT Imaging

The combination of blockchain with federated learning for the detection of Covid-19 with the use of a deep learning model would provide complete data security and a trustworthy model. When it comes to training a deep learning (DL) model, one of the fundamental concerns is maintaining personal anonymity. Another limitation of a deep learning model is the need for a significant amount of data for training. The standard techniques for machine learning entail consolidating all of the training data onto a single device or into a data center. So, decentralized data management is important and is the characteristic of federated learning (FL). In federated learning, it is necessary to train an algorithm over a large number of decentralized end devices or even servers that have the capacity to store a local set of data. The healthcare industry may benefit from federated learning because it enables the protection of sensitive data at the source and provides improved data diversity by obtaining data from a variety of sites. Blockchain technology would provide a scheme to manage the massive amounts of data that are now being collected. In addition to this, the blockchain may be used to develop trustworthy data exchange protocols within the health care ecosystem that also protect patients' privacy. The major purpose of the blockchain is to act as a centralized database for the FL system, which is completely decentralized and protects users' anonymity. This paper will review the combination of FL and blockchain in the process of Covid 19 detection to create a powerful operational foundation for the Covid 19 detection and management system.
Improving Nighttime Semantic Segmentation with Dual-Domain Mixed Attention and Shallow Feature Enhancement

Semantic segmentation in nighttime scenes has garnered considerable attention in the field of computer vision. Recent studies have begun to utilize frequency information to tackle this task. However, existing methods based on frequency solely consider the unidirectional guidance of frequency domain information on spatial features, disregarding the impact of spatial features on the frequency domain information. Moreover, the color and contour information of nighttime images is not prominent, leading to an insufficient contribution of shallow features in nighttime semantic segmentation networks. To address these issues, we propose a dual-domain mixed attention (DDMA) module and a shallow feature enhancement (SFE) module for nighttime semantic segmentation. Specifically, the DDMA module performs a domain transformation on the saliency learned from spatial and frequency domain features, enabling the cross-domain fusion of information in both frequency and spatial domains. The SFE module is employed to enhance the expressive capacity of shallow features within semantic segmentation networks. Extensive experiments on the NightCity, ACDC-Night and BDD100k-Night datasets showed effective improvement in nighttime semantic segmentation performance compared to methods using the same baseline, achieving state-of-the-art results.
Enhancing Chinese Event Causality Identification with Supervised Contrastive Learning

Event causality identification is a significant task in natural language processing, aiming to discern causal relationships between pairs of events within a given text. Identifying causal relationships is helpful to sort out the causes and effects of events. It is of great significance to the development of logical reasoning, building event relationship diagrams, and question answering systems. Existing research predominantly relies on pre-trained models to encode events and obtain vector representations.However, these representations obtained in such a manner are anisotropic and fail to adequately convey the semantic information of events. To alleviate this problem, we propose a supervised contrastive learning-based event causality identification model. We construct positive and negative example pairs to improve the generalization ability and robustness of the model. We also design a supervised contrastive loss function to jointly train with the cross-entropy loss function to identify the causal relationships between events. The experimental results show that our supervised contrastive learning-based model achieves superior accuracy in identifying causal relationships between events, with an F1 score of 90.48% and an accuracy of 93.14%.
A Hybrid Blockchain-Based Approach for Intelligent Transportation System

Due to urbanization and an increase in people's lifestyles, the no. of vehicles is increasing. Efficient transportation and traffic management systems have become increasingly prominent. In recent years, advances have been made in the Internet of Vehicles (IoV) field. This has paved the way for the development of intelligent transportation systems (ITS). Advancements in communication systems, the Internet of Things, and cybersecurity have made it possible to achieve vehicular communication. With the introduction of smart cities, smart vehicles, and V2X technology, the development of a suitable environment for ITS has become more efficient. However, the existing system is centralized and poses security and privacy risks for IoV communication. Blockchain technology has been proposed to overcome security and privacy issues. Blockchain brings features such as decentralization, transparency, security, and immutability and cannot be corrupted. Communication between entities will become secure and tamper-proof. Hence, the system can operate smoothly in a trustless manner. The purpose of this article is to conduct a literature survey, put forth the latest advancements in the blockchain for ITS, and suggest a suitable solution for the same.
Design of integrated intelligent information system for manned spacecraft based on cloud platform and smart home technology

For manned spacecrafts, astronauts have become an important part of the whole system, and the human-machine interaction ability of spacecraft information system will directly affect the astronauts’ work efficiency, even the success of the mission. In addition, with the improvement of spacecraft payload support capability, spacecraft information system needs to forward, calculate and store lots of experimental data when processing its own control instructions and state information. And how to make full use of system capability to process data in real time is also the key part of information system design. Combined with the mission requirements of future manned spacecraft, an integrated intelligent information system design method based on cloud platform and smart home technology is proposed, which comprehensively uses system resources to meet the requirements of spacecraft control and state monitoring, long-term astronaut residence, and experimental data processing. The effectiveness of the method is proved by software simulation and spacecraft on-orbit test.
A People Counting System Using Convolutional Neural Network for Avoiding Public Gathering and Maintaining Social Distancing

Image processing and computer vision are two essential components of object identification. It is utilized in a variety of technological and economic domains, including laboratory, agriculture, research, and manufacturing, as well as medical diagnosis. In this work, we discussed the various strategies as well as the issues encountered in picture categorization and object counting. It is a difficult technique that is dependent on a variety of circumstances. Our goal is to investigate various approaches utilized in perception and item counting. Furthermore, we addressed specific use cases of this module in real-world applications. One of the biggest challenges in the retail industry is measuring how many customers enter a store throughout the day. For example in a pandemic situation likely, that occurred in the form of covid-19 few years back, maintaining track of people movements and social distancing rules in a commercial setting is crucial in the current circumstances because only way to stay safe in this situation is social distancing. In order to lessen the possibility of disease transmission, social distancing is a public health technique that seeks to keep sick people from being in close proximity to healthy people. It is a system where we use a webcam or we can give our video or images to count the number of people in a frame and if the number of people in the frame exceeds a certain limit, then it gives an alert. In this work, we are using for human detection. It is an object detection algorithm which is trained on the image dataset. The detections are very fast and thus can be used in a real-time scenario.
Custom-Built Deep Learning Architecture for Identifying Malaria Parasites in Blood Smear Images

The Plasmodium parasite, a form of protozoan parasite, invades red blood cells in the human body and is responsible for causing malaria. Malaria is a potentially lethal disease. Malaria diagnosis must be accurate and rapid in order to treat and control the disease efficiently. It is a major public health problem, specifically in tropical areas, and fast and explicit diagnosis is critical for effective treatment. However, due to low contrast, overlapping cells, and other artefacts, visual evaluation of blood smears can be difficult. A Convolutional Neural Networks (CNN) system for accurately detecting parasites associated with malaria in blood smear pictures is proposed in this research. Thousands of photos of diseased and unaffected blood cells made up the data set used in this investigation. Multiple pools and convolutional layers are included in the proposed CNN model, which follows fully linked layers for classification. The model's effectiveness was assessed comprehensively by employing well-established metrics, including The metrics used to evaluate the performance include accuracy and loss etc. This evaluation facilitated a thorough and comprehensive analysis. The findings revealed that the CNN model exhibited exceptional capability in accurately identifying and categorizing malaria cells, achieving an impressive accuracy rate of 0.93. These results clearly highlight the significant promise of this approach for diagnosing and managing malaria.
Marwari (Heritage Script) OCR Using Attention Based Encoder-Decoder Architecture

The Marwari language is home to a vast collection of historical handwritten documents that require preservation and digitalization to deepen our understanding of Marwari history. Unfortunately, the digitization of Marwari language documents has seen limited progress thus far. To address this, deep learning techniques offer valuable solutions for text generation and expediting the digitization process. This study pioneers the recognition of printed line images in the Marwari and tackles three major challenges in Marwari OCR. Firstly, unlike Hindi, the Marwari language lacks distinct word boundaries, resulting in longer interconnected words compared to other Indian languages. Moreover, handwritten form is the predominant format for Marwari documents. Lastly, the presence of plain documents adds to the complexity of line segmentation, as the text is often written without any specific formatting or structure. In this paper, we propose a novel approach utilizing DNN to overcome the first challenge. Due to the absence of an actual annotated dataset, 74,339 synthetic Marwari line images are generated with augmentation using in-house developed Bikaner font. Our method harnesses the CNN-Transformer architecture, chosen for its flexibility in hyperparameter tuning, for text recognition. The model takes Marwari line images as input, utilizing a CNN to extract visual features, which are then fed to the transformer decoder for text generation. We evaluate our approach using an encoder based on ResNet18 and a transformer decoder, achieving an impressive accuracy of 99.53% on the test set. The primary contribution of this work lies in demonstrating the successful recognition of extremely lengthy interconnected words which will benefit the research community.
Fusion of Transformer Model and Skeleton Detection Model for Abnormal Human Activity Detection with Transfer Learning

Abnormal activity detection is an area of study that focuses on finding abnormal activities like theft, vandalism, abuse, etc in video and CCTV footage. In this paper, a combination of skeleton based and transformer-based approach is used. The skeletons are used to enrich the motion of human bodies in the video. The transformer is used to model dynamic convolutions which helps to overcome the problems associated with fixed size kernels. To preserve the background information the architecture also uses feature extraction. The methodology used in the paper, also proposed a way to use transfer learning in the transformer network. The proposed framework provides excellent results outperforming many of existing methodologies. ROC (AUC score) attained by the proposed method for some classes of actions are up to 97.71%, which is even higher than current state of art approaches. The average ROC (AUC score) of our method is better than many of the approaches which are considered in the paper.
Real-Time Pedestrian Detection Using Local Regression Based YOLOv6

Most video surveillance systems are employed to keep tabs on individuals. It was impossible for old human identification algorithms, which relied on background and foreground modelling, to cope with a crowd. Video surveillance systems have reached a new milestone with the development of strong and highly effective pedestrian recognition algorithms. Utilizing current deep learning methodologies, these algorithms provide highly discriminative features that may be utilized to get strong conclusions from actual images and video footage on the go. They have to cope with difficulties like identifying individuals in a crowd, overcoming the challenge of enclosing enough human bodies in the front, and seeing diverse facial expressions. Detection and classification problems may be combined into one challenge utilizing convolution neural networks in our study. Our starting point is the YOLO-LR model, in which we provide a highly fast method for learning a single neural network to do all of the aforementioned tasks. Faster than existing models like the FAST R-CNN and hence suitable for use in real-time surveillance and other video monitoring systems, this technique demonstrated competitive results. It has a number of benefits, but there are also acknowledged limitations that prevent it from being used to pictures of varying resolutions. In addition, it makes it difficult to separate tiny, close-up human figures in groups, which is critical to our work since the photographs we use are of poor quality and often include dense clusters of individuals. To address the issues outlined above, we progressively alter network design, train it on a difficult pedestrian dataset, and finally get the LR to recognize little pedestrians in actual settings.
Image Aesthetics Assessment for Virtual Cinematography of Cloud-based Performing Arts Scenes

Cloud-based performing arts is an innovative form of traditional performing arts that embraces new artistic expression. Lens language is widely used in the production of performing arts programs, which helps to enhance the artistic expression of performance content. One of the cores of its aesthetics expression is image composition. However, great majority current intelligent cinematography methods tend to overlook the learning of lens language attributes, resulting in the inability to obtain camera images that effectively reflect the composition and cinematographic intention. Therefore, in the cloud-based performing arts scenes, we propose an aesthetics assessment framework for virtual cinematography, which is designed to select the optimal camera position and angle to make its view image composition closer to the designed composition patterns until aesthetics assessment results meet expectations.
Improved Deep Learning Model for Aspect Words Extraction

Deep learning has recently been successful in numerous academic areas, including social network filtering, image processing, machine translation, and text mining. There is a lot of user-generated content available on various social media sites. that can be used for precise opinion analysis of the shifting market dynamics. Aspect word extraction (AWE) task detects the targets of user opinions from the text in aspect-level sentiment analysis (ABSA). The majority of aspect retrieval methods depend on a certain domain or context. Additionally, it is difficult to identify the most pertinent aspect phrases that are domain-specific because there is a deficiency of topic and situational knowledge. The current methods fail to extract some useful aspect terms because they are unable to identify noun phrase dependencies over time. In order to enhance the AWE challenge, this research suggests a combined language patterns with deep learning methods. On typical review datasets, results from studies and experiments are examined for many kinds of aspects.