Reviewerly/exHarmony huggingface dataset:
Few-shot Medical Image Classification with MAML Based on Dice Loss

In medical image processing based on deep learning, medical image datasets are usually few-shot and unbalanced between classes. This will cause the model to overfit and be biased towards the class with more samples during training and the accuracy of the class with fewer samples will decrease. As a result, the overall accuracy will also decrease. To address these issues, the model-agnostic meta-learning based on dice loss is proposed. The algorithm avoids the need for a large amount of data to be driven and can quickly adapt to new tasks with only a small amount of labeled data. Meanwhile, the objective function can be optimized autonomously in the direction of a small number of classes during training. Finally, good results are achieved on two few-shot datasets of medical images. Our method can provide a solution to achieve good results with less data cost in medical image processing.
Underwater Drowning People Detection Based On Bottleneck Transformer And Feature Pyramid Network

Emergency rescue is a critical security guarantee for transportation and engineering operations at sea. As an important equipment for the emergency rescue mission, under-water robots could detect drowning people automatically, which can effectively improve operational efficiency. Due to the severe attenuation of light underwater, optical imaging devices such as cameras cannot obtain information from long-distance scenarios. This paper proposed an object detection network based on forward looking sonar images. Due to the performance limitations of the processer in the robots, the proposed network is designed to be lightweight based on the Bottleneck Transformer and Feature Pyramid Networks to detect drowning people underwater instead of large networks. The experimental results show that the proposed network can ensure relatively good accuracy with fast detection speed.
Daily Solar Radiation Prediction Using LSTM Neural Networks

The integration of solar energy with the smart grids and existing infrastructure makes it a cost-effective and environmentally-friendly solution to address the growing energy need. To make use of the potential of solar energy, several challenges such as the stability of generated energy and the supply-demand imbalance must be overcome. In this regard, an accurate forecast model for global solar radiation (GSR) can be useful for power generation planning and system reliability. The GSR estimate is regarded as the most significant and critical element in defining solar system characteristics, thus, it is crucial in predicting the generated energy. This work, therefore, employs long-short-term memory (LSTM) as a deep learning method to successfully estimate solar irradiance and capture the stochastic fluctuations. In this respect, the measurement data (from year 2021) obtained from the station installed in Dicle University (Turkey), Science and Technology Application and Research Centre (DUBTAM) were used, and the efficiency of the proposed method was evaluated.
A Lightweight Stemmer for Telugu Language

Stemming is the process of reducing inflected words to their word stem, base, or root structure in semantic morphology and data recovery. It delivers the best capitulate when contrasted with different stemmers and it has less error rate. But as per the research, till now there are no proper Telugu language tools for stemmer. Telugu is a language belongs to the Dravidian. The resources for this language are also low. So, a rule-based stemmer is used for the analysis of Telugu language. This article has proposed new rules for the development of stemming for Telugu language.
Virtual Mouse to Enhance User Experience and Increase Accessibility

A User Interface is a form of establishing a platform in which a human interacts with machines. There have been different types of user interfaces evolving over the period in line with the rapid growth of technology. Some of the most popular input devices used are mice, keyboards, touchscreens and styluses. A graphical interface is user friendly and, as a result, is widely used. Systems of contactless communication surfaces for interactions have been introduced to decrease the spread of germs and combat diseases like covid-19. This system also can be utilized by disabled people who still retain motor function in the hand and forearm. This paper put forward an AI-assisted virtual mouse system where these drawbacks are solved by utilizing a webcam/built-in camera for recording the motions of the hand and translating them into mouse actions via ML algorithms. The mouse actions are performed based on hand gestures, which are used to control the computer virtually. The hand detection algorithm is based on a deep learning model. So, the proposed system can reduce the spread of germs and help a computer be more accessible to people with special needs.
A Survey and Taxonomy of Anti-Phishing Techniques for Detecting Fake Websites

Phishing is a threat it causes damage to the Organization, Users, Employees, etc., by stealing sensitive information like Username, Password, Credit card numbers, CVVs, and other details. Fake Websites are widely used to steal data by sending URL to a large number of users through emails SMS, and Popups. Fake websites also called phishing websites which are very similar the Trusted and Famous websites. Phishing websites have a very less life span but every year Phishing sites are gradually increasing. In 2016, loss of 4.2 billion to the organization and a 1.25 lakh crores financial loss in India in 2019. Anti-Phishing Working Group detected the phishing websites by collecting, and analyzing the Emails containing URLs and reported. We cannot identify by seeing the User interface of websites. Many methods are existed to identify Phishing websites. Widely used methods are Blacklist or Whitelist approach, Content-based approach, URLs based, Visual Similarity, Machine Learning models, etc., every method requires different inputs. Inputs are URLs, Source Codes, Website text data, Website URLs statistical data, Images on websites. This survey mainly focuses on the various methodologies, Technologies, and Accuracy of the approaches.
Facial Recognition Impact in Smartphone Sector

In recent times, technological advancement brings many benefits for people and facial recognition technology is one of them. Nowadays, S martphone users increasingly use facial recognition features that help in verifying identity of users for different purposes, for example, accessing their device before making any payment from their phones. This study paper has analysed impact of facial recognition systems in S martphone sectors by discussing its advantages and challenges to understand both its negative and positive impact. Literature review section has provided relevant information on selected topic as it has elaborated that different large organisation have their own face recognition software for Apple, Facebook, and Google. Additionally, it has mentioned that there are two different types of facial recognition software such as camera-based and infrared; however, regarding effectiveness, infrared works more appropriate rather than camera-based recognition technologies. The paper has also elaborated that this technology offers automating time-tracking systems and it has touch less sign options, which help them in limiting physical contact with others. Result of this study showed that facial recognition helps to protect any type of unauthorised access in a Smartphone as well as it could prevent cyber threats by recognising face of S martphone users. Additionally, due to this software's fast identification feature, it could successfully verify faces of people within a few seconds. However, as this software is based on artificial intelligence, unlike other applications, it is highly vulnerable as hackers can hack Smartphones if strong algorithms are more used. Lastly, to prevent this issue a strong AI infrastructure and model security is required in future.
Fast R-CNN based Masked Face Recognition for Access Control System

Masked face recognition is becoming an essential requirement in most of the facial recognition based access control and authentication systems, particularly after the Covid-19 pandemic. The work analyses the capability of Region Based Convolutional Neural Networks (R-CNN) for masked face detection and demonstrates a facial recognition system with R-CNN in hardware. R-CNN uses Region Proposal Networks (RPN) that can extract non-occluded region on an image and feed it to a Deep Neural Network for recognition. The R-CNN classifier running on the region containing the non-occluded part of the face will be used for classification in case of a masked face. In case of recognition of unmasked face, the classifier will be run on the region containing the face. By this way, the system will be able to recognize face for both cases. Python modules like opencv, numpy have been used for image pre-processing, while Tensorflow has been used for image classification. Custom dataset is used for training. The trained deep learning model is evaluated using a confusion matrix heat-map which can be used to know the reliability of the model. The demonstration system consists of a Raspberry Pi module connected to a door actuator and a camera. On successful authentication, the system opens the door.
A Survey of Medical Image Processing and its Applications

At present time, medical field is rapidly broadening as a result of inventive image processing technologies. Medical images are exclusively important because they provide a pathway for disease diagnosis and therapeutic analysis in the healthcare profession. Medical image acquisition, image enhancement, segmentation, feature extraction, and classification are the five major processing steps in a general medical image processing system. Based on image processing methodologies, this article presents a comprehensive study on medical image analysis and its application on various forms of medical images. We also discuss the challenges that scholars face during successful execution and provide an overview of the ups and downs of existing algorithms. This case study highlights the key terminologies to be noted or the future challenges that are to be considered in the image processing techniques.
Hand Gesture Recognition using AI Algorithm for Hearing Impaired

HRI can be challenging to facilitate communication between humans and robots. This is because robots cannot understand human language without a mediator, and this causes problems for HRI when it comes to hearing impaired patients, and elderly persons. In today's world HGR plays an important role and the HGR system is widely used in many applications throughout the world. HGR can help establish transmission between humans and machines by helping these groups of people. ML is part of AI that focuses on the evolution of a system that rely on data. The main issue of HGR is that the machine doesn't recognize the human language directly and human machine interaction is in need of media for conveying which can be recognized by machine and as well as humans, to help the hearing impaired persons and elderly persons, so HGR as transmitting media is required to give instructions to computer. This project is based on HGR as an input method using two clustering methods, Fuzzy C Means clustering and Decision K Means clustering. The Euclidean distance will be used to compare the different data elements in order to get an output at the end of the project. The output had higher accuracies compared to other algorithms.
Vietnamese Scene Text Detection and Recognition using Deep Learning: An Empirical Study

Scene text detection and recognition are vital challenging tasks in computer vision, which are to detect and recognize sequences of texts in natural scenes. Recently, researchers have investigated a lot of state-of-the-art methods to improve the accuracy and efficiency of text detection and recognition. However, there has been little research on text detection and recognition in natural scenes in Vietnam. In this paper, a deep learning-based empirical investigation of Vietnamese scene text detection and recognition is presented. Firstly, four detection models including differentiable binarization network (DBN), pyramid mask text detector (PMTD), pixel aggregation network (PAN), and Fourier contour embedding network (FCEN), are employed to detect text regions from the images. Then, four text recognition models including convolutional recurrent neural network (CRNN), self-attention text recognition network (SATRN), no-recurrence sequence-to-sequence text recognizer (NRTR), and RobustScanner (RS) are also investigated to recognize the texts. Moreover, data augmentation methods are also applied to enrich data for improving the accuracy and enhancing the performance of scene text detection and recognition. To validate the effectiveness of scene text detection and recognition models, the VinText dataset is employed for evaluation. Empirical results show that PMTD and SATRN achieve the highest scores among the others for text detection and recognition, respectively. For knowledge-sharing, our implementation is publicly available at https://github.com/ThorPham/VN_scene_text_detection_recognition.
Yoga Pose Classification using Angle Heuristic Approach

Pose estimation plays vital role in various applications related to fitness. Yoga aims to development of all-round personality. In the past, it is used to be performed under the supervision of expert. In this fast and continuously changing world, yoga asanas can be performed utilizing the presented solution in this research work. Here, yoga asanas are classified by using Mediapipe and Angle heuristic approach. Mediapipe employs Blazepose model to identify 33 body keypoints, which are more than the number of points determined by conventional approaches of keypoint detection methods.
DR-Unet++: An Approach for Left Ventricle Segmentation from Magnetic Resonance Images

Precise and automatic segmentation of the left ventricle (LV) in cardiac magnetic resonance imaging (MRI) can help cardiologists make accurate diagnoses and decisions. In the revolution of Deep Learning, many LV image segmentation models have been developed and shown promising performance. Nevertheless, segmentation of the LV is still a nontrivial task due to challenges in the cardiac MR images such as the presence of intensity inhomogeneity, clutter, and object size variations. In this study, we propose DR- Unet++, an enhanced DR-Unet architecture, for cardiac segmentation tasks. The DR-Unet++ model, which utilizes advanced Deep Learning techniques, provides better feature extraction for the segmentation of desired segmented objects. Our tests on the ACDC dataset and Sunnybrook Cardiac dataset show that DR-Unet++ outperforms other modern models in terms of Dice coefficient and IoU metric and demonstrates its potential in biomedical image segmentation tasks.
KINETIC'S as an innovative learning media based on Android in thermodynamic

This research aims to create a medium of learning in which these media can help students understand the concept of thermodynamics. Products developed by researchers is based instructional media android application using APP-Inventor platform. The method used is research and development with 4D models and data collection instruments using observation and questionnaires. Likert scale used to measure attitudes, opinions someone or a group of social phenomena in bio polar continuum scale, which is shown to the left end (lower numbers) as a negative answer, while on the left (high numbers) as a positive answer. Likert scale was designed to evaluate the response or responses of respondents regarding the questions given. The questions posed to the respondents using a scale of 1 up to d 5 for ordinal data. The subject of this research is the students of State Islamic University Sunan Gunung Jati Bandung as many as 15 respondents. Based on trial results showed leaning media ratings based android application is entered in both categories. The define stage, which is based on an analysis of learning needs, requires teaching materials that are easily accessible to students for distance learning and the right solution is by way of teachers developing teaching materials based on Android. Thus, this product can be said to be feasible for the learning activities.
Greedy Integration Based Multi-Frame Detection Algorithm in Radar Systems

In this paper, we address the problem of detection and tracking of dim targets in radar systems through the use of multi-frame detection (MFD) techniques. We first study the energy expansion problem during multi-frame integration for classical MFD methods, which shows that the extended energy peak envelope poses a great challenge for target detection in nonhomogeneous backgrounds and multi-target cases. Next, a greedy integration based MFD algorithm is proposed, which separates the overall multi-frame joint maximization into two processes of confidence building and extended energy suppression. The proposed algorithm can eliminate the energy expansion intrinsically and achieve better detection performance with a lower implementation complexity. In addition, we extend the proposed algorithm to radar systems by deriving the accurate nonlinear conversion relationships between target states of Cartesian coordinates and echo measurements of polar coordinates. In radar scenarios, the proposed algorithm is capable to make detection adaptively after multi-frame integration through the use of traditional constant false alarm rate (CFAR) procedures. Finally, numerical results and tests with real radar data are presented to demonstrate the effectiveness of the proposed algorithm.
PCFN: Progressive Cross-Modal Fusion Network for Human Pose Transfer

The goal of human pose transfer is to transfer the human in the image from the original pose to the desired one. Existing methods utilizing progressive manner have achieved great success. However, they fail to remove background distraction and preserve appearance details in synthesized images since the correlation between the image and pose is not fully exploit. To this end, we propose a novel progressive cross-modal fusion network (PCFN), which consists of multiple cascaded cross-modal fusion blocks (CMFBs). Each CMFB comprises a feature fusion module (FFM) and a cross-modal module (CMM) to take full advantage of appearance and shape information. From an overall perspective, FFM fully exploits the correlation between image features and pose features through the residual gated convolution. Benefitting from feature integration and dynamic selection, CMFB can extract useful information from the image-pose stream. From a local perspective, CMM utilizes the feature-conditioned gated convolution and the pose-guided heterogeneous attention mechanism to update all codes in a crossing manner and enhance the interaction between fusion information and structural information. Qualitative and quantitative experiments demonstrate the superiority of PCFN compared to state-of-the-art methods, which can transfer the correct human features and increase the authenticity of the generated images. At the same time, PCFN can also be applied to supplement the dataset for person re-identification (ReID). PCFN works well for human pose transfer, and our usage of the gated convolution and the attention mechanism also provides references for other conditional generation tasks.
DMVC: Decomposed Motion Modeling for Learned Video Compression

Inter prediction is the critical component in hybrid coding framework to deal with the temporal redundancy. Most of the neural video coding methods typically follow the motion compensation based inter coding scheme, establishing motion vector (MV) as the central role. In this paper, we innovatively propose an efficient motion modeling approach by inherently decomposing it into two components, the intrinsic motion and the compensatory motion. The intrinsic motion originates from the implicit spatiotemporal context hidden in the historical sequence, which can be intuitively captured free of bits. On the top of it, the compensatory motion acts a role of structural refinement and texture enhancement as a form of side information. In particular, the inter prediction is performed in the feature space as a manner of progressive temporal transition, conditioned on the decomposed motion. By the motion decomposition paradigm, we innovatively answer the question of motion representation, compensation and coding in the learned video compression framework. With the temporal prediction, the remaining pixel residue is signaled to obtain the reconstruction. Extensive experimental results demonstrate that the proposed method achieves state-of-the-art coding performance on par with other end-to-end coding methods, and outperforms versatile video coding (VVC) under low-delay P (LDP) configuration in terms of MS-SSIM metric.
Compiler of Reed–Solomon Codec for 400-Gb/s IEEE 802.3bs Standard

Error correction is often indispensable in a modern digital communication system that transmits data at a very high speed. Recently published IEEE Std 802.3bs requires an astounding throughput of 400 Gb/s while using the Reed–Solomon code (RS-Code) to protect the integrity of the transmitted data. An RS-Codec supporting such a high throughput demands a significant silicon area. Improper decisions on the parameters of the parallel architecture could lead to unnecessarily high costs in the implementation. We have developed a compiler to solve this problem. First, the Codec satisfying IEEE Std 802.3bs using RS(544, 514) is parameterized, in a way that the throughput can be boosted on demand by setting some “configuration.” Second, an area- and power-efficient RS-Codec design satisfying a target throughput using a specific process can be inferred by our compiler in just minutes, and thereby easy process migration is supported. Experimental results using 28 and 90-nm CMOS processes are presented to demonstrate their effectiveness.
National Repository of Academic Texts in the system of open science: current state and future development

The value of open science for the development of science, education, and society as a whole. Legal prerequisites of openness in Ukraine. The infrastructure of open science and the place of e-repositories in it. National Repository of Academic Texts: History, Mission, Key Features, Database of Academic Texts, Target Audience. Tasks related to the development of NRAT, integration with local repositories, and international cooperation.
Determination of vehicle speed from recorded video using the open‐source software Kinovea

Video devices of different kind often record traffic accidents, including vehicle-pedestrian collisions and hit-and-run accidents. In these cases, the vehicle speed is valuable information because it can assist the investigators in an accident reconstruction. This paper examines the use of Kinovea, an open-source video annotation tool designed for sport analysis, to estimate vehicle speed in forensic videos. Kinovea does not require a complex methodology, and it can be used to make the calculation easily. A series of vehicle driving experiments using an appropriately calibrated speed radar system (so called Scout Speed) were carried out, and measurements were compared with the estimated speed. In controlled conditions, the comparison of Scout reference speed and calculated average vehicle speed by means of Kinovea found an average difference of 0.43 km/h, with a margin of error of ±0.64 km/h. In addition, further preliminary tests were carried out to check the reliability of the measurements under lower resolution conditions. Also, in these cases the calculations were in line with the ground truth. Therefore, in the tested conditions, Kinovea demonstrated to be an easy and reliable tool available for forensic video examiners. Further tests need to be conducted in order to address the applicability of the measurement technique with true CCTV/surveillance video recordings.