Reviewerly/exHarmony huggingface dataset:
Depth Comparison of Objects in 2D Images Using Mask RCNN

Getting distance of an object from a single 2D image has always been a task. Due to various reasons, it was difficult to compare from images whether an object is closer or farther from camera. In this paper, we propose an idea to compare multiple images taken from same focal length cameras and specifying the distance of an object in those images with respect to each other. Our dataset contains images of palm of hand with particular distance from camera, and the output difference can specify in which image the palm is closer to camera as compared to others and vice versa. For this model, we are using Mask RCNN to recognize the object; in our case, it has been trained to identify palm, and then giving the output of masked RCNN to a depth identifier model to specify the distance of the palm from the camera. Directly using depth identifier model cannot give correct output as distance of background from camera results in different value for distance of targeted object in different images. So, we will be using mask RCNN to specify which part of image depth model should find distance from the camera. In the final step, we take the output of the depth model and take the mean of the output generated by it and compare the means of various images to specify relative distance from each other.
An Augmented Reality-Based Mobile Application for Drug Prescribing Information System: ARPECTUS

In many countries, drugs are available without any supervision, leading to misdiagnosis and mistreatment, and health and economic problems. Package inserts allow people to use drugs correctly and effectively. However, research shows that most users do not read the package inserts, and most of those who read them have difficulty understanding them because they contain too many medical terms. Another disadvantage for drug users is that reading drug leaflets is time-consuming and tedious. By the lack of a study on augmented reality-based package inserts in this area is the main motivation for study. A mobile application, ARPECTUS, was proposed to make up for the deficiency and to eliminate the disadvantages of the current package insert systems; also, the proposed ARPECTUS was evaluated from different perspectives. The proposed mobile application uses one of the state-of-the-art technologies, augmented reality, to make package insert more intelligible and user-friendly. The application reads the QR code on the drug package and involves a virtual assistant that provides the user with the important information in the package insert as well as the expiration date not specified in it.
Incremental Learning of Object Detector with Limited Training Data

State of the art Deep learning models, despite being at par to the human level in some of the challenging tasks, still suffer badly when they are put in the condition where they have to learn with time. This open challenge problem of making deep learning model learn with time is referred in the literature as Lifelong Learning, Incremental Learning or Continual Learning. In each increment, new classes/tasks are introduced to the existing model and trained on them while maintaining the accuracy of the previously learned classes/tasks. But accuracy of the deep learning model on the previously learned classes/tasks decreases with each increment. The main reason behind this accuracy drop is catastrophic forgetting, an inherent flaw in the deep learning models, where weights learned during the past increments, get disturbed while learning the new classes/tasks from new increment. Several approaches have been proposed to mitigate or avoid this catastrophic forgetting, such as the use of knowledge distillation, rehearsal over previous classes, or dedicated paths for different increments, etc. In this work, we have proposed a novel approach based on transfer learning methodology, which uses a combination of pre-trained shared and fixed network as a backbone, along with a dedicated network extension in incremental setting for the learning of new tasks incrementally. The results have shown that our approach has better performance in two ways. First, our model has significantly better overall incremental accuracy than that of the best in class model in different incremental configurations. Second, our approach achieves better results while maintaining properties of true incremental learning algorithm i.e. successful avoidance of the catastrophic forgetting issue and complete eradication of the need of saved exemplars or retraining phases, which are required by the current state of the art model to maintain performance.
Representation Learning for Image Retrieval through 3D CNN and Manifold Ranking

Despite of the substantial success of Convolutional Neural Networks (CNNs) on many recognition and representation tasks, such models are very reliant on huge amount of data to allow effective training. In order to improve the generalization ability of CNNs, several approaches have been proposed, including variations of data augmentation strategies. With the goal of achieving more effective retrieval results on unsupervised learning scenarios, we propose a representation learning approach which exploits a rank-based formulation to build a more comprehensive data representation. The proposed model uses 2D and 3D CNNs trained by transfer learning and fuse both representations through a rank-based formulation based on manifold learning algorithms. Our approach was evaluated on an unsupervised image retrieval scenario applied to action recognition datasets. The experimental results indicated that significant effectiveness gains can be obtained on various datasets, reaching +56.93% of relative gains on MAP scores.
A Speech Recognition Algorithm of Speaker-Independent Chinese Isolated Words Based on RNN-LSTM and Attention Mechanism

The speech recognition technology of isolated words is one of the widely used speech recognition technologies at present. The isolated Words Speech Recognition technology for speaker-independent is selected as the research object of the paper. And the corresponding speech database of Chinese isolated words is established. Through the research and experiment of existing speech recognition algorithms for Chinese isolated words, a model of Chinese isolated words speech recognition algorithm which based on recurrent neural networks (RNN), long short-term memory (LSTM) and attention mechanism is proposed. Attention mechanism is introduced by the algorithm model, which is used for weight adjustment, training and optimizing parameter for the combined model. The experimental results show that, compared with other algorithm models, the proposed algorithm model which based on RNN-LSTM and attention mechanism has better recognition performance for Chinese isolated words, and can effectively improve the speech recognition efficiency of Chinese isolated words for speaker-independent.
DEVELOPMENT OF A MATHEMATICAL MODEL FOR A NEURAL NETWORK FOR RECOGNIZING TEXT INFORMATION

The article considers a mathematical model of a system that provides recognition of images that represent text or use similar information in the generation process.
Three dimensional visualization of simulations of liquids and solids

Abstract Visualization in three dimensions is invaluable for understanding the nature of condensed and fluid systems, but it is not always easy. In nature it is hard to view sample interiors, but on computers it is possible. We describe and contrast two opposite approaches - “smoke” visualization for viewing interiors of liquid samples and interactive WebGL for solids and molecules. Both are extensions of earlier Technion Computational Physics group projects and complement and are interoperable with the recent SimPhoNy Fp7 project. They require only desktop hardware and software accessible to students. Examples and standalone instructions for both are presented, starting with sample creation and concluding with image galleries.
Research on computer assisted smart pension using structural equation model and diamond model

In recent years, with the continuous increase of the elderly population in China, the degree of aging is deepening. Based on the theoretical framework of diamond model, this paper analyzes the current situation of smart pension industry, taking the four basic factors as the breakthrough point. Then, through the literature analysis, combined with the research purpose, we summarized and sorted out the Likert five scale corresponding to the research variables and formed the final questionnaire for distribution and collection, and tested the reliability and validity of the data. Next, this paper uses the structural equation model to make an empirical analysis of the smart pension industry, and gradually modifies the model. Finally, we put forward suggestions on the intelligent pension industry from the top-level design, talent training, and the development mode of the intelligent pension industry, so as to promote the better development of the intelligent pension industry.
Investigation of the neural networks effectiveness in recognizing moving drones

The aim of the work was to study the recognition efficiency of a moving unmanned aerial vehicle (drone) using various neural networks. Models of fully connected and convolutional neural networks had been created and trained making it possible to classify 12 types of moving objects. Sets of images, such as drones, fragments of tree foliage, grass, clouds and insects had been created to train neural networks. The results of the work are: recommendations for choosing the type and structure of the neural network, estimation of the recognition range for high-resolution images.
On the generalized modal Θ-valent Reed-Muller codes

In this paper, we present the parameters of the mΘ generalized Reed-Muller codes. To determine these parameters, we consider the dual codes of the rth order Generalized Reed-Muller codes [5] and the chrysippian modal Θ -valent representation of [7].
Uyghur Language Recognition Method based on BIGRU_IDCNN_ATT_CRF

Named entity recognition plays a very important role in the field of natural language processing. Aiming at the special semantic morphology and scarcity of data in Uyghur named entity recognition, a neural network model based on BIGRU_IDCNN_ATT_CRF is proposed. First, extract the long-dependent semantic information of the Uyghur language context through the bidirectional gated recurrent neural network (BIGRU), and then uses the word vector through iterated dilated convolutional neural network (IDCNN) to increase the perception field to reduce the number of neurons and training parameters. Then use the self-attention mechanism to weight the features extracted from BIGRU_IDCNN to strengthen key features and weaken useless features. Finally, Conditional Random Field (CRF) is used for label prediction. It is concluded through experiments that the accuracy, recall and F1 value of this model on the Uyghur language data set are 85.0%, 84.3% and 84.58%, respectively, which can significantly improve the Uyghur language recognition task compared with the existing models.
Development of Textual Analysis using Machine Learning to Improve the Sentiment Classification

Abstract Rapid development in Internet and the increase in online information, the technology demanded for intelligently classifying the textual data has become significant role in Information Retrieval Process. Based on given query in the search box, the response from the internet has made open to the public. Thus, the scope of text mining is being explored by several researchers. Sentiment Analysis is one of the most popular process for analysing user opinions and feelings, and since, online communication has become the fast ever growing medium for expressing thoughts, therefore, there have been development in text classification to improve sentiment analysis. In this paper, some of the prior works on sentiment analysis and the advancements in text classification have been discussed.
TRAFFIC SIGNAL RECOGNITION ON AN IMAGE USING CONVOLUTIONAL NEURAL NETWORK

The purpose of this work is to develop an intelligent system for recognizing traffic signals. To achieve this, DetectNet was applied, using an interface for learning, which was developed by NVIDIA. With their help, the disadvantages of this approach were identified, and therefore it was necessary to consider another option for solving this problem.
USING WAVELETS WITH A RECTANGULAR AMPLITUDE-FREQUENCY RESPONSE TO FILTER SIGNALS

The wavelet transform is the transmission of a signal through a bandpass filter. The design of wavelets with a rectangular amplitude-frequency response makes it possible to obtain almost ideal digital filters. The wavelet transform is calculated in the frequency domain using the fast Fourier transform.
Machine Learning For Risk Assessment In Emergency Care: A Data-Oriented Survey

During last decades, Machine Learning has been increasingly applied to multiple medical fields including the Emergency Care. In this survey article, we give a functional overview of the chain of Emergency Care, and we introduce different kinds of data being available at different levels of the chain. Then, we present recent works about Machine Learning applications to risk and acuity evaluation of emergency incidents in both pre-hospital and in-hospital processes.
Learning-based End-to-End Video Compression Using Predictive Coding

Driven by the growing demand for video applications, deep learning techniques have become alternatives for implementing end-to-end encoders to achieve applicable compression rates. Conventional video codecs exploit both spatial and temporal correlation. However, due to some restrictions (e.g. computational complexity), they are commonly limited to linear transformations and translational motion estimation. Autoencoder models open up the way for exploiting predictive end-to-end video codecs without such limitations. This paper presents an entire learning-based video codec that exploits spatial and temporal correlations. The presented codec extends the idea of P-frame prediction presented in our previous work. The architecture adopted for I-frame coding is defined by a variational autoencoder with non-parametric entropy modeling. Besides an entropy model parameterized by a hyperprior, the inter-frame encoder architecture has two other independent networks, responsible for motion estimation and residue prediction. Experimental results indicate that some improvements still have to be incorporated into our codec to overcome the all-intra coding set up regarding the traditional algorithms High Efficiency Video Coding (HEVC) and Versatile Video Coding (VVC).
Action Classification from Egocentric Videos Using Reinforcement Learning-based Pose Estimation

We propose a novel action classification method using egocentric pose estimation based on reinforcement learning. In the field of egocentric video analysis, action classification and pose estimation have actively been studied. However, there have been few works that use pose estimation for action classification from egocentric videos. Since egocentric videos do not contain human pose information, it will be effective to introduce the estimated pose information into egocentric action classification methods. As the first step of this study, we train a pose estimation model using flow and humanoid information. Then an action classification model is trained by inputting the estimated pose sequence information into a Bidirectional Long Short-Term Memory network. Experimental results show that the proposed method is effective for the action classification from egocentric videos.
Review of Remote Audit in Occupational Safety and Health Management System through the Mixed-Reality Spectrum

The Occupational Safety and Health Management System (OSHMS) is a collection of interconnected or interacting elements that work together to define and implement Occupational Safety and Health (OSH) policies and objectives. Employers must guarantee that this system is effectively implemented to safeguard employees from hazards in order to attain those goals and eliminating work-related injuries, illnesses, diseases, incidents and deaths by using the guideline given by the International Labor Organization (ILO) as well as the Department of Safety and Health (DOSH) of Malaysia. Auditing is among the vital process in analyzing the performance of OSHMS. The audit process is part of the evaluation element, as described by ILO where the auditor needs to evaluate the OSH performance. However, due to the Covid-19 pandemic situation, the traditional audit could not be conducted, thus the remote audit is considered to be a viable option to ensure the audit process continues. Therefore, this paper intends to understand how the OSHMS especially in the university context can be remotely audited using Mixed Reality (MR) applications through a review of the literature. The findings indicate appropriate contexts of discussion for the study and also identified MR technology namely the 360-degree panoramic view technology that could be further studiedThe paper's contribution is a better knowledge of the OSHMS remote audit process through the usage of MR applications in a specialized setting. The findings of this study would be beneficial to the stakeholders in the relevant context particularly in the university in their investment decision-making for a future-ready ecosystem by digitising their enterprises.
Semantic-Aware Lossless Data Compression for Deep Learning Recommendation Model (DLRM)

As the architectures and capabilities of deep neural networks evolve, they become more sophisticated to train and use. Deep Learning Recommendation Model (DLRM), a new neural network for recommendation systems, introduces challenging requirements for deep neural network training and inference. The size of the DLRM model is typically large and not able to fit on a single GPU memory. Unlike other deep neural networks, DLRM requires both model-parallel and data-parallel for the bottom part and top part of the model when running on multiple GPUs. Due to the hybrid-parallel model, the all-to-all communication is used for welding the top and bottom parts together. We have observed that the all-to-all communication is costly and is a bottleneck in the DLRM training/inference. In this paper, we propose a novel approach to reduce the communication volume by using DLRM&#x2019;s properties to compress the transferred data without information loss. We demonstrate benefits of our method by training DLRM MLPerf on eight AMD Instinc$\mathrm{t}^{\mathrm{T}\mathrm{M}}$ MI100 accelerators. The experimental results show 59&#x0025; and 38&#x0025; improvement in the time-to-solution of the DLRM MLPerf training for FP32 and mixed-precision, respectively.
Counting the Number of Moving Vehicles by Its Type Based on Computer Vision

Traffic jam, lack of adequate information of the traffic low used for development of road infrastructure to reduce traffic jam, and possibility of human error in the execution of a heavy traffic survey become the primary background of this study. By using information technology, open up an opportunity for the development of monitoring system computer vision based. This study will calculate the total amount of moving vehicles based on its type with computer vision based with staged: ROI selection, image segmentation with Gaussian Mixture Model method, filtering process, blob detection and tracking, and vehicles classification with Fuzzy Clustering Means. Implementation of an application using visual studio 2010. The output comprises result of classification and total amount vehicles based on its type. The test application divided into a few test scenarios, namely test 1, test 2, test 3 and test 4. The accuracy obtained on each test are 36.27%, 50.47%, 60.75%, and 67.00% respectively.
Remote-Sensing Image Tile-Level Annotation Based on Discriminative Features and Expressive Visual Word Descriptors

Remote-sensing images play a crucial role in a wide range of applications and have been receiving significant attention. In recent years, great efforts have been made in developing various methods for intelligent interpretation of remote-sensing images. Generally speaking, machine learning-based methods of remote-sensing image interpretation require a large number of labeled samples and there are still not enough annotated datasets in the field of remote sensing. However, manual annotation of remote-sensing images is usually labor-intensive and requires expert knowledge and the accuracy of annotation results is relatively low. The goal of this paper is to propose a novel tile-level annotation method of remote-sensing images to obtain remote-sensing datasets which are well-labeled and contain accurate semantic concepts. Firstly, we use a set of images with defined semantic concepts to represent the training set and divide them into several nonoverlapping regions. Secondly, the color features, texture features, and spatial features of each region are extracted, and discriminative features are obtained by the weight optimization feature fusion method. Then, the features are quantized into visual words by applying a density-based clustering center selection method and an isolated feature point elimination method. And the remote-sensing images can be represented by a series of visual words. Finally, the LDA model is used to calculate the probabilities of semantic categories for each region. The experiments are conducted on remote-sensing images which demonstrate that our proposed method can achieve good performance on remote-sensing image tile-level annotation. The implications of our research can obtain annotated datasets with accurate semantic concepts for intelligent interpretation of remote-sensing images.
On Inverse Operators in Dynamic Epistemic Logic

We extend Dynamic Epistemic Logic with inverse operators $$\langle \!\langle \alpha ^{-1}\rangle \!\rangle $$ of an action $$\alpha $$ along the line of tense logics. The meaning of the formula $$\langle \!\langle \alpha ^{-1}\rangle \!\rangle \varphi $$ is ‘ $$\varphi $$ is the case before an action $$\alpha $$ ’. This augmentation of expressivity enables us to capture important aspects of communication actions. We also propose its semantics using model transition systems provided in our previous work, which are a suitable framework for interpreting inverse operators. In this framework, we give several soundness/completeness correspondences, which lead to modular proofs of completeness of public announcement logic and epistemic action logic of Baltag-Moss-Solecki extended with inverse operators with respect to suitable classes of MTSs.
Human Activity Recognition from Accelerometer Data: Axis-Wise Versus Axes-Resultant Feature Extraction

Benefitting from the development of pervasive computing, recent years have witnessed a variety of meaningful human-centric applications, where automating the recognition of human activities plays a central role in bridging the gap between sensing data and high-level services. Accelerometer-based activity recognizer often remains a priority due to its recognition performance, low costs, and portability, however, few studies systematically investigate how to extract and use features from the time-series sensor data and further compare their discriminant power. To this end, we herein propose two different ways of extracting features and exploring their combinations. Specifically, we take as a resultant axis or separate channels the accelerometer axes and then extract axes-resultant and axis-wise features. Afterwards, we evaluate the cases where the two feature sets are used separately or jointly. Finally, we conduct comparative experiments on two public activity recognition datasets with five different classification models in terms of four performance metrics. Results show that the use of axis-wise features outperforms its competitor in the majority across the datasets and that their joint use generally leads to enhanced accuracy.