Reviewerly/exHarmony huggingface dataset:
Daily life human activities recognition using a novel AR‐DenseNet

SUMMARY With the introduction of mobile‐wearable gadgets, the embedding of health‐tracking capabilities has advanced dramatically. The Covid‐19 years have accelerated research toward wearable sensor‐based health monitoring. One of the most important applications in health monitoring is human activity identification. Daily behaviors like walking, sitting and jogging, as well as crucial activities such as falling forward and backward, provide a barrier in HAR (human action recognition) since they are semantically comparable. Previously developed deep learning algorithms have addressed some of these issues. However, these algorithms are hindered by a lack of training data. To cope with nonuniform samples in the human activities data, which can lead to overfitting results, this work introduces the synthetic minority oversampling approach. This article proposes a unique configuration of stacked convolutional neural network (CNN)‐AR‐DenseNet. With Bayesian optimization, the parameters of AR‐DenseNet are optimally optimized. When compared to state‐of‐the‐art stacked CNN network methods, the classification accuracy improved by up to 3.22%, with a substantial improvement of 5.8% over the standard CNN algorithm.
AMB-Wnet: Embedding attention model in multi-bridge Wnet for exploring the mechanics of disease

In recent years, progressive application of convolutional neural networks in image processing has successfully filtered into medical diagnosis. As a prerequisite for images detection and classification, object segmentation in medical images has attracted a great deal of attention. This study is based on the fact that most of the analysis of pathological diagnoses requires nuclei detection as the starting phase for obtaining an insight into the underlying biological process and further diagnosis. In this paper, we introduce an embedded attention model in multi-bridge Wnet (AMB-Wnet) to achieve suppression of irrelevant background areas and obtain good features for learning image semantics and modality to automatically segment nuclei, inspired by the 2018 Data Science Bowl. The proposed architecture, consisting of the redesigned down sample group, up-sample group, and middle block (a new multiple-scale convolutional layers block), is designed to extract different level features. In addition, a connection group is proposed instead of skip-connection to transfer semantic information among different levels. In addition, the attention model is well embedded in the connection group, and the performance of the model is improved without increasing the amount of calculation. To validate the model's performance, we evaluated it using the BBBC038V1 data sets for nuclei segmentation. Our proposed model achieves 85.83% F1-score, 97.81% accuracy, 86.12% recall, and 83.52% intersection over union. The proposed AMB-Wnet exhibits superior results compared to the original U-Net, MultiResUNet, and recent Attention U-Net architecture.
Web Application Based Exam Hall Seating Management System

The Exam Hall Seating Management System has been created for the institution to make the process of allocating examination halls easier by avoiding the last rush to check on to their hall allotted. The software is developed to generate the exam hall details such as class, time, date and subject of the examination report before exam starts. In this proposed system, a website is developed for admin and students, and is useful for a particular college or school. Most of the students face hassle for locating the exam corridor. So, this system helps the students to check their examination halls which in turn identify the rooms which are allocated for them without delay. Corridor information has an overall quantity of halls to be had in the institution and the call of the hall. Batch information carries numerous branch details belongs to the group degree and the exam timings info has general timing allocated to students. Therefore, the examination hall can be easily found without any delay using the information provided in the website containing location of exam hall, timing of exam, and the upcoming exam details also.
Stock text topic recognition based on Stu-BERT

Expert stock comments are important basis for accurately predicting stock trends. How to capture the theme of expert stock reviews effectively is an important issue in text classification. The Bidirectional Encoder Representations from Transformers (BERT) model has been widely used for text classification. However, BERT has some limitations. (1) The information extraction of stock comments beyond the limit of the fixed length in model is incomplete. (2) The features extracted from the model are not comprehensive enough. (3) The application efficiency of BERT model is low due to the large number of parameters. To tackle above issues, we propose a Student Bidirectional Encoder Representations from Transformers (Stu-BERT) model for accurately identifying of stock comments. Specifically, we firstly intercept at beginning and end of stock comments beyond the fixed length to improve access to information. Secondly, we fuse all the features of the last layer of the hidden layer in model to improve the topic recognition accuracy. In addition, we distill the BERT model to get Stu-BERT model, which enhance the practicability of applying it to the topic identification. Experimental results on real data demonstrate the effectiveness of the proposed method.
OfflineMOT: A Python package for multiple objects detection and tracking from bird view stationary drone videos

The topic of multiple objects tracking (MOT) is still considered an open research area (Dendorfer et al., 2020).Among the many available methods for this problem, it is worth mentioning Deep Sort (Wojke et al., 2017), where a detection and tracking steps are done in real time and for different types of scenes and areas (for example for pedestrians movement or for vehicles tracking).Another state-of-the-art method is Tracktor (Bergmann et al., 2019), where tracking is done by repetitive detections on all the frames in the video.
Application of using R language to call SNNS neural network learning platform function in econometric prediction: take the price fluctuation prediction of gold and bitcoin as an example

Maximizing returns is a constant pursuit of the financial market. In this paper, we show the relevant models we designed to realize the optimal allocation and combination of gold, bitcoin and cash. Advanced forecasting technology can promote the maturity of transactions. Our model takes the lead in introducing the rolling learning prediction method of feedforward multilayer neural network (MLP), which provides a method for accurately predicting market fluctuations from another way. We improved the original algorithm and changed the unified learning method to the rolling learning method based on the existing data before each day. Our practice has proved that our technology has better prediction effect for assets with less market volatility. For example, for gold, a safe haven asset, our prediction accuracy is 99.5%; For products with large market fluctuations, the prediction accuracy is relatively low. For example, for speculative assets with large market fluctuations such as bitcoin, our prediction accuracy is also as high as 97%.
Visualization of Electromagnetic Spectrum Based on Distributed Database

By building distributed spectrum detection nodes, collaborative detection of electromagnetic spectrum is realized. The efficiency and reliability of spectrum detection are improved, and the acquisition and processing of signal source data is realized in a more cost-effective electromagnetic spectrum detection system. Aiming at the problem of signal source data storage and visual analysis, this paper proposes a distributed electromagnetic spectrum database design in the electromagnetic spectrum detection node based on embedded system. After the signal source parameters are estimated in the cooperative detection, the data is packaged and sent to other detection nodes through multicast. Through the distributed database consistency protocol, the data consistency and data sharing of the spectrum database are realized. On the web side, access the spectrum database through the interface and read the data for visual analysis to form a two-dimensional spectrum map.
A Performance Optimization of Quantum Computing Simulation using FPGA

We use FPGA to optimize the simulation of quantum computing in two aspects. (a) The if-else state is used in place of tensor product calculation. This allows the tensor product of each quantum operator to be generated in a single clock cycle. (b) The pre-calculated lookup ROM is used for estimating the sine and cosine values. This facilitates the computation of quantum gates that are related to angle. To validate our work, we implement our design in VerilogHDL. The performance is evaluated using an FPGA simulator. The result shows a dramatic improvement in the simulation process comparing to those of simulation on classical computers.
An Integration of Various Virtual Environments to Overcome Claustrophobia using Virtual Reality

The fear of confined spaces is termed as claustrophobia, which is one of the most common phobias. There are numerous ways to treat it. Virtual Reality Exposure Therapy (VRET) is the most commonly used method among them. The purpose of this study is to treat Claustrophobia using VRET. This is accomplished by immersing people in non-hazardous and user-friendly virtual environments. Those situations would elicit their Claustrophobic fear, encouraging them to conquer it. It's based on the premise the more you expose yourself to what frightens you, the less you will fear it. The effects of numerous elements such as tidiness and messiness, space wideness and narrowness, color, and brightness on virtual environments such as a claustrophobic house, Tunnels, Trial rooms, Public Restroom have been researched. Teleportation allows users to move about inside these virtual environments. It is accomplished through gaze-based interactions, which do not induce as much nausea as other methods. Google Cardboard can be used to view the project as an app in android mobile.
A Web Application for Tracing, Selling and Sustaining Ownership in Blockchain enabled Art Marketplace

Blockchain is an evolving technology that can possibly reform the worldwide business and form a Decentralized network for various pragmatic applications. Generally, Blockchain has a property of supporting untrusted ecosystems, thereby bringing in Tamper Proof and transparent transaction history of the data stored as a ledger. This paper is all about Art - Blockchain, which is a coordinated exchanging framework for artists to sell their artworks to customers dependent on Blockchain. This idea includes the front-end and the back-end for regular artists - user interactions, Smart contracts for the Blockchain and other trading services. As the Indian Art Marketplace is one of the largest trading industries, this paper suggests a better and Secured Trading environment eliminating Copyright issues and false trading.
Multimedia Based Medical Big Data Analysis of Leg Swing Strike Effect in Wushu Sanda Sports

In order to explore the effect of leg swing in Wushu Sanda, a method based on multimedia medical big data is proposed. The joint torque of hip joint is less than that of healthy subjects. The peak values of the horizontal and vertical components of the joint reaction force are approximately the same as those of the sound subject, and a comparison is made between the two kinds of leg swing strike effects of Wushu Sanda to select the leg swing strike which is more suitable for Wushu Sanda. These results show that the turning strategy of Wushu Sanda on the stairs can be used to swing the legs after turning with similar or smaller hip joint dynamics. Multimedia medical big data analysis has the greatest impact on the variability of simulation dynamic parameters. Six test students turned their knees and turned their heels forward, which is better than the technology without turning their knees and turned to swing their legs in the air. At the moment of striking, the running track of the front striking feet in the air is generally shorter. Therefore, the technology of turning their knees and turning their heels forward has higher concealment and better striking effect. It is proved that the research results may improve the inertia and usability of Wushu Sanda process design.
Fast 3D body reconstruction of continuous 2D human joint data

The development of methods and tools for 3D body reconstruction has become an important research area in computer animation. Relevant research shows that 3D reconstruction directly depends on monocular video data needs a lot of computation. Therefore, existing methods can’t meet the requirements of fast real-time 3D body joint computing. With the help of deep learning and pattern recognition, we can easily obtain a large number of continuous two-dimensional joint data from video. In this paper, we propose a fast 3D body reconstruction method with continuous two-dimensional human joint data. We use the expected position to solve the problem of Ambiguity from monocular data restoration. Base on the predefined body pose, we can get continuous 3D joint data quickly, and our result show this method has a good performance in the situation of rapid change of motion amplitude and speed.
Preprocessing succinct non-interactive arguments for rank-1 constraint satisfiability from holographic proofs

We design a new preprocessing succinct non-interactive argument (SNARG) for rank-1 constraint satisfiability (R1CS) from holographic Interactive Oracle Proofs (IOPs). The protocol is secure in the random oracle model (ROM). We achieve polylogarithm time verifier and polylogarithm proof size. Our construction consists of two parts: (1) a holographic IOP for R1CS with linear size proof and logarithmic query complexity. (2) A cryptography complier converts holographic IOPs to SNARGs. In the first part, univariate sumcheck and low degree testing play an important role in the holographic IOP. In the second part, we use Merkle tree scheme and Fiat-Shamir heuristic to achieve succinctness and non-interactivity.
Autonomous Cost-Effective Robotic Exploration and Mapping for Disaster Reconnaissance

Natural calamities like earthquake and tsunami wreak havoc on life of so many people and the infrastructure, thus saving people who are trapped in the affected region has been a very difficult task for rescuers. This work aims to develop a system which will ease this task for the rescuers by using behavior based multiple robots that will explore, map, localize, path plan and navigate in an unknown environment. Various incarnations of pre-existing meta-heuristic algorithms are used to develop a cost-effective solution in this work. A random walk exploration is used to explore the entire environment effectively which reduces the computational cost and time. To reduce the complexities of mapping, binary occupancy grid is used for the map layout, which is not as efficient as compared to its counterparts but better, where time is the crucial factor. To identify the efficient path to navigate from source to destination positions in the environment, Particle Swarm Optimization algorithm is used in this paper. The last and the most crucial step of the system is navigation towards the destination. By integrating INS and GPS algorithms, the robot can get an almost perfect algorithm for navigation which is not prone to errors. Autonomous robot exploration, mapping and navigation are achieved by integrating the proposed algorithms to work simultaneously. The effectiveness of the proposed algorithms is analyzed through simulations in Webots virtual simulator environment, and the results are presented. Adapting the proposed exploration technique for swarm of robots is a prospective future research direction of this paper.
Development of a Neuro-fuzzy Model of a Polymerizer Reactor

The article proposes an universal algorithm for the synthesis of fuzzy models of industrial control objects using adaptive neuro-fuzzy inference system. A typical polymerizer reactor is used as an example to create a model. The stages of synthesis a fuzzy model, the initial conditions of modeling, the block diagram of the model and the results of simulation modeling are given. A good convergence of the results of the fuzzy model and the initial data is shown. It is shown that the presented algorithm for the synthesis of fuzzy models can be adapted to any control object such as a reactor.
Rayleigh Distribution-based Edge Detection in SAR Images

Edge detection in Synthetic Aperture Radar (SAR) images is challenging task in remote sensing as the images contain significant speckle noise. Many ratio-based edge detectors have been developed in recent years to effectively identify the edges in SAR images. However, an automatic threshold value selection in edge detection is a critical issue. In this paper, a Rayleigh distribution-based edge detection method is proposed for the automatic selection of threshold value. This method is very effective to automatically identify the true edges and reject the false edges. Experiments are performed on different SAR images to verify the effectiveness of the developed method.
Deep Learning based Surveillance system for Tracking unknown Faces and Movements

Security of an area where only authorized people are allowed is one of the crucial issues. Research is constantly going on how to keep track of personnel that are not authorized and are entering the area. Neural networks are being used in many surveillance systems. Deep Learning can also be used for image analysis and pattern recognition. Facial Recognition with great accuracy is one of its applications. In this paper, a deep learning-based model is proposed to keep track of entry of unknown people in any organization by comparing the facial features of every person with those present in our database and saving the records for future verification if required.
Transformer with sequence relative position for continuous sign language translation

Sign language is the main way for the hearing-impaired people, a huge special group, to communicate with others in society. The use of new information technology in sign language recognition and translation is helpful for smooth communication between hearing impaired and healthy people. With the development of the Transformer network and attention mechanism in machine translation, the study has entered a new process. Aiming at the phenomenon of longer-term dependency, based on Transformer, we propose a continuous sign language translation model that incorporates the sequence relative position into the attention mechanism, replacing the original absolute position encoding. Combining with movement characteristics, we use image difference technology to dynamically calculate difference threshold and use image blur detection to adaptively extract key frames. Experimental results on RWTH-PHOENIX-Weather 2014T Dataset verify the effectiveness of the proposed model.
Innovative Practices Track: New Methods for System Level Test of Image Projection and Radar VLSI Systems

OverviewThere are two presentations in this session which describe non-conventional test methods. The first presentation is about testing micro-mirror devices used for imaging and light projection applications. The second presentation is about new directions in testing radar devices. Together they highlight new challenges in testing these systems in the context of the applications that they are incorporated into.
Cross-Border Practice of Intelligent Documentary Image Quality Improvement Algorithm in the Internet Era

Starting from the basic concept of documentary, this paper analyzes some trivial new features and characteristics of documentary along the historical development of documentary. By summarizing these characteristics, it analyzes the shortcomings of documentary clarity. In response to this problem, an algorithm model for image quality improvement of ultra-high-definition display devices is envisaged. Based on the deep network model, combined with super-resolution reconstruction technology and motion picture compensation technology, the perceptual quality of the video source is improved to match the ultra-high-definition display. High-definition display equipment, making the details and colors of the display screen infinitely close to the real.
Rediscovered: The Shabti of Senseneb, a Museum Provenance Study

In 2021 the Chau Chak Wing Museum, University of Sydney, received a new donation of an Egyptian shabti in a model coffin. It was given to the donor’s grandfather by the Sixth Earl of Carnarvon in gratitude for the safe return of artifacts accidentally left in an item of furniture sold through a London auction house in the mid-1920s. The shabti was originally discovered by the Earl’s father during his excavations of the Theban Valley, in the tomb of Tetiky, an important administrator of the early New Kingdom. This paper presents the curatorial investigation of the artifact’s provenance, contextualizes the artifact as part of an assemblage that is dispersed globally in museum collections, and determines how it came to be hidden in a cupboard. This paper hopes to serve as a case study in recontextualizing archaeological artifacts as part of dispersed assemblages in multiple collections through detailed interconnected provenance studies.
Hyper-Hyponymic Relations in Computer Linguistics Terminology

The aim of the study is to reveal the features of gender-aspect relations in the terminology of computer linguistics on the basis of their comprehensive analysis. The article researches the structural organization of computer linguistics terms based on the processes of hyper-hyponymy, shows the relationship between the terms within the framework of privative and equivalent oppositions. The scientific novelty of the work lies in the fact that a classification of gender-aspect relations in the terminology of computer linguistics formed according to various characteristics has been compiled for the first time. As a result of the study, the types of hierarchical structures have been identified representing the patterns of hyper-hyponymic relationships between the elements of the terminological system of computer linguistics.
Real-Time Handgun Detection in Surveillance Videos based on Deep Learning Approach

Closed-circuit television (CCTV) systems are vital to prevent security threats to public safety. Nowadays, the use of weapons causes a big security threat in public places and it creates a lot of violence. The quick detection of handguns in public places is very important to avoid or reduce risks. Some places in the world have a lot of crimes that are committed with handguns, even though guns aren't allowed. Closed-circuit television (CCTV) has been used a lot to keep an eye on these situations; it's time to keep an eye on the images. This task is typically carried out by a human, who is more prone to forgetting them due to fatigue or being distracted by something else. Detecting handguns on their own is very important in catching people who use guns to do bad things. Deep-learning based object detectors can't find handguns of different sizes in an unrestricted area. In this work, deep learning techniques are used for automatic detection of handguns in an unconstrained environment through video surveillance footage.