Reviewerly/exHarmony huggingface dataset:
Data Augmentation Technique to Expand Road Dataset Using Mask RCNN and Image Inpainting

Data augmentation is a common technique to increase dataset. This study proposes a method that generates image data of empty road dataset. Data-driven approach is a popular approach to training an intelligent model. Various existing researches or new researches that require the empty road to train their data-driven model will be benefited by using this method. The method uses image inpainting to remove vehicles from input images. The model detects the vehicle using Mask RCNN and using image inpainting the model removes the detected object. To increase the efficiency of the method morphological transformation was used. The morphological transformation is also adjusted depending on the output and may need several iterations to ensure better results. The experiment results uphold the efficiency of the method.
Design and Implementation of Remote Intelligent Service Platform for Railway Signal System

Nowadays, as an important development direction in the railway field, railway informatization construction has injected strong impetus into supporting the rapid development of China Railway. As a key advanced control technology for train operation, railway signal system requires the development and maintenance of the full life cycle. Therefore, a remote intelligent service platform for railway signal system is proposed. The platform has the characteristics of multiple concurrency, low latency and high intelligence. It uses virtualized cloud computing technology to support multiple users online, uses 5G and encryption technology for remote authorization login, and uses artificial intelligence technology for intelligent training and problem solving. The platform is of great significance for supporting scientific deployment in the railway field, transforming traditional office mode, and strengthening the maintenance of full life cycle of railway signal system.
Volumization and Inverse Volumization for Low-Altitude Airspace Geofencing

The number of Unmanned Aircraft System (UAS) operations is expected to increase dramatically with emerging applications in package delivery, search and rescue, recreation, photography, and surveillance. UAS Traffic Management (UTM) has been proposed to safely and efficiently manage low-altitude airspace. Airspace geofencing is a key capability for safe flight. Geofenced airspace volumes can be allocated to safely contain compatible UAS flight operations within a fly-zone (keep-in geofence) and assure avoidance of no-fly zones (keep-out geofences). This paper proposes operational volumization and inverse volumization algorithms to support airspace geofence management for UTM. Layered polygon geofence volumes with constant-altitude floors and ceilings enclose user-input waypoint-based flight plans, and a family of conflict-free flight planning solutions is proposed for inverse volumization. Monte Carlo simulations statistically validate our geofencing algorithms and identify corner cases.
What Users Think of COVID-19 Contact-Tracing Apps: An Analysis of Eight European Apps

We mine insights from the user reviews of COVID-19 contact-tracing apps from eight European countries to determine what end users think of the apps and the main problems that have been reported.
VehiPose: a multi-scale framework for vehicle pose estimation

Vehicle pose estimation is useful for applications such as self-driving cars, traffic monitoring, and scene analysis. Recent developments in computer vision and deep learning have achieved significant progress in human pose estimation, but little of this work has been applied to vehicle pose. We propose VehiPose, an efficient architecture for vehicle pose estimation, based on a multi-scale deep learning approach that achieves high accuracy vehicle pose estimation while maintaining manageable network complexity and modularity. The VehiPose architecture combines an encoder-decoder architecture with a waterfall atrous convolution module for multi-scale feature representation. Our approach aims to reduce the loss due to successive pooling layers and preserve the multiscale contextual and spatial information in the encoder feature representations. The waterfall module generates multiscale features, as it leverages the efficiency of progressive filtering while maintaining wider fields-of-view through the concatenation of multiple features. This multi-scale approach results in a robust vehicle pose estimation architecture that incorporates contextual information across scales and performs the localization of vehicle keypoints in an end-to-end trainable network.
English Teaching Ability Evaluation Based on Fuzzy K-means Clustering Algorithm

Abstract With the continuous progress of my country’s social economy and the continuous improvement of people’s material living standards, people’s demand for teaching standards is increasing. In recent years, many higher vocational colleges in our country have hired many foreign teachers when opening English classes. Higher vocational colleges are different from traditional schools. They pay particular attention to cultivating students’ skills. Using foreign teachers in English classrooms can enable students to understand English knowledge and improve their later use ability. However, in English classroom teaching, too many foreign teachers have brought a lot of problems to the teaching ability evaluation system of English foreign teachers and caused difficulties to the teaching management of higher vocational colleges. Based on the fuzzy K-means clustering algorithm, this paper has launched a series of research and discussion on the evaluation of English teaching ability. In the context of the modern big data era, in order to improve the teaching ability of English teachers, improve the quality of college teachers, and provide students with better teaching services, teachers can start with the main factors that influence and influence the improvement of college English teaching ability. In this paper, by designing a college English teaching ability evaluation system, based on the establishment of the constraint parameter index analysis model, the ability evaluation of the big data information model is carried out, and the ability constraint function information is extracted. A combination of K-means clustering algorithms, clustering and integrating proficiency index parameters, completing the preparation of an educational resource allocation plan, and achieving scientific evaluation of the English language ability. The experimental results show that the use of the K-means fuzzy clustering algorithm to evaluate the English language proficiency has an excellent information fusion analysis capability. This improves the accuracy of educational ability evaluation and the efficiency of educational resources.
Meta Analysis of Deep Learning Models for Doodle Recognition

Digital life has augmented the human horizons, code-driven systems have spread to more than half of the world’s inhabitants owing to ambient information and connectivity. Successful combination & integration of human intelligence and machine, also known as Artificial Intelligence continues to impact the future of virtually every industry. From the Harappan civilization till the 21st century, visuals have played a crucial role in our life. The use of hand-made sketches is both frequent and persistent, which empowers human beings with greater retention and the free exchange of ideas and information. This mode of communication is both effective and helpful as witnessed across various communities. Considering the noisiness of the drawings, as the illustrators are often untrained and focus on ideas rather than the technicalities of art, doodle recognition is considered to have significant consequences in computer vision, Natural Language Processing, digit recognition, pattern recognition, and speech recognition. In this paper, we aim to implement various techniques of deep learning to analyze and classify doodles into predefined labels and estimate their efficacy to eventually improve classification accuracy. We have used data from the Google Quick Draw dataset to train and test the various Deep learning Models.
Hand Gesture Recognition System using Modified SVM and Hybrid Ensemble Classifier

Hand Gesture Recognition (HGR) methods have gained tremendous interest in the past few years. The technique of HGR allows humans to connect with the system and interact naturally, thereby avoiding the involvement of any mechanical amenities. Automatic control of home appliances in smart home is an important application of a HGR system. In this paper, we propose a new HGR system using Speeded Up Robust Feature (SURF) as the feature descriptor. Bag of Feature (BoF) algorithm is employed to generate visual histogram of the SURF features and to generate a unified vector by mapping to the visual vocabulary. The initial stage of classification is performed by the proposed modified Support Vector Machine (SVM) classifier. In the second stage a classifier fusion model called as hybrid ensemble classifier obtained by combining K-Nearest Neighbour (KNN) and the modified SVM classifier is used. The experimental results show that the proposed hybrid ensemble and the modified SVM classifier provides better results compared to the individual classifiers.
NASGuard: A Novel Accelerator Architecture for Robust Neural Architecture Search (NAS) Networks

Due to the wide deployment of deep learning applications in safety-critical systems, robust and secure execution of deep learning workloads is imperative. Adversarial examples, where the inputs are carefully designed to mislead the machine learning model is among the most challenging attacks to detect and defeat. The most dominant approach for defending against adversarial examples is to systematically create a network architecture that is sufficiently robust. Neural Architecture Search (NAS) has been heavily used as the de facto approach to design robust neural network models, by using the accuracy of detecting adversarial examples as a key metric of the neural network’s robustness. While NAS has been proven effective in improving the robustness (and accuracy in general), the NAS-generated network models run noticeably slower on typical DNN accelerators than the hand-crafted networks, mainly because DNN accelerators are not optimized for robust NAS-generated models. In particular, the inherent multi-branch nature of NAS-generated networks causes unacceptable performance and energy overheads.To bridge the gap between the robustness and performance efficiency of deep learning applications, we need to rethink the design of AI accelerators to enable efficient execution of robust (auto-generated) neural networks. In this paper, we propose a novel hardware architecture, NASGuard, which enables efficient inference of robust NAS networks. NASGuard leverages a heuristic multi-branch mapping model to improve the efficiency of the underlying computing resources. Moreover, NASGuard addresses the load imbalance problem between the computation and memory-access tasks from multi-branch parallel computing. Finally, we propose a topology-aware performance prediction model for data prefetching, to fully exploit the temporal and spatial localities of robust NAS-generated architectures. We have implemented NASGuard with Verilog RTL. The evaluation results show that NASGuard achieves an average speedup of 1.74× over the baseline DNN accelerator.
Succinct Encoding of Binary Strings Representing Triangulations

Abstract We consider the problem of designing a succinct data structure for representing the connectivity of planar triangulations. The main result is a new succinct encoding achieving the information-theory optimal bound of 3.24 bits per vertex, while allowing efficient navigation. Our representation is based on the bijection of Poulalhon and Schaeffer (Algorithmica, 46(3):505–527, 2006) that defines a mapping between planar triangulations and a special class of spanning trees, called PS-trees . The proposed solution differs from previous approaches in that operations in planar triangulations are reduced to operations in particular parentheses sequences encoding PS-trees. Existing methods to handle balanced parentheses sequences have to be combined and extended to operate on such specific sequences, essentially for retrieving matching elements. The new encoding supports extracting the d neighbors of a query vertex in O ( d ) time and testing adjacency between two vertices in O (1) time. Additionally, we provide an implementation of our proposed data structure. In the experimental evaluation, our representation reaches up to 7.35 bits per vertex, improving the space usage of state-of-the-art implementations for planar embeddings.
Prism-based solar system optimization adopting stochastic light demands

Solar energy is considered as one of the most propitious alternatives among various renewable resources to preserve the environment and attain sustainable energy. However, Stochastic light demand in a building and sunlight variations increases the difficulties of installing a solar system and the light transmission. This paper proposes the optimal allocation model for a prism-based solar concentrator system to collect sunlight and satisfy lighting. A bi-layer stochastic optimization model is presented where the upper layer will maximize the profit of the energy provider by coordinating the prism allocation design and sunlight assignment. The lower layer can facilitate an affordable budget for the light user to minimize the expenditure. The model is solved using a non-sorting genetic algorithm to simultaneously determine the optimal prism installation and light transmission route. The contribution and novelty of this study pivots on proposing the joint optimal system design for the sunlight prism installation and sunlight delivery plans for a prism-based solar concentrator system. The present model resolved the difficulties of installing a solar concentrator system and scheduling the light transmission against stochastic light demand in a building.
Analyze and develop a model for sentimental reviews of e-government services using deep learning algorithms with CNN framework

Nowadays the government can introduce many new schemes through online and uploaded in their official web portal. Publics can able to access and avail those facilities on internet by reading news and notifications of such schemes. In order to improve their governance the reviews of the public will be very significant. Since these reviews will help the government to take better decisions. By achieving this, the government may get peoples reviews about such schemes. In the existing system reviews such as manual, oral and somewhere online modes were available for facilitating e-government services. But Artificial Intelligence based techniques like facial recognition and sentimental reviews of the public is not incorporated in the current scenario. So in order to facilitate the government to provide better decision the software based deep learning algorithm called Convolution Neural Networks (CNN) is implemented to analysing the sentimental reviews of e-Government services. In this framework three models were developed to implement a concept of multiple CNN models in which first model can recognize people’s hand written digits, second model can detect sentiments from text sentence which can be given by people about government services, third model can detect sentiment from person face image. This paper involves analyze some of the applications to make the user friendly. In order to make the application more accessible and navigations the well-suited browser, need to be selected. The navigations could be done from one screen to the other in sequence and also help the users to reduce the typing action. Using this method, we can reduce the human interventions in the analysing the reviews and consolidated the result which specifies the overall conclusion of the service.
Immersive Technology as A Tool for Sustainable Architecture

Abstract Immersive technology in architecture has begun to be used in the design process. Immersive technology is a computing technology that blurs the boundaries between the real world and the digital world or the simulated world, so that users can feel an atmosphere similar to the real world. Immersive technology includes Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). This paper discusses how immersive technology can be used as a tool for analyzing sustainable architecture. Methodology in this paper uses a literature study research method, the data obtained are compiled, analyzed, and concluded, to get conclusions about the use of immersive technology in architecture. From several literature studies conducted, it can be concluded that immersive technology can be used as a tool to improve architectural sustainability.
A Light Chaotic Encryption Algorithm for Real-Time Video Encryption

With the increasing application of video, security problems caused by video leakage also become more prominent. Due to the randomness, ergodicity, determinism, and sensitivity to initial conditions of chaotic systems, it is possible to generate a large number of uncorrelated pseudo-random chaotic sequences, so chaotic encryption has become an important research direction for video security. This chapter improves on the basis of chaotic encryption algorithm based on logistic mapping and proposes an optimized chaotic encryption algorithm. The algorithm introduces tent mapping, which complements the logistic mapping to achieve superiority, thereby obtaining better performance than the two algorithms. On this basis, a prototype system for real-time video transmission was designed and implemented to verify the feasibility of the encryption algorithm.
Design and Development of a Prototype Model for Monitoring of Automatic Platform with Train Arrival Using IoT

In the recent survey it was noticed that the most disadvantage and challenging thing in Indian railways is climbing up the overhead steps for the old and physically challenged people. Second thing is the collision of the overhead bridges due to the large number of crowd over it. To avoid overhead bridge some people will jump on to the track and runs towards the other platform, which is very risky. So, our main aim of this project is to create a temporary bridge between two platforms. The motivation to create this project is to avoid the overhead bridges for old and physically challenged people. Here we are using the new concept of movable temporary railway platform. For the successful operation of the system we use IR sensors, H-bridge, microcontroller and a speaker. IR sensors are used to sense the arrival and departure of the train. Microcontroller is used to operate the entire system. H-bridge is used to rotate the motor in forward and backward motion. Motor is used to rotate the temporary platform. After each and every movement of platform, it is updated and displayed through blynk application. And there will an announcement through speaker.
Digital topology with fixed point

The theory of fixed point is considered a very relevant and powerful tool in various studies for the last 60 years. In particular, the concept of fixed point and corresponding techniques have been applied in wide ranges of Physics, Chemistry, Biology, Engineering, Game Theory, and Economics. Fixed Point theory is itself an exquisite mixture of analysis, topology, and geometry. The present paper deals with some random fixed results in complete DMS forα-β-ψ-expansive mapping. These results can be used for image processing and automatic control system in specified conditions. The obtained results satisfies well known basic results of fixed point theory.
A full-reference laparoscopic video quality assessment algorithm

In this paper, we introduce a full-reference quality assessment model for laparoscopic videos. The perceived quality of medical imaging in general is of upmost importance, as visual degradations may lead to severe negative impacts on diagnostic accuracy. Laparoscopy, also known as keyhole surgery, is a camera-assisted operation performed in the abdomen or the pelvis of the patient. Unlike the conventional utilization of telescopic rod lens systems, digital laparoscopy uses a miniature digital camera at the end of the laparoscope, and therefore, the surgeon fully relies on the quality of the medical video. In our scientific contribution, we utilize different image quality measures for each frame of the laparoscopic videos. We implement a regression neural network architecture on the frame-level features with the associated mean opinion score as labels. Finally, we calculate the average of the predicted frame-level scores to compute the overall quality score. The performance of the proposed model is evaluated on the well-known LVQ laparoscopic video dataset. The evaluation results confirm that our model is competitive with the state-of-the-art 2D full-reference and no-reference supervised algorithms. Furthermore, the model demonstrates robust performance across all distortion types of the dataset.
Design of basic logic gates using optical threshold logic

Threshold Logic Gate (TLG) is a potential alternative of Boolean Logic circuit. It is one of the fundamental components of neural network. An Optical Threshold Logic Gate (OTLG) has been designed in this paper, using a reflector telescopic system and a resonant Fabry-Perot cavity. Threshold logic gate consists of a weighted summing unit followed by a comparator. In the proposed OTLG, the weight of the input signal is varied using a polarizer-analyser combination. The threshold value, which is the reference of the comparator, is applied into the Fabry–Perot cavity with the help of a Beam splitter . AND, OR and NOT gates are implemented by setting the threshold values of the proposed OTLG.The system has been modelled analytically and studied using MATLAB and FINESSE. The simulation results for OTLG based Basic Gates are presented here.
Tweet Sentiment Analysis for Predicting the Symptoms Effect Level Regarding COVID-19

From the end of 2019, numerous comments and opinions relating to the COVID-19 pandemic have been posted on Twitter. The number of opinions rapidly increased since the countries began implementing social isolation and reduction. In these comments, users often express different emotions regarding COVID-19 signs and symptoms, the majority of which are sadness and fear sentiments. It is important to determine the symptom effect level for the emotions of symptomatic persons based on their opinions. However, no study analyzes the tweets' sentiment related to the COVID-19 topic to predict the symptoms effect level. Therefore, in this study, we present a method to predict the symptoms effect level based on the sentiment analysis of symptomatic persons according to the following steps. First, the sentiments in tweets are analyzed by using a combination of the text representation model and convolutional neural network. Second, a topic modeling model is built based on the latent Dirichlet allocation algorithm to group symptoms into small clusters that conform to sadness and fear sentiments. Finally, the symptom effect level is predicted based on the probability distribution of the symptoms in each sentiment cluster. Experiments using tweets promise that the proposed method achieves significant results toward the accuracy and obtained information.