Reviewerly/exHarmony huggingface dataset:
Applications of Deep Learning for Vehicle Detection for Smart Transportation Systems

The most important task in designing an intelligent transportation system is the robust and efficient detection of all the vehicles presents in the frame sequence. As there is huge development going on in terms of computer vision and deep learning techniques, a lot of new techniques and algorithms have been proposed for doing the same. Also with the high accessibility of video data, the training has become easier and more accurate. In this paper, a brief review of the literature in the area of vehicle detection and classification is presented. This paper also discuss the different challenges faced in on-road vehicle detection due to weather and driving conditions, camera illuminations, and occlusion. Then, it discuss various methods proposed for overcoming all these challenges using deep learning. Future scope is also presented so that efforts may be focused toward making more robust smart systems.
Pressure, Temperature and Humidity Monitoring System Using the Arty Platform

Abstract The paper aims to present the usefulness of Field Programmable Gate Array - based systems, the elasticity of hardware design for reuse, and the use of hardware and software resources. The proposed measurement system is a starting point for the design of a weather station capable of interpreting the variation of the size of atmospheric pressure, temperature, and humidity, which could make local weather predictions.
Trust in Google - A Textual Analysis of News Articles About Cyberbullying

Aim/Purpose: Cyberbullying (CB) is an ongoing phenomenon that affects youth in negative ways. Using online news articles to provide information to schools can help with the development of comprehensive cyberbullying prevention campaigns, and in restoring faith in news reporting. The inclusion of online news also allows for increased awareness of cybersafety issues for youth. Background: CB is an inherent problem of information delivery and security. Textual analysis provides input into prevention and training efforts to combat the issue. Methodology: Text extraction and text analysis methods of term and concept extraction; text link analysis and sentiment analysis are performed on a body of news articles. Contribution: News articles are determined to be a major source of information for comprehensive cyberbullying prevention campaigns. Findings: Online news articles are relatively neutral in their sentiment; terms and topic extraction provide fertile ground for information presentation and context. Recommendation for Researchers: Researchers should seek support for research projects that extract timely information from online news articles. Future Research: Refinement of the terms and topics analytic model, as well as a system development approach for information extraction of online CB news.
PXR2020: Re-Seeing the Possibilities of Theatre in Virtual, Augmented, and Mixed Reality

What new insights and opportunities does virtual reality (VR) offer to theatre and performance? In turn, what potential might theatre offer to VR creators? This potentially fruitful cross-pollination was precisely what prompted host organizations Single Thread Theatre Company and Electric Company Theatre to program the inaugural Performance and XR Symposium in October 2020, a conference designed to foster and explore the relationship between theatre and XR (a catch-all term for media created with virtual, augmented, and mixed reality). The experience of the conference was one of re-seeing the possibilities of performance in a new media form: discussions around access, spectatorship, and ethics—topics familiar to and well trodden by theatre artists and scholars—were reinvigorated in the context of virtual reality.
Research Progress Of Bolt Loose Detection Technology Based On Convolutional Neural Network

Convolutional neural networks have powerful generalization and expression capabilities for extracting deep-level features of images. Their emergence has further promoted the development of artificial intelligence, and greatly improved the image recognition and detection effects and computer operating speed. With the advent of the global intelligent era, image recognition and detection technology based on convolutional neural networks has emerged in various fields of workpiece detection, and it has also brought challenges for professionals in this field to optimize intelligent algorithms. In this paper, the recognition process of the convolutional neural network and the current research status of the convolutional neural network in the recognition of bolt looseness images are described and prospected.
Internet of Things: Shaping Healthcare during COVID-19 Pandemic

Outbreak of coronavirus (COVID-19) has resulted in lockdowns and many healthcare systems around the world getting overwhelmed. In time of pandemic, Internet of Things (IoT) and, especially, Internet of Medical Things (IoMT) can be leveraged to ease the strain put onto local hospitals and healthcare providers through preventive medicine and telemedicine. This paper offers state-of-the-art review of current use of Internet of Things within healthcare systems in the context of tackling COVID-19. It also suggests future research for battling COVID-19 through IoT, using it for purposes such as social distancing, contact tracing, health monitoring, and early diagnosing.
Interpreting high negation in Negative Interrogatives: the role of the Other

Abstract This paper presents an account of the peculiar properties of Negative Interrogatives (NI). In uttering Don’t you speak Italian? , the speakers is biased towards the underlying positive proposition, expects a positive answer, and seeks a confirmation of that expectation from the hearer. What’s more, NI involves uncertainty with respect to p , a novel observation that we bring to the fore by comparison with epistemic modals. Using a framework by which speech-acts are derived by the two operators Speaker and Other representing sources of information, the high negative is assigned to the Other projection, the positive proposition being left under the responsibility of the Speaker. Thus, the NI is motivated by evidence contradicting the speaker’s belief, which is expressed by the negation that is attributed to another source and that therefore has full referential force. Because the questions is addressed by the Speaker in spite of evidence that <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" overflow="scroll"><m:mrow><m:mo>¬</m:mo><m:mi>p</m:mi></m:mrow></m:math> ${\neg}p$ , NI displays uncertainty as to p and gives rise to a confirmation request. With direct mapping from syntax to semantics and pragmatics, the parsimonious account thus explains the morphosyntactic and interpretative properties of Negative Interrogatives.
Research on Decoupling Motion Path Planning of Two Manipulators of Live Working Robot Based on Hybrid RRT Algorithm

Double manipulator path planning is the basis for overhead line live working robot to complete the task. The previous dual manipulator motion path planning method has some problems, such as low success rate, low convergence speed, unstable guidance, and easy to lead to path discontinuity in complex environment. Therefore, this paper proposes a decoupling motion planning method for two manipulators of live working robot based on hybrid RRT (Rapidly-Exploring Random Trees) algorithm. The hybrid RRT algorithm combines target offset RRT algorithm, bidirectional RRT algorithm and dynamic expansion RRT algorithm. MATLAB robot toolbox is used to simulate the motion path planning of 6-DOF double manipulator of overhead line live working robot. The results show that the method proposed in this paper can realize the loose coupling and efficient collision free motion planning of double manipulator of live working robot.
Smart Contracts Implementation Based on Bidirectional Encoder Representations from Transformers

The distribution and immutability properties of blockchains made it possible to use them in various fields, such as Supply Chain, finance and health. The automation of the creation and execution of transactions in a blockchain in a decentralized and transparent manner is realized through Smart Contracts programming codes. This paper presents the implementation of Smart Contracts in specific manufacturing Supply Chains and discusses their life cycle and impact on the Supply Chain management. The presented application deals with the possibility of transforming natural language contracts of a given Supply Chain to automated Smart Contracts that makes the Supply Chain management faster and safer. A first solution is proposed based on Bidirectional Encoder Representations from Transformers (BERT) model and limited to the implementation of Smart Contracts of the Supply Chain legal contracts. Also described here is the ways of extracting contract elements from legal contracts by applying the BERT Deep Learning method on annotated contract dataset of a corpus of 13000 annotations over 510 contracts.
Prediction of Optimal Solvers for Sparse Linear Systems Using Deep Learning

Solving sparse linear systems is a key task in a number of computational problems, such as data analysis and simulations, and majorly determines overall execution time. Choosing a suitable iterative solver algorithm, however, can significantly improve time-to-completion. We present a deep learning approach designed to predict the optimal iterative solver for a given sparse linear problem. For this, we detail useful linear system features to drive the prediction process, the metrics we use to quantify the iterative solvers' time-to-approximation performance and a comprehensive experimental evaluation of the prediction quality of the neural network. Using a hyperparameter optimization and an ablation study on the SuiteSparse matrix collection we have inferred the importance of distinct features, achieving a top-1 classification accuracy of 60%.
A Novel Model of SAR Image Edge Enhancement and Despeckling

There have been analyzed and invented multifarious models in past decade for SAR (Synthetic-Aperture Radar) photo edge enhancement as well as for despeckling. But certain existing models and numerous set of rules are not capable to provide higher accuracy as demanded in modern systems due to the technological alteration constantly and have numerous limitations that demand more attention in present times for better photo edge enhancement and despeckling in more pragmatic manner. In this present investigation a novel model is build that jointly offers better results for SAR photo edge enhancement as well as despeckling in comparison to existing approaches. In this proposed model, the NLM (Nonlocal-Means) filter was utilized in order to handle the issues related to the diverse images despeckling. Because of mammoth dimensions numerous pictures, certain pictures demands the segmentation of the pictures prior to processing. But effective segmentation of the SAR photos has become challenging due to multifarious factors namely higher speckle noise, lower contrasts and many more. In order to, efficiently resolve existing threats found in prior art this model was developed for pragmatic solutions and better accuracy. Although, multifarious investigators have worked and developed numerous models as well as set of rules in last decade, but still there are gigantic likelihoods to deep dive and explore more in this domain for novel methods due to rapid alteration in the technologies.
Gender Identification Using Ensemble Linear Discriminant Analysis Algorithm Based on Facial Features

A wide range of applications rely on the capacity to extract information about people from images. Person identification for the surveillance or access control, gender and age estimation for constructing user models, and facial expression recognition are some of the most common applications that could provide relevant data for evaluating man–machine interfaces. In addition to the previously listed recognition tasks, early works were largely associated with psychological analysis, a method in which humans identify gender from faces studied. Our proposed system presents gender and age classification based on facial images using dimensionality reduction technique, linear discriminant analysis (LDA), along with subspace ensemble learning. Our experiments and results show that LDA and ensemble learning techniques can be used for estimating gender and age.
Hybrid Whale Optimization Algorithm with Differential Evolution and Chaotic Map Operations

Original Whale Optimization Algorithm (WOA) is a meta-heuristic optimization one generated by imitating whale hunting. It has achieved good performance in many optimization problems. However, WOA is prone to lose the diversity of solutions in the iterative process and fall into local optima, which makes the optimization process stagnate. This work aims to solve this problem by combining the chaos theory and the idea of a Differential Evolution (DE) algorithm to enhance the randomness and diversity of solutions generated by WOA, so as not to fall into a locally optimal solution in the iteration as far as possible. Specifically, this work proposes a Chaotic Differential WOA (CDWOA), which is tested with 10 benchmark functions, and final optimization results are obtained to demonstrate its performance. In addition, experiments with higher dimensions are conducted to evaluate the performance of CDWOA. The running time of CDWOA for each problem is tested to demonstrate the efficiency of CDWOA. The comparison of experimental results shows that it outperforms its typical peers in solving both low-dimensional and high-dimensional problems of single-objective optimization.
Tamil Language Handwritten Document Digitization and Analysis of the Impact of Data Augmentation Using Generative Adversarial Networks (GANs) on the Accuracy of CNN Model

Recognition of handwritten documents is an essential part of today’s world for various reasons such as analysis, data extraction, etc. Recognition of handwritten in Indian regional languages, for, e.g., Tamil character is very difficult, due to variations in size, style, and orientation angle. Limited availability of handwritten character dataset makes it difficult to achieve high accuracy for all the characters present in the language. In this paper, we propose a deep learning approach to recognize and classify handwritten characters of the Tamil language. The proposed system can be divided into five phases: expansion of the dataset and data augmentation with the help of Generative Adversarial Networks (GAN), preprocessing of the input image to reduce the noise, segmentation of the characters, feature extraction, and classification of the character done using Convolutional Neural Networks (CNN). We have achieved an accuracy of 94.03% and with data augmentation using GAN, an accuracy of 97% was achieved. The proposed system, along with the recognition of characters, also converts the image of the handwritten document into an editable word document or a PDF file.
Comparative performance analysis of AES encryption algorithm for various LVCMOS on different FPGAs

Purpose Privacy and security of personal data is the prime concern in any communication. Security algorithms play a crucial role in privacy preserving and are used extensively. Therefore, these algorithms need to be effective as well as energy-efficient. Advanced Encryption Standards (AES) is one of the efficient security algorithms. The principal purpose of this research is to design Energy efficient implementation of AES, as it is one of the important aspects for a step toward green computing. Design/methodology/approach This paper presents a low voltage complementary metal oxide semiconductor (LVCMOS) based energy efficient architecture for AES encryption algorithm on Field Programmable Gate Array (FPGA) platform. The experiments are performed for five different FPGAs at different input/output standards of LVCMOS. Experiments are performed separately at two frequencies (default and 1.6 GHz). Findings The comparative study of total on-chip power consumption for different frequency suggested that LVCMOS12 performed best for all the FPGAs. Also, Kintex-7 Low Voltage was found to be the best performing FPGA. At 1.6 GHz frequency, the authors observed 55% less on-chip power consumption when switched from Artix-7 with LVCMOS33 (maximum power consuming combination) to Kintex-7 Low Voltage with LVCMOS12. Mathematical models are developed for the proposed design. Originality/value The green implementation of AES algorithm based on LVCMOS standards has not been explored yet by researchers. The energy efficient implementation of AES will certainly be beneficial for society as it will consume less power and dissipate lesser heat to environment.
Recycling forward and backward frequency-multiplexed modes in a waveguide coupled to phased time-perturbed microrings for low-footprint neuromorphic computing

Optical structures can serve as low-power high-capacity alternatives of electronic processors for more efficient neuromorphic computing, but can suffer from large footprints and weak scalability. In this work, properly phased time-perturbed microrings side-coupled to a waveguide are utilized to realize a compact processor for linear transformations. We build up a synthetic frequency dimension to provide sufficient degrees of freedom, where the linear time-varying structures enable the linear intermixing and transformation of frequency-multiplexed data. Moreover, non-reciprocal and asymmetric flow of data in the forward and backward modes, due to phasing of the perturbations, helped to build up another synthetic dimension and to avoid physically repeating the processing elements, thus enabling a much more compact and scalable linear processor.
PR Wallet-Based Blockchain Access Protocol to Secure EHRs

With the increase in the amount of data generated, especially in the healthcare sectors, one demands a revolution in the way data and records are being handled in this sector. The patients demand to gain instant access to their health data whenever the need calls for. Many applications have promised their clients for such instant access. But the centralized systems driving the healthcare industry, which comes handy to these instant access data, pose yet another concern for privacy and security of these patients' health records. These extremely sensitive health data risks are being compromised by malicious intents and hence further attract cybercrimes. Legacy systems concerned with all the record-keeping and tracking of the patient's data are centralized and often need some third-party access to the data. These systems are also confined to an institution or an authority governing them, and hence, it becomes difficult to share this sensitive information with some external concerned legal authorities, even in the case of any emergency. Blockchain, simply defined as a distributed ledger, has the characteristics to provide a decentralized, privacy-preserving environment in which records can be searched and shared easily with the concerned authorities. Although many institutions are contributing to developing blockchain as a means to secure electronic health records (EHRs), there have been discussions on its usability, especially like how elderly people or people with certain illnesses will be able to authorize institutions. Hence, in this chapter, to address this issue, we introduce a patient record (PR) wallet and a new protocol to access the hybrid blockchain. We shall further introduce a similar application that addresses some of the common issues in this modal.
Path Analysis on Writing Anxiety, Writing Attitude, Language Awareness, and Writing Achievement of University Students

During the teaching and learning process in writing context, EFL learners have their own intrinsic problems. They tend to experience some psychological tensions such as writing anxiety, writing attitude and language awareness. In line, some academical issues they need to handle in writing context are text organization, grammar, spelling, punctuation and ideas development. The aim was to find out the direct and indirect contribution among the variables. Thus, writing anxiety and writing attitude were the exogenous variable, then language awareness as mediated variable, while the writing achievement as the endogenous variable. This study applied the combination of multiple regression and mediated model as a part of Path Analysis. There were four instruments used, namely SLWAI, ATWQ, LALQ, and TOEFL Independent Writing Test. These instruments were fulfilled by 100 participants of fourth semester English Department students. The data was collected online through Google Form, Google Meet and personal chat on WhatsApp. The result shows that there was low significant direct contribution of writing anxiety toward writing achievement. Meanwhile, there was no significant direct contribution of writing attitude toward writing achievement. In addition, there was a significant direct contribution of language awareness toward writing achievement. Besides, the result of indirect contribution revealed, there was no indirect contribution of writing anxiety through writing attitude toward writing achievement. Then, the indirect contribution of writing attitude through language awareness toward writing achievement was found out significant. Lastly, it was revealed that there was no indirect contribution of writing anxiety through language awareness toward writing achievement.
Design of two-stage federal learning incentive mechanism under specific indicators

As a privacy-focused distributed machine learning, federated learning can not only train models effectively but also prevent data sets from being leaked easily. However, like crowdsensing perception and other technologies, participants often lack the motivation to learn and the quality of participation is not high. Therefore, this paper mainly designs a two-stage federal learning incentive mechanism based on the Stackelberg game model under a specific model accuracy index. Firstly, we combine data quality and data quantity to construct a federal learning incentive mechanism model under specific indicators. Then, we conduct a two-stage Stackelberg game analysis on the incentive mechanism model based on the utility function construction of the server platform and data island. In the first stage, the platform server is the leader and the data island is the follower. The second stage is a Nash equilibrium game between data islands. Finally, we construct the objective function of the server platform and data island, namely utility maximization, deduce the optimal equilibrium solution of the two-stage game, and determine the optimal strategy of the platform server and data island.