Reviewerly/exHarmony huggingface dataset:
Research Chinese-Urdu Machine Translation Based on Deep Learning

&lt;span&gt;Urdu is Pakistan 's national language. However, Chinese expertise is very negligible in Pakistan and the Asian nations. Yet fewer research has been undertaken in the area of computer translation on Chinese to Urdu. In order to solve the above problems, we designed of an electronic dictionary for Chinese-Urdu, and studied the sentence-level machine translation technology which is based on deep learning. The Design of an electronic dictionary Chinese-Urdu machine translation system we collected and constructed an electronic dictionary containing 24000 entries from Chinese to Urdu. For Sentence we used English as an intermediate language, and based on the existing parallel corpus of Chinese to English and English to Urdu, we constructed a bilingual parallel corpus containing 66000 sentences from Chinese to Urdu. The Corpus has trained by using two NMT Models (LSTM,Transformer Model) and the above two translation model were compared to the desired translation, with the help of bilingual valuation understudy (BLEU) score. On NMT, The LSTM Model is gain of 0.067 to 0.41 in BLEU score while on Transformer model, there is gain of 0.077 to 0.52 in BLEU which is better than from LSTM Model score. Furthermore, we compared the proposed model with Google and Microsoft translation.&lt;/span&gt;
Adaptation of Big Data: an empirical investigation for sustainability of education

The current study aims at developing a framework to assess big data use for education and encompassing the theoretical background of knowledge sharing and diffusion of innovations in the educational environment.This study hypothesizes that age and cultural diversity, and motivators can influence knowledge sharing, whereas the constructs of relative advantage, trialability, complexity, observability would impact innovations.Thus, innovations influence knowledge sharing and would be positively associated with behavioural intention to use big data and sustainability for education.This study utilized a version of knowledge sharing model and Diffusion of Innovations (DOI) theory as the study framework and implemented quantitative approach for data analysis by collecting 494 responses from university students who were elected using stratified random sampling technique.The data were processed using eleven factors to unveil and understand the predictors of big data use for education sustainability.The study adopts the quantitative approach and employs structure equation modelling (SEM) to data analysis.According to the study's findings, age and cultural diversity and motivators significantly determine knowledge management sharing, while relative advantage, trialability, complexity, and observability have a positive impact on innovations.The adoption of innovations, knowledge sharing, and big data are able to capture 78.9% of sustainability phenomenon on education.Further, the study concludes by reporting findings and implications for research and practitioners.
EARTHQUAKE EARLY WARNING AND RAPID RESPONSE SYSTEM BASED ON SMART SEISMIC AND MONITORING SENSORS EMBEDDED IN A COMMUNICATION PLATFORM AND COUPLED WITH BIM MODELS

This paper describes the concept of an innovative, interdisciplinary, user-oriented earthquake warning and rapid response system coupled with a structural health monitoring system (SHM), capable to detect structural damages in real time.The novel system is based on interconnected decentralized seismic and structural health monitoring sensors.It is developed and will be exemplarily applied on critical infrastructures in Lower Rhine Region, in particular on a road bridge and within a chemical industrial facility.A communication network is responsible to exchange information between sensors and forward warnings and status reports about infrastructures' health condition to the concerned recipients (e.g., facility operators, local authorities).Safety measures such as emergency shutdowns are activated to mitigate structural damages and damage propagation.Local monitoring systems of the infrastructures are integrated in BIM models.The visualization of sensor data and the graphic representation of the detected damages provide spatial content to sensors data and serve as a useful and effective tool for the decision-making processes after an earthquake in the region under consideration.
Prior to and in the Course of Covid-19 Pandemic: Exploring Learners’ Experiences of Learning English through Narrative Lens

The language learning largely took place in the classroom, especially prior to Covid-19 pandemic and that this occurrence resulted in simplicity for the learners to learn and interact with each other because they could directly make in contact with their classmates and their teachers. However, since the pandemic developed in March 2020, all the learning process was subsequently altered into online learning. To date, studies on language learning has been undertaken by the researchers both at intra and international level. Despite the existence of these former studies, little work reports the comprehensive picture of language learning experiences prior to and during Covid-19 pandemic. To fill this lacuna, the current article reports on a narrative inquiry of EFL learners’ experiences of learning English prior to and during Covid-19 pandemic. Drawing upon data from a semi-structured interview with two Indonesian EFL learners of the sixth semester majoring in English education program, the finding indicates that EFL learners have sustained multifaceted learning experiences prior to and during the pandemic. They have gone through face-to-face learning prior to the pandemic, asynchronous learning during the pandemic and hybrid learning experience during the Covid-19 pandemic. This article concludes with suggestions for promoting hybrid learning model in the EFL setting, particularly in a higher education during the Covid-19 pandemic.
Investigation of CNN-Based Acoustic Modeling for Continuous Hindi Speech Recognition

Recently, Convolutional Neural Network (CNN) gains more popularity over hybrid Deep Neural Network (DNN) and Hidden Markov Model (HMM) based acoustic models. CNN has the ability to deal with speech signals and it makes appropriate choice for the Automatic Speech Recognition (ASR) system. The sparse connectivity, weight sharing, and pooling allow CNN to handle a slight position shift in the frequency domain. This property helps to manage speaker and environment variations. CNN works well for speech recognition, but it was not appropriately examined for the Hindi speech recognition system. The activation functions and optimization techniques play a vital role in CNN to achieve high accuracy. In this work, we investigate the impact of various activation functions and optimization techniques in the Hindi-ASR system. All the experiments were performed on the Hindi speech dataset developed by TIFR, with the help of the Kaldi and Pytorch-Kaldi toolkit. The experiment results show that the ELU activation function with Rmsprop optimization techniques gives the best Word Error Rate (WER) 14.56%.
Construction of the Open Oral Evaluation Model Based on the Neural Network

According to the problem of low efficiency and low scoring accuracy of the traditional oral language scoring system, this study builds an open oral language evaluation model based on the basic principles of deep learning technology. Firstly, the basic methods of the convolutional neural network (CNN) and long short-term memory (LSTM) neural network are introduced. Then, we combine the convolutional neural network (CNN) and long short-term memory (LSTM) neural network to design an open oral scoring model based on CNN + LSTM, which divides the oral evaluation model into the speech scoring model and text scoring model and makes a specific implementation of two scoring models, respectively. An experimental environment is then built to preprocess the data, and finally, the model built in this study is trained and simulated. The experimental results show that the CNN + LSTM network evaluation model has a better comprehensive scoring performance, higher scoring efficiency, and higher accuracy and has feasibility and practicability.
Exploratory Study of Visual Enhancement to Display Smart Apps on Android Phones for Selasar Imaji Library

The attractive appearance of the system applications on the Android phone is quite important. There is an initial premise that the "Imaji Library" application that the Selasar Imaji Library owns is less attractive to users because it is not attractive. This study aims to explore an attractive appearance based on studies that are filtered to application users. Through Research and Development (R&amp;D) activities, by 1) capturing questionnaire results from users to find out what is less attractive from the application display; 2) study of the search for the preferred visual appearance; 3) designing applications; and 4) application testing to librarians. The results of the exploration activities are in the form of (1) a new prototype design of the "Imaji Library" application display, (2) the development of application features in terms of procurement, processing, tracking, and member and circulation management activities. The implication of this research is helpful for librarians' convenience in managing libraries.
Recent Developments in Path Planning for Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) have demonstrated their effectiveness in performing diverse missions at significantly lower costs compared to the human beings. UAVs have the capabilities to reach and execute mission in those areas that are very difficult for humans to even reach such as forest, deserts, and mines. Integration of the latest technologies including reactive controls, sense and avoid, and onboard computations have strengthened their dominance further in various practical missions. Besides the innovative applications, the use of UAVs imposes several challenges, and one of those challenges is computing a low-cost path for aerial mission by avoiding obstacles as well as satisfying certain performance objectives (a.k.a path planning (PP)). To this end, this chapter provides a concise overview of various aspects concerning to PP including basics introduction of the subject matter, categorization of the PP approaches and problems, taxonomy of the essential components of the PP, performance objectives of the PP approaches, recent algorithms that have been proposed for PP in known and unknown environments, and future prospects of research in this area considering the emerging technologies. With this chapter, we aim to provide sufficient knowledge about one of the essential components of robotics technology (i.e., navigation) for researchers.
Modeling Context-Guided Visual and Linguistic Semantic Feature for Video Captioning

It has received increasing attention to exploiting temporal visual features and corresponding descriptions in video captioning. Most of the existing models generate the captioning words merely depend on video temporal structure, ignoring fine-grained complete scene information. And the traditional long-short term memory (LSTM) in recent models is used as decoder to generate sentences, the last generated states in previous hidden ones are used to do work directly to predict the current word. This may lead to the predicted word highly related to the last generated hidden state other than the overall context information. To model the temporal aspects of activities typically shown in the video and better capture long-range context information, we propose a novel video captioning framework via context-guided semantic features model (CSF). Specifically, to maximum information flow, several previous and future information are aggregated to guide the current token by the semantic loss in the encoding and decoding phase respectively. The visual and linguistic information are corrected by fusing the surrounding information. Extensive experiments conducted on MSVD and MSR-VTT video captioning datasets demonstrate the effectiveness of our method compared with state-of-the-art approaches.
Answer-Aware Question Generation from Tabular and Textual Data using T5

Automatic Question Generation (AQG) systems are applied in a myriad of domains to generate questions from sources such as documents, images, knowledge graphs to name a few. With the rising interest in such AQG systems, it is equally important to recognize structured data like tables while generating questions from documents. In this paper, we propose a single model architecture for question generation from tables along with text using “Text-to-Text Transfer Transformer” (T5) - a fully end-to-end model which does not rely on any intermediate planning steps, delexicalization, or copy mechanisms. We also present our systematic approach in modifying the ToTTo dataset, release the augmented dataset as TabQGen along with the scores achieved using T5 as a baseline to aid further research.
Enhancing the Security of Caesar Cipher Algorithm by Designing a Hybrid Cryptography System

Cryptography is the art of protecting information by transforming it into an unreadable format.It is about constructing and analyzing protocols that prevent third parties or the public from reading private message.Caesar cipher is a substitution cipher that implements monoalphabetic substitutions.It is a very old encryption technique and major disadvantage of caesar cipher is the repeating nature of its keys.In this paper an advanced encryption algorithm is proposed which improves the security of caesar method by combining traditional playfair approach and a modern cipher method like stream cipher.When applying the proposed method, three different key is used which makes the algorithm more strong.
Automatic Text Summarization for Urdu Roman Language by Using Fuzzy Logic

In the new era of technology, there is the redundancy of information in the internet world, which gives a hard time for users to contain the willed outcome it, to crack this hardship we need an automated process that riddle and search the obtained facts. Text summarization is one of the normal methods to solve problems. The target of the single document epitome is to raise the possibilities of data. we have worked mostly on extractive stationed text summarization. Sentence scoring is the method usually used for extractive text summarization. In this paper, we built an Urdu Roman Language Dataset which has thirty thousand articles. We follow the Fuzzy good judgment technique to clear up the hassle of text summarization. The fuzzy logic approach model delivers Fuzzy rules which have uncertain property weight and produce an acceptable outline. Our approach is to use Cosine similarity with Fuzzy logic to suppress the extra data from the summary to boost the proposed work. We used the standard Testing Method for Fuzzy Logic Urdu Roman Text Summarization and then compared our Machine-generated summary with the help of ROUGE and BLEU Score Method. The result shows that the Fuzzy Logic approach is better than the preceding avenue by a meaningful edge.
Blockchain Technology for Industry 4.0 Applications

Today, blockchain and Internet of Things (IoT) are the two major technologies which play vital roles in building a smart society. The applications of these technologies make them popular as they are widely used in our day-to-day life. With the use of these latest technologies, Industry 4.0 shows the path for improving the performance of modern factories. To add decentralization, security and trust to modern fields, blockchain is one of the most promising technologies. In order to develop Industry 4.0 applications, there are various benefits and challenges which arises with use of these technologies. In this paper, we explain the usage of blockchain technology with various industry aspects along with IoT, which is very popular and auspicious technology used now a days. There still exists some security problems and challenges resulting while implementing blockchain to Industry 4.0 which opens a forum for future research direction.
Remote Recovery of Sound from Speckle Pattern Video Based on Convolutional LSTM

In the field of security surveillance, remotely acquire the sound signal of the target is an attractive research topic. The research has broad application prospects, such as counter-terrorism, rescue, medical monitoring, and so on. To obtain clear and accurate sound signal of the target, we propose a method based on convolutional LSTM network to recover the sound. The principle of our method consists of two steps. First, we record the speckle images of target remotely. Then we utilize the convolutional LSTM network to extract the subtle movement from speckle images. The results demonstrate that our network is superior to convolutional neural network in the accuracy and efficiency of processing temporal-spatial speckle image data. The influence of different sampling rates on sound extraction is revealed through appropriate experimental settings. In addition, we also reveal the principle that our network has stronger generalization ability than convolutional neural network. Benefit from the powerful generalization ability of the network, our method could perform accurate and robust sound extraction to unseen objects. The excellent performance of our method proves that it is a significant development in the field of remote sound acquisition.
An Intelligent Control Model of Credit Line Computing in Intelligence Health-Care Systems

Technologies such as machine learning and artificial intelligence have brought about a tremendous change to biomedical computing and intelligence health care. As a principal component of the intelligence healthcare system, the hospital information system (HIS) has provided great convenience to hospitals and patients, but incidents of leaking private information of patients through HIS occasionally occur at times. Therefore, it is necessary to properly control excessive access behavior. To reduce the risk of patient privacy leakage when medical data are accessed, this article proposes a dynamic permission intelligent access control model that introduces credit line calculation. According to the target given by the doctor in HIS and the actual access record, the International Classification of Diseases (ICD)-10 code is used to describe the degree of correlation, and the rationality of the access is formally described by a mathematical formula. The concept of intelligence healthcare credit lines is redefined with relevance and time Windows. The access control policy matches the corresponding credit limit and credit interval according to the authorization rules to achieve the purpose of intelligent control. Finally, with the actual data provided by a Grade-III Level-A hospital in Kunming, the program code is written through machine learning and biomedical computing-related technologies to complete the experimental test. The experiment proves that the intelligent access control model based on credit computing proposed in this study can play a role in protecting the privacy of patients to a certain extent.
Towards the Co-Simulation of Charge Qubits: A Methodology Grounding on an Equivalent Circuit Representation

This paper presents a methodology to describe quantum mechanical states of charge qubits, realized as coupled quantum dots occupied by single electrons, using equivalent electrical circuits. We explain how to construct all equations starting from low-level simulations of wave functions and interpret the relationship between the parameters appearing in the resulting model and the parameters of the physical system. An efficient methodology to obtain 2D wave functions is presented. Results obtained in this study using a circuit simulator correspond to quantum dot arrays fabricated in a CMOS technology and recently published in the literature. The generalization of quantum mechanical equations, their conversion to equivalent circuits, and numerical examples are discussed.
cushLEPOR uses LABSE distilled knowledge to improve correlation with human translation evaluations

Human evaluation has always been expensive while researchers struggle to trust the automatic metrics. To address this, we propose to customise traditional metrics by taking advantages of the pre-trained language models (PLMs) and the limited available human labelled scores. We first re-introduce the hLEPOR metric factors, followed by the Python portable version we developed which achieved the automatic tuning of the weighting parameters in hLEPOR metric. Then we present the customised hLEPOR (cushLEPOR) which uses LABSE distilled knowledge model to improve the metric agreement with human judgements by automatically optimised factor weights regarding the exact MT language pairs that cushLEPOR is deployed to. We also optimise cushLEPOR towards human evaluation data based on MQM and pSQM framework on English-German and Chinese-English language pairs. The experimental investigations show cushLEPOR boosts hLEPOR performances towards better agreements to PLMs like LABSE with much lower cost, and better agreements to human evaluations including MQM and pSQM scores, and yields much better performances than BLEU (data available at \url{this https URL}).
Looking for Confirmations: An Effective and Human-Like Visual Dialogue Strategy

Generating goal-oriented questions in Visual Dialogue tasks is a challenging and longstanding problem.State-Of-The-Art systems are shown to generate questions that, although grammatically correct, often lack an effective strategy and sound unnatural to humans.Inspired by the cognitive literature on information search and cross-situational word learning, we design Confirm-it, a model based on a beam search re-ranking algorithm that guides an effective goal-oriented strategy by asking questions that confirm the model's conjecture about the referent.We take the GuessWhat?! game as a case-study.We show that dialogues generated by Confirm-it are more natural and effective than beam search decoding without re-ranking.
Chemical imaging and machine learning for sub‐classification of oesophageal tissue histology

Abstract Fourier Transform Infrared (FTIR) based chemical imaging is a powerful, non‐destructive and label‐free biophotonic technique, which spatially acquires bio‐molecularly relevant information in histopathology. Cancer detection with objective chemical imaging techniques is relatively well established, though detection of pre‐cancer stages within a continuum from normal tissue to cancer remains challenging. Here machine learning with chemical imaging was used to provide an objective classification pipeline for oesophageal tissues pathologically classified as normal, oesophagitis, dysplasia, Barrett's disease and cancer. Spectral images were segmented using a k ‐means cluster validity indices approach and clustered spectra were classified using partial least squares discriminant analysis. Classification performances approached a receiver operator characteristic area‐under‐the‐curve (ROC‐AUC) of 0.90 for binary classification tasks (eg, normal vs Barrett's). Isolated histopathological substructures were identified which delivered a ROC‐AUC in of ~0.69 in classifying into each of the five‐classes. This work may provide the means to assist pathologist diagnoses of intermediate pre‐cancer stages.
Artificial Intelligence versus End-User Development: A Panel on What Are the Tradeoffs in Daily Automations?

Artificial Intelligence (AI) and End-User Development (EUD) look at automation from two different perspectives. The former tends to provide fully automatic solutions, the latter aims to empower users to directly create what they want. We need both, but it is still unclear how to combine them to obtain effective every-day automations that meet the flexible and dynamic user needs. The panel aims to stimulate the Human-Computer Interaction community to think more carefully about such aspects and the possible approaches to address them.