Reviewerly/exHarmony huggingface dataset:
Hong Kong Learner's Perceptions, Experiences and Motivation with Mobile App Vocabulary Learning

Second- and foreign language learners in higher education often have limited time for vocabulary development and retention, which may constitute a major challenge. This article reports on undergraduate students' perceptions of a vocabulary learning application developed in-house, Alphabet vs Aliens @PolyU. 14 undergraduate students at an English-medium university in Hong Kong participated. A qualitative methodology of semi-structured interviews captured a holistic overview of student perceptions of the app. The results suggest that Hong Kong university students are strongly motivated to acquire second-language (L2) vocabulary. Participants also indicated a preference for mobile applications that use gamified features. The study offers considerations for future research into apps for English language learning..
Online Health Communities: The Impact of AI Conversational Agents on Users

The literature lacks evidence on the acceptability of AI conversational agents (chatbots) and the motivations for their adoption in healthcare industry. This paper aims to examine the acceptance of these chatbots based on the UTAUT model in Online Health Communities (OHCs) and to explore what kind of impact these particular features have on the users’ intentions, and the actual use of these communities. Based on a quantitative methodology approach, we rely on the UTAUT model to study OHCs users’ behavior and intentions towards such AI conversational agents/chatbots. The study shows that the UTAUT has proved to be a strong and reliable model for evaluating the adoption and application of AI conversational agents (chatbots) in OHCs. A questionnaire was employed to collect data, and respondents are chosen using the cluster sampling approach. On a 7 Likert scale, respondents were asked to select which choice best suited their reaction to any of the topics presented. A total of 632 answers from 62 countries were received, with 443 of them being complete. Many tests were used to examine the data such as the bivariate and multivariate analysis. Since the returned p-value for most of the hypotheses tested was 0.05, the majority of the hypotheses tested were accepted. Findings showed the interrelations between AI conversational agents/chatbots and OHCs on users’ Behavioral Intention (BI). The main constructs of the UTAUT model (Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions) had a significant impact on the participants’ BI and Usage Behavior (UB) for AI conversational agents/chatbots in OHCs. As for moderators, gender and age had no effect on BI and UB. Understanding the main factors that have a significant impact on users’ intentions to use chatbots in OHCs determines the significance of those results.
Towards a Better Practice in Water Sampling: Case Studies on Used in Practice Geothermal Waters

Sampling state-of-art is specified in guidelines, but there is a permanent need to minimise the risk to the water sample quality, in particular of geothermal waters. The proper sampling influences, i.a., the uncertainty of chemical scaling and corrosivity models in installations, reservoir geothermometry or environmental assessments. Influence of filters (nylon, PVDF, MCE), containers (HDPE, borosilicate glass) and preservation on 34 major, minor and trace elements in samples from two used in practice geothermal waters was studied. Results of replicated chemical analyses in duplicated samples of geothermal waters and blank deionised water were subjected to statistical analyses, including non-parametric (Kruskal-Wallis, Dunn’s, Kolmogorov-Smirnov, Whitney U, Wolfowitz Runs) tests. PVC housing of MCE syringe filters used contaminates water samples in antimony. Borosilicate glass increases aluminium, copper, lead and selenium concentrations in samples. Acidification protects against changes in concentrations of numerous elements (i.a. Al, B, Ba, Cu, Fe, Ni, Pb) in solution.
An Adaptive Approach to Detect Face Mask in Real Time using Convolutional NeuralNetwork (CNN) Model

The corona virus diseases are sparkling an astonishing level of scientific collaboration around the world. Artificial intelligence (AI) association with machine learning and deep learning could greatly assist in fighting corona virus in a number of paths. Machine learning allows research scholar, researchers and clinicians to make assessment on various amounts of datasets to foresee the spread of corona virus, which serve as an early warning mechanism for potential epidemics. In many countries, people are required by protocol to wear a mask in public. These rules and laws are designed as measures to exponentially increase the number of diseases count and death cases in many regions. However, the procedure of observing many people is becoming increasingly difficult. Here, we present a model for mask face detection based on deep learning and computer vision. The proposed model can be embedded with a surveillance camera to block the spread of corona diseases, detecting people wearing masks but not face masks. This model integrates deep learning and classic machine learning methods using open computer vision, tensor flow and keras.
Projeto: Cerrado: Unfolding Spaces

In this paper, the authors discuss using sound spatialization techniques to enhance immersiveness in sound composition. The artistic goal is to set up an expanded environment wherein people can experience Nature's complexity, which underlies what is possible to perceive. The creative process in this study evokes a continuous gathering of field recordings into artistic works. Henceforth, the authors intend to focus this discussion on sound displacement as a creative strategy to employ an expanded environment.
A Non-negative Matrix Factorization for IVUS Image Classification Using Various Kernels of SVM

Intravascular ultrasound (IVUS) is a medical methodology. It is a specially constructed catheter with a miniaturized Ultrasound Probe attached to the catheter’s distal end is a medical imaging technique. An efficient method for IVUS image classification using non-negative matrix factorization (NNMF) and various support vector machine (SVM) kernels are presented in this study. The input IVUS images are given to NNMF for feature extraction and stored in the feature database. Finally, SVM kernels like linear, polynomial, quadratic and radial basis function (RBF) are used for prediction. The system produces a classification accuracy of 94% by using NNMF and different SVM kernels.
The Neural Network Based Ontology Detection on Unstructured Data Flow

The paper proposes a new approach to solving the problem of automatic analysis and identification of defect ontologies in solids, primarily in railway. Automatic identification of defects is an urgent task in the construction of a fully automated railway transport control system, which includes such an essential component as ensuring the safety of railway traffic. The use of neural networks is proposed as a method and the practical applicability of convolutional neural network architecture for these tasks is shown. The main results were verified at the level of the Kuibyshevskaya Railway Directorate of the Russian Railways. The results showed that the neural network does not miss any defects, but there remain problems with false positives answers of the system. Overall, the results of the prototype already exceed those of a novice flaw detectorist. In terms of practical significance, the work will be used in the future process of automation of railway management processes, which will eventually completely abandon manual labor in defectoscopy and move to a fully automated process to identify defects in the railway.
Similarity Computing Method of Poly-Encoders Short Texts in Both Chinese and Lao Based on Part of Speech Characteristics

Similarity computing of short texts in both Chinese and Lao is a basic task in bilingual research of the Chinese and Lao, which is widely used in the fields of text matching and QA question answering. The corpus resources of the Lao language are scarce, and the research results by current methods are poor. Therefore, we propose a similarity calculation method by incorporating bilingual language features and combining the common part-of-speech features of Chinese and Lao bilinguals to obtain more semantic information from the limited training corpus. At the same time, we get the text-matching information by the Poly network to further improve the accuracy of semantic vector representation, and finally, the similarity calculation model of Chinese and Lao bilingual short texts is constructed. Experimental results show that the proposed method achieves better results than the existing methods (74.95%).
Rustpi: A Rust-powered Reliable Micro-kernel Operating System

Rustpi is a micro-kernel operating system implemented in Rust to explore how modern language features can help to build a reliable operating system. In our system, isolations between micro-kernel servers are achieved by Rust language instead of expensive hardware mechanisms. Moreover, Rust language features such as control-flow integrity and unwinding enable hardware transient fault detection and error recovery without resource leaking. Rustpi creatively integrates these features to enhance its reliability. Moreover, our design is also applicable to other Rust micro-kernel systems or even the Linux kernel.
Accuracy Verification of 3D Motion Analysis System Using Smart-phone Monocular Camera

PURPOSE This study aimed to verify the accuracy of three-dimensional (3D) motion data produced through artificial intelligence-based user motion recognition technology with images obtained using a smartphone monocular camera. This was done to explore the possibility of developing an application that can improve the reliability of the measurement of physical activity performing motions and feedback provision.METHODS To check the accuracy of the artificial intelligence-based 3D motion analysis system that utilized a semi-supervised learning method, a commercialized 3D infrared motion analysis system measured and compared motions on three movement planes, motions with limited joint movement, and fast motions in a wide moving range.RESULTS The motions on the coronal and sagittal planes produced through the 3D motion analysis application showed very high measurement accuracy; however, the accuracy of the measurement of motions on the horizontal plane, which could not be measured directly with a camera, was relatively lower than that of the coronal and sagittal planes. Accuracy in measuring 3D motion was moderate in moving motions and low in motions with limited joint movement.CONCLUSIONS For the developed 3D motion analysis system to be used in online physical education, the types of physical activities included in the program should be comprehensively composed through the analysis of the content system of the physical education curriculum and the resultant physical activities.
A Personalized Cross-Platform Post Style Transfer Method Based on Transformer and Bi-Attention Mechanism

To meet different social purposes, users usually share content related to the same topic or event to multiple social media platforms (cross-platform content sharing). As the differences of social norms and audiences among these social ecosystems, there are differences in the use of words and expressions in different platforms, resulting in different language styles among different platforms. In reality, it is usually difficult for users to grasp the consistency between the language style of posts to be published and that of a platform as the problem of context collapse. To address this problem, firstly, we conduct an study to investigate users' content sharing practices across two Chinese popular social media platforms (Douban and Weibo). The results indicate that: 1) there are significant linguistic differences between different platforms; 2) users' content sharing practices are personalized, and the style of their newly shared content is correlated with their historical posts. Secondly, based on the above findings, we propose a personalized cross-platform post style transfer model. The model can automatically transfer users' posts from one platform's language style to the target platform's language style, while preserving the content and reflecting users' personalized characteristics as much as possible. Experiments on the datasets collected from Douban and Weibo show that our model generally outperforms other comparison models on both style transfer and personalization metrics.
Image Quality Improve by Super Resolution Generative Adversarial Networks

In this research, we analyze the methods to improve the quality of the pictures. Machine learning technique is used to achieve the effect of converting low-pixeled pictures into high pixeled ones. It is convenient and can be used in many circumstances, such as engineering projects and medical surveys. This study uses the SRGAN to train the model, which has the generator network and the discriminator network. The generator network is used to generate high-resolution images, and the discriminator network is used to judge the authenticity of the image generated. Our network has trained the network with 8× upscaling factors and eventually obtains predominant achievement for dealing with detailed textures like trees, cars, and animal fur. Our network can recover low-quality pictures perpetual satisfying, and the loss of this model is low. Our research provides model-based strategic support for image quality improvement and gets a good picture resolution increase in the tests.
A Domain Based Approach to Semantic Lexicon Expansion

Abstract Current approaches to the expansion of semantic lexicons for corpus annotation are somewhat ad hoc in nature and do not generally offer a systematic means of identifying areas for development within one’s lexicon. The present paper sets forward a domain based approach to semantic lexicon expansion, targeting UCREL’s Semantic Analysis System (USAS). First, an updated version of the lexicon is compared to representative corpora to ascertain areas of underrepresentation in a novel method which we call K-FLUX analysis. Second, an example set of underrepresented types are targeted for development using domain specific corpora. Collectively, the results show that some corpora are more successful than others in supplementing the existing USAS lexicon. The paper discusses the various factors that should be borne in mind when utilising the proposed method before concluding with how findings might inform future developments of the lexicon, and crucially, the semantic system on which it is based.
Liveable city from the perpective of disaster management

Abstract Research purpose is provide an overall image of what a liveable city from the perspective of disaster management. Disaster is seen as an event that can disrupt and threaten people’s lives, while a liveable city is an idea to create a city that is economically qualified. environment dan equity of live. The initial stage begins with the theoretical aspect and the final stage with the practical aspect, is attempt to provide answers to the following questions: can liveable cities be applied to disaster-risk areas and what kind of concept will be applied to disaster-vulnerable areas. The elements of liveable cities which are generally different in each country because they are adapted to the geographical conditions and demographics of each region. The method of this study uses literature studies and case studies. Disaster management from the beginning must be taken into consideration in planning a city, so as to minimize losses due to disasters and the people who live in it can live side by side with nature. The results of this study explain that the application of the concept of a liveable city seen from disaster management is not only the urgency of fulfilling the requirements of a liveable city that is ready to face disasters but also views that liveable urban planning as a city ecosystem that has appropriate values and culture and is comfortable to live in so that it creates sustainable city.
Use case of Paillier Homomorphic Algorithm for Electronic-Voting Systems

In modern age, there are requirements for not only sending and receiving confidential data over the public network or for storing them in an online server but also to process such data in the open without compromising the confidentiality of the data or its originator. This calls for cryptographic algorithms with special features like homomorphic properties. Paillier cryptosystem is an example of such an algorithm. A use case of e-voting system using Paillier cryptosystem is discussed in this paper. Algorithm execution and analysis are done using Python programming language. A few useful properties of the implemented Paillier algorithm are verified and found satisfactory. A new voting scheme based on two stages of encryption and a digital envelope is also proposed for critical security requirements.
Location Privacy Protection Techniques for Mobile Location-Based Services. A Survey

Nowadays a large number of mobile devices are in use and this has been exacerbated by the technologies embedded in them. The embedded technologies allow the devices to access and use Location-Based Services (LBS) as a way to improve service delivery by service providers. The issue of LBS privacy security comes in two ways which makes it complex. On one hand, for a user to receive LBS his/her location must be known to the service providers. While on the other hand, if this information falls in wrong hands it can be used in wrong ways and harm the respective users. This paper surveyed location privacy techniques developed by different authors and made a critical evaluation of the techniques to give recommendations on their use and for further studies.
Eight Wave Mixing Process is suitable to make Single Photon Source for Quantum Cryptography

Abstract Single photon source for use in quantum cryptography should show higher order photon antibunching. We have analyzed non classical effect i.e. higher order photon antibunching in pump mode of eight-wave mixing non linear optical process using short time interaction technique and also the dependence of non classicality on the number of photons prior to interaction in non linear medium. Further we have reported that higher the order of photon antibunching, higher will be the probability to generate single photon source utilized in quantum cryptography.
Comparative Study of Different Salp Swarm Algorithm Improvements for Feature Selection Applications

Recently, many optimization algorithms have been applied for Feature Selection (FS) problems and still showing very promising results. Moreover, Salp Swarm Algorithm (SSA) is one of the most sophisticated meta-heuristic swarm-based optimization algorithms. As SSA is proving its efficiency, it has undergone several improvements in order to make its performance as better as it could be. In this context, this paper presents a comparative study of the Salp Swarm Algorithm variants for feature selection application. These different improvement approaches aim to reduce the number of features and eliminate the non-useful ones. This study focuses on three binary versions of SSA, namely Binary Salp Swarm Algorithm (BSSA), Chaotic Salp Swarm Algorithm (CSSA) and Dynamic Salp Swarm Algorithm (DSSA). For this purpose, 13 UCI benchmark datasets were used. Based on the comparative results we can conclude that the DSSA approach enhances the performance of the SSA algorithm and outperforms other similar approaches in the literature.
A Fair Incentive Mechanism in Federated Learning

Federated learning (FL) has shown great potential for addressing the challenge of isolated data islands while preserving data privacy. However, not everyone is willing to participate in federated learning to share their data computation results. Providing incentives is a common way to promote the success of federated learning. The design of this paper focuses on the incentive mechanism based on the reverse auction. First, an incentive model for crowdsourcing systems is constructed. Secondly, an auction-based incentive mechanism is proposed by combining the concepts of reverse auction and VCG auction. Simulation results indicate that the proposed incentive mechanism can effectively improve the fairness of the bids. In this paper, we present a VCG-based FL incentive mechanism, named LVCG, specifically designed for incentivizing data owners to contribute all their data and truthfully report their costs in FL settings.
Dehazing Based on Long-Range Dependence of Foggy Images

Deep neural networks (DNNs) with long-range dependence (LRD) have attracted more and more attention recently. However, LRD of DNNs is proposed from the view on gradient disappearance in training, which lacks theory analysis. In order to prove LRD of foggy images, the Hurst parameters of over 1,000 foggy images in SOTS are computed and discussed. Then, the Residual Dense Block Group (RDBG), which has additional long skips among two Residual Dense Blocks to fit LRD of foggy images, is proposed. The Residual Dense Block Group can significantly improve the details of dehazing image in dense fog and reduce the artifacts of dehazing image.
SNAG: SDN-Managed Network Architecture for GridFTP Transfers Using Application-Awareness

Increasingly, academic campus networks support large-scale data transfer workflows for data-intensive science. These data transfers rely on high-performance, scalable, and reliable protocols for moving large amounts of data over a high-bandwidth, high-latency network. GridFTP is a widely used protocol for wide area network (WAN) data movement. However, as the GridFTP protocol does not share connection information with the network-layer, network operators have reduced flexibility, particularly in identifying/managing flows across the network. We address this problem by deploying a production “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">application-aware</i> ” software defined network (SDN) for managing GridFTP transfers for data-intensive science workflows. We first propose a novel application-aware architecture called SNAG (SDN-managed Network Architecture for GridFTP transfers). SNAG combines <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">application-layer and network-layer collaboration</i> (termed “application-awareness”) with SDN-enabled network management to classify, monitor and to manage network resources actively. Until now, our SNAG deployment has successfully classified over <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1.5 Billion</i> GridFTP connections at the Holland Computing Center (HCC), University of Nebraska-Lincoln (UNL). Next, we develop an application-aware SDN system to provide differentiated network services for distributed computing workflows. At HCC, we also demonstrate how our system ensures the quality of service (QoS) for high-throughput workflows such as Compact Muon Solenoid (CMS) and Laser Interferometer Gravitational-Wave Observatory (LIGO). Further, we also demonstrate how application-aware SDN can be exploited to create <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">policy-driven</i> approaches to achieve accurate resource accounting for each workflow. We present strategies for implementing differentiated network services and discuss their capacity improvement benefits. Lastly, we provide some guidelines and recommendations for developing application-aware SDN architectures for general-purpose applications.