Reviewerly/exHarmony huggingface dataset:
Linguistic resources for paraphrase generation in portuguese: a lexicon-grammar approach

This paper presents a new linguistic resource for the generation of paraphrases in Portuguese, based on the lexicon-grammar framework. The resource components include: (i) a lexicon-grammar based dictionary of 2100 predicate nouns co-occurring with the support verb ser de ‘be of’, such as in ser de uma ajuda inestimável ‘be of invaluable help’; (ii) a lexicon-grammar based dictionary of 6000 predicate nouns co-occurring with the support verb fazer ‘do’ or ‘make’, such as in fazer uma comparação ‘make a comparison’; and (iii) a lexicon-grammar based dictionary of about 5000 human intransitive adjectives co-occurring with the copula verbs ser and/or estar ‘be’, such as in ser simpático ‘be kind’ or estar entusiasmado ‘be enthusiastic’. A set of local grammars explore the properties described in linguistic resources, enabling a variety of text transformation tasks for paraphrasing applications. The paper highlights the different complementary and synergistic components and integration efforts, and presents some preliminary evaluation results on the inclusion of such resources in the eSPERTo paraphrase generation system.
OAE-EEKNN: An Accurate and Efficient Automatic Modulation Recognition Method for Underwater Acoustic Signals

The automatic modulation recognition (AMR) enables the receiver to automatically recognize the modulation type of the received signal for achieving correct demodulation. However, the noise and interference in the underwater acoustic channel greatly influence the features extracted from the signals. In this letter, we proposed the optimizing autoencoder (OAE) and the evaluation enhanced K-nearest neighbors (EEKNN) algorithms. The combination of OAE and EEKNN not only improves the feature discrimination but also avoids the misjudgment caused by abnormal samples and realizes accurate and efficient AMR. The experimental results of the data measured in the South China sea show that the proposed method successfully recognizes eight modulation types. The recognition accuracy is up to 99.25%, and the recognition time is only 3.48 ms.
A Fast Image Compression Algorithm Based on Wavelet Transform

With multimedia becoming widely popular, the conflict between mass data and finite memory devices has been continuously intensified; so, it requires more convenient, efficient and high-quality transmission and storage technology and meanwhile, this is also the researchers’ pursuit for highly efficient compression technology and it is the fast image transmission that is what people really seek. This paper mainly further studies wavelet analysis and fractal compression coding, proposes a fast image compression coding method based on wavelet transform and fractal theory, and provides the theoretical basis and specific operational approaches for the algorithm. It makes use of the smoothness of wavelet, the high compression ratio of fractal compression coding and the high quality of reconstructed image. It firstly processes the image through wavelet transform. Then it introduces fractal features and classifies the image according to the features of image sub-blocks. Each class selects the proper features. In this way, for any sub-block, it only needs to search the best-matched block in a certain class according to the corresponding features. With this method, it can effectively narrow the search in order to speed up coding and build the relation of inequality between the sub-block and the matching mean square error. So, it can effectively combine wavelet transform with fractal theory and further improves the quality of reconstructed image. By comparing the simulation experiment, it objectively analyzes the performance of algorithm and proves that the proposed algorithm has higher efficiency.
Analysis of Performance Evaluation Model for Higher Education Informatization by Means of PLS-BP Model

In order to improve the intelligent level of higher education informatization, a novel performance evaluation model of higher education informatization by means of PLS-BP model is proposed. The principles of partial least squares (PLS) method and back propagation (BP) neural network are introduced firstly. The main components of the data are extracted by PLS method, and then combined with BP neural network, the PLS-BP model is constructed. At the end of the article, the PLS-BP evaluation model is simulated and analyzed. From the comparison results, the error between the PLS-BP evaluation model and traditional performance evaluation is relatively small, and the maximum relative error is 2.32%. The comparison results verify the accuracy of the PLS-BP model. This model presented in this paper provides a new way for the performance evaluation of higher education informatization.
A Systematic Literature Review of New Trends in Self-expression Caused by Emojis and Memes

Emoji and memes are a new way to present one's emotion, are popular among young people.It is vital nowadays because almost everyone today largely relies on social networks and sends emojis and memes.Lots of research about emoji and memes have been conducted.Here, this paper summarizes new trends brought by their appearance in China.The article is written using the method of literature review.Based on the research, this paper reveals three typical trends of selfexpression in China society: emoji and memes create new ways to shape one's self-image because of their characteristics and the "looking glass self" effect; emojis and memes cause generation gaps due to physical difference and different growth background; emoji and memes lead to obscure boundaries between diverse social circles.The research about emojis and memes enables us to know more about why people can easily use emojis and memes to shape a persona and why there are differences between elderly groups and young groups when they use emojis and memes, and why the use of emoji blurs the social sphere.By doing that, this paper, from a micro perspective, examines the trend of each case and examples as individual trends.Also, from a macro perspective, this paper shows that emojis bring infinite possibilities to society, enrich people's social channels, and ultimately affect the structural changes of social circles.
Research on Intelligent Supervision and Application System of Food Traceability Based on Blockchain and Artificial intelligence

The lack of transparency in the production and circulation of commodities and the lack of corresponding supervision have led to endless problems such as food safety, counterfeit and shoddy products, loss and damage of commodities, and damage to the rights and interests of consumers. The traditional centralized database traceability monitoring system has serious problems of data trust, data fragmentation, difficulty in accountability, and low enthusiasm of merchants. In order to solve the traditional system problems, it is proposed to build an intelligent supervision system model for food traceability based on blockchain and artificial intelligence. Blockchain technology can effectively make up and improve the shortcomings of the existing commodity traceability technology, and achieve full process control and real-time storage. Forensic forensics, increase transparency, prevent counterfeiting, and increase consumer trust; AI uses industry-sharing data to perform big data analysis to guide companies in business decisions; at the same time, in order to increase user stickiness and increase the ecological environment of the platform, the article proposes to increase Blockchain and artificial intelligence and application ecology form a more practical and complete integrated system model of traceability, supervision and application. Finally, using FISCO BCOS as a blockchain platform development platform, the validity of the model is verified, and it can provide a certain reference for food traceability companies, software R&D companies, and government regulatory agencies.
Quantum Reservoir Computing for Speckle Disorder Potentials

Quantum reservoir computing is a machine learning approach designed to exploit the dynamics of quantum systems with memory to process information. As an advantage, it presents the possibility to benefit from the quantum resources provided by the reservoir combined with a simple and fast training strategy. In this work, this technique is introduced with a quantum reservoir of spins and it is applied to find the ground state energy of an additional quantum system. The quantum reservoir computer is trained with a linear model to predict the lowest energy of a particle in the presence of different speckle disorder potentials. The performance of the task is analyzed with a focus on the observable quantities extracted from the reservoir and it is shown to be enhanced when two-qubit correlations are employed.
Alibaba: the battle against counterfeits

Learning outcomes The case will help to understand the concept of online marketplaces and the working of their business model through third party selling. The case highlights how third party selling opens door to online counterfeiting. The case well presents the need of integrity and ethics in business. It showcases how Alibaba took responsibility, designed various initiatives to curb the problem and emerged as a global face for anti-counterfeiting actions. Case overview/synopsis Manufacturing and selling of counterfeits have become easier than ever with the wide and easy reach of technology. Internet has smoothened the sale of such fake replicas around the globe. Alibaba Group faced serious problem of counterfeit selling across its various websites. The various challenges were degrading global image, rising number of fake products and numerous lawsuits filed against the company. The case study will help readers to understand the critical aspects of counterfeiting and decisions involved to run such models where the platform is not a direct seller but just an online marketplace. It emphasises how technology and brand collaboration can be used as a means to identify and remove fake product listings from such platforms, thereby preserving the integrity of business. The case also stresses the need to preserve intellectual property rights and exclusivity of original brands. Complexity academic level Senior Undergraduate, MBA and Executive MBA. Supplementary materials Teaching notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes. Subject code CSS 8: Marketing.
Key Ergonomics Requirements and Possible Mechanical Solutions for Augmented Reality Head-Mounted Displays in Surgery

In the context of a European project, we identified over 150 requirements for the development of an augmented reality (AR) head-mounted display (HMD) specifically tailored to support highly challenging manual surgical procedures. The requirements were established by surgeons from different specialties and by industrial players working in the surgical field who had strong commitments to the exploitation of this technology. Some of these requirements were specific to the project, while others can be seen as key requirements for the implementation of an efficient and reliable AR headset to be used to support manual activities in the peripersonal space. The aim of this work is to describe these ergonomic requirements that impact the mechanical design of the HMDs, the possible innovative solutions to these requirements, and how these solutions have been used to implement the AR headset in surgical navigation. We also report the results of a preliminary qualitative evaluation of the AR headset by three surgeons.
Introduction to the Special Issue on Digital Touch: Reshaping Interpersonal Communicative Capacity and Touch Practices

introduction Share on Introduction to the Special Issue on Digital Touch: Reshaping Interpersonal Communicative Capacity and Touch Practices Editors: Sara Price UCL Knowledge Lab, University College London UCL Knowledge Lab, University College LondonSearch about this author , Nadia Bianchi-Berthouze UCLIC, University College London, City UCLIC, University College London, CitySearch about this author , Carey Jewitt UCL Knowledge Lab, University College London UCL Knowledge Lab, University College LondonSearch about this author , Jürgen Steimle Department of Computer Science, Saarland University Department of Computer Science, Saarland UniversitySearch about this author Authors Info & Claims ACM Transactions on Computer-Human InteractionVolume 29Issue 3June 2022 Article No.: 18pp 1–8https://doi.org/10.1145/3505591Online:09 February 2022Publication History 1citation286DownloadsMetricsTotal Citations1Total Downloads286Last 12 Months286Last 6 weeks28 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Multi-Modular Approach for Sign Language and Speech Recognition for Deaf-Mute People

"ENGINEER", established in 1973 by The Institution of Engineers, is a Journal for dissemination of Engineering knowledge, published quarterly.Cover DescriptionCover images show a schematic diagram of a Pumped-Storage Hydroelectricity (PSH) facility and one of the promising sites for PSH in Sri Lanka. The latter image was obtained from the publicly available JICA report on “Development Planning on Optimal Power Generation for Peak Demand in Sri Lanka”. The Institution of Engineers, Sri Lanka acknowledges JICA for the image.If you are interested in contributing an Original Technical Paper based on research by the author(s) (intended for Section I) or an Original Article of Professional or Technical interest related to Engineering. (Section II) to this journal please go through the publication checklist which can be downloaded from here.
Towards Explainable Meta-learning

Meta-learning is a field that aims at discovering how different machine learning algorithms perform on a wide range of predictive tasks. Such knowledge speeds up the hyperparameter tuning or feature engineering. With the use of surrogate models, various aspects of the predictive task such as meta-features, landmarker models, etc., are used to predict expected performance. State-of-the-art approaches focus on searching for the best meta-model but do not explain how these different aspects contribute to its performance. However, to build a new generation of meta-models, we need a deeper understanding of the importance and effect of meta-features on model tunability. This paper proposes techniques developed for eXplainable Artificial Intelligence (XAI) to examine and extract knowledge from black-box surrogate models. To our knowledge, this is the first paper that shows how post-hoc explainability can be used to improve meta-learning.
Infrastructure Capacity Planning in Digitalization of Educational Services

Capacity planning of the infrastructure is a difficult task performed during the virtualization of the IT infrastructure. It is the planning of the necessary hardware to which the existing systems will be transferred, and later the future systems that will work in a virtualized environment. Infrastructure capacity planning in digitalization of educational services is a process in which the IT infrastructure is needed to meet not only the current workload but also the future workload and to follow further requirements. In case the process of planning is well performed, it could more fully support the development of emerging technologies in learning. The traditional IT infrastructure, bound by the physical limitations of the devices, does not meet the requirements for flexible changes, for quick recovery after a problem. This leads to difficult management, to weaknesses in the implementation of educational processes. Virtualization could solve most of these problems for modern IT infrastructures. It provides highly efficient and reliable operation of operating systems and applications that do not depend on the computer system.
Microcontroller Systems in Education for Sustainable Development Service. A Qualitative Thematic Meta-Analysis

Goal of the present paper is to answer the research question: Can the proper use of Microcontroller Board |Systems as educational tools help achieve the goals of Education for Sustainable Development? In order to answer the aforementioned question, a meta-analysis of the qualitative strategy of four recently published relevant papers was utilized. The technique of Thematic Analysis was used. The results show that the proper adaptation of Microcontroller Board Systems in the educational process can adequately help achieve the goals of the Education for Sustainable Development. In order to achieve these goals in the pedagogical framework, the Microcontroller System can be adapted in such a way as to serve the approaches of holistic &amp; systemic thinking, HASS&amp;STEM interdisciplinarity and multidisciplinarity, constructivist approach to knowledge, cooperative learning, critical thinking, cultivation of values, and learning satisfaction of the Education for Sustainable Development.
On keystrokes as Continuous User Biometric Authentication

Authentication of a user through an ID and password is generally done at the start of a session. But the continuous authentication system observe the genuineness of the user throughout the entire session, and not at login only. In this paper, we propose the usage of keystroke dynamics as biometric trait for continuous user authentication in desktop platform. Biometric Authentication involves mainly three phases named as enrollment phase, verification phase and identification phase. The identification phase marks the accessed user as an authenticated only if the input pattern matches with the profile pattern otherwise the system is logout. The proposed Continuous User Biometric Authentication (CUBA) System is based on free text input from keyboard. There is no restriction on input data during Enrolment, Verification, and Identification phase. Unsupervised One-class Support Vector Machine is used to classify the authenticated user’s input from all the other inputs. This continuous authentication system can be used in many areas like in Un-proctored online examination systems, Intrusion &amp; Fraud Detection Systems, Areas where user alertness is required for entire period e.g. Controlling Air Traffic etc.
Naked eye direction of arrival estimation with a Fresnel lens

Direction of arrival (DoA) estimation is of primary importance in a broad range of wireless applications, where electromagnetic waves play a role. While a vast majority of existing techniques is based on phase lag comparison in antenna arrays, intensity-based approaches are valuable in a range of low budget applications. Here we demonstrate a direct visible to a naked eye DoA device, based on a Fresnel zone plate lens, aperture, and a light-emitting diode indicator. Being a low budget device, it still allows achieving up to 90° angle of view, 19° of angular resolution, and 11° of angular accuracy at 10 GHz operational frequency. The demonstrated approach provides fast DoA visualization and can be used to adjust point-to-point communication links, identify radio wave pollution sources at home conditions and several others.
Blockchain-based Smart Contract with Machine Learning for Insurance Claim Verification

Blockchain is a decentralized, immutable, peer-to-peer network which provides trust, transparency, and tamper resistance and these features have led to various applications such as bitcoin and ethereum. Various structures of Blockchains are designed in fields such as e-health as it can facilitate safe and secure storage of the patient’s information in the health care system. Data integration problem in healthcare can be overcome by bringing a decentralized system in the hospital organization. This paper focuses on the use of Blockchains in managing medical records for insurance claim. By removing the central administrator, the suggested architecture uses Ethereum smart contracts to create a tamperproof and transparent healthcare system and ensure the integrity of sensitive patient data. Additionally, the smart contract keeps the patient well-informed and governs the communication between all the participants in the network. The proposed approach provides a solution to eliminate fraudulent insurance claims using Machine Learning (ML) with Blockchain. Machine Learning techniques such as Random Forest Classifier, Support Vector Machines are used along with Python and Solidity languages and an accuracy of 88% is achieved on the healthcare data set.
Robust Supervised and Semisupervised Least Squares Regression Using <i>ℓ</i> <sub>2,<i>p</i> </sub>-Norm Minimization

Least squares regression (LSR) is widely applied in statistics theory due to its theoretical solution, which can be used in supervised, semisupervised, and multiclass learning. However, LSR begins to fail and its discriminative ability cannot be guaranteed when the original data have been corrupted and noised. In reality, the noises are unavoidable and could greatly affect the error construction in LSR. To cope with this problem, a robust supervised LSR (RSLSR) is proposed to eliminate the effect of noises and outliers. The loss function adopts <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2,p}$ </tex-math></inline-formula> -norm ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0&lt; p\leq 2$ </tex-math></inline-formula> ) instead of square loss. In addition, the probability weight is added to each sample to determine whether the sample is a normal point or not. Its physical meaning is very clear, in which if the point is normal, the probability value is 1; otherwise, the weight is 0. To effectively solve the concave problem, an iterative algorithm is introduced, in which additional weights are added to penalize normal samples with large errors. We also extend RSLSR to robust semisupervised LSR (RSSLSR) to fully utilize the limited labeled samples. A large number of classification performances on corrupted data illustrate the robustness of the proposed methods.
Document Cluster Analysis Based on Parameter Tuning of Spectral Graphs

Partitioning a setMenon, Remya R. K. of objects in a particular domainAshok, Astha into different subsets or clusters can be done usingArya, S. unsupervised learning methods. The algorithms compete to increase the intra-cluster similarity with an objective to find top-quality clusters in a limited time frame. High-dimensional data are very relevant to a wide range of areas but to make analysis and easy understanding we need to converge it to a low-dimensional representation. Here, we implement a spectral clustering algorithm for document data set in which we are able to cluster semantically similar documents with respect to the presence of semantically similar terms. Some documents will be similar in a particular subspace but dissimilar in other. The documents that belong to a union of low-dimensional subspaces are found from a collection. We get a cluster map which has a weighted link between the nodes indicating the strength of similarity between clusters in different subspaces. We apply principal component analysis (PCA) as a feature extraction step. There are no assumptions made about the structure or shape of the clusters. We analyse the cluster formation using homogeneity, inertia and Silhouette score for varying parameters (Epsilon-neighbourhood graph/K-nearest neighbour/fully connected, epsilon value, number of clusters) of spectral clustering.