Reviewerly/exHarmony huggingface dataset:
Text Data Pre-Processing for Time-series Modelling

The paper proposes an algorithm of text data representation for time-series econometric modeling. We link two areas in empirical econometrics, text mining and time-series modeling, and provide a multi-step methodology for processing qualitative text data for quantitative time-series models. We also present an empirical example of text data processing of research article meta-data in economics and show its applicability to sentiment analysis using annual data from 1933-2015
Automatic extension of a symbolic mobile manipulation skill set

Symbolic planning can provide an intuitive interface for non-expert users to operate autonomous robots by abstracting away much of the low-level programming. However, symbolic planners assume that the initially provided abstract domain and problem descriptions are closed and complete. This means that they are fundamentally unable to adapt to changes in the environment or tasks that are not captured by the initial description. We propose a method that allows an agent to automatically extend the abstract description of its skill set upon encountering such a situation. We introduce strategies for generalizing from previous experience, completing sequences of key actions and discovering preconditions to ensure computational efficiency. The resulting system is evaluated on a symbolic planning benchmark task and on object rearrangement tasks in simulation. Compared to a Monte Carlo Tree Search baseline, our strategies for efficient search have on average a 25% higher success rate at a 67% faster runtime. Code is available at https://github.com/ethz-asl/high_level_planning.
Real Time Indian Sign Language Detection System

Sign language, as a different form of the communication language, is important to large groups of people in society. There are different signs in each sign language with variability in hand shape, motion profile, and position of the hand, face, and body parts contributing to each sign. A challenging area of computer vision research is the recognition of visual sign languages. In recent years, deep learning techniques have significantly improved the many models that have been suggested by various academics. We examine the deep learning-based vision-based models for sign language recognition. An important advancement in enhancing communication between the deaf and the overall populace is a real-time sign language detector. We are glad to present the development and application of a model for recognizing sign language based on a Convolutional neural network (CNN). We utilized a pre-trained SSD mobile net V2 architecture trained on our own dataset in Order to apply transfer learning to the task
The implementation of Shannon-limited polar codes-based information reconciliation for quantum key distribution

Abstract Quantum key distribution (QKD) gives a way to generate unconditionally secure keys for two remote users, Alice and Bob. Information reconciliation (IR), which can correct the errors caused by the imperfections of the QKD systems, is a critical component in QKD. Due to the high-security requirements and large volumes of data processing, robustness and efficiency are two main factors that must be considered for the implementation of IR. The polar codes-based IR has several potential advantages, such as capacity to reach Shannon-limit, high IR efficiency, and low computational complexity. Although CPU-based IR is always implemented in most of the previous works, it is not the optimal implementation in terms of performance and power dissipation. To the best of our knowledge, there is still no work to build a special-purpose hardware module for polar codes-based IR. In this paper, a dedicated design of hardware accelerator is first proposed for polar codes-based IR, in which the block-checked successive cancellation list (SCL) algorithm is used to verify the consistency of the sifted keys, and the overall failure probability of IR is significantly reduced. The proposed design is constructed into a partially-unrolled parallel architecture to accelerate the core decoder as well as balance the resource utilization. Furthermore, the hardware implementation is completed based on Xilinx Zynq UltraScale+ XCZU5EV MPSoC platform and achieves an IR throughput of 15 Mbps with a block length of 2 12 , while less than <?CDATA $20\%$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>20</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> of the amount of on-chip resources are used in other previous designs of SCL decoder. The proposed design can provide a real-time, low-cost solution for IR in QKD systems, and enhance the performance of QKD.
Identification of Trolling in Memes Using Convolutional Neural Networks

In this paper, we applied a convolutional neural network based on classifier to classify the given input image whether it was trolled image or a normal image, which creates a suggested priority based on its input, which includes vectored image data and emotion values. We applied the traditional methods of natural language processing to preprocess the text. In particular, we utilized the Tesseract library to extract text from meme images for the classification of troll memes. The tokenization method is used to prepossess meme image text data. We extracted the features from the texts by Word2Vec modelling. The classification was done only on the basis of the displayed text. We do not consider memes that do not contain any text. The results of our study indicate that the suggested model performs effectively with 98 percent accuracy rates.
Real-World Data for Interdisciplinary Health Care Research

Real-word data (RWD) refer to data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources, including electronic health records, medical claims data, and patient-generated data. Data sets that combine personal health data stored in different sources can provide a more complete picture of an individual's health and can be used to improve population health through research and practice. The 2-tiered aim of this article is to provide a brief introduction to using RWD in health care research and to present a case study that demonstrates data curation and data merge from different sources while highlighting the benefits and limitations of using RWD. The current digital health ecosystem and value-based care approach highlight the need to use RWD to catalyze the advancement of health care research and practice. This is an excellent field that nurse researchers can lead, as they have an innate understanding of such data and data sources.
Implementing a Personal Librarian Program to Rebuild Connections With Medical Students

Health sciences librarians may be experiencing a fundamental sense of disconnection from medical students as the reliance on online resources and post-pandemic acceptance of remote learning chip away at use of the physical library. In response, librarians have investigated a variety of virtual approaches to counter the loss of face-to-face contact with patrons. Numerous reports in the literature explore ways to build virtual relationships with patrons. This case study discusses a project by the Savitt Medical Library of the University of Nevada, Reno School of Medicine to implement a Personal Librarian Program that promoted communication between librarians and learners.
A NEW COMPUTER-AIDED DIAGNOSIS OF PRECISE MALARIA PARASITE DETECTION IN MICROSCOPIC IMAGES USING A DECISION TREE MODEL WITH SELECTIVE OPTIMAL FEATURES

Malaria is a life-threatening mosquito-borne disease. Recently, the number of malaria cases has increased worldwide, threatening vulnerable populations. Malaria is responsible for a high rate of morbidity and mortality in people all around the world. Each year, many people, die from this disease, according to the World Health Organization (WHO). Thick and thin blood smears are used to determine parasite habitation and computer-aided diagnosis (CADx) techniques using machine learning (ML) are being used to assist. CADx reduces traditional diagnosis time, lessens socio-economic impact, and improves quality of life. This study develops a simplified model with selective features to reduce processing power and further shorten diagnostic time, which is important to resource-constrained areas. To improve overall classification results, we use a decision tree (DT)-based approach with image pre-processing called optimal features to identify optimal features. Various feature selection and extraction techniques are used, including information gain (IG). Our proposed model is compared to a benchmark state-of-art classification model. For an unseen dataset, our proposed model achieves accuracy, precision, recall, F-score, and processing time of 0.956, 0.949, 0.964, 0.956, and 9.877 s, respectively. Furthermore, our proposed model’s training time is less than those of the state-of-the-art classification model, while the performance metrics are comparable.
CLIL e intercomprensione: due approcci compatibili al servizio della linguistica romanza

Intercomprehension (IC), that is, a multilingual approach that aims to improve users’ receptive skills in various similar languages, so that everyone can express themselves in their own language while understanding the other's, has proven its effectiveness especially at the university level. On the other hand, CLIL is now a well-established approach in secondary schools, but much less so used in universities where courses are often implemented in English but without the support of CLIL methodology. This paper reflects on the reasons behind the success of each approach, and it proposes an integrated CLIL + IC approach through the few existing or upcoming projects.
Il profilo dell’insegnante di lingue minoritarie, tra standardizzazione delle competenze e varietà dei modelli scolastici

Our paper is based on this observation: there is a link between the quality of the language teaching, and the visibility and recognition of the skills of the teachers. In communities identified by a minority language, school and education have a key role in their maintenance and enhancement; at this time, though, it isn’t possible to drawn up a satisfactory result about the quality of minority language (ML) teaching in Italy; this is also because of the lack of a common and issued profile of the skills of the teachers.
‘Di necessità, virtù’: insegnamento linguistico, tecnologia e ricerca-azione al tempo del COVID-19 Uno studio di caso

The long lockdown period has forced the world of education to work from home, a circumstance that created inconveniences and considerable problems. At the same time, however, distance education has proved to be an opportunity to explore and exploit the potential of technology, engaging teachers and students in the search for creative solutions in response to contingent difficulties. An emblematic experience is presented in this case study, as representative of the vocation of some teachers to take on the role of researcher as well, starting from the concrete problems and needs of the classroom and drawing on theory to find appropriate answers, through a bottom-up process. In this specific case, the need to deliver an Italian L2 course online became an opportunity to pave a systematic constructivist action-research path, focused on the innovative solutions that technologies can offer to improve the teaching practice and enhance the cooperative and social aspects of language learning.
On the role of politeness in online human–human tutoring

Abstract Researchers have demonstrated that dialogue‐based intelligent tutoring systems (ITS) can be effective in assisting students in learning. However, little research has attempted to explore the necessity of equipping dialogue‐based ITS with one of the most important capabilities of human tutors, that is, maintaining polite interactions with students, which is essential to provide students with a pleasant learning experience. In this study, we examined the role of politeness by analysing a large‐scale real‐world dataset consisting of over 14K online human–human tutorial dialogues. Specifically, we employed linguistic theories of politeness to characterise the politeness levels of tutor–student‐generated utterances, investigated the correlation between the politeness levels of tutors' utterances and students' problem‐solving performance and quantified the power of politeness in predicting students' problem‐solving performance by applying Gradient Tree Boosting. The study results showed that: (i) in the effective tutorial sessions (ie, sessions in which students successfully solved problems), tutors tended to be very polite at the start of a tutorial session and become more direct to guide students as the session progressed; (ii) students with better performance in solving problems tended to be more polite at the beginning and the end of a tutorial session than their counterparts who failed to solve problems; (iii) the correlation between tutors' polite expressions and students' performance was not evident in non‐instructional communication; and (iv) politeness alone cannot adequately reveal students' problem‐solving performance, and thus other factors (eg, sentiment contained in utterances) should also be taken into account. Practitioner notes What is already known about this topic Human–human tutoring is acknowledged as an effective instructional method. Polite expression can help strengthen the relationship between tutors and students. Polite expression can promote students' learning achievements in many educational contexts. What this paper adds By considering the students' prior progress on a problem‐based learning task, we demonstrated the extent to which tutors and students display politeness in tutoring dialogues. Tutors' polite expressions might not correlate with students' problem‐solving performance in online human–human tutoring dialogues. Politeness alone was insufficient to predict the students' performance. Implications for practice Tutors might consider using words with positive sentiment values to express politeness to students with prior progress, which might encourage those students to make a further effort. The polite strategy of expressing indirect requests could help tutors mitigate the sense of directness, but this strategy should be carefully used in delivering instructional hints, especially for students without prior progress. To better assist students without prior progress, tutors might consider using more direct expression to explicitly guide students.
L’insegnamento esplicito nello sviluppo della competenza pragmatica Il caso dei segnali discorsivi

The paper presents a brief review of research in the field of L2/LS pragmatic teaching, focusing on the effects of explicit versus implicit approach in learning situations that focus on discourse markers (DMs). The literature on the subject offers a vast panorama of studies that take into consideration different types of structuring of the didactic intervention. Although studies on the development of DMs have shown that input flood and frequent exposure to specific target forms support their acquisition, explicit instruction along with input flood has been shown to positively influence the overall frequency and use of DMs.
La lingua straniera veicolare nella scuola dell’infanzia

The present paper focuses on some theoretical and methodological issues regarding preschool familiarisation to foreign languages. In particular, it highlights the contribution provided by Carmel Mary Coonan on this topic, as well as the implications for the next generations of scholars who have conducted and continue to conduct research in this thriving field of Educational Linguistics.
A Block chain Based Crypto Currency Exchange &amp; Recommendation System

Block chain technology has become extremely popular in recent years, largely because of how safe and decentralized it is. Block chain technology has opened up new prospects in the financial sector with the rise of crypto currencies and non-fungible tokens (NFTs). However, it can be challenging for investors to understand and make wise investment decisions given the abundance of crypto currencies and NFTs available. We suggest an Internet Computer block chain-based crypto currency recommendation and NFT exchange platform that makes use of REST APIs in order to solve this problem. The platform makes use of machine learning algorithms to analyze user data, offer investments based on their goals for investing, risk tolerance, and other preferences. The site also functions as a marketplace where NFTs may be bought and sold. Users can trade NFTs without relying on centralized intermediaries thanks to the Internet Computer block chain's secure and decentralized ecosystem. While keeping ownership and control over their assets, users can securely swap NFTs and other digital assets. To provide a user-friendly interface for accessing investment suggestions and NFT trading functions, the platform makes use of REST APIs. Because of the REST APIs, a larger audience can engage with the platform in a standardized and scalable manner. We make use of the special characteristics of the Internet Computer block chain, such as smart contracts and canisters, to guarantee the security and dependability of the platform. On the Internet Computer block chain, code is executed and data is managed by canisters, which are scalable and secure computing units. In conclusion, our Internet Computer block chain-based crypto currency recommendation and NFT exchange platform, which uses REST APIs, provides a safe and scalable option for investors looking to trade NFTs and make informed investment decisions. Users benefit from a personalized and secure investing experience thanks to the platform's usage of machine learning algorithms and the distinctive characteristics of the Internet Computer block chain Key Word: Block chain technology, Crypto currencies, Non-fungible tokens (NFTs), REST APIs, Internet Computer block chain, Investment recommendations.
Exploration of “English Plus” teaching mode under the background of blockchain construction

Abstract English is the most widely used language in the world. For Chinese students, fluent English will have a positive impact on their future study and work. However, with the progress and development of science and technology, the traditional college English education model can no longer meet the needs of the current social development, so it is necessary to use advanced teaching methods such as information technology, intelligence and big data to improve the teaching quality. The inclusion of blockchain technology in the category of local innovation is the official start of the central government’s promotion for the development and application of blockchain technology in China. Under this background, big data, as a new technology, can effectively improve the teaching efficiency of college English and promote the comprehensive development of college students. This paper analyses the problems existing in college English teaching and discusses the innovative strategies of college English teaching combined with the wide application of big data.
Quantum Regularized Least Squares

Linear regression is a widely used technique to fit linear models and finds widespread applications across different areas such as machine learning and statistics. In most real-world scenarios, however, linear regression problems are often ill-posed or the underlying model suffers from overfitting, leading to erroneous or trivial solutions. This is often dealt with by adding extra constraints, known as regularization. In this paper, we use the frameworks of block-encoding and quantum singular value transformation (QSVT) to design the first quantum algorithms for quantum least squares with general $\ell_2$-regularization. These include regularized versions of quantum ordinary least squares, quantum weighted least squares, and quantum generalized least squares. Our quantum algorithms substantially improve upon prior results on quantum ridge regression (polynomial improvement in the condition number and an exponential improvement in accuracy), which is a particular case of our result. To this end, we assume approximate block-encodings of the underlying matrices as input and use robust QSVT algorithms for various linear algebra operations. In particular, we develop a variable-time quantum algorithm for matrix inversion using QSVT, where we use quantum eigenvalue discrimination as a subroutine instead of gapped phase estimation. This ensures that substantially fewer ancilla qubits are required for this procedure than prior results. Owing to the generality of the block-encoding framework, our algorithms are applicable to a variety of input models and can also be seen as improved and generalized versions of prior results on standard (non-regularized) quantum least squares algorithms.
Evaluation on real-time self-organization algorithm of forest cultivation, tea planting and tea processing for sustainable ecosystem management of agricultural water resources

Abstract Agricultural water resources are water resources that can be used for agricultural production, including surface water, groundwater and soil water. The sustainable use of water resources is of great significance to agriculture and rural economy. In the context of climate change and the increasing crisis of water resources, how to effectively use forest resources and other natural resources to achieve sustainable management of agricultural water ecosystems has become an increasingly urgent issue. Therefore, this paper proposed a real-time self-organization algorithm for forest cultivation, tea planting and tea processing for sustainable ecosystem management of agricultural water resources. This paper first described the sustainable ecosystem of agricultural water resources, carried out optimization analysis on tea planting and processing technology, using the genetic optimization algorithm based on decision tree and then carried out an experimental analysis on a tea garden. It can be seen from the data that the average monthly temperature of the tea garden cultivated by the algorithm in this paper was between 13.4 and 20.5 °C and the average annual temperature was 16.16 °C. However, the average monthly temperature of the traditional cultivation method was between 11.5 and 16.8 °C and the average annual temperature was 13.64 °C.
Heterogeneous scene matching based on the gradient direction distribution field

Abstract Heterogeneous scene matching is a key technology in the field of computer vision. The image rotation problem is popular and difficult in the field of heterogeneous scene matching. In this paper, a heterogeneous scene matching method based on the gradient direction distribution field is proposed, and distributed field theory is introduced into heterogeneous scene matching for the first time. First, the distribution field of the gradient direction is constructed and fuzzified, and then the effective regions are selected. Then, the distribution field of the main direction is defined to solve the matching errors due to the existence of rotational transformations between heterogeneous source images. Third, the chi-square distance is introduced as a similarity measure. Finally, the hill-climbing method search strategy, which greatly improves the efficiency of the algorithm, is adopted. Experimental results on 8 pairs of infrared and visible heterogeneous images demonstrate that the proposed method outperforms the other state-of-the-art region-based matching methods in terms of the robustness, accuracy, and real-time performance.