Reviewerly/exHarmony huggingface dataset:
SeAttE: An Embedding Model Based on Separating Attribute Space for Knowledge Graph Completion

Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is the task of inferring missing facts based on existing ones. Knowledge graph embedding, representing entities and relations in the knowledge graphs with high-dimensional vectors, has made significant progress in link prediction. The tensor decomposition models are an embedding family with good performance in link prediction. The previous tensor decomposition models do not consider the problem of attribute separation. These models mainly explore particular regularization to improve performance. No matter how sophisticated the design of tensor decomposition models is, the performance is theoretically under the basic tensor decomposition model. Moreover, the unnoticed task of attribute separation in the traditional models is just handed over to the training. However, the amount of parameters for this task is tremendous, and the model is prone to overfitting. We investigate the design approaching the theoretical performance of tensor decomposition models in this paper. The observation that measuring the rationality of specific triples means comparing the matching degree of the specific attributes associated with the relations is well-known. Therefore, the comparison of actual triples needs first to separate specific attribute dimensions, which is ignored by existing models. Inspired by this observation, we design a novel tensor ecomposition model based on Separating Attribute space for knowledge graph completion (SeAttE). The major novelty of this paper is that SeAttE is the first model among the tensor decomposition family to consider the attribute space separation task. Furthermore, SeAttE transforms the learning of too many parameters for the attribute space separation task into the structure’s design. This operation allows the model to focus on learning the semantic equivalence between relations, causing the performance to approach the theoretical limit. We also prove that RESCAL, DisMult and ComplEx are special cases of SeAttE in this paper. Furthermore, we classify existing tensor decomposition models for subsequent researchers. Experiments on the benchmark datasets show that SeAttE has achieved state-of-the-art among tensor decomposition models.
Influence of Learning Engagement on Learning Effect under a Virtual Reality (VR) Environment

Abstract—In this study, the differences generated by the scenario resources under a virtual reality (VR) environment to the learning effect and learning engagement were explored. Next, the influences of learning engagement (including emotional engagement and cognitive engagement) on the immediate test performance and the one-week-later test performance were tested. Finally, the mediating role played by the VR technology in the facilitating effect of learning engagement on the learning effect was analyzed. Results reveal that the emotional engagement (3.366) and cognitive engagement (3.854) in the experimental group under the VR environment are higher than those (3.325 and 3.618, respectively) in the control group. The difference between the control group and experimental group in the aspect of immediate test performance is significant at the 0.01 level (t=−3.388, p=0.002), and the same significance level is manifested in the aspect of one-week-later test performance (t=-3.126, p=0.003). Whether VR-based teaching was adopted plays a complete mediating role in the influences of cognitive engagement on the immediate test performance and the one-week-later test performance, but it does not play any mediating role in the influences of emotional engagement on the immediate test performance and the one-week-later test performance. The conclusions have important reference values for giving full play to VR-based teaching design, encouraging front-line teachers to participate in customizing VR-based immersive teaching resources, optimizing the VR learning contents highlighting emotional elements, and designing multichannel perceived knowledge contexts.
THE NEXUS BETWEEN ORGANISATIONAL CAPABILITIES, ORGANISATIONAL READINESS AND REVERSE SUPPLY CHAIN ADOPTION

Successful Reverse Supply Chain Adoption (RSC) adoption requires a clear identification and understanding of Organizational Capabilities, assets, and resources internally and externally. Towards this end, this research assesses the nexus between organisational capabilities and RSC Adoption with a focus on the mediation role of organisational readiness. A survey method was used to achieve the research objectives. Different manager levels from the Jordanian industrial sector were selected by using a convenient sample technique. SMART PLS 3 was used to conduct Structural Equation Modelling (SEM). The outcomes represent a significant influence for internal organisational capabilities on the RSC adoption. As a mediation result, organisational readiness has a mediation role between internal organisational capabilities and the RSC adoption.
A Survey on Quantum Computational Finance for Derivatives Pricing and VaR

Abstract We review the state of the art and recent advances in quantum computing applied to derivative pricing and the computation of risk estimators like Value at Risk. After a brief description of the financial derivatives, we first review the main models and numerical techniques employed to assess their value and risk on classical computers. We then describe some of the most popular quantum algorithms for pricing and VaR. Finally, we discuss the main remaining challenges for the quantum algorithms to achieve their potential advantages.
RECENT CNN-BASED TECHNIQUES FOR BREAST CANCER HISTOLOGY IMAGE CLASSIFICATION

Histology images are extensively used by pathologists to assess abnormalities and detect malignancy in breast tissues. On the other hand, Convolutional Neural Networks (CNN) are by far, the privileged models for image classification and interpretation. Based on these two facts, we surveyed the recent CNN-based methods for breast cancer histology image analysis. The survey focuses on two major issues usually faced by CNN-based methods namely the design of an appropriate CNN architecture and the lack of a sufficient labelled dataset for training the model. Regarding the design of the CNN architecture, methods examining breast histology images adopt three main approaches: Designing manually from scratch the CNN architecture, using pre-trained models and adopting an automatic architecture design. Methods addressing the lack of labelled datasets are grouped into four categories: methods using pre-trained models, methods using data augmentation, methods adopting weakly supervised learning and those adopting feedforward filter learning. Research works from each category and reported performance are presented in this paper. We conclude the paper by indicating some future research directions related to the analysis of histology images.
Reading Better?: Enhancing Thai EFL Secondary School Students&amp;#39; Reading Comprehension Abilities with the Use of Graphic Organizers

The purpose of this study is to examine the effectiveness of utilizing graphic organizers to enhance Thai EFL students&amp;#39; reading comprehension abilities and their opinions regarding using graphic organizers for reading comprehension. To gather information from sixty-four upper secondary school students at a public school in Bangkok, this research study used 1) instructional instruments: ten reading passages from a coursebook and ten graphic organizers designed by the researcher; and 2) research instruments: two English reading comprehension tests with multiple-choice, true/false, and short-answer questions, and focus-group interviews about students&amp;#39; opinions toward English reading instruction using graphic organizers. The data were analyzed using descriptive statistics, including mean scores, normalized gains, standard deviations, and inferential statistics, namely the dependent t-test and Cohen&amp;rsquo;s d. The results indicated that the mean score on the reading comprehension post-test was significantly higher than the mean score on the pre-test at the 0.05 level of significance, and the experimental group acquired a higher score (M=87.00, SD=10.55) than the control group (M=51.18, SD=12.127) with the effect size of .846. Students exhibited a favorable opinion toward English reading instruction employing graphic organizers; and the average normalized gain was in the high gain range, g&amp;gt; = 0.78. Additionally, this research established that English reading instruction employing graphic organizers benefited students with low reading abilities and improved their abilities to comprehend what they read. The findings of this study provide potential avenues for improving students&amp;#39; reading comprehension.&#x0D; &amp;nbsp;
Perspectives on Covid19 Safety Protocols among Non-Native English Speaking Teachers and Students

This study sought to elicit global perspectives on Covid19 safety protocols when communicated in English to teachers and students of other languages. Adherence through comprehension of safety protocols by non-native English speaking students and teachers of higher education institutions (HEIs) is central to this investigation. Covid19 pandemic resulted in an unprecedented impact on the education sector. Since the well-being of students and teachers against any form of risk is a priority, safety protocols should be communicated effectively using comprehensible language. However, there is a valid observation on the lack of inquiries on how HEIs communicate, facilitate and implement those safety protocols to ensure adherence. Hence, the quantitative research approach was employed to address the problem presented. Primary data were collected through a structured questionnaire from 450 global student and teacher respondents. The respondents were chosen through a referral sampling technique, also known as a snowball, they joined the online survey, which took place for a month. The descriptive correlational research design was used, and the data were treated using descriptive and inferential statistical tools. Results revealed that out of eighteen components of safety protocols for educational institutions, wearing face masks is clearly understood and was noted as the most prevalently observed. The high level of knowledge and awareness on safety protocols among respondents is linked to massive information dissemination. The step-wise regression model identified only two significant predictors for a conforming perspective: wearing a face mask and physical distancing. The rest of the pre-identified protocols do not significantly affect or influence the perspectives. These results suggest that the respondents adhere to all protocols and agree that wearing face masks is their foremost concern. A negative perspective is indicated toward those who resist following the Covid19 protocols, suggesting that the respondents are aware of the importance of safety protocols in reducing positive cases if religiously observed. This study concludes that protocols for any crisis should be maintained and institutionalised because they serve their purpose better with proper implementation. Furthermore, English as a medium for communicating those health and safety is not a barrier and did not interfere with the respondents’ understanding and adherence. Therefore, the language of the protocols is within the grasp of the respondents, which is attributed to the success of its implementation.
Efficient NFS Model for Risk Estimation in a Risk-Based Access Control Model

Providing a dynamic access control model that uses real-time features to make access decisions for IoT applications is one of the research gaps that many researchers are trying to tackle. This is because existing access control models are built using static and predefined policies that always give the same result in different situations and cannot adapt to changing and unpredicted situations. One of the dynamic models that utilize real-time and contextual features to make access decisions is the risk-based access control model. This model performs a risk analysis on each access request to permit or deny access dynamically based on the estimated risk value. However, the major issue associated with building this model is providing a dynamic, reliable, and accurate risk estimation technique, especially when there is no available dataset to describe risk likelihood and impact. Therefore, this paper proposes a Neuro-Fuzzy System (NFS) model to estimate the security risk value associated with each access request. The proposed NFS model was trained using three learning algorithms: Levenberg–Marquardt (LM), Conjugate Gradient with Fletcher–Reeves (CGF), and Scaled Conjugate Gradient (SCG). The results demonstrated that the LM algorithm is the optimal learning algorithm to implement the NFS model for risk estimation. The results also demonstrated that the proposed NFS model provides a short and efficient processing time, which can provide timeliness risk estimation technique for various IoT applications. The proposed NFS model was evaluated against access control scenarios of a children’s hospital, and the results demonstrated that the proposed model can be applied to provide dynamic and contextual-aware access decisions based on real-time features.
Improving Biomedical Information Retrieval with Neural Retrievers

Information retrieval (IR) is essential in search engines and dialogue systems as well as natural language processing tasks such as open-domain question answering. IR serve an important function in the biomedical domain, where content and sources of scientific knowledge may evolve rapidly. Although neural retrievers have surpassed traditional IR approaches such as TF-IDF and BM25 in standard open-domain question answering tasks, they are still found lacking in the biomedical domain. In this paper, we seek to improve information retrieval (IR) using neural retrievers (NR) in the biomedical domain, and achieve this goal using a three-pronged approach. First, to tackle the relative lack of data in the biomedical domain, we propose a template-based question generation method that can be leveraged to train neural retriever models. Second, we develop two novel pre-training tasks that are closely aligned to the downstream task of information retrieval. Third, we introduce the ``Poly-DPR'' model which encodes each context into multiple context vectors. Extensive experiments and analysis on the BioASQ challenge suggest that our proposed method leads to large gains over existing neural approaches and beats BM25 in the small-corpus setting. We show that BM25 and our method can complement each other, and a simple hybrid model leads to further gains in the large corpus setting.
“Is Whole Word Masking Always Better for Chinese BERT?”: Probing on Chinese Grammatical Error Correction

Whole word masking (WWM), which masks all subwords corresponding to a word at once, makes a better English BERT model (Sennrich et al., 2016).For the Chinese language, however, there is no subword because each token is an atomic character.The meaning of a word in Chinese is different in that a word is a compositional unit consisting of multiple characters.Such difference motivates us to investigate whether WWM leads to better context understanding ability for Chinese BERT.To achieve this, we introduce two probing tasks related to grammatical error correction and ask pretrained models to revise or insert tokens in a masked language modeling manner.We construct a dataset including labels for 19,075 tokens in 10,448 sentences.We train three Chinese BERT models with standard character-level masking (CLM), WWM, and a combination of CLM and WWM, respectively.Our major findings are as follows: First, when one character needs to be inserted or replaced, the model trained with CLM performs the best.Second, when more than one character needs to be handled, WWM is the key to better performance.Finally, when being fine-tuned on sentence-level downstream tasks, models trained with different masking strategies perform comparably.
Cultivation of Teenagers’ Digital Media Literacy and Network Legal Literacy in the Era of Digital Virtual Technology

Digital virtual technology with media survival as an important representation, with the rapid development of the new media technology represented by the Internet, digital virtual technology with its entertainment, personalized service, open sharing of content, form of real-time interaction and other characteristics strongly attract the young group. In the digital virtual era, teenagers’ digital media literacy has not only become one of the essential qualities for them to cope with global competition, but also an essential quality for college students to realize lifelong learning. Therefore, it is not only necessary but also urgent to carry out systematic digital media literacy education for adolescent groups. This study first discusses that the typical illegal phenomenon in the network is the problem caused by the lack of network legal awareness, and analyzes the level of media literacy of the subject media in these cases and whether the network legal awareness is weak. Finally, from the perspective of school education, social publicity and national system support, this paper puts forward some Suggestions on how to improve teenagers’ media literacy and cultivate their network legal literacy, so as to create a harmonious network environment. Establish a scientific and reasonable indicator system of youth network literacy, and build a youth network literacy training mechanism.
Geospatial Web Services Discovery through Semantic Annotation of WPS

This paper presents an approach to GWS (GeospatialWeb Service) discovery through the semantic annotation of WPS (Web Processing Service) service descriptions. The rationale behind this work is that search engines that use appropriate semantic-based similarity measures in the matching process are more accurate in terms of precision and recall than those based on syntactic matching alone. The lack of semantics in the description of services using a standard such as WPS prevents the use of such a matching process and is considered a limitation of GWS discovery. The GWS discovery approach presented is based on the consideration of semantics in the service description method and in the matching process. The description of services is based on a semantic lightweight meta-model instantiated in the WPS 2.0 standard, extending the description of the service through metadata tags. The matching process is performed in three steps (functionality matching step, I/O (Input/Output) matching step and non-functional matching step). Its core is a semantic similarity measure that combines logical and non-logical matching methods. Finally, the paper presents the results of an experiment applying the proposed discovery approach on a GWS corpus, showing promising results and the added value of the three-step matching process.
Extraction of Drug Review Polarity Using Sentimental Analysis

The social networking site and user sites contain a large amount of information with users' feelings and opinions in various fields.For example, pharmaceutical companies provide users with text reviews and drug numeric ratings.Anyhow, these text-oriented reviews may not always be consistent with numerical values.In this project, we use different sentiment analysis models to differentiate drug user rating emotions and compare their accuracy.Various machine learning models including Logistic Regression, XG boost, and Naïve Bayes classifiers are being implemented by pushing it with drug reviews as inputs.In this, the XG boost model performed much better than other models with a total accuracy of 79.7%.This study has shown that these classification models can be used to separate drug reviews and identify the overall polarity or sentiment of the consumers.Since the main focus of this study was to separate the text reviews, the data were segregated according to each polarity.The division of binary classification was chosen instead of multiple classes as the purpose of this project was to identify the best and worst points/polarity.With the help of this polarity, it helps the nutritionist and the doctor to get updated with the in-market polarity of a particular drug and its public response.
Training and Educational Innovation: An Evaluative Perspective of the Digital Teaching Competence

This article analyzes the assessment of the four development levels of the Digital Teaching Competence (DTC) to recognize the needs and formulation of challenges in training and educational innovation required in the pedagogical practices of university professors under the current context of the Covid-19 pandemic. The study used an empirical-analytical methodology with a non-experimental, transactional, descriptive design. The sample design was probabilistic, estimated with 95% confidence and 5% error among 252 teachers from various faculties of the University of La Guajira (Colombia). The selected instrument corresponded to the rubric's application to evaluate the university professor's digital teaching competence in Latin America. Among the study's significant results, we highlight that the rubric presented a high Cronbach's alpha reliability (α: 0.947). In the general assessment of DTC development, it was estimated that 78.2% of teachers are in the first two levels of DTC development assessment (Beginning and Middle). The evaluation rubric allows identifying challenges and opportunities that teacher training must address to advance the professional development of professors.
Pragmatic Inferences and Moral Factors in Treaty Interpretation—Applying Experimental Linguistics to International Law

Abstract The article argues, based on results from massive online survey experiments, that, just as the utterances from ordinary conversation, legal rules can convey a surplus meaning, which is more than just the amalgam of the meanings of the words which are employed in the legal rule’s formulation. More precisely, the experiments check whether a typology of the types of this surplus meaning—pragmatic typology—describes adequately the psychological processing of, not only everyday speech, but also legal rules. In two experiments—total N = 733—we find that in morally neutral cases the pragmatic typology adequately describes the psychological processes involved in the interpretation of a legal rule. However, we also find that in morally valenced cases, it is rather the moral inferences carried by participants that shape the pragmatic inferences than the other way around.
The voice synthesis business: 2022 update

Abstract In the past few years, high-quality automated text-to-speech synthesis has effectively become a commodity, with easy access to cloud-based APIs provided by a number of major players. At the same time, developments in deep learning have broadened the scope of voice synthesis functionalities that can be delivered, leading to a growth in the range of commercially viable use cases. We take a look at the technology features and use cases that have attracted attention and investment in the past few years, identifying the major players and recent start-ups in the space.
Human Judgement as a Compass to Navigate Automatic Metrics for Formality Transfer

Although text style transfer has witnessed rapid development in recent years, there is as yet no established standard for evaluation, which is performed using several automatic metrics, lacking the possibility of always resorting to human judgement.We focus on the task of formality transfer, and on the three aspects that are usually evaluated: style strength, content preservation, and fluency.To cast light on how such aspects are assessed by common and new metrics, we run a human-based evaluation and perform a rich correlation analysis.We are then able to offer some recommendations on the use of such metrics in formality transfer, also with an eye to their generalisability (or not) to related tasks. 1