Reviewerly/exHarmony huggingface dataset:
A systematic review of the literature on the application of blockchain in the health supply chain

Objective of the study: This article aims to present a systematic literature review on the application of blockchain in the healthcare supply chain.Relevance / originality: The relevance and originality consist in the presentation of a systematic literature review that focuses and considers the concepts of blockchain, healthcare and supply chain as equally important, in the studied time frame.Methodology / approach: The systematic literature review followed a protocol based on Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), performing the search in eight databases, selecting conference and journal articles based on their content in sequential steps.Main results: As a result, 122 publications were selected. The number of publications in the field has been found to grow rapidly, and sensors and the Internet of Things (IoT) are two prominent minor topics. Three research challenges were raised.Theoretical / methodological contributions: Provide understanding to academics and professionals in the field about the state of the art of applying the blockchain in the healthcare supply chain, both in terms of the evolution of the literature, as well as in terms of raising new research.Social / management contributions: Raising discussions on solutions to healthcare supply chain issues, involving for example data integrity and privacy in electronic medical reports, drug counterfeiting and the use of sensors and the internet of things.
Investigating the Use of AR Glasses for Content Annotation on Mobile Devices

Mobile devices such as smartphones and tablets have limited display size and input capabilities that make a variety of tasks challenging. Coupling the mobile device with Augmented Reality eyewear such as smartglasses can help address some of these challenges. In the specific context of digital content annotation tasks, this combination has the potential to enhance the user experience on two fronts. First, annotations can be offloaded into the air around the mobile device, freeing precious screen real-estate. Second, as smartglasses often come equipped with a variety of sensors including a camera, users can annotate documents with pictures or videos of their environment, captured on the spot, hands-free, and from the wearer's perspective. We present AnnotAR, a prototype that we use as a research probe to assess the viability of this approach to digital content annotation. We use AnnotAR to gather users' preliminary feedback in a laboratory setting, and to showcase how it could support real-world use cases.
Art Design Teaching Based on the Multidata Fusion Algorithm and Virtual Simulation Technology

Virtual Reality (VR) technologies are widely applied to teaching art design. VR has been created with high-level techniques that create the artificial environment to support virtual aesthetics. Especially ceramics art design requires technical advancement to enhance business individuals. However, the user requires the virtual reality experience to improve the art design teaching performance. During the teaching process, reality learning patterns, experiences, and fusion ceramic product design are required to enhance art design teaching. So, in this research, modern art design has been optimized using virtual reality with a deep learning architecture for craftsmanship, style, ideals, creative aesthetics, cultural ramifications, and inherited designers to create an effective model. The deep learning-assisted gate array algorithm (GAA) is used to optimize the modern art structure in system design. Therefore, this approach reveals some experimental findings that produce better performance benefits than saving time and resources in traditional manual detection systems.
Construction of Computer English Corpus Assisted by Internet of Things Information Perception and Interaction Technology

Aiming at the situation that multiple sensing devices in the sensing layer sense the same sensing quantity, multiple redundant measurement data will be generated. In this paper, an improved Kalman filtering algorithm is used. The algorithm combines with numerical test theory to remove abnormal error data before filtering, thereby reducing the error of filtering result and improving its reliability. The parallel multiprotocol processing mechanism of the gateway is designed, and the IoT gateway has a hierarchical modular processing design for different protocol signals of the heterogeneous perception layer. The web interaction function with good platform versatility is designed to provide a visual interaction interface for the application layer, which is convenient for the application layer terminal to monitor and manage the perception layer equipment. This paper realizes the retrieval and query function of keywords, wrong sentences, and English character errors. This corpus enriches the less existing Madagascar corpus and establishes a separate English character error query database. This paper describes the overall process of corpus from preliminary preparation to establishment and the related use instructions of corpus, makes a statistical analysis of the current corpus data, and puts forward some suggestions for building a computer English corpus according to the analysis results.
Cross-Cultural Understanding in Learning English at IAIN Takengon Students

Culture is the identity of a country, and as a differentiator between one country and another, the purpose of studying cross-cultural understanding is to understand the differences between cultures in other countries so that there will be no misunderstanding of the culture of one country and another. Therefore, it is essential for students, especially English students. This research is descriptive quantitative research with interviews and observations. The results show that the understanding of the 6th-semester students of the English study program about cross-cultural and language is excellent. In observations and interviews, students understand the cultural rules inherent in other countries, the concept of discipline and respecting time in other countries, and how to respect one's privacy. And students understand the relationship between language and culture.
Issues in the Implementation of 21st Century Learning Skills in Malaysian ESL Classrooms

: Implementing 21st century learning skills in the English language classrooms requires both teachers and students to learn and adapt new knowledge and approaches during the teaching and learning process. This research seeks to identify the issues related to the implementation of 21st century learning skills in the Malaysian English as a Second Language (ESL) classrooms. Through semi-structured interviews and written surveys, this qualitative study involved five School Specialist Improvement Coaches (SISCs) and 116 English language teachers. The findings of the study highlighted important issues such as teachers' resistance and misconstrued perspectives towards 21st century learning skills. The results implicate that more comprehensive measures are needed to ensure effective implementation of 21st century learning skills in Malaysian ESL classrooms. The measures would assist in addressing issues which can hinder the implementation of 21st century learning skills.
Visualization of Materials Science Topics in Publications of Institutional Repository using Natural Language Processing

SAMURAI (NIMS 2022), a directory service of the National Institute for Materials Science (NIMS) researchers in Japan was launched in 2009 following the development of NIMS institutional repository (Tanifuji et al. 2019). The concept is to synchronize between profile information of researchers and their publications which are self-archived in the repository system. The SAMURAI was renewed in 2017 with interoperable functions with ORCID. SAMURAI supports various links to not only individual articles and patents, but also to databases such as KAKEN (Database of Grants-in-Aid for Scientific Research by NII). The service has yielded fully identified authors of journal articles from research members of NIMS by implementing a unique ResearcherID. Through this directory, NIMS is promoting materials research, supporting management of its researchers activities, and introducing NIMS researchers and their work to the public. In this work, we present an application to describe each researcher's output topics automatically from the archived research papers in the repository, by implementing materials science specific natural language processing developed in our study (Dieb et al. 2021) that visualizes the research trend of each SAMURAI researchers. The approach can maximize information absorbance for general audience and fully corresponds to open science policy. A list of publications' digital object identifiers (DOIs DOI 2022) for each researcher was constructed from his profile in SAMURAI. (In SAMURAI, the DOIs are stored in a PostgreSQL database). Using the DOI, recent publications were retrieved from NIMS text data mining platform (TDM-PF) in their XML format which were mainly available from 2003. Representative topic terms from their research publications that are related to materials science and engineering were extracted. We utilize term frequency analysis and automatic extraction for materials names to extract these necessary informative terms. Additionally, domain knowledge resources such as dictionaries were used. Data was preprocessed using noise reduction such as removing general English language stop words and physical units filtering. Such words do not have significance on their own. Word cloud approach was used for visualization (Fig. 1). This work brings us an opportunity to apply our NLP experience to mine information from research papers for public knowledge as a step towards data-driven materials science.
One for all: One-stage referring expression comprehension with dynamic reasoning

Referring Expression Comprehension (REC) is one of the most important tasks in visual reasoning that requires a model to detect the target object referred by a natural language expression. Among the proposed pipelines, the one-stage Referring Expression Comprehension (OSREC) has become the dominant trend since it merges the region proposal and selection stages. Many state-of-the-art OSREC models adopt a multi-hop reasoning strategy because a sequence of objects is frequently mentioned in a single expression which needs multi-hop reasoning to analyze the semantic relation. However, one unsolved issue of these models is that the number of reasoning steps needs to be pre-defined and fixed before inference, ignoring the varying complexity of expressions. In this paper, we propose a Dynamic Multi-step Reasoning Network, which allows the reasoning steps to be dynamically adjusted based on the reasoning state and expression complexity. Specifically, we adopt a Transformer module to memorize & process the reasoning state and a Reinforcement Learning strategy to dynamically infer the reasoning steps. The work achieves the state-of-the-art performance or significant improvements on several REC datasets, ranging from RefCOCO (+, g) with short expressions, to Ref-Reasoning, a dataset with long and complex compositional expressions.
APPLICATION OF PRIVACY-PRESERVING DATA PUBLISHING IN TERTIARY INSTITUTIONS OF KEBBI STATE USING GENERALIZATION AND SUPPRESSION

The research was conducted in the field of publishing data to preserve confidentiality. Several educational datasets have been used to address privacy and utility. The sample questionnaires served to investigate the level of privacy awareness and enforcement in the records of students in tertiary institutions in Kebbi State, Nigeria. The benchmark datasets were obtained from Kebbi State Polytechnic Dakin-gari. K-anonymity and l-diversity models were used with k configurations and suppression limits of 10 and 50% in the ARX 3.9.0 de-anonymization environment. The work evaluates data privacy, quality, and execution time for each k value and suppressions limit. Experimental results demonstrate that the higher the suppression the more balanced exists between privacy and utility. It was observed that suppression of 50% provides less anonymization time irrespective of k compared to k values in suppression = 10%. This was proved to be due to less time it takes anonymization to be completed Also, from respondents, 92% of students’ records were kept permanently in plain and, issued to third parties like that—with no privacy guarantee. This poses privacy threats to datasets.
Semantic similarity information discrimination for video captioning

Video captioning is a task that aims to automatically describe objects and their actions in videos using natural language sentences. The correct understanding of vision and language information is critical for video captioning tasks. Many existing methods usually fuse different features to generate sentences. However, the sentences have many improper nouns and verbs. Inspired by the successes of fine-grained visual recognition, we treat the problem of improper words to discriminate semantic similarity information. In this paper, we designed a semantic bilinear block (SBB) to widen the gap between the probability of existing and nonexistent words, which can capture more fine-grained features to discriminate semantic information. Moreover, our designed linear attention block (LAB) implements the channelwise attention for the 1-D feature by simplifying the squeeze-and-excitation structure. Furthermore, we designed a semantic discrimination network (SDN) that integrates the LAB and SBB into video encoder and decoder to leverage successful channelwise attention and discriminate semantic similarity information for better video captioning. Experiments on two widely used datasets, MSVD and MSR-VTT, demonstrate that our proposed SDN can achieve better performance than state-of-the-art methods.
TDO-Spider Taylor ChOA: An Optimized Deep-Learning-Based Sentiment Classification and Review Rating Prediction

Modern review websites, namely Yelp and Amazon, permit the users to post online reviews for numerous businesses, services and products. Currently, online reviewing is an imperative task in the manipulation of shopping decisions produced by customers. These reviews afford consumers experience and information regarding the superiority of the product. The prevalent method of strengthening online review evolution is the performance of Sentiment Classification, which is an attractive domain in industrial and academic research. The review helps various domains, and it is problematic to collect interpreted training data. In this paper, an effectual Review Rating Prediction and Sentiment Classification was developed. Here, a Gated Recurrent Unit (GRU) was employed for the Sentiment Classification process, whereas a Hierarchical Attention Network (HAN) was applied for Review Rating Prediction. The significant features, such as statistical, SentiWordNet and classification features, were extracted for the Sentiment Classification and Review Rating Prediction process. Moreover, the GRU was trained by the designed TD-Spider Taylor ChOA approach, and the HAN was trained by the designed Jaya-TDO approach. The experimental results show that the proposed Jaya-TDO technique attained a better performance of 0.9425, 0.9654 and 0.9538, and that TD-Spider Taylor ChOA achieved 0.9524, 0.9698 and 0.9588 in terms of the precision, recall and F-measure.
PEMEROLEHAN BAHASA ANAK USIA 3-4 TAHUN (STUDI KASUS PADA SISWA PAUD PITARA PONDOK CABE ILIR, TANGERANG SELATAN)

The purpose of this study was to describe language acquisition in children aged 3-4 years. The subjects of this study were 3 boys from PAUD Pitara Pondok Cabe Ilir, Pamulang, South Tangerang aged about 3-4 years. The focus of this research is the acquisition at the level of fontology to syntax. Data was collected by using documentation, interview, listening, and note-taking techniques. This study applies a qualitative descriptive method. The results showed that language acquisition in children aged 3-5 years occurred in the fields of phonology, syntax, and semantics. In terms of phonology, subjects E, A, and R have mastered most of the consonant phonemes. However, there are also some phonemes that cannot be pronounced perfectly. In terms of morphology, subjects E, A, and R have mastered the form of words that are obtained or mastered are monomorphemic word forms (one morpheme) and language acquisition in the syntactic aspect, indicating that there is a phenomenon of the production of simple sentences or called minor sentences.
A risk factor attention-based model for cardiovascular disease prediction

Cardiovascular disease (CVD) is a serious disease that endangers human health and is one of the main causes of death. Therefore, using the patient's electronic medical record (EMR) to predict CVD automatically has important application value in intelligent assisted diagnosis and treatment, and is a hot issue in intelligent medical research. However, existing methods based on natural language processing can only predict CVD according to the whole or part of the context information of EMR.Given the deficiencies of the existing research on CVD prediction based on EMRs, this paper proposes a risk factor attention-based model (RFAB) to predict CVD by utilizing CVD risk factors and general EMRs text, which adopts the attention mechanism of a deep neural network to fuse the character sequence and CVD risk factors contained in EMRs text. The experimental results show that the proposed method can significantly improve the prediction performance of CVD, and the F-score reaches 0.9586, which outperforms the existing related methods.RFAB focuses on the key information in EMR that leads to CVD, that is, 12 risk factors. In the stage of risk factor identification and extraction, risk factors are labeled with category information and time attribute information by BiLSTM-CRF model. In the stage of CVD prediction, the information contained in risk factors and their labels is fused with the information of character sequence in EMR to predict CVD. RFAB makes well use of the fine-grained information contained in EMR, and also provides a reliable idea for predicting CVD.
PUPILS ATTITUDE TOWARDS DIDACTIC CAREER

In this article we present the results of a stage within a pedagogical experiment on improving the attitude of high school students towards the teaching career. Being a fairly current topic, but also quite complicated, based on the fact that it is a long process and it is difficult in a short period of time to form positive attitudes towards an activity, but this is not impossible. The attitude of the pupils towards the teaching career can be different, depending on many factors, from which its external manifestation in the form of opinion or actions, can be unstable and be different from each other. We assume that capitalizing on a model of guiding pupils in the teaching career would have a positive impact on the level of attitude of pupils towards the teaching profession. In this regard, in this article we mention the tools that have been applied in this regard, and have demonstrated their effectiveness with positive comparative values.
Parasitic Knowledge Infrastructures: Data Reuse by Anthropogenic Climate Change Skeptics

ABSTRACT Stakeholders from academia, industry, funding agencies, and scholarly publishing are increasingly investing in open data partially in the hope that it will democratize science and promote more diverse data reuse. However, fewer studies examine how unconventional communities outside academia and industry use open data. Through an investigative digital ethnography, I observed the data practices of anthropogenic climate change (ACC) skeptics, specifically how they discuss, evaluate, and reuse open data. This poster focuses on the knowledge infrastructure that affords the data practices of ACC skeptics. I argue that ACC skeptics are building a parasitic knowledge infrastructure on the back of the climate science knowledge infrastructure it often seeks to discredit. Understanding the infrastructure that supports skeptics' data reuse can inform how we design policies and infrastructure to actualize open data's promises while minimizing its perils.
Prediction of Click-through Rate of Marketing Advertisements Using Deep Learning

Aiming at the defect that the click-through rate of marketing advertisements cannot provide accurate prediction results for the company in time in the marketing strategy of Internet companies, this paper uses a deep learning algorithm to establish a prediction model for the click-through rate of marketing advertisements. The suggested model is called high-order cross-feature network (HCN). Furthermore, this paper also introduces the combination of feature vectors into the graph structure and as the nodes in the graph; therefore, the graph neural network (GNN) is used to obtain the high-level representation ability of structured data more fully. Through numerical simulations, we observed that HCN has the capability to provide Internet companies with more accurate advertising business information, user information, and advertising content. Moreover, HCN model is more reasonable to adjust the advertising strategy and can provide better user experience. The simulation outcomes indicate that the suggested HCN approach has noble adaptability and high correctness in forecasting the click-through rate of marketing advertisements. We observed that this improvement, in terms of predictions precisions and accuracies, can be as high as 17.52% higher than the deep neural network (DNN) method and 10.45% higher than the factorization network (FM) approach.
Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks

With the recent prevalence of reinforcement learning (RL), there have been tremendous interests in utilizing RL for ads allocation in recommendation platforms (e.g., e-commerce and news feed sites). To achieve better allocation, the input of recent RL-based ads allocation methods is upgraded from point-wise single item to list-wise item arrangement. However, this also results in a high-dimensional space of state-action pairs, making it difficult to learn list-wise representations with good generalization ability. This further hinders the exploration of RL agents and causes poor sample efficiency. To address this problem, we propose a novel RL-based approach for ads allocation which learns better list-wise representations by leveraging task-specific signals on Meituan food delivery platform. Specifically, we propose three different auxiliary tasks based on reconstruction, prediction, and contrastive learning respectively according to prior domain knowledge on ads allocation. We conduct extensive experiments on Meituan food delivery platform to evaluate the effectiveness of the proposed auxiliary tasks. Both offline and online experimental results show that the proposed method can learn better list-wise representations and achieve higher revenue for the platform compared to the state-of-the-art baselines.