Reviewerly/exHarmony huggingface dataset:
The Shortcomings of Video Conferencing Technology, Methods for Revealing Them, and Emerging XR Solutions

Abstract Video conferencing has become a central part of our daily lives, thanks to the COVID-19 pandemic. Unfortunately, so have its many limitations, resulting in poor support for communicative and social behavior and ultimately, “Zoom fatigue.” New technologies will be required to address these limitations, including many drawn from mixed reality (XR). In this paper, our goals are to equip and encourage future researchers to develop and test such technologies. Toward this end, we first survey research on the shortcomings of video conferencing systems, as defined before and after the pandemic. We then consider the methods that research uses to evaluate support for communicative behavior, and argue that those same methods should be employed in identifying, improving, and validating promising video conferencing technologies. Next, we survey emerging XR solutions to video conferencing's limitations, most of which do not employ head-mounted displays. We conclude by identifying several opportunities for video conferencing research in a post-pandemic, hybrid working environment.
Development and Effects of a Virtual Reality Simulation Nursing Education Program Combined With Clinical Practice Based on an Information Processing Model

The need to strengthen patient human rights and create a patient-centered healthcare environment is growing. Also as science and technology develop, new educational methods using virtual reality in nursing education are emerging. This study aimed to develop a virtual reality simulation nursing education program related to postoperative patient nursing based on an information processing model and to verify its effectiveness. Clinical practice-linked virtual reality simulation nursing education was conducted for a total of 4 weeks. Nursing students were divided into an experimental group (n = 22) experiencing virtual reality simulation combined with clinical practice and a control group (n = 22) having routine clinical practice. The analytical results of this study indicated that the information processing model-based virtual reality simulation nursing education program was effective in improving nursing students' performance confidence and clinical decision-making ability. Therefore, the virtual reality simulation program developed in this study can provide basic data for the development of a simulation curriculum in the future and can contribute to the development of clinical competency as a professional nurse by improving the performance confidence and clinical decision-making ability of nursing students.
The epistemic machinery of educational platforms

ABSTRACTThis paper adds an algorithmic epistemology perspective to previous works that examine the datafication of subjective social and emotional characteristics, perceptions, and behaviours. The paper employs a comparative epistemological approach to explore two behavioural educational platforms: RedCritter Teacher and Panorama Education. We unpack these platforms’ epistemic arrays of data extraction, interfaces, justifications, and representations and the knowledge arrangements they aim to constitute in schools. Borrowing Knorr-Cetina’s notion of ‘epistemic machineries,’ which entails the material and discursive apparatuses of knowledge production in specific ‘epistemic cultures,’ we analyse the platforms’ empirical apparatuses, ontological assumptions, and referential constructions. This exploration unearths two different digital epistemes: a gamified, engagement-based recognition system; and a bureaucratic-scientific framework that aims to produce actionable data for educators and schools. We explore the ramifications of these different human-machine entanglements and question how these bring to bare on pertaining to school life, such as human agency, knowledge practices, and educational decision-making.KEYWORDS: Educational platformsSELepistemic machinerystudents’ charactersdatafication Disclosure statementNo potential conflict of interest was reported by the author(s).
Retracted: Construction of the Public Management Performance Assessment Algorithm Using Fuzzy Clustering

[This retracts the article DOI: 10.1155/2022/1272099.].
Retracted: Clinical Observation of the Effect of Nasal Breathing on Nonanalgesic and Sedative Gastroscopy

[This retracts the article DOI: 10.1155/2022/8603625.].
The Use of Games in Citizen Science Based on Findings from the EyeWire User Study

The article addresses the use of games for citizen research. Following the results of the EyeWire user research, the authors attempt to answer the question of the impact of introductory game training on task performance, identify the areas with the most significant effect on participants’ performance, and assess users’ impressions and level of engagement in the proposed working model. A survey method was used to investigate user impressions. Fixation data were obtained from eye-tracking studies. The research shows, that users with experience with computer games do better in scientific discovery games. The main reasons to engage in this type of project are the need for learning development and self-development. The results indicate a significant cognitive strain on users, notably in the initial phase of solving tasks independently. It infers the conclusion that this should be considered when designing such programs and the pace of introducing the user to its functions should be adjusted.
Retracted: MRI Imaging Omics and Risk Factors Analysis of PWMD in Premature Infants Based on Fuzzy Clustering Algorithm

[This retracts the article DOI: 10.1155/2022/8624617.].
A cloud based enhanced CPABE framework for efficient user and attribute-level revocation

Outsourcing massive amounts of data to the cloud service provider (CSP) has raised various security concerns for data confidentiality and access control. The ciphertext policy attribute based encryption (CPABE) scheme allows data owners to impose access control on their cloud-resident sensitive data. This paper has studied the approaches adopted to revoke users by the existing bilinear pairing cryptography based CPABE schemes. The existing studies have suggested solutions to revocation either by updating the non-revoked users’ keys or updating the ciphertext. Such approaches increase computational overhead for resource-constrained devices. In addition, a few studies have discussed the possibility of the CSP becoming dishonest and colluding with the revoked users. The likelihood of a collusion attack caused by the CSP and the revoked users also needs extensive attention. The development of the proposed proxy-based framework aims to extend the existing CPABE scheme and simplify the revocation of access rights at the user and attribute level with scalability, dynamicity, collusion resistance, and forward/backward secrecy. The proposed framework uses bilinear pairing cryptography and LSSS as an access structure. Furthermore, the security and performance analysis of the proposed framework reflects that it is implementable, better, and more secure than the existing work.
Hardware acceleration design of the SHA-3 for high throughput and low area on FPGA

Abstract In sensitive communications, the cryptographic hash function plays a crucial role, including in the military, healthcare, and banking, ensuring secure transmission by verifying data integrity and carrying out other vital tasks. Compared to other cryptographic hash algorithms, such as SHA-1 and SHA-2, the Keccak hash function (SHA-3) boasts superior hardware performance and is more resilient to modern cryptanalysis techniques. Nonetheless, hardware performance enhancements, such as boosting speed or reducing area usage, are constantly required. This research focuses on increasing the Keccak hash algorithm’s throughput rate by introducing a novel architecture that reduces the total number of clock cycles required to obtain the result of a hash function. Additionally, the new simplified structure of the round constant (RC) generator design assures a reasonably low area and achieves the highest throughput and efficiency. Thus, when implemented, it achieved the highest throughput of 19.515 Gbps, 24.428 Gbps, 33.393 Gbps, and 36.358 Gbps on FPGA devices with the Virtex-5, Artix-7, Virtex-6, and Virtex-7, respectively. Finally, our approach is compared to recently published designs.
Resource-Centric Goal Model Slicing for Detecting Feature Interactions

Feature interaction (FI) occurs when the requirements are satisfied by the features in isolation but not in composition. We present a novel approach to FI detection via a lightweight modeling of two features’ resource dependency. Our preliminary study on two Zoom features shows three types of resource dependency: produce-and-use, state-changing, and mutual-exclusion. We present the testing pattern associated with each type, report the FI testing results, and discuss our long-term directions toward using real-world software’s features to ground and evaluate requirements engineering research.
COUNTER in context: a case study on journal package usage

This study assesses electronic journal usage for subscription packages and discusses the challenges for librarians to interpret COUNTER data accurately. By correlating usage and holding data from two different types of vendor, COUNTER 5 Title Master Report and Electronic Holdings Report, the authors were able to review journal usage in a larger context and gain insights into how usage was affected by other factors, such as journal access models and vendor platform design. While the findings showed a high percentage of overall usage for subscription journals, one surprising result was the usage of non-subscribed journal titles in all COUNTER metrics.
Distress Signal Recognition Using Pose Estimation and Neural Networks

Detecting real-time distress signals in public places can help authorities react promptly in life-threatening circumstances or violent encounters and assist those in distress. The most common way to ask for help is to shout or flap your hands above your head, often called a “waving” action. This study aims to deploy distress signal recognition software on existing surveillance systems or cameras to automatically identify risky situations and alert the authorities. This will reduce human interaction and the constant monitoring of numerous surveillance feeds. In this paper, we propose two different methods for distress signal recognition. Both approaches use pose estimation models, which are used to identify and locate skeletal points on the human body. In the first approach, we propose an algorithm that detects a series of features that are used to identify a distress signal action. In the second approach, we propose a neural network-based approach to recognize distress signals. The neural network is trained on a dataset that we have constructed by identifying major features calculated using the skeletal points from the pose estimation model. Our proposed approach uses significantly fewer training data compared to the traditional convolutional neural network (CNN) approaches that use image or video data for action recognition. In this paper, we compare and assess the two approaches and demonstrate their effectiveness on a real-world dataset.
Manfaat chatGPT dalam hubungan manusia dengan alam: menggunakan chatGPT di bidang pertanian

ChatGPT, a powerful language model, claims to be able to analyze data and provide actionable agricultural insights. According to him, this data can be used for crop forecasting, soil analysis, plant disease and pest identification, precision farming and irrigation scheduling. ChatGPT states that it can be used to generate accurate and timely reports, alerts, and insights that can help businesses make more informed decisions, improve results, and improve customer service. In this article the author explores the potential use of GPT chat in agriculture, according to him. The author admits asking chatGPT questions about its uses for agriculture. Some of its stated uses are possible now and some are potential for the future. The author has analyzed and edited GPT chat replies.
Named Entity Recognition for peer-review disambiguation in academic publishing

In recent years, there has been a constant increase in the number of scientific peer-reviewed articles published. Each of these articles has to go through a laborious process, from peer review, through author revision rounds, to the final decision made by the editor-in-chief. Lacking time and being under pressure with diverse research tasks, senior scientists need new tools to automate parts of their activities. In this paper, we propose a new approach based on named entity recognition that is able to annotate review comments in order to extract meaningful information about changes requested by reviewers. This research focuses on deep learning models that are achieving state-of-the-art results in many natural language processing tasks. Exploring the performance of BERT-based and XLNet models on the review comments annotation task, a “review-annotation“ model based on SciBERT was trained, able to achieve an F1 score of 0.87. Its usage allows different players in the academic publishing process to better understand the review request. In addition, the correlation of the requested and the actual changes is made possible, allowing the final decision-maker to strengthen the article evaluation.
Long Text Classification Using Pre-trained Language Model for a Low-Resource Language

Text classification is an essential task of Natural Language Processing (NLP) that intends to classify texts into predefined classes. Most recent studies show that transformer-based pre-trained language models such as BERT and RoBERTa have achieved state-of-the-art performance in several downstream NLP tasks. Despite their advantages, these models suffer from one primary drawback of the restricted input size. Because of this limitation, they cannot operate the entire input long texts. This paper presents an approach that utilizes the self-attention mechanism to address the bottleneck of most pre-trained language models of long input texts in the case of Amharic, regarded as a low-resourced language. Specifically, our method carefully investigates the significance of each word in the dataset using a self-attention mechanism. Then identify and select the most relevant words according to their attention scores. Finally, we train our model on the filtered text. Our results show that the approach achieves better performance in terms of accuracy compared to the baseline model.
Improved Consistency in Price Negotiation Dialogue System Using Parameterized Action Space with Generative Adversarial Imitation Learning

A price negotiation dialogue system should not only gain profit but also pay attention to the negotiation process, such as the consistency of price proposals. In particular, when using a parameterized action space to make price proposals as a continuous action, the proposals can be more flexible but potentially inconsistent. In this study, we propose introducing Generative Adversarial Imitation Learning (GAIL) to price negotiation dialogues with a parameterized action space. To the best of our knowledge, this is the first case study to introduce GAIL in parameterized action space. In addition, we work on extending the dialogue act to maintain consistency, and on combining parameteried action reinforcement learning(RL) and GAIL by using Multi-Critic. The proposed method is applied to the CRAIGSLISTBARGAIN negotiation task, which is a practical negotiation task and is trained in a multi-agent format as a seller and buyer without using a simulator, and is evaluated by interacting with agents that combine supervised learning and rule-based methods. The results show that GAIL can reduce price inconsistencies better than RL with the designed reward function for consistency maintenance or the combination of RL and behavior cloning. Furthermore, we confirmed that the combined method of RL with the designed reward function and GAIL can reduce price inconsistencies the most and also enhance profit-seeking abilities.
Blockchain-based Certificate Management with Multi-Party Authentication

The authenticity and reliability of user identity are the premise of secure network communication. Public key infrastructure (PKI) issues certificates through certificate authority (CA) and provides users with secure identity management services. In traditional PKI systems, the CA is given much power, and the compromise of CA will lead to a single point of failure. How to prevent the CA from issuing fraudulent certificates has become a vital issue. In this paper, we propose a blockchain-based certificate management scheme. The proposed scheme separates the identity verification process from certificate issuance, and distributes the verification task to multiple registration authorities (RAs). This can prevent the attacker from issuing fraudulent certificates by controlling one CA or RA. Besides, the proposed scheme requires the subject of a certificate to store information about the certificate on the blockchain. Only if the corresponding record can be found on the blockchain, the certificate will be considered validate. As a result, the impersonation attack can be prevented. Simulation results demonstrate that the proposed certificate management scheme is feasible.
Discovering Coherent Topics from Urdu Text: A Comparative Study of Statistical Models, Clustering Techniques and Word Embedding

The volume of data on the internet is continuously expanding due to the abundance of news sources, journals, blogs, contents, and other online publications. The use of Urdu online has grown significantly, much like other languages. Information retrieval (IR) is getting more challenging as data amount rises. The natural language processing (NLP) technique of topic modelling (TM) is crucial for extracting themes or aspects from text. Although there is a long tradition of TM in both English and other western languages, Urdu falls behind in terms of sophisticated NLP tools and resources for TM. The rich morphology of the Urdu language makes TM a challenging task. In this study, we developed a framework of TM and analysed word embedding, statistical models, and clustering techniques for Urdu documents. The aim of this work is to evaluate and compare three distinct approaches based on the coherence measure of extracted topics. The findings of a thorough experiment and evaluation demonstrate that word embedding fails to extract coherent topics in Urdu language, and that the average coherence measure of topics retrieved by clustering approaches outperforms that discovered through statistical models.
A Content-Based Dataset Recommendation System for Biomedical Datasets

Nowadays, with the rapid development of cloud data and online collaboration platforms, there is a growing trend among researchers to make their data publicly available for experimental reproducibility and data reusability. On one hand, sharing data with collaborators increases the visibility of the work. On the other hand, the abundance of data on multiple platforms makes it hard for researchers to find data relevant to their own research. To overcome this challenge, a dataset recommendation system capable of finding relevant datasets from multiple resources would be helpful. In the past two decades, few dataset recommendation methods have been implemented, that are mostly domain-specific or simply recommend datasets based on keywords. We believe a general dataset recommender system that recommends datasets with information either extracted from another dataset or supplied by researchers can enhance researchers’ efficiency in searching for relevant data and significantly improve their research efficiency. This work adopts an information retrieval (IR) paradigm for dataset recommendation. By extracting summary information from each dataset and generating a profile for each, we use and compare multiple content-based recommendation methods to recommend the most-relevant datasets in GEO, SRA, and several other repositories. Our results and evaluations prove the usefulness and need for such system.
Educational Group Formation Problem Resolution Based on an Improved Swarm Particle Optimization Using Fuzzy Knowledge

In educational context, instructors usually partition students into collaborative learning teams to perform collaborative learning tasks. Indeed, one of the grouping criteria most utilized by instructors is based on the students’ roles and on forming similar teams according to the roles of their members which is costly and complex. In this paper, we address the optimization problem of forming automatically learning teams by minimizing the knowledge-difference cost among formed teams. The knowledge index of each group depends on the Belbin roles of their students’ members in the form of a sum of students’ fuzzy rating indexes. The proposed algorithm is called improved particle swarm optimization with multi-parent order crossover (IPSOMPOX). The multi-parent order crossover is used in IPSOMPOX in order to investigate new solutions in the search space and to accelerate the convergence of the proposed algorithm to the best global solution. To evaluate the performance of the proposed algorithm, we apply it on several different experiments with different numbers of teams and students. The proposed algorithm is compared with the standard PSO and has proved better performance are shown in our results.
Offensive Language Detection in Spanish Social Media: Testing From Bag-of-Words to Transformers Models

Social networks allow us to communicate with people around the world. However, some users usually take advantage of anonymity for writing offensive comments to others, which might affect those who receive offensive messages or discourage the use of these networks. However, it is impossible to manually check every message. This has promoted several proposals for automatic detection systems. Current state-of-the-art systems are based on the transformers’ architecture and most of the work has been focused on the English language. However, these systems do not pay too much attention to the unbalanced nature of data, since there are fewer offensive comments than non-offensive in a real environment. Besides, these previous works have not studied the impact on the final results of pre-processing or the corpora used for pre-training the models. In this work, we propose and evaluate a series of automatic methods aimed at detecting offensive language in Spanish texts addressing the unbalanced nature of data. We test different learning models, from those based on classical Machine Learning algorithms using Bag-of-Words as data representation to those based in large language models and neural networks such as transformers, paying more attention to minor classes and the corpora used for pre-training the transformer-based models. We show how transformer-based models continue obtaining the best results, but we improved previous results by a 6,2% by adding new steps of pre-processing and using models pre-trained with Spanish social-media data, setting new state-of-the-art results.