Reviewerly/exHarmony huggingface dataset:
Design and Implementation of WebXR Health Learning Module Application

The Corona-Virus Disease Pandemic of 2019 (COVID-19) has impacted every part of our lives, including schooling. Measures to prevent virus transmission have also been taken, such as the Work From Home policy. It caused various problems in understanding the lesson, especially for medical students, because most medical schools have a lot of practical lessons. Unfortunately, the practical lesson can not be done in this situation. To keep clinical learning on track in the face of a pandemic, the Environment must adjust to better train future clinicians. The authors of this work develop an immersive platform of medical learning to aid medical education in the clinical evaluation future and competency development in the healthcare education field via virtual education via a web-based extended reality (XR) experience (WebXR). This WebXR provides standard operating procedures for doing intramuscular injection and has various features like creating an account to access the Environment of intramuscular injection, giving the explanation of what VR, AR, and MR are, and also explaining more about intramuscular injection based on medical module handbooks. The authors decided to interact with the object using pointing, clicking, and dragging. This platform is a multi-cross XR platform which means it can be run on mobile VR devices, PCVR, android as AR devices, Magic Leap 1, and Hololens 2 as MR devices, so the WebXR will be a flexible web that can be accessed with various devices. Therefore, it gives advantages because of the flexibility. The authors evaluated the system using the PIECES framework and some questionnaires to give a better result.
Optimized Deep Learning Model for Lung Cancer Prediction Using ANN Algorithm

Cancer is disease in which there is unnatural growth of abnormal cells that infest healthy cells in the body. Lung cancer invokes imbalance in cells which affects the lungs. Prediction of lung cancer is very necessary at the early stages especially in populated and less income countries. Specialists use clinical traditional procedures such as blood test and other therapies. Artificial intelligence era has started, now with the help of machine learning algorithm and deep learning algorithms it is possible to develop computer aided diagnosis mechanism. This research study has considered four machine learning algorithms such as Bayes Net, Naive Bayes, Decision Tree, Random Forest and one deep learning algorithm Artificial Neural Network to predict lung cancer at early stages. For this purpose, state of art parameters are measured for different algorithms and evaluated on lung cancer dataset. Accuracy as a prime parameter is evaluated and compared for all-the five algorithms. As per experimental result, Artificial Neural Network is best learning algorithm with accuracy of 92.23%. Further accuracy in Artificial Neural Network is evaluated for one, two and three hidden layers and compared. Artificial Neural Network with one layer has highest accuracy in experiment conducted.
The Impact of Escape Room Gamification Using a Teleconferencing Platform on Pharmacy Student Learning

The purpose of this study is to design and implement a virtual escape room game using the Zoom video conferencing platform to enhance third professional doctor of pharmacy students' knowledge of self-management of sexual health and pregnancy prevention and to assess student's perception of the activity.Students were divided into five pre-assigned breakout rooms of five to six students each using the Zoom video conferencing platform, following the conclusion of the self-care sexual health and pregnancy prevention lecture. Once in the breakout room, students worked as a group to complete seven activities to gain access to a code needed to "escape the room." At the completion of the activity, students were asked to fill out a perceptions survey.Twenty-six students participated in the virtual escape room activity, with all students completing the activity within the allotted time. Overall, students expressed positive attitudes toward the escape room activity, reporting it helped them check their knowledge of the subject, remain motivated, and connect with their classmates.Overall, the virtual escape room had a positive impact on student learning and application of subject material.
BiSeNet-oriented context attention model for image semantic segmentation

When the traditional semantic segmentation model is adopted, the different feature importance of feature maps is ignored in the feature extraction stage, which results in the detail loss, and affects the segmentation effect. In this paper, we propose a BiSeNet-oriented context attention model for image semantic segmentation. In the BiSeNet, the spatial path is utilized to extract more low-level features to solve the problem of information loss in deep network layers. Context attention mechanism is used to mine high-level implied semantic features of images. Meanwhile, the focus loss is used as the loss function to improve the final segmentation effect by reducing the internal weighting. Finally, we conduct experiments on open data sets, and the results show that pixel accuracy, average pixel accuracy, and average Intersection-over-Union are greatly improved compared with other state-of-theart semantic segmentation models. It effectively improves the accuracy of feature extraction, reduces the loss of feature details, and improves the final segmentation effect.
A hybrid Transformer approach for Chinese NER with features augmentation

Named entity recognition (NER) plays an important role in many downstream tasks of natural language processing, such as knowledge extraction and information retrieval. NER of Chinese is more challenging than that of English due to lack of the explicit word boundary. Features augmentation is a potential way to improve NER model of Chinese. Pre-trained models can implicitly preserve prior knowledge with additional features. This paper proposes a hybrid Transformer approach, which first utilize the fused additional features embeddings (e.g. char embeddings, bigram embeddings, lattice embeddings and BERT embeddings) as distributed representations to augment the representation ability of model. In addition, a new training strategy named DF strategy is proposed to efficiently fine-tune Bidirectional Encoder Representations from Transformers (BERT) and other embeddings in balance. Then, the proposed model can perceive the relations of features by introducing relative position embeddings to an additional adapted Transformer encoder. Lastly, a standard Conditional Random Field is used to alleviate the obvious tag errors. The proposed model is applied to four representative Chinese datasets to investigate its performance. Experiments results show that the proposed model outperforms the other popular models in terms of accuracy. The proposed BL-BTC model can effectively improve the recognition performance of formal and informal texts.
What Soft Skills Does the Software Industry *Really* Want? An Exploratory Study of Software Positions in New Zealand

Background: Soft skills of software professionals (e.g., communication, interpersonal skills) significantly contribute to project and product success. Aims: We aim to understand (a) what are relevant soft skills in software engineering, (b) how soft skills relate to types of software engineering positions, and (c) how soft skills relate to characteristics of hiring organizations. We focus on organizations in New Zealand, a country with a relatively small but growing software sector characterized by a skills shortage and embedded in a bi-cultural context. Method: We used a qualitative research method and manually analyzed 530 job adverts from New Zealand’s largest job portal for technology-related positions. We identified soft skills following an inductive approach, i.e., without a pre-defined set of soft skills. Results: We found explicit references to soft skills in 82% of adverts. We identified 17 soft skills and proposed a contextualized software engineering description. Communication-related soft skills are most in demand, regardless of the type of position. Soft skills related to broader human or societal values (e.g., empathy or cultural awareness) or distributed development are not frequently requested. Soft skills do not depend on company size or core business. Conclusions: Employers explicitly ask for soft skills. Our findings support previous studies that highlight the importance of communication. Characteristics specific to New Zealand do not impact the demand for soft skills. Our findings benefit researchers in human aspects of software engineering and to those responsible for staff, curricula and professional development.
DevOps Practitioners’ Perceptions of the Low-code Trend

Background: DevOps is currently one of the main trends in software development. Low-Code is also an emerging tendency that, combined with DevOps, may offer significant value to software businesses by improving the process. However, how DevOps practices and low-code are combined is little known. Aim: This study aims to understand the practitioner's perspectives on low-code trends. Method: Twelve interviews with IT professionals who deal with low-code in the context of DevOps were conducted. Then, a grounded theory approach was used to theme the interview quotes into emergent categories. Results: The main result of this exploratory study reveals that such an approach is the most common response to the skill shortages of software professionals. Conclusion: This study suggests the emergence of DevOps and low-code could significantly contribute to the development of quality products with low-cost and time.
Balancing Privacy and Flexibility of Cloud-based Personal Health Records Sharing System

The Internet of Things and cloud services have been widely adopted in many applications, and personal health records (PHR) can provide tailored medical care. The PHR data is usually stored on cloud servers for sharing. Weighted ABE is a practical and flexible technique to protect PHR data. Under a weighted ABE policy, the data user's attributes will be “scored”, if and only if the score reaches the threshold value, they can access the data. However, while this approach offers a flexible access policy, the data owners have difficulty controlling their privacy, especially sharing PHR data in collaborative e-health systems. This paper aims to find a balance between privacy and flexibility and proposes an AND-weighted ABE scheme in cloud-based personal health records sharing systems. The proposed scheme can meet both privacy and flexibility. Only when the data user satisfies the scored-based policy and is in the specified organization(s), can the data user access the PHR data. Besides, we give the security proof and the performance evaluation of the proposed scheme. The security proof and performance analysis show that the proposed scheme can efficiently and securely share PHR data in cloud service.
Decision Boundary Visualization for Counterfactual Reasoning

Abstract Machine learning algorithms are widely applied to create powerful prediction models. With increasingly complex models, humans' ability to understand the decision function (that maps from a high‐dimensional input space) is quickly exceeded. To explain a model's decisions, black‐box methods have been proposed that provide either non‐linear maps of the global topology of the decision boundary, or samples that allow approximating it locally. The former loses information about distances in input space, while the latter only provides statements about given samples, but lacks a focus on the underlying model for precise ‘What‐If'‐reasoning. In this paper, we integrate both approaches and propose an interactive exploration method using local linear maps of the decision space. We create the maps on high‐dimensional hyperplanes—2D‐slices of the high‐dimensional parameter space—based on statistical and personal feature mutability and guided by feature importance. We complement the proposed workflow with established model inspection techniques to provide orientation and guidance. We demonstrate our approach on real‐world datasets and illustrate that it allows identification of instance‐based decision boundary structures and can answer multi‐dimensional ‘What‐If'‐questions, thereby identifying counterfactual scenarios visually.
Anomaly identification of critical power plant facilities based on YOLOX-CBAM

Aiming at the abnormal phenomena of steam leakage, oil leakage and water leakage in the key facilities of the power plant, this paper proposes an anomaly detection method based on YOLOX and improves the network and parameters, and the improved network can be better applied to the anomaly detection of power plants. YOLOX is an improved version of the YOLO series, using Anchor-Free, Mosaic, SimOTA and other methods to improve the network. This article adds CBAM (Convolutional Block Attention Module) on this basis and the final experiment shows that the mAP of the YOLOX-CBAM model is 2.7% higher than that of YOLOX and the detection speed is only 1.4 FPS slower.
Collective Digital Factories for Buildings: Stigmergic Collaboration Through Cryptoeconomics

The chapter describes conceptually how a blockchain (BC), through smart contracts (SC) and tokenisation, can act as a stigmergic information layer for the creation of collective digital factories in construction. The chapter focuses on the orchestration of a series of design agents and tools in the design of buildings; however, the presented framework can be extended to the whole lifecycle of the AEC industry. Furthermore, a cryptoeconomics-like strategy for the AEC industry is explored, based on smart contracts, having the potential to operationalise the stigmergic coordination via token incentive mechanisms. We expect that stigmergic coordination through cryptoeconomic incentives on the blockchain is a better fit for the fragmented nature of the construction industry, compared to current modes of organisation; consequently, the scope for presenting this strategy is threefold: the incentives mechanism can lead to an increase in productivity, a reduction in both whole-lifecycle carbon and waste, and a decentralised governance through smart contracts. While blockchain and decentralised ledger technologies have proven to have the potential to be embedded deeply as an information governance layer in many industries, the scope within the paper is limited to the digital aspects of the AEC industry, forming what we call “collective digital factories”. An engagement strategy of the manufacturing sector of the AEC industry is presented with arising open questions discussed at the end.
An Explainable Lattice based Fertility Treatment Outcome Prediction Model for TeleFertility

The global trends of women in the reproductive age have significantly altered due to their personal and career development engagements besides adoption of contraceptive methods. Since women are extending birth to their late ages where natural conception is quite hard besides other factors, it has globally boosted the fertility service market which is a projected 41.4 billion industry by 2026. Despite the growing market for fertility services, infertility evaluation is still uncomfortable, expensive, inaccessible and ambiguous for both the customers and the fertility service providers. In this work, we deploy Machine Learning and Explainable Artificial Intelligence to predict the outcomes of fertility treatment using interpretable Machine Learning Lattice Models for predictive, preventive and precision reproductive medicine. We also introduce the concept of Quantum Lattice Learning in Artificial Intelligence for Machine Learning Interpretability.
Outlining and Filling: Hierarchical Query Graph Generation for Answering Complex Questions Over Knowledge Graphs

Query graph construction aims to construct the correct executable SPARQL on the KG to answer natural language questions. Although recent methods have achieved good results using neural network-based query graph ranking, they suffer from three new challenges when handling more complex questions: 1) complicated SPARQL syntax, 2) huge search space, and 3) locally ambiguous query graphs. In this paper, we provide a new solution. As a preparation, we extend the query graph by treating each SPARQL clause as a subgraph consisting of vertices and edges and define a unified graph grammar called AQG to describe the structure of query graphs. Based on these concepts, we propose a novel end-to-end model that performs hierarchical autoregressive decoding to generate query graphs. The high-level decoding generates an AQG as a constraint to prune the search space and reduce the locally ambiguous query graph. The bottom-level decoding accomplishes the query graph construction by selecting appropriate instances from the preprepared candidates to fill the slots in the AQG. The experimental results show that our method greatly improves the SOTA performance on complex KGQA benchmarks. Equipped with pre-trained models, the performance of our method is further improved, achieving SOTA for all three datasets used.
Graph Convolutional Networks for Fast Text Classification

Recently, lots of studies have explored text classification methods based on graph convolutional neural network (GCN) technology. Compared with traditional deep learning methods, graph convolutional neural networks can capture global information while processing complex graph-structured data. However, when the previous GCN method deals with text classification problems, the entire corpus is often constructed as a complex heterogeneous graph. Such a graph structure faces the problems of the huge amount of calculation and long network training time when learning text representation. To solve the above problems, we simplified the construction of the adjacency matrix of the text heterogeneous graph based on Text GCN to reduce the amount of calculation during training. In addition, by constructing a special feature matrix, the graph convolutional neural network can extract a better text representation during training, while reducing the dimension of the feature matrix. The model performs text classification tasks on three data sets of R8, R52, and Ohsumed. The results show that the training speed of the proposed model on the three data sets of R8, R52, and Ohsumed is improved compared with the benchmark method (Text GCN) 71.5%, 72.6%, 78.6%. At the same time, the proposed model achieves an accuracy comparable to Text GCN on three data sets.
Design and Construction of a Knowledge Database for Learning Japanese Grammar Using Natural Language Processing and Machine Learning Techniques

This work describes the design and construction of a computer-aided foreign language learning grammar knowledge database for learning Japanese sentence patterns using natural language processing (NLP) and machine learning techniques. This grammar knowledge database can be applied for leaners of Japanese as a second language (JSL) to study the Japanese sentence patterns for Japanese Language Proficiency Test (JLPT). Selection of Example sentence is a challenging problem when constructing the database, because it is difficult to detect the Japanese sentence patterns from the example sentences. To solve this problem, NLP and machine learning technologies are applied to automatically detect the Japanese sentence patterns from the input example sentences by proposing an approach which integrates Conditional Random Fields (CRF) machine learning algorithm and manual rules. Several experiments were conducted and the experimental results demonstrated the validity of the proposed methods by using NLP and machine learning techniques on the design and construction of the Japanese grammar knowledge database. This database can be considered as an effective supplementary tool for learning Japanese grammar, and as a further improvement in the effectiveness of current intelligent computer-aided Japanese language learning system.
Agent-based Modelling of Bitcoin Consensus without Block Rewards

Trust is key to the efficient functioning of any fiat or crypto-currency and so is for the consensus algorithm behind the functioning of blockchain systems. By an arbitrary design choice, Bitcoin and most Proof-of- Work (PoW) blockchains have a limited supply. Once block rewards vanish, only transaction fees will remain as an incentive for miners to partake in the verification process. In this paper, we analyse the impact that miners bargaining over block composition has on consensus in the absence of block rewards: in this situation, competing blocks at the same height may be more attractive to peers by including less transactions (i.e. sharing the mempool). The mining and acceptance of blocks can be modelled as an Ulti-matum Game, where miners' strategies represent their fairness sentiment. Extending previous Literature, our study focuses on the effect of the transaction arrival rate on global consensus in the system and whether local consensus is formed under certain assumptions about the strategies of miners. We find that consensus is threatened when the supply of transactions is low and stable consensus only emerges when the amount of unconfirmed transactions remains sufficient. In addition, when miners are set with randomised strategies, it is more difficult for the system to achieve consensus. Our research suggests that transitioning from a block reward incentive to a transaction fee incentive may weaken and even destroy the consensus of PoW-based systems.
Optimal Mining: Maximizing Bitcoin Miners' Revenues from Transaction Fees

Following the Bitcoin model, many modern blockchains reward their miners in two ways: (i) a base reward for each block that is mined, and (ii) the transaction fees of those transactions that are included in the mined block. The base reward is fixed by the respective blockchain's protocol and is not under the miner's control. Hence, for a miner who wishes to maximize earnings, the fundamental problem is to form a valid block with maximal total transaction fees and then try to mine it. Moreover, in many protocols, including Bitcoin itself, the base reward halves at predetermined intervals, hence increasing the importance of maximizing transaction fees and mining an optimal block. This problem is further complicated by the fact that transactions can be prerequisites of each other or have conflicts (in case of double-spending). In this work, we consider the problem of forming an optimal block, i.e. a valid block with maximal total transaction fees, given a set of unmined transactions. The problem is known to be NP-hard. As such, there is no hope in solving it efficiently for general instances. However, we observe that its real-world instances are quite sparse, i.e. the transactions have very few dependencies and conflicts. Using this fact, and exploiting a well-known graph sparsity parameter, namely pathwidth, we present an exact linear-time parameterized algorithm that is applicable to the real-world instances and obtains optimal results. We also provide an experimental evaluation demonstrating that our approach outperforms current Bitcoin miners in practice, obtaining a significant increase in transaction fee revenues.