Reviewerly/exHarmony huggingface dataset:
LEVERAGING DIGITAL LEARNING AND WORK-BASED LEARNING TO ENHANCE EMPLOYEE SKILLS IN SMALL AND MEDIUM ENTERPRISES

Small and medium enterprises (SMEs) play a critical role in the European economy, accounting for the majority of businesses and employment opportunities. However, with rapidly changing technology and global competition, it is becoming increasingly important for SMEs to stay competitive. This paper explores the role of digital learning and work-based learning (WBL) in helping European SMEs remain competitive in the 21st century. Digital learning and work-based learning are two of the most innovative and influential approaches to education and training in the 21st century. Both approaches leverage technology and real-world experience to help students and workers acquire the skills they need to succeed in their careers. The paper argues that by leveraging technology and real-world experience, SMEs can upskill their employees and stay ahead of the curve. The paper concludes that a combination of digital learning and work-based learning has the potential to transform the way SMEs approach employee development, allowing them to remain competitive in a rapidly changing business landscape. This paper utilizes a mixed-methods approach, incorporating both a literature review and the results of a survey, to explore the role of digital learning and work-based learning.
A Web DApp for Efficient Organ Donation Management System: Leveraging Centralized Wallet Architecture as Backend

It's hard to understand the level of urgency and desperation someone feels when their loved one needs an organ transplant but they can't find a suitable donor. Organ donation is a critical procedure that can potentially save lives, but there are several obstacles that make it challenging, such as a shortage of donors and complicated systems of third-party intermediaries. Blockchain technology offers many features, including decentralization, transparency, and privacy that make it an important tool for digital automation. The proposed system is a decentralized web-based application that offers an effective platform for hospitals, organ recipients and donors to connect. The architecture allows users to interact with a smart contract on the blockchain without having to manage their own keys, while still maintaining the security and transparency advantages of using a decentralised system. As a result, it can offer a good balance between decentralisation and convenience. The use of and off-chain databases will improve data security, accessibility, and overall efficiency. Using all of these features can solve several Interplanetary File System healthcare sector problems such as eliminating the complicated network of intermediaries and improving transaction traceability. Moreover, this solution is cost-effective and can help patients avoid the expensive cost of transplantation.
Blockchain Applications in Higher Education: A Systematic Literature Review

The widespread usage of the Internet and the abrupt outbreak of the COVID-19 pandemic have compelled higher education institutes (HEIs) worldwide to shift their academic and administrative affairs online. These unprecedented changes have caused issues in relation to trust, identity authentication, and stakeholder engagement that cripple HEIs' effective digitalisation of educational practices. Additionally, the ecosystem of HEIs has become more complex because it involves various heterogeneous information systems and the diverse demands of decentralised stakeholders, such as lecturers, students, and alumni. To address these emerging problems, blockchain has become a potentially disruptive technology that can facilitate practical innovation and provide opportunities for the re-engineering of traditional HEIs. Therefore, the aim of this study is to consolidate the fragmented knowledge of blockchain applications in higher education based on the current literature of related research domains, blockchain benefits, and additional challenges for future studies. The paper consists of a systematic literature review following the PRISMA guidelines and an analysis of 79 peer-reviewed articles published between January 2018 and December 2022 by making an extensive literature search of Scopus, Taylor and Francis Online, and Web of Science databases. The results illustrate that blockchain is being explored and utilised to create new interventions to promote the sharing, securing, and management of data and knowledge and to enhance the coordination and trust between internal and external stakeholders of HEIs. This paper contributes to the stimulation of the awareness and interest of researchers and practitioners to leverage blockchain technology in their future work in order to facilitate the digital transformation of higher education.
Introducing Dreams of Dali in a Tertiary Education ESP Course: Technological and Pedagogical Implementations

Technology-enhanced teaching and learning environments transform the learning experience and increase student interest and engagement in the lesson. Virtual Reality (VR) has been identified as a multimodal medium that offers highly interactive and fully immersive experiences through which students can access a variety of meanings and enter a new social learning space which transcends classroom boundaries. VR also constitutes an innovative digital tool for foreign language (FL) learning in tertiary education through which teachers can promote a more situated learning context. Based on the multimodal affordances of the VR application Dreams of Dali, this paper aims to propose meaningful ways the application can be embedded in an English-for-Specific-Purposes (ESP) course in tertiary education, foregrounding the delivery of pedagogical content, creating student immersive literacy practices, and leading to alternative ways of conceptualising meanings. The immersive environment in Dreams of Dali can pose an instructional design tool which can increase familiarisation with course-related content in English for Fine Arts and help simulate an authentic surrealistic environment that deviates from static art-related images and passive surrealist representations of the art movement in the classroom.
A Deep Learning Approach for Locating Microalgae in Image Captured by Smartphone and Digital Microscope Camera

The utilization of microalgae as a bioindicator for water quality assessment has traditionally relied on the expertise of morphological identification. With the advent of technological advancements, particularly the smartphone camera, there has been increasing interest in using it as a tool for various fields of microbiological and medical diagnostics, including image analysis of microalgae. Despite the growing popularity of smartphone-based image analysis, the image quality of smartphone cameras for detecting microalgae has yet to be thoroughly evaluated. Therefore, the purpose of this study was to investigate the suitability of smartphone-based camera for collecting microalgae images and compare it to that of a digital microscope camera. Additionally, the effect of different microscopic magnifications at 100x and 400x on model performance was thoroughly studied. Four models, YOLOv5, RetinaNet, EfficientDet, and Faster-RCNN, were trained on microalgae image datasets taken by both a smartphone camera (SC) and a digital microscope camera (DMC) under 100x and 400x magnification. A total of 11,608 images were divided into three parts: train (70%), validation (10%), and test (20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ), with the test dataset separated into four parts for each device and magnification. The results of the study indicated that Faster-RCNN was the best model for microalgae detection, with the highest average precision <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{AP}^{0.5})$</tex> values of 0.60, 0.84, 0.91, and 0.96 at 100x magnification of SC and DMC and 400x magnification of SC and DMC, respectively. The comparison between the image quality of SC and DMC at each magnification revealed that the SC was less suitable for microalgae identification, particularly at 100x magnification. Conversely, the 400x magnification of SC had the potential to identify microalgae through the use of smartphone-based photography, as well as digital microscope camera.
The social grounds of self-tracking in insurance: A mixed-method approach to adoption and use

Scholars have explored the role of self-tracking in mediating people's values, perceptions, and practices. But little is known about its institutionalised forms, although it is becoming a routine component of health policies and insurance programs. Furthermore, the role of structural elements such as sociodemographic variables, socialisations, and trajectories has been neglected. Using both quantitative (n = 818) and qualitative (n = 44) data gathered from users and non-users of an insurance program's self-tracking intervention, and drawing from Bourdieu's theoretical framework, we highlight the impact of users' social background on the adoption and use of the technology. We show that older, poorer, and less educated individual are less likely to adopt the technology, and describe four prototypical categories of users, the meritocrats, the litigants, the scrutinisers and the good-intentioned. Each category displays different reasons and ways to use the technology that are grounded in users' socialisations and life trajectories. Results suggest that too much emphasis may have been put on self-tracking's transformative powers and not enough on its reproductive inertia, with important consequences for both scholars, designers, and public health stakeholders.
Prevalensi Bakteri Methicillin-Resistant Staphylococcus aureus (MRSA) Pada Peralatan Laboratorium

Staphylococcus aureus bacteria are common flora bacteria that are both commensal and opportunistic pathogens in humans. They are commonly treated with antibiotics, which can lead to bacterial resistance, such as the emergence of Methicillin-Resistant Staphylococcus aureus (MRSA) bacteria, which are resistant to the β -lactam antibiotics that are currently widespread to a variety of communities, such as the campus community, which is equipped with several facilities such as laboratories. The microbiology laboratory is a practicum and research laboratory that uses materials in the form of microorganisms, hence it has the potential to transmit MRSA bacteria. The aim of this study is to find out if MRSA bacteria are present on microbiology lab equipment and how prevalent they are. This study used observational research with a descriptive research design and a purposive sampling strategy. The results of bacterial isolation on MSA medium from fourteen laboratory equipment revealed that thirteen devices exhibited bacterial growth, whereas one device had none. Seven bacterial isolates with S. aureus-like features are detected growing in three of the thirteen equipment, the LAF, incubator, and manual autoclave. The seven bacterial isolates were identified as having round colony shape, glistening, opaque, convex yellow pigmentation, round cell morphology, clustered like grapes and purple in color, positive for coagulase and catalase, and resistance to methicillin antibiotics (MRSA bacteria). It can be concluded that of the fourteen-laboratory equipment examined, three instruments, namely the LAF, incubator, and manual autoclave, were found to be overgrown with MRSA bacteria, with the prevalence of MRSA bacteria in microbiology laboratory equipment reaching 21.4%. Keywords: laboratory equipment, MRSA, prevalence, S. aureus
NFE-PCN: A Node Feature Enhanced Embedding Framework for Pattern Change in Dynamic Network

Dynamic networks are complex networks as their structures and node features change over time. However, they can better represent the real world, thus attracting the interest of researchers. Although realistic dynamic networks often exhibit changes in their patterns, the existing dynamic network models tend to classify all the snapshots as having the same pattern to learn during their embedding. These embedding models ignore a large amount of information about the patterns of dynamic networks. So, it is necessary to design a dedicated framework for learning the patterns of dynamic networks. Accordingly, this paper proposes a new framework, namely the NFE-PCN framework for effectively extracting information about the change in the patterns of networks. Specifically, the framework first determines the pattern in which the dynamic network snapshot is located, and then enhances the node information between networks by maintaining the same pattern. We conduct experiments with both real and artificial datasets for predicting links and classifying nodes. The obtained results show that the existing model under this framework decreases the computational effort in dynamic network embedding. The performance in the network embedding is improved by up to 29%, which is quite significant.
Fast Approximate Convex Hull Construction in Networks via Node Embedding

Geodesic convexity in networks is an intrinsic property of graphs. It aids in distinguishing between real-world networks and random graphs. One possible application is recommending new connections in a collaborative network by searching for them in the so-called convex hull, which is a minimal subgraph containing all the shortest paths between its nodes. However, the existing algorithms for constructing convex hulls from subsets of nodes involve extensive search over subgraphs and have poor scalability. Thus, they become inapplicable to large graphs such as social networks. In this paper, we propose a new approach for fast convex hull construction for a subset of nodes on a network using graph embeddings. We apply the well-known convexity concept in embedding space to a similar problem for geometric learning on a graph, optimizing the process of finding all the shortest paths in the induced subgraph. To preserve the metric characteristics of a network, we train a graph neural network with an L1-distance loss. As a result, the trained model enables us to approximately verify the convexity of subgraphs in linear time, contrary to the previous approaches, which have cubic complexity.
Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving

Deep neural networks (DNNs) have proven their capabilities in the past years and play a significant role in environment perception for the challenging application of automated driving. They are employed for tasks such as detection, semantic segmentation, and sensor fusion. Despite tremendous research efforts, several issues still need to be addressed that limit the applicability of DNNs in automated driving. The bad generalization of DNNs to unseen domains is a major problem on the way to a safe, large-scale application, because manual annotation of new domains is costly, particularly for semantic segmentation. For this reason, methods are required to adapt DNNs to new domains without labeling effort. This task is termed unsupervised domain adaptation (UDA). While several different domain shifts challenge DNNs, the shift between synthetic and real data is of particular importance for automated driving, as it allows the use of simulation environments for DNN training. We present an overview of the current state of the art in this research field. We categorize and explain the different approaches for UDA. The number of considered publications is larger than any other survey on this topic. We also go far beyond the description of the UDA state-of-the-art, as we present a quantitative comparison of approaches and point out the latest trends in this field. We conduct a critical analysis of the state-of-the-art and highlight promising future research directions. With this survey, we aim to facilitate UDA research further and encourage scientists to exploit novel research directions.
An Unsupervised Framework With Attention Mechanism and Embedding Perturbed Encoder for Non-Parallel Text Sentiment Style Transfer

Text sentiment style transfer aims to extract the sentiment words from a sentence and transfer them into another expected sentiment style while retaining the original sentence's content. However, previous works have not achieved satisfactory performance on the text sentiment style transfer task, especially for non-parallel text. In this paper, a novel framework with the attention mechanism and embedding perturbed encoder is proposed to improve the performance of non-parallel text sentiment style transfer. Firstly, the reverse attention mechanism is adopted to disentangle the sentiment style information from the latent representation. And then an embedding perturbed encoder is designed to append an adjustable noise to the embedding space to make the latent representation more semantic. Finally, the attention mechanism is introduced to give different weights for generated words during the decoding process, so that the model can focus on those high-weight words to enhance the quality of sentiment style transfer. Experiments on the corpora of Yelp and IMDB demonstrate that the suggested framework outperforms previous works on the aspects of sentiment style transfer accuracy, content preservation and language fluency.
Supervised Contrastive Learned Deep Model for Question Continuation Evaluation

Question continuation evaluation (QCE) is a branch task of dialogue act prediction (DAP) in the natural language processing area, which is aimed at predicting whether each question in a dialogue is worthy of being followed-up under a specific context. QCE is important for communication, education, and even entertainment. Regrettably, QCE has always been disregarded as an auxiliary task for conversational machine reading comprehension. QCE involves more information and relationships than the original DAP task, making it more complex. Moreover, the classification of QCE inherently renders the samples confusing. In this article, a transformer long short-term memory (LSTM)-based supervised contrastive learned model for QCE is proposed to automatically distribute QCE labels. This model is mainly constructed with transformer encoder blocks and LSTM modules, and supervised contrastive learning (SCL) is innovatively introduced to the training process. This model is good at extracting both information about corpora and the relationships among corpora, and SCL alleviates any confusion. With the only applicable dataset, i.e., Question Answering in Context (QuAC), experiments are conducted. This model is proven to perform well and is robust to missing data. The performance is 2.3% (accuracy) and 12.2% (macro-F1 score) higher than baselines from QuAC and only decreases by approximately 2.3% when 10% data remain.
Sketch face recognition based on light semantic Transformer network

Abstract Sketch face recognition has a wide range of applications in criminal investigation, but it remains a challenging task due to the small‐scale sample and the semantic deficiencies caused by cross‐modality differences. The authors propose a light semantic Transformer network to extract and model the semantic information of cross‐modality images. First, the authors employ a meta‐learning training strategy to obtain task‐related training samples to solve the small sample problem. Then to solve the contradiction between the high complexity of the Transformer and the small sample problem of sketch face recognition, the authors build the light semantic transformer network by proposing a hierarchical group linear transformation and introducing parameter sharing, which can extract highly discriminative semantic features on small–scale datasets. Finally, the authors propose a domain‐adaptive focal loss to reduce the cross‐modality differences between sketches and photos and improve the training effect of the light semantic Transformer network. Extensive experiments have shown that the features extracted by the proposed method have significant discriminative effects. The authors’ method improves the recognition rate by 7.6% on the UoM‐SGFSv2 dataset, and the recognition rate reaches 92.59% on the CUFSF dataset.
Cloud Secure Storage Mechanism Based on Data Dispersion and Encryption

The widespread use and significant capability of the cloud storage service have demonstrated how important it is to the quick growth of cloud computing. However, there are still important security incidents that happen as a result of management negligence and malicious attack, resulting in the release of sizable amounts of sensitive data in the cloud storage tier. This paper proposes a CSSM, or secure cloud storage mechanism, to guarantee the confidentiality of data saved in the cloud. A technique to prevent data leakage at the storage layer was added by CSSM. Encrypted, scattered, and distributed storage is carried out via data dispersion and dispersed storage. In order to avoid the leaking of cryptographic materials, CSSM also adopted a hierarchical management strategy and coupled the user password with the exchange of secrets. The experimental findings suggest that the suggested mechanism. It can store a lot of cloud data efficiently without adding a lot of overhead, making it excellent for protecting data security in the storage layer against leaking. Users find it acceptable that it only takes 646 seconds to upload or download a 5G-size file with CSSM.
Learning-Based End-to-End Navigation for Planetary Rovers Considering Non-Geometric Hazards

Autonomous navigation plays an increasingly crucial role in rover-based planetary missions. End-to-end navigation approaches developed upon deep reinforcement learning have enabled great adaptability in complex environments. However, most existing works focus on geometric obstacle avoidance thus have limited capability to cope with ubiquitous non-geometric hazards, such as sinkage and slippage. Autonomous navigation in unstructured harsh environments remains a great challenge requiring further in-depth study. In this letter, a DRL-based navigation method is proposed to autonomously guide a planetary rover towards goals via hazard-free paths with low wheel slip ratios. We introduce an end-to-end network architecture, in which the visual perception and the wheel-terrain interaction are fused to learn the representation of terrain mechanical properties implicitly and further facilitate policy learning for non-geometric hazard avoidance. Our approach outperforms baseline methods in simulation evaluation with superior avoidance capabilities against geometric obstacles and non-geometric hazards. Experiments conducted at a Mars emulation site suggest the successful deployment of our approach on a planetary rover prototype and the capacity of dealing with locomotion risks in real-world navigation tasks.
Learning Causal Graphs in Manufacturing Domains Using Structural Equation Models

Many production processes are characterized by numerous and complex cause-and-effect relationships. Since they are only partially known, they pose a challenge to effective process control. In this work we present how Structural Equation Models can be used for deriving cause-and-effect relationships from the combination of prior knowledge and process data in the manufacturing domain. Compared to earlier applications, we do not assume linear relationships leading to more informative results. Furthermore, our results indicate that including expert knowledge seems to be able to reduce the difference between the learned cause-effect relationships and the expert assessment, thus opening a promising direction for future research on manufacturing processes.
An FPGA-implemented Method for Real-time Multi-dimensional Feature Extraction of Sequence Image Targets

目标区域多维特征的建模表征是实现目标跟踪搜索的前提。传统的特征提取方法通常先连通出目标区域，再计算目标特征，实时性不强。提出了一种基于像素的连通域标记及目标特征同步计算方法，建立特征传递机制，利用标号映射表关联标号表与特征属性表，并在区域邻接时进行标号合并和特征属性同步传递计算，保证了目标区域连通完成即可输出目标区域特征。给出一种基于FPGA硬件设计的实现方案。仿真测试结果显示，此方法具有以下几方面优势：连通域标记耗时少，仅为(<i>L</i> × <i>W</i> + 2<i>n</i>/<i>m</i>) × <i>T</i><sub>clk</sub>，接近理论最小值(<i>L</i> × <i>W</i>) × <i>T</i><sub>clk</sub>；利用循环缓存区存储图像，资源消耗小；标记与计算并行处理，提升了检测跟踪效率；多目标特征经测试验证结果准确，可有效支持后续的目标跟踪检测；具有理论与实用价值。