Reviewerly/exHarmony huggingface dataset:
Fire Risk Assessment of Urban Utility Tunnels Based on Improved Cloud Model and Evidence Theory

In order to accurately assess the fire risk of urban utility tunnels, an evaluation method based on the improved cloud model and evidence theory is proposed. Firstly, an evaluation index system for the fire risk of urban utility tunnels is constructed from five aspects: fire prevention, fire control, emergency evacuation, personnel prevention and control, and safety management. Secondly, because of the randomness and fuzziness of fire risk assessment, the improved cloud model with cloud entropy optimization is used to calculate the index membership degree. The uncertainty focal elements are introduced to satisfy the basic probability assignment in evidence theory. Then, the improved evidence theory with dynamic and static weights is applied to fuse the information of the evidence and determine the final evaluation results. It avoids the possible paradoxes of the combination of strong conflict evidence in traditional evidence theory and improves the credibility of the evaluation results. Finally, the feasibility and superiority of the proposed method are verified by an example analysis, which provides a new idea for the fire risk assessment of urban utility tunnels.
Levy flight incorporated hybrid learning model for gravitational search algorithm

Gravitational search algorithm (GSA) is a widely used meta-heuristic algorithm for global optimization. Its strong social interaction abilities and easy to implement nature make it more applicable than its contemporaries. However, multi-modality always remains a challenging task for GSA search mechanism due to its incapabilities towards premature convergence. This paper proposes a novel GSA variant called ‘Levy flight incorporated gravitational search algorithm with an adaptive spiral strategy (LevyGSA)’ to address the shortcomings of GSA with the following developments: First, a levy flight associated position update strategy for elite agents of the swarm is proposed for a better interior search. Secondly, an adaptive spiral update strategy is introduced for the rest swarm to balance the trade-off between exploration and exploitation for a robust search. Finally, a dimensional reduction based strategy for enhancing the local search around the known global optimal region is introduced. The proposed algorithm is tested over 23 classical test problems and 30 CEC 2014 test problems. The numerical results demonstrate the outstanding performance of the proposed algorithm through which it outperforms the well-known existing meta-heuristics along with recent GSA variants. Furthermore, finding more accurate solutions for five engineering design problems validates its applicability in real-world scenarios.
O2M-UDA: Unsupervised dynamic domain adaptation for one-to-multiple medical image segmentation

One-to-multiple medical image segmentation aims to directly test a segmentation model trained with the medical images of a one-domain site on those of a multiple-domain site, suffering from segmentation performance degradation on multiple domains. This process avoids additional annotations and helps improve the application value of the model. However, no successful one-to-multiple unsupervised domain adaptation (O2M-UDA) work has been reported in one-to-multiple medical image segmentation due to its inherent challenges: distribution differences among multiple target domains (among-target differences) caused by different scanning equipment and distribution differences between one source domain and multiple target domains (source–target differences). In this paper, we propose an O2M-UDA framework called dynamic domain adaptation (DyDA), for one-to-multiple medical image segmentation, which has two innovations: (1) dynamic credible sample strategy (DCSS) dynamically extracts credible samples from the target site and iteratively updates their number, thus iteratively expanding the generalization boundary of the model and minimizing the among-target differences; (2) hybrid uncertainty learning (HUL) reduces the voxel-level and domain-level uncertainty simultaneously, thus minimizing the source–target differences from the detail and entire perspective concurrently. Experiments on two one-to-multiple medical image segmentation tasks have been conducted to demonstrate the performance of the proposed DyDA. The proposed DyDA achieved competitive segmentation results and high adaptation with an average of 83.8% and 48.1% dice for the two tasks, respectively, which has improved by 21.7% and 9.2% compared with no adaptation, respectively. The code developed in this study code can be downloaded at https://github.com/ZoeyJiang/DyDA.
A Blockchain Supported Model for Secure Exchange of Land Ownership: An Innovative Approach

Due to the duplicity and inefficiencies in the current land registration system, the land records are not secured, and citizens face the brunt of this. Similar to this, thousands of people are dealing with this situation. Fraudsters use numerous faults in the current property registration process to swindle both the public and the government. The implementation of a secure land registration using Blockchain and majority consensus is examined in this study. The adoption of Blockchain by the land register significantly lowers security issues. Since each block is linked to the hash of the one before it, each block’s hash value will be unique. It uses a hashing algorithm known as SHA256. The security of the data linked with each transaction is improved by the Proof of Work (PoW) method with SHA256. Each hash in the message digest is fixed for all the blocks and represents the complete collection of transactions contained in these blocks. The responsibility for mining new blocks, chaining with existing Blockchain and validating transactions falls on a node. Hashes are used to link transactions together via merge trees, which requires less storage. Thus, a 99% decrease in the human record-keeping effort is offered by the suggested usage of Blockchain for land registration.
Sarcasm Identification in text with deep learning models and Glove word embedding

In social media, sarcasm is frequently found to convey a negative opinion employing positive or exaggerated positive terms. Sarcasm is necessary for and advantageous for many Natural Language Processing (NLP) algorithms. Unless explicitly constructed to compensate for it, sarcasm can readily fool sentiment analysis tools. Sarcasm abounds in viewer stuff, such as Facebook postings and Tweets. It’s quite difficult to spot sarcasm without a complete awareness of the event, the wider problem, and the context. Memory-based network models, notably Long Short Term Networks (LSTM), Bidirectional Long-Short Memory (Bi-LSTM), and Convolution Neural Network (CNN) algorithms, can identify sarcastic remarks in a corpus. We assess the efficacy of competing Deep Learning algorithms for text sarcasm identification using the SarcasmV2 corpus. Based on the results, we can infer that for sarcasm detection, the Bidirectional Long Short Term Memory Network (Bi-LSTM) model provides the best performance. The suggested approach involving deep networks is consistent towards many traditional approaches for sarcasm detection, and based on specific standard performance indicators, the current model outperforms these approaches.
Classifying News Based on Indonesian News Using LightGBM

There were several news categories that present editors with challenges. Some news categories, such megapolitan, national, celebrity, news, lifestyle, and economics, used vocabulary that was quite similar to that of the other categories. International required an editor to be familiar with the article's contents in order for it to be uploaded and placed in the proper category. We had to first digest the news before we could label it compared to other kinds of data. The text mining approach, which attempts to make text or documents may be processed so that it will aid in the process of news classification, will be used to categorize and determine the type of news in this context. The Light Gradient Boosted Machine (LightGBM) model was used in this study to increase the gradient point with a learning stage and obtain the optimal value. This model's training process was intended to be quick while consuming less storage space and processing information more accurately. The accuracy of the classifications made using a confusion matrix to quantify the findings of this investigation, which were news type classifications, was 86%.
Digital Technology for Inclusive Education: Reflecting on the Role of Teachers

This chapter focuses on the role of teachers and their experiences of digital technology as a key social justice issue for the twenty-first century, where the role of the teacher is constructed as critical if schools are to provide inclusive and equitable education for all pupils. The factors which impact on teachers’ use of digital technologies are critically examined through a theoretical framework of Nancy Fraser’s writing about social justice, where Fraser focuses on the social structures and institutions which enable equitable participation and inclusion in society. This chapter draws examples from two research studies which explored the experiences of teachers in Scotland and their engagement with digital technology.
BERT-LID: Leveraging BERT to Improve Spoken Language Identification

Language identification is the task of automatically determining the identity of a language conveyed by a spoken segment. It has a profound impact on the multilingual interoperability of an intelligent speech system. Despite language identification attaining high accuracy on medium or long utterances (>3 s), the performance on short utterances (<=1s) is still far from satisfactory. We propose a BERT-based language identification system (BERT-LID) to improve language identification performance, especially on short-duration speech segments. We extend the original BERT model by taking the phonetic posteriorgrams (PPG) derived from the front-end phone recognizer as input. Then we deployed the optimal deep classifier followed by it for language identification. Our BERT-LID model can improve the baseline accuracy by about 6.5% on long-segment identification and 19.9% on short-segment identification, demonstrating our BERT-LID’s effectiveness to language identification.
A bibliometric analysis of Metaverse research using VOSviewer

This research tracked the knowledge frameworks of the Metaverse academic field. To this end, we employed “metaverse” as the keyword to gather articles from Web of Science and performed bibliometric analyses using VOSviewer. Concretely, we explored productive and highly-cited authors, institutes, countries/regions, and journals/conference proceedings. For a deeper understanding of the state of Metaverse research, we also reviewed highly-cited literature and performed keyword-occurrence analysis. Overall, this is one of the first studies providing a comprehensive understanding of knowledge structures in the context of Metaverse research.
Category Based Sentiment Analysis for Basic Skin-Cares Using LDA and SVM Approach

Aspect Based Sentiment Analysis (ABSA) is a sentiment analysis technique of identifying sentiment value towards a specific aspect associated with a given target. However, in some cases, several aspects can be classified into one category group. This is because some words have different spellings, even though they refer to the same aspect. The problem is the dataset for category-based categories is still limited, especially for low-resource languages such as Bahasa Indonesia (Indonesian Language). In fact, dataset generation is the most time-consuming step rather than the process of building the classification model. This study aims to build a category-based sentiment analysis model starting from the dataset generation, model classification generation, and model evaluation. The case study in this research is sentiment analysis for the basic skin-care category in Bahasa Indonesia. The dataset is generated from raw data, which is the user’s review on the female-daily website (www.female-daily.com). We implemented the Latent Dirichlet Allocation (LDA) algorithm to extract the aspect from each review and implemented TextBlob library to annotate the polarity of each review. The research uses a Support Vector Machine algorithm to generate a model classification. Based on the research, the LDA can extract and also can group the same aspect into the same category. The support Vector Machine method is a fairly accurate method for conducting sentiment analysis. In this study, the accuracy value of 86.9% for training data and 83.9% for test data from all categories were obtained using the K-Fold Cross Validation evaluation method with 5 folds.
A Multi-Source Transfer Joint Matching Method for Inter-Subject Motor Imagery Decoding

Individual differences among different subjects pose a great challenge to motor imagery (MI) decoding. Multi-source transfer learning (MSTL) is one of the most promising ways to reduce individual differences, which can utilize rich information and align the data distribution among different subjects. However, most MSTL methods in MI-BCI combine all data in the source subjects into a single mixed domain, which will ignore the effect of important samples and the large differences in multiple source subjects. To address these issues, we introduce transfer joint matching and improve it to multi-source transfer joint matching (MSTJM) and weighted MSTJM (wMSTJM). Different from previous MSTL methods in MI, our methods align the data distribution for each pair of subjects, and then integrate the results by decision fusion. Besides that, we design an inter-subject MI decoding framework to verify the effectiveness of these two MSTL algorithms. It mainly consists of three modules: covariance matrix centroid alignment in the Riemannian space, source selection in the Euclidean space after tangent space mapping to reduce negative transfer and computation overhead, and further distribution alignment by MSTJM or wMSTJM. The superiority of this framework is verified on two common public MI datasets from BCI competition IV. The average classification accuracy of the MSTJM and wMSTJ methods outperformed other state-of-the-art methods by at least 4.24% and 2.62% respectively. It's promising to advance the practical applications of MI-BCI.
Siamese Graph Attention Networks for robust visual object tracking

Siamese-based trackers usually convert the object tracking task into a similarity matching problem between the target template and the search region. Since fixed or manually updated templates are not robust when tracking moving objects with dramatically changing appearance, this paper proposes an improved siamese graph attention network with adaptive template update called SiamGT. By establishing spatiotemporal and context dependencies between historical images and search regions, a frame selection mechanism is added to improve the richness of information. In addition, a graph attention network with residual connections is used in the template update mechanism which enables the propagation and aggregation of information to generate robust templates. Extensive experimental results on challenging benchmarks such as UAV123, OTB100, and VOT2019 demonstrate that the proposed SiamGT has achieved state-of-the-art performance in visual object tracking.
Crowd Density Estimation and Mapping Method Based on Surveillance Video and GIS

Aiming at the problem that the existing crowd counting methods cannot achieve accurate crowd counting and map visualization in a large scene, a crowd density estimation and mapping method based on surveillance video and GIS (CDEM-M) is proposed. Firstly, a crowd semantic segmentation model (CSSM) and a crowd denoising model (CDM) suitable for high-altitude scenarios are constructed by transfer learning. Then, based on the homography matrix between the video and remote sensing image, the crowd areas in the video are projected to the map space. Finally, according to the distance from the crowd target to the camera, the camera inclination, and the area of the crowd polygon in the geographic space, a BP neural network for the crowd density estimation is constructed. The results show the following: (1) The test accuracy of the CSSM was 96.70%, and the classification accuracy of the CDM was 86.29%, which can achieve a high-precision crowd extraction in large scenes. (2) The BP neural network for the crowd density estimation was constructed, with an average error of 1.2 and a mean square error of 4.5. Compared to the density map method, the MAE and RMSE of the CDEM-M are reduced by 89.9 and 85.1, respectively, which is more suitable for a high-altitude camera. (3) The crowd polygons were filled with the corresponding number of points, and the symbol was a human icon. The crowd mapping and visual expression were realized. The CDEM-M can be used for crowd supervision in stations, shopping malls, and sports venues.
Participatory design research for the development of real-time simulation models in healthcare

The implementation challenges for modelling and simulation in health and social care are well-known and understood. Yet increasing availability of data and a better understanding of the value of Operational Research (OR) applications are strengthening opportunities to support healthcare delivery. Participative approaches in healthcare modelling have shown value through stakeholder engagement and commitment towards co-creation of models and knowledge but are limited in focus on model design and development. For simulation modelling, a participative design research methodology can support development for sustained use, emphasising model usefulness and usability using iterative cycles of development and evaluation. Within a structured methodology, measures of success are built into the design process, focusing on factors which contribute to success, with implicit goals of implementation and improvement. We illustrate this through a participative case study which demonstrates development of the component parts of a real-time simulation model aimed at reducing emergency department crowding.
A federated semi‐supervised learning approach for network traffic classification

Summary The classification of network traffic, which involves classifying and identifying the type of network traffic, is the most fundamental step to network service improvement and modern network management. Classic machine learning and deep learning methods have widely adopted in the field of network traffic classification. However, there are two major challenges in practice. One is the user privacy concern in cross‐domain traffic data sharing for the purpose of training a global classification model, and the other is the difficulty to obtain large amount of labeled data for training. In this paper, we propose a novel approach using federated semi‐supervised learning for network traffic classification, in which the federated server and clients from different domains work together to train a global classification model. Among them, unlabeled data are used on the client side, and labeled data are used on the server side. The experimental results derived from a public dataset show that the accuracy of the proposed approach can reach 97.81%, and the accuracy gap between the federated learning approach and the centralized training method is minimal.
Radical infrastructure: Building beyond the failures of past imaginaries for networked communication

Ongoing political, environmental, and economic crises require infrastructures that can respond to crises in ways that do not replicate and reinforce inequality. To this end, we use a case study method of analysis that compares the authors’ previous work on Internet infrastructure at the levels of development, governance, and use to explore how these imaginaries promote or impede people-centered change in the development and maintenance of Internet infrastructure. This theoretical work puts the three existing cases in conversation to better understand how Internet infrastructure alternatives presented as radical, new, or non-hierarchical present shortcomings and opportunities, so that it might be more possible to imagine better, more truly radical, people-centered alternatives. From this comparison, we close our discussion with three heuristics for radical infrastructure: the need for pushing for alternative ensembles of support, busting the myth of technosolutionism, re-politicizing Internet infrastructure, and encouraging technical communities to build around cooperativity, not connectivity.
Additional predictions for forensic DNA phenotyping of externally visible characteristics using the ForenSeq and Imagen kits

Abstract Multiplex DNA typing methods using massively parallel sequencing can be used to predict externally visible characteristics (EVCs) in forensic DNA phenotyping through the analysis of single‐nucleotide polymorphisms. The focus of EVC determination has focused on hair color, eye color, and skin tone as well as visible biogeographical ancestry features. In this study, we researched off‐label applications beyond what is currently marketed by the manufacturer of the Verogen ForenSeq kit primer set B and Imagen primer set E SNP loci. We investigated additional EVC predictions by examining published genome wide sequencing studies and reported allele‐specific gene expression and predictive values. We have identified 15 SNPs included in the ForenSeq kit panel and Imagen kits that have additional EVC prediction capabilities beyond what is published in the Verogen manuals. The additional EVCs that can be predicted include hair graying, ephelides hyperpigmented spots, dermatoheliosis, facial pigmented spots, standing height, pattern balding, helix‐rolling ear morphology, hair shape, hair thickness, facial morphology, eyebrow thickness, sarcoidosis, obesity, vitiligo, and tanning propensity. The loci can be used to augment and refine phenotype predictions with software such as MetaHuman for missing persons, cold case, and historic case investigations.