Reviewerly/exHarmony huggingface dataset:
A comparative study of anomaly detection methods for gross error detection problems

The chemical industry requires highly accurate and reliable measurements to ensure smooth operation and effective monitoring of processing facilities. However, measured data inevitably contains errors from various sources. Traditionally in flow systems, data reconciliation through mass balancing is applied to reduce error by estimating balanced flows. However, this approach can only handle random errors. For non-random errors (called gross errors, GEs) which are caused by measurement bias, instrument failures, or process leaks, among others, this approach would return incorrect results. In recent years, many gross error detection (GED) methods have been proposed by the research community. It is recognised that the basic principle of GED is a special case of the detection of outliers (or anomalies) in data analytics. With the developments of Machine Learning (ML) research, patterns in the data can be discovered to provide effective detection of anomalous instances. In this paper, we present a comprehensive study of the application of ML-based Anomaly Detection methods (ADMs) in the GED context on a number of synthetic datasets and compare the results with several established GED approaches. We also perform data transformation on the measurement data and compare its associated results to the original results, as well as investigate the effects of training size on the detection performance. One class Support Vector Machine outperformed other ADMs and five selected statistical tests for GED on Accuracy, F1 Score, and Overall Power while Interquartile Range (IQR) method obtained the best selectivity outcome among the top 6 AMDs and the five statistical tests. The results indicate that ADMs can potentially be applied to GED problems.
ISTOA: An Improved Sooty Tern Optimization Algorithm for Multilevel Threshold Image Segmentation

This paper introduces an improved version of well-known Sooty Tern Optimization Algorithm (STOA). The improved version combines Opposition based learning (OBL) to introduce the Improved Sooty Tern Optimization Algorithm (ISTOA). The OBL strategy increases population diversity and avoids falling into local solutions. The efficiency of the proposed ISTOA is verified on multilevel threshold segmentation based on the objective functions of Kapur, and its performance is compared with the original algorithm and another metaheuristic algorithm. Experimental results reveal that the proposed ISTOA outperforms other algorithms in terms of fitness, peak signal-to-noise ratio, structural similarity, and segmentation findings.
Automatic ICD-10 coding: Deep semantic matching based on analogical reasoning

ICD-10 has been widely used in statistical analysis of mortality rates and medical reimbursement. Automatic ICD-10 coding is desperately needed because manually assigning codes is expensive, time-consuming, and labor-intensive. Diagnoses described in medical records differ significantly from those used in ICD-10 classification, making it impossible for existing automatic coding techniques to perform well enough to support medical billing, resource allocation, and research requirements. Meanwhile, most of the current automatic coding approaches are oriented toward English ICD-10. This method for automatically assigning ICD-10 codes to diagnoses extracted from Chinese discharge records was provided in this paper.First, BERT creates word representations of the two texts. Second, the context representation layer incorporates contextual information into the representation of each time step of the word representations using a bidirectional Long Short-Term Memory. Third, the matching layer compares each contextual embedding of the uncoded diagnosis record against a weighted version of all contextual character embeddings of the manually coded diagnosis record. The matching strategy is element-wise subtraction and element-wise multiplication and then through a neural network layer. Fourth, the matching vectors are combined using a one-layer convolutional neural network. A sigmoid is then used to output matching results.To evaluate the proposed method, 1,003,558 manually coded primary diagnoses were gathered from the homepage of the discharge medical records. The experimental results showed that the proposed method outperformed popular deep semantic matching algorithms, such as DSSM, ConvNet, ESIM, and ABCNN, and demonstrated state-of-the-art results in a single text matching with an accuracy of 0.986, a precision of 0.979, a recall of 0.983, and an F1-score of 0.981.The automatic ICD-10 coding of Chinese diagnoses is successful when using the proposed deep semantic matching approach based on analogical reasoning.
A research of convolutional neural network model deployment in low- to medium-performance microcontrollers

Artificial intelligence internet of things (AIoT) is a technology that came into being under the development of artificial intelligence (AI) and Internet of things (IOT) where deep learning is vigorously promoted and used. Compared with the traditional concept of the Internet of things, the main difference of AIoT technology is that it applies interconnected devices which are embedded with the capacity of neural network model reasoning to the perception layer, this reduce reliance on edge servers (especially for neural network model training or reasoning). Thus, the edge devices of the system will get a more intelligent execution power. For the IOT system structures that have been built at present, most of the interconnection devices in the sensing layer, such as data acquisition nodes or execution nodes, are designed with the low and medium performance microcontroller unit as the processing core. After using the technology such like lightweight neural network and global average pooling, we succeed in deploying the convolutional neural network model to the low and medium performance microcontroller. Thus, the original node can get the reasoning result of neural network model in offline state and use it as a decision element for the operation of the system whit a simple modification of the program.
ResE: A Fast and Efficient Neural Network-Based Method for Link Prediction

In this study, we present a novel embedding model, named ResE, for predicting links in knowledge graphs. ResE employs depth-separable convolution and residual blocks, integrated with channel attention mechanisms. ResE surpasses previously published models, including the closely related TransE model, by achieving the satisfactory mean rank (MR) and the excellent Hits@10 scores on both WN18RR and FB15K-237 benchmarks. ResE is a promising model for knowledge graph completion tasks, with potential for further investigation and extension to new applications such as user-oriented relationship modeling. Although comparatively shallow compared to computer vision convolutional architectures, future work may explore deeper convolutional models. ResE exhibits remarkable performance and outperforms existing approaches, thus setting a new benchmark for knowledge graph completion. The outcomes of our study illustrate the effectiveness of incorporating depth-separable convolution and residual blocks, accompanied by channel attention mechanisms, in modeling knowledge graphs. These findings highlight ResE’s potential to push the boundaries of cutting-edge in this domain.
Label-Free Model Evaluation with Out-of-Distribution Detection

In recent years, label-free model evaluation has been developed to estimate the performance of models on unlabeled test sets. However, we find that existing methods perform poorly in environments with out-of-distribution (OOD) data. To address this issue, we propose a novel automatic model evaluation method using OOD detection to reduce the impact of OOD data on model evaluation. Specifically, we use the representation of datasets to train a neural network for accuracy prediction and employ energy-based OOD detection to exclude OOD data during testing. We conducted experiments on several benchmark datasets with varying amounts of OOD data (SVHN, ISUN, ImageNet, and LSUN) and demonstrated that our method reduces the RMSE compared to existing methods by at least 1.27%. Additionally, we tested our method on transformed datasets and datasets with a high proportion of OOD data, and the results show its robustness.
Audience design and egocentrism in reference production during human-computer dialogue

Our current understanding of the mechanisms that underpin language production in human-computer dialogue (HCD) is sparse. What work there is in the field of human-computer interaction (HCI) supposes that people tend to adapt their language allocentrically, taking into account the perceived limitations of their partners, when talking to computers. Yet, debates in human-human dialogue (HHD) research suggest that people may also act egocentrically when producing language in dialogue. Our research aims to identify whether, similar to HHD, users also produce egocentric language within speech-based HCD interactions and how this behaviour compares to interaction with human dialogue partners. Such knowledge benefits the field of HCI by better understanding the mechanisms present in language production during HCD, which can be used to build more nuanced theories and models of user behaviour to inform research and design of speech interfaces. Through two controlled experiments using an adapted director-matcher task similar to those used in research on perspective-taking in psycholinguistics, we show that people do take the computer's perspective into account less (i.e. behave more egocentrically) during HCD than in HHD (Experiment 1). However, this egocentric effect is eliminated when computers are framed as separate interlocutors rather than computers integrated in the interactive system and where differences in perspective are made salient, leading to similar levels of perspective-taking as with human partners (Experiment 2). We discuss the findings, emphasising potential explanations for this effect, focusing on how egocentric and allocentric production processes may interact, along with the impact of partner roles and the division of labour in HCD as an underlying explanation for the effects seen.
A Conceptual Model to Share Resources and Align Goals: Building Blockchain Application to Support Care Continuity Outside a Hospital

The increased use of advanced technologies by consumers and hospitals is moving care closer to patients, and the challenge is one of how patient data can be shared with external care providers and patients. To support care continuity, patient data include both clinical data used by external care providers and non-clinical data used by social care providers. Care coordination of a patient outside a hospital requires peer-to-peer connectivity among a number of these clinical and social care providers, using a digital platform that aligns their goals and assigns their resource sharing responsibilities. With no single entity supporting such care coordination, most hospitals currently distribute this responsibility to several of its provider partners and patients. Such a division of responsibility with no real time feedback leads to discontinuous resource sharing, localized data analysis, and challenges in tailoring care to improve health outcomes. The goal of this paper is to propose a blockchain architecture model that uses a number of constructs for creating and assigning ownership to patient data so it can support peer-to-peer resource sharing and uses smart contracts to support goal alignment. Using two blockchain applications implemented in Hyperledger and illustrating their potential representation using the constructs in multi-chain, we develop a conceptual model for developing blockchain applications in general to support continuity of care. The generalizability of this model is illustrated by applying these constructs to four additional healthcare applications. Finally, we conclude the paper with a discussion of the limitations and directions for future research.
HDGN: Heat diffusion graph network for few-shot learning

A heat diffusion graph network (HDGN) is proposed in this paper, which retains more similar graph signals in the spectral domain, for few-shot learning. Convolution on the graph is essentially the filtering of the graph signal. Most existing graph-network-based few-shot learning methods process graph signals with high-pass filters to get the difference in information. However, the low-frequency similar information is usually more valuable in the few-shot tasks. A joint low-pass filter is constructed to filter low-frequency graph signals, that is, heat kernel convolution aggregates similar information from neighboring nodes. The obtained low-frequency similarity information is utilized to update the representations of nodes on the graph. In addition, a more robust mixed metric is designed to dynamically update the edge feature of the graph. Predicting Unknown Nodes on Graphs by Alternating Updates of Node Representation and Edge Matrix. The experimental results also demonstrate that HDGN achieves better performance for the few-shot classification task.
Lightweight detection network for arbitrary-oriented vehicles in UAV imagery via precise positional information encoding and bidirectional feature fusion

Unmanned aerial vehicles (UAVs) open up new opportunities for transportation monitoring. However, the vehicle targets in UAV images are situated in dynamic scenarios, such as uncertain background, dramatically varying arrangement density, multi-scale, and arbitrary-oriented. Most strategies for UAV-based monitoring require complex manoeuvring and still lack accurate abilities and lightweight structures. Consequently, designing effective detection methods with both speed and accuracy is challenging. This paper proposes a lightweight YOLO-based arbitrary-oriented vehicle detector via precise positional information encoding and bidirectional feature fusion to address the above issues. First, an additional angular classification prediction branch is added to the YOLO head network to significantly improve the detection performance for arbitrary-oriented vehicles without incurring the extra computational complexity and burden. Second, a C3 module embedded coordinate attention (C3CA) is presented to capture long-range dependencies and preserve vehicles’ precise positional information in feature maps. Then, a fully connected bidirectional feature fusion module (FC-BiFPN) is applied at the neck of the YOLO detection framework, which is helpful for multi-scale vehicle detection. This module can efficiently aggregate features at different resolutions and automatically enhance information interaction. Finally, experiments and comparisons on vehicle and remote sensing datasets demonstrate that our approach outperforms the state-of-the-art methods in balancing precision and efficiency. In addition, the overall network design follows the lightweight concept, which better meets the real-time requirements of the UAV urban traffic monitoring platform in realistic scenarios.
The impact of the first millennial teachers on education: views held by different generations of teachers

The first people considered digital natives, the millennials, have already entered the teaching profession. As a result, we are faced with a remarkable generational diversity. This survey aimed to explore the generational change in teachers and the beginning of the incorporation of the first millennials (digital natives) into teaching. It was carried out through a qualitative study using focus groups and interviews with a total of 147 teachers. The main results found establish a generational clash between migrants and digital natives. This difference is present in the use and understanding of ICTs in the teaching task across the different teaching generations and in a generational diversity within the educational centres that has not been seen so far. However, this difference between teachers is also a condition that facilitates exchange between teachers of different generations. Junior teachers help veteran teachers in the use of ICTs and veteran teachers provide the expertise that new recruits lack.
An ontological data model to support urban flood disaster response

This work aims to develop an ontological data model to support urban flood disaster response. The preliminary studies of the existing flood ontologies revealed that the developed ontologies do not express the relationships between the different levels of administration involved during flood disasters. So, an ontology named Flood Disaster Support Ontology (FDSO) was developed. The FDSO acts as a data model that can accommodate the various organisations and agents of different levels of the administrative agencies that are part of the flood disaster management process. It depicts their relationships and their activities specifically during an urban flood disaster response. The ontology also organises the resources and the information about them that are possessed by different organisations and may be used by first responders to carry out the activities such as rescue and relief. An amalgamation of two existing methodologies Yet Another Methodology for Ontology Development (YAMO) and NeOn was used to construct the ontology. The ontology was evaluated syntactically using reasoners along with the OOPS!, an OntOlogy pitfall scanner, whereas the semantic evaluation was done through SPARQL queries. The process yielded satisfactory results.
Target-Oriented Data Annotation for Emotion and Sentiment Analysis in Tourism Related Social Media Data

Social media have been a valuable data source for studying people’s opinions, intentions, and behaviours. Such a data source incorporating advanced big data analysis methods, such as machine-operated emotion and sentiment analysis, will open unprecedented opportunities for innovative data-driven destination monitoring and management. However, a big challenge any machine-operated text analysis method faces is the ambiguity of the natural languages, which may cause an expression to have different meanings in different contexts. In this work, we address the ambiguity challenge by proposing a context-aware dictionary-based target-oriented emotion and sentiment analysis method that incorporates inputs from both humans and machines to introduce an alternative approach to measuring emotions and sentiment in limited tourism-related data. The study makes a methodological contribution by creating a target dictionary specifically for tourism sentiment analysis. To demonstrate the performance of the proposed method, a case of target-oriented emotion and sentiment analysis of posts from Twitter for the Gold Coast of Australia as a tourist destination was considered. The results suggest that Twitter data cover a broad range of destination attributes and can be a valuable source for comprehensive monitoring of tourist experiences at a destination.
Evaluation of High Resolution WRF Solar

The amount of solar irradiation that reaches Earth’s surface is a key quantity of solar energy research and is difficult to predict, because it is directly affected by the changing constituents of the atmosphere. The numerical weather prediction (NWP) model performs computational simulations of the evolution of the entire atmosphere to forecast the future state of the atmosphere based on the current state. The Weather Research and Forecasting (WRF) model is a mesoscale NWP. WRF solar is an augmented feature of WRF, which has been improved and configured specifically for solar energy applications. The aim of this paper is to evaluate the performance of the high resolution WRF solar model and compare the results with the low resolution WRF solar and Global Forecasting System (GFS) models. We investigate the performance of WRF solar for a high-resolution spatial domain of resolution 1 × 1 km and compare the results with a 3 × 3 km domain and GFS. The results show error metrices rMAE {23.14%, 24.51%, 27.75%} and rRMSE {35.69%, 36.04%, 37.32%} for high resolution WRF solar, coarse domain WRF solar and GFS, respectively. This confirms that high resolution WRF solar performs better than coarse domain and in general. WRF solar demonstrates statistically significant improvement over GFS.
Quantum Error-Correcting Codes Based on Orthogonal Arrays

In this paper, by using the Hamming distance, we establish a relation between quantum error-correcting codes ((N,K,d+1))s and orthogonal arrays with orthogonal partitions. Therefore, this is a generalization of the relation between quantum error-correcting codes ((N,1,d+1))s and irredundant orthogonal arrays. This relation is used for the construction of pure quantum error-correcting codes. As applications of this method, numerous infinite families of optimal quantum codes can be constructed explicitly such as ((3,s,2))s for all si≥3, ((4,s2,2))s for all si≥5, ((5,s,3))s for all si≥4, ((6,s2,3))s for all si≥5, ((7,s3,3))s for all si≥7, ((8,s2,4))s for all si≥9, ((9,s3,4))s for all si≥11, ((9,s,5))s for all si≥9, ((10,s2,5))s for all si≥11, ((11,s,6))s for all si≥11, and ((12,s2,6))s for all si≥13, where s=s1⋯sn and s1,…,sn are all prime powers. The advantages of our approach over existing methods lie in the facts that these results are not just existence results, but constructive results, the codes constructed are pure, and each basis state of these codes has far less terms. Moreover, the above method developed can be extended to construction of quantum error-correcting codes over mixed alphabets.
Replace, absorb, serve: Data scientists talk about their aspired jurisdiction

How do data scientists frame their relations with domain experts? This study focuses on data scientists’ aspired professional jurisdiction and their multiple narratives regarding data science’s relations to other fields of expertise. Based on the analysis of 60 open-ended, in-depth interviews with data scientists, data science professors, and managers in Israel, the findings show that data scientists institutionalize three narratives regarding their relations with domain experts: (a) replace experts, (b) absorb experts’ knowledge, and (c) provide a service to experts. These three narratives construct data scientists’ expertise as universal and omnivorous; namely, they are relevant to many domains and allow data scientists to be flexible in their claim for authority.
An AI-Enhanced Solution for Large-Scale Deliberation Mapping and Explainable Reasoning

This work aims to respond to the profound lack of dialogue between citizenship and policy making institutions by proposing a novel solution that enables the transition to inclusive, transparent, accountable and trustworthy deliberation practices. The proposed solution builds on cutting-edge AI tools and technologies to develop a sustainable digital platform, and bridges theories from the fields of argumentation and digital democracy. It may transform scattered islands of emerging knowledge and practices, as well as fragmented discussion threads, into an integrated and coherent dialogue, and provides mechanisms for expanding this dialogue and converting it into tangible actions. Much attention is paid to issues related to knowledge extraction, knowledge graph-based representation of large-scale deliberation, argument mining, aggregation and visualization, as well as to explanation and awareness services about the evolution and outcome of a deliberation.