Reviewerly/exHarmony huggingface dataset:
Rapid Detection of Iron Ore and Mining Areas Based on MSSA-BNVTELM, Visible—Infrared Spectroscopy, and Remote Sensing

The accuracy and rapidity of total iron content (TFE) analysis can accelerate iron ore production. Although the conventional TFE detection methods are accurate, its detection speed presents difficulties in meeting production requirements. Therefore, this paper proposes a method of TFE detection based on reflectance spectroscopy (wavelength range: 340–2500 nm) and remote sensing. Firstly, spectral experiments were conducted on iron ore using the HR SVC-1024 spectrometer to obtain spectral data for each sample. Then, the spectra were smoothed and dimensionally reduced by using wavelet transform and principal component analysis. To improve the detection accuracy of TFE, a two hidden layer extreme learning machine with variable neuron nodes based on an improved sparrow search algorithm and batch normalization optimization (MSSA-BNVTELM) is proposed. According to the experimental results, MSSA-BNVTELM exhibited superior detection accuracy in comparison to other algorithms. In addition, this research established a remote sensing detection model using Sentinel-2 data and MSSA-BNVTEM to detect the distribution of TFE in the mining area. The distribution of TFE in the mine area was plotted based on the detection results. The results show that the remote sensing of the mine area can be useful for detection of the TFE distribution, providing assistance for the mining plan.
Reduction of False Positives for Runtime Errors in C/C++ Software: A Comparative Study

In software development, early defect detection using static analysis can be performed without executing the source code. However, defects are detected on a non-execution basis, thus resulting in a higher ratio of false positives. Recently, studies have been conducted to effectively perform static analyses using machine learning (ML) and deep learning (DL) technologies. This study examines the techniques for detecting runtime errors used in existing static analysis tools and the causes and rates of false positives. It analyzes the latest static analysis technologies that apply machine learning/deep learning to decrease false positives and compares them with existing technologies in terms of effectiveness and performance. In addition, machine-learning/deep-learning-based defect detection techniques were implemented in experimental environments and real-world software to determine their effectiveness in real-world software.
Low-Light Image Contrast Enhancement with Adaptive Noise Attenuator for Augmented Vehicle Detection

The rapid progress in deep learning technologies has accelerated the use of object detection models, but most models do not operate satisfactorily in low-light environments. As a result, many studies have been conducted on image enhancement techniques aiming to make objects more visible by increasing contrast, but the process of image enhancement may negatively impact detection as it further strengthens unwanted noises due to indirect factors of light reflection such as overall low brightness, streetlamps, and neon signboards. Therefore, in this study, we propose a technique for improving the performance of object detection in low-light environments. The proposed technique inverts a low-light image to make it similar to a hazy image and then uses a haze removal algorithm based on entropy and fidelity to increase image contrast, clarifying the boundary between the object and the background. In the next step, we used the adaptive 2D Wiener filter (A2WF) to attenuate the noise accidentally strengthened during the image enhancement process and reinforced the boundary between the object and the background to increase detection performance. The test evaluation results showed that the proposed image enhancement scheme significantly increased image perception performance with the perception-based image quality evaluator being 12.73% lower than existing image enhancement techniques. In a comparison of vehicle detection performance, the proposed technique for enhancing nighttime images combined with the detection model proved its effectiveness by increasing the average precision by up to 18.63% against existing detection methods.
Siamese Network Tracker Based on Multi-Scale Feature Fusion

The main task in visual object tracking is to track a moving object in an image sequence. In this process, the object’s trajectory and behavior can be described by calculating the object’s position, velocity, acceleration, and other parameters or by memorizing the position of the object in each frame of the corresponding video. Therefore, visual object tracking can complete many more advanced tasks, has great performance in relation to real scenes, and is widely used in automated driving, traffic monitoring, human–computer interaction, and so on. Siamese-network-based trackers have been receiving a great deal of attention from the tracking community, but they have many drawbacks. This paper analyzes the shortcomings of the Siamese network tracker in detail, uses the method of feature multi-scale fusion to improve the Siamese network tracker, and proposes a new target-tracking framework to address its shortcomings. In this paper, a feature map with low-resolution but strong semantic information and a feature map with high-resolution and rich spatial information are integrated to improve the model’s ability to depict an object, and the problem of scale change is solved by fusing features at different scales. Furthermore, we utilize the 3D Max Filtering module to suppress repeated predictions of features at different scales. Finally, our experiments conducted on the four tracking benchmarks OTB2015, VOT2016, VOT2018, and GOT10K show that the proposed algorithm effectively improves the tracking accuracy and robustness of the system.
Textural mapping and building a paragenetic interpretation of hydrothermal veins

Abstract Paragenetic interpretation, defined as the process of constraining the order in which the phases (minerals) comprising a rock formed, are fundamental in all disciplines requiring a detailed mapping of the genetic relationships between the investigated crystal(s) and their surrounding phases. Ore geology, geochronology, petrological and fluid inclusion studies are underpinned by a robust interpretation of the paragenesis. Without this understanding, the interpretation of the analytical data will be greatly restricted and potentially wrong. Textural mapping is particularly useful for studying phases precipitated into hydrothermal veins because the host many metallic ore deposits. Understanding the paragenesis and the association of ore minerals feeds directly into the approach taken towards exploration, mining operation and ore processing. Apart from ore deposits, precipitation of hydrothermal phases occurs in any setting where hot fluids interact with rock or sediment (e.g. CCS storage; geothermal facilities). This paper summarizes the principles for developing a robust paragenetic interpretation, including demonstrating the practical workflow via a case study from a sulfide-bearing vein near Loch Tay, Scotland. The principles and the workflow outlined in this paper are applicable to any discipline and route of investigation that necessitates a robust understanding of the relationships of the phases in a rock.
Corruption and New Insights in Lebanon

This research study aims to provide new insights into the issue of corruption in Lebanon by examining its impact on various sectors of the country, the role of the government in tackling corruption, and the effectiveness of current anti-corruption measures. Lebanon is ranked 149th out of 180 countries in Transparency International’s 2020 Corruption Perceptions Index, highlighting the widespread nature of the issue. To achieve the research objectives, we adopted a mixed-methods approach that involved both quantitative and qualitative data collection methods. Specifically, we conducted an online questionnaire that received responses from a diverse sample of Lebanese citizens, including government officials, business leaders, and the general public. We also conducted interviews with key stakeholders, including representatives from civil society organizations and government agencies. The study’s findings reveal that corruption has a significant impact on the country’s economy, businesses, electricity, and pollution levels. The study also highlights the challenges faced by the government in effectively addressing corruption and the limitations of current anti-corruption measures. Based on our analysis, we provide policy recommendations that can help improve the effectiveness of anti-corruption efforts in Lebanon.
Deep Learning for Natural Language Processing

With the constantly growing number of topical or sentiment-bearing texts and dialogs on the Web, the demand for automatic language or text analysis algorithms continues to expand. This chapter discusses about advanced deep learning techniques for classical and hot research directions in the field of natural language processing, including text classification, sentiment analysis, and task-oriented dialog systems. In text classification, we focus on tasks of multi-label text classification and extreme multi-label text classification, which allow for automatically annotates the texts with the most relevant labels. In sentiment analysis, we look into aspect-based sentiment analysis that makes automatic extraction of fine-grained sentiment information from texts, and multimodal sentiment analysis that classifies people’s opinions or attitudes from multimedia data through fusion techniques. In dialog system, we introduce how deep learning techniques work in pipeline mode and end-to-end mode for task-oriented dialog system. In this chapter, the rapidly evolving state of the research on the three topics is reviewed. Furthermore, trends in the research on deep learning for natural language processing are identified, and a discussion about future advances is provided.
Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making

The emergency department (ED) is a fast-paced environment responsible for large volumes of patients with varied disease acuity. Operational pressures on EDs are increasing, which creates the imperative to efficiently identify patients at imminent risk of acute deterioration. The aim of this study is to systematically compare the performance of machine learning algorithms based on logistic regression, gradient boosted decision trees, and support vector machines for predicting imminent clinical deterioration for patients based on cross-sectional patient data extracted from electronic patient records (EPR) at the point of entry to the hospital. We apply state-of-the-art machine learning methods to predict early patient deterioration, based on their first recorded vital signs, observations, laboratory results, and other predictors documented in the EPR. Clinical deterioration in this study is measured by in-hospital mortality and/or admission to critical care. We build on prior work by incorporating interpretable machine learning and fairness-aware modelling, and use a dataset comprising 118, 886 unplanned admissions to Salford Royal Hospital, UK, to systematically compare model variations for predicting mortality and critical care utilisation within 24 hours of admission. We compare model performance to the National Early Warning Score 2 (NEWS2) and yield up to a 0.366 increase in average precision, up to a [Formula: see text] reduction in daily alert rate, and a median 0.599 reduction in differential bias amplification across the protected demographics of age and sex. We use Shapely Additive exPlanations to justify the models' outputs, verify that the captured data associations align with domain knowledge, and pair predictions with the causal context of each patient's most influential characteristics. Introducing our modelling to clinical practice has the potential to reduce alert fatigue and identify high-risk patients with a lower NEWS2 that might be missed currently, but further work is needed to trial the models in clinical practice. We encourage future research to follow a systematised approach to data-driven risk modelling to obtain clinically applicable support tools.
Sustainability and robust decision-support strategy for multi-echelon supply chain system against disruptions

ABSTRACTDue to the higher nonlinearity and parametric sensitivity of multi-echelon supply chains, minor changes may result in a completely different evolution of supply chain capabilities. Realizing resilient and sustainable development goals for enterprise logistics requires great insights into management schemes in a volatile market. This paper deals with dynamic characteristics and resilience issues of managing multi-echelon supply chain systems against disruptions. Discrete-time nonlinear systems with time delay are extensively evaluated via computation of Lyapunov exponent, bifurcation diagram, permutation entropy, and Poincaré map. For time-series evolution, forecasting and predictability are assessed by the Hurst exponent. An echo state network (ESN) will be utilized to predict the future states of the supply chain dynamics. Based on a decision-support strategy with an ESN model, a resilience strategy concerning disturbing factors is implemented to ensure a sustainable supply chain network. Numerical simulations are demonstrated to validate dynamical behaviors and verify the efficiency of control theory with machine learning, guaranteeing the resilience and sustainability of a supply chain against market volatility. The test results demonstrate how machine learning algorithms can be integrated with management schemes. Finally, the presented methodology can ensure more supply chain visibility and traceability under highly uncertain markets.KEYWORDS: Supply chain managementdiscrete-time dynamicsresilience and sustainabilitydecision-support strategymachine learning Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementAll data generated or analyzed during this study are included in this article (and its supplementary information files).Additional informationFundingThis research was supported by Korea Institute of Marine Science & Technology Promotion (KIMST) funded by the Ministry of Oceans and Fisheries, Korea [grant number 20220573].
Augmented Reality Visualization of Autonomous Mobile Robot Change Detection in Uninstrumented Environments

The creation of information transparency solutions to enable humans to understand robot perception is a challenging requirement for autonomous and artificially intelligent robots to impact a multitude of domains. By taking advantage of comprehensive and high-volume data from robot teammates’ advanced perception and reasoning capabilities, humans will be able to make better decisions, with significant impacts from safety to functionality. We present a solution to this challenge by coupling augmented reality (AR) with an intelligent mobile robot that is autonomously detecting novel changes in an environment. We show that the human teammate can understand and make decisions based on information shared via AR by the robot. Sharing of robot-perceived information is enabled by the robot’s online calculation of the human’s relative position, making the system robust to environments without external instrumentation such as GPS. Our robotic system performs change detection by comparing current metric sensor readings against a previous reading to identify differences. We experimentally explore the design of change detection visualizations and the aggregation of information, the impact of instruction on communication understanding, the effects of visualization and alignment error, and the relationship between situated 3D visualization in AR and human movement in the operational environment on shared situational awareness in human-robot teams. We demonstrate this novel capability and assess the effectiveness of human-robot teaming in crowdsourced data-driven studies, as well as an in-person study where participants are equipped with a commercial off-the-shelf AR headset and teamed with a small ground robot which maneuvers through the environment. The mobile robot scans for changes, which are visualized via AR to the participant. The effectiveness of this communication is evaluated through accuracy and subjective assessment metrics to provide insight into interpretation and experience.
Deep Learning-Based Detection Technology for SQL Injection Research and Implementation

Amid the incessant evolution of the Internet, an array of cybersecurity threats has surged at an unprecedented rate. A notable antagonist within this plethora of attacks is the SQL injection assault, a prevalent form of Internet attack that poses a significant threat to web applications. These attacks are characterized by their extensive variety, rapid mutation, covert nature, and the substantial damage they can inflict. Existing SQL injection detection methods, such as static and dynamic detection and command randomization, are principally rule-based and suffer from low accuracy, high false positive (FP) rates, and false negative (FN) rates. Contemporary machine learning research on SQL injection attack (SQLIA) detection primarily focuses on feature extraction. The effectiveness of detection is heavily reliant on the precision of feature extraction, leading to a deficiency in tackling more intricate SQLIA. To address these challenges, we propose a novel SQLIA detection approach harnessing the power of an enhanced TextCNN and LSTM. This method begins by vectorizing the samples in the corpus and then leverages an improved TextCNN to extract local features. It then employs a Bidirectional LSTM (Bi-LSTM) network to decipher the sequence information inherent in the samples. Given LSTM’s modest effectiveness for relatively long sequences, we further integrate an attention mechanism, reducing the distance between any two words in the sequence to one, thereby enhancing the model’s effectiveness. Moreover, pre-trained word vector features acquired via BERT for transfer learning are incorporated into the feature section. Comparative experimental results affirm the superiority of our deep learning-based SQLIA detection approach, as it effectively elevates the SQLIA recognition rate while reducing both FP and FN rates.
Leveraging Language Models for Inpatient Diagnosis Coding

Medical coding plays an essential role in medical billing, health resource planning, clinical research and quality assessment. Automated coding systems offer promising solutions to streamline the coding process, improve accuracy and reduce the burden on medical coders. To date, there has been limited research focusing on inpatient diagnosis coding using an extensive comprehensive dataset and encompassing the full ICD-10 code sets. In this study, we investigate the use of language models for coding inpatient diagnoses and examine their performance using an institutional dataset comprising 230,645 inpatient admissions and 8677 diagnosis codes spanning over a six-year period. A total of three language models, including two general-purpose models and a domain-specific model, were evaluated and compared. The results show competitive performance among the models, with the domain-specific model achieving the highest micro-averaged F1 score of 0.7821 and the highest mean average precision of 0.8097. Model performance varied by disease and condition, with diagnosis codes with larger sample sizes producing better results. The rarity of certain diseases and conditions posed challenges to accurate coding. The results also indicated the potential difficulties of the model with long clinical documents. Our models demonstrated the ability to capture relevant associations between diagnoses. This study advances the understanding of language models for inpatient diagnosis coding and provides insights into the extent to which the models can be used.
Study on the National Identity Education Intentions of Pre-Service Teachers: Evidence from PLS-SEM and fsQCA

National identity education is a form of education that fosters a stable sense of national identity among citizens and plays a crucial role in the sustainable development of the country. However, with the deepening of economic globalisation and cultural pluralism, pre-service teachers, in their dual roles as school students and prospective teachers, have encountered challenges in practicing their intention to implement national identity education. This study constructed a model of influencing pre-service teachers’ intention to implement national identity education based on the theory of planned behaviour (TPB) and the cognitive evaluation theory (CET) with the aim of explaining and analysing the development path of pre-service teachers’ educational intentions. The model was validated using PLS-SEM and fsQCA on data from 280 pre-service teachers in teacher training colleges in northern China. The PLS-SEM results indicated that subjective norms, relatedness need, and study resources directly and positively influenced pre-service teachers’ educational intentions, and study resources could also indirectly influence educational intentions through subjective norms and a need for relatedness. The fsQCA results indicated that a single variable was unable to predict and explain educational intentions. A total of five ways of influencing educational intentions emerged from the group analysis. Based on the pre-service teachers’ propensity to demand learning resources and their competence, pre-service teachers were categorised into four types—basic development pathway, internally driven pathway, competence-driven pathway and resource-supported pathway—and recommendations were made according to the degree of reliance on resources of different types of pre-service teachers as a way of providing theoretical and data support for the sustainable development of the country.
Enhanced local addressability of a spin array with local exchange pulses and global microwave driving

We theoretically propose a strategy to address an individual spin in a large array of spin qubits with a random distribution of $g$ factors by employing a combination of single-qubit and swap gates facilitated by a global microwave field and local exchange pulses. Consequently, only the target qubit undergoes the desired operation and all other qubits return to their original states, even qubits that share the same Larmor frequency as the target. Gate fidelities above 99% can thus be maintained for arrays containing tens of qubits.
A neutral zone classifier for three classes with an application to text mining

Abstract A classifier may be limited by its conditional misclassification rates more than its overall misclassification rate. In the case that one or more of the conditional misclassification rates are high, a neutral zone may be introduced to decrease and possibly balance the misclassification rates. In this paper, a neutral zone is incorporated into a three‐class classifier with its region determined by controlling conditional misclassification rates. The neutral zone classifier is illustrated with a text mining application that classifies written comments associated with student evaluations of teaching.