Reviewerly/exHarmony huggingface dataset:
Digital holographic technique based breast cancer detection using transfer learning method

The digital holographic technique is an interferometric method that provides comprehensive information on morphological traits such as cell layer thickness and shape as well as access to biophysical attributes of cells like refractive index, dry mass, and volume. This method helps characterize sample structures in three dimensions both statically and dynamically, even for transparent objects like living biological cells. This research work captures the digital holograms of breast tissues and analyzes the malignancy of the tissue using a deep learning technique. It enables dynamic measurement of the sample under investigation. Different transfer learning models such as Inception, DenseNet, SqueezeNet, VGG, and ResNet are incorporated in this work. The parameters accuracy, precision, sensitivity, and F1 score of different models are compared and found that the ResNet model outperforms better compared to other models.
Adaptive learning rate optimization algorithms with dynamic bound based on Barzilai-Borwein method

The training effect of the neural network model is directly influenced by optimization algorithms. The Barzilai-Borwein(BB) method is used in the stochastic gradient descent (SGD) and other deep learning optimization algorithms because of its outstanding performance in terms of convergence speed. In order to improve the stability of BB step size and the output quality of network model in deep learning, this paper presents two improved optimization algorithms based on the BB method: BBbound and AdaBBbound. BBbound reduces the floating BB step size by generating the upper bound of the step in the current iteration. It avoids the occasional occurrence of long steps; AdaBBbound is an adaptive gradient method based on BB method with dynamic bound. The algorithm has a fast convergence effect in the early stage, then calculates learning rate and smoothly transitions to SGD by setting the better conditions of the BB method. We analyzed the performance of the two algorithms based on the initial conditions and the learning rate change curve. Meanwhile, we tested our algorithms on popular network models such as ResNet and DenseNet. The results showed that the new optimization algorithms have achieved high stability and significantly improved the performance of the network model.
Artificial Intelligence and Economic Development: An Evolutionary Investigation and Systematic Review

In today’s environment of the rapid rise of artificial intelligence (AI), debate continues about whether it has beneficial effects on economic development. However, there is only a fragmented perception of what role and place AI technology actually plays in economic development (ED). In this paper, we pioneer the research by focusing our detective work and discussion on the intersection of AI and economic development. Specifically, we adopt a two-step methodology. At the first step, we analyze 2211 documents in the AI&ED field using the bibliometric tool Bibliometrix, presenting the internal structure and external characteristics of the field through different metrics and algorithms. In the second step, a qualitative content analysis of clusters calculated from the bibliographic coupling algorithm is conducted, detailing the content directions of recently distributed topics in the AI&ED field from different perspectives. The results of the bibliometric analysis suggest that the number of publications in the field has grown exponentially in recent years, and the most relevant source is the “Sustainability” journal. In addition, deep learning and data mining-related research are the key directions for the future. On the whole, scholars dedicated to the field have developed close cooperation and communication across the board. On the other hand, the content analysis demonstrates that most of the research is centered on the five facets of intelligent decision-making, social governance, labor and capital, Industry 4.0, and innovation. The results provide a forward-looking guide for scholars to grasp the current state and potential knowledge gaps in the AI&ED field.
THE CORRELATION BETWEEN COGNITIVE READING STRATEGIES AND STUDENTS’ ENGLISH PROFICIENCY TEST SCORE

One of the factors that influence the success of language learning is learning strategy, with language learning strategies learners not only get learning achievement, but also can improve language skills. This study aims to determine whether there is a correlation between cognitive reading strategies and English proficiency test scores. The population of this research was the third year students of the Department of English Education at private University. Quantitative methods were used to collect data. A self-report questionnaire consisting of 25 items was administered to 40 students. The reading strategy was evaluated under three headings: pre-reading, while-reading, and post-reading in both sections. The data collected from the questionnaire were statistically analyzed using SPSS. The result of this study indicated that there were positive correlation between cognitive reading strategies and English proficiency test scores. Students were usually and sometimes used cognitive reading strategy while reading a text.
Detection of geochemical anomalies related to mineralization using the Random Forest model optimized by the Competitive Mechanism and Beetle Antennae Search

This study aimed to detect the geochemical anomalies related to Fe-mineralization in the Hunjiang area in Jilin Province, China. For this purpose, a Random Forest model was optimized from two aspects of parameters' search and seeking the optimal solution by integrating the "Beetle Antennae Search" and "Competitive Mechanism". The optimized Random Forest model was compared with the original Random Forest model in the detection of geochemical anomalies related to Fe-mineralization. The results showed that the optimized Random Forest model avoids some limitations of the original Random Forest model, with significant advantages in the detection of mineralization-related anomaly information from exploration geochemical data. It allowed automatic optimization of parameters by integrating two heuristic searching processes. Simultaneously, it focussed more on the quest for the best global optimal solution. Through the case study, it is found that the optimized Random Forest algorithm significantly improved the overall performance for the detection of geochemical anomalies related to Fe-mineralization. The geochemical anomalies detected by the optimized Random Forest model showed close spatial correlation with known iron polymetallic deposits. Therefore, using the optimized Random Forest model to detect geochemical anomalies related to mineralization is of great importance. It also provides a better supporting foundation for geochemical exploration and mineral resource exploration in the Hunjiang area.
The Effect of Financial Digital Transformation on Financial Performance: The Intermediary Effect of Information Symmetry and Operating Costs

The ability of A-share listed companies to adhere to the digital economy and achieve long-term corporate benefits amidst an uncertain external environment through financial digital transformation remains a crucial concern for entrepreneurs and scholars. The objective of this study was to scrutinize the effect of financial digital transformation on financial performance among 2566 of China’s A-share listed companies in the hopes of providing informative recommendations for businesses that are currently undertaking or planning to undertake financial digital transformation. To investigate the mechanism by which financial performance among A-share listed companies was influenced by digital transformation, a panel data regression model was utilized. The findings suggest that, first and foremost, financial digital transformation significantly boosts corporate financial performance for A-share listed companies, and this enhancement is sustainable over time. For every 1% growth in financial digital transformation, corporate financial performance improves by 1.1%. Corporate financial performance is projected to improve by 29.8% during the next three financial years. Secondly, information symmetry and operational expenses function as intermediaries in the financial digital transformation process that affects firm financial performance. For every 1% increase in financial digital transformation, the information symmetry level of firms grows by 15.1%, while the operational cost rate declines by 0.8%. Thirdly, information disclosure and operating costs play a chained intermediary role. Every 1% increase in the level of information symmetry reduces the operating cost rate by 2.3%. Fourthly, in comparison to enterprises in eastern provinces, businesses located in central and western provinces are better positioned to improve their financial performance by undertaking a financial digital transformation.
PREIUD: An Industrial Control Protocols Reverse Engineering Tool Based on Unsupervised Learning and Deep Neural Network Methods

The security of industrial control systems relies on the communication and data exchange capabilities provided by industrial control protocols, which can be complex, and may even use encryption. Reverse engineering these protocols has become an important topic in industrial security research. In this paper, we present PREIUD, a reverse engineering tool for industrial control protocols, based on unsupervised learning and deep neural network methods. The reverse process is divided into stages. First, we use the bootstrap voting expert algorithm to infer the keyword segment boundaries of the protocols, considering the symmetry properties. Then, we employ a bidirectional long short-term memory conditional random field with an attention mechanism to classify the protocols and extract their format and semantic features. We manually constructed data sample sets for six commonly used industrial protocols, and used them to train and test our model, comparing its performance to two advanced protocol reverse tools, MSERA and Discoverer. Our results showed that PREIUD achieved an average accuracy improvement of 7.4% compared to MSERA, and 15.4% compared to Discoverer, while also maintaining a balance between computational conciseness and efficiency. Our approach represents a significant advancement in the field of industrial control protocol reverse engineering, and we believe it has practical implications for securing industrial control systems.
A Semantic Learning-Based SQL Injection Attack Detection Technology

Over the years, injection vulnerabilities have been at the top of the Open Web Application Security Project Top 10 and are one of the most damaging and widely exploited types of vulnerabilities against web applications. Structured Query Language (SQL) injection attack detection remains a challenging problem due to the heterogeneity of attack loads, the diversity of attack methods, and the variety of attack patterns. It has been demonstrated that no single model can guarantee adequate security to protect web applications, and it is crucial to develop an efficient and accurate model for SQL injection attack detection. In this paper, we propose synBERT, a semantic learning-based detection model that explicitly embeds the sentence-level semantic information from SQL statements into an embedding vector. The model learns representations that can be mapped to SQL syntax tree structures, as evidenced by visualization work. We gathered a wide range of datasets to assess the classification performance of the synBERT, and the results show that our approach outperforms previously proposed models. Even on brand-new, untrained models, accuracy can reach 90% or higher, indicating that the model has good generalization performance.
Improved Low-Depth SHA3 Quantum Circuit for Fault-Tolerant Quantum Computers

To build a secure cryptography system in the post-quantum era, one must find the minimum security parameters against quantum attacks by estimating the quantum resources of a fault-tolerant quantum computer. In a fault-tolerant quantum computer, errors must reach an acceptable level for practical uses according to error detection and error correction processes. However, these processes utilize additional quantum resources. As the depth of the quantum circuit increases, the computation time per qubit increases together with the processing errors. Therefore, in terms of errors in quantum circuits, it is a fundamental requirement to reduce the depth by trading off the number of qubits. This paper proposes novel low-depth SHA3 quantum circuit implementations for fault-tolerant quantum computers to reduce errors. The proposed SHA3 quantum circuit was implemented with the aim of optimizing the quantum circuit depth through a trade-off between the number of qubits, the quantum gate, and the quantum depth in each function. Compared to other state-of-art techniques, the proposed method achieved T-depth and full-depth reductions of 30.3% and 80.05%, respectively. We believe that this work will contribute to the establishment of minimum security parameters for SHA3 in the quantum era.
Proposal and Definition of an Intelligent Clinical Decision Support System Applied to the Screening and Early Diagnosis of Breast Cancer

Breast cancer is the most frequently diagnosed tumor pathology on a global scale, being the leading cause of mortality in women. In light of this problem, screening programs have been implemented on the population at risk in the form of mammograms, starting in the 20th century. This has considerably reduced the associated deaths, as well as improved the prognosis of the patients who suffer from this disease. In spite of this, the evaluation of mammograms is not without certain variability and depends, to a large extent, on the experience and training of the medical team carrying out the assessment. With the aim of supporting the evaluation process of mammogram images and improving the diagnosis process, this work presents the design, development and proof of concept of a novel intelligent clinical decision support system, grounded on two predictive approaches that work concurrently. The first of them applies a series of expert systems based on fuzzy inferential engines, geared towards the treatment of the characteristics associated with the main findings present in mammograms. This allows the determination of a series of risk indicators, the Symbolic Risks, related to the risk of developing breast cancer according to the different findings. The second one implements a classification machine learning algorithm, which using data related to mammography findings as well as general patient information determines another metric, the Statistical Risk, also linked to the risk of developing breast cancer. These risk indicators are then combined, resulting in a new indicator, the Global Risk. This could then be corrected using a weighting factor according to the BI-RADS category, allocated to each patient by the medical team in charge. Thus, the Corrected Global Risk is obtained, which after interpretation can be used to establish the patient's status as well as generate personalized recommendations. The proof of concept and software implementation of the system were carried out using a data set with 130 patients from a database from the School of Medicine and Public Health of the University of Wisconsin-Madison. The results obtained were encouraging, highlighting the potential use of the application, albeit pending intensive clinical validation in real environments. Moreover, its possible integration in hospital computer systems is expected to improve diagnostic processes as well as patient prognosis.
Interpretable Dropout Prediction: Towards XAI-Based Personalized Intervention

Abstract Student drop-out is one of the most burning issues in STEM higher education, which induces considerable social and economic costs. Using machine learning tools for the early identification of students at risk of dropping out has gained a lot of interest recently. However, there has been little discussion on dropout prediction using interpretable machine learning (IML) and explainable artificial intelligence (XAI) tools.In this work, using the data of a large public Hungarian university, we demonstrate how IML and XAI tools can support educational stakeholders in dropout prediction. We show that complex machine learning models – such as the CatBoost classifier – can efficiently identify at-risk students relying solely on pre-enrollment achievement measures, however, they lack interpretability. Applying IML tools, such as permutation importance (PI), partial dependence plot (PDP), LIME, and SHAP values, we demonstrate how the predictions can be explained both globally and locally. Explaining individual predictions opens up great opportunities for personalized intervention, for example by offering the right remedial courses or tutoring sessions. Finally, we present the results of a user study that evaluates whether higher education stakeholders find these tools interpretable and useful.
Creation, Analysis and Evaluation of AnnoMI, a Dataset of Expert-Annotated Counselling Dialogues

Research on the analysis of counselling conversations through natural language processing methods has seen remarkable growth in recent years. However, the potential of this field is still greatly limited by the lack of access to publicly available therapy dialogues, especially those with expert annotations, but it has been alleviated thanks to the recent release of AnnoMI, the first publicly and freely available conversation dataset of 133 faithfully transcribed and expert-annotated demonstrations of high- and low-quality motivational interviewing (MI)—an effective therapy strategy that evokes client motivation for positive change. In this work, we introduce new expert-annotated utterance attributes to AnnoMI and describe the entire data collection process in more detail, including dialogue source selection, transcription, annotation, and post-processing. Based on the expert annotations on key MI aspects, we carry out thorough analyses of AnnoMI with respect to counselling-related properties on the utterance, conversation, and corpus levels. Furthermore, we introduce utterance-level prediction tasks with potential real-world impacts and build baseline models. Finally, we examine the performance of the models on dialogues of different topics and probe the generalisability of the models to unseen topics.
Using deep learning in an embedded system for real-time target detection based on images from an unmanned aerial vehicle: vehicle detection as a case study

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ParallelChain: a scalable healthcare framework with low‐energy consumption using blockchain

Abstract The blockchain‐enabled healthcare system assists in tackling the inherent issues of centralized healthcare systems, such as data stewardship, single point of failure, and data integrity. However, existing blockchain‐enabled healthcare systems suffer from high‐energy consumption, low transaction throughput, and poor scalability. This work proposes the healthcare framework ParallelChain to tackle the low scalability and high‐energy consumption issue. ParallelChain divides the network into clusters, enabling linear scalability of nodes. The parallel execution of transactions in each cluster improves the throughput. The leader selection process of ParallelChain does not require solving computer‐intensive puzzles, making it more energy efficient than proof‐of‐work. The performance of ParallelChain is validated using metrics such as data transfer, processing time, consensus delay, block congestion, and message exchange. It shows that the amount of data transmitted is 4.6 times more in Bitcoin than ParallelChain with varying nodes. The number of messages exchanged is reduced by 48%, and the processing time is reduced by 2.17 and 1.7 times for varying nodes and block sizes, respectively.
Dynamic Multi-Objective Optimization Framework With Interactive Evolution for Sequential Recommendation

In contrast to traditional recommender systems which usually pay attention to users' general and long-term preferences, sequential recommendation (SR) can model users' dynamic intents based on their behaviour sequences and suggest the next item(s) to them. However, most of existing sequential models learn the ranking score of an item only based on its relevance property, and the personalized user demands in terms of different learning objectives, such as diversity, tail novelty or recency, which have been played essential roles in multi-objective recommendation (MOR), are often neglected in SR. In this paper, we first discuss the importance of considering multiple different objectives within a learning model for recommender system. Next, to capture users' objective-level preferences by utilizing interactive information in the sequential context, we propose a novel Dynamic Multi-objective Recommendation (DMORec) framework with interactive evolution for SR. In particular, DMORec formulates a dynamic multi-objective optimization task to simultaneously optimize more than two varying objectives in an interactive recommendation process. Moreover, to resolve this optimization task in SR, we develop an evolutionary algorithm with supervised learning approach to obtain the Pareto-optimal solutions of the formulated problem. Comprehensive experiments on four real-world datasets demonstrate the effectiveness of the proposed DMORec for dynamic multi-objective recommendation in sequential recommender systems.
Data heterogeneity in federated learning with Electronic Health Records: Case studies of risk prediction for acute kidney injury and sepsis diseases in critical care

With the wider availability of healthcare data such as Electronic Health Records (EHR), more and more data-driven based approaches have been proposed to improve the quality-of-care delivery. Predictive modeling, which aims at building computational models for predicting clinical risk, is a popular research topic in healthcare analytics. However, concerns about privacy of healthcare data may hinder the development of effective predictive models that are generalizable because this often requires rich diverse data from multiple clinical institutions. Recently, federated learning (FL) has demonstrated promise in addressing this concern. However, data heterogeneity from different local participating sites may affect prediction performance of federated models. Due to acute kidney injury (AKI) and sepsis' high prevalence among patients admitted to intensive care units (ICU), the early prediction of these conditions based on AI is an important topic in critical care medicine. In this study, we take AKI and sepsis onset risk prediction in ICU as two examples to explore the impact of data heterogeneity in the FL framework as well as compare performances across frameworks. We built predictive models based on local, pooled, and FL frameworks using EHR data across multiple hospitals. The local framework only used data from each site itself. The pooled framework combined data from all sites. In the FL framework, each local site did not have access to other sites' data. A model was updated locally, and its parameters were shared to a central aggregator, which was used to update the federated model's parameters and then subsequently, shared with each site. We found models built within a FL framework outperformed local counterparts. Then, we analyzed variable importance discrepancies across sites and frameworks. Finally, we explored potential sources of the heterogeneity within the EHR data. The different distributions of demographic profiles, medication use, and site information contributed to data heterogeneity.