Reviewerly/exHarmony huggingface dataset:
Transformation of the idea of technological determinism in digital era

The paper is devoted to the consideration of the features of a contemporary digital era from the standpoint of identifying the key factors of development in its basis, among which the author singles out technologies of production and socialization of information and social communication technologies. Scientific and technological progress has always been the locomotive of economic and social progress affecting the possibilities of social and communicative relations in a society and in its main subsystems, including science, engineering and technologies. Modern digital era brings its own changes into the correlation of scientific-technological and socio-communicative factors of progress, endowing modern technologies of social communication with the possibility of a significant reverse influence on scientific, technological and socio-economic structures of society. From this point of view, the author draws attention to the trend of transformation of the concept of technological determinism, according to which the prospects for the development of society are literally determined by the achievements of science, engineering and technology, and the latter in this context acquire self-sufficient value and autonomy. The classical idea of technological determinism is implemented within the framework of an instrumental understanding of socio-technological success. The trend of transformation of this idea is carried out because new forms of social and communicative relations are included into scientific and technological practice and act as interactive regulators of social actions and decision-making, also in the field of scientific, technical and technological development.
The neural network revolution of artificial intelligence and it's development options

The civilization development, which ensures the science and technology progress, has led to the theories of algorithms and control creation and the achievement of technological capabilities to create "smart" machines, primarily based on information and computing technologies. But, just as in chemistry until the first half of the 19th century there was no doubt that “life force” was needed to obtain organic substances, so it is now widely believed that all modern successes in artificial intelligence are only an imitation of mental activity, which "for real" can occur only in a living person brain. To substantiate this opinion, it is indicated that the created networks of formal neurons not only have work algorithms that differ markedly from the living neural networks functioning, but also their learning processes properties are also radically different. Yes, this is true, but modern successes in the development of "narrow" artificial intelligence provide a basis for understanding the existing unresolved problems and allow us to look for ways to overcome them. The main step is to abandon the imitation of problem solving methods based on the knowledge that a person has and the transition to the acquisition and accumulation of new knowledge through interaction with the environment. But this does not mean that the civilizational knowledge accumulated by mankind should be discarded. On the contrary, it is the existing knowledge that should serve as the basis for obtaining new ones.
The transformation of 21st century science: From Parmenides to Heraclitus

Modern science has entered a deep crisis. This manifests itself in the excessive differentiation of knowledge, the disappearance of a holistic picture of the world, the lack of progress in solving deep problems. The way out of the crisis is possible when scientists master the synergetic style of thinking, i.e. thinking with “moving thought forms.” The paradigm of Parmenides, accepted in scientific thinking, is gradually being replaced by the paradigm of Heraclitus. The thesis “The One is essentially immobile” is replaced by the thesis “Everything is movement.” Fixed concepts become a special case of “moving concepts.” In logic, the law of identity can be replaced by the law of generalized identity. The new rationalism will require the transformation of a number of traditions of scientific knowledge.
PEDAGOGICAL COMPETENCE DEVELOPING OF PSYCHOLOGY TEACHER (MASTER’S DEGREE): EXPERIENCE OF UKRAINE

The labour market demands psychologists to be competitive and able to solve professional tasks. In our opinion, competent Psychology teachers should be motivated for professional development in the constantly changing war and post-war times. The article presents the author’s ideas about the development of the pedagogical competence of Psychology teachers (master’s students) in the current conditions of Ukrainian universities, which is the result of a longitudinal scientific and pedagogical study, which began in 2008 on the basis of Ukrainian universities. The changing standards of the teaching profession and the new challenges of the time became the basis for writing this article. The methodology is a literature review which focuses on the analysis of the international experience in training teachers in Psychology. After evaluating international pedagogical and scientific experiences, the author concluded that developing pedagogical competence requires organizing the educational process with a focus on formal, informal, and non-formal methods of obtaining higher education. The text presents a concise overview of the conditions and methods for developing the pedagogical competence of Psychology teachers, including the importance of cultivating reflective abilities as a vital aspect of competence.
Dynamic Optical Networks as Arcade Games: Lessons Learnt and Next Steps

We transformed the dynamic routing and spectrum allocation problem into an Arcade-like game and trained a reinforcement learning agent to play it. Preliminary results show the agent outperforms the K-SP-FF heuristic.
An Empirical Study on HDP Approaches

Software defect prediction has always been a hot research topic in the field of software engineering owing to its capability of allocating limited resources reasonably. Compared with cross-project defect prediction (CPDP), heterogeneous defect prediction (HDP) further relaxes the limitation of defect data used for prediction, permitting different metric sets to be contained in the source and target projects. However, there is still a lack of a holistic understanding of existing HDP studies due to different evaluation strategies and experimental settings. In this section, we provide an empirical study on HDP approaches. We review the research status systematically and compare the HDP approaches proposed from 2014 to June 2018. Furthermore, we also investigate the feasibility of HDP approaches in CPDP. Through extensive experiments on 30 projects from five datasets, we have the following findings: (1) metric transformation-based HDP approaches usually result in better prediction effects, while metric selection-based approaches have better interpretability. Overall, the HDP approach proposed by Li et al. (CTKCCA) currently has the best performance. (2) Handling class imbalance problems can boost the prediction effects, but the improvements are usually limited. In addition, utilizing mixed project data cannot improve the performance of HDP approaches consistently since the label information in the target project is not used effectively. (3) HDP approaches are feasible for cross-project defect prediction in which the source and target projects have the same metric set.
Cross-Project Defect Prediction

The challenge of CPDP methods is the distribution difference between the data from different projects. Transfer learning can transfer the knowledge from the source domain to the target domain with the aim to minimize the domain difference between different domains. However, most existing methods reduce the distribution discrepancy in the original feature space, where the features are high-dimensional and nonlinear, which makes it hard to reduce the distribution distance between different projects. In this chapter, we proposed a manifold embedded distribution adaptation (MDA) approach to narrow the distribution gap in manifold feature subspace. For cross-project SDP, we found that the class imbalanced source usually leads to misclassification of defective instances. However, only one work has paid attention to this cross-project class imbalance problem. Subclass discriminant analysis (SDA), an effective feature learning method, is introduced to solve the problems. It can learn features with more powerful classification ability from original metrics. Within-project and cross-project class imbalance problems greatly affect prediction performance, and we provide a unified and effective prediction framework for both problems. We call CPDP in this scenario as cross-project semi-supervised defect prediction (CSDP). Although some within-project semi-supervised defect prediction (WSDP) methods have been developed in recent years, there still exists much room for improvement on prediction performance. We aim to provide a unified and effective solution for both CSDP and WSDP problems. We introduce the semi-supervised dictionary learning technique and propose a cost-sensitive kernelized semi-supervised dictionary learning (CKSDL) approach. CKSDL can make full use of the limited labeled defect data and a large amount of unlabeled data in the kernel space.
Introduction

With the increasing pressures of expediting software projects that is always increasing in size and complexity to meet rapidly changing business needs, quality assurance activities such as fault prediction models have thus become extremely important. The main purpose of a fault prediction model is the effective allocation or prioritization of quality assurance effort (test effort and code inspection effort). Construction of these prediction models are mostly dependent on historical or previous software project data referred to as a dataset.
Writing systems

Abstract Chapter 3 introduces the history of writing in Korean and Japanese, which involves borrowing of the Chinese script and syntax and development of different writing systems more suitable to the morphosyntax and phonology of the two languages. Several methods of adapting Chinese characters to write texts (e.g., by changing word order and adding auxiliary elements to mark grammatical functions) were attempted by Koreans and shared with Japanese. The Japanese developed syllabic phonographic scripts, i.e., Hiragana and Katakana, by the 10th century by simplifying Chinese characters and established a writing system in which both the Chinese and phonographic characters are used. The Koreans stayed closer to the authentic Chinese writing until they invented an alphabetic phonographic script Hangŭl in the 15th century, which allowed Korean texts to fully represent Korean morphosyntax and phonology. In principle, Korean texts are written solely in Hangŭl without mixing Chinese characters today.
Preface

Extract As linguists both working on Korean and Japanese, we have often encountered the need for a textbook on Korean/Japanese linguistics for advanced undergraduates, graduate students, and instructors who teach Korean/Japanese language and Korean/Japanese linguistics. We bring different perspectives to the task: one as a teacher of Korean/Japanese and specialist on Korean/Japanese second language learning, one as a researcher in comparative syntax and morphology. This textbook has grown out of the lecture notes for the ‘Structure of Korean’ and ‘Structure of Japanese’ courses that we have taught over the past few decades. Korean and Japanese linguistics attracts the interest of not only students, researchers, and teachers working on the language, but of students and scholars of general linguistics. Korean is well known for such features as its three-way distinction among obstruents without a voicing distinction, case stacking, and multiple nominative and accusative constructions. The Korean writing system has long been a topic of interest to linguists and to those interested in writing systems in general. Japanese is often noted for its direct and indirect passives and the use of multiple writing systems in the same text. Both languages, due to their agglutinative nature, have overt morphological suffixes for grammatical functions including topic and nominative markers and elaborate honorific systems, which fascinates both morphologists and syntacticians. Their relative clauses appear to lack a gap, and yet they prohibit extraction from certain constructions, behaving similarly to European languages, which invites further investigation.
Survey and Experiments on Biomedical Pre-Trained Language Models for Named Entity Recognition

The use of language models and deep learning has arisen as promising solution for Named Entity Recognition in biomedicine due to the outperforming results in the last years. However, several challenges remain under research, such as training under different conditions to detect biomedical entities in texts, and to choose the best model for each entity. Besides, since the number of detected genetic variations is growing, the recognition of mutation entities is highly demanded. In the present work, we fine-tuned and compared the performance of publicly available relevant pre-trained language models under the same condition to detect biomedical entities. In addition, we applied an extra fine-tuning step, and observed an outstanding F1 improvement for mutation entities.
Evaluating SHAP’s Robustness in Precision Medicine: Effect of Filtering and Normalization

Local interpretation of explainable AI, SHAP (SHapley Additive exPlanations), in disease classification problems offers significant feature scores for each sample, potentially identifying precision medicine targets. Tailoring treatments based on individual genetic and molecular targets can enhance therapeutic outcomes while minimizing side effects. However, the suitability of SHAP's local interpretation at the patient level remains uncertain. It generates different sets of patient-specific genes in various runs, even with consistent overall accuracies. This uncertainty challenges the reliability of SHAP’s local interpretations for precision medicine applications. Not only that, different filtering criteria and normalization techniques may influence the contribution scores of patient-specific features. To validate our hypothesis, SHAP was applied to machine learning algorithms from different genres to identify patient-specific feature contributions from the breast cancer subtype classification problem. The program underwent multiple runs to assess the robustness of SHAP.Our study demonstrates that shallow machine learning algorithms, like Logistic Regression, consistently provided stable and reliable results across multiple runs. In contrast, complex machine learning models like XGBoost and MLP exhibited inconsistencies across different runs. Moreover, we found that data normalization techniques, particularly z-score and min-max normalization, had a minimal effect on the performance of XGBoost models. Our study also shows that the accuracy scores of complex machine learning models remained relatively constant across different runs but produced different sets of patient-specific features. In conclusion, our findings underscore the importance of selecting appropriate filtering and normalization techniques, given the variability in SHAP results across different runs. Our study indicates that combining SHAP with shallow machine learning algorithms yields more stable and dependable results compared to complex machine learning approaches.
Privacy Risks Analysis and Mitigation in Federated Learning for Medical Images

Federated learning (FL) is gaining increasing popularity in the medical domain for analyzing medical images, which is considered an effective technique to safeguard sensitive patient data and comply with privacy regulations. However, several recent studies have revealed that the default settings of FL may leak private training data under privacy attacks. Thus, it is still unclear whether and to what extent such privacy risks of FL exist in the medical domain, and if so, “how to mitigate such risks?”. In this paper, first, we propose a holistic framework for Medical data Privacy risk analysis and mitigation in Federated Learning (MedPFL) to analyze privacy risks and develop effective mitigation strategies in FL for protecting private medical data. Second, we demonstrate the substantial privacy risks of using FL to process medical images, where adversaries can easily perform privacy attacks to reconstruct private medical images accurately. Third, we show that the defense approach of adding random noises may not always work effectively to protect medical images against privacy attacks in FL, which poses unique and pressing challenges associated with medical data for privacy protection.
CAMPEOD: A Cross Attention-Based Multi-Scale Patch Embedding Organoid Detection Model

In medical research, organoids, which exhibit structural and functional resemblances to authentic organs, offer a substantial avenue for delving into aspects encompassing physiology, pathophysiology, diseases, and pharmaceutical screening. Critical insights into drug responsiveness are often gleaned from these organoids' dimensional and configurational disparities. However, conventional detection methodologies reliant upon fluorescent labeling engender potential hazards to organoid integrity, thereby impinging upon their intrinsic dynamic attributes. Traditional bounding-box detection methodologies fall short in encapsulating intricate morphological particulars, and certain deep-learning approaches grapple with the intricate task of capturing multi-scale data, particularly when tasked with discerning organoid structures characterized by marked shape and size heterogeneities. In a bid to surmount these constraints, our study introduces CAMPEOD, an innovative framework that synergistically amalgamates multi-scale attributes derived from organoid specimens, employing cross-attention mechanisms. This novel approach effectively obviates superfluous background interference and image noise, thereby endowing an automated, finely-tuned dissection of organoid samples. Significantly, this segmentation process ensures congruence with authentic organoid quantities and morphological characteristics. By facilitating comprehensive scrutiny of microscopy images of organoid samples on a large scale, CAMPEOD assumes considerable implications for the realm of pharmaceutical screening and ailment emulation.
A Multi-View Learning-Based Bayesian Ruleset Extraction Algorithm For Accurate Hepatotoxicity Prediction

The interpretable machine learning method is important in drug discovery. Unlike traditional ensemble learning methods, this paper proposes an interpretable algorithm based on Bayesian rule extraction to obtain reliable and explainable results for hepatotoxicity prediction. To extract information from different types of omics data, our algorithm employs a multi-view learning strategy to enhance performance. Specifically, a random forest is trained in each view, and then the Bayesian rule extraction algorithm is designed to select an optimal rule subset, controlling the size and accuracy of the ruleset through probabilities. These rule sets are integrated through multi-view voting to get the final decisions. The performance of our algorithm is tested on the hepatotoxicity dataset, demonstrating that compared to traditional machine learning algorithms and rule-based algorithms, our approach maintains excellent performance while achieving high interpretability in most cases. Our python source code and the related Supplementary Materials are available at: github.com/MLDMXM2017/MV-BRS.
Medical Image Segmentation Using Dual Branch Networks with Embedded Attention Mechanism

Medical applications heavily rely on medical image segmentation for tasks such as disease detection, treatment planning, and surgical navigation. While CNNs excel at local feature extraction, they have limitations in capturing global features. Transformer-based models are effective in global feature extraction but struggle with local features. TransUnet combines CNN and Transformer but lacks sufficient global and local feature extraction due to its serial connection approach. TransFuse addresses this by designing parallel Transformer and CNN branches for global and local feature extraction. The self-attention of ViT, the core network of the Transformer branch, has a computational complexity that is directly correlated with the square of the image size, which means it cannot meet the need for multi-level information in complicated segmentation tasks. To overcome these limitations, we propose SR-Unet, a novel dual-branch medical image segmentation network. It merges Transformer and CNN branches in the encoder for optimal global and local feature extraction, utilizing the Swin Transformer as the Transformer branch backbone. Furthermore, we integrate CBAM(Convolutional Block Attention Module) in the decoder’s fusion block to fully merge global and local features and eliminate redundant and irrelevant information, thereby enhancing segmentation accuracy. Experimental results on the Synapse, CXML, and BUSI datasets demonstrate that SR-Unet outperforms TransFuse. It achieves higher DSC metrics by 3.47%, 0.16%, and 0.35%, and significantly better HD metrics by 41.21%, 6.98%, and 11.33%.
Automatic Large-scale Data Generation for Open-topic Biomedical Event Relation Extraction

Biomedical event relation extraction (BioERE) plays an important role in many downstream biological applications. Recent efforts based on supervised learning from small hand-labeled data usually suffer from low coverage of relation topics and limited scale. These shortages make supervised methods hard to achieve competitive performances and predict unseen relations in other topics. Thus, we explore the open-topic BioERE task to simultaneously extract event relations of multiple topics based on the automatically labeled large-scale training data via detecting key meta paths using distant supervision. The experimental results show that the quality of the generated data is competitive to enhance the performances of the open-topic BioERE models.
Tackling Challenges of Low-texture and Illumination Variations for Endoscopy Self-supervised Monocular Depth Estimation

Extracting 3D environmental insights from endoscopy images holds immense value for minimally invasive surgical procedures. The Self-supervised Monocular Depth Estimation (SMDE) framework is promising for achieving this objective. However, existing methods struggle with low-texture and drastic illumination fluctuations in endoscopic images. To tackle this, we incorporate Photometric Aliagnment method based on pixel-wise Color Offset, and propose a carefully designed Color Offset penalty based on Reconstruction Confidence. We furthur apply an Auto-Mask mechanism and a cutting-edge backbone to enhance the performance. Our experiments employ a dataset of airway intubation images captured with a low-resolution and near-field electronic bronchoscope. The experimental results unequivocally highlight the exceptional performance of our approach, achieving root mean square error of 1.64 ± 0.22 mm after point cloud registration, which decreases by 16% than the current SoTA. Furthermore, we introduce an innovative evaluation metric rooted in Photometric Alignment Symmetry, and conduct ablation experiments on this metric. The ablation experiments furthur validate the effectiveness of proposed modules. This study underscores the efficacy of the Reconstruction Confidence-based Color Offset penalty and symmetric alignment evaluations in extracting 3D information from low-resolution and near-field endoscopy images.