Reviewerly/exHarmony huggingface dataset:
AUC margin loss for limited, imbalanced and noisy medical image diagnosis – a case study on CheXpert5000

Abstract The AUC margin loss is a valuable loss function for medical image classification as it addresses the problems of imbalanced and noisy labels. It is used by the current winner of the CheXpert competition. The CheXpert dataset is a large dataset (200k+ images), however datasets in the range of 1k-10k medical datasets are much more common. This raises the question if optimizing AUC margin loss also is effective in scenarios with limited data.We compare AUC margin loss optimization to binary cross-entropy on limited, imbalanced and noisy CheXpert5000, a subset of CheXpert dataset. We show that AUC margin loss is beneficial for limited data and considerably improves accuracy in the presence of label noise. It also improves out-of-box calibration.
Exploring the Ethereum Blockchain

Blockchain technology binds the idea of distributed ledgers and decentralization of assets into providing secure and fully ensured transparency of the data history. This technology consists of a P2P or peer-to-peer architecture of nodes that keeps a copy of the blockchain thereby enabling them to validate every transaction that is taking place in the network and has influenced developers and enthusiasts alike to adopt a decentralised mechanism. It is a robust architecture and makes hacking impossible. Consensus mechanisms, smart contracts, gas fees, different security protocols, Web3 wallets, oracles are some of the concepts and features that make blockchains immutable and secure. This chapter covers major contemporary concepts and terms profoundly. Ethereum blockchain network is dealt as the basic and foundational example that can be explained comfortably with it. Content and examples are related to solidity programming language so that even a novice can write his or her first smart contract on the Ethereum blockchain.
Smart Recommendation for Unanimous People

Creativity has become one of the optimal technologies to solve human life problems, and technology isused for facilitating human needs. People always seek and be more comfortable based on a similar mindset. Which in return helps them to build new ideas and thoughts. With the popularity of social networks and social media, many users like to share their reviews, ratings, experiences, and images. The factors that are most considered by social media platforms, like influence, search content and interest based on friends, bring connectivity for smart recommendation systems to establish the relation between the users with the help of the data collected from the users. Social factors like interpersonal interest similarity, interpersonal influence, and personal interest these factors are taken into consideration before recommending friends to users. This smart recommendation model, which we have used, is based on Latent Dirichlet Allocation (LDA) algorithm. The category of personal interest will help the users to club together and can recommend people to the user based on individualities. The interpersonal influence helps users to connect based on theirinterest towards learning and innovation; they can connect and discuss regarding their common interests.
Artificial Intelligence and Machine Learning in Forensic Accounting

This paper reviews the application of artificial intelligence (AI) and machine learning (ML) for fraud detection in forensic accounting. We analyze commonly used supervised learning algorithms like support vector machines (SVMs), random forests, and neural networks. Unsupervised techniques are also discussed, including clustering, anomaly detection, and association rule mining. For feature engineering, natural language processing (NLP) enables the analysis of unstructured text data, while deep learning methods like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can extract features from raw data. Empirical results demonstrate the high accuracy of ensemble models combining multiple algorithms compared to individual models. However, challenges remain regarding model interpretability, bias, and regulatory compliance. Overall, AI and ML can enhance forensic accounting through automated analysis of massive datasets and identification of complex fraudulent patterns. Further research into ethical AI and standardized implementation is needed to realize the potential of these emerging technologies fully.
A Hybrid Model Based on Samples Difficulty for Imbalanced Data Classification

Imbalanced data classification is a challenging problem with wide applications in machine learning and data mining. Most researchers attempt to solve this problem from the data level or algorithm level. Nevertheless, these methods have their limitations. In addition, most of them focus on dealing with the imbalance in the number of data samples while ignoring the imbalance caused by sample difficulty. Thus, we design a hybrid model to handle this problem. Our model integrates data space improvement, sample selection, sampling strategy, and loss function. To evaluate the performance of our hybrid model, we conduct experiments on several real-world imbalanced datasets. The experimental results prove that our hybrid model is effective.
Disruptive Money: Exploring the Future of Corporate Cryptocurrency

Researchers considered a future in which corporate cryptocurrency (crypto), a type of fungible digital token sponsored by multinational corporations, is in widespread use and asked experts and external stakeholders to identify strategic approaches to evolving technological and economic trends underpinning this potential future. This analysis pays special attention to the balance of global benefits and risks that corporate crypto presents.
Analysis of Learning Needs for Visual Communication Design Students for the Latest Graphics Teaching Materials

This research aims to analyze the learning needs of students in the Visual Communication Design Program in developing teaching materials for the latest graphics topics. The research methods employed include qualitative and quantitative descriptive analysis methods. The research steps involve identifying learning needs through data collection, data analysis, and the interpretation of graphic learning materials, through interviews, observations, questionnaires, and literature review. The findings of this research will provide comprehensive insights into students' perspectives and preferences regarding the Graphics course. The collected data will be used to support the development of teaching materials that are more aligned with students' needs and relevant to current industry trends. This research is expected to make a significant contribution to improving the quality of education in the Visual Communication Design Program and preparing students to face the challenges in the ever-evolving world of graphic design. The research shows that students need teaching materials on graphic topics that are easy to understand, contain the latest and industry-relevant content, and have clear and engaging visualizations. Based on these findings, the researcher suggests that the development of graphic teaching materials should prioritize students' learning needs as the primary target.
The Effect of Guessing Picture Application on the Students’ Vocabulary Mastery of 7th Graders at SMPN 1 Plosoklaten

Vocabulary is important in English because it is the main capital for learning various skills in a language. Therefore, teaching techniques are needed to teach vocabulary appropriately. Teaching vocabulary can be done easily through descriptive text because students will produce a lot of vocabulary to describe something. Moreover, interesting and creative teaching media uses technology will help the teaching of descriptive text, namely Guessing Picture Application. The purpose of this study is to find out the effect of Guessing Picture Application in the students' vocabulary mastery of 7th graders at SMPN 1 Plosoklaten. This research used quantitative approach of research method by using quasi-experimental design, Guessing Picture Application to teach vocabulary in experimental class and Guessing Game to teach vocabulary in control class. The data collection used three steps, namely pre-test, treatments, post-test. Then, the researcher found the result of students’ achievement after they were taught by Guessing Picture Application or Guessing Game (Guessing Picture without Application). This research used ANCOVA in SPSS to analyze the data. The results of this study showed that the significance was 0.014. It was less than 0.05 (0.014 &lt; 0.05). It means this study found a significant difference between students who were taught using Guessing Picture Application and students who were taught by Guessing Game. Therefore, it could be concluded that Guessing Picture Application effectively used in teaching vocabulary since the student could memorize vocabulary better than by using Guessing Game, especially in teaching descriptive text for the seventh-grade students of SMPN 1 Plosoklaten.
Exploring Text Messages by Using Emojis on Indonesian Whatsapp

WhatsApp is a free messaging and calling application that can be downloaded to mobile phones all over the world that supports the feature. It is straightforward, trustworthy, and secure. WhatsApp enables us to exchange text, voice, and audio-visual (video) messages. However, text message recipients often fail to understand the intended meaning of a text message because the text messages are not provided with intonation and direct expression, resulting in the recipient’s difficulty interpreting the message's intended meaning. This study aimed to find out the functions of emojis in WhatsApp text messages. Interpretative qualitative was used as the research methodology in this study. The data source used is the results of screenshots of chat activities on the WhatsApp application. The study came up with the findings that the primary function of emojis in WhatsApp text messages is to show and emphasize emotion, expression, and feeling like happiness, sadness, apology, embarrassment, support, anger, mockery, gratitude, criticism, humorous, etc. that cannot be spoken verbally or conveyed/shown directly.
Artificial Intelligence vs. Natural Stupidity

The number of articles, contributions, TV reports and tweets, squeaks and cheeps on the social networks that deal with the emerging and invasive role of artificial intelligence (AI) in several aspects of our life is increasing enormously, day by day.&#x0D; Like for other hot issues, the use of AI has bright and dark sides. Some are sincerely excited by the potential beneficial outcomes of its applications, others are scared by the potential drawbacks, including some challenging limitations to human freedom.&#x0D; AI is an incredibly powerful machine. It can make calculations and infer conclusions starting from huge datasets and with such a speed that is absolutely inconceivable for a human being. However the history of technology teaches us that the problem is always in the mind and in the hands of the user and, particularly in this case, of the developer. It is not necessary to be sluggishly reluctant to accept innovations and changes to advance serious doubts on the consequences of AI. Some of these effects are, at the moment, unforeseeable.
The characteristics of artificial intelligence model description language and its research direction

As an important branch of the development of computer science, artificial intelligence not only belongs to the basic component of computer science and technology research, but also has been widely used in computer engineering. As the basic environment for the research of artificial intelligence system, it is the focus of Chinese researchers to deeply explore the intelligent model and describe the characteristics and future development direction of language. Therefore, after understanding the development trend of artificial intelligence and intelligent language, this paper mainly studies how to use modeling elements in UML to describe infrastructure, cooperation model, information model and behavior model in business models, and describes language and algorithm generation according to intelligent models oriented to multi-objective problems, providing an effective basis for artificial intelligence language research in the new era.
Goods Procurement Audit System Information At PT. Global Satria Aji

As an effort to realize ideal information for companies, the implementation of reliable information technology governance is needed by companies. This study aims to audit the application of information technology governance of the goods procurement system at PT. Global Satria Aji, because the company PT. Satria Aji Global wants to check profit or loss and also wants to minimize losses related to the goods procurement system. The problem that is often experienced by companies is a system that is deemed unable to accommodate all the data needed when incoming data increases. This study uses the COBIT 5 framework using the EDM01, APO01, APO02, APO07, DSS01 and MEA01 process domains. The calculation of the average maturity level of the six domains used is 1.58 in decimal numbers or 158% in percent. In the study it was found that the biggest discrepancy occurred in the APO01 domain where the difference between the expected conditions and the conditions that occurred reached 2.04. The recommended improvement is to provide a consistent management approach so as to enable corporate governance requirements to be met.
Examples of Beginner Tasks of the Preprofessional Education Program According to the Original Method of Sergey Andriaka (the First Half-Year of Academic Drawing Training)

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Impact of information and communication technologies in the teaching-learning process in rural area educational institutions

New technologies have become a necessary element in the teaching-learning process, being a highly supportive tool for teachers in being able to transmit knowledge to students. Currently, there is a lack of implementation of new technologies to achieve motivation objectives in students. The objective is to identify the barriers that exist between teachers and the implementation of ICT in education, seeking to promote and explore the participation of students in the educational process. The methodology used was descriptive to know the elements involved in this process, in addition to observation and bibliographic review. The result was that teachers need training on new technologies, and the economic difficulties that students and teachers present in the teaching-learning process were identified.
Knowledge Distillation with Feature Enhancement Mask

Knowledge distillation transfers knowledge from the teacher model to the student model, aiming to improve the student’s performance. Previous methods mainly focus on effective feature transformations and alignments to improve distillation efficiency and reduce information loss. However, these approaches ignore the differences between different pixels and layers, which contribute differently during distillation. To this end, we propose the novel knowledge distillation with feature enhancement mask (FEM). The FEM consists of two components: pixel-level feature enhancement mask and layer-level dynamic importance. The pixel-level feature enhancement mask treats target object and non-target object differently during distillation to help the student capture the teacher’s crucial features. The layer-level dynamic importance dynamically regulates the effect of each layer in distillation. Extensive experiments on CIFAR-100 indicate that FEM can help the student capture the teacher’s crucial features and outperforms previous One-to-One distillation methods even One-to-Many distillation methods.
A Graph Neural Network-Based Smart Contract Vulnerability Detection Method with Artificial Rule

As blockchain technology advances, the security of smart contracts has become increasingly crucial. However, most of smart contract vulnerability detection tools available on the market currently rely on artificial-predefined vulnerability rules, which result in suboptimal generalization ability and detection accuracy. Deep learning-based methods usually treat smart contracts as token sequences, which limit the utilization of structural information and the integration of artificial rules. To mitigate these issues, we propose a novel smart contract vulnerability detection method. First, we propose an approach for constructing contract graph to capture vital structural information, such as control- and data- flow. Then, we employ a Wide & Deep learning model to integrate the structural feature, sequencial feature, and artificial rules for smart contract vulnerability detection. Extensive experiments show that the proposed method performs exceptionally well in detecting four different types of vulnerabilities. The results demonstrate that integrating structural information and artificial rules can significantly improve the effectiveness of smart contract vulnerability detection.
STAN: Spatio-Temporal Alignment Network for No-Reference Video Quality Assessment

With the rapid development of multimedia user-generated content (UGC), the number of videos characterized by multiple resolutions and frame rates has increased. The development of efficient no-reference video quality assessment (NR-VQA) models for UGC with these features is a challenging task. Although previous studies have proposed solutions that combine multi-scale spatial and multi-rate motion information, existing NR-VQA models simply connect multi-attribute features obtained separately, often leading to information loss and distortion. In this study, we developed a spatiotemporal alignment network addressing the limitations of existing methods. Our model includes two modules, one for multi-rate motion feature alignment and the other for multi-scale spatial feature alignment. Moreover, by introducing a Lagrangian multi-rate and multi-scale attention-based alignment mechanism, multiple features can be effectively processed without incurring in unwanted information conflict or loss. By associating spatial and time-series information, our attention-based feature-alignment module enhances low-quality spatial regions around subject objects, thus, improving the performance of the model significantly. Our experimental results demonstrated that the proposed model outperforms other existing methods on several benchmark datasets, proving its effectiveness and generalizability. Code will be available at: https://github.com/945402003/STAN-VQA .
Visible-Infrared Person Re-identification via Modality Augmentation and Center Constraints

Visible-infrared person re-identification (VI-ReID) is a tricky cross-modality retrieval problem, where main challenge is the significant modality discrepancy. Existing methods mainly focus on bridging the relation between modalities by shared representation learning in the common embedding space. However, due to the outliers, these methods often struggle to build compact clustering subspaces. Besides, these methods also suffer from modality imbalance problem caused by more visible (VIS) modality images than infrared (IR) modality images, which induces the features of IR modality have to face an extra modality bias during the test phase. In this paper, we propose a novel modality augmentation and center constraints (MACC) framework, where grayscale channel joint random patch is adopted to generate semantic-rich images that are more similar to IR modality images in style, which reduces the modality discrepancy and, to some extent, alleviates the modality imbalance problem. In addition, based on the idea of partition, we design a fine-grained feature mining module (FFMM) to mine nuanced but discriminative information within each part, which is benefit to further alleviate the modality discrepancy. Meanwhile, to encourage more compact clustering, we propose a novel compact center clustering (C $$^3$$ ) loss to achieve intra-modality and inter-modality compact clustering. Apart from C $$^3$$ loss, we also introduce the center distribution consistencies (CDC) loss to align the feature vectors and prediction logits by optimizing the standard deviation and JS divergence, respectively. Extensive experiments on two public datasets demonstrate that our method achieves new state-of-the-art performance. The code will be available at https://github.com/cq0907/MACC .
The Implementation Of Digital Resources As Supporting Learning Media In Teaching Narrative Writing Text

Digital Resources is supporting learning media that utilize technology in delivering material such as video, website, e-book, and slideshows. This research aims to investigates the implementation of digital resources as supporting learning media in teaching narrative writing text. This research used qualitative method by using descriptive qualitative design. The data was obtained through observation and interview. The subject of this research were two English teachers in Vocational High School Ibnu Khaldun at 10th Grade the academic years of 2022/2023 who implemented digital resources in teaching narrative writing text. The research findings of this research show that the English teachers have used digital resources very well, but it still needs to optimize the implementation of digital resources for the further learning
THE HISTORY OF PHILOSOPHY AS THE OBJECT OF PHILOSOPHICAL REFLECTION

International scientific conference «History, political science, philosophy, and sociology: development trends in the 21st century» : conference proceedings (September 6–7, 2023, Wloclawek, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 96 pages.
Bayesian nonparametric latent class analysis with different item types

Traditional latent class analysis (LCA) requires deciding on the number of classes in advance. Model selection criteria are then employed to find the best number of classes by going through a class enumeration process. However, this process neglects the uncertainty of the number of classes, often leading to over- or under-fitting. Moreover, different criteria can result in different optimal models, making it challenging to reach a consensus on the best criterion. By incorporating the Dirichlet process mixture (DPM) model, Bayesian nonparametric LCA is an alternative that circumvents class enumeration by assuming an infinite number of classes and inferring the number of classes from the data. The objective of the current article is to introduce an extended DPM-LCA approach that can cluster individuals based on observed variables measured on various metrics. Through a simulation study, we evaluated the performance of the DPM-LCA in terms of model selection, parameter recovery, and classification accuracy in comparison with the conventional approach. For easy implementation, a hands-on tutorial with the R package nimble is provided. The DPM-LCA approach is illustrated using three real data examples.
Mathematical Structures of Different Categories of Quantum Fields

Quantum field theory is a covering quantum theory that applies to high-energy processes where particles are created and destroyed. Properties of a quantum field that represents an elementary particle and a quantum field that mediates interaction between particles are analyzed. This analysis relies on fundamental physical principles. The mathematical structure of these fields proves that they are completely different physical objects. A further analysis proves that a quantum field that represents an elementary massive particle and a quantum field that represents a massless particle have a completely different mathematical structure. The results are used in an examination of free spin-1/2 elementary massive particles and other free elementary particles that have an integral spin. Inherent inconsistencies are found for elementary massive particles that have an integral spin and for the Majorana neutrino. The analysis also proves that interaction mediating fields do not represent a genuine particle. In particular, the electroweak theory of the W \(\pm\) and the Z particles has no coherent basis.