Reviewerly/exHarmony huggingface dataset:
Exploring Public Data Vulnerabilities in Semi-Supervised Learning Models through Gray-box Adversarial Attack

Authors:
raw_author_name: Young-Joo Suh
raw_affiliation_string: Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang 37673, Republic of Korea

Abstract: Semi-supervised learning (SSL) models, integrating labeled and unlabeled data, have gained prominence in vision-based tasks, yet their susceptibility to adversarial attacks remains underexplored. This paper unveils the vulnerability of SSL models to gray-box adversarial attacks—a scenario where the attacker has partial knowledge of the model. We introduce an efficient attack method, Gray-box Adversarial Attack on Semi-supervised learning (GAAS), which exploits the dependency of SSL models on publicly available labeled data. Our analysis demonstrates that even with limited knowledge, GAAS can significantly undermine the integrity of SSL models across various tasks, including image classification, object detection, and semantic segmentation, with minimal access to labeled data. Through extensive experiments, we exhibit the effectiveness of GAAS, comparing it to white-box attack scenarios and underscoring the critical need for robust defense mechanisms. Our findings highlight the potential risks of relying on public datasets for SSL model training and advocate for the integration of adversarial training and other defense strategies to safeguard against such vulnerabilities.
Uncertainty Quantification of Machine Learning Model Performance via Anomaly-Based Dataset Dissimilarity Measures

Authors:
raw_author_name: Gabriele Incorvaia
raw_affiliation_string: Thales UK - Research, Technology & Innovation, Reading RG2 6GF, UK
raw_author_name: Darryl Hond
raw_affiliation_string: Thales UK - Research, Technology & Innovation, Reading RG2 6GF, UK
raw_author_name: Hamid Asgari
raw_affiliation_string: Thales UK - Research, Technology & Innovation, Reading RG2 6GF, UK

Abstract: The use of Machine Learning (ML) models as predictive tools has increased dramatically in recent years. However, data-driven systems (such as ML models) exhibit a degree of uncertainty in their predictions. In other words, they could produce unexpectedly erroneous predictions if the uncertainty stemming from the data, choice of model and model parameters is not taken into account. In this paper, we introduce a novel method for quantifying the uncertainty of the performance levels attained by ML classifiers. In particular, we investigate and characterize the uncertainty of model accuracy when classifying out-of-distribution data that are statistically dissimilar from the data employed during training. A main element of this novel Uncertainty Quantification (UQ) method is a measure of the dissimilarity between two datasets. We introduce an innovative family of data dissimilarity measures based on anomaly detection algorithms, namely the Anomaly-based Dataset Dissimilarity (ADD) measures. These dissimilarity measures process feature representations that are derived from the activation values of neural networks when supplied with dataset items. The proposed UQ method for classification performance employs these dissimilarity measures to estimate the classifier accuracy for unseen, out-of-distribution datasets, and to give an uncertainty band for those estimates. A numerical analysis of the efficacy of the UQ method is conducted using standard Artificial Neural Network (ANN) classifiers and public domain datasets. The results obtained generally demonstrate that the amplitude of the uncertainty band associated with the estimated accuracy values tends to increase as the data dissimilarity measure increases. Overall, this research contributes to the verification and run-time performance prediction of systems composed of ML-based elements.
Analysing lexical semantic changes in chinese educational texts by integrating machine learning methods

Authors:
raw_author_name: Xiaohui Shen
raw_affiliation_string: School of Chinese Language and Literature, Jiaozuo Normal College, Jiaozuo, Henan, China

Abstract: Analyzing Lexical Semantic Changes (LSC) in Educational Texts (ET) refers to examining how the meanings of words, terms, or phrases used in ET have evolved. It involves learning shifts in the semantic content, connotations, and language associations within educational resources such as textbooks, research articles, and instructional content. The analysis can reveal how educational models, pedagogical methods, and terminology have transformed in response to technological innovations, societal changes, and pedagogical developments. This analysis provides visions into the dynamic nature of educational discourse, helping researchers, educators, and policymakers understand how language has adapted to reflect changes in educational paradigms and the broader educational context. This research investigates the semantic analysis and classification performance within ET, employing the innovative Decision Tree + Feed Forward Neural Networks (DT + FFNNs) framework. This research shows the dynamic semantic relationships inherent in educational terminology by diverse semantic similarity measures and contextualized embeddings. It looks at how educational language changes to reflect changes in society, technology, and pedagogy. The study uses a DT + FFNN framework for semantic analysis and classification. The study uses several embeddings and semantic similarity metrics, and Spearman’s Correlation Coefficient (SCC) is employed to evaluate their effectiveness. This study highlights the DT + FFNN framework’s capacity to capture complex semantics in an educational setting and offers insights into the adaptive nature of educational discourse. SCC serves as a guiding metric, offering insights into the efficiency of several embeddings and measures. The findings show the pivotal role of fine-tuning in significantly enhancing the accuracy of DT + FFNNs across measures, revealing its remarkable potential in capturing semantics within an educational context.
Research on discourse role recognition in task-oriented collaborative dialogue

Authors:
raw_author_name: Liqian Shan
raw_affiliation_string: College of Information Science and Engineering, Xinjiang University, Urumqi, China
raw_author_name: Hui Zhao
raw_affiliation_string: School of Cyber Science and Engineering, College of Computer Science and Technology, Xinjiang University, Urumqi, China

Abstract: Task-oriented collaborative dialogues have become an indispensable form of communication in our daily work and learning, in which participants exchange ideas and share information to advance goals. It is crucial to automatically analyze participants’ contributions and understand these dialogues relative to individuals with limited attention spans. In this paper, seven Discourse Role (DR) labels are designed to describe discourse’s different roles in collaborative dialogues for goal achievement. We collected about 11K discourses from a publicly available dialogue corpus and annotated them with DR tags to construct a dataset named MRDR (Meeting Recorder Discourse Role). In addition, this paper proposes a novel hierarchical model, STTAHM (Speaker Turn and Topic-Aware Hierarchical Model), for Discourse Role classification. The model is equipped to perceive speaker turn and dialogue topic and can effectively capture the discourse’s local and global semantic information. Experimental results show that our proposed method is effective on the constructed dataset, and the accuracy of Discourse Role classification reaches 86.99%.
TSRNet: A Trans-Scale and Refined Low-Light Image Enhancement Network

Authors:
raw_author_name: Qi Mu
raw_affiliation_string: College of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an 710054, China
raw_author_name: Xinyue Wang
raw_affiliation_string: College of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an 710054, China
raw_author_name: Zhanli Li
raw_affiliation_string: College of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an 710054, China

Abstract: Retinex-based deep learning methods show good low-light enhancement performance and are mainstream approaches in this field. However, the current methods for enhancing low-light images are insufficient in accurately separating illumination and comprehensively restoring degraded information, especially in images with uneven or extremely low illumination levels. This situation often leads to the over-enhancement of bright regions, a loss of detail, and color distortion in the final images. To address these issues, we improved three subnetworks in the classic KinD network, and proposed a trans-scale and refined low-light image enhancement network. Compared with KinD, our method shows more precise image decomposition performance, enhancing the expressiveness of the reflection and illumination components in order to better depict image details, colors, and lighting information. For reflectance restoration, we use a U-shaped network for cross-scale denoising, incorporating attention mechanisms and a color saturation loss to restore image textures and colors. For light adjustment, we apply fine-grained light adjustment approaches to simultaneously enhance brightness in dark areas and prevent excessive enhancement in bright areas. The experimental results demonstrate that with the LOL dataset, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of TSRNet are improved by 2–31% and 5–34%, respectively, when compared with the mainstream methods.
Using iterative 2δ‐technique and 4δ‐outlier test for evaluating the geochemical background concentrations of trace elements in topsoil at Cao Bang province, Vietnam

Authors:
raw_author_name: Anh Tuan Tran
raw_affiliation_string: Institute of Geological Science Vietnam Academy of Science and Technology Hanoi Vietnam

Abstract: Abstract This study investigated the geochemical background values for seven trace elements in topsoil samples collected from highland areas within Cao Bang province, Vietnam. Total concentrations of these elements were determined, revealing the following order of mean concentration: Zn &gt; Cr &gt; Pb &gt; Cu &gt; As &gt; Ni &gt; Co. The background values were calculated using two different methods including iterative 2δ‐technique and 4δ‐outlier test. The 4δ‐outlier test yielded the following background concentrations (dry weight) for the seven elements: As (24.41 ± 12.42 mg kg −1 ), Co (15.64 ± 10.57 mg kg −1 ), Cr (49.31 ± 33.41 mg kg −1 ), Cu (29.77 ± 27.14 mg kg −1 ), Ni (22.52 ± 13.33 mg kg −1 ), Pb (38.06 ± 31.48 mg kg −1 ), Zn (71.79 61.09 mg kg −1 ). The 2δ‐technique, on the other hand, resulted in the following background concentrations (dry weight) for the same elements: As (8.41 ± 8.57 mg kg −1 ), Co (5.06 ± 4.31 mg kg −1 ), Cr (26.72 ± 26.20 mg kg −1 ), Cu (25.31 ± 24.81 mg kg −1 ), Ni (8.02 ± 7.83 mg kg −1 ), Pb (28.12 ± 27.58 mg kg −1 ), Zn (51.91 ± 49.44 mg kg −1 ). These results provide valuable baseline data for future environmental assessments and monitoring programs in the region. They also highlight the potential impact of human activities, such as mining, on the geochemical landscape of highland areas.
Revolutionizing Supply Chain With Machine Learning and Blockchain Integration

Authors:
raw_author_name: S. Balasubramani
raw_affiliation_string: Koneru Lakshmaiah Education Foundation, India
raw_author_name: R. Dhanalakshmi
raw_affiliation_string: Vellore Institute of Technology, Chennai, India
raw_author_name: L. Kavisankar
raw_affiliation_string: SRM Institute of Science and Technology, Kattankulathur, India
raw_author_name: Digvijay Pandey
raw_affiliation_string: Department of Technical Education, Government of Uttar Pradesh, India

Abstract: Efficient supply chain management has emerged as a crucial determinant of organizational performance in the contemporary dynamic corporate environment. The incorporation of nascent technology, such as machine learning and blockchain, is revolutionizing how enterprises manage their supply chain operations. By examining extensive datasets, machine learning algorithms can predict future demand, optimize inventory levels, and improve the planning of routes. By discerning regularities and deviations within datasets, these algorithms facilitate enterprises in making well-informed choices and managing potential hazards. Additionally, the utilization of machine learning facilitates the automation of monotonous jobs, hence mitigating the occurrence of human fallibility and augmenting the overall efficacy of supply chain operations. The utilization of blockchain technology, renowned for its decentralized and unalterable ledger system, effectively tackles several critical issues encountered in the realm of supply chain management.
The Financial Dynamics of AI-Enhanced Supply Chain Management

Authors:
raw_author_name: T. J. Nagalakshmi
raw_affiliation_string: Saveetha Institute of Medical and Technical Sciences, Saveetha University, India
raw_author_name: A. Shameem
raw_affiliation_string: AMET Business School, AMET University, India
raw_author_name: Sorabh Lakhanpal
raw_affiliation_string: Lovely Professional University, India
raw_author_name: Mohit Tiwari
raw_affiliation_string: Bharati Vidyapeeth's College of Engineering, Delhi, India
raw_author_name: Joshuva Arockia Dhanraj
raw_affiliation_string: Hindustan Institute of Technology and Science, India

Abstract: This chapter explores the evolving landscape of AI-enhanced supply chain management, emphasizing the pivotal role of artificial intelligence (AI) in optimizing supply chain operations. It commences with an introduction that defines AI-enhanced supply chain management and underscores the importance of AI in this context. Subsequently, the chapter delves into the multifaceted role of AI in supply chain management, elucidating its diverse benefits. These advantages encompass enhanced supply chain efficiency, streamlined inventory and transportation management, and improved revenue generation through superior customer service and data-driven demand forecasting.
Synthetic Hard Negative Samples for Contrastive Learning

Authors:
raw_author_name: Hengkui Dong
raw_affiliation_string: School of Computer Science & Technology, School of Software, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
raw_author_name: Xianzhong Long
raw_affiliation_string: School of Computer Science & Technology, School of Software, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
raw_author_name: Yun Li
raw_affiliation_string: School of Computer Science & Technology, School of Software, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China

Abstract: Abstract Contrastive learning has emerged as an essential approach in self-supervised visual representation learning. Its main goal is to maximize the similarities between augmented versions of the same image (positive pairs), while minimizing the similarities between different images (negative pairs). Recent studies have demonstrated that harder negative samples, i.e., those that are more challenging to differentiate from the anchor sample perform a more crucial function in contrastive learning. However, many existing contrastive learning methods ignore the role of hard negative samples. In order to provide harder negative samples for the network model more efficiently. This paper proposes a novel feature-level sample sampling method, namely sampling synthetic hard negative samples for contrastive learning (SSCL). Specifically, we generate more and harder negative samples by mixing them through linear combination and ensure their reliability by debiasing. Finally, we execute weighted sampling of these negative samples. Compared to state-of-the-art methods, our method can provide more high-quality negative samples. Experiments show that SSCL improves the classification performance on different image datasets and can be readily integrated into existing methods.
Utilizing Machine Learning in Legal Management to Enhance Supply Chain

Authors:
raw_author_name: Binay Kumar Pandey
raw_affiliation_string: College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, India
raw_author_name: Asif Iqubal Shah
raw_affiliation_string: St. Xavier's University, Kolkata, India
raw_author_name: Pankaj Dadheech
raw_affiliation_string: Swami Keshvanand Institute of Technology, Management, and Gramothan, India
raw_author_name: Blessy Thankachan
raw_affiliation_string: School of Computer Applications, JECRC University, Jaipur, India
raw_author_name: Dharmesh Dhabliya
raw_affiliation_string: Department of Information Technology, Vishwakarma Institute of Information Technology, India
raw_author_name: A. Shaji George
raw_affiliation_string: Almarai Company, Saudi Arabia

Abstract: Innovative legal and supply chain complexity solutions include machine learning in legal management and intelligent supply chain governance. Complex laws and supply chains necessitate data-driven initiatives. Effective supply chain governance needs transparency, accountability, and risk management. Complex data-rich legal administration requires machine learning. Legal research, document analysis, and predictive analytics benefit from machine learning. Supply chain governance requires compliance and risk management. Machine learning principles demonstrate lawyers can switch careers. Innovation in legal tech comes from AI, blockchain, and cybersecurity. This novel machine learning strategy for legal and change and training management requires careful planning and implementation.
Extension of the kinematics problem about the meeting of two bodies

Authors:
raw_author_name: Ľ Zelenický
raw_affiliation_string: Department of Physics, Faculty of Natural Sciences and Informatics, Constantine the Philosopher University in Nitra, Tr. A. Hlinku 1, 949 74 Nitra, Slovak Republic

Abstract: Abstract The well-known problem about the meeting of two bodies starting from two different points and moving along their line is extended by the general case when the bodies move in a plane at a non-zero angle to the given line. The task is solved graphically, analytically and using GeoGebra. This approach makes it possible to use IT technologies close to students in both standard and distance education.
Innovative technologies of physical and mathematical education implemented by using of fractal approach

Authors:
raw_author_name: V Seben
raw_affiliation_string: Department of Physics, Mathematics & Technics, Faculty of Humanities and Natural Science, University of Preshov, str.17.Novembra 1, 08116, Preshov, Slovakia
raw_author_name: N Yurkovych
raw_affiliation_string: Department of Solid State Electronics & Information Security, Faculty of Physics, Uzhhorod National University, str.Voloshyna 54, 88000, Uzhhorod, Ukraine
raw_author_name: M Mar’yan
raw_affiliation_string: Department of Solid State Electronics & Information Security, Faculty of Physics, Uzhhorod National University, str.Voloshyna 54, 88000, Uzhhorod, Ukraine

Abstract: Abstract The implementation of the fractal approach to the teaching of physical and mathematical disciplines using computer modeling is substantiated.The formation of a fractal structure is established and iterations are defined that reflect the integrity and spontaneity of information presentation. The involvement of students of related specialties from two universities – University of Presov (Faculty of Humanities and Natural Sciences) and Uzhhorod National University (Faculty of Physics) – demonstrates the feasibility of using the fractal approach in training future teachers and scientists of natural sciences in higher educational institutions.
Mentoring as a way to the professional development of in-service physics teachers

Authors:
raw_author_name: Katarína Kozelková
raw_affiliation_string: Faculty of Science, Pavol Jozef Šafárik University in Košice, Šrobárova 1, 040 01 Košice, Slovakia

Abstract: Abstract Nowadays education is facing new challenges based on the changes in the world and society. The generation of pupils has changed and their needs are different to what the teachers were used to. The change not only in teaching and learning methods but also in the way of thinking is necessary. To help teachers to find a different point of view on education and implement the changes, the Regional teachers’ support centres were established. The first two launched in 2021 and within three years there should be 40 centres across Slovakia. The main idea is to provide individual support for primary and lower secondary teachers as well as training in five main topics -i) school curriculum, ii) assessment and evaluation of pupils’ learning outcomes, iii) educational process and teaching style, iv) personalization and inclusive education, v) digitalization. Individual support is provided by a mentor in the form of class observations and mentoring conversations. The physics teachers can be involved and work on professional self-development in the topics they feel they want to improve. The paper reports on the mentors’ activities with the main focus held on the possibility of the in-service physics teachers’ professional development.
The entry profile of a pre-service physics teachers and the development of their professional competences

Authors:
raw_author_name: M Kireš
raw_affiliation_string: Institute of Physic Faculty of Science, Pavol Jozef Šafárik University in Košice, Slovakia, Park Angelinum 9, 041 54 Košice, Slovakia

Abstract: Abstract In the paper, we reflect on various factors of motivation of pre-service physics teacher. Based on the data obtained from the entrance questionnaires of incoming students for the previous four years and a structured interview with current students studying in all grades, we create a professional entry profile of a future physics teacher student. Based on international standards, current trends, current and future needs of school practice, we characterize the professional competences of a physics teacher graduate. In the conditions of the Slovak educational space, we are looking for a breakthrough with the updated description of the field of study teaching and pedagogical sciences and standards for study programs. We present the goals and key elements of the university training of future physics teachers, educational methods, educational content and links to pedagogical practice.
Affordances of technology-supported trails in education of pre-service primary teachers in physics

Authors:
raw_author_name: L Valovicova
raw_affiliation_string: Department of Physics, Faculty of Natural Sciences and Informatics, Constantine the Philosopher University in Nitra, Tr. A. Hlinku 1, 949 01 Nitra, Slovakia
raw_author_name: V Bockova
raw_affiliation_string: Department of Mathematics, Faculty of Natural Sciences and Informatics, Constantine the Philosopher University in Nitra, Tr. A. Hlinku 1, 949 01 Nitra, Slovakia

Abstract: Abstract Outdoor activities focused on solving problems related to concrete objects, supported by technology, also known as trails, offer opportunities to develop not only content knowledge, but also the soft-skill of learners. The learner can be a pupil or a pre-service teacher, student of initial teacher education programme. Current students of initial teacher education programme are usually members of Generation Z and their education should be tailored to the needs of this generation. In this paper we designed an interdisciplinary trail connecting mathematics and science, particularly physics, knowledge and compared the solution process of pre-service teachers for primary schools and the primary pupils. The strategies almost did not differ, but pre-service teachers demonstrated deeper understanding of concept of measure.