Reviewerly/exHarmony huggingface dataset:
FastAL: Fast Evaluation Module for Efficient Dynamic Deep Active Learning Using Broad Learning System

Authors:
raw_author_name: Shuzhou Sun
raw_affiliation_string: College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China
raw_author_name: Huali Xu
raw_affiliation_string: Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
raw_author_name: Yan Li
raw_affiliation_string: College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China
raw_author_name: Ping Li
raw_affiliation_string: Department of Computing and the School of Design, The Hong Kong Polytechnic University, Hong Kong, china
raw_author_name: Bin Sheng
raw_affiliation_string: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

Abstract: State-of-the-art Active Learning (AL) methods often encounter challenges associated with a hysteretic learning process and an expensive data sampling mechanism. The former implies that data selection in the ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> + 1)-th round is solely based on the learned model’s results in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> -th round. The latter involves using model inference to calculate data value (e.g., uncertainty estimation based on model inference), which can be cumbersome, particularly when working with large datasets or Deep Neural Networks (DNNs). To address these challenges, we propose FastAL, an efficient and dynamic deep AL framework. Our approach includes an efficient method for calculating data value from the frequency domain perspective, generating multiple candidates. Then, we introduce the Fast Evaluation Module, which directly calculates each candidate’s contribution to future model training and selects the best options. In addition, current AL methods, particularly those based on uncertainty, are susceptible to data bias, which implies that selected data may not represent the original unlabeled data adequately. To alleviate this issue, we propose the De-similar Module, which removes partially similar data. The above three modules are model-agnostic and thus can be seamlessly integrated into any Active Learning framework. We conducted rigorous experiments on various benchmark datasets to validate our approach’s effectiveness. Our results demonstrate that FastAL outperforms other state-of-the-art methods by a significant margin, including those based on uncertainty, diversity, and expected model change.
Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images

Authors:
raw_author_name: Pablo Morales-Álvarez
raw_affiliation_string: Department of Computer Science and AI, University of Granada, Spain
raw_author_name: Arne Schmidt
raw_affiliation_string: Department of Computer Science and AI, University of Granada, Spain
raw_author_name: José Miguel Hernández-Lobato
raw_affiliation_string: Department of Engineering, University of Cambridge, UK
raw_author_name: Rafael Molina
raw_affiliation_string: Department of Computer Science and AI, University of Granada, Spain

Abstract: In the last years, the weakly supervised paradigm of multiple instance learning (MIL) has become very popular in many different areas. A paradigmatic example is computational pathology, where the lack of patch-level labels for whole-slide images prevents the application of supervised models. Probabilistic MIL methods based on Gaussian Processes (GPs) have obtained promising results due to their excellent uncertainty estimation capabilities. However, these are general-purpose MIL methods that do not take into account one important fact: in (histopathological) images, the labels of neighboring patches are expected to be correlated. In this work, we extend a state-of-the-art GP-based MIL method, which is called VGPMIL-PR, to exploit such correlation. To do so, we develop a novel coupling term inspired by the statistical physics Ising model. We use variational inference to estimate all the model parameters. Interestingly, the VGPMIL-PR formulation is recovered when the weight that regulates the strength of the Ising term vanishes. The performance of the proposed method is assessed in two real-world problems of prostate cancer detection. We show that our model achieves better results than other state-of-the-art probabilistic MIL methods. We also provide different visualizations and analysis to gain insights into the influence of the novel Ising term. These insights are expected to facilitate the application of the proposed model to other research areas.
The development of orthography and phonology coupling in the ventral occipito-temporal cortex and its relation to reading.

Authors:
raw_author_name: Jin Wang
raw_affiliation_string: Department of Psychology and Human Development, Vanderbilt University.

Abstract: The left ventral occipito-temporal (lvOT) cortex is considered to house the brain's representation of orthography (i.e., the spelling patterns of words). Because letter-sound coupling is crucial in reading, we investigated the engagement of the lvOT cortex in processing phonology (i.e., the sound patterns of words) as a function of reading acquisition. We tested 47 Polish children both at the beginning of formal literacy instruction and 2 years later. During functional magnetic resonance imaging, children performed auditory phonological tasks from small to large grain size levels (i.e., single phoneme, rhyme). We showed that orthographically relevant lvOT areas activated during small-grain size phonological tasks were skill-dependent, perhaps due to the relatively transparent mappings between orthography and phonology in Polish. We also studied activation pattern similarity between processing visual and auditory word stimuli in the lvOT. We found that a higher similarity level was observed in the anterior lvOT compared to the posterior lvOT after 2 years of schooling. This is consistent with models proposing a posterior-to-anterior shift in word processing during reading acquisition. We argue that the development of orthography-phonology coupling at the brain level reflects writing system-specific effects and a more universal pathway of the left vOT development in reading acquisition. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
A Preliminary Investigation into Search and Matching for Tumor Discrimination in World Health Organization Breast Taxonomy Using Deep Networks

Authors:
raw_author_name: Abubakr Shafique
raw_affiliation_string: Kimia Lab, University of Waterloo, Canada; Rhazes Lab, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA
raw_author_name: Ricardo Gonzalez
raw_affiliation_string: Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
raw_author_name: Liron Pantanowitz
raw_affiliation_string: Department of Pathology, University of Pittsburgh Medical Center, PA, USA
raw_author_name: Puay Hoon Tan
raw_affiliation_string: Luma Medical Centre, Singapore
raw_author_name: Alberto Machado
raw_affiliation_string: International Agency for Research on Cancer, Lyon, France
raw_author_name: Ian A. Cree
raw_affiliation_string: International Agency for Research on Cancer, Lyon, France
raw_author_name: Hamid R. Tizhoosh
raw_affiliation_string: Kimia Lab, University of Waterloo, Canada; Rhazes Lab, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA

Abstract: Breast cancer is one of the most common cancers affecting women worldwide. They include a group of malignant neoplasms with a vari- ety of biological, clinical, and histopathological characteristics. There are more than 35 different histological forms of breast lesions that can be classified and diagnosed histologically according to cell morphology, growth, and architecture patterns. Recently, deep learning, in the field of artificial intelligence, has drawn a lot of attention for the computerized representation of medical images. Searchable digital atlases can provide pathologists with patch matching tools allowing them to search among evidently diagnosed and treated archival cases, a technology that may be regarded as computational second opinion. In this study, we indexed and analyzed the WHO breast taxonomy (Classification of Tumours 5th Ed.) spanning 35 tumour types. We visualized all tumour types using deep fea- tures extracted from a state-of-the-art deep learning model, pre-trained on millions of diagnostic histopathology images from the TCGA reposi- tory. Furthermore, we test the concept of a digital “atlas” as a referencefor search and matching with rare test cases. The patch similarity search within the WHO breast taxonomy data reached over 88% accuracy when validating through “majority vote” and more than 91% accuracy when validating using top-n tumour types. These results show for the first time that complex relationships among common and rare breast lesions can be investigated using an indexed digital archive.
Graph representation learning method based on three-way partial order structure

Authors:
raw_author_name: Enliang Yan
raw_affiliation_string: School of Artificial Intelligence, South China Normal University, Foshan 528225, Guangdong, China
raw_author_name: Shikuan Hao
raw_affiliation_string: School of Artificial Intelligence, South China Normal University, Foshan 528225, Guangdong, China
raw_author_name: Tao Zhang
raw_affiliation_string: School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, Hebei, China
raw_author_name: Tianyong Hao
raw_affiliation_string: School of Computer Science, South China Normal University, Guangzhou 510631, Guangdong, China
raw_author_name: Qiliang Chen
raw_affiliation_string: School of Basic Medicine, Guangzhou University of Chinese Medicine, Guangzhou 510006, Guangdong, China
raw_author_name: Jianping Yu
raw_affiliation_string: School of Foreign Studies, Yanshan University, Qinhuangdao 066004, Hebei, China

Abstract: In the era of big data, handling massive datasets to extract valuable information has become increasingly critical. Knowledge representation emerges as a pivotal method to address this challenge. In the domain of knowledge representation, there exist two primary approaches: symbolic representation and vector representation. The integration of symbolic and vector representations to harness their respective strengths has become the cutting-edge approach to address challenges in the field of knowledge representation. This paper proposes a method that integrates a partial order formal structure analysis (POFSA) with graph representation learning. Specifically, we initially construct three-way partial order structure graphs, then create an attribute graph based on this structure, which can be processed by the graph representation learning methods. Finally, we utilize the graph representation learning to construct embeddings for three-way attribute partial order structure diagram (APOSD). We comprehensively assesse these embeddings across eight different datasets and present the results. The experiments indicate the feasibility of our proposed approach which is proven a novel approach of combining symbolic and vector representations for handling complex data and implicit knowledge.
Blockchains for SMEs: A Fit-Viability perspective moderated by organizational innovation diffusion for supply chain performance

Authors:
raw_author_name: Lai-Wan Wong
raw_affiliation_string: School of Computing and Data Science, Xiamen University Malaysia
raw_author_name: Garry Wei-Han Tan
raw_affiliation_string: Faculty of Business, Design, and Arts, Swinburne University of Technology Sarawak Campus, Malaysia; Graduate Business School, UCSI University, Kuala Lumpur, Malaysia; School of Economics and Management, Yunnan Normal University, China
raw_author_name: Keng-Boon Ooi
raw_affiliation_string: FORE School of Management, New Delhi, India; Faculty of Business, Design, and Arts, Swinburne University of Technology Sarawak Campus, Malaysiaa; Graduate Business School, UCSI Universityy, Kuala Lumpur, Malaysia
raw_author_name: Hing Kai Chan
raw_affiliation_string: Department of Entrepreneurship, Marketing and Management Systems, University of Nottingham Ningbo China, China

Abstract: This paper empirically examines the suitability of blockchain for improving supply chain performance in small-medium enterprises through the theoretical lens of the fit-viability model against the impact of organisation innovation diffusion level Data gathered from 206 respondents are analysed using partial least square-structured equation modelling (PLS-SEM) and artificial neural network (ANN). The findings of this study show empirically that enhanced supply chain connectivity, information sharing, and supply chain visibility are fit for enhancing supply chain performance. Economics, management support, and technology infrastructure are important for blockchain viability. Further, an organisation's innovation diffusion level affects the fitness of blockchain for supply chain performance but not viability. The transformational effects of blockchain are mainly centred on its security and trust-enhancing features. However, its deployment continues to be marred by technical complexities and often, scenarios that seem opportune may be a poor fit. This study is among the few that consider the impact of blockchain on supply chain performance through a fit-viability lens against the effect of organisation innovation diffusion level.
Korean Syntactic Complexity Analyzer (KOSCA): An NLP application for the analysis of syntactic complexity in second language production

Authors:
raw_author_name: Haerim Hwang
raw_affiliation_string: The Chinese University of Hong Kong, Hong Kong
raw_author_name: Hyunwoo Kim
raw_affiliation_string: Yonsei University, Republic of Korea

Abstract: Given the lack of computational tools available for assessing second language (L2) production in Korean, this study introduces a novel automated tool called the Korean Syntactic Complexity Analyzer (KOSCA) for measuring syntactic complexity in L2 Korean production. As an open-source graphic user interface (GUI) developed in Python, KOSCA provides seven indices of syntactic complexity, including traditional and Korean-specific ones. Its validity was tested by investigating whether the syntactic complexity indices measured by it in L2 written and spoken production could explain the variability of L2 Korean learners’ proficiency. The results of mixed-effects regression analyses showed that all seven indices significantly accounted for learner proficiency in Korean. Subsequent stepwise multiple regression analyses revealed that the syntactic complexity indices explained 56.0% of the total variance in proficiency for the written data and 54.4% for the spoken data. These findings underscore the validity of the syntactic complexity indices measured by KOSCA as reliable indicators of L2 Korean proficiency, which can serve as a valuable resource for researchers and educators in the field of L2 Korean learning and assessment.
Enhanced Aquila optimizer based on tent chaotic mapping and new rules

Authors:
raw_author_name: Youfa Fu
raw_affiliation_string: Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, 550025, Guizhou, China
raw_author_name: Dan Liu
raw_affiliation_string: Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, 550025, Guizhou, China
raw_author_name: Shengwei Fu
raw_affiliation_string: Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, 550025, Guizhou, China
raw_author_name: Jiadui Chen
raw_affiliation_string: Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, 550025, Guizhou, China
raw_author_name: Ling He
raw_affiliation_string: Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, 550025, Guizhou, China

Abstract: Abstract Metaheuristic algorithms, widely applied across various domains due to their simplicity and strong optimization capabilities, play a crucial role in problem-solving. While the Aquila Optimizer is recognized for its effectiveness, it often exhibits slow convergence rates and susceptibility to local optima in certain scenarios. To address these concerns, this paper introduces an enhanced version, termed Tent-enhanced Aquila Optimizer (TEAO). TEAO incorporates the Tent chaotic map to initialize the Aquila population, promoting a more uniform distribution within the solution space. To balance exploration and exploitation, novel formulas are proposed, accelerating convergence while ensuring precision. The effectiveness of the TEAO algorithm is validated through a comprehensive comparison with 14 state-of-the-art algorithms using 23 classical benchmark test functions. Additionally, to assess the practical feasibility of the approach, TEAO is applied to six constrained engineering problems and benchmarked against the performance of the same 14 algorithms. All experimental results consistently demonstrate that TEAO outperforms other advanced algorithms in terms of solution quality and stability, establishing it as a more competitive choice for optimization tasks.
AI Model for Industry Classification Based on Website Data

Authors:
raw_author_name: Timotej Jagrič
raw_affiliation_string: Institute of Finance and Artificial Intelligence, Faculty of Economics and Business, University of Maribor, Razlagova 14, 2000 Maribor, Slovenia

Abstract: This paper presents a broad study on the application of the BERT (Bidirectional Encoder Representations from Transformers) model for multiclass text classification, specifically focusing on categorizing business descriptions into 1 of 13 distinct industry categories. The study involved a detailed fine-tuning phase resulting in a consistent decrease in training loss, indicative of the model’s learning efficacy. Subsequent validation on a separate dataset revealed the model’s robust performance, with classification accuracies ranging from 83.5% to 92.6% across different industry classes. Our model showed a high overall accuracy of 88.23%, coupled with a robust F1 score of 0.88. These results highlight the model’s ability to capture and utilize the nuanced features of text data pertinent to various industries. The model has the capability to harness real-time web data, thereby enabling the utilization of the latest and most up-to-date information affecting to the company’s product portfolio. Based on the model’s performance and its characteristics, we believe that the process of relative valuation can be drastically improved.
Prompt Engineering or Fine-Tuning? A Case Study on Phishing Detection with Large Language Models

Authors:
raw_author_name: Fouad Trad
raw_affiliation_string: Electrical and Computer Engineering, American University of Beirut, Beirut 1107-2020, Lebanon
raw_author_name: Ali Chehab
raw_affiliation_string: Electrical and Computer Engineering, American University of Beirut, Beirut 1107-2020, Lebanon

Abstract: Large Language Models (LLMs) are reshaping the landscape of Machine Learning (ML) application development. The emergence of versatile LLMs capable of undertaking a wide array of tasks has reduced the necessity for intensive human involvement in training and maintaining ML models. Despite these advancements, a pivotal question emerges: can these generalized models negate the need for task-specific models? This study addresses this question by comparing the effectiveness of LLMs in detecting phishing URLs when utilized with prompt-engineering techniques versus when fine-tuned. Notably, we explore multiple prompt-engineering strategies for phishing URL detection and apply them to two chat models, GPT-3.5-turbo and Claude 2. In this context, the maximum result achieved was an F1-score of 92.74% by using a test set of 1000 samples. Following this, we fine-tune a range of base LLMs, including GPT-2, Bloom, Baby LLaMA, and DistilGPT-2—all primarily developed for text generation—exclusively for phishing URL detection. The fine-tuning approach culminated in a peak performance, achieving an F1-score of 97.29% and an AUC of 99.56% on the same test set, thereby outperforming existing state-of-the-art methods. These results highlight that while LLMs harnessed through prompt engineering can expedite application development processes, achieving a decent performance, they are not as effective as dedicated, task-specific LLMs.
Automated Assessment and Feedback in Higher Education Using Generative AI

Authors:
raw_author_name: Fawad Naseer
raw_affiliation_string: Beaconhouse International College, Pakistan
raw_author_name: Nafees Ayub
raw_affiliation_string: Government College University, Faisalabad, Pakistan
raw_author_name: Muhammad Waleed Afzal
raw_affiliation_string: Beaconhouse International College, Pakistan

Abstract: This chapter explores the integration of generative AI in higher education assessment, addressing the inadequacies of traditional methods in meeting the diverse needs of contemporary learners. It highlights the potential of AI technologies, such as natural language processing and computer vision, to offer personalized, scalable, and insightful evaluations. The chapter critically examines both the enhanced capabilities introduced by AI in educational settings and the ethical challenges it poses. Emphasizing the need for a balanced approach, it suggests synergizing AI's analytical strengths with human expertise to ensure equitable and effective assessments. This work aims to guide educators, administrators, and policymakers through the complexities of AI adoption in academic evaluation, focusing on maintaining academic integrity and inclusivity while leveraging the transformative potential of AI in education.