Reviewerly/exHarmony huggingface dataset:
Anchor-based scalable multi-view subspace clustering

Authors:
raw_author_name: Mingrui Yang
raw_affiliation_string: Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education, China; School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
raw_author_name: Xi Wang
raw_affiliation_string: Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education, China; School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
raw_author_name: Wei Song
raw_affiliation_string: Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education, China; School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China

Abstract: Multi-view clustering is a major topic in pattern recognition and machine learning. Common multi-view clustering algorithms construct similarity graphs from original samples and use them to perform spectral clustering. The time complexity of the singular value decomposition process in graph construction and spectral clustering is high, leading to high computational and memory costs. In addition, subsequent K-means clustering is sensitive to the initial points, yielding unstable clustering results. To address these issues, this study proposes a novel approach to reduce time overhead and memory space from two perspectives. First, a new anchor selection method is proposed to reduce the dimension of the original data by lowering the cost. Second, the self-representation matrix of multi-views is fused into a consistent graph matrix using the post-fusion technique, and the fused graph is directly processed in postprocessing. Furthermore, the proposed method directly obtains clustering results based on the connectivity of the fusion graph, eliminating the need for K-means postprocessing, which avoids the issue of unstable clustering results. Experimental results on artificial and real multi-view datasets indicate that the proposed algorithm is superior to existing algorithms.
Remittance inflows and financial development IN Sub-Saharan African countries: Does governance matter?

Authors:
raw_author_name: N.M. Odhiambo
raw_affiliation_string: Department of Economics, University of South Africa, P.O Box 392, UNISA, 0003, Pretoria, South Africa.
raw_author_name: Mercy T. Musakwa
raw_affiliation_string: Department of Economics, University of South Africa, P.O Box 392, UNISA, 0003, Pretoria, South Africa.

Abstract: <h2>Abstract</h2><h3>Background</h3> The surge in remittances in most sub-Saharan African countries motivated this study to establish whether remittance inflows enhance financial development and whether governance plays any significant mediating role in the nexus between financial development and remittance inflows. <h3>Purpose</h3> The study examines the impact of remittance inflows on bank-based financial development in 26 sub-Saharan African (SSA) countries using three proxies of bank-based financial development. The study also examines whether government regulatory quality and effectiveness modulate the impact of remittance on bank-based financial development. <h3>Method</h3> The generalised method of moments (GMM) estimation technique is used to examine this linkage. <h3>Results</h3> The results show that when financial development is proxied by liquid liabilities and bank deposits, remittance inflow is found to have an unconditional positive impact on bank-based financial development, while governance is found to reinforce the positive relationship between remittance and financial development. However, when financial development is proxied by deposit money bank assets, remittance is found to have no profound effect on bank-based financial development, but it was found to interact with government effectiveness to yield a positive influence on financial development. <h3>Conclusion</h3> Overall, remittance inflows were found to have an overwhelming positive impact on the banking sector development. It was also found that good governance generally tends to reinforce the positive impact of remittances on banking sector development. <h3>Novelty</h3> This study adds value to the extant literature by providing answers to the role that governance plays in enhancing the impact of remittances on financial development in sub-Saharan African countries.
Tswinpose: Enhanced monocular 3D human pose estimation with JointFlow

Authors:
raw_author_name: Muyu Li
raw_affiliation_string: School of Control Science and Engineering, Dalian University of Technology, Dalian, 116024, Liaoning, China
raw_author_name: Henan Hu
raw_affiliation_string: School of Mechanical Engineering, Dalian Jiaotong University, Dalian, 116028, Liaoning, China
raw_author_name: Xudong Zhao
raw_affiliation_string: School of Control Science and Engineering, Dalian University of Technology, Dalian, 116024, Liaoning, China
raw_author_name: Hong Yan
raw_affiliation_string: Centre for Intelligent Multidimensional Data Analysis Limited, Hong Kong, China; Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China

Abstract: Monocular estimation of 3D human poses is challenging due to ambiguity in depths and partial occlusion. Most recent works define this as a 2D-to-3D lifting task, taking 2D key point sequences and using spatial and temporal relationships. However, prior works focus on capturing spatio-temporal correlations but ignore the motion of joints that is needed for continuous estimation. To extend the potential of 2D-to-3D pose estimation, we propose TSwinPose, which learns multi-scale spatio-temporal representations from 2D key point locations and patterns of motion. The input 2D key point sequences are enhanced by JointFlow, which encodes the motion of each human joint. Based on Swin-Transformer, we designed a temporal domain Swin-Unet structure to model multi-scale spatio-temporal relationships of human joints across different temporal windows. The final 3D pose generated by multi-stage representations is consistent temporally and has a higher accuracy. Experiments conducted on three benchmark datasets, Human3.6M, MPI-INF-3DHP, and HumanEva-I, demonstrate that TSwinPose achieves performance that is on par with state-of-the-art methods. Moreover, the introduction of JointFlow as a plug-in extension enhances performance significantly, particularly benefiting long-term 2D-to-3D lifting human pose estimation methods.
Secure decentralized firmware update delivery service for Internet of Things

Authors:
raw_author_name: Yustus Eko Oktian
raw_affiliation_string: IoT Research Center, Pusan National University, Busan, Republic of Korea
raw_author_name: Thi-Thu-Huong Le
raw_affiliation_string: IoT Research Center, Pusan National University, Busan, Republic of Korea
raw_author_name: Howon Kim
raw_affiliation_string: School of Computer Science and Engineering, Pusan National University, Busan, Republic of Korea

Abstract: With the increasing number of Internet of Things devices, it is crucial to keep them up-to-date to prevent cyber attacks. Traditional centralized delivery is not suitable for scaling and also can be too expensive to run for small vendors. Thus, finding a fully secure, scalable, yet cost-efficient firmware update distribution strategy is always an open research problem. This paper tries to answer those problems by proposing Patchman, a secure decentralized firmware update delivery service for the Internet of Things ecosystem leveraging blockchain. When a new firmware patch is available, vendors make a bid in the smart contract for anyone to join as firmware distributors. For each successful delivery to targeted devices, distributors are rewarded with tokens. Meanwhile, devices gain a reputation score every time they successfully install an update. To ensure robustness and fairness, we develop secure fair exchange protocols using verifiable proof-of-delivery and proof-of-installation. Those proofs can be traded in the blockchain for rewards and reputation scores increase, proving that the proof-holders have successfully processed a firmware delivery and firmware installation task. This way, the firmware update delivery can be executed safely without centralized third-party control. Our evaluation results show that our implementation complies with the five security goals that we envision. We also have successfully punished malicious actions by confiscating their deposits and requiring them to pay up to four times of base deposit value when they join the next update task, ensuring the fairness of our protocol. Furthermore, we generate low processing delay overhead compared to existing works that rely on Zero-Knowledge Proofs. The gas usage consumption from our approach also produced a competitive result despite our works supporting more features than existing works, ensuring the efficiency of our proposal.
Applying large language models and chain-of-thought for automatic scoring

Authors:
raw_author_name: Ehsan Latif
raw_affiliation_string: AI4STEM Education Center, University of Georgia, Athens, GA, USA
raw_author_name: Xuansheng Wu
raw_affiliation_string: School of Computing, University of Georgia, Athens, GA, USA
raw_author_name: Ninghao Liu
raw_affiliation_string: School of Computing, University of Georgia, Athens, GA, USA
raw_author_name: Xiaoming Zhai
raw_affiliation_string: AI4STEM Education Center, University of Georgia, Athens, GA, USA

Abstract: This study investigates the application of large language models (LLMs), specifically GPT-3.5 and GPT-4, with Chain-of-Though (CoT) in the automatic scoring of student-written responses to science assessments. We focused on overcoming the challenges of accessibility, technical complexity, and lack of explainability that have previously limited the use of artificial intelligence-based automatic scoring tools among researchers and educators. With a testing dataset comprising six assessment tasks (three binomial and three trinomial) with 1650 student responses, we employed six prompt engineering strategies to automatically score student responses. The six strategies combined zero-shot or few-shot learning with CoT, either alone or alongside item stem and scoring rubrics, developed based on a novel approach, WRVRT (prompt writing, reviewing, validating, revising, and testing). Results indicated that few-shot (acc = 0.67) outperformed zero-shot learning (acc = 0.60), with 12.6% increase. CoT, when used without item stem and scoring rubrics, did not significantly affect scoring accuracy (acc = 0.60). However, CoT prompting paired with contextual item stems and rubrics proved to be a significant contributor to scoring accuracy (13.44% increase for zero-shot; 3.7% increase for few-shot). We found a more balanced accuracy across different proficiency categories when CoT was used with a scoring rubric, highlighting the importance of domain-specific reasoning in enhancing the effectiveness of LLMs in scoring tasks. We also found that GPT-4 demonstrated superior performance over GPT -3.5 in various scoring tasks when combined with the single-call greedy sampling or ensemble voting nucleus sampling strategy, showing 8.64% difference. Particularly, the single-call greedy sampling strategy with GPT-4 outperformed other approaches. This study also demonstrates the potential of LLMs in facilitating explainable and interpretable automatic scoring, emphasizing that CoT enhances accuracy and transparency, particularly when used with item stem and scoring rubrics.
Search Space Division Method for Wrapper Feature Selection on High-Dimensional Data Classification

Authors:
raw_author_name: Abhilasha Chaudhuri
raw_affiliation_string: Department of Computer Science and Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujrat, India

Abstract: Feature selection (FS) is an essential pre-processing technique for high-dimensional data. Wrapper-based FS techniques are known for their superior performance over filter FS. However, when the dimensionality of data is very high the wrapper techniques become computationally very expensive. To solve this problem of scalability, this paper proposes the concept of search space division (SSD) which leads to smaller search spaces and hence reduced computational cost. The proposed SSD approach is generic in nature and can be integrated with any wrapper-based FS technique. The SSD approach divides search space into multiple parts and the wrapper-based FS is independently applied to each part. To facilitate the interaction of features over the complete search space, all feature subsets obtained from each part are combined and the wrapper-based FS is again applied to get the final feature subset. Moreover, a new wrapper FS technique named Binary Rao (BRAO) Algorithm has been proposed. BRAO is based on the metaphor-less, parameter-less meta-heuristic optimization algorithm Rao-1. The proposed combination of SSD and BRAO algorithm named SSDR has produced better classification accuracies with a smaller number of features in a much shorter time as compared to other well-regarded wrapper FS techniques as well as classical FS techniques over thirteen benchmark high-dimensional datasets.
How to create a successful mobile learning strategy for medical education during lockdowns?

Authors:
raw_author_name: Masomeh Kalantarion
raw_affiliation_string: Department of Medical Education, School of Medical Education and Learning Technologies, Shahid Beheshti University of Medical Sciences, Tehran, Iran
raw_author_name: Soleiman Ahmady
raw_affiliation_string: Department of Medical Education, Virtual School of Medical Education and Management, Shahid Beheshti University of Medical Sciences, Tehran, Iran
raw_author_name: Per Kallestrup
raw_affiliation_string: Centre for Global Health, Department of Public Health, Aarhus University, Aarhus, Denmark
raw_author_name: Marzieh Katibeh
raw_affiliation_string: Centre for Global Health, Department of Public Health, Aarhus University, Aarhus, Denmark
raw_author_name: Mohammad Mehdi Sadoughi
raw_affiliation_string: Ophthalmic Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Beheshti University of Medical Sciences, Tehran, Iran
raw_author_name: Nasrin Khajeali
raw_affiliation_string: Department of Medical Education, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

Abstract: BACKGROUND: Mobile learning has played an important role during the COVID-19 pandemic and medical schools now consider it as an effective educational method in current and future crises. In this qualitative study, an attempt was made to demonstrate the principles of designing a mobile learning strategy in medical education from the perspective of experts. MATERIALS AND METHODS: The study was conducted by the qualitative content analysis method. The data were collected from July 2022 to Feb 2023. Twelve participants were included in this study from Iran’s medical universities, consisting of two members of the Higher Council of Virtual Education, three educational directors, three clinical faculty members, two faculty members specializing in e-learning and medical education, an educational vice, and a dean. Data were collected using semi-structured interviews and analyzed by Granheim and Lundman’s (2004) method. RESULTS: Out of twelve participants in the study, eight (66%) were males and four (44%) females. Data were classified into eight categories and one theme. Based on the participants’ experiences, the main theme, that is, “the principles of medical education design in mobile learning,” included pedagogical component, interactive design, effective and comprehensive analysis, achieving objectives with the mobile learning platform, generating micro- and interactive e-content, teaching-learning interactive methods, course implementation and interactive evaluation at both micro- and macro-levels. CONCLUSION: Data analysis revealed that in addition to the eight principles in the medical education design in mobile learning, the participants prioritized the two principles of pedagogical component and interactive design over other principles in educational design. Using a successful mobile learning strategy in situations of restrictions limiting physical presence may improve the quality of medical education.
Machine learning in neurological disorders: A multivariate LSTM and AdaBoost approach to Alzheimer's disease time series analysis

Authors:
raw_author_name: Seyed Shahrestani
raw_affiliation_string: School of Computer, Data and Mathematical Sciences Western Sydney University Sydney Australia
raw_author_name: Mahmoud Elkhodr
raw_affiliation_string: School of Engineering and Technology Central Queensland University Sydney Australia

Abstract: Abstract Introduction Alzheimer's disease (AD) is a progressive brain disorder that impairs cognitive functions, behavior, and memory. Early detection is crucial as it can slow down the progression of AD. However, early diagnosis and monitoring of AD's advancement pose significant challenges due to the necessity for complex cognitive assessments and medical tests. Methods This study introduces a data acquisition technique and a preprocessing pipeline, combined with multivariate long short‐term memory (M‐LSTM) and AdaBoost models. These models utilize biomarkers from cognitive assessments and neuroimaging scans to detect the progression of AD in patients, using The AD Prediction of Longitudinal Evolution challenge cohort from the Alzheimer's Disease Neuroimaging Initiative database. Results The methodology proposed in this study significantly improved performance metrics. The testing accuracy reached 80% with the AdaBoost model, while the M‐LSTM model achieved an accuracy of 82%. This represents a 20% increase in accuracy compared to a recent similar study. Discussion The findings indicate that the multivariate model, specifically the M‐LSTM, is more effective in identifying the progression of AD compared to the AdaBoost model and methodologies used in recent research.
Comparative analysis of various models for image classification on Cifar-100 dataset

Authors:
raw_author_name: YuYu Zheng
raw_affiliation_string: School of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, ShangHai, 200093, China
raw_author_name: JunMing Chen
raw_affiliation_string: Department of Mathematics, University of California, Los Angeles, Los Angeles, 90095, United States

Abstract: Abstract Nowadays, people developed various convolutional neural network (CNN) based models for computer vision. Some famous models, such as GoogLeNet, Residual Network (ResNet), Visual Geometry Group (VGG), and You Only Look Once (YOLO), have different architecture and performances. Determining which model to use may be a troublesome problem for those just starting to study image classification. To solve this problem, we introduce the GoogLeNet, ResNet-18, and VGG-16 models, comparing their architecture, features, and performance. Then we give our suggestions based on the test results to help beginners choose a suitable model. We conducted experiments to train and test GoogLeNet, ResNet-18, and VGG-16 on the Cifar-100 datasets with the same hyperparameters. Based on the test results (test accuracy, average test loss, training loss), we analyze the figures for trends, key points, increase rate, and other features. Then we combine the architecture of each model to make our conclusions. The experimental results show that ResNet-18 can be a good choice when training the model with the Cifar-100 datasets because it performs well after training and has a low time complexity. ResNet-18 also has the fastest convergence speed. GoogLeNet would be the second choice because it functions similarly to ResNet-18 and is even better. However, training GoogLeNet is a time-consuming task. VGG is not recommended in this experiment because it has the worst performance and similar training complexity compared with ResNet-18.
Decentralized Face Identification with Hierarchical Navigable Small World on Blockchain

Authors:
raw_author_name: Yiting Chen
raw_affiliation_string: Department of Computer Software and Theory, Donghua University, Shanghai 201620, China

Abstract: Abstract This paper presents a novel method for decentralized storage in deep-learning-based face recognition systems using the Hierarchical Navigable Small World (HNSW) algorithm. The proposed solution utilizes Ethereum smart contracts, which acts as highly available data storage systems for storing identifiable data for authorized personnel. In addition, the solution is integrated with a centralized vector database that is in charge of vector indexing, searching and associating face embeddings to an identity on the Ethereum blockchain with anonymous hashes. Vector indexing and search processes involve different machine learning algorithms that enable computations to be carried out in a reasonable time with good matching accuracy. Specifically, we compared different approaches and selected the HNSW algorithm. Accordingly, we successfully implemented a prototype of a reliable and privacy-focused decentralized face identification system for areas under government surveillance, such as customs inspection sites. In our measurements, the system could handle 20,000 face vectors easily with high matching accuracy, and the performance could be further improved using more powerful hardware. Finally, we also propose additional methods to further scale up the system to handle millions of face vectors.
Graph sampling based deep metric learning for cross-view geo-localization

Authors:
raw_author_name: Haozhang Jia
raw_affiliation_string: School of Information Technology, University of Science and Technology of China Hefei, Anhui, China

Abstract: Abstract Cross-view geo-localization has emerged as a novel computer vision task that has garnered increasing attention. This is primarily attributed to its practical significance in the domains of drone navigation and drone-view localization. Moreover, the work is particularly demanding due to its inherent requirement for cross-domain matching. There are generally two ways to train a neural network to match similar satellite and drone-view images: presentation learning with classifiers and identity loss, and metric learning with pairwise matching within mini-batches. The first takes extra computing and memory costs in large-scale learning, so this paper follows a person-reidentification method called QAConv-GS, and implements a graph sampler to mine the hardest data to form mini-batches, and a QAConv module with extra attention layers appended to compute similarity between image pairs. Batch-wise OHEM triplet loss is then used for model training. With these implementations and adaptions combined, this paper significantly improves the state of the art on the challenging University-1652 dataset.