Reviewerly/exHarmony huggingface dataset:
Engaging Community College Students in Computer Engineering Research through Design and Implementation of a Versatile Gesture Control Interface

Authors:
raw_author_name: Christopher Thomas
raw_author_name: Amelito G Enriquez
raw_affiliation_string: Canada College
raw_author_name: Cheng Chen
raw_affiliation_string: San Francisco State University
raw_author_name: Hao Jiang
raw_affiliation_string: San Francisco State University
raw_author_name: Xiaorong Zhang
raw_affiliation_string: San Francisco State University

Abstract: Abstract Given the important role of community colleges in undergraduate education, in order to increase the recruitment of STEM students and improve undergraduate STEM education, it is crucial to develop strategies to inspire community college students' interest in STEM. With support from the Department of Education Minority Science and Engineering Improvement Program (MSEIP), a cooperative internship program between a community college and a public comprehensive university was developed to engage community college students in leading-edge engineering research. In summer 2016, five sophomore students from the community college participated in a ten-week computer engineering research internship project in a research lab at the four-year university. This internship project aimed to develop an intelligent electromyography (EMG)-based gesture control interface which deciphers EMG signals collected from forearm muscles to identify users' intended hand and arm movements. The gesture control interface has great potential to provide natural human-machine interaction in a variety of applications, from assistive devices through rehabilitation therapy to virtual reality (VR). The developed interface provides easy connection with a commercial EMG-based armband Myo and a modular software engine for customizable gesture recognition as well as a special pipeline for converting recognition decisions into control commands for external applications. The students also conducted usability testing of the gesture control interface on human subjects by using it to control a first-person shooter (FPS) virtual reality game which was developed by another community college student intern. The project provided a great opportunity for the student interns to gain research experience and learn valuable knowledge in human-machine interfaces, EMG signal processing, and gesture recognition. It also helped them improve their skills in experimental design, data analysis, scientific writing and presentation, as well as teamwork and time management. The outcome of this project indicated that the internship program was an effective method for inspiring community college students' interest in computer engineering research and strengthening their confidence and capability in pursuing an engineering profession.
What do we know about cryptocurrency investment? An empirical study of its adoption among Indian retail investors

Authors:
raw_author_name: Jitender Kumar
raw_affiliation_string: Department of Management Studies, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, India

Abstract: Purpose This study aims to examine the cryptocurrency adoption (CA) level among Indian retail investors who use cryptocurrency as an investment and mode of transaction. Design/methodology/approach Through self-administered survey questionnaires, data is collected from 397 retail investors of Haryana (India). This study adopted a quantitative method using partial least squares structural equation modeling (PLS-SEM). Findings This paper offered a robust model with a high explanatory value for CA in which four of the five proposed factors of diffusion of innovation theory (trialability, compatibility, complexity and observability) and one of the two proposed factors of consumer behavioral theory (perceived value) significantly influences CA. More specifically, the absence of regulatory support is a barrier to the broad adoption of cryptocurrencies, as its regulations are necessary to mitigate or minimize uncertain outcomes. Research limitations/implications This research primarily focuses on CA in India. Thus, it can be extended to cover diverse other countries for more precise results. Practical implications The results provide insights to the government to design the policies, better regulate and make investment strategies that can ultimately enhance CA. In addition, the study’s results also inform financial educators, policymakers, employers and academicians about the significance of several variables affecting CA in India. Social implications From a social standpoint, this study is an advance that directs central banks and governments to develop, regulate and manage digital currencies and implement a digital currency ecosystem. Moreover, the results assist in understanding investors’ perceptions and decision-making perspectives toward cryptocurrencies through the country’s digitalization. Originality/value This paper fills the study gap to assist policymakers and cryptocurrency experts in broadening their knowledge base and recognizing prioritized intentions. Additionally, this study provides a theoretical model with the latent variable for a present and pertinent matter.
Alteration Mapping for Porphyry Cu Targeting in the Western Chagai Belt, Pakistan, Using ZY1-02D Spaceborne Hyperspectral Data

Authors:
raw_author_name: Lei Liu
raw_affiliation_string: 1 School of Earth Science and Resources, Chang’an University, Xi’an, Shaanxi 710054, China
raw_author_name: Chuntao Yin
raw_affiliation_string: 1 School of Earth Science and Resources, Chang’an University, Xi’an, Shaanxi 710054, China
raw_author_name: Jilu Feng
raw_affiliation_string: 4 Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton T6G2E3, Canada
raw_author_name: Huishan Zhang
raw_affiliation_string: 3 Xi’an Center of Geological Survey, China Geological Survey, Xi’an, Shaanxi 710054, China

Abstract: Abstract The Chagai porphyry Cu belt in Pakistan is an important metallogenic terrain extending approximately 400 km in an east-west direction. Most of the known porphyry Cu deposits, such as the world-class Reko Diq deposit and Saindak deposit, are located in the western Chagai belt. In this study, the ZY1-02D hyperspectral data acquired by a recently launched spaceborne imaging spectrometer with 166 bands within a 0.4- to 2.5-μm spectral region were used to map mineral information over 8,000 km2 for exploring potential targets of porphyry Cu mineralization in the western Chagai belt. False color composite, spectral angle mapper, and wavelength position mapping methods were used in this research leading to the identification of a series of alteration minerals (including muscovite [sericite], kaolinite, alunite, epidote, chlorite, and calcite) from ZY1-02D data. The alteration mineral maps derived from ZY1-02D data match well with the known deposits and field inspections. Twenty-three new targets were identified as potential porphyry Cu mineralization targets for further exploration in the study area. Three targets, north of Saindak, Koh-i-Sultan, and Durban Chah, and six alteration sites in the southwest of Durban Chah, were inspected in the field, and Cu-Au mineralization was confirmed in all these inspected areas. As the ZY1-02D hyperspectral data covers most of the land area of the earth, this study provides new insights for mineral exploration and lithologic mapping in remote regions.
Cross-scale multi-instance learning for pathological image diagnosis

Authors:
raw_author_name: Ruining Deng
raw_affiliation_string: Vanderbilt University, Nashville, TN 37215, USA
raw_author_name: Can Cui
raw_affiliation_string: Vanderbilt University, Nashville, TN 37215, USA
raw_author_name: Lucas W. Remedios
raw_affiliation_string: Vanderbilt University, Nashville, TN 37215, USA
raw_author_name: Shunxing Bao
raw_affiliation_string: Vanderbilt University, Nashville, TN 37215, USA
raw_author_name: R. Michael Womick
raw_affiliation_string: The University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
raw_author_name: Sophie Chiron
raw_affiliation_string: Vanderbilt University Medical Center, Nashville, TN 37232, USA
raw_author_name: Joseph T. Roland
raw_affiliation_string: Vanderbilt University Medical Center, Nashville, TN 37232, USA
raw_author_name: Ken S. Lau
raw_affiliation_string: Vanderbilt University, Nashville, TN 37215, USA
raw_author_name: Qi Liu
raw_affiliation_string: Vanderbilt University Medical Center, Nashville, TN 37232, USA
raw_author_name: Keith T. Wilson
raw_affiliation_string: Vanderbilt University Medical Center, Nashville, TN 37232, USA; Veterans Affairs Tennessee Valley Healthcare System, Nashville, TN 37212, USA
raw_author_name: Lori A. Coburn
raw_affiliation_string: Vanderbilt University Medical Center, Nashville, TN 37232, USA; Veterans Affairs Tennessee Valley Healthcare System, Nashville, TN 37212, USA
raw_author_name: Bennett A. Landman
raw_affiliation_string: Vanderbilt University Medical Center, Nashville, TN 37232, USA; Vanderbilt University, Nashville, TN 37215, USA
raw_author_name: Yuankai Huo
raw_affiliation_string: Vanderbilt University, Nashville, TN 37215, USA

Abstract: Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20× magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed; (2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention; (3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.
exKidneyBERT: a language model for kidney transplant pathology reports and the crucial role of extended vocabularies

Authors:
raw_author_name: Tiancheng Yang
raw_affiliation_string: Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada
raw_author_name: Ilia Sucholutsky
raw_affiliation_string: Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada
raw_author_name: Kuang-Yu Jen
raw_affiliation_string: Department of Pathology and Laboratory Medicine, University of California, Davis, Sacramento, CA, United States of America
raw_author_name: Matthias Schonlau
raw_affiliation_string: Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada

Abstract: Background Pathology reports contain key information about the patient’s diagnosis as well as important gross and microscopic findings. These information-rich clinical reports offer an invaluable resource for clinical studies, but data extraction and analysis from such unstructured texts is often manual and tedious. While neural information retrieval systems (typically implemented as deep learning methods for natural language processing) are automatic and flexible, they typically require a large domain-specific text corpus for training, making them infeasible for many medical subdomains. Thus, an automated data extraction method for pathology reports that does not require a large training corpus would be of significant value and utility. Objective To develop a language model-based neural information retrieval system that can be trained on small datasets and validate it by training it on renal transplant-pathology reports to extract relevant information for two predefined questions: (1) “What kind of rejection does the patient show?”; (2) “What is the grade of interstitial fibrosis and tubular atrophy (IFTA)?” Methods Kidney BERT was developed by pre-training Clinical BERT on 3.4K renal transplant pathology reports and 1.5M words. Then, exKidneyBERT was developed by extending Clinical BERT’s tokenizer with six technical keywords and repeating the pre-training procedure. This extended the model’s vocabulary. All three models were fine-tuned with information retrieval heads. Results The model with extended vocabulary, exKidneyBERT, outperformed Clinical BERT and Kidney BERT in both questions. For rejection, exKidneyBERT achieved an 83.3% overlap ratio for antibody-mediated rejection (ABMR) and 79.2% for T-cell mediated rejection (TCMR). For IFTA, exKidneyBERT had a 95.8% exact match rate. Conclusion ExKidneyBERT is a high-performing model for extracting information from renal pathology reports. Additional pre-training of BERT language models on specialized small domains does not necessarily improve performance. Extending the BERT tokenizer’s vocabulary library is essential for specialized domains to improve performance, especially when pre-training on small corpora.
Human-AI collaboration patterns in AI-assisted academic writing

Authors:
raw_author_name: Andy Nguyen
raw_affiliation_string: Learning and Educational Technology (LET) Research Lab, University of Oulu, Oulu, Finland

Abstract: Artificial Intelligence (AI) has increasingly influenced higher education, notably in academic writing where AI-powered assisting tools offer both opportunities and challenges. Recently, the rapid growth of generative AI (GAI) has brought its impacts into sharper focus, yet the dynamics of its utilisation in academic writing remain largely unexplored. This paper focuses on examining the nature of human-AI interactions in academic writing, specifically investigating the strategies doctoral students employ when collaborating with a GAI-powered assisting tool. This study involves 626 recorded activities on how ten doctoral students interact with GAI-powered assisting tool during academic writing. AI-driven learning analytics approach was adopted for three layered analyses: (1) data pre-processing and analysis with quantitative content analysis, (2) sequence analysis with Hidden Markov Model (HMM) and hierarchical sequence clustering, and (3) pattern analysis with process mining. Findings indicate that doctoral students engaging in iterative, highly interactive processes with the GAI-powered assisting tool generally achieve better performance in the writing task. In contrast, those who use GAI merely as a supplementary information source, maintaining a linear writing approach, tend to get lower writing performance. This study points to the need for further investigations into human-AI collaboration in learning in higher education, with implications for tailored educational strategies and solutions.
Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm

Authors:
raw_author_name: Seyedali Mirjalili
raw_affiliation_string: Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Adelaide, Australia
raw_author_name: Nima Khodadadi
raw_affiliation_string: Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL, USA

Abstract: Abstract The novelty of this article lies in introducing a novel stochastic technique named the Hippopotamus Optimization (HO) algorithm. The HO is conceived by drawing inspiration from the inherent behaviors observed in hippopotamuses, showcasing an innovative approach in metaheuristic methodology. The HO is conceptually defined using a trinary-phase model that incorporates their position updating in rivers or ponds, defensive strategies against predators, and evasion methods, which are mathematically formulated. It attained the top rank in 115 out of 161 benchmark functions in finding optimal value, encompassing unimodal and high-dimensional multimodal functions, fixed-dimensional multimodal functions, as well as the CEC 2019 test suite and CEC 2014 test suite dimensions of 10, 30, 50, and 100 and Zigzag Pattern benchmark functions, this suggests that the HO demonstrates a noteworthy proficiency in both exploitation and exploration. Moreover, it effectively balances exploration and exploitation, supporting the search process. In light of the results from addressing four distinct engineering design challenges, the HO has effectively achieved the most efficient resolution while concurrently upholding adherence to the designated constraints. The performance evaluation of the HO algorithm encompasses various aspects, including a comparison with WOA, GWO, SSA, PSO, SCA, FA, GOA, TLBO, MFO, and IWO recognized as the most extensively researched metaheuristics, AOA as recently developed algorithms, and CMA-ES as high-performance optimizers acknowledged for their success in the IEEE CEC competition. According to the statistical post hoc analysis, the HO algorithm is determined to be significantly superior to the investigated algorithms. The source codes of the HO algorithm are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/160088-hippopotamus-optimization-algorithm-ho .
AI-Based Sports Highlight Generation for Social Media

Authors:
raw_author_name: Cise Midoglu
raw_affiliation_string: SimulaMet, Forzasys, Norway
raw_author_name: Saeed Shafiee Sabet
raw_affiliation_string: Forzasys, Norway
raw_author_name: Sushant Gautam
raw_affiliation_string: SimulaMet, OsloMet, Norway
raw_author_name: Tomas Kupka
raw_affiliation_string: Forzasys, Norway
raw_author_name: Pål Halvorsen
raw_affiliation_string: SimulaMet, OsloMet, Forzasys, Norway

Abstract: Social media plays a significant role for sports organizations with millions of active fans, but publishing highlights is often a tedious manual operation. With the development of AI, new tools are available for content generation and personalization to engage audiences. We propose an AI-based multimedia production framework for the automatic publishing of soccer and ice hockey highlights on social media, and disseminate our experiences with developing dedicated pipelines for event detection and classification, player detection and tracking, highlight clipping, cropping, thumbnail generation, game summarization, caption generation, and social media sharing.
Video Quality Assessment with Texture Information Fusion for Streaming Applications

Authors:
raw_author_name: Vignesh V. Menon
raw_affiliation_string: Video Communication and Applications Dept Fraunhofer HHI, Berlin, Germany
raw_author_name: Prajit T. Rajendran
raw_affiliation_string: CEA, List, Palaiseau Université Paris-Saclay, Paris, France
raw_author_name: Reza Farahani
raw_affiliation_string: Institute of Information Technology, Alpen-Adria-Universität, Klagenfurt, Klagenfurt, Austria
raw_author_name: Klaus Schoeffmann
raw_affiliation_string: Institute of Information Technology, Alpen-Adria-Universität, Klagenfurt, Austria
raw_author_name: Christian Timmerer
raw_affiliation_string: Institute of Information Technology, Alpen-Adria-Universität, Klagenfurt, Austria

Abstract: The rise in video streaming applications has increased the demand for video quality assessment (VQA). In 2016, Netflix introduced Video Multi-Method Assessment Fusion (VMAF), a full reference VQA metric that strongly correlates with perceptual quality, but its computation is time-intensive. We propose a Discrete Cosine Transform (DCT)-energy-based VQA with texture information fusion (VQ-TIF) model for video streaming applications that determines the visual quality of the reconstructed video compared to the original video. VQ-TIF extracts Structural Similarity (SSIM) and spatiotemporal features of the frames from the original and reconstructed videos and fuses them using a long short-term memory (LSTM)-based model to estimate the visual quality. Experimental results show that VQ-TIF estimates the visual quality with a Pearson Correlation Coefficient (PCC) of 0.96 and a Mean Absolute Error (MAE) of 2.71, on average, compared to the ground truth VMAF scores. Additionally, VQ-TIF estimates the visual quality at a rate of 9.14 times faster than the state-of-the-art VMAF implementation, along with an 89.44 % reduction in energy consumption, assuming an Ultra HD (2160p) display resolution.
Unsupervised clustering-based domain adaptation for estimating occupancy and recognizing activities in smart buildings

Authors:
raw_author_name: Jawher Dridi
raw_affiliation_string: Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Canada
raw_author_name: Manar Amayri
raw_affiliation_string: Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Canada
raw_author_name: Nizar Bouguila
raw_affiliation_string: Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Canada

Abstract: Smart buildings rely on estimating the number of occupants and recognizing their behaviors to provide several benefits such as energy efficiency, HVAC (Heating, Ventilation, and Air Conditioning) systems optimization, and health safety. Examining the behavior and the counts of residents in different building areas can provide efficient management of resources (heating, cooling, lighting), and energy distribution across the building. Artificial intelligence (AI) and machine learning (ML) can exploit Internet of Things (IoT) data, such as power consumption data, to provide occupancy estimation (OE) and activity recognition (AR). Obtaining labeled smart building data can be very challenging because it is costly, sensitive, and time-consuming. Data scarcity is a serious problem for research development since few labeled data samples may lead to weak classifiers that do not satisfy real-world applications. Researchers have examined domain adaptation (DA) approaches to share knowledge from source domains where labeled data is abundant to target domains where labeled is scarce. Furthermore, researchers have considered a particular case of DA which is the unsupervised method that solves the case of unavailability of labeled data in target domains. Most previous research in domain adaptation has focused on minimizing the discrepancy between source and target feature distributions. In this work, we adapt and evaluate deep unsupervised domain adaptation (UDA) methods based on clustering (UCDA) to estimate the number of occupants and recognize their activities. We have adapted three methods called Structurally Regularized Deep Clustering (SRDC), Hybrid Structurally Regularized Deep Clustering (H-SRDC), and Distilled Discriminative Clustering for Domain Adaptation (DisClusterDA). We have used OE and AR datasets to evaluate the efficiency of the adapted techniques. The findings, with accuracy up to 99%, make these methods open for potential real-world applications. To further enhance the current research, we have provided the code in the following repository: https://github.com/JawDri/Unsupervised-Clustering-based-DA-for-OE-and-AR.git