Reviewerly/exHarmony huggingface dataset:
Toward a Universal Dependencies Treebank of Old English: Representing the Morphological Relatedness of Un-Derivatives

Authors:
raw_author_name: Javier Martín Arista
raw_affiliation_string: Department of Modern Languages, Faculty of Humanities and Education, University of La Rioja, 26006 Logroño, Spain

Abstract: This article deals with one of the aspects involved in the compilation of a treebank of Old English within the framework of Universal Dependencies. More specifically, this study addresses the question of how to account for the remarkable degree of Old English morphological relatedness in a type of treebank designed to stress syntactic similarities across languages. The solution proposed and assessed in this study is the addition of an extra field of annotation for morphological relatedness. The data of this analysis comprise 1106 derivatives attaching the prefix un-. Out of these, there are around 80 morphologically complex nouns, adjectives, verbs, and adverbs whose derivation cannot be described gradually, 33 of which are unique formations or hapax legomena according to the attestations provided by the Dictionary of Old English Corpus. The main conclusion is that the specification of short-distance and long-distance morphological relatedness provides the Old English treebank with a paradigmatic dimension that can be particularly relevant for languages with relatively generalised and transparent derivational morphology.
Evaluating how extended reality delivery device and preservice teacher major impact presence in immersive learning environments

Authors:
raw_author_name: Enrico Gandolfi
raw_affiliation_string: School of Teaching, Learning and Curriculum Studies Kent State University Kent Ohio USA
raw_author_name: Richard E. Ferdig
raw_affiliation_string: School of Teaching, Learning and Curriculum Studies Kent State University Kent Ohio USA
raw_author_name: Karl W. Kosko
raw_affiliation_string: School of Teaching, Learning and Curriculum Studies Kent State University Kent Ohio USA

Abstract: Teacher education has begun to embrace the use of 360 videos to improve preservice teachers' (PSTs) engagement and immersion. While recent research on such use is promising overall, there are specific questions that have been left unanswered about the construct of presence in 360 videos. More specifically, research has yet to fully explore how video delivery devices and PST characteristics may impact presence. The purpose of this study was to respond to this gap in the literature by examining PST major, delivery device (ie, head mounted display vs. flat screen), and the interaction between the two in informing presence. A total of 93 PSTs watched 360 videos and then completed a questionnaire. Findings indicated that presence in head‐mounted displays was related to emotions and agency. Conversely, presence with flat screens meant familiarity and control. Moreover, PST major was a predictor of presence, suggesting that 360 videos should be personalized according to PST career and professional goals. Finally, the interaction effect of preparing to teach K‐12 mathematics and wearing a head‐mounted display had a negative and statistically significant effect on participants' perceived presence, thus indicating a non‐cumulative effect. Practitioner notes What is already known about this topic 360 videos are increasingly used to support preservice teacher training. 360 videos for teacher education foster immersion, presence, and noticing skills. What this paper adds Empirical evidence that 360 videos experienced with head‐mounted display facilitate agency and emotional involvement. Empirical evidence that 360 videos experienced with flat screens facilitate feelings of control. Data showed that preservice teachers' content area (math) was a positive predictor of presence in 360 videos. Implications for practice and/or policy Head‐mounted displays show promise as a delivery mode for 360 videos in teacher education. 360 videos should be personalized according to PST career and professional goals. The eXtended Reality Presence Scale is confirmed as a robust instrument to measure presence in immersive environments. More research is needed to evaluate the scalability of this approach in other contexts.
Cascaded Searching Reinforcement Learning Agent for Proposal-Free Weakly-Supervised Phrase Comprehension

Authors:
raw_author_name: Yaodong Wang
raw_affiliation_string: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
raw_author_name: Lili Yue
raw_affiliation_string: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
raw_author_name: Maoqing Li
raw_affiliation_string: School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Abstract: Phrase comprehension (PC) aims to locate a specific object in an image according to a given linguistic query. The existing PC methods work in either a fully supervised or proposal-based weakly supervised manner, which rely explicitly or implicitly on expensive region annotations. In order to completely remove the dependence on the supervised region information, this paper proposes to address PC in a proposal-free weakly supervised training paradigm. To this end, we developed a novel cascaded searching reinforcement learning agent (CSRLA). Concretely, we first leveraged a visual language pre-trained model to generate a visual–textual cross-modal attention heatmap. Accordingly, a coarse salient initial region of the referential target was located. Then, we formulated the visual object grounding as a Markov decision process (MDP) in a reinforcement learning framework, where an agent was trained to iteratively search for the target’s complete region from the salient local region. Additionally, we developed a novel confidence discrimination reward function (ConDis_R) to constrain the model to search for a complete and exclusive object region. The experimental results on three benchmark datasets of Refcoco, Refcoco+, and Refcocog demonstrated the effectiveness of our proposed method.
Development of an Automated Downtime System for Small and Medium sized Enterprises

Authors:
raw_author_name: Andrew Johnston
raw_affiliation_string: School of Mechanical & Aerospace Engineering, Queen’s University Belfast, Belfast BT7 1NN, UK

Abstract: The implementation of Industry 4.0 can be a challenge for SMEs as often the existing equipment cannot be simply replaced or data cannot be easily extracted from existing equipment. A system that automatically records downtime with minimal operator interaction can be beneficial by giving a clear picture of how a manufacturing process is running, along with providing information on duration, frequency and reasons for stoppages. When this information is available, further analysis can be performed to identify the main reasons for downtime and identify which sections of the production line are more prone to stopping; therefore, corrective actions can be put in place to reduce this. Off-the-shelf IoT equipment was used to develop this system, which reads the status from the signal towers of the line. These data were recorded in an SQL database and an interactive user interface was developed so that downtime codes could be assigned to stoppages and downtime data could be presented in different ways. This case study has shown that a small investment in IoT can make a huge improvement on the ability to monitor and analyse the downtime of a machining line for an SME.
Teachable Q&amp;A Agent: The Effect of Chatbot Training by Students on Reading Interest and Engagement

Authors:
raw_author_name: Chen-Chung Liu
raw_affiliation_string: National Central University, Taoyuan, Taiwan
raw_author_name: Wan-Jun Chen
raw_affiliation_string: National Central University, Taoyuan, Taiwan
raw_author_name: Chia-Hui Chang
raw_affiliation_string: National Central University, Taoyuan, Taiwan
raw_author_name: Hung-Ming Lin
raw_affiliation_string: Chang Gung University of Science and Technology, Taoyuan, Taiwan

Abstract: Reading requires appropriate strategies to spark initial interest and sustain engagement. One promising strategy is the pedagogical approach of learning-by-teaching, transforming learners into active participants. Integrating this approach into digitalized and individualized reading contexts has the potential to foster the development of young readers. Currently, AI techniques are primarily used in chatbots as tutors, with limited focus on tutee chatbots that employ the learning-by-teaching pedagogy. Therefore, this study adopted a teachable Q&amp;A agent and probed into the effect of chatbot training, employing AI techniques and utilizing student-generated questions and answers, with the aim of enhancing students’ reading interest and engagement. Ninety-five fifth graders participated in a 9-week reading program. A quasi-experimental design was conducted. The results proved that incorporating a learning-by-teaching approach into the chatbot training activity significantly enhanced their reading interest and engagement. However, the quantity of certain question types is negatively correlated with interest and engagement. This implies that asking diverse questions poses a certain level of challenge to young readers, which requires deliberate training and incubation. Additionally, the identification of four distinct student clusters exhibited the affordances and limitations of tutee chatbots for reading.
Computer vision for transit travel time prediction: an end-to-end framework using roadside urban imagery

Authors:
raw_author_name: Awad Abdelhalim
raw_affiliation_string: Department of Urban Studies and Planning, Massachusetts Institute of Technology, 77 Mass Ave, Cambridge, MA, 02139, USA
raw_author_name: Jinhua Zhao
raw_affiliation_string: Department of Urban Studies and Planning, Massachusetts Institute of Technology, 77 Mass Ave, Cambridge, MA, 02139, USA

Abstract: Abstract Accurate travel time estimation is paramount for providing transit users with reliable schedules and dependable real-time information. This work is the first to utilize roadside urban imagery to aid transit agencies and practitioners in improving travel time prediction. We propose and evaluate an end-to-end framework integrating traditional transit data sources with a roadside camera for automated image data acquisition, labeling, and model training to predict transit travel times across a segment of interest. First, we show how the General Transit Feed Specification real-time data can be utilized as an efficient activation mechanism for a roadside camera unit monitoring a segment of interest. Second, automated vehicle location data is utilized to generate ground truth labels for the acquired images based on the observed transit travel time percentiles across the camera-monitored segment during the time of image acquisition. Finally, the generated labeled image dataset is used to train and thoroughly evaluate a Vision Transformer (ViT) model to predict a discrete transit travel time range (band). The results of this exploratory study illustrate that the ViT model is able to learn image features and contents that best help it deduce the expected travel time range with an average validation accuracy ranging between 80 and 85%. We assess the interpretability of the ViT model’s predictions and showcase how this discrete travel time band prediction can subsequently improve continuous transit travel time estimation. The workflow and results presented in this study provide an end-to-end, scalable, automated, and highly efficient approach for integrating traditional transit data sources and roadside imagery to improve the estimation of transit travel duration. This work also demonstrates the added value of incorporating real-time information from computer-vision sources, which are becoming increasingly accessible and can have major implications for improving transit operations and passenger real-time information.
Multi-Channel Hypergraph Collaborative Filtering with Attribute Inference

Authors:
raw_author_name: Yutong Jiang
raw_affiliation_string: School of Mechanical, Electrical, and Information Engineering, Shandong University, Weihai 264209, China
raw_author_name: Yuhan Gao
raw_affiliation_string: Department of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; Lishui Institute of Hangzhou Dianzi University, Lishui 323000, China
raw_author_name: Yaoqi Sun
raw_affiliation_string: Department of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; Lishui Institute of Hangzhou Dianzi University, Lishui 323000, China
raw_author_name: Shuai Wang
raw_affiliation_string: Department of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; Lishui Institute of Hangzhou Dianzi University, Lishui 323000, China
raw_author_name: Chenggang Yan
raw_affiliation_string: Department of Automation, Hangzhou Dianzi University, Hangzhou 310018, China

Abstract: In the field of collaborative filtering, attribute information is often integrated to improve recommendations. However, challenges remain unaddressed. Firstly, existing data modeling methods often fall short of appropriately handling attribute information. Secondly, attribute data are often sparse and can potentially impact recommendation performance due to the challenge of incomplete correspondence between the attribute information and the recommendations. To tackle these challenges, we propose a hypergraph collaborative filtering with attribute inference (HCFA) framework, which segregates attribute and user behavior information into distinct channels and leverages hypergraphs to capture high-order correlations among vertices, offering a more natural approach to modeling. Furthermore, we introduce behavior-based attribute confidence (BAC) for assessing the reliability of inferred attributes concerning the corresponding behaviors and update the most credible portions to enhance recommendation quality. Extensive experiments conducted on three public benchmarks demonstrate the superiority of our model. It consistently outperforms other state-of-the-art approaches, with ablation experiments further confirming the effectiveness of our proposed method.
Staff Affection, the Key Factor in the Loyalty of University Students using Virtual Library Services that are Accessible by a Physical Access Space, in Developing Countries

Authors:
raw_author_name: María Esther Trinidad Domínguez
raw_affiliation_string: Department of Economic and Social Sciences, Universidad Tecnológica de Santiago, Dominican Republic

Abstract: Virtual libraries are essential in strengthening research and education, especially in universities in the Dominican Republic. Despite facing various challenges, they offer services in physical spaces. This study examines the relevance of system quality, service quality, information quality, and staff interaction on student satisfaction and loyalty toward these physical virtual libraries. A survey of 531 students was analyzed using SPSS and PLS-SEM. Findings show that staff interaction positively affects user loyalty, though not satisfaction. Additionally, system quality, information quality, and service quality continue to positively influence user satisfaction, which, in turn, affects user loyalty. This research deepens our understanding of staff's influence on user loyalty in developing countries' virtual libraries.
UHD Database Focus on Smart Cities and Smart Transport

Authors:
raw_author_name: Lukas Sevcik
raw_affiliation_string: Faculty of Electrical Engineering and Information Technology, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia
raw_author_name: Miroslav Uhrina
raw_affiliation_string: Faculty of Electrical Engineering and Information Technology, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia
raw_author_name: Jaroslav Frnda
raw_affiliation_string: Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia

Abstract: “Smart city” refers to a modern solution to organizing a city’s services, using cloud technologies to collect and evaluate large amounts of data, including data from camera systems. Smart city management covers several areas that can be implemented separately, but only their combination can realize the overall desired smart city function. One of the core areas of smart city automation is smart city transport. Transportation is a crucial system in any city, and this is why it needs to be monitored. The primary objective of this publication is to generate top-notch 4K UHD video sequences that are solely dedicated to showcasing smart cities and their transportation systems. The resulting comprehensive database will be made accessible to all professionals in the field, who can utilize it for extensive research purposes. Additionally, all the reference video sequences will be transcoded into various quality settings by altering critical parameters like the resolution, compression standard, and bit rate. The ultimate aim is to determine the best combination of video parameters and their respective settings based on the measured values. This in-depth evaluation will ensure that each video sequence is of the highest quality and provides an unparalleled experience for the service providers offering the service. The video sequences captured will be analyzed for quality assessments in smart cities or smart transport technologies. The database will also include objective and subjective ratings, along with information about the dynamics determined by spatial and temporal information. This will enable a comparison of the subjective evaluation of a selected sample of our respondents with the work of other researchers, who may evaluate it with a different sample of evaluators. The assumption of our future research is to predict the subjective quality based on the type of sequence determined by its dynamicity.
CACTUS: a Comprehensive Abstraction and Classification Tool for Uncovering Structures

Authors:
raw_author_name: Luca Gherardini
raw_affiliation_string: Sano Centre for Personalised Computational Medicine, Krakow Personal Health Data Science Team, Poland and Queen’s University Belfast, Belfast [1]The Wellcome-Wolfson Institute for Experimental Medicine [0]School of Medicine, Dentistry and Biomedical Science, Northern Ireland, the United Kingdom
raw_author_name: Varun Ravi Varma
raw_affiliation_string: Sano Centre for Personalised Computational Medicine, Krakow Personal Health Data Science Team, Poland
raw_author_name: Karol Capała
raw_affiliation_string: Sano Centre for Personalised Computational Medicine, Krakow Personal Health Data Science Team, Poland
raw_author_name: Roger Woods
raw_affiliation_string: Queen’s University Belfast, Belfast School of Electronics, Electrical Engineering, and Computer Science, Northern Ireland, the United Kingdom
raw_author_name: Jose Sousa
raw_affiliation_string: Sano Centre for Personalised Computational Medicine, Krakow Personal Health Data Science Team, Poland and Queen’s University Belfast, Belfast [1]Institute of Clinical Sciences [0]Centre for Public Health, Northern Ireland, the United Kingdom

Abstract: The availability of large data sets is providing the impetus for driving many current artificial intelligent developments. However, specific challenges arise in developing solutions that exploit small data sets, mainly due to practical and cost-effective deployment issues, as well as the opacity of deep learning models. To address this, the Comprehensive Abstraction and Classification Tool for Uncovering Structures (CACTUS) is presented as a means of improving secure analytics by effectively employing explainable artificial intelligence. CACTUS achieves this by providing additional support for categorical attributes, preserving their original meaning, optimising memory usage, and speeding up the computation through parallelisation. It exposes to the user the frequency of the attributes in each class and ranks them by their discriminative power. Performance is assessed by applying it to various domains, including Wisconsin Diagnostic Breast Cancer, Thyroid0387, Mushroom, Cleveland Heart Disease, and Adult Income data sets.
An Optimal Edge-weighted Graph Semantic Correlation Framework for Multi-view Feature Representation Learning

Authors:
raw_author_name: Lei Gao
raw_affiliation_string: Toronto Metropolitan University, Toronto, Canada
raw_author_name: Zheng Guo
raw_affiliation_string: Toronto Metropolitan University, Toronto, Canada
raw_author_name: Ling Guan
raw_affiliation_string: Toronto Metropolitan University, Toronto, Canada

Abstract: In this paper, we present an optimal edge-weighted graph semantic correlation (EWGSC) framework for multi-view feature representation learning. Different from most existing multi-view representation methods, local structural information and global correlation in multi-view feature spaces are exploited jointly in the EWGSC framework, leading to a new and high quality multi-view feature representation. Specifically, a novel edge-weighted graph model is first conceptualized and developed to preserve local structural information in each of the multi-view feature spaces. Then, the explored structural information is integrated with a semantic correlation algorithm, labeled multiple canonical correlation analysis (LMCCA), to form a powerful platform for effectively exploiting local and global relations across multi-view feature spaces jointly. We then theoretically verified the relation between the upper limit on the number of projected dimensions and the optimal solution to the multi-view feature representation problem. To validate the effectiveness and generality of the proposed framework, we conducted experiments on five data sets of different scales, including visual-based (University of California Irvine (UCI) iris database, Olivetti Research Lab (ORL) face database and Caltech 256 database), text-image-based (Wiki database) and video-based (Ryerson Multimedia Lab (RML) audio-visual emotion database) examples. The experimental results show the superiority of the proposed framework on multi-view feature representation over state-of-the-art algorithms.