Reviewerly/exHarmony huggingface dataset:
“Putting your money where your mouth is”: An empirical study on buyers’ preferences and willingness to pay for blockchain-enabled sustainable supply chain transparency

Authors:
raw_author_name: Aneesh Banerjee
raw_affiliation_string: Bayes Business School (formerly Cass), City, University of London, 106 Bunhill Row, London, EC1Y 8TZ, United Kingdom
raw_author_name: Feng Li
raw_affiliation_string: Bayes Business School (formerly Cass), City, University of London, 106 Bunhill Row, London, EC1Y 8TZ, United Kingdom

Abstract: This paper investigates how buyers assess the importance of various attributes of supply chain sustainability disclosed by suppliers. These include different types of disclosure (i.e., product, process, and sourcing network), self- and third-party verified disclosure, partial and full disclosure, as well as the attributes associated with information disclosure using blockchain technology: immutability and update frequency. Building on concepts in signalling theory and inter-organisational trust, our research uses a choice-based conjoint experimental design to elicit responses from 234 managers with decision-making roles in procurement. Using this design, we calculate the relative importance of attributes, part-worth utility, and marginal willingness to pay, and test hypotheses about buyer preferences and willingness to pay. Our research reveals that buyers prefer suppliers with sustainability signals that span across different types of disclosure and methods of disclosure. It emphasises the importance of how sustainability information is disclosed, highlighting buyer trust in self-disclosure and a preference for comprehensive, regularly updated information. However, we find mixed results for buyers' willingness to pay. For instance, buyers prefer third-party verified supply chain transparency, but we do not find a significantly higher willingness to pay for such information compared to self-disclosure. The implications suggest a competitive advantage for suppliers adopting voluntary disclosure, prioritising disclosure based on buyer preferences, and recognising the limited direct impact of blockchain technology. Our research contributes to advancing our understanding of information disclosure in supply chain transparency and presents new avenues of inquiry into the value of blockchain-enabled platforms in supply chain sustainability reporting.
Assessing the Ability of Genetic Programming for Feature Selection in Constructing Dispatching Rules for Unrelated Machine Environments

Authors:
raw_author_name: Marko Đurasević
raw_affiliation_string: Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia
raw_author_name: Domagoj Jakobović
raw_affiliation_string: Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia
raw_author_name: Stjepan Picek
raw_affiliation_string: Digital Security Group, Radboud University, 6525 XZ Nijmegen, The Netherlands
raw_author_name: Luca Mariot
raw_affiliation_string: Semantics, Cybersecurity and Services Group, University of Twente, 7522 NB Enschede, The Netherlands

Abstract: The automated design of dispatching rules (DRs) with genetic programming (GP) has become an important research direction in recent years. One of the most important decisions in applying GP to generate DRs is determining the features of the scheduling problem to be used during the evolution process. Unfortunately, there are no clear rules or guidelines for the design or selection of such features, and often the features are simply defined without investigating their influence on the performance of the algorithm. However, the performance of GP can depend significantly on the features provided to it, and a poor or inadequate selection of features for a given problem can result in the algorithm performing poorly. In this study, we examine in detail the features that GP should use when developing DRs for unrelated machine scheduling problems. Different types of features are investigated, and the best combination of these features is determined using two selection methods. The obtained results show that the design and selection of appropriate features are crucial for GP, as they improve the results by about 7% when only the simplest terminal nodes are used without selection. In addition, the results show that it is not possible to outperform more sophisticated manually designed DRs when only the simplest problem features are used as terminal nodes. This shows how important it is to design appropriate composite terminal nodes to produce high-quality DRs.
Atom-based Optical Polarization Modulator

Authors:
raw_author_name: Ruiqi Wang
raw_author_name: Peiyu Yang
raw_author_name: Weiping Zhang

Abstract: In this work, we employ 87Rb atoms as rotation media to manipulate the polarization of optical fields in both magnetic and magnetic-free environments. Employing the nonlinear magneto-optical rotation mechanism, we achieve a state-of-the-art magneto-optical rotation coefficient of 1.74×108 rad⋅T-1⋅m-1 which is four orders of magnitude higher than commonly employed materials. Additionally, in a magnetic-free environment, we achieve all-optical cross-polarization modulation between the pump and probe light via Rb atoms. The nonlinear magneto-optical rotation configuration introduces inventive techniques for a new type of magneto-optical modulator while the all-optical configuration paves the way for exploring photonic integrated circuit (PIC) devices free from disruptions caused by electrical or magnetic crosstalk.
AR Fitting Room and Beyond

Authors:
raw_author_name: Yeqin Zhao

Abstract: &lt;p&gt;Over the years, the AR technology has started to bloom, more and more customers are enjoying using their mobile device to experience this advanced technology. This research project mainly focuses on creating an AR integrated fitting room mobile application or prototype which offers a more detailed and refined virtual try-on experience. It has a five-stage process: Detection, detect user’s body features and setup referencing points. Selection, we will generate a specific body type for users to choose according to the user information we gathered. Interaction, fashion items can be freely try-on on the user's virtual body. Acquiring, users can simply go to the retailer website through our app. Social hub, where users can share their fashion try-on and share it within the community. This application can be utilized in physical retail fashion stores, fashion e-commerce, and basically anywhere with a mobile device. The design of the AR fitting room will be focusing on improving user flow, customer satisfaction, brand engagement and brand acceptance.&lt;/p&gt;
Board 372: Remote Engaged Student Learning through Hands-on Internet of Things

Authors:
raw_author_name: Lifford McLauchlan
raw_affiliation_string: Texas A&M University, Kingsville
raw_author_name: David Hicks
raw_author_name: Mehrube Mehrubeoglu
raw_affiliation_string: Texas A&M University, Corpus Christi

Abstract: Abstract Remote learning environments have become integral to higher educational institutions even when most courses, particularly in STEM, have returned to face-to-face or hybrid instruction. Although the impetus for this project was the COVID-19 pandemic which forced institutions of higher education everywhere to move to an online remote teaching and learning format, and as such negatively affected STEM fields which require hands-on labs and access to instrumentation, remote learning still remains part of most courses today. We describe continuing efforts to create learning environments and materials to support remote hands-on engaged student learning off-campus at two Hispanic Serving Institutions (HSIs) to enable and enhance student learning beyond the institutional walls. The approach utilizes Internet of Things (IoT) kits as remote learning tools that are provided to students to allow hands-on learning experiences at students' own chosen environment and time. Problem- and Project-based Learning approaches are utilized to achieve student learning outcomes, and equip the students with the problem-solving skills necessary in STEM fields. Team-based projects engage students from multiple disciplines and demographics to work on projects, make individual contributions, and integrate their work with that of their team members. General feedback from students has been positive. Assessment of student performance also demonstrates the effectiveness of IoT-based learning in engineering and computer science disciplines.
Deep Neural Network-Oriented Indicator Method for Inverse Scattering Problems Using Partial Data

Authors:
raw_author_name: Jiguang Sun
raw_affiliation_string: Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA
raw_author_name: Juan Liu
raw_affiliation_string: Department of Mathematics, Jinan University, Guangzhou 510632, China

Abstract: We consider the inverse scattering problem to reconstruct an obstacle using partial far-field data due to one incident wave. A simple indicator function, which is negative inside the obstacle and positive outside of it, is constructed and then learned using a deep neural network (DNN). The method is easy to implement and effective as demonstrated by numerical examples. Rather than developing sophisticated network structures for the classical inverse operators, we reformulate the inverse problem as a suitable operator such that standard DNNs can learn it well. The idea of the DNN-oriented indicator method can be generalized to treat other partial data inverse problems.
Integrating joint latent class mixed models and Bayesian network for uncovering clinical subgroups of COVID-19 patients

Authors:
raw_author_name: Pietro E. Cippà
raw_affiliation_string: Department of Medicine, Division of Nephrology, Ente Ospedaliero Cantonale, Bellinzona, Switzerland; Faculty of Medicine, University of Zurich, Zurich, Switzerland
raw_author_name: Clelia Di Serio
raw_affiliation_string: Department of Clinical Pharmacology and Toxicology, University Hospital Zurich, Zurich, Switzerland; University Centre for Statistics in the Biomedical Sciences (CUSSB) and School of Psychology, Vita-Salute San Raffaele University, Milan, Italy

Abstract: When modelling the dynamics of biomarkers in biomedical studies, it is essential to identify homogeneous clusters of patients and analyse them from a precision medicine perspective. This need has emerged as crucial and urgent during the COVID-19 pandemic: early understanding of symptoms and patient heterogeneity has significant implications for prevention, early diagnosis, effective management, and treatment. Additionally, biomarker progression may be associated with clinically relevant time-toevent data. Therefore, statistical models are necessary to gain insight into complex disease mechanisms by properly accounting for unobservable heterogeneity in patients while jointly modelling longitudinal and time-to-event data. In this study, we leverage the key features of Latent Class modelling and Bayesian Network approaches and propose a unified framework to (a) uncover homogeneous subgroups of patients concerning their longitudinal and survival data and (b) describe patient subgroups within a multivariate framework.
An Interactive Application for Hand Muscle Fatigue Relief Based on Gesture Recognition

Authors:
raw_author_name: Siyu Pang
raw_affiliation_string: Zhejiang University of Technology, Hangzhou, China; xingbaixi@zjut.edu.cn
raw_author_name: Baixi Xing
raw_affiliation_string: Zhejiang University of Technology, Hangzhou, China; xingbaixi@zjut.edu.cn
raw_author_name: Pan Zou
raw_affiliation_string: Staatlichen Akademie der Bildenden Künste Stuttgart, Stuttgart, Germany; panzou@gmx.de
raw_author_name: Hailong Zhang
raw_affiliation_string: Zhejiang University of Technology, Hangzhou, China; xingbaixi@zjut.edu.cn

Abstract: Prolonged use of a keyboard or mouse can lead to continuous bending and twisting of the wrist, which can lead to muscle fatigue and pain. Finger exercises can reduce the hand muscle fatigue effectively. However, the repetitive exercises will bring boring and tedious experience. In this paper, we developed a hand rehabilitation exercise system based on gesture recognition technology and gamification design, which provides a more enjoyable, personalized and effective rehabilitation experience. The hand fatigue relief system was designed with the fusion of game elements and gesture interaction, which can enhance users’ engagement and their rehabilitation motivation.
Integrated Profitability Evaluation for a Newsboy-Type Product in Own Brand Manufacturers

Authors:
raw_author_name: Tse-Min Tseng
raw_affiliation_string: Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City 242062, Taiwan
raw_author_name: Chun Lin
raw_affiliation_string: Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City 242062, Taiwan

Abstract: Effective inventory management depends on accurate estimates of product profitability to formulate ordering and manufacturing strategies. The achievable capacity index (ACI) is a simple yet efficient approach to measuring the profitability of newsboy-type products with normally distributed demand, wherein profitability is presented as the probability of achieving the target profit under the optimal ordering quantity. Unfortunately, the ACI is applicable only to retail stores with a single demand. In the current study, we addressed the issue of measuring the integrated profitability of newsboy-type products sold in multiple locations with independent demand levels, such as own-branding-and-manufacture (OBM) companies with multiple owned channels. We began by formulating profitability in accordance with multiple independent normal demands, and then developed an integrated ACI (IACI) to simplify expression. We also derived the statistical properties of the unbiased estimator to determine the true IACI in situations where demand patterns are unknown. Finally, we conducted hypothesis testing to determine whether the integrated profitability meets a stipulated minimum level. For convenience, we tabulated the critical values as a function of sample size, confidence level, the number of channels, and the stipulated minimum level. One can make decisions simply by estimating the IACI based on historical demand data from all channels and then looking up the critical value in the corresponding tables. Consequently, the proposed methods make it possible for OBM managers to address integrated profitability evaluation, which is effective in deciding the optimal timing to pull unprofitable items from the shelves by looking up generic tables. Furthermore, we also performed numerical and sensitivity analyses for a real-world case to illustrate the applicability and some managerial implications of the proposed scheme.
Errors in memory recall, ToT, SoT, FoK and BoP. Operational classification

Authors:
raw_author_name: María del Rocío Hernández Pozo
raw_affiliation_string: Universidad Nacional Autónoma de México

Abstract: The evocation of memory is a critical cognitive process that involves the activation of engrams, which in turn evoke concepts. During memory recall, errors often occur, some of which are well-known, such as the tip-of-the-tongue (ToT) phenomenon or slip of the tongue (SoT). A related variable is verbal taxonomy closely associated with ToT and SoT, as well as the anxiety variable, which has been linked to the occurrence of these phenomena. The results of this research identify errors unrelated to verbs, leading to the operationalization of previously unidentified phenomena called Bias of Perception (BoP) and feeling of Knowing (FoK). This paper explores these errors and their systematic occurrence. In summary, this document seeks to increase understanding of errors that occur during memory recall, their causes, and their implications for cognitive processes.
Radar High-Resolution Range Profile Rejection Based on Deep Multi-Modal Support Vector Data Description

Authors:
raw_author_name: Yue Dong
raw_affiliation_string: National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
raw_author_name: Penghui Wang
raw_affiliation_string: Hangzhou Institute of Technology, Xidian University, Hangzhou 311200, China; Institute of Information Sensing, Xidian University, Xi’an 710071, China; National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
raw_author_name: Ming Fang
raw_affiliation_string: Shanghai Aerospace Electronic Technology Institute, Shanghai 201109, China
raw_author_name: Yifan Guo
raw_affiliation_string: Shanghai Aerospace Electronic Technology Institute, Shanghai 201109, China
raw_author_name: Lili Cao
raw_affiliation_string: Shanghai Aerospace Electronic Technology Institute, Shanghai 201109, China
raw_author_name: Junkun Yan
raw_affiliation_string: Hangzhou Institute of Technology, Xidian University, Hangzhou 311200, China; National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
raw_author_name: Hongwei Liu
raw_affiliation_string: Hangzhou Institute of Technology, Xidian University, Hangzhou 311200, China; Institute of Information Sensing, Xidian University, Xi’an 710071, China; National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Abstract: Radar Automatic Target Recognition (RATR) based on high-resolution range profile (HRRP) has received intensive attention in recent years. In practice, RATR usually needs not only to recognize in-library samples but also to reject out-of-library samples. However, most rejection methods lack a specific and accurate description of the underlying distribution of HRRP, which limits the effectiveness of the rejection task. Therefore, this paper proposes a novel rejection method for HRRP, named Deep Multi-modal Support Vector Data Description (DMMSVDD). On the one hand, it forms a more compact rejection boundary with the Gaussian mixture model in consideration of the high-dimensional and multi-modal structure of HRRP. On the other hand, it captures the global temporal information and channel-dependent information with a dual attention module to gain more discriminative structured features, which are optimized jointly with the rejection boundary. In addition, a semi-supervised extension is proposed to refine the boundary with available out-of-library samples. Experimental results based on measured data show that the proposed methods demonstrate significant improvement in the HRRP rejection performance.
Individual- vs. Multiple-Objective Strategies for Targeted Sentiment Analysis in Finances Using the Spanish MTSA 2023 Corpus

Authors:
raw_author_name: Ronghao Pan
raw_affiliation_string: Facultad de Informática, Universidad de Murcia, Campus de Espinardo, 30100 Murcia, Spain
raw_author_name: José Antonio García-Díaz
raw_affiliation_string: Facultad de Informática, Universidad de Murcia, Campus de Espinardo, 30100 Murcia, Spain
raw_author_name: Rafael Valencia-García
raw_affiliation_string: Facultad de Informática, Universidad de Murcia, Campus de Espinardo, 30100 Murcia, Spain

Abstract: Multitarget sentiment analysis extracts the subjective polarity of text from multiple targets simultaneously in a given context. This approach is useful in finance, where opinions about different entities affect the target differently. Examples of possible targets are other companies and society. However, typical multitarget solutions are resource-intensive due to the need to deploy multiple classification models for each target. An alternative to this is the use of multiobjective training approaches, where a single model is capable of handling multiple targets. In this work, we propose the Spanish MTSACorpus 2023, a novel corpus for multitarget sentiment analysis in finance, and we evaluate its reliability with several large language models for multiobjective training. To this end, we compare three design approaches: (i) a Main Economic Target (MET) detection model based on token classification plus a multiclass classification model for sentiment analysis for each target; (ii) a MET detection model based on token classification but replacing the sentiment analysis models with a multilabel classification model; and (iii) using seq2seq-type models, such as mBART and mT5, to return a response sequence containing the MET and the sentiments of different targets. Based on the computational resources required and the performance obtained, we consider the fine-tuned mBART to be the best approach, with a mean F1 of 80.300%.
Covert communication in power Internet of Things

Authors:
raw_author_name: Yang Li
raw_affiliation_string: China Three Gorges University (China); State Grid Yichang Electric Power Supply Company (China)
raw_author_name: Jia Zhu
raw_affiliation_string: State Grid Yichang Electric Power Supply Company (China)
raw_author_name: Yonglin Liu
raw_affiliation_string: State Grid Yichang Electric Power Supply Company (China)
raw_author_name: Jian Ding
raw_affiliation_string: State Grid Yichang Electric Power Supply Company (China)
raw_author_name: Jia Yang
raw_affiliation_string: China Three Gorges University (China)

Abstract: Covert communication refers to achieving high-level secure transmission by protecting communication behaviour. This paper primarily centres on the examination of covert communication in the scene of the power Internet of Things (PIoT). Specifically, we explore the scenario where power users (PU) transmit information to the master station (MS) while being exposed to potential eavesdropping. In addition, we assume that Willie has noise uncertainty when detecting communication behaviour, which makes it difficult for Willie to accurately detect communication behaviour, and significantly affects the detection error probability (DEP) of Willie and the overall covert performance of the proposed model. It is worth noting that the simulation results indicate that the presence of noise uncertainty can enable MS and PU to successfully achieve covert transmission. By leveraging this uncertainty, MS and PU can effectively protect their communication behaviour and minimize the risk detected by potential Willie, which provides a solution for high-level secure communication in the field of PIoT and effectively protects the privacy of PU.