Reviewerly/exHarmony huggingface dataset:
Quasi-Real-Time Inversion of Large Earthquake Rupture Process Based on Earthquake Early Warning System in China: Applications to 2017 MW 6.5 Jiuzhaigou and 2022 MW 6.7 Menyuan Earthquakes

Authors:
raw_author_name: Yuan-Yuan Liang
raw_affiliation_string: Shandong Urban Construction Vocational College, Lvyou road 4657 Jinan 250103, P. R. China
raw_author_name: Zhe Wu
raw_affiliation_string: Qinghai Earthquake Agency, Chaidamu road 153 Xining 810005, P. R. China
raw_author_name: Yonghong Li
raw_affiliation_string: Shandong Earthquake Agency, Gangxi road 2066 Jinan 250109, P. R. China
raw_author_name: Haitao Yin
raw_affiliation_string: Shandong Earthquake Agency, Gangxi road 2066 Jinan 250109, P. R. China

Abstract: The study of seismic ruptures is crucial for understanding major earthquake events. Historically, research on large earthquake rupture processes has required a long time to be published. However, recent advancements in earthquake early warning (EEW) systems have allowed for more rapid analyses of earthquake rupture inversions. The widespread and dense distribution of EEW stations provides in China an opportunity to study and invert rupture processes in near-real time. Two notable earthquakes were studied using this technology: the 2017 Jiuzhaigou MW 6.5 earthquake in Sichuan Province, China, and the 2022 Menyuan MW 6.7 earthquake in Qinghai Province, China. In both instances, numerous strong-motion sensors captured the seismic events and transmitted waveform data to data analysis centers in real time. Following automated site selection and preparation procedures, the rupture processes of these earthquakes were analyzed and released within 30[Formula: see text]min of the event origin. This rapid response time demonstrates that the seismic rupture process determined through inversion using existing EEW systems can serve as a guide for emergency rescue work after earthquakes.
MCRPL: A Pretrain, Prompt, and Fine-tune Paradigm for Non-overlapping Many-to-one Cross-domain Recommendation

Authors:
raw_author_name: Hao Liu
raw_affiliation_string: Shandong Normal University, Jinan, China
raw_author_name: Lei Guo
raw_affiliation_string: Shandong Normal University, Jinan, China
raw_author_name: Lei Zhu
raw_affiliation_string: Shandong Normal University, Jinan, China
raw_author_name: Min Gao
raw_affiliation_string: Chongqing University, Chongqing, China
raw_author_name: Hongzhi Yin
raw_affiliation_string: The University of Queensland, Brisbane, Australia

Abstract: Cross-domain Recommendation is the task that tends to improve the recommendations in the sparse target domain by leveraging the information from other rich domains. Existing methods of cross-domain recommendation mainly focus on overlapping scenarios by assuming users are totally or partially overlapped, which are taken as bridges to connect different domains. However, this assumption does not always hold, since it is illegal to leak users’ identity information to other domains. Conducting Non-overlapping MCR (NMCR) is challenging, since (1) the absence of overlapping information prevents us from directly aligning different domains, and this situation may get worse in the MCR scenario, and (2) the distribution between source and target domains makes it difficult for us to learn common information across domains. To overcome the above challenges, we focus on NMCR and devise MCRPL as our solution. To address Challenge 1, we first learn shared domain-agnostic and domain-dependent prompts and pre-train them in the pre-training stage. To address Challenge 2, we further update the domain-dependent prompts with other parameters kept fixed to transfer the domain knowledge to the target domain. We conduct experiments on five real-world domains, and the results show the advance of our MCRPL method compared with several recent SOTA baselines. Moreover, our source codes have been publicly released. 1
Wizard of Props: Mixed Reality Prototyping with Physical Props to Design Responsive Environments

Authors:
raw_author_name: Yuzhen Zhang
raw_affiliation_string: Information Science, Cornell Tech, United States
raw_author_name: Ruixiang Han
raw_affiliation_string: Indiana University Bloomington, United States
raw_author_name: Ran Zhou
raw_affiliation_string: ATLAS Institute, University of Colorado, Boulder, United States
raw_author_name: Peter Gyory
raw_affiliation_string: ATLAS Institute, University of Colorado Boulder, United States
raw_author_name: Clement Zheng
raw_affiliation_string: Division of Industrial Design, National University of Singapore, Singapore
raw_author_name: Patrick C. Shih
raw_affiliation_string: Department of Informatics, Indiana University Bloomington, United States
raw_author_name: Ellen Yi-Luen Do
raw_affiliation_string: ATLAS Institute, University of Colorado Boulder, United States
raw_author_name: Malte F Jung
raw_affiliation_string: Cornell University, United States
raw_author_name: Wendy Ju
raw_affiliation_string: Information Science, Cornell Tech, United States
raw_author_name: Daniel Leithinger
raw_affiliation_string: ATLAS Institute, University of Colorado, Boulder, United States

Abstract: Driven by the vision of future responsive environments, where everyday surroundings can perceive human behaviors and respond through intelligent robotic actuation, we propose Wizard of Props (WoP): a human-centered design workflow for creating expressive, implicit, and meaningful interactions. This collaborative experience prototyping approach integrates full-scale physical props with Mixed Reality (MR) to support ideation, prototyping, and rapid testing of responsive environments. We present two design explorations that showcase our investigations of diverse design solutions based on varying technology resources, contextual considerations, and target audiences. Design Exploration One focuses on mixed environment building, where we observe fluid prototyping methods. In Design Exploration Two, we explore how novice designers approach WoP, and illustrate their design ideas and behaviors. Our findings reveal that WoP complements conventional design methods, enabling intuitive body-storming, supporting flexible prototyping fidelity, and fostering expressive environment-human interactions through in-situ improvisational performance.
Spectral Total-variation Processing of Shapes—Theory and Applications

Authors:
raw_author_name: Jonathan Brokman
raw_affiliation_string: Technion - Israel Institute of Technology, Haifa, Israel
raw_author_name: Martin Burger
raw_affiliation_string: Helmholtz Imaging, Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany and Fachbereich Mathematik, Universität Hamburg, Hamburg, Germany
raw_author_name: Guy Gilboa
raw_affiliation_string: Technion - Israel Institute of Technology, Haifa, Israel

Abstract: We present a comprehensive analysis of total variation (TV) on non-Euclidean domains and its eigenfunctions. We specifically address parameterized surfaces, a natural representation of the shapes used in 3D graphics. Our work sheds new light on the celebrated Beltrami and Anisotropic TV flows and explains experimental findings from recent years on shape spectral TV [Fumero et al. 2020 ] and adaptive anisotropic spectral TV [Biton and Gilboa 2022 ]. A new notion of convexity on surfaces is derived by characterizing structures that are stable throughout the TV flow, performed on surfaces. We establish and numerically demonstrate quantitative relationships between TV, area, eigenvalue, and eigenfunctions of the TV operator on surfaces. Moreover, we expand the shape spectral TV toolkit to include zero-homogeneous flows, leading to efficient and versatile shape processing methods. These methods are exemplified through applications in smoothing, enhancement, and exaggeration filters. We introduce a novel method that, for the first time, addresses the shape deformation task using TV. This deformation technique is characterized by the concentration of deformation along geometrical bottlenecks, shown to coincide with the discontinuities of eigenfunctions. Overall, our findings elucidate recent experimental observations in spectral TV, provide a diverse framework for shape filtering, and present the first TV-based approach to shape deformation.
Prerequisite-Enhanced Category-Aware Graph Neural Networks for Course Recommendation

Authors:
raw_author_name: Jianshan Sun
raw_affiliation_string: School of Management, Hefei University of Technology, Hefei, China and KeyLaboratory of Process Optimization and Intelligent Decision Making, Ministry of Education, Hefei, China
raw_author_name: Kun Yuan
raw_affiliation_string: School of Management, Hefei University of Technology, Hefei, China and IntelligentInterconnected Systems Laboratory of Anhui Province (Hefei University of Technology), Hefei, China
raw_author_name: Yuanchun Jiang
raw_affiliation_string: School of Management, Hefei University of Technology, Hefei, China and Key Laboratory of Philosophy and Social Sciences for Cyberspace Behaviour and Management, Hefei, China
raw_author_name: Jie Cao
raw_affiliation_string: School of Management, Hefei University of Technology, Hefei, China

Abstract: The rapid development of Massive Open Online Courses (MOOCs) platforms has created an urgent need for an efficient personalized course recommender system that can assist learners of all backgrounds and levels of knowledge in selecting appropriate courses. Currently, most existing methods utilize a sequential recommendation paradigm that captures the user’s learning interests from their learning history, typically through recurrent or graph neural networks. However, fewer studies have explored how to incorporate principles of human learning at both the course and category levels to enhance course recommendations. In this article, we aim at addressing this gap by introducing a novel model, named Prerequisite-Enhanced Catory-Aware Graph Neural Network (PCGNN), for course recommendation. Specifically, we first construct a course prerequisite graph that reflects the human learning principles and further pre-train the course prerequisite relationships as the base embeddings for courses and categories. Then, to capture the user’s complex learning patterns, we build an item graph and a category graph from the user’s historical learning records, respectively: (1) the item graph reflects the course-level local learning transition patterns and (2) the category graph provides insight into the user’s long-term learning interest. Correspondingly, we propose a user interest encoder that employs a gated graph neural network to learn the course-level user interest embedding and design a category transition pattern encoder that utilizes GRU to yield the category-level user interest embedding. Finally, the two fine-grained user interest embeddings are fused to achieve precise course prediction. Extensive experiments on two real-world datasets demonstrate the effectiveness of PCGNN compared with other state-of-the-art methods.
An Analysis of the Ingredients for Learning Interpretable Symbolic Regression Models with Human-in-the-loop and Genetic Programming

Authors:
raw_author_name: Giorgia Nadizar
raw_affiliation_string: University of Trieste, Trieste, Italy and Centrum Wiskunde &amp; Informatica, Amsterdam, The Netherlands
raw_author_name: Luigi Rovito
raw_affiliation_string: University of Trieste, Trieste, Italy
raw_author_name: Andrea De Lorenzo
raw_affiliation_string: University of Trieste, Trieste, Italy
raw_author_name: Eric Medvet
raw_affiliation_string: University of Trieste, Trieste, Italy
raw_author_name: Marco Virgolin
raw_affiliation_string: Centrum Wiskunde &amp; Informatica, Amsterdam, The Netherlands

Abstract: Interpretability is a critical aspect to ensure a fair and responsible use of machine learning (ML) in high-stakes applications. Genetic programming (GP) has been used to obtain interpretable ML models because it operates at the level of functional building blocks: if these building blocks are interpretable, there is a chance that their composition (i.e., the entire ML model) is also interpretable. However, the degree to which a model is interpretable depends on the observer. Motivated by this, we study a recently-introduced human-in-the-loop system that allows the user to steer GP’s generation process to their preferences, which shall be online-learned by an artificial neural network (ANN). We focus on the generation of ML models as analytical functions (i.e., symbolic regression) as this is a key problem in interpretable ML, and propose a two-fold contribution. First, we devise more general representations for the ML models for the ANN to learn upon, to enable the application of the system to a wider range of problems. Second, we delve into a deeper analysis of the system’s components. To this end, we propose an incremental experimental evaluation, aimed at (1) studying the effectiveness by which an ANN can capture the perceived interpretability for simulated users, (2) investigating how the GP’s outcome is affected across different simulated user feedback profiles, and (3) determining whether humans participants would prefer models that were generated with or without their involvement. Our results pose clarity on pros and cons of using a human-in-the-loop approach to discover interpretable ML models with GP.
Fault prediction of unmanned aerial vehicles based on entropy weight fusion and temporal graph convolutional network with non-Euclidian structure

Authors:
raw_author_name: shaohui zhang
raw_affiliation_string: School of Mechanical Engineering, Dongguan University of Technology, Dongguan, People's Republic of China
raw_author_name: Jinglun Liang
raw_affiliation_string: School of Mechanical Engineering, Dongguan University of Technology, Dongguan, People's Republic of China

Abstract: Abstract Predicting weak and hidden faults in unmanned aerial vehicles (UAVs) is challenging due to their variable operation conditions and complex mechanisms. Conventional neural network models process the multisensory data in the form of Euclidean structure, the intrinsic connections among the individual data points are easy to be disregarded. Additionally, multisensory data are always directly fed into the model without adequately considering the importance or contribution of each sensor. Hence, an UAV fault prediction method is proposed by combining entropy weight fusion with a temporal graph convolutional network (GCN) to address the above problems. Firstly, the importance of multisensory data of UAVs are evaluated by each entropy value, and the multisensory data fusion is further realized by multiplying corresponding signal and entropy weight. Secondly, the multisensory data combined with fusion data are transferred together into graph-structure by adjacent matrix based on the node connection between different sensor data. Finally, the graph-structure data with non-Euclidian distance properties are input into temporal GCN to both capture the spatial and temporal relationship of the data, achieving better fault prediction results of UAVs. It is demonstrated that the proposed method is both applicable and superior in characterizing and predicting fault time series information of UAVs through parameter analysis and comparison studies with various existing algorithms.
MEASUREMENT OF EMPLOYEE INFORMATION SECURITY AWARENESS: CASE STUDY AT FINANCIAL INSTITUTION

Authors:
raw_author_name: Achmad Nizar Hidayanto
raw_affiliation_string: Universitas Indonesia

Abstract: The lack of awareness regarding information security among employees in financial institutions can have detrimental impacts on both customers and the institution itself, both financially and in terms of trust. Therefore, this research aims to assess the information security awareness at PT XYZ, a financial institution, in order to identify the existing level of awareness, which will be used to provide recommendations. The method applied in this research is quantitative, using a questionnaire as a tool for data collection and distribution with a voluntary sampling technique among PT XYZ employees concerning their awareness of information security. The assessment of information security awareness covers 8 specific aspects, with 7 of them drawing sources from HAIS-Q and another 1 referring to the KAMI Index, using the Analytic Hierarchy Process (AHP) for weighting each area. The total number of respondents participating in this research is 52. The research results affirm that PT XYZ employees have a positive awareness of information security, indicating that there are no urgent actions needed at present. However, there are specific areas with potential for improvement, hence recommendations are provided to enhance and sustain information security awareness among employees.
Semi-device-independent quantum random number generator with a broadband squeezed state of light

Authors:
raw_author_name: Jialin Cheng
raw_affiliation_string: State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Opto-Electronics, Shanxi University, Taiyuan, 030006, China
raw_author_name: Shaocong Liang
raw_affiliation_string: State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Opto-Electronics, Shanxi University, Taiyuan, 030006, China
raw_author_name: Jiliang Qin
raw_affiliation_string: State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Opto-Electronics, Shanxi University, Taiyuan, 030006, China
raw_author_name: Jiatong Li
raw_affiliation_string: State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Opto-Electronics, Shanxi University, Taiyuan, 030006, China
raw_author_name: Zhihui Yan
raw_affiliation_string: State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Opto-Electronics, Shanxi University, Taiyuan, 030006, China
raw_author_name: Xiaojun Jia
raw_affiliation_string: State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Opto-Electronics, Shanxi University, Taiyuan, 030006, China
raw_author_name: Changde Xie
raw_affiliation_string: State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Opto-Electronics, Shanxi University, Taiyuan, 030006, China
raw_author_name: Kunchi Peng
raw_affiliation_string: Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, Shanxi, 030006, China

Abstract: Abstract Random numbers are a basic ingredient of simulation algorithms and cryptography, and play a significant part in computer simulation and information processing. One prominent feature of a squeezed light is its lower fluctuation and more randomness in a pair of orthogonal oriented quadratures, thus it prompts a significant application in not only quantum information and quantum precision measurement but also an excellent entropy source for true random number generation. Here we report a generation of a high-efficiency semi-device-independent quantum random number based on a broadband squeezed light, where a reliable randomness source is unnecessary and a noisy local oscillator is allowed for homodyne detection. The equivalent generation of private random bits is at a rate of 580.7 Mbps. In addition, the use of squeezed light at 1.3 μm enables the transmission of entropy sources and local oscillators at the metropolitan scale, thus expanding the potential applications of quantum random number generators based on non-classical state of light.
Patenting artificial intelligence: the High Court’s decision in <i>Emotional Perception</i>

Authors:
raw_author_name: Plamen Dinev
raw_affiliation_string: Lecturer in Law, Royal Holloway

Abstract: Abstract In Emotional Perception, the EWHC held that an artificial neural network implementing a media recommendation system did not engage the ‘computer program’ exclusion under the Patents Act 1977. Following the ruling, the UK Intellectual Property Office suspended its existing rules on AI patents and issued fresh guidance on the issue.
Training children's cognitive functions using eye-tracking technology

Authors:
raw_author_name: A.B. Rebreikina
raw_affiliation_string: Sirius University of Science and Technology

Abstract: &lt;p style="text-align: justify;"&gt;The active development of eye-tracking, that is, recording of eye movements, gives opportunities for the creation of new approaches to the improving of cognitive functions and learning. Eye-tracking based methods can be useful not only for children with impaired motor and speech functions, but also for children with attention disorders, since gaze control is closely related to attention. Feedback from the gaze accuracy can improve the effectiveness of attention training techniques. In this paper, we describe studies of the effectiveness of the use of correctional techniques based on eye-tracking in infants (healthy group and groups with various risks for cognitive impairment) and in children with developmental disorders (autism, Rett syndrome, attention deficit hyperactivity disorder). In almost all of the studies reviewed, positive effects of training were observed, i. e. both the productivity of the tasks performance and sustained attention were improved. A number of studies have revealed the transfer of the training effect to untrained functions, which is an important indicator of the effectiveness of correction techniques. All this indicates the perspective of development in this direction. However, much work is to be done, such as selecting and creating the most effective tasks, determining the optimal frequency and duration of training for different clinical groups.&lt;/p&gt;
A Robust Sequential Recommendation Model Based on Multiple Feedback Behavior Denoising and Trusted Neighbors

Authors:
raw_author_name: Hongyun Cai
raw_affiliation_string: School of Cyber Security and Computer, Hebei University, Baoding, 071000, China
raw_author_name: Jie Meng
raw_affiliation_string: School of Cyber Security and Computer, Hebei University, Baoding, 071000, China
raw_author_name: Shilin Yuan
raw_affiliation_string: School of Cyber Security and Computer, Hebei University, Baoding, 071000, China
raw_author_name: Jichao Ren
raw_affiliation_string: Key Laboratory on High Trusted Information System in Hebei Province, Hebei University, Baoding, 071000, China

Abstract: Abstract At present, most of the personalized sequential recommendations utilize users’ implicit positive feedback (such as clicks) to predict user behavior, ignoring the impact of implicit negative feedback and explicit feedback on the accuracy of recommendation results prediction. In this paper, we propose a robust sequence recommendation model based on multi feedback behavior denoising and trusted neighbors, which utilizes multiple feedback behavior data for feature denoising and considers trusted nearest neighbor information to improve model performance. Firstly, by learning the feature representations and interactions of various types of feedback, explicit feedback is used to map and purify implicit feedback with the same and different attributes, resulting in unbiased user performance. Then, we design a filter attention network to identify highly trusted neighbor information. Finally, we integrate pure user interest representations and trusted nearest neighbor representations to improve the accuracy and robustness of the model. The experimental results on two publicly available datasets show that the proposed sequential recommendation model can achieve superior results to baseline methods in both AUC and RelaImpr.