Reviewerly/exHarmony huggingface dataset:
Local business environment and firm innovation: evidence from China

Authors:
raw_author_name: Jiapeng Wu
raw_affiliation_string: School of Accounting, Tianjin University of Finance and Economics, Tianjin, China
raw_author_name: Cheng Xu
raw_affiliation_string: International Business School Suzhou, Xi’an Jiaotong-Liverpool University, Suzhou, China
raw_author_name: Yanqi Sun
raw_affiliation_string: School of Economics and Management, Beijing Institute of Petrochemical Technology, Beijing, China

Abstract: Purpose This paper aims to investigate the influence of the regional business environment on local firm innovation, considering various dimensions such as administrative, financial and legal environments. Design/methodology/approach Multiple regression analysis is employed to analyze archival data for firms listed on Chinese stock markets. Findings We find that the optimizations of the administrative and financial environments positively affect firm innovation, whereas the legal environment does not exert a similar impact. Our analysis also reveals that the business environment’s optimization significantly influences innovation in firms that are small, non-state-owned and operating in high-tech industries. Furthermore, the business environment acts as a moderating variable in the relationship between firm innovation and firm value. Research limitations/implications This study contributes to a more comprehensive understanding of institutional-level determinants of firm innovation, highlighting the nuances of the legal environment and the importance of context-specific analysis, especially in emerging markets like China. Practical implications Developing countries can significantly enhance firm innovation by improving the business environment, including the optimization of administrative and financial systems, reducing transaction costs and ensuring capital supply. Tailored legal frameworks and alternative institutional strategies may also be explored. Social implications This study explicitly emphasizes the governmental role in promoting firm innovation, shedding light on policy formulation and strategic alignment with local administrative policies. Originality/value To the best of our knowledge, this paper is the first to explore the relationship between the business environment and firm innovation using World Bank indicators in an emerging market context, providing novel insights into the unique dynamics of legal, financial and administrative sub-environments.
Understanding user perceptions toward marketing in the metaverse

Authors:
raw_author_name: Heetae Yang
raw_affiliation_string: Department of Management Information Systems, Hannam University, Daejeon, South Korea
raw_author_name: Sang-Yeal Han
raw_affiliation_string: Convergence SW Research, Software Policy and Research Institute, Seongnam, South Korea

Abstract: Purpose This study develops a comprehensive research model and investigates the significant factors affecting positive marketing outcomes in the Metaverse through perceived social benefits and trust. Design/methodology/approach The authors propose a new research model based on social exchange theory (SET) and examine the impact of cost and reward factors. Using 327 survey samples collected from current Metaverse users in South Korea, dual-stage analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) and an artificial neural network (ANN) were employed to test the study’s hypotheses. Findings The results showed that perceived social benefit and trust had significant mediating effects on marketing outcomes, such as loyalty to the seller, product/service attitude, and purchase intention. All antecedents, except perceived performance risk, had a crucial impact on the two mediators. The most interesting finding of this study is the positive influence of knowledge-seeking efforts on perceived social benefits. Originality/value This study is the first empirical research to examine the effectiveness of marketing in the Metaverse. It also proposes a new theoretical model based on SET to investigate users’ behavioral intentions regarding marketing in the Metaverse, and confirms its explanatory power. Moreover, the results of this study also offer suggestions to brands on how to market to consumers in the Metaverse.
Argo data anomaly detection based on transformer and Fourier transform

Authors:
raw_author_name: Yongguo Jiang
raw_affiliation_string: College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China

Abstract: Argo data is multidimensional observational data from ocean floats, which has long been plagued by data anomalies. Currently, the anomaly detection problem in Argo data still faces many challenges, as anomalies may involve complex relationships between multiple variables, leading to suboptimal performance with traditional machine learning methods. Thanks to the advancement of deep learning, it has become the predominant methodology for anomaly detection, demonstrating notable performance. The Transformer model has shown significant potential in the field of data anomaly detection, with its core Self-Attention mechanism capable of learning relationships between variables. We introduce Fast Fourier Transform (FFT) into the Transformer model, enabling the model to better capture periodic patterns and complex relationships in multivariate data, learning normal data patterns to improve the method for Argo data anomaly detection. Through experiments conducted on three public datasets and the Argo dataset, the enhanced model outperforms the original model in terms of performance. This also demonstrates the potential of FFT in multidimensional data anomaly detection, providing new insights into addressing anomaly detection challenges in real-world complex datasets.
Integrating Egocentric and Robotic Vision for Object Identification Using Siamese Networks and Superquadric Estimations in Partial Occlusion Scenarios

Authors:
raw_author_name: Fernando Díaz-de-María
raw_affiliation_string: Signal Theory and Communications Department, University Carlos III, Av de la Universidad, 30, 28911 Madrid, Spain
raw_author_name: Carlos Balaguer
raw_affiliation_string: System Engineering and Automation Department, University Carlos III, Av de la Universidad, 30, 28911 Madrid, Spain

Abstract: This paper introduces a novel method that enables robots to identify objects based on user gaze, tracked via eye-tracking glasses. This is achieved without prior knowledge of the objects’ categories or their locations and without external markers. The method integrates a two-part system: a category-agnostic object shape and pose estimator using superquadrics and Siamese networks. The superquadrics-based component estimates the shapes and poses of all objects, while the Siamese network matches the object targeted by the user’s gaze with the robot’s viewpoint. Both components are effectively designed to function in scenarios with partial occlusions. A key feature of the system is the user’s ability to move freely around the scenario, allowing dynamic object selection via gaze from any position. The system is capable of handling significant viewpoint differences between the user and the robot and adapts easily to new objects. In tests under partial occlusion conditions, the Siamese networks demonstrated an 85.2% accuracy in aligning the user-selected object with the robot’s viewpoint. This gaze-based Human–Robot Interaction approach demonstrates its practicality and adaptability in real-world scenarios.
A Dual Fusion Pipeline to Discover Tactical Knowledge Guided by Implicit Graph Representation Learning

Authors:
raw_author_name: Xiaodong Wang
raw_affiliation_string: School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
raw_author_name: Pei He
raw_affiliation_string: School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
raw_author_name: Hongjing Yao
raw_affiliation_string: School of Cybersecurity, Northwestern Polytechnical University, Xi’an 710072, China
raw_author_name: Jiwei Wang
raw_affiliation_string: School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
raw_author_name: Yangming Guo
raw_affiliation_string: School of Cybersecurity, Northwestern Polytechnical University, Xi’an 710072, China

Abstract: Discovering tactical knowledge aims to extract tactical data derived from battlefield signal data, which is vital in information warfare. The learning and reasoning from battlefield signal information can help commanders make effective decisions. However, traditional methods are limited in capturing sequential and global representation due to their reliance on prior knowledge or feature engineering. The current models based on deep learning focus on extracting implicit behavioral characteristics from combat process data, overlooking the embedded martial knowledge within the recognition of combat intentions. In this work, we fill the above challenge by proposing a dual fusion pipeline introducing graph representation learning into sequence learning to construct tactical behavior sequence graphs expressing implicit martial knowledge, named TBGCN. Specifically, the TBGCN utilizes graph representation learning to represent prior knowledge by building a graph to induce deep learning paradigms, and sequence learning finds the hidden representation from the target’s serialized data. Then, we employ a fusion module to merge two such representations. The significance of integrating graphs with deep learning lies in using the artificial experience of implicit graph structure guiding adaptive learning, which can improve representation ability and model generalization. Extensive experimental results demonstrate that the proposed TBGCN can effectively discover tactical knowledge and significantly outperform the traditional and deep learning methods.
Latency-Constrained Neural Architecture Search Method for Efficient Model Deployment on RISC-V Devices

Authors:
raw_author_name: Mingxi Xiang
raw_affiliation_string: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
raw_author_name: Rui Ding
raw_affiliation_string: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
raw_author_name: Haijun Liu
raw_affiliation_string: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
raw_author_name: Xichuan Zhou
raw_affiliation_string: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China

Abstract: The rapid development of the RISC-V instruction set architecture (ISA) has garnered significant attention in the realm of deep neural network applications. While hardware-aware neural architecture search (NAS) methods for ARM, X86, and GPUs have been extensively explored, research specifically targeting RISC-V remains limited. In light of this, we propose a latency-constrained NAS (LC-NAS) method specifically designed for RISC-V. This method enables efficient network searches without the requirement of network training. Concretely, in the training-free NAS framework, we introduce an RISC-V latency evaluation module that includes two implementations: a lookup table and a latency predictor based on a deep neural network. To obtain real latency data, we have designed a specialized data collection pipeline for RISC-V devices, which allows for precise end-to-end hardware latency measurements. We validate the effectiveness of our method in the NAS-Bench-201 search space. Experimental results demonstrate that our method can efficiently search for latency-constrained networks for RISC-V devices within seconds while maintaining high accuracy. Additionally, our method can easily integrate with existing training-free NAS approaches.
A categorical interpretation of state merging algorithms for DFA inference

Authors:
raw_author_name: Juan Miguel Vilar
raw_affiliation_string: Institute of New Imaging Technologies, Universitat Jaume I, Av. de Vicent Sos Baynat s/n, 12071, Castelló de la Plana, Spain

Abstract: We use Category Theory to interpret the family of algorithms for inference of DFAs that work by merging states. This interpretation allows us to characterize the structure of the search space and to define criteria for the convergence of these algorithms to the correct DFA. We also prove that the well-known EDSM algorithm does not identify DFAs in the limit.
Complexity of a class of first-order objective-function-free optimization algorithms

Authors:
raw_author_name: S. Gratton
raw_affiliation_string: INP, IRIT, Université de Toulouse, Toulouse, France
raw_author_name: Ph. L. Toint
raw_affiliation_string: NAXYS, University of Namur, Namur, Belgium

Abstract: A parametric class of trust-region algorithms for unconstrained non-convex optimization is considered where the value of the objective function is never computed. The class contains a deterministic version of the first-order Adagrad method typically used for minimization of noisy function, but also allows the use of (possibly approximate) second-order information when available. The rate of convergence of methods in the class is analysed and is shown to be identical to that known for first-order optimization methods using both function and gradients values, recovering existing results for purely-first order variants and improving the explicit dependence on problem dimension. This rate is shown to be essentially sharp. A new class of methods is also presented, for which a slightly worse and essentially sharp complexity result holds. Limited numerical experiments show that the new methods' performance may be comparable to that of standard steepest descent, despite using significantly less information, and that this performance is relatively insensitive to noise.
Locked entropy in partially coherent optical fields

Authors:
raw_author_name: Mitchell Harling
raw_affiliation_string: PROBE Laboratory, School of Engineering, Brown University, Providence, Rhode Island 02912, USA
raw_author_name: Varun A. Kelkar
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA
raw_author_name: Kimani C. Toussaint
raw_affiliation_string: PROBE Laboratory, School of Engineering, Brown University, Providence, Rhode Island 02912, USA
raw_author_name: Ayman F. Abouraddy
raw_affiliation_string: CREOL, The College of Optics and Photonics, University of Central Florida, Orlando, Florida 32816, USA

Abstract: The authors study partially coherent optical fields, for which polarization and the spatial degrees of freedom (DoF) are relevant, by exploring the rank of the associated coherence matrix. They show that rank-2 fields can always be rendered separable and can concentrate entropy entirely into one DoF, whereas all rank-3 fields are nonseparable and always have some locked entropy in one DoF.
Investigating the Impact of Prompt Engineering on the Performance of Large Language Models for Standardizing Obstetric Diagnosis Text: Comparative Study

Authors:
raw_author_name: Lei Wang
raw_affiliation_string: BGI Research, Shenzhen, China
raw_author_name: Suling Zhao
raw_affiliation_string: BGI Research, Shenzhen, China
raw_author_name: Hanlin Lv
raw_affiliation_string: BGI Research, Shenzhen, China

Abstract: Background The accumulation of vast electronic medical records (EMRs) through medical informatization creates significant research value, particularly in obstetrics. Diagnostic standardization across different health care institutions and regions is vital for medical data analysis. Large language models (LLMs) have been extensively used for various medical tasks. Prompt engineering is key to use LLMs effectively. Objective This study aims to evaluate and compare the performance of LLMs with various prompt engineering techniques on the task of standardizing obstetric diagnostic terminology using real-world obstetric data. Methods The paper describes a 4-step approach used for mapping diagnoses in electronic medical records to the International Classification of Diseases, 10th revision, observation domain. First, similarity measures were used for mapping the diagnoses. Second, candidate mapping terms were collected based on similarity scores above a threshold, to be used as the training data set. For generating optimal mapping terms, we used two LLMs (ChatGLM2 and Qwen-14B-Chat [QWEN]) for zero-shot learning in step 3. Finally, a performance comparison was conducted by using 3 pretrained bidirectional encoder representations from transformers (BERTs), including BERT, whole word masking BERT, and momentum contrastive learning with BERT (MC-BERT), for unsupervised optimal mapping term generation in the fourth step. Results LLMs and BERT demonstrated comparable performance at their respective optimal levels. LLMs showed clear advantages in terms of performance and efficiency in unsupervised settings. Interestingly, the performance of the LLMs varied significantly across different prompt engineering setups. For instance, when applying the self-consistency approach in QWEN, the F1-score improved by 5%, with precision increasing by 7.9%, outperforming the zero-shot method. Likewise, ChatGLM2 delivered similar rates of accurately generated responses. During the analysis, the BERT series served as a comparative model with comparable results. Among the 3 models, MC-BERT demonstrated the highest level of performance. However, the differences among the versions of BERT in this study were relatively insignificant. Conclusions After applying LLMs to standardize diagnoses and designing 4 different prompts, we compared the results to those generated by the BERT model. Our findings indicate that QWEN prompts largely outperformed the other prompts, with precision comparable to that of the BERT model. These results demonstrate the potential of unsupervised approaches in improving the efficiency of aligning diagnostic terms in daily research and uncovering hidden information values in patient data.
Rethinking Attention Mechanisms in Vision Transformers with Graph Structures

Authors:
raw_author_name: Hyeongjin Kim
raw_affiliation_string: Department of Computer Engineering, Keimyung University, Daegu 42601, Republic of Korea
raw_author_name: Byoung Chul Ko
raw_affiliation_string: Department of Computer Engineering, Keimyung University, Daegu 42601, Republic of Korea

Abstract: In this paper, we propose a new type of vision transformer (ViT) based on graph head attention (GHA). Because the multi-head attention (MHA) of a pure ViT requires multiple parameters and tends to lose the locality of an image, we replaced MHA with GHA by applying a graph to the attention head of the transformer. Consequently, the proposed GHA maintains both the locality and globality of the input patches and guarantees the diversity of the attention. The proposed GHA-ViT commonly outperforms pure ViT-based models using small-sized CIFAR-10/100, MNIST, and MNIST-F datasets and a medium-sized ImageNet-1K dataset in scratch training. A Top-1 accuracy of 81.7% was achieved for ImageNet-1K using GHA-B, which is a base model with approximately 29 M parameters. In addition, with CIFAR-10/100, the existing ViT and parameters are reduced 17-fold and the performance increased by 0.4/4.3%, respectively. The proposed GHA-ViT shows promising results in terms of the number of parameters and operations and the level of accuracy in comparison with other state-of-the-art ViT-lightweight models.
Efficient Error and Variance Estimation for Randomized Matrix Computations

Authors:
raw_author_name: Ethan N. Epperly
raw_affiliation_string: Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125 USA.
raw_author_name: Joel A. Tropp
raw_affiliation_string: Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125 USA.

Abstract: .Randomized matrix algorithms have become workhorse tools in scientific computing and machine learning. To use these algorithms safely in applications, they should be coupled with posterior error estimates to assess the quality of the output. To meet this need, this paper proposes two diagnostics: a leave-one-out error estimator for randomized low-rank approximations and a jackknife resampling method to estimate the variance of the output of a randomized matrix computation. Both of these diagnostics are rapid to compute for randomized low-rank approximation algorithms such as the randomized SVD and randomized Nyström approximation, and they provide useful information that can be used to assess the quality of the computed output and guide algorithmic parameter choices.Keywordsjackknife resampling low-rank approximation error estimation randomized algorithmsMSC codes62F4065F5568W20
Graph neural networks: a survey on the links between privacy and security

Authors:
raw_author_name: Faqian Guan
raw_affiliation_string: School of Computer Science, China University of Geosciences, No. 388 Lumo Road, Wuhan, 430074, Hubei, People’s Republic of China
raw_author_name: Tianqing Zhu
raw_affiliation_string: School of Computer Science, University of Technology Sydney, 15 Broadway, Sydney, NSW, 2008, Australia
raw_author_name: Wanlei Zhou
raw_affiliation_string: School of Data Science, City University of Macau, Taipei, Macau
raw_author_name: Kim-Kwang Raymond Choo
raw_affiliation_string: Department of Information Systems and Cyber Security, University of Texas at San Antonio, San Antonio, TX, 78249-0631, USA

Abstract: Abstract Graph neural networks (GNNs) are models that capture the dependencies between graph data by passing messages between graph nodes and they have been widely used to process graph data that contains relational information. Example application areas include social networks, recommendation systems, and life sciences. However, like all neural networks, there are underpinning security and privacy concerns associated with GNN deployments in practice. For example, attackers can perturb a graph’s data to undermine a model’s effectiveness, or they can steal the model’s data and/or parameters, thus threatening the privacy of the model. In this survey, we provide a comprehensive review of recent research efforts on security and/or privacy in GNNs. We also systematically describe the distinctions and relationships between security and privacy, as well as providing an outlook on future directions of research in this area.
Minimal orthonormal bases for pure quantum state estimation

Authors:
raw_author_name: Leonardo Zambrano
raw_affiliation_string: ICFO - Institut de Ciencies Fotoniques, The Barcelona Institute of Science and Technology, 08860 Castelldefels, Barcelona, Spain
raw_author_name: Luciano Pereira
raw_affiliation_string: Instituto de Física Fundamental IFF-CSIC, Calle Serrano 113b, Madrid 28006, Spain
raw_author_name: Aldo Delgado
raw_affiliation_string: Instituto Milenio de Investigación en Óptica y Departamento de Física, Facultad de Ciencias Físicas y Matemáticas, Universidad de Concepción, Casilla 160-C, Concepción, Chile

Abstract: We present an analytical method to estimate pure quantum states using a minimum of three measurement bases in any finite-dimensional Hilbert space. This is optimal as two bases are insufficient to construct an informationally complete positive operator-valued measurement (IC-POVM) for pure states. We demonstrate our method using a binary tree structure, providing an algorithmic path for implementation. The performance of the method is evaluated through numerical simulations, showcasing its effectiveness for quantum state estimation.