Reviewerly/exHarmony huggingface dataset:
GPONet: A Two-Stream Gated Progressive Optimization Network for Salient Object Detection

Authors:
raw_author_name: Yugen Yi
raw_affiliation_string: School of Software, Jiangxi Normal University, Nanchang, 330022, China
raw_author_name: Ningyi Zhang
raw_affiliation_string: School of Software, Jiangxi Normal University, Nanchang, 330022, China
raw_author_name: Wei Zhou
raw_affiliation_string: College of Computer Science, Shenyang Aerospace University, Shenyang, 110136 China
raw_author_name: Yanjiao Shi
raw_affiliation_string: School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai, 201418, China
raw_author_name: Gengsheng Xie
raw_affiliation_string: School of Software, Jiangxi Normal University, Nanchang, 330022, China
raw_author_name: Jianzhong Wang
raw_affiliation_string: College of Information Science and Technology, Northeast Normal University, Changchun 130117, China

Abstract: The salient object detection task is to locate and detect salient regions in images, which is widely applied in various fields. In this paper, we propose a gated progressive optimization network (GPONet) for salient object detection. Firstly, to extract salient regions more accurately, we design a multi-level feature fusion module with a gating mechanism named gate fusion network (GFN). GFN focuses on the semantic information of high-level features as well as the detailed information of low-level features, enabling purposeful delivery of high-level features to low-level features. The gate fusion unit (GFU) is also able to maintain valid information and suppress redundant information in the fusion process. Secondly, while some existing methods have shown that the additional edge supervision can facilitate salient object detection, edge pixels are often much less common than non-edge pixels, leading to the challenge of class imbalance. To overcome this issue, we introduce detail labels that provide additional internal details as a supplementary supervisory signal. Combining these labels with proposed Detail Perception Loss enables our network to learn edge information of salient objects more effectively. To complement each other and guide information exchange between the two branches, we propose a cross guide module (CGM) to control the information flow transfer between them. Finally, we develop a simple and efficient attention fusion strategy to merge the prediction maps of the two branches to generate the final salient prediction map. Extensive experimental results validate that our method reaches optimal or comparable performance on several mainstream datasets. The code of GPONet is available from https://github.com/antonie-z/GPONet
The Future of Competitive Advantage in Oman: Integrating Green Product Innovation, AI, and Intellectual Capital in Business Strategies

Authors:
raw_author_name: Fadi Abdelfattah
raw_affiliation_string: Modern College of Business and Science (MCBS), Muscat, Oman
raw_author_name: Mohammed Salah
raw_affiliation_string: Modern College of Business and Science (MCBS), Muscat, Oman
raw_author_name: Khalid Dahleez
raw_affiliation_string: Dean College of Business Administration, A’Sharqiyah University, Ibra, Oman

Abstract: This study delves into the dynamics of green product innovation, artificial intelligence (AI) adaption, and intellectual capital, investigating their impact on the competitiveness of firms in Oman. It emphasizes the crucial role of government intervention and R&D investments in this process. Based on the responses of 214 top managers in Oman, the research employs structural equation modeling to analyze the intricate relationships between these factors. The findings underscore a significant positive correlation between green innovation, AI implementation, and intellectual capital, with government involvement and R&D investments as vital moderators. This study provides a novel perspective on the synergy of technology, innovation, and intellectual capital in developing economies. It offers essential insights for business leaders, policymakers, and scholars, highlighting the necessity of integrating advanced technologies and sustainable practices in business strategies to achieve competitive advantage. The research adds to the existing body of knowledge on innovation and competitiveness. It offers practical implications for enhancing firm performance in Oman and similar emerging markets.
DeepCover: Advancing RNN test coverage and online error prediction using state machine extraction

Authors:
raw_author_name: Fathiyeh Faghih
raw_affiliation_string: University of Tehran, Tehran, Iran

Abstract: Recurrent neural networks (RNNs) have emerged as powerful tools for processing sequential data in various fields, including natural language processing and speech recognition. However, the lack of explainability in RNN models has limited their interpretability, posing challenges in understanding their internal workings. To address this issue, this paper proposes a methodology for extracting a state machine (SM) from an RNN-based model to provide insights into its internal function. The proposed SM extraction algorithm was assessed using four newly proposed metrics: Purity, Richness, Goodness, and Scale. The proposed methodology along with its assessment metrics contribute to increasing explainability in RNN models by providing a clear representation of their internal decision making process through the extracted SM. In addition to improving the explainability of RNNs, the extracted SM can be used to advance testing and and monitoring of the primary RNN-based model. To enhance RNN testing, we introduce six model coverage criteria based on the extracted SM, serving as metrics for evaluating the effectiveness of test suites designed to analyze the primary model. We also propose a tree-based model to predict the error probability of the primary model for each input based on the extracted SM. We evaluated our proposed online error prediction approach using the MNIST dataset and Mini Speech Commands dataset, achieving an area under the curve (AUC) exceeding 80% for the receiver operating characteristic (ROC) chart.
Confidence Intervals for Parameters of Unobserved Events

Authors:
raw_author_name: Amichai Painsky
raw_affiliation_string: Department of Industrial Engineering, Tel Aviv University, Israel

Abstract: Consider a finite sample from an unknown distribution over a countable alphabet. Unobserved events are alphabet symbols which do not appear in the sample. Estimating the probabilities of unobserved events is a basic problem in statistics and related fields, which was extensively studied in the context of point estimation. In this work we introduce a novel interval estimation scheme for unobserved events. Our proposed framework applies selective inference, as we construct confidence intervals (CIs) for the desired set of parameters. Interestingly, we show that obtained CIs are dimension-free, as they do not grow with the alphabet size. Further, we show that these CIs are (almost) tight, in the sense that they cannot be further improved without violating the prescribed coverage rate. We demonstrate the performance of our proposed scheme in synthetic and real-world experiments, showing a significant improvement over the alternatives. Finally, we apply our proposed scheme to large alphabet modeling. We introduce a novel simultaneous CI scheme for large alphabet distributions which outperforms currently known methods while maintaining the prescribed coverage rate.
Global panel data on World Governance and State Fragility from 2006 to 2022

Authors:
raw_author_name: Alejandro Vega-Muñoz
raw_affiliation_string: Facultad de Ciencias Empresariales, Universidad Arturo Prat, 1110939 Iquique, Chile; Public Policy Observatory, Universidad Autónoma de Chile, 7500912 Santiago, Chile

Abstract: This global panel dataset contains information collected from two different sources (The Fund for Peace and World Bank), on the governance and stability levels of 178 countries between 2006 and 2022. The dataset includes information on 1) Cohesion (security apparatus (C1), factionalized elites (C2), and group grievance (C3)), 2) Economic (economy (E1), economic inequality (E2), and human flight and brain drain (E3)), 3) Political (state legitimacy (P1), public services (P2), and human rights (P3)) indicators, 4) Social and cross-cutting (demographic pressures (S1), refugees and internally displaced persons (S2), and external intervention (X1)), and 5) Governance (voice and accountability (G1), political stability and absence of violence/terrorism (G2), government effectiveness (G3), regulatory quality (G4), rule of law (G5), and control of corruption (G6)). Data analysis was carried out using SPSS version 29 software to ensure a complete description of the data (labels, type and measure of variables, and uniformity of decimals), as well as the imputation possibility of missing data, which will allow future researchers to study both cross-sectional and longitudinal relationships between the five types of indicators and the eighteen indicators reported.
Intelligent Customer Service System Optimization Based on Artificial Intelligence

Authors:
raw_author_name: Zhong Wu
raw_affiliation_string: Wuhan Business University, China
raw_author_name: Qiping She
raw_affiliation_string: Hubei University of Economics, China
raw_author_name: Chuan Zhou
raw_affiliation_string: Wuhan Institute of Shipbuilding Technology, China

Abstract: To elevate the intelligence of customer service dialogue systems, this article proposes an intelligent customer service system comprising chat dialogue subsystems, task-oriented multi-turn dialogue subsystems, single-turn dialogue subsystems, and an integration model. Firstly, to enhance diversity of responses and improve user experience, particularly in casual chat scenarios, this article presents a Seq2Seq-based approach for multi-answer responses, allowing for more expressive emotional expression in responses. Secondly, to address situations where customers cannot articulate their needs in a single sentence during multi-turn dialogues, this article designs a task-oriented multi-turn dialogue module. It employs intent recognition and slot filling to maintain contextual information throughout the conversation, aiding customers in problem resolution. Lastly, to overcome the current limitation of intelligent customer service models providing relatively one-dimensional answers in specific domains.
Regularized Discrete Optimal Transport for Class-Imbalanced Classifications

Authors:
raw_author_name: Jiqiang Chen
raw_affiliation_string: School of Mathematics and Physics, Hebei University of Engineering, Handan 056038, China
raw_author_name: Jie Wan
raw_affiliation_string: Laboratory for Space Environment and Physical Sciences, Harbin Institute of Technology, Harbin 150001, China

Abstract: Imbalanced class data are commonly observed in pattern analysis, machine learning, and various real-world applications. Conventional approaches often resort to resampling techniques in order to address the imbalance, which inevitably alter the original data distribution. This paper proposes a novel classification method that leverages optimal transport for handling imbalanced data. Specifically, we establish a transport plan between training and testing data without modifying the original data distribution, drawing upon the principles of optimal transport theory. Additionally, we introduce a non-convex interclass regularization term to establish connections between testing samples and training samples with the same class labels. This regularization term forms the basis of a regularized discrete optimal transport model, which is employed to address imbalanced classification scenarios. Subsequently, in line with the concept of maximum minimization, a maximum minimization algorithm is introduced for regularized discrete optimal transport. Subsequent experiments on 17 Keel datasets with varying levels of imbalance demonstrate the superior performance of the proposed approach compared to 11 other widely used techniques for class-imbalanced classification. Additionally, the application of the proposed approach to water quality evaluation confirms its effectiveness.
Overview and empirical analysis of wealth decentralization in blockchain networks

Authors:
raw_author_name: Ernestas Filatovas
raw_affiliation_string: Institute of Data Science and Digital Technologies Vilnius University, Akademijos str. 4, Vilnius, LT-08663, Lithuania
raw_author_name: Remigijus Paulavičius
raw_affiliation_string: Institute of Data Science and Digital Technologies Vilnius University, Akademijos str. 4, Vilnius, LT-08663, Lithuania

Abstract: The decentralization paradigm has made blockchain one of the most disruptive technologies today. When evaluating the level of decentralization, the key metric for most public blockchain networks is the degree of decentralization of the resources responsible for determining who generates the blocks. In turn, it facilitates a greater understanding of both security and scalability on a blockchain. This work provides an overview of the current state-of-the-art on wealth decentralization, which has not yet received the attention it deserves. We collect data, calculate various wealth decentralization metrics, and compare our results with research on the same methodology. As the amount of data for various blockchains increases rapidly, it is helpful to have techniques to aggregate data for statistical analysis. We introduce and provide conservative estimates of decentralized group metrics based on the reduced data and compare them with full-data measurements. Our research considers both the Layer 1 blockchains of Bitcoin and Ethereum, along with Layer 2 blockchains such as Arbitrum, Optimism, and Polygon.
Projective Synchronization of Inertial Quaternion-Valued Neural Networks via Non-reduced Order Approach

Authors:
raw_author_name: Yue Yu
raw_affiliation_string: School of Mathematics and Statistics, Nantong University, Nantong, 226019, China
raw_author_name: Jinde Cao
raw_affiliation_string: School of Mathematics, Southeast University, Nanjing, 210096, China

Abstract: Abstract In this article, the issue on projective synchronization of delayed inertial quaternion-valued neural networks (IQVNNs) is investigated. Different from most existing literature, we adopt the non-reduced order approach to deal with IQVNNs described by second order differential equations. By introducing a novel Lyapunov functional, several sufficient criteria are presented in component form to ensure the projective synchronization between master–slave systems. A numerical experiment demonstrates the feasibility of control strategy as well as the correctness of theoretical results.
Innovating Sustainability

Authors:
raw_author_name: JunYi Liu
raw_affiliation_string: Zhejiang Normal University, China

Abstract: In today's global society, carbon neutrality has become a focal point of concern. Greenhouse gas emissions and rising atmospheric temperatures are triggering various extreme weather events, sea level rise, and ecological imbalances. These changes not only affect the stability and sustainable development of human society but also pose a serious threat to the Earth's ecosystems and biodiversity. Faced with this global challenge, finding effective solutions has become urgent. This article aims to propose a comprehensive artificial intelligence design approach to address issues related to carbon neutrality. This method integrates technologies from fields such as computer vision, natural language processing, and deep learning to achieve a comprehensive understanding of environmental conservation and innovative solutions. Specifically, the authors first use a visual module to extract features from images, which helps capture important information in the images. Next, we employ the ALBEF model for cross-modal alignment, enabling better collaboration between images and textual information.
The Dynamic Connectedness Between Environmental Attention and Green Cryptocurrency

Authors:
raw_author_name: Asma Salman
raw_affiliation_string: American University in the Emirates, UAE

Abstract: The results indicate a dynamic pattern of interconnectedness throughout history. Based on the findings, the transmission of volatility exhibited a higher magnitude during the period of COVID-19. The issue of high transmission volatility due to limited diversification options concerns investors, green stakeholders, and policymakers alike. This article proposes various potential areas for future research. The ICEA index can potentially assist businesses operating in environmentally sensitive sectors make well-informed policy decisions. It includes sectors such as environmental green bonds, and commodities. Consideration should be given to implementing blockchain technology, as it can consume less power in this particular scenario. By employing a time-frequency paradigm, this study is able to incorporate the investment horizon, a crucial factor to be taken into account when making financial judgments. The advancement of this research could be facilitated by directing our attention toward the implications of our findings on portfolios and developing appropriate measures for their evaluation.
Feature Extraction of Dialogue Text Based on Big Data and Machine Learning

Authors:
raw_author_name: Xuelin Liu
raw_affiliation_string: Weifang University, China
raw_author_name: Hua Zhang
raw_affiliation_string: Weifang University, China
raw_author_name: Yue Cheng
raw_affiliation_string: Adamson University, Philippines

Abstract: In this article, a dialogue text feature extraction model based on big data and machine learning is constructed, which transforms the high-dimensional space of text features into the low-dimensional space that is easy to process, so that the best feature words can be selected to represent the document set. Tests show that in most cases, the classification accuracy of this model is higher than 88%, and the recall rate is higher than 85%, thus achieving the goal of higher classification accuracy with less computation. When extracting the features of dialogue texts, there is no need for preprocessing, just count the data such as lexical composition, sentence length and sentence-to-sentence relationship of the target text, and make linear analysis to obtain key indicators and weights. Based on this, the classification model can achieve good results, thus effectively reducing the workload and computation of text classification.
Trajectory optimization with hybrid probabilistic roadmap approach to achieve time efficient navigation of unmanned vehicles in unstructured environment

Authors:
raw_author_name: Ravinder Singh
raw_affiliation_string: Department of Electrical Engineering, Netaji Subhas University of Technology, New Delhi, India

Abstract: Purpose This paper aims to focus on solving the path optimization problem by modifying the probabilistic roadmap (PRM) technique as it suffers from the selection of the optimal number of nodes and deploy in free space for reliable trajectory planning. Design/methodology/approach Traditional PRM is modified by developing a decision-making strategy for the selection of optimal nodes w.r.t. the complexity of the environment and deploying the optimal number of nodes outside the closed segment. Subsequently, the generated trajectory is made smoother by implementing the modified Bezier curve technique, which selects an optimal number of control points near the sharp turns for the reliable convergence of the trajectory that reduces the sum of the robot’s turning angles. Findings The proposed technique is compared with state-of-the-art techniques that show the reduction of computational load by 12.46%, the number of sharp turns by 100%, the number of collisions by 100% and increase the velocity parameter by 19.91%. Originality/value The proposed adaptive technique provides a better solution for autonomous navigation of unmanned ground vehicles, transportation, warehouse applications, etc.
Capital flight, institutional quality and real sector in sub-Saharan African countries

Authors:
raw_author_name: Taiwo Akinlo
raw_affiliation_string: Department of Economics, Faculty of Arts and Social Science, Adeyemi Federal University of Education, Ondo, Nigeria
raw_author_name: Busayo Olubunmi Aderounmu
raw_affiliation_string: Department of Economics and Development Studies, Covenant University, Ota, Nigeria

Abstract: Purpose This study aims to provide an empirical investigation into rising capital flight and the role of institutional quality to mitigate its effect on the real sector in sub-Saharan Africa (SSA). Design/methodology/approach The study uses the system generalized method of moments and uses data spanning from 1989 to 2020 from 26 SSA countries. Findings The findings show that capital flight has no direct impact on the real sector while institutional quality adversely impacted the agricultural and industrial sectors. The study also found that institutional quality is unable to mitigate the effect of capital flight on the industrial sector. Originality/value This study investigates if institutional quality mitigates the impact of capital flight on the real sector proxied by industrial value-added and agriculture value-added.
Full Spatial Characterization of Entangled Structured Photons

Authors:
raw_author_name: Xiaoqin Gao
raw_affiliation_string: Emerging Technologies Division, National Research Council of Canada, K1A 0R6, Ottawa, Ontario, Canada; Nexus for Quantum Technologies, University of Ottawa, K1N 5N6 Ottawa, Ontario, Canada
raw_author_name: Yingwen Zhang
raw_affiliation_string: National Research Council of Canada, 100 Sussex Drive, K1A 0R6 Ottawa, Ontario, Canada; Nexus for Quantum Technologies, University of Ottawa, K1N 5N6 Ottawa, Ontario, Canada
raw_author_name: Alessio D’Errico
raw_affiliation_string: Nexus for Quantum Technologies, University of Ottawa, K1N 5N6 Ottawa, Ontario, Canada
raw_author_name: Alicia Sit
raw_affiliation_string: Nexus for Quantum Technologies, University of Ottawa, K1N 5N6 Ottawa, Ontario, Canada
raw_author_name: Khabat Heshami
raw_affiliation_string: National Research Council of Canada, 100 Sussex Drive, K1A 0R6 Ottawa, Ontario, Canada; Nexus for Quantum Technologies, University of Ottawa, K1N 5N6 Ottawa, Ontario, Canada
raw_author_name: Ebrahim Karimi
raw_affiliation_string: National Research Council of Canada, 100 Sussex Drive, K1A 0R6 Ottawa, Ontario, Canada; Nexus for Quantum Technologies, University of Ottawa, K1N 5N6 Ottawa, Ontario, Canada

Abstract: Vector modes are fully polarized modes of light with spatially varying polarization distributions, and they have found widespread use in numerous applications such as microscopy, metrology, optical trapping, nanophotonics, and communications. The entanglement of such modes has attracted significant interest, and it has been shown to have tremendous potential in expanding existing applications and enabling new ones. However, due to the complex spatially varying polarization structure of entangled vector modes (EVMs), a complete entanglement characterization of these modes remains challenging and time consuming. Here, we have used a time-tagging event camera to demonstrate the ability to completely characterize the entanglement of EVMs. Leveraging the camera's capacity to provide independent measurements for each pixel, we simultaneously characterize the entanglement of approximately $2.6\ifmmode\times\else\texttimes\fi{}{10}^{6}$ modes between a bipartite EVM through measuring only 16 observables in polarization. We reveal that EVMs can naturally generate various polarization-entangled Bell states. This achievement is an important milestone in high-dimensional entanglement characterization of structured light, and it could significantly impact the implementation of related quantum technologies. The potential applications of this technique are extensive, and it could pave the way for advancements in quantum communication, quantum imaging, and other areas where structured entangled photons play a crucial role.