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关键词： Machine tools  

摘要： A great deal of expertise on the part of the machine operator is currently required for the acceptance of machine tools and the setting of control parameters. In order to accelerate the process and reduce dependence on this knowledge, an assistance system is needed that supports the operator and can learn from him. This article presents a concept for a system that combines machine acceptance and parameter setting. © 2025, Author. All rights reserved.

关键词： Semantics   Visualization   Pedestrians   Natural languages   Training   Feature extraction   Cameras   Bridges   Identification of persons   Computer science   CLIP   high-level semantics   information integration   visible-infrared person re-identification  

摘要： Visible-infrared person re-identification (VIReID) primarily deals with matching identities across person images from different modalities. Due to the modality gap between visible and infrared images, cross-modality identity matching poses significant challenges. Recognizing that high-level semantics of pedestrian appearance, such as gender, shape, and clothing style, remain consistent across modalities, this paper intends to bridge the modality gap by infusing visual features with high-level semantics. Given the capability of Contrastive Language-Image Pre-training (CLIP) to sense high-level semantic information corresponding to visual representations, we explore the application of CLIP within the domain of VIReID. Consequently, we propose a CLIP-Driven Semantic Discovery Network (CSDN) that consists of Modality-specific Prompt Learner, Semantic Information Integration (SII), and High-level Semantic Embedding (HSE). Specifically, considering the diversity stemming from modality discrepancies in language descriptions, we devise bimodal learnable text tokens to capture modality-private semantic information for visible and infrared images, respectively. Additionally, acknowledging the complementary nature of semantic details across different modalities, we integrate text features from the bimodal language descriptions to achieve comprehensive semantics. Finally, we establish a connection between the integrated text features and the visual features across modalities. This process embed rich high-level semantic information into visual representations, thereby promoting the modality invariance of visual representations. The effectiveness and superiority of our proposed CSDN over existing methods have been substantiated through experimental evaluations on multiple widely used benchmarks.

关键词： Training   Games   Data models   Computer science   Servers   Resource management   Federated learning   Optimization   Privacy   Energy consumption   Stackelberg game   resource allocation   incentive mechanism   federated learning  

摘要： Federated learning (FL) is an emerging distributed-learning technology that allows a requester to hire a set of independent FL workers for a model training task. The fact that FL workers are not obliged to do the training task calls for an incentive mechanism to motivate workers. However, existing incentive mechanisms for FL mainly were designed for single requester. A few studies considered multiple requesters but constrained a worker's potential to train multiple models simultaneously. In this paper, we remove the constraint to investigate the interplays among requesters in addition to inherent interplays among workers and between requesters and workers. Two crucial issues in this setting are whether the interplays lead to a stable (i.e., attainable) outcome and how to find it. We model the problem as seeking an equilibrium in a multi-leader multi-follower Stackelberg game and propose an exact method based on the derivation and backward induction to identify the Stackelberg equilibrium. We show how to adapt the proposed method to scenarios with capacity/budget constraints and privacy concerns. We have done a series of analyses to study the interplays of workers and requesters in Stackelberg equilibria. The results confirm the first-mover advantage of the requesters and reveal the possibility of mutual benefits between the requesters and workers.

关键词： Perturbation methods   Measurement   Observers   Predictive models   Distortion   Noise   Feature extraction   Computer science   Visualization   Visual systems   Adversarial stealthiness assessment   adversarial attack   classification  

摘要： Assessing the stealthiness of adversarial perturbations is challenging due to the lack of appropriate evaluation metrics. Existing evaluation metrics, e.g., L-p norms or Image Quality Assessment (IQA), fall short of assessing the pixel-level stealthiness of subtle adversarial perturbations since these metrics are primarily designed for traditional distortions. To bridge this gap, we present the first comprehensive study on the subjective and objective assessment of the stealthiness of adversarial perturbations from a visual perspective at a pixel level. Specifically, we propose new subjective assessment criteria for human observers to score adversarial stealthiness in a fine-grained manner. Then, we create a large-scale adversarial example dataset comprising 10586 pairs of clean and adversarial samples encompassing twelve state-of-the-art adversarial attacks. To obtain the subjective scores according to the proposed criterion, we recruit 60 human observers, and each adversarial example is evaluated by at least 15 observers. The mean opinion score of each adversarial example is utilized for labeling. Finally, we develop a three-stage objective scoring model that mimics human scoring habits to predict adversarial perturbation's stealthiness. Experimental results demonstrate that our objective model exhibits superior consistency with the human visual system, surpassing commonly employed metrics like PSNR and SSIM.

关键词： Artificial intelligence   scientific research   scientific research   science ethics   science ethics   computer science   computer science   physics   physics   medicine   medicine   psychology   psychology   mathematics   mathematics   geography   geography   agriculture   agriculture  

摘要： The recent rise in relevance and diffusion of Artificial Intelligence (AI)-based systems and the increasing number and power of applications of AI methods invites a profound reflection on the impact of these innovative systems on scientific research and society at large. The Universal Scientific Education and Research Network (USERN), an organization that promotes initiatives to support interdisciplinary science and education across borders and actively works to improve science policy, collects here the vision of its Advisory Board members, together with a selection of AI experts, to summarize how we see developments in this exciting technology impacting science and society in the foreseeable future. In this review, we first attempt to establish clear definitions of intelligence and consciousness, then provide an overview of AI's state of the art and its applications. A discussion of the implications, opportunities, and liabilities of the diffusion of AI for research in a few representative fields of science follows this. Finally, we address the potential risks of AI to modern society, suggest strategies for mitigating those risks, and present our conclusions and recommendations.

ISBN: (数字)9783111436425;9783111436579 ISBN: (纸本)9783111436388

摘要： Imaging Science consists of three core interconnected pillars – Image Processing, Computer Vision, and Pattern Recognition. This book showcases groundbreaking applications and provides solid foundation in the theoretical underpinnings and low-level processing techniques of each field. Dive into practical examples showcasing the power of pattern recognition algorithms and emerge equipped to contribute to the vibrant research communities.

关键词： Training   Probabilistic logic   Metalearning   Representation learning   Predictive models   Data augmentation   Computer science   Uncertainty   Testing   Target recognition   Binary classifier   class boundary   domain expansion   edge map   open-set   out of distribution   single-source domain generalization  

摘要： Open-set single-source domain generalization aims to use a single-source domain to learn a robust model that can be generalized to unknown target domains with both domain shifts and label shifts. The scarcity of the source domain and the unknown data distribution of the target domain pose a great challenge for domain-invariant feature learning and unknown class recognition. In this article, we propose a novel learning approach based on domain expansion and boundary growth to expand the scarce source samples and enlarge the boundaries across the known classes that indirectly broaden the boundary between the known and unknown classes. Specifically, we achieve domain expansion by employing both background suppression and style augmentation on the source data to synthesize new samples. Then we force the model to distill consistent knowledge from the synthesized samples so that the model can learn domain-invariant information. Furthermore, we realize boundary growth across classes by using edge maps as an additional modality of samples when training multi-binary classifiers. In this way, it enlarges the boundary between the inliers and outliers, and consequently improves the unknown class recognition during open-set generalization. Extensive experiments show that our approach can achieve significant improvements and reach state-of-the-art performance on several cross-domain image classification datasets.

关键词： Project management   Software   Organizations   Systematic literature review   Interviews   Cultural differences   Costs   Computer science   Technological innovation   Lead   GSD projects   CSFs   software project management   interrelationships   ISM   MICMAC  

摘要： In current era of technological advancement, global software development (GSD) is expanding to an increasing extent. This rise is driven by the advantages of low-cost development, access to expert resources, proximity to markets and clients, as well as options for innovation and the exchange of best practices. However, the various key aspects of GSD, including geographical dispersion, work ethics, time zone differences and cultural variability make project management more complex in a GSD setting. This study harnesses the critical success factors (CSFs) approach to identify the model for successful management of GSD projects. Sixteen CSFs have been determined using literature review, expert judgement, and exhaustive interviews. The interrelationship among the identified CSFs has been modeled using interpretive structural modeling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) analysis. The proposed model has been validated using detailed interviews, conducted with twelve professionals from industry and academia. The expert participants were carefully chosen based on criteria which include working experience, roles, and participation in GSD projects. This research computes CSFs of software project management in GSD by assessing their driving and relative strengths. Furthermore, these CSFs are categorized using MICMAC analysis. The previous research studies lack in exploring the mutual influence of CSFs and their hierarchical relationships. With the application of ISM and MICMAC analysis, the proposed study offers understanding of these interdependencies, providing great insights into their driving and dependent function. These insights enhance knowledge in GSD project management by allowing practitioners to prioritize key criteria and formulate potent management strategies.

关键词： Neural networks  

摘要： With the advancement of educational informatization, the integration of English subject and computer science has become an effective way to improve primary school students' comprehensive English application ability and interdisciplinary literacy. The purpose of this article is to explore the practice and challenges of integrated teaching of English and computer science in primary schools under the framework of CLIL (Content and Language Integrated Learning). In this article, BPNN (Back Propagation Neural Network) model is used to quantitatively analyze the application effect of CLIL instructional mode in ELT (English Language Teaching) in primary schools. By collecting the teaching data under the traditional instructional mode and CLIL instructional mode, this article compares the differences of students' English scores, classroom participation, language ability, intercultural communication ability and learning interest. The results show that CLIL instructional mode shows advantages in improving students' abilities in all aspects, especially in improving students' English scores and intercultural communication ability. This model also stimulates students' interest in learning and promotes their comprehensive development. The integrated teaching of English and computer science in primary schools under the CLIL framework is an effective instructional mode, but it also faces some challenges. In the future, we should fully consider these challenges and take measures to deal with them. © 2025 International Association for Housing Science. All rights reserved.

关键词： Artificial Intelligence   Ethics   Bibliometrics   Biological System Modeling   Reliability   Industries   Privacy   Explainable AI   Ecosystems   Analytical Models   Bibliometrics   Responsible Artificial Intelligence RAI   Topic Analysis   Responsible Artificial Intelligence   Government Initiatives   Artificial Intelligence Techniques   Knowledge Discovery   Primary Technique   Explainable Artificial Intelligence   Principles Of Responsibility   Machine Learning   Humanitarian   Web Of Science   Computer Science   Internet Of Things   Data Privacy   Human Computer Interaction   Digital Networks   Privacy Protection   Conference Proceedings   Recommender Systems   Artificial Intelligence Algorithms   Artificial Intelligence Systems   Digital Object Identifier   Blockchain   Bibliometric Indicators   Development Of Artificial Intelligence   Fundamental Technique   Security Protection   Online Privacy   Neural Network   Cloud Computing  

摘要： Shifting the focus from principles to practical implementation, responsible artificial intelligence (AI) has garnered considerable attention across academia, industry, and society at large. Despite being in its nascent stages, this emerging field grapples with nebulous concepts and intricate knowledge frameworks. By analyzing three prevailing concepts-explainable AI, trustworthy AI, and ethical AI, this study defined responsible AI (RAI) and identified its core principles. Methodologically, this study successfully demonstrated the implementation of leveraging AI's capabilities into bibliometrics for enhanced knowledge discovery and the cross-validation of experimentally examined models with domain insights. Empirically, this study investigated 17 799 research articles contributed by the AI community since 2015. This involves recognizing key technological players and their relationships, unveiling the topical landscape and hierarchy of RAI, charting its evolution, and elucidating the interplay between the responsibility principles and primary AI techniques. An analysis of a core cohort comprising 380 articles from multiple disciplines captures the most recent advancements in RAI. As one of the pioneering bibliometric studies dedicated to exploring RAI, this study will provide comprehensive macro-level insights, enhancing the understanding of RAI while furnishing valuable knowledge support for AI regulation and governance initiatives. © IEEE. 2020 IEEE.