查看更多>>摘要：Investigators publish new report on Machine Learning - Intelligent Systems. According to news reporting originating from Wuhan, People's Republic of China, by NewsRx correspondents, research stated, “The detection of anomalies in high-dimensional time-series has always played a crucial role in the domain of system security. Recently, with rapid advancements in transformer model and graph neural network (GNN) technologies, spatiotemporal modeling approaches for anomaly detection tasks have been greatly improved.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Centre for Research in Cyber Security, Singapore University of Technology and Design.
查看更多>>摘要：Data detailed on Machine Learning have been presented. According to news reporting out of Shanghai, People's Republic of China, by NewsRx editors, research stated, “Because machine learning models, especially black-box malicious models vulnerable to attribute inference attacks, are capable of generating a great deal of privacy leakage, recent work has focused on assessing these models in an attempt to prevent unexpected attribute privacy leakage. While there has been some success at model privacy risk evaluations, these traditional solutions are almost brittle in practice because they not only require white-box access to obtain model feature layer outputs but also their evaluation results are heavily influenced by the training dataset and the model structure, leading to difficulty in generalization."
查看更多>>摘要：Research findings on robotics are discussed in a new report. According to news reporting out of Urumqi, People's Republic of China, by NewsRx editors, research stated, “Handwriting robots as an application of Imitation Learning (IL).” Our news reporters obtained a quote from the research from Xinjiang University: “However, most methods have poor accuracy of trajectory generation under task constraints, and models are less robust to changes in demonstration data. This paper proposes an IL algorithm named Bagging in Hidden Semi-Markov Model (BHSMM). The demonstration data is first divided into several sub-datasets, and each sub-dataset is encoded into several basic learning models by Hidden Semi-Markov Models (HSMM). Then the relationship between the task constraint points and the basic learning models is used to derive the weights. Finally, the trajectories adapted to the task constraints are generated based on the weights. We conducted experiments on the handwritten dataset LASA and compared the accuracy error with the original HSMM method."
查看更多>>摘要：Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Karachi, Pakistan, by NewsRx editors, research stated, “Pre-labeled data is typically required for supervised machine learning. A limited number of object classes in the majority of open access and pre-annotated datasets make them unsuitable for certain tasks, even though they are readily available for training machine learning algorithms. For custom models, previously available pre-annotated data is typically insufficient, so gathering and preparing training data is necessary for the majority of real-world applications.”
查看更多>>摘要：New study results on robotics have been published. According to news reporting originating from the University Rovira i Virgili by NewsRx correspondents, research stated, “Industry 4.0 profoundly impacts the insurance sector, as evidenced by the significant growth of insurtech. One of these technologies is chatbots, which enable policyholders to seamlessly manage their active insurance policies.” The news editors obtained a quote from the research from University Rovira i Virgili: “This paper analyses policyholders' attitude toward conversational bots in this context. To achieve this objective, we employed a structured survey involving policyholders. The survey aimed to determine the average degree of acceptance of chatbots for contacting the insurer to take action such as claim reporting. We also assessed the role of variables of the technology acceptance model, perceived usefulness, and perceived ease of use, as well as trust, in explaining attitude and behavioral intention. We have observed a low acceptance of insureds to implement insurance procedures with the assistance of a chatbot. The theoretical model proposed to explain chatbot acceptance provides good adjustment and prediction capability. Even though the three assessed factors are relevant for explaining attitude toward interactions with conversational robots and behavioral intention to use them, the variable trust exhibited the greatest impact. The findings of this paper have fair potential theoretical and practical implications.”
查看更多>>摘要：Research findings on Machine Learning - Intelligent Systems are discussed in a new report. According to news originating from Wuhan, People's Republic of China, by NewsRx correspondents, research stated, “The present-day globalized economy and diverse market demands have compelled an increasing number of manufacturing enterprises to move toward the distributed manufacturing pattern and the model of multi-variety and small-lot. Taking these two factors into account, this study investigates an extension of the distributed hybrid flowshop scheduling problem (DHFSP), called the distributed hybrid flowshop scheduling problem with consistent sublots (DHFSP_CS).” Funders for this research include Natural Science Foundation of Shandong Province, National Natural Science Foundation of China (NSFC), Natural Science Foundation of Shandong Province, Guangyue Young Scholar Innova-tion Team of Liaocheng University.
查看更多>>摘要：Investigators discuss new findings in Robotics. According to news originating from Shenyang, People's Republic of China, by NewsRx correspondents, research stated, “In concrete construction, vibration is an essential process used to ensure the structural soundness and long-term durability of concrete structures. Automating concrete vibration monitoring enables far more precise control compared to subjective human judgments.” Funders for this research include University Innovation Team of Liaoning Province, China Construction Eighth Engineering Division Co., Ltd., China Scholarship Council. Our news journalists obtained a quote from the research from Northeastern University, “In this paper, a control mode for concrete vibration time used on the vibrating robot is proposed. The control mode utilizes YOLOv8 model to recognize the best-vibrating position and eliminate the rebar part of concrete surface image and employs attention-enhanced squeeze-and-excitation neural network to regress the vibrating completion degree. After deployment to the embedded system of vibrating robot, experimental results demonstrate superior performance of the proposed method over state-ofthe-art algorithms with the highest recognition rate and maximum R2 value. The control mode enhances vibration quality while liberating workers from manual tasks.”
查看更多>>摘要：Research findings on Robotics are discussed in a new report. According to news originating from Henan, People's Republic of China, by NewsRx correspondents, research stated, “Herein, we synthesized anemone-like copper-based metal-organic frameworks (MOFs) loaded with gold-palladium nanoparticles (AuPd@Cu-MOFs) and polyethylenimine-reduced graphene oxide/gold-silver nanosheet composites (PEI-rGO/AuAg NSs) for the first time to construct the sensor and to detect T-2 toxin (T-2) using triple helix molecular switch (THMS) and signal amplification by swing-arm robot. The aptasensor used PEI-rGO/hexagonal AuAg NSs as the electrode modification materials and anemone-like AuPd@Cu-MOFs as the signal materials.” Funders for this research include National Natural Science Foundation of China (NSFC), Program for Science and Technology Innovation Talents in Universities of Henan Province, Natural Science Foundation of Henan Province of China, Innovative Funds Plan of Henan University of Technology, Open Fund from Research Platform of Grain Information Processing Center in Henan University of Technology, Double First-Class Project for Postgraduate-Cultivating Innovation Platform Establishment Programme of Henan University of Technology, Double First-Class Project for Postgraduate Academic Innovation Enhancement Programme of Henan University of Technology.
查看更多>>摘要：Researchers detail new data in Support Vector Machines. According to news reporting originating from Zhenjiang, People's Republic of China, by NewsRx correspondents, research stated, “In order to solve the problems of low integration, low reliability, and high cost caused by mechanical sensors used in bearingless synchronous reluctance motor (PMa-BSynRM) control system, a novel displacement self-sensing control method using a least square support vector machine (LSSVM) left inverse system is proposed. First, the working principle of the PMa-BSynRM is introduced and the mathematical model of the PMa-BSynRM is derived.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news editors obtained a quote from the research from Jiangsu University, “Second, the observation principle of the left-inverse system of the PMa-BSynRM is explained and the left-invertibility of the displacement subsystem is proved. Thirdly, the improved NSGA-II algorithm is utilized to optimize the regularization parameter and the bandwidth of LSSVM, and the displacement self-sensing control system is constructed. The simulations of speed variation and anti-interference are performed, which proves the dynamic tracking performance of the displacement. Finally, the static suspension, speed variation and anti-interference experiments are carried out.”
查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news reporting out of Milan, Italy, by NewsRx editors, research stated, “Cardiovascular disease is a leading global cause of mortality. The potential cardiotoxic effects of chemicals from different classes, such as environmental contaminants, pesticides, and drugs can significantly contribute to effects on health.” Funders for this research include European Union's Horizon 2020 Research And Innovation Program. Our news correspondents obtained a quote from the research from Mario Negri Institute for Pharmacological Research: “The same chemical can induce cardiotoxicity in different ways, following various Adverse Outcome Pathways (AOPs). In addition, the potential synergistic effects between chemicals further complicate the issue. In silico methods have become essential for tackling the problem from different perspectives, reducing the need for traditional in vivo testing, and saving valuable resources in terms of time and money. Artificial intelligence (AI) and machine learning (ML) are among today's advanced approaches for evaluating chemical hazards. They can serve, for instance, as a first-tier component of Integrated Approaches to Testing and Assessment (IATA). This study employed ML and AI to assess interactions between chemicals and specific biological targets within the AOP networks for cardiotoxicity, starting with molecular initiating events (MIEs) and progressing through key events (KEs). We explored methods to encode chemical information in a suitable way for ML and AI. We started with commonly used approaches in Quantitative Structure-Activity Relationship (QSAR) methods, such as molecular descriptors and different types of fingerprint.”
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