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    Study Findings on Artificial Intelligence Are Outlined in Reports from Zhejiang Gongshang University (Artificial Intelligence and the Skill Premium: a Numerical Analysis of Theoretical Models)

    67-68页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on Artificial Intelligence have been published. According to news originating from Hangzhou, People's Repub lic of China, by NewsRx correspondents, research stated, "As a new engine in gui ding China's high-quality economic development, it is important to study whether the development of artificial intelligence (AI) will increase the skill premium and affect labor income inequality. Based on Acemoglu and Restrepo's (2018a) ta sk-based model, this study constructs a multi-sector dynamic general equilibrium (DGE) model to analyze the impact and mechanism of AI on the skill premium and performs a numerical simulation using China's industrial panel data from 2010 to 2019." Funders for this research include National Office of Philosophy and Social Scien ces, ZheJiang Social Science Foundation, Natural Science Foundation of Zhejiang Province, National Statistical Science Project of China, Zhejiang Provincial Sta tistical Science Project, Characteristic & Preponderant Discipline of Key Construction Universities in Zhejiang province (Zhejiang Gongshang Unive rsity -Statistics). Our news journalists obtained a quote from the research from Zhejiang Gongshang University, "The results show that AI widens the skill premium by substituting l ow-skilled labor with industrial robots and performing high-skilled labor tasks. The mechanism analysis reveals that AI also affects the skill premium by influe ncing factor flow and structural transformation."
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    New Research on Robotics and Mechatronics from Tokyo University of Agriculture a nd Technology Summarized (Automatic Design of Serial Linkage Using Virtual Screw Joint)

    67-67页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in robotics and mechatronics. According to news originating from Tokyo, Japan, by N ewsRx correspondents, research stated, "Here, an automatic design method for a s erial link mechanism is proposed." Financial supporters for this research include Japan Society For The Promotion o f Science. Our news reporters obtained a quote from the research from Tokyo University of A griculture and Technology: "This method outputs all kinematic parameters of join ts, position, orientation, number, and type of joint (revolute or prismatic). Se veral studies have been conducted on optimizing only the positions and direction s of joints for a desired path. However, automatically determining the numbers a nd types of joints requires an excessive calculation time owing to the complicit y of kinematics. To handle heavy computation, a virtual screw joint (VSJ) is int roduced based on a screw axis and the product of exponentials formula. Screw joi nts have the advantage of including both rotation and translation. First, an add itional joint is optimized as a VSJ." According to the news editors, the research concluded: "Then, adopting its posit ion and orientation and selecting a revolute or prismatic joint facilitate an ef ficient design process. To demonstrate the effectiveness of this study, two task motions addressed in a related work are adopted as target paths. Consequently, the proposed method automatically generates serial linkages that contain both re volute and prismatic joints and can follow along desired paths."
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    Researchers from National Institute of Information and Communications Technology Describe Research in Robotics (Phantom in the opera: adversarial music attack f or robot dialogue system)

    68-69页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ro botics. According to news originating from Kyoto, Japan, by NewsRx correspondent s, research stated, "This study explores the vulnerability of robot dialogue sys tems' automatic speech recognition (ASR) module to adversarial music attacks." The news correspondents obtained a quote from the research from National Institu te of Information and Communications Technology: "Specifically, we explore music as a natural camouflage for such attacks. We propose a novel method to hide gho st speech commands in a music clip by slightly perturbing its raw waveform. We a pply our attack on an industry-popular ASR model, namely the time-delay neural n etwork (TDNN), widely used for speech and speaker recognition. Our experiment de monstrates that adversarial music crafted by our attack can easily mislead indus try-level TDNN models into picking up ghost commands with high success rates. Ho wever, it sounds no different from the original music to the human ear."
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    University of Toulouse Researcher Broadens Understanding of Robotics (A survey o n socially aware robot navigation: Taxonomy and future challenges)

    69-70页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on robotics have been published. According to news reporting out of Toulouse, France, by NewsRx editors, research stated, "Socially aware robot navigation is gaining popularity with the increase in delivery and assistive robots." Financial supporters for this research include Ministerio De Ciencia E Innovacio n; Spanish Government (Mcin/aei) And European Union; European Project Canopies; Agence Nationale De La Recherche; Horizon Europe Framework Programme. The news reporters obtained a quote from the research from University of Toulous e: "The research is further fueled by a need for socially aware navigation skill s in autonomous vehicles to move safely and appropriately in spaces shared with humans. Although most of these are ground robots, drones are also entering the f ield. In this paper, we present a literature survey of the works on socially awa re robot navigation in the past 10 years. We propose four different faceted taxo nomies to navigate the literature and examine the field from four different pers pectives." According to the news editors, the research concluded: "Through the taxonomic re view, we discuss the current research directions and the extending scope of appl ications in various domains. Further, we put forward a list of current research opportunities and present a discussion on possible future challenges that are li kely to emerge in the field."
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    New Findings from University of Peradeniya in the Area of Machine Learning Publi shed (A new frontier in streamflow modeling in ungauged basins with sparse data: A modified generative adversarial network with explainable AI)

    70-70页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on artificial intelligenc e is the subject of a new report. According to news reporting from Peradeniya, S ri Lanka, by NewsRx journalists, research stated, "Streamflow forecasting is cru cial for effective water resource planning and early warning systems, especially in regions with complex hydrological behaviors and uncertainties. While machine learning (ML) has gained popularity for streamflow prediction, many studies hav e overlooked the predictability of future events considering anthropogenic, stat ic physiographic, and dynamic climate variables." Our news reporters obtained a quote from the research from University of Peraden iya: "This study, for the first time, used a modified generative adversarial net work (GAN) based model to predict streamflow. The adversarial training concept m odifies and enhances the existing data to embed featureful information enough to capture extreme events rather than generating synthetic data instances. The mod el was trained using (sparse data) a combination of anthropogenic, static physio graphic, and dynamic climate variables obtained from an ungauged basin to predic t monthly streamflow. The GAN-based model was interpreted for the first time usi ng local interpretable model-agnostic explanations (LIME), explaining the decisi onmaking process of the GAN-based model. The GAN-based model achieved R2 from 0 .933 to 0.942 in training and 0.93-0.94 in testing. Also, the extreme events in the testing period have been reasonably well captured. The LIME explanations gen erally adhere to the physical explanations provided by related work." According to the news editors, the research concluded: "This approach looks prom ising as it worked well with sparse data from an ungauged basin. The authors sug gest this approach for future research work that focuses on machine learning-bas ed streamflow predictions."
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    Studies from Chinese Academy of Sciences Further Understanding of Robotics (A No vel Positioning Accuracy Improvement Method for Polishing Robot Based On Levenbe rg-marquardt and Opposition-based Learning Squirrel Search Algorithm)

    71-71页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on Robotics. Acc ording to news reporting originating from Chengdu, People's Republic of China, b y NewsRx correspondents, research stated, "Achieving highprecision manufacturin g of optical components requires improving the absolute positioning accuracy of the robot to the highest possible level. Identifying the robot's kinematic param eters and compensating for kinematic errors are effective methods for improving the robot's positioning accuracy." Our news editors obtained a quote from the research from the Chinese Academy of Sciences, "This paper proposes a hybrid algorithm that combines the Levenberg-Ma rquardt algorithm and an oppositionbased learning squirrel search algorithm to identify the kinematic parameters of a polishing robot. Firstly, the Levenberg-M arquardt algorithm is utilized to solve the suboptimal values of kinematic param eter deviations. Secondly, an opposition-based learning strategy is integrated i nto the standard squirrel search algorithm to increase the diversity of the popu lation and prevent the population from getting stuck in local optima. The subopt imal values obtained by the Levenberg-Marquardt algorithm are subsequently used as the central values to generate the initial population for the opposition-base d learning squirrel search algorithm, which helps identify more accurate kinemat ic parameter deviations. Ultimately, the kinematic parameters of the robot are e ffectively calibration. The calibration experimental results showed that the pro posed method achieved a high level of calibration accuracy, resulting in a 62.61 % improvement in absolute positioning error compared to before cal ibration." According to the news editors, the research concluded: "Offline machining experi ments have validated the effectiveness of LM-OBLSSA in reducing deviations in th e dwell points of optical components during the machining process."
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    Imperial College London Reports Findings in Artificial Intelligence (Post-COVID highlights: Challenges and solutions of artificial intelligence techniques for s wift identification of COVID-19)

    72-72页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating from Londo n, United Kingdom, by NewsRx correspondents, research stated, "Since the onset o f the COVID-19 pandemic in 2019, there has been a concerted effort to develop co st-effective, non-invasive, and rapid AI-based tools. These tools were intended to alleviate the burden on healthcare systems, control the rapid spread of the v irus, and enhance intervention outcomes, all in response to this unprecedented g lobal crisis." Our news editors obtained a quote from the research from Imperial College London , "As we transition into a post-COVID era, we retrospectively evaluate these pro posed studies and offer a review of the techniques employed in AI diagnostic mod els, with a focus on the solutions proposed for different challenges. This revie w endeavors to provide insights into the diverse solutions designed to address t he multifaceted challenges that arose during the pandemic." According to the news editors, the research concluded: "By doing so, we aim to p repare the AI community for the development of AI tools tailored to address publ ic health emergencies effectively." This research has been peer-reviewed.
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    Thammasat University Researcher Discusses Research in Machine Translation (A Stu dy for Enhancing Low-resource Thai-Myanmar-English Neural Machine Translation)

    72-73页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ma chine translation. According to news reporting out of Thammasat University by Ne wsRx editors, research stated, "Several methodologies have recently been propose d to enhance the performance of low-resource Neural Machine Translation (NMT)."Our news reporters obtained a quote from the research from Thammasat University: "However, these techniques have yet to be explored thoroughly in low-resource T hai and Myanmar languages. Therefore, we first applied augmentation techniques s uch as SwitchOut and Ciphertext Based Data Augmentation (CipherDAug) to improve NMT performance in these languages. We secondly enhanced the NMT performance by fine-tuning the pre-trained Multilingual Denoising BART model (mBART), where BAR T denotes Bidirectional and Auto-Regressive Transformer. We implemented three NM T systems: namely, Transformer+SwitchOut, Multi-source Transformer+CipherDAug, a nd fine-tuned mBART in the bidirectional translations of Thai-English-Myanmar la nguage pairs from the ASEAN-MT corpus. Experimental results showed that Multi-so urce Transformer+CipherDAug significantly improved BLEU, ChrF, and TER scores ov er the first baseline Transformer and second baseline Edit-Based Transformer (ED ITOR). The model achieved notable BLEU scores: 37.9 (English-to-Thai), 42.7 (Tha i-to-English), 28.9 (English-to- Myanmar), 31.2 (Myanmar-to-English), 25.3 (Thai- to-Myanmar), and 25.5 (Myanmar-to-Thai)."
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    Findings from China University of Petroleum in Machine Learning Reported (Machin e Learning Models for Predicting Asphaltene Stability Based On Saturates-aromati cs-resins-asphaltenes)

    73-74页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting out of Beijing, People's Republic of Chin a, by NewsRx editors, research stated, "Asphaltene precipitation is one of the c hallenging flow assurance problems as it can cause permeability impairment and p ipeline blockages by depositing on the surface of well tubing, flowlines, and he at exchangers. The cost of remediating an unexpected asphaltene problem is expen sive and time-consuming wherever offshore or on land." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from the China Universit y of Petroleum, "Thus, the provision of asphaltene precipitation is vital and an effective approach is stability screening for monitoring asphaltene precipitati on problems. In this study, asphaltene stability performance in crude oil was ev aluated using six machine learning (ML) techniques, namely decision tree (DT), N aive Bayes (NB), support vector machine (SVM), artificial neural networks (ANN), random forest (RF), and k-nearest neighbor (KNN). A large stability data contai ning 186 crude oil samples of known stability were used to design the classifica tion models for predicting asphaltene stability. The inputs to the models were t he content of saturates, aromatics, resins, and asphaltenes (SARA); and the outp ut was stability. The classification results showed that the best classification model is the KNN classifier, and it has an accuracy of 82%, area u nder the curve (AUC) of 83%, precision of 75%, and f1- score of 83%. Also, three empirical correlations with high accuracy including stability index (SI), stability crossplot (SCP), and asphaltene stabi lity predicting model (ANJIS) were utilized comparatively with the ML models to evaluate asphaltene stability. Results revealed that the KNN classifier has supe rior performance in this work with an accuracy of 80%, a precision of 82%, and an f1-score of 79%."
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    Recent Findings in Artificial Intelligence Described by a Researcher from Northe ast Petroleum University (Research on energy-saving strategies in the architectu ral design of the Eastern Railway in the era of artificial intelligence)

    74-75页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on artificial intelligence is now available. According to news reporting originating from Heilongjiang, Pe ople's Republic of China, by NewsRx correspondents, research stated, "This paper takes the air conditioning energy consumption in railroad building design as th e main object of analysis and uses an orthogonal experimental design method to s tudy the influencing factors and effects of energy consumption in the Middle Eas t railroad construction design." Our news journalists obtained a quote from the research from Northeast Petroleum University: "Stochastic working condition simulation is carried out for the typ ical models of passenger stations in five climate zones to obtain the data set o f the passenger station energy consumption model, and the multiple linear regres sion prediction models of air conditioning energy consumption in different regio ns are established. Combined with the hour-by-hour average outdoor temperature, hour-by-hour passenger flow and lighting and related equipment data in the waiti ng hall of a city in July, the air-conditioning cold load of railroad passenger stations is simulated and analyzed."
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