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Robotics & Machine Learning Daily News

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Robotics & Machine Learning Daily News/Journal Robotics & Machine Learning Daily News
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    Tsinghua University Reports Findings in Robotics (Influence of a robotic compani on on women's food choices: Evidence from an imaginary task)

    56-57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 Robotics is the subjec t of a report. According to news reporting out of Beijing, People's Republic of China, by NewsRx editors, research stated, "Previous research has demonstrated t he influence of commensal dining between humans on food choices, whereas we cond ucted two studies to examine how the presence of a robot might influence people' s choices between meat-heavy and vegetable-forward meals in imaginary scenarios. In Study 1, participants were instructed to choose three desirable dishes from a set of two meat and two vegetable dishes while they imagined eating alone, wit h a human, or with a robot." Financial support for this research came from National Natural Science Foundatio n of China. Our news journalists obtained a quote from the research from Tsinghua University , "Although the meat dishes were rated as more palatable and pleasant, the femal e participants chose fewer meat-heavy meals when eating alone or with a robot th an when eating with a human, whereas no such effect was observed for the male pa rticipants. We also replicated these patterns in Study 2, as the female particip ants chose fewer meat-heavy meals when eating with a robot and a human than when eating with two humans."
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    New Robotics Findings from National Taiwan University of Science and Technology Described (Road Anomaly Detection with Unknown Scenes Using DifferNet-Based Auto matic Labeling Segmentation)

    57-57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 reporting from Taipei, Taiwan, by NewsRx journalists, research stated, "Obstacle avoidance is essential for the effective operation of autonomous mobile robots, enabling them to detect and navigate around obstacles in their environment." Funders for this research include Ministry of Science And Technology, Taiwan. The news journalists obtained a quote from the research from National Taiwan Uni versity of Science and Technology: "While deep learning provides significant ben efits for autonomous navigation, it typically requires large, accurately labeled datasets, making the data's preparation and processing time-consuming and labor -intensive. To address this challenge, this study introduces atransfer learning (TL)-based automatic labeling segmentation (ALS) framework. This framework util izes a pretrained attention-based network, DifferNet, to efficiently perform sem antic segmentation tasks on new, unlabeled datasets. DifferNet leverages prior k nowledge from the Cityscapes dataset to identify high-entropy areas as road obst acles by analyzing differences between the input and resynthesized images. The r esulting road anomaly map was refined using depth information to produce a robus t drivable area and map of road anomalies. Several off-the-shelf RGB-D semantic segmentation neural networks were trained using pseudo-labels generated by the A LS framework, with validation conducted on the GMRPD dataset."
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    Beijing Normal University-Hong Kong Baptist University United International Coll ege Reports Findings in Artificial Intelligence (Enhancing educational Q& A systems using a Chaotic Fuzzy Logic-Augmented large language model)

    58-58页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Machine Learning-Artificial Int elligence is the subject of a report. According to news reporting originating in Zhuhai, People's Republic of China, by NewsRx journalists, research stated, "On line question-and-answer (Q&A) platforms are frequently replete wit h extensive human resource support. This study proposes a novel methodology of a customized large language model (LLM) called Chaotic LLM-based Educational Q& A System (CHAQS) to navigate the complexities associated with intelligent Q& A systems for the educational sector." The news reporters obtained a quote from the research from Beijing Normal Univer sity-Hong Kong Baptist University United International College, "It uses an expa nsive dataset comprising over 383,000 educational data pairs, an intricate fine- tuning process encompassing p-tuning v2, low-rank adaptation (LRA), and strategi es for parameter freezing at an open-source large language model ChatGLM as a ba seline model. In addition, Fuzzy Logic is implemented to regulate parameters and the system's adaptability with the Lee Oscillator to refine the model's respons e variability and precision. Experiment results showed a 5.12% imp rovement in precision score, an 11% increase in recall metric, and an 8% improvement in the F1 score as compared to other models."
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    Studies from Telecom Paris Provide New Data on Machine Learning (Machine Learnin g Techniques for Blind Beam Alignment in mmWave Massive MIMO)

    59-59页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on artificial intelligen ce have been presented. According to news originating from Paris, France, by New sRx correspondents, research stated, "This paper proposes methods for Machine Le arning (ML)-based Beam Alignment (BA), using low-complexity ML models, and achie ves a small pilot overhead." Financial supporters for this research include Telecom Paris, L'institut Polytec hnique De Paris, France. Our news correspondents obtained a quote from the research from Telecom Paris: " We assume a single-user massive mmWave MIMO, Uplink, using a fully analog archit ecture. Assuming large-dimension codebooks of possible beam patterns at U E and B S , this data-driven and model-based approach aims to partially and blindly so und a small subset of beams from these codebooks. The proposed BA is blind (no C SI), based on Received Signal Energies (RSEs), and circumvents the need for exha ustively sounding all possible beams. A sub-sampled subset of beams is then used to train several ML models such as low-rank Matrix Factorization (MF), non-negative MF (NMF), and shallow Multi-Layer Perceptron (MLP). We provide an extensive mathematical description of these models and the algorithms for each of them. O ur extensive numerical results show that, by sounding only 10 % of the beams from the U E and B S codebooks, the proposed ML tools are able to acc urately predict the non-sounded beams through multiple transmitted power regimes ."
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    Study Findings on Machine Learning Are Outlined in Reports from Visvesvaraya Nat ional Institute of Technology (Prediction of Rainfall and Groundwater Using Mach ine Learning Algorithms for Nagpur Division)

    59-60页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Fresh data on Machine Learning are presented in a new report. According to news reporting out of Maharashtra, India, by NewsRx ed itors, research stated, "Rainfall and groundwater predictions are important for water resource planning and also to reduce the consequences of catastrophes like drought and floods. In the present study, Rainfall Anomaly Index (RAI) was esti mated for 20 years period (2001-2020) to calculate the positive and negative ano malies."
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    Reports from PSG College of Technology Advance Knowledge in Machine Learning (Fe dassess: Analysis for Efficient Communication and Security Algorithms Over Vario us Federated Learning Frameworks and Mitigation of Label-flipping Attack)

    60-61页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on Machine Learning have be en published. According to news reporting out of Tamil Nadu, India, by NewsRx ed itors, research stated, "Federated learning is an upcoming concept used widely i n distributed machine learning. Federated learning (FL) allows a large number of users to learn a single machine learning model together while the training data is stored on individual user devices." Our news journalists obtained a quote from the research from the PSG College of Technology, "Nonetheless, federated learning lessens threats to data privacy. Ba sed on iterative model averaging, our study suggests a feasible technique for th e federated learning of deep networks with improved security and privacy. We als o undertake athorough empirical evaluation while taking various FL frameworks a nd averaging algorithms into consideration. Secure multi party computation, secu re aggregation, and differential privacy are implemented to improve the security and privacy in a federated learning environment. In spite of advancements, conc erns over privacy remain in FL, as the weights or parameters of atrained model may reveal private information about the data used for training. Our work demons trates that FL can be prone to label-flipping attack and a novel method to preve nt label-flipping attack has been proposed. We compare standard federated model aggregation and optimization methods, FedAvg and FedProx using benchmark data se ts. Experiments are implemented in two different FL frameworks-Flower and PySy ft and the results are analyzed. Our experiments confirm that classification acc uracy increases in FL framework over a centralized model and the model performan ce is better after adding all the security and privacy algorithms."
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    Wuhan University of Science and Technology Researchers Provide Details of New St udies and Findings in the Area of Machine Learning (Tunnel construction worker s afety state prediction and management system based on AHP and anomaly detection ...)

    61-62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 artificial in telligence. According to news originating from Hubei, People's Republic of China , by NewsRx editors, the research stated, "Tunnels represent complex, highrisk, and technically demanding underground construction projects. The safety of cons truction workers in tunnels is influenced by various factors, including physiolo gical indicators, tunnel dimensions, and internal environmental conditions." Our news reporters obtained a quote from the research from Wuhan University of S cience and Technology: "Analyzing safety based solely on static factors is inade quate for modern tunnel engineering safety management requirements. To address t his challenge, this paper provides a comprehensive analysis of factors impacting safety and employs the Analytic Hierarchy Process (AHP) to identify seven signi ficant factors with high importance: body temperature, heart rate, internal temp erature, internal humidity, CO concentration, chlorine concentration, and the re lative positioning of personnel. Considering these factors essential for assessi ng worker safety, we introduce a novel model named Tunnel-APH-AD. For training m odels aimed at anomaly detection, we performed data augmentation and utilized fo ur distinct machine learning models. Additionally, ensemble learning techniques were applied to aggregate the predictions from individual models, thereby enhanc ing the effectiveness of detecting safety states for tunnel workers. We also eva luated the performance of these models on out-of-distribution (OOD) samples to t est their robustness and generalizability. The experimental results indicate that, under similar ventilation and tunnel conditions, the ensemble learning model exhibits superior overall performance compared to individual models, underscorin g the effectiveness of model combination in improving the accuracy and reliabili ty of safety alerts."
      原文链接:
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    Recent Findings in Robotics Described by Researchers from University of Minnesot a (Talk Through It: End User Directed Manipulation Learning)

    62-63页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Robotics are presented i n a new report. According to news originating from Minneapolis, Minnesota, by Ne wsRx correspondents, research stated, "Training robots to perform a huge range o f tasks in many different environments is immensely difficult. Instead, we propo se selectively training robots based on end-user preferences." Financial support for this research came from Minnesota Robotics Institute. Our news journalists obtained a quote from the research from the University of M innesota, "Given a factory model that lets an end user instruct a robot to perfo rm lower-level actions (e.g. ‘Move left'), we show that end users can collect de monstrations using language to train their home model for higher-level tasks spe cific to their needs (e.g. ‘Open the top drawer and put the block inside'). We d emonstrate this framework on robot manipulation tasks using RLBench environments . Our method results in a 13 % improvement in task success rates co mpared to a baseline method. We also explore the use of the large vision-languag e model (VLM), Bard, to automatically break down tasks into sequences of lower-l evel instructions, aiming to bypass end-user involvement."
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    New Findings in Machine Learning Described from Chalmers University of Technolog y (Development of a machine learning model to improve estimates of material stoc k and embodied emissions of roads)

    63-64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 ar tificial intelligence. According to news reporting originating from Gothenburg, Sweden, by NewsRx correspondents, research stated, "Material flow analysis is an important tool for estimating material flows and embedded emissions of transpor t infrastructure." Financial supporters for this research include Stiftelsen For Miljostrategisk Fo rskning. The news correspondents obtained a quote from the research from Chalmers Univers ity of Technology: "Missing attributes tend to be a major barrier to accurate es timates. In this study a machine learning model is developed to estimate the mis sing data in a statistics dataset of roads, to enable a bottomup material stock and flow analysis. The proposed approach was applied to the Swedish road networ k to predict missing data for road width in the statistical dataset. The predict ed hybrid dataset was then used to estimate material stocks, flows, and embodied emissions from Year 2020 to Year 2045 using decarbonization scenarios with a su pply chain perspective. The study demonstrates that machine learning models can be used to enable national-level material stock and flow analyses of roads. Mult iple machine learning algorithms were tested, and the best performing model achi eved an R2 value of 0.784."
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    Helwan University Researcher Provides Details of New Studies and Findings in the Area of Machine Learning (Using Machine Learning Techniques to Predict the Surf ace Roughness of Titanium Alloys)

    64-64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 out of Cairo, Egyp t, by NewsRx editors, research stated, "Production engineering focuses on design ing, optimizing, and managing manufacturing processes to produce goods efficient ly. Turning is a machining process where a cutting tool removes material from a rotating workpiece to create cylindrical shapes." The news correspondents obtained a quote from the research from Helwan Universit y: "Key parameters include cutting speed, feed rate, and depth of cut. Surface r oughness is a key challenge in turning, impacting product quality. Achieving the desired finish is crucial for tight tolerance and performance. Engineers use op timization techniques to minimize roughness. Advancements in tool materials and technology help address roughness challenges for improved efficiency in turning. Predictive models for surface roughness are vital for optimizing machining proc esses, ensuring quality, and enhancing performance. They guide decision-making, improve efficiency, and drive innovation in manufacturing."
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