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    Findings from Technical University Munich (TU Munich) Broaden Understanding of Robotics and Automation [Enhanced Dexterity Maps (Edm): a New Map for Manipulator Capability Analysis]

    76-76页
    查看更多>>摘要：Research findings on Robotics - Robotics and Automation are discussed in a new report. According to news reporting from Munich, Germany, by NewsRx journalists, research stated, “The ability of a manipulator to compute a geometry-aware quality index for general tasks with different joint configurations is essential. Such workspace assessment is a well-known and studied field in existing robotics literature, often deployed through embodied structures such as voxelized maps.” Financial support for this research came from Lighthouse Initiative Geriatronics. The news correspondents obtained a quote from the research from Technical University Munich (TU Munich), “Notwithstanding, existing literature solely focuses on the assessment of a single pose (endeffector), neglecting the whole-body structure and its dexterity, which allows for secondary task optimization, nullspace motion, body placement, and improved manipulability. The proposed enhanced dexterity maps (EDM) aims to close these gaps using an augmented data structure. It offers a systematic analysis of disjoint flip solutions and accommodates additional performance metrics.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Data on Robotics Detailed by Researchers at Shenzhen University (Neural Packing: From Visual Sensing To Reinforcement Learning)

    77-77页
    查看更多>>摘要：Investigators publish new report on Robotics. According to news reporting originating in Shenzhen, People’s Republic of China, by NewsRx journalists, research stated, “We present a novel learning framework to solve the transport-and-packing (TAP) problem in 3D. It constitutes a full solution pipeline from partial observations of input objects via RGBD sensing and recognition to final box placement, via robotic motion planning, to arrive at a compact packing in a target container.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of Guangdong Province, Shenzhen Science and Technology Program, DEGP Innovation Team, Natural Sciences and Engineering Research Council of Canada (NSERC). The news reporters obtained a quote from the research from Shenzhen University, “The technical core of our method is a neural network for TAP, trained via reinforcement learning (RL), to solve the NP-hard combinatorial optimization problem. Our network simultaneously selects an object to pack and determines the final packing location, based on a judicious encoding of the continuously evolving states of partially observed source objects and available spaces in the target container, using separate encoders both enabled with attention mechanisms. The encoded feature vectors are employed to compute the matching scores and feasibility masks of different pairings of box selection and available space configuration for packing strategy optimization. Extensive experiments, including ablation studies and physical packing execution by a real robot (Universal Robot UR5e), are conducted to evaluate our method in terms of its design choices, scalability, generalizability, and comparisons to baselines, including the most recent RL-based TAP solution.”
      原文链接:
    • NETL
    • NSTL
    • Assoc Computing Machinery

    Studies from University of Belgrade Yield New Data on Artificial Intelligence (The Analysis and Reexamination of Functionalism From the Perspective of Artificial Intelligence)

    78-78页
    查看更多>>摘要：Investigators publish new report on Artificial Intelligence. According to news reporting originating from Belgrade, Serbia, by NewsRx correspondents, research stated, “This paper examines the role of machine functionalism, as one of the most popular positions within the philosophy of mind, in the context of the development of artificial intelligence. Our analysis starts from the idea that machine functionalism is a theory that is largely consistent with the principles behind the strong AI thesis.” Our news editors obtained a quote from the research from the University of Belgrade, “However, we will see that there is a convincing counter-argument against such claims, and we will problematize this issue. Also, by testing ChatGPT, as the most popular publicly available AI tool, we will make an effort to figure out whether the strong AI thesis could currently even be considered a potentially sustainable principle.” According to the news editors, the research concluded: “Since ChatGPT fails the Turing test our conclusion is that the strong AI thesis cannot be upheld even in principle, implying that the current state of this AI tool does not yet provide strong enough arguments in favor of machine functionalism.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    New Machine Learning Findings from Technical University of Kosice Described (Comparison of Machine Learning Approaches for Sentiment Analysis in Slovak)

    78-79页
    查看更多>>摘要：Fresh data on artificial intelligence are presented in a new report. According to news reporting from Technical University of Kosice by NewsRx journalists, research stated, “The process of determining and understanding the emotional tone expressed in a text, with a focus on textual data, is referred to as sentiment analysis. This analysis facilitates the identification of whether the overall sentiment is positive, negative, or neutral.” Financial supporters for this research include Ministry of Education, Science, Research And Sport of The Slovak Republic; Slovak Research And Development Agency; Faculty of Electrical Engineering And Informatics, Tu Kosice. The news reporters obtained a quote from the research from Technical University of Kosice: “Sentiment analysis on social networks seeks valuable insight into public opinions, trends, and user sentiments. The main motivation is to enable informed decisions and an understanding of the dynamics of online discourse by businesses and researchers. Additionally, sentiment analysis plays a vital role in the field of hate speech detection, aiding in the identification and mitigation of harmful content on social networks. In this paper, studies on the sentiment analysis of texts in the Slovak language, as well as in other languages, are introduced. The primary aim of the paper, aside from releasing the “SentiSK” dataset to the public, is to evaluate our dataset by comparing its results with those of other existing datasets in the Slovak language. The “SentiSK” dataset, consisting of 34,006 comments, was created, specified, and annotated for the task of sentiment analysis. The proposed approach involved the utilization of three datasets in the Slovak language, with nine classification methods trained and compared in two defined tasks.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Ente Ospedaliero Cantonale (EOC) Reports Findings in Robotics (Concomitant training in robotic and laparoscopic liver resections of low-to-intermediate difficulty score: a retrospective analysis of the learning curve)

    79-80页
    查看更多>>摘要：New research on Robotics is the subject of a report. According to news originating from Lugano, Switzerland, by NewsRx correspondents, research stated, “In the setting of minimally invasive liver surgery (MILS), training in robotic liver resections (RLR) usually follows previous experience in laparoscopic liver resections (LLR). The aim of our study was to assess the learning curve of RLR in case of concomitant training with LLR.” Our news journalists obtained a quote from the research from Ente Ospedaliero Cantonale (EOC), “We analyzed consecutive RLRs and LLRs by a surgeon trained simultaneously in both techniques (Surg1); while a second surgeon trained only in LLRs was used as control (Surg2). A regression model was used to adjust for confounders and a Cumulative Sum (CUSUM) analysis was carried out to assess the learning phases according to operative time and difficulty of the procedures (IWATE score). Two-hundred-forty-five procedures were identified (RobSurg1, n = 75, LapSurg1, n = 102, LapSurg2, n = 68). Mean IWATE was 4.0, 4.3 and 5.8 (p ＜0.001) in each group. The CUSUM analysis of the adjusted operative times estimated the learning phase in 40 cases (RobSurg1), 40 cases (LapSurg1), 48 cases (LapSurg2); for IWATE score it was 38 cases (RobSurg1), 33 cases (LapSurg1), 38 cases (LapSurg2) respectively. Our preliminary experience showed a similar learning curve of 40 cases for low and intermediate difficulty RLR and LLR.”
      原文链接:
    • NETL
    • NSTL

    Research Triangle Institute Reports Findings in Marijuana Laws (Using publicly available data to predict recreational cannabis legalization at the county-level: A machine learning approach)

    80-81页
    查看更多>>摘要：New research on Marijuana/Cannabis - Marijuana Laws is the subject of a report. According to news reporting originating in Research Triangle Park, North Carolina, by NewsRx journalists, research stated, “There is substantial geographic variability in local cannabis policies within states that have legalized recreational cannabis. This study develops an interpretable machine learning model that uses county-level population demographics, sociopolitical factors, and estimates of substance use and mental illness prevalences to predict the legality of recreational cannabis sales within each U.S. county.” The news reporters obtained a quote from the research from Research Triangle Institute, “We merged data and selected 14 model inputs from the 2010 Census, 2012 County Presidential Data from the MIT Elections Lab, and Small Area Estimates from the National Surveys on Drug Use and Health (NSDUH) from 2010 to 2012 at the county level. County policies were labeled as having recreational cannabis legal (RCL) if the sale of recreational cannabis was allowed anywhere in the county in 2014, resulting in 92 RCL and 3002 non-RCL counties. We used synthetic data augmentation and minority oversampling techniques to build an ensemble of 1000 logistic regressions on random sub-samples of the data, withholding one state at a time and building models from all remaining states. Performance was evaluated by comparing the predicted policy conditions with the actual outcomes in 2014. When compared to the actual RCL policies in 2014, the ensemble estimated predictions of counties transitioning to RCL had a macro f1 average score of 0.61. The main factors associated with legalizing county-level recreational cannabis sales were the prevalences of past-month cannabis use and past-year cocaine use. By leveraging publicly available data from 2010 to 2012, our model was able to achieve appreciable discrimination in predicting counties with legal recreational cannabis sales in 2014, however, there is room for improvement.”
      原文链接:
    • NETL
    • NSTL

    Findings on Robotics Reported by Investigators at Swiss Federal Institute of Technology (Non-destructive Corrosion Inspection of Reinforced Concrete Structures Using an Autonomous Flying Robot)

    81-82页
    查看更多>>摘要：Investigators publish new report on Robotics. According to news reporting out of Zurich, Switzerland, by NewsRx editors, research stated, “Autonomous non-destructive testing (NDT) on reinforced concrete structures has a large potential to overcome the limitations of current routine inspection techniques, often not capable of detecting corrosion at an early stage. Here, the development and validation of two probes, tailored to acquire contact-based NDT data with the help of a hexacopter, is presented.” Financial support for this research came from ETH Research Grant. Our news journalists obtained a quote from the research from the Swiss Federal Institute of Technology, “Each probe allows the combined measurement of two essential parameters in the condition assessment: half-cell potentials and concrete electrical resistivity. Strategies are presented to monitor probe functionality during operation and detect loss of contact between probe and structure, which is considered essential for autonomous NDT. The presented approach enables effective and reliable autonomous corrosion inspection, surpassing traditional visual inspections by localizing corrosion at an early stage, allowing engineers a better planning of maintenance.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Swiss Federal Institute of Technology Zurich (ETH) Reports Findings in Robotics (Dexterous helical magnetic robot for improved endovascular access)

    82-83页
    查看更多>>摘要：New research on Robotics is the subject of a report. According to news reporting originating in Zurich, Switzerland, by NewsRx journalists, research stated, “Treating vascular diseases in the brain requires access to the affected region inside the body. This is usually accomplished through a minimally invasive technique that involves the use of long, thin devices, such as wires and tubes, that are manually maneuvered by a clinician within the bloodstream.” The news reporters obtained a quote from the research from the Swiss Federal Institute of Technology Zurich (ETH), “By pushing, pulling, and twisting, these devices are navigated through the tortuous pathways of the blood vessels. The outcome of the procedure heavily relies on the clinician’s skill and the device’s ability to navigate to the affected target region in the bloodstream, which is often inhibited by tortuous blood vessels. Sharp turns require high flexibility, but this flexibility inhibits translation of proximal insertion to distal tip advancement. We present a highly dexterous, magnetically steered continuum robot that overcomes pushability limitations through rotation. A helical protrusion on the device’s surface engages with the vessel wall and translates rotation to forward motion at every point of contact. An articulating magnetic tip allows for active steerability, enabling navigation from the aortic arch to millimeter-sized arteries of the brain.”
      原文链接:
    • NETL
    • NSTL
    • Amer Assoc Advancement Science

    Researchers at University of Aizu Zero in on Machine Learning (Human Activity Recognition via Wi-Fi and Inertial Sensors With Machine Learning)

    83-84页
    查看更多>>摘要：Research findings on artificial intelligence are discussed in a new report. According to news reporting out of the University of Aizu by NewsRx editors, research stated, “Human activity recognition (HAR) plays a crucial role in human-computer interaction, smart home, health monitoring and elderly care. However, existing methods typically utilize camera, radio frequency (RF) signals or wearable devices for activity recognition.” Funders for this research include Japan Society For The Promotion of Science (Jsps) Kakenhi; Jka Foundation; New Energy And Industrial Technology Development Organization (Nedo) Intensive Support For Young Promising Researchers. The news correspondents obtained a quote from the research from University of Aizu: “Each singlesensor modality has its inherent limitations, like camera-based methods having blind spots, Wi-Fi-based methods depending on the environment and the inconvenience of wearing Inertial Measurement Unit (IMU) devices. In this paper, we propose a HAR system that leverages three types of sensor combinations: Wi-Fi, IMU and a hybrid of Wi-Fi+IMU. We utilize the Channel State Information (CSI) provided by Wi-Fi and the accelerometer and gyroscope data from IMU devices to capture activity characteristics. Then, we employ six machine learning algorithms to recognize eight types of daily activities. These algorithms include Support Vector Machine (SVM), Multi-layer Perceptron (MLP), Decision Tree, Random Forest, Logistic Regression and k-Nearest Neighbors (kNN). Additionally, we investigate the accuracy of hand gesture recognition using different sensor combinations and analyze the calculation speed of each combination. We conduct a survey to collect user feedback on the performance of various sensor combinations in our HAR system. The results show that the combination of CSI+IMU yields the best HAR recognition accuracy, with a accuracy of 89.38%. The SVM algorithm consistently performs well across all systems, especially excelling in the CSI+IMU system supported by energy and average Fast Fourier Transform (FFT) features.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Investigators at Nanyang Technological University Describe Findings in Machine Learning (An Interpretable English Reading Proficiency Detection Model In an Online Learning Environment: a Study Based On Eye Movement)

    84-85页
    查看更多>>摘要：Researchers detail new data in Machine Learning. According to news originating from Singapore, Singapore, by NewsRx correspondents, research stated, “Aiming at the low English reading proficiency of ESL (English as second language) students in online learning environments, this study proposed an eye-movement-based machine learning monitoring model to detect English reading proficiency in real time. Eye-movement data from 43 students while completing online English reading tasks were recorded and 31 eye-movement features were extracted from the taxonomy of fixation, saccade, movement direction and gaze velocity.” Financial support for this research came from Singapore Maritime Institute Research Project. Our news journalists obtained a quote from the research from Nanyang Technological University, “During the model training phase, LightGBM achieved an accuracy of 96.51 % in detection. An interpretable model, SHAP (SHapley Additive exPlanation), was used to explain the main effects of eye-movement features in detection, where high gaze velocity, absolute saccade direction, and average saccade duration were found to be strong indicators of English reading proficiency. Furthermore, SHAP analysis allows the identification of individual factors contributing to differences in English reading proficiency.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier