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 Robotic s. According to news originating from Beijing, People's Republic of China, by Ne wsRx correspondents, research stated, "The Five Hundred Meter Spherical Radio Te lescope (FAST) is the largest spherical radio telescope in the world, and there are more than 2,000 prism targets distributed on its reflector that require regu lar maintenance. These prism targets are screwed into the corresponding threaded holes by target bolts." Financial support for this research came from National Key Research and Developm ent Program of China.
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 out of Yibin, People's Republic of China, by NewsRx editors, research stated, "This paper proposes a precise motion control strategy for a three-wheeled mobile robot with two driven rear wheels and one st eered front wheel so that an obstacle avoidance motion task is able to be well i mplemented." Funders for this research include National Natural Science Foundation of China. The news journalists obtained a quote from the research from Yibin University: " Initially, the motion laws under nonholonomic constraints are expounded for the three-wheeled mobile robot in order to facilitate the derivation of its dynamic model. Subsequently, a prescribed target curve is converted into a speed target through the nonholonomic constraint of zero lateral speed. A modified dynamical tracking target that is aligned with the dynamic model is then developed based o n the relative curvature of the prescribed curve. By applying this dynamical tra cking target, path tracking precision is enhanced through appropriate selection of a yaw motion speed target, thus preventing speed errors from accumulating dur ing relative curvature tracking."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Artificial Intelligence is the su bject of a report. According to news originating from Providence, Rhode Island, by NewsRx correspondents, research stated, "Symptoms of normal pressure hydrocep halus (NPH) are sometimes refractory to shunt placement, with limited ability to predict improvement for individual patients. We evaluated an MRI-based artifici al intelligence method to predict post-shunt NPH symptom improvement." Our news journalists obtained a quote from the research, "NPH patients who under went magnetic resonance imaging (MRI) prior to shunt placement at a single cente r (2014-2021) were identified. Twelvemonth post-shunt improvement in modified R ankin Scale (mRS), incontinence, gait, and cognition were retrospectively abstra cted from clinical documentation. 3D deep residual neural networks were built on skull stripped T2-weighted and fluid attenuated inversion recovery (FLAIR) imag es. Predictions based on both sequences were fused by additional network layers. Patients from 2014-2019 were used for parameter optimization, while those from 2020-2021 were used for testing. Models were validated on an external validation dataset from a second institution (n=33). Of 249 patients, n=201 and n=185 were included in the T2-based and FLAIR-based models according to imaging availabili ty. The combination of T2- weighted and FLAIR sequences offered the best performa nce in mRS and gait improvement predictions relative to models trained on imagin g acquired using only one sequence, with AUROC values of 0.7395 [0.5765-0.9024] for mRS and 0.8816 [0.8030- 0.9602] for gait. For urinary incontinence and cognition, com bined model performances on predicting outcomes were similar to FLAIR-only perfo rmance, with AUROC values of 0.7874 [0.6845-0.8903] and 0.7230 [0.5600-0.8859]. Application of a combined algorithm using both T2-weighted and FLAIR sequences offered the bes t image-based prediction of post-shunt symptom improvement, particularly for gai t and overall function in terms of mRS."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Punjab, Pakist an, by NewsRx editors, research stated, "Driving while drowsy poses significant risks, including reduced cognitive function and the potential for accidents, whi ch can lead to severe consequences such as trauma, economic losses, injuries, or death." Our news journalists obtained a quote from the research from Khwaja Fareed Unive rsity of Engineering and Information Technology: "The use of artificial intellig ence can enable effective detection of driver drowsiness, helping to prevent acc idents and enhance driver performance. This research aims to address the crucial need for real-time and accurate drowsiness detection to mitigate the impact of fatigue-related accidents. Leveraging ultra-wideband radar data collected over f ive minutes, the dataset was segmented into one-minute chunks and transformed in to grayscale images. Spatial features are retrieved from the images using a two- dimensional Convolutional Neural Network. Following that, these features were us ed to train and test multiple machine learning classifiers. The ensemble classif ier RF-XGB-SVM, which combines Random Forest, XGBoost, and Support Vector Machin e using a hard voting criterion, performed admirably with an accuracy of 96.6% ."
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 artificial in telligence have been published. According to news reporting originating from Aub urn, Alabama, by NewsRx correspondents, research stated, "The use of wearable se nsors, such as inertial measurement units (IMUs), and machine learning for human intent recognition in health-related areas has grown considerably. However, the re is limited research exploring how IMU quantity and placement affect human mov ement intent prediction (HMIP) at the joint level." Financial supporters for this research include United States Army Combat Capabil ities And Development Command. The news editors obtained a quote from the research from Auburn University: "The objective of this study was to analyze various combinations of IMU input signal s to maximize the machine learning prediction accuracy for multiple simple movem ents. We trained a Random Forest algorithm to predict future joint angles across these movements using various sensor features. We hypothesized that joint angle prediction accuracy would increase with the addition of IMUs attached to adjace nt body segments and that non-adjacent IMUs would not increase the prediction ac curacy. The results indicated that the addition of adjacent IMUs to current join t angle inputs did not significantly increase the prediction accuracy (RMSE of 1 .92° vs. 3.32° at the ankle, 8.78° vs. 12.54° at the knee, and 5.48° vs. 9.67° a t the hip). Additionally, including non-adjacent IMUs did not increase the predi ction accuracy (RMSE of 5.35° vs. 5.55° at the ankle, 20.29° vs."
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 originating from the Universitas Islam Negeri by NewsRx correspondents, research stated, "This study aims to understand the feelings of newly converted Muslims when they narrated their pre- and post- conversion using the Machine Learning model and qualitative approach. The data s et analyzed in this paper comes from in-depth interviews with 12 mualaf/ newly c onverted Muslims from various backgrounds." Our news journalists obtained a quote from the research from Universitas Islam N egeri: "All recorded interviews were transcribed and filtered to remove any unne cessary or misaligned data to ensure that the data was fully aligned with the in terview questions. To analyze emotional changes, we utilize natural language pro cessing (NLP) algorithms, which enable us to extract and interpret emotional con tent from textual data sources, such as personal narratives. The analysis was pe rformed in Google Colab and utilizing XLM-EMO, a fine-tuned multilingual emotion detection model that detects joy, anger, fear, and sadness emotions from text. The model was chosen because it supports Bahasa, as our interview was conducted in Bahasa. Furthermore, the model also has the best accuracy amongst its competi tors, namely LS-EMO and UJ-Combi. The model also has great performance, with the overall average Macro-F1s for XLMRoBERTa- large, XLM-RoBERTa-base, and XLM-Twit ter-base are .86, .81, and .84. Furthermore, two psychologists compared emotion detection results from the XLM-EMO model to the raw input data, and an inductive content analysis was performed. This approach allowed us to identify the reason ing behind the emotions deemed pertinent and intriguing for our investigation. T his study showed that Sadness is the most dominant emotion, constituting 46.67% of the total emotions in the pre-conversion context."
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 originating from Nottingham, United Kingdom , by NewsRx editors, the research stated, "Introduction & The abil ity of policymakers to positively transform food environments requires robust em pirical evidence that can inform decisions. At present, there is limited data on food-insecurity in the UK that can be used to inform interventions by local aut horities, due to the prohibitive costs and logistical challenges of administerin g longitudinal surveys." The news correspondents obtained a quote from the research from Nottingham Unive rsity Business School: "This study builds on existing research and a key pilot s tudy developed in partnership between Olio - a food-sharing app with 7 million r egistered users as of 2023, the University of Nottingham and Havering Council in 2020, which resulted in the world's first map prototype of food-insecurity. Obj ectives & Approach Our approach leverages Machine Learning methods applied to unprecedented food-acquisition behavioural data and open area-level deprivation statistics to model and predict individuals' experience of food-inse curity across London. We used Olio's extensive network of users to distribute 2, 849 surveys, asking respondents across London about their experiences of food-in security. The survey was distributed online, adapting the US Department of Agric ulture Food Security module. Respondents were asked about their experiences, inc luding (1) eating smaller meals or skipping meals, (2) being hungry but being un able to eat, and (3) not eating for a whole day, because they could not afford f ood or because they could not get access to food. Using the household, rather th an the individual-level version of the food insecurity module helped shed light on the experience of vulnerable groups - such as children. Relevance to Digital Footprints The survey responses provided a ground truth about users' experiences of destitution. Deprivation metrics and digital footprint data in the form of f ood-acquisition behavioural data were then used in a Random Forests Machine Lear ning model to predict whether households were experiencing foodinsecurity, achi eving high accuracy. Food-sharing data from almost 50,000 London-based users act ive on Olio's platform were then used to identify relevant food-seeking behaviou rs and aggregate recognised instances of food-insecurity at neighbourhood (MSOA) level."
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 Machine Trans lation have been published. According to news reporting out of Kunming, People's Republic of China, by NewsRx editors, research stated, "In recent years, the ut ilization of deep models has significantly enhanced the performance of neural ma chine translation (NMT). Nevertheless, the uneven distribution of data leads to critical challenges." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Yunnan Provincial Science and Technology Major Special Proje ct, Yunnan Provincial Basic Research Programme Project. Our news journalists obtained a quote from the research from Kunming University, "Specifically, lowfrequency words severely affect translation performance. Thi s is especially in low-resource language translation, where the training of low- frequency words is inadequate. To address this issue, we use syntactic and word frequency information to enhance the performance of encoding representations of input sequence. we propose a simple approach called Dynamic Routing Attention (D RA). When processing different words, DRA dynamically adjusts the Self-attention weight based on word frequency and source syntactic, which enables the encoder Self-attention to focus on the surrounding words and the words with syntactic as sociations rather than the current word solely. Consequently, our method improve s the representation capability of the encoder in processing sentences containin g low-frequency words. Using Transformer RPR model as a baseline model, we demon strate the effectiveness of our method with the experiments on machine translati on tasks of WMT14 English-German, IWLST14 English-German, IWLST14 English-Vietna mese, and TED talk Thai-Chinese."
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 Machine Learn ing have been published. According to news originating from Changchun, People's Republic of China, by NewsRx correspondents, research stated, "Anaerobic ferment ation for hydrogen production is influenced by various environmental factors tha t can limit microbial activity. However, machine learning (ML) shows significant potential in explaining the complexity of biological processes." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), National Science and Technology Council, Taiwan, Science and Technology Program of Jilin Province, Scientific research project of Ecological Environment Department of Jilin Province, Key Laboratory of Songliao Aquatic En vironment, Ministry of Education (Openend Funds), Jilin Provincial Science and T echnology Department Science and Technology Innovation and Entrepreneurship outs tanding talent program for young and middle-aged.
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 brain-based d evices have been published. According to news reporting out of First Hospital of China Medical University by NewsRx editors, research stated, "In recent years, with the shift of focus in metaverse research toward content exchange and social interaction, breaking through the current bottleneck of audio-visual media inte raction has become an urgent issue." Our news reporters obtained a quote from the research from First Hospital of Chi na Medical University: "The use of brain-machine interfaces for sensory simulati on is one of the proposed solutions. Currently, brain-machine interfaces have de monstrated irreplaceable potential as physiological signal acquisition tools in various fields within the metaverse. This study explores three application scena rios: generative art in the metaverse, serious gaming for healthcare in metavers e medicine, and brain-machine interface applications for facial expression synth esis in the virtual society of the metaverse. It investigates existing commercia l products and patents (such as MindWave Mobile, GVS, and Galea), draws analogie s with the development processes of network security and neurosecurity, bioethic s and neuroethics, and discusses the challenges and potential issues that may ar ise when brain-machine interfaces mature and are widely applied."
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