查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Wading through the staggering amount o f social media content being produced every second to find the nastiest bits is no task for humans alone. Even with the newest deep-learning tools at their disposal, the employees who id entify and review problematic posts can be overwhelmed and often traumatized by what they encounter every day. Gigworking annotators, who analyze and label dat a to help improve machine learning, can be paid pennies per unit worked. In a new Concordia-led paper published in IEEE Technology and Society Magazine, researchers argue that supporting these human workers is essential and requires a constant re-evaluation of the techniques and tools they use to identify toxic content. The authors examine social, policy and technical approaches to automatic toxicit y detection and consider their shortcomings while also proposing potential solut ions. "We want to know how well current moderating techniques, which involve both mach ine learning and human annotators of toxic language, are working," says Ketra Sc hmitt, one of the paper's co-authors and an associate professor with the Centre for Engineering in Society at the Gina Cody School of Engineering and Computer S cience.
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 Berkeley, Cali fornia, by NewsRx editors, the research stated, "As artificial intelligence (AI) systems become more prevalent in various domains, ensuring their trustworthines s and efficiency becomes increasingly important. This article presents an analys is of the tradeoffs between different dimensions of trustworthy AI, including tr ansparency, robustness, fairness, accountability, efficiency, privacy, and human interaction." Our news journalists obtained a quote from the research from the University of C alifornia Berkeley, "The challenges and opportunities that arise when striving f or a balance among these dimensions are also discussed. Recommendations for achi eving an optimal balance are also offered." According to the news editors, the research concluded: "By understanding and add ressing these tradeoffs, we can foster AI systems that perform efficiently but a lso maintain user trust and societal values."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Data detailed on artificial intelligence have bee n presented. According to news originating from Falmouth University by NewsRx ed itors, the research stated, "The emergence of artificial intelligence (AI) in ac ademia has prompted various debates on the uses, threats, and limitations of too ls that can create text for numerous academic purposes." The news correspondents obtained a quote from the research from Falmouth Univers ity: "Critics argue that these advancements may provide opportunities for cheati ng and plagiarism and even replace the art of writing entirely. To reclaim the c reativity and depth that academic writing holds, we propose both an innovative a pproach to safeguard the creativity and depth of academic writing and a scaffold ed way to enhance success in terms of authenticity for the author and, by extens ion, meaning for the reader. This novel conceptual algorithmic trickle filter mo del aims to inform successful academic writing and embody the writer's agency-a task too sophisticated for current AI tools. Our model provides a scaffolded dec ision-making process in a highly personal, flexible, and iterative individual wr iting development tool applied in a health-conscious way."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Central Nervous System Diseases and Conditions - Temporal Lobe Epilepsy is the subject of a report. Ac cording to news reporting out of Changsha, People's Republic of China, by NewsRx editors, research stated, "Structural-functional coupling (SFC) has shown great promise in predicting postsurgical seizure recurrence in patients with temporal lobe epilepsy (TLE). In this study, we aimed to clarify the global alterations in SFC in TLE patients and predict their surgical outcomes using SFC features." Financial supporters for this research include National Natural Science Foundati on of China, National Basic Research Program of China. Our news journalists obtained a quote from the research from Central South Unive rsity, "This study analyzed presurgical diffusion and functional magnetic resona nce imaging data from 71 TLE patients and 48 healthy controls (HCs). TLE patient s were categorized into seizure-free (SF) and non-seizure-free (nSF) groups base d on postsurgical recurrence. Individual functional connectivity (FC), structura l connectivity (SC), and SFC were quantified at the regional and modular levels. The data were compared between the TLE and HC groups as well as among the TLE, SF, and nSF groups. The features of SFC, SC, and FC were categorized into three datasets: the modular SFC dataset, regional SFC dataset, and SC/FC dataset. Each dataset was independently integrated into a cross-validated machine learning mo del to classify surgical outcomes. Compared with HCs, the visual and subcortical modules exhibited decoupling in TLE patients (p <.05). Mu ltiple default mode network (DMN)-related SFCs were significantly higher in the nSF group than in the SF group (p <.05). Models trained us ing the modular SFC dataset demonstrated the highest predictive performance. The final prediction model achieved an area under the receiver operating characteri stic curve of .893 with an overall accuracy of .887. Presurgical hyper-SFC in th e DMN was strongly associated with postoperative seizure recurrence."
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 originating from Madrid, Spain, by NewsRx correspondents, research stated, "The irruption of advanced technologi es and the limited knowledge of software architectures are making it difficult f or many small and medium-sized manufacturing companies to keep up with what is b eing called the fourth industrial revolution (Industry 4.0, Industry of the Futu re). Container orchestration platforms provide layers of simplification for key requirements such as interoperability, security, and privacy, and provide mechan isms that allow companies and technology providers to focus on their specific fu nctionalities and goals, instead of investing considerable time and effort in th e underlying platform on which the solution will operate." Our news journalists obtained a quote from the research from Universidad Politec nica de Madrid, "This article focuses on these platforms and the issues when dev eloping them, and proposes a risk- and goaloriented hybrid meta-framework for s ecurity and privacy analysis. The meta-framework uses well-known security and pr ivacy standards and frameworks as a reference and can be used to understand asse ts and requirements and, in particular, to select and configure countermeasures. For practical evaluation of the meta-framework, it was applied to a real case."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators discuss new findings in artificial intelligence. According to news reporting originating from Recife, Brazil, by Ne wsRx correspondents, research stated, "Precision agriculture requires accurate m ethods for classifying crops and soil cover in agricultural production areas." Financial supporters for this research include Cnpq; Facepe; Ministry of Integra tion And Regional Development; Capes-print/ufrpe; Foundation For Science And Tec hnology, I.P.. The news editors obtained a quote from the research from Federal Rural Universit y of Pernambuco: "The study aims to evaluate three machine learning-based classi fiers to identify intercropped forage cactus cultivation in irrigated areas usin g Unmanned Aerial Vehicles (UAV). It conducted a comparative analysis between mu ltispectral and visible Red-Green-Blue (RGB) sampling, followed by the efficienc y analysis of Gaussian Mixture Model (GMM), K-Nearest Neighbors (KNN), and Rando m Forest (RF) algorithms. The classification targets included exposed soil, mulc hing soil cover, developed and undeveloped forage cactus, moringa, and gliricidi a in the Brazilian semiarid. The results indicated that the KNN and RF algorithm s outperformed other methods, showing no significant differences according to th e kappa index for both Multispectral and RGB sample spaces. In contrast, the GMM showed lower performance, with kappa index values of 0.82 and 0.78, compared to RF 0.86 and 0.82, and KNN 0.86 and 0.82. The KNN and RF algorithms performed we ll, with individual accuracy rates above 85% for both sample space s."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on intelligent syste ms have been published. According to news originating from Salerno, Italy, by Ne wsRx editors, the research stated, "In the last year, Large Language Models (LLM s) have transformed the way of tackling problems, opening up new perspectives in various works and research fields, due to their ability to generate and underst and human languages." Our news reporters obtained a quote from the research from University of Salerno : "In this regard, the recent release of Claude 2.0 has contributed to the proce ssing of more complex prompts. In this scenario, the goal of this paper is to ev aluate the effectiveness of Claude 2.0 in a specific classification task. In par ticular, we considered the Forest cover-type problem, concerning the prediction of a cover-type value according to the geospatial characterization of target wor ldwide areas. To this end, we propose a novel iterative prompt template engineer ing approach, which integrates files by exploiting prompts and evaluates the qua lity of responses provided by the LLM. Moreover, we conducted several comparativ e analyses to evaluate the effectiveness of Claude 2.0 with respect to online an d batch learning models." According to the news editors, the research concluded: "The results demonstrated that, although some online and batch models performed better than Claude 2.0, t he new iterative prompt engineering approach improved the quality of responses, leading to better performance with increases ranging from 14% to 3 2 % in terms of accuracy, precision, recall, and F1-score."
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 reporting originating from Arrasate M ondragon, Spain, by NewsRx correspondents, research stated, "In the transition t owards more sustainable ships, electric motors (EM) are being used in ship propu lsion systems to reduce emissions and increase efficiency. The safe operation of ships is crucial, and prognostics and health management applications have emerg ed as effective solutions to transit towards monitored reliable systems." Funders for this research include Research Council of Norway, Juan de la Cierva Incorporacion Fellowship, Spanish State Research Agency, Basque Government. Our news editors obtained a quote from the research from Mondragon University, " In this context, this paper presents a probabilistic EM prognostics model integr ating data-driven operational models and physics-informed degradation models. Fi rstly, motor torque and winding temperature are estimated through connected mach ine learning models, which are based on operational and meteorological data. Ope rational and meteorological variables drive the EM degradation model and enable the analysis of EM degradation under different operational and environmental con ditions. Subsequently, EM remaining useful life (RUL) is predicted within a prob abilistic Monte-Carlo approach combining the thermal-stress model along with the associated uncertainties. The methodology is tested on a real case study of the OV Ryvingen vessel, with collected data during voyages along the Norwegian coas t."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting out of Gorlitz, Germany, by N ewsRx editors, research stated, "High-content image-based screening is widely us ed in Drug Discovery and Systems Biology. However, sample preparation artefacts may significantly deteriorate the quality of image-based screening assays." Financial supporters for this research include Sachsisches Staatsministerium fur Wissenschaft und Kunst, Bundesministerium fur Bildung und Forschung. Our news journalists obtained a quote from the research from Center for Advanced Systems Understanding (CASUS), "While detection and circumvention of such artef acts could be addressed using modern-day machine learning and deep learning algo rithms, this is widely impeded by the lack of suitable datasets. To address this , here we present a purpose-created open dataset of high-content microscopy samp le preparation artefact. It consists of high-content microscopy of laboratory du st titrated on fixed cell culture specimens imaged with fluorescence filters cov ering the complete spectral range. To ensure this dataset is suitable for superv ised machine learning tasks like image classification or segmentation we propose rule-based annotation strategies on categorical and pixel levels."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news reporting out of Hangzhou, People' s Republic of China, by NewsRx editors, research stated, "Solid-state batteries are currently developing into one of the most promising battery types for both t he electrification of transport and for energy storage applications due to their high energy density and safe operating behaviour." The news journalists obtained a quote from the research from Zhejiang University : "The performance of solid-state batteries is largely determined by the manufac turing process, particularly in the production of electrodes. However, efficient ly analysing the effects of key manufacturing features and predicting the mass l oading of electrodes in the early stages of battery manufacturing remain a major challenge. In this study, a machine-learning-based approach is proposed to effe ctively analyse the importance of manufacturing features and accurately predict the mass loading of electrodes. Specifically, the importance of four key feature s during the manufacturing process of solid-state batteries is first quantified and analysed using a machine-learning-based method to analyse the importance of features. Then, four effective machinelearning- based regression methods, includ ing decision tree, boosted decision tree, support vector regression and Gaussian process regression, are used to predict the mass loading of the electrodes in t he mixing and coating stages. The comparative results show that the developed ma chine-learning-based approach is able to provide a satisfactory prediction of th e electrode mass loading of a solid-state battery with 0.995 R2 while successful ly quantifying the importance of four key features in the early manufacturing st ages." According to the news reporters, the research concluded: "Due to the advantages of its datadriven nature, the developed machine-learning-based approach can eff iciently assist engineers in monitoring/ predicting the electrode mass loading of solid-state batteries and analysing/quantifying the importance of manufacturing features of interest. This could benefit the production of solid-state batterie s for further energy storage applications."
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