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 originating from Sydney, Australia, by NewsRx correspondents, research stated, "The persistence of SMS spam remains a significant challenge, highlighting the need for research aimed at developing systems capable of effectively handling the evasive strategies used by spammers. Such research efforts are important for safeguarding the general public from the detrimental impact of SMS spam." Our news editors obtained a quote from the research from Macquarie University: "In this study, we aim to highlight the challenges encountered in the current landscape of SMS spam detection and filtering. To address these challenges, we present a new SMS dataset comprising more than 68K SMS messages with 61% legitimate (ham) SMS and 39% spam messages. Notably, this dataset, we release for further research, represents the largest publicly available SMS spam dataset to date. To characterize the dataset, we perform a longitudinal analysis of spam evolution. We then extract semantic and syntactic features to evaluate and compare the performance of well-known machine learning based SMS spam detection methods, ranging from shallow machine learning approaches to advanced deep neural networks. We investigate the robustness of existing SMS spam detection models and popular anti-spam services against spammers' evasion techniques. Our findings reveal that the majority of shallow machine learning based techniques and anti-spam services exhibit inadequate performance when it comes to accurately classifying SMS spam messages."
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 reporting originating from Fukushima, Japan, by NewsRx correspondents, research stated, "In the decommissioning of the Fukushima Daiichi Nuclear Power Station, radiation dose calculations necessitate a 3D model of the workspace are performed to determine suitable measures for reducing exposure." Our news editors obtained a quote from the research from Japan Atomic Energy Agency: "However, the construction of a 3D model from a 3D point cloud is a costly endeavor. To separate the geometrical shape regions on 3D point cloud, we are developing a structure discrimination method using 3D and 2D deep learning to contribute to the advancement of 3D modeling automation technology. In this paper, we present a method for transferring and fusing labels to handle 2D prediction labels in 3D space." According to the news editors, the research concluded: "We propose an exhaustive label fusion method designed for plant facilities with intricate structures. Through evaluation on a mock-up plant dataset, we confirmed the method's effective performance."
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 the subject of a report. According to news reporting originating in Vilnius, Lithuania, by NewsRx journalists, research stated, "Scanning electrochemical microscopy (SECM) is a scanning probe microscope with an ultramicroelectrode (UME) as a probe. The technique is advantageous in the characterization of the electrochemical properties of surfaces." The news reporters obtained a quote from the research from Vilnius Gediminas Technical University, "However, the limitations, such as slow imaging and many functions depending on the user, only allow us to use some of the possibilities. Therefore, we applied visual recognition and machine learning to detect micro-objects from the image and determine their electrochemical activity. The reconstruction of the image from several approach curves allows it to scan faster and detect active areas of the sample. Therefore, the scanning time and presence of the user is diminished." According to the news reporters, the research concluded: "An automated scanning electrochemical microscope with visual recognition has been developed using commercially available modules, relatively low-cost components, design, software solutions proven in other fields, and an original control and data fusion algorithm."
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 Machine Learning. According to news originating from Shenzhen, People's Republic of China, by NewsRx correspondents, research stated, "The evolution of pathway enzymes enhances the biosynthesis of high-value chemicals, crucial for pharmaceutical, and agrochemical applications. However, unpredictable evolutionary landscapes of pathway genes often hinder successful evolution." Funders for this research include National Key Research and Development Program of China, National Natural Science Foundation of China. Our news journalists obtained a quote from the research from the Chinese Academy of Sciences, "Here, the presence of complex epistasis is identifued within the representative naringenin biosynthetic pathway enzymes, hampering straightforward directed evolution. Subsequently, a biofoundry-assisted strategy is developed for pathway bottlenecking and debottlenecking, enabling the parallel evolution of all pathway enzymes along a predictable evolutionary trajectory in six weeks. This study then utilizes a machine learning model, ProEnsemble, to further balance the pathway by optimizing the transcription of individual genes. The broad applicability of this strategy is demonstrated by constructing an Escherichia coli chassis with evolved and balanced pathway genes, resulting in 3.65 g L-1 naringenin. The optimized naringenin chassis also demonstrates enhanced production of other flavonoids. This approach can be readily adapted for any given number of enzymes in the specific metabolic pathway, paving the way for automated chassis construction in contemporary biofoundries. A biofoundry-assisted strategy for pathway bottlenecking and debottlenecking enables the parallel evolution of all pathway enzymes along a predictable evolutionary trajectory. A machine learning model can further relax the epistasis of the evolved pathway by optimizing the corresponding promoter combinations."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators discuss new findings in Machine Learning. According to news reporting from San Vicente del Raspeig, Spain, by NewsRx journalists, research stated, "The rise of Artificial Intelligence (AI) and Deep Learning has led to Machine Learning (ML) becoming a common practice in academia and enterprise. However, a successful ML project requires deep domain knowledge as well as expertise in a plethora of algorithms and data processing techniques." Funders for this research include AETHER-UA project, Spanish Government, Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital (Generalitat Valenciana), University of Alicante, Lucentia Lab Spin-off Company. The news correspondents obtained a quote from the research from the University of Alicante, "This leads to a stronger dependency and need for communication between developers and stakeholders where numerous requirements come into play. More specifically, in addition to functional requirements such as the output of the model (e.g. classification, clustering or regression), ML projects need to pay special attention to a number of non-functional and quality aspects particular to ML. These include explainability, noise robustness or equity among others. Failure to identify and consider these aspects will lead to inadequate algorithm selection and the failure of the project. In this sense, capturing ML requirements becomes critical. Unfortunately, there is currently an absence of ML requirements modeling approaches. Therefore, in this paper we present the first i* extension for capturing ML requirements and apply it to two real-world projects. Our study covers two main objectives for ML requirements: (i) allows domain experts to specify objectives and quality aspects to be met by the ML solution, and (ii) facilitates the selection and justification of the most adequate ML approaches. Our case studies show that our work enables better ML algorithm selection, preprocessing implementation tailored to each algorithm, and aids in identifying missing data."
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 intelligence have been published. According to news reporting out of Technical University Dortmund (TU Dortmund) by NewsRx editors, research stated, "The development dynamics of any new technology are usually associated with promises of its special performance and completely new application possibilities." Our news reporters obtained a quote from the research from Technical University Dortmund (TU Dortmund): "This is especially true for artificial intelligence (AI), prompting this contribution to inquire into which particular special features the technology promises. However, the imprecise rhetoric of that promise becomes apparent. Although it appears simple, clear, and convincing, it is fundamentally difficult to dispute and introduces multitudes of ambiguity, relying on fuzzy conceptual metaphors, very unspecific assessments, implicit misconceptions, technological determinism, and exaggerations of the future opportunities AI offers for economic and social progress."
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 Intelligence have been presented. According to news originating from Provo, Utah, by NewsRx editors, the research stated, "Artificial intelligence revolutionizes nursing informatics and healthcare by enhancing patient outcomes and healthcare access while streamlining nursing workflow." Our news journalists obtained a quote from the research from Brigham Young University, "These advancements, while promising, have sparked debates on traditional nursing ethics like patient data handling and implicit bias. The key to unlocking the next frontier in holistic nursing care lies in nurses navigating the delicate balance between artificial intelligence and the core values of empathy and compassion."
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 Machine Learning. According to news reporting from Montreal, Canada, by NewsRx journalists, research stated, "Given the democratic nature of open source development, code review and issue discussions may be uncivil. Incivility, defined as features of discussion that convey an unnecessarily disrespectful tone, can have negative consequences to open source communities." Financial support for this research came from CGIAR. The news correspondents obtained a quote from the research from Polytechnique Montreal, "To prevent or minimize these negative consequences, open source platforms have included mechanisms for removing uncivil language from the discussions. However, such approaches require manual inspection, which can be overwhelming given the large number of discussions. To help open source communities deal with this problem, in this paper, we aim to compare six classical machine learning models with BERT to detect incivility in open source code review and issue discussions. Furthermore, we assess if adding contextual information in the previous email/comment improves the models' performance and how well the models perform in a cross-platform setting. We found that BERT performs better than classical machine learning models, with a best F1-score of 0.95. Furthermore, classical machine learning models tend to underperform to detect tone-bearing and civil discussions. Our results show that adding the previous email/comment to BERT did not improve its performance and that none of the analyzed classifiers had an outstanding performance in a cross-platform setting."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Robotics are discussed in a new report. According to news reporting originating in Nanjing, People's Republic of China, by NewsRx journalists, research stated, "The robotic arm has the characteristics of multi-degree-of-freedom motion and can perform complex tasks, making it the first choice to replace manual operations in space environment. However, traditional robotic arms still face many challenges in achieving both lightweight and high precision." Financial support for this research came from National Natural Science Foundation of China (NSFC). The news reporters obtained a quote from the research from the Nanjing University of Aeronautics and Astronautics, "To overcome this, we present an integrated stator -rotor piezoelectric actuator that integrates structural and functional design, enabling substantial weight reduction and high motion accuracy. The proposed design meets the requirements of the aerospace field, making it an ideal replacement for manual operations in space. The mechanism is composed of two sets of orthogonally linked rotary joints with driven arm joints enabling rotation in two vertical directions. To reduce the impact of clamping on vibration characteristics, the vibration modes and structural parameters are optimized through simulation. The size of the fabricated prototype is 160 x 60 x 60 mm, and its weight is only 24 g. The experimental results show that the maximum motion speed of the mechanism is 620 deg/s and the stalling torque is 20 mN m at 300 Vpp. The minimum resolution can reach 25 mu rad in pulse mode, and a startup and shutdown response time of 45 ms and 31 ms at 200 Vpp, respectively."
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 from Algiers, Algeria, by NewsRx journalists, research stated, "This paper presents a new algorithm for relocating sensors in a Wireless Sensor and Robots Network (WSRN) using a mobile robot. The goal is to repair coverage holes using redundant sensors that are caused by an initial random deployment." The news correspondents obtained a quote from the research from the University of Science and Technology, "The holes are repaired without prior knowledge of their positions or that of the redundant sensors. The existing solutions mainly focus either on how to optimally repair holes by determining to where relocate redundant sensors, or how to build a repair path with assumption that the positions of holes and redundant sensors are known. In both scenarios, the literature lacked the optimization of the robot's path for its initial exploration to identify both the holes and redundant sensors. Our proposed solution introduces an efficient robot trajectory that utilizes stochastic paths that adhere to the principles of light reflection. This trajectory serves the dual purpose of identifying redundant sensors and detecting as well as repairing coverage holes. We achieve this by incorporating the law of large numbers into the light reflection principal, enabling the robot to move randomly while adhering to the pathways of light reflection to efficiently relocate the redundant sensors. This approach results in a highly efficient and effective sensor relocation process. The effectiveness of the proposed solution is assessed across multiple parameters, including relocation time, the length of the relocation path, the robot average moves, and the total energy consumption required to cover holes with varying carrying capacity, dimensions of region of interest, coverage ratio and exit angles of reflection. Through a series of extensive simulations, we provide compelling evidence that our proposed solution distinctly surpasses the existing state-of-the-art approaches. This notable advantage becomes evident in multiple aspects: from reduced relocation time and shorter relocation path length to minimized total energy consumption."
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