It is generally believed that oil does not absorb microwave energy due to the small dielectric loss factor (only 1/100 that of water). Here, we compared the heating rates of four cooking oils versus water in a microwa...
详细信息
The development and optimisation of energy-efficient communication protocols that are specifically adapted for Internet of Things (IoT) devices is the focus of this research. The proliferation of interoperable bias ha...
详细信息
ISBN:
(数字)9798350365092
ISBN:
(纸本)9798350365108
The development and optimisation of energy-efficient communication protocols that are specifically adapted for Internet of Things (IoT) devices is the focus of this research. The proliferation of interoperable bias has given rise to the converse girding energy consumption, which has come a consummate concern in the contemporary period. The primary end of this bid is to probe innovative approaches and protocols with the capacity to reduce the energy consumption of Internet of Effects(IoT) connections. As a result, the bias will retain the capability to attain an extended functional lifetime while coincidently producing a lowered environmental footmark. The presented protocols aim to achieve a result that balances the utilisation of energy coffers with the conservation of dependable communication. A thorough examination of the presently employed protocols, coupled with the perpetration of sophisticated optimisation ways, will be the styles employed to achieve this thing. Theprovides a significant donation to the development and prosecution of communication protocols that effectively address the unique energy constraints encountered by Internet of effects(10T)- connected ecosystems. This contributes to the enhancement of effectiveness and sustainability in the world of networked bias, which is expanding at an accelerated rate.
Referring to solution programs written by other users is helpful for learners in programming education. However, current online judge systems just list all solution programs submitted by users for references, and the ...
详细信息
This paper investigates the Agile development processes in tech companies, focusing on strategies for effective backlog management, tool utilization for progress tracking, Agile methodology integration, stakeholder in...
This paper investigates the Agile development processes in tech companies, focusing on strategies for effective backlog management, tool utilization for progress tracking, Agile methodology integration, stakeholder involvement, and handling challenges in the tech domain. The study reveals the adaptability of Agile methodologies to diverse organizational needs and objectives, emphasizing their significance in the technical industry. Companies prioritize backlog tasks through subtask breakdown, Story Points, and priority tags, aligning with Agile principles. The integration of Agile involves Jira and Scrum, enhancing development efficiency. Stakeholders and team members engage through planning and design sessions, ensuring collaboration. Challenges are addressed through regular meetings, technical discussions, and transparency. Knowledge transfer sessions keep teams updated. Data security is maintained through NDA agreements and robust security measures. Metrics like story points and KPIs are tracked for Agile success evaluation. The paper concludes by highlighting the importance of aligning process models with organizational requirements.
Research on software reliability growth models (SRGMs) has been extensively conducted for decades, and the models were often developed based on two assumptions: (1) once the errors are detected, they can be completely...
详细信息
In this paper, as the restriction of a rooted labeled unordered tree (tree, for short), we introduce a rooted labeled k-caterpillar(k-caterpillar, for short) that is a tree T whose number of the leaves in the tree obt...
详细信息
The Linux kernel extensively uses the Berkeley Packet Filter (BPF) to allow user-written BPF applications to execute in the kernel space. The BPF employs a verifier to check the security of user-supplied BPF code stat...
详细信息
For large-scale cyber-physical systems, the collaboration of spatially distributed sensors is often needed to perform the state estimation process. Privacy concerns naturally arise from disclosing sensitive measuremen...
详细信息
Accurate surgical video semantic segmentation is vital for computer-aided surgery. Semi-supervised algorithms produce pseudo labels to solve the problem of the lack of labels, as it is very difficult to obtain the pix...
Accurate surgical video semantic segmentation is vital for computer-aided surgery. Semi-supervised algorithms produce pseudo labels to solve the problem of the lack of labels, as it is very difficult to obtain the pixel-level segmentation labels from doctors or researchers. However, most of the algorithms consider the videos as independent images, which cannot solve some issues caused by complex surgery scenarios, such as blurred instruments. The paper proposes a novel Cross Supervision of Inter-frame (CSI) method using inter-frame information from surgery video to crosswise supervise semantic segmentation. Specifically, we design Inter-frame Information Transformation (I2T) modules to transfer features with class prototypes between continuous frames mutually. Besides, we utilize ground truth to supervise inter-frame features for labeled frames, and for unlabeled frames, we propose a cross pseudo loss and a pixel-wise contrastive loss as the constraints. Extensive experiments are performed on a publicly available cataract surgery dataset, which proves that our CSI method improves the segmentation accuracy after considering the inter-frame information.
In an era where both the general public and established news outlets increasingly rely on social media for real-time information, the abundance of rumors offers a huge difficulty. False information can have far-reachi...
In an era where both the general public and established news outlets increasingly rely on social media for real-time information, the abundance of rumors offers a huge difficulty. False information can have far-reaching implications, affecting individuals, communities, and even entire countries. To address this issue, a low-cost, self-regulating, and forward-thinking rumor detection technique is required. This research performs an intensive analysis of the performance of three robust machine learning algorithms, including XGBoost, SVM, and Random Forest, as well as two deep learning-based transformers, namely BERT and DistilBert. In this research, the models are trained and evaluated on a combined dataset comprising data from Twitter15 and Twitter16 datasets. Support Vector Machine (SVM) and Random Forest exhibit the best accuracy among classical machine learning models, reaching 89.05%. In comparison, among the transformer-based deep learning models, BERT achieves the best accuracy of 90.20%. In conclusion, the learning-based transformers beat its competitors in terms of accuracy, recall, precision, and F-measure, proving its efficacy in minimizing the detrimental impact of rumors on people, communities, and society as a whole.
暂无评论