In this paper, the electromagnetic response of a planar array of electric ring resonators combining with a cross structure on a substrate is investigated. The retrieved effective dielectric parameters from scattering ...
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Drones have drawn considerable attention as the agents in wireless data collection for agricultural applications, by virtue of their three-dimensional mobility and dominant line-of-sight communication channels. Existi...
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Drones have drawn considerable attention as the agents in wireless data collection for agricultural applications, by virtue of their three-dimensional mobility and dominant line-of-sight communication channels. Existing works mainly exploit dedicated drones via deployment and maintenance, which is insufficient regarding resource and cost-efficiency. In contrast, leveraging existing delivery drones for the data collection on their way of delivery, called delivery drones’ piggybacking, is a promising solution. For achieving such cost-efficiency, drone scheduling inevitably stands in front, but the delivery missions involved have escalated it to a wholly different and unexplored problem. As an attempt, we first survey 514 delivery workers and conduct field experiments; noticeably, the collection cost, which mostly comes from the energy consumption of drones’ piggybacking, is determined by the decisions on package-route scheduling and data collection time distribution. Based on such findings, we build a new model that jointly optimizes these two decisions to maximize data collection amount, subject to the collection budget and delivery constraints. Further model analysis finds it a Mixed Integer Non-Linear Programming problem, which is NP-hard. The major challenge stems from interdependence entangling the two decisions. For this point, we propose Delta, a \(\frac{1}{9+\delta }\)-approximation delivery drone scheduling algorithm. The key idea is to devise an approximate collection time distribution scheme leveraging energy slicing, which transforms the complex problem with two interdependent variables into a submodular function maximization problem only with one variable. The theoretical proofs and extensive evaluations verify the effectiveness and the near-optimal performance of Delta.
Following two successful events in Guilin, People’s Republic of China (KSEM 2006) and in Melbourne, Australia (KSEM 2007) the third event in this conference series was held for the first time in Europe, namely, in Vi...
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ISBN:
(数字)9783642104886
ISBN:
(纸本)9783642104879
Following two successful events in Guilin, People’s Republic of China (KSEM 2006) and in Melbourne, Australia (KSEM 2007) the third event in this conference series was held for the first time in Europe, namely, in Vienna, Austria. KSEM 2009 aimed to be a communication platform and meeting ground for research on knowledge science, engineering and management, attracting high-quality, state-of-the-art publications from all over the world. It offers an exceptional opportunity for presenting original work, technological advances, practical problems and concerns of the research community. The importance of studying “knowledge” from different viewpoints such as science, engineering and management has been widely acknowledged. The accelerating pace of the "Internet age" challenges organizations to compress communication and innovation cycles to achieve a faster return on investment for knowledge. Thus, next-generation business solutions must be focused on supporting the creation of value by adding knowledge-rich components as an integral part to the work process. Therefore, an integrated approach is needed, which combines issues from a large array of knowledge fields such as science, engineering and management. Based on the reviews by the members of the Program Committee and the additional reviewers, 42 papers were selected for this year’s conference. Additionally, two discussion panels dealing with “Knowware: The Third Star after Hardware and Software” and “Required Knowledge for Delivering Services” took place under the auspices of the conference. The papers and the discussions covered a great variety of approaches of knowledge science, management and engineering, thus making KSEM a unique conference.
The rise of social media has led to vast amounts of user-generated content, with emotions ranging from joy to anger. Negative comments often target individuals, communities, or brands, prompting successful efforts to ...
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The rise of social media has led to vast amounts of user-generated content, with emotions ranging from joy to anger. Negative comments often target individuals, communities, or brands, prompting successful efforts to detect harmful speech such as hate speech, cyberbullying, and abuse. Recently, another type of speech referred to as ‘Hope Speech’ has gained attention from the research community. Hope speech consists of positive affirmations or words of reassurance, encouragement, consolation or motivation offered to the affected individual/ community during the lean periods of life. However, there has been relatively less research focused on the detection of hope speech, more particularly in low-resource languages. This paper, therefore, attempts to develop an ensemble model for detecting hope speech in some low-resource languages. data for four different languages, namely English, Kannada, Malayalam and Tamil are obtained and experimented with different deep learning-based models. An ensemble model is proposed to combine the advantages of the better performing models. Experimental results demonstrate the superior performance of the proposed Ensemble (LSTM, mBERT, XLM-RoBERTa) model compared to individual models based on data from all four languages (weighted average F1-score for English is 0.93; for Kannada is 0.74; for Malayalam is 0.82; and for Tamil is 0.60). Thus, the proposed ensemble model proves to be a suitable approach for hope speech detection in the given low resource languages.
This book features high-quality research papers presented at the 6th International Conference on Computational Intelligence in Pattern Recognition (CIPR 2024), held at Maharaja Sriram Chandra Bhanja Deo University (MS...
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ISBN:
(数字)9789819780907
ISBN:
(纸本)9789819780891
This book features high-quality research papers presented at the 6th International Conference on Computational Intelligence in Pattern Recognition (CIPR 2024), held at Maharaja Sriram Chandra Bhanja Deo University (MSCB University), Baripada, Odisha, India, during March 15–16, 2024. It includes practical development experiences in various areas of data analysis and pattern recognition, focusing on soft computing technologies, clustering and classification algorithms, rough set and fuzzy set theory, evolutionary computations, neural science and neural network systems, image processing, combinatorial pattern matching, social network analysis, audio and video data analysis, data mining in dynamic environments, bioinformatics, hybrid computing, big dataanalytics, and deep learning. It also provides innovative solutions to the challenges in these areas and discusses recent developments.
This book constitutes the refereed proceedings of the 6th International Conference on Provable Security, ProvSec 2012, held in Chengdu, China, in September 2012. The 16 full papers and 4 short papers presented were ca...
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ISBN:
(数字)9783642332722
ISBN:
(纸本)9783642332715
This book constitutes the refereed proceedings of the 6th International Conference on Provable Security, ProvSec 2012, held in Chengdu, China, in September 2012. The 16 full papers and 4 short papers presented were carefully reviewed and selected from 66 submissions. The papers are grouped in topical sections on signature schemes, foundations, leakage resilence and key escrow, encryption schemes, and information theoretical security.
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