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Paid Peering, Settlement-Free Peering, or Both?

Paid Peering, Settlement-Free Peering, or Both?

引用

IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Xin Wang Yinlong Xu Richard T. B. Ma School of Computer Science and Technology University of Science and Technology of China School of Computing National University of Singapore

ISBN: (纸本)9781538641293

With the rapid growth of congestion-sensitive and data-intensive applications, traditional settlement-free peering agreements with best-effort delivery often do not meet the QoS requirements of content providers (CPs). Meanwhile, Internet access providers (IAPs) feel that revenues from end-users are not sufficient to recoup the upgrade costs of network infrastructures. Consequently, some IAPs have begun to offer CPs a new type of peering agreement, called paid peering, under which they provide CPs with better data delivery quality for a fee. In this paper, we model a network platform where an IAP makes decisions on the peering types offered to CPs and the prices charged to CPs and end-users. We study the optimal peering schemes for the IAP, i.e., to offer CPs both the paid and settlement-free peering to choose from or only one of them, as the objective is profit or welfare maximization. Our results show that 1) the IAP should always offer the paid and settlement-free peering under the profit-optimal and welfare-optimal schemes, respectively, 2) whether to simultaneously offer the other peering type is largely driven by the type of data traffic, e.g., text or video, and 3) regulators might want to encourage the IAP to allocate more network capacity to the settlement-free peering for increasing user welfare.

关键词： Internet Data models Regulators Pricing Resource management Elasticity Sensitivity

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The role of data cap in two-part pricing under market competition

The role of data cap in two-part pricing under market compet...

引用

IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Xin Wang Richard T. B. Ma Yinlong Xu School of Computer Science and Technology University of Science and Technology of China School of Computing National University of Singapore

Internet services are traditionally priced at flat rates; however, many Internet service providers (ISPs) have recently shifted towards two-part tariffs where a data cap is imposed to restrain data demand from heavy users and usage over the data cap is charged based on a per-unit fee. Although two-part tariffs could generally increase the revenue for ISPs, the role of data cap and its optimal pricing structures are not well understood. In this paper, we study the impact of data cap on the optimal two-part pricing schemes for congestion-prone service markets, e.g., broadband or cloud services. In particular, we characterize users' optimal data demand and derive the equilibrium market share of ISPs under market competition. Based on this equilibrium model, we characterize the optimal structure of the two-part tariffs under market competition. Our results reveal that 1) the data cap provides a mechanism for ISPs to transition between flat-rate and pay-as-you-go type of schemes, 2) with growing data demand and network capacity, the optimal two-part structure will move towards usage-based schemes with diminishing data caps, and 3) under intense market competition, the optimal two-part structure will move in an opposite direction towards flat-rate schemes with higher data caps.

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Performance evaluation of fingerprint biometrics systems for e-business access control 6th

Performance evaluation of fingerprint biometrics systems for...

引用

6th International Conference on e-Infrastructure and e-Services for Developing Countries, AFRICOMM 2014

作者： Esan, O.A. Osunmakinde, I.O. Ngwira, S.M. Department of Computer System Engineerining Tshwane University of Technology Soshanguve Campus Pretoria South Africa School of Computing College of Science Engineering and Technology University of South Africa UNISA Pretoria South Africa

ISBN: (纸本)9783319168852

The performance of a fingerprint authentication system in electronic business depends on the accurate analysis of the quality of input fingerprints. This paper investigates distorted and misaligned fingerprints caused by environmental noise such as oil, wrinkles, dry skin, dirt and displacement of the query fingerprint with the database fingerprint template during matching. The noisy, distorted and/ or misaligned fingerprint produced as a 2-D on x-y image, is enhanced and optimized using a new hybrid modified Gabor filter-hierarchal structure check (MGFHSC) system model based on an MGF integrated with a HSC. Our findings indicate that structural matching is experimentally confirmed as a reliable matching technique in a fingerprint authentication system, as distorted fingerprints can easily be matched, even when the pixel value detected is low, while the rotational and geometrical transformation that affects fingerprint authentication is addressed. The MGF-HSC approach minimizes false fingerprint matching and the dominant effect of distortion and misalignment of fingerprints to an acceptable level. Our approach, which is benchmarked with publicly available methods, deals with 60% heavy distortions and misaligned fingerprint and exhibits reliable matching performance for promising e-business security work in terms of visual quality, false rejection rate and false acceptance rate. © Institute for computer sciences, Social Informatics and Telecommunications Engineering 2015.

关键词： Biometrics

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Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction 38

Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary r...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Luo, Haoran Haihong, E. Yang, Yuhao Yao, Tianyu Guo, Yikai Tang, Zichen Zhang, Wentai Peng, Shiyao Wan, Kaiyang Song, Meina Lin, Wei Zhu, Yifan Tuan, Luu Anh School of Computer Science Beijing University of Posts and Telecommunications China School of Automation Science and Electrical Engineering Beihang University China Beijing Institute of Computer Technology and Application China Inspur Group Co. Ltd. China College of Computing and Data Science Nanyang Technological University Singapore

Beyond traditional binary relational facts, n-ary relational knowledge graphs (NKGs) are comprised of n-ary relational facts containing more than two entities, which are closer to real-world facts with broader applications. However, the construction of NKGs remains at a coarse-grained level, which is always in a single schema, ignoring the order and variable arity of entities. To address these restrictions, we propose Text2NKG, a novel fine-grained n-ary relation extraction framework for n-ary relational knowledge graph construction. We introduce a span-tuple classification approach with hetero-ordered merging and output merging to accomplish fine-grained n-ary relation extraction in different arity. Furthermore, Text2NKG supports four typical NKG schemas: hyper-relational schema, event-based schema, role-based schema, and hypergraph-based schema, with high flexibility and practicality. The experimental results demonstrate that Text2NKG achieves state-of-the-art performance in F1 scores on the fine-grained n-ary relation extraction benchmark. Our code and datasets are publicly available. © 2024 Neural information processing systems foundation. All rights reserved.

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Wireless Sensor Networks: Revolutionizing Explosive Detection 5

Wireless Sensor Networks: Revolutionizing Explosive Detectio...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Sujith Kumar, P.S. Anand, C. Vasuki, N. Ramesh, V. Santhoshkumar, S.P. Sree Buddha College of Engineering Department of Computer Science and Engineering Kerala Alappuzha India Nandha College of Technology Department of Information Technology Erode India Government College of Engineering Department of Computer Science and Engineering Erode India School of Computing Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Avadi India

ISBN: (纸本)9798331518097

One of the important technologies for protecting civilian life from terrorist attacks is explosive detection techniques. The global incidence of terrorist activities has been considerably reduced by the development of explosive detection techniques, which has been made possible by ongoing technology improvements. Wireless sensor networks (WSNs) are one of these developments that has become an innovative tool for explosives detection. This work explores recent empirical research on the application of WSNs to explosives detection and includes statistical analysis of the investigations. Wireless sensor network with compatible specialized sensors is required for effective explosive detection. Three main wireless sensor system aspects that are relevant to explosives detection are covered in this research. It first looks at how scalable wireless sensors are in terms of explosives detection technologies. In the second section, it looks at the mobility and communication capabilities of WSNs and sensors in explosives detecting systems. Finally, it highlights potential hazards and weaknesses and addresses the crucial topic of data security inside wireless sensor networks used for explosive detection. The study also suggests incorporating drone assisted detection as a novel way to improve the coverage and effectiveness of explosives detection systems, adding to the continuous advancement of security technologies meant to shield civilian populations from terrorist threats. © 2024 IEEE.

关键词： Terrorism

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Support vector guided dictionary learning

引用

13th European Conference on computer Vision, ECCV 2014

作者： Wang, Ping Cai, Sijia Zuo, Wangmeng Zhang, Lei Cai, Sijia School of Science Tianjin University China School of Computer Science and Technology Harbin Institute of Technology China Dept. Of Computing The Hong Kong Polytechnic University China Dept. Of Applied Mathematics Xidian University China

ISBN: (纸本)9783319105925

Discriminative dictionary learning aims to learn a dictionary from training samples to enhance the discriminative capability of their coding vectors. Several discrimination terms have been proposed by assessing the prediction loss (e.g., logistic regression) or class separation criterion (e.g., Fisher discrimination criterion) on the coding vectors. In this paper, we provide a new insight on discriminative dictionary learning. Specifically, we formulate the discrimination term as the weighted summation of the squared distances between all pairs of coding vectors. The discrimination term in the state-of-the-art Fisher discrimination dictionary learning (FDDL) method can be explained as a special case of our model, where the weights are simply determined by the numbers of samples of each class. We then propose a parameterization method to adaptively determine the weight of each coding vector pair, which leads to a support vector guided dictionary learning (SVGDL) model. Compared with FDDL, SVGDL can adaptively assign different weights to different pairs of coding vectors. More importantly, SVGDL automatically selects only a few critical pairs to assign non-zero weights, resulting in better generalization ability for pattern recognition tasks. The experimental results on a series of benchmark databases show that SVGDL outperforms many state-of-the-art discriminative dictionary learning methods. ©Springer International Publishing Switzerland 2014.

关键词： Pattern recognition

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Intelligent Analysis on Frameworks for Mobile App Development 5

Intelligent Analysis on Frameworks for Mobile App Developmen...

引用

5th International Conference on Smart Systems and Inventive technology, ICSSIT 2023

作者： Gowri, S. Pappa, C. Kanmani Tamilvizhi, T. Nelson, Leema Surendran, R. Institute of Science and Technology School of Computing Sathyabama Chennai India Veltech Rangarajan Dr Sagunthala RandD Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665474672

Establishing new mobile applications that are adaptable across multiple cross platforms is a strenuous task. There are several frameworks for mobile application development existing in the market which has got its own tremendous features and benefits, but choosing the right framework for app development is very crucial for any developer. This study compares the benefits and efficiency of the three most popular and preferred frameworks by the end-users;React-Native, Flutter, and Ionic Frameworks and choose the most optimized and suitable framework for mobile app development. These designed frameworks are platform independent, and the yield was evaluated and appeared to be satisfactory by the users in the respective platform. These methodologies have been implemented using Javascript, Dart, HTML and CSS. © 2023 IEEE.

关键词： Flutter (aerodynamics)

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Semi-supervised Crowd Counting via Self-training on Surrogate Tasks 1

引用

16th European Conference on computer Vision, ECCV 2020

作者： Liu, Yan Liu, Lingqiao Wang, Peng Zhang, Pingping Lei, Yinjie College of Electronics and Information Engieering Sichuan University Chengdu China School of Computer Science The University of Adelaide Adelaide Australia School of Computing and Information Technology University of Wollongong Wollongong Australia School of Artificial Intelligence Dalian University of Technology Dalian China

ISBN: (数字)9783030585556

ISBN: (纸本)9783030585549

Most existing crowd counting systems rely on the availability of the object location annotation which can be expensive to obtain. To reduce the annotation cost, one attractive solution is to leverage a large number of unlabeled images to build a crowd counting model in semi-supervised fashion. This paper tackles the semi-supervised crowd counting problem from the perspective of feature learning. Our key idea is to leverage the unlabeled images to train a generic feature extractor rather than the entire network of a crowd counter. The rationale of this design is that learning the feature extractor can be more reliable and robust towards the inevitable noisy supervision generated from the unlabeled data. Also, on top of a good feature extractor, it is possible to build a density map regressor with much fewer density map annotations. Specifically, we proposed a novel semi-supervised crowd counting method which is built upon two innovative components: (1) a set of inter-related binary segmentation tasks are derived from the original density map regression task as the surrogate prediction target;(2) the surrogate target predictors are learned from both labeled and unlabeled data by utilizing a proposed self-training scheme which fully exploits the underlying constraints of these binary segmentation tasks. Through experiments, we show that the proposed method is superior over the existing semi-supervised crowd counting method and other representative baselines. © 2020, Springer Nature Switzerland AG.

关键词： computer vision

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IntNet: A novel framework using reconstructed mutation samples and a biologically informed neural network for pathway analysis

IntNet: A novel framework using reconstructed mutation sampl...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhou, Li Li, Jie Tan, Weilong Harbin Institute of Technology School of Computer Science and Technology Harbin China National Key Laboratory of Smart Farming Technology and Systems College of Computing Harbin Institute of Technology Harbin China Technology Center for Medicines Center for Medicines Nanjing China

ISBN: (纸本)9798350386226

Most pathway analysis methods based on simple nucleotide variations overlook mutations occurring in genes outside the pathway, fail to address mutation heterogeneity with targeted measures, and do not consider the functional relationships among gene components within biomolecular networks when modeling the contributions of biomarkers to pathways. So, we proposed a novel framework, named IntNet, for pathway analysis based on simple nucleotide variations. The core components of IntNet are sample reconstruction (SC) and a new biologically informed neural network model (BINN). To address the difficulty of extracting effective features caused by the mutation heterogeneity, IntNet reconstructs mutation samples based on the hypothesis that mutations in patients with the same phenotype may cluster in similar network regions. BINN uses the human gene network and expanded biological pathways to construct its neural network architecture. BINN implements mutation signaling propagation along the human gene network and integration within the pathways. IntNet's performance was validated using multiple cancer datasets. Experimental results demonstrated that IntNet could identify more accurate and reliable pathways. © 2024 IEEE.

关键词： Nucleotides

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LI4 : Label-Infused Iterative Information Interacting based Fact Verification in Question-answering Dialogue 30

LI4 : Label-Infused Iterative Information Interacting based ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Xiaocheng Wang, Chang Zhao, Guoping Su, Xiaohong Faculty of Computing Harbin Institute of Technology Harbin China School of Computer Science Engineering South China University of Technology Guangzhou China Zhengzhou Esunny Information Technology Co. Ltd. Zhengzhou China

ISBN: (纸本)9782493814104

Fact verification constitutes a pivotal application in the effort to combat the dissemination of disinformation, a concern that has recently garnered considerable attention. However, previous studies in the field of fact verification, particularly those focused on question-answering dialogue, have exhibited limitations, such as failing to fully exploit the potential of question structures and ignoring relevant label information during the verification process. In this paper, we introduce Label-Infused Iterative Information Interacting (LI4), a novel approach designed for the task of question-answering dialogue based fact verification. LI4 consists of two meticulously designed components, namely the Iterative Information Refining and Filtering Module (IIRF) and the Fact Label Embedding Module (FLEM). The IIRF uses the Interactive Gating Mechanism to iteratively filter out the noise of question and evidence, concurrently refining the claim information. The FLEM is conceived to strengthen the understanding ability of the model towards labels by injecting label knowledge. We evaluate the performance of the proposed LI4 on HEALTHVER, FAVIQ, and COLLOQUIAL. The experimental results confirm that our LI4 model attains remarkable progress, manifesting as a new state-of-the-art performance. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Refining

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